0)\\n photos = list(activity.photos)\\n self.assertEqual(len(photos), 1)\\n self.assertEqual(len(photos), activity.photo_count)\\n self.assertIsInstance(photos[0], model.ActivityPhoto)\",\n \"def create_fake_data():\\n s = Story.objects.create(body=\\\"farfrompuken is so awesome, i just love it.\\\",\\n author_name=\\\"jared nuzzolillo\\\")\\n\\n img_urls = (\\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/u-B.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/u-D.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/l-f.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/u-K.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/l-m.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/u-P.png\\\",\\n \\\"https://dl.dropboxusercontent.com/u/190173/typequoting/images/u-T.png\\\",)\\n\\n for i, img_url in enumerate(img_urls):\\n StoryImage.objects.create(\\n story=s,\\n image=Image.objects.create(\\n url=img_url,\\n name=\\\"name %d\\\" % i,\\n author_name=\\\"Johnny %d Times\\\" % i,\\n author_url=\\\"http://somewherecool%d\\\" % i,\\n uppercase=\\\"uppercase %d\\\" % i,\\n typeface=\\\"typeface %d\\\" % i))\",\n \"def test_delete_image_by_wrong_tag(self, test_image):\\n tag = f\\\"{TEST_IMAGE_NAME}:wrong_tag\\\"\\n assert image_exists(TEST_IMAGE_NAME)\\n assert not delete_image(tag, force=True)\\n assert image_exists(TEST_IMAGE_NAME)\\n\\n # now delete using that tag, both tags will be gone because it's the same image.\\n build_test_image(tag=tag)\\n assert image_exists(TEST_IMAGE_NAME)\\n assert image_exists(tag)\\n assert delete_image(tag, force=True)\\n assert not image_exists(TEST_IMAGE_NAME)\\n assert not image_exists(tag)\",\n \"def copy_images(apps, schema_editor):\\n\\n FieldImage = apps.get_model('field_wagtail', 'FieldImage')\\n Image = apps.get_model('wagtailimages', 'Image')\\n django_content_type = apps.get_model('contenttypes', 'contenttype')\\n tagged_item_model = apps.get_model('taggit', 'TaggedItem')\\n\\n images = Image.objects.all()\\n new_images = []\\n for image in images:\\n new_images.append(FieldImage(\\n id=image.id,\\n title=image.title,\\n file=image.file,\\n width=image.width,\\n height=image.height,\\n created_at=image.created_at,\\n focal_point_x=image.focal_point_x,\\n focal_point_y=image.focal_point_y,\\n focal_point_width=image.focal_point_width,\\n focal_point_height=image.focal_point_height,\\n file_size=image.file_size,\\n collection=image.collection,\\n uploaded_by_user=image.uploaded_by_user,\\n alt_text=''\\n ))\\n\\n FieldImage.objects.bulk_create(new_images)\\n\\n ct_extended_model, created = django_content_type.objects.get_or_create(\\n app_label='field_wagtail',\\n model='fieldimage'\\n )\\n ct_wagtail_model = django_content_type.objects.get(\\n app_label='wagtailimages',\\n model='image'\\n )\\n\\n tagged_item_model.objects.filter(\\n content_type_id=ct_wagtail_model.id).update(\\n content_type_id=ct_extended_model.id\\n )\",\n \"def test_aws_service_api_image_get(self):\\n pass\",\n \"def test_image_obj_creado_con_exito(self):\\n self.assertTrue(self.image_model.objects.get(pk=self.image_obj.pk))\",\n \"def test_deletion_of_user_photo_succeeds(self):\\n\\t\\tself.name = 'media.png'\\n\\t\\tself.image = File(open('static/img/media.png', 'rb'))\\n\\t\\tself.created_image = UserPhoto(image=self.image, name=self.name, created_by=self.user)\\n\\t\\tself.created_image.save()\\t\\t\\t\\n\\t\\tresponse = self.client.delete('/api/modify_photo/?id={}'.format(self.created_image.id))\\n\\t\\tself.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\",\n \"def test_retrieve_tags_assigned_unique(self):\\n tag1 = Tag.objects.create(user = self.user,name='Breakfast')\\n tag2 = Tag.objects.create(user=self.user,name='Lunch')\\n\\n recipe1 = Recipe.objects.create(user=self.user,title='Goose Liver on toast',price=5.00,time_minutes=15)\\n recipe2 = Recipe.objects.create(user = self.user,title='Egg Benedict',price=5.00,time_minutes=15)\\n recipe1.tags.add(tag1)\\n recipe2.tags.add(tag1)\\n\\n res = self.client.get(TAGS_URL,{'assigned_only':1})\\n self.assertEqual(len(res.data),1)\",\n \"def aug_test(self, imgs, img_metas, **kwargs):\\n pass\",\n \"def aug_test(self, imgs, img_metas, **kwargs):\\n pass\",\n \"def testimage_handler(self):\\n\\t\\t\\n\\t\\tthings = Thing.objects.all()\\n\\t\\tif len( things ):\\n\\t\\t\\tthing = things[0]\\n\\t\\telse:\\n\\t\\t\\tc = Client()\\n\\t\\t\\tdata = parse_qs( 'title=&tags=&lattitude=32.82248&longitude=-96.762986&duration=&parent=&privacy=U&lifespan=&format=txt' )\\n\\t\\t\\tdata[ 'media' ] = open( MEDIA_ROOT + 'unittest_image.jpg' )\\n\\t\\t\\tc.post( '/api/place/', data )\\n\\t\\t\\t\\n\\t\\t\\tthing = Thing.objects.all()[0]\\n\\n\\t\\t\\n\\t\\turi = thing.media.replace( 'http://' + DOMAIN, '' )\\n\\t\\t\\n\\t\\tc = Client()\\n\\t\\tresponse = c.get( uri )\\n\\t\\tself.failUnlessEqual(response.status_code, 200)\",\n \"def test_record_emotions_post_adds_emotion_to_db(self):\\n from emotion_emotions.views import RecordEmotions\\n num = len(Emotion.objects.all())\\n request = self.request.post('', data={'image': 'data:base64,FAKE'})\\n request.user = self.dan\\n view = RecordEmotions(request=request)\\n view.post(request)\\n new_num = len(Emotion.objects.all())\\n self.assertEqual(num + 1, new_num)\",\n \"def fetch_photo_id_image(self):\\r\\n if settings.FEATURES.get('AUTOMATIC_VERIFY_STUDENT_IDENTITY_FOR_TESTING'):\\r\\n return\\r\\n\\r\\n self.photo_id_key = self.original_verification(self.user).photo_id_key\\r\\n self.save()\",\n \"def forward_test(self, img, img_metas, **kwargs):\",\n \"def simple_test(self, img, img_meta, **kwargs):\\n pass\",\n \"def test_answer_meta_image_uses_category_image_if_no_social_image(self):\\n category = baker.make(Category, category_image=self.test_image)\\n page = self.page1\\n page.category.add(category)\\n page.save_revision()\\n self.assertEqual(page.meta_image, self.test_image)\",\n \"def test_get_image_id(self):\\n img_id = str(uuid.uuid4())\\n img_name = 'myfakeimage'\\n self.my_image.id = img_id\\n self.my_image.name = img_name\\n self.sahara_client.images.get.return_value = self.my_image\\n self.sahara_client.images.find.side_effect = [[self.my_image], []]\\n\\n self.assertEqual(img_id, self.sahara_plugin.get_image_id(img_id))\\n self.assertEqual(img_id, self.sahara_plugin.get_image_id(img_name))\\n self.assertRaises(exception.EntityNotFound,\\n self.sahara_plugin.get_image_id, 'noimage')\\n\\n calls = [mock.call(name=img_name),\\n mock.call(name='noimage')]\\n self.sahara_client.images.get.assert_called_once_with(img_id)\\n self.sahara_client.images.find.assert_has_calls(calls)\",\n \"def test_price_breakdown_by_id_image(self):\\n pass\",\n \"def test_Image():\\n assert Image(cur, \\\"Simple_Linear\\\").detect_image() == True\\n assert Image(cur, \\\"Logistic_Linear\\\").detect_image() == False\\n assert Image(cur, \\\"Simple_Linear\\\").date == \\\"2021-04-20\\\"\\n assert Image(cur, \\\"Breslow-Day_Test\\\").source == \\\"Course BIOSTAT703 slide\\\"\",\n \"def test_create_recipe_with_tags(self):\\n tag1 = sample_tag(user=self.user,name='vegan')\\n tag2 = sample_tag(user=self.user, name='dessert')\\n payload = {\\n 'title':'cheesecake',\\n 'tag':[tag1.id,tag2.id],\\n 'time_minutes':60,\\n 'price':10.00,\\n }\\n res = self.client.post(RECIPE_URL,payload)\\n self.assertEqual(res.status_code,status.HTTP_201_CREATED)\\n recipe = Recipe.objects.get(id=res.data['id'])\\n tags = recipe.tag.all()\\n self.assertEqual(len(tags),2)\\n self.assertIn(tag1,tags)\\n self.assertIn(tag2,tags)\",\n \"def get_images_by_vulnerability(self, **kwargs):\\n ...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66079843","0.6477032","0.6326245","0.6268388","0.6267517","0.6212116","0.6183669","0.61595887","0.6151405","0.61384684","0.60875714","0.60794526","0.6066342","0.60580915","0.6017938","0.60149026","0.600587","0.6003988","0.5976658","0.59595704","0.5959389","0.5944568","0.5924291","0.59211135","0.589627","0.5887148","0.5880393","0.58649886","0.5861153","0.5850407","0.5849973","0.58262974","0.58257467","0.58053976","0.5800949","0.5773536","0.5770676","0.57614267","0.57304215","0.57161194","0.5712388","0.57110626","0.56890166","0.56807595","0.5666246","0.5664621","0.5661664","0.5655272","0.5654474","0.5653362","0.5652592","0.5636366","0.5632572","0.5630062","0.56242555","0.5621509","0.56148183","0.5608346","0.5607644","0.5586847","0.55742043","0.5573187","0.5560907","0.554996","0.55474967","0.55376804","0.5520847","0.55140877","0.5509492","0.55073214","0.5492122","0.5487059","0.54864615","0.54864377","0.5484148","0.5479218","0.54647034","0.5459926","0.5452578","0.5435263","0.54266876","0.54238445","0.5417995","0.5416855","0.54159874","0.5414571","0.54141396","0.5411324","0.5411324","0.54096735","0.54073346","0.5403596","0.53911453","0.5390099","0.5376477","0.5375408","0.5370571","0.5363878","0.5361499","0.536111"],"string":"[\n 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\"0.5608346\",\n \"0.5607644\",\n \"0.5586847\",\n \"0.55742043\",\n \"0.5573187\",\n \"0.5560907\",\n \"0.554996\",\n \"0.55474967\",\n \"0.55376804\",\n \"0.5520847\",\n \"0.55140877\",\n \"0.5509492\",\n \"0.55073214\",\n \"0.5492122\",\n \"0.5487059\",\n \"0.54864615\",\n \"0.54864377\",\n \"0.5484148\",\n \"0.5479218\",\n \"0.54647034\",\n \"0.5459926\",\n \"0.5452578\",\n \"0.5435263\",\n \"0.54266876\",\n \"0.54238445\",\n \"0.5417995\",\n \"0.5416855\",\n \"0.54159874\",\n \"0.5414571\",\n \"0.54141396\",\n \"0.5411324\",\n \"0.5411324\",\n \"0.54096735\",\n \"0.54073346\",\n \"0.5403596\",\n \"0.53911453\",\n \"0.5390099\",\n \"0.5376477\",\n \"0.5375408\",\n \"0.5370571\",\n \"0.5363878\",\n \"0.5361499\",\n \"0.536111\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56813097"},"document_rank":{"kind":"string","value":"43"}}},{"rowIdx":95679,"cells":{"query":{"kind":"string","value":"Method to be implemented"},"document":{"kind":"string","value":"def update_with_fit_args(self, **kwargs):\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __call__(self):\n raise NotImplementedError","def support(self):","def __call__(self):\n raise NotImplementedError()","def __call__(self):\r\n raise NotImplementedError('override me')","def __call__(self):\n\t\treturn","def __call__(self) -> None:","def __call__( self ):\n pass","def __call__(self):\n pass","def __call__(self):\n pass","def implement(self):\n\t#@DEBUG remove comments","def use(self):","def function(self):\n raise NotImplementedError","def regular(self):","def override(self):\n return None","def degibber(self):","def test(self):\n raise NotImplementedError","def intuit(self):\n raise NotImplemented()","def __call__(self, *args, **kwargs):\r\n raise 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run(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def initialize(self):\",\n \"def init(self):\",\n \"def init(self):\",\n \"def processing(self):\\n pass\",\n \"def offering(self):\\r\\n raise NotImplementedError()\",\n \"def _prepare(self):\",\n \"def _prepare(self):\",\n \"def present(self):\",\n \"def __call__(self) -> dict:\\n\\t\\tpass\",\n \"def call(self):\",\n \"def _build_impl(self):\",\n \"def _init(self):\\n raise NotImplementedError\",\n \"def run(self):\\n raise NotImplementedError # implement in subclass\",\n \"def target(self):\",\n \"def bad(self):\\n raise NotImplementedError\",\n \"def bad(self):\\n raise NotImplementedError\",\n \"def check(self):\\n raise NotImplementedError\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def _run(self):\\n raise NotImplementedError\",\n \"def __init__(self):\\r\\n\\t\\tpass\",\n \"def base(self):\\n raise NotImplementedError()\",\n \"def initialise(self):\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\",\n \"def extension (self):\\n assert False, \\\"To be implemented by child\\\"\",\n \"def perform(self):\\n pass\",\n \"def _init(self):\",\n \"def mezclar_bolsa(self):\",\n \"def __init__(self):\\n raise NotImplementedError(\\\"This class cannot be instantiated!\\\")\",\n \"def result(self):\",\n \"def result(self):\",\n \"def run(self): \\r\\n return\",\n \"def __init__():\",\n \"def perform(self):\\n raise NotImplementedError\",\n \"def __init__ (self) :\",\n \"def __call__(self):\",\n \"def __call__(self):\",\n \"def apply(self): # pragma: no cover\\n raise NotImplementedError\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78358424","0.7723889","0.7493241","0.7416625","0.7341111","0.7135667","0.7118768","0.71088976","0.71088976","0.70884323","0.69750255","0.68996793","0.6808303","0.6796849","0.6753698","0.67092526","0.66899514","0.6654799","0.6654799","0.6654799","0.6641924","0.6641924","0.6641924","0.6641924","0.6596571","0.6596571","0.6588812","0.6571001","0.6568187","0.65485066","0.6520751","0.65138245","0.6494921","0.64902943","0.6467395","0.6467395","0.6465909","0.6441366","0.6441366","0.6441366","0.6424249","0.6420371","0.6420371","0.64175504","0.6413171","0.64078313","0.64028096","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6389797","0.6385707","0.6385707","0.6385707","0.6385707","0.6385517","0.6385517","0.63816535","0.6370596","0.63703585","0.63703585","0.6363289","0.6358765","0.63450044","0.6337269","0.63343555","0.63336754","0.6328508","0.6327035","0.6327035","0.6318311","0.6309866","0.6309866","0.63090646","0.6301731","0.63002306","0.62871397","0.62871397","0.62871397","0.62871397","0.62871397","0.62790257","0.6277439","0.627327","0.6270304","0.62677425","0.626764","0.626764","0.6250167","0.6249451","0.6246711","0.62440634","0.6239104","0.6239104","0.6233122"],"string":"[\n \"0.78358424\",\n \"0.7723889\",\n \"0.7493241\",\n \"0.7416625\",\n \"0.7341111\",\n \"0.7135667\",\n \"0.7118768\",\n \"0.71088976\",\n \"0.71088976\",\n \"0.70884323\",\n \"0.69750255\",\n \"0.68996793\",\n \"0.6808303\",\n \"0.6796849\",\n \"0.6753698\",\n \"0.67092526\",\n \"0.66899514\",\n \"0.6654799\",\n \"0.6654799\",\n \"0.6654799\",\n \"0.6641924\",\n \"0.6641924\",\n \"0.6641924\",\n \"0.6641924\",\n \"0.6596571\",\n \"0.6596571\",\n \"0.6588812\",\n \"0.6571001\",\n \"0.6568187\",\n \"0.65485066\",\n \"0.6520751\",\n \"0.65138245\",\n \"0.6494921\",\n \"0.64902943\",\n \"0.6467395\",\n \"0.6467395\",\n \"0.6465909\",\n \"0.6441366\",\n \"0.6441366\",\n \"0.6441366\",\n \"0.6424249\",\n \"0.6420371\",\n \"0.6420371\",\n \"0.64175504\",\n \"0.6413171\",\n \"0.64078313\",\n \"0.64028096\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6389797\",\n \"0.6385707\",\n \"0.6385707\",\n \"0.6385707\",\n \"0.6385707\",\n \"0.6385517\",\n \"0.6385517\",\n \"0.63816535\",\n \"0.6370596\",\n \"0.63703585\",\n \"0.63703585\",\n \"0.6363289\",\n \"0.6358765\",\n \"0.63450044\",\n \"0.6337269\",\n \"0.63343555\",\n \"0.63336754\",\n \"0.6328508\",\n \"0.6327035\",\n \"0.6327035\",\n \"0.6318311\",\n \"0.6309866\",\n \"0.6309866\",\n \"0.63090646\",\n \"0.6301731\",\n \"0.63002306\",\n \"0.62871397\",\n \"0.62871397\",\n \"0.62871397\",\n \"0.62871397\",\n \"0.62871397\",\n \"0.62790257\",\n \"0.6277439\",\n \"0.627327\",\n \"0.6270304\",\n \"0.62677425\",\n \"0.626764\",\n \"0.626764\",\n \"0.6250167\",\n \"0.6249451\",\n \"0.6246711\",\n \"0.62440634\",\n \"0.6239104\",\n \"0.6239104\",\n \"0.6233122\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95680,"cells":{"query":{"kind":"string","value":"Explore a dataset dataset pandas dataset prj_info dictionnary containing projet information (response...)"},"document":{"kind":"string","value":"def explore_data(data,prj_info,TMP=1234):\r\n print(\" Data file rows and columns are : \", data.shape)\r\n #Open pdf\r\n pp = PdfPages(prj_info['OUTPUT_PATH'] + \"exploration_\" + str(TMP) + \".pdf\")\r\n\r\n #Plot average\r\n plot_average_reponse(data,prj_info,pp,TMP)\r\n\r\n #Close pdf\r\n pp.close()\r\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extract_project_details(\n df,\n copy_columns=info_columns,\n column_rename_dict=info_column_rename_dict,\n use_record=0,\n record_index=\"PID_Index\",\n):\n df_details = df.copy().loc[df[record_index] == use_record][copy_columns]\n\n if column_rename_dict:\n df_details = df_details.copy().rename(columns=column_rename_dict)\n\n return df_details.set_index(\"PID\")","def personnel_projects_table(personnel_search_table_selected_indices, selected_funding, selected_year, rows):\n personnel_outcome_data, personnel_name = personnel_outcomes_helper(personnel_search_table_selected_indices, selected_funding, selected_year, rows)\n if personnel_outcome_data is not None:\n personnel_outcome_data = personnel_outcome_data[[\"type\", \"pub_title\"]]\n return personnel_outcome_data.to_dict('records')\n\n return pd.DataFrame(data=None, columns=[\"type\", \"pub_title\"]).to_dict('records') # None cannot be passed back as it will cause an error.","def _createDataFromProject(self):\n data = []\n project_dict = self._project.asDict()\n for phase_id, phase_dict in project_dict['phases'].items():\n for fitable_id, fitable_dict in phase_dict['cell'].items():\n data.append({\n 'id': ['phases', phase_id, 'cell', fitable_id],\n 'label': '{} {}'.format(phase_id, fitable_id),\n 'value': fitable_dict['value'],\n 'error': fitable_dict['error'],\n 'refine': fitable_dict['refine']\n })\n return data","def getProjectData(self) -> ghidra.framework.model.ProjectData:\n ...","def getIndicePreciosInternosAlPorMayorBase2015(self):\n #Obtener la url de descarga del cvs\n urlPackage=\"https://datos.gob.ar/api/3/action/package_show?id=sspm-indice-precios-internos-basicos-al-por-mayor-dic-2015-100\"\n s=requests.get(urlPackage).content\n objJson = json.loads(s)\n resultado = objJson['result']['resources']\n selector = 0\n ultimoResultado = resultado[selector]\n urlDescarga = ultimoResultado['url']\n descripcion = ultimoResultado['description']\n print(\"Descargando: {}\".format(descripcion))\n print(\"Archivo: {}\".format(urlDescarga))\n \n #Descargar la url con cvs y generar pandas dataframe\n contenidoCVS = requests.get(urlDescarga).content\n flujoCVS = io.StringIO(contenidoCVS.decode('utf-8'))\n df_temp = pd.read_csv(flujoCVS)\n \n #transform string to datetime\n df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore')\n df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date\n #set index\n df_temp.set_index('indice_tiempo', inplace=True)\n \n return df_temp","def get_reanalysis_projects_by_accession(self, accession):\n request_url = self.api_base_url + \"projects/reanalysis/\" + accession\n headers = {\"Accept\": \"application/JSON\"}\n response = Util.get_api_call(request_url, headers)\n return response.json()","def getCitationsData():\n # Follows https://github.com/simonw/irma-scrapers/issues/1\n citationsResponse = requests.get(\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\", headers=headers).json()\n treeEntry = [t for t in citationsResponse[\"tree\"] if t[\"path\"] == \"references.json\"][0] \n citations = json.loads(base64.b64decode(requests.get(treeEntry[\"url\"]).json()[\"content\"]))\n\n citationsDF = pd.DataFrame(citations)\n citationsDF[\"Covid19-review_paperLink\"] = citationsDF.id.apply(lambda x: \"https://greenelab.github.io/covid19-review/#ref-\" + x)\n citationsDF = citationsDF[[\"DOI\", \"title\", \"issued\", \"container-title\", \"URL\", \"Covid19-review_paperLink\"]]\n citationsDF.rename(columns={\"DOI\": \"doi\", \"issued\": \"date\", \"container-title\": \"publication\"}, inplace=True)\n\n # Convert date to string\n def dateStringFromDateParts(row):\n try:\n dateParts = row['date']['date-parts'][0]\n if len(dateParts) == 3:\n return \"-\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\n elif len(dateParts) == 2:\n return \"-\".join([str(dateParts[1]), str(dateParts[0])])\n elif len(dateParts) == 1:\n return str(dateParts[0])\n else:\n return\n except:\n return\n\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\n\n citationsDF.set_index(\"doi\", inplace=True)\n return citationsDF","def extract_details( session_requests, job_id ):\n \n url_prefix = CONFIG[\"url_prefix\"]\n \n #Extract html from web\n url = CONFIG[\"url_jobno\"] + str(job_id)\n tree = scrape_html(session_requests, url)\n \n #Extact description\n title = \"; \".join(tree.xpath(\"//p[@class='listheader']/text()\"))\n description = \"; \".join(tree.xpath(\"//p//text()\")) #more than one element\n \n #Extract files\n num_file = int(tree.xpath(\"count(//p[contains(text(),'Job Description Document :')]//a)\"))\n loop_range = min(num_file, (MAX_NUM_OF_FILES - 1))\n \n file_link = [\"NA\"] * MAX_NUM_OF_FILES\n file_name = [\"NA\"] * MAX_NUM_OF_FILES\n down_file_name = [\"NA\"] * MAX_NUM_OF_FILES\n \n if (num_file > (MAX_NUM_OF_FILES - 1)):\n file_link[(MAX_NUM_OF_FILES - 1)] = \"More than 9 files\"\n file_name[(MAX_NUM_OF_FILES - 1)] = \"More than 9 files\"\n \n for i in range(loop_range):\n file_link[i] = url_prefix + tree.xpath(\"//p[contains(text(),'Job Description Document :')]//a/@href\")[i]\n file_name[i] = tree.xpath(\"//p[contains(text(),'Job Description Document :')]//a/text()\")[i]\n \n ext = find_file_extention(file_name[i])\n down_file_name[i] = download_file(session_requests, file_link[i], job_id, i, ext)\n \n # dataframe\n row_names_link = init_file_dataframe()[1]\n row_names_name = init_file_dataframe()[2]\n row_names_down = init_file_dataframe()[3]\n \n df_link = np.transpose(pd.DataFrame(file_link, row_names_link))\n df_name = np.transpose(pd.DataFrame(file_name, row_names_name))\n df_down = np.transpose(pd.DataFrame(down_file_name, row_names_down))\n \n df_file = pd.DataFrame(data = {\"job_title\": [title], \"description\": [description], \"num_of_file\": [loop_range]})\n df_file = pd.concat([df_file.reset_index(drop=True), df_link], axis=1, sort=False)\n df_file = pd.concat([df_file.reset_index(drop=True), df_name], axis=1, sort=False)\n df_file = pd.concat([df_file.reset_index(drop=True), df_down], axis=1, sort=False)\n \n return df_file","def get_data(self, **kwargs):\n\n self.data = {}\n #node_data = ''\n #link_data = ''\n templates_data = self.request_from_server('templates')\n self.templates = templates_data\n project_data = self.request_from_server('projects')\n for project in project_data:\n project_name = project['name']\n if 'project_name' in kwargs:\n if project_name != kwargs['project_name']:\n continue\n\n self.data[project_name] = {}\n self.data[project_name]['project_id'] = project['project_id']\n self.data[project_name]['nodes'] = {}\n node_data = self.request_from_server('projects/{}/nodes'.format(project['project_id']))\n link_data = self.request_from_server('projects/{}/links'.format(project['project_id']))\n for node in node_data:\n node_name = node['name']\n self.data[project_name]['nodes'][node_name] = {}\n self.data[project_name]['nodes'][node_name]['node_id'] = node['node_id']\n self.data[project_name]['nodes'][node_name]['template_id'] = node['template_id']\n self.data[project_name]['nodes'][node_name]['node_type'] = node['node_type']\n self.data[project_name]['nodes'][node_name]['console_port'] = node['console']\n self.data[project_name]['nodes'][node_name]['console_session'] = None\n self.data[project_name]['nodes'][node_name]['x'] = node['x']\n self.data[project_name]['nodes'][node_name]['y'] = node['y']\n self.data[project_name]['nodes'][node_name]['ports'] = {}\n if project['status'] != 'closed':\n self.data[project_name]['nodes'][node_name]['status'] = node['status']\n for port in node['ports']:\n port_name = port['short_name']\n self.data[project_name]['nodes'][node_name]['ports'][port_name] = {}\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['adapter_number'] = port['adapter_number']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['port_number'] = port['port_number']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_type'] = port['link_type']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = None\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = False\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = None\n for link in link_data:\n for link_node in link['nodes']:\n if node['node_id'] == link_node['node_id']:\n if link_node['label']['text'] == port_name:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = link['link_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = True\n if link['nodes'].index(link_node) == 0:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][1]['node_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][1]['node_id'])\n else:\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][0]['node_id']\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][0]['node_id'])","def _parse_project_params(self, response_tree, data):\n # if self.project == 'TWC':\n # data['lithium'] = response_tree.find('lithiumresults').text\n\n if self.project == 'ILAR':\n data['ilar_title'] = response_tree.find('ilartitle').text\n data['ilar_rank'] = int(response_tree.find('ilarrank').text)\n\n if response_tree.find('ilarresponse').text is None:\n data['ilar_response'] = None\n else:\n data['ilar_response'] = response_tree.find(\n 'ilarresponse').text.encode('ascii', 'ignore')","def fetch_data(self) -> pd.DataFrame:\r\n os.chdir(r'\\\\192.168.8.90\\投研部\\Jessica\\test_data')\r\n if self.tic in ['RB.CCRI', 'HC.CCRI', 'I.CCRI', 'J.CCRI', 'JM.CCRI', 'ZC.CCRI']:\r\n f = pd.read_hdf('data.h5', 'snc')\r\n if self.tic in ['CU.CCRI', 'ZN.CCRI', 'AL.CCRI', 'NI.CCRI']:\r\n f = pd.read_hdf('data.h5', 'met')\r\n data = f.loc[f.loc[:, 'sec_code'] == self.tic, :]\r\n # extract I.CCRI data\r\n table = pd.pivot_table(data, index=['date'], columns=['factor_code'], values='factor_value')\r\n table = table.sort_values(by='date')\r\n \r\n return table","def yaml_parser_preprocess_pipeline_input_parameter_get_description_df(config):\n df= pandas.DataFrame.from_dict(config['query_system']['profile_description'], orient = 'index')\n df['query_protein_name'] = df.index\n return df","def extract_data():\n raw_data = pd.read_csv(\"../../../resource/DataVisualization/vaccinations.csv\")\n raw_data = raw_data[[\"location\", \"date\", \"people_fully_vaccinated_per_hundred\"]]\n raw_data.date = pd.to_datetime(raw_data.date, format=\"%Y-%m-%d\")\n min_date = raw_data.date.min()\n raw_data.date = raw_data.date-min_date\n raw_data.date = pd.Series([x.days for x in raw_data.date])\n raw_data.drop(raw_data.loc[raw_data.people_fully_vaccinated_per_hundred.isnull()].index,\n axis=0, inplace=True)\n raw_data[\"people_fully_vaccinated_per_hundred\"] /= 100\n\n data_dict = dict()\n for country in raw_data.location.unique():\n if len(raw_data.loc[raw_data.location == country]) >= 100:\n tmp_data = raw_data.loc[raw_data.location == country]\n tmp_data.drop(\"location\", axis=1, inplace=True)\n data_dict[country] = {\"data\":tmp_data}\n else:\n raw_data.drop(raw_data.loc[raw_data.location ==\n country].index, inplace=True)\n return data_dict, min_date, raw_data","def _createDataFromProject(self):\n project_dict = self._project.asDict()\n data_blocks = {}\n headers_blocks = {}\n for experiment_id, experiment_dict in project_dict['experiments'].items():\n data = []\n headers = []\n for data_id, data_list in experiment_dict['calculated'].items():\n headers.append(data_id)\n data.append(data_list)\n headers_blocks[experiment_id] = headers\n data_transposed = [*zip(*data)]\n data_blocks[experiment_id] = data_transposed\n return headers_blocks, data_blocks","def _createDataFromProject(self, project):\n data = []\n headers = []\n project_dict = project.asDict()\n for experiment_id, experiment_dict in project_dict['experiments'].items():\n for data_id, data_list in experiment_dict['measured'].items():\n headers.append(data_id)\n data.append(data_list)\n data_transposed = [*zip(*data)]\n return headers, data_transposed","def lifedata_hpi():\n\n # A constant that defines the record fields that we wish to retrieve.\n FIELDS = {\n '_id': False, 'Country': True, 'CountryCode': True, 'Life Expectancy': True, 'Well-being(0-10)': True, 'Happy Life Years': True, 'Happy Planet Index': True,\n 'Population': True, 'GDP/capita': True, 'Governance Rank(1 - highest gov.)': True\n }\n\n # Open a connection to MongoDB using a with statement such that the\n # connection will be closed as soon as we exit the with statement\n with MongoClient(MONGODB_HOST, MONGODB_PORT) as conn:\n # Define which collection we wish to access\n collection = conn[DBS_NAME][COLLECTION_NAME]\n # Retrieve a result set only with the fields defined in FIELDS\n # and limit the the results to 55000\n projects = collection.find(projection=FIELDS, limit=55000)\n # Convert projects to a list in a JSON object and return the JSON data\n return json.dumps(list(projects))","def xnat_workflow_info_show(args):\n\trequest_url = \"http://\" + args.server + \"/data/services/workflows/workflowid/\" + args.workflow_id + \"?format=json\"\n\tprint(\"xnat_workflow_info show: request_url: \" + request_url)\n\tresponse = requests.get(request_url, auth=(args.username, args.password))\n\tif (response.status_code != 200):\n\t\tprint(\"Cannot get response from request: \" + request_url)\n\t\tsys.exit(1)\n\n\tjson_response = json.loads(response.text)\n\tjson_items = json_response['items']\n\ti = 0\n\tfor json_item in json_items:\n\t\ti = i + 1\n\t\tprint i\n\n\t\t# meta\n\t\tjson_meta = json_item['meta']\n\t\tisHistory = json_meta['isHistory']\n\t\ttype = json_meta['xsi:type']\n\t\tstart_date = json_meta['start_date']\n\n\t\tprint \" isHistory: \" + str(isHistory)\n\t\tprint \" type: \" + type\n\t\tprint \" start_date: \" + start_date\n\t\n\t\t# children\n\t\t#json_children = json_item['children']\n\t\t#print \" children\"\n\t\t#print json_children\n\n\t\t# data_fields\n\t\tjson_data_fields = json_item['data_fields']\n\t\tstatus = json_data_fields['status']\n\t\tworkflow_id = json_data_fields['wrk_workflowData_id']\n\t\tdata_type = json_data_fields['data_type']\n\t\tlaunch_time = json_data_fields['launch_time']\n\t\tExternalID = json_data_fields['ExternalID']\n\t\tpipeline_name = json_data_fields['pipeline_name']\n\t\tID = json_data_fields['ID']\n\t\n\t\tprint \" status: \" + status\n\t\tprint \" workflow_id: \" + str(workflow_id)\n\t\tprint \" data_type: \" + data_type\n\t\tprint \" launch_time: \" + launch_time\n\t\tprint \" ExternalID: \" + ExternalID\n\t\tprint \" pipeline_name: \" + pipeline_name\n\t\tprint \" ID: \" + ID\n\n\t\tprint \" All Data Fields:\"\n\t\tprint \" \" + str(json_data_fields)","def _get_data(self):\n project_name, experiment_id = self.parent._get_parent_identifiers()\n\n self._data = self.repository.get_dataframe_data(\n project_name, self.id, experiment_id=experiment_id\n )","def get_details(i, df):\n print(\"Name:\", df.name.loc[i])\n print(\"Gender:\", df.gender.loc[i])\n print(\"Age in months:\", df.age.loc[i])\n print(\"Breed:\", df.breed1_desc.loc[i].title().replace(\"_\",\" \"),df.breed2_desc.loc[i].title().replace(\"_\",\" \"))\n print(\"Color/s:\", df.color1_desc.loc[i].title(), df.color2_desc.loc[i].title(), df.color3_desc.loc[i].title())\n print(\"Fur Length:\", df.fur_length.loc[i])\n print(\"Vaccinated:\", df.vaccinated.loc[i])\n print(\"Dewormed:\", df.dewormed.loc[i])\n print(\"Spayed or Neutered:\", df.sterilized.loc[i])\n print(\"Health:\", df.health.loc[i])\n print(\"No. of Pets in this Listing:\", df.quantity.loc[i])\n if df.fee.loc[i] == 0:\n print(\"Adoption Fee: FREE\")\n else:\n print(\"Adoption Fee: MYR\", round(df.fee.loc[i],2))\n print(\"Location :\", df.state_desc.loc[i].title().replace(\"_\",\" \"))\n print(\"Description :\", df.description.loc[i])\n print_images(i, df)","def main():\n\n # Instantiate the class\n my_data = DataProject(TRAIN_URL, TEST_URL, HEADER_URL)\n\n # Check which columns contain missing data\n print(my_data.get_na_cols('train_df'))\n\n # We see 'workclass', 'occupation', 'native-country' all have missing\n # values. However, these are all categorical columns. Let's try\n # seeing what we can from the numerical columns\n print(my_data.train_df.describe())\n\n \"\"\"see:\n age goes from 17-90, but doesn't look too surprising.\n fnlwgt is a weight that is given to each line to show how representative\n of the full population it is (at least, how representative of the\n state it was drawn from??).\n education-num is actually a classification of different categories of\n education, and maps one-to-one with that column. I will drop this next\n capital-gain and capital-loss -- these seem reasonable, but are highly\n skewed to the right (up to the 75% is 0). So let's take a look at that\n distribution to see what it looks like\n hours-per-week shows nothing too extrodinary, except that the max is\n 99 hours a week! If this person is doing this weekly, although it is\n do-able, this is incredible!\n \"\"\"\n \n # as we noted in our observation, education-num is just a numbering\n # of the education factors. So, remove, since it doesn't give any extra\n # information\n my_data.train_df.drop('education-num', axis=1, inplace=True)\n my_data.test_df.drop('education-num', axis=1, inplace=True)\n\n # Now, let's see if we can spot anything if we visualize the data.\n # Note, the scatter_matrix method grabs all numerical columns and shows\n # a nice grid of plots to make it easy to see the distribution of the\n # values in these columns, and any correlations that might exist between\n # columns.\n #\n # (PS, I realize if we are just executing the script directly, the plots\n # will just flash by, but I didn't want to clutter the HD with saved\n # images, so I assume you'll be following along this part by just running\n # the things in main() one by one)\n pd.plotting.scatter_matrix(my_data.train_df)\n plt.show()\n\n \"\"\"see:\n A good amount of binning of age and hours-per-week into discrete values\n (probably integer values for each). Also, perhaps an overabundance of\n people at age 90 and that work 99 hours a week, but these are probably\n individuals that go even higher, but were truncated at these values.\n Overall, nothing really out of the ordinary, except for in capital-gain,\n it looks like there is a big jump from most of the values up to 99999\n (nothing between ~50k to 99,999), with the vast majority of the\n values at 0 (makes sense, since if we look at the capital-gain vs\n capital-loss plot, it looks like we can only have non-zero values for\n one of those categories.) Let's try looking at captial-gain without\n the capital-loss individuals\n \"\"\"\n\n # Plot capital-gains without people that have a capital-loss (who are given\n # capital-gain of 0)\n my_data.train_df['capital-gain']\\\n [my_data.train_df['capital-gain'] != 0].hist()\n plt.show()\n\n \"\"\"see:\n True enough, it looks like there is an over-abundance of values for\n individuals with a capital-gain of 99,999. Let's have pandas calculate\n the quartiles to confirm.\n \"\"\"\n\n # Get the quartiles for the capital-gain column, excluding the capital-loss\n # individuals\n my_data.train_df['capital-gain']\\\n [my_data.train_df['capital-gain'] != 0].describe()\n\n \"\"\"Indeed, it looks like the 99,999 values are outliers, as the 75% of\n the non-0 values is at 14,084.\n \"\"\"\n\n # I was originally going to try to fit a gaussian distribution to the\n # histogram to re-assign the 99,9999 individuals based on it, but if\n # we take a look at:\n my_data.train_df['capital-gain'][\n (my_data.train_df['capital-gain'] != 0) &\n (my_data.train_df['capital-gain'] < 75000)].hist(bins=100)\n plt.show()\n\n # We see this is a tri-modal distribution (it'll be interesting to\n # see what makes up the groups at the 7-8 thousand range, and the group\n # at ~15 tousand).So, instead, replace with a value randomly picked from\n # the exact non-zero, non-99,9999 distribution. First, need to grab\n # these values\n good_val = my_data.train_df['capital-gain'][\n (my_data.train_df['capital-gain'] != 0) &\n (my_data.train_df['capital-gain'] < 99999)].values\n\n # Now, define a function that will return a random instance of\n # the good values\n def get_good_val(vals=good_val):\n \"\"\"quick helper function to allow easy re-assigning of the outlier\n values in the capital-gain column, preserving the non-zero\n distribution.\n\n Args:\n vals (np.ndarray): The non-zero, non-99,999 values from the\n capital-gain column\n\n Returns:\n int: A random value from the distribution\n \"\"\"\n\n # Return a value from the distribution randomly\n return vals[rand.randint(0, (vals.shape[0] - 1))]\n\n # Now, use the helper function to reset outlier values based on the\n # non-zero, non-outlier distribution\n # First, define a mask to save the conditions we want\n mask = (my_data.train_df['capital-gain'] == 99999)\n\n # Now, for these outlier values, reset them based on the good values\n # distribution\n my_data.train_df.loc[mask, 'capital-gain'] = (\n my_data.train_df[mask]['capital-gain'].apply(lambda x: get_good_val())\n )\n\n # Check the distribution that it looks ok\n my_data.train_df[my_data.train_df['capital-gain'] != 0]['capital-gain']\\\n .hist(bins=40)\n plt.show()\n\n # Looking at the test dataset:\n pd.plotting.scatter_matrix(my_data.test_df)\n plt.show()\n\n # It looks like we have a similar problem, so repeat the procedure\n # For the test set, using the test set's distribution\n good_val = my_data.test_df['capital-gain'][\n (my_data.test_df['capital-gain'] != 0) &\n (my_data.test_df['capital-gain'] < 99999)].values\n\n # Define the mask\n mask = (my_data.test_df['capital-gain'] == 99999)\n\n # Now, for these outlier values, reset them based on the good values\n # distribution\n my_data.test_df.loc[mask, 'capital-gain'] = (\n my_data.test_df[mask]['capital-gain'].apply(\n lambda x: get_good_val(good_val))\n )\n\n # Check the distribution that it looks ok\n my_data.test_df[my_data.test_df['capital-gain'] != 0]['capital-gain']\\\n .hist(bins=40)\n plt.show()","def generate_personnel_trend_graph(selected_funding, selected_year, selected_row_indices, rows, search_ids_personnel, search_ids_professors_keywords, personnel_name, search_keywords):\n\n names = []\n personnel = pd.DataFrame(rows)\n if selected_row_indices:\n names = personnel[\"Personnel\"].iloc[selected_row_indices]\n\n\n filtered_data = funding_data[\n funding_data.start_year.isin(selected_year)\n # & funding_data['Submitting Institution Name:'].isin(selected_uni)\n # & funding_data['Project Status:'].isin(selected_award_status)\n & funding_data['Program Cycle:'].isin(selected_funding)]\n\n\n selected_personnel_data = filtered_data.loc[filtered_data[\"Lead Investigator:\"].isin(names)]\n\n\n\n # Getting the projects of the selected names from personnel_data\n\n personnel_data_filtered = personnel_data\n personnel_data_filtered = personnel_data_filtered[\n personnel_data_filtered[\"Proposal Number:\"].isin(filtered_data[\"Proposal Number:\"])]\n\n personnel_data_filtered = personnel_data_filtered[personnel_data_filtered[\"investigator\"].isin(names)]\n\n # A pandas dataframe containg the list of projects for each selected personnel\n\n if search_keywords and len(search_keywords) > 0:\n if len(search_ids_professors_keywords) > 0:\n search_ids_professors_keywords = json.loads(search_ids_professors_keywords)\n search_ids_professors_keywords = np.array(search_ids_professors_keywords)\n\n # Using the dataset only to get relevant names of the personnel who are involved in the project\n # from personnel_data\n filtered_data = filtered_data[filtered_data._id.isin(search_ids_professors_keywords[0:, 0])]\n list_of_projects = filtered_data[\"Proposal Number:\"]\n personnel_data_filtered = personnel_data_filtered[\n personnel_data_filtered[\"Proposal Number:\"].isin(list_of_projects)]\n\n # print(\"Personnel Projects list is \", personnel_projects_list)\n # personnel_names = personnel_data_filtered[\"investigator\"].unique()\n # print(\"Personnel Who have worked in this field are \", personnel_names)\n\n personnel_projects_list = personnel_data_filtered.groupby('investigator')['Proposal Number:'].apply(list)\n print(\"Personnel Projects list is \", personnel_projects_list)\n\n\n\n\n # if personnel_name and len(personnel_name) > 0:\n # if len(search_ids_personnel) > 0:\n # # Using the dataset only to get relevant names of the personnel who are involved in the project\n # # from personnel_data\n #\n # personnel_data_filtered = personnel_data_filtered[personnel_data_filtered._id.isin(search_ids_personnel)]\n #\n # # Filtering the datasets based on search ids and filtered_data\n # # Find a better way to do filters by join?\n #\n # filtered_data = filtered_data[\n # filtered_data[\"Proposal Number:\"].isin(personnel_data_filtered[\"Proposal Number:\"])]\n #\n # personnel_projects_list = personnel_data_filtered[[\"investigator\", \"Proposal Number:\"]].groupby(\n # 'investigator').agg({\"Proposal Number:\": lambda x: [].append(x)})\n #\n # print(\"Personnel Projects list is \", personnel_projects_list)\n # personnel_names = personnel_data_filtered[\"investigator\"].unique()\n # print(\"Personnel Who have worked in this field are \", personnel_names)\n\n\n def make_project_count_dict(df):\n \"\"\"\n From the DF that is passed ( containing one personnel with all his projects, calculates the number of years a\n project was active,\n :param df:\n :return: An Ordered Dictionary with the number of projects active per year for that personnel.\n \"\"\"\n year_lists = []\n null_date_info_projects = 0\n for i in range(len(df)):\n start_date = df[\"Start Date:\"].iloc[i]\n end_date = df[\"End Date:\"].iloc[i]\n if (start_date == -1) or (end_date == -1):\n null_date_info_projects += 1\n continue\n year_lists.append(list(range(start_date.year, end_date.year + 1))) # +1 because the project is active that year. It needs to show on graph\n print(year_lists)\n year_count_dict = OrderedDict.fromkeys(range(2000, datetime.now().year + 5), 0)\n print(year_count_dict)\n for i in year_lists:\n for j in i:\n year_count_dict[j] += 1\n return year_count_dict, null_date_info_projects\n\n def make_traces(names, personnel_projects_list):\n traces = []\n null_date_info_projects = 0\n for i in names:\n list_of_projects = personnel_projects_list.loc[i]\n # list_of_projects = personnel_projects_list[\"Proposal Number:\"].loc[personnel_projects_list[\"investigator\"] == i]\n list_of_project_records = filtered_data.loc[filtered_data[\"Proposal Number:\"].isin(list_of_projects)] # Passing the DF containing all the projects that a personnel was involved with\n personnel_year_count_dict, null_date_info_projects = make_project_count_dict(list_of_project_records)\n sample_trace_object = go.Scatter(\n x=list(personnel_year_count_dict.keys()),\n y=list(personnel_year_count_dict.values()),\n opacity=0.7,\n # Adding the null_date_info_projects into the name\n name=i +\" \"+ str(null_date_info_projects),\n line=dict(\n # color=('rgb(22, 96, 167)'),\n width=4,\n shape='hv'\n # colorscale='YlGnBu'\n ),\n mode='lines+markers'\n )\n traces.append(sample_trace_object)\n return traces\n\n traces = make_traces(names, personnel_projects_list)\n\n\n # years = [i for i in range(2008,2018)]\n # numbers = random.choices(population=[0, 1, 2, 3], k=len(years))\n\n # personnel = \"Doctor Name\"\n\n # trace1 = go.Scatter(\n # x=years,\n # y=numbers,\n # name='Professor Name Here',\n # line=dict(\n # color=('rgb(22, 96, 167)'),\n # width=4,\n # ),\n # mode='lines+markers'\n # )\n\n\n return {\n 'data': traces,\n 'layout': go.Layout(\n # autosize=False,\n title='Projects in a year for selected personnel',\n xaxis=dict(\n # tickangle=-45,\n zeroline=False, # no thick y=0 line\n showgrid=False, # no horizontal grid lines\n showticklabels=True, # no y-axis tick labels\n dtick=1,\n ),\n yaxis=dict(\n # type='log'\n dtick=1,\n ),\n showlegend=True,\n # legend=go.Legend(\n # x=0,\n # y=1.0\n # ),\n # legend=dict(x=.75, y=1),\n # bargap =0.5,\n # width = 100,\n # autosize=F,\n margin=go.layout.Margin(l=40, r=0, t=30, b=40),\n # paper_bgcolor='rgb(233,233,233)', # set paper (outside plot)\n plot_bgcolor='rgb(192,192,192)', # plot color to grey\n )\n }","def _deep_data(self, url):\n def _nested_persons(persons):\n _persons = list()\n for person_ in persons:\n person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]\n person = {'name': person_[0][0].title()}\n if len(person_[0]) == 2:\n person['age'] = person_[0][1]\n\n if len(person_[1]) > 0:\n person['addressLocality'] = person_[1][0].title()\n if len(person_[1]) == 2:\n person['addressRegion'] = person_[1][1].upper()\n\n _persons.append(person)\n return _persons\n\n with self.driver(self.DRIVER_DIR) as driver:\n driver.get(url)\n driver.fullscreen_window()\n time.sleep(2)\n txt = driver.page_source\n\n soup = bs(txt, 'html.parser')\n\n profile_data = soup.find(type=\"application/ld+json\")\n if profile_data is None:\n self._raise_site_schema_change()\n profile_data = profile_data.string\n profile_data = json.loads(profile_data, strict=False)\n profile_data['@id'] = profile_data.pop('@id').split('/')[-1]\n\n try:\n about = profile_data.pop('about')\n for k, v in about.items():\n profile_data[k] = v\n except KeyError:\n pass\n\n name_ = profile_data.pop('name')\n profile_data['name'] = name_\n\n name_ = name_.split()\n profile_data['givenName'] = name_[0]\n profile_data['middleName'] = ' '.join(name_[1:-1])\n profile_data['familyName'] = name_[-1]\n\n if soup.find(class_='rep-vcard-score') is not None:\n profile_data['reputation_score'] = \"{min}-{max}\".format(\n min=soup.find(class_='rep-vcard-min').text,\n max=soup.find(class_='rep-vcard-max').text\n )\n\n address = list()\n address_ = soup.find_all(class_='card-address')\n for a in address_:\n street_address, locality_region_postal, *misc = [_.text for _ in a.find_all(class_='block-container')]\n address_locality, locality_region_postal = locality_region_postal.split(',')\n address_region, postal_code = locality_region_postal.split()\n address.append({\n 'streetAddress': street_address,\n 'addressLocality': address_locality,\n 'addressRegion': address_region,\n 'postalCode': postal_code,\n })\n\n profile_data['address'] = address\n\n personal_details = soup.find(class_='card-personal-details')\n if personal_details is not None:\n personal_details = personal_details.find_all(class_='item-container')\n personal_details = [detail.text.split(': ') for detail in personal_details]\n personal_details = [_ for _ in personal_details if len(_) == 2]\n personal_details = {detail.lower().replace(' ', '_'): value for\n detail, value in personal_details if value != 'Add Info'}\n\n birth_date = personal_details.pop('date_of_birth')\n if len(birth_date) > 0:\n profile_data['birthDate'] = birth_date\n\n for key_, value_ in personal_details.items():\n profile_data[key_] = value_\n\n # Education\n schools_ = soup.find(class_='card-education')\n if schools_ is not None:\n schools = list()\n schools_ = schools_.find_all(class_='card-content')\n for school in schools_:\n school = [detail.text.split(': ') for detail in school.find_all(class_='item-container')]\n school = {detail.lower().replace(' ', '_'): value for\n detail, value in school if value != 'Add Info'}\n\n if len(school) == 0:\n continue\n\n school['@type'] = 'EducationalOrganization'\n school['name'] = school.pop('school')\n school['streetAddress'], school['addressLocality'] = school.pop('city').split(', ')\n schools.append(school)\n\n # Work\n employers = soup.find(class_='card-job')\n if employers is not None:\n works_for = list()\n employers = employers.find_all(class_='card-content')\n for employer in employers:\n employer = [detail.text.split(': ') for detail in employer.find_all(class_='item-container')]\n employer = {detail.lower().replace(' ', '_'): value for\n detail, value in employer if value != 'Add Info'}\n\n if len(employer) == 0:\n continue\n\n employer['@type'] = 'Organization'\n try:\n employer['name'] = employer.pop('company')\n except KeyError:\n continue\n\n if len(employer.get('city', '')) > 0:\n employer['streetAddress'], employer['addressLocality'] = employer.pop('city').split(', ')\n\n works_for.append(employer)\n\n if len(works_for) > 0:\n profile_data['worksFor'] = works_for\n\n # Automobiles\n automobiles = soup.find(class_='card-auto')\n if automobiles is not None:\n owns = list()\n automobiles = automobiles.find_all(class_='card-content')\n for automobile in automobiles:\n automobile = [detail.text.split(': ') for detail in automobile.find_all(class_='item-container')]\n automobile = {detail.lower().replace(' ', '_'): value for\n detail, value in automobile if value != 'Add Info'}\n\n if len(automobile) == 0:\n continue\n\n automobile['@type'] = 'Product'\n automobile['model'] = ' '.join([\n automobile.pop('year'),\n automobile.pop('make'),\n automobile.pop('model')\n ])\n owns.append(automobile)\n\n if len(owns) > 0:\n profile_data['owns'] = owns\n\n profile_data['relatedTo'] = _nested_persons(soup.find_all(class_='relative-container'))\n profile_data['neighbors'] = _nested_persons(soup.find_all(class_='neighbor-container'))\n\n # Photos\n profile_data['pictures'] = list({photo['src'] for photo in soup.find_all(class_='profile-picture-holder')})\n return profile_data","def get_data():\n \n \"\"\" Prepare variables\"\"\"\n urls = {\"cases\": \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv\",\n \"deaths\": \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv\",\n \"recovered\": \"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv\"}\n\n localnames = {\"cases\": \"Cases.csv\",\n \"deaths\": \"Deaths.csv\",\n \"recovered\": \"Recovered.csv\"}\n\n dfs = {\"cases\": None,\n \"deaths\": None,\n \"recovered\": None}\n\n \"\"\" Download\"\"\"\n for key in urls.keys():\n url = urls[key]\n localname = localnames[key]\n urllib.request.urlretrieve(url, localname)\n\n \"\"\" Load variables\"\"\"\n for key in dfs.keys():\n dfs[key] = pd.read_csv(localnames[key])\n \n \"\"\" Return\"\"\"\n return(dfs)","def get_individual_df(nombre):\n clusters = []\n contadores = []\n for k, v in mydic[nombre][\"contador\"].items():\n clusters.append(k)\n contadores.append(v)\n return pd.DataFrame({\"CODIGO_POSTAL\": clusters, f\"contadores_{nombre}\": contadores})","def get_metadata(datas):\n\n required_profile_key, required_item_key = 'basics', 'category'\n utility_matrix = []\n\n item_details, profile_details = {}, {}\n\n for _ , data in datas.items():\n profile_name, item_name = None, None\n\n # extracted profile data for user, because two user may have the same nick name\n # so we will concatenate user nick name and its profile link to form the unique one.\n if type(data) is dict and required_profile_key in data.keys() :\n profile_name = \"%s|%s\" % (data[required_profile_key].get('name',''),\n data[required_profile_key].get('profile',''))\n\n profile_details[profile_name] = get_profile_detail(data)\n print ('extracted data of profile: %s ...' % data[required_profile_key].get('name',''))\n\n # for item\n if type(data) is dict and required_item_key in data.keys():\n if hasattr(data[required_item_key],'items'):\n for k,v in data[required_item_key].items():\n\n item_detail = get_item_detail(v)\n print ('extracted data for category %s ...' % k)\n\n #item_detail_to_str = json.dumps(item_detail) #\" ; \".join(list(set(item_detail)))\n if k in item_details:\n item_details[k]['work'].append(item_detail['work'])\n item_details[k]['skill'].append(item_detail['skill'])\n else:\n item_details[k] = {}\n item_details[k]['work'] = [item_detail['work']]\n item_details[k]['skill'] = [item_detail['skill']]\n\n utility_matrix.append({\n 'profile': profile_name,\n 'item': k,\n 'rating':v['point']\n })\n\n return utility_matrix, \\\n {k:{'work':json.dumps(v['work']),'skill':json.dumps(v['skill'])} for k,v in item_details.items()}, \\\n profile_details","def parse_info(df, idx, root):\n df_row = df.iloc[idx]\n\n img_id = str(df_row['id'])\n img_name = id_to_path(root, img_id)\n landmark_id = df_row.landmark_id\n return img_name, landmark_id","def read_json_stats(url, parameters, tunnus):\r\n \r\n\r\n import requests \r\n from pyjstat import pyjstat\r\n from collections import OrderedDict\r\n import pandas as pd\r\n \r\n \r\n #query from JSON to be able to flatten data to one level\r\n col_list=[]\r\n for j in parameters[\"query\"]:\r\n col_list.append(j['code'])\r\n \r\n \r\n col_list.remove(tunnus)\r\n\r\n response = requests.post(url, json=parameters)\r\n if response.status_code == 200:\r\n try:\r\n json_data = response.json(object_pairs_hook=OrderedDict)\r\n except:\r\n print('Json error !!!!')\r\n return\r\n else:\r\n print('Error: Status code: {}'.format(response.status_code))\r\n return\r\n \r\n results = pyjstat.from_json_stat(json_data)\r\n stat=pd.DataFrame(results[0])\r\n stat = pd.pivot_table(stat, values='value', index=[tunnus],columns=col_list)\r\n stat.fillna(0,inplace=True)\r\n stat.reset_index(level=[0], inplace=True)\r\n if len(col_list) > 1:\r\n stat.columns = stat.columns.map(' '.join)\r\n stat.columns = stat.columns.str.strip()\r\n return(stat)","def precipitation():\n\n return jsonify(prcp_df)","def _info(self) -> tfds.core.DatasetInfo:\n features = tfds.features.FeaturesDict({\n \"tokens\":\n tfds.features.Sequence(tfds.features.Text()),\n \"tags\":\n tfds.features.Sequence(\n tfds.features.ClassLabel(names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ])),\n \"langs\":\n tfds.features.Sequence(tfds.features.Text()),\n \"spans\":\n tfds.features.Sequence(tfds.features.Text()),\n })\n return tfds.core.DatasetInfo(\n builder=self,\n description=_DESCRIPTION,\n features=features,\n supervised_keys=None,\n homepage=\"https://github.com/afshinrahimi/mmner\",\n citation=_CITATION,\n )","def get_task_flow_data(jeditaskid):\n data = []\n # get datasets\n datasets = []\n dquery = {'jeditaskid': jeditaskid, 'type__in': ['input', 'pseudo_input'], 'masterid__isnull': True}\n datasets.extend(JediDatasets.objects.filter(**dquery).values('jeditaskid', 'datasetname', 'type'))\n\n dataset_dict = {}\n for d in datasets:\n dname = d['datasetname'] if ':' not in d['datasetname'] else d['datasetname'].split(':')[1]\n dataset_dict[dname] = {'replica': {}, 'jobs': {}}\n\n # get jobs aggregated by status, computingsite and proddblock (input dataset name)\n jobs = []\n jquery = {'jeditaskid': jeditaskid, 'prodsourcelabel__in': ['user', 'managed'], }\n extra_str = \"( processingtype not in ('pmerge') )\"\n jvalues = ['proddblock', 'computingsite', 'jobstatus']\n jobs.extend(Jobsarchived4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\n jobs.extend(Jobsarchived.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\n jobs.extend(Jobsactive4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\n jobs.extend(Jobsdefined4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\n jobs.extend(Jobswaiting4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\n\n if len(jobs) > 0:\n for j in jobs:\n if len(j['proddblock']) > 0:\n dname = j['proddblock'] if ':' not in j['proddblock'] else j['proddblock'].split(':')[1]\n else:\n dname = next(iter(dataset_dict)) if len(dataset_dict) > 0 else 'pseudo_dataset'\n if j['computingsite'] is not None and j['computingsite'] != '':\n if j['computingsite'] not in dataset_dict[dname]['jobs']:\n dataset_dict[dname]['jobs'][j['computingsite']] = {}\n job_state = j['jobstatus'] if j['jobstatus'] in const.JOB_STATES_FINAL else 'active'\n if job_state not in dataset_dict[dname]['jobs'][j['computingsite']]:\n dataset_dict[dname]['jobs'][j['computingsite']][job_state] = 0\n dataset_dict[dname]['jobs'][j['computingsite']][job_state] += j['njobs']\n\n # get RSE for datasets\n replicas = []\n if len(datasets) > 0:\n dids = []\n for d in datasets:\n if d['type'] == 'input':\n did = {\n 'scope': d['datasetname'].split(':')[0] if ':' in d['datasetname'] else d['datasetname'].split('.')[0],\n 'name': d['datasetname'].split(':')[1] if ':' in d['datasetname'] else d['datasetname'],\n }\n dids.append(did)\n\n rw = ruciowrapper()\n replicas = rw.getRSEbyDID(dids)\n\n if replicas is not None and len(replicas) > 0:\n for r in replicas:\n if r['name'] in dataset_dict:\n dataset_dict[r['name']]['replica'][r['rse']] = {\n 'state': r['state'],\n 'available_pct': round(100.0 * r['available_length']/r['length'], 1) if r['length'] > 0 else 0\n }\n\n # transform data for plot and return\n return executeTF({'data': {'datasets': dataset_dict, } })","def print_record_project_count(dataframe, dataset=\"full\"):\n if dataset == \"full\":\n print(\n \"For the ORIGINAL cleansed data, containing all available NYC capital \"\n \"projects change records:\\n\"\n )\n\n elif dataset == \"all\":\n print(\n \"For the data containing start and end data for all available \"\n \"NYC capital projects for the ENTIRE INTERVAL of changes \"\n \"covered in the ORIGINAL data:\\n\"\n )\n\n else:\n print(\n \"For the final {} data, containing the {} split of 3-year \"\n \"project data used in this analysis:\\n\".format(\n dataset.upper(), dataset\n )\n )\n\n # entries\n print(f\"\\tNumber of dataset records: {len(dataframe)}\")\n\n # num projects\n print(\n f\"\\tNumber of unique projects in dataset: {dataframe['PID'].nunique()}\\n\"\n )","def define_info_dict():\n\n d = {\n \"PRED\": {\n \"COLUMN\": [\"predicted_class\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Predicted class: somatic, germline, artifact\",\n },\n \"PROB\": {\n \"COLUMN\": [\"prob_s\", \"prob_g\", \"prob_a\"],\n \"Number\": \"3\",\n \"Type\": \"Float\",\n \"Description\": \"Prediction probability of \"\n \"being somatic, germline, artifact in this order\",\n },\n \"SNP\": {\n \"COLUMN\": [\"is_on_db\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\",\n },\n \"ANNO\": {\n \"COLUMN\": [\"annotation\"],\n \"Number\": \".\",\n \"Type\": \"String\",\n \"Description\": \"Indel annotation formatted as \"\n \"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\"\n \"Delimited by comma for multiple isoforms\",\n },\n \"COSMIC_CNT\": {\n \"COLUMN\": [\"cosmic_cnt\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"COSMIC count in v89\",\n },\n \"MAXMAF\": {\n \"COLUMN\": [\"max_maf\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Maximum minor allele frequency (MAF) \"\n \"reported in dbSNP, ClinVar and gnomAD non-cancer population\",\n },\n \"COMMON\": {\n \"COLUMN\": [\"is_common\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Common in dbSNP or MAXMAF > 0.01\",\n },\n \"CLIN\": {\n \"COLUMN\": [\"clin_info\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"ClinVar annotation formatted as ClinicalSignificance|Condition\",\n },\n \"ICP\": {\n \"COLUMN\": [\"indel_complexity\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel complexity: mismatches around the indel measured by edit distance\",\n },\n \"DSM\": {\n \"COLUMN\": [\"dissimilarity\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Dissimilarity: edit distance between indel and flanking sequences\",\n },\n \"ISZ\": {\n \"COLUMN\": [\"indel_size\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Indel size\",\n },\n \"REP\": {\n \"COLUMN\": [\"repeat\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Repeat: count of the indel-sequence repeats in flanking region\",\n },\n \"UQM\": {\n \"COLUMN\": [\"is_uniq_mapped\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by uniquely mapped reads\",\n },\n \"NEB\": {\n \"COLUMN\": [\"is_near_boundary\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Near exon boundary\",\n },\n \"EQX\": {\n \"COLUMN\": [\"equivalence_exists\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Equivalent alignments exist for the indel\",\n },\n \"BID\": {\n \"COLUMN\": [\"is_bidirectional\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Supported by forward and reverse reads\",\n },\n \"MTA\": {\n \"COLUMN\": [\"is_multiallelic\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Multialleleic\",\n },\n \"FRM\": {\n \"COLUMN\": [\"is_inframe\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"In-frame indel\",\n },\n \"SPL\": {\n \"COLUMN\": [\"is_splice\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in splice region\",\n },\n \"TRN\": {\n \"COLUMN\": [\"is_truncating\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Truncating indel\",\n },\n \"CDD\": {\n \"COLUMN\": [\"is_in_cdd\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Located in conserved domain\",\n },\n \"LOC\": {\n \"COLUMN\": [\"indel_location\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Relative indel location within the transcript coding region\",\n },\n \"NMD\": {\n \"COLUMN\": [\"is_nmd_insensitive\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insensitive to nonsense mediated decay\",\n },\n \"IPG\": {\n \"COLUMN\": [\"ipg\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Indels per gene\",\n },\n \"LEN\": {\n \"COLUMN\": [\"cds_length\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Coding sequence length. Median value if multiple isoforms exist\",\n },\n \"LC\": {\n \"COLUMN\": [\"lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Linguistic complexity: diversity of k-mers in flanking 50-bp region\",\n },\n \"LLC\": {\n \"COLUMN\": [\"local_lc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\",\n },\n \"GC\": {\n \"COLUMN\": [\"gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"GC-content in flanking 50-bp region\",\n },\n \"LGC\": {\n \"COLUMN\": [\"local_gc\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local GC-content in flanking 6-bp region\",\n },\n \"SG\": {\n \"COLUMN\": [\"strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"DNA bond strength of 2-mers in flanking 50-bp region\",\n },\n \"LSG\": {\n \"COLUMN\": [\"local_strength\"],\n \"Number\": \"1\",\n \"Type\": \"Float\",\n \"Description\": \"Local DNA bond strength of 2-mers in flanking 6-bp region\",\n },\n \"INS\": {\n \"COLUMN\": [\"is_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Insertion\",\n },\n \"ATI\": {\n \"COLUMN\": [\"is_at_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of A or T\",\n },\n \"ATD\": {\n \"COLUMN\": [\"is_at_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of A or T\",\n },\n \"GCI\": {\n \"COLUMN\": [\"is_gc_ins\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single insertion of G or C\",\n },\n \"GCD\": {\n \"COLUMN\": [\"is_gc_del\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Single deletion of G or C\",\n },\n \"ALTC\": {\n \"COLUMN\": [\"alt_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Alt count: count of unique reads supporting ALT allele\",\n },\n \"REFC\": {\n \"COLUMN\": [\"ref_count\"],\n \"Number\": \"1\",\n \"Type\": \"Integer\",\n \"Description\": \"Ref count: count of unique reads supporting REF allele\",\n },\n \"RCF\": {\n \"COLUMN\": [\"reclassified\"],\n \"Number\": \"0\",\n \"Type\": \"Flag\",\n \"Description\": \"Reclassification applied\",\n },\n \"RQB\": {\n \"COLUMN\": [\"filtered\", \"rescued\"],\n \"Number\": \"1\",\n \"Type\": \"String\",\n \"Description\": \"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\",\n },\n }\n\n return d","def job_data(driver):\n job_info = {\n \"job_title\" : \"h1.title\",\n \"company\" : \"span.company\",\n \"location\" : \"h3.location\",\n \"employment_type\" : \"div.employment div.content div.rich-text\",\n \"industry\" : \"div.industry div.content div.rich-text\",\n \"experience\" : \"div.experience div.content div.rich-text\",\n \"job_function\" : \"div.function div.content div.rich-text\",\n \"description\" : \"div.summary div.content div.description-section div.rich-text\"\n }\n # click the 'read more' button to reveal more about the job posting\n try:\n driver.find_element_by_css_selector(\"button#job-details-reveal\").click()\n except Exception as e:\n print(\"error in attempting to click 'reveal details' button\")\n print(e)\n for key, selector in job_info.items():\n try:\n job_info[key] = driver.find_element_by_css_selector(selector).text\n except Exception as e:\n job_info[key] = \"\"\n pass\n return job_info","def get_infos(self):\n infos = dict()\n infos['dataset'] = self._dataset_name()\n infos['task'] = self.task\n if self.task == 'sep_clean':\n data_license = [librispeech_license]\n else:\n data_license = [librispeech_license, wham_noise_license]\n infos['licenses'] = data_license\n return infos","def get_data(self):\n\n all_data = OrderedDict()\n projects = [Path(proj) for proj in glob(str(self.data_path.joinpath(\"*\"))) if Path(proj).is_dir()]\n\n for project in projects:\n files = []\n \n # Read all csv files and save them as a list in files\n for ver in glob(str(project.joinpath(\"*.csv\"))):\n files.extend(pd.read_csv(ver, usecols=['time', 'buggy']).values.tolist())\n \n # Create a pandas dataframe from the csv sorted by datetime\n df = pd.DataFrame(files, columns=['Time', 'Bugs']).sort_values(by='Time').reset_index(drop=True)\n \n # Convert time to Pandas DateTime format\n df['Time'] = pd.to_datetime(df['Time']) \n \n # Group bug counts by week starting on monday\n df = df.reset_index().set_index('Time').groupby(\n [pd.Grouper(freq='W-MON')])[\"Bugs\"].sum().astype(int).reset_index()\n \n df = df.set_index('Time')\n # Save the data to dictionary\n all_data.update(OrderedDict({project.name: df}))\n\n return all_data","def get_doctor_info(url, html):\n \n # create a dictionary to save data\n doctor_info = {}\n \n # parsed html by using lxml\n # In the following processing\n # the one ending in org is the raw data in the HTML\n # the one ending in inf is the processed data by extracting from raw data\n select = etree.HTML(html)\n \n # part 1: get basic information about your doctor\n # attribute: Name, Title, Hospital, Department\n name_org = select.xpath('//div[@class=\"profile-text\"]//h1[@class=\"doctor-name\"]//text()')\n name_inf = name_org[0].strip()\n \n title_org = select.xpath('//div[@class=\"profile-text\"]//span[@class=\"positon\"]//text()')\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\n title_inf = ' '.join(title_inf)\n \n hospital_department_org = select.xpath('//div[@class=\"profile-text\"]//p[@class=\"doctor-faculty\"]//text()')\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\n hospital_inf = hospital_department_inf[0]\n department_inf = hospital_department_inf[1]\n \n doctor_info['姓名'] = name_inf\n doctor_info['职称'] = title_inf\n doctor_info['医院'] = hospital_inf\n doctor_info['科室'] = department_inf\n \n # part2: get header format data\n org = select.xpath('//div[@class=\"profile-sta\"]//text()')\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\n for i in range(len(inf)//2):\n doctor_info[inf[2*i]] = inf[2*i + 1]\n \n \n # part3: get sidebar format data\n org_1 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-label\"]//text()')\n org_2 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-data\"]//text()')\n for i in range(len(org_1)):\n doctor_info[org_1[i][:-1]] = org_2[i]\n \n # part4: get body format data\n honour_org = select.xpath('//div[@class=\"honour-header\"]//text()')\n honour_inf = ''.join([i.strip() for i in honour_org])\n \n honour_detail_org = select.xpath('//li[@class=\"honour-title\"]//text()')\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\n honour_detail_inf = ' '.join(honour_detail_inf)\n \n satisfaction_org = select.xpath('//div[@class=\"item-body\"]//div[@class=\"satisfaction clearfix\"]//i[@class=\"sta-num\"]//text()')\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\n \n resume_org = select.xpath('//div[@class=\"good-at-text\"]//text()')\n resume_inf = [i.strip() for i in resume_org]\n if len(resume_inf) <= 20:\n resume_inf = ''.join(resume_inf)\n resume_inf = ''.join(resume_inf[:20])\n \n star_org = select.xpath('//div[@class=\"experience-row clearfix\"]//span[@class=\"experience-label\"]//text()')\n star_inf = 1 if len(star_org) >= 1 else 0\n\n doctor_info['好大夫届数'] = honour_inf\n doctor_info['好大夫具体年份'] = honour_detail_inf\n doctor_info['简历'] = resume_inf \n doctor_info['诊后服务星'] = star_inf\n try:\n doctor_info['疗效满意度'] = satisfaction_inf[0]\n doctor_info['态度满意度'] = satisfaction_inf[1]\n except:\n pass\n \n # part5: personal url\n personal_url = url\n doctor_info['医生个人链接'] = personal_url\n \n return doctor_info","def get_data(self):\n\n # Import json data into a pandas dataframe\n df = pd.read_json(\"./nutrients.json\", lines=True)\n\n return df","def _gather_deep_data(self):\n\n cleaned_data_from_website = list()\n\n for i, search_result in self.data_from_website.iterrows():\n cleaned_data_from_website.append(self._deep_data(search_result.url))\n\n cleaned_data_from_website = pd.DataFrame(cleaned_data_from_website)\n if len(cleaned_data_from_website) == 0:\n cleaned_data_from_website['@id'] = '0'\n cleaned_data_from_website.set_index('@id', inplace=True)\n self.data_from_website = cleaned_data_from_website","def get_infos(self):\n infos = dict()\n infos[\"dataset\"] = self.dataset_name\n infos[\"task\"] = \"separate_noisy\"\n infos[\"licenses\"] = [librispeech_license, tac_license]\n return infos","def _91_render_plant_data_to_projects(records, **params):\n for plant_record in [record for record in records if record[\"Type\"] == \"Plant\"]:\n key = (plant_record[\"Power Plant Name\"], plant_record[\"Project Name\"])\n project_records = [\n record\n for record in records\n if record[\"Type\"] == \"Project\"\n and record[\"Power Plant Name\"] == plant_record[\"Power Plant Name\"]\n ]\n if len(project_records) == 0:\n log.error(\"NO PROJECT RECORDS FOR %s\" % (key,))\n for project_record in project_records:\n for field in plant_record.keys():\n if project_record.get(field) in [None, '']:\n project_record[field] = plant_record[field]\n return records","def queryFlywheel(project):\n\n # Create info dict with entries for each subject.\n info = dict()\n\n # Loop through subjects in project\n #for sub in subjects:\n for sub in project.subjects():\n\n # Loop through sessions in subject\n for ses in sub.sessions():\n ses = ses.reload()\n\n # Loop through acquisitions in session\n for acq in ses.acquisitions():\n acq = acq.reload()\n\n # Loop through files in acquisition\n for f in acq.files:\n \n # Skip over non-nifti files\n if f.type != 'nifti':\n next\n\n # Get Flywheel fileId to use as unique identifier\n fileId = f.id\n\n # Try to get timestamp (sometimes DateTime field isn't present.) \n try:\n timestamp = f.info['AcquisitionDateTime']\n except KeyError:\n try:\n timestamp = f.info['AcquisitionDate']\n # Set to None if field isn't present\n except:\n timestamp = pd.NaT\n \n # Try to get series number (sometimes field isn't present.) \n try:\n seriesNum = f.info['SeriesNumber']\n # Set to None if field isn't present\n except:\n np.NaN \n # Add the folowing metadata to study info dict:\n # fileID: [subId, sesId, acqLabel, fileName, seriesNum, timestamp]\n info[fileId] = [sub.label, ses.label, acq.label, f.name, seriesNum, timestamp]\n \n # Return project info dict\n return info","def get_org_projects_info(org_link):\n response = get_response(org_link)\n if response.ok:\n soup = BeautifulSoup(response.text, 'html.parser')\n projects_li = soup.find_all(\n 'li', attrs={'layout': True}\n )\n project_info = []\n for proj_html in projects_li:\n proj_info = {}\n proj_title = proj_html.get('aria-label').replace('\\n', '')\n proj_desc = proj_html.find(\n 'div', attrs={'class': 'archive-project-card__content'}).text.replace('\\t', '')\n proj_relative_link = proj_html.select('a')[0].get('href')\n proj_full_link = HOME_PAGE + proj_relative_link\n proj_info['title'] = proj_title\n proj_info['description'] = proj_desc\n proj_info['link'] = proj_full_link\n project_info.append(proj_info)\n return project_info","def extract_data(self, root, path, tag):\n data = []\n element = root.xpath(path)\n if element:\n url = self.PODEROPEDIA_BASE_URL + element[0].get('data-w2p_remote', None)\n if url:\n self.logger.debug('Querying {} from {}'.format(tag, url))\n try:\n response = self.session.get(url)\n response.raise_for_status()\n content = response.content\n html_tree = etree.HTML(content, parser=self.parser)\n if html_tree is None:\n return data\n rows = html_tree.xpath('.//*[starts-with(@id, \"collapse\")]/div/table/tr')\n for row in rows:\n target = target_name = target_path = relationship = None\n when = where = where_name = where_path = source = None\n row_id = row.get('id', '')\n cells = row.getchildren()\n idx = 0\n while idx < len(cells) - 1:\n try:\n cell_text = text_strip(cells[idx])\n except AttributeError:\n cell_text = ''\n sources = cells[idx].xpath('.//*[@class=\"fuente\"]')\n if len(sources) > 0:\n source = process_sources(cells[idx])\n elif cell_text == 'es' or cell_text == 'fue':\n when = cell_text\n idx = idx - 1\n target = cells[idx].find('a')\n if target is not None:\n target_path = target.get('href', None)\n target_name = text_strip(target)\n idx = idx + 2\n relationship = text_strip(cells[idx])\n elif cell_text == 'a' or cell_text == 'de':\n idx = idx - 1\n relationship = text_strip(cells[idx])\n idx = idx + 2\n target = cells[idx].find('a')\n if target is not None:\n target_path = target.get('href', None)\n target_name = text_strip(target)\n elif cell_text.startswith('desde'):\n when = cell_text\n elif 'es pasado' in cell_text:\n when = cell_text\n else:\n try:\n ignore = int(cell_text)\n when = cell_text\n except ValueError:\n potential_date = cell_text.split(' ')[0]\n try:\n ignore = datetime.strptime(potential_date, '%d-%m-%Y')\n when = cell_text\n except ValueError:\n try:\n ignore = datetime.strptime(potential_date, '%m-%Y')\n when = cell_text\n except ValueError:\n pass\n idx = idx + 1\n entry = {\n 'type': tag,\n 'target_path': target_path,\n 'relationship': relationship,\n 'when': when,\n 'where': where,\n 'source': source\n }\n data.append(entry)\n self.logger.debug('{}: {}'.format(tag, entry))\n except (requests.exceptions.HTTPError, etree.ParserError):\n self.logger.info('Something bad happened', exc_info=True)\n return data","def getMonthlyPRinfo(df):\n\n new_df = df.filter(['Merged_YM', 'node.title', 'node.url'], axis=1)\n new_df.groupby('Merged_YM')\n new_df.to_csv('PR_Info_Monthly.csv', index=False)","def fetch_game_info(df: pd.DataFrame) -> pd.DataFrame:\n data_dict = defaultdict(list)\n len = df.shape[0]\n\n with alive_bar(len) as bar:\n for _, row in df.iterrows():\n gid = row['id']\n\n html = fetch_url(TWITCH_TRCK_URL + 'games/' + gid, hint='game')\n divs = html.find_all('div', {'class': 'g-x-s-block'})\n for div in divs:\n # Give a initial value as None\n # so that the program won't raise exception for length\n val, label = (None, None)\n val = div.find('div', {'class': 'g-x-s-value'}).text.strip()\n label = div.find('div', {'class': 'g-x-s-label'}).text.strip()\n if ('@' in label):\n (label, date) = label.split('@')\n label = label.strip()\n val += date\n data_dict[label].append(val)\n\n bar()\n\n df = df.assign(**data_dict)\n return df","def set_data(self):\n data = [\n {\"ASN1P-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"ASN1P\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n {\"asn1rt-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"asn1rt\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n {\"cadessignature-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"cadessignature\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n { \"JCP-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"JCP\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }},\n { \"JCPRequest-1.0.jar\": {\n \"groupId\": \"ru.lanit.jcp\",\n \"artifactId\": \"JCPRequest\",\n \"version\": \"1.0\",\n \"packaging\": \"jar\"\n }}\n ]\n return data","def print_info(self):\n\n n_metabolites = len(self.metabolites)\n n_reactions = len(self.reactions)\n n_constraints = len(self.constraints)\n n_variables = len(self.variables)\n\n info = pd.DataFrame(columns=['value'])\n info.loc['name'] = self.name\n info.loc['description'] = self.description\n info.loc['num constraints'] = n_constraints\n info.loc['num variables'] = n_variables\n info.loc['num metabolites'] = n_metabolites\n info.loc['num reactions'] = n_reactions\n info.index.name = 'key'\n\n print(info)","def attach_data(df: pd.DataFrame) -> pd.DataFrame:\n # load in parties and constituents data\n # if data is missing ask if scraping is wanted to be performed\n\n with open(os.path.join(instancepath, f\"parties.json\"), \"r\", encoding='utf-8') as json_file:\n parties = json.load(json_file)\n with open(os.path.join(instancepath, f\"constituencies.json\"), \"r\", encoding='utf-8') as json_file:\n constituencies = json.load(json_file)\n \n\n \"\"\" #tätä glob hommaa en saanu toimiin, käy for loopin sisäl vaan yhen kerran ja hakee vaan yhden tiedoston\n # load the scraped data to its own data frame\n df_scraped = pd.DataFrame(columns=['first_name', 'last_name', 'election_number', 'image', 'election_promise_1', 'party', 'constituency'])\n i = 1\n with suppress(KeyError,FileNotFoundError):\n for filename in glob(f\"{instancepath}/candidate*.json\"):\n print(\"jee\")\n with open(filename, \"r\", encoding='utf-8') as json_file:\n candidate = json.load(json_file)\n party_name = None\n constituency_name = None\n for part in parties:\n if part['id'] == candidate[\"party_id\"]:\n party_name = part['name_fi']\n\n for consti in constituencies:\n if consti['id'] == candidate[\"constituency_id\"]:\n constituency_name = consti['name_fi']\n\n df_scraped = df_scraped.append({'first_name': candidate['first_name'], \n 'last_name': candidate['last_name'], \n 'election_number': candidate['election_number'], \n 'image': candidate['image'], \n 'election_promise_1': candidate['info']['election_promise_1'],\n 'party': party_name,\n 'constituency': constituency_name}, \n ignore_index = True)\n #except (FileNotFoundError, KeyError):\n \"\"\"\n\n # load the scraped data to its own data frame\n df_scraped = pd.DataFrame(columns=['first_name', 'last_name', 'election_number', 'image', 'election_promise_1', 'party', 'constituency'])\n i = 1\n while i <= 3000: # files are named arfter candidate ids and ids range is between 1 and 3000\n with suppress(KeyError,FileNotFoundError):\n with open(os.path.join(instancepath, f\"candidate\" + str(i) + \".json\"), \"r\", encoding='utf-8') as json_file:\n candidate = json.load(json_file)\n party_name = None\n constituency_name = None\n for part in parties:\n if part['id'] == candidate[\"party_id\"]:\n party_name = part['name_fi']\n\n for consti in constituencies:\n if consti['id'] == candidate[\"constituency_id\"]:\n constituency_name = consti['name_fi']\n\n df_scraped = df_scraped.append({\n 'first_name': candidate['first_name'].strip(), \n 'last_name': candidate['last_name'].strip(), \n 'election_number': candidate['election_number'], \n 'image': candidate['image'], \n 'election_promise_1': candidate['info']['election_promise_1'],\n 'party': party_name.strip(),\n 'constituency': constituency_name}, \n ignore_index = True)\n i += 1\n\n\n # loading in data of each individual constituent\n # the ids range from 1 to 14\n j = 1\n constituentArray = []\n constituentArray.append(\"\")\n while j <= 14:\n try:\n with open(os.path.join(instancepath, f'constituent' + str(j) + '.json'), \"r\", encoding='utf-8') as json_file:\n constituentArray.append(json.load(json_file))\n except FileNotFoundError:\n constituentArray.append(\"\")\n j += 1\n\n for i, row in df.iterrows():\n promise = row['Mitkä ovat kolme vaalilupaustasi? Vaalilupaus 1:']\n constituency = row['constituency']\n party = row['party']\n df = search_andsetvalues(promise, constituency, party, df, i, df_scraped)\n return df","def _fetch_poverty_data(year=None):\n global _SIMPLECOUNT_COLUMNS\n\n try:\n if year is None:\n year = _get_max_year([30, 31]) + 1\n ext = 'txt' if year > 2003 else 'dat'\n url = f'https://www2.census.gov/programs-surveys/saipe/datasets/{year}/{year}-state-and-county/est{str(year)[2:]}-il.{ext}'\n\n raw = pd.read_table(url, header=None, skiprows=1, names=['raw'])\n pattern = '.{3}(?P<fips>.{3}).(?P<all>.{8}).{34}(?P<minor>.{8}).*'\n \n filtered = raw['raw'].str.extract(pattern)\n filtered['year'] = year\n filtered['fips'] = filtered['fips'].astype(int) + 1 / 2\n filtered.columns = ['fk_simplecount_county', '1200', '1201', 'year']\n\n pivoted = pd.melt(\n filtered.astype(int),\n id_vars = ['fk_simplecount_county', 'year'],\n value_vars=['1200', '1201'],\n var_name = 'fk_simplecount_indicator'\n )\n\n return pivoted[_SIMPLECOUNT_COLUMNS]\n except HTTPError as e:\n if e.code == 404:\n raise ValueError(\"WARNING: Poverty data is up to date.\")\n except:\n raise","def getexperimentinfo(expid):\n rdata = {}\n rdata['expId'] = expid\n res = requests.get(scbd_server_address + '/experiments/get_details', json=rdata)\n if res.status_code == 200:\n outstr = ''\n for cres in res.json()['details']:\n outstr += cres[0] + ':' + cres[1] + '<br>'\n # details=res.json()['details']\n return outstr\n return []","def filter_page_data(self, document_data: dict) -> dict:\n name = (\n document_data[\"project\"][\"name\"]\n )\n short_description = (\n document_data[\"project\"][\"shortDescription\"]\n )\n created = (\n document_data[\"project\"][\"created\"]\n )\n # due_date = (\n # document_data[\"project\"][\"dueDate\"]\n # )\n changeset_comment = (\n document_data[\"project\"][\"changesetComment\"]\n )\n instructions = (\n document_data[\"project\"][\"externalSource\"][\"instructions\"]\n )\n per_task_instructions = (\n document_data[\"project\"][\"externalSource\"][\"perTaskInstructions\"]\n )\n imagery = (\n document_data[\"project\"][\"externalSource\"][\"imagery\"]\n )\n license = (\n document_data[\"project\"][\"externalSource\"][\"license\"]\n )\n url = (\n document_data[\"project\"][\"url\"]\n )\n # metrics = (\n # document_data[\"platform\"][\"metrics\"]\n # )\n # quality_assurance = (\n # document_data[\"platform\"][\"qualityAssurance\"]\n # )\n users = (\n document_data[\"project\"][\"users\"]\n )\n project_page_data = {\n \"project\": {\n \"name\": name,\n \"shortDescription\": short_description,\n \"created\": created,\n # \"due_date\": due_date,\n \"changesetComment\": changeset_comment,\n \"externalSource\": {\n \"instructions\": instructions,\n \"perTaskInstructions\": per_task_instructions,\n \"imagery\": imagery,\n \"license\": license\n },\n \"url\": url,\n \"users\": users\n }\n # \"metrics\": metrics,\n # \"quality_assurance\": quality_assurance,\n }\n return project_page_data","def extract_data():\n client = MongoClient(HOST, PORT)\n collection = client[DB][COLLECTION]\n df = pd.DataFrame(collection.find().limit(10))\n return df","def subjectinfo(subject_id):\n import pandas as pd\n from nipype.interfaces.base import Bunch\n \n def construct_sj(trialinfo, subject_id, run_num, cond_name):\n \"\"\"construct df\"\"\"\n df_sj = trialinfo[(trialinfo['subject']==int(subject_id)) & (trialinfo['session']==int(run_num))]\n sj_info = pd.DataFrame()\n sj_info['onset'] = df_sj['runtime']\n sj_info['duration'] = 0.\n sj_info['weight'] = 1.\n trial_type = df_sj['seq'].replace({1:'Low', 2:'High'})\n sj_info['trial_type'] = trial_type\n sj_info_cond = sj_info[sj_info['trial_type']==cond_name]\n return sj_info_cond\n\n def select_confounds(subject_id, run_num):\n \"\"\"import confounds tsv files\"\"\"\n confounds_dir = f'/data/sub-%02d/func/' % int(subject_id)\n confounds_file = confounds_dir+f'sub-%02d_task-tsl_run-%d_desc-confounds_timeseries.tsv' % (int(subject_id), int(run_num))\n conf_df = pd.read_csv(confounds_file, sep='\\t')\n return conf_df\n\n def confounds_regressor(conf_df, conf_names):\n \"\"\"select confounds for regressors\"\"\"\n conf_select = conf_df[conf_names].loc[4:].fillna(0) # ignore first 4 dummy scans\n conf_select_list = [conf_select[col].values.tolist() for col in conf_select] \n return conf_select_list\n\n def find_runs(subject_id):\n \"\"\"find available runs from func\"\"\"\n from glob import glob\n func_dir = f'/output/smooth_nomask/preproc/sub-%02d/' % int(subject_id) \n func_files = glob(func_dir+'*bold.nii')\n runs = []\n for f in func_files:\n tmp = f.split('/')\n run = tmp[5].split('_')[2].split('-')[1]\n runs.append(int(run))\n return sorted(runs)\n \n conf_names = ['csf','white_matter','global_signal',\n 'dvars','std_dvars','framewise_displacement', 'rmsd',\n 'a_comp_cor_00', 'a_comp_cor_01', 'a_comp_cor_02', 'a_comp_cor_03', 'a_comp_cor_04', 'a_comp_cor_05', 'cosine00', 'cosine01', 'cosine02', 'cosine03', 'cosine04', 'cosine05',\n 'trans_x', 'trans_y', 'trans_z', 'rot_x','rot_y','rot_z']\n\n alltrialinfo = pd.read_csv('/code/data/fmri_behavioural_new.csv')\n alltrialinfo.head()\n \n subject_info = []\n onset_list = []\n condition_names = ['High', 'Low']\n runs = find_runs(subject_id)\n print(runs)\n for run in runs:\n for cond in condition_names:\n run_cond = construct_sj(alltrialinfo, subject_id, run, cond)\n onset_run_cond = run_cond['onset'].values\n onset_list.append(sorted(onset_run_cond))\n\n subject_info = []\n for r in range(len(runs)):\n onsets = [onset_list[r*2], onset_list[r*2+1]]\n regressors_all = select_confounds(subject_id, runs[r])\n regressors = confounds_regressor(regressors_all, conf_names)\n\n subject_info.insert(r,\n Bunch(conditions=condition_names,\n onsets=onsets,\n durations=[[0], [0]],\n regressors=regressors,\n regressor_names=conf_names,\n amplitudes=None,\n tmod=None,\n pmod=None))\n\n return subject_info # this output will later be returned to infosource","def getStudyInfo(self, study_id, web_app_user_id):\n con = self.getMetadataDatabaseConnection()\n results = con.cursor()\n con.cursor().callproc('qiime_assets.get_study_info', [study_id, web_app_user_id, results])\n study_info = {}\n for row in results:\n study_info['submit_to_insdc'] = row[0]\n study_info['investigation_type'] = row[1]\n study_info['project_name'] = row[2]\n study_info['experimental_factor'] = row[3]\n study_info['study_alias'] = row[4]\n study_info['study_title'] = row[5]\n study_info['study_type'] = row[6]\n study_info['study_abstract'] = row[7]\n study_info['study_description'] = row[8]\n study_info['center_name'] = row[9]\n study_info['center_project_name'] = row[10]\n study_info['project_id'] = row[11]\n study_info['pmid'] = row[12]\n study_info['metadata_complete'] = row[13]\n study_info['sff_complete'] = row[14]\n study_info['mapping_file_complete'] = row[15]\n study_info['miens_compliant'] = row[16]\n study_info['can_delete'] = row[17]\n study_info['avg_emp_score'] = row[18]\n study_info['user_emp_score'] = row[19]\n study_info['number_samples_promised'] = row[20]\n study_info['number_samples_collected'] = row[21]\n study_info['principal_investigator'] = row[22]\n study_info['sample_count'] = row[23] \n study_info['lab_person'] = row[24] \n study_info['lab_person_contact'] = row[25]\n study_info['emp_person'] = row[26]\n study_info['first_contact'] = row[27]\n study_info['most_recent_contact'] = row[28]\n study_info['sample_type'] = row[29]\n study_info['has_physical_specimen'] = row[30]\n study_info['has_extracted_data'] = row[31]\n study_info['timeseries'] = row[32]\n study_info['spatial_series'] = row[33]\n study_info['principal_investigator'] = row[34]\n study_info['principal_investigator_contact'] = row[35]\n study_info['default_emp_status'] = row[36]\n study_info['funding'] = row[37]\n study_info['includes_timeseries'] = row[38]\n study_info['sample_count'] = row[39]\n study_info['ebi_study_accession'] = row[40]\n study_info['locked'] = row[41]\n study_info['vamps_id'] = row[42]\n return study_info","def get_projects(session):\n cursuses = [1, 21] # cursus ids from which to get the projects\n project_names = []\n\n for cursus in cursuses:\n # Get all the projects from 1 cursus, very slow process because projects endpoint contains\n # a lot of information\n projects = get_all_pages(session, f'/cursus/{cursus}/projects', 100, {'filter[exam]': False})\n for project in projects:\n # Create dictionary containing project id and project name ans set in bigger dict\n project_names.append({'id': project['id'], 'name': project['name']})\n\n return project_names","def download_potholes():\n\n\tlink = \"https://data.cityofchicago.org/api/views/7as2-ds3y/rows.csv?accessType=DOWNLOAD\"\n\tdf = pd.read_csv(link)\n\tdf = df[(df.STATUS == \"Open\") | (df.STATUS == \"Open - Dup\")]\n\tdf = df[[\"LATITUDE\", \"LONGITUDE\"]]\n\tdf = df.dropna(axis =0, subset=[\"LATITUDE\", \"LONGITUDE\"])\n\treturn df","def _build_single_study_info(study, info, study_proc, proc_samples):\n PI = StudyPerson(info['principal_investigator_id'])\n status = study.status\n if info['publication_doi'] is not None:\n pmids = get_pubmed_ids_from_dois(info['publication_doi']).values()\n info['pmid'] = \", \".join([pubmed_linkifier([p]) for p in pmids])\n info['publication_doi'] = \", \".join([doi_linkifier([p])\n for p in info['publication_doi']])\n\n else:\n info['publication_doi'] = \"\"\n info['pmid'] = \"\"\n if info[\"number_samples_collected\"] is None:\n info[\"number_samples_collected\"] = 0\n info[\"shared\"] = _get_shared_links_for_study(study)\n # raw data is any artifact that is not Demultiplexed or BIOM\n\n info[\"num_raw_data\"] = len([a for a in study.artifacts()\n if a.artifact_type not in ['Demultiplexed',\n 'BIOM']])\n info[\"status\"] = status\n info[\"study_id\"] = study.id\n info[\"pi\"] = study_person_linkifier((PI.email, PI.name))\n del info[\"principal_investigator_id\"]\n del info[\"email\"]\n # Build the proc data info list for the child row in datatable\n info[\"proc_data_info\"] = []\n for data_type, proc_datas in viewitems(study_proc[study.id]):\n info[\"proc_data_info\"].extend([\n _build_single_proc_data_info(pd_id, data_type, proc_samples[pd_id])\n for pd_id in proc_datas])\n return info","def example_data():\n return {\n # \"$schema\": \"\",\n \"id\": \"12345-abcde\",\n \"metadata\": {\n \"title\": \"My record\",\n \"date\": \"2020-09-20\",\n },\n \"pids\": {\n \"oaiid\": {\"value\": \"\", \"provider\": \"local\"},\n },\n }","def get_data_dict(self):\n\n print('---------repo creation date------------------')\n print('start date', self.start_date)\n print('---------------------------------------------')\n print('------Commits----------')\n print(self.commits)\n print('--------------------------------------------')\n\n df = pd.DataFrame.from_records(self.commits) # pylint: disable=invalid-name\n df.date = pd.to_datetime(df.date, utc=True, unit='s')\n df.set_index('date', inplace=True)\n df.index = df.index.floor('D')\n\n date_range = pd.date_range(start=self.start_date, end=self.end_date, freq=self.date_unit)\n date_range = date_range.floor('D')\n\n grouped = df.groupby('author')\n new_df = pd.DataFrame(index=date_range)\n for name, group in grouped:\n new_df[name] = group.groupby('date').size()\n new_df.fillna(0, inplace=True)\n\n return {\n 'x': new_df.index.strftime('%Y-%m-%d').tolist(),\n 'y': new_df.columns.tolist(),\n 'z': new_df.T.values.astype('int32').tolist()\n }","def make_df_from_json(json_files, out_file):\n table = [[\"name\", \n \"cik\", \n \"city\",\n \"state\",\n \"street1\",\n \"street2\",\n \"zip_code\",\n \"year_of_incorp\", \n \"min_inv\", \n \"tot_off\", \n \"tot_sold\", \n \"tot_rem\", \n \"ind_group_type\", \n \"has_non_accred\", \n \"num_non_accred\", \n \"tot_num_inv\"\n ]] \n\n for json_dict in json_files:\n\n with open(json_dict, \"rb\") as f:\n data = json.load(f)\n print(json_dict)\n\n for i, key in enumerate(data):\n # if i % 1000 == 0:\n # print(i)\n entry = data[key] \n if entry == {}:\n #print(\"missing entry {0}\".format(i))\n continue\n row = []\n\n primary_issuer = entry[\"Primary Issuer\"]\n cik = primary_issuer[\"cik\"]\n name = primary_issuer[\"entity_name\"]\n phone = primary_issuer[\"phone\"]\n year_of_incorp = primary_issuer[\"year_of_incorp\"]\n address = primary_issuer[\"address\"]\n city = address[\"city\"]\n state = address[\"state\"]\n street1 = address[\"street1\"]\n street2 = address[\"street2\"]\n zip_code = address[\"zip_code\"]\n\n secondary_issuers = entry[\"Secondary Issuers\"]\n related_people = entry[\"Related People\"]\n \n offering_data = entry[\"Offering Data\"]\n min_inv = offering_data[\"min_investment_accepted\"]\n tot_off = offering_data[\"total_offering_amount\"]\n tot_sold = offering_data[\"total_amount_sold\"]\n tot_rem = offering_data[\"total_remaining\"]\n ind_group_type = offering_data[\"ind_group_type\"]\n has_non_accred = offering_data[\"has_non_accred\"]\n num_non_accred = offering_data[\"num_non_accred\"]\n tot_num_inv = offering_data[\"tot_num_inv\"] \n\n row = [name, \n cik, \n city,\n state,\n street1,\n street2,\n zip_code,\n year_of_incorp,\n min_inv,\n tot_off,\n tot_sold,\n tot_rem,\n ind_group_type,\n has_non_accred,\n num_non_accred,\n tot_num_inv\n ]\n\n table.append(row)\n\n df = pd.DataFrame(table)\n df.to_csv(out_file)\n\n return 0","def search_andsetvalues(promise, constituency, party, df: pd.DataFrame, i, df_scraped) -> pd.DataFrame:\n candidates = df_scraped.loc[(df_scraped['constituency'] == constituency) & (df_scraped['party'] == party)] #& \n\n for j, row in candidates.iterrows():\n if compare_promises(row[\"election_promise_1\"][\"fi\"], row[\"election_promise_1\"][\"se\"], \n row[\"election_promise_1\"][\"en\"], promise):\n df.at[i,'name'] = row[\"first_name\"] + \" \" + row[\"last_name\"]\n df.at[i, 'number'] = str(row[\"election_number\"]) \n df.at[i,'image'] = \"https://ehdokaskone.yle.webscale.fi/\" + row['image']\n return df","def download(self, verbose):\n # Download datasets\n if verbose:\n print(\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\")\n # Vaccinations\n v_rec_cols = [\n \"date\", \"location\", \"iso_code\", \"total_vaccinations\", \"people_vaccinated\", \"people_fully_vaccinated\"]\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\"location\", \"vaccines\"])\n v_df = v_rec_df.merge(v_loc_df, how=\"left\", on=\"location\")\n # Tests\n pcr_rec_cols = [\"ISO code\", \"Date\", \"Daily change in cumulative total\", \"Cumulative total\"]\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\n pcr_df = pcr_df.rename(columns={\"ISO code\": \"iso_code\", \"Date\": \"date\"})\n pcr_df[\"cumsum\"] = pcr_df.groupby(\"iso_code\")[\"Daily change in cumulative total\"].cumsum()\n pcr_df = pcr_df.assign(tests=lambda x: x[\"Cumulative total\"].fillna(x[\"cumsum\"]))\n # Combine data (vaccinations/tests)\n df = v_df.set_index([\"iso_code\", \"date\"])\n df = df.combine_first(pcr_df.set_index([\"iso_code\", \"date\"]).loc[:, [\"tests\"]])\n df = df.reset_index()\n # Location (country/province)\n df[\"location\"] = df[\"location\"].replace(\n {\n # COG\n \"Congo\": \"Republic of the Congo\",\n }\n )\n df = df.loc[~df[\"iso_code\"].str.contains(\"OWID_\")]\n df[\"location\"] = df.groupby(\"iso_code\")[\"location\"].bfill()\n df.loc[df[\"location\"] == df[\"iso_code\"], \"location\"] = None\n df.loc[df[\"location\"].isna(), \"location\"] = df.loc[df[\"location\"].isna(), \"iso_code\"].apply(\n lambda x: coco.convert(x, to=\"name_short\", not_found=None))\n df[self.PROVINCE] = self.UNKNOWN\n return df","def _request_info(self):\n \n current_date = str(datetime.datetime.now(pytz.timezone('US/Mountain')))\n json_dct = {'search_title': self.job_title, \\\n 'search_location': self.job_location, \\\n 'search_date': current_date, 'job_site': 'indeed'}\n # Holds the actual CSS selector as the key and the label I want to store\n # the info. as as the key. \n possible_attributes = {'.jobtitle': \"job_title\", '.company': \"company\", \\\n '.location': \"location\", '.date': \"date\", \\\n '.iaLabel': \"easy_apply\"}\n for k, v in possible_attributes.iteritems(): \n res = self.row.select(k)\n if res: \n json_dct[v] = res[0].text\n # Now let's grab the href and pass that on to another function to \n # get that info. \n href = self.row.find('a').get('href')\n json_dct['href'] = href\n json_dct['posting_txt'] = self._query_href(href)\n\n return json_dct","def viewdata(data):\n\n print('_' * 50)\n print('Number of Results: ' + str(data[0]['numResults']))\n print('\\nSearchURL: ' + data[0]['searchURL'])\n print('_' * 50)\n\n i = 1\n for m in data[1]:\n print(str(i) + '. ')\n for n in m:\n print(str(n) + ': ' + str(m[n]))\n i += 1\n print('\\n')","def data_dict0():\n\n # 0- Sample from detectron2 -> 5 different sections.\n info_val0 = [{\"date_created\": \"2020-03-15 04:59:45.442988\",\n \"description\": \"Automatically generated COCO json file for Detectron2.\"}]\n images0 = [{\"id\": \"image\", \"width\": 100,\n \"height\": 100, \"file_name\": \"image.png\"}]\n annotations0 = [{\"id\": 1, \"image_id\": \"image\", \"bbox\": [70.0, 30.0, 30.0, 40.0],\n \"area\": 1200.0, \"iscrowd\": 0, \"category_id\": 0}]\n categories0 = [{\"id\": 0, \"name\": \"first\"}]\n licence0 = 'null'\n\n return [{\"info\": info_val0,\n \"images\": images0,\n \"annotations\": annotations0,\n \"categories\": categories0,\n \"licenses\": licence0}]","def _info(self) -> tfds.core.DatasetInfo:\n return tfds.core.DatasetInfo(\n builder=self,\n description=_DESCRIPTION,\n features=tfds.features.FeaturesDict({\n 'id': tfds.features.Text(),\n 'program': tfds.features.Text(),\n 'date': tfds.features.Text(),\n 'url': tfds.features.Text(),\n 'summary': tfds.features.Text(),\n 'utt': tfds.features.Sequence(tfds.features.Text()),\n 'speaker': tfds.features.Sequence(tfds.features.Text()),\n }),\n supervised_keys=('utt', 'summary'),\n homepage='https://github.com/zcgzcgzcg1/MediaSum',\n citation=_CITATION,\n )","def precip():\n date_prcp=session.query(measurements.date,measurements.prcp).all()\n date_prcp_df=pd.DataFrame(date_prcp).set_index('date')\n date_prcp_dict=date_prcp_df.to_dict()\n return jsonify(date_prcp_dict)","def precipitation():\r\n # Query all measurements\r\n results = session.query(Measurement).all()\r\n\r\n # Create a dictionary from the row data and append to a list of all_precipitation\r\n all_precipitation = []\r\n\r\n for row in results:\r\n precipitation_dict = { row.date : row.prcp}\r\n all_precipitation.append(precipitation_dict)\r\n # print(all_precipitation)\r\n return jsonify(all_precipitation)","def dataset_grabber(sess, link):\n json_dict = sess.get(link).json()\n if '.geojson' in link:\n dataset = gpd.GeoDataFrame.from_features(json_dict['features'])\n else:\n dataset = pd.DataFrame(json_dict)\n return dataset","def get_pr_info(num):\r\n url = \"https://api.github.com/repos/edx/edx-platform/pulls/{num}\".format(num=num)\r\n username, token = get_github_creds()\r\n headers = {\r\n \"Authorization\": \"token {}\".format(token),\r\n \"User-Agent\": \"edx-release\",\r\n }\r\n response = requests.get(url, headers=headers)\r\n result = response.json()\r\n if not response.ok:\r\n raise requests.exceptions.RequestException(result[\"message\"])\r\n return result","def fetch(url, header_path, id, ip, dbase, targets_table):\n # url = 'http://esimbad/testGSAV7/reslabo?FENID=resLaboPatDitep&NIP={}' \\\n # '&STARTDATE={}&ENDDATE={}'\n\n # header_path = '~/workspace/data/biology/header.csv'\n # constant names specific to our database\n KEY1 = 'id'\n KEY2 = 'NIP'\n C1J1 = 'C1J1'\n\n header = pd.read_csv(header_path, sep=';', encoding='latin1').columns\n\n\n engine = get_engine(id, ip, dbase)\n\n df_ids = sql2df(engine, targets_table)[[KEY1, 'nip', C1J1]]\n df_ids.rename({'nip': KEY2}, inplace=True, axis=1)\n df_ids['patient_id'] = df_ids[KEY1]\n\n cols = [KEY2, 'Analyse', 'Resultat', 'Date prelvt']\n df_res = pd.DataFrame(data=None, columns=cols)\n\n for index, row in df_ids.iterrows():\n nip = row[KEY2].replace(' ', '')\n patient_id = row['patient_id']\n c1j1_date = row[C1J1].date()\n start_date = c1j1_date - timedelta(weeks=8)\n\n c1j1 = str(c1j1_date).replace('-', '')\n start = str(start_date).replace('-', '')\n\n req = requests.get(url.format(nip, start, c1j1))\n values = BeautifulSoup(req.content, 'html.parser').body.text\n\n new_df = pd.read_csv(StringIO(values), sep=';', header=None,\n index_col=False, names=header)\n new_df = new_df.loc[:, cols + ['LC']] # remove LC\n\n # normalize nip\n new_df[KEY2] = row[KEY2]\n # new_df[KEY2] = new_df[KEY2].map(str)\n # new_df[KEY2] = [nip[:4] + '-' + nip[4:] for nip in new_df[KEY2]]\n\n new_df.drop('LC', axis=1, inplace=True)\n\n df_res = pd.concat([df_res, new_df], axis=0,\n sort=False, ignore_index=True)\n\n return df_res","def extract_all_lazy():\n\n\t#Construct filepaths: Data COMP_INFO_1\n\tdata_ci1_name = \"DATA_2016_COMP_INFO_1.csv\"\n\tdata_ci1_fullname = os.path.join(files_location, data_ci1_name)\n\t#Data COMP_INFO_2\n\tdata_ci2_name = \"DATA_2016_COMP_INFO_2.csv\"\n\tdata_ci2_fullname = os.path.join(files_location, data_ci2_name)\n\t#Data PROPERTY INFO\n\tdata_pi_name = \"DATA_2016_PROPERTY_INFO_ST.csv\"\n\tdata_pi_fullname = os.path.join(files_location, data_pi_name)\n\t#Data General Info\n\tdata_gi_name = \"DATA_2016_GENERAL_INFO.csv\"\n\tdata_gi_fullname = os.path.join(files_location, data_gi_name)\n\n\t#Read & Process COMP_INFO\n\tdata_ci1 = pd.read_csv(data_ci1_fullname, skiprows=2, usecols = constants.keep_columns_CI, encoding='ISO-8859-1')\n\tdata_ci2 = pd.read_csv(data_ci2_fullname, skiprows=2, usecols = constants.keep_columns_CI, encoding='ISO-8859-1')\n\n\tdata_ci = data_ci1.append(data_ci2)\n\tdata_ci['QUESTION'] = data_ci['QUESTION'].replace(constants.ci_mapping)\n\t# Take only the survey questions mapped\n\tdata_ci = data_ci[data_ci['QUESTION'].isin(constants.ci_mapping.values())]\n\tdata_ci = data_ci.set_index(['PROPERTY_CODE','PROPERTY_NAME','JOB_CODE','POSITION'])\n\tdata_ci = data_ci.pivot(columns=\"QUESTION\")\n\tdata_ci.columns = [\"_\".join(pair) for pair in data_ci.columns]\n\tdata_ci = data_ci.reset_index()\n\n\t#Read & Process Property Info data\n\tdata_pi = pd.read_csv(data_pi_fullname, usecols = constants.keep_columns_PI, encoding='ISO-8859-1')\n\t#survey_type_transformed = transform.surveytype_categorical(data_pi)\n\t#data_pi = pd.merge(data_pi, survey_type_transformed, on=['PROPERTY_CODE'])\n\n\t#Read & Process General Info\n\tdata_gi = pd.read_csv(data_gi_fullname, skiprows = 2, usecols = constants.keep_columns_GI, encoding='ISO-8859-1')\n\tdata_gi['QUESTION'] = data_gi['QUESTION'].replace(constants.gi_mapping)\n\t# Take onl the survey questions mapped\n\tdata_gi = data_gi[data_gi['QUESTION'].isin(constants.gi_mapping.values())]\n\tdata_gi = data_gi.set_index(['PROPERTY_CODE','PROPERTY_NAME'])\n\tdata_gi = data_gi.pivot(columns=\"QUESTION\")\n\tdata_gi.columns = [\"_\".join(pair) for pair in data_gi.columns]\n\tdata_gi = data_gi.reset_index()\n\n\t#This frame needs to be reworked\n\td_ci = pd.merge(data_gi, data_pi, on = ['PROPERTY_CODE','PROPERTY_NAME'])\n\td_ci = pd.merge(d_ci, data_ci, on = ['PROPERTY_CODE','PROPERTY_NAME'],suffixes= ['_ci','_gi'])\n\n\t#Observations by Dimensions to determine top X markets\n\t#Can this be in a better position?\n\td_ci = d_ci[~(d_ci['PROPERTY_NAME'].isin(constants.del_rows_property_name))]\n\td_ci['POSITION'] = d_ci['POSITION'].astype(str)\n\n\tpayload = {}\n\tpayload['gi'] = data_gi\n\tpayload['pi'] = data_pi\n\tpayload['ci'] = data_ci\n\tpayload['d_ci'] = d_ci\n\n\treturn payload","def _fetch_idjj_data(year=None):\n try:\n if year is None:\n year = _get_max_year([11, 12, 34, 35]) + 1\n\n database = 'PrisonMain'\n tbl_admit = 'IDJJ_Admissions'\n tbl_exit = 'IDJJ_exits'\n cols = 'Age, SFY, County, sex, race, admtypo, OFFTYPE9, hclass'\n condition = f'SFY = {year}'\n\n df_a = _fetch_from_ms_sql_server(database, tbl_admit, cols, condition)\n df_e = _fetch_from_ms_sql_server(database, tbl_exit, 'Exit'+cols, condition)\n\n return (\n pd.DataFrame()\n .append(_tranform_idjj(df_a))\n .append(_tranform_idjj(df_a, age1720=True))\n .append(_tranform_idjj(df_e, exit=True))\n .append(_tranform_idjj(df_e, age1720=True, exit=True))\n )\n except:\n raise","def fetch_tikhonov_data(dpath='/tmp/glm-tools'):\n if os.path.exists(dpath):\n shutil.rmtree(dpath)\n os.mkdir(dpath)\n\n base_url = \"https://raw.githubusercontent.com/glm-tools/datasets/master\"\n url = os.path.join(base_url, \"tikhonov/fixations.csv\")\n fname = os.path.join(dpath, 'fixations.csv')\n urllib.urlretrieve(url, fname)\n fixations_df = pd.read_csv(fname)\n\n url = os.path.join(base_url, \"tikhonov/probes.csv\")\n fname = os.path.join(dpath, 'probes.csv')\n urllib.urlretrieve(url, fname)\n probes_df = pd.read_csv(fname)\n\n url = os.path.join(base_url, \"tikhonov/spiketimes.csv\")\n fname = os.path.join(dpath, 'spiketimes.csv')\n urllib.urlretrieve(url, fname)\n spikes_df = pd.read_csv(fname, header=None)\n\n return fixations_df, probes_df, spikes_df","def get_metadata_list(path, file, task_description, dataset_description):\n df = pd.read_csv(os.path.join(path, file), sep='\\t')\n\n for column in COMMON_FIELDS:\n if column not in df.columns:\n logging.getLogger('zenodo_upload').critical('File {} is missing column {}'.format(os.path.join(path, file), column))\n exit(1)\n\n df = df[(df['DOI'].str.lower() == 'new') | (df['DOI'].isna())]\n if df.empty:\n return None\n\n metadata_list = []\n for index, row in df.iterrows():\n title = ' '.join([row['Software'], row['Version'], task_description, 'of the', dataset_description + ', samples ' + row['SamplesUsed']])\n\n row_copy= row.copy().drop(COMMON_FIELDS).dropna()\n if 'Description' in row_copy.index:\n description = row_copy.pop('Description') + '<br>'\n else:\n description = ''\n description += '<strong>Software: </strong>' + row['Software'] + '<br>'\n description += '<strong>SoftwareVersion: </strong>' + str(row['Version']) + '<br>'\n description += '<strong>DataURL: </strong> https://data.cami-challenge.org/participate'\n for item in row_copy.iteritems():\n if len(description) > 0:\n description = description + '<br>'\n description = description + str('<strong>' + item[0]) + ':</strong> ' + str(item[1])\n\n creators_metadata = get_creators_metadata(row)\n metadata = {\n 'metadata': {\n 'title': title,\n 'upload_type': 'dataset',\n 'communities': [{'identifier': 'cami'}],\n 'description': description,\n 'creators': creators_metadata,\n 'access_right': 'open',\n 'license': 'cc-by',\n 'version': row['Version'],\n 'keywords': KEYWORDS + [task_description, dataset_description]\n },\n 'files': row['FileName']\n }\n metadata_list.append(metadata)\n return metadata_list","def data_frame_info(self):\r\n print(self.dataframe_name)\r\n print(self.data_frame.info())","def _read_projects_df_from_db(self, include_stats=True):\n\n projects_df = None\n\n # TODO: should cursor be created here or no?\n # https://www.datacamp.com/community/tutorials/tutorial-postgresql-python # noqa\n # shows creation of a cursor even though no methods are called on it\n with self._transaction.dict_cursor():\n # TODO: is there a better way to access this?\n conn = self._transaction._conn\n\n queries = _PROJECT_SQLS if include_stats else [_PROJECT_SQLS[0]]\n for stats_keys, sql_source in queries:\n if callable(sql_source):\n curr_sql = sql_source(*stats_keys)\n else:\n curr_sql = sql_source.format(*stats_keys)\n\n curr_df = pd.read_sql(curr_sql, conn)\n\n if projects_df is None:\n projects_df = curr_df\n else:\n # left join here: the first query produces a df with a\n # COMPLETE list of all projects, whereas subsequent\n # queries only return info on projects relevant to their\n # computed statistic.\n projects_df = pd.merge(projects_df, curr_df,\n how=\"left\", on=[\"project_id\"])\n\n # make the project_id the index of the final data frame, but\n # do NOT drop the project_id column from the data frame.\n projects_df.set_index('project_id', drop=False, inplace=True)\n return projects_df","def get_info(now):\n url = 'https://api.stackexchange.com/2.2/info'\n payload = {\n 'site': 'stackoverflow'\n }\n result = requests.get(url, params=payload)\n # print(result.url) # https://api.stackexchange.com/2.2/info?site=stackoverflow\n result = result.json()\n items = result['items'][0]\n items_seq = {k:[v] for k, v in items.items()}\n df = pd.DataFrame(items_seq)\n df['time_requested'] = now\n return df","def get_organisation_metadata() -> pd.DataFrame:\n return GETTER.organisationmetadata","def identify_primary_reference_datasets(conn, log):\n\n primary_ref = {}\n\n primary_ref['refimg_id_ip'] = phot_db.find_primary_reference_image_for_field(conn)\n\n query = 'SELECT facility, filter, software FROM reference_images WHERE refimg_id=\"'+str(primary_ref['refimg_id_ip'])+'\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n\n primary_ref['facility_id'] = t['facility'][0]\n primary_ref['software_id'] = t['software'][0]\n\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\"ip\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n primary_ref['ip'] = t['filter_id'][0]\n\n for f in ['rp', 'gp']:\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\"'+f+'\"'\n t = phot_db.query_to_astropy_table(conn, query, args=())\n primary_ref[f] = t['filter_id'][0]\n\n query = 'SELECT refimg_id FROM reference_images WHERE facility=\"'+str(primary_ref['facility_id'])+\\\n '\" AND software=\"'+str(primary_ref['software_id'])+\\\n '\" AND filter=\"'+str(t['filter_id'][0])+'\"'\n qs = phot_db.query_to_astropy_table(conn, query, args=())\n\n if len(qs) > 0:\n primary_ref['refimg_id_'+f] = qs['refimg_id'][0]\n else:\n log.info('WARNING: Database contains no primary reference image data in filter '+f)\n\n log.info('Identified the primary reference datasets for this field as:')\n for key, value in primary_ref.items():\n log.info(str(key)+' = '+str(value))\n\n return primary_ref","async def get_project_info(project_urls):\n project_info = []\n for url in project_urls:\n soup = await get_page(url)\n about = soup.find_all(\"p\")\n title = soup.find(\"h3\").text\n student = about[0].text.splitlines()[2].strip()\n details = about[1].text\n name = about[0].find(\"a\").text\n project_info.append({'Organization': name, 'title': title,\n 'student': student, 'details': details,\n 'link': url})\n\n return project_info","def update_datatable(clickData, selected_year, search_ids_keywords, search_keywords, selected_funding):\n\n prof_name = clickData['points'][0]['customdata']\n print(\"selected professor for network graph is \", prof_name)\n\n # TODO: Handle no search_ids or Nonetype search ids DONE\n # print(\" Search IDS ************* 8********** ****** here are this\", search_ids_keywords[0:,0])\n\n professor_data = funding_data[(funding_data['Lead Investigator:'] == prof_name)\n & (funding_data.start_year.isin(selected_year))\n # & (funding_data._id.isin(search_ids_keywords[0:,0]))\n & (funding_data['Program Cycle:'].isin(selected_funding))]\n\n if search_keywords and len(search_keywords) > 0:\n search_ids_keywords = json.loads(search_ids_keywords)\n search_ids_keywords = np.array(search_ids_keywords)\n professor_data = professor_data[(professor_data._id.isin(search_ids_keywords[0:, 0]))]\n\n\n # collaborating_personnel_counts = collaborating_personnel.investigator.value_counts()\n professor_data = professor_data[\n [\"Proposal Number:\", \"Proposal Title:\"]]\n\n # [\"Proposal Number:\", \"Submitting Institution Name:\", \"Project Status:\", \"Proposal Title:\"]]\n return professor_data.to_dict(\"rows\")\n # return professor_data","def info(self):\n for key, value in self.dataset['info'].items():\n print('{}: {}'.format(key, value))","def extract_depto(succId = 439) :\n deps_api = 'https://www.lacomer.com.mx/lacomer-api/api/v1/public/header/inicio?cambioSucc=false&succFmt=100&succId={}'.format(succId)\n deps_json = json.loads(requests.get(deps_api).text)['departamentos']\n deps_list = list(deps_json.keys())\n deps_df = []\n\n for depto in deps_list :\n \n tmp = pd.DataFrame(deps_json[depto])\n tmp['dept'] = depto\n deps_df.append(tmp)\n \n deps_df = pd.concat(deps_df)\n deps_df['dept'].unique()\n\n sucursales = extract_sucursales()\n deps_df['succId'] = succId\n deps_df['sucursal'] = sucursales.loc[sucursales['id'] == succId,'sucursal'].iloc[0]\n\n return deps_df","def create_df(link=config.api_link, key=config.api_key, master_file = 'pvd_crime_master.csv'):\n #only want reports we don't already have, so what is the most recent date in the master\n master = pd.read_csv(master_file, nrows=1)\n most_recent = pd.to_datetime(master['reported_date'])[0]\n most_recent_format = most_recent.strftime('%Y-%m-%dT%H:%M:%S.000')\n\n headers = {'Authentication': key} #api_key\n \n query = \"SELECT * WHERE reported_date > '\"+most_recent_format+\"' ORDER BY reported_date LIMIT 13000\"\n\n params = {'$query': query}\n\n response = requests.get(link, headers=headers, params=params) #json data\n response_json = response.json() #json data as list of dictionaries\n \n #create and return pandas DataFrame of json response\n\n return pd.DataFrame(response_json)","def load_data():\n df = pd.read_csv(\"https://raw.githubusercontent.com/Andrea-Giuliani/Python-Project/master/data/final_dataset.csv\",sep=',') \n return df","def test_project_detail(self):\n rv = self.app.get(\"/Assignment0\")\n self.assertIn(\"Assignment0\", rv.data)\n self.assertIn(\"2015-02-04 21:57:12.156363\", rv.data)\n self.assertIn(\"221\", rv.data)\n self.assertIn(\"commit assignment0\", rv.data)\n\n self.assertIn(\"Assignment0/Procfile\", rv.data)\n self.assertIn(\"Assignment0/README.md\", rv.data)","def getDatasetsProjectsFromImages(queryService, imageIds):\n ids = \",\".join([str(i) for i in imageIds])\n \n query_string = \"select i from Image i join fetch i.datasetLinks idl join fetch idl.parent d join fetch d.projectLinks pl join fetch pl.parent where i.id in (%s)\" % ids\n\n images = queryService.findAllByQuery(query_string, None)\n results = {}\n \n for i in images: # order of images not same as imageIds\n pdList = []\n imageId = i.getId().getValue()\n for link in i.iterateDatasetLinks():\n dataset = link.parent\n dName = dataset.getName().getValue()\n if dataset.sizeOfProjectLinks() == 0:\n pdList.append((\"\", dName))\n for dpLink in dataset.iterateProjectLinks():\n project = dpLink.parent\n pName = project.getName().getValue()\n pdList.append((pName, dName))\n results[imageId] = pdList\n \n # make sure the map contains all the imageIds\n for iId in imageIds:\n if iId not in results:\n results[iId] = []\n return results","def load_landkreis_information():\n client = MongoClient(f'mongodb://{os.getenv(\"USR_\")}:{os.getenv(\"PWD_\")}@{os.getenv(\"REMOTE_HOST\")}:{os.getenv(\"REMOTE_PORT\")}/{os.getenv(\"AUTH_DB\")}')\n db = client[os.getenv(\"MAIN_DB\")]\n lk_collection = db[\"lk_overview\"]\n data = pd.DataFrame(list(lk_collection.find()))\n return data","def get_dataset_details(name, analyst):\n\n template = None\n allowed_sources = user_sources(analyst)\n dataset_object = Dataset.objects(name = name,\n source__name__in=allowed_sources).first()\n if not dataset_object:\n error = (\"Either no data exists for this dataset\"\n \" or you do not have permission to view it.\")\n template = \"error.html\"\n args = {'error': error}\n return template, args\n\n dataset_object.sanitize_sources(username=\"%s\" % analyst,\n sources=allowed_sources)\n\n # remove pending notifications for user\n remove_user_from_notification(\"%s\" % analyst, dataset_object.id, 'Dataset')\n\n # subscription\n subscription = {\n 'type': 'Dataset',\n 'id': dataset_object.id,\n 'subscribed': is_user_subscribed(\"%s\" % analyst,\n 'Dataset',\n dataset_object.id),\n }\n\n #objects\n objects = dataset_object.sort_objects()\n\n #relationships\n relationships = dataset_object.sort_relationships(\"%s\" % analyst, meta=True)\n\n # relationship\n relationship = {\n 'type': 'Datset',\n 'value': dataset_object.id\n }\n\n #comments\n comments = {'comments': dataset_object.get_comments(),\n 'url_key':dataset_object.name}\n\n # favorites\n favorite = is_user_favorite(\"%s\" % analyst, 'Dataset', dataset_object.id)\n\n # services\n service_list = get_supported_services('Dataset')\n\n # analysis results\n service_results = dataset_object.get_analysis_results()\n\n args = {'dataset': dataset_object,\n 'objects': objects,\n 'relationships': relationships,\n 'comments': comments,\n 'favorite': favorite,\n 'relationship': relationship,\n 'subscription': subscription,\n 'name': dataset_object.name,\n 'service_list': service_list,\n 'service_results': service_results}\n\n return template, args","def build_person_df(person):\n data = {\n \"act\" : [],\n \"modes\": [],\n \"start_time\": [],\n \"end_time\": [],\n \"dur\": [],\n }\n for component in person.plan.day:\n data[\"act\"].append(component.act.lower().title())\n if isinstance(component, activity.Leg):\n data[\"modes\"].append(component.mode.lower().title())\n else:\n data[\"modes\"].append(None)\n data[\"start_time\"].append(component.start_time.hour + component.start_time.minute/60)\n data[\"end_time\"].append(component.end_time.hour + component.end_time.minute/60)\n data[\"dur\"].append(component.duration.total_seconds()/3600)\n df = pd.DataFrame(data)\n df['pid'] = person.pid\n\n return df","def download(self, verbose):\n\n # Download datasets\n if verbose:\n print(\"Retrieving datasets from COVID-19 Open Data by Google Cloud Platform https://github.com/GoogleCloudPlatform/covid-19-open-data\")\n # Index\n i_cols = [\"location_key\", \"country_name\", \"subregion1_name\", \"subregion2_name\", \"iso_3166_1_alpha_3\"]\n i_df = pd.read_csv(self.URL_I, usecols=i_cols)\n # Mobility\n m_df = pd.read_csv(self.URL_M)\n m_df = (m_df.set_index([\"date\", \"location_key\"]) + 100).reset_index()\n # Combine data\n df = m_df.merge(i_df, how=\"left\", on=\"location_key\")\n # Location (country/province)\n df = df.loc[df[\"subregion2_name\"].isna()]\n df[self.PROVINCE] = df[\"subregion1_name\"].fillna(self.UNKNOWN).apply(unidecode)\n df[\"country_name\"] = df[\"country_name\"].replace(\n {\n # CIV\n \"Ivory Coast\": \"Cote d'Ivoire\",\n }\n )\n return df","def extract_details(df):\n df_RSinfo = df[['pentamer', 'Step details', 'RouteScore details',\n 'Isolated', 'RouteScore', 'log(RouteScore)']]\n\n last3_rxns = ['Buchwald_deprotection', 'Buchwald', 'SNAr']\n for rxn in last3_rxns:\n df_RSinfo[rxn] = [next(step for step in row[-3:] if step['reaction'] == rxn) for row in df['Step details']]\n\n for key in df_RSinfo['RouteScore details'][0].keys():\n df_RSinfo[key] = [row[key] for row in df['RouteScore details']]\n\n return df_RSinfo","async def get_all_record():\n # X_new = item.to_df()\n # item_str = item.to_string()\n # project_code = int(item_str[item_str.find('=')+1:])\n pg = PostgreSQL()\n return_json = pg.fetch_all_records()\n return return_json","def _info(self) -> tfds.core.DatasetInfo:\n features = {\n # sequence of [RGB, depth] images\n \"image\": tfds.features.Sequence(\n tfds.features.Image(shape=(600, 800, 3)), length=2,\n ),\n # sequence of image features for [RGB, depth]\n \"images\": tfds.features.Sequence(\n tfds.features.FeaturesDict(\n {\n \"file_name\": tfds.features.Text(),\n \"height\": tf.int64,\n \"width\": tf.int64,\n \"id\": tf.int64,\n },\n ),\n length=2,\n ),\n # both modalities share the same categories\n \"categories\": tfds.features.Sequence(\n tfds.features.FeaturesDict(\n {\n \"id\": tf.int64, # {'pedstrian':1, 'vehicles':2, 'trafficlight':3, 'patch':4}\n \"name\": tfds.features.Text(),\n }\n )\n ),\n # both modalities share the same objects\n \"objects\": tfds.features.Sequence(\n {\n \"id\": tf.int64,\n \"image_id\": tf.int64,\n \"area\": tf.int64, # un-normalized area\n \"boxes\": tfds.features.BBoxFeature(), # normalized bounding box [ymin, xmin, ymax, xmax]\n \"labels\": tfds.features.ClassLabel(num_classes=5),\n \"is_crowd\": tf.bool,\n }\n ),\n # these data only apply to the \"green screen patch\" objects, which both modalities share\n \"patch_metadata\": tfds.features.FeaturesDict(\n {\n \"gs_coords\": tfds.features.Sequence(\n tfds.features.Tensor(\n shape=[2], dtype=tf.int64\n ), # green screen vertices in (x,y)\n length=4, # always rectangle shape\n ),\n \"cc_ground_truth\": tfds.features.Tensor(\n shape=[24, 3], dtype=tf.float32\n ), # colorchecker color ground truth\n \"cc_scene\": tfds.features.Tensor(\n shape=[24, 3], dtype=tf.float32\n ), # colorchecker colors in a scene\n # binarized segmentation mask of patch.\n # mask[x,y] == 1 indicates patch pixel; 0 otherwise\n \"mask\": tfds.features.Tensor(shape=[600, 800, 3], dtype=tf.uint8),\n \"shape\": tfds.features.Text(),\n }\n ),\n }\n\n return tfds.core.DatasetInfo(\n builder=self,\n description=_DESCRIPTION,\n features=tfds.features.FeaturesDict(features),\n citation=_CITATION,\n )","def names():\n\n df = pd.read_sql_query(f\"SELECT * FROM olympics_raw\", con = engine)\n print(df.head())\n \n\n # return jsonify(all_olympians)\n return jsonify(df.to_dict(orient='records'))","def getprojects(self):\n resp = self.conn.request('GET', self.URLS['allprojects'], dict(api_key=self.api_key))\n data = resp.data.decode('utf-8')\n jdata = json.loads(data)['projects']\n # Convert nested JSON documents\n for project_index in range(len(jdata)):\n for field in ('options_json', 'templates_json'):\n jdata[project_index][field] = json.loads(jdata[project_index][field])\n # Pass project details dictionaries to constructors, return array\n return [PhProject(self, project) for project in jdata]","def get_data(self):\n def _clean_search_hit(search_hit):\n \"\"\"\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\n\n :param search_hit:\n :return Dictionary: A dictionary with the cleaned data\n \"\"\"\n\n hit_name = search_hit.find(class_='hit-name')\n hit_url = hit_name.get('href')\n hit_id = hit_url.split('/')[-1]\n name = hit_name.get_text().split(',')[0].title().split()\n\n current_city = search_hit.find(class_='hit-location').get_text().upper()\n\n # Find all Addresses for search result.\n try:\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\n address = list({a.text.upper().replace('.', '') for a in address})\n except AttributeError:\n address = list()\n\n # find the address that is most likely the current main address.\n try:\n address.insert(0, address.pop(address.index(current_city)))\n except ValueError:\n address.insert(0, current_city)\n\n address = [\n {\n '@type': 'PostalAddress',\n 'addressLocality': locality.title(),\n 'addressRegion': region\n } for locality, region in [a.split(', ') for a in address]]\n\n work_location = {'@type': 'Place'}\n try:\n work_location['name'] = search_hit\\\n .find(class_='hit-work')\\\n .find(class_='hit-values')\\\n .get_text()\\\n .title()\n except AttributeError:\n work_location['name'] = ''\n\n alumni_of = {'@type': 'EducationalOrganization'}\n try:\n alumni_of['name'] = search_hit\\\n .find(class_='hit-high-school')\\\n .find(class_='hit-values')\\\n .get_text().title()\n except AttributeError:\n pass\n\n return {\n '@id': hit_id,\n '@type': 'Person',\n 'name': ' '.join(name),\n 'givenName': name[0],\n 'middleName': ' '.join(name[1:-1]),\n 'familyName': name[-1],\n 'url': hit_url,\n 'address': address,\n 'workLocation': work_location,\n 'alumniOf': alumni_of,\n }\n\n def _refine_search(search_str, options):\n \"\"\"\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\n press the option if found. Returns Boolean for found status\n\n :param search_str: str of the desired option.\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\n :return:\n \"\"\"\n search_str = search_str.upper()\n logging.info(f'Looking for \\'{search_str}\\'')\n try:\n for option in options:\n option_text = option.text.upper()\n logging.info(f'Option Checked: {option_text}')\n if search_str in option_text:\n option.click()\n time.sleep(2)\n logging.info(f'Option Selected: {option_text}')\n return True\n else:\n return False\n except AttributeError:\n return True\n except StaleElementReferenceException as e:\n ChromeCrash(e)\n\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\n driver.get(self.url)\n\n \"\"\"\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \n \"\"\"\n driver.fullscreen_window()\n\n # Refine the search by State\n address_region = self.person.get('addressRegion', '')\n address_region = STATES.get(address_region.upper(), address_region.upper())\n region_options = driver\\\n .find_element_by_class_name(\"STATE\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_region, region_options):\n return False\n\n # Narrow the search by pressing a City option\n address_locality = self.person.get('addressLocality').title()\n locality_options = driver\\\n .find_element_by_class_name(\"CITY\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_locality, locality_options):\n return False\n\n \"\"\"\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\n be done in steps with a pause between movements to allow for loading. \n Here it will first get the current location on the page, attempt to move down the page, and then check to\n see if the location changed.\n \"\"\"\n\n if self.auto_scroll and len(driver.find_elements_by_class_name(\"ais-InfiniteHits-item\")) > 15:\n current_height, new_height = 0, driver.execute_script(\"return document.body.scrollHeight\")\n\n while new_height != current_height:\n # Scroll down to the bottom of the page\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # Wait to load page\n time.sleep(SCROLL_PAUSE_TIME)\n\n # Calculate new scroll height and compare with last scroll height\n current_height, new_height = new_height, driver.execute_script(\"return document.body.scrollHeight\")\n\n page_source = driver.page_source\n page_soup = bs(page_source, 'html.parser')\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\n for i, search_result in enumerate(search_results):\n search_results[i] = _clean_search_hit(search_result)\n\n self.data_from_website = pd.DataFrame(search_results)\n self.data_from_website.set_index('@id', inplace=True)\n return True","def glass_pandas(self):\n # pandas.set_option('display.width', 120)\n # TODO timeit (git_implementation) vs (my_implementation)\n # * df = pd.DataFrame(json.loads(r.text))\n # * df = df.set_index('t')\n # * df.index = pd.to_datetime(df.index, unit='s')\n # * df = df.sort_index()\n # * s = df.v\n # * s.name = '_'.join(url.split('/')[-2:])\n # * return s\n # for elem in self.loaded:\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\n # try:\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\n # framed = pandas.DataFrame(\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\n # for k in frame_keys} for iters in range(len(_data))],\n # columns=frame_keys)\n # except KeyError:\n # framed = pandas.DataFrame(_data)\n # framed.set_index('t', inplace=True)\n # framed.index = pandas.to_datetime(\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\n # framed.sort_index(inplace=True)\n # framed.name = _metric\n # print(framed.name)\n # print(framed)","def get_info_from_row(r):\n cells = r.select(\"td\") \n # we want this data point if theres a zip, save all the info\n # including lat, long, type, \n if len(cells) > 10 and cells[2].select(\"a\"):\n id_ = re.sub(\"[^\\w\\. ]\", \"\",cells[0].text)\n data_anchor = cells[2].select(\"a\")[0]\n href = data_anchor[\"href\"]\n desc = cells[3].text\n type_ = cells[4].text\n med_ec = cells[8].text\n flow = re.sub(\"[^\\w\\.]\", \"\",cells[9].text)\n lat = re.sub(\"[^\\w\\.]\", \"\",cells[10].text)\n lon = re.sub(\"[^\\w\\.]\", \"\",cells[11].text)\n \n return {\"id\":id_, \"desc\":desc, \"type\":type_, \"med_ec\":med_ec, \"flow\":flow, \"lat\":lat, \"lon\":lon, \"href\":href}"],"string":"[\n \"def extract_project_details(\\n df,\\n copy_columns=info_columns,\\n column_rename_dict=info_column_rename_dict,\\n use_record=0,\\n record_index=\\\"PID_Index\\\",\\n):\\n df_details = df.copy().loc[df[record_index] == use_record][copy_columns]\\n\\n if column_rename_dict:\\n df_details = df_details.copy().rename(columns=column_rename_dict)\\n\\n return df_details.set_index(\\\"PID\\\")\",\n \"def personnel_projects_table(personnel_search_table_selected_indices, selected_funding, selected_year, rows):\\n personnel_outcome_data, personnel_name = personnel_outcomes_helper(personnel_search_table_selected_indices, selected_funding, selected_year, rows)\\n if personnel_outcome_data is not None:\\n personnel_outcome_data = personnel_outcome_data[[\\\"type\\\", \\\"pub_title\\\"]]\\n return personnel_outcome_data.to_dict('records')\\n\\n return pd.DataFrame(data=None, columns=[\\\"type\\\", \\\"pub_title\\\"]).to_dict('records') # None cannot be passed back as it will cause an error.\",\n \"def _createDataFromProject(self):\\n data = []\\n project_dict = self._project.asDict()\\n for phase_id, phase_dict in project_dict['phases'].items():\\n for fitable_id, fitable_dict in phase_dict['cell'].items():\\n data.append({\\n 'id': ['phases', phase_id, 'cell', fitable_id],\\n 'label': '{} {}'.format(phase_id, fitable_id),\\n 'value': fitable_dict['value'],\\n 'error': fitable_dict['error'],\\n 'refine': fitable_dict['refine']\\n })\\n return data\",\n \"def getProjectData(self) -> ghidra.framework.model.ProjectData:\\n ...\",\n \"def getIndicePreciosInternosAlPorMayorBase2015(self):\\n #Obtener la url de descarga del cvs\\n urlPackage=\\\"https://datos.gob.ar/api/3/action/package_show?id=sspm-indice-precios-internos-basicos-al-por-mayor-dic-2015-100\\\"\\n s=requests.get(urlPackage).content\\n objJson = json.loads(s)\\n resultado = objJson['result']['resources']\\n selector = 0\\n ultimoResultado = resultado[selector]\\n urlDescarga = ultimoResultado['url']\\n descripcion = ultimoResultado['description']\\n print(\\\"Descargando: {}\\\".format(descripcion))\\n print(\\\"Archivo: {}\\\".format(urlDescarga))\\n \\n #Descargar la url con cvs y generar pandas dataframe\\n contenidoCVS = requests.get(urlDescarga).content\\n flujoCVS = io.StringIO(contenidoCVS.decode('utf-8'))\\n df_temp = pd.read_csv(flujoCVS)\\n \\n #transform string to datetime\\n df_temp['indice_tiempo'] = pd.to_datetime(df_temp['indice_tiempo'], format='%Y-%m-%d', errors='ignore')\\n df_temp['indice_tiempo'] = df_temp['indice_tiempo'].dt.date\\n #set index\\n df_temp.set_index('indice_tiempo', inplace=True)\\n \\n return df_temp\",\n \"def get_reanalysis_projects_by_accession(self, accession):\\n request_url = self.api_base_url + \\\"projects/reanalysis/\\\" + accession\\n headers = {\\\"Accept\\\": \\\"application/JSON\\\"}\\n response = Util.get_api_call(request_url, headers)\\n return response.json()\",\n \"def getCitationsData():\\n # Follows https://github.com/simonw/irma-scrapers/issues/1\\n citationsResponse = requests.get(\\\"https://api.github.com/repos/greenelab/covid19-review/git/trees/output\\\", headers=headers).json()\\n treeEntry = [t for t in citationsResponse[\\\"tree\\\"] if t[\\\"path\\\"] == \\\"references.json\\\"][0] \\n citations = json.loads(base64.b64decode(requests.get(treeEntry[\\\"url\\\"]).json()[\\\"content\\\"]))\\n\\n citationsDF = pd.DataFrame(citations)\\n citationsDF[\\\"Covid19-review_paperLink\\\"] = citationsDF.id.apply(lambda x: \\\"https://greenelab.github.io/covid19-review/#ref-\\\" + x)\\n citationsDF = citationsDF[[\\\"DOI\\\", \\\"title\\\", \\\"issued\\\", \\\"container-title\\\", \\\"URL\\\", \\\"Covid19-review_paperLink\\\"]]\\n citationsDF.rename(columns={\\\"DOI\\\": \\\"doi\\\", \\\"issued\\\": \\\"date\\\", \\\"container-title\\\": \\\"publication\\\"}, inplace=True)\\n\\n # Convert date to string\\n def dateStringFromDateParts(row):\\n try:\\n dateParts = row['date']['date-parts'][0]\\n if len(dateParts) == 3:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[2]), str(dateParts[0])])\\n elif len(dateParts) == 2:\\n return \\\"-\\\".join([str(dateParts[1]), str(dateParts[0])])\\n elif len(dateParts) == 1:\\n return str(dateParts[0])\\n else:\\n return\\n except:\\n return\\n\\n citationsDF.date = citationsDF.apply(dateStringFromDateParts, axis=1)\\n\\n citationsDF.set_index(\\\"doi\\\", inplace=True)\\n return citationsDF\",\n \"def extract_details( session_requests, job_id ):\\n \\n url_prefix = CONFIG[\\\"url_prefix\\\"]\\n \\n #Extract html from web\\n url = CONFIG[\\\"url_jobno\\\"] + str(job_id)\\n tree = scrape_html(session_requests, url)\\n \\n #Extact description\\n title = \\\"; \\\".join(tree.xpath(\\\"//p[@class='listheader']/text()\\\"))\\n description = \\\"; \\\".join(tree.xpath(\\\"//p//text()\\\")) #more than one element\\n \\n #Extract files\\n num_file = int(tree.xpath(\\\"count(//p[contains(text(),'Job Description Document :')]//a)\\\"))\\n loop_range = min(num_file, (MAX_NUM_OF_FILES - 1))\\n \\n file_link = [\\\"NA\\\"] * MAX_NUM_OF_FILES\\n file_name = [\\\"NA\\\"] * MAX_NUM_OF_FILES\\n down_file_name = [\\\"NA\\\"] * MAX_NUM_OF_FILES\\n \\n if (num_file > (MAX_NUM_OF_FILES - 1)):\\n file_link[(MAX_NUM_OF_FILES - 1)] = \\\"More than 9 files\\\"\\n file_name[(MAX_NUM_OF_FILES - 1)] = \\\"More than 9 files\\\"\\n \\n for i in range(loop_range):\\n file_link[i] = url_prefix + tree.xpath(\\\"//p[contains(text(),'Job Description Document :')]//a/@href\\\")[i]\\n file_name[i] = tree.xpath(\\\"//p[contains(text(),'Job Description Document :')]//a/text()\\\")[i]\\n \\n ext = find_file_extention(file_name[i])\\n down_file_name[i] = download_file(session_requests, file_link[i], job_id, i, ext)\\n \\n # dataframe\\n row_names_link = init_file_dataframe()[1]\\n row_names_name = init_file_dataframe()[2]\\n row_names_down = init_file_dataframe()[3]\\n \\n df_link = np.transpose(pd.DataFrame(file_link, row_names_link))\\n df_name = np.transpose(pd.DataFrame(file_name, row_names_name))\\n df_down = np.transpose(pd.DataFrame(down_file_name, row_names_down))\\n \\n df_file = pd.DataFrame(data = {\\\"job_title\\\": [title], \\\"description\\\": [description], \\\"num_of_file\\\": [loop_range]})\\n df_file = pd.concat([df_file.reset_index(drop=True), df_link], axis=1, sort=False)\\n df_file = pd.concat([df_file.reset_index(drop=True), df_name], axis=1, sort=False)\\n df_file = pd.concat([df_file.reset_index(drop=True), df_down], axis=1, sort=False)\\n \\n return df_file\",\n \"def get_data(self, **kwargs):\\n\\n self.data = {}\\n #node_data = ''\\n #link_data = ''\\n templates_data = self.request_from_server('templates')\\n self.templates = templates_data\\n project_data = self.request_from_server('projects')\\n for project in project_data:\\n project_name = project['name']\\n if 'project_name' in kwargs:\\n if project_name != kwargs['project_name']:\\n continue\\n\\n self.data[project_name] = {}\\n self.data[project_name]['project_id'] = project['project_id']\\n self.data[project_name]['nodes'] = {}\\n node_data = self.request_from_server('projects/{}/nodes'.format(project['project_id']))\\n link_data = self.request_from_server('projects/{}/links'.format(project['project_id']))\\n for node in node_data:\\n node_name = node['name']\\n self.data[project_name]['nodes'][node_name] = {}\\n self.data[project_name]['nodes'][node_name]['node_id'] = node['node_id']\\n self.data[project_name]['nodes'][node_name]['template_id'] = node['template_id']\\n self.data[project_name]['nodes'][node_name]['node_type'] = node['node_type']\\n self.data[project_name]['nodes'][node_name]['console_port'] = node['console']\\n self.data[project_name]['nodes'][node_name]['console_session'] = None\\n self.data[project_name]['nodes'][node_name]['x'] = node['x']\\n self.data[project_name]['nodes'][node_name]['y'] = node['y']\\n self.data[project_name]['nodes'][node_name]['ports'] = {}\\n if project['status'] != 'closed':\\n self.data[project_name]['nodes'][node_name]['status'] = node['status']\\n for port in node['ports']:\\n port_name = port['short_name']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name] = {}\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['adapter_number'] = port['adapter_number']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['port_number'] = port['port_number']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_type'] = port['link_type']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = None\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = False\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = None\\n for link in link_data:\\n for link_node in link['nodes']:\\n if node['node_id'] == link_node['node_id']:\\n if link_node['label']['text'] == port_name:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['link_id'] = link['link_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['in_use'] = True\\n if link['nodes'].index(link_node) == 0:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][1]['node_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][1]['node_id'])\\n else:\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to_id'] = link['nodes'][0]['node_id']\\n self.data[project_name]['nodes'][node_name]['ports'][port_name]['connected_to'] = self.get_node_name_from_id(project_name,link['nodes'][0]['node_id'])\",\n \"def _parse_project_params(self, response_tree, data):\\n # if self.project == 'TWC':\\n # data['lithium'] = response_tree.find('lithiumresults').text\\n\\n if self.project == 'ILAR':\\n data['ilar_title'] = response_tree.find('ilartitle').text\\n data['ilar_rank'] = int(response_tree.find('ilarrank').text)\\n\\n if response_tree.find('ilarresponse').text is None:\\n data['ilar_response'] = None\\n else:\\n data['ilar_response'] = response_tree.find(\\n 'ilarresponse').text.encode('ascii', 'ignore')\",\n \"def fetch_data(self) -> pd.DataFrame:\\r\\n os.chdir(r'\\\\\\\\192.168.8.90\\\\投研部\\\\Jessica\\\\test_data')\\r\\n if self.tic in ['RB.CCRI', 'HC.CCRI', 'I.CCRI', 'J.CCRI', 'JM.CCRI', 'ZC.CCRI']:\\r\\n f = pd.read_hdf('data.h5', 'snc')\\r\\n if self.tic in ['CU.CCRI', 'ZN.CCRI', 'AL.CCRI', 'NI.CCRI']:\\r\\n f = pd.read_hdf('data.h5', 'met')\\r\\n data = f.loc[f.loc[:, 'sec_code'] == self.tic, :]\\r\\n # extract I.CCRI data\\r\\n table = pd.pivot_table(data, index=['date'], columns=['factor_code'], values='factor_value')\\r\\n table = table.sort_values(by='date')\\r\\n \\r\\n return table\",\n \"def yaml_parser_preprocess_pipeline_input_parameter_get_description_df(config):\\n df= pandas.DataFrame.from_dict(config['query_system']['profile_description'], orient = 'index')\\n df['query_protein_name'] = df.index\\n return df\",\n \"def extract_data():\\n raw_data = pd.read_csv(\\\"../../../resource/DataVisualization/vaccinations.csv\\\")\\n raw_data = raw_data[[\\\"location\\\", \\\"date\\\", \\\"people_fully_vaccinated_per_hundred\\\"]]\\n raw_data.date = pd.to_datetime(raw_data.date, format=\\\"%Y-%m-%d\\\")\\n min_date = raw_data.date.min()\\n raw_data.date = raw_data.date-min_date\\n raw_data.date = pd.Series([x.days for x in raw_data.date])\\n raw_data.drop(raw_data.loc[raw_data.people_fully_vaccinated_per_hundred.isnull()].index,\\n axis=0, inplace=True)\\n raw_data[\\\"people_fully_vaccinated_per_hundred\\\"] /= 100\\n\\n data_dict = dict()\\n for country in raw_data.location.unique():\\n if len(raw_data.loc[raw_data.location == country]) >= 100:\\n tmp_data = raw_data.loc[raw_data.location == country]\\n tmp_data.drop(\\\"location\\\", axis=1, inplace=True)\\n data_dict[country] = {\\\"data\\\":tmp_data}\\n else:\\n raw_data.drop(raw_data.loc[raw_data.location ==\\n country].index, inplace=True)\\n return data_dict, min_date, raw_data\",\n \"def _createDataFromProject(self):\\n project_dict = self._project.asDict()\\n data_blocks = {}\\n headers_blocks = {}\\n for experiment_id, experiment_dict in project_dict['experiments'].items():\\n data = []\\n headers = []\\n for data_id, data_list in experiment_dict['calculated'].items():\\n headers.append(data_id)\\n data.append(data_list)\\n headers_blocks[experiment_id] = headers\\n data_transposed = [*zip(*data)]\\n data_blocks[experiment_id] = data_transposed\\n return headers_blocks, data_blocks\",\n \"def _createDataFromProject(self, project):\\n data = []\\n headers = []\\n project_dict = project.asDict()\\n for experiment_id, experiment_dict in project_dict['experiments'].items():\\n for data_id, data_list in experiment_dict['measured'].items():\\n headers.append(data_id)\\n data.append(data_list)\\n data_transposed = [*zip(*data)]\\n return headers, data_transposed\",\n \"def lifedata_hpi():\\n\\n # A constant that defines the record fields that we wish to retrieve.\\n FIELDS = {\\n '_id': False, 'Country': True, 'CountryCode': True, 'Life Expectancy': True, 'Well-being(0-10)': True, 'Happy Life Years': True, 'Happy Planet Index': True,\\n 'Population': True, 'GDP/capita': True, 'Governance Rank(1 - highest gov.)': True\\n }\\n\\n # Open a connection to MongoDB using a with statement such that the\\n # connection will be closed as soon as we exit the with statement\\n with MongoClient(MONGODB_HOST, MONGODB_PORT) as conn:\\n # Define which collection we wish to access\\n collection = conn[DBS_NAME][COLLECTION_NAME]\\n # Retrieve a result set only with the fields defined in FIELDS\\n # and limit the the results to 55000\\n projects = collection.find(projection=FIELDS, limit=55000)\\n # Convert projects to a list in a JSON object and return the JSON data\\n return json.dumps(list(projects))\",\n \"def xnat_workflow_info_show(args):\\n\\trequest_url = \\\"http://\\\" + args.server + \\\"/data/services/workflows/workflowid/\\\" + args.workflow_id + \\\"?format=json\\\"\\n\\tprint(\\\"xnat_workflow_info show: request_url: \\\" + request_url)\\n\\tresponse = requests.get(request_url, auth=(args.username, args.password))\\n\\tif (response.status_code != 200):\\n\\t\\tprint(\\\"Cannot get response from request: \\\" + request_url)\\n\\t\\tsys.exit(1)\\n\\n\\tjson_response = json.loads(response.text)\\n\\tjson_items = json_response['items']\\n\\ti = 0\\n\\tfor json_item in json_items:\\n\\t\\ti = i + 1\\n\\t\\tprint i\\n\\n\\t\\t# meta\\n\\t\\tjson_meta = json_item['meta']\\n\\t\\tisHistory = json_meta['isHistory']\\n\\t\\ttype = json_meta['xsi:type']\\n\\t\\tstart_date = json_meta['start_date']\\n\\n\\t\\tprint \\\" isHistory: \\\" + str(isHistory)\\n\\t\\tprint \\\" type: \\\" + type\\n\\t\\tprint \\\" start_date: \\\" + start_date\\n\\t\\n\\t\\t# children\\n\\t\\t#json_children = json_item['children']\\n\\t\\t#print \\\" children\\\"\\n\\t\\t#print json_children\\n\\n\\t\\t# data_fields\\n\\t\\tjson_data_fields = json_item['data_fields']\\n\\t\\tstatus = json_data_fields['status']\\n\\t\\tworkflow_id = json_data_fields['wrk_workflowData_id']\\n\\t\\tdata_type = json_data_fields['data_type']\\n\\t\\tlaunch_time = json_data_fields['launch_time']\\n\\t\\tExternalID = json_data_fields['ExternalID']\\n\\t\\tpipeline_name = json_data_fields['pipeline_name']\\n\\t\\tID = json_data_fields['ID']\\n\\t\\n\\t\\tprint \\\" status: \\\" + status\\n\\t\\tprint \\\" workflow_id: \\\" + str(workflow_id)\\n\\t\\tprint \\\" data_type: \\\" + data_type\\n\\t\\tprint \\\" launch_time: \\\" + launch_time\\n\\t\\tprint \\\" ExternalID: \\\" + ExternalID\\n\\t\\tprint \\\" pipeline_name: \\\" + pipeline_name\\n\\t\\tprint \\\" ID: \\\" + ID\\n\\n\\t\\tprint \\\" All Data Fields:\\\"\\n\\t\\tprint \\\" \\\" + str(json_data_fields)\",\n \"def _get_data(self):\\n project_name, experiment_id = self.parent._get_parent_identifiers()\\n\\n self._data = self.repository.get_dataframe_data(\\n project_name, self.id, experiment_id=experiment_id\\n )\",\n \"def get_details(i, df):\\n print(\\\"Name:\\\", df.name.loc[i])\\n print(\\\"Gender:\\\", df.gender.loc[i])\\n print(\\\"Age in months:\\\", df.age.loc[i])\\n print(\\\"Breed:\\\", df.breed1_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"),df.breed2_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"))\\n print(\\\"Color/s:\\\", df.color1_desc.loc[i].title(), df.color2_desc.loc[i].title(), df.color3_desc.loc[i].title())\\n print(\\\"Fur Length:\\\", df.fur_length.loc[i])\\n print(\\\"Vaccinated:\\\", df.vaccinated.loc[i])\\n print(\\\"Dewormed:\\\", df.dewormed.loc[i])\\n print(\\\"Spayed or Neutered:\\\", df.sterilized.loc[i])\\n print(\\\"Health:\\\", df.health.loc[i])\\n print(\\\"No. of Pets in this Listing:\\\", df.quantity.loc[i])\\n if df.fee.loc[i] == 0:\\n print(\\\"Adoption Fee: FREE\\\")\\n else:\\n print(\\\"Adoption Fee: MYR\\\", round(df.fee.loc[i],2))\\n print(\\\"Location :\\\", df.state_desc.loc[i].title().replace(\\\"_\\\",\\\" \\\"))\\n print(\\\"Description :\\\", df.description.loc[i])\\n print_images(i, df)\",\n \"def main():\\n\\n # Instantiate the class\\n my_data = DataProject(TRAIN_URL, TEST_URL, HEADER_URL)\\n\\n # Check which columns contain missing data\\n print(my_data.get_na_cols('train_df'))\\n\\n # We see 'workclass', 'occupation', 'native-country' all have missing\\n # values. However, these are all categorical columns. Let's try\\n # seeing what we can from the numerical columns\\n print(my_data.train_df.describe())\\n\\n \\\"\\\"\\\"see:\\n age goes from 17-90, but doesn't look too surprising.\\n fnlwgt is a weight that is given to each line to show how representative\\n of the full population it is (at least, how representative of the\\n state it was drawn from??).\\n education-num is actually a classification of different categories of\\n education, and maps one-to-one with that column. I will drop this next\\n capital-gain and capital-loss -- these seem reasonable, but are highly\\n skewed to the right (up to the 75% is 0). So let's take a look at that\\n distribution to see what it looks like\\n hours-per-week shows nothing too extrodinary, except that the max is\\n 99 hours a week! If this person is doing this weekly, although it is\\n do-able, this is incredible!\\n \\\"\\\"\\\"\\n \\n # as we noted in our observation, education-num is just a numbering\\n # of the education factors. So, remove, since it doesn't give any extra\\n # information\\n my_data.train_df.drop('education-num', axis=1, inplace=True)\\n my_data.test_df.drop('education-num', axis=1, inplace=True)\\n\\n # Now, let's see if we can spot anything if we visualize the data.\\n # Note, the scatter_matrix method grabs all numerical columns and shows\\n # a nice grid of plots to make it easy to see the distribution of the\\n # values in these columns, and any correlations that might exist between\\n # columns.\\n #\\n # (PS, I realize if we are just executing the script directly, the plots\\n # will just flash by, but I didn't want to clutter the HD with saved\\n # images, so I assume you'll be following along this part by just running\\n # the things in main() one by one)\\n pd.plotting.scatter_matrix(my_data.train_df)\\n plt.show()\\n\\n \\\"\\\"\\\"see:\\n A good amount of binning of age and hours-per-week into discrete values\\n (probably integer values for each). Also, perhaps an overabundance of\\n people at age 90 and that work 99 hours a week, but these are probably\\n individuals that go even higher, but were truncated at these values.\\n Overall, nothing really out of the ordinary, except for in capital-gain,\\n it looks like there is a big jump from most of the values up to 99999\\n (nothing between ~50k to 99,999), with the vast majority of the\\n values at 0 (makes sense, since if we look at the capital-gain vs\\n capital-loss plot, it looks like we can only have non-zero values for\\n one of those categories.) Let's try looking at captial-gain without\\n the capital-loss individuals\\n \\\"\\\"\\\"\\n\\n # Plot capital-gains without people that have a capital-loss (who are given\\n # capital-gain of 0)\\n my_data.train_df['capital-gain']\\\\\\n [my_data.train_df['capital-gain'] != 0].hist()\\n plt.show()\\n\\n \\\"\\\"\\\"see:\\n True enough, it looks like there is an over-abundance of values for\\n individuals with a capital-gain of 99,999. Let's have pandas calculate\\n the quartiles to confirm.\\n \\\"\\\"\\\"\\n\\n # Get the quartiles for the capital-gain column, excluding the capital-loss\\n # individuals\\n my_data.train_df['capital-gain']\\\\\\n [my_data.train_df['capital-gain'] != 0].describe()\\n\\n \\\"\\\"\\\"Indeed, it looks like the 99,999 values are outliers, as the 75% of\\n the non-0 values is at 14,084.\\n \\\"\\\"\\\"\\n\\n # I was originally going to try to fit a gaussian distribution to the\\n # histogram to re-assign the 99,9999 individuals based on it, but if\\n # we take a look at:\\n my_data.train_df['capital-gain'][\\n (my_data.train_df['capital-gain'] != 0) &\\n (my_data.train_df['capital-gain'] < 75000)].hist(bins=100)\\n plt.show()\\n\\n # We see this is a tri-modal distribution (it'll be interesting to\\n # see what makes up the groups at the 7-8 thousand range, and the group\\n # at ~15 tousand).So, instead, replace with a value randomly picked from\\n # the exact non-zero, non-99,9999 distribution. First, need to grab\\n # these values\\n good_val = my_data.train_df['capital-gain'][\\n (my_data.train_df['capital-gain'] != 0) &\\n (my_data.train_df['capital-gain'] < 99999)].values\\n\\n # Now, define a function that will return a random instance of\\n # the good values\\n def get_good_val(vals=good_val):\\n \\\"\\\"\\\"quick helper function to allow easy re-assigning of the outlier\\n values in the capital-gain column, preserving the non-zero\\n distribution.\\n\\n Args:\\n vals (np.ndarray): The non-zero, non-99,999 values from the\\n capital-gain column\\n\\n Returns:\\n int: A random value from the distribution\\n \\\"\\\"\\\"\\n\\n # Return a value from the distribution randomly\\n return vals[rand.randint(0, (vals.shape[0] - 1))]\\n\\n # Now, use the helper function to reset outlier values based on the\\n # non-zero, non-outlier distribution\\n # First, define a mask to save the conditions we want\\n mask = (my_data.train_df['capital-gain'] == 99999)\\n\\n # Now, for these outlier values, reset them based on the good values\\n # distribution\\n my_data.train_df.loc[mask, 'capital-gain'] = (\\n my_data.train_df[mask]['capital-gain'].apply(lambda x: get_good_val())\\n )\\n\\n # Check the distribution that it looks ok\\n my_data.train_df[my_data.train_df['capital-gain'] != 0]['capital-gain']\\\\\\n .hist(bins=40)\\n plt.show()\\n\\n # Looking at the test dataset:\\n pd.plotting.scatter_matrix(my_data.test_df)\\n plt.show()\\n\\n # It looks like we have a similar problem, so repeat the procedure\\n # For the test set, using the test set's distribution\\n good_val = my_data.test_df['capital-gain'][\\n (my_data.test_df['capital-gain'] != 0) &\\n (my_data.test_df['capital-gain'] < 99999)].values\\n\\n # Define the mask\\n mask = (my_data.test_df['capital-gain'] == 99999)\\n\\n # Now, for these outlier values, reset them based on the good values\\n # distribution\\n my_data.test_df.loc[mask, 'capital-gain'] = (\\n my_data.test_df[mask]['capital-gain'].apply(\\n lambda x: get_good_val(good_val))\\n )\\n\\n # Check the distribution that it looks ok\\n my_data.test_df[my_data.test_df['capital-gain'] != 0]['capital-gain']\\\\\\n .hist(bins=40)\\n plt.show()\",\n \"def generate_personnel_trend_graph(selected_funding, selected_year, selected_row_indices, rows, search_ids_personnel, search_ids_professors_keywords, personnel_name, search_keywords):\\n\\n names = []\\n personnel = pd.DataFrame(rows)\\n if selected_row_indices:\\n names = personnel[\\\"Personnel\\\"].iloc[selected_row_indices]\\n\\n\\n filtered_data = funding_data[\\n funding_data.start_year.isin(selected_year)\\n # & funding_data['Submitting Institution Name:'].isin(selected_uni)\\n # & funding_data['Project Status:'].isin(selected_award_status)\\n & funding_data['Program Cycle:'].isin(selected_funding)]\\n\\n\\n selected_personnel_data = filtered_data.loc[filtered_data[\\\"Lead Investigator:\\\"].isin(names)]\\n\\n\\n\\n # Getting the projects of the selected names from personnel_data\\n\\n personnel_data_filtered = personnel_data\\n personnel_data_filtered = personnel_data_filtered[\\n personnel_data_filtered[\\\"Proposal Number:\\\"].isin(filtered_data[\\\"Proposal Number:\\\"])]\\n\\n personnel_data_filtered = personnel_data_filtered[personnel_data_filtered[\\\"investigator\\\"].isin(names)]\\n\\n # A pandas dataframe containg the list of projects for each selected personnel\\n\\n if search_keywords and len(search_keywords) > 0:\\n if len(search_ids_professors_keywords) > 0:\\n search_ids_professors_keywords = json.loads(search_ids_professors_keywords)\\n search_ids_professors_keywords = np.array(search_ids_professors_keywords)\\n\\n # Using the dataset only to get relevant names of the personnel who are involved in the project\\n # from personnel_data\\n filtered_data = filtered_data[filtered_data._id.isin(search_ids_professors_keywords[0:, 0])]\\n list_of_projects = filtered_data[\\\"Proposal Number:\\\"]\\n personnel_data_filtered = personnel_data_filtered[\\n personnel_data_filtered[\\\"Proposal Number:\\\"].isin(list_of_projects)]\\n\\n # print(\\\"Personnel Projects list is \\\", personnel_projects_list)\\n # personnel_names = personnel_data_filtered[\\\"investigator\\\"].unique()\\n # print(\\\"Personnel Who have worked in this field are \\\", personnel_names)\\n\\n personnel_projects_list = personnel_data_filtered.groupby('investigator')['Proposal Number:'].apply(list)\\n print(\\\"Personnel Projects list is \\\", personnel_projects_list)\\n\\n\\n\\n\\n # if personnel_name and len(personnel_name) > 0:\\n # if len(search_ids_personnel) > 0:\\n # # Using the dataset only to get relevant names of the personnel who are involved in the project\\n # # from personnel_data\\n #\\n # personnel_data_filtered = personnel_data_filtered[personnel_data_filtered._id.isin(search_ids_personnel)]\\n #\\n # # Filtering the datasets based on search ids and filtered_data\\n # # Find a better way to do filters by join?\\n #\\n # filtered_data = filtered_data[\\n # filtered_data[\\\"Proposal Number:\\\"].isin(personnel_data_filtered[\\\"Proposal Number:\\\"])]\\n #\\n # personnel_projects_list = personnel_data_filtered[[\\\"investigator\\\", \\\"Proposal Number:\\\"]].groupby(\\n # 'investigator').agg({\\\"Proposal Number:\\\": lambda x: [].append(x)})\\n #\\n # print(\\\"Personnel Projects list is \\\", personnel_projects_list)\\n # personnel_names = personnel_data_filtered[\\\"investigator\\\"].unique()\\n # print(\\\"Personnel Who have worked in this field are \\\", personnel_names)\\n\\n\\n def make_project_count_dict(df):\\n \\\"\\\"\\\"\\n From the DF that is passed ( containing one personnel with all his projects, calculates the number of years a\\n project was active,\\n :param df:\\n :return: An Ordered Dictionary with the number of projects active per year for that personnel.\\n \\\"\\\"\\\"\\n year_lists = []\\n null_date_info_projects = 0\\n for i in range(len(df)):\\n start_date = df[\\\"Start Date:\\\"].iloc[i]\\n end_date = df[\\\"End Date:\\\"].iloc[i]\\n if (start_date == -1) or (end_date == -1):\\n null_date_info_projects += 1\\n continue\\n year_lists.append(list(range(start_date.year, end_date.year + 1))) # +1 because the project is active that year. It needs to show on graph\\n print(year_lists)\\n year_count_dict = OrderedDict.fromkeys(range(2000, datetime.now().year + 5), 0)\\n print(year_count_dict)\\n for i in year_lists:\\n for j in i:\\n year_count_dict[j] += 1\\n return year_count_dict, null_date_info_projects\\n\\n def make_traces(names, personnel_projects_list):\\n traces = []\\n null_date_info_projects = 0\\n for i in names:\\n list_of_projects = personnel_projects_list.loc[i]\\n # list_of_projects = personnel_projects_list[\\\"Proposal Number:\\\"].loc[personnel_projects_list[\\\"investigator\\\"] == i]\\n list_of_project_records = filtered_data.loc[filtered_data[\\\"Proposal Number:\\\"].isin(list_of_projects)] # Passing the DF containing all the projects that a personnel was involved with\\n personnel_year_count_dict, null_date_info_projects = make_project_count_dict(list_of_project_records)\\n sample_trace_object = go.Scatter(\\n x=list(personnel_year_count_dict.keys()),\\n y=list(personnel_year_count_dict.values()),\\n opacity=0.7,\\n # Adding the null_date_info_projects into the name\\n name=i +\\\" \\\"+ str(null_date_info_projects),\\n line=dict(\\n # color=('rgb(22, 96, 167)'),\\n width=4,\\n shape='hv'\\n # colorscale='YlGnBu'\\n ),\\n mode='lines+markers'\\n )\\n traces.append(sample_trace_object)\\n return traces\\n\\n traces = make_traces(names, personnel_projects_list)\\n\\n\\n # years = [i for i in range(2008,2018)]\\n # numbers = random.choices(population=[0, 1, 2, 3], k=len(years))\\n\\n # personnel = \\\"Doctor Name\\\"\\n\\n # trace1 = go.Scatter(\\n # x=years,\\n # y=numbers,\\n # name='Professor Name Here',\\n # line=dict(\\n # color=('rgb(22, 96, 167)'),\\n # width=4,\\n # ),\\n # mode='lines+markers'\\n # )\\n\\n\\n return {\\n 'data': traces,\\n 'layout': go.Layout(\\n # autosize=False,\\n title='Projects in a year for selected personnel',\\n xaxis=dict(\\n # tickangle=-45,\\n zeroline=False, # no thick y=0 line\\n showgrid=False, # no horizontal grid lines\\n showticklabels=True, # no y-axis tick labels\\n dtick=1,\\n ),\\n yaxis=dict(\\n # type='log'\\n dtick=1,\\n ),\\n showlegend=True,\\n # legend=go.Legend(\\n # x=0,\\n # y=1.0\\n # ),\\n # legend=dict(x=.75, y=1),\\n # bargap =0.5,\\n # width = 100,\\n # autosize=F,\\n margin=go.layout.Margin(l=40, r=0, t=30, b=40),\\n # paper_bgcolor='rgb(233,233,233)', # set paper (outside plot)\\n plot_bgcolor='rgb(192,192,192)', # plot color to grey\\n )\\n }\",\n \"def _deep_data(self, url):\\n def _nested_persons(persons):\\n _persons = list()\\n for person_ in persons:\\n person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]\\n person = {'name': person_[0][0].title()}\\n if len(person_[0]) == 2:\\n person['age'] = person_[0][1]\\n\\n if len(person_[1]) > 0:\\n person['addressLocality'] = person_[1][0].title()\\n if len(person_[1]) == 2:\\n person['addressRegion'] = person_[1][1].upper()\\n\\n _persons.append(person)\\n return _persons\\n\\n with self.driver(self.DRIVER_DIR) as driver:\\n driver.get(url)\\n driver.fullscreen_window()\\n time.sleep(2)\\n txt = driver.page_source\\n\\n soup = bs(txt, 'html.parser')\\n\\n profile_data = soup.find(type=\\\"application/ld+json\\\")\\n if profile_data is None:\\n self._raise_site_schema_change()\\n profile_data = profile_data.string\\n profile_data = json.loads(profile_data, strict=False)\\n profile_data['@id'] = profile_data.pop('@id').split('/')[-1]\\n\\n try:\\n about = profile_data.pop('about')\\n for k, v in about.items():\\n profile_data[k] = v\\n except KeyError:\\n pass\\n\\n name_ = profile_data.pop('name')\\n profile_data['name'] = name_\\n\\n name_ = name_.split()\\n profile_data['givenName'] = name_[0]\\n profile_data['middleName'] = ' '.join(name_[1:-1])\\n profile_data['familyName'] = name_[-1]\\n\\n if soup.find(class_='rep-vcard-score') is not None:\\n profile_data['reputation_score'] = \\\"{min}-{max}\\\".format(\\n min=soup.find(class_='rep-vcard-min').text,\\n max=soup.find(class_='rep-vcard-max').text\\n )\\n\\n address = list()\\n address_ = soup.find_all(class_='card-address')\\n for a in address_:\\n street_address, locality_region_postal, *misc = [_.text for _ in a.find_all(class_='block-container')]\\n address_locality, locality_region_postal = locality_region_postal.split(',')\\n address_region, postal_code = locality_region_postal.split()\\n address.append({\\n 'streetAddress': street_address,\\n 'addressLocality': address_locality,\\n 'addressRegion': address_region,\\n 'postalCode': postal_code,\\n })\\n\\n profile_data['address'] = address\\n\\n personal_details = soup.find(class_='card-personal-details')\\n if personal_details is not None:\\n personal_details = personal_details.find_all(class_='item-container')\\n personal_details = [detail.text.split(': ') for detail in personal_details]\\n personal_details = [_ for _ in personal_details if len(_) == 2]\\n personal_details = {detail.lower().replace(' ', '_'): value for\\n detail, value in personal_details if value != 'Add Info'}\\n\\n birth_date = personal_details.pop('date_of_birth')\\n if len(birth_date) > 0:\\n profile_data['birthDate'] = birth_date\\n\\n for key_, value_ in personal_details.items():\\n profile_data[key_] = value_\\n\\n # Education\\n schools_ = soup.find(class_='card-education')\\n if schools_ is not None:\\n schools = list()\\n schools_ = schools_.find_all(class_='card-content')\\n for school in schools_:\\n school = [detail.text.split(': ') for detail in school.find_all(class_='item-container')]\\n school = {detail.lower().replace(' ', '_'): value for\\n detail, value in school if value != 'Add Info'}\\n\\n if len(school) == 0:\\n continue\\n\\n school['@type'] = 'EducationalOrganization'\\n school['name'] = school.pop('school')\\n school['streetAddress'], school['addressLocality'] = school.pop('city').split(', ')\\n schools.append(school)\\n\\n # Work\\n employers = soup.find(class_='card-job')\\n if employers is not None:\\n works_for = list()\\n employers = employers.find_all(class_='card-content')\\n for employer in employers:\\n employer = [detail.text.split(': ') for detail in employer.find_all(class_='item-container')]\\n employer = {detail.lower().replace(' ', '_'): value for\\n detail, value in employer if value != 'Add Info'}\\n\\n if len(employer) == 0:\\n continue\\n\\n employer['@type'] = 'Organization'\\n try:\\n employer['name'] = employer.pop('company')\\n except KeyError:\\n continue\\n\\n if len(employer.get('city', '')) > 0:\\n employer['streetAddress'], employer['addressLocality'] = employer.pop('city').split(', ')\\n\\n works_for.append(employer)\\n\\n if len(works_for) > 0:\\n profile_data['worksFor'] = works_for\\n\\n # Automobiles\\n automobiles = soup.find(class_='card-auto')\\n if automobiles is not None:\\n owns = list()\\n automobiles = automobiles.find_all(class_='card-content')\\n for automobile in automobiles:\\n automobile = [detail.text.split(': ') for detail in automobile.find_all(class_='item-container')]\\n automobile = {detail.lower().replace(' ', '_'): value for\\n detail, value in automobile if value != 'Add Info'}\\n\\n if len(automobile) == 0:\\n continue\\n\\n automobile['@type'] = 'Product'\\n automobile['model'] = ' '.join([\\n automobile.pop('year'),\\n automobile.pop('make'),\\n automobile.pop('model')\\n ])\\n owns.append(automobile)\\n\\n if len(owns) > 0:\\n profile_data['owns'] = owns\\n\\n profile_data['relatedTo'] = _nested_persons(soup.find_all(class_='relative-container'))\\n profile_data['neighbors'] = _nested_persons(soup.find_all(class_='neighbor-container'))\\n\\n # Photos\\n profile_data['pictures'] = list({photo['src'] for photo in soup.find_all(class_='profile-picture-holder')})\\n return profile_data\",\n \"def get_data():\\n \\n \\\"\\\"\\\" Prepare variables\\\"\\\"\\\"\\n urls = {\\\"cases\\\": \\\"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv\\\",\\n \\\"deaths\\\": \\\"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv\\\",\\n \\\"recovered\\\": \\\"https://github.com/CSSEGISandData/COVID-19/raw/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv\\\"}\\n\\n localnames = {\\\"cases\\\": \\\"Cases.csv\\\",\\n \\\"deaths\\\": \\\"Deaths.csv\\\",\\n \\\"recovered\\\": \\\"Recovered.csv\\\"}\\n\\n dfs = {\\\"cases\\\": None,\\n \\\"deaths\\\": None,\\n \\\"recovered\\\": None}\\n\\n \\\"\\\"\\\" Download\\\"\\\"\\\"\\n for key in urls.keys():\\n url = urls[key]\\n localname = localnames[key]\\n urllib.request.urlretrieve(url, localname)\\n\\n \\\"\\\"\\\" Load variables\\\"\\\"\\\"\\n for key in dfs.keys():\\n dfs[key] = pd.read_csv(localnames[key])\\n \\n \\\"\\\"\\\" Return\\\"\\\"\\\"\\n return(dfs)\",\n \"def get_individual_df(nombre):\\n clusters = []\\n contadores = []\\n for k, v in mydic[nombre][\\\"contador\\\"].items():\\n clusters.append(k)\\n contadores.append(v)\\n return pd.DataFrame({\\\"CODIGO_POSTAL\\\": clusters, f\\\"contadores_{nombre}\\\": contadores})\",\n \"def get_metadata(datas):\\n\\n required_profile_key, required_item_key = 'basics', 'category'\\n utility_matrix = []\\n\\n item_details, profile_details = {}, {}\\n\\n for _ , data in datas.items():\\n profile_name, item_name = None, None\\n\\n # extracted profile data for user, because two user may have the same nick name\\n # so we will concatenate user nick name and its profile link to form the unique one.\\n if type(data) is dict and required_profile_key in data.keys() :\\n profile_name = \\\"%s|%s\\\" % (data[required_profile_key].get('name',''),\\n data[required_profile_key].get('profile',''))\\n\\n profile_details[profile_name] = get_profile_detail(data)\\n print ('extracted data of profile: %s ...' % data[required_profile_key].get('name',''))\\n\\n # for item\\n if type(data) is dict and required_item_key in data.keys():\\n if hasattr(data[required_item_key],'items'):\\n for k,v in data[required_item_key].items():\\n\\n item_detail = get_item_detail(v)\\n print ('extracted data for category %s ...' % k)\\n\\n #item_detail_to_str = json.dumps(item_detail) #\\\" ; \\\".join(list(set(item_detail)))\\n if k in item_details:\\n item_details[k]['work'].append(item_detail['work'])\\n item_details[k]['skill'].append(item_detail['skill'])\\n else:\\n item_details[k] = {}\\n item_details[k]['work'] = [item_detail['work']]\\n item_details[k]['skill'] = [item_detail['skill']]\\n\\n utility_matrix.append({\\n 'profile': profile_name,\\n 'item': k,\\n 'rating':v['point']\\n })\\n\\n return utility_matrix, \\\\\\n {k:{'work':json.dumps(v['work']),'skill':json.dumps(v['skill'])} for k,v in item_details.items()}, \\\\\\n profile_details\",\n \"def parse_info(df, idx, root):\\n df_row = df.iloc[idx]\\n\\n img_id = str(df_row['id'])\\n img_name = id_to_path(root, img_id)\\n landmark_id = df_row.landmark_id\\n return img_name, landmark_id\",\n \"def read_json_stats(url, parameters, tunnus):\\r\\n \\r\\n\\r\\n import requests \\r\\n from pyjstat import pyjstat\\r\\n from collections import OrderedDict\\r\\n import pandas as pd\\r\\n \\r\\n \\r\\n #query from JSON to be able to flatten data to one level\\r\\n col_list=[]\\r\\n for j in parameters[\\\"query\\\"]:\\r\\n col_list.append(j['code'])\\r\\n \\r\\n \\r\\n col_list.remove(tunnus)\\r\\n\\r\\n response = requests.post(url, json=parameters)\\r\\n if response.status_code == 200:\\r\\n try:\\r\\n json_data = response.json(object_pairs_hook=OrderedDict)\\r\\n except:\\r\\n print('Json error !!!!')\\r\\n return\\r\\n else:\\r\\n print('Error: Status code: {}'.format(response.status_code))\\r\\n return\\r\\n \\r\\n results = pyjstat.from_json_stat(json_data)\\r\\n stat=pd.DataFrame(results[0])\\r\\n stat = pd.pivot_table(stat, values='value', index=[tunnus],columns=col_list)\\r\\n stat.fillna(0,inplace=True)\\r\\n stat.reset_index(level=[0], inplace=True)\\r\\n if len(col_list) > 1:\\r\\n stat.columns = stat.columns.map(' '.join)\\r\\n stat.columns = stat.columns.str.strip()\\r\\n return(stat)\",\n \"def precipitation():\\n\\n return jsonify(prcp_df)\",\n \"def _info(self) -> tfds.core.DatasetInfo:\\n features = tfds.features.FeaturesDict({\\n \\\"tokens\\\":\\n tfds.features.Sequence(tfds.features.Text()),\\n \\\"tags\\\":\\n tfds.features.Sequence(\\n tfds.features.ClassLabel(names=[\\n \\\"O\\\",\\n \\\"B-PER\\\",\\n \\\"I-PER\\\",\\n \\\"B-ORG\\\",\\n \\\"I-ORG\\\",\\n \\\"B-LOC\\\",\\n \\\"I-LOC\\\",\\n ])),\\n \\\"langs\\\":\\n tfds.features.Sequence(tfds.features.Text()),\\n \\\"spans\\\":\\n tfds.features.Sequence(tfds.features.Text()),\\n })\\n return tfds.core.DatasetInfo(\\n builder=self,\\n description=_DESCRIPTION,\\n features=features,\\n supervised_keys=None,\\n homepage=\\\"https://github.com/afshinrahimi/mmner\\\",\\n citation=_CITATION,\\n )\",\n \"def get_task_flow_data(jeditaskid):\\n data = []\\n # get datasets\\n datasets = []\\n dquery = {'jeditaskid': jeditaskid, 'type__in': ['input', 'pseudo_input'], 'masterid__isnull': True}\\n datasets.extend(JediDatasets.objects.filter(**dquery).values('jeditaskid', 'datasetname', 'type'))\\n\\n dataset_dict = {}\\n for d in datasets:\\n dname = d['datasetname'] if ':' not in d['datasetname'] else d['datasetname'].split(':')[1]\\n dataset_dict[dname] = {'replica': {}, 'jobs': {}}\\n\\n # get jobs aggregated by status, computingsite and proddblock (input dataset name)\\n jobs = []\\n jquery = {'jeditaskid': jeditaskid, 'prodsourcelabel__in': ['user', 'managed'], }\\n extra_str = \\\"( processingtype not in ('pmerge') )\\\"\\n jvalues = ['proddblock', 'computingsite', 'jobstatus']\\n jobs.extend(Jobsarchived4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\\n jobs.extend(Jobsarchived.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\\n jobs.extend(Jobsactive4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\\n jobs.extend(Jobsdefined4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\\n jobs.extend(Jobswaiting4.objects.filter(**jquery).extra(where=[extra_str]).values(*jvalues).annotate(njobs=Count('pandaid')))\\n\\n if len(jobs) > 0:\\n for j in jobs:\\n if len(j['proddblock']) > 0:\\n dname = j['proddblock'] if ':' not in j['proddblock'] else j['proddblock'].split(':')[1]\\n else:\\n dname = next(iter(dataset_dict)) if len(dataset_dict) > 0 else 'pseudo_dataset'\\n if j['computingsite'] is not None and j['computingsite'] != '':\\n if j['computingsite'] not in dataset_dict[dname]['jobs']:\\n dataset_dict[dname]['jobs'][j['computingsite']] = {}\\n job_state = j['jobstatus'] if j['jobstatus'] in const.JOB_STATES_FINAL else 'active'\\n if job_state not in dataset_dict[dname]['jobs'][j['computingsite']]:\\n dataset_dict[dname]['jobs'][j['computingsite']][job_state] = 0\\n dataset_dict[dname]['jobs'][j['computingsite']][job_state] += j['njobs']\\n\\n # get RSE for datasets\\n replicas = []\\n if len(datasets) > 0:\\n dids = []\\n for d in datasets:\\n if d['type'] == 'input':\\n did = {\\n 'scope': d['datasetname'].split(':')[0] if ':' in d['datasetname'] else d['datasetname'].split('.')[0],\\n 'name': d['datasetname'].split(':')[1] if ':' in d['datasetname'] else d['datasetname'],\\n }\\n dids.append(did)\\n\\n rw = ruciowrapper()\\n replicas = rw.getRSEbyDID(dids)\\n\\n if replicas is not None and len(replicas) > 0:\\n for r in replicas:\\n if r['name'] in dataset_dict:\\n dataset_dict[r['name']]['replica'][r['rse']] = {\\n 'state': r['state'],\\n 'available_pct': round(100.0 * r['available_length']/r['length'], 1) if r['length'] > 0 else 0\\n }\\n\\n # transform data for plot and return\\n return executeTF({'data': {'datasets': dataset_dict, } })\",\n \"def print_record_project_count(dataframe, dataset=\\\"full\\\"):\\n if dataset == \\\"full\\\":\\n print(\\n \\\"For the ORIGINAL cleansed data, containing all available NYC capital \\\"\\n \\\"projects change records:\\\\n\\\"\\n )\\n\\n elif dataset == \\\"all\\\":\\n print(\\n \\\"For the data containing start and end data for all available \\\"\\n \\\"NYC capital projects for the ENTIRE INTERVAL of changes \\\"\\n \\\"covered in the ORIGINAL data:\\\\n\\\"\\n )\\n\\n else:\\n print(\\n \\\"For the final {} data, containing the {} split of 3-year \\\"\\n \\\"project data used in this analysis:\\\\n\\\".format(\\n dataset.upper(), dataset\\n )\\n )\\n\\n # entries\\n print(f\\\"\\\\tNumber of dataset records: {len(dataframe)}\\\")\\n\\n # num projects\\n print(\\n f\\\"\\\\tNumber of unique projects in dataset: {dataframe['PID'].nunique()}\\\\n\\\"\\n )\",\n \"def define_info_dict():\\n\\n d = {\\n \\\"PRED\\\": {\\n \\\"COLUMN\\\": [\\\"predicted_class\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Predicted class: somatic, germline, artifact\\\",\\n },\\n \\\"PROB\\\": {\\n \\\"COLUMN\\\": [\\\"prob_s\\\", \\\"prob_g\\\", \\\"prob_a\\\"],\\n \\\"Number\\\": \\\"3\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Prediction probability of \\\"\\n \\\"being somatic, germline, artifact in this order\\\",\\n },\\n \\\"SNP\\\": {\\n \\\"COLUMN\\\": [\\\"is_on_db\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Present on SNP database (modified dbSNP/gnomAD (default) or user-provided database)\\\",\\n },\\n \\\"ANNO\\\": {\\n \\\"COLUMN\\\": [\\\"annotation\\\"],\\n \\\"Number\\\": \\\".\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel annotation formatted as \\\"\\n \\\"GeneSymbol|RefSeqAccession|CodonPos|IndelEffect\\\"\\n \\\"Delimited by comma for multiple isoforms\\\",\\n },\\n \\\"COSMIC_CNT\\\": {\\n \\\"COLUMN\\\": [\\\"cosmic_cnt\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"COSMIC count in v89\\\",\\n },\\n \\\"MAXMAF\\\": {\\n \\\"COLUMN\\\": [\\\"max_maf\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Maximum minor allele frequency (MAF) \\\"\\n \\\"reported in dbSNP, ClinVar and gnomAD non-cancer population\\\",\\n },\\n \\\"COMMON\\\": {\\n \\\"COLUMN\\\": [\\\"is_common\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Common in dbSNP or MAXMAF > 0.01\\\",\\n },\\n \\\"CLIN\\\": {\\n \\\"COLUMN\\\": [\\\"clin_info\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"ClinVar annotation formatted as ClinicalSignificance|Condition\\\",\\n },\\n \\\"ICP\\\": {\\n \\\"COLUMN\\\": [\\\"indel_complexity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel complexity: mismatches around the indel measured by edit distance\\\",\\n },\\n \\\"DSM\\\": {\\n \\\"COLUMN\\\": [\\\"dissimilarity\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Dissimilarity: edit distance between indel and flanking sequences\\\",\\n },\\n \\\"ISZ\\\": {\\n \\\"COLUMN\\\": [\\\"indel_size\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Indel size\\\",\\n },\\n \\\"REP\\\": {\\n \\\"COLUMN\\\": [\\\"repeat\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Repeat: count of the indel-sequence repeats in flanking region\\\",\\n },\\n \\\"UQM\\\": {\\n \\\"COLUMN\\\": [\\\"is_uniq_mapped\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by uniquely mapped reads\\\",\\n },\\n \\\"NEB\\\": {\\n \\\"COLUMN\\\": [\\\"is_near_boundary\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Near exon boundary\\\",\\n },\\n \\\"EQX\\\": {\\n \\\"COLUMN\\\": [\\\"equivalence_exists\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Equivalent alignments exist for the indel\\\",\\n },\\n \\\"BID\\\": {\\n \\\"COLUMN\\\": [\\\"is_bidirectional\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Supported by forward and reverse reads\\\",\\n },\\n \\\"MTA\\\": {\\n \\\"COLUMN\\\": [\\\"is_multiallelic\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Multialleleic\\\",\\n },\\n \\\"FRM\\\": {\\n \\\"COLUMN\\\": [\\\"is_inframe\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"In-frame indel\\\",\\n },\\n \\\"SPL\\\": {\\n \\\"COLUMN\\\": [\\\"is_splice\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in splice region\\\",\\n },\\n \\\"TRN\\\": {\\n \\\"COLUMN\\\": [\\\"is_truncating\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Truncating indel\\\",\\n },\\n \\\"CDD\\\": {\\n \\\"COLUMN\\\": [\\\"is_in_cdd\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Located in conserved domain\\\",\\n },\\n \\\"LOC\\\": {\\n \\\"COLUMN\\\": [\\\"indel_location\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Relative indel location within the transcript coding region\\\",\\n },\\n \\\"NMD\\\": {\\n \\\"COLUMN\\\": [\\\"is_nmd_insensitive\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insensitive to nonsense mediated decay\\\",\\n },\\n \\\"IPG\\\": {\\n \\\"COLUMN\\\": [\\\"ipg\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Indels per gene\\\",\\n },\\n \\\"LEN\\\": {\\n \\\"COLUMN\\\": [\\\"cds_length\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Coding sequence length. Median value if multiple isoforms exist\\\",\\n },\\n \\\"LC\\\": {\\n \\\"COLUMN\\\": [\\\"lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Linguistic complexity: diversity of k-mers in flanking 50-bp region\\\",\\n },\\n \\\"LLC\\\": {\\n \\\"COLUMN\\\": [\\\"local_lc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local linguistic complexity: diversity of k-mers in flanking 6-bp region\\\",\\n },\\n \\\"GC\\\": {\\n \\\"COLUMN\\\": [\\\"gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"GC-content in flanking 50-bp region\\\",\\n },\\n \\\"LGC\\\": {\\n \\\"COLUMN\\\": [\\\"local_gc\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local GC-content in flanking 6-bp region\\\",\\n },\\n \\\"SG\\\": {\\n \\\"COLUMN\\\": [\\\"strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"DNA bond strength of 2-mers in flanking 50-bp region\\\",\\n },\\n \\\"LSG\\\": {\\n \\\"COLUMN\\\": [\\\"local_strength\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Float\\\",\\n \\\"Description\\\": \\\"Local DNA bond strength of 2-mers in flanking 6-bp region\\\",\\n },\\n \\\"INS\\\": {\\n \\\"COLUMN\\\": [\\\"is_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Insertion\\\",\\n },\\n \\\"ATI\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of A or T\\\",\\n },\\n \\\"ATD\\\": {\\n \\\"COLUMN\\\": [\\\"is_at_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of A or T\\\",\\n },\\n \\\"GCI\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_ins\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single insertion of G or C\\\",\\n },\\n \\\"GCD\\\": {\\n \\\"COLUMN\\\": [\\\"is_gc_del\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Single deletion of G or C\\\",\\n },\\n \\\"ALTC\\\": {\\n \\\"COLUMN\\\": [\\\"alt_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Alt count: count of unique reads supporting ALT allele\\\",\\n },\\n \\\"REFC\\\": {\\n \\\"COLUMN\\\": [\\\"ref_count\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"Integer\\\",\\n \\\"Description\\\": \\\"Ref count: count of unique reads supporting REF allele\\\",\\n },\\n \\\"RCF\\\": {\\n \\\"COLUMN\\\": [\\\"reclassified\\\"],\\n \\\"Number\\\": \\\"0\\\",\\n \\\"Type\\\": \\\"Flag\\\",\\n \\\"Description\\\": \\\"Reclassification applied\\\",\\n },\\n \\\"RQB\\\": {\\n \\\"COLUMN\\\": [\\\"filtered\\\", \\\"rescued\\\"],\\n \\\"Number\\\": \\\"1\\\",\\n \\\"Type\\\": \\\"String\\\",\\n \\\"Description\\\": \\\"Indel used to rescue this entry formatted as CHROM:POS:REF:ALT\\\",\\n },\\n }\\n\\n return d\",\n \"def job_data(driver):\\n job_info = {\\n \\\"job_title\\\" : \\\"h1.title\\\",\\n \\\"company\\\" : \\\"span.company\\\",\\n \\\"location\\\" : \\\"h3.location\\\",\\n \\\"employment_type\\\" : \\\"div.employment div.content div.rich-text\\\",\\n \\\"industry\\\" : \\\"div.industry div.content div.rich-text\\\",\\n \\\"experience\\\" : \\\"div.experience div.content div.rich-text\\\",\\n \\\"job_function\\\" : \\\"div.function div.content div.rich-text\\\",\\n \\\"description\\\" : \\\"div.summary div.content div.description-section div.rich-text\\\"\\n }\\n # click the 'read more' button to reveal more about the job posting\\n try:\\n driver.find_element_by_css_selector(\\\"button#job-details-reveal\\\").click()\\n except Exception as e:\\n print(\\\"error in attempting to click 'reveal details' button\\\")\\n print(e)\\n for key, selector in job_info.items():\\n try:\\n job_info[key] = driver.find_element_by_css_selector(selector).text\\n except Exception as e:\\n job_info[key] = \\\"\\\"\\n pass\\n return job_info\",\n \"def get_infos(self):\\n infos = dict()\\n infos['dataset'] = self._dataset_name()\\n infos['task'] = self.task\\n if self.task == 'sep_clean':\\n data_license = [librispeech_license]\\n else:\\n data_license = [librispeech_license, wham_noise_license]\\n infos['licenses'] = data_license\\n return infos\",\n \"def get_data(self):\\n\\n all_data = OrderedDict()\\n projects = [Path(proj) for proj in glob(str(self.data_path.joinpath(\\\"*\\\"))) if Path(proj).is_dir()]\\n\\n for project in projects:\\n files = []\\n \\n # Read all csv files and save them as a list in files\\n for ver in glob(str(project.joinpath(\\\"*.csv\\\"))):\\n files.extend(pd.read_csv(ver, usecols=['time', 'buggy']).values.tolist())\\n \\n # Create a pandas dataframe from the csv sorted by datetime\\n df = pd.DataFrame(files, columns=['Time', 'Bugs']).sort_values(by='Time').reset_index(drop=True)\\n \\n # Convert time to Pandas DateTime format\\n df['Time'] = pd.to_datetime(df['Time']) \\n \\n # Group bug counts by week starting on monday\\n df = df.reset_index().set_index('Time').groupby(\\n [pd.Grouper(freq='W-MON')])[\\\"Bugs\\\"].sum().astype(int).reset_index()\\n \\n df = df.set_index('Time')\\n # Save the data to dictionary\\n all_data.update(OrderedDict({project.name: df}))\\n\\n return all_data\",\n \"def get_doctor_info(url, html):\\n \\n # create a dictionary to save data\\n doctor_info = {}\\n \\n # parsed html by using lxml\\n # In the following processing\\n # the one ending in org is the raw data in the HTML\\n # the one ending in inf is the processed data by extracting from raw data\\n select = etree.HTML(html)\\n \\n # part 1: get basic information about your doctor\\n # attribute: Name, Title, Hospital, Department\\n name_org = select.xpath('//div[@class=\\\"profile-text\\\"]//h1[@class=\\\"doctor-name\\\"]//text()')\\n name_inf = name_org[0].strip()\\n \\n title_org = select.xpath('//div[@class=\\\"profile-text\\\"]//span[@class=\\\"positon\\\"]//text()')\\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\\n title_inf = ' '.join(title_inf)\\n \\n hospital_department_org = select.xpath('//div[@class=\\\"profile-text\\\"]//p[@class=\\\"doctor-faculty\\\"]//text()')\\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\\n hospital_inf = hospital_department_inf[0]\\n department_inf = hospital_department_inf[1]\\n \\n doctor_info['姓名'] = name_inf\\n doctor_info['职称'] = title_inf\\n doctor_info['医院'] = hospital_inf\\n doctor_info['科室'] = department_inf\\n \\n # part2: get header format data\\n org = select.xpath('//div[@class=\\\"profile-sta\\\"]//text()')\\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\\n for i in range(len(inf)//2):\\n doctor_info[inf[2*i]] = inf[2*i + 1]\\n \\n \\n # part3: get sidebar format data\\n org_1 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-label\\\"]//text()')\\n org_2 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-data\\\"]//text()')\\n for i in range(len(org_1)):\\n doctor_info[org_1[i][:-1]] = org_2[i]\\n \\n # part4: get body format data\\n honour_org = select.xpath('//div[@class=\\\"honour-header\\\"]//text()')\\n honour_inf = ''.join([i.strip() for i in honour_org])\\n \\n honour_detail_org = select.xpath('//li[@class=\\\"honour-title\\\"]//text()')\\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\\n honour_detail_inf = ' '.join(honour_detail_inf)\\n \\n satisfaction_org = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"satisfaction clearfix\\\"]//i[@class=\\\"sta-num\\\"]//text()')\\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\\n \\n resume_org = select.xpath('//div[@class=\\\"good-at-text\\\"]//text()')\\n resume_inf = [i.strip() for i in resume_org]\\n if len(resume_inf) <= 20:\\n resume_inf = ''.join(resume_inf)\\n resume_inf = ''.join(resume_inf[:20])\\n \\n star_org = select.xpath('//div[@class=\\\"experience-row clearfix\\\"]//span[@class=\\\"experience-label\\\"]//text()')\\n star_inf = 1 if len(star_org) >= 1 else 0\\n\\n doctor_info['好大夫届数'] = honour_inf\\n doctor_info['好大夫具体年份'] = honour_detail_inf\\n doctor_info['简历'] = resume_inf \\n doctor_info['诊后服务星'] = star_inf\\n try:\\n doctor_info['疗效满意度'] = satisfaction_inf[0]\\n doctor_info['态度满意度'] = satisfaction_inf[1]\\n except:\\n pass\\n \\n # part5: personal url\\n personal_url = url\\n doctor_info['医生个人链接'] = personal_url\\n \\n return doctor_info\",\n \"def get_data(self):\\n\\n # Import json data into a pandas dataframe\\n df = pd.read_json(\\\"./nutrients.json\\\", lines=True)\\n\\n return df\",\n \"def _gather_deep_data(self):\\n\\n cleaned_data_from_website = list()\\n\\n for i, search_result in self.data_from_website.iterrows():\\n cleaned_data_from_website.append(self._deep_data(search_result.url))\\n\\n cleaned_data_from_website = pd.DataFrame(cleaned_data_from_website)\\n if len(cleaned_data_from_website) == 0:\\n cleaned_data_from_website['@id'] = '0'\\n cleaned_data_from_website.set_index('@id', inplace=True)\\n self.data_from_website = cleaned_data_from_website\",\n \"def get_infos(self):\\n infos = dict()\\n infos[\\\"dataset\\\"] = self.dataset_name\\n infos[\\\"task\\\"] = \\\"separate_noisy\\\"\\n infos[\\\"licenses\\\"] = [librispeech_license, tac_license]\\n return infos\",\n \"def _91_render_plant_data_to_projects(records, **params):\\n for plant_record in [record for record in records if record[\\\"Type\\\"] == \\\"Plant\\\"]:\\n key = (plant_record[\\\"Power Plant Name\\\"], plant_record[\\\"Project Name\\\"])\\n project_records = [\\n record\\n for record in records\\n if record[\\\"Type\\\"] == \\\"Project\\\"\\n and record[\\\"Power Plant Name\\\"] == plant_record[\\\"Power Plant Name\\\"]\\n ]\\n if len(project_records) == 0:\\n log.error(\\\"NO PROJECT RECORDS FOR %s\\\" % (key,))\\n for project_record in project_records:\\n for field in plant_record.keys():\\n if project_record.get(field) in [None, '']:\\n project_record[field] = plant_record[field]\\n return records\",\n \"def queryFlywheel(project):\\n\\n # Create info dict with entries for each subject.\\n info = dict()\\n\\n # Loop through subjects in project\\n #for sub in subjects:\\n for sub in project.subjects():\\n\\n # Loop through sessions in subject\\n for ses in sub.sessions():\\n ses = ses.reload()\\n\\n # Loop through acquisitions in session\\n for acq in ses.acquisitions():\\n acq = acq.reload()\\n\\n # Loop through files in acquisition\\n for f in acq.files:\\n \\n # Skip over non-nifti files\\n if f.type != 'nifti':\\n next\\n\\n # Get Flywheel fileId to use as unique identifier\\n fileId = f.id\\n\\n # Try to get timestamp (sometimes DateTime field isn't present.) \\n try:\\n timestamp = f.info['AcquisitionDateTime']\\n except KeyError:\\n try:\\n timestamp = f.info['AcquisitionDate']\\n # Set to None if field isn't present\\n except:\\n timestamp = pd.NaT\\n \\n # Try to get series number (sometimes field isn't present.) \\n try:\\n seriesNum = f.info['SeriesNumber']\\n # Set to None if field isn't present\\n except:\\n np.NaN \\n # Add the folowing metadata to study info dict:\\n # fileID: [subId, sesId, acqLabel, fileName, seriesNum, timestamp]\\n info[fileId] = [sub.label, ses.label, acq.label, f.name, seriesNum, timestamp]\\n \\n # Return project info dict\\n return info\",\n \"def get_org_projects_info(org_link):\\n response = get_response(org_link)\\n if response.ok:\\n soup = BeautifulSoup(response.text, 'html.parser')\\n projects_li = soup.find_all(\\n 'li', attrs={'layout': True}\\n )\\n project_info = []\\n for proj_html in projects_li:\\n proj_info = {}\\n proj_title = proj_html.get('aria-label').replace('\\\\n', '')\\n proj_desc = proj_html.find(\\n 'div', attrs={'class': 'archive-project-card__content'}).text.replace('\\\\t', '')\\n proj_relative_link = proj_html.select('a')[0].get('href')\\n proj_full_link = HOME_PAGE + proj_relative_link\\n proj_info['title'] = proj_title\\n proj_info['description'] = proj_desc\\n proj_info['link'] = proj_full_link\\n project_info.append(proj_info)\\n return project_info\",\n \"def extract_data(self, root, path, tag):\\n data = []\\n element = root.xpath(path)\\n if element:\\n url = self.PODEROPEDIA_BASE_URL + element[0].get('data-w2p_remote', None)\\n if url:\\n self.logger.debug('Querying {} from {}'.format(tag, url))\\n try:\\n response = self.session.get(url)\\n response.raise_for_status()\\n content = response.content\\n html_tree = etree.HTML(content, parser=self.parser)\\n if html_tree is None:\\n return data\\n rows = html_tree.xpath('.//*[starts-with(@id, \\\"collapse\\\")]/div/table/tr')\\n for row in rows:\\n target = target_name = target_path = relationship = None\\n when = where = where_name = where_path = source = None\\n row_id = row.get('id', '')\\n cells = row.getchildren()\\n idx = 0\\n while idx < len(cells) - 1:\\n try:\\n cell_text = text_strip(cells[idx])\\n except AttributeError:\\n cell_text = ''\\n sources = cells[idx].xpath('.//*[@class=\\\"fuente\\\"]')\\n if len(sources) > 0:\\n source = process_sources(cells[idx])\\n elif cell_text == 'es' or cell_text == 'fue':\\n when = cell_text\\n idx = idx - 1\\n target = cells[idx].find('a')\\n if target is not None:\\n target_path = target.get('href', None)\\n target_name = text_strip(target)\\n idx = idx + 2\\n relationship = text_strip(cells[idx])\\n elif cell_text == 'a' or cell_text == 'de':\\n idx = idx - 1\\n relationship = text_strip(cells[idx])\\n idx = idx + 2\\n target = cells[idx].find('a')\\n if target is not None:\\n target_path = target.get('href', None)\\n target_name = text_strip(target)\\n elif cell_text.startswith('desde'):\\n when = cell_text\\n elif 'es pasado' in cell_text:\\n when = cell_text\\n else:\\n try:\\n ignore = int(cell_text)\\n when = cell_text\\n except ValueError:\\n potential_date = cell_text.split(' ')[0]\\n try:\\n ignore = datetime.strptime(potential_date, '%d-%m-%Y')\\n when = cell_text\\n except ValueError:\\n try:\\n ignore = datetime.strptime(potential_date, '%m-%Y')\\n when = cell_text\\n except ValueError:\\n pass\\n idx = idx + 1\\n entry = {\\n 'type': tag,\\n 'target_path': target_path,\\n 'relationship': relationship,\\n 'when': when,\\n 'where': where,\\n 'source': source\\n }\\n data.append(entry)\\n self.logger.debug('{}: {}'.format(tag, entry))\\n except (requests.exceptions.HTTPError, etree.ParserError):\\n self.logger.info('Something bad happened', exc_info=True)\\n return data\",\n \"def getMonthlyPRinfo(df):\\n\\n new_df = df.filter(['Merged_YM', 'node.title', 'node.url'], axis=1)\\n new_df.groupby('Merged_YM')\\n new_df.to_csv('PR_Info_Monthly.csv', index=False)\",\n \"def fetch_game_info(df: pd.DataFrame) -> pd.DataFrame:\\n data_dict = defaultdict(list)\\n len = df.shape[0]\\n\\n with alive_bar(len) as bar:\\n for _, row in df.iterrows():\\n gid = row['id']\\n\\n html = fetch_url(TWITCH_TRCK_URL + 'games/' + gid, hint='game')\\n divs = html.find_all('div', {'class': 'g-x-s-block'})\\n for div in divs:\\n # Give a initial value as None\\n # so that the program won't raise exception for length\\n val, label = (None, None)\\n val = div.find('div', {'class': 'g-x-s-value'}).text.strip()\\n label = div.find('div', {'class': 'g-x-s-label'}).text.strip()\\n if ('@' in label):\\n (label, date) = label.split('@')\\n label = label.strip()\\n val += date\\n data_dict[label].append(val)\\n\\n bar()\\n\\n df = df.assign(**data_dict)\\n return df\",\n \"def set_data(self):\\n data = [\\n {\\\"ASN1P-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"ASN1P\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n {\\\"asn1rt-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"asn1rt\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n {\\\"cadessignature-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"cadessignature\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n { \\\"JCP-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"JCP\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }},\\n { \\\"JCPRequest-1.0.jar\\\": {\\n \\\"groupId\\\": \\\"ru.lanit.jcp\\\",\\n \\\"artifactId\\\": \\\"JCPRequest\\\",\\n \\\"version\\\": \\\"1.0\\\",\\n \\\"packaging\\\": \\\"jar\\\"\\n }}\\n ]\\n return data\",\n \"def print_info(self):\\n\\n n_metabolites = len(self.metabolites)\\n n_reactions = len(self.reactions)\\n n_constraints = len(self.constraints)\\n n_variables = len(self.variables)\\n\\n info = pd.DataFrame(columns=['value'])\\n info.loc['name'] = self.name\\n info.loc['description'] = self.description\\n info.loc['num constraints'] = n_constraints\\n info.loc['num variables'] = n_variables\\n info.loc['num metabolites'] = n_metabolites\\n info.loc['num reactions'] = n_reactions\\n info.index.name = 'key'\\n\\n print(info)\",\n \"def attach_data(df: pd.DataFrame) -> pd.DataFrame:\\n # load in parties and constituents data\\n # if data is missing ask if scraping is wanted to be performed\\n\\n with open(os.path.join(instancepath, f\\\"parties.json\\\"), \\\"r\\\", encoding='utf-8') as json_file:\\n parties = json.load(json_file)\\n with open(os.path.join(instancepath, f\\\"constituencies.json\\\"), \\\"r\\\", encoding='utf-8') as json_file:\\n constituencies = json.load(json_file)\\n \\n\\n \\\"\\\"\\\" #tätä glob hommaa en saanu toimiin, käy for loopin sisäl vaan yhen kerran ja hakee vaan yhden tiedoston\\n # load the scraped data to its own data frame\\n df_scraped = pd.DataFrame(columns=['first_name', 'last_name', 'election_number', 'image', 'election_promise_1', 'party', 'constituency'])\\n i = 1\\n with suppress(KeyError,FileNotFoundError):\\n for filename in glob(f\\\"{instancepath}/candidate*.json\\\"):\\n print(\\\"jee\\\")\\n with open(filename, \\\"r\\\", encoding='utf-8') as json_file:\\n candidate = json.load(json_file)\\n party_name = None\\n constituency_name = None\\n for part in parties:\\n if part['id'] == candidate[\\\"party_id\\\"]:\\n party_name = part['name_fi']\\n\\n for consti in constituencies:\\n if consti['id'] == candidate[\\\"constituency_id\\\"]:\\n constituency_name = consti['name_fi']\\n\\n df_scraped = df_scraped.append({'first_name': candidate['first_name'], \\n 'last_name': candidate['last_name'], \\n 'election_number': candidate['election_number'], \\n 'image': candidate['image'], \\n 'election_promise_1': candidate['info']['election_promise_1'],\\n 'party': party_name,\\n 'constituency': constituency_name}, \\n ignore_index = True)\\n #except (FileNotFoundError, KeyError):\\n \\\"\\\"\\\"\\n\\n # load the scraped data to its own data frame\\n df_scraped = pd.DataFrame(columns=['first_name', 'last_name', 'election_number', 'image', 'election_promise_1', 'party', 'constituency'])\\n i = 1\\n while i <= 3000: # files are named arfter candidate ids and ids range is between 1 and 3000\\n with suppress(KeyError,FileNotFoundError):\\n with open(os.path.join(instancepath, f\\\"candidate\\\" + str(i) + \\\".json\\\"), \\\"r\\\", encoding='utf-8') as json_file:\\n candidate = json.load(json_file)\\n party_name = None\\n constituency_name = None\\n for part in parties:\\n if part['id'] == candidate[\\\"party_id\\\"]:\\n party_name = part['name_fi']\\n\\n for consti in constituencies:\\n if consti['id'] == candidate[\\\"constituency_id\\\"]:\\n constituency_name = consti['name_fi']\\n\\n df_scraped = df_scraped.append({\\n 'first_name': candidate['first_name'].strip(), \\n 'last_name': candidate['last_name'].strip(), \\n 'election_number': candidate['election_number'], \\n 'image': candidate['image'], \\n 'election_promise_1': candidate['info']['election_promise_1'],\\n 'party': party_name.strip(),\\n 'constituency': constituency_name}, \\n ignore_index = True)\\n i += 1\\n\\n\\n # loading in data of each individual constituent\\n # the ids range from 1 to 14\\n j = 1\\n constituentArray = []\\n constituentArray.append(\\\"\\\")\\n while j <= 14:\\n try:\\n with open(os.path.join(instancepath, f'constituent' + str(j) + '.json'), \\\"r\\\", encoding='utf-8') as json_file:\\n constituentArray.append(json.load(json_file))\\n except FileNotFoundError:\\n constituentArray.append(\\\"\\\")\\n j += 1\\n\\n for i, row in df.iterrows():\\n promise = row['Mitkä ovat kolme vaalilupaustasi? Vaalilupaus 1:']\\n constituency = row['constituency']\\n party = row['party']\\n df = search_andsetvalues(promise, constituency, party, df, i, df_scraped)\\n return df\",\n \"def _fetch_poverty_data(year=None):\\n global _SIMPLECOUNT_COLUMNS\\n\\n try:\\n if year is None:\\n year = _get_max_year([30, 31]) + 1\\n ext = 'txt' if year > 2003 else 'dat'\\n url = f'https://www2.census.gov/programs-surveys/saipe/datasets/{year}/{year}-state-and-county/est{str(year)[2:]}-il.{ext}'\\n\\n raw = pd.read_table(url, header=None, skiprows=1, names=['raw'])\\n pattern = '.{3}(?P<fips>.{3}).(?P<all>.{8}).{34}(?P<minor>.{8}).*'\\n \\n filtered = raw['raw'].str.extract(pattern)\\n filtered['year'] = year\\n filtered['fips'] = filtered['fips'].astype(int) + 1 / 2\\n filtered.columns = ['fk_simplecount_county', '1200', '1201', 'year']\\n\\n pivoted = pd.melt(\\n filtered.astype(int),\\n id_vars = ['fk_simplecount_county', 'year'],\\n value_vars=['1200', '1201'],\\n var_name = 'fk_simplecount_indicator'\\n )\\n\\n return pivoted[_SIMPLECOUNT_COLUMNS]\\n except HTTPError as e:\\n if e.code == 404:\\n raise ValueError(\\\"WARNING: Poverty data is up to date.\\\")\\n except:\\n raise\",\n \"def getexperimentinfo(expid):\\n rdata = {}\\n rdata['expId'] = expid\\n res = requests.get(scbd_server_address + '/experiments/get_details', json=rdata)\\n if res.status_code == 200:\\n outstr = ''\\n for cres in res.json()['details']:\\n outstr += cres[0] + ':' + cres[1] + '<br>'\\n # details=res.json()['details']\\n return outstr\\n return []\",\n \"def filter_page_data(self, document_data: dict) -> dict:\\n name = (\\n document_data[\\\"project\\\"][\\\"name\\\"]\\n )\\n short_description = (\\n document_data[\\\"project\\\"][\\\"shortDescription\\\"]\\n )\\n created = (\\n document_data[\\\"project\\\"][\\\"created\\\"]\\n )\\n # due_date = (\\n # document_data[\\\"project\\\"][\\\"dueDate\\\"]\\n # )\\n changeset_comment = (\\n document_data[\\\"project\\\"][\\\"changesetComment\\\"]\\n )\\n instructions = (\\n document_data[\\\"project\\\"][\\\"externalSource\\\"][\\\"instructions\\\"]\\n )\\n per_task_instructions = (\\n document_data[\\\"project\\\"][\\\"externalSource\\\"][\\\"perTaskInstructions\\\"]\\n )\\n imagery = (\\n document_data[\\\"project\\\"][\\\"externalSource\\\"][\\\"imagery\\\"]\\n )\\n license = (\\n document_data[\\\"project\\\"][\\\"externalSource\\\"][\\\"license\\\"]\\n )\\n url = (\\n document_data[\\\"project\\\"][\\\"url\\\"]\\n )\\n # metrics = (\\n # document_data[\\\"platform\\\"][\\\"metrics\\\"]\\n # )\\n # quality_assurance = (\\n # document_data[\\\"platform\\\"][\\\"qualityAssurance\\\"]\\n # )\\n users = (\\n document_data[\\\"project\\\"][\\\"users\\\"]\\n )\\n project_page_data = {\\n \\\"project\\\": {\\n \\\"name\\\": name,\\n \\\"shortDescription\\\": short_description,\\n \\\"created\\\": created,\\n # \\\"due_date\\\": due_date,\\n \\\"changesetComment\\\": changeset_comment,\\n \\\"externalSource\\\": {\\n \\\"instructions\\\": instructions,\\n \\\"perTaskInstructions\\\": per_task_instructions,\\n \\\"imagery\\\": imagery,\\n \\\"license\\\": license\\n },\\n \\\"url\\\": url,\\n \\\"users\\\": users\\n }\\n # \\\"metrics\\\": metrics,\\n # \\\"quality_assurance\\\": quality_assurance,\\n }\\n return project_page_data\",\n \"def extract_data():\\n client = MongoClient(HOST, PORT)\\n collection = client[DB][COLLECTION]\\n df = pd.DataFrame(collection.find().limit(10))\\n return df\",\n \"def subjectinfo(subject_id):\\n import pandas as pd\\n from nipype.interfaces.base import Bunch\\n \\n def construct_sj(trialinfo, subject_id, run_num, cond_name):\\n \\\"\\\"\\\"construct df\\\"\\\"\\\"\\n df_sj = trialinfo[(trialinfo['subject']==int(subject_id)) & (trialinfo['session']==int(run_num))]\\n sj_info = pd.DataFrame()\\n sj_info['onset'] = df_sj['runtime']\\n sj_info['duration'] = 0.\\n sj_info['weight'] = 1.\\n trial_type = df_sj['seq'].replace({1:'Low', 2:'High'})\\n sj_info['trial_type'] = trial_type\\n sj_info_cond = sj_info[sj_info['trial_type']==cond_name]\\n return sj_info_cond\\n\\n def select_confounds(subject_id, run_num):\\n \\\"\\\"\\\"import confounds tsv files\\\"\\\"\\\"\\n confounds_dir = f'/data/sub-%02d/func/' % int(subject_id)\\n confounds_file = confounds_dir+f'sub-%02d_task-tsl_run-%d_desc-confounds_timeseries.tsv' % (int(subject_id), int(run_num))\\n conf_df = pd.read_csv(confounds_file, sep='\\\\t')\\n return conf_df\\n\\n def confounds_regressor(conf_df, conf_names):\\n \\\"\\\"\\\"select confounds for regressors\\\"\\\"\\\"\\n conf_select = conf_df[conf_names].loc[4:].fillna(0) # ignore first 4 dummy scans\\n conf_select_list = [conf_select[col].values.tolist() for col in conf_select] \\n return conf_select_list\\n\\n def find_runs(subject_id):\\n \\\"\\\"\\\"find available runs from func\\\"\\\"\\\"\\n from glob import glob\\n func_dir = f'/output/smooth_nomask/preproc/sub-%02d/' % int(subject_id) \\n func_files = glob(func_dir+'*bold.nii')\\n runs = []\\n for f in func_files:\\n tmp = f.split('/')\\n run = tmp[5].split('_')[2].split('-')[1]\\n runs.append(int(run))\\n return sorted(runs)\\n \\n conf_names = ['csf','white_matter','global_signal',\\n 'dvars','std_dvars','framewise_displacement', 'rmsd',\\n 'a_comp_cor_00', 'a_comp_cor_01', 'a_comp_cor_02', 'a_comp_cor_03', 'a_comp_cor_04', 'a_comp_cor_05', 'cosine00', 'cosine01', 'cosine02', 'cosine03', 'cosine04', 'cosine05',\\n 'trans_x', 'trans_y', 'trans_z', 'rot_x','rot_y','rot_z']\\n\\n alltrialinfo = pd.read_csv('/code/data/fmri_behavioural_new.csv')\\n alltrialinfo.head()\\n \\n subject_info = []\\n onset_list = []\\n condition_names = ['High', 'Low']\\n runs = find_runs(subject_id)\\n print(runs)\\n for run in runs:\\n for cond in condition_names:\\n run_cond = construct_sj(alltrialinfo, subject_id, run, cond)\\n onset_run_cond = run_cond['onset'].values\\n onset_list.append(sorted(onset_run_cond))\\n\\n subject_info = []\\n for r in range(len(runs)):\\n onsets = [onset_list[r*2], onset_list[r*2+1]]\\n regressors_all = select_confounds(subject_id, runs[r])\\n regressors = confounds_regressor(regressors_all, conf_names)\\n\\n subject_info.insert(r,\\n Bunch(conditions=condition_names,\\n onsets=onsets,\\n durations=[[0], [0]],\\n regressors=regressors,\\n regressor_names=conf_names,\\n amplitudes=None,\\n tmod=None,\\n pmod=None))\\n\\n return subject_info # this output will later be returned to infosource\",\n \"def getStudyInfo(self, study_id, web_app_user_id):\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor()\\n con.cursor().callproc('qiime_assets.get_study_info', [study_id, web_app_user_id, results])\\n study_info = {}\\n for row in results:\\n study_info['submit_to_insdc'] = row[0]\\n study_info['investigation_type'] = row[1]\\n study_info['project_name'] = row[2]\\n study_info['experimental_factor'] = row[3]\\n study_info['study_alias'] = row[4]\\n study_info['study_title'] = row[5]\\n study_info['study_type'] = row[6]\\n study_info['study_abstract'] = row[7]\\n study_info['study_description'] = row[8]\\n study_info['center_name'] = row[9]\\n study_info['center_project_name'] = row[10]\\n study_info['project_id'] = row[11]\\n study_info['pmid'] = row[12]\\n study_info['metadata_complete'] = row[13]\\n study_info['sff_complete'] = row[14]\\n study_info['mapping_file_complete'] = row[15]\\n study_info['miens_compliant'] = row[16]\\n study_info['can_delete'] = row[17]\\n study_info['avg_emp_score'] = row[18]\\n study_info['user_emp_score'] = row[19]\\n study_info['number_samples_promised'] = row[20]\\n study_info['number_samples_collected'] = row[21]\\n study_info['principal_investigator'] = row[22]\\n study_info['sample_count'] = row[23] \\n study_info['lab_person'] = row[24] \\n study_info['lab_person_contact'] = row[25]\\n study_info['emp_person'] = row[26]\\n study_info['first_contact'] = row[27]\\n study_info['most_recent_contact'] = row[28]\\n study_info['sample_type'] = row[29]\\n study_info['has_physical_specimen'] = row[30]\\n study_info['has_extracted_data'] = row[31]\\n study_info['timeseries'] = row[32]\\n study_info['spatial_series'] = row[33]\\n study_info['principal_investigator'] = row[34]\\n study_info['principal_investigator_contact'] = row[35]\\n study_info['default_emp_status'] = row[36]\\n study_info['funding'] = row[37]\\n study_info['includes_timeseries'] = row[38]\\n study_info['sample_count'] = row[39]\\n study_info['ebi_study_accession'] = row[40]\\n study_info['locked'] = row[41]\\n study_info['vamps_id'] = row[42]\\n return study_info\",\n \"def get_projects(session):\\n cursuses = [1, 21] # cursus ids from which to get the projects\\n project_names = []\\n\\n for cursus in cursuses:\\n # Get all the projects from 1 cursus, very slow process because projects endpoint contains\\n # a lot of information\\n projects = get_all_pages(session, f'/cursus/{cursus}/projects', 100, {'filter[exam]': False})\\n for project in projects:\\n # Create dictionary containing project id and project name ans set in bigger dict\\n project_names.append({'id': project['id'], 'name': project['name']})\\n\\n return project_names\",\n \"def download_potholes():\\n\\n\\tlink = \\\"https://data.cityofchicago.org/api/views/7as2-ds3y/rows.csv?accessType=DOWNLOAD\\\"\\n\\tdf = pd.read_csv(link)\\n\\tdf = df[(df.STATUS == \\\"Open\\\") | (df.STATUS == \\\"Open - Dup\\\")]\\n\\tdf = df[[\\\"LATITUDE\\\", \\\"LONGITUDE\\\"]]\\n\\tdf = df.dropna(axis =0, subset=[\\\"LATITUDE\\\", \\\"LONGITUDE\\\"])\\n\\treturn df\",\n \"def _build_single_study_info(study, info, study_proc, proc_samples):\\n PI = StudyPerson(info['principal_investigator_id'])\\n status = study.status\\n if info['publication_doi'] is not None:\\n pmids = get_pubmed_ids_from_dois(info['publication_doi']).values()\\n info['pmid'] = \\\", \\\".join([pubmed_linkifier([p]) for p in pmids])\\n info['publication_doi'] = \\\", \\\".join([doi_linkifier([p])\\n for p in info['publication_doi']])\\n\\n else:\\n info['publication_doi'] = \\\"\\\"\\n info['pmid'] = \\\"\\\"\\n if info[\\\"number_samples_collected\\\"] is None:\\n info[\\\"number_samples_collected\\\"] = 0\\n info[\\\"shared\\\"] = _get_shared_links_for_study(study)\\n # raw data is any artifact that is not Demultiplexed or BIOM\\n\\n info[\\\"num_raw_data\\\"] = len([a for a in study.artifacts()\\n if a.artifact_type not in ['Demultiplexed',\\n 'BIOM']])\\n info[\\\"status\\\"] = status\\n info[\\\"study_id\\\"] = study.id\\n info[\\\"pi\\\"] = study_person_linkifier((PI.email, PI.name))\\n del info[\\\"principal_investigator_id\\\"]\\n del info[\\\"email\\\"]\\n # Build the proc data info list for the child row in datatable\\n info[\\\"proc_data_info\\\"] = []\\n for data_type, proc_datas in viewitems(study_proc[study.id]):\\n info[\\\"proc_data_info\\\"].extend([\\n _build_single_proc_data_info(pd_id, data_type, proc_samples[pd_id])\\n for pd_id in proc_datas])\\n return info\",\n \"def example_data():\\n return {\\n # \\\"$schema\\\": \\\"\\\",\\n \\\"id\\\": \\\"12345-abcde\\\",\\n \\\"metadata\\\": {\\n \\\"title\\\": \\\"My record\\\",\\n \\\"date\\\": \\\"2020-09-20\\\",\\n },\\n \\\"pids\\\": {\\n \\\"oaiid\\\": {\\\"value\\\": \\\"\\\", \\\"provider\\\": \\\"local\\\"},\\n },\\n }\",\n \"def get_data_dict(self):\\n\\n print('---------repo creation date------------------')\\n print('start date', self.start_date)\\n print('---------------------------------------------')\\n print('------Commits----------')\\n print(self.commits)\\n print('--------------------------------------------')\\n\\n df = pd.DataFrame.from_records(self.commits) # pylint: disable=invalid-name\\n df.date = pd.to_datetime(df.date, utc=True, unit='s')\\n df.set_index('date', inplace=True)\\n df.index = df.index.floor('D')\\n\\n date_range = pd.date_range(start=self.start_date, end=self.end_date, freq=self.date_unit)\\n date_range = date_range.floor('D')\\n\\n grouped = df.groupby('author')\\n new_df = pd.DataFrame(index=date_range)\\n for name, group in grouped:\\n new_df[name] = group.groupby('date').size()\\n new_df.fillna(0, inplace=True)\\n\\n return {\\n 'x': new_df.index.strftime('%Y-%m-%d').tolist(),\\n 'y': new_df.columns.tolist(),\\n 'z': new_df.T.values.astype('int32').tolist()\\n }\",\n \"def make_df_from_json(json_files, out_file):\\n table = [[\\\"name\\\", \\n \\\"cik\\\", \\n \\\"city\\\",\\n \\\"state\\\",\\n \\\"street1\\\",\\n \\\"street2\\\",\\n \\\"zip_code\\\",\\n \\\"year_of_incorp\\\", \\n \\\"min_inv\\\", \\n \\\"tot_off\\\", \\n \\\"tot_sold\\\", \\n \\\"tot_rem\\\", \\n \\\"ind_group_type\\\", \\n \\\"has_non_accred\\\", \\n \\\"num_non_accred\\\", \\n \\\"tot_num_inv\\\"\\n ]] \\n\\n for json_dict in json_files:\\n\\n with open(json_dict, \\\"rb\\\") as f:\\n data = json.load(f)\\n print(json_dict)\\n\\n for i, key in enumerate(data):\\n # if i % 1000 == 0:\\n # print(i)\\n entry = data[key] \\n if entry == {}:\\n #print(\\\"missing entry {0}\\\".format(i))\\n continue\\n row = []\\n\\n primary_issuer = entry[\\\"Primary Issuer\\\"]\\n cik = primary_issuer[\\\"cik\\\"]\\n name = primary_issuer[\\\"entity_name\\\"]\\n phone = primary_issuer[\\\"phone\\\"]\\n year_of_incorp = primary_issuer[\\\"year_of_incorp\\\"]\\n address = primary_issuer[\\\"address\\\"]\\n city = address[\\\"city\\\"]\\n state = address[\\\"state\\\"]\\n street1 = address[\\\"street1\\\"]\\n street2 = address[\\\"street2\\\"]\\n zip_code = address[\\\"zip_code\\\"]\\n\\n secondary_issuers = entry[\\\"Secondary Issuers\\\"]\\n related_people = entry[\\\"Related People\\\"]\\n \\n offering_data = entry[\\\"Offering Data\\\"]\\n min_inv = offering_data[\\\"min_investment_accepted\\\"]\\n tot_off = offering_data[\\\"total_offering_amount\\\"]\\n tot_sold = offering_data[\\\"total_amount_sold\\\"]\\n tot_rem = offering_data[\\\"total_remaining\\\"]\\n ind_group_type = offering_data[\\\"ind_group_type\\\"]\\n has_non_accred = offering_data[\\\"has_non_accred\\\"]\\n num_non_accred = offering_data[\\\"num_non_accred\\\"]\\n tot_num_inv = offering_data[\\\"tot_num_inv\\\"] \\n\\n row = [name, \\n cik, \\n city,\\n state,\\n street1,\\n street2,\\n zip_code,\\n year_of_incorp,\\n min_inv,\\n tot_off,\\n tot_sold,\\n tot_rem,\\n ind_group_type,\\n has_non_accred,\\n num_non_accred,\\n tot_num_inv\\n ]\\n\\n table.append(row)\\n\\n df = pd.DataFrame(table)\\n df.to_csv(out_file)\\n\\n return 0\",\n \"def search_andsetvalues(promise, constituency, party, df: pd.DataFrame, i, df_scraped) -> pd.DataFrame:\\n candidates = df_scraped.loc[(df_scraped['constituency'] == constituency) & (df_scraped['party'] == party)] #& \\n\\n for j, row in candidates.iterrows():\\n if compare_promises(row[\\\"election_promise_1\\\"][\\\"fi\\\"], row[\\\"election_promise_1\\\"][\\\"se\\\"], \\n row[\\\"election_promise_1\\\"][\\\"en\\\"], promise):\\n df.at[i,'name'] = row[\\\"first_name\\\"] + \\\" \\\" + row[\\\"last_name\\\"]\\n df.at[i, 'number'] = str(row[\\\"election_number\\\"]) \\n df.at[i,'image'] = \\\"https://ehdokaskone.yle.webscale.fi/\\\" + row['image']\\n return df\",\n \"def download(self, verbose):\\n # Download datasets\\n if verbose:\\n print(\\\"Retrieving datasets from Our World In Data https://github.com/owid/covid-19-data/\\\")\\n # Vaccinations\\n v_rec_cols = [\\n \\\"date\\\", \\\"location\\\", \\\"iso_code\\\", \\\"total_vaccinations\\\", \\\"people_vaccinated\\\", \\\"people_fully_vaccinated\\\"]\\n v_rec_df = pd.read_csv(self.URL_V_REC, usecols=v_rec_cols)\\n v_loc_df = pd.read_csv(self.URL_V_LOC, usecols=[\\\"location\\\", \\\"vaccines\\\"])\\n v_df = v_rec_df.merge(v_loc_df, how=\\\"left\\\", on=\\\"location\\\")\\n # Tests\\n pcr_rec_cols = [\\\"ISO code\\\", \\\"Date\\\", \\\"Daily change in cumulative total\\\", \\\"Cumulative total\\\"]\\n pcr_df = pd.read_csv(self.URL_P_REC, usecols=pcr_rec_cols)\\n pcr_df = pcr_df.rename(columns={\\\"ISO code\\\": \\\"iso_code\\\", \\\"Date\\\": \\\"date\\\"})\\n pcr_df[\\\"cumsum\\\"] = pcr_df.groupby(\\\"iso_code\\\")[\\\"Daily change in cumulative total\\\"].cumsum()\\n pcr_df = pcr_df.assign(tests=lambda x: x[\\\"Cumulative total\\\"].fillna(x[\\\"cumsum\\\"]))\\n # Combine data (vaccinations/tests)\\n df = v_df.set_index([\\\"iso_code\\\", \\\"date\\\"])\\n df = df.combine_first(pcr_df.set_index([\\\"iso_code\\\", \\\"date\\\"]).loc[:, [\\\"tests\\\"]])\\n df = df.reset_index()\\n # Location (country/province)\\n df[\\\"location\\\"] = df[\\\"location\\\"].replace(\\n {\\n # COG\\n \\\"Congo\\\": \\\"Republic of the Congo\\\",\\n }\\n )\\n df = df.loc[~df[\\\"iso_code\\\"].str.contains(\\\"OWID_\\\")]\\n df[\\\"location\\\"] = df.groupby(\\\"iso_code\\\")[\\\"location\\\"].bfill()\\n df.loc[df[\\\"location\\\"] == df[\\\"iso_code\\\"], \\\"location\\\"] = None\\n df.loc[df[\\\"location\\\"].isna(), \\\"location\\\"] = df.loc[df[\\\"location\\\"].isna(), \\\"iso_code\\\"].apply(\\n lambda x: coco.convert(x, to=\\\"name_short\\\", not_found=None))\\n df[self.PROVINCE] = self.UNKNOWN\\n return df\",\n \"def _request_info(self):\\n \\n current_date = str(datetime.datetime.now(pytz.timezone('US/Mountain')))\\n json_dct = {'search_title': self.job_title, \\\\\\n 'search_location': self.job_location, \\\\\\n 'search_date': current_date, 'job_site': 'indeed'}\\n # Holds the actual CSS selector as the key and the label I want to store\\n # the info. as as the key. \\n possible_attributes = {'.jobtitle': \\\"job_title\\\", '.company': \\\"company\\\", \\\\\\n '.location': \\\"location\\\", '.date': \\\"date\\\", \\\\\\n '.iaLabel': \\\"easy_apply\\\"}\\n for k, v in possible_attributes.iteritems(): \\n res = self.row.select(k)\\n if res: \\n json_dct[v] = res[0].text\\n # Now let's grab the href and pass that on to another function to \\n # get that info. \\n href = self.row.find('a').get('href')\\n json_dct['href'] = href\\n json_dct['posting_txt'] = self._query_href(href)\\n\\n return json_dct\",\n \"def viewdata(data):\\n\\n print('_' * 50)\\n print('Number of Results: ' + str(data[0]['numResults']))\\n print('\\\\nSearchURL: ' + data[0]['searchURL'])\\n print('_' * 50)\\n\\n i = 1\\n for m in data[1]:\\n print(str(i) + '. ')\\n for n in m:\\n print(str(n) + ': ' + str(m[n]))\\n i += 1\\n print('\\\\n')\",\n \"def data_dict0():\\n\\n # 0- Sample from detectron2 -> 5 different sections.\\n info_val0 = [{\\\"date_created\\\": \\\"2020-03-15 04:59:45.442988\\\",\\n \\\"description\\\": \\\"Automatically generated COCO json file for Detectron2.\\\"}]\\n images0 = [{\\\"id\\\": \\\"image\\\", \\\"width\\\": 100,\\n \\\"height\\\": 100, \\\"file_name\\\": \\\"image.png\\\"}]\\n annotations0 = [{\\\"id\\\": 1, \\\"image_id\\\": \\\"image\\\", \\\"bbox\\\": [70.0, 30.0, 30.0, 40.0],\\n \\\"area\\\": 1200.0, \\\"iscrowd\\\": 0, \\\"category_id\\\": 0}]\\n categories0 = [{\\\"id\\\": 0, \\\"name\\\": \\\"first\\\"}]\\n licence0 = 'null'\\n\\n return [{\\\"info\\\": info_val0,\\n \\\"images\\\": images0,\\n \\\"annotations\\\": annotations0,\\n \\\"categories\\\": categories0,\\n \\\"licenses\\\": licence0}]\",\n \"def _info(self) -> tfds.core.DatasetInfo:\\n return tfds.core.DatasetInfo(\\n builder=self,\\n description=_DESCRIPTION,\\n features=tfds.features.FeaturesDict({\\n 'id': tfds.features.Text(),\\n 'program': tfds.features.Text(),\\n 'date': tfds.features.Text(),\\n 'url': tfds.features.Text(),\\n 'summary': tfds.features.Text(),\\n 'utt': tfds.features.Sequence(tfds.features.Text()),\\n 'speaker': tfds.features.Sequence(tfds.features.Text()),\\n }),\\n supervised_keys=('utt', 'summary'),\\n homepage='https://github.com/zcgzcgzcg1/MediaSum',\\n citation=_CITATION,\\n )\",\n \"def precip():\\n date_prcp=session.query(measurements.date,measurements.prcp).all()\\n date_prcp_df=pd.DataFrame(date_prcp).set_index('date')\\n date_prcp_dict=date_prcp_df.to_dict()\\n return jsonify(date_prcp_dict)\",\n \"def precipitation():\\r\\n # Query all measurements\\r\\n results = session.query(Measurement).all()\\r\\n\\r\\n # Create a dictionary from the row data and append to a list of all_precipitation\\r\\n all_precipitation = []\\r\\n\\r\\n for row in results:\\r\\n precipitation_dict = { row.date : row.prcp}\\r\\n all_precipitation.append(precipitation_dict)\\r\\n # print(all_precipitation)\\r\\n return jsonify(all_precipitation)\",\n \"def dataset_grabber(sess, link):\\n json_dict = sess.get(link).json()\\n if '.geojson' in link:\\n dataset = gpd.GeoDataFrame.from_features(json_dict['features'])\\n else:\\n dataset = pd.DataFrame(json_dict)\\n return dataset\",\n \"def get_pr_info(num):\\r\\n url = \\\"https://api.github.com/repos/edx/edx-platform/pulls/{num}\\\".format(num=num)\\r\\n username, token = get_github_creds()\\r\\n headers = {\\r\\n \\\"Authorization\\\": \\\"token {}\\\".format(token),\\r\\n \\\"User-Agent\\\": \\\"edx-release\\\",\\r\\n }\\r\\n response = requests.get(url, headers=headers)\\r\\n result = response.json()\\r\\n if not response.ok:\\r\\n raise requests.exceptions.RequestException(result[\\\"message\\\"])\\r\\n return result\",\n \"def fetch(url, header_path, id, ip, dbase, targets_table):\\n # url = 'http://esimbad/testGSAV7/reslabo?FENID=resLaboPatDitep&NIP={}' \\\\\\n # '&STARTDATE={}&ENDDATE={}'\\n\\n # header_path = '~/workspace/data/biology/header.csv'\\n # constant names specific to our database\\n KEY1 = 'id'\\n KEY2 = 'NIP'\\n C1J1 = 'C1J1'\\n\\n header = pd.read_csv(header_path, sep=';', encoding='latin1').columns\\n\\n\\n engine = get_engine(id, ip, dbase)\\n\\n df_ids = sql2df(engine, targets_table)[[KEY1, 'nip', C1J1]]\\n df_ids.rename({'nip': KEY2}, inplace=True, axis=1)\\n df_ids['patient_id'] = df_ids[KEY1]\\n\\n cols = [KEY2, 'Analyse', 'Resultat', 'Date prelvt']\\n df_res = pd.DataFrame(data=None, columns=cols)\\n\\n for index, row in df_ids.iterrows():\\n nip = row[KEY2].replace(' ', '')\\n patient_id = row['patient_id']\\n c1j1_date = row[C1J1].date()\\n start_date = c1j1_date - timedelta(weeks=8)\\n\\n c1j1 = str(c1j1_date).replace('-', '')\\n start = str(start_date).replace('-', '')\\n\\n req = requests.get(url.format(nip, start, c1j1))\\n values = BeautifulSoup(req.content, 'html.parser').body.text\\n\\n new_df = pd.read_csv(StringIO(values), sep=';', header=None,\\n index_col=False, names=header)\\n new_df = new_df.loc[:, cols + ['LC']] # remove LC\\n\\n # normalize nip\\n new_df[KEY2] = row[KEY2]\\n # new_df[KEY2] = new_df[KEY2].map(str)\\n # new_df[KEY2] = [nip[:4] + '-' + nip[4:] for nip in new_df[KEY2]]\\n\\n new_df.drop('LC', axis=1, inplace=True)\\n\\n df_res = pd.concat([df_res, new_df], axis=0,\\n sort=False, ignore_index=True)\\n\\n return df_res\",\n \"def extract_all_lazy():\\n\\n\\t#Construct filepaths: Data COMP_INFO_1\\n\\tdata_ci1_name = \\\"DATA_2016_COMP_INFO_1.csv\\\"\\n\\tdata_ci1_fullname = os.path.join(files_location, data_ci1_name)\\n\\t#Data COMP_INFO_2\\n\\tdata_ci2_name = \\\"DATA_2016_COMP_INFO_2.csv\\\"\\n\\tdata_ci2_fullname = os.path.join(files_location, data_ci2_name)\\n\\t#Data PROPERTY INFO\\n\\tdata_pi_name = \\\"DATA_2016_PROPERTY_INFO_ST.csv\\\"\\n\\tdata_pi_fullname = os.path.join(files_location, data_pi_name)\\n\\t#Data General Info\\n\\tdata_gi_name = \\\"DATA_2016_GENERAL_INFO.csv\\\"\\n\\tdata_gi_fullname = os.path.join(files_location, data_gi_name)\\n\\n\\t#Read & Process COMP_INFO\\n\\tdata_ci1 = pd.read_csv(data_ci1_fullname, skiprows=2, usecols = constants.keep_columns_CI, encoding='ISO-8859-1')\\n\\tdata_ci2 = pd.read_csv(data_ci2_fullname, skiprows=2, usecols = constants.keep_columns_CI, encoding='ISO-8859-1')\\n\\n\\tdata_ci = data_ci1.append(data_ci2)\\n\\tdata_ci['QUESTION'] = data_ci['QUESTION'].replace(constants.ci_mapping)\\n\\t# Take only the survey questions mapped\\n\\tdata_ci = data_ci[data_ci['QUESTION'].isin(constants.ci_mapping.values())]\\n\\tdata_ci = data_ci.set_index(['PROPERTY_CODE','PROPERTY_NAME','JOB_CODE','POSITION'])\\n\\tdata_ci = data_ci.pivot(columns=\\\"QUESTION\\\")\\n\\tdata_ci.columns = [\\\"_\\\".join(pair) for pair in data_ci.columns]\\n\\tdata_ci = data_ci.reset_index()\\n\\n\\t#Read & Process Property Info data\\n\\tdata_pi = pd.read_csv(data_pi_fullname, usecols = constants.keep_columns_PI, encoding='ISO-8859-1')\\n\\t#survey_type_transformed = transform.surveytype_categorical(data_pi)\\n\\t#data_pi = pd.merge(data_pi, survey_type_transformed, on=['PROPERTY_CODE'])\\n\\n\\t#Read & Process General Info\\n\\tdata_gi = pd.read_csv(data_gi_fullname, skiprows = 2, usecols = constants.keep_columns_GI, encoding='ISO-8859-1')\\n\\tdata_gi['QUESTION'] = data_gi['QUESTION'].replace(constants.gi_mapping)\\n\\t# Take onl the survey questions mapped\\n\\tdata_gi = data_gi[data_gi['QUESTION'].isin(constants.gi_mapping.values())]\\n\\tdata_gi = data_gi.set_index(['PROPERTY_CODE','PROPERTY_NAME'])\\n\\tdata_gi = data_gi.pivot(columns=\\\"QUESTION\\\")\\n\\tdata_gi.columns = [\\\"_\\\".join(pair) for pair in data_gi.columns]\\n\\tdata_gi = data_gi.reset_index()\\n\\n\\t#This frame needs to be reworked\\n\\td_ci = pd.merge(data_gi, data_pi, on = ['PROPERTY_CODE','PROPERTY_NAME'])\\n\\td_ci = pd.merge(d_ci, data_ci, on = ['PROPERTY_CODE','PROPERTY_NAME'],suffixes= ['_ci','_gi'])\\n\\n\\t#Observations by Dimensions to determine top X markets\\n\\t#Can this be in a better position?\\n\\td_ci = d_ci[~(d_ci['PROPERTY_NAME'].isin(constants.del_rows_property_name))]\\n\\td_ci['POSITION'] = d_ci['POSITION'].astype(str)\\n\\n\\tpayload = {}\\n\\tpayload['gi'] = data_gi\\n\\tpayload['pi'] = data_pi\\n\\tpayload['ci'] = data_ci\\n\\tpayload['d_ci'] = d_ci\\n\\n\\treturn payload\",\n \"def _fetch_idjj_data(year=None):\\n try:\\n if year is None:\\n year = _get_max_year([11, 12, 34, 35]) + 1\\n\\n database = 'PrisonMain'\\n tbl_admit = 'IDJJ_Admissions'\\n tbl_exit = 'IDJJ_exits'\\n cols = 'Age, SFY, County, sex, race, admtypo, OFFTYPE9, hclass'\\n condition = f'SFY = {year}'\\n\\n df_a = _fetch_from_ms_sql_server(database, tbl_admit, cols, condition)\\n df_e = _fetch_from_ms_sql_server(database, tbl_exit, 'Exit'+cols, condition)\\n\\n return (\\n pd.DataFrame()\\n .append(_tranform_idjj(df_a))\\n .append(_tranform_idjj(df_a, age1720=True))\\n .append(_tranform_idjj(df_e, exit=True))\\n .append(_tranform_idjj(df_e, age1720=True, exit=True))\\n )\\n except:\\n raise\",\n \"def fetch_tikhonov_data(dpath='/tmp/glm-tools'):\\n if os.path.exists(dpath):\\n shutil.rmtree(dpath)\\n os.mkdir(dpath)\\n\\n base_url = \\\"https://raw.githubusercontent.com/glm-tools/datasets/master\\\"\\n url = os.path.join(base_url, \\\"tikhonov/fixations.csv\\\")\\n fname = os.path.join(dpath, 'fixations.csv')\\n urllib.urlretrieve(url, fname)\\n fixations_df = pd.read_csv(fname)\\n\\n url = os.path.join(base_url, \\\"tikhonov/probes.csv\\\")\\n fname = os.path.join(dpath, 'probes.csv')\\n urllib.urlretrieve(url, fname)\\n probes_df = pd.read_csv(fname)\\n\\n url = os.path.join(base_url, \\\"tikhonov/spiketimes.csv\\\")\\n fname = os.path.join(dpath, 'spiketimes.csv')\\n urllib.urlretrieve(url, fname)\\n spikes_df = pd.read_csv(fname, header=None)\\n\\n return fixations_df, probes_df, spikes_df\",\n \"def get_metadata_list(path, file, task_description, dataset_description):\\n df = pd.read_csv(os.path.join(path, file), sep='\\\\t')\\n\\n for column in COMMON_FIELDS:\\n if column not in df.columns:\\n logging.getLogger('zenodo_upload').critical('File {} is missing column {}'.format(os.path.join(path, file), column))\\n exit(1)\\n\\n df = df[(df['DOI'].str.lower() == 'new') | (df['DOI'].isna())]\\n if df.empty:\\n return None\\n\\n metadata_list = []\\n for index, row in df.iterrows():\\n title = ' '.join([row['Software'], row['Version'], task_description, 'of the', dataset_description + ', samples ' + row['SamplesUsed']])\\n\\n row_copy= row.copy().drop(COMMON_FIELDS).dropna()\\n if 'Description' in row_copy.index:\\n description = row_copy.pop('Description') + '<br>'\\n else:\\n description = ''\\n description += '<strong>Software: </strong>' + row['Software'] + '<br>'\\n description += '<strong>SoftwareVersion: </strong>' + str(row['Version']) + '<br>'\\n description += '<strong>DataURL: </strong> https://data.cami-challenge.org/participate'\\n for item in row_copy.iteritems():\\n if len(description) > 0:\\n description = description + '<br>'\\n description = description + str('<strong>' + item[0]) + ':</strong> ' + str(item[1])\\n\\n creators_metadata = get_creators_metadata(row)\\n metadata = {\\n 'metadata': {\\n 'title': title,\\n 'upload_type': 'dataset',\\n 'communities': [{'identifier': 'cami'}],\\n 'description': description,\\n 'creators': creators_metadata,\\n 'access_right': 'open',\\n 'license': 'cc-by',\\n 'version': row['Version'],\\n 'keywords': KEYWORDS + [task_description, dataset_description]\\n },\\n 'files': row['FileName']\\n }\\n metadata_list.append(metadata)\\n return metadata_list\",\n \"def data_frame_info(self):\\r\\n print(self.dataframe_name)\\r\\n print(self.data_frame.info())\",\n \"def _read_projects_df_from_db(self, include_stats=True):\\n\\n projects_df = None\\n\\n # TODO: should cursor be created here or no?\\n # https://www.datacamp.com/community/tutorials/tutorial-postgresql-python # noqa\\n # shows creation of a cursor even though no methods are called on it\\n with self._transaction.dict_cursor():\\n # TODO: is there a better way to access this?\\n conn = self._transaction._conn\\n\\n queries = _PROJECT_SQLS if include_stats else [_PROJECT_SQLS[0]]\\n for stats_keys, sql_source in queries:\\n if callable(sql_source):\\n curr_sql = sql_source(*stats_keys)\\n else:\\n curr_sql = sql_source.format(*stats_keys)\\n\\n curr_df = pd.read_sql(curr_sql, conn)\\n\\n if projects_df is None:\\n projects_df = curr_df\\n else:\\n # left join here: the first query produces a df with a\\n # COMPLETE list of all projects, whereas subsequent\\n # queries only return info on projects relevant to their\\n # computed statistic.\\n projects_df = pd.merge(projects_df, curr_df,\\n how=\\\"left\\\", on=[\\\"project_id\\\"])\\n\\n # make the project_id the index of the final data frame, but\\n # do NOT drop the project_id column from the data frame.\\n projects_df.set_index('project_id', drop=False, inplace=True)\\n return projects_df\",\n \"def get_info(now):\\n url = 'https://api.stackexchange.com/2.2/info'\\n payload = {\\n 'site': 'stackoverflow'\\n }\\n result = requests.get(url, params=payload)\\n # print(result.url) # https://api.stackexchange.com/2.2/info?site=stackoverflow\\n result = result.json()\\n items = result['items'][0]\\n items_seq = {k:[v] for k, v in items.items()}\\n df = pd.DataFrame(items_seq)\\n df['time_requested'] = now\\n return df\",\n \"def get_organisation_metadata() -> pd.DataFrame:\\n return GETTER.organisationmetadata\",\n \"def identify_primary_reference_datasets(conn, log):\\n\\n primary_ref = {}\\n\\n primary_ref['refimg_id_ip'] = phot_db.find_primary_reference_image_for_field(conn)\\n\\n query = 'SELECT facility, filter, software FROM reference_images WHERE refimg_id=\\\"'+str(primary_ref['refimg_id_ip'])+'\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n\\n primary_ref['facility_id'] = t['facility'][0]\\n primary_ref['software_id'] = t['software'][0]\\n\\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\\\"ip\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n primary_ref['ip'] = t['filter_id'][0]\\n\\n for f in ['rp', 'gp']:\\n query = 'SELECT filter_id, filter_name FROM filters WHERE filter_name=\\\"'+f+'\\\"'\\n t = phot_db.query_to_astropy_table(conn, query, args=())\\n primary_ref[f] = t['filter_id'][0]\\n\\n query = 'SELECT refimg_id FROM reference_images WHERE facility=\\\"'+str(primary_ref['facility_id'])+\\\\\\n '\\\" AND software=\\\"'+str(primary_ref['software_id'])+\\\\\\n '\\\" AND filter=\\\"'+str(t['filter_id'][0])+'\\\"'\\n qs = phot_db.query_to_astropy_table(conn, query, args=())\\n\\n if len(qs) > 0:\\n primary_ref['refimg_id_'+f] = qs['refimg_id'][0]\\n else:\\n log.info('WARNING: Database contains no primary reference image data in filter '+f)\\n\\n log.info('Identified the primary reference datasets for this field as:')\\n for key, value in primary_ref.items():\\n log.info(str(key)+' = '+str(value))\\n\\n return primary_ref\",\n \"async def get_project_info(project_urls):\\n project_info = []\\n for url in project_urls:\\n soup = await get_page(url)\\n about = soup.find_all(\\\"p\\\")\\n title = soup.find(\\\"h3\\\").text\\n student = about[0].text.splitlines()[2].strip()\\n details = about[1].text\\n name = about[0].find(\\\"a\\\").text\\n project_info.append({'Organization': name, 'title': title,\\n 'student': student, 'details': details,\\n 'link': url})\\n\\n return project_info\",\n \"def update_datatable(clickData, selected_year, search_ids_keywords, search_keywords, selected_funding):\\n\\n prof_name = clickData['points'][0]['customdata']\\n print(\\\"selected professor for network graph is \\\", prof_name)\\n\\n # TODO: Handle no search_ids or Nonetype search ids DONE\\n # print(\\\" Search IDS ************* 8********** ****** here are this\\\", search_ids_keywords[0:,0])\\n\\n professor_data = funding_data[(funding_data['Lead Investigator:'] == prof_name)\\n & (funding_data.start_year.isin(selected_year))\\n # & (funding_data._id.isin(search_ids_keywords[0:,0]))\\n & (funding_data['Program Cycle:'].isin(selected_funding))]\\n\\n if search_keywords and len(search_keywords) > 0:\\n search_ids_keywords = json.loads(search_ids_keywords)\\n search_ids_keywords = np.array(search_ids_keywords)\\n professor_data = professor_data[(professor_data._id.isin(search_ids_keywords[0:, 0]))]\\n\\n\\n # collaborating_personnel_counts = collaborating_personnel.investigator.value_counts()\\n professor_data = professor_data[\\n [\\\"Proposal Number:\\\", \\\"Proposal Title:\\\"]]\\n\\n # [\\\"Proposal Number:\\\", \\\"Submitting Institution Name:\\\", \\\"Project Status:\\\", \\\"Proposal Title:\\\"]]\\n return professor_data.to_dict(\\\"rows\\\")\\n # return professor_data\",\n \"def info(self):\\n for key, value in self.dataset['info'].items():\\n print('{}: {}'.format(key, value))\",\n \"def extract_depto(succId = 439) :\\n deps_api = 'https://www.lacomer.com.mx/lacomer-api/api/v1/public/header/inicio?cambioSucc=false&succFmt=100&succId={}'.format(succId)\\n deps_json = json.loads(requests.get(deps_api).text)['departamentos']\\n deps_list = list(deps_json.keys())\\n deps_df = []\\n\\n for depto in deps_list :\\n \\n tmp = pd.DataFrame(deps_json[depto])\\n tmp['dept'] = depto\\n deps_df.append(tmp)\\n \\n deps_df = pd.concat(deps_df)\\n deps_df['dept'].unique()\\n\\n sucursales = extract_sucursales()\\n deps_df['succId'] = succId\\n deps_df['sucursal'] = sucursales.loc[sucursales['id'] == succId,'sucursal'].iloc[0]\\n\\n return deps_df\",\n \"def create_df(link=config.api_link, key=config.api_key, master_file = 'pvd_crime_master.csv'):\\n #only want reports we don't already have, so what is the most recent date in the master\\n master = pd.read_csv(master_file, nrows=1)\\n most_recent = pd.to_datetime(master['reported_date'])[0]\\n most_recent_format = most_recent.strftime('%Y-%m-%dT%H:%M:%S.000')\\n\\n headers = {'Authentication': key} #api_key\\n \\n query = \\\"SELECT * WHERE reported_date > '\\\"+most_recent_format+\\\"' ORDER BY reported_date LIMIT 13000\\\"\\n\\n params = {'$query': query}\\n\\n response = requests.get(link, headers=headers, params=params) #json data\\n response_json = response.json() #json data as list of dictionaries\\n \\n #create and return pandas DataFrame of json response\\n\\n return pd.DataFrame(response_json)\",\n \"def load_data():\\n df = pd.read_csv(\\\"https://raw.githubusercontent.com/Andrea-Giuliani/Python-Project/master/data/final_dataset.csv\\\",sep=',') \\n return df\",\n \"def test_project_detail(self):\\n rv = self.app.get(\\\"/Assignment0\\\")\\n self.assertIn(\\\"Assignment0\\\", rv.data)\\n self.assertIn(\\\"2015-02-04 21:57:12.156363\\\", rv.data)\\n self.assertIn(\\\"221\\\", rv.data)\\n self.assertIn(\\\"commit assignment0\\\", rv.data)\\n\\n self.assertIn(\\\"Assignment0/Procfile\\\", rv.data)\\n self.assertIn(\\\"Assignment0/README.md\\\", rv.data)\",\n \"def getDatasetsProjectsFromImages(queryService, imageIds):\\n ids = \\\",\\\".join([str(i) for i in imageIds])\\n \\n query_string = \\\"select i from Image i join fetch i.datasetLinks idl join fetch idl.parent d join fetch d.projectLinks pl join fetch pl.parent where i.id in (%s)\\\" % ids\\n\\n images = queryService.findAllByQuery(query_string, None)\\n results = {}\\n \\n for i in images: # order of images not same as imageIds\\n pdList = []\\n imageId = i.getId().getValue()\\n for link in i.iterateDatasetLinks():\\n dataset = link.parent\\n dName = dataset.getName().getValue()\\n if dataset.sizeOfProjectLinks() == 0:\\n pdList.append((\\\"\\\", dName))\\n for dpLink in dataset.iterateProjectLinks():\\n project = dpLink.parent\\n pName = project.getName().getValue()\\n pdList.append((pName, dName))\\n results[imageId] = pdList\\n \\n # make sure the map contains all the imageIds\\n for iId in imageIds:\\n if iId not in results:\\n results[iId] = []\\n return results\",\n \"def load_landkreis_information():\\n client = MongoClient(f'mongodb://{os.getenv(\\\"USR_\\\")}:{os.getenv(\\\"PWD_\\\")}@{os.getenv(\\\"REMOTE_HOST\\\")}:{os.getenv(\\\"REMOTE_PORT\\\")}/{os.getenv(\\\"AUTH_DB\\\")}')\\n db = client[os.getenv(\\\"MAIN_DB\\\")]\\n lk_collection = db[\\\"lk_overview\\\"]\\n data = pd.DataFrame(list(lk_collection.find()))\\n return data\",\n \"def get_dataset_details(name, analyst):\\n\\n template = None\\n allowed_sources = user_sources(analyst)\\n dataset_object = Dataset.objects(name = name,\\n source__name__in=allowed_sources).first()\\n if not dataset_object:\\n error = (\\\"Either no data exists for this dataset\\\"\\n \\\" or you do not have permission to view it.\\\")\\n template = \\\"error.html\\\"\\n args = {'error': error}\\n return template, args\\n\\n dataset_object.sanitize_sources(username=\\\"%s\\\" % analyst,\\n sources=allowed_sources)\\n\\n # remove pending notifications for user\\n remove_user_from_notification(\\\"%s\\\" % analyst, dataset_object.id, 'Dataset')\\n\\n # subscription\\n subscription = {\\n 'type': 'Dataset',\\n 'id': dataset_object.id,\\n 'subscribed': is_user_subscribed(\\\"%s\\\" % analyst,\\n 'Dataset',\\n dataset_object.id),\\n }\\n\\n #objects\\n objects = dataset_object.sort_objects()\\n\\n #relationships\\n relationships = dataset_object.sort_relationships(\\\"%s\\\" % analyst, meta=True)\\n\\n # relationship\\n relationship = {\\n 'type': 'Datset',\\n 'value': dataset_object.id\\n }\\n\\n #comments\\n comments = {'comments': dataset_object.get_comments(),\\n 'url_key':dataset_object.name}\\n\\n # favorites\\n favorite = is_user_favorite(\\\"%s\\\" % analyst, 'Dataset', dataset_object.id)\\n\\n # services\\n service_list = get_supported_services('Dataset')\\n\\n # analysis results\\n service_results = dataset_object.get_analysis_results()\\n\\n args = {'dataset': dataset_object,\\n 'objects': objects,\\n 'relationships': relationships,\\n 'comments': comments,\\n 'favorite': favorite,\\n 'relationship': relationship,\\n 'subscription': subscription,\\n 'name': dataset_object.name,\\n 'service_list': service_list,\\n 'service_results': service_results}\\n\\n return template, args\",\n \"def build_person_df(person):\\n data = {\\n \\\"act\\\" : [],\\n \\\"modes\\\": [],\\n \\\"start_time\\\": [],\\n \\\"end_time\\\": [],\\n \\\"dur\\\": [],\\n }\\n for component in person.plan.day:\\n data[\\\"act\\\"].append(component.act.lower().title())\\n if isinstance(component, activity.Leg):\\n data[\\\"modes\\\"].append(component.mode.lower().title())\\n else:\\n data[\\\"modes\\\"].append(None)\\n data[\\\"start_time\\\"].append(component.start_time.hour + component.start_time.minute/60)\\n data[\\\"end_time\\\"].append(component.end_time.hour + component.end_time.minute/60)\\n data[\\\"dur\\\"].append(component.duration.total_seconds()/3600)\\n df = pd.DataFrame(data)\\n df['pid'] = person.pid\\n\\n return df\",\n \"def download(self, verbose):\\n\\n # Download datasets\\n if verbose:\\n print(\\\"Retrieving datasets from COVID-19 Open Data by Google Cloud Platform https://github.com/GoogleCloudPlatform/covid-19-open-data\\\")\\n # Index\\n i_cols = [\\\"location_key\\\", \\\"country_name\\\", \\\"subregion1_name\\\", \\\"subregion2_name\\\", \\\"iso_3166_1_alpha_3\\\"]\\n i_df = pd.read_csv(self.URL_I, usecols=i_cols)\\n # Mobility\\n m_df = pd.read_csv(self.URL_M)\\n m_df = (m_df.set_index([\\\"date\\\", \\\"location_key\\\"]) + 100).reset_index()\\n # Combine data\\n df = m_df.merge(i_df, how=\\\"left\\\", on=\\\"location_key\\\")\\n # Location (country/province)\\n df = df.loc[df[\\\"subregion2_name\\\"].isna()]\\n df[self.PROVINCE] = df[\\\"subregion1_name\\\"].fillna(self.UNKNOWN).apply(unidecode)\\n df[\\\"country_name\\\"] = df[\\\"country_name\\\"].replace(\\n {\\n # CIV\\n \\\"Ivory Coast\\\": \\\"Cote d'Ivoire\\\",\\n }\\n )\\n return df\",\n \"def extract_details(df):\\n df_RSinfo = df[['pentamer', 'Step details', 'RouteScore details',\\n 'Isolated', 'RouteScore', 'log(RouteScore)']]\\n\\n last3_rxns = ['Buchwald_deprotection', 'Buchwald', 'SNAr']\\n for rxn in last3_rxns:\\n df_RSinfo[rxn] = [next(step for step in row[-3:] if step['reaction'] == rxn) for row in df['Step details']]\\n\\n for key in df_RSinfo['RouteScore details'][0].keys():\\n df_RSinfo[key] = [row[key] for row in df['RouteScore details']]\\n\\n return df_RSinfo\",\n \"async def get_all_record():\\n # X_new = item.to_df()\\n # item_str = item.to_string()\\n # project_code = int(item_str[item_str.find('=')+1:])\\n pg = PostgreSQL()\\n return_json = pg.fetch_all_records()\\n return return_json\",\n \"def _info(self) -> tfds.core.DatasetInfo:\\n features = {\\n # sequence of [RGB, depth] images\\n \\\"image\\\": tfds.features.Sequence(\\n tfds.features.Image(shape=(600, 800, 3)), length=2,\\n ),\\n # sequence of image features for [RGB, depth]\\n \\\"images\\\": tfds.features.Sequence(\\n tfds.features.FeaturesDict(\\n {\\n \\\"file_name\\\": tfds.features.Text(),\\n \\\"height\\\": tf.int64,\\n \\\"width\\\": tf.int64,\\n \\\"id\\\": tf.int64,\\n },\\n ),\\n length=2,\\n ),\\n # both modalities share the same categories\\n \\\"categories\\\": tfds.features.Sequence(\\n tfds.features.FeaturesDict(\\n {\\n \\\"id\\\": tf.int64, # {'pedstrian':1, 'vehicles':2, 'trafficlight':3, 'patch':4}\\n \\\"name\\\": tfds.features.Text(),\\n }\\n )\\n ),\\n # both modalities share the same objects\\n \\\"objects\\\": tfds.features.Sequence(\\n {\\n \\\"id\\\": tf.int64,\\n \\\"image_id\\\": tf.int64,\\n \\\"area\\\": tf.int64, # un-normalized area\\n \\\"boxes\\\": tfds.features.BBoxFeature(), # normalized bounding box [ymin, xmin, ymax, xmax]\\n \\\"labels\\\": tfds.features.ClassLabel(num_classes=5),\\n \\\"is_crowd\\\": tf.bool,\\n }\\n ),\\n # these data only apply to the \\\"green screen patch\\\" objects, which both modalities share\\n \\\"patch_metadata\\\": tfds.features.FeaturesDict(\\n {\\n \\\"gs_coords\\\": tfds.features.Sequence(\\n tfds.features.Tensor(\\n shape=[2], dtype=tf.int64\\n ), # green screen vertices in (x,y)\\n length=4, # always rectangle shape\\n ),\\n \\\"cc_ground_truth\\\": tfds.features.Tensor(\\n shape=[24, 3], dtype=tf.float32\\n ), # colorchecker color ground truth\\n \\\"cc_scene\\\": tfds.features.Tensor(\\n shape=[24, 3], dtype=tf.float32\\n ), # colorchecker colors in a scene\\n # binarized segmentation mask of patch.\\n # mask[x,y] == 1 indicates patch pixel; 0 otherwise\\n \\\"mask\\\": tfds.features.Tensor(shape=[600, 800, 3], dtype=tf.uint8),\\n \\\"shape\\\": tfds.features.Text(),\\n }\\n ),\\n }\\n\\n return tfds.core.DatasetInfo(\\n builder=self,\\n description=_DESCRIPTION,\\n features=tfds.features.FeaturesDict(features),\\n citation=_CITATION,\\n )\",\n \"def names():\\n\\n df = pd.read_sql_query(f\\\"SELECT * FROM olympics_raw\\\", con = engine)\\n print(df.head())\\n \\n\\n # return jsonify(all_olympians)\\n return jsonify(df.to_dict(orient='records'))\",\n \"def getprojects(self):\\n resp = self.conn.request('GET', self.URLS['allprojects'], dict(api_key=self.api_key))\\n data = resp.data.decode('utf-8')\\n jdata = json.loads(data)['projects']\\n # Convert nested JSON documents\\n for project_index in range(len(jdata)):\\n for field in ('options_json', 'templates_json'):\\n jdata[project_index][field] = json.loads(jdata[project_index][field])\\n # Pass project details dictionaries to constructors, return array\\n return [PhProject(self, project) for project in jdata]\",\n \"def get_data(self):\\n def _clean_search_hit(search_hit):\\n \\\"\\\"\\\"\\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\\n\\n :param search_hit:\\n :return Dictionary: A dictionary with the cleaned data\\n \\\"\\\"\\\"\\n\\n hit_name = search_hit.find(class_='hit-name')\\n hit_url = hit_name.get('href')\\n hit_id = hit_url.split('/')[-1]\\n name = hit_name.get_text().split(',')[0].title().split()\\n\\n current_city = search_hit.find(class_='hit-location').get_text().upper()\\n\\n # Find all Addresses for search result.\\n try:\\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\\n address = list({a.text.upper().replace('.', '') for a in address})\\n except AttributeError:\\n address = list()\\n\\n # find the address that is most likely the current main address.\\n try:\\n address.insert(0, address.pop(address.index(current_city)))\\n except ValueError:\\n address.insert(0, current_city)\\n\\n address = [\\n {\\n '@type': 'PostalAddress',\\n 'addressLocality': locality.title(),\\n 'addressRegion': region\\n } for locality, region in [a.split(', ') for a in address]]\\n\\n work_location = {'@type': 'Place'}\\n try:\\n work_location['name'] = search_hit\\\\\\n .find(class_='hit-work')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text()\\\\\\n .title()\\n except AttributeError:\\n work_location['name'] = ''\\n\\n alumni_of = {'@type': 'EducationalOrganization'}\\n try:\\n alumni_of['name'] = search_hit\\\\\\n .find(class_='hit-high-school')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text().title()\\n except AttributeError:\\n pass\\n\\n return {\\n '@id': hit_id,\\n '@type': 'Person',\\n 'name': ' '.join(name),\\n 'givenName': name[0],\\n 'middleName': ' '.join(name[1:-1]),\\n 'familyName': name[-1],\\n 'url': hit_url,\\n 'address': address,\\n 'workLocation': work_location,\\n 'alumniOf': alumni_of,\\n }\\n\\n def _refine_search(search_str, options):\\n \\\"\\\"\\\"\\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\\n press the option if found. Returns Boolean for found status\\n\\n :param search_str: str of the desired option.\\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\\n :return:\\n \\\"\\\"\\\"\\n search_str = search_str.upper()\\n logging.info(f'Looking for \\\\'{search_str}\\\\'')\\n try:\\n for option in options:\\n option_text = option.text.upper()\\n logging.info(f'Option Checked: {option_text}')\\n if search_str in option_text:\\n option.click()\\n time.sleep(2)\\n logging.info(f'Option Selected: {option_text}')\\n return True\\n else:\\n return False\\n except AttributeError:\\n return True\\n except StaleElementReferenceException as e:\\n ChromeCrash(e)\\n\\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\\n driver.get(self.url)\\n\\n \\\"\\\"\\\"\\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \\n \\\"\\\"\\\"\\n driver.fullscreen_window()\\n\\n # Refine the search by State\\n address_region = self.person.get('addressRegion', '')\\n address_region = STATES.get(address_region.upper(), address_region.upper())\\n region_options = driver\\\\\\n .find_element_by_class_name(\\\"STATE\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_region, region_options):\\n return False\\n\\n # Narrow the search by pressing a City option\\n address_locality = self.person.get('addressLocality').title()\\n locality_options = driver\\\\\\n .find_element_by_class_name(\\\"CITY\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_locality, locality_options):\\n return False\\n\\n \\\"\\\"\\\"\\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\\n be done in steps with a pause between movements to allow for loading. \\n Here it will first get the current location on the page, attempt to move down the page, and then check to\\n see if the location changed.\\n \\\"\\\"\\\"\\n\\n if self.auto_scroll and len(driver.find_elements_by_class_name(\\\"ais-InfiniteHits-item\\\")) > 15:\\n current_height, new_height = 0, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n while new_height != current_height:\\n # Scroll down to the bottom of the page\\n driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n\\n # Wait to load page\\n time.sleep(SCROLL_PAUSE_TIME)\\n\\n # Calculate new scroll height and compare with last scroll height\\n current_height, new_height = new_height, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n page_source = driver.page_source\\n page_soup = bs(page_source, 'html.parser')\\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\\n for i, search_result in enumerate(search_results):\\n search_results[i] = _clean_search_hit(search_result)\\n\\n self.data_from_website = pd.DataFrame(search_results)\\n self.data_from_website.set_index('@id', inplace=True)\\n return True\",\n \"def glass_pandas(self):\\n # pandas.set_option('display.width', 120)\\n # TODO timeit (git_implementation) vs (my_implementation)\\n # * df = pd.DataFrame(json.loads(r.text))\\n # * df = df.set_index('t')\\n # * df.index = pd.to_datetime(df.index, unit='s')\\n # * df = df.sort_index()\\n # * s = df.v\\n # * s.name = '_'.join(url.split('/')[-2:])\\n # * return s\\n # for elem in self.loaded:\\n # _metric, _data = elem[1]['_metrics'], elem[1]['_data']\\n # try:\\n # frame_keys = ['t'] + list(_data[0]['o'].keys())\\n # framed = pandas.DataFrame(\\n # data=[{k: (_data[iters]['t'] if k in 't' else _data[iters]['o'][k])\\n # for k in frame_keys} for iters in range(len(_data))],\\n # columns=frame_keys)\\n # except KeyError:\\n # framed = pandas.DataFrame(_data)\\n # framed.set_index('t', inplace=True)\\n # framed.index = pandas.to_datetime(\\n # framed.index.to_flat_index(), unit='s', infer_datetime_format=True)\\n # framed.sort_index(inplace=True)\\n # framed.name = _metric\\n # print(framed.name)\\n # print(framed)\",\n \"def get_info_from_row(r):\\n cells = r.select(\\\"td\\\") \\n # we want this data point if theres a zip, save all the info\\n # including lat, long, type, \\n if len(cells) > 10 and cells[2].select(\\\"a\\\"):\\n id_ = re.sub(\\\"[^\\\\w\\\\. ]\\\", \\\"\\\",cells[0].text)\\n data_anchor = cells[2].select(\\\"a\\\")[0]\\n href = data_anchor[\\\"href\\\"]\\n desc = cells[3].text\\n type_ = cells[4].text\\n med_ec = cells[8].text\\n flow = re.sub(\\\"[^\\\\w\\\\.]\\\", \\\"\\\",cells[9].text)\\n lat = re.sub(\\\"[^\\\\w\\\\.]\\\", \\\"\\\",cells[10].text)\\n lon = re.sub(\\\"[^\\\\w\\\\.]\\\", \\\"\\\",cells[11].text)\\n \\n return {\\\"id\\\":id_, \\\"desc\\\":desc, \\\"type\\\":type_, \\\"med_ec\\\":med_ec, \\\"flow\\\":flow, \\\"lat\\\":lat, \\\"lon\\\":lon, \\\"href\\\":href}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58241713","0.57872564","0.5712627","0.5473365","0.5464034","0.5438289","0.54039174","0.53523386","0.5309826","0.5266796","0.5227246","0.5216134","0.51905394","0.5185898","0.51729375","0.5161594","0.5143175","0.5138437","0.5124035","0.51153386","0.5106635","0.50948983","0.50927824","0.5091926","0.5083201","0.5079717","0.5075052","0.5072244","0.50654215","0.5063841","0.50576365","0.5042167","0.5040933","0.50373906","0.503705","0.50113255","0.50070816","0.49856043","0.4983203","0.49799192","0.49774805","0.4973956","0.49721754","0.49721128","0.49691868","0.49553695","0.49522576","0.49443406","0.49419528","0.4935744","0.49345595","0.49308005","0.49210006","0.4917369","0.49171233","0.49121323","0.4904817","0.48938093","0.48916945","0.488792","0.48875734","0.48809645","0.48737332","0.4873445","0.48659647","0.48619986","0.48603666","0.48582882","0.4856893","0.48565707","0.4856216","0.48549998","0.48486587","0.4848305","0.48401514","0.48368624","0.48360723","0.4834831","0.48332337","0.48314106","0.48296654","0.48280975","0.4827655","0.48218325","0.4817879","0.48126706","0.48084152","0.48048773","0.48045903","0.48041013","0.48031673","0.4799518","0.47983","0.4796307","0.4795676","0.4795338","0.4789771","0.47863775","0.4785633","0.47847873"],"string":"[\n \"0.58241713\",\n \"0.57872564\",\n \"0.5712627\",\n \"0.5473365\",\n \"0.5464034\",\n \"0.5438289\",\n \"0.54039174\",\n \"0.53523386\",\n \"0.5309826\",\n \"0.5266796\",\n \"0.5227246\",\n \"0.5216134\",\n \"0.51905394\",\n \"0.5185898\",\n \"0.51729375\",\n \"0.5161594\",\n \"0.5143175\",\n \"0.5138437\",\n \"0.5124035\",\n \"0.51153386\",\n \"0.5106635\",\n \"0.50948983\",\n \"0.50927824\",\n \"0.5091926\",\n \"0.5083201\",\n \"0.5079717\",\n \"0.5075052\",\n \"0.5072244\",\n \"0.50654215\",\n \"0.5063841\",\n \"0.50576365\",\n \"0.5042167\",\n \"0.5040933\",\n \"0.50373906\",\n \"0.503705\",\n \"0.50113255\",\n \"0.50070816\",\n \"0.49856043\",\n \"0.4983203\",\n \"0.49799192\",\n \"0.49774805\",\n \"0.4973956\",\n \"0.49721754\",\n \"0.49721128\",\n \"0.49691868\",\n \"0.49553695\",\n \"0.49522576\",\n \"0.49443406\",\n \"0.49419528\",\n \"0.4935744\",\n \"0.49345595\",\n \"0.49308005\",\n \"0.49210006\",\n \"0.4917369\",\n \"0.49171233\",\n \"0.49121323\",\n \"0.4904817\",\n \"0.48938093\",\n \"0.48916945\",\n \"0.488792\",\n \"0.48875734\",\n \"0.48809645\",\n \"0.48737332\",\n \"0.4873445\",\n \"0.48659647\",\n \"0.48619986\",\n \"0.48603666\",\n \"0.48582882\",\n \"0.4856893\",\n \"0.48565707\",\n \"0.4856216\",\n \"0.48549998\",\n \"0.48486587\",\n \"0.4848305\",\n \"0.48401514\",\n \"0.48368624\",\n \"0.48360723\",\n \"0.4834831\",\n \"0.48332337\",\n \"0.48314106\",\n \"0.48296654\",\n \"0.48280975\",\n \"0.4827655\",\n \"0.48218325\",\n \"0.4817879\",\n \"0.48126706\",\n \"0.48084152\",\n \"0.48048773\",\n \"0.48045903\",\n \"0.48041013\",\n \"0.48031673\",\n \"0.4799518\",\n \"0.47983\",\n \"0.4796307\",\n \"0.4795676\",\n \"0.4795338\",\n \"0.4789771\",\n \"0.47863775\",\n \"0.4785633\",\n \"0.47847873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5347947"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":95681,"cells":{"query":{"kind":"string","value":"Plot average response for all variables in dataset and save plot in pdf dataset pandas dataset prj_info dictionnary containing projet information (response...)"},"document":{"kind":"string","value":"def plot_average_reponse(data,prj_info,pp=PdfPages(\"exploration.pdf\"),TMP=1234,bins=20):\r\n #Copy data\r\n data = data.copy()\r\n #Slice data\r\n data = data.sample(n = min(10000,data.shape[0]),random_state=1234)\r\n #Colnames\r\n var_to_plot = list(set(data.columns.values)-set(prj_info['PRJ_COLUMN'].values()))\r\n\r\n #Loop figure\r\n pbar = ProgressBar()\r\n for var in pbar(var_to_plot):\r\n #Bins\r\n if data[var].dtype.name != \"category\" and len(data[var].unique())>bins:\r\n data[\"var_new\"] = pd.qcut(data[var], bins, duplicates='drop')\r\n else:\r\n data[\"var_new\"] = data[var].astype(str)\r\n data_plot = data.groupby(\"var_new\").agg({prj_info['PRJ_COLUMN'][\"RESPONSE\"]: 'mean', \"var_new\": 'count'})\r\n\r\n #Table\r\n data_plot = data.groupby(\"var_new\").agg({prj_info['PRJ_COLUMN'][\"RESPONSE\"]: 'mean', \"var_new\": 'count'})\r\n\r\n #Build plot\r\n f, ax = plt.subplots()\r\n ax2 =ax.twinx()\r\n sns.barplot(x=data_plot.index.tolist(), y=\"var_new\",data=data_plot,ax=ax, color=\"dodgerblue\")\r\n sns.pointplot(x=data_plot.index.tolist(), y=prj_info['PRJ_COLUMN'][\"RESPONSE\"], data=data_plot,ax=ax2, color=\"chartreuse\")\r\n ax.set_xlabel(var)\r\n ax.set_ylabel(var)\r\n ax2.set_ylabel(prj_info['PRJ_COLUMN'][\"RESPONSE\"])\r\n plt.title(\"Average reponse by \" + var)\r\n plt.setp(ax.xaxis.get_majorticklabels(), rotation=60)\r\n \r\n pp.savefig(f)\r\n\r\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def avg_response_report(df, var_list, y_obs, y_est, file):\n page = PdfPages(file)\n for var in var_list:\n avg_response(df, var, y_obs, y_est, show=False)\n page.savefig()\n page.close()","def explore_data(data,prj_info,TMP=1234):\r\n print(\" Data file rows and columns are : \", data.shape)\r\n #Open pdf\r\n pp = PdfPages(prj_info['OUTPUT_PATH'] + \"exploration_\" + str(TMP) + \".pdf\")\r\n\r\n #Plot average\r\n plot_average_reponse(data,prj_info,pp,TMP)\r\n\r\n #Close pdf\r\n pp.close()\r\n return None","def do_plot(self):\n years = sorted(set(self.prediction_df_without_covid19['Year']))\n predict_without_covid_country = self.prediction_df_without_covid19[\n self.prediction_df_without_covid19['Country'].isin([self.country])].sort_values(['Year'],\n ascending=[True])\n predict_with_covid_country = self.prediction_df_with_covid19[\n self.prediction_df_with_covid19['Country'].isin([self.country])].sort_values(['Year'],\n ascending=[True])\n # ------------------------------------------------------------------------------------------------------\n pa = \\\n predict_without_covid_country.loc[predict_without_covid_country['Year'] == 1990][\n 'Total_CO2_Emissions'].values[\n 0]\n x = []\n for i in range(len(years)):\n x.append(pa * 0.6)\n # ------------------------------------------------------------------------------------------------------\n fig = Figure()\n ax = fig.subplots()\n ax.grid(True, alpha=0.3)\n # plot_title = 'Total CO2 Emissions predicted from 2019-2030 for ' + self.country\n plot_title = 'Total ' + '$CO_2$' + ' Emissions predicted from 2019-2030 for ' + self.country\n label_country_without_covid = 'Total CO2 emissions without covid'\n label_country_with_covid = 'Total CO2 emissions with Covid-19'\n # ------------------------------------------------------------------------------------------------------\n params = {'mathtext.default': 'regular'}\n rcParams.update(params)\n rcParams['font.size'] = 7\n rcParams['lines.markersize'] = 4\n rcParams['figure.figsize'] = [7, 4]\n rcParams['figure.dpi'] = 150\n rcParams['font.family'] = 'Verdana'\n rcParams[\"font.weight\"] = \"normal\"\n font = {'family': 'Verdana',\n 'color': 'xkcd:darkgreen',\n 'weight': 'normal',\n 'size': 9,\n }\n colors = rcParams['axes.prop_cycle'].by_key()['color']\n l1, = ax.plot(years, predict_without_covid_country['Total_CO2_Emissions'], color='xkcd:dark blue green',\n marker='o',\n label=label_country_without_covid)\n l2, = ax.plot(years, predict_with_covid_country['Total_CO2_Emissions'], color='xkcd:neon pink', marker='.',\n label=label_country_with_covid)\n l3, = ax.plot(years, x, color='xkcd:orchid', marker='1')\n print('without covid: ', predict_without_covid_country['Total_CO2_Emissions'].values)\n print('with covid: ', predict_with_covid_country['Total_CO2_Emissions'].values)\n ax.set_xlabel('Years', fontdict=font)\n ax.set_ylabel('Emissions (Gg)', fontdict=font)\n ax.set_title(plot_title, fontsize=12, fontweight='normal')\n ax.patch.set_facecolor('xkcd:green')\n ax.set_facecolor('xkcd:pale green')\n fig.legend((l1, l2, l3), ('Prediction without Covid19', 'Prediction with Covid19', 'Paris Agreement'),\n bbox_to_anchor=(0.907, 0.89))\n fig.savefig(OUTPUT_GRAPH_PATH)","def data_visualization(df):\r\n\r\n # Visualizing the target variable\r\n plt.figure(figsize=(14, 10))\r\n plt.title(\"Count of bike sharing according to dates\")\r\n plt.plot(df['dteday'], df['cnt'])\r\n #plt.show()\r\n plt.savefig(\"Raw data visualization.png\")\r\n\r\n # box plot for visualizing outliers\r\n fig=px.box(df, y=\"cnt\", notched=True,title='Box plot of the count variable')\r\n #fig.show()\r\n plt.savefig(\"Box Plot.png\")\r\n\r\n # point plot for hourly utilization\r\n for column in ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']:\r\n hist = px.histogram(df, x=column, y='cnt')\r\n hist.show()\r\n plt.savefig(\"Histogram plots for each column.png\")\r\n sns.pointplot(x=df['hr'], y='cnt', data=df);\r\n plt.title(\"Hourly Utilization\")\r\n plt.ylabel(\"Bike Shares\", fontsize=12)\r\n plt.xlabel(\"Hour\", fontsize=12)\r\n plt.savefig(\"Hourly Utilization point plot.png\", dpi=300, bbox_inches='tight')\r\n\r\n # line plot for hourly utilization\r\n for c in ['holiday','season','workingday']:\r\n sns.lineplot(data=df,x='hr',y='cnt',hue=c)\r\n plt.title('Hourly plot vs count')\r\n plt.savefig(\"Hour vs count plot_main features.png\",dpi=300, bbox_inches='tight')\r\n\r\n # point plots for humidity vs count\r\n sns.pointplot(x='hum', y='cnt', data=df)\r\n plt.title(\"Amount of bike shares vs humidity\", fontsize=25)\r\n plt.xlabel(\"Humidity (%)\", fontsize=20)\r\n plt.ylabel('count of bike shares', fontsize=20)\r\n plt.locator_params(axis='x', nbins=10)\r\n plt.savefig(\"Pointplot of humidity vs count.png\",dpi=300, bbox_inches='tight')\r\n\r\n # box plots of whole df\r\n bx=px.box(df, y=\"cnt\")\r\n bx.show()\r\n\r\n # feature correlation plot\r\n corrs = abs(df.corr())\r\n sns.heatmap(corrs, annot=True)\r\n plt.title(\"Feature Correlation\")\r\n plt.savefig(\"Feature_correlation.png\", dpi=300, bbox_inches='tight')\r\n return plt","def plot_data(data, par, par_names, par_fixed, output_dir='./'):\n\n datasets = dict()\n\n for data_point in data:\n experiment_name = data_point.par['experiment_name']\n datasets.setdefault(experiment_name, list()).append(data_point)\n\n for experiment_name, dataset in datasets.items():\n\n # ##### Matplotlib ######\n\n name_pdf = ''.join([experiment_name, '.pdf'])\n name_pdf = os.path.join(output_dir, name_pdf)\n\n name_txt = ''.join([experiment_name, '.fit'])\n name_txt = os.path.join(output_dir, name_txt)\n\n print(\" * {} [.fit]\".format(name_pdf))\n\n # #######################\n\n data_grouped = group_data(dataset)\n profiles, r2_min, r2_max = compute_profiles(data_grouped)\n ymin, ymax = set_lim([r2_min, r2_max], 0.10)\n\n with PdfPages(name_pdf) as file_pdf, open(name_txt, 'w') as file_txt:\n\n for (_index, id_), profile in sorted(profiles.items()):\n write_profile(id_, profile, file_txt)\n\n ###### Matplotlib ######\n\n fig = plt.figure(1, frameon=True)\n ax = fig.add_subplot(111)\n\n ax.axhline(0, color='black', alpha=0.87)\n\n ########################\n\n frq, r2_cal, r2_exp, r2_erd, r2_eru = profile[0]\n\n ax.plot(\n frq,\n r2_cal,\n linestyle='-',\n color=red200,\n zorder=2,\n )\n\n ax.errorbar(\n frq,\n r2_exp,\n yerr=[r2_erd, r2_eru],\n fmt='o',\n color=red500,\n zorder=3,\n )\n\n xmin, xmax = set_lim(frq, 0.10)\n\n ax.set_xlim(xmin, xmax)\n ax.set_ylim(ymin, ymax)\n\n ax.xaxis.set_major_locator(MaxNLocator(6))\n ax.yaxis.set_major_locator(MaxNLocator(6))\n\n ax.set_xlabel(r'$\\mathregular{\\nu_{CPMG} \\ (Hz)}$')\n ax.set_ylabel(\n r'$\\mathregular{R_{2,eff} \\ (s^{-1})}$')\n\n ax.set_title('{:s}'.format(id_.upper()))\n\n fig.tight_layout()\n\n ########################\n\n file_pdf.savefig()\n plt.close()\n\n ########################\n\n return","def _plot_marginal_pdfs( res, nbins=101, **kwargs):\n\tfrom matplotlib import pyplot as pl\n\timport numpy as np\n\n\tnparam = len(res.vparam_names)\n\t# nrow = np.sqrt( nparam )\n\t# ncol = nparam / nrow + 1\n\tnrow, ncol = 1, nparam\n\n\tpdfdict = _get_marginal_pdfs( res, nbins )\n\n\tfig = plt.gcf()\n\tfor parname in res.vparam_names :\n\t\tiax = res.vparam_names.index( parname )+1\n\t\tax = fig.add_subplot( nrow, ncol, iax )\n\n\t\tparval, pdf, mean, std = pdfdict[parname]\n\t\tax.plot( parval, pdf, **kwargs )\n\t\tif np.abs(std)>=0.1:\n\t\t\tax.text( 0.95, 0.95, '%s %.1f +- %.1f'%( parname, np.round(mean,1), np.round(std,1)),\n\t\t\t\t\t ha='right',va='top',transform=ax.transAxes )\n\t\telif np.abs(std)>=0.01:\n\t\t\tax.text( 0.95, 0.95, '%s %.2f +- %.2f'%( parname, np.round(mean,2), np.round(std,2)),\n\t\t\t\t\t ha='right',va='top',transform=ax.transAxes )\n\t\telif np.abs(std)>=0.001:\n\t\t\tax.text( 0.95, 0.95, '%s %.3f +- %.3f'%( parname, np.round(mean,3), np.round(std,3)),\n\t\t\t\t\t ha='right',va='top',transform=ax.transAxes )\n\t\telse :\n\t\t\tax.text( 0.95, 0.95, '%s %.3e +- %.3e'%( parname, mean, std),\n\t\t\t\t\t ha='right',va='top',transform=ax.transAxes )\n\n\tplt.draw()","def plot(self):\n # get data without totals\n data = self.woe_report[self.woe_report.index != 'total']\n # setup panel\n fig, axs = plt.subplots(1, 3, figsize=(12, 3))\n plt.subplots_adjust(wspace=0.3)\n # first chart\n data['P(Hi|A)'].plot(ax=axs[0], linewidth=3, alpha=0.7)\n data['P(Hi|Ā)'].plot(ax=axs[0], linewidth=3, alpha=0.7)\n axs[0].set_title('Probability distribution')\n axs[0].set_xlabel(data.index.name)\n axs[0].set_ylabel('probability')\n axs[0].legend(['P(Hi|A)', 'P(Hi|Ā)'])\n # second chart\n data['weight-of-evidence'].plot(ax=axs[1], linewidth=3, alpha=0.7)\n axs[1].set_title('WoE')\n axs[1].set_xlabel(data.index.name)\n axs[1].set_ylabel('WoE')\n # third chart\n data['information-value'].plot(ax=axs[2], linewidth=3, alpha=0.7)\n axs[2].set_title('Information value')\n axs[2].set_ylabel('IV')","def plot_individual(xdict, ydict, xprop, yprop, documents, spline):\n figure_array = {}\n for item in documents:\n xlabel = \"\\\\textbf{\" + label_dict[xprop] + \"}\"\n ylabel = \"\\\\textbf{\" + label_dict[yprop] + \"}\"\n x = xdict[item[\"path_id\"]]\n y = ydict[item[\"path_id\"]]\n # fig_title = item[\"path_id\"] + \"(\" + item[\"pretty_formula\"] + \")\" # Individual traces\n # fig_title = yprop + item[\"cation_type\"] # Plot by cation\n fig_title = yprop # All together\n figure_array[item[\"path_id\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\n ax = figure_array[item[\"path_id\"]].add_subplot(111) \n ax.scatter(x,y, s=70, zorder=2, color=color_dict[item[\"cation_type\"]], linewidths=2.5, edgecolors='black')\n if spline:\n tck = interpolate.splrep(x, y, s=0)\n xnew = np.arange(0, 100, 0.1)\n splfit = interpolate.splev(xnew, tck, der=0)\n x = xnew\n y = splfit\n if item[\"path_id\"][-3:] == \"002\":\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]], linestyle='dashed')\n elif item[\"path_id\"][-3:] == \"003\":\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]], linestyle='dotted')\n else:\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]])\n ax.set_xlabel(xlabel, fontsize=24)\n # ax.set_ylim([0,1200])\n # ax.set_xlim([0,100])\n ax.set_ylabel(ylabel, fontsize=24)\n ax.tick_params(axis='x', labelsize=22)\n ax.tick_params(axis='y', labelsize=22)\n border_width = 2\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\n plt.tight_layout()\n plt.legend(loc='best', prop={'size': 14})\n plt.rc('text', usetex=True)\n plt.rc('font', family='sans-serif')\n plt.tight_layout()\n plt.show()","def three_PDF_plots(res=200,table_exts=[''],**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n GR = glo.global_results()\n\n fig, axs = plt.subplots(3, sharex='col',\\\n figsize=(8,15),facecolor='w',\\\n gridspec_kw={'hspace': 0, 'wspace': 0})\n\n # First print cell data distribution\n i = 0\n for gal_index in zip(p.gal_index):\n ax1 = axs[i]\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\n df = gal_ob.cell_data.get_dataframe()\n lognH = np.log10(df.nH)\n hist = np.histogram(lognH[df.nH.values > 0],bins=200,weights=df.m[df.nH.values > 0])\n hist1 = np.asarray(hist[0]) # histogram\n hist2 = np.asarray(hist[1]) # bin edges\n hist1 = hist1*1./sum(hist1)\n ax1.plot(hist2[0:len(hist1)],hist1,drawstyle='steps',ls='-',lw=1.5,\\\n alpha=0.7,color=p.color[0],label='Original cell distribution')\n \n for table_ext,ls,color in zip(table_exts,['--',':'],p.color[1::]):\n if '_M10' in table_ext: lab = 'Mach = 10'\n if '_arepoPDF_M51' in table_ext: lab = 'AREPO parametrized PDF'\n PDF(gal_index,color=color,table_ext=table_ext,ls=ls,res=200,add=True,ax=ax1,label=lab,ow=p.ow)\n \n if i == 0: ax1.legend(loc='upper right',fontsize=12)\n if i == 2: ax1.set_xlabel(getlabel('lnH'))\n ax1.set_ylabel('Mass fraction per bin')\n\n i += 1\n\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'cell_data/PDFs/'): os.mkdir(p.d_plot + 'cell_data/PDFs/') \n plt.savefig(p.d_plot + 'cell_data/PDFs/simple_PDF_%s%s%s_x3.png' % (p.sim_name,p.sim_run,p.table_ext), format='png', dpi=250, facecolor='w')","def plot_data(self):","def plot(self):\n # Get data\n #print(self.file_name)\n fig, ax = plb.subplots(1,1,figsize=(18,20))\n for key,value in self.testTrend.items():\n x = np.arange(len(self.data_array))\n y = np.asarray(value)\n plb.plot(x,y, label=key)\n ax.scatter(x, y)\n for i in range(0, len(value)):\n ax.annotate(str(i), (x[i], y[i]))\n # Title\n plb.title(self.file_name)\n # Legend\n plb.legend(bbox_to_anchor=(.05, 1), loc='best', borderaxespad=0.)\n # x ticks\n plb.xticks(np.arange(min(x), max(x) + 1, 2.0))\n #plb.ylim(-250, 1)\n # Show image\n plb.show()","def resultPlots(record):\n record.createDataFrames()\n \n atmPlot(record)\n clientPlot(record)\n transactionPlot(record)","def analysis_plot(predictions, ys):\n fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\n\n residuals = ys - predictions\n\n # Plot 1 - Predicted vs Actual\n sns.scatterplot(predictions, ys, ax=ax1)\n ax1.set_title('Predicted vs Actual', fontsize=20)\n ax1.set(xlabel='Predicted Ys', ylabel='Actual Ys')\n\n # Plot 2 - Residuals PLot (predicted vs residuals)\n sns.scatterplot(predictions, residuals, ax=ax2)\n ax2.set_title('Residuals Plot', fontsize=20)\n ax2.set(xlabel='Predicted Ys', ylabel='Residuals')\n\n # Plot 3 - QQ Plot\n sm.qqplot(residuals, ax=ax3, line='s')\n ax3.set_title('QQ Plot- Distribution of Residuals', fontsize=20)\n\n plt.show();","def plot_parameter_evolution(analyses, pdf=False):\n ncs = np.arange(11, 15)\n genes = set(analyses.gene)\n constructs = set(analyses.construct)\n long_labels = {'bac': 'bac', 'no_pr': 'no pr', 'no_sh': 'no sh'}\n gene_long = {'hb': 'hunchback', 'kn': 'knirps', 'sn': 'snail'}\n y_label = {'j': 'Normalized flux $j$',\n 'rho': 'Site occupation density $\\\\rho$', 'tau': 'Residence time $\\\\tau$ (s)', 'alpha_comb': 'Initiation rate $\\\\alpha$ (pol/min)'}\n\n # Add extra jiggle to be able to distinguish overlapping data points\n x_jiggle = 0.04\n x_shifts = np.array([-1, 0, 1]) * x_jiggle\n\n # Plot parameters\n capsize = 0\n markersize = 4\n lw = 1 # line width\n\n for gene in genes:\n grouped_data = analyses.groupby(by=['gene', 'construct', 'nc'])\n all_means = grouped_data.mean()\n all_stds = grouped_data.std(ddof=1)\n all_ps = analyses.groupby(by=['gene', 'nc']).first()\n\n for quantity in ['j', 'rho', 'tau', 'alpha_comb']:\n ymaxs = {'j': 0.36, 'rho': 0.27, 'tau': 103, 'alpha_comb': 12}\n num = 12\n set_figure_size(num=num, rows=1, page_width_frac=0.5, clear=True, height_factor=0.7)\n fig, ax = plt.subplots(1, 1, num=num, clear=True)\n avg_data, std_data = {}, {}\n for construct in constructs:\n if quantity in ['rho', 'j']:\n avg_data[construct] = all_means.loc[(\n gene, construct, slice(None)), quantity].values\n std_data[construct] = all_stds.loc[(\n gene, construct, slice(None)), quantity].values\n\n elif quantity in ['tau', 'alpha_comb']:\n avg_data[construct] = all_means.loc[(\n gene, construct, slice(None)), quantity].values\n std_data[construct] = np.sqrt(\n all_means.loc[(gene, construct, slice(None)), quantity + 'V'].values)\n\n # Prepare a marker generator and plot the data with errorbars\n marker_gen = itertools.cycle(markers_additivity)\n for i, construct in enumerate(constructs):\n m = next(marker_gen)\n plt.errorbar(\n ncs + x_shifts[i], avg_data[construct],\n yerr=std_data[construct],\n fmt='-' + m, color=colors_additivity[construct],\n capsize=capsize, label=long_labels[construct],\n markersize=markersize, lw=lw)\n\n # Adjust plot\n plt.xlabel('Nuclear cycle')\n plt.ylabel(y_label[quantity])\n plt.ylim(ymin=0, ymax=ymaxs[quantity])\n\n plt.xticks(ncs)\n plt.title(gene_long[gene])\n\n plt.tight_layout()\n plt.show()\n\n # Save figure\n figname = 'additivity_' + quantity + '_' + gene\n figpath = os.path.join(figures_folder, figname)\n fig.savefig(figpath + '.png', pad_inches=0, bbox_inches='tight')\n if pdf:\n fig.savefig(figpath + '.pdf', pad_inches=0, bbox_inches='tight')","def Diagnostic_plot3(self):\n\n floc = glob.glob('/home/mxs191/Desktop/MathewSchofield/TRG/DetTest/DetTest1_results/Info2Save/*.csv')\n fig = plt.figure()\n plt.rc('font', size=18)\n #fig, ax = generalPlot(xaxis=r'$\\nu / \\mu$Hz', yaxis=r'$P_{\\rm det}$')\n gs = gridspec.GridSpec(1, 2, width_ratios=(4,1))\n ax = fig.add_subplot(gs[0])\n\n for idx, i in enumerate(floc):\n\n d = pd.read_csv(i)\n\n if idx == 0:\n fullpdet = d[['f0', 'Pdet_Kepler', 'Pdet_TESS365', 'Pdet_TESS27']]\n else:\n fullpdet = pd.concat([ fullpdet,\\\n d[['f0', 'Pdet_Kepler', 'Pdet_TESS365', 'Pdet_TESS27']] ])\n\n plt.scatter(d['f0'], d['Pdet_Kepler'], color='b',\\\n label=r\"$\\rm Kepler - 4\\ yrs$\" if idx == 0 else '')\n plt.scatter(d['f0'], d['Pdet_TESS365'], color='orange',\\\n label=r'$\\rm TESS - 1\\ yr$' if idx == 0 else '')\n plt.scatter(d['f0'], d['Pdet_TESS27'], color='g',\\\n label=r'$\\rm TESS - 27\\ days$' if idx == 0 else '')\n\n plt.axhline(fullpdet['Pdet_Kepler'].median(), color='b')\n plt.axhline(fullpdet['Pdet_TESS365'].median(), color='orange')\n plt.axhline(fullpdet['Pdet_TESS27'].median(), color='g')\n ax.legend(loc='lower right')\n plt.ylim([0,1])\n ax.set_ylabel(r'$P_{\\rm det}$')\n ax.set_xlabel(r'$\\nu / \\mu \\rm Hz$')\n\n bx = fig.add_subplot(gs[1])\n import seaborn as sns\n bw = 0.4\n sns.kdeplot(fullpdet['Pdet_Kepler'].values, shade=True, vertical=True, \\\n ax=bx, color='b', bw=bw)\n sns.kdeplot(fullpdet['Pdet_TESS365'].values, shade=True, vertical=True, \\\n ax=bx, color='orange', bw=bw)\n sns.kdeplot(fullpdet['Pdet_TESS27'].values, shade=True, vertical=True, \\\n ax=bx, color='g', bw=bw)\n bx.set_ylim([0.0,1.0])\n bx.set_xticks([])\n bx.set_yticks([])\n bx.set_xlabel(r'$\\rm Density$')\n plt.tight_layout()\n\n plt.show()\n fig.savefig(os.getcwd() + os.sep + 'DetTest1_plots' + os.sep +\\\n 'DetTest_Diagnostic_plot3.pdf')\n sys.exit()","def plot_pdf(pop_name, pop_val, pop_file, full_pop_file, outdir='.'):\n try:\n plt.style.use(\n \"https://gist.githubusercontent.com/avivajpeyi/4d9839b1ceb7d3651cbb469bc6b0d69b/raw/4ee4a870126653d542572372ff3eee4e89abcab0/publication.mplstyle\")\n except Exception:\n pass\n\n plt.close('all')\n all = pd.read_csv(full_pop_file, sep=\" \")\n all['cos_theta_1'] = all['cos_tilt_1']\n all = process_samples(all)\n sub = pd.read_csv(pop_file, sep=\" \")\n sub = process_samples(sub)\n sub['cos_theta_1'] = sub['cos_tilt_1']\n\n fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n for ax, l in zip(axes, [\"cos_theta_1\", \"cos_theta_12\"]):\n ax.hist(all[l], density=True, histtype='step', color=\"tab:blue\", label=\"ALL\", lw=2, alpha=0.8)\n ax.scatter(all[l], [0 for _ in all[l]], color=\"tab:blue\",marker=\"+\")\n ax.hist(sub[l], density=True, histtype='step', color=\"tab:purple\", label=\"HIGH SNR\", lw=2, alpha=0.6)\n ax.scatter(sub[l], [0 for _ in sub[l]], color=\"tab:purple\", marker=\"+\")\n\n x = np.linspace(-1, 1, 100)\n y1 = TruncatedNormal(mu=1, sigma=pop_val[0], minimum=-1, maximum=1).prob(x)\n y2 = TruncatedNormal(mu=1, sigma=pop_val[1], minimum=-1, maximum=1).prob(x)\n axes[1].plot(x, y2, color='tab:gray', zorder=-10, lw=3, label=\"TRUE\")\n axes[0].plot(x, y1, color='tab:gray', zorder=-10, lw=3)\n\n for i in range(len(axes)):\n if (i == 0):\n axes[i].set_xlabel(r\"$\\cos\\ \\theta_1$\")\n axes[i].set_ylabel(\"PDF\")\n else:\n axes[i].set_xlabel(r\"$\\cos\\ \\theta_{12}$\")\n axes[i].set_yticklabels([])\n axes[i].legend()\n axes[i].grid(False)\n axes[i].set_xlim(-1, 1)\n\n plt.suptitle(f\"POP {pop_name}\")\n plt.tight_layout()\n plt.savefig(f\"{outdir}/pop_trues_{pop_name}.png\")","def analyse_plots(plot_dict, data_dict) :\n for component in [ '_x', '_y' ] :\n z_pos = array.array( 'd' )\n trans_pos = array.array( 'd' )\n errors = array.array( 'd' )\n zeros = array.array( 'd' )\n\n plot = plot_dict['beam_positions'+component]\n\n for i in range( plot.GetXaxis().GetNbins()+2 ) :\n projection = plot.ProjectionY( \\\n 'profile'+component+'_pro_'+str(i), i, i )\n if projection.GetEntries() == 0 :\n continue\n\n pro_mean, pro_mean_err, pro_std, pro_std_err = \\\n scifi.fit_gaussian( projection )\n\n errors.append( pro_mean_err )\n trans_pos.append( pro_mean )\n z_pos.append( data_dict['station_positions'][ i-6 ] )\n zeros.append(0.0)\n\n position_graph = ROOT.TGraphErrors( len(zeros), z_pos, trans_pos, \\\n zeros, errors )\n position_graph.SetName('beam_profile'+component)\n plot_dict['beam_profile'+component] = position_graph\n\n profile_x = plot_dict['beam_profile_x']\n profile_y = plot_dict['beam_profile_y']\n\n up_x_func = ROOT.TF1( \"up_fit_x\", \"pol1\", -5000.0, 0.0 )\n up_y_func = ROOT.TF1( \"up_fit_y\", \"pol1\", -5000.0, 0.0 )\n down_x_func = ROOT.TF1( \"down_fit_x\", \"pol1\", 0.0, 5000.0 )\n down_y_func = ROOT.TF1( \"down_fit_y\", \"pol1\", 0.0, 5000.0 )\n\n up_fit_x = profile_x.Fit( 'up_fit_x', \"QSR\" )\n up_fit_y = profile_y.Fit( 'up_fit_y', \"QSR\" )\n down_fit_x = profile_x.Fit( 'down_fit_x', \"QSR\" )\n down_fit_y = profile_y.Fit( 'down_fit_y', \"QSR\" )\n\n plot_dict['beam_profile_x_up_fit'] = up_x_func\n plot_dict['beam_profile_y_up_fit'] = up_y_func\n plot_dict['beam_profile_x_down_fit'] = down_x_func\n plot_dict['beam_profile_y_down_fit'] = down_y_func\n\n\n up_beam_gra_x = up_x_func.GetParameter(1)\n up_beam_gra_x_err = up_x_func.GetParError(1)\n up_beam_gra_y = up_y_func.GetParameter(1)\n up_beam_gra_y_err = up_y_func.GetParError(1)\n\n up_beam_pos_x = data_dict['station_positions'][-1]*up_beam_gra_x + up_x_func.GetParameter(0)\n up_beam_pos_x_err = up_x_func.GetParError(0)\n up_beam_pos_y = data_dict['station_positions'][-1]*up_beam_gra_y + up_y_func.GetParameter(0)\n up_beam_pos_y_err = up_y_func.GetParError(0)\n\n up_beam_rot_x = math.atan( up_beam_gra_x )\n up_beam_rot_x_err = up_beam_gra_x_err # Approx linear\n up_beam_rot_y = math.atan( up_beam_gra_y )\n up_beam_rot_y_err = up_beam_gra_y_err # Approx linear\n\n\n\n down_beam_gra_x = down_x_func.GetParameter(1)\n down_beam_gra_x_err = down_x_func.GetParError(1)\n down_beam_gra_y = down_y_func.GetParameter(1)\n down_beam_gra_y_err = down_y_func.GetParError(1)\n\n down_beam_pos_x = data_dict['station_positions'][1]*down_beam_gra_x + down_x_func.GetParameter(0)\n down_beam_pos_x_err = down_x_func.GetParError(0)\n down_beam_pos_y = data_dict['station_positions'][1]*down_beam_gra_y + down_y_func.GetParameter(0)\n down_beam_pos_y_err = down_y_func.GetParError(0)\n\n down_beam_rot_x = math.atan( down_beam_gra_x )\n down_beam_rot_x_err = down_beam_gra_x_err # Approx linear\n down_beam_rot_y = math.atan( down_beam_gra_y )\n down_beam_rot_y_err = down_beam_gra_y_err # Approx linear\n\n\n# down_pos_x = down_beam_pos_x - data_dict['station_positions'][1]*up_beam_gra_x + up_x_func.GetParameter(0)\n# down_pos_x_err = math.sqrt( up_x_func.GetParError(0)**2 + down_beam_pos_x_err**2 )\n# down_pos_y = down_beam_pos_y - data_dict['station_positions'][1]*up_beam_gra_y + up_y_func.GetParameter(0)\n# down_pos_y_err = math.sqrt( up_y_func.GetParError(0)**2 + down_beam_pos_y_err**2 )\n\n length = TRACKER_SEPARATION\n down_pos_x = down_beam_pos_x - ( up_beam_pos_x + length*up_beam_gra_x )\n down_pos_x_err = math.sqrt( up_beam_pos_x_err**2 + down_beam_pos_x_err**2 + (length*up_beam_gra_x_err)**2 )\n down_pos_y = down_beam_pos_y - ( up_beam_pos_y + length*up_beam_gra_y )\n down_pos_y_err = math.sqrt( up_beam_pos_y_err**2 + down_beam_pos_y_err**2 + (length*up_beam_gra_y_err)**2 )\n\n down_rot_x = down_beam_rot_x - up_beam_rot_x\n down_rot_x_err = math.sqrt( down_beam_rot_x_err**2 + up_beam_rot_x_err**2 )\n down_rot_y = down_beam_rot_y - up_beam_rot_y\n down_rot_y_err = math.sqrt( down_beam_rot_y_err**2 + up_beam_rot_y_err**2 )\n\n\n print\n print \"Incoming Beam Misalignments:\"\n print\n print \"Displacement and rotation of beam with respect to upstream tracker:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( up_beam_pos_x, up_beam_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( up_beam_pos_y, up_beam_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( up_beam_rot_x*1000.0, up_beam_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( up_beam_rot_y*1000.0, up_beam_rot_y_err*1000.0 )\n print\n\n print\n print \"Downstream Tracker Beam Misalignments:\"\n print\n print \"Displacement and rotation of beam with respect to downstream tracker:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_beam_pos_x, down_beam_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_beam_pos_y, down_beam_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_beam_rot_x*1000.0, down_beam_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_beam_rot_y*1000.0, down_beam_rot_y_err*1000.0 )\n print\n\n print\n print \"Downstream Tracker Alignment:\"\n print\n print \"Displacement and rotation of between the two trackers:\"\n print\n print \"X Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_pos_x, down_pos_x_err )\n print \"Y Position = {0:0.3f} +/- {1:0.3f} mm\".format( down_pos_y, down_pos_y_err )\n print\n print \"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_rot_x*1000.0, down_rot_x_err*1000.0 )\n print \"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\".format( down_rot_y*1000.0, down_rot_y_err*1000.0 )\n print","def plot_results(self, a):\n import matplotlib.pyplot as plt\n fig, axes = plt.subplots(nrows=len(a.data_vars), sharex='all', sharey='all')\n for ax, var in zip(axes, a.data_vars):\n data = a[var]\n plt.sca(ax)\n data.plot(x='time', cmap=plt.cm.viridis_r, yincrease=False, robust=True)\n plt.show()","def plot_data(data, param_choice, args):\n \n ### set general plot properties\n \n savebase = '/usr/users/iff_th2/duman/Cells_in_LAMMPS/POVRAY/'\n #downlim = -1\n #uplim = sim.lx/4.\n num_ticks = 5\n ax_len = 1.0 # Length of one subplot square box\n ax_b = 0.0 # Beginning/offset of the subplot in the box\n ax_sep = 0.0 # Separation length between two subplots\n total_subplots_in_x = 1 # Total number of subplots \n fig = plt.figure()\n subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x) \n ax0 = subp.addSubplot()\n \n name = ''\n pname = ''\n if param_choice == 'areak': \n name = 'AREAK'\n pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \\\n '_kappa_' + str(args.kappa)\n xlab = '$\\kappa_A$'\n tit = '$\\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \\\n ',$\\kappa=$' + str(args.kappa)\n elif param_choice == 'eps':\n name = 'EPS'\n pname = name + '_fp_' + str(args.fp) + '_areak_' + str(args.areak) + \\\n '_kappa_' + str(args.kappa)\n xlab = '$\\epsilon$'\n tit = '$f_m=$' + str(args.fp) + ',$\\kappa_A=$' + str(args.areak) + \\\n ',$\\kappa=$' + str(args.kappa) \n elif param_choice == 'fp':\n name = 'FP'\n pname = name + '_eps_' + str(args.eps) + '_areak_' + str(args.areak) + \\\n '_kappa_' + str(args.kappa)\n xlab = '$f_{m}$'\n tit = '$\\epsilon=$' + str(args.eps) + ',$\\kappa_A=$' + str(args.areak) + \\\n ',$\\kappa=$' + str(args.kappa) \n elif param_choice == 'kappa':\n name = 'KAPPA'\n pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \\\n '_areak_' + str(args.areak)\n xlab = '$\\kappa$'\n tit = '$\\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \\\n ',$\\kappa_A=$' + str(args.areak) \n base = savebase + name + '/'\n os.system(\"mkdir -p \" + base) \n \n ### plot \n\n subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x) \n ax0 = subp.addSubplot()\n \n x = data.keys()\n y = [1 for j in range(len(data.keys()))]\n print x\n ax0.scatter(x, y)\n ax0.set_xscale('log')\n ax0.set_yscale('log')\n \n for j, p in enumerate(data.keys()):\n \n fname = data[p] \n \n if os.path.exists(fname):\n arr_hand = read_png(fname)\n \n zoom=0.099\n imagebox = OffsetImage(arr_hand, zoom=zoom)\n\n xy = [x[j], y[j]] # coordinates to position this image\n\n ab = AnnotationBbox(imagebox, xy,\n xybox=(0., -0.),\n xycoords='data',\n boxcoords=\"offset points\",frameon=1,pad=.1) \n \n ax0.add_artist(ab)\n \n ### title\n \n ax0.set_title(tit, fontsize=30)\n \n ### labels\n \n ax0.set_xlabel(xlab, fontsize=30)\n #ax0.set_ylabel(\"$F_{s}(q,\\\\Delta t)$\", fontsize=40)\n\n ### limits\n\n #ax0.set_xlim((-1, 15))\n ax0.set_ylim((0.9999, 1.0001))\n \n ax0.grid(1, color='#cccccc', linestyle='--')\n ax0.set_frame_on(False)\n ax0.get_xaxis().tick_bottom()\n ax0.axes.get_yaxis().set_visible(False)\n xmin, xmax = ax0.get_xaxis().get_view_interval()\n ymin, ymax = ax0.get_yaxis().get_view_interval()\n ax0.add_artist(Line2D((xmin, xmax), (ymin, ymin), color='black', linewidth=2)) \n ### ticks\n \n #ax0.xaxis.set_ticks(np.linspace(0, 15, num_ticks, endpoint=True))\n #ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))\n plt.setp(ax0.get_yticklabels(),visible=False) \n ax0.tick_params(axis='both', which='major', labelsize=30)\n \n ### legend\n\n# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \\\n# prop={'size': 20}, mode=\"expand\", frameon=False)\n \n ### save \n \n savepath = base + \"images_per_\" + pname + \".pdf\"\n print savepath\n plt.savefig(savepath, dpi=300, bbox_inches='tight', pad_inches=0.08) \n fig.clf() \n \n return","def multi_plot(data, fname=None):\n for entry in data['data']:\n plt.plot(entry['x'], entry['y'], label=entry['label'])\n\n plt.title(data['title'])\n plt.xlabel(data['x_label'])\n plt.ylabel(data['y_label'])\n\n #plt.legend(loc='best')\n\n Plotter.show(data['title'], fname=fname)","def plot_sample(self):\n print(u'plot_sample()')\n data_set = self.data_sets[1]\n scenario = u'Greedy Search'\n titles = [u'Collaborative Filtering', u'Content-based']\n fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n for i, rec_type in enumerate(data_set.missions):\n graph = data_set.folder_graphs + rec_type + '_' + str(15) + u'.txt'\n for strategy in Strategy.strategies:\n m = data_set.missions[rec_type][graph][strategy][scenario]\n m.compute_stats()\n ppl.plot(axes[i], np.arange(STEPS_MAX + 1),\n m.stats, label=strategy, linewidth=2)\n axes[i].set_xlabel(u'#Hops')\n axes[i].set_ylabel(u'Success Ratio')\n axes[i].set_ylim(0, 85)\n axes[i].set_xlim(0, STEPS_MAX * 1.01)\n axes[i].set_title(titles[i])\n ppl.legend(axes[i], loc=0)\n\n\n # plt.suptitle(u'Greedy Search on the BookCrossing for N=15',\n # size='xx-large', x=0.5)\n fig.subplots_adjust(left=0.08, right=0.97, top=0.9)\n\n plt.savefig('plots/sample.png')\n plt.savefig('plots/sample.pdf')","def plot_prodata_psf(self,font_size=28,img_name='prodata_psf.pdf',img_id=0):\n rawimage = self.raw_image\n dataimage = self.data\n len_mask = self.lens_mask\n plu_mask_out = self.plu_mask\n\n fig, (ax1, ax2, ax3, ax4,ax5) = plt.subplots(1, 5, figsize=(19, 10))\n ax1.imshow((rawimage), origin='lower', cmap=\"gist_heat\")\n ax1.set_title('Original Image', fontsize=font_size)\n ax1.text(rawimage.shape[0] * 0.55, rawimage.shape[0] * 0.8, 'ID='+repr(img_id), size=12, color='white',\n weight=\"bold\")\n ax1.text(rawimage.shape[0] * 0.2, rawimage.shape[0] * 0.05, 'observation', size=20, color='white', weight=\"bold\")\n ax1.axis('off')\n #\n ax2.imshow((dataimage), origin='lower', cmap=\"gist_heat\")\n ax2.set_title('Image Data', fontsize=font_size)\n ax2.text(dataimage.shape[0] * 0.2, dataimage.shape[0] * 0.05, 'image data', size=20, color='white', weight=\"bold\")\n ax2.axis('off')\n #\n ax3.imshow(len_mask, origin='lower')\n ax3.set_title('Lens light', fontsize=font_size)\n ax3.axis('off')\n #\n ax4.imshow(plu_mask_out, origin='lower')\n ax4.set_title('Mask', fontsize=font_size)\n ax4.axis('off')\n#\n psf=self.psf\n ax5.imshow(np.log10(psf), origin='lower', cmap=\"gist_heat\")\n ax5.set_title('lg(PSF)', fontsize=font_size)\n ax5.axis('off')\n\n plt.show()\n fig.savefig(img_name)\n return 0","def plot_actual_vs_predicted_by_equations(df, x_variable, y_variables, plot_title):\n #Plot results\n df.plot(x=x_variable, y=y_variables, title=plot_title)\n plt.show()","def plot_data_stats(data_dict, data_bxtxn, data_dt):\n print(onp.mean(onp.sum(data_bxtxn, axis=1)), \"spikes/second\")\n f = plt.figure(figsize=(12,4))\n plt.subplot(141)\n plt.hist(onp.mean(data_bxtxn, axis=1).ravel()/data_dt);\n plt.xlabel('spikes / sec')\n plt.subplot(142)\n plt.imshow(data_dict['hiddens'][0,:,:].T)\n plt.xlabel('time')\n plt.ylabel('neuron #')\n plt.title('Sample trial rates')\n plt.subplot(143);\n plt.imshow(data_bxtxn[0,:,:].T)\n plt.xlabel('time')\n plt.ylabel('neuron #')\n plt.title('spikes')\n plt.subplot(144)\n plt.stem(onp.mean(onp.sum(data_bxtxn, axis=1), axis=0));\n plt.xlabel('neuron #')\n plt.ylabel('spikes / sec');\n return f","def plot_all(show=True):\n fig, axes = plt.subplots(max_iterations, 1, figsize=(6, 12))\n for t in range(max_iterations):\n with open('results/%s/df_%d.pkl' % (id, t), 'rb') as f:\n df = pickle.load(f)\n with open('results/%s/w_%d.pkl' % (id, t), 'rb') as f:\n w = pickle.load(f)\n axes[t].hist2d(x=df['vision'], y=df['metab'], weights=w, density=True,\n bins=((xticks, yticks)), cmap='magma')\n axes[t].set_ylabel('max metabolism')\n axes[t].set_xticks(vision_domain)\n axes[t].set_yticks((2, 3, 4))\n axes[3].set_xlabel('max vision')\n fig.tight_layout()\n if show:\n plt.show()\n else:\n plt.savefig('results/%s/abc_results.pdf' % id)","def plot(self, **kwargs):\n\n # get colors\n colors = kwargs.get(\"colors\", GW_OBSERVATORY_COLORS)\n\n # get Result samples\n self._samples = {\n label: value.posterior\n for label, value in self.results.items()\n if isinstance(value, Result)\n }\n\n # get Grid posteriors\n self._grids = {\n label: [value, value.ln_evidence] # store grid and log evidence\n for label, value in self.results.items()\n if isinstance(value, Grid)\n }\n\n # apply offsets for slightly nicer plots axes\n self.parameter_offsets = {parameter: 0.0 for parameter in self.parameters}\n if len(self._grids) == 0 and len(self._samples) == 1:\n for label in self._samples:\n for parameter in self.parameters:\n srange = [\n np.min(self._samples[label][parameter]),\n np.max(self._samples[label][parameter]),\n ]\n label_suffix = \"\"\n\n # offset values\n median = np.median(self._samples[label][parameter])\n relwidth = np.abs((srange[1] - srange[0]) / median)\n\n if relwidth < 1e-4:\n offsetstr = f\"{median:.4e}\"\n a, b = offsetstr.split(\"e\")\n\n if np.abs(int(b)) < 3:\n offsetstr = f\"{median:.4f}\"\n offset = float(offsetstr)\n else:\n offset = float(offsetstr)\n offsetstr = a + rf\"\\!\\times\\!10^{{{int(b)}}}\"\n\n self.parameter_offsets[parameter] = offset\n\n self._samples[label][parameter] -= offset\n label_suffix = rf\" [${{\\scriptstyle {offsetstr}}}$]\"\n\n self.latex_labels[parameter] += label_suffix\n\n colordicts = []\n for j, res in enumerate([self._samples, self._grids]):\n colordicts.append({})\n for i, key in enumerate(res):\n if key in colors:\n colordicts[-1][key] = colors[key]\n elif key.lower() == \"joint\":\n # if using \"Joint\" as the multi-detector analysis key, set the color to black\n colordicts[-1][key] = \"k\"\n else:\n # use PESummary color cycle\n colordicts[-1][key] = list(colorcycle)[\n (j * 2 + i) % len(colorcycle)\n ]\n\n # store original keywords arguments\n origkwargs = kwargs.copy()\n\n # plot samples\n fig = None\n if len(self._samples) > 0:\n kwargs[\"colors\"] = list(colordicts[0].values())\n if self._num_parameters == 1:\n fig = self._1d_plot_samples(**kwargs)\n elif self._num_parameters == 2 and self.plottype != \"corner\":\n fig = self._2d_plot_samples(**kwargs)\n else:\n fig = self._nd_plot_samples(**kwargs)\n\n # restore keywords\n kwargs = origkwargs\n\n if len(self._grids) > 0:\n kwargs[\"colors\"] = list(colordicts[1].values())\n if fig is not None and \"fig\" not in kwargs:\n kwargs[\"fig\"] = fig\n if self._num_parameters == 1:\n fig = self._1d_plot_grid(**kwargs)\n elif self._num_parameters == 2 and self.plottype != \"corner\":\n fig = self._2d_plot_grid(**kwargs)\n else:\n fig = self._nd_plot_grid(**kwargs)\n\n # add further figure information\n if self._num_parameters == 1:\n ax = fig.gca()\n\n # set figure bounds if outside defaults\n if self.parameters[0] in DEFAULT_BOUNDS:\n _set_axes_limits(ax, self.parameters[0], axis=\"x\")\n\n # add injection values\n if self.injection_parameters is not None:\n if self.injection_parameters[self.parameters[0]] is not None:\n ax.axvline(\n (\n self.injection_parameters[self.parameters[0]]\n - self.parameter_offsets[self.parameters[0]]\n ),\n color=kwargs.get(\"injection_color\", \"k\"),\n linewidth=1,\n )\n elif self._num_parameters == 2:\n if \"triangle\" in self.plottype:\n a1, a2, a3 = fig[1:]\n order = [\"x\", \"y\"] if self.plottype == \"triangle\" else [\"y\", \"x\"]\n params = (\n self.parameters[:2]\n if self.plottype == \"triangle\"\n else self.parameters[1::-1]\n )\n\n # set figure bounds if outside defaults\n for param, axes, axis in zip(params, [[a1, a2], [a2, a3]], order):\n for ax in axes:\n _set_axes_limits(ax, param, axis=axis)\n\n self.fig = fig\n return self.fig","def ratio(gb_data, data_depcode, data_ratio_hospitalises,current_date, data_hospitalises, current_date_file, min_value_80p , nbhospitalises_80p) :\n start = time.time()\n fig, ax = plt.subplots(figsize=(12, 8))\n\n plt.title(f\"Ratio of in-hospital deaths to hospitalizations : {current_date}\", fontsize=20)\n plt.ylabel(\"Total number of deceases / Total number of hospitalized\")\n plt.xlabel(\"Total number of hospitalized\")\n\n for i, txt in enumerate(data_depcode):\n if (data_hospitalises[i] > data_hospitalises.max() * 0.20):\n ax.annotate(txt, (data_hospitalises[i], data_ratio_hospitalises[i]), xytext=(data_hospitalises[i] + 20, data_ratio_hospitalises[i])) \n\n plt.axhline(data_ratio_hospitalises.mean(), color='green', linestyle='--', label=f'average death ratio ({data_ratio_hospitalises.mean():.2f}%)')\n\n plt.axvline(min_value_80p, color='pink', linestyle='-', label=f\"80% of the number of hospitalized people in France are on the right side of the line ({nbhospitalises_80p:.0f} hospitalized)\")\n\n ax.scatter(data_hospitalises, data_ratio_hospitalises)\n\n ax.annotate('updated chart',xy=(1, 0), xytext=(-15, 10), fontsize=15,\n xycoords='axes fraction', textcoords = 'offset points',\n bbox=dict(facecolor = 'white', alpha = 0.9),\n horizontalalignment = 'right', verticalalignment = 'bottom')\n\n ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f%%'))\n plt.legend()\n\n current_date_file = gb_data['date'].max().strftime('%Y%m%d')\n end = time.time()\n print(\"Time spent on ratio plot: {0:.5f} s.\".format(end - start)) \n plt.show()","def ResBeam_Stats_plot(n, header_bmaj, header_bmin): \n\n file_dir = 'SpectralCube_BeamLogs'\n basename = '/beamlog.image.restored.' + imagebase + field\n\n # use different basename for the Milky Way range\n if not glob.glob(file_dir + basename +'*.txt'):\n basename = '/beamlog.image.restored.' + imagebase + 'MilkyWay.' + field\n\n \n BEAM_THRESHOLD = []\n \n title1 = 'Restoring beam bmaj standard deviation [arcsec]'\n plt_name1 = 'BmajStdev.png'\n saved_fig1 = fig_dir+'/'+plt_name1\n\n title2 = 'Restoring beam bmin standard deviation [arcsec]'\n plt_name2 = 'BminStdev.png'\n saved_fig2 = fig_dir+'/'+plt_name2\n\n title3 = 'Maximum ratio of beam area'\n plt_name3 = 'max_ratioBA.png'\n saved_fig3 = fig_dir+'/'+plt_name3\n\n title4 = 'Minimum ratio of beam area' \n plt_name4 = 'min_ratioBA.png'\n saved_fig4 = fig_dir+'/'+plt_name4\n \n params = {'axes.labelsize': 10,\n 'axes.titlesize': 10,\n 'font.size':10}\n\n pylab.rcParams.update(params)\n\n beamXPOS, beamYPOS = BeamPosition()\n fig1, ax1 = plt.subplots()\n fig2, ax2 = plt.subplots()\n fig3, ax3 = plt.subplots()\n fig4, ax4 = plt.subplots()\n \n for i in range(0,36):\n bnum = n[i]\n infile = file_dir + basename +'.beam%02d.txt'%(bnum)\n bmaj_stdev, bmin_stdev, beam_threshold, max_ratio_BA, min_ratio_BA = cal_ResBeam_Stats(infile, header_bmaj, header_bmin)\n BEAM_THRESHOLD.append(beam_threshold)\n\n ax1.scatter([beamXPOS[i]], [beamYPOS[i]], s=1400, edgecolors='black', facecolors='none')\n ax1.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\n ax1.text(beamXPOS[i], beamYPOS[i]-0.02, round(bmaj_stdev, 3), va='center', ha='center', fontsize=8, color='blue')\n\n ax2.scatter([beamXPOS[i]], [beamYPOS[i]], s=1400, edgecolors='black', facecolors='none')\n ax2.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\n ax2.text(beamXPOS[i], beamYPOS[i]-0.02, round(bmin_stdev,3), va='center', ha='center', fontsize=8, color='blue')\n\n maxplot = ax3.scatter([beamXPOS[i]], [beamYPOS[i]], s=1300, c=[max_ratio_BA], cmap='summer', edgecolors='black', vmin=0, vmax=1.1)\n ax3.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\n ax3.text(beamXPOS[i], beamYPOS[i]-0.02, round(max_ratio_BA,3), va='center', ha='center', fontsize=8, color='blue')\n \n minplot = ax4.scatter([beamXPOS[i]], [beamYPOS[i]], s=1300, c=[min_ratio_BA], cmap='summer', edgecolors='black', vmin=0, vmax=1.1)\n ax4.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\n ax4.text(beamXPOS[i], beamYPOS[i]-0.02, round(min_ratio_BA,3), va='center', ha='center', fontsize=8, color='blue')\n \n ax1.set_xlim(0,0.7)\n ax1.set_ylim(0,1.4)\n ax1.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\n ax1.set_title(title1)\n\n ax2.set_xlim(0,0.7)\n ax2.set_ylim(0,1.4)\n ax2.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\n ax2.set_title(title2)\n\n ax3.set_xlim(0,0.7)\n ax3.set_ylim(0,1.4)\n ax3.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\n ax3.set_title(title3)\n plt.colorbar(maxplot, ax=ax3)\n\n ax4.set_xlim(0,0.7)\n ax4.set_ylim(0,1.4)\n ax4.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\n ax4.set_title(title4)\n plt.colorbar(minplot, ax=ax4)\n\n fig1.savefig(saved_fig1, bbox_inches='tight')\n fig2.savefig(saved_fig2, bbox_inches='tight')\n fig3.savefig(saved_fig3, bbox_inches='tight')\n fig4.savefig(saved_fig4, bbox_inches='tight')\n\n plt.close('all')\n\n return saved_fig1, saved_fig2, plt_name1, plt_name2, saved_fig3, saved_fig4, plt_name3, plt_name4, BEAM_THRESHOLD","def plot(model, results, filename):\n\n # c = model.compartments.get_one(id='c')\n #\n # rna_1 = model.species_types.get_one(id='rna_1').species.get_one(compartment=c)\n # rna_2 = model.species_types.get_one(id='rna_2').species.get_one(compartment=c)\n # rna_3 = model.species_types.get_one(id='rna_3').species.get_one(compartment=c)\n #\n pops = results.get('populations')\n time = pops.index\n pop_rna_1 = pops['rna_1[c]']\n pop_rna_2 = pops['rna_2[c]']\n pop_rna_3 = pops['rna_3[c]']\n\n pop_atp = pops['atp[c]']\n pop_gtp = pops['gtp[c]']\n pop_utp = pops['ctp[c]']\n pop_ctp = pops['utp[c]']\n\n pop_amp = pops['amp[c]']\n pop_gmp = pops['gmp[c]']\n pop_ump = pops['cmp[c]']\n pop_cmp = pops['ump[c]']\n\n print(pop_rna_1, pop_atp, pop_gtp, pop_utp, pop_ctp)\n\n fig1, axes1 = pyplot.subplots(nrows=3, ncols=1)\n\n axes1[0].plot(time / 3600, pop_rna_1)\n axes1[0].plot(time / 3600, pop_rna_2)\n axes1[0].plot(time / 3600, pop_rna_3)\n axes1[0].set_xlim((time[0] / 3600, time[-1] / 3600))\n axes1[0].set_ylim((0., 10.0))\n axes1[0].legend(loc='upper right')\n\n axes1[1].plot(time / 3600, pop_atp)\n axes1[1].plot(time / 3600, pop_gtp)\n axes1[1].plot(time / 3600, pop_utp)\n axes1[1].plot(time / 3600, pop_ctp)\n axes1[1].set_xlim((time[0] / 3600, time[-1] / 3600))\n # axes1[1].set_ylim((0., 10.0))\n axes1[1].legend(loc='upper right')\n\n axes1[2].plot(time / 3600, pop_amp)\n axes1[2].plot(time / 3600, pop_gmp)\n axes1[2].plot(time / 3600, pop_ump)\n axes1[2].plot(time / 3600, pop_cmp)\n axes1[2].set_xlim((time[0] / 3600, time[-1] / 3600))\n # axes1[2].set_ylim((0., 10.0))\n axes1[2].legend(loc='upper right')\n\n fig1.savefig(filename.format('species'))\n pyplot.close(fig1)","def pratn_writer(clf, y_true, y_prob, eval_folder, i=''):\n if type(y_true[0]) is not np.ndarray: y_true = [y_true]\n if type(y_prob[0]) is not np.ndarray: y_prob = [y_prob]\n\n img_dir = eval_folder+'/images/'\n if not os.path.exists(img_dir):\n os.makedirs(img_dir)\n\n clf_name = str(clf)[:str(clf).index('(')]+str(i)\n\n enum_list = range(0, len(y_true))\n\n fig, ax1 = plt.subplots()\n ax1.set_xlabel('percent of population')\n ax1.set_ylabel('precision', color='b')\n ax2 = ax1.twinx()\n ax2.set_ylabel('recall', color='r')\n\n mean_prec = 0.0\n mean_rec = 0.0\n mean_pct_above = np.linspace(0,1,1000)\n\n for i in enum_list:\n prec, rec, thres = metrics.precision_recall_curve(y_true[i], y_prob[i])\n prec = prec[:-1]\n rec = rec[:-1]\n skip_size = int(thres.shape[0]/1000.0)\n if skip_size == 0: skip_size = 1\n\tplotting_thres = thres[0::skip_size][::-1]\n plotting_prec = prec[0::skip_size][::-1]\n plotting_rec = rec[0::skip_size][::-1]\n\n how_many = float(len(y_true[i]))\n\n pct_above = [(y_prob[i][y_prob[i] >= value].shape[0])/how_many\n for value in plotting_thres]\n\n pct_above = np.array(pct_above)\n mean_prec += interp(mean_pct_above, pct_above, plotting_prec)\n mean_rec += interp(mean_pct_above, pct_above, plotting_rec)\n\n #ax1.plot(pct_above, plotting_prec, 'b')\n #ax2.plot(pct_above, plotting_rec, 'r')\n\n mean_prec /= len(y_true)\n mean_rec /= len(y_true)\n\n mean_prec[-1] = np.mean([np.mean(enu) for enu in y_true])\n mean_rec[-1] = 1.0\n\n ax1.plot(mean_pct_above, mean_prec, 'b')\n ax2.plot(mean_pct_above, mean_rec, 'r')\n plt.title('Precision, Recall vs % Population')\n plt.savefig(img_dir+'PRATN_Curve_'+clf_name+'.png')","def plot_apertures(image, ext=1):\n\n hdu = fits.open(image)\n apfile = './database/ape' + image.strip('.fits') + '_{:d}'.format(ext)\n\n b = np.array(\n [i.split()[3:] for i in open(apfile).readlines() if 'begin' in i])\n\n apid = b[:, 0]\n x = np.array([float(i) for i in b[:, 2]])\n\n sci_exts = np.array([i for i in range(len(hdu)) if hdu[i].name == 'SCI'])\n data = hdu[sci_exts[len(sci_exts)/2]].data\n\n profile = np.average(data, 1)\n\n fig = plt.figure(1)\n ax = fig.add_subplot(111)\n\n pmax = profile.max()\n\n ax.plot(np.arange(len(profile))+1, profile/pmax)\n ax.set_ylim(0, 1.1)\n\n for i, j in enumerate(apid):\n ax.annotate(j, xy=(x[i], 1), ha='center')\n ax.axvline(x[i], alpha=.3)\n\n plt.show()","def initial_plots(runs):\n for run in runs.keys():\n meta = runs[run]\n plot_pdfs(meta)\n plot_priorsamps(meta)\n plot_ivals(meta)\n# if meta.truNz is not None:\n# plot_true(meta)\n timesaver(meta,'iplot',meta.key)","def PlotEX(df, eom=None, show_plot=True):\n # Ensure 'Study' is in header.\n # TODO: Add code.\n # Obtain exceedence probabilities.\n if eom:\n df_excd = stats.AnnualExceedence(df, eom=eom)\n else:\n df_excd = stats.MonthlyExceedence(df)\n # Get list of variables.\n var_list = set(df_excd.columns.get_level_values('Part B'))\n # Determine number of axes in the figure.\n n_var = len(var_list)\n n_row = np.int(np.around(np.sqrt(n_var)))\n if n_row**2 >= n_var:\n n_col = n_row\n else:\n n_col = n_row + 1\n fig, ax = plt.subplots(nrows=n_row, ncols=n_col, figsize=(16, 9))\n # Remove extra axes, if necessary.\n if n_row * n_col != n_var:\n n_empty = n_row * n_col - n_var\n for k in range(n_empty):\n ax[-1, -(1+k)].remove()\n # Initialize variables for updating axes.\n cur_row = 0\n cur_col = 0\n # Plot each variable.\n for var in var_list:\n df_exc = df_excd.xs(var, level='Part B', axis=1)\n # Initialize parameters for current axis.\n y_min = np.min(df_exc.values)\n y_max = np.max(df_exc.values)\n col_var = df_exc.columns\n # Select current axis.\n if n_row == 1 and n_col == 1:\n ax_cur = ax\n elif n_row == 1:\n ax_cur = ax[cur_col]\n else:\n ax_cur = ax[cur_row, cur_col]\n # Plot study results for each variable.\n for c in col_var:\n sr_plot = df_exc[c]\n label_c = '{} {}'.format(*c[:2])\n ax_cur.plot(sr_plot, label=label_c, linewidth=1.0, alpha=0.7)\n # Set plot title.\n if eom:\n month_name = dt.date(1900, eom, 1).strftime('%B')\n temp_title = 'Exceedence Plot of {} (End of {})'\n ax_cur.set_title(temp_title.format(var, month_name))\n else:\n ax_cur.set_title('Exceedence Plot of {} (All Months)'.format(var))\n # Modify x-axis and y-axis.\n label_c = set(df_exc.columns.get_level_values('Part C'))\n _unit = df_exc.columns.get_level_values('Units')\n _dtyp = df_exc.columns.get_level_values('Data Type')\n unit_d = list(zip(_unit, _dtyp))\n combo_unit = [' '.join(i) for i in unit_d]\n label_u = set(combo_unit)\n ax_cur.set_ylabel('{} ({})'.format(r'/'.join(label_c),\n r'/'.join(label_u)))\n ax_cur.set_ylim(bottom=y_min, top=y_max)\n ax_cur.set_xlabel('Exceedance Probability')\n ax_cur.spines['right'].set_visible(False)\n ax_cur.spines['top'].set_visible(False)\n # Set legend.\n ax_cur.legend(title='Study, Part F')\n # Update current axis.\n cur_col += 1\n if cur_col >= n_col:\n cur_col = 0\n cur_row += 1\n # Adjust layout.\n plt.tight_layout()\n # Add figure notes.\n t = PlotChartNotes()\n if n_row * n_col != n_var:\n x_pos = (1 - n_empty / n_col) + 0.025\n y_pos = (1 / n_row) - 0.025\n plt.figtext(x_pos, y_pos, t, ha='left', va='top', wrap=True)\n else:\n plt.figtext(0.05, 0, t, ha='left', va='bottom', wrap=True)\n plt.subplots_adjust(bottom=0.2)\n # Show plot, if requested.\n if show_plot:\n plt.show()\n # Return figure and axes.\n return fig, ax","def plot_ave(results_list):\n x_range = range(len(results_list[0]))\n err_x, err_y, std_list = [], [], []\n\n for i in x_range:\n if i % 10 == 0:\n #get average for each generation\n column = [] \n for result in results_list:\n column.append(result[i])\n average = np.average(column)\n \n std_dev = np.std(column)\n err_x.append(i)\n err_y.append(average)\n std_list.append(std_dev)\n\n pylab.errorbar(err_x, err_y, yerr=std_list)\n pylab.show()","def plot_individual_tm(xdict, ydict, xprop, yprop, documents, spline):\n figure_array = {}\n for item in documents:\n xlabel = \"\\\\textbf{\" + label_dict[xprop] + \"}\"\n ylabel = \"\\\\textbf{\" + label_dict[yprop] + \"}\"\n print str(item[\"path_id\"])\n x = xdict[item[\"path_id\"]]\n y = ydict[item[\"path_id\"]]\n # fig_title = item[\"path_id\"] + \"(\" + item[\"pretty_formula\"] + \")\" # Individual traces\n fig_title = yprop + item[\"cation_type\"] # Plot by cation\n figure_array[item[\"path_id\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\n ax = figure_array[item[\"path_id\"]].add_subplot(111)\n ax.scatter(x,y, s=70, zorder=2, color=tm_color_dict[item[\"tm_type\"][0]], linewidths=2.5, edgecolors='black',\n label=item[\"tm_type\"][0])\n if spline:\n tck = interpolate.splrep(x, y, s=0)\n xnew = np.arange(0, 100, 0.1)\n splfit = interpolate.splev(xnew, tck, der=0)\n x = xnew\n y = splfit\n if item[\"path_id\"][-3:] == \"002\":\n ax.plot(x, y, linewidth=2.5, zorder=1, color=tm_color_dict[item[\"tm_type\"][0]], linestyle='dashed')\n else:\n ax.plot(x, y, linewidth=2.5, zorder=1, color=tm_color_dict[item[\"tm_type\"][0]])\n ax.set_xlabel(xlabel, fontsize=24)\n # ax.set_ylim([0,1200])\n # ax.set_xlim([7,22])\n ax.set_ylabel(ylabel, fontsize=24)\n ax.tick_params(axis='x', labelsize=22)\n ax.tick_params(axis='y', labelsize=22)\n border_width = 2\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\n plt.tight_layout()\n plt.legend(loc='best', prop={'size': 14})\n plt.rc('text', usetex=True)\n plt.rc('font', family='sans-serif')\n plt.tight_layout()\n plt.show()","def plot_morphism_output(data, outdir):\n\n # show the distributions for each variable separately\n for col in data.columns:\n ProfilePlotter._plot_1d(data[col], outfile = os.path.join(outdir, col + \".pdf\"))\n\n # later, maybe also show 2d plots etc.","def plot_p1(data, documentation):\r\n fig, ax = plt.subplots(1)\r\n data = process_data_p1(data)\r\n sns.lineplot(x=\"Academic Year\", y=\"MD_EARN_WNE_P10\", hue=\"CONTROL\",\r\n data=data, ax=ax,\r\n palette=sns.color_palette([\"#9b59b6\", \"#3498db\", \"#e74c3c\"]))\r\n fig.suptitle(\"Yearly Changes of Average Median Earnings of Former \" +\r\n \"Students per College Control Type\")\r\n ax.set(ylabel=\"Average Median Earnings ($)\")\r\n handles, legends = ax.get_legend_handles_labels()\r\n legends = replace_legend_value_mappings(legends, documentation)\r\n ax.legend(handles, legends, loc=\"lower left\")\r\n fig.savefig(join_curdir(\"results\", \"plot_p1.png\"))","def analyze(data):\n # perform fit\n regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\n print(regr_results.tvalues)\n \n fig = plt.figure(figsize=(6, 9))\n\n # plot predicted vs. measured mosquito populations from fitted model \n ax0 = fig.add_subplot(3, 1, 1)\n\n parameters = regr_results.params\n predicted = (parameters['Intercept'] + \n parameters['temperature'] * data['temperature'] + \n parameters['rainfall'] * data['rainfall'])\n\n ax0.plot(predicted, data['mosquitos'], 'gd')\n\n ax0.set_xlabel('predicted mosquito population')\n ax0.set_ylabel('measured mosquito population')\n \n # plot mosquitos vs. temperature\n ax1 = fig.add_subplot(3, 1, 2)\n\n ax1.plot(data['temperature'], data['mosquitos'], 'ro')\n ax1.set_xlabel('temperature')\n ax1.set_ylabel('mosquitos')\n\n # plot mosquitos vs. rainfall\n ax2 = fig.add_subplot(3, 1, 3)\n\n ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\n ax2.set_xlabel('rainfall')\n ax2.set_ylabel('mosquitos')\n \n # adjust layout of axes according to label placement\n plt.tight_layout()\n \n return fig","def avg_response(df, x, y_obs, y_est, save=False, show=True):\n\n fig, ax1 = plt.subplots(figsize=(15,15))\n\n ax2 = ax1.twinx()\n\n x_name = x\n if df[x].dtype == \"int\":\n x = df[x].astype(\"category\")\n elif df[x].dtype == \"float\":\n x = pd.cut(df[x], bins=10)\n\n metrics = {\"mean\":\"mean\", \"std err\":\"sem\", \"count\":\"count\"}\n df_grouped = df.groupby([x])[y_obs, y_est].agg(metrics)\n \n x_vals = range(len(df_grouped))\n y_vals = df_grouped[\"mean\"][y_est]\n ax1.errorbar(x_vals, y_vals,yerr=df_grouped[\"std err\"][y_est], fmt='-',\n marker='o',color=\"R\", mec='black', ms=10, mew=2, linewidth=4, \n capsize=10, elinewidth=2)\n\n y_vals = df_grouped[\"mean\"][y_obs]\n ax1.plot(x_vals, y_vals, '-', label=y_obs, marker='o',\n color = \"G\",mec='black', ms=10, mew=2, linewidth=4)\n\n y_vals = df_grouped[\"count\"][y_obs]\n ax2.bar(x_vals,y_vals, color='DarkSlateGray', alpha = 0.25)\n\n ax1.set_xlim(x_vals[0]-0.2,x_vals[-1]+1)\n x_levels = list(y_vals.index)\n plt.xticks(x_vals, x_levels)\n ax1.set_xticklabels(x_levels, rotation=45)\n ax1.grid(False)\n ax2.grid(False)\n font_size = 20\n ax1.set_xlabel(x_name, fontsize=font_size)\n ax1.set_ylabel(y_obs, fontsize=font_size)\n ax2.set_ylabel(\"count\", fontsize=font_size)\n plt.title(\"Average {y} for groups of {x}\".format(x=x_name, y=y_obs), \n fontsize=font_size+5)\n ax1.legend([y_obs, y_est], fontsize=font_size-2)\n if save:\n fig.savefig(\"/home/edward/work/repos/prometheus/python/plots/avg_response/{}.png\".\n format(x_name), bbox_inches='tight')\n if show:\n plt.show()","def plot_data(X, y, X_pred, clf, title):\n \n figsize(10.2, 5.1)\n X2 = [[x] for x in linspace(0, max(X_pred)[0] * 1.15, 50)]\n days = clf.predict(X_pred)\n scatter(X, y, color='black')\n scatter(X_pred, days, color='red')\n plot(X2, clf.predict(X2), color='blue')\n \n i = 0\n len_data = len(X_pred)\n xytext = (5, -10)\n for data in zip(X_pred, days):\n i = i + 1\n dat = purchase_date + timedelta(int(round(data[1], 0)))\n\n annotate(dat.strftime('%b %d %Y'), xy=(data[0][0], data[1]), xycoords='data', xytext=xytext, textcoords='offset points')\n \n pylab.ylim([0, int(clf.predict(X2).max()) + 1])\n pylab.xlim([0, max(X2)[0]])\n ax=pylab.gca()\n ax.yaxis.set_major_formatter(NullFormatter())\n ax.yaxis.set_minor_formatter(NullFormatter())\n pylab.axes().set_xlabel('Miles')\n pylab.axes().set_ylabel('Time')\n pylab.axes().set_title(title)","def plotPRC(yscore, true, datasets, title, outfile):\n \n fig = plt.figure()\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.0])\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n plt.title(title)\n \n for i in range(len(datasets)):\n precision, recall, _ = precision_recall_curve(true[i], yscore[i][:,1])\n prc_auc = average_precision_score(true[i], yscore[i][:,1])\n plt.plot(recall, precision, label=datasets[i]+' (area = %0.2f)' % (prc_auc),linewidth=1)\n \n plt.legend(loc=\"lower right\")\n \n pdfplot = PdfPages(outfile);\n pdfplot.savefig(fig)\n pdfplot.close()","def plotFittingResults(self):\n _listFitQ = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitQ()]\n _listFitValues = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitValues()]\n _listExpQ = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataQ()]\n _listExpValues = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataValues()]\n\n #_listExpStdDev = None\n #if self.getDataInput().getExperimentalDataStdDev():\n # _listExpStdDev = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataStdDev()]\n #if _listExpStdDev:\n # pylab.errorbar(_listExpQ, _listExpValues, yerr=_listExpStdDev, linestyle='None', marker='o', markersize=1, label=\"Experimental Data\")\n # pylab.gca().set_yscale(\"log\", nonposy='clip')\n #else: \n # pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label=\"Experimental Data\")\n\n pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label=\"Experimental Data\")\n pylab.semilogy(_listFitQ, _listFitValues, label=\"Fitting curve\")\n pylab.xlabel('q')\n pylab.ylabel('I(q)')\n pylab.suptitle(\"RMax : %3.2f. Fit quality : %1.3f\" % (self.getDataInput().getRMax().getValue(), self.getDataOutput().getFitQuality().getValue()))\n pylab.legend()\n pylab.savefig(os.path.join(self.getWorkingDirectory(), \"gnomFittingResults.png\"))\n pylab.clf()","def plotResults(results):\n e = results['eclean'] - results['eCTI']\n e1 = results['e1clean'] - results['e1CTI']\n e2 = results['e2clean'] - results['e2CTI']\n\n print 'Delta e, e_1, e_2:', np.mean(e), np.mean(e1), np.mean(e2)\n print 'std e, e_1, e_2:', np.std(e), np.std(e1), np.std(e2)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.hist(e, bins=15, label='$e$', alpha=0.5)\n ax.hist(e1, bins=15, label='$e_{2}$', alpha=0.5)\n ax.hist(e2, bins=15, label='$e_{1}$', alpha=0.5)\n ax.set_xlabel(r'$\\delta e$ [no CTI - CDM03 corrected]')\n plt.legend(shadow=True, fancybox=True)\n plt.savefig('ellipticityDelta.pdf')\n plt.close()\n\n r2 = (results['R2clean'] - results['R2CTI'])/results['R2clean']\n print 'delta R2 / R2: mean, std ', np.mean(r2), np.std(r2)\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.hist(r2, bins=15, label='$R^{2}$')\n ax.set_xlabel(r'$\\frac{\\delta R^{2}}{R^{2}_{ref}}$ [no CTI - CDM03 corrected]')\n plt.legend(shadow=True, fancybox=True)\n plt.savefig('sizeDelta.pdf')\n plt.close()","def plot_results(outputs_table_totals, elec_benefits, gas_benefits):\n summer_months = [6, 7, 8, 9]\n shoulder_months = [3, 4, 5, 10]\n winter_months = [11, 12, 1, 2]\n peak_hours = [16, 17, 18, 19, 20]\n pct_hours_in_summer = 2928 / 8760\n pct_hours_in_shoulder = 2952 / 8760\n pct_hours_in_winter = 2880 / 8760\n\n trc_costs_record = outputs_table_totals[\"TRC Costs ($)\"]\n pac_costs_record = outputs_table_totals[\"PAC Costs ($)\"]\n trc_record = outputs_table_totals[\"TRC\"]\n pac_record = outputs_table_totals[\"PAC\"]\n lifecycle_net_mwh = outputs_table_totals[\"Electricity Lifecycle Net Savings (MWh)\"]\n lifecycle_net_therms = outputs_table_totals[\"Gas Lifecycle Net Savings (Therms)\"]\n lifecycle_net_ghg = outputs_table_totals[\"Total Lifecycle GHG Savings (Tons)\"]\n\n # Getting variables for plots\n elec_benefits_cols = (\n [\"hourly_savings\"] + ACC_COMPONENTS_ELECTRICITY + [\"av_csts_levelized\"]\n )\n\n elec_benefits_hour_month_year = (\n elec_benefits.groupby([\"hour_of_day\", \"year\", \"month\"])\n .agg(\n {\n **{component: \"sum\" for component in ACC_COMPONENTS_ELECTRICITY},\n **{\n \"hourly_savings\": \"sum\",\n \"marginal_ghg\": \"sum\",\n \"av_csts_levelized\": \"mean\",\n },\n }\n )\n .reset_index()\n )\n\n total_benefits = list(\n elec_benefits_hour_month_year.groupby([\"hour_of_day\"])[\"total\"].sum()\n )\n\n summer_benefits = list(\n elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n ]\n .groupby([\"hour_of_day\"])[\"total\"]\n .sum()\n )\n summer_peak_benefits = elec_benefits_hour_month_year[\"total\"][\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n & (elec_benefits_hour_month_year[\"hour_of_day\"].isin(peak_hours))\n ].sum()\n shoulder_benefits = list(\n elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(shoulder_months))\n ]\n .groupby([\"hour_of_day\"])[\"total\"]\n .sum()\n )\n winter_benefits = list(\n elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(winter_months))\n ]\n .groupby([\"hour_of_day\"])[\"total\"]\n .sum()\n )\n total_savings = list(\n elec_benefits_hour_month_year.groupby([\"hour_of_day\"])[\"hourly_savings\"].sum()\n )\n summer_savings = list(\n elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n ]\n .groupby([\"hour_of_day\"])[\"hourly_savings\"]\n .sum()\n )\n shoulder_savings = list(\n elec_benefits_hour_month_year[\n ((elec_benefits_hour_month_year[\"month\"].isin(shoulder_months)))\n ]\n .groupby([\"hour_of_day\"])[\"hourly_savings\"]\n .sum()\n )\n summer_peak_savings = elec_benefits_hour_month_year[\"hourly_savings\"][\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n & (elec_benefits_hour_month_year[\"hour_of_day\"].isin(peak_hours))\n ].sum()\n winter_savings = list(\n elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(winter_months))\n ]\n .groupby([\"hour_of_day\"])[\"hourly_savings\"]\n .sum()\n )\n total_av_csts_avg = list(\n elec_benefits_hour_month_year.groupby([\"hour_of_day\"])[\n \"av_csts_levelized\"\n ].mean()\n )\n summer_av_csts_avg = list(\n pct_hours_in_summer\n * elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n ]\n .groupby([\"hour_of_day\"])[\"av_csts_levelized\"]\n .mean()\n )\n summer_peak_av_csts_avg = elec_benefits_hour_month_year[\"av_csts_levelized\"][\n (elec_benefits_hour_month_year[\"month\"].isin(summer_months))\n & (elec_benefits_hour_month_year[\"hour_of_day\"].isin(peak_hours))\n ].mean()\n shoulder_av_csts_avg = list(\n pct_hours_in_shoulder\n * elec_benefits_hour_month_year[\n ((elec_benefits_hour_month_year[\"month\"].isin(shoulder_months)))\n ]\n .groupby([\"hour_of_day\"])[\"av_csts_levelized\"]\n .mean()\n )\n winter_av_csts_avg = list(\n pct_hours_in_winter\n * elec_benefits_hour_month_year[\n (elec_benefits_hour_month_year[\"month\"].isin(winter_months))\n ]\n .groupby([\"hour_of_day\"])[\"av_csts_levelized\"]\n .mean()\n )\n\n elec_benefits_sum_by_hod = (\n elec_benefits[elec_benefits_cols].groupby(elec_benefits[\"hour_of_day\"]).sum()\n )\n elec_benefits_hoy = (\n elec_benefits[elec_benefits_cols]\n .groupby(elec_benefits[\"hour_of_year\"])\n .sum()\n .cumsum()\n .reset_index()\n )\n sav_avcsts_288 = (\n elec_benefits.groupby([\"hour_of_day\", \"month\"])\n .agg(\n {\n **{component: \"sum\" for component in ACC_COMPONENTS_ELECTRICITY},\n **{\n \"hourly_savings\": \"sum\",\n \"marginal_ghg\": \"sum\",\n \"av_csts_levelized\": \"mean\",\n },\n }\n )\n .reset_index()\n )\n sav_avcsts_288 = sav_avcsts_288[\n [\"hour_of_day\", \"month\", \"hourly_savings\", \"total\", \"marginal_ghg\"]\n ]\n ghgsav = sav_avcsts_288.pivot(\"hour_of_day\", \"month\", \"marginal_ghg\")\n sav = sav_avcsts_288.pivot(\"hour_of_day\", \"month\", \"hourly_savings\")\n avcsts = sav_avcsts_288.pivot(\"hour_of_day\", \"month\", \"total\")\n\n # savings load shape plot\n fig0, (ax1, ax2, ax3) = plt.subplots(\n 1, 3, figsize=(18, 5), sharex=True, sharey=True\n )\n plt.subplots_adjust(wspace=0, hspace=0)\n axs = [ax1, ax2, ax3]\n hod = elec_benefits_sum_by_hod.index\n legend_labels1 = [\"Summer\"]\n legend_labels2 = [\"Shoulder\"]\n legend_labels3 = [\"Winter\"]\n\n ax1.plot(\n hod,\n summer_savings,\n c=\"firebrick\",\n linewidth=5,\n marker=\"$\\u25EF$\",\n markersize=13,\n linestyle=\"-\",\n )\n ax2.plot(\n hod,\n shoulder_savings,\n c=\"royalblue\",\n linewidth=5,\n marker=\"$\\u2206$\",\n markersize=13,\n linestyle=\"-\",\n )\n ax3.plot(\n hod,\n winter_savings,\n c=\"green\",\n linewidth=5,\n marker=\"$\\u25A1$\",\n markersize=13,\n linestyle=\"-\",\n )\n ax1.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n ax2.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n ax3.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n # Shade peak region\n ax1.axvspan(16, 21, alpha=0.2, color=\"grey\")\n\n leg1 = ax1.legend(legend_labels1, fontsize=14, loc=\"upper left\", frameon=False)\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\n text.set_color(line.get_color())\n leg2 = ax2.legend(legend_labels2, fontsize=14, loc=\"upper left\", frameon=False)\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\n text.set_color(line.get_color())\n leg3 = ax3.legend(legend_labels3, fontsize=14, loc=\"upper left\", frameon=False)\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\n text.set_color(line.get_color())\n\n ax1.set_ylabel(\"Savings (MWh/hr)\", size=16)\n ax2.set_xlabel(\"Hour of Day\", size=16)\n\n if max(summer_savings + shoulder_savings + winter_savings) < 0:\n ymax = 0\n else:\n ymax = max(summer_savings + shoulder_savings + winter_savings)\n if min(summer_savings + shoulder_savings + winter_savings) > 0:\n ymin = 0\n else:\n ymin = min(summer_savings + shoulder_savings + winter_savings)\n\n # Tick and lebel parameters\n ax1.set_ylim(ymin * 1.08, ymax * 1.08)\n ax1.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax2.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax3.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax1.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax2.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax3.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\n ax1.set_xticks(np.arange(0, 24, step=4))\n ax1.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax1.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\n ax2.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax2.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax2.xaxis.set_minor_locator(AutoMinorLocator())\n ax3.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\n\n # Set plot title, size, and position\n ax1.set_title(\"Seasonal Savings Load Shapes\", size=18, loc=\"left\").set_position(\n [0, 1.03]\n )\n\n # benefits_seasonal_shape_plot\n fig1, (ax1, ax2, ax3) = plt.subplots(\n 1, 3, figsize=(18, 5), sharex=True, sharey=True\n )\n plt.subplots_adjust(wspace=0, hspace=0)\n axs = [ax1, ax2, ax3]\n hod = elec_benefits_sum_by_hod.index\n legend_labels1 = [\"Summer\"]\n legend_labels2 = [\"Shoulder\"]\n legend_labels3 = [\"Winter\"]\n\n ax1.plot(\n hod,\n summer_benefits,\n c=\"firebrick\",\n linewidth=5,\n marker=\"$\\u2B24$\",\n markersize=13,\n linestyle=\":\",\n )\n ax2.plot(\n hod,\n shoulder_benefits,\n c=\"royalblue\",\n linewidth=5,\n marker=\"$\\u25B2$\",\n markersize=13,\n linestyle=\":\",\n )\n ax3.plot(\n hod,\n winter_benefits,\n c=\"green\",\n linewidth=5,\n marker=\"$\\u25A0$\",\n markersize=13,\n linestyle=\":\",\n )\n ax1.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n ax2.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n ax3.axhline(y=0, color=\"gray\", linewidth=1, linestyle=\"--\")\n # Shade peak region\n ax1.axvspan(16, 21, alpha=0.2, color=\"grey\")\n\n leg1 = ax1.legend(legend_labels1, fontsize=15, loc=\"upper left\", frameon=False)\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\n text.set_color(line.get_color())\n leg2 = ax2.legend(legend_labels2, fontsize=15, loc=\"upper left\", frameon=False)\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\n text.set_color(line.get_color())\n leg3 = ax3.legend(legend_labels3, fontsize=15, loc=\"upper left\", frameon=False)\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\n text.set_color(line.get_color())\n\n ax1.set_ylabel(\"TRC Benefits ($/hr)\", size=16)\n ax2.set_xlabel(\"Hour of Day\", size=16)\n\n if max(summer_benefits + shoulder_benefits + winter_benefits) < 0:\n ymax = 0\n else:\n ymax = max(summer_benefits + shoulder_benefits + winter_benefits)\n if min(summer_benefits + shoulder_benefits + winter_benefits) > 0:\n ymin = 0\n else:\n ymin = min(summer_benefits + shoulder_benefits + winter_benefits)\n\n # Tick and label parameters\n ax1.set_ylim(ymin * 1.08, ymax * 1.08)\n ax1.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax2.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax3.set_yticks(\n np.arange(\n ymin * 1.08,\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax1.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax2.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax3.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\n ax1.set_xticks(np.arange(0, 24, step=4))\n ax1.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax1.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\n ax2.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax2.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax2.xaxis.set_minor_locator(AutoMinorLocator())\n ax3.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=7, width=2, labelsize=14\n )\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\n\n # Set plot title, size, and position\n ax1.set_title(\n \"Seasonal TRC Benefits by Hour ($)\", size=18, loc=\"left\"\n ).set_position([0, 1.03])\n\n # sum_hourly_plot\n fig2 = plt.figure(figsize=(12, 7), dpi=250)\n ax = fig2.gca()\n colors = [\n \"royalblue\",\n \"black\",\n \"pink\",\n \"firebrick\",\n \"gray\",\n \"darkviolet\",\n \"darkorange\",\n \"green\",\n \"saddlebrown\",\n ]\n legend_labels = []\n x = 1\n while x <= len(ACC_COMPONENTS_ELECTRICITY[1:]):\n if x == 1:\n ax.bar(\n hod,\n elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]],\n color=colors[x - 1],\n )\n legend_labels.append(\n re.findall(\n \".*Name: (.*),\",\n str(elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]]),\n )[0]\n )\n x += 1\n else:\n ax.bar(\n hod,\n elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]],\n bottom=elec_benefits_sum_by_hod.iloc[:, 2 : x + 1].sum(axis=1),\n color=colors[x - 1],\n )\n legend_labels.append(\n re.findall(\n \".*Name: (.*),\",\n str(elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]]),\n )[0]\n )\n x += 1\n\n # Set x and y limits based on min and max values\n ymax = elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).max()\n if elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).min() > 0:\n ymin = 0\n else:\n ymin = elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).min()\n\n ax.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax.set_ylim(ymin * 1.1, ymax * 1.08)\n\n # Set x and y axis labels\n ax.set_xlabel(\"Hour of Day\", size=17, labelpad=5)\n ax.set_ylabel(\"$ Avoided Costs\", size=17)\n\n # Set plot title, size, and position\n ax.set_title(\n \"Sum of Electric Avoided Costs by Component and Hour of Day\",\n size=17,\n loc=\"left\",\n )\n\n # Tick and lebel parameters\n ax.tick_params(bottom=True, top=False, left=True, right=False)\n ax.set_xticks(np.arange(0, 24, step=4))\n ax.set_yticks(\n np.arange(\n int(round(ymin * 1.1, 0)),\n ymax * 1.08,\n step=max(round(ymax - ymin, 2) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n\n # Minor ticks\n ax.xaxis.set_minor_locator(AutoMinorLocator())\n ax.yaxis.set_minor_locator(AutoMinorLocator())\n\n # Legend\n plt.legend(\n legend_labels,\n bbox_to_anchor=(1, 1),\n fontsize=12,\n loc=\"upper left\",\n frameon=False,\n )\n\n # avoided_cost_summary_plot\n fig3, (ax1, ax2, ax3) = plt.subplots(\n 3, 1, figsize=(6, 10), sharex=True, sharey=False\n )\n axs = [ax1, ax2, ax3]\n hod = elec_benefits_sum_by_hod.index\n legend_labels = [\"Total\", \"Summer\", \"Shoulder\", \"Winter\"]\n\n ax1.plot(\n hod,\n total_benefits,\n c=\"royalblue\",\n marker=\"$\\u25EF$\",\n markersize=10,\n linewidth=3,\n linestyle=\"-\",\n )\n ax1.plot(hod, summer_benefits, c=\"darkorchid\", linewidth=1, linestyle=\"--\")\n ax1.plot(hod, shoulder_benefits, c=\"olivedrab\", linewidth=1, linestyle=\":\")\n ax1.plot(hod, winter_benefits, c=\"teal\", linewidth=1, linestyle=\"-\")\n ax2.plot(\n hod,\n total_savings,\n c=\"firebrick\",\n marker=\"$\\u2206$\",\n markersize=10,\n linewidth=3,\n linestyle=\"-\",\n )\n ax2.plot(hod, summer_savings, c=\"darkorchid\", linewidth=1, linestyle=\"--\")\n ax2.plot(hod, shoulder_savings, c=\"olivedrab\", linewidth=1, linestyle=\":\")\n ax2.plot(hod, winter_savings, c=\"teal\", linewidth=1, linestyle=\"-\")\n ax3.plot(\n hod,\n total_av_csts_avg,\n c=\"green\",\n marker=\"$\\u25A0$\",\n markersize=10,\n linewidth=3,\n linestyle=\"-\",\n )\n ax3.plot(hod, summer_av_csts_avg, c=\"darkorchid\", linewidth=1, linestyle=\"--\")\n ax3.plot(hod, shoulder_av_csts_avg, c=\"olivedrab\", linewidth=1, linestyle=\":\")\n ax3.plot(hod, winter_av_csts_avg, c=\"teal\", linewidth=1, linestyle=\"-\")\n\n leg1 = ax1.legend(legend_labels, fontsize=11, loc=\"upper left\", frameon=False)\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\n text.set_color(line.get_color())\n leg2 = ax2.legend(legend_labels, fontsize=11, loc=\"upper left\", frameon=False)\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\n text.set_color(line.get_color())\n leg3 = ax3.legend(legend_labels, fontsize=11, loc=\"upper left\", frameon=False)\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\n text.set_color(line.get_color())\n\n ax3.set_xticks(np.arange(0, 24, step=4))\n ax3.set_xlabel(\"Hour of Day\", size=14, labelpad=5)\n ax3.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=12\n )\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\n\n ax1.set_ylabel(\"TRC Benefits ($)\", size=14)\n ax2.set_ylabel(\"Savings (MWh)\", size=14)\n ax3.set_ylabel(\"Av. Cost ($/MWh)\", size=14)\n\n if max(total_benefits + summer_benefits + shoulder_benefits + winter_benefits) < 0:\n ymax1 = 0\n else:\n ymax1 = max(\n total_benefits + summer_benefits + shoulder_benefits + winter_benefits\n )\n if min(total_benefits + summer_benefits + shoulder_benefits + winter_benefits) > 0:\n ymin1 = 0\n else:\n ymin1 = min(\n total_benefits + summer_benefits + shoulder_benefits + winter_benefits\n )\n if max(total_savings + summer_savings + shoulder_savings + winter_savings) < 0:\n ymax2 = 0\n else:\n ymax2 = max(total_savings + summer_savings + shoulder_savings + winter_savings)\n if min(total_savings + summer_savings + shoulder_savings + winter_savings) > 0:\n ymin2 = 0\n else:\n ymin2 = min(total_savings + summer_savings + shoulder_savings + winter_savings)\n if (\n max(\n total_av_csts_avg\n + summer_av_csts_avg\n + shoulder_av_csts_avg\n + winter_av_csts_avg\n )\n < 0\n ):\n ymax3 = 0\n else:\n ymax3 = max(\n total_av_csts_avg\n + summer_av_csts_avg\n + shoulder_av_csts_avg\n + winter_av_csts_avg\n )\n if (\n min(\n total_av_csts_avg\n + summer_av_csts_avg\n + shoulder_av_csts_avg\n + winter_av_csts_avg\n )\n > 0\n ):\n ymin3 = 0\n else:\n ymin3 = min(\n total_av_csts_avg\n + summer_av_csts_avg\n + shoulder_av_csts_avg\n + winter_av_csts_avg\n )\n\n # Tick and lebel parameters\n ax1.set_ylim(ymin1 * 1.08, ymax1 * 1.08)\n ax2.set_ylim(ymin2 * 1.08, ymax2 * 1.08)\n ax3.set_ylim(ymin3 * 1.08, ymax3 * 1.08)\n\n ax1.set_yticks(\n np.arange(\n ymin1 * 1.08,\n ymax1 * 1.08,\n step=max(round(ymax1 - ymin1, 3) / 5, int((round(ymax1 - ymin1, 0)) / 4)),\n )\n )\n ax2.set_yticks(\n np.arange(\n ymin2 * 1.08,\n ymax2 * 1.08,\n step=max(round(ymax2 - ymin2, 3) / 5, int((round(ymax2 - ymin2, 0)) / 4)),\n )\n )\n ax3.set_yticks(\n np.arange(\n ymin3 * 1.08,\n ymax3 * 1.08,\n step=max(round(ymax3 - ymin3, 3) / 5, int((round(ymax3 - ymin3, 0)) / 4)),\n )\n )\n\n ax1.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=12\n )\n ax2.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=12\n )\n ax3.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=12\n )\n\n # Shade peak region\n ax1.axvspan(16, 21, alpha=0.2, color=\"grey\")\n ax2.axvspan(16, 21, alpha=0.2, color=\"grey\")\n ax3.axvspan(16, 21, alpha=0.2, color=\"grey\")\n\n # Print key information\n plt.annotate(\n \"Electric Benefits = $\" + str(round(elec_benefits[\"total\"].sum(), 2)),\n xy=(350, 530),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Gas Benefits = $\" + str(round(gas_benefits, 2)),\n xy=(350, 505),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Total Benefits = $\"\n + str(round(elec_benefits[\"total\"].sum() + gas_benefits, 2)),\n xy=(350, 480),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"TRC Costs = $\" + str(trc_costs_record),\n xy=(350, 455),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"PAC Costs = $\" + str(pac_costs_record),\n xy=(350, 430),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"TRC = \" + str(trc_record),\n xy=(350, 405),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"PAC = \" + str(pac_record),\n xy=(350, 380),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Net Lifecycle Electric Savings = \" + str(lifecycle_net_mwh) + \" MWh\",\n xy=(350, 335),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Net Lifecycle Gas Savings = \" + str(lifecycle_net_therms) + \" Therms\",\n xy=(350, 310),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Net Lifecycle GHG Savings = \" + str(lifecycle_net_ghg) + \" Tons\",\n xy=(350, 285),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n str(round(100 * ((summer_peak_savings) / sum(total_savings)), 1))\n + \"% MWh savings during summer peak period\",\n xy=(350, 260),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n str(round(100 * ((summer_peak_benefits) / sum(total_benefits)), 1))\n + \"% Electric TRC benefits from summer peak period\",\n xy=(350, 235),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Electric Benefits per MWh = $\"\n + str(round(elec_benefits[\"total\"].sum() / lifecycle_net_mwh, 2)),\n xy=(350, 210),\n xycoords=\"axes points\",\n fontsize=18,\n )\n plt.annotate(\n \"Typical Avoided Cost per MWh = $\"\n + str(round(elec_benefits[\"av_csts_levelized\"].mean(), 2)),\n xy=(350, 145),\n xycoords=\"axes points\",\n fontsize=18,\n )\n\n # Set plot title, size, and position\n ax1.set_title(\n \"Savings and Avoided Cost Profiles\", size=16, loc=\"left\"\n ).set_position([0, 1.03])\n\n # marginal_ghg_savings_plot\n cmp = sns.diverging_palette(16, 260, l=35, n=25, as_cmap=True)\n\n fig4 = plt.figure(figsize=(8, 6), dpi=100)\n ax1 = fig4.gca()\n y_ticks = [\n 0,\n \"\",\n 2,\n \"\",\n 4,\n \"\",\n 6,\n \"\",\n 8,\n \"\",\n 10,\n \"\",\n 12,\n \"\",\n 14,\n \"\",\n 16,\n \"\",\n 18,\n \"\",\n 20,\n \"\",\n 22,\n ]\n hmp = sns.heatmap(ghgsav, cmap=cmp, ax=ax1, yticklabels=y_ticks, center=0.00)\n ax1.set_xlabel(\"Month\", size=15)\n ax1.set_ylabel(\"Hour of Day\", size=15)\n ax1.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=13\n )\n ax1.tick_params(\n which=\"major\",\n axis=\"y\",\n direction=\"out\",\n length=6,\n width=2,\n labelsize=13,\n rotation=0,\n )\n ax1.set_title(\"Electric GHG Savings by Month and Hour\", size=15, loc=\"left\", pad=8)\n cbar1 = hmp.collections[0].colorbar\n cbar1.ax.tick_params(labelsize=14)\n plt.annotate(\"Sum GHG\", xy=(370, 352), xycoords=\"axes points\", fontsize=12)\n plt.annotate(\"Savings (Tons)\", xy=(370, 336), xycoords=\"axes points\", fontsize=12)\n\n # month_hour_savings_benefits_plot\n fig5, (ax1, ax2) = plt.subplots(1, 2, figsize=(21, 10), dpi=200)\n y_ticks = [\n 0,\n \"\",\n 2,\n \"\",\n 4,\n \"\",\n 6,\n \"\",\n 8,\n \"\",\n 10,\n \"\",\n 12,\n \"\",\n 14,\n \"\",\n 16,\n \"\",\n 18,\n \"\",\n 20,\n \"\",\n 22,\n ]\n fleft = sns.heatmap(sav, cmap=cmp, ax=ax1, yticklabels=y_ticks, center=0.00)\n fright = sns.heatmap(avcsts, cmap=cmp, ax=ax2, yticklabels=y_ticks, center=0.00)\n ax1.set_xlabel(\"Month\", size=22)\n ax1.set_ylabel(\"Hour of Day\", size=22)\n ax2.set_xlabel(\"Month\", size=22)\n ax2.set_ylabel(\"Hour of Day\", size=22)\n ax1.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=18\n )\n ax1.tick_params(\n which=\"major\",\n axis=\"y\",\n direction=\"out\",\n length=6,\n width=2,\n labelsize=18,\n rotation=0,\n )\n ax2.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=18\n )\n ax2.tick_params(\n which=\"major\",\n axis=\"y\",\n direction=\"out\",\n length=6,\n width=2,\n labelsize=18,\n rotation=0,\n )\n ax1.set_title(\n \"MWh Savings by Month and Hour\", size=24, loc=\"left\", pad=15\n ).set_position([0, 1.1])\n ax2.set_title(\"$ Benefits by Month and Hour\", size=24, loc=\"left\", pad=15)\n fig4.tight_layout(pad=2.0)\n cbar1 = fleft.collections[0].colorbar\n cbar1.ax.tick_params(labelsize=18)\n cbar2 = fright.collections[0].colorbar\n cbar2.ax.tick_params(labelsize=18)\n plt.annotate(\"Sum MWh\", xy=(-200, 585), xycoords=\"axes points\", fontsize=20)\n plt.annotate(\"Savings\", xy=(-193, 560), xycoords=\"axes points\", fontsize=20)\n plt.annotate(\"Sum TRC\", xy=(435, 585), xycoords=\"axes points\", fontsize=20)\n plt.annotate(\"Benefits\", xy=(442, 560), xycoords=\"axes points\", fontsize=20)\n\n # savings_benefits_cumulative_sum_plot\n fig6 = plt.figure(figsize=(12, 7), dpi=250)\n ax1 = fig6.gca()\n ax1.plot(\n elec_benefits_hoy[\"hour_of_year\"],\n elec_benefits_hoy[\"hourly_savings\"],\n color=\"royalblue\",\n linewidth=3,\n )\n ax2 = ax1.twinx()\n ax2.plot(\n elec_benefits_hoy[\"hour_of_year\"],\n elec_benefits_hoy[\"total\"],\n color=\"firebrick\",\n linewidth=3,\n linestyle=\"--\",\n )\n ax2.axhline(y=0, color=\"gray\", linewidth=0.7, linestyle=\"--\")\n\n # Set x and y limits based on min and max values\n\n if (\n elec_benefits_hoy[\"hourly_savings\"].max() >= 0\n and elec_benefits_hoy[\"total\"].max() >= 0\n ):\n ymax1 = elec_benefits_hoy[\"hourly_savings\"].max()\n ymax2 = elec_benefits_hoy[\"total\"].max()\n elif (\n elec_benefits_hoy[\"hourly_savings\"].max() < 0\n and elec_benefits_hoy[\"total\"].max() < 0\n ):\n ymax1 = 0\n ymax2 = 0\n elif (\n elec_benefits_hoy[\"hourly_savings\"].max() < 0\n and elec_benefits_hoy[\"total\"].max() > 0\n ):\n ymax1 = (\n -1\n * elec_benefits_hoy[\"hourly_savings\"].min()\n * (\n elec_benefits_hoy[\"total\"].max()\n / (elec_benefits_hoy[\"total\"].max() - elec_benefits_hoy[\"total\"].min())\n )\n / (\n 1\n - elec_benefits_hoy[\"total\"].max()\n / (elec_benefits_hoy[\"total\"].max() - elec_benefits_hoy[\"total\"].min())\n )\n )\n ymax2 = elec_benefits_hoy[\"total\"].max()\n else:\n ymax1 = 0\n ymax2 = (\n -1\n * elec_benefits_hoy[\"total\"].min()\n * (\n elec_benefits_hoy[\"hourly_savings\"].max()\n / (\n elec_benefits_hoy[\"hourly_savings\"].max()\n - elec_benefits_hoy[\"hourly_savings\"].min()\n )\n )\n )\n\n if (\n elec_benefits_hoy[\"hourly_savings\"].min() <= 0\n and elec_benefits_hoy[\"total\"].min() <= 0\n ):\n ymin1 = elec_benefits_hoy[\"hourly_savings\"].min()\n ymin2 = elec_benefits_hoy[\"total\"].min()\n elif (\n elec_benefits_hoy[\"hourly_savings\"].min() > 0\n and elec_benefits_hoy[\"total\"].min() > 0\n ):\n ymin1 = 0\n ymin2 = 0\n elif (\n elec_benefits_hoy[\"hourly_savings\"].min() > 0\n and elec_benefits_hoy[\"total\"].min() < 0\n ):\n ymin1 = (\n -1\n * elec_benefits_hoy[\"hourly_savings\"].max()\n * (\n elec_benefits_hoy[\"total\"].min()\n / (elec_benefits_hoy[\"total\"].min() - elec_benefits_hoy[\"total\"].max())\n )\n / (\n 1\n - elec_benefits_hoy[\"total\"].min()\n / (elec_benefits_hoy[\"total\"].min() - elec_benefits_hoy[\"total\"].max())\n )\n )\n ymin2 = elec_benefits_hoy[\"total\"].min()\n else:\n ymin1 = 0\n ymin2 = (\n -1\n * elec_benefits_hoy[\"total\"].min()\n * (\n elec_benefits_hoy[\"hourly_savings\"].min()\n / (\n elec_benefits_hoy[\"hourly_savings\"].min()\n - elec_benefits_hoy[\"hourly_savings\"].min()\n )\n )\n )\n\n # Set x and y axis limits\n ax1.set_xlim(-340, 9000)\n ax1.set_ylim(ymin1 * 1.08, ymax1 * 1.08)\n ax2.set_ylim(ymin2 * 1.08, ymax2 * 1.08)\n\n # Set x and y axis labels\n ax1.set_xlabel(\"Hour of Year\", size=17, labelpad=5)\n ax1.set_ylabel(\"Net Lifecycle Savings (MWh)\", size=17)\n ax2.set_ylabel(\"$ TRC Benefits\", size=17, rotation=-90, labelpad=20)\n\n # Set plot title, size, and position\n ax1.set_title(\n \"Cumulative Savings and TRC Benefits by Hour of Year\",\n size=17,\n loc=\"left\",\n pad=8,\n )\n\n # Tick and lebel parameters\n ax1.set_xticks(np.arange(0, 8760, step=1000))\n ax1.set_yticks(\n np.arange(\n int(round(ymin1 * 1.1, 0)),\n ymax1 * 1.08,\n step=max(round(ymax1 - ymin1, 2) / 5, int((round(ymax1 - ymin1, 0)) / 4)),\n )\n )\n ax1.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax1.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n\n ax2.set_xticks(np.arange(0, 8760, step=1000))\n ax2.set_yticks(\n np.arange(\n int(round(ymin2 * 1.1, 0)),\n ymax2 * 1.08,\n step=max(round(ymax2 - ymin2, 2) / 5, int((round(ymax2 - ymin2, 0)) / 4)),\n )\n )\n ax2.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax2.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n\n # Minor ticks\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\n ax2.yaxis.set_minor_locator(AutoMinorLocator())\n\n # Legend\n ax1.legend(\n [\"Savings\"],\n fontsize=12,\n bbox_to_anchor=(0.02, 1),\n loc=\"upper left\",\n frameon=False,\n )\n ax2.legend(\n [\"TRC Beneftis\"],\n fontsize=12,\n bbox_to_anchor=(0.02, 0.95),\n loc=\"upper left\",\n frameon=False,\n )\n\n fig7 = plt.figure(figsize=(12, 7), dpi=250)\n ax = fig7.gca()\n colors1 = [\n \"black\",\n \"royalblue\",\n \"black\",\n \"pink\",\n \"firebrick\",\n \"gray\",\n \"darkviolet\",\n \"darkorange\",\n \"green\",\n \"saddlebrown\",\n ]\n legend_labels2 = []\n\n ax.plot(\n elec_benefits_hoy[\"hour_of_year\"],\n elec_benefits_hoy[ACC_COMPONENTS_ELECTRICITY[0]],\n color=colors1[0],\n linewidth=3,\n )\n legend_labels2.append(ACC_COMPONENTS_ELECTRICITY[0])\n x = 1\n while x <= len(ACC_COMPONENTS_ELECTRICITY) - 2:\n ax.plot(\n elec_benefits_hoy[\"hour_of_year\"],\n elec_benefits_hoy[ACC_COMPONENTS_ELECTRICITY[x]],\n color=colors1[x],\n )\n legend_labels2.append(ACC_COMPONENTS_ELECTRICITY[x])\n x += 1\n\n # Set x and y limits based on min and max values\n if max(elec_benefits_hoy.iloc[:, 2:x].max()) < 0:\n ymax = 0\n else:\n ymax = max(elec_benefits_hoy.iloc[:, 2:x].max())\n if min(elec_benefits_hoy.iloc[:, 2:x].min()) > 0:\n ymin = 0\n else:\n ymin = min(elec_benefits_hoy.iloc[:, 2:x].min())\n\n ax.set_xlim(-340, 9000)\n ax.set_ylim(ymin * 1.1, ymax * 1.08)\n\n # Set x and y axis labels\n ax.set_xlabel(\"Hour of Year\", size=17, labelpad=5)\n ax.set_ylabel(\"$ TRC Benefits\", size=17)\n\n # Set plot title, size, and position\n ax.set_title(\n \"Sum of Avoided Costs by Component and Hour of Day\", size=17, loc=\"left\"\n )\n\n # Tick and lebel parameters\n ax.set_xticks(np.arange(0, 8760, step=1000))\n ax.set_yticks(\n np.arange(\n int(round(ymin * 1.1, 0)),\n ymax * 1.08,\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\n )\n )\n ax.tick_params(\n which=\"major\", axis=\"x\", direction=\"out\", length=6, width=2, labelsize=14\n )\n ax.tick_params(\n which=\"major\", axis=\"y\", direction=\"out\", length=6, width=2, labelsize=14\n )\n\n # Minor ticks\n ax.xaxis.set_minor_locator(AutoMinorLocator())\n ax.yaxis.set_minor_locator(AutoMinorLocator())\n\n # Legend\n plt.legend(\n legend_labels2,\n bbox_to_anchor=(1, 1),\n fontsize=12,\n loc=\"upper left\",\n frameon=False,\n )","def get_safety_vars_plot(self):\n if 'safety_vars_stats' not in self.stats:\n raise ValueError('No safety vars statistics present in this evaluator.')\n\n safety_vars = self.stats['safety_vars_stats'][0].keys()\n n_plots = len(safety_vars)\n fig, axes = plt.subplots(n_plots, 1, figsize=(8, 6 * n_plots))\n\n for idx, var in enumerate(safety_vars):\n series = collections.defaultdict(list)\n for ep in self.stats['safety_vars_stats']:\n for stat in ep[var]:\n series[stat].append(ep[var][stat])\n ax = axes[idx]\n for stat in ['min', 'max']:\n ax.plot(np.squeeze(np.array(series[stat])), label=stat)\n x = range(len(series['mean']))\n\n mean = np.squeeze(np.array(series['mean']))\n std_dev = np.squeeze(np.array(series['std_dev']))\n ax.plot(x, mean, label='Value')\n ax.fill_between(\n range(len(series['mean'])), mean - std_dev, mean + std_dev, alpha=0.3)\n ax.set_title('Stats for {}'.format(var))\n ax.legend()\n ax.spines['top'].set_visible(False)\n\n ax.xaxis.set_ticks_position('bottom')\n ax.set_xlabel('Episode #')\n ax.set_ylabel('Magnitude')\n ax.plot()\n return fig","def plot_single(\n data_dict: dict,\n keys: str,\n x_ticks: List[str] = ['2015', '2016', '2017', '2018', '2019', '2020'],\n show_preprint: bool = False,\n title_text: str = '',\n figpath: str = 'comparison_plot.pdf',\n logscale=False,\n) -> None:\n\n sns.set_palette(sns.color_palette('colorblind', 10))\n plt.rcParams.update({'hatch.color': 'w'})\n plt.rcParams['figure.facecolor'] = 'white'\n plt.figure(figsize=(8, 5))\n\n arxiv, biorxiv, pubmed, medrxiv, chemrxiv, preprint = [], [], [], [], [], []\n\n for key in keys:\n try:\n arxiv.append(data_dict[key]['arxiv'])\n biorxiv.append(data_dict[key]['biorxiv'])\n medrxiv.append(data_dict[key]['medrxiv'])\n chemrxiv.append(data_dict[key]['chemrxiv'])\n pubmed.append(data_dict[key]['pubmed'])\n except KeyError:\n raise KeyError(\n f'Did not find all DBs for {key}, only found {data_dict[key].keys()}'\n )\n preprint.append(arxiv[-1] + biorxiv[-1] + medrxiv[-1] + chemrxiv[-1])\n\n ind = np.arange(len(arxiv[0])) # the x locations for the groups\n width = [0.75] * len(ind) # the width of the bars: can also be len(x) sequence\n fnc = np.log10 if logscale else np.copy\n\n plts = []\n legend_plts = []\n if show_preprint:\n bars = [pubmed, preprint]\n legend_platform = ['PubMed', 'Preprint']\n if logscale:\n sums = np.array(pubmed) + np.array(preprint)\n logsums = np.log10(sums)\n bars = [pubmed * logsums / sums, preprint * logsums / sums]\n\n else:\n bars = [pubmed, arxiv, biorxiv, chemrxiv, medrxiv]\n legend_platform = ['PubMed', 'ArXiv', 'BiorXiv', 'ChemRxiv', 'MedRxiv']\n if logscale:\n sums = (\n np.array(pubmed)\n + np.array(arxiv)\n + np.array(biorxiv)\n + np.array(chemrxiv)\n + np.array(medrxiv)\n )\n logsums = np.log10s(sums)\n bars = [\n pubmed * logsums / sums,\n arxiv * logsums / sums,\n biorxiv * logsums / sums,\n chemrxiv * logsums / sums,\n medrxiv * logsums / sums,\n ]\n for idx in range(len(keys)):\n bottom = 0\n\n for bidx, b in enumerate(bars):\n if idx == 0:\n p = plt.bar(\n ind,\n b[idx],\n width,\n linewidth=1,\n edgecolor='k',\n bottom=bottom,\n )\n else:\n p = plt.bar(\n ind,\n b[idx],\n width,\n color=next(iter(plts[bidx])).get_facecolor(),\n linewidth=1,\n edgecolor='k',\n bottom=bottom,\n )\n\n bottom += b[idx]\n plts.append(p)\n legend_plts.append(\n plt.bar(ind, np.zeros((len(ind),)), color='k', bottom=bottom)\n )\n\n plt.ylabel('Counts', size=17) if not logscale else plt.ylabel(\n 'Counts (log scale)', size=17\n )\n plt.xlabel('Years', size=17)\n plt.title(title_text, size=17)\n # Customize minor tick labels\n\n plt.xticks(ind, x_ticks, size=14)\n ymax = plt.gca().get_ylim()[1]\n if logscale:\n yticks = np.arange(1, ymax).astype(int)\n plt.yticks(yticks, np.power(10, yticks))\n\n plt.tick_params(axis='y', labelsize=17)\n\n plt.legend(\n legend_platform,\n prop={'size': 14},\n loc='upper left',\n title='Platform:',\n title_fontsize=17,\n ncol=1,\n )\n\n get_step_size = lambda x: round(x / 10, -math.floor(math.log10(x)) + 1)\n ymax = plt.gca().get_ylim()[1]\n\n for y_step in plt.yticks()[0]:\n plt.hlines(y_step, xmax=10, xmin=-1, color='black', linewidth=0.1)\n plt.xlim([-0.5, len(ind)])\n plt.ylim([0, ymax * 1.02])\n\n plt.tight_layout()\n plt.savefig(figpath)\n plt.show()","def frame_stats(df, file=\"./frame_stats.pdf\", fontsize=16):\n \n temp = df\n \n matplotlib.rcParams.update({'font.size': 12})\n fig, (ax1, ax2,ax3) = plt.subplots(3, figsize=(10,8), sharex=True)\n\n ax1.plot(temp.index, temp.noise, ':')\n ax1.plot(temp.index, [.1 for i in temp.index], '--',color='k')\n ax1.fill_between(temp.index, 0.01,0.045, facecolor='blue', alpha=0.1)\n ax1.set( ylabel='Noise')\n ax1.yaxis.label.set_size(fontsize)\n\n ax2.plot(temp.index, temp.pseudo_ent/10, ':')\n ax2.plot(temp.index, [0.01 for i in temp.index], '--',color='k')\n ax2.set(ylabel='Signal',)\n ax2.yaxis.label.set_size(fontsize)\n ax2.xaxis.label.set_size(fontsize)\n\n ax3.plot(temp.index, temp[\"2dhist95pre\"]/10, ':')\n ax3.plot(temp.index, temp[\"2dhist99pre\"]/10, ':')\n ax3.fill_between(temp.index,temp[\"2dhist95pre\"]/10,temp[\"2dhist99pre\"]/10, facecolor='blue', alpha=0.1)\n ax3.set( ylabel='Narrow-band')#xlabel='Frame index',\n ax3.yaxis.label.set_size(fontsize)\n ax3.set_ylim(0,1)\n\n plt.xlabel(\"Time (15 minute intervals)\",fontsize=fontsize)\n \n plt.xlim(0,100)\n plt.subplots_adjust(hspace=0.1)\n fig.savefig(file,bbox_inches='tight')","def summaryPlot(df):\n import datetime as dt\n import matplotlib.pyplot as plt\n import matplotlib as mpl\n import numpy as np\n import pandas as pd\n from numpy import array\n import matplotlib.patches as mpatches\n import seaborn as sns\n from matplotlib.pyplot import figure\n\n class color:\n # Allows for bolded and underlined text\n BOLD = \"\\033[1m\"\n UNDERLINE = \"\\033[4m\"\n END = \"\\033[0m\"\n\n # Reads df and fills empty values\n df.index = pd.to_datetime(df.date)\n df = df.drop(\"date\", axis=1)\n df_all = df.resample(\"1D\")\n df_all = df_all.fillna(method=\"ffill\")\n\n dataPoints = [\"pm25\", \"co\", \"so2\", \"pm10\", \"o3\", \"no2\", \"nox\", \"wd\", \"ws\"]\n\n i = 0\n sub = 1\n while i < 9:\n # Plots line and histogram plots for ecery polutant\n # in the correct location based on subplot\n plt.figure(1, figsize=(50, 50))\n plt.subplot(9, 2, sub)\n sub = sub + 1\n a = df_all[dataPoints[i]].plot.line(color=\"gold\")\n a.axes.get_xaxis().set_visible(False)\n a.yaxis.set_label_position(\"left\")\n plt.ylabel(dataPoints[i], fontsize=75, bbox=dict(facecolor=\"whitesmoke\"))\n # print(df['pm25'].max())\n\n plt.subplot(9, 2, sub)\n sub = sub + 1\n plt.hist(df_all[dataPoints[i]], bins=50, color=\"green\")\n i = i + 1\n i = 0\n while i < 9:\n # Calculates statistics\n nDf = df[dataPoints[i]]\n missing = nDf.isna().sum() + sum(n < 0 for n in nDf)\n minVal = nDf.min()\n maxVal = nDf.max()\n meanVal = nDf.mean()\n medianVal = nDf.median()\n percentile = nDf.quantile(0.95)\n print(\"---------------\")\n print(color.BOLD + color.UNDERLINE + dataPoints[i] + color.END)\n print(\"min = \" + str(0))\n print(\"max = \" + str(maxVal))\n print(\"missing = \" + str(missing))\n print(\"mean = \" + str(meanVal))\n print(\"median = \" + str(medianVal))\n print(\"95th percentile = \" + str(percentile))\n i = i + 1","def plot_PCA():\n X, languages = prepare_data_matrix()\n #print(X)\n eigenvectors, eigenvalues=power_iteration_two_components(X)\n explain = explained_variance_ratio(X, eigenvectors, eigenvalues)\n X=project_to_eigenvectors(X,eigenvectors)\n\n #print(X)\n plt.title('Explained variance: %.3f' % explain)\n plt.scatter(X[:,0], X[:,1])\n for i in range(len(X)):\n plt.text(X[i,0], X[i,1], languages[i][:3])\n plt.show()","def plot_results(infer_images, inference_predicted_class, inference_predictions, class_names=['plants', 'water']):\n plt.style.use(['dark_background', 'bmh'])\n rc('figure', figsize=(8, 8), max_open_warning=False)\n rc('axes', facecolor='none')\n plt.figure(figsize=(15, 15))\n\n for i, (infer_img, _) in enumerate(infer_images.take(10)):\n ax = plt.subplot(5, 2, i + 1)\n plt.imshow(infer_img.numpy()/255)\n\n # Find the predicted class from predictions\n m = \"Predicted: {}, {:.2f}%\".format(\n class_names[inference_predicted_class[i]], inference_predictions[i]*100)\n plt.title(m)\n plt.axis(\"off\")\n plt.show()","def plot_prec_value1(self):\n# self.query_dict={'code':code.value,'exchange':exchange.value,\\\n# 'structure':struct.value,'element':element.value,'properties':prop.value}\n# print ('POSTING', self.query_dict)\n# self.query_api(endpoint='evk')\n\n #layout_doc.children[4].children[0] = self.plot_pade_figure()\n\n\n self.query_dict={'code':code.value,'exchange':exchange.value,\\\n 'structure':struct.value,'element':element.value,'properties':prop.value}\n print ('POSTING', self.query_dict)\n self.query_api(endpoint='evk')\n\n layout_doc.children[4].children[0] = self.plot_pade_figure()","def plotPaperFigures(folder='results/'):\n #model Example\n _AiryDisc(amplitude=1e6, center_x=10.0, center_y=10.0, radius=0.5, focus=0.4, size=21)\n _CCDkernel(CCDx=10, CCDy=10, width_x=0.35, width_y=0.4, size=21)\n plotModelExample()\n\n #simulation figures\n theta1 = (2.e5, 9.9, 10.03, 0.41, 0.51, 10., 10., 0.291, 0.335)\n try:\n _plotDifferenceIndividualVsJoined(individuals='simulatedResults/RunI*.pkl',\n joined='results/simulated800nmJoint.pkl',\n title='Simulated Data: CCD PSF Recovery', truthx=theta1[7], truthy=theta1[8],\n requirementE=None, requirementFWHM=None, requirementR2=None)\n _plotModelResiduals(id='RunI1', folder='simulatedResults/', out='Residual1.pdf', individual=True)\n except:\n print 'No simulated data to plot...'\n\n #real data\n _plotDifferenceIndividualVsJoined(individuals=folder+'I800nm?.pkl', joined=folder+'J800nm.pkl', title='800nm',\n FWHMlims=(7.3, 11.8))\n\n _plotModelResiduals(id='I800nm2', folder=folder, out='ResidualData.pdf', individual=True)\n _plotModelResiduals(id='RunI2', folder='simulatedResults/', out='Residual2.pdf', individual=True)\n\n #_plotModelResiduals(id='G600nm0', folder=folder, out='ResidualG600.pdf', individual=True)\n #_plotModelResiduals(id='G700nm0', folder=folder, out='ResidualG700.pdf', individual=True)\n _plotModelResiduals(id='G800nm0', folder=folder, out='ResidualG800.pdf', individual=True)\n #_plotModelResiduals(id='G890nm0', folder=folder, out='ResidualG890.pdf', individual=True)\n\n #wavelength dependency\n plotLambdaDependency()\n\n #brighter fatter\n plotBrighterFatter()","def run_and_plot(self):\n self.raw_processing()\n self.data_averaging_and_cleaning()\n\n print(self.organized_names)\n print(self.organized_film)\n print(self.organized_plank)\n\n height = self.organized_film\n bars = tuple(self.organized_names.copy())\n y_pos = np.arange(len(bars))\n\n plt.bar(y_pos, height)\n plt.xticks(y_pos, bars)\n plt.xlabel('TH% in 100ul water/TH mixture')\n plt.ylabel('CFU/mL count')\n plt.title('Experiment 2.5 (Sucrose Concentration) 7 Aug 2018')\n\n plt.show()\n\n height2 = self.organized_plank\n\n plt.bar(y_pos, height2)\n plt.xticks(y_pos, bars)\n plt.xlabel('TH% in 100ul water/TH mixture')\n plt.ylabel('Proportion of Biofilm CFUs to Planktonic CFUs')\n plt.title('Experiment 2.5 (Sucrose Concentration) 7 Aug 2018')\n\n plt.show()","def _plotModelResiduals(id='simulated800nmJoint1', folder='results/', out='Residual.pdf', individual=False):\n #data\n if individual:\n data = pf.getdata(folder+id+'small.fits')\n data[data < 1] = 1.\n data = np.log10(data)\n else:\n data = pf.getdata(folder+id+'datafit.fits')\n data[data < 1] = 1.\n data = np.log10(data)\n #model\n model = pf.getdata(folder+id+'model.fits ')\n model[model < 1] = 1.\n model = np.log10(model)\n #residual\n residual = pf.getdata(folder+id+'residual.fits')\n #squared residual\n residualSQ = pf.getdata(folder+id+'residualSQ.fits')\n\n max = np.max((data.max(), model.max()))\n\n #figure\n fig = plt.figure(figsize=(12, 12))\n ax1 = fig.add_subplot(221)\n ax2 = fig.add_subplot(222)\n ax3 = fig.add_subplot(223)\n ax4 = fig.add_subplot(224)\n ax = [ax1, ax2, ax3, ax4]\n fig.subplots_adjust(hspace=0.05, wspace=0.3, top=0.95, bottom=0.02, left=0.02, right=0.9)\n ax1.set_title('Data')\n ax2.set_title('Model')\n ax3.set_title('Residual')\n ax4.set_title('$L^{2}$ Residual')\n\n im1 = ax1.imshow(data, interpolation='none', vmax=max, origin='lower', vmin=0.1)\n im2 = ax2.imshow(model, interpolation='none', vmax=max, origin='lower', vmin=0.1)\n im3 = ax3.imshow(residual, interpolation='none', origin='lower', vmin=-100, vmax=100)\n im4 = ax4.imshow(residualSQ, interpolation='none', origin='lower', vmin=0., vmax=10)\n\n divider = make_axes_locatable(ax1)\n cax1 = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n divider = make_axes_locatable(ax2)\n cax2 = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n divider = make_axes_locatable(ax3)\n cax3 = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n divider = make_axes_locatable(ax4)\n cax4 = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n cbar1 = plt.colorbar(im1, cax=cax1)\n cbar1.set_label(r'$\\log_{10}(D_{i, j} \\quad [e^{-}]$)')\n cbar2 = plt.colorbar(im2, cax=cax2)\n cbar2.set_label(r'$\\log_{10}(M_{i, j} \\quad [e^{-}]$)')\n cbar3 = plt.colorbar(im3, cax=cax3)\n cbar3.set_label(r'$M_{i, j} - D_{i, j} \\quad [e^{-}]$')\n cbar4 = plt.colorbar(im4, cax=cax4)\n cbar4.set_label(r'$\\frac{(M_{i, j} - D_{i, j})^{2}}{\\sigma_{CCD}^{2}}$')\n\n for tmp in ax:\n plt.sca(tmp)\n plt.xticks(visible=False)\n plt.yticks(visible=False)\n\n plt.savefig(out)\n plt.close()","def PlotEnsemblePredictions(self,ens, sampling_freq=10, fontSizeLabels=24, fontSizeLegend=20,rescale=1.,pow10first=False):\n pylab.figure()\n\tpylab.axes([0.15,0.35,0.95-0.15,0.95-0.35])\n\tdata_experiments = self.data.experiments\n for independentValues in data_experiments:\n Xdata = self.data.X[independentValues]\n Ydata = self.data.Y[independentValues]\n\t Xtheory = scipy.logspace(scipy.log10(min(Xdata)),scipy.log10(max(Xdata)),num=100)\n\t pointType = self.data.pointType[independentValues]\n\t errorBar = self.data.errorBar[independentValues]\n\t mean_theory = scipy.zeros(len(Xtheory))\n\t std_theory = scipy.zeros(len(Xtheory))\n for i in range(0, num_to_count):\n\t\t ens_theory = self.theory.Y(Xtheory,ens[i*sampling_freq],independentValues)\n\t\t mean_theory += ens_theory\n\t\t std_theory += (ens_theory)**2\n\t mean_theory = mean_theory/(1.0*num_to_count)\n\t std_theory = scipy.sqrt((std_theory-num_to_count*mean_theory**2)/(num_to_count-1.))\n\t pylab.loglog(Xdata,Ydata,pointType[1])\n\t lb = self.getLabel(self.theory.independentNames,independentValues,pow10first=pow10first)\n\t pylab.errorbar(Xdata,Ydata, yerr=errorBar, fmt=pointType,label=lb)\n\t pylab.loglog(Xtheory,mean_theory,pointType[0])\n\t axis_dep=self.getAxis(Xdata,Ydata)\n\t #upper_bound = mean_theory+std_theory\n\t #lower_bound = mean_theory-std_theory\n\t upper_bound = scipy.exp(scipy.log(mean_theory) + scipy.log(1.+std_theory/mean_theory)*rescale)\n\t lower_bound = scipy.exp(scipy.log(mean_theory)+scipy.log(1.-std_theory/mean_theory)*rescale)\n\t for i in range(0, len(lower_bound)):\n\t\t if lower_bound[i]<=0:\n\t\t\t lower_bound[i]=10.**(-16)\n\t pylab.fill_between(Xtheory,lower_bound,y2=upper_bound,color=pointType[0],alpha=0.2)\n\n\t for i, Ax in enumerate(axis_dep):\n\t\t ax0[i] =i%2 and max(ax0[i],Ax) or min(ax0[i],Ax)\n\tpylab.axis(tuple(ax0))\n\tpylab.legend(loc=(-0.15,-0.52),ncol=3)","def setup_plot(self):\n\n # Get all the healthy, immune, infected, and dead people seperately \n healthy_x = self.putil.population.get_all_healthy()[:, index.x_axis]\n healthy_y = self.putil.population.get_all_healthy()[:, index.y_axis]\n infected_x = self.putil.population.get_all_infected()[:, index.x_axis]\n infected_y = self.putil.population.get_all_infected()[:, index.y_axis]\n immune_x = self.putil.population.get_all_recovered()[:, index.x_axis]\n immune_y = self.putil.population.get_all_recovered()[:, index.y_axis]\n dead_x = self.putil.population.get_all_dead()[:, index.x_axis]\n dead_y = self.putil.population.get_all_dead()[:, index.y_axis]\n total_infected = self.putil.size - len(healthy_x)\n total_hospitalized = len(self.putil.persons[self.putil.persons[:,index.hospitalized] == 3])\n \n # Current healthcare status\n self.healthcare_status = \"Normal\"\n \n # Scatter plots to plot people\n self.scat = self.ax.scatter(healthy_x,\n healthy_y, vmin=0, vmax=1,\n cmap=\"jet\", c=\"lightsteelblue\", s=10)\n self.scat2 = self.ax.scatter(infected_x,\n infected_y, vmin=0, vmax=1,\n cmap=\"jet\", c=\"indianred\", s=10)\n self.scat3 = self.ax.scatter(immune_x,\n immune_y, vmin=0, vmax=1,\n cmap=\"jet\", c=\"mediumseagreen\", s=10)\n self.scat4 = self.ax.scatter(dead_x,\n dead_y, vmin=0, vmax=1,\n cmap=\"jet\", c=\"indigo\", s=10)\n # Lists for line graph\n self.infected = []\n self.infected_total = []\n self.deaths = []\n self.frames = []\n self.immunes = []\n self.infected.append(len(infected_x))\n self.deaths.append(len(dead_x))\n self.infected_total.append(self.putil.size - len(healthy_x))\n self.immunes.append(len(immune_x))\n self.frames.append(0)\n\n # Line graph plotting number\n self.total_infected, = self.ax1.plot(self.frames, self.infected_total)\n self.currently_infected, = self.ax1.plot(self.frames, self.infected, c=\"indianred\", label='Currently Infected')\n self.total_deaths, = self.ax1.plot(self.frames, self.deaths, c=\"indigo\", label='Total Dead')\n self.total_immune, = self.ax1.plot(self.frames, self.immunes, c=\"mediumseagreen\", label='Total Immune')\n\n # Code below prints statistics \n if(self.putil.enforce_social_distance_at > 0):\n self.ax1.plot([self.putil.enforce_social_distance_at]*2, [0,self.putil.size],c=\"gold\", label=\"Social Distancing\")\n self.social_distancing_info = (\"At frame \" + str(self.putil.enforce_social_distance_at))\n self.social_distancing_num = str(int(self.putil.social_distance_per * self.putil.size)) + \" or \" + str(self.putil.social_distance_per*100)+\"%\"\n else:\n self.social_distancing_info = (\"Disabled\")\n self.social_distancing_num = \"0 or 0%\"\n\n if(self.putil.enforce_mask_wearing_at > 0):\n self.ax1.plot([self.putil.enforce_mask_wearing_at]*2, [0,self.putil.size],c=\"hotpink\", label=\"Mask Mandate\")\n self.mask_wearing_info = \"At frame \" + str(self.putil.enforce_mask_wearing_at) \n else:\n self.mask_wearing_info = \"Disabled\"\n\n self.ax1.tick_params(axis=\"y\",direction=\"in\", pad=3)\n self.ax1.plot([0,1000],[self.putil.virus.total_healthcare_capacity]*2, c=\"silver\")\n self.ax1.get_xaxis().set_visible(False)\n self.ax1.legend(prop={'size': 8},loc='upper right')\n self.ax2.text(0,1,\"Statistics\", fontsize='large' , fontweight='bold')\n self.ax2.text(0,-0.5, \"Frame:\\nCurrently Infected:\\nHealthy People:\\nImmune People:\\nTotal Deaths:\\nHealthcare Conditions:\")\n self.ax2.text(0.54,-0.5, \"Population:\\nMasks Wearing:\\nSocial Distancing:\\nPeople Distancing:\\nTotal Infected:\\n\")\n self.ax.text(0,1.06, \"Simulation\", fontsize='xx-large' , fontweight='bold')\n self.text = self.ax2.text(0.33, -0.5, \"%i \\n%i \\n%s \\n%s \\n%s \\n%s\" %(0,len(infected_x),str(len(healthy_x)) + \" or 0%\", str(len(immune_x)) + \" or 0%\",str(len(dead_x)) + \" or 0%\",self.healthcare_status))\n self.text2 = self.ax2.text(0.81,-0.5,\"%d \\n%s \\n%s \\n%s \\n%s\\n\" % (self.putil.size, self.mask_wearing_info, self.social_distancing_info, self.social_distancing_num , total_infected))\n\n return self.scat, self.scat2, self.scat3, self.scat4, self.currently_infected, self.total_infected,","def vaccination_population_plot(r_zero,\n data_file_name='vaccination_data_solution2020-06-16.pkl',\n fig_file_name='model_solution.pdf'):\n df = pd.read_pickle(data_file_name)\n fig, ((ax_s, ax_e, ax_cl), (ax_i_s, ax_i_a, ax_r), (ax_d, ax_v, ax_t)) = plt.subplots(nrows=3, ncols=3)\n #\n n_whole = 1.0 # without rescaling population\n t = df['time']\n ax_s = plt.subplot(331)\n ax_s.plot(t, n_whole * df['s'], label=\"s\")\n ax_s.legend(loc=0)\n #\n ax_e = plt.subplot(332)\n ax_e.plot(t, n_whole * df['e'], label=\"e\")\n ax_e.legend(loc=0)\n #\n cl = n_whole * (df['s'] + df['e'] + df['i_s'] +\n df['i_a'] + df['r'] + df['d'] +\n df['v'] + df['treat'])\n #\n ax_cl = plt.subplot(333)\n ax_cl.plot(t, cl, label=\"cl\")\n ax_cl.legend(loc=0)\n #\n ax_i_s = plt.subplot(334)\n ax_i_s.plot(t, n_whole * df['i_s'], label=\"i_s\")\n ax_i_s.legend(loc=0)\n #\n ax_i_a = plt.subplot(335)\n ax_i_a.plot(t, n_whole * df['i_a'], label=\"i_a\")\n ax_i_a.legend(loc=0)\n #\n ax_r = plt.subplot(336)\n ax_r.plot(t, n_whole * df['r'], label=\"r\")\n ax_r.legend(loc=0)\n #\n ax_d = plt.subplot(337)\n ax_d.plot(t, n_whole * df['d'], label=\"d\")\n ax_d.legend(loc=0)\n #\n ax_v = plt.subplot(338)\n ax_v.plot(t, n_whole * df['v'], label=\"v\")\n ax_v.legend(loc=0)\n #\n ax_t = plt.subplot(339)\n ax_t.plot(t, n_whole * df['treat'], label=\"treat\")\n ax_t.legend(loc=0)\n #\n plt.tight_layout()\n fig.suptitle(\"R0: \" + str(r_zero))\n plt.savefig(fig_file_name)\n plt.show()\n return","def plot_for_pop(exp,correctp,data_crop,IV,DV,bins,xticklabels,xlabel,ylabel,robust=0):\n \n\n \n if not os.path.exists('plots'):\n os.makedirs('plots')\n \n if exp == 'POP1':\n logLossmedian=1.386294\n colors = [\"orangered\", \"silver\"]\n else:\n logLossmedian=1.098612\n colors = [\"steelblue\", \"silver\"]\n \n customPalette = sns.set_palette(sns.color_palette(colors))\n plt.rc('font', size=15) \n \n if IV=='Final.balance':\n correctp.loc[correctp['logLoss.streak'] == 0]\n if IV=='logLoss.streak':\n correctp.loc[correctp['Final.balance'] == 0]\n if IV == 'logResult':\n correctp.loc[correctp['Final.balance'] == 0]\n\n # Predicted\n IV_p=correctp.groupby(['group',IV]).mean().loc[:,'predicted']\n IV_p=IV_p.unstack(level=0)\n IV_p=pd.DataFrame(IV_p)\n if exp == 'POP1':\n #IV_p.reset_index(inplace=True)\n IV_p=IV_p.loc[:,['Voucher predicted','Cash predicted']]\n print(IV_p)\n # Raw Data\n # Create arbitrary bins and check N in each\n data_crop.loc[:,'IV.binned']=pd.cut(data_crop.loc[:,IV], \n bins=bins)\n IV_nbins=pd.cut(data_crop.loc[:,IV], \n bins=bins).value_counts()\n IV_data=data_crop.groupby(['Participant','IV.binned','group']).mean().loc[:,DV]\n IV_data=pd.DataFrame(IV_data)\n\n IV_data = IV_data.stack(level=0).reset_index(level=0) \\\n .reset_index().drop('level_2',axis=1) \\\n .rename(columns={0:'DV'})\n print(IV_nbins)\n IV_nbins.to_csv('plots/'+IV+'binN.csv',header=False )\n # Build figure\n fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,4),sharey=True)\n #boxplot ax1\n if exp=='POP1':\n sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',\n ax=ax1,palette=customPalette,linewidth=1,fliersize=5, hue_order=['Voucher data','Cash data'])\n else:\n sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',\n ax=ax1,palette=customPalette,linewidth=1,fliersize=5)\n if IV == 'Trial.type.binary':\n IV_p.plot.bar(ax=ax2)\n ax1.xaxis.set_ticklabels(['Win','Loss'])\n ax2.xaxis.set_ticklabels(['Win','Loss'],rotation=0)\n else:\n IV_p.plot(ax=ax2)\n ax1.set_ylabel(ylabel)\n ax2.set_ylabel(ylabel)\n ax1.set_xlabel(xlabel)\n ax2.set_xlabel(xlabel)\n fig.legend(title=None,loc=1,frameon=False)\n ax1.legend().set_visible(False)\n ax2.legend().set_visible(False)\n if DV == 'logISI':\n ax1.set_ylim(bottom=5,top=9.5)\n # Change yaxis labels for boxplot - use minor ticks to define edges and turn off major\n xbins=[]\n for i in np.arange(-0.5, 100,1):\n xbins.append(i)\n if len(xbins)==len(xticklabels):\n break\n if IV != 'Trial.type.binary':\n ax1.xaxis.set_ticks(xbins,minor=True)\n ax1.xaxis.set_ticklabels(xticklabels,minor=True)\n ax1.xaxis.set_ticks([])\n ax1.xaxis.set_tick_params(which='minor',length=4)\n sns.despine(top=True,right=True)\n plt.tight_layout()\n if robust==1:\n fig.savefig(fname='plots/robust_'+IV +'.png',format='png',transparent=True)\n fig.savefig(fname='plots/robust_'+IV + '.eps',format='eps',transparent=True)\n else:\n fig.savefig(fname='plots/'+IV +'.png',format='png',transparent=True)\n fig.savefig(fname='plots/'+IV + '.eps',format='eps',transparent=True)","def plot_ave_profile(f,y):\n favex = np.mean(f, axis=1) # Horizontal average\n\n # Plot\n fig, ax = plt.subplots(1,1, figsize=(15,9))\n ax.plot(favex, y)\n\n # Label\n ax.set_xlabel(r'$\\overline{T}$')\n ax.set_ylabel(r'$y$')\n fig.tight_layout()\n\n # Save\n fig.savefig('Tave.pdf')\n \n plt.show()","def p_plot(data,pv_index=0,alpha=0.05):\n ####if it's a pd.dataframe, rename to col header\n if isinstance(data, pd.DataFrame):\n if isinstance(pv_index, int):\n pv_index = data.columns.get_values()[pv_index]\n data =data.rename(columns ={pv_index: \"p_value\"})\n if not (np.issubdtype(data['p_value'].dtypes, np.number)):\n raise TypeError(\"Please ensure you have specified the column index of numeric p-values.\")\n ###or make a vector a pd.dataframe\n else:\n data = pd.DataFrame({\"p_value\": data})\n \n if (data[\"p_value\"].max()> 1) or (data[\"p_value\"].max()< 0):\n raise ProbabilityError(\"One or more p-values is not between 0 and 1!\")\n \n m = len(data['p_value'])\n\n data = data.sort_values('p_value',ascending=True)\n data['rank'] = np.arange(1,len(data['p_value'])+1)\n data['critical_value'] = data['rank']*alpha/m\n\n fig = plt.clf()\n plt.scatter(data['rank'],data['p_value'],color='black')\n plt.axhline(y=alpha,label='Bonferroni')\n plt.plot(data['rank'],data['critical_value'],label='BH',color='red')\n plt.legend()\n plt.title(\"Bonferroni vs BH\")\n plt.xlabel(\"Rank\")\n plt.ylabel(\"p(k)\")\n return fig","def main(logy):\n filep = sys.stdin\n dataf = pd.read_csv(filep, sep=\" \", index_col=0, header=None)\n dataf.plot(logy=logy)\n print(dataf)\n plt.savefig(\"scaling.pdf\")","def show_results(self):\n\n N = split_list(self.N)\n # create subplot\n fig = make_subplots(rows=1,cols=2,\n subplot_titles=('Fish population', 'Harvested fish'),\n specs=[[{'type': 'xy'}, {'type': 'pie'}]])\n #Add population line graph\n fig.add_trace(go.Scatter(y=N['odds'], x=np.linspace(1, 11, 6), name='odd year population',\n hovertemplate =\n 'Year: %{x}'+ '<br>Pop: %{y}'),\n row=1, col=1)\n fig.add_trace(go.Scatter(y=N['evens'], x=np.linspace(2, 12, 6), name='even year population',\n hovertemplate =\n 'Year: %{x}'+ '<br>Pop: %{y}'),\n row=1, col=1)\n fig.update_xaxes(title_text=\"year\", row=1, col=1)\n fig.update_yaxes(title_text=\"population\", row=1, col=1)\n\n # cannot use 'paper' as yref due to bug in sublplot.\n fig.add_shape(type='line',\n xref='x', yref='y',\n x0=2.5, y0=-10, x1=2.5, y1=1000,\n line=dict(color='Black', width=3),\n row=1, col=1)\n\n # create pie chart\n colors = ['#636EFA', '#EF553B'] \n labels = ['total odd year harvest', 'total even year harvest']\n M = split_list(self.harvest_record)\n values = [sum(M['odds']), sum(M['evens'])]\n fig.add_trace(go.Pie(labels=labels, values=values, hoverinfo='label', textinfo='value', marker=dict(colors=colors)), \n row=1, col=2)\n\n # add title\n fig.update_layout(title_text='Results') \n fig.write_html(\"fish_trap_simulation.html\")\n\n \n return fig","def plot_spatial_res(data_obj, test_num):\n\n #---------------------------------------------------------------------------------------------#\n # Go through each file [image] and plot the spatial resolution ROI images and MTF data graphs #\n #---------------------------------------------------------------------------------------------#\n for i in range(len(data_obj.files)):\n\n fig = new_pdf_page(data_obj.pdf_obj) #Create a new pdf page\n plt.axis('off')\n\n # Create and plot the (centered) title of the page - that also states the MTF20 values\n str1 = 'Test ' + str(test_num) + ': Spatial Resolution \\n' + \\\n 'MTF20x = ' + \"{0:.2f}\".format(data_obj.MTF_data.MTF20_x) + \\\n '\\n MTF20y = ' + \"{0:.2f}\".format(data_obj.MTF_data.MTF20_y)\n plt.suptitle(str1)\n\n #-----------------------------------------------#\n # Plot the main image in the middle of the page #\n #-----------------------------------------------#\n ax1 = fig.add_subplot(312)\n plt.imshow(data_obj.img_data[i].lead_foil_ROI.img) #Display the ROI image of the lead foil\n\n # Display the title of the ROI image - the orientation\n plt.title('Orientation ' + str(data_obj.img_data[i].orientation))\n plt.xticks([]) # labels\n plt.yticks([])\n ax = plt.gca()\n ax.xaxis.set_ticks_position('none') # tick markers\n ax.yaxis.set_ticks_position('none')\n\n if data_obj.img_data[i].orientation % 2 == 1:\n plt.xlabel('x axis')\n plt.ylabel('y axis')\n else:\n plt.xlabel('y axis')\n plt.ylabel('x axis')\n\n #-----------------------------#\n # Display the Horizontal plot #\n #-----------------------------#\n ax2 = fig.add_subplot(325)\n plt.ylabel('MTFx')\n plt.xlabel('Spatial frequency (cycles/mm)')\n plt.plot(data_obj.img_data[i].MTF_obj.MTF_x_f, data_obj.img_data[i].MTF_obj.MTF_x)\n text = 'MTF20x = ' + \"{0:.2f}\".format(data_obj.img_data[i].MTF_obj.MTF20_x)\n\n ax2.annotate(text,\n xy=(data_obj.img_data[i].MTF_obj.MTF20_x, 0.2),\n xytext=(np.max(data_obj.img_data[i].MTF_obj.MTF_x_f), 0.7),\n arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth=4),\n horizontalalignment='right')\n\n #---------------#\n # Vertical plot #\n #---------------#\n ax3 = fig.add_subplot(326)\n plt.ylabel('MTFy')\n plt.xlabel('Spatial frequency (cycles/mm)')\n plt.plot(data_obj.img_data[i].MTF_obj.MTF_y_f, data_obj.img_data[i].MTF_obj.MTF_y)\n text = 'MTF20y = ' + \"{0:.2f}\".format(data_obj.img_data[i].MTF_obj.MTF20_y)\n\n ax3.annotate(text,\n xy=(data_obj.img_data[i].MTF_obj.MTF20_y, 0.2),\n xytext=(np.max(data_obj.img_data[i].MTF_obj.MTF_y_f), 0.7),\n arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth=4),\n horizontalalignment='right')","def plot(self, dtables, figs, **kwargs):\n self.safe_update(**kwargs)\n sumtable = dtables['biasoscorr_stats']\n figs.plot_stat_color('mean-s', sumtable['s_correl_mean'].reshape(9, 16))\n figs.plot_stat_color('mean-p', sumtable['p_correl_mean'].reshape(9, 16))","def dofigure5():\n required = {'Plastocyanin (Phormidium)':[61,92],\n 'Plastocyanin (Anabaena variabilis)':[62,92],\n 'Bovine Beta-Lactoglobulin':[64,114]}\n \n for p in DB.getRecs():\n prot = DB[p]['name']\n if prot in required:\n Eh = data=DB[p]['1H NMR']\n En = data=DB[p]['15N NMR']\n for d in Eh.datasets:\n hdata = Eh.getDataset(d)\n num = int(t.getResidueFields(hdata)['res_num'])\n #print d, type(num), required[prot]\n if num in required[prot]:\n print num\n ndata = En.getDataset(d)\n Eh.plotDatasets(d, filename=d+'.png')\n return","def analyse():\n health = db.execute(\"SELECT * FROM health WHERE user_ID = :id\", id=session[\"user_id\"])\n phone = db.execute(\"SELECT * FROM usage WHERE user_ID = :id\", id=session[\"user_id\"])\n hdata, pdata = []\n # get relevant values for health data then put values into two array\n for i in range(3):\n health1 = [health[i][h] for h in health[i]]\n hdata.append(health1[2:6])\n hdata = np.array(hdata)\n hdata[[0, 2]] = hdata[[2, 0]]\n print(np.mean(hdata, axis = 0))\n print(np.std(hdata, axis = 0))\n print(np.median(hdata, axis = 0))\n\n # get relevant values for phone data then put values into 2D array\n for i in range(3):\n phone1 = [phone[i][h] for h in phone[i]]\n pdata.append(phone1[2:7] + phone1[9:14])\n pdata = np.array(pdata)\n red = pdata[...,2]\n dep = []\n print(red)\n for i in hdata[...,0]:\n dep.append(int(i))\n dep = np.array(dep)\n print(dep)\n # pearson\n print (np.corrcoef(red, dep ))\n\n\n\n return render_template(\"stats.html\",)","def show_stats(self):\n if len(self.keypoints) == 0:\n logger.warning(\"No keypoints yet: running process before display\")\n self.process()\n import pylab\n f = pylab.figure()\n ax = f.add_subplot(1, 1, 1)\n ax.plot(self.keypoints.sigma, self.keypoints.I, '.r')\n ax.set_xlabel(\"Sigma\")\n ax.set_ylabel(\"Intensity\")\n ax.set_title(\"Peak repartition\")\n f.show()","def plot_precision_figure(self):\n\n data_analysis = DatabaseData(dataframe=self.plot_data)\n prop_data, energy_data, M, C, pred_energy, pred_property = \\\n data_analysis.create_precision_bokeh_compat(self.prop_data, self.energy_data, properties=self.properties)\n p = figure(plot_height=400, plot_width=400,tools=\"pan,wheel_zoom,box_zoom,reset,previewsave\",\\\n x_axis_type=\"log\", y_axis_type=\"log\", x_axis_label='Energy Convergence (meV/atom)', title='Slope M is {0}'.format(str(M)) )\n p.line(pred_energy, pred_property, color='red')\n p.circle(self.energy_data, self.prop_data, color='blue',size=5, line_alpha=0)\n #p.multi_line(xs_err, ys_err, color='black')\n if self.properties == 'B':\n p.yaxis.axis_label = 'Bulk Modulus B (%)'\n elif self.properties == 'dB':\n p.yaxis.axis_label = 'Bulk Modulus Pressure Derivative (%)'\n elif self.properties == 'Multiple':\n p.yaxis.axis_label = \"V0, B, B' (%)\"\n elif self.properties == 'V0':\n p.yaxis.axis_label = 'Volume (%)'\n\n return p","def gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total):\n with PdfPages(name) as pdf:\n total_xuf = []\n total_yuf = []\n total_xf = []\n total_yf = []\n for entry in uf_dict:\n print 'Making plot for ' + entry\n xuf, yuf = zip(*uf_dict[entry])\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ax1.scatter(xuf, yuf, c='#ad4851', marker='o', label='initial structures')\n try:\n xf, yf = zip(*f_dict[entry])\n ax1.scatter(xf, yf, c='orange', marker='x', label='selected structures')\n except ValueError:\n xf = []\n yf = []\n plt.legend(loc='upper right')\n plt.title(entry, fontsize=30)\n plt.xlim(min_x, max_x)\n plt.ylim(min_y, max_y)\n plt.xlabel(axes[0], fontsize=20)\n plt.ylabel(axes[1], fontsize=20)\n pdf.savefig(fig)\n plt.close()\n\n if total:\n total_xuf.extend(xuf)\n total_yuf.extend(yuf)\n total_xf.extend(xf)\n total_yf.extend(yf)\n\n if histogram:\n bins = np.linspace(min_y, max_y, num=10)\n plt.hist(yuf, bins, alpha=0.5, color='b', label='initial structures')\n try:\n plt.hist(yf, bins, alpha=0.5, color='orange', label='selected structures')\n except ValueError:\n pass\n plt.legend(loc='upper right')\n plt.title(entry, fontsize=30)\n plt.xlabel(axes[1], fontsize=20)\n plt.ylabel('Frequency', fontsize=20)\n pdf.savefig()\n plt.close()\n\n if total:\n print 'Making composite plot'\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ax1.scatter(total_xuf, total_yuf, c='#ad4851', marker='o', label='initial structures')\n ax1.scatter(total_xf, total_yf, c='orange', marker='x', label='selected structures')\n plt.legend(loc='upper right')\n plt.title('Composite Plot', fontsize=30)\n plt.xlim(min_x, max_x)\n plt.ylim(min_y, max_y)\n plt.xlabel(axes[0], fontsize=20)\n plt.ylabel(axes[1], fontsize=20)\n pdf.savefig(fig)\n plt.close()","def plot(self,experiment_id = None,image_path = None):\n c = self.cursor()\n where_experiment_id = ''\n if not experiment_id is None:\n if isinstance(experiment_id, list):\n exp_ids = ','.join([ str(f) for f in experiment_id ])\n where_experiment_id = ' WHERE id in ({})'.format(exp_ids)\n else:\n where_experiment_id = ' WHERE id = {}'.format(experiment_id)\n c.execute(\n 'SELECT exp_id,exp_name,exp_description,var_name FROM experiment'\n + where_experiment_id\n )\n experiments = c.fetchall()\n exp_count = len(experiments)\n fig, axs = plt.subplots(exp_count)\n if exp_count == 1:\n axs = [axs]\n trend = lambda a,b: np.poly1d(np.polyfit(a, b, 1))(a)\n for i in range(exp_count):\n axs[i].set_title(experiments[i]['exp_name'])\n axs[i].set_xlabel(experiments[i]['exp_description'])\n # build x-axis \n x_axis = []\n c.execute(\n '''\n SELECT val FROM fact\n WHERE var_name = ?\n AND exp_id = ?\n ORDER BY step_id ASC\n ''',\n (\n experiments[i]['var_name'],\n experiments[i]['exp_id']\n )\n )\n x_axis = [r['val'] for r in c.fetchall()]\n c.execute(\n '''\n SELECT DISTINCT var_name FROM fact \n WHERE exp_id = ? AND var_name != ?\n ORDER BY var_name ASC\n ''',\n (experiments[i]['exp_id'],experiments[i]['var_name'])\n )\n variables = [r['var_name'] for r in c.fetchall()]\n for variable in variables:\n c.execute(\n '''\n SELECT val FROM fact\n WHERE exp_id = ? AND var_name = ?\n ORDER BY step_id ASC \n ''',\n (experiments[i]['exp_id'], variable)\n )\n y_axis = [r['val'] for r in c.fetchall()]\n axs[i].scatter(x_axis, y_axis)\n axs[i].plot(x_axis,trend(x_axis, y_axis),label=variable)\n axs[i].legend()\n fig.tight_layout()\n # save into image on headless machine\n if not image_path is None:\n plt.savefig(image_path)\n else:\n try:\n plt.show()\n except:\n plt.savefig(\"plot.png\") \n self.commit()","def plot_data(df, title=\"normalized Stock prices\", ylabel=\"Price\", xlabel=\"Date\" ):\n plt.clf()\n ax = df.plot(title=title, fontsize=12)\n ax.set_xlabel(xlabel)\n ax.set_ylabel(ylabel)\n plt.savefig('files/output/'+title+'.png')","def plotting(dataframe, prod_num):\n fig, axs = plt.subplots(2, sharex=True)\n axs[0].plot(dataframe['STU'])\n axs[1].plot(dataframe['STU'].diff().dropna())\n axs[0].set_title(\"Time Series of Product\" + f\"_{prod_num}\")\n axs[1].set_title(\"Differenced Time Series of Product\" + f\"_{prod_num}\")\n plt.savefig(\"Time Series of Product\" + f\"_{prod_num}\" + \".pdf\")","def plot(plot_count, density_map, padding, name):\n density_map = density_map[padding:-1 - padding, padding:-1 - padding]\n print(\"max density = %g\" % (np.amax(density_map)))\n print(\"mean density = %g\" % (np.mean(density_map)))\n\n fig = plt.figure()\n ax = fig.gca(projection='3d')\n\n x = np.arange(density_map.shape[0])\n y = np.arange(density_map.shape[1])\n\n x, y = np.meshgrid(x, y)\n ax.plot_surface(x, y, density_map, alpha=0.8)\n\n ax.set_xlabel('x')\n ax.set_ylabel('y')\n ax.set_zlabel('density')\n\n #plt.tight_layout()\n plt.savefig(name + \".3d.%d.png\" % (plot_count))\n plt.close()\n\n #plt.clf()\n\n #fig, ax = plt.subplots()\n\n #ax.pcolor(density_map)\n\n ## Loop over data dimensions and create text annotations.\n ##for i in range(density_map.shape[0]):\n ## for j in range(density_map.shape[1]):\n ## text = ax.text(j, i, density_map[i, j],\n ## ha=\"center\", va=\"center\", color=\"w\")\n #fig.tight_layout()\n #plt.savefig(name+\".2d.%d.png\" % (plot_count))\n #plt.close()","def generate_results(self, test_no, test_dict):\n g_s = gridspec.GridSpec(4, 2, wspace=0.2, hspace=1.5)\n fig = plt.figure(figsize=(20, 6))\n fig.suptitle('Experiment Results', y=0.93)\n\n x_val = np.arange(1, self.iters+1)\n\n ax1 = plt.subplot(g_s[0:3, :1], label = 'Mean Rewards')\n ax1.set_title('Mean Rewards')\n ax1.scatter(x_val, self.mean_rewards, s=5)\n ax1.set(xlabel='Iteration', ylabel='Mean Reward')\n\n ax2 = plt.subplot(g_s[0:3, 1:])\n ax2.scatter(x_val, self.sub_goals, s=5, label='Sub-optimal Goal')\n ax2.scatter(x_val, self.opt_goals, s=5, label='Optimal Goal')\n ax2.set_title('Goal Success Percentage by Type')\n ax2.set(xlabel='Iteration', ylabel='Success Percentage (%)')\n ax2.legend(loc=0)\n\n cells = list(test_dict.values())\n cells = [str(i) for i in cells]\n columns = list(test_dict.keys())\n ax3 = plt.subplot(g_s[3:, :])\n ax3.axis('off')\n ax3.table(cellText=[cells], colLabels=columns, loc='center', cellLoc='center')\n\n plt.savefig(f'results/charts/Test_{test_no}.png', bbox_inches='tight')","def plot_collective(xdict, ydict, xprop, yprop, documents):\n x_ion = {\"Mg\": [], \"Ca\": [], \"Zn\": [], \"Li\": [], \"Na\": []}\n y_ion = {\"Mg\": [], \"Ca\": [], \"Zn\": [], \"Li\": [], \"Na\": []}\n for item in documents:\n if item[\"path_id\"][-3:] == \"001\":\n x_ion[item[\"cation_type\"]].append(xdict[item[\"path_id\"]])\n y_ion[item[\"cation_type\"]].append(ydict[item[\"path_id\"]])\n fig = plt.figure(figsize=(6,6), dpi=plotting_dpi)\n ax = fig.add_subplot(111)\n for ion in [\"Mg\", \"Ca\", \"Zn\", \"Li\", \"Na\"]:\n ax.scatter(x_ion[ion], y_ion[ion], s=70, zorder=2, color=color_dict[ion], linewidths=2.5, edgecolors='black',\n label=ion)\n xlabel = \"\\\\textbf{\" + label_dict[xprop] + \"}\"\n ylabel = \"\\\\textbf{\" + label_dict[yprop] + \"}\"\n \n # # Plot lines for fitting, if useful\n # x2 = np.arange(-700, 3300, 1)\n # ax.plot(x2, x2)\n \n # # For setting axis boundaries\n # ax.set_xlim([-700, 3500])\n # ax.set_ylim([0,100])\n \n # Plot display settings\n ax.set_xlabel(xlabel, fontsize=24)\n ax.set_ylabel(ylabel, fontsize=24)\n ax.tick_params(axis='x', labelsize=22)\n ax.tick_params(axis='y', labelsize=22)\n border_width = 2\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\n plt.tight_layout()\n plt.legend(loc='best', prop={'size':10})\n # plt.legend(loc='best')\n plt.rc('text', usetex=True)\n plt.rc('font', family='sans-serif')\n plt.show()","def plotObsExpSigEnrichment(f):\n mat = pd.read_csv(f, sep=\"\\t\", index_col=0)\n fig, ax = pylab.subplots()\n ax.plot(mat.index,\n mat[\"observed\"] * 100,\n color=colors[0],\n label=\"observed\")\n ax.plot(mat.index,\n mat[\"expected\"] * 100,\n color=colors[1],\n label=\"expected\")\n ax.set_xlabel(\"Percentage of Bins\")\n ax.set_ylabel(\"Percetange of PETs\")\n ax.legend(loc=\"upper left\")\n #ax2 = ax.twinx()\n #ax2.plot( mat.index, mat[\"Obs/Exp\"],color=colors[2],label=\"Obs/Exp\")\n #ax2.set_ylabel(\"Obs/Exp\")\n #for t in ax2.get_yticklabels():\n # t.set_color(colors[2])\n pylab.savefig(\"%s.pdf\" % (f.replace(\".txt\", \"\")))","def plot_modelparametercollections(plotname, parametercollection_SF, parametercollection_AGN,\n stat_SF, stat_AGN, AGNcol='blue',SFcol='red', constraintsstr=None,\n fluxratiodictionarylist=None, verbose=True):\n\n Nobj = len(parametercollection_SF)\n if verbose: print(' - Will generate plots of NEOGAL \"PDFs\" for all '+str(Nobj)+' objects in parameter collections')\n for oo in np.arange(Nobj):\n objid = parametercollection_SF[oo]['id']\n if verbose:\n infostr = ' plotting info for '+str(objid)+' ('+str(\"%.5d\" % (oo+1))+' / '+str(\"%.5d\" % Nobj)+') '\n sys.stdout.write(\"%s\\r\" % infostr)\n sys.stdout.flush()\n plotname_obj = plotname.replace('.pdf','_id'+str(objid)+'.pdf')\n # if verbose: print(' - Generating the figure '+plotname_obj)\n figuresize_x = 6\n figuresize_y = 5\n fig = plt.figure(figsize=(figuresize_x,figuresize_y))\n Fsize = 9\n LW = 2\n plt.rc('text', usetex=True) # enabling LaTex rendering of text\n plt.rc('font', family='serif',size=Fsize) # setting text font\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n\n left = 0.10 # the left side of the subplots of the figure\n right = 0.95 # the right side of the subplots of the figure\n bottom = 0.10 # the bottom of the subplots of the figure\n top = 0.90 # the top of the subplots of the figure\n wspace = 1.50 # the amount of width reserved for blank space between subplots\n hspace = 0.50 # the amount of height reserved for white space between subplots\n plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)\n\n Nrows, Ncols = 3, 6\n ylabel = 'Number of NEOGAL SF ('+str(SFcol)+') and AGN ('+str(AGNcol)+') models'\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n Nmodels_SF = float(len(parametercollection_SF[oo]['Zgas']))\n Nmodels_AGN = float(len(parametercollection_AGN[oo]['Zgas']))\n\n titlestr = 'Models satisfying ID='+str(objid)+' cuts: SF='+str(Nmodels_SF)+'; AGN='+str(Nmodels_AGN)\n if (Nmodels_AGN > 0) & (Nmodels_SF > 0):\n Nmodels_ratio = Nmodels_SF/Nmodels_AGN\n titlestr_addition = '; SF/AGN='+str(\"%.4f\" % Nmodels_ratio)\n titlestr = titlestr+titlestr_addition\n\n if fluxratiodictionarylist is not None:\n constraints = fluxratiodictionarylist[oo]\n constraintslist = [key+':['+str(\"%.2f\" % constraints[key][0])+','+str(\"%.2f\" % constraints[key][1])+']'\n for key in constraints.keys() if key not in ['id']]\n\n if len(constraintslist) < 4:\n constraintsstr = '; '.join(constraintslist)\n elif (len(constraintslist) > 3) & (len(constraintslist) < 7):\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])\n elif (len(constraintslist) > 6) & (len(constraintslist) < 10):\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])+\\\n '\\n'+'; '.join(constraintslist[6:])\n else:\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])+\\\n '\\n'+'; '.join(constraintslist[6:9])+'\\n'+'; '.join(constraintslist[9:])\n\n constraintsstr = constraintsstr.replace('10000000000.00','1e10')\n\n titlestr = titlestr+'\\n'+constraintsstr\n # titlestr = r'{\\fontsize{'+str(Fsize)+'pt}{3em}\\selectfont{}{'+titlestr+'\\r}{\\fontsize{'+str((Fsize-2.))+'pt}{3em}\\selectfont{}('+constraintsstr+'}'\n\n # plt.text(x=0.5, y=0.94, s=titlestr, fontsize=Fsize, ha=\"center\", transform=fig.transFigure)\n # plt.text(x=0.5, y=0.88, s=constraintsstr, fontsize=Fsize-2, ha=\"center\", transform=fig.transFigure)\n # fig.title(titlestr,fontsize=Fsize)\n fig.suptitle(titlestr,fontsize=Fsize-2)\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Zgas\n plt.subplot(Nrows, Ncols, (1,3))\n\n bindefs = np.array([0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.014,\n 0.017, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07])-0.00001\n\n plotkey = 'Zgas'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xscale('log')\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([0.00001,0.1])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # logUs\n plt.subplot(Nrows, Ncols, (4,6))\n\n bindefs = np.arange(-4.75, -0.25, 0.5)\n\n plotkey = 'logUs'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-5,-0.5])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # xid\n plt.subplot(Nrows, Ncols, (7,9))\n\n bindefs = np.array([0.0, 0.2, 0.4, 0.6])\n\n plotkey = 'xid'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-0.05,0.65])\n plt.ylabel(ylabel)\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # nh\n plt.subplot(Nrows, Ncols, (10,12))\n\n bindefs = 10**np.array([1.5, 2.5, 3.5, 4.5])\n\n plotkey = 'nh'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n plt.xscale('log')\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([10,1e5])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # COCOsol\n plt.subplot(Nrows, Ncols, (13,14))\n\n #bindefs = np.array([0.10, 0.14, 0.20, 0.27, 0.38, 0.52, 0.72, 1.00, 1.40])\n bindefs = np.arange(0.05,1.5,0.06)\n\n plotkey = 'COCOsol'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\n stat_SF[oo][plotkey],None,\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([0.00,1.55])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # mup\n plt.subplot(Nrows, Ncols, (15,16))\n\n bindefs = np.array([0,200,400])\n\n plotkey = 'mup'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\n stat_SF[oo][plotkey],None,\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-10,410])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # alpha\n plt.subplot(Nrows, Ncols, (17,18))\n\n bindefs = np.array([-2.15,-1.85,-1.55,-1.25,-0.95])\n\n plotkey = 'alpha'\n Nbins = 10\n nm.plot_modelparametercollections_addhist(None,parametercollection_AGN[oo][plotkey],\n None,stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=None,Nbins=Nbins)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-2.2,-0.9])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n plt.savefig(plotname_obj)\n plt.clf()\n plt.close('all')\n # if verbose: print(' - Successfully saved figure to file')\n if verbose: print('\\n done...')","def plot_PDs(output_path,M_max,max_dim,normalized=False,output_name=None):\n plots_folder = os.path.join(output_path,'plots')\n if output_name is None: \n aux = ['PDs']\n input_file = os.path.join(output_path,'outputs_PDS.csv')\n else:\n aux = [output_name,'PDs']\n input_file = os.path.join(output_path,'%s_PDS.csv' % output_name)\n if normalized is True:\n aux.append('normalized')\n plot_file_name = '-'.join(aux)+'.png'\n plot_file_name = os.path.join(plots_folder,plot_file_name)\n\n \n data = pd.read_csv(input_file,index_col = 0)\n \n\n fig_size_aux = 15/(3-max_dim)\n plt.figure(figsize=(fig_size_aux,5))\n if(normalized):\n for i in range(1,max_dim+2):\n plt.subplot(1,max_dim+1,i, aspect='equal')\n plt.plot(data[data.dimH==i-1].birth.values/np.float(M_max),data[data.dimH==i-1].death.values/np.float(M_max),'bo',alpha=.3,label='dim %i'%(i-1))\n plt.xlim((0,1)); plt.ylim((0,1))\n frame = plt.legend(loc=4,frameon=True,title='normalized')\n frame = frame.get_frame()\n frame.set_edgecolor('black')\n plt.xlabel('birth'); plt.ylabel('death')\n plt.plot([0, 1], [0, 1], ls=\"--\", c=\".3\")\n plt.suptitle('Persistence Diagrams')\n plt.savefig(plot_file_name)\n print 'Saved PDs plots in %s'%plot_file_name\n else:\n for i in range(1,max_dim+2):\n plt.subplot(1,max_dim+1,i, aspect='equal')\n plt.plot(data[data.dimH==i-1].birth.values,data[data.dimH==i-1].death.values,'bo',alpha=.3,label='dim %i'%(i-1))\n plt.xlim((0,M_max)); plt.ylim((0,M_max))\n frame = plt.legend(loc=4,frameon=True)\n frame = frame.get_frame()\n frame.set_edgecolor('black')\n plt.xlabel('birth'); plt.ylabel('death')\n plt.plot([0, M_max], [0, M_max], ls=\"--\", c=\".3\")\n plt.suptitle('Persistence Diagrams')\n plt.savefig(plot_file_name)\n print 'Saved PDs plots in %s'%plot_file_name\n return()","def PlotMA(df, show_plot=True):\n # Ensure 'Study' is in header.\n # TODO: Add code.\n # Obtain monthly means.\n df_ave = stats.MonthlyMean(df)\n # Get list of variables.\n var_list = set(df_ave.columns.get_level_values('Part B'))\n # Determine number of axes in the figure.\n n_var = len(var_list)\n n_row = np.int(np.around(np.sqrt(n_var)))\n if n_row**2 >= n_var:\n n_col = n_row\n else:\n n_col = n_row + 1\n fig, ax = plt.subplots(nrows=n_row, ncols=n_col, figsize=(16, 9))\n # Remove extra axes, if necessary.\n if n_row * n_col != n_var:\n n_empty = n_row * n_col - n_var\n for k in range(n_empty):\n ax[-1, -(1+k)].remove()\n # Initialize variables for updating axes.\n cur_row = 0\n cur_col = 0\n var_width = 0.9\n idx = np.arange(12)\n x_min = 0\n x_max = 12\n # Plot each variable.\n for var in var_list:\n df_var = df_ave.xs(var, level='Part B', axis=1)\n # Initialize parameters for current axis.\n y_min = np.min(df_var.values) * 0.95\n y_max = np.max(df_var.values) * 1.05\n col_var = df_var.columns\n bar_width = var_width / len(col_var)\n # Select current axis.\n if n_row == 1 and n_col == 1:\n ax_cur = ax\n elif n_row == 1:\n ax_cur = ax[cur_col]\n else:\n ax_cur = ax[cur_row, cur_col]\n # Plot study results for each variable.\n for c in col_var:\n idx_c = idx + 0.05 + col_var.get_loc(c) * bar_width\n val_c = df_var[c].values\n lab_c = '{} {}'.format(*c[:2])\n ax_cur.bar(idx_c, val_c, bar_width, label=lab_c, align='edge')\n # Set plot title.\n ax_cur.set_title('Monthly Averages of {}'.format(var))\n # Modify x-axis and y-axis.\n ax_cur.set_xticks(idx + 0.5)\n tick_labels = list(df_ave.index)\n ax_cur.set_xticklabels(tick_labels)\n label_c = set(df_var.columns.get_level_values('Part C'))\n _unit = df_var.columns.get_level_values('Units')\n _dtyp = df_var.columns.get_level_values('Data Type')\n unit_d = list(zip(_unit, _dtyp))\n combo_unit = [' '.join(i) for i in unit_d]\n label_u = set(combo_unit)\n ax_cur.set_ylabel('{} ({})'.format(r'/'.join(label_c),\n r'/'.join(label_u)))\n ax_cur.set_ylim(bottom=y_min, top=y_max)\n ax_cur.set_xlim(left=x_min, right=x_max)\n ax_cur.set_xlabel('Month')\n ax_cur.spines['right'].set_visible(False)\n ax_cur.spines['top'].set_visible(False)\n # Set legend.\n ax_cur.legend(title='Study, Part A')\n # Update current axis.\n cur_col += 1\n if cur_col >= n_col:\n cur_col = 0\n cur_row += 1\n # Adjust layout.\n plt.tight_layout()\n # Add figure notes.\n t = PlotChartNotes()\n if n_row * n_col != n_var:\n x_pos = (1 - n_empty / n_col) * 1.05\n y_pos = (1 / n_row) * 0.95\n plt.figtext(x_pos, y_pos, t, ha='left', va='top', wrap=True)\n else:\n plt.figtext(0.05, 0, t, ha='left', va='bottom', wrap=True)\n plt.subplots_adjust(bottom=0.2)\n # Show plot, if requested.\n if show_plot:\n plt.show()\n # Return figure and axes.\n return fig, ax","def logit_model_plots(ds,Population = 'Population_%',Event_rate ='Event_rate',decile ='Band',Cumulative_Non_Event = 'Cumulative_Non_Event_%',Cumulative_Event= 'Cumulative_Event_%',sample_type ='Development'):\n \n import matplotlib.pyplot as plt\n fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(15, 4))\n _= ax1.plot(plot_df[Cumulative_Non_Event],plot_df[Cumulative_Event])\n _= ax1.set_ylabel(Cumulative_Non_Event)\n _= ax1.set_title('Gini Curve : '+str(sample_type) +' sample')\n _= ax1.set_xlabel(Cumulative_Event)\n\n _= plot_df[Population].plot(kind='bar', color='b', width = 0.35,legend=True , label = Population)\n _= plot_df[Event_rate].plot(kind='line',color ='r', secondary_y=True,legend=True, label = Event_rate)\n _= ax2.set_xticklabels(plot_df[decile])\n _= ax2.set_ylim(0,plot_df[Event_rate].max()*0.15)\n _= ax2.right_ax.set_ylim(0,plot_df[Event_rate].max()*1.5)\n _= ax2.right_ax.set_ylabel(Event_rate)\n _= ax2.set_ylabel(Population)\n _= ax2.set_title('Decile Wise Event Rate : ' +str(sample_type) +' sample')\n _= ax2.set_xlabel(decile)\n plt.show()","def ploter(self):\n if len(self.dataset[self.first_title]) != 2:\n print('plot is only avilable for two features')\n return\n x_axis = []\n y_axis = []\n for title in self.dataset:\n x_axis.append(self.dataset[title][0])\n y_axis.append(self.dataset[title][1])\n plt.plot(x_axis, y_axis, 'o')\n plt.show()","def hotspot_fields_plot(self, results_dict, tas_bound=None, pr_bound=None):\n sorted_keys = [(f\"{period}_{season}_{variable}\"\n f\"_{project}_{results_dict['scenario']}\")\n for variable in self.variables\n for period in self.cfg[\"future_periods\"]\n for project in self.projects for season in self.seasons]\n sorted_keys = [\n sorted_keys[:len(sorted_keys) // 2],\n sorted_keys[len(sorted_keys) // 2:]\n ]\n ancestor_files_var = [[\n ancestor_file for ancestor_file in results_dict[\"ancestors\"]\n if f\"/{var}_\" in ancestor_file\n ] for var in self.variables]\n for ancestor_files, keys, variable in zip(ancestor_files_var,\n sorted_keys, self.variables):\n fig = plt.figure(figsize=(14.4, 3.4),\n constrained_layout=True,\n dpi=300)\n plt.gcf().subplots_adjust()\n # bound colorbar to abs(max) value on the map\n style = self.cb_bounds(variable, results_dict, keys,\n [tas_bound, pr_bound])\n # plot each panel\n fill, frame = self._hotspot_fields_plot_panels(\n results_dict, fig, keys, style)\n # plot figtexts\n self._hotspot_fields_plot_figtexts(results_dict['scenario'], frame)\n # plot line\n self._hotspot_fields_plot_line(fig, frame)\n # plot colorbar\n cbar = plt.colorbar(fill,\n plt.gcf().add_axes([0.25, 0.125, 0.5, 0.04]),\n orientation=\"horizontal\",\n extend=\"both\")\n if variable == \"pr\":\n cbar.set_label(\"%\")\n against_region = (\n f\"{self.cfg['region'][2]}$^o$ N-\"\n f\"{self.cfg['region'][3]}$^o$ N latitudinal belt\")\n else:\n cbar.set_label(\n self.formatter(str(results_dict[keys[-1]].units)))\n against_region = \"global\"\n\n # plot title and save\n self._hotspot_fields_plot_save(against_region, variable,\n results_dict['scenario'],\n ancestor_files)","def make_N4255_plots(data_obj, aspect_corr=1.0, title_pages=False):\n\n print(\"Generating plots...\")\n\n #Create color maps\n cmap = plt.get_cmap('jet')\n cmap = plt.get_cmap('gray')\n\n #Call the function to create the title page of the pdf document\n plot_front_title(data_obj)\n\n #-----------------------------------------------------------------------#\n # Initialize the position variables for the text and graphs on the pdf. #\n #-----------------------------------------------------------------------#\n y0 = 0.9\n dy = [0.03, 0.025]\n\n ha = 'left'\n va = 'center'\n fs = 10\n dfs = 2\n\n # metric name value unc min\n xpos = [0.0, 0.4, 0.5, 0.75]\n yi = y0 - 0.1 # The position of the text on the y access, which is constantly updated as more text is added\n\n #-----------------------------------------------------------------------------------#\n # Plot the 'summary' page listing all the tests and the overall results - TEXT ONLY #\n #-----------------------------------------------------------------------------------#\n\n #Create the title of the page\n plot_overall_text(data_obj, yi, xpos, ha, va, fs)\n\n #Plot the overall results text of the first test, Penetration\n yi = yi - dy[0]\n plot_pen_text(data_obj, 1, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the first test, Organic Material Detection\n yi = yi - dy[0]\n plot_BSNR_text(data_obj, 2, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the third test, Spatial Resolution\n yi = yi - dy[0]\n plot_spatial_text(data_obj, 3, yi, yi - dy[1], xpos, ha, va, fs, dfs)\n yi = yi - dy[1] #Make sure the local yi is updated\n\n #Plot the overall results text of the fourth test, Dynamic Range\n yi = yi - dy[0]\n plot_dyn_text(data_obj, 4, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the fifth test, NEQ Noise\n yi = yi - dy[0]\n plot_noise_text(data_obj, 5, yi, dy, xpos, ha, va, fs, dfs)\n yi = yi - (dy[1] * 2) #Make sure to update yi, as it was only locally changed in 'plot_noise_text()'\n\n #Plot the overall results text of the sixth test, Flatness of field\n yi = yi - dy[0]\n plot_ff_text(data_obj, 6, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the seventh test, Image Extent\n yi = yi - dy[0]\n plot_extent_text(data_obj, 7, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the eighth test, Image Area\n yi = yi - dy[0]\n plot_area_text(data_obj, 8, yi, xpos, ha, va, fs, dfs)\n\n #Plot the overall results text of the ninth test, Aspect Ratio\n yi = yi - dy[0]\n plot_a_ratio_text(data_obj, 9, yi, xpos, ha, va, fs, dfs)\n\n #--------------------------------------------------#\n # Plot the footnotes for the overall results page. #\n #--------------------------------------------------#\n plot_overall_footnotes(xpos, ha, va, fs, dfs)\n\n\n #-----------------#\n # Plot the images #\n #-----------------#\n plot_images(data_obj, fs) #Plot the images to the pdf\n\n plot_image_footnotes(data_obj, xpos, ha, va, fs, dfs) #Add in the footnotes to the pdf\n\n\n #-------------------#\n # Penetration plots #\n #-------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 1: Penetration\")\n\n #Call the function to plot the Steel Penetration results to the pdf\n plot_steel_pen_N4255(data_obj, 1)\n\n\n #------------#\n # BSNR plots #\n #------------#\n if title_pages:\n new_title_page(data_obj, \"Test 2: Organic Material Detection\")\n\n # Call the function to plot the Organic Material Detection results to the pdf\n plot_BSNR(data_obj, 2, cmap)\n\n\n #--------------------#\n # Spatial Resolution #\n #--------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 3: Spatial Resolution\")\n\n # Call the function to plot the Spatial Resolution results to the pdf\n plot_spatial_res(data_obj, 3)\n\n #---------------#\n # Dynamic Range #\n #---------------#\n if title_pages:\n new_title_page(data_obj, \"Test 4: Dynamic Range\")\n\n # Call the function to plot the Dynamic Range results to the pdf\n plot_dynamic_range(data_obj, 4)\n\n #-------#\n # Noise #\n #-------#\n if title_pages:\n new_title_page(data_obj, \"Test 5: Noise (NEQ)\")\n\n # Call the function to plot the Noise (NEQ) results to the pdf\n plot_noise(data_obj, 5)\n\n #-------------------#\n # Flatness of field #\n #-------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 6: Flatness of Field\")\n\n # Call the function to plot the Flatness of Field results to the pdf\n plot_field_flatness(data_obj, 6)\n\n #--------------#\n # Image extent #\n #--------------#\n if title_pages:\n new_title_page(data_obj, \"Test 7: Image Extent\")\n\n # Call the function to plot the Image Extent results to the pdf\n plot_image_extent(data_obj, 7)\n\n\n #------------#\n # Image Area #\n #------------#\n if title_pages:\n fig = new_pdf_page(data_obj.pdf_obj)\n plt.axis('off')\n plt.text(0.5, 0.5, 'Test 8: Image Area', ha='center', va='center', fontsize=20)\n str1 = str(data_obj.image_area[0]) + ' by ' + str(data_obj.image_area[1]) + ' pixels'\n plt.text(0.5, 0.4, str1, ha='center', va='center', fontsize=12)\n\n #--------------#\n # Aspect Ratio #\n #--------------#\n if title_pages:\n new_title_page(data_obj, \"Test 9: Aspect Ratio\")\n\n #Call the function to plot the Aspect Ratio results to the pdf\n plot_aspect_ratio(data_obj, 9, cmap, aspect_corr)\n\n fig = new_pdf_page(data_obj.pdf_obj, open_fig=False)","def covariance_plot(catl_pd, param_dict, proj_dict, plot_only_feat=False,\n fig_fmt='pdf', figsize=(10, 8), fig_number=2):\n file_msg = param_dict['Prog_msg']\n plot_dict = param_dict['plot_dict']\n ## Filename\n fname = os.path.join( proj_dict['figure_dir'],\n 'Fig_{0}_{1}_feature_covariance.{2}'.format(\n fig_number,\n param_dict['catl_str_fig'],\n fig_fmt))\n ##\n ## Paper Figure\n fname_paper = os.path.join( proj_dict['paper_fig_dir'],\n 'Figure_02.{0}'.format(fig_fmt))\n ## Renaming properties\n catl_pd_copy = catl_pd.copy()\n ## Dropping columns\n cols_drop = ['GG_pointing', 'GG_haloid_point', 'GG_mhalo_point', \n 'g_brightest', 'GG_r_rms']\n catl_pd_copy.drop(cols_drop, axis=1, inplace=True)\n ## Reordering columns\n mhalo_key = 'M_h'\n # Saving data from Halo mass\n gal_mhalo_arr = catl_pd_copy[mhalo_key].values\n # Removing Halo mass\n catl_pd_copy.drop(mhalo_key, axis=1, inplace=True)\n # Inserting it back again to the DataFrame\n catl_pd_copy.insert(0, mhalo_key, gal_mhalo_arr)\n ##\n ## Rearranging columns\n df_cols_new = [ 'M_h','dist_centre_group', 'M_r', 'logssfr', 'g_galtype',\n 'g_r', 'GG_mr_brightest', 'GG_mr_ratio',\n 'GG_M_r', 'GG_logssfr', 'GG_shape', 'GG_ngals',\n 'GG_rproj', 'GG_r_tot', 'GG_r_med', 'GG_sigma_v',\n 'GG_sigma_v_rmed', 'GG_M_group', 'GG_mdyn_rproj',\n 'GG_dist_cluster']\n catl_pd_copy = catl_pd_copy.loc[:, df_cols_new]\n # Plotting only features if applicable\n if plot_only_feat:\n feat_cols = param_dict['ml_args']._feature_cols()\n catl_pd_copy = catl_pd_copy.loc[:,feat_cols]\n ## Renaming\n catl_pd_copy.rename(columns=param_dict['feat_cols_dict'], inplace=True)\n ## Selecting certain columns only\n ## Figure details\n plt.clf()\n plt.close()\n fig = plt.figure(figsize=figsize)\n ax1 = fig.add_subplot(111, facecolor='white')\n fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,\n wspace=0.02, hspace=0.02)\n cax = fig.add_axes([0.30, 0.85, 0.30, 0.05])\n ## Correlation\n corr = catl_pd_copy.corr()\n # Generate a mask for the upper triangle\n mask = num.zeros_like(corr, dtype=num.bool)\n mask[num.triu_indices_from(mask)] = True\n # Generate a custom diverging colormap\n cmap = sns.diverging_palette(220, 10, as_cmap=True)\n # Draw the heatmat with the mask and correct aspect ratio\n g = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1.0, vmin=-1., center=0,\n square=True, linewidths=0.5, cbar=False, ax=ax1)\n cbar = fig.colorbar(g.get_children()[0], cax=cax, orientation='horizontal')\n cbar.set_label(r'$\\Leftarrow$ Correlation $\\Rightarrow$',\n fontsize=plot_dict['size_label'])\n cbar.ax.tick_params(labelsize=20)\n g.yaxis.set_tick_params(labelsize=25)\n g.xaxis.set_tick_params(labelsize=25)\n ##\n ## Saving figure\n if fig_fmt=='pdf':\n plt.savefig(fname, bbox_inches='tight')\n plt.savefig(fname_paper, bbox_inches='tight')\n else:\n plt.savefig(fname, bbox_inches='tight', dpi=400)\n plt.savefig(fname_paper, bbox_inches='tight', dpi=400)\n ##\n ##\n print('{0} Figure saved as: {1}'.format(file_msg, fname))\n print('{0} Paper Figure saved as: {1}'.format(file_msg, fname_paper))\n plt.clf()\n plt.close()","def output_results(self, filename):\n\n self.data.plot(title='Result of applying {} onto data set'.format(self.transformations[-1]))\n plt.savefig(\"results/{}.png\".format(filename))\n plt.close()","def visualizePredictions(testData,knn_predictions):\r\n testData.visualize.scatterPlot('Petal length','Petal width')\r\n testData.dataDict[testData.reference] = knn_predictions\r\n testData.visualize.scatterPlot('Petal length','Petal width')\r\n\r\n pass","def make_F792_plots(data_obj, title_pages=False):\n\n print(\"Generating plots...\")\n\n # Create color maps\n cmap = plt.get_cmap('jet')\n cmap = plt.get_cmap('gray')\n\n # Call the\n plot_front_title(data_obj)\n\n # -----------------------------------------------------------------------#\n # Initialize the position variables for the text and graphs on the pdf. #\n # -----------------------------------------------------------------------#\n y0 = 0.9\n dy = [0.03, 0.025]\n\n ha = 'left'\n va = 'center'\n fs = 10\n dfs = 2\n\n # metric name value unc min\n xpos = [0.0, 0.4, 0.5, 0.75]\n yi = y0 - 0.1 # The position of the text on the y access, which is constantly updated as more text is added\n\n # -----------------------------------------------------------------------------------#\n # Plot the 'summary' page listing all the tests and the overall results - TEXT ONLY #\n # -----------------------------------------------------------------------------------#\n\n # Create the title of the page\n plot_overall_text(data_obj, yi, xpos, ha, va, fs)\n\n #Plot the overall results text of the first test, Steel Differentiation\n\n\n # Plot the overall results text of the second test, Penetration\n yi = yi - dy[0]\n plot_pen_text(data_obj, 2, yi, xpos, ha, va, fs, dfs)\n\n # Plot the overall results text of the third test, Organic Material Detection\n yi = yi - dy[0]\n plot_BSNR_text(data_obj, 3, yi, xpos, ha, va, fs, dfs)\n\n # Plot the overall results text of the fourth test, Spatial Resolution\n yi = yi - dy[0]\n plot_spatial_text(data_obj, 4, yi, yi - dy[1], xpos, ha, va, fs, dfs)\n yi = yi - dy[1] # Make sure the local yi is updated\n\n # Plot the overall results text of the fifth test, Dynamic Range\n yi = yi - dy[0]\n plot_dyn_text(data_obj, 5, yi, xpos, ha, va, fs, dfs)\n\n # Plot the overall results text of the sixth test, Noise\n yi = yi - dy[0]\n plot_noise_text(data_obj, 6, yi, dy, xpos, ha, va, fs, dfs)\n yi = yi - (dy[1] * 2) # Make sure to update yi, as it was only locally changed in 'plot_noise_text()'\n\n # --------------------------------------------------#\n # Plot the footnotes for the overall results page. #\n # --------------------------------------------------#\n plot_overall_footnotes(xpos, ha, va, fs, dfs, standard=\"ASTM F792\")\n\n\n #---------------------------------------------------------#\n # Plot the cropped and rotated images from the processing #\n #---------------------------------------------------------#\n plot_images(data_obj, fs) # Plot the images to the pdf\n\n plot_image_footnotes(data_obj, xpos, ha, va, fs, dfs) # Add in the footnotes to the pdf\n\n # NOTE: Above image plotting the same, with the same footnotes, for F792???\n\n #-----------------------------#\n # Steel differentiation plots #\n #-----------------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 1: Steel Differentiation\")\n\n #Call the function to plot the Steel Differentiation results to the pdf\n\n\n #-------------------#\n # Penetration plots #\n #-------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 2: Penetration\")\n\n # Call the function to plot the Steel Penetration results to the pdf\n #plot_steel_pen(data_obj, 2)\n\n #------------#\n # BSNR plots #\n #------------#\n if title_pages:\n new_title_page(data_obj, \"Test 3: Organic Material Detection\")\n\n # Call the function to plot the Organic Material Detection results to the pdf\n plot_BSNR(data_obj, 3, cmap)\n\n #--------------------#\n # Spatial Resolution #\n #--------------------#\n if title_pages:\n new_title_page(data_obj, \"Test 4: Spatial Resolution\")\n\n # Call the function to plot the Spatial Resolution results to the pdf\n plot_spatial_res(data_obj, 4)\n\n #---------------#\n # Dynamic Range #\n #---------------#\n if title_pages:\n new_title_page(data_obj, \"Test 5: Dynamic Range\")\n\n # Call the function to plot the Dynamic Range results to the pdf\n plot_dynamic_range(data_obj, 5)\n\n #-------#\n # Noise #\n #-------#\n if title_pages:\n new_title_page(data_obj, \"Test 6: Noise (NEQ)\")\n\n # Call the function to plot the Noise (NEQ) results to the pdf\n plot_noise(data_obj, 6)\n\n fig = new_pdf_page(data_obj.pdf_obj, open_fig=False)","def analyze_data(self, bandwith=1):\n data = {\"Topple Count\": self.topple_count, \"Fallen mass\": self.mass_fallen_count}\n print(data)\n\n self.plot()\n self.plot(type='mass')\n self.plot(type='topple')\n self.plot(type='histogram',bandwith=bandwith)\n self.plot(type='pdf',bandwith=bandwith)","def plot(var):\n # MISSCHIEN KUNNEN WE HIER NOG IETS MEE\n # total_dead = len(train_data[\"Survived\"] == 0)\n # total_survived = len(train_data[\"Survived\"] == 1)\n # died = train_data[train_data[\"Survived\"] == 0][var].value_counts() / total_dead\n # survived = train_data[train_data[\"Survived\"] == 1][var].value_counts() / total_survived\n sns.set()\n sns.set_color_codes(\"pastel\")\n\n # order bars for family size variable\n if var == \"FamSize\":\n sns.barplot(x=var, y=\"Survived\", data=train_data, color=\"b\",\\\n capsize=.1, errwidth=.7, order=[\"alone\", 1, 2, 3, \"4 or more\"]).\\\n tick_params(labelsize=18)\n else:\n sns.barplot(x=var, y=\"Survived\", data=train_data, color=\"b\",\\\n capsize=.1, errwidth=1.1).tick_params(labelsize=18)\n\n # plot style properties\n ax = plt.gca()\n\n for ax in plt.gcf().axes:\n x = ax.get_xlabel()\n y = ax.get_ylabel()\n ax.set_xlabel(x, fontsize=20)\n ax.set_ylabel(y, fontsize=20)\n\n plt.title(\"Ratio of survivors for variable \" + str(var), fontsize=22)\n t = ax.title\n t.set_position([.5, 1.05])\n plt.ylim([0, 1])\n plt.subplots_adjust(bottom=.15, left=.15)\n plt.savefig(\"results/survived_\" + str(var) + \".png\", bbox_inches=\"tight\")\n\n plt.show()","def plot_ppplot(obj1,sheet1,variable1,obj2,sheet2,variable2,title,opath):\n p1 = np.percentile(obj1.me[sheet1][variable1],range(0,101,1))\n p2 = np.percentile(obj2.me[sheet2][variable2],range(0,101,1))\n p1c = np.cumsum(np.array(p1))/np.cumsum(np.array(p1)).max()\n p2c = np.cumsum(np.array(p2))/np.cumsum(np.array(p2)).max()\n fig = plt.figure(figsize=(8,8),dpi=120)\n plt.scatter(p1c,p2c,color='#566c73',s=30)\n plt.plot([0,1],[0,1],color='red',alpha=0.3)\n plt.xlim(0,1)\n plt.ylim(0,1)\n plt.grid()\n plt.xlabel(sheet1+'_'+variable1)\n plt.ylabel(sheet2+'_'+variable2)\n plt.title(title)\n plt.savefig(opath+'.png')\n plt.close()","def plot_observed_predictions(self):\n \n # Plot of X vs Y\n fig = plt.figure(figsize=(15,5))\n plt.subplot(1,3,1) \n for k in self.phd_filter['estimated_positions'].keys():\n plt.plot(self.phd_filter['estimated_positions'][k][0], self.phd_filter['estimated_positions'][k][1], 'bx')\n plt.xlabel(\"X\",fontsize=20)\n plt.ylabel(\"Y\",fontsize=20)\n \n # Plot of time vs X\n plt.subplot(1,3,2)\n for k in self.phd_filter['estimated_positions'].keys(): \n plt.plot(k*np.ones(self.phd_filter['estimated_positions'][k].shape[1]), self.phd_filter['estimated_positions'][k][0], 'bx')\n plt.xlabel(\"time\",fontsize=20)\n plt.ylabel(\"X\",fontsize=20)\n plt.xlim(0,self.n_time_steps+1)\n\n # Plot of time vs Y\n plt.subplot(1,3,3)\n for k in self.phd_filter['estimated_positions'].keys():\n plt.plot(k*np.ones(self.phd_filter['estimated_positions'][k].shape[1]), self.phd_filter['estimated_positions'][k][1], 'bx')\n plt.xlabel(\"time\",fontsize=20)\n plt.ylabel(\"Y\",fontsize=20)\n plt.xlim(0,self.n_time_steps+1)\n plt.show();","def plot_gmm_preds(x, z, with_supervision, plot_id):\n plt.figure(figsize=(12, 8))\n plt.title('{} GMM Predictions'.format('Semi-supervised' if with_supervision else 'Unsupervised'))\n plt.xlabel('x_1')\n plt.ylabel('x_2')\n\n for x_1, x_2, z_ in zip(x[:, 0], x[:, 1], z):\n color = 'gray' if z_ < 0 else PLOT_COLORS[int(z_)]\n alpha = 0.25 if z_ < 0 else 0.75\n plt.scatter(x_1, x_2, marker='.', c=color, alpha=alpha)\n\n file_name = 'pred{}_{}.pdf'.format('_ss' if with_supervision else '', plot_id)\n save_path = os.path.join('.', file_name)\n plt.savefig(save_path)","def plot_gmm_preds(x, z, with_supervision, plot_id):\n plt.figure(figsize=(12, 8))\n plt.title('{} GMM Predictions'.format('Semi-supervised' if with_supervision else 'Unsupervised'))\n plt.xlabel('x_1')\n plt.ylabel('x_2')\n\n for x_1, x_2, z_ in zip(x[:, 0], x[:, 1], z):\n color = 'gray' if z_ < 0 else PLOT_COLORS[int(z_)]\n alpha = 0.25 if z_ < 0 else 0.75\n plt.scatter(x_1, x_2, marker='.', c=color, alpha=alpha)\n\n file_name = 'pred{}_{}.pdf'.format('_ss' if with_supervision else '', plot_id)\n save_path = os.path.join('.', file_name)\n plt.savefig(save_path)","def plot4(self, plog=False):\n\n probs = pd.read_csv(self.probfile)\n\n plt.rc('font', size=14)\n fig, ax = plt.subplots()\n plt.plot(self.ds.freq, self.snr, 'k-', alpha=0.5, zorder=1)\n\n # plot the SNR range to search across when finding snr_modes\n for idx, line in enumerate(self.ds.mode_id['f0']):\n w = np.exp(self.ds.mode_id['w0'][idx])\n plt.axvline(x=line-w, color='b', linestyle='-', alpha=0.4)\n plt.axvline(x=line+w, color='b', linestyle='-', alpha=0.4)\n\n # overplot the predicted SNR values at the modes\n plt.scatter(probs['f0'], probs['SNR_Kepler'], label='Kepler - 4yrs', alpha=1, zorder=2)\n plt.scatter(probs['f0'], probs['SNR_TESS365'], label='TESS - 1 yr', alpha=1, zorder=3)\n plt.scatter(probs['f0'], probs['SNR_TESS27'], label='TESS - 27 days', alpha=1, zorder=4)\n\n if plog:\n plt.xscale('log')\n plt.yscale('log')\n plt.xlabel(r'$\\nu$ / $\\rm \\mu Hz$')\n plt.ylabel(r'SNR')\n\n mn = min(star.ds.mode_id['f0']) -\\\n (max(star.ds.mode_id['f0'])-min(star.ds.mode_id['f0']))/7.\n mx = max(star.ds.mode_id['f0']) +\\\n (max(star.ds.mode_id['f0'])-min(star.ds.mode_id['f0']))/7.\n plt.xlim([mn,mx])\n\n plt.legend()\n plt.title('KIC ' + str(self.ds.epic))\n plt.show()\n fig.savefig(os.getcwd() + os.sep + 'DetTest1_plots' + os.sep +\\\n 'plot4_SNR' + self.ds.epic + '.pdf')","def plot_data(indf, prefix='html'):\n list_of_plots = []\n# scatter_matrix(indf)\n# pl.savefig('scatter_matrix.png')\n# list_of_plots.append('scatter_matrix.png')\n\n for col in indf:\n pl.clf()\n# cond = indf[col].notnull()\n# v = indf[cond][col]\n v = indf[col]\n# nent = len(v)\n# hmin, hmax = v.min(), v.max()\n# xbins = np.linspace(hmin,hmax,nent)\n# hmin, hmax, nbin = BOUNDS[col]\n# xbins = np.linspace(hmin, hmax, nbin)\n v.hist(bins=20, histtype='step', normed=True, log=True)\n pl.title(col)\n pl.savefig('%s_hist.png' % col)\n list_of_plots.append('%s_hist.png' % col)\n\n create_html_page_of_plots(list_of_plots, prefix)\n return","def display(self):\r\n \r\n plt.rcParams['font.size'] = 14\r\n plt.rcParams['axes.linewidth'] = 1.2 # 1.2 for single plot, 0.5 for all 6\r\n plt.rcParams['lines.linewidth'] = 20.0 # Aah, this doesn't work because line width is changed later on\r\n\r\n cwd = os.getcwd() # Gets current working directory.\r\n cwd = cwd.replace('\\\\', '/')\r\n path = cwd + directory # This is the folder all the results are stored in.\r\n \r\n if type(array_element) == str:\r\n dataframes = [file + array_element] # This is to pass a single csv file\r\n else:\r\n dataframes = [file + i for i in array_element] # This is a list so you can pass multiple csv files to be overlayed on the same plot.\r\n\r\n colours = ['black', 'darkred', 'darkmagenta', 'darkturquoise', 'saddlebrown'] # Array of colours for the lines.\r\n\r\n dfE = pd.read_csv(cwd + \"/experimental_data.csv\") # Reads in the experimental data as a pandas dataframe.\r\n\r\n # Rescale the x-axis of the experimental data.\r\n ratio_of_capacities = 272.4 / 338.313338 # experimental maximum capacity / theoretical maximum capacity\r\n dfE[\"x_theo\"] = ratio_of_capacities * dfE[\"x\"]\r\n # 'x' is the experimental x and 'x_theo' is the theoretical x.\r\n\r\n # Second derivative of enthalpy for experimental data. One w/ respect to the experimental x and one w/ respect to theoretical x.\r\n secder_enthalpy_experimental_x = np.gradient(np.array(dfE['Enthalpy dH/dx']), np.array(dfE['x']))\r\n secder_enthalpy_experimental_x_theo = np.gradient(np.array(dfE['Enthalpy dH/dx']), np.array(dfE['x_theo']))\r\n dfE['secder enthalpy x'] = secder_enthalpy_experimental_x\r\n dfE['secder enthalpy x theo'] = secder_enthalpy_experimental_x_theo\r\n\r\n # vertical shift on p.m. entropy for vibrational effect\r\n vibrational_shift = 0.0108 # eV K this includes being multiplied by the ratio of capacities.\r\n dfE[\"Entropy dS/dx\"] = (dfE[\"Entropy dS/dx\"]) - vibrational_shift\r\n\r\n # Integrates the p.m. entropy\r\n entropy_list_experimental = integrate.cumtrapz(dfE['Entropy dS/dx'], dfE['x'],\r\n initial=0) # Contains the entropy values\r\n dfE['Entropy'] = entropy_list_experimental\r\n\r\n dfE['x_new'] = ((dfE['x_theo'] - dfE['x_theo'].iloc[0]) * dfE['x_theo'][73]) / (dfE['x_theo'][73] - dfE['x_theo'].iloc[0]) # Rescales the line so that the experimental data starts at 0.\r\n dfE['x'] = ((dfE['x'] - dfE['x'].iloc[0]) * dfE['x'][73]) / (dfE['x'][73] - dfE['x'].iloc[0]) # Same as above but for experimental x axis.\r\n\r\n # Calculates the analytical solution\r\n points = 1000\r\n x_pos = np.linspace(0, 1, points) # x for p.m. entropy\r\n y_pos = np.linspace(0, 1, points) # y for p.m. etropy\r\n s_x = np.linspace(0, 1, points) # x for entropy\r\n s_y = np.linspace(0, 1, points) # y for entropy\r\n l = 0.329217689 # This must be the same as what was used in the main script\r\n R = -0.0000862 # eV/K.Site\r\n T = 288 # K\r\n for index, x in enumerate(x_pos):\r\n if x < l:\r\n s_y[index] = (R * (x * np.log(x / l) - (x - l) * np.log((l - x) / l))) * T\r\n y_pos[index] = T * R * (np.log(x / l) - np.log((l - x) / l))\r\n else:\r\n s_y[index] = (R * l * (\r\n (x / l - 1) * np.log(x / l - 1) + (1 - x) / l * np.log((1 - x) / l) - (1 - l) / l * np.log(\r\n (1 - l) / l))) * T\r\n y_pos[index] = T * R * (np.log(x / l - 1) - np.log(1 / l - x / l))\r\n\r\n # Calculates the single solid state entropy\r\n x_ent = np.linspace(0, 1, points)\r\n y_ent = np.linspace(0, 1, points)\r\n for index, x in enumerate(x_ent):\r\n y_ent[index] = T * R * (x * np.log(x) + (1-x) * np.log(1-x))\r\n \r\n \"\"\"\r\n #\r\n #\r\n # Create plot and formats\r\n #\r\n #\r\n \"\"\"\r\n \r\n fig, axes = plt.subplots(nrows=num_row, ncols=num_col, constrained_layout=True, squeeze=False)\r\n # squeeze=False is needed to prevent errors when plotting a single subplot\r\n plt.rc('legend', fontsize=13, handlelength=1)\r\n plt.rc('tick')\r\n lw = 1.5 # Line width\r\n \r\n plt.tick_params(bottom=True, top=True, left=True, right=True)\r\n plt.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\r\n plt.tick_params(direction='in', width=1.2, length=4.5, pad=3) # For single plot\r\n # plt.tick_params(direction='in', width=1, length=4.5, pad=3) # For multiple plots\r\n\r\n marker_list = ['v', '^', 'p', 'o']\r\n mark_size = 3 #0.7 for 6 plots\r\n \r\n colours = ['#176ba0', '#af4bce', 'orangered', '#48a11b', '#3caea3'] #'#af4bce'\r\n common_legend = ['400 Averaging Steps', '800 Averaging Steps', '2000 Averaging Steps']\r\n \r\n if num_col==2 and num_row==3: # This will work when using the original axes dimensions (3 rows, 2 columns)\r\n placement = dict([\r\n ('voltage', axes[0, 0]),\r\n ('dS/dx', axes[0, 1]),\r\n ('dQ/dV', axes[1, 0]),\r\n ('dH/dx', axes[1, 1]),\r\n ('S', axes[2, 0]),\r\n ('d/dx(dH/dx)', axes[2, 1])\r\n ])\r\n else: # If axes dimensions are different, I'm probably trying to plot one graph\r\n \"\"\"\r\n If plotting more than one graph, the position on the plot in the subplot can be adjusted\r\n by appropriately altering the axes[] parameter. For the graphs that are not being plotted, \r\n leave their position as axes[0, 0].\r\n \"\"\"\r\n placement = dict([\r\n ('voltage', axes[0, 0]),\r\n ('dS/dx', axes[0, 0]),\r\n ('dQ/dV', axes[0, 0]),\r\n ('dH/dx', axes[0, 0]),\r\n ('S', axes[0, 0]),\r\n ('d/dx(dH/dx)', axes[0, 0])\r\n ])\r\n \r\n # Plots all of the experimental data\r\n if experimental_plot == True:\r\n if pick_plot['voltage'] == True:\r\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['voltage'], x='x_new', y='OCV')\r\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['voltage'], x='x', y='OCV')\r\n \r\n if pick_plot['dS/dx'] == True:\r\n ax2 = dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dS/dx'], x='x_new', y='Entropy dS/dx')\r\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['dS/dx'], x='x', y='Entropy dS/dx')\r\n \r\n if pick_plot['dQ/dV'] == True:\r\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dQ/dV'], x='OCV', y='dQdV') \r\n \r\n if pick_plot['dH/dx'] == True:\r\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dH/dx'], x='x_new', y='Enthalpy dH/dx')\r\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['dH/dx'], x='x', y='Enthalpy dH/dx')\r\n \r\n if pick_plot['S'] == True:\r\n ax5 = dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['S'], x='x_new', y='Entropy')\r\n \r\n if pick_plot['d/dx(dH/dx)'] == True:\r\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['d/dx(dH/dx)'], x='x_new', y='secder enthalpy x theo')\r\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['d/dx(dH/dx)'], x='x', y='secder enthalpy x')\r\n\r\n # Iterate through all the data to be plotted\r\n if simulation_plot == True:\r\n for count, df in enumerate(dataframes):\r\n df1 = pd.read_csv(path + df) # reads file into a dataframe.\r\n \r\n df1 = df1.replace(0, np.nan).dropna(axis=0, how='all') # For the rows with all '0' entries they are replaced with 'nan' and then these rows are dropped.\r\n df1 = df1.replace(np.nan, 0) # As some legitimate 0 entries such as 0 volts we flip back the remaining from 'nan' to 0.\r\n \r\n # Integrates the p.m. entropy\r\n entropy_list = integrate.cumtrapz(df1['Partial molar entropy'], df1['Total mole fraction'],\r\n initial=0) # Contains the entropy values\r\n df1['Entropy'] = entropy_list\r\n \r\n # Rescale voltage profile and p.m. enthalpy by the chain rule.\r\n df1[\"adjusted voltage\"] = df1[\"Chemical potential\"] * ratio_of_capacities\r\n df1[\"adjusted enthalpy\"] = df1[\"Partial molar enthalpy\"] * ratio_of_capacities\r\n df1[\"adjusted entropy\"] = df1[\"Partial molar entropy\"] * ratio_of_capacities\r\n df1[\"adjusted dq/de\"] = df1[\"dq/de\"] * (1/ratio_of_capacities)**2\r\n \r\n # Differentiate the p.m. enthalpy to get the second derivative.\r\n pm_enthalpy = np.array(df1['adjusted enthalpy'])\r\n mole_fraction = np.array(df1['Total mole fraction'])\r\n secder_enthalpy = np.gradient(pm_enthalpy, mole_fraction)\r\n df1['secder enthalpy'] = secder_enthalpy\r\n \r\n if pick_plot['voltage'] == True:\r\n ax1 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['voltage'], x='Total mole fraction', y='adjusted voltage')\r\n ax1.set_xlim([0, 1])\r\n ax1.set_xlabel('Na content $[x]$')\r\n ax1.set_ylabel('Voltage $[V]$')\r\n ax1.legend(common_legend) \r\n # ax1.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\r\n \r\n if pick_plot['dS/dx'] == True:\r\n ax2 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['dS/dx'], x='Total mole fraction', y='adjusted entropy')\r\n # ax2.plot(x_pos, y_pos, linewidth=lw, color='red') # Plots the ideal p.m. entropy\r\n ax2.set_xlim([0, 1])\r\n ax2.set_xlabel('Na content $[x]$')\r\n ax2.set_ylabel('$\\\\frac{dS}{dx}$ $[eV K/site]$')\r\n ax2.legend(common_legend) \r\n # ax2.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data', 'Analytical solution'])\r\n \r\n if pick_plot['dQ/dV'] == True:\r\n ax3 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['dQ/dV'], x='Chemical potential', y='adjusted dq/de') \r\n ax3.set_xlim([-0.1, 1])\r\n ax3.set_xlabel('Voltage $[V]$')\r\n ax3.set_ylabel('$\\\\frac{dQ}{dV}$ [$\\mathregular{eV^{-1}}$]')\r\n ax3.legend(common_legend)\r\n # ax3.legend(['Experimental data', 'Monte Carlo Data'])\r\n \r\n if pick_plot['dH/dx'] == True:\r\n ax4 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['dH/dx'], x='Total mole fraction', y='adjusted enthalpy')\r\n ax4.set_xlim([0, 1])\r\n ax4.set_xlabel('Na content $[x]$')\r\n ax4.set_ylabel('$\\\\frac{dH}{dx}$ $[eV/site]$')\r\n ax4.legend(common_legend) \r\n # ax4.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\r\n \r\n if pick_plot['d/dx(dH/dx)'] == True:\r\n ax5 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['d/dx(dH/dx)'], x='Total mole fraction', y='secder enthalpy')\r\n ax5.set_xlim([0, 1])\r\n ax5.set_ylim([0, 6])\r\n ax5.set_xlabel('Na content $[x]$')\r\n ax5.set_ylabel('$\\\\frac{d^2H}{dx^2}$ $[eV/site]$')\r\n ax5.legend(common_legend)\r\n \r\n # ax5.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\r\n \r\n if pick_plot['S'] == True:\r\n ax6 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\r\n markersize=mark_size, ax=placement['S'], x='Total mole fraction', y='Entropy')\r\n \r\n # ax6.plot(s_x, s_y, linewidth=lw, color='red') # Plots the entropy for l=0.32...\r\n # ax6.plot(x_ent, y_ent, linewidth=lw, color='grey') # Plots the entropy for solid state solution.\r\n ax6.set_xlim([0, 1])\r\n ax6.set_xlabel('Na content $[x]$')\r\n ax6.set_ylabel('S $[eV K/site]$')\r\n ax6.legend(common_legend)\r\n # ax6.legend(['Experimental data', 'Monte Carlo data', 'Analytical solution', 'Solid state solution'], loc='upper right', bbox_to_anchor=(0.75, 0.5))\r\n \r\n \r\n\r\n # parameter_file = open(path + \"/Input_arguments_\" + uid + \".txt\", \"w\")\r\n # parameter_file.write(str(self.args))\r\n # parameter_file.close()\r\n\r\n # manager = plt.get_current_fig_manager()\r\n # # manager.resize(*manager.window.maxsize())\r\n # # fig_path = cwd + \"/Na_plot_results.png\"\r\n # # plt.savefig(path + \"/Na_monte_carlo_plot_\" + uid + \".png\")\r\n # plt.show()\r\n \r\n plt.savefig(\"Varying sps Overlaid Plots - dQ_dV\", dpi = 300)\r\n\r\n plt.show()","def grafCasosAtivos(tipPDF):\n\n\t\tdf = pd.read_csv('dadosPaises.csv') #dataFrame que recebe a tabela de dados\n\t\tdf = df.sort_values(by=['Casos Ativos'], ascending=False) #ordena pela coluna 'casos ativos' de forma descendente\n\t\tdf2 = df[:5] # Separa em um novo dataframe as 5 primeiras linhas\n\n\t\tfig, ax = plt.subplots()\n\n\t\tindex = np.arange(len(df2['Casos Ativos'])) #indice para definir as posicoes dos labels\n\t\tbar_width = 0.45 #grossura da barra\n\t\tscore_label = np.arange(0,50000,5000) #array com volares a serem apresentados no eixo y\n\t\tbar = ax.bar(index, df2['Casos Ativos'], bar_width, label='Casos Ativos',color='#ff4500')\n\t\t\n\t\t#setting axis labels\n\t\tax.set_xticks(index) #distancia entre barras\n\t\tax.set_xticklabels(df2['Dados do País']) #label das barras\n\t\tax.set_yticks(score_label) #distancia entre marcas do eixo y\n\t\tax.set_yticklabels(score_label) #labels das marcas do eixo y\n\t\tax.legend() #legenda\n\n\t\t#ciclo para plotar os labels\n\t\tfor i in index:\n\t\t\tax.annotate('{}'.format(bar[i].get_height()), #string a ser plotada\n\t\t\t\t\t\txy=(bar[i].get_x(),bar[i].get_height()), #local da plotagem do label\n\t\t\t\t\t\txytext=(0,2), #movimentacao adicional do label\n\t\t\t\t\t\ttextcoords='offset points',\n\t\t\t\t\t\tva='bottom'\n\t\t\t\t\t\t)\n\n\t\tplt.title('Paises com mais casos ativos')\n\n\t\t#Se true, o tipo selecionado sera pdf, senao png\n\t\tif tipPDF:\n\t\t\tnomFig = 'grafico.pdf'\n\t\telse :\n\t\t\tnomFig = 'grafico.png'\t\n\t\tplt.savefig(nomFig) #salva a imagem (poder ser salvo como pdf mudando o nome do arquivo","def plot_data_pca(data_dict):\n f = plt.figure()\n ndata, ntime, nhidden = data_dict['hiddens'].shape\n\n print('Number of data examples: ', ndata)\n print('Number of timesteps: ', ntime)\n print('Number of data dimensions: ', nhidden)\n pca = PCA(n_components=100)\n pca.fit(onp.reshape(data_dict['hiddens'], [ndata * ntime, nhidden]))\n\n plt.plot(onp.arange(1, 16), onp.cumsum(pca.explained_variance_ratio_)[0:15],\n '-o');\n plt.plot([1, 15], [0.95, 0.95])\n plt.xlabel('PC #')\n plt.ylabel('Cumulative Variance')\n plt.xlim([1, 15])\n plt.ylim([0.3, 1]);\n return f","def figure_2(df):\n\n sns.set(style=\"white\", palette=sns.color_palette(\"cubehelix\", 6))\n f, axes = plt.subplots(4, 1, figsize=(6, 9)) # , sharex=True)\n sns.despine(top=True, bottom=True)\n # f.suptitle(\"Etiology of Central Events, Grouped by %Central Events\")\n\n OSA_pure_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \"Mainly OSA\"], return_df=True).sort_values(\"Dx\"))\n OSA_predom_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \"Combined OSA/CSA\"], return_df=True).sort_values(\"Dx\"))\n CSA_predom_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \"Predominantly CSA\"], return_df=True).sort_values(\"Dx\"))\n CSA_pure_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \"Pure CSA\"], return_df=True).sort_values(\"Dx\"))\n\n # Create count plot for each #CSA on the left, then a Pie Chart with proportion on the right\n\n hatches = ['///', 'xxx', '---', '', '']\n face_color = ['white', 'white', 'white', 'white', 'dimgrey']\n\n # Pure OSA\n bar = sns.barplot(x=\"Count\", y=\"Dx\", data=OSA_pure_hist, ax=axes[3])\n for i, this_bar in enumerate(bar.patches):\n # Set a different hatch for each bar\n this_bar.set_edgecolor('black')\n this_bar.set_facecolor(face_color[i])\n this_bar.set_hatch(hatches[i])\n axes[3].set(xlabel=\"Number of Patients\", ylabel=\"<10% CSA\")\n\n # Predom OSA\n bar = sns.barplot(x=\"Count\", y=\"Dx\", data=OSA_predom_hist, ax=axes[2])\n for i, this_bar in enumerate(bar.patches):\n # Set a different hatch for each bar\n this_bar.set_edgecolor('black')\n this_bar.set_facecolor(face_color[i])\n this_bar.set_hatch(hatches[i])\n axes[2].set(xlabel=\"\", ylabel=\"10-49.9% CSA\")\n\n # Predom CSA\n bar = sns.barplot(x=\"Count\", y=\"Dx\", data=CSA_predom_hist, ax=axes[1])\n for i, this_bar in enumerate(bar.patches):\n # Set a different hatch for each bar\n this_bar.set_edgecolor('black')\n this_bar.set_facecolor(face_color[i])\n this_bar.set_hatch(hatches[i])\n axes[1].set(xlabel=\"\", ylabel=\"50-90% CSA\")\n\n # Pure CSA\n bar = sns.barplot(x=\"Count\", y=\"Dx\", data=CSA_pure_hist, ax=axes[0])\n for i, this_bar in enumerate(bar.patches):\n # Set a different hatch for each bar\n this_bar.set_edgecolor('black')\n this_bar.set_facecolor(face_color[i])\n this_bar.set_hatch(hatches[i])\n axes[0].set(xlabel=\"\", ylabel=\">90% CSA\")\n\n # Combined X axis for L side\n axes[3].get_shared_x_axes().join(axes[3], axes[2], axes[1], axes[0])\n axes[0].set_xticklabels(\"\")\n axes[1].set_xticklabels(\"\")\n axes[2].set_xticklabels(\"\")\n # Leave bottom aka [3,0] labels in\n\n # Resize all\n axes[0].autoscale()\n axes[1].autoscale()\n axes[2].autoscale()\n axes[3].autoscale()\n\n f.tight_layout(rect=[0, 0, 1, 1]) # .95 to leave space for title\n f.savefig('Figure 2 - etio by perc csa', dpi=100)\n # plt.show()","def plot_word_class_pr_genre(df):\n df['nouns'] = df['nouns'] * 100\n df['verbs'] = df['verbs'] * 100\n df['adverbs'] = df['adverbs'] * 100\n # plotting nouns\n plotting_helper_method('nouns', 'genre', df)\n plt.title('Amount of nouns pr song pr. genre')\n plt.xlabel(\"Amount of nouns in each song\")\n plt.ylabel('Genre')\n plt.legend()\n plt.show()\n # plt.savefig('src/visualization/feature_plots/nouns_pr_genre_plot')\n\n # plotting verbs\n plotting_helper_method('verbs', 'genre', df)\n plt.title('Amount of verbs pr song pr. genre')\n plt.xlabel('Amount of verbs in each song')\n plt.ylabel('Genre')\n plt.legend()\n plt.show()\n # plt.savefig('src/visualization/feature_plots/verbs_pr_genre_plot')\n\n # plotting adverbs\n plotting_helper_method('adverbs', 'genre', df)\n plt.title('Amount of adverbs pr song pr. genre')\n plt.xlabel('Amount of adverbs in each song')\n plt.ylabel('Genre')\n plt.legend()\n plt.show()\n # plt.savefig('src/visualization/feature_plots/adverbs_pr_genre_plot')"],"string":"[\n \"def avg_response_report(df, var_list, y_obs, y_est, file):\\n page = PdfPages(file)\\n for var in var_list:\\n avg_response(df, var, y_obs, y_est, show=False)\\n page.savefig()\\n page.close()\",\n \"def explore_data(data,prj_info,TMP=1234):\\r\\n print(\\\" Data file rows and columns are : \\\", data.shape)\\r\\n #Open pdf\\r\\n pp = PdfPages(prj_info['OUTPUT_PATH'] + \\\"exploration_\\\" + str(TMP) + \\\".pdf\\\")\\r\\n\\r\\n #Plot average\\r\\n plot_average_reponse(data,prj_info,pp,TMP)\\r\\n\\r\\n #Close pdf\\r\\n pp.close()\\r\\n return None\",\n \"def do_plot(self):\\n years = sorted(set(self.prediction_df_without_covid19['Year']))\\n predict_without_covid_country = self.prediction_df_without_covid19[\\n self.prediction_df_without_covid19['Country'].isin([self.country])].sort_values(['Year'],\\n ascending=[True])\\n predict_with_covid_country = self.prediction_df_with_covid19[\\n self.prediction_df_with_covid19['Country'].isin([self.country])].sort_values(['Year'],\\n ascending=[True])\\n # ------------------------------------------------------------------------------------------------------\\n pa = \\\\\\n predict_without_covid_country.loc[predict_without_covid_country['Year'] == 1990][\\n 'Total_CO2_Emissions'].values[\\n 0]\\n x = []\\n for i in range(len(years)):\\n x.append(pa * 0.6)\\n # ------------------------------------------------------------------------------------------------------\\n fig = Figure()\\n ax = fig.subplots()\\n ax.grid(True, alpha=0.3)\\n # plot_title = 'Total CO2 Emissions predicted from 2019-2030 for ' + self.country\\n plot_title = 'Total ' + '$CO_2$' + ' Emissions predicted from 2019-2030 for ' + self.country\\n label_country_without_covid = 'Total CO2 emissions without covid'\\n label_country_with_covid = 'Total CO2 emissions with Covid-19'\\n # ------------------------------------------------------------------------------------------------------\\n params = {'mathtext.default': 'regular'}\\n rcParams.update(params)\\n rcParams['font.size'] = 7\\n rcParams['lines.markersize'] = 4\\n rcParams['figure.figsize'] = [7, 4]\\n rcParams['figure.dpi'] = 150\\n rcParams['font.family'] = 'Verdana'\\n rcParams[\\\"font.weight\\\"] = \\\"normal\\\"\\n font = {'family': 'Verdana',\\n 'color': 'xkcd:darkgreen',\\n 'weight': 'normal',\\n 'size': 9,\\n }\\n colors = rcParams['axes.prop_cycle'].by_key()['color']\\n l1, = ax.plot(years, predict_without_covid_country['Total_CO2_Emissions'], color='xkcd:dark blue green',\\n marker='o',\\n label=label_country_without_covid)\\n l2, = ax.plot(years, predict_with_covid_country['Total_CO2_Emissions'], color='xkcd:neon pink', marker='.',\\n label=label_country_with_covid)\\n l3, = ax.plot(years, x, color='xkcd:orchid', marker='1')\\n print('without covid: ', predict_without_covid_country['Total_CO2_Emissions'].values)\\n print('with covid: ', predict_with_covid_country['Total_CO2_Emissions'].values)\\n ax.set_xlabel('Years', fontdict=font)\\n ax.set_ylabel('Emissions (Gg)', fontdict=font)\\n ax.set_title(plot_title, fontsize=12, fontweight='normal')\\n ax.patch.set_facecolor('xkcd:green')\\n ax.set_facecolor('xkcd:pale green')\\n fig.legend((l1, l2, l3), ('Prediction without Covid19', 'Prediction with Covid19', 'Paris Agreement'),\\n bbox_to_anchor=(0.907, 0.89))\\n fig.savefig(OUTPUT_GRAPH_PATH)\",\n \"def data_visualization(df):\\r\\n\\r\\n # Visualizing the target variable\\r\\n plt.figure(figsize=(14, 10))\\r\\n plt.title(\\\"Count of bike sharing according to dates\\\")\\r\\n plt.plot(df['dteday'], df['cnt'])\\r\\n #plt.show()\\r\\n plt.savefig(\\\"Raw data visualization.png\\\")\\r\\n\\r\\n # box plot for visualizing outliers\\r\\n fig=px.box(df, y=\\\"cnt\\\", notched=True,title='Box plot of the count variable')\\r\\n #fig.show()\\r\\n plt.savefig(\\\"Box Plot.png\\\")\\r\\n\\r\\n # point plot for hourly utilization\\r\\n for column in ['season', 'yr', 'mnth', 'holiday', 'weekday', 'workingday', 'weathersit']:\\r\\n hist = px.histogram(df, x=column, y='cnt')\\r\\n hist.show()\\r\\n plt.savefig(\\\"Histogram plots for each column.png\\\")\\r\\n sns.pointplot(x=df['hr'], y='cnt', data=df);\\r\\n plt.title(\\\"Hourly Utilization\\\")\\r\\n plt.ylabel(\\\"Bike Shares\\\", fontsize=12)\\r\\n plt.xlabel(\\\"Hour\\\", fontsize=12)\\r\\n plt.savefig(\\\"Hourly Utilization point plot.png\\\", dpi=300, bbox_inches='tight')\\r\\n\\r\\n # line plot for hourly utilization\\r\\n for c in ['holiday','season','workingday']:\\r\\n sns.lineplot(data=df,x='hr',y='cnt',hue=c)\\r\\n plt.title('Hourly plot vs count')\\r\\n plt.savefig(\\\"Hour vs count plot_main features.png\\\",dpi=300, bbox_inches='tight')\\r\\n\\r\\n # point plots for humidity vs count\\r\\n sns.pointplot(x='hum', y='cnt', data=df)\\r\\n plt.title(\\\"Amount of bike shares vs humidity\\\", fontsize=25)\\r\\n plt.xlabel(\\\"Humidity (%)\\\", fontsize=20)\\r\\n plt.ylabel('count of bike shares', fontsize=20)\\r\\n plt.locator_params(axis='x', nbins=10)\\r\\n plt.savefig(\\\"Pointplot of humidity vs count.png\\\",dpi=300, bbox_inches='tight')\\r\\n\\r\\n # box plots of whole df\\r\\n bx=px.box(df, y=\\\"cnt\\\")\\r\\n bx.show()\\r\\n\\r\\n # feature correlation plot\\r\\n corrs = abs(df.corr())\\r\\n sns.heatmap(corrs, annot=True)\\r\\n plt.title(\\\"Feature Correlation\\\")\\r\\n plt.savefig(\\\"Feature_correlation.png\\\", dpi=300, bbox_inches='tight')\\r\\n return plt\",\n \"def plot_data(data, par, par_names, par_fixed, output_dir='./'):\\n\\n datasets = dict()\\n\\n for data_point in data:\\n experiment_name = data_point.par['experiment_name']\\n datasets.setdefault(experiment_name, list()).append(data_point)\\n\\n for experiment_name, dataset in datasets.items():\\n\\n # ##### Matplotlib ######\\n\\n name_pdf = ''.join([experiment_name, '.pdf'])\\n name_pdf = os.path.join(output_dir, name_pdf)\\n\\n name_txt = ''.join([experiment_name, '.fit'])\\n name_txt = os.path.join(output_dir, name_txt)\\n\\n print(\\\" * {} [.fit]\\\".format(name_pdf))\\n\\n # #######################\\n\\n data_grouped = group_data(dataset)\\n profiles, r2_min, r2_max = compute_profiles(data_grouped)\\n ymin, ymax = set_lim([r2_min, r2_max], 0.10)\\n\\n with PdfPages(name_pdf) as file_pdf, open(name_txt, 'w') as file_txt:\\n\\n for (_index, id_), profile in sorted(profiles.items()):\\n write_profile(id_, profile, file_txt)\\n\\n ###### Matplotlib ######\\n\\n fig = plt.figure(1, frameon=True)\\n ax = fig.add_subplot(111)\\n\\n ax.axhline(0, color='black', alpha=0.87)\\n\\n ########################\\n\\n frq, r2_cal, r2_exp, r2_erd, r2_eru = profile[0]\\n\\n ax.plot(\\n frq,\\n r2_cal,\\n linestyle='-',\\n color=red200,\\n zorder=2,\\n )\\n\\n ax.errorbar(\\n frq,\\n r2_exp,\\n yerr=[r2_erd, r2_eru],\\n fmt='o',\\n color=red500,\\n zorder=3,\\n )\\n\\n xmin, xmax = set_lim(frq, 0.10)\\n\\n ax.set_xlim(xmin, xmax)\\n ax.set_ylim(ymin, ymax)\\n\\n ax.xaxis.set_major_locator(MaxNLocator(6))\\n ax.yaxis.set_major_locator(MaxNLocator(6))\\n\\n ax.set_xlabel(r'$\\\\mathregular{\\\\nu_{CPMG} \\\\ (Hz)}$')\\n ax.set_ylabel(\\n r'$\\\\mathregular{R_{2,eff} \\\\ (s^{-1})}$')\\n\\n ax.set_title('{:s}'.format(id_.upper()))\\n\\n fig.tight_layout()\\n\\n ########################\\n\\n file_pdf.savefig()\\n plt.close()\\n\\n ########################\\n\\n return\",\n \"def _plot_marginal_pdfs( res, nbins=101, **kwargs):\\n\\tfrom matplotlib import pyplot as pl\\n\\timport numpy as np\\n\\n\\tnparam = len(res.vparam_names)\\n\\t# nrow = np.sqrt( nparam )\\n\\t# ncol = nparam / nrow + 1\\n\\tnrow, ncol = 1, nparam\\n\\n\\tpdfdict = _get_marginal_pdfs( res, nbins )\\n\\n\\tfig = plt.gcf()\\n\\tfor parname in res.vparam_names :\\n\\t\\tiax = res.vparam_names.index( parname )+1\\n\\t\\tax = fig.add_subplot( nrow, ncol, iax )\\n\\n\\t\\tparval, pdf, mean, std = pdfdict[parname]\\n\\t\\tax.plot( parval, pdf, **kwargs )\\n\\t\\tif np.abs(std)>=0.1:\\n\\t\\t\\tax.text( 0.95, 0.95, '%s %.1f +- %.1f'%( parname, np.round(mean,1), np.round(std,1)),\\n\\t\\t\\t\\t\\t ha='right',va='top',transform=ax.transAxes )\\n\\t\\telif np.abs(std)>=0.01:\\n\\t\\t\\tax.text( 0.95, 0.95, '%s %.2f +- %.2f'%( parname, np.round(mean,2), np.round(std,2)),\\n\\t\\t\\t\\t\\t ha='right',va='top',transform=ax.transAxes )\\n\\t\\telif np.abs(std)>=0.001:\\n\\t\\t\\tax.text( 0.95, 0.95, '%s %.3f +- %.3f'%( parname, np.round(mean,3), np.round(std,3)),\\n\\t\\t\\t\\t\\t ha='right',va='top',transform=ax.transAxes )\\n\\t\\telse :\\n\\t\\t\\tax.text( 0.95, 0.95, '%s %.3e +- %.3e'%( parname, mean, std),\\n\\t\\t\\t\\t\\t ha='right',va='top',transform=ax.transAxes )\\n\\n\\tplt.draw()\",\n \"def plot(self):\\n # get data without totals\\n data = self.woe_report[self.woe_report.index != 'total']\\n # setup panel\\n fig, axs = plt.subplots(1, 3, figsize=(12, 3))\\n plt.subplots_adjust(wspace=0.3)\\n # first chart\\n data['P(Hi|A)'].plot(ax=axs[0], linewidth=3, alpha=0.7)\\n data['P(Hi|Ā)'].plot(ax=axs[0], linewidth=3, alpha=0.7)\\n axs[0].set_title('Probability distribution')\\n axs[0].set_xlabel(data.index.name)\\n axs[0].set_ylabel('probability')\\n axs[0].legend(['P(Hi|A)', 'P(Hi|Ā)'])\\n # second chart\\n data['weight-of-evidence'].plot(ax=axs[1], linewidth=3, alpha=0.7)\\n axs[1].set_title('WoE')\\n axs[1].set_xlabel(data.index.name)\\n axs[1].set_ylabel('WoE')\\n # third chart\\n data['information-value'].plot(ax=axs[2], linewidth=3, alpha=0.7)\\n axs[2].set_title('Information value')\\n axs[2].set_ylabel('IV')\",\n \"def plot_individual(xdict, ydict, xprop, yprop, documents, spline):\\n figure_array = {}\\n for item in documents:\\n xlabel = \\\"\\\\\\\\textbf{\\\" + label_dict[xprop] + \\\"}\\\"\\n ylabel = \\\"\\\\\\\\textbf{\\\" + label_dict[yprop] + \\\"}\\\"\\n x = xdict[item[\\\"path_id\\\"]]\\n y = ydict[item[\\\"path_id\\\"]]\\n # fig_title = item[\\\"path_id\\\"] + \\\"(\\\" + item[\\\"pretty_formula\\\"] + \\\")\\\" # Individual traces\\n # fig_title = yprop + item[\\\"cation_type\\\"] # Plot by cation\\n fig_title = yprop # All together\\n figure_array[item[\\\"path_id\\\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\\n ax = figure_array[item[\\\"path_id\\\"]].add_subplot(111) \\n ax.scatter(x,y, s=70, zorder=2, color=color_dict[item[\\\"cation_type\\\"]], linewidths=2.5, edgecolors='black')\\n if spline:\\n tck = interpolate.splrep(x, y, s=0)\\n xnew = np.arange(0, 100, 0.1)\\n splfit = interpolate.splev(xnew, tck, der=0)\\n x = xnew\\n y = splfit\\n if item[\\\"path_id\\\"][-3:] == \\\"002\\\":\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]], linestyle='dashed')\\n elif item[\\\"path_id\\\"][-3:] == \\\"003\\\":\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]], linestyle='dotted')\\n else:\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]])\\n ax.set_xlabel(xlabel, fontsize=24)\\n # ax.set_ylim([0,1200])\\n # ax.set_xlim([0,100])\\n ax.set_ylabel(ylabel, fontsize=24)\\n ax.tick_params(axis='x', labelsize=22)\\n ax.tick_params(axis='y', labelsize=22)\\n border_width = 2\\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\\n plt.tight_layout()\\n plt.legend(loc='best', prop={'size': 14})\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='sans-serif')\\n plt.tight_layout()\\n plt.show()\",\n \"def three_PDF_plots(res=200,table_exts=[''],**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n GR = glo.global_results()\\n\\n fig, axs = plt.subplots(3, sharex='col',\\\\\\n figsize=(8,15),facecolor='w',\\\\\\n gridspec_kw={'hspace': 0, 'wspace': 0})\\n\\n # First print cell data distribution\\n i = 0\\n for gal_index in zip(p.gal_index):\\n ax1 = axs[i]\\n gal_ob = gal.galaxy(GR=GR, gal_index=gal_index)\\n df = gal_ob.cell_data.get_dataframe()\\n lognH = np.log10(df.nH)\\n hist = np.histogram(lognH[df.nH.values > 0],bins=200,weights=df.m[df.nH.values > 0])\\n hist1 = np.asarray(hist[0]) # histogram\\n hist2 = np.asarray(hist[1]) # bin edges\\n hist1 = hist1*1./sum(hist1)\\n ax1.plot(hist2[0:len(hist1)],hist1,drawstyle='steps',ls='-',lw=1.5,\\\\\\n alpha=0.7,color=p.color[0],label='Original cell distribution')\\n \\n for table_ext,ls,color in zip(table_exts,['--',':'],p.color[1::]):\\n if '_M10' in table_ext: lab = 'Mach = 10'\\n if '_arepoPDF_M51' in table_ext: lab = 'AREPO parametrized PDF'\\n PDF(gal_index,color=color,table_ext=table_ext,ls=ls,res=200,add=True,ax=ax1,label=lab,ow=p.ow)\\n \\n if i == 0: ax1.legend(loc='upper right',fontsize=12)\\n if i == 2: ax1.set_xlabel(getlabel('lnH'))\\n ax1.set_ylabel('Mass fraction per bin')\\n\\n i += 1\\n\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'cell_data/PDFs/'): os.mkdir(p.d_plot + 'cell_data/PDFs/') \\n plt.savefig(p.d_plot + 'cell_data/PDFs/simple_PDF_%s%s%s_x3.png' % (p.sim_name,p.sim_run,p.table_ext), format='png', dpi=250, facecolor='w')\",\n \"def plot_data(self):\",\n \"def plot(self):\\n # Get data\\n #print(self.file_name)\\n fig, ax = plb.subplots(1,1,figsize=(18,20))\\n for key,value in self.testTrend.items():\\n x = np.arange(len(self.data_array))\\n y = np.asarray(value)\\n plb.plot(x,y, label=key)\\n ax.scatter(x, y)\\n for i in range(0, len(value)):\\n ax.annotate(str(i), (x[i], y[i]))\\n # Title\\n plb.title(self.file_name)\\n # Legend\\n plb.legend(bbox_to_anchor=(.05, 1), loc='best', borderaxespad=0.)\\n # x ticks\\n plb.xticks(np.arange(min(x), max(x) + 1, 2.0))\\n #plb.ylim(-250, 1)\\n # Show image\\n plb.show()\",\n \"def resultPlots(record):\\n record.createDataFrames()\\n \\n atmPlot(record)\\n clientPlot(record)\\n transactionPlot(record)\",\n \"def analysis_plot(predictions, ys):\\n fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 5))\\n\\n residuals = ys - predictions\\n\\n # Plot 1 - Predicted vs Actual\\n sns.scatterplot(predictions, ys, ax=ax1)\\n ax1.set_title('Predicted vs Actual', fontsize=20)\\n ax1.set(xlabel='Predicted Ys', ylabel='Actual Ys')\\n\\n # Plot 2 - Residuals PLot (predicted vs residuals)\\n sns.scatterplot(predictions, residuals, ax=ax2)\\n ax2.set_title('Residuals Plot', fontsize=20)\\n ax2.set(xlabel='Predicted Ys', ylabel='Residuals')\\n\\n # Plot 3 - QQ Plot\\n sm.qqplot(residuals, ax=ax3, line='s')\\n ax3.set_title('QQ Plot- Distribution of Residuals', fontsize=20)\\n\\n plt.show();\",\n \"def plot_parameter_evolution(analyses, pdf=False):\\n ncs = np.arange(11, 15)\\n genes = set(analyses.gene)\\n constructs = set(analyses.construct)\\n long_labels = {'bac': 'bac', 'no_pr': 'no pr', 'no_sh': 'no sh'}\\n gene_long = {'hb': 'hunchback', 'kn': 'knirps', 'sn': 'snail'}\\n y_label = {'j': 'Normalized flux $j$',\\n 'rho': 'Site occupation density $\\\\\\\\rho$', 'tau': 'Residence time $\\\\\\\\tau$ (s)', 'alpha_comb': 'Initiation rate $\\\\\\\\alpha$ (pol/min)'}\\n\\n # Add extra jiggle to be able to distinguish overlapping data points\\n x_jiggle = 0.04\\n x_shifts = np.array([-1, 0, 1]) * x_jiggle\\n\\n # Plot parameters\\n capsize = 0\\n markersize = 4\\n lw = 1 # line width\\n\\n for gene in genes:\\n grouped_data = analyses.groupby(by=['gene', 'construct', 'nc'])\\n all_means = grouped_data.mean()\\n all_stds = grouped_data.std(ddof=1)\\n all_ps = analyses.groupby(by=['gene', 'nc']).first()\\n\\n for quantity in ['j', 'rho', 'tau', 'alpha_comb']:\\n ymaxs = {'j': 0.36, 'rho': 0.27, 'tau': 103, 'alpha_comb': 12}\\n num = 12\\n set_figure_size(num=num, rows=1, page_width_frac=0.5, clear=True, height_factor=0.7)\\n fig, ax = plt.subplots(1, 1, num=num, clear=True)\\n avg_data, std_data = {}, {}\\n for construct in constructs:\\n if quantity in ['rho', 'j']:\\n avg_data[construct] = all_means.loc[(\\n gene, construct, slice(None)), quantity].values\\n std_data[construct] = all_stds.loc[(\\n gene, construct, slice(None)), quantity].values\\n\\n elif quantity in ['tau', 'alpha_comb']:\\n avg_data[construct] = all_means.loc[(\\n gene, construct, slice(None)), quantity].values\\n std_data[construct] = np.sqrt(\\n all_means.loc[(gene, construct, slice(None)), quantity + 'V'].values)\\n\\n # Prepare a marker generator and plot the data with errorbars\\n marker_gen = itertools.cycle(markers_additivity)\\n for i, construct in enumerate(constructs):\\n m = next(marker_gen)\\n plt.errorbar(\\n ncs + x_shifts[i], avg_data[construct],\\n yerr=std_data[construct],\\n fmt='-' + m, color=colors_additivity[construct],\\n capsize=capsize, label=long_labels[construct],\\n markersize=markersize, lw=lw)\\n\\n # Adjust plot\\n plt.xlabel('Nuclear cycle')\\n plt.ylabel(y_label[quantity])\\n plt.ylim(ymin=0, ymax=ymaxs[quantity])\\n\\n plt.xticks(ncs)\\n plt.title(gene_long[gene])\\n\\n plt.tight_layout()\\n plt.show()\\n\\n # Save figure\\n figname = 'additivity_' + quantity + '_' + gene\\n figpath = os.path.join(figures_folder, figname)\\n fig.savefig(figpath + '.png', pad_inches=0, bbox_inches='tight')\\n if pdf:\\n fig.savefig(figpath + '.pdf', pad_inches=0, bbox_inches='tight')\",\n \"def Diagnostic_plot3(self):\\n\\n floc = glob.glob('/home/mxs191/Desktop/MathewSchofield/TRG/DetTest/DetTest1_results/Info2Save/*.csv')\\n fig = plt.figure()\\n plt.rc('font', size=18)\\n #fig, ax = generalPlot(xaxis=r'$\\\\nu / \\\\mu$Hz', yaxis=r'$P_{\\\\rm det}$')\\n gs = gridspec.GridSpec(1, 2, width_ratios=(4,1))\\n ax = fig.add_subplot(gs[0])\\n\\n for idx, i in enumerate(floc):\\n\\n d = pd.read_csv(i)\\n\\n if idx == 0:\\n fullpdet = d[['f0', 'Pdet_Kepler', 'Pdet_TESS365', 'Pdet_TESS27']]\\n else:\\n fullpdet = pd.concat([ fullpdet,\\\\\\n d[['f0', 'Pdet_Kepler', 'Pdet_TESS365', 'Pdet_TESS27']] ])\\n\\n plt.scatter(d['f0'], d['Pdet_Kepler'], color='b',\\\\\\n label=r\\\"$\\\\rm Kepler - 4\\\\ yrs$\\\" if idx == 0 else '')\\n plt.scatter(d['f0'], d['Pdet_TESS365'], color='orange',\\\\\\n label=r'$\\\\rm TESS - 1\\\\ yr$' if idx == 0 else '')\\n plt.scatter(d['f0'], d['Pdet_TESS27'], color='g',\\\\\\n label=r'$\\\\rm TESS - 27\\\\ days$' if idx == 0 else '')\\n\\n plt.axhline(fullpdet['Pdet_Kepler'].median(), color='b')\\n plt.axhline(fullpdet['Pdet_TESS365'].median(), color='orange')\\n plt.axhline(fullpdet['Pdet_TESS27'].median(), color='g')\\n ax.legend(loc='lower right')\\n plt.ylim([0,1])\\n ax.set_ylabel(r'$P_{\\\\rm det}$')\\n ax.set_xlabel(r'$\\\\nu / \\\\mu \\\\rm Hz$')\\n\\n bx = fig.add_subplot(gs[1])\\n import seaborn as sns\\n bw = 0.4\\n sns.kdeplot(fullpdet['Pdet_Kepler'].values, shade=True, vertical=True, \\\\\\n ax=bx, color='b', bw=bw)\\n sns.kdeplot(fullpdet['Pdet_TESS365'].values, shade=True, vertical=True, \\\\\\n ax=bx, color='orange', bw=bw)\\n sns.kdeplot(fullpdet['Pdet_TESS27'].values, shade=True, vertical=True, \\\\\\n ax=bx, color='g', bw=bw)\\n bx.set_ylim([0.0,1.0])\\n bx.set_xticks([])\\n bx.set_yticks([])\\n bx.set_xlabel(r'$\\\\rm Density$')\\n plt.tight_layout()\\n\\n plt.show()\\n fig.savefig(os.getcwd() + os.sep + 'DetTest1_plots' + os.sep +\\\\\\n 'DetTest_Diagnostic_plot3.pdf')\\n sys.exit()\",\n \"def plot_pdf(pop_name, pop_val, pop_file, full_pop_file, outdir='.'):\\n try:\\n plt.style.use(\\n \\\"https://gist.githubusercontent.com/avivajpeyi/4d9839b1ceb7d3651cbb469bc6b0d69b/raw/4ee4a870126653d542572372ff3eee4e89abcab0/publication.mplstyle\\\")\\n except Exception:\\n pass\\n\\n plt.close('all')\\n all = pd.read_csv(full_pop_file, sep=\\\" \\\")\\n all['cos_theta_1'] = all['cos_tilt_1']\\n all = process_samples(all)\\n sub = pd.read_csv(pop_file, sep=\\\" \\\")\\n sub = process_samples(sub)\\n sub['cos_theta_1'] = sub['cos_tilt_1']\\n\\n fig, axes = plt.subplots(1, 2, figsize=(10, 5))\\n for ax, l in zip(axes, [\\\"cos_theta_1\\\", \\\"cos_theta_12\\\"]):\\n ax.hist(all[l], density=True, histtype='step', color=\\\"tab:blue\\\", label=\\\"ALL\\\", lw=2, alpha=0.8)\\n ax.scatter(all[l], [0 for _ in all[l]], color=\\\"tab:blue\\\",marker=\\\"+\\\")\\n ax.hist(sub[l], density=True, histtype='step', color=\\\"tab:purple\\\", label=\\\"HIGH SNR\\\", lw=2, alpha=0.6)\\n ax.scatter(sub[l], [0 for _ in sub[l]], color=\\\"tab:purple\\\", marker=\\\"+\\\")\\n\\n x = np.linspace(-1, 1, 100)\\n y1 = TruncatedNormal(mu=1, sigma=pop_val[0], minimum=-1, maximum=1).prob(x)\\n y2 = TruncatedNormal(mu=1, sigma=pop_val[1], minimum=-1, maximum=1).prob(x)\\n axes[1].plot(x, y2, color='tab:gray', zorder=-10, lw=3, label=\\\"TRUE\\\")\\n axes[0].plot(x, y1, color='tab:gray', zorder=-10, lw=3)\\n\\n for i in range(len(axes)):\\n if (i == 0):\\n axes[i].set_xlabel(r\\\"$\\\\cos\\\\ \\\\theta_1$\\\")\\n axes[i].set_ylabel(\\\"PDF\\\")\\n else:\\n axes[i].set_xlabel(r\\\"$\\\\cos\\\\ \\\\theta_{12}$\\\")\\n axes[i].set_yticklabels([])\\n axes[i].legend()\\n axes[i].grid(False)\\n axes[i].set_xlim(-1, 1)\\n\\n plt.suptitle(f\\\"POP {pop_name}\\\")\\n plt.tight_layout()\\n plt.savefig(f\\\"{outdir}/pop_trues_{pop_name}.png\\\")\",\n \"def analyse_plots(plot_dict, data_dict) :\\n for component in [ '_x', '_y' ] :\\n z_pos = array.array( 'd' )\\n trans_pos = array.array( 'd' )\\n errors = array.array( 'd' )\\n zeros = array.array( 'd' )\\n\\n plot = plot_dict['beam_positions'+component]\\n\\n for i in range( plot.GetXaxis().GetNbins()+2 ) :\\n projection = plot.ProjectionY( \\\\\\n 'profile'+component+'_pro_'+str(i), i, i )\\n if projection.GetEntries() == 0 :\\n continue\\n\\n pro_mean, pro_mean_err, pro_std, pro_std_err = \\\\\\n scifi.fit_gaussian( projection )\\n\\n errors.append( pro_mean_err )\\n trans_pos.append( pro_mean )\\n z_pos.append( data_dict['station_positions'][ i-6 ] )\\n zeros.append(0.0)\\n\\n position_graph = ROOT.TGraphErrors( len(zeros), z_pos, trans_pos, \\\\\\n zeros, errors )\\n position_graph.SetName('beam_profile'+component)\\n plot_dict['beam_profile'+component] = position_graph\\n\\n profile_x = plot_dict['beam_profile_x']\\n profile_y = plot_dict['beam_profile_y']\\n\\n up_x_func = ROOT.TF1( \\\"up_fit_x\\\", \\\"pol1\\\", -5000.0, 0.0 )\\n up_y_func = ROOT.TF1( \\\"up_fit_y\\\", \\\"pol1\\\", -5000.0, 0.0 )\\n down_x_func = ROOT.TF1( \\\"down_fit_x\\\", \\\"pol1\\\", 0.0, 5000.0 )\\n down_y_func = ROOT.TF1( \\\"down_fit_y\\\", \\\"pol1\\\", 0.0, 5000.0 )\\n\\n up_fit_x = profile_x.Fit( 'up_fit_x', \\\"QSR\\\" )\\n up_fit_y = profile_y.Fit( 'up_fit_y', \\\"QSR\\\" )\\n down_fit_x = profile_x.Fit( 'down_fit_x', \\\"QSR\\\" )\\n down_fit_y = profile_y.Fit( 'down_fit_y', \\\"QSR\\\" )\\n\\n plot_dict['beam_profile_x_up_fit'] = up_x_func\\n plot_dict['beam_profile_y_up_fit'] = up_y_func\\n plot_dict['beam_profile_x_down_fit'] = down_x_func\\n plot_dict['beam_profile_y_down_fit'] = down_y_func\\n\\n\\n up_beam_gra_x = up_x_func.GetParameter(1)\\n up_beam_gra_x_err = up_x_func.GetParError(1)\\n up_beam_gra_y = up_y_func.GetParameter(1)\\n up_beam_gra_y_err = up_y_func.GetParError(1)\\n\\n up_beam_pos_x = data_dict['station_positions'][-1]*up_beam_gra_x + up_x_func.GetParameter(0)\\n up_beam_pos_x_err = up_x_func.GetParError(0)\\n up_beam_pos_y = data_dict['station_positions'][-1]*up_beam_gra_y + up_y_func.GetParameter(0)\\n up_beam_pos_y_err = up_y_func.GetParError(0)\\n\\n up_beam_rot_x = math.atan( up_beam_gra_x )\\n up_beam_rot_x_err = up_beam_gra_x_err # Approx linear\\n up_beam_rot_y = math.atan( up_beam_gra_y )\\n up_beam_rot_y_err = up_beam_gra_y_err # Approx linear\\n\\n\\n\\n down_beam_gra_x = down_x_func.GetParameter(1)\\n down_beam_gra_x_err = down_x_func.GetParError(1)\\n down_beam_gra_y = down_y_func.GetParameter(1)\\n down_beam_gra_y_err = down_y_func.GetParError(1)\\n\\n down_beam_pos_x = data_dict['station_positions'][1]*down_beam_gra_x + down_x_func.GetParameter(0)\\n down_beam_pos_x_err = down_x_func.GetParError(0)\\n down_beam_pos_y = data_dict['station_positions'][1]*down_beam_gra_y + down_y_func.GetParameter(0)\\n down_beam_pos_y_err = down_y_func.GetParError(0)\\n\\n down_beam_rot_x = math.atan( down_beam_gra_x )\\n down_beam_rot_x_err = down_beam_gra_x_err # Approx linear\\n down_beam_rot_y = math.atan( down_beam_gra_y )\\n down_beam_rot_y_err = down_beam_gra_y_err # Approx linear\\n\\n\\n# down_pos_x = down_beam_pos_x - data_dict['station_positions'][1]*up_beam_gra_x + up_x_func.GetParameter(0)\\n# down_pos_x_err = math.sqrt( up_x_func.GetParError(0)**2 + down_beam_pos_x_err**2 )\\n# down_pos_y = down_beam_pos_y - data_dict['station_positions'][1]*up_beam_gra_y + up_y_func.GetParameter(0)\\n# down_pos_y_err = math.sqrt( up_y_func.GetParError(0)**2 + down_beam_pos_y_err**2 )\\n\\n length = TRACKER_SEPARATION\\n down_pos_x = down_beam_pos_x - ( up_beam_pos_x + length*up_beam_gra_x )\\n down_pos_x_err = math.sqrt( up_beam_pos_x_err**2 + down_beam_pos_x_err**2 + (length*up_beam_gra_x_err)**2 )\\n down_pos_y = down_beam_pos_y - ( up_beam_pos_y + length*up_beam_gra_y )\\n down_pos_y_err = math.sqrt( up_beam_pos_y_err**2 + down_beam_pos_y_err**2 + (length*up_beam_gra_y_err)**2 )\\n\\n down_rot_x = down_beam_rot_x - up_beam_rot_x\\n down_rot_x_err = math.sqrt( down_beam_rot_x_err**2 + up_beam_rot_x_err**2 )\\n down_rot_y = down_beam_rot_y - up_beam_rot_y\\n down_rot_y_err = math.sqrt( down_beam_rot_y_err**2 + up_beam_rot_y_err**2 )\\n\\n\\n print\\n print \\\"Incoming Beam Misalignments:\\\"\\n print\\n print \\\"Displacement and rotation of beam with respect to upstream tracker:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( up_beam_pos_x, up_beam_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( up_beam_pos_y, up_beam_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( up_beam_rot_x*1000.0, up_beam_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( up_beam_rot_y*1000.0, up_beam_rot_y_err*1000.0 )\\n print\\n\\n print\\n print \\\"Downstream Tracker Beam Misalignments:\\\"\\n print\\n print \\\"Displacement and rotation of beam with respect to downstream tracker:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_beam_pos_x, down_beam_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_beam_pos_y, down_beam_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_beam_rot_x*1000.0, down_beam_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_beam_rot_y*1000.0, down_beam_rot_y_err*1000.0 )\\n print\\n\\n print\\n print \\\"Downstream Tracker Alignment:\\\"\\n print\\n print \\\"Displacement and rotation of between the two trackers:\\\"\\n print\\n print \\\"X Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_pos_x, down_pos_x_err )\\n print \\\"Y Position = {0:0.3f} +/- {1:0.3f} mm\\\".format( down_pos_y, down_pos_y_err )\\n print\\n print \\\"X Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_rot_x*1000.0, down_rot_x_err*1000.0 )\\n print \\\"Y Rotation = {0:0.3f} +/- {1:0.3f} mrad\\\".format( down_rot_y*1000.0, down_rot_y_err*1000.0 )\\n print\",\n \"def plot_results(self, a):\\n import matplotlib.pyplot as plt\\n fig, axes = plt.subplots(nrows=len(a.data_vars), sharex='all', sharey='all')\\n for ax, var in zip(axes, a.data_vars):\\n data = a[var]\\n plt.sca(ax)\\n data.plot(x='time', cmap=plt.cm.viridis_r, yincrease=False, robust=True)\\n plt.show()\",\n \"def plot_data(data, param_choice, args):\\n \\n ### set general plot properties\\n \\n savebase = '/usr/users/iff_th2/duman/Cells_in_LAMMPS/POVRAY/'\\n #downlim = -1\\n #uplim = sim.lx/4.\\n num_ticks = 5\\n ax_len = 1.0 # Length of one subplot square box\\n ax_b = 0.0 # Beginning/offset of the subplot in the box\\n ax_sep = 0.0 # Separation length between two subplots\\n total_subplots_in_x = 1 # Total number of subplots \\n fig = plt.figure()\\n subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x) \\n ax0 = subp.addSubplot()\\n \\n name = ''\\n pname = ''\\n if param_choice == 'areak': \\n name = 'AREAK'\\n pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \\\\\\n '_kappa_' + str(args.kappa)\\n xlab = '$\\\\kappa_A$'\\n tit = '$\\\\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \\\\\\n ',$\\\\kappa=$' + str(args.kappa)\\n elif param_choice == 'eps':\\n name = 'EPS'\\n pname = name + '_fp_' + str(args.fp) + '_areak_' + str(args.areak) + \\\\\\n '_kappa_' + str(args.kappa)\\n xlab = '$\\\\epsilon$'\\n tit = '$f_m=$' + str(args.fp) + ',$\\\\kappa_A=$' + str(args.areak) + \\\\\\n ',$\\\\kappa=$' + str(args.kappa) \\n elif param_choice == 'fp':\\n name = 'FP'\\n pname = name + '_eps_' + str(args.eps) + '_areak_' + str(args.areak) + \\\\\\n '_kappa_' + str(args.kappa)\\n xlab = '$f_{m}$'\\n tit = '$\\\\epsilon=$' + str(args.eps) + ',$\\\\kappa_A=$' + str(args.areak) + \\\\\\n ',$\\\\kappa=$' + str(args.kappa) \\n elif param_choice == 'kappa':\\n name = 'KAPPA'\\n pname = name + '_eps_' + str(args.eps) + '_fp_' + str(args.fp) + \\\\\\n '_areak_' + str(args.areak)\\n xlab = '$\\\\kappa$'\\n tit = '$\\\\epsilon=$' + str(args.eps) + ',$f_m=$' + str(args.fp) + \\\\\\n ',$\\\\kappa_A=$' + str(args.areak) \\n base = savebase + name + '/'\\n os.system(\\\"mkdir -p \\\" + base) \\n \\n ### plot \\n\\n subp = misc_tools.Subplots(fig, ax_len, ax_sep, ax_b, total_subplots_in_x) \\n ax0 = subp.addSubplot()\\n \\n x = data.keys()\\n y = [1 for j in range(len(data.keys()))]\\n print x\\n ax0.scatter(x, y)\\n ax0.set_xscale('log')\\n ax0.set_yscale('log')\\n \\n for j, p in enumerate(data.keys()):\\n \\n fname = data[p] \\n \\n if os.path.exists(fname):\\n arr_hand = read_png(fname)\\n \\n zoom=0.099\\n imagebox = OffsetImage(arr_hand, zoom=zoom)\\n\\n xy = [x[j], y[j]] # coordinates to position this image\\n\\n ab = AnnotationBbox(imagebox, xy,\\n xybox=(0., -0.),\\n xycoords='data',\\n boxcoords=\\\"offset points\\\",frameon=1,pad=.1) \\n \\n ax0.add_artist(ab)\\n \\n ### title\\n \\n ax0.set_title(tit, fontsize=30)\\n \\n ### labels\\n \\n ax0.set_xlabel(xlab, fontsize=30)\\n #ax0.set_ylabel(\\\"$F_{s}(q,\\\\\\\\Delta t)$\\\", fontsize=40)\\n\\n ### limits\\n\\n #ax0.set_xlim((-1, 15))\\n ax0.set_ylim((0.9999, 1.0001))\\n \\n ax0.grid(1, color='#cccccc', linestyle='--')\\n ax0.set_frame_on(False)\\n ax0.get_xaxis().tick_bottom()\\n ax0.axes.get_yaxis().set_visible(False)\\n xmin, xmax = ax0.get_xaxis().get_view_interval()\\n ymin, ymax = ax0.get_yaxis().get_view_interval()\\n ax0.add_artist(Line2D((xmin, xmax), (ymin, ymin), color='black', linewidth=2)) \\n ### ticks\\n \\n #ax0.xaxis.set_ticks(np.linspace(0, 15, num_ticks, endpoint=True))\\n #ax0.yaxis.set_ticks(np.linspace(0, uplim, num_ticks, endpoint=True))\\n plt.setp(ax0.get_yticklabels(),visible=False) \\n ax0.tick_params(axis='both', which='major', labelsize=30)\\n \\n ### legend\\n\\n# ax0.legend(bbox_to_anchor=(1.005, 0.,0.65, 1.), loc=2, borderaxespad=0., \\\\\\n# prop={'size': 20}, mode=\\\"expand\\\", frameon=False)\\n \\n ### save \\n \\n savepath = base + \\\"images_per_\\\" + pname + \\\".pdf\\\"\\n print savepath\\n plt.savefig(savepath, dpi=300, bbox_inches='tight', pad_inches=0.08) \\n fig.clf() \\n \\n return\",\n \"def multi_plot(data, fname=None):\\n for entry in data['data']:\\n plt.plot(entry['x'], entry['y'], label=entry['label'])\\n\\n plt.title(data['title'])\\n plt.xlabel(data['x_label'])\\n plt.ylabel(data['y_label'])\\n\\n #plt.legend(loc='best')\\n\\n Plotter.show(data['title'], fname=fname)\",\n \"def plot_sample(self):\\n print(u'plot_sample()')\\n data_set = self.data_sets[1]\\n scenario = u'Greedy Search'\\n titles = [u'Collaborative Filtering', u'Content-based']\\n fig, axes = plt.subplots(1, 2, figsize=(10, 5))\\n for i, rec_type in enumerate(data_set.missions):\\n graph = data_set.folder_graphs + rec_type + '_' + str(15) + u'.txt'\\n for strategy in Strategy.strategies:\\n m = data_set.missions[rec_type][graph][strategy][scenario]\\n m.compute_stats()\\n ppl.plot(axes[i], np.arange(STEPS_MAX + 1),\\n m.stats, label=strategy, linewidth=2)\\n axes[i].set_xlabel(u'#Hops')\\n axes[i].set_ylabel(u'Success Ratio')\\n axes[i].set_ylim(0, 85)\\n axes[i].set_xlim(0, STEPS_MAX * 1.01)\\n axes[i].set_title(titles[i])\\n ppl.legend(axes[i], loc=0)\\n\\n\\n # plt.suptitle(u'Greedy Search on the BookCrossing for N=15',\\n # size='xx-large', x=0.5)\\n fig.subplots_adjust(left=0.08, right=0.97, top=0.9)\\n\\n plt.savefig('plots/sample.png')\\n plt.savefig('plots/sample.pdf')\",\n \"def plot_prodata_psf(self,font_size=28,img_name='prodata_psf.pdf',img_id=0):\\n rawimage = self.raw_image\\n dataimage = self.data\\n len_mask = self.lens_mask\\n plu_mask_out = self.plu_mask\\n\\n fig, (ax1, ax2, ax3, ax4,ax5) = plt.subplots(1, 5, figsize=(19, 10))\\n ax1.imshow((rawimage), origin='lower', cmap=\\\"gist_heat\\\")\\n ax1.set_title('Original Image', fontsize=font_size)\\n ax1.text(rawimage.shape[0] * 0.55, rawimage.shape[0] * 0.8, 'ID='+repr(img_id), size=12, color='white',\\n weight=\\\"bold\\\")\\n ax1.text(rawimage.shape[0] * 0.2, rawimage.shape[0] * 0.05, 'observation', size=20, color='white', weight=\\\"bold\\\")\\n ax1.axis('off')\\n #\\n ax2.imshow((dataimage), origin='lower', cmap=\\\"gist_heat\\\")\\n ax2.set_title('Image Data', fontsize=font_size)\\n ax2.text(dataimage.shape[0] * 0.2, dataimage.shape[0] * 0.05, 'image data', size=20, color='white', weight=\\\"bold\\\")\\n ax2.axis('off')\\n #\\n ax3.imshow(len_mask, origin='lower')\\n ax3.set_title('Lens light', fontsize=font_size)\\n ax3.axis('off')\\n #\\n ax4.imshow(plu_mask_out, origin='lower')\\n ax4.set_title('Mask', fontsize=font_size)\\n ax4.axis('off')\\n#\\n psf=self.psf\\n ax5.imshow(np.log10(psf), origin='lower', cmap=\\\"gist_heat\\\")\\n ax5.set_title('lg(PSF)', fontsize=font_size)\\n ax5.axis('off')\\n\\n plt.show()\\n fig.savefig(img_name)\\n return 0\",\n \"def plot_actual_vs_predicted_by_equations(df, x_variable, y_variables, plot_title):\\n #Plot results\\n df.plot(x=x_variable, y=y_variables, title=plot_title)\\n plt.show()\",\n \"def plot_data_stats(data_dict, data_bxtxn, data_dt):\\n print(onp.mean(onp.sum(data_bxtxn, axis=1)), \\\"spikes/second\\\")\\n f = plt.figure(figsize=(12,4))\\n plt.subplot(141)\\n plt.hist(onp.mean(data_bxtxn, axis=1).ravel()/data_dt);\\n plt.xlabel('spikes / sec')\\n plt.subplot(142)\\n plt.imshow(data_dict['hiddens'][0,:,:].T)\\n plt.xlabel('time')\\n plt.ylabel('neuron #')\\n plt.title('Sample trial rates')\\n plt.subplot(143);\\n plt.imshow(data_bxtxn[0,:,:].T)\\n plt.xlabel('time')\\n plt.ylabel('neuron #')\\n plt.title('spikes')\\n plt.subplot(144)\\n plt.stem(onp.mean(onp.sum(data_bxtxn, axis=1), axis=0));\\n plt.xlabel('neuron #')\\n plt.ylabel('spikes / sec');\\n return f\",\n \"def plot_all(show=True):\\n fig, axes = plt.subplots(max_iterations, 1, figsize=(6, 12))\\n for t in range(max_iterations):\\n with open('results/%s/df_%d.pkl' % (id, t), 'rb') as f:\\n df = pickle.load(f)\\n with open('results/%s/w_%d.pkl' % (id, t), 'rb') as f:\\n w = pickle.load(f)\\n axes[t].hist2d(x=df['vision'], y=df['metab'], weights=w, density=True,\\n bins=((xticks, yticks)), cmap='magma')\\n axes[t].set_ylabel('max metabolism')\\n axes[t].set_xticks(vision_domain)\\n axes[t].set_yticks((2, 3, 4))\\n axes[3].set_xlabel('max vision')\\n fig.tight_layout()\\n if show:\\n plt.show()\\n else:\\n plt.savefig('results/%s/abc_results.pdf' % id)\",\n \"def plot(self, **kwargs):\\n\\n # get colors\\n colors = kwargs.get(\\\"colors\\\", GW_OBSERVATORY_COLORS)\\n\\n # get Result samples\\n self._samples = {\\n label: value.posterior\\n for label, value in self.results.items()\\n if isinstance(value, Result)\\n }\\n\\n # get Grid posteriors\\n self._grids = {\\n label: [value, value.ln_evidence] # store grid and log evidence\\n for label, value in self.results.items()\\n if isinstance(value, Grid)\\n }\\n\\n # apply offsets for slightly nicer plots axes\\n self.parameter_offsets = {parameter: 0.0 for parameter in self.parameters}\\n if len(self._grids) == 0 and len(self._samples) == 1:\\n for label in self._samples:\\n for parameter in self.parameters:\\n srange = [\\n np.min(self._samples[label][parameter]),\\n np.max(self._samples[label][parameter]),\\n ]\\n label_suffix = \\\"\\\"\\n\\n # offset values\\n median = np.median(self._samples[label][parameter])\\n relwidth = np.abs((srange[1] - srange[0]) / median)\\n\\n if relwidth < 1e-4:\\n offsetstr = f\\\"{median:.4e}\\\"\\n a, b = offsetstr.split(\\\"e\\\")\\n\\n if np.abs(int(b)) < 3:\\n offsetstr = f\\\"{median:.4f}\\\"\\n offset = float(offsetstr)\\n else:\\n offset = float(offsetstr)\\n offsetstr = a + rf\\\"\\\\!\\\\times\\\\!10^{{{int(b)}}}\\\"\\n\\n self.parameter_offsets[parameter] = offset\\n\\n self._samples[label][parameter] -= offset\\n label_suffix = rf\\\" [${{\\\\scriptstyle {offsetstr}}}$]\\\"\\n\\n self.latex_labels[parameter] += label_suffix\\n\\n colordicts = []\\n for j, res in enumerate([self._samples, self._grids]):\\n colordicts.append({})\\n for i, key in enumerate(res):\\n if key in colors:\\n colordicts[-1][key] = colors[key]\\n elif key.lower() == \\\"joint\\\":\\n # if using \\\"Joint\\\" as the multi-detector analysis key, set the color to black\\n colordicts[-1][key] = \\\"k\\\"\\n else:\\n # use PESummary color cycle\\n colordicts[-1][key] = list(colorcycle)[\\n (j * 2 + i) % len(colorcycle)\\n ]\\n\\n # store original keywords arguments\\n origkwargs = kwargs.copy()\\n\\n # plot samples\\n fig = None\\n if len(self._samples) > 0:\\n kwargs[\\\"colors\\\"] = list(colordicts[0].values())\\n if self._num_parameters == 1:\\n fig = self._1d_plot_samples(**kwargs)\\n elif self._num_parameters == 2 and self.plottype != \\\"corner\\\":\\n fig = self._2d_plot_samples(**kwargs)\\n else:\\n fig = self._nd_plot_samples(**kwargs)\\n\\n # restore keywords\\n kwargs = origkwargs\\n\\n if len(self._grids) > 0:\\n kwargs[\\\"colors\\\"] = list(colordicts[1].values())\\n if fig is not None and \\\"fig\\\" not in kwargs:\\n kwargs[\\\"fig\\\"] = fig\\n if self._num_parameters == 1:\\n fig = self._1d_plot_grid(**kwargs)\\n elif self._num_parameters == 2 and self.plottype != \\\"corner\\\":\\n fig = self._2d_plot_grid(**kwargs)\\n else:\\n fig = self._nd_plot_grid(**kwargs)\\n\\n # add further figure information\\n if self._num_parameters == 1:\\n ax = fig.gca()\\n\\n # set figure bounds if outside defaults\\n if self.parameters[0] in DEFAULT_BOUNDS:\\n _set_axes_limits(ax, self.parameters[0], axis=\\\"x\\\")\\n\\n # add injection values\\n if self.injection_parameters is not None:\\n if self.injection_parameters[self.parameters[0]] is not None:\\n ax.axvline(\\n (\\n self.injection_parameters[self.parameters[0]]\\n - self.parameter_offsets[self.parameters[0]]\\n ),\\n color=kwargs.get(\\\"injection_color\\\", \\\"k\\\"),\\n linewidth=1,\\n )\\n elif self._num_parameters == 2:\\n if \\\"triangle\\\" in self.plottype:\\n a1, a2, a3 = fig[1:]\\n order = [\\\"x\\\", \\\"y\\\"] if self.plottype == \\\"triangle\\\" else [\\\"y\\\", \\\"x\\\"]\\n params = (\\n self.parameters[:2]\\n if self.plottype == \\\"triangle\\\"\\n else self.parameters[1::-1]\\n )\\n\\n # set figure bounds if outside defaults\\n for param, axes, axis in zip(params, [[a1, a2], [a2, a3]], order):\\n for ax in axes:\\n _set_axes_limits(ax, param, axis=axis)\\n\\n self.fig = fig\\n return self.fig\",\n \"def ratio(gb_data, data_depcode, data_ratio_hospitalises,current_date, data_hospitalises, current_date_file, min_value_80p , nbhospitalises_80p) :\\n start = time.time()\\n fig, ax = plt.subplots(figsize=(12, 8))\\n\\n plt.title(f\\\"Ratio of in-hospital deaths to hospitalizations : {current_date}\\\", fontsize=20)\\n plt.ylabel(\\\"Total number of deceases / Total number of hospitalized\\\")\\n plt.xlabel(\\\"Total number of hospitalized\\\")\\n\\n for i, txt in enumerate(data_depcode):\\n if (data_hospitalises[i] > data_hospitalises.max() * 0.20):\\n ax.annotate(txt, (data_hospitalises[i], data_ratio_hospitalises[i]), xytext=(data_hospitalises[i] + 20, data_ratio_hospitalises[i])) \\n\\n plt.axhline(data_ratio_hospitalises.mean(), color='green', linestyle='--', label=f'average death ratio ({data_ratio_hospitalises.mean():.2f}%)')\\n\\n plt.axvline(min_value_80p, color='pink', linestyle='-', label=f\\\"80% of the number of hospitalized people in France are on the right side of the line ({nbhospitalises_80p:.0f} hospitalized)\\\")\\n\\n ax.scatter(data_hospitalises, data_ratio_hospitalises)\\n\\n ax.annotate('updated chart',xy=(1, 0), xytext=(-15, 10), fontsize=15,\\n xycoords='axes fraction', textcoords = 'offset points',\\n bbox=dict(facecolor = 'white', alpha = 0.9),\\n horizontalalignment = 'right', verticalalignment = 'bottom')\\n\\n ax.yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f%%'))\\n plt.legend()\\n\\n current_date_file = gb_data['date'].max().strftime('%Y%m%d')\\n end = time.time()\\n print(\\\"Time spent on ratio plot: {0:.5f} s.\\\".format(end - start)) \\n plt.show()\",\n \"def ResBeam_Stats_plot(n, header_bmaj, header_bmin): \\n\\n file_dir = 'SpectralCube_BeamLogs'\\n basename = '/beamlog.image.restored.' + imagebase + field\\n\\n # use different basename for the Milky Way range\\n if not glob.glob(file_dir + basename +'*.txt'):\\n basename = '/beamlog.image.restored.' + imagebase + 'MilkyWay.' + field\\n\\n \\n BEAM_THRESHOLD = []\\n \\n title1 = 'Restoring beam bmaj standard deviation [arcsec]'\\n plt_name1 = 'BmajStdev.png'\\n saved_fig1 = fig_dir+'/'+plt_name1\\n\\n title2 = 'Restoring beam bmin standard deviation [arcsec]'\\n plt_name2 = 'BminStdev.png'\\n saved_fig2 = fig_dir+'/'+plt_name2\\n\\n title3 = 'Maximum ratio of beam area'\\n plt_name3 = 'max_ratioBA.png'\\n saved_fig3 = fig_dir+'/'+plt_name3\\n\\n title4 = 'Minimum ratio of beam area' \\n plt_name4 = 'min_ratioBA.png'\\n saved_fig4 = fig_dir+'/'+plt_name4\\n \\n params = {'axes.labelsize': 10,\\n 'axes.titlesize': 10,\\n 'font.size':10}\\n\\n pylab.rcParams.update(params)\\n\\n beamXPOS, beamYPOS = BeamPosition()\\n fig1, ax1 = plt.subplots()\\n fig2, ax2 = plt.subplots()\\n fig3, ax3 = plt.subplots()\\n fig4, ax4 = plt.subplots()\\n \\n for i in range(0,36):\\n bnum = n[i]\\n infile = file_dir + basename +'.beam%02d.txt'%(bnum)\\n bmaj_stdev, bmin_stdev, beam_threshold, max_ratio_BA, min_ratio_BA = cal_ResBeam_Stats(infile, header_bmaj, header_bmin)\\n BEAM_THRESHOLD.append(beam_threshold)\\n\\n ax1.scatter([beamXPOS[i]], [beamYPOS[i]], s=1400, edgecolors='black', facecolors='none')\\n ax1.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\\n ax1.text(beamXPOS[i], beamYPOS[i]-0.02, round(bmaj_stdev, 3), va='center', ha='center', fontsize=8, color='blue')\\n\\n ax2.scatter([beamXPOS[i]], [beamYPOS[i]], s=1400, edgecolors='black', facecolors='none')\\n ax2.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\\n ax2.text(beamXPOS[i], beamYPOS[i]-0.02, round(bmin_stdev,3), va='center', ha='center', fontsize=8, color='blue')\\n\\n maxplot = ax3.scatter([beamXPOS[i]], [beamYPOS[i]], s=1300, c=[max_ratio_BA], cmap='summer', edgecolors='black', vmin=0, vmax=1.1)\\n ax3.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\\n ax3.text(beamXPOS[i], beamYPOS[i]-0.02, round(max_ratio_BA,3), va='center', ha='center', fontsize=8, color='blue')\\n \\n minplot = ax4.scatter([beamXPOS[i]], [beamYPOS[i]], s=1300, c=[min_ratio_BA], cmap='summer', edgecolors='black', vmin=0, vmax=1.1)\\n ax4.text(beamXPOS[i], beamYPOS[i]+0.02, n[i], va='center', ha='center')\\n ax4.text(beamXPOS[i], beamYPOS[i]-0.02, round(min_ratio_BA,3), va='center', ha='center', fontsize=8, color='blue')\\n \\n ax1.set_xlim(0,0.7)\\n ax1.set_ylim(0,1.4)\\n ax1.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\\n ax1.set_title(title1)\\n\\n ax2.set_xlim(0,0.7)\\n ax2.set_ylim(0,1.4)\\n ax2.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\\n ax2.set_title(title2)\\n\\n ax3.set_xlim(0,0.7)\\n ax3.set_ylim(0,1.4)\\n ax3.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\\n ax3.set_title(title3)\\n plt.colorbar(maxplot, ax=ax3)\\n\\n ax4.set_xlim(0,0.7)\\n ax4.set_ylim(0,1.4)\\n ax4.tick_params(axis='both',which='both', bottom=False,top=False,right=False,left=False,labelbottom=False, labelleft=False)\\n ax4.set_title(title4)\\n plt.colorbar(minplot, ax=ax4)\\n\\n fig1.savefig(saved_fig1, bbox_inches='tight')\\n fig2.savefig(saved_fig2, bbox_inches='tight')\\n fig3.savefig(saved_fig3, bbox_inches='tight')\\n fig4.savefig(saved_fig4, bbox_inches='tight')\\n\\n plt.close('all')\\n\\n return saved_fig1, saved_fig2, plt_name1, plt_name2, saved_fig3, saved_fig4, plt_name3, plt_name4, BEAM_THRESHOLD\",\n \"def plot(model, results, filename):\\n\\n # c = model.compartments.get_one(id='c')\\n #\\n # rna_1 = model.species_types.get_one(id='rna_1').species.get_one(compartment=c)\\n # rna_2 = model.species_types.get_one(id='rna_2').species.get_one(compartment=c)\\n # rna_3 = model.species_types.get_one(id='rna_3').species.get_one(compartment=c)\\n #\\n pops = results.get('populations')\\n time = pops.index\\n pop_rna_1 = pops['rna_1[c]']\\n pop_rna_2 = pops['rna_2[c]']\\n pop_rna_3 = pops['rna_3[c]']\\n\\n pop_atp = pops['atp[c]']\\n pop_gtp = pops['gtp[c]']\\n pop_utp = pops['ctp[c]']\\n pop_ctp = pops['utp[c]']\\n\\n pop_amp = pops['amp[c]']\\n pop_gmp = pops['gmp[c]']\\n pop_ump = pops['cmp[c]']\\n pop_cmp = pops['ump[c]']\\n\\n print(pop_rna_1, pop_atp, pop_gtp, pop_utp, pop_ctp)\\n\\n fig1, axes1 = pyplot.subplots(nrows=3, ncols=1)\\n\\n axes1[0].plot(time / 3600, pop_rna_1)\\n axes1[0].plot(time / 3600, pop_rna_2)\\n axes1[0].plot(time / 3600, pop_rna_3)\\n axes1[0].set_xlim((time[0] / 3600, time[-1] / 3600))\\n axes1[0].set_ylim((0., 10.0))\\n axes1[0].legend(loc='upper right')\\n\\n axes1[1].plot(time / 3600, pop_atp)\\n axes1[1].plot(time / 3600, pop_gtp)\\n axes1[1].plot(time / 3600, pop_utp)\\n axes1[1].plot(time / 3600, pop_ctp)\\n axes1[1].set_xlim((time[0] / 3600, time[-1] / 3600))\\n # axes1[1].set_ylim((0., 10.0))\\n axes1[1].legend(loc='upper right')\\n\\n axes1[2].plot(time / 3600, pop_amp)\\n axes1[2].plot(time / 3600, pop_gmp)\\n axes1[2].plot(time / 3600, pop_ump)\\n axes1[2].plot(time / 3600, pop_cmp)\\n axes1[2].set_xlim((time[0] / 3600, time[-1] / 3600))\\n # axes1[2].set_ylim((0., 10.0))\\n axes1[2].legend(loc='upper right')\\n\\n fig1.savefig(filename.format('species'))\\n pyplot.close(fig1)\",\n \"def pratn_writer(clf, y_true, y_prob, eval_folder, i=''):\\n if type(y_true[0]) is not np.ndarray: y_true = [y_true]\\n if type(y_prob[0]) is not np.ndarray: y_prob = [y_prob]\\n\\n img_dir = eval_folder+'/images/'\\n if not os.path.exists(img_dir):\\n os.makedirs(img_dir)\\n\\n clf_name = str(clf)[:str(clf).index('(')]+str(i)\\n\\n enum_list = range(0, len(y_true))\\n\\n fig, ax1 = plt.subplots()\\n ax1.set_xlabel('percent of population')\\n ax1.set_ylabel('precision', color='b')\\n ax2 = ax1.twinx()\\n ax2.set_ylabel('recall', color='r')\\n\\n mean_prec = 0.0\\n mean_rec = 0.0\\n mean_pct_above = np.linspace(0,1,1000)\\n\\n for i in enum_list:\\n prec, rec, thres = metrics.precision_recall_curve(y_true[i], y_prob[i])\\n prec = prec[:-1]\\n rec = rec[:-1]\\n skip_size = int(thres.shape[0]/1000.0)\\n if skip_size == 0: skip_size = 1\\n\\tplotting_thres = thres[0::skip_size][::-1]\\n plotting_prec = prec[0::skip_size][::-1]\\n plotting_rec = rec[0::skip_size][::-1]\\n\\n how_many = float(len(y_true[i]))\\n\\n pct_above = [(y_prob[i][y_prob[i] >= value].shape[0])/how_many\\n for value in plotting_thres]\\n\\n pct_above = np.array(pct_above)\\n mean_prec += interp(mean_pct_above, pct_above, plotting_prec)\\n mean_rec += interp(mean_pct_above, pct_above, plotting_rec)\\n\\n #ax1.plot(pct_above, plotting_prec, 'b')\\n #ax2.plot(pct_above, plotting_rec, 'r')\\n\\n mean_prec /= len(y_true)\\n mean_rec /= len(y_true)\\n\\n mean_prec[-1] = np.mean([np.mean(enu) for enu in y_true])\\n mean_rec[-1] = 1.0\\n\\n ax1.plot(mean_pct_above, mean_prec, 'b')\\n ax2.plot(mean_pct_above, mean_rec, 'r')\\n plt.title('Precision, Recall vs % Population')\\n plt.savefig(img_dir+'PRATN_Curve_'+clf_name+'.png')\",\n \"def plot_apertures(image, ext=1):\\n\\n hdu = fits.open(image)\\n apfile = './database/ape' + image.strip('.fits') + '_{:d}'.format(ext)\\n\\n b = np.array(\\n [i.split()[3:] for i in open(apfile).readlines() if 'begin' in i])\\n\\n apid = b[:, 0]\\n x = np.array([float(i) for i in b[:, 2]])\\n\\n sci_exts = np.array([i for i in range(len(hdu)) if hdu[i].name == 'SCI'])\\n data = hdu[sci_exts[len(sci_exts)/2]].data\\n\\n profile = np.average(data, 1)\\n\\n fig = plt.figure(1)\\n ax = fig.add_subplot(111)\\n\\n pmax = profile.max()\\n\\n ax.plot(np.arange(len(profile))+1, profile/pmax)\\n ax.set_ylim(0, 1.1)\\n\\n for i, j in enumerate(apid):\\n ax.annotate(j, xy=(x[i], 1), ha='center')\\n ax.axvline(x[i], alpha=.3)\\n\\n plt.show()\",\n \"def initial_plots(runs):\\n for run in runs.keys():\\n meta = runs[run]\\n plot_pdfs(meta)\\n plot_priorsamps(meta)\\n plot_ivals(meta)\\n# if meta.truNz is not None:\\n# plot_true(meta)\\n timesaver(meta,'iplot',meta.key)\",\n \"def PlotEX(df, eom=None, show_plot=True):\\n # Ensure 'Study' is in header.\\n # TODO: Add code.\\n # Obtain exceedence probabilities.\\n if eom:\\n df_excd = stats.AnnualExceedence(df, eom=eom)\\n else:\\n df_excd = stats.MonthlyExceedence(df)\\n # Get list of variables.\\n var_list = set(df_excd.columns.get_level_values('Part B'))\\n # Determine number of axes in the figure.\\n n_var = len(var_list)\\n n_row = np.int(np.around(np.sqrt(n_var)))\\n if n_row**2 >= n_var:\\n n_col = n_row\\n else:\\n n_col = n_row + 1\\n fig, ax = plt.subplots(nrows=n_row, ncols=n_col, figsize=(16, 9))\\n # Remove extra axes, if necessary.\\n if n_row * n_col != n_var:\\n n_empty = n_row * n_col - n_var\\n for k in range(n_empty):\\n ax[-1, -(1+k)].remove()\\n # Initialize variables for updating axes.\\n cur_row = 0\\n cur_col = 0\\n # Plot each variable.\\n for var in var_list:\\n df_exc = df_excd.xs(var, level='Part B', axis=1)\\n # Initialize parameters for current axis.\\n y_min = np.min(df_exc.values)\\n y_max = np.max(df_exc.values)\\n col_var = df_exc.columns\\n # Select current axis.\\n if n_row == 1 and n_col == 1:\\n ax_cur = ax\\n elif n_row == 1:\\n ax_cur = ax[cur_col]\\n else:\\n ax_cur = ax[cur_row, cur_col]\\n # Plot study results for each variable.\\n for c in col_var:\\n sr_plot = df_exc[c]\\n label_c = '{} {}'.format(*c[:2])\\n ax_cur.plot(sr_plot, label=label_c, linewidth=1.0, alpha=0.7)\\n # Set plot title.\\n if eom:\\n month_name = dt.date(1900, eom, 1).strftime('%B')\\n temp_title = 'Exceedence Plot of {} (End of {})'\\n ax_cur.set_title(temp_title.format(var, month_name))\\n else:\\n ax_cur.set_title('Exceedence Plot of {} (All Months)'.format(var))\\n # Modify x-axis and y-axis.\\n label_c = set(df_exc.columns.get_level_values('Part C'))\\n _unit = df_exc.columns.get_level_values('Units')\\n _dtyp = df_exc.columns.get_level_values('Data Type')\\n unit_d = list(zip(_unit, _dtyp))\\n combo_unit = [' '.join(i) for i in unit_d]\\n label_u = set(combo_unit)\\n ax_cur.set_ylabel('{} ({})'.format(r'/'.join(label_c),\\n r'/'.join(label_u)))\\n ax_cur.set_ylim(bottom=y_min, top=y_max)\\n ax_cur.set_xlabel('Exceedance Probability')\\n ax_cur.spines['right'].set_visible(False)\\n ax_cur.spines['top'].set_visible(False)\\n # Set legend.\\n ax_cur.legend(title='Study, Part F')\\n # Update current axis.\\n cur_col += 1\\n if cur_col >= n_col:\\n cur_col = 0\\n cur_row += 1\\n # Adjust layout.\\n plt.tight_layout()\\n # Add figure notes.\\n t = PlotChartNotes()\\n if n_row * n_col != n_var:\\n x_pos = (1 - n_empty / n_col) + 0.025\\n y_pos = (1 / n_row) - 0.025\\n plt.figtext(x_pos, y_pos, t, ha='left', va='top', wrap=True)\\n else:\\n plt.figtext(0.05, 0, t, ha='left', va='bottom', wrap=True)\\n plt.subplots_adjust(bottom=0.2)\\n # Show plot, if requested.\\n if show_plot:\\n plt.show()\\n # Return figure and axes.\\n return fig, ax\",\n \"def plot_ave(results_list):\\n x_range = range(len(results_list[0]))\\n err_x, err_y, std_list = [], [], []\\n\\n for i in x_range:\\n if i % 10 == 0:\\n #get average for each generation\\n column = [] \\n for result in results_list:\\n column.append(result[i])\\n average = np.average(column)\\n \\n std_dev = np.std(column)\\n err_x.append(i)\\n err_y.append(average)\\n std_list.append(std_dev)\\n\\n pylab.errorbar(err_x, err_y, yerr=std_list)\\n pylab.show()\",\n \"def plot_individual_tm(xdict, ydict, xprop, yprop, documents, spline):\\n figure_array = {}\\n for item in documents:\\n xlabel = \\\"\\\\\\\\textbf{\\\" + label_dict[xprop] + \\\"}\\\"\\n ylabel = \\\"\\\\\\\\textbf{\\\" + label_dict[yprop] + \\\"}\\\"\\n print str(item[\\\"path_id\\\"])\\n x = xdict[item[\\\"path_id\\\"]]\\n y = ydict[item[\\\"path_id\\\"]]\\n # fig_title = item[\\\"path_id\\\"] + \\\"(\\\" + item[\\\"pretty_formula\\\"] + \\\")\\\" # Individual traces\\n fig_title = yprop + item[\\\"cation_type\\\"] # Plot by cation\\n figure_array[item[\\\"path_id\\\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\\n ax = figure_array[item[\\\"path_id\\\"]].add_subplot(111)\\n ax.scatter(x,y, s=70, zorder=2, color=tm_color_dict[item[\\\"tm_type\\\"][0]], linewidths=2.5, edgecolors='black',\\n label=item[\\\"tm_type\\\"][0])\\n if spline:\\n tck = interpolate.splrep(x, y, s=0)\\n xnew = np.arange(0, 100, 0.1)\\n splfit = interpolate.splev(xnew, tck, der=0)\\n x = xnew\\n y = splfit\\n if item[\\\"path_id\\\"][-3:] == \\\"002\\\":\\n ax.plot(x, y, linewidth=2.5, zorder=1, color=tm_color_dict[item[\\\"tm_type\\\"][0]], linestyle='dashed')\\n else:\\n ax.plot(x, y, linewidth=2.5, zorder=1, color=tm_color_dict[item[\\\"tm_type\\\"][0]])\\n ax.set_xlabel(xlabel, fontsize=24)\\n # ax.set_ylim([0,1200])\\n # ax.set_xlim([7,22])\\n ax.set_ylabel(ylabel, fontsize=24)\\n ax.tick_params(axis='x', labelsize=22)\\n ax.tick_params(axis='y', labelsize=22)\\n border_width = 2\\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\\n plt.tight_layout()\\n plt.legend(loc='best', prop={'size': 14})\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='sans-serif')\\n plt.tight_layout()\\n plt.show()\",\n \"def plot_morphism_output(data, outdir):\\n\\n # show the distributions for each variable separately\\n for col in data.columns:\\n ProfilePlotter._plot_1d(data[col], outfile = os.path.join(outdir, col + \\\".pdf\\\"))\\n\\n # later, maybe also show 2d plots etc.\",\n \"def plot_p1(data, documentation):\\r\\n fig, ax = plt.subplots(1)\\r\\n data = process_data_p1(data)\\r\\n sns.lineplot(x=\\\"Academic Year\\\", y=\\\"MD_EARN_WNE_P10\\\", hue=\\\"CONTROL\\\",\\r\\n data=data, ax=ax,\\r\\n palette=sns.color_palette([\\\"#9b59b6\\\", \\\"#3498db\\\", \\\"#e74c3c\\\"]))\\r\\n fig.suptitle(\\\"Yearly Changes of Average Median Earnings of Former \\\" +\\r\\n \\\"Students per College Control Type\\\")\\r\\n ax.set(ylabel=\\\"Average Median Earnings ($)\\\")\\r\\n handles, legends = ax.get_legend_handles_labels()\\r\\n legends = replace_legend_value_mappings(legends, documentation)\\r\\n ax.legend(handles, legends, loc=\\\"lower left\\\")\\r\\n fig.savefig(join_curdir(\\\"results\\\", \\\"plot_p1.png\\\"))\",\n \"def analyze(data):\\n # perform fit\\n regr_results = sm.OLS.from_formula('mosquitos ~ temperature + rainfall', data).fit()\\n print(regr_results.tvalues)\\n \\n fig = plt.figure(figsize=(6, 9))\\n\\n # plot predicted vs. measured mosquito populations from fitted model \\n ax0 = fig.add_subplot(3, 1, 1)\\n\\n parameters = regr_results.params\\n predicted = (parameters['Intercept'] + \\n parameters['temperature'] * data['temperature'] + \\n parameters['rainfall'] * data['rainfall'])\\n\\n ax0.plot(predicted, data['mosquitos'], 'gd')\\n\\n ax0.set_xlabel('predicted mosquito population')\\n ax0.set_ylabel('measured mosquito population')\\n \\n # plot mosquitos vs. temperature\\n ax1 = fig.add_subplot(3, 1, 2)\\n\\n ax1.plot(data['temperature'], data['mosquitos'], 'ro')\\n ax1.set_xlabel('temperature')\\n ax1.set_ylabel('mosquitos')\\n\\n # plot mosquitos vs. rainfall\\n ax2 = fig.add_subplot(3, 1, 3)\\n\\n ax2.plot(data['rainfall'], data['mosquitos'], 'bs')\\n ax2.set_xlabel('rainfall')\\n ax2.set_ylabel('mosquitos')\\n \\n # adjust layout of axes according to label placement\\n plt.tight_layout()\\n \\n return fig\",\n \"def avg_response(df, x, y_obs, y_est, save=False, show=True):\\n\\n fig, ax1 = plt.subplots(figsize=(15,15))\\n\\n ax2 = ax1.twinx()\\n\\n x_name = x\\n if df[x].dtype == \\\"int\\\":\\n x = df[x].astype(\\\"category\\\")\\n elif df[x].dtype == \\\"float\\\":\\n x = pd.cut(df[x], bins=10)\\n\\n metrics = {\\\"mean\\\":\\\"mean\\\", \\\"std err\\\":\\\"sem\\\", \\\"count\\\":\\\"count\\\"}\\n df_grouped = df.groupby([x])[y_obs, y_est].agg(metrics)\\n \\n x_vals = range(len(df_grouped))\\n y_vals = df_grouped[\\\"mean\\\"][y_est]\\n ax1.errorbar(x_vals, y_vals,yerr=df_grouped[\\\"std err\\\"][y_est], fmt='-',\\n marker='o',color=\\\"R\\\", mec='black', ms=10, mew=2, linewidth=4, \\n capsize=10, elinewidth=2)\\n\\n y_vals = df_grouped[\\\"mean\\\"][y_obs]\\n ax1.plot(x_vals, y_vals, '-', label=y_obs, marker='o',\\n color = \\\"G\\\",mec='black', ms=10, mew=2, linewidth=4)\\n\\n y_vals = df_grouped[\\\"count\\\"][y_obs]\\n ax2.bar(x_vals,y_vals, color='DarkSlateGray', alpha = 0.25)\\n\\n ax1.set_xlim(x_vals[0]-0.2,x_vals[-1]+1)\\n x_levels = list(y_vals.index)\\n plt.xticks(x_vals, x_levels)\\n ax1.set_xticklabels(x_levels, rotation=45)\\n ax1.grid(False)\\n ax2.grid(False)\\n font_size = 20\\n ax1.set_xlabel(x_name, fontsize=font_size)\\n ax1.set_ylabel(y_obs, fontsize=font_size)\\n ax2.set_ylabel(\\\"count\\\", fontsize=font_size)\\n plt.title(\\\"Average {y} for groups of {x}\\\".format(x=x_name, y=y_obs), \\n fontsize=font_size+5)\\n ax1.legend([y_obs, y_est], fontsize=font_size-2)\\n if save:\\n fig.savefig(\\\"/home/edward/work/repos/prometheus/python/plots/avg_response/{}.png\\\".\\n format(x_name), bbox_inches='tight')\\n if show:\\n plt.show()\",\n \"def plot_data(X, y, X_pred, clf, title):\\n \\n figsize(10.2, 5.1)\\n X2 = [[x] for x in linspace(0, max(X_pred)[0] * 1.15, 50)]\\n days = clf.predict(X_pred)\\n scatter(X, y, color='black')\\n scatter(X_pred, days, color='red')\\n plot(X2, clf.predict(X2), color='blue')\\n \\n i = 0\\n len_data = len(X_pred)\\n xytext = (5, -10)\\n for data in zip(X_pred, days):\\n i = i + 1\\n dat = purchase_date + timedelta(int(round(data[1], 0)))\\n\\n annotate(dat.strftime('%b %d %Y'), xy=(data[0][0], data[1]), xycoords='data', xytext=xytext, textcoords='offset points')\\n \\n pylab.ylim([0, int(clf.predict(X2).max()) + 1])\\n pylab.xlim([0, max(X2)[0]])\\n ax=pylab.gca()\\n ax.yaxis.set_major_formatter(NullFormatter())\\n ax.yaxis.set_minor_formatter(NullFormatter())\\n pylab.axes().set_xlabel('Miles')\\n pylab.axes().set_ylabel('Time')\\n pylab.axes().set_title(title)\",\n \"def plotPRC(yscore, true, datasets, title, outfile):\\n \\n fig = plt.figure()\\n plt.xlim([0.0, 1.0])\\n plt.ylim([0.0, 1.0])\\n plt.xlabel('Recall')\\n plt.ylabel('Precision')\\n plt.title(title)\\n \\n for i in range(len(datasets)):\\n precision, recall, _ = precision_recall_curve(true[i], yscore[i][:,1])\\n prc_auc = average_precision_score(true[i], yscore[i][:,1])\\n plt.plot(recall, precision, label=datasets[i]+' (area = %0.2f)' % (prc_auc),linewidth=1)\\n \\n plt.legend(loc=\\\"lower right\\\")\\n \\n pdfplot = PdfPages(outfile);\\n pdfplot.savefig(fig)\\n pdfplot.close()\",\n \"def plotFittingResults(self):\\n _listFitQ = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitQ()]\\n _listFitValues = [tmp.getValue() for tmp in self.getDataOutput().getScatteringFitValues()]\\n _listExpQ = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataQ()]\\n _listExpValues = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataValues()]\\n\\n #_listExpStdDev = None\\n #if self.getDataInput().getExperimentalDataStdDev():\\n # _listExpStdDev = [tmp.getValue() for tmp in self.getDataInput().getExperimentalDataStdDev()]\\n #if _listExpStdDev:\\n # pylab.errorbar(_listExpQ, _listExpValues, yerr=_listExpStdDev, linestyle='None', marker='o', markersize=1, label=\\\"Experimental Data\\\")\\n # pylab.gca().set_yscale(\\\"log\\\", nonposy='clip')\\n #else: \\n # pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label=\\\"Experimental Data\\\")\\n\\n pylab.semilogy(_listExpQ, _listExpValues, linestyle='None', marker='o', markersize=5, label=\\\"Experimental Data\\\")\\n pylab.semilogy(_listFitQ, _listFitValues, label=\\\"Fitting curve\\\")\\n pylab.xlabel('q')\\n pylab.ylabel('I(q)')\\n pylab.suptitle(\\\"RMax : %3.2f. Fit quality : %1.3f\\\" % (self.getDataInput().getRMax().getValue(), self.getDataOutput().getFitQuality().getValue()))\\n pylab.legend()\\n pylab.savefig(os.path.join(self.getWorkingDirectory(), \\\"gnomFittingResults.png\\\"))\\n pylab.clf()\",\n \"def plotResults(results):\\n e = results['eclean'] - results['eCTI']\\n e1 = results['e1clean'] - results['e1CTI']\\n e2 = results['e2clean'] - results['e2CTI']\\n\\n print 'Delta e, e_1, e_2:', np.mean(e), np.mean(e1), np.mean(e2)\\n print 'std e, e_1, e_2:', np.std(e), np.std(e1), np.std(e2)\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.hist(e, bins=15, label='$e$', alpha=0.5)\\n ax.hist(e1, bins=15, label='$e_{2}$', alpha=0.5)\\n ax.hist(e2, bins=15, label='$e_{1}$', alpha=0.5)\\n ax.set_xlabel(r'$\\\\delta e$ [no CTI - CDM03 corrected]')\\n plt.legend(shadow=True, fancybox=True)\\n plt.savefig('ellipticityDelta.pdf')\\n plt.close()\\n\\n r2 = (results['R2clean'] - results['R2CTI'])/results['R2clean']\\n print 'delta R2 / R2: mean, std ', np.mean(r2), np.std(r2)\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.hist(r2, bins=15, label='$R^{2}$')\\n ax.set_xlabel(r'$\\\\frac{\\\\delta R^{2}}{R^{2}_{ref}}$ [no CTI - CDM03 corrected]')\\n plt.legend(shadow=True, fancybox=True)\\n plt.savefig('sizeDelta.pdf')\\n plt.close()\",\n \"def plot_results(outputs_table_totals, elec_benefits, gas_benefits):\\n summer_months = [6, 7, 8, 9]\\n shoulder_months = [3, 4, 5, 10]\\n winter_months = [11, 12, 1, 2]\\n peak_hours = [16, 17, 18, 19, 20]\\n pct_hours_in_summer = 2928 / 8760\\n pct_hours_in_shoulder = 2952 / 8760\\n pct_hours_in_winter = 2880 / 8760\\n\\n trc_costs_record = outputs_table_totals[\\\"TRC Costs ($)\\\"]\\n pac_costs_record = outputs_table_totals[\\\"PAC Costs ($)\\\"]\\n trc_record = outputs_table_totals[\\\"TRC\\\"]\\n pac_record = outputs_table_totals[\\\"PAC\\\"]\\n lifecycle_net_mwh = outputs_table_totals[\\\"Electricity Lifecycle Net Savings (MWh)\\\"]\\n lifecycle_net_therms = outputs_table_totals[\\\"Gas Lifecycle Net Savings (Therms)\\\"]\\n lifecycle_net_ghg = outputs_table_totals[\\\"Total Lifecycle GHG Savings (Tons)\\\"]\\n\\n # Getting variables for plots\\n elec_benefits_cols = (\\n [\\\"hourly_savings\\\"] + ACC_COMPONENTS_ELECTRICITY + [\\\"av_csts_levelized\\\"]\\n )\\n\\n elec_benefits_hour_month_year = (\\n elec_benefits.groupby([\\\"hour_of_day\\\", \\\"year\\\", \\\"month\\\"])\\n .agg(\\n {\\n **{component: \\\"sum\\\" for component in ACC_COMPONENTS_ELECTRICITY},\\n **{\\n \\\"hourly_savings\\\": \\\"sum\\\",\\n \\\"marginal_ghg\\\": \\\"sum\\\",\\n \\\"av_csts_levelized\\\": \\\"mean\\\",\\n },\\n }\\n )\\n .reset_index()\\n )\\n\\n total_benefits = list(\\n elec_benefits_hour_month_year.groupby([\\\"hour_of_day\\\"])[\\\"total\\\"].sum()\\n )\\n\\n summer_benefits = list(\\n elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"total\\\"]\\n .sum()\\n )\\n summer_peak_benefits = elec_benefits_hour_month_year[\\\"total\\\"][\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n & (elec_benefits_hour_month_year[\\\"hour_of_day\\\"].isin(peak_hours))\\n ].sum()\\n shoulder_benefits = list(\\n elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(shoulder_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"total\\\"]\\n .sum()\\n )\\n winter_benefits = list(\\n elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(winter_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"total\\\"]\\n .sum()\\n )\\n total_savings = list(\\n elec_benefits_hour_month_year.groupby([\\\"hour_of_day\\\"])[\\\"hourly_savings\\\"].sum()\\n )\\n summer_savings = list(\\n elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"hourly_savings\\\"]\\n .sum()\\n )\\n shoulder_savings = list(\\n elec_benefits_hour_month_year[\\n ((elec_benefits_hour_month_year[\\\"month\\\"].isin(shoulder_months)))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"hourly_savings\\\"]\\n .sum()\\n )\\n summer_peak_savings = elec_benefits_hour_month_year[\\\"hourly_savings\\\"][\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n & (elec_benefits_hour_month_year[\\\"hour_of_day\\\"].isin(peak_hours))\\n ].sum()\\n winter_savings = list(\\n elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(winter_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"hourly_savings\\\"]\\n .sum()\\n )\\n total_av_csts_avg = list(\\n elec_benefits_hour_month_year.groupby([\\\"hour_of_day\\\"])[\\n \\\"av_csts_levelized\\\"\\n ].mean()\\n )\\n summer_av_csts_avg = list(\\n pct_hours_in_summer\\n * elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"av_csts_levelized\\\"]\\n .mean()\\n )\\n summer_peak_av_csts_avg = elec_benefits_hour_month_year[\\\"av_csts_levelized\\\"][\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(summer_months))\\n & (elec_benefits_hour_month_year[\\\"hour_of_day\\\"].isin(peak_hours))\\n ].mean()\\n shoulder_av_csts_avg = list(\\n pct_hours_in_shoulder\\n * elec_benefits_hour_month_year[\\n ((elec_benefits_hour_month_year[\\\"month\\\"].isin(shoulder_months)))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"av_csts_levelized\\\"]\\n .mean()\\n )\\n winter_av_csts_avg = list(\\n pct_hours_in_winter\\n * elec_benefits_hour_month_year[\\n (elec_benefits_hour_month_year[\\\"month\\\"].isin(winter_months))\\n ]\\n .groupby([\\\"hour_of_day\\\"])[\\\"av_csts_levelized\\\"]\\n .mean()\\n )\\n\\n elec_benefits_sum_by_hod = (\\n elec_benefits[elec_benefits_cols].groupby(elec_benefits[\\\"hour_of_day\\\"]).sum()\\n )\\n elec_benefits_hoy = (\\n elec_benefits[elec_benefits_cols]\\n .groupby(elec_benefits[\\\"hour_of_year\\\"])\\n .sum()\\n .cumsum()\\n .reset_index()\\n )\\n sav_avcsts_288 = (\\n elec_benefits.groupby([\\\"hour_of_day\\\", \\\"month\\\"])\\n .agg(\\n {\\n **{component: \\\"sum\\\" for component in ACC_COMPONENTS_ELECTRICITY},\\n **{\\n \\\"hourly_savings\\\": \\\"sum\\\",\\n \\\"marginal_ghg\\\": \\\"sum\\\",\\n \\\"av_csts_levelized\\\": \\\"mean\\\",\\n },\\n }\\n )\\n .reset_index()\\n )\\n sav_avcsts_288 = sav_avcsts_288[\\n [\\\"hour_of_day\\\", \\\"month\\\", \\\"hourly_savings\\\", \\\"total\\\", \\\"marginal_ghg\\\"]\\n ]\\n ghgsav = sav_avcsts_288.pivot(\\\"hour_of_day\\\", \\\"month\\\", \\\"marginal_ghg\\\")\\n sav = sav_avcsts_288.pivot(\\\"hour_of_day\\\", \\\"month\\\", \\\"hourly_savings\\\")\\n avcsts = sav_avcsts_288.pivot(\\\"hour_of_day\\\", \\\"month\\\", \\\"total\\\")\\n\\n # savings load shape plot\\n fig0, (ax1, ax2, ax3) = plt.subplots(\\n 1, 3, figsize=(18, 5), sharex=True, sharey=True\\n )\\n plt.subplots_adjust(wspace=0, hspace=0)\\n axs = [ax1, ax2, ax3]\\n hod = elec_benefits_sum_by_hod.index\\n legend_labels1 = [\\\"Summer\\\"]\\n legend_labels2 = [\\\"Shoulder\\\"]\\n legend_labels3 = [\\\"Winter\\\"]\\n\\n ax1.plot(\\n hod,\\n summer_savings,\\n c=\\\"firebrick\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u25EF$\\\",\\n markersize=13,\\n linestyle=\\\"-\\\",\\n )\\n ax2.plot(\\n hod,\\n shoulder_savings,\\n c=\\\"royalblue\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u2206$\\\",\\n markersize=13,\\n linestyle=\\\"-\\\",\\n )\\n ax3.plot(\\n hod,\\n winter_savings,\\n c=\\\"green\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u25A1$\\\",\\n markersize=13,\\n linestyle=\\\"-\\\",\\n )\\n ax1.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax2.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax3.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n # Shade peak region\\n ax1.axvspan(16, 21, alpha=0.2, color=\\\"grey\\\")\\n\\n leg1 = ax1.legend(legend_labels1, fontsize=14, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\\n text.set_color(line.get_color())\\n leg2 = ax2.legend(legend_labels2, fontsize=14, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\\n text.set_color(line.get_color())\\n leg3 = ax3.legend(legend_labels3, fontsize=14, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\\n text.set_color(line.get_color())\\n\\n ax1.set_ylabel(\\\"Savings (MWh/hr)\\\", size=16)\\n ax2.set_xlabel(\\\"Hour of Day\\\", size=16)\\n\\n if max(summer_savings + shoulder_savings + winter_savings) < 0:\\n ymax = 0\\n else:\\n ymax = max(summer_savings + shoulder_savings + winter_savings)\\n if min(summer_savings + shoulder_savings + winter_savings) > 0:\\n ymin = 0\\n else:\\n ymin = min(summer_savings + shoulder_savings + winter_savings)\\n\\n # Tick and lebel parameters\\n ax1.set_ylim(ymin * 1.08, ymax * 1.08)\\n ax1.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax2.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax3.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\\n ax1.set_xticks(np.arange(0, 24, step=4))\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax1.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax2.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax2.xaxis.set_minor_locator(AutoMinorLocator())\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\\n\\n # Set plot title, size, and position\\n ax1.set_title(\\\"Seasonal Savings Load Shapes\\\", size=18, loc=\\\"left\\\").set_position(\\n [0, 1.03]\\n )\\n\\n # benefits_seasonal_shape_plot\\n fig1, (ax1, ax2, ax3) = plt.subplots(\\n 1, 3, figsize=(18, 5), sharex=True, sharey=True\\n )\\n plt.subplots_adjust(wspace=0, hspace=0)\\n axs = [ax1, ax2, ax3]\\n hod = elec_benefits_sum_by_hod.index\\n legend_labels1 = [\\\"Summer\\\"]\\n legend_labels2 = [\\\"Shoulder\\\"]\\n legend_labels3 = [\\\"Winter\\\"]\\n\\n ax1.plot(\\n hod,\\n summer_benefits,\\n c=\\\"firebrick\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u2B24$\\\",\\n markersize=13,\\n linestyle=\\\":\\\",\\n )\\n ax2.plot(\\n hod,\\n shoulder_benefits,\\n c=\\\"royalblue\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u25B2$\\\",\\n markersize=13,\\n linestyle=\\\":\\\",\\n )\\n ax3.plot(\\n hod,\\n winter_benefits,\\n c=\\\"green\\\",\\n linewidth=5,\\n marker=\\\"$\\\\u25A0$\\\",\\n markersize=13,\\n linestyle=\\\":\\\",\\n )\\n ax1.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax2.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax3.axhline(y=0, color=\\\"gray\\\", linewidth=1, linestyle=\\\"--\\\")\\n # Shade peak region\\n ax1.axvspan(16, 21, alpha=0.2, color=\\\"grey\\\")\\n\\n leg1 = ax1.legend(legend_labels1, fontsize=15, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\\n text.set_color(line.get_color())\\n leg2 = ax2.legend(legend_labels2, fontsize=15, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\\n text.set_color(line.get_color())\\n leg3 = ax3.legend(legend_labels3, fontsize=15, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\\n text.set_color(line.get_color())\\n\\n ax1.set_ylabel(\\\"TRC Benefits ($/hr)\\\", size=16)\\n ax2.set_xlabel(\\\"Hour of Day\\\", size=16)\\n\\n if max(summer_benefits + shoulder_benefits + winter_benefits) < 0:\\n ymax = 0\\n else:\\n ymax = max(summer_benefits + shoulder_benefits + winter_benefits)\\n if min(summer_benefits + shoulder_benefits + winter_benefits) > 0:\\n ymin = 0\\n else:\\n ymin = min(summer_benefits + shoulder_benefits + winter_benefits)\\n\\n # Tick and label parameters\\n ax1.set_ylim(ymin * 1.08, ymax * 1.08)\\n ax1.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax2.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax3.set_yticks(\\n np.arange(\\n ymin * 1.08,\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\\n ax1.set_xticks(np.arange(0, 24, step=4))\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax1.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax2.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax2.xaxis.set_minor_locator(AutoMinorLocator())\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=7, width=2, labelsize=14\\n )\\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\\n\\n # Set plot title, size, and position\\n ax1.set_title(\\n \\\"Seasonal TRC Benefits by Hour ($)\\\", size=18, loc=\\\"left\\\"\\n ).set_position([0, 1.03])\\n\\n # sum_hourly_plot\\n fig2 = plt.figure(figsize=(12, 7), dpi=250)\\n ax = fig2.gca()\\n colors = [\\n \\\"royalblue\\\",\\n \\\"black\\\",\\n \\\"pink\\\",\\n \\\"firebrick\\\",\\n \\\"gray\\\",\\n \\\"darkviolet\\\",\\n \\\"darkorange\\\",\\n \\\"green\\\",\\n \\\"saddlebrown\\\",\\n ]\\n legend_labels = []\\n x = 1\\n while x <= len(ACC_COMPONENTS_ELECTRICITY[1:]):\\n if x == 1:\\n ax.bar(\\n hod,\\n elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]],\\n color=colors[x - 1],\\n )\\n legend_labels.append(\\n re.findall(\\n \\\".*Name: (.*),\\\",\\n str(elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]]),\\n )[0]\\n )\\n x += 1\\n else:\\n ax.bar(\\n hod,\\n elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]],\\n bottom=elec_benefits_sum_by_hod.iloc[:, 2 : x + 1].sum(axis=1),\\n color=colors[x - 1],\\n )\\n legend_labels.append(\\n re.findall(\\n \\\".*Name: (.*),\\\",\\n str(elec_benefits_sum_by_hod[ACC_COMPONENTS_ELECTRICITY[x]]),\\n )[0]\\n )\\n x += 1\\n\\n # Set x and y limits based on min and max values\\n ymax = elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).max()\\n if elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).min() > 0:\\n ymin = 0\\n else:\\n ymin = elec_benefits_sum_by_hod.iloc[:, 2:x].sum(axis=1).min()\\n\\n ax.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax.set_ylim(ymin * 1.1, ymax * 1.08)\\n\\n # Set x and y axis labels\\n ax.set_xlabel(\\\"Hour of Day\\\", size=17, labelpad=5)\\n ax.set_ylabel(\\\"$ Avoided Costs\\\", size=17)\\n\\n # Set plot title, size, and position\\n ax.set_title(\\n \\\"Sum of Electric Avoided Costs by Component and Hour of Day\\\",\\n size=17,\\n loc=\\\"left\\\",\\n )\\n\\n # Tick and lebel parameters\\n ax.tick_params(bottom=True, top=False, left=True, right=False)\\n ax.set_xticks(np.arange(0, 24, step=4))\\n ax.set_yticks(\\n np.arange(\\n int(round(ymin * 1.1, 0)),\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 2) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n\\n # Minor ticks\\n ax.xaxis.set_minor_locator(AutoMinorLocator())\\n ax.yaxis.set_minor_locator(AutoMinorLocator())\\n\\n # Legend\\n plt.legend(\\n legend_labels,\\n bbox_to_anchor=(1, 1),\\n fontsize=12,\\n loc=\\\"upper left\\\",\\n frameon=False,\\n )\\n\\n # avoided_cost_summary_plot\\n fig3, (ax1, ax2, ax3) = plt.subplots(\\n 3, 1, figsize=(6, 10), sharex=True, sharey=False\\n )\\n axs = [ax1, ax2, ax3]\\n hod = elec_benefits_sum_by_hod.index\\n legend_labels = [\\\"Total\\\", \\\"Summer\\\", \\\"Shoulder\\\", \\\"Winter\\\"]\\n\\n ax1.plot(\\n hod,\\n total_benefits,\\n c=\\\"royalblue\\\",\\n marker=\\\"$\\\\u25EF$\\\",\\n markersize=10,\\n linewidth=3,\\n linestyle=\\\"-\\\",\\n )\\n ax1.plot(hod, summer_benefits, c=\\\"darkorchid\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax1.plot(hod, shoulder_benefits, c=\\\"olivedrab\\\", linewidth=1, linestyle=\\\":\\\")\\n ax1.plot(hod, winter_benefits, c=\\\"teal\\\", linewidth=1, linestyle=\\\"-\\\")\\n ax2.plot(\\n hod,\\n total_savings,\\n c=\\\"firebrick\\\",\\n marker=\\\"$\\\\u2206$\\\",\\n markersize=10,\\n linewidth=3,\\n linestyle=\\\"-\\\",\\n )\\n ax2.plot(hod, summer_savings, c=\\\"darkorchid\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax2.plot(hod, shoulder_savings, c=\\\"olivedrab\\\", linewidth=1, linestyle=\\\":\\\")\\n ax2.plot(hod, winter_savings, c=\\\"teal\\\", linewidth=1, linestyle=\\\"-\\\")\\n ax3.plot(\\n hod,\\n total_av_csts_avg,\\n c=\\\"green\\\",\\n marker=\\\"$\\\\u25A0$\\\",\\n markersize=10,\\n linewidth=3,\\n linestyle=\\\"-\\\",\\n )\\n ax3.plot(hod, summer_av_csts_avg, c=\\\"darkorchid\\\", linewidth=1, linestyle=\\\"--\\\")\\n ax3.plot(hod, shoulder_av_csts_avg, c=\\\"olivedrab\\\", linewidth=1, linestyle=\\\":\\\")\\n ax3.plot(hod, winter_av_csts_avg, c=\\\"teal\\\", linewidth=1, linestyle=\\\"-\\\")\\n\\n leg1 = ax1.legend(legend_labels, fontsize=11, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg1.get_lines(), leg1.get_texts()):\\n text.set_color(line.get_color())\\n leg2 = ax2.legend(legend_labels, fontsize=11, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg2.get_lines(), leg2.get_texts()):\\n text.set_color(line.get_color())\\n leg3 = ax3.legend(legend_labels, fontsize=11, loc=\\\"upper left\\\", frameon=False)\\n for line, text in zip(leg3.get_lines(), leg3.get_texts()):\\n text.set_color(line.get_color())\\n\\n ax3.set_xticks(np.arange(0, 24, step=4))\\n ax3.set_xlabel(\\\"Hour of Day\\\", size=14, labelpad=5)\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=12\\n )\\n ax3.set_xlim(hod.min() - hod.max() * 0.04, hod.max() * 1.04)\\n ax3.xaxis.set_minor_locator(AutoMinorLocator())\\n\\n ax1.set_ylabel(\\\"TRC Benefits ($)\\\", size=14)\\n ax2.set_ylabel(\\\"Savings (MWh)\\\", size=14)\\n ax3.set_ylabel(\\\"Av. Cost ($/MWh)\\\", size=14)\\n\\n if max(total_benefits + summer_benefits + shoulder_benefits + winter_benefits) < 0:\\n ymax1 = 0\\n else:\\n ymax1 = max(\\n total_benefits + summer_benefits + shoulder_benefits + winter_benefits\\n )\\n if min(total_benefits + summer_benefits + shoulder_benefits + winter_benefits) > 0:\\n ymin1 = 0\\n else:\\n ymin1 = min(\\n total_benefits + summer_benefits + shoulder_benefits + winter_benefits\\n )\\n if max(total_savings + summer_savings + shoulder_savings + winter_savings) < 0:\\n ymax2 = 0\\n else:\\n ymax2 = max(total_savings + summer_savings + shoulder_savings + winter_savings)\\n if min(total_savings + summer_savings + shoulder_savings + winter_savings) > 0:\\n ymin2 = 0\\n else:\\n ymin2 = min(total_savings + summer_savings + shoulder_savings + winter_savings)\\n if (\\n max(\\n total_av_csts_avg\\n + summer_av_csts_avg\\n + shoulder_av_csts_avg\\n + winter_av_csts_avg\\n )\\n < 0\\n ):\\n ymax3 = 0\\n else:\\n ymax3 = max(\\n total_av_csts_avg\\n + summer_av_csts_avg\\n + shoulder_av_csts_avg\\n + winter_av_csts_avg\\n )\\n if (\\n min(\\n total_av_csts_avg\\n + summer_av_csts_avg\\n + shoulder_av_csts_avg\\n + winter_av_csts_avg\\n )\\n > 0\\n ):\\n ymin3 = 0\\n else:\\n ymin3 = min(\\n total_av_csts_avg\\n + summer_av_csts_avg\\n + shoulder_av_csts_avg\\n + winter_av_csts_avg\\n )\\n\\n # Tick and lebel parameters\\n ax1.set_ylim(ymin1 * 1.08, ymax1 * 1.08)\\n ax2.set_ylim(ymin2 * 1.08, ymax2 * 1.08)\\n ax3.set_ylim(ymin3 * 1.08, ymax3 * 1.08)\\n\\n ax1.set_yticks(\\n np.arange(\\n ymin1 * 1.08,\\n ymax1 * 1.08,\\n step=max(round(ymax1 - ymin1, 3) / 5, int((round(ymax1 - ymin1, 0)) / 4)),\\n )\\n )\\n ax2.set_yticks(\\n np.arange(\\n ymin2 * 1.08,\\n ymax2 * 1.08,\\n step=max(round(ymax2 - ymin2, 3) / 5, int((round(ymax2 - ymin2, 0)) / 4)),\\n )\\n )\\n ax3.set_yticks(\\n np.arange(\\n ymin3 * 1.08,\\n ymax3 * 1.08,\\n step=max(round(ymax3 - ymin3, 3) / 5, int((round(ymax3 - ymin3, 0)) / 4)),\\n )\\n )\\n\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=12\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=12\\n )\\n ax3.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=12\\n )\\n\\n # Shade peak region\\n ax1.axvspan(16, 21, alpha=0.2, color=\\\"grey\\\")\\n ax2.axvspan(16, 21, alpha=0.2, color=\\\"grey\\\")\\n ax3.axvspan(16, 21, alpha=0.2, color=\\\"grey\\\")\\n\\n # Print key information\\n plt.annotate(\\n \\\"Electric Benefits = $\\\" + str(round(elec_benefits[\\\"total\\\"].sum(), 2)),\\n xy=(350, 530),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Gas Benefits = $\\\" + str(round(gas_benefits, 2)),\\n xy=(350, 505),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Total Benefits = $\\\"\\n + str(round(elec_benefits[\\\"total\\\"].sum() + gas_benefits, 2)),\\n xy=(350, 480),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"TRC Costs = $\\\" + str(trc_costs_record),\\n xy=(350, 455),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"PAC Costs = $\\\" + str(pac_costs_record),\\n xy=(350, 430),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"TRC = \\\" + str(trc_record),\\n xy=(350, 405),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"PAC = \\\" + str(pac_record),\\n xy=(350, 380),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Net Lifecycle Electric Savings = \\\" + str(lifecycle_net_mwh) + \\\" MWh\\\",\\n xy=(350, 335),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Net Lifecycle Gas Savings = \\\" + str(lifecycle_net_therms) + \\\" Therms\\\",\\n xy=(350, 310),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Net Lifecycle GHG Savings = \\\" + str(lifecycle_net_ghg) + \\\" Tons\\\",\\n xy=(350, 285),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n str(round(100 * ((summer_peak_savings) / sum(total_savings)), 1))\\n + \\\"% MWh savings during summer peak period\\\",\\n xy=(350, 260),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n str(round(100 * ((summer_peak_benefits) / sum(total_benefits)), 1))\\n + \\\"% Electric TRC benefits from summer peak period\\\",\\n xy=(350, 235),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Electric Benefits per MWh = $\\\"\\n + str(round(elec_benefits[\\\"total\\\"].sum() / lifecycle_net_mwh, 2)),\\n xy=(350, 210),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n plt.annotate(\\n \\\"Typical Avoided Cost per MWh = $\\\"\\n + str(round(elec_benefits[\\\"av_csts_levelized\\\"].mean(), 2)),\\n xy=(350, 145),\\n xycoords=\\\"axes points\\\",\\n fontsize=18,\\n )\\n\\n # Set plot title, size, and position\\n ax1.set_title(\\n \\\"Savings and Avoided Cost Profiles\\\", size=16, loc=\\\"left\\\"\\n ).set_position([0, 1.03])\\n\\n # marginal_ghg_savings_plot\\n cmp = sns.diverging_palette(16, 260, l=35, n=25, as_cmap=True)\\n\\n fig4 = plt.figure(figsize=(8, 6), dpi=100)\\n ax1 = fig4.gca()\\n y_ticks = [\\n 0,\\n \\\"\\\",\\n 2,\\n \\\"\\\",\\n 4,\\n \\\"\\\",\\n 6,\\n \\\"\\\",\\n 8,\\n \\\"\\\",\\n 10,\\n \\\"\\\",\\n 12,\\n \\\"\\\",\\n 14,\\n \\\"\\\",\\n 16,\\n \\\"\\\",\\n 18,\\n \\\"\\\",\\n 20,\\n \\\"\\\",\\n 22,\\n ]\\n hmp = sns.heatmap(ghgsav, cmap=cmp, ax=ax1, yticklabels=y_ticks, center=0.00)\\n ax1.set_xlabel(\\\"Month\\\", size=15)\\n ax1.set_ylabel(\\\"Hour of Day\\\", size=15)\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=13\\n )\\n ax1.tick_params(\\n which=\\\"major\\\",\\n axis=\\\"y\\\",\\n direction=\\\"out\\\",\\n length=6,\\n width=2,\\n labelsize=13,\\n rotation=0,\\n )\\n ax1.set_title(\\\"Electric GHG Savings by Month and Hour\\\", size=15, loc=\\\"left\\\", pad=8)\\n cbar1 = hmp.collections[0].colorbar\\n cbar1.ax.tick_params(labelsize=14)\\n plt.annotate(\\\"Sum GHG\\\", xy=(370, 352), xycoords=\\\"axes points\\\", fontsize=12)\\n plt.annotate(\\\"Savings (Tons)\\\", xy=(370, 336), xycoords=\\\"axes points\\\", fontsize=12)\\n\\n # month_hour_savings_benefits_plot\\n fig5, (ax1, ax2) = plt.subplots(1, 2, figsize=(21, 10), dpi=200)\\n y_ticks = [\\n 0,\\n \\\"\\\",\\n 2,\\n \\\"\\\",\\n 4,\\n \\\"\\\",\\n 6,\\n \\\"\\\",\\n 8,\\n \\\"\\\",\\n 10,\\n \\\"\\\",\\n 12,\\n \\\"\\\",\\n 14,\\n \\\"\\\",\\n 16,\\n \\\"\\\",\\n 18,\\n \\\"\\\",\\n 20,\\n \\\"\\\",\\n 22,\\n ]\\n fleft = sns.heatmap(sav, cmap=cmp, ax=ax1, yticklabels=y_ticks, center=0.00)\\n fright = sns.heatmap(avcsts, cmap=cmp, ax=ax2, yticklabels=y_ticks, center=0.00)\\n ax1.set_xlabel(\\\"Month\\\", size=22)\\n ax1.set_ylabel(\\\"Hour of Day\\\", size=22)\\n ax2.set_xlabel(\\\"Month\\\", size=22)\\n ax2.set_ylabel(\\\"Hour of Day\\\", size=22)\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=18\\n )\\n ax1.tick_params(\\n which=\\\"major\\\",\\n axis=\\\"y\\\",\\n direction=\\\"out\\\",\\n length=6,\\n width=2,\\n labelsize=18,\\n rotation=0,\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=18\\n )\\n ax2.tick_params(\\n which=\\\"major\\\",\\n axis=\\\"y\\\",\\n direction=\\\"out\\\",\\n length=6,\\n width=2,\\n labelsize=18,\\n rotation=0,\\n )\\n ax1.set_title(\\n \\\"MWh Savings by Month and Hour\\\", size=24, loc=\\\"left\\\", pad=15\\n ).set_position([0, 1.1])\\n ax2.set_title(\\\"$ Benefits by Month and Hour\\\", size=24, loc=\\\"left\\\", pad=15)\\n fig4.tight_layout(pad=2.0)\\n cbar1 = fleft.collections[0].colorbar\\n cbar1.ax.tick_params(labelsize=18)\\n cbar2 = fright.collections[0].colorbar\\n cbar2.ax.tick_params(labelsize=18)\\n plt.annotate(\\\"Sum MWh\\\", xy=(-200, 585), xycoords=\\\"axes points\\\", fontsize=20)\\n plt.annotate(\\\"Savings\\\", xy=(-193, 560), xycoords=\\\"axes points\\\", fontsize=20)\\n plt.annotate(\\\"Sum TRC\\\", xy=(435, 585), xycoords=\\\"axes points\\\", fontsize=20)\\n plt.annotate(\\\"Benefits\\\", xy=(442, 560), xycoords=\\\"axes points\\\", fontsize=20)\\n\\n # savings_benefits_cumulative_sum_plot\\n fig6 = plt.figure(figsize=(12, 7), dpi=250)\\n ax1 = fig6.gca()\\n ax1.plot(\\n elec_benefits_hoy[\\\"hour_of_year\\\"],\\n elec_benefits_hoy[\\\"hourly_savings\\\"],\\n color=\\\"royalblue\\\",\\n linewidth=3,\\n )\\n ax2 = ax1.twinx()\\n ax2.plot(\\n elec_benefits_hoy[\\\"hour_of_year\\\"],\\n elec_benefits_hoy[\\\"total\\\"],\\n color=\\\"firebrick\\\",\\n linewidth=3,\\n linestyle=\\\"--\\\",\\n )\\n ax2.axhline(y=0, color=\\\"gray\\\", linewidth=0.7, linestyle=\\\"--\\\")\\n\\n # Set x and y limits based on min and max values\\n\\n if (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].max() >= 0\\n and elec_benefits_hoy[\\\"total\\\"].max() >= 0\\n ):\\n ymax1 = elec_benefits_hoy[\\\"hourly_savings\\\"].max()\\n ymax2 = elec_benefits_hoy[\\\"total\\\"].max()\\n elif (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].max() < 0\\n and elec_benefits_hoy[\\\"total\\\"].max() < 0\\n ):\\n ymax1 = 0\\n ymax2 = 0\\n elif (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].max() < 0\\n and elec_benefits_hoy[\\\"total\\\"].max() > 0\\n ):\\n ymax1 = (\\n -1\\n * elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n * (\\n elec_benefits_hoy[\\\"total\\\"].max()\\n / (elec_benefits_hoy[\\\"total\\\"].max() - elec_benefits_hoy[\\\"total\\\"].min())\\n )\\n / (\\n 1\\n - elec_benefits_hoy[\\\"total\\\"].max()\\n / (elec_benefits_hoy[\\\"total\\\"].max() - elec_benefits_hoy[\\\"total\\\"].min())\\n )\\n )\\n ymax2 = elec_benefits_hoy[\\\"total\\\"].max()\\n else:\\n ymax1 = 0\\n ymax2 = (\\n -1\\n * elec_benefits_hoy[\\\"total\\\"].min()\\n * (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].max()\\n / (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].max()\\n - elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n )\\n )\\n )\\n\\n if (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].min() <= 0\\n and elec_benefits_hoy[\\\"total\\\"].min() <= 0\\n ):\\n ymin1 = elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n ymin2 = elec_benefits_hoy[\\\"total\\\"].min()\\n elif (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].min() > 0\\n and elec_benefits_hoy[\\\"total\\\"].min() > 0\\n ):\\n ymin1 = 0\\n ymin2 = 0\\n elif (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].min() > 0\\n and elec_benefits_hoy[\\\"total\\\"].min() < 0\\n ):\\n ymin1 = (\\n -1\\n * elec_benefits_hoy[\\\"hourly_savings\\\"].max()\\n * (\\n elec_benefits_hoy[\\\"total\\\"].min()\\n / (elec_benefits_hoy[\\\"total\\\"].min() - elec_benefits_hoy[\\\"total\\\"].max())\\n )\\n / (\\n 1\\n - elec_benefits_hoy[\\\"total\\\"].min()\\n / (elec_benefits_hoy[\\\"total\\\"].min() - elec_benefits_hoy[\\\"total\\\"].max())\\n )\\n )\\n ymin2 = elec_benefits_hoy[\\\"total\\\"].min()\\n else:\\n ymin1 = 0\\n ymin2 = (\\n -1\\n * elec_benefits_hoy[\\\"total\\\"].min()\\n * (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n / (\\n elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n - elec_benefits_hoy[\\\"hourly_savings\\\"].min()\\n )\\n )\\n )\\n\\n # Set x and y axis limits\\n ax1.set_xlim(-340, 9000)\\n ax1.set_ylim(ymin1 * 1.08, ymax1 * 1.08)\\n ax2.set_ylim(ymin2 * 1.08, ymax2 * 1.08)\\n\\n # Set x and y axis labels\\n ax1.set_xlabel(\\\"Hour of Year\\\", size=17, labelpad=5)\\n ax1.set_ylabel(\\\"Net Lifecycle Savings (MWh)\\\", size=17)\\n ax2.set_ylabel(\\\"$ TRC Benefits\\\", size=17, rotation=-90, labelpad=20)\\n\\n # Set plot title, size, and position\\n ax1.set_title(\\n \\\"Cumulative Savings and TRC Benefits by Hour of Year\\\",\\n size=17,\\n loc=\\\"left\\\",\\n pad=8,\\n )\\n\\n # Tick and lebel parameters\\n ax1.set_xticks(np.arange(0, 8760, step=1000))\\n ax1.set_yticks(\\n np.arange(\\n int(round(ymin1 * 1.1, 0)),\\n ymax1 * 1.08,\\n step=max(round(ymax1 - ymin1, 2) / 5, int((round(ymax1 - ymin1, 0)) / 4)),\\n )\\n )\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax1.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n\\n ax2.set_xticks(np.arange(0, 8760, step=1000))\\n ax2.set_yticks(\\n np.arange(\\n int(round(ymin2 * 1.1, 0)),\\n ymax2 * 1.08,\\n step=max(round(ymax2 - ymin2, 2) / 5, int((round(ymax2 - ymin2, 0)) / 4)),\\n )\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax2.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n\\n # Minor ticks\\n ax1.xaxis.set_minor_locator(AutoMinorLocator())\\n ax1.yaxis.set_minor_locator(AutoMinorLocator())\\n ax2.yaxis.set_minor_locator(AutoMinorLocator())\\n\\n # Legend\\n ax1.legend(\\n [\\\"Savings\\\"],\\n fontsize=12,\\n bbox_to_anchor=(0.02, 1),\\n loc=\\\"upper left\\\",\\n frameon=False,\\n )\\n ax2.legend(\\n [\\\"TRC Beneftis\\\"],\\n fontsize=12,\\n bbox_to_anchor=(0.02, 0.95),\\n loc=\\\"upper left\\\",\\n frameon=False,\\n )\\n\\n fig7 = plt.figure(figsize=(12, 7), dpi=250)\\n ax = fig7.gca()\\n colors1 = [\\n \\\"black\\\",\\n \\\"royalblue\\\",\\n \\\"black\\\",\\n \\\"pink\\\",\\n \\\"firebrick\\\",\\n \\\"gray\\\",\\n \\\"darkviolet\\\",\\n \\\"darkorange\\\",\\n \\\"green\\\",\\n \\\"saddlebrown\\\",\\n ]\\n legend_labels2 = []\\n\\n ax.plot(\\n elec_benefits_hoy[\\\"hour_of_year\\\"],\\n elec_benefits_hoy[ACC_COMPONENTS_ELECTRICITY[0]],\\n color=colors1[0],\\n linewidth=3,\\n )\\n legend_labels2.append(ACC_COMPONENTS_ELECTRICITY[0])\\n x = 1\\n while x <= len(ACC_COMPONENTS_ELECTRICITY) - 2:\\n ax.plot(\\n elec_benefits_hoy[\\\"hour_of_year\\\"],\\n elec_benefits_hoy[ACC_COMPONENTS_ELECTRICITY[x]],\\n color=colors1[x],\\n )\\n legend_labels2.append(ACC_COMPONENTS_ELECTRICITY[x])\\n x += 1\\n\\n # Set x and y limits based on min and max values\\n if max(elec_benefits_hoy.iloc[:, 2:x].max()) < 0:\\n ymax = 0\\n else:\\n ymax = max(elec_benefits_hoy.iloc[:, 2:x].max())\\n if min(elec_benefits_hoy.iloc[:, 2:x].min()) > 0:\\n ymin = 0\\n else:\\n ymin = min(elec_benefits_hoy.iloc[:, 2:x].min())\\n\\n ax.set_xlim(-340, 9000)\\n ax.set_ylim(ymin * 1.1, ymax * 1.08)\\n\\n # Set x and y axis labels\\n ax.set_xlabel(\\\"Hour of Year\\\", size=17, labelpad=5)\\n ax.set_ylabel(\\\"$ TRC Benefits\\\", size=17)\\n\\n # Set plot title, size, and position\\n ax.set_title(\\n \\\"Sum of Avoided Costs by Component and Hour of Day\\\", size=17, loc=\\\"left\\\"\\n )\\n\\n # Tick and lebel parameters\\n ax.set_xticks(np.arange(0, 8760, step=1000))\\n ax.set_yticks(\\n np.arange(\\n int(round(ymin * 1.1, 0)),\\n ymax * 1.08,\\n step=max(round(ymax - ymin, 3) / 5, int((round(ymax - ymin, 0)) / 4)),\\n )\\n )\\n ax.tick_params(\\n which=\\\"major\\\", axis=\\\"x\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n ax.tick_params(\\n which=\\\"major\\\", axis=\\\"y\\\", direction=\\\"out\\\", length=6, width=2, labelsize=14\\n )\\n\\n # Minor ticks\\n ax.xaxis.set_minor_locator(AutoMinorLocator())\\n ax.yaxis.set_minor_locator(AutoMinorLocator())\\n\\n # Legend\\n plt.legend(\\n legend_labels2,\\n bbox_to_anchor=(1, 1),\\n fontsize=12,\\n loc=\\\"upper left\\\",\\n frameon=False,\\n )\",\n \"def get_safety_vars_plot(self):\\n if 'safety_vars_stats' not in self.stats:\\n raise ValueError('No safety vars statistics present in this evaluator.')\\n\\n safety_vars = self.stats['safety_vars_stats'][0].keys()\\n n_plots = len(safety_vars)\\n fig, axes = plt.subplots(n_plots, 1, figsize=(8, 6 * n_plots))\\n\\n for idx, var in enumerate(safety_vars):\\n series = collections.defaultdict(list)\\n for ep in self.stats['safety_vars_stats']:\\n for stat in ep[var]:\\n series[stat].append(ep[var][stat])\\n ax = axes[idx]\\n for stat in ['min', 'max']:\\n ax.plot(np.squeeze(np.array(series[stat])), label=stat)\\n x = range(len(series['mean']))\\n\\n mean = np.squeeze(np.array(series['mean']))\\n std_dev = np.squeeze(np.array(series['std_dev']))\\n ax.plot(x, mean, label='Value')\\n ax.fill_between(\\n range(len(series['mean'])), mean - std_dev, mean + std_dev, alpha=0.3)\\n ax.set_title('Stats for {}'.format(var))\\n ax.legend()\\n ax.spines['top'].set_visible(False)\\n\\n ax.xaxis.set_ticks_position('bottom')\\n ax.set_xlabel('Episode #')\\n ax.set_ylabel('Magnitude')\\n ax.plot()\\n return fig\",\n \"def plot_single(\\n data_dict: dict,\\n keys: str,\\n x_ticks: List[str] = ['2015', '2016', '2017', '2018', '2019', '2020'],\\n show_preprint: bool = False,\\n title_text: str = '',\\n figpath: str = 'comparison_plot.pdf',\\n logscale=False,\\n) -> None:\\n\\n sns.set_palette(sns.color_palette('colorblind', 10))\\n plt.rcParams.update({'hatch.color': 'w'})\\n plt.rcParams['figure.facecolor'] = 'white'\\n plt.figure(figsize=(8, 5))\\n\\n arxiv, biorxiv, pubmed, medrxiv, chemrxiv, preprint = [], [], [], [], [], []\\n\\n for key in keys:\\n try:\\n arxiv.append(data_dict[key]['arxiv'])\\n biorxiv.append(data_dict[key]['biorxiv'])\\n medrxiv.append(data_dict[key]['medrxiv'])\\n chemrxiv.append(data_dict[key]['chemrxiv'])\\n pubmed.append(data_dict[key]['pubmed'])\\n except KeyError:\\n raise KeyError(\\n f'Did not find all DBs for {key}, only found {data_dict[key].keys()}'\\n )\\n preprint.append(arxiv[-1] + biorxiv[-1] + medrxiv[-1] + chemrxiv[-1])\\n\\n ind = np.arange(len(arxiv[0])) # the x locations for the groups\\n width = [0.75] * len(ind) # the width of the bars: can also be len(x) sequence\\n fnc = np.log10 if logscale else np.copy\\n\\n plts = []\\n legend_plts = []\\n if show_preprint:\\n bars = [pubmed, preprint]\\n legend_platform = ['PubMed', 'Preprint']\\n if logscale:\\n sums = np.array(pubmed) + np.array(preprint)\\n logsums = np.log10(sums)\\n bars = [pubmed * logsums / sums, preprint * logsums / sums]\\n\\n else:\\n bars = [pubmed, arxiv, biorxiv, chemrxiv, medrxiv]\\n legend_platform = ['PubMed', 'ArXiv', 'BiorXiv', 'ChemRxiv', 'MedRxiv']\\n if logscale:\\n sums = (\\n np.array(pubmed)\\n + np.array(arxiv)\\n + np.array(biorxiv)\\n + np.array(chemrxiv)\\n + np.array(medrxiv)\\n )\\n logsums = np.log10s(sums)\\n bars = [\\n pubmed * logsums / sums,\\n arxiv * logsums / sums,\\n biorxiv * logsums / sums,\\n chemrxiv * logsums / sums,\\n medrxiv * logsums / sums,\\n ]\\n for idx in range(len(keys)):\\n bottom = 0\\n\\n for bidx, b in enumerate(bars):\\n if idx == 0:\\n p = plt.bar(\\n ind,\\n b[idx],\\n width,\\n linewidth=1,\\n edgecolor='k',\\n bottom=bottom,\\n )\\n else:\\n p = plt.bar(\\n ind,\\n b[idx],\\n width,\\n color=next(iter(plts[bidx])).get_facecolor(),\\n linewidth=1,\\n edgecolor='k',\\n bottom=bottom,\\n )\\n\\n bottom += b[idx]\\n plts.append(p)\\n legend_plts.append(\\n plt.bar(ind, np.zeros((len(ind),)), color='k', bottom=bottom)\\n )\\n\\n plt.ylabel('Counts', size=17) if not logscale else plt.ylabel(\\n 'Counts (log scale)', size=17\\n )\\n plt.xlabel('Years', size=17)\\n plt.title(title_text, size=17)\\n # Customize minor tick labels\\n\\n plt.xticks(ind, x_ticks, size=14)\\n ymax = plt.gca().get_ylim()[1]\\n if logscale:\\n yticks = np.arange(1, ymax).astype(int)\\n plt.yticks(yticks, np.power(10, yticks))\\n\\n plt.tick_params(axis='y', labelsize=17)\\n\\n plt.legend(\\n legend_platform,\\n prop={'size': 14},\\n loc='upper left',\\n title='Platform:',\\n title_fontsize=17,\\n ncol=1,\\n )\\n\\n get_step_size = lambda x: round(x / 10, -math.floor(math.log10(x)) + 1)\\n ymax = plt.gca().get_ylim()[1]\\n\\n for y_step in plt.yticks()[0]:\\n plt.hlines(y_step, xmax=10, xmin=-1, color='black', linewidth=0.1)\\n plt.xlim([-0.5, len(ind)])\\n plt.ylim([0, ymax * 1.02])\\n\\n plt.tight_layout()\\n plt.savefig(figpath)\\n plt.show()\",\n \"def frame_stats(df, file=\\\"./frame_stats.pdf\\\", fontsize=16):\\n \\n temp = df\\n \\n matplotlib.rcParams.update({'font.size': 12})\\n fig, (ax1, ax2,ax3) = plt.subplots(3, figsize=(10,8), sharex=True)\\n\\n ax1.plot(temp.index, temp.noise, ':')\\n ax1.plot(temp.index, [.1 for i in temp.index], '--',color='k')\\n ax1.fill_between(temp.index, 0.01,0.045, facecolor='blue', alpha=0.1)\\n ax1.set( ylabel='Noise')\\n ax1.yaxis.label.set_size(fontsize)\\n\\n ax2.plot(temp.index, temp.pseudo_ent/10, ':')\\n ax2.plot(temp.index, [0.01 for i in temp.index], '--',color='k')\\n ax2.set(ylabel='Signal',)\\n ax2.yaxis.label.set_size(fontsize)\\n ax2.xaxis.label.set_size(fontsize)\\n\\n ax3.plot(temp.index, temp[\\\"2dhist95pre\\\"]/10, ':')\\n ax3.plot(temp.index, temp[\\\"2dhist99pre\\\"]/10, ':')\\n ax3.fill_between(temp.index,temp[\\\"2dhist95pre\\\"]/10,temp[\\\"2dhist99pre\\\"]/10, facecolor='blue', alpha=0.1)\\n ax3.set( ylabel='Narrow-band')#xlabel='Frame index',\\n ax3.yaxis.label.set_size(fontsize)\\n ax3.set_ylim(0,1)\\n\\n plt.xlabel(\\\"Time (15 minute intervals)\\\",fontsize=fontsize)\\n \\n plt.xlim(0,100)\\n plt.subplots_adjust(hspace=0.1)\\n fig.savefig(file,bbox_inches='tight')\",\n \"def summaryPlot(df):\\n import datetime as dt\\n import matplotlib.pyplot as plt\\n import matplotlib as mpl\\n import numpy as np\\n import pandas as pd\\n from numpy import array\\n import matplotlib.patches as mpatches\\n import seaborn as sns\\n from matplotlib.pyplot import figure\\n\\n class color:\\n # Allows for bolded and underlined text\\n BOLD = \\\"\\\\033[1m\\\"\\n UNDERLINE = \\\"\\\\033[4m\\\"\\n END = \\\"\\\\033[0m\\\"\\n\\n # Reads df and fills empty values\\n df.index = pd.to_datetime(df.date)\\n df = df.drop(\\\"date\\\", axis=1)\\n df_all = df.resample(\\\"1D\\\")\\n df_all = df_all.fillna(method=\\\"ffill\\\")\\n\\n dataPoints = [\\\"pm25\\\", \\\"co\\\", \\\"so2\\\", \\\"pm10\\\", \\\"o3\\\", \\\"no2\\\", \\\"nox\\\", \\\"wd\\\", \\\"ws\\\"]\\n\\n i = 0\\n sub = 1\\n while i < 9:\\n # Plots line and histogram plots for ecery polutant\\n # in the correct location based on subplot\\n plt.figure(1, figsize=(50, 50))\\n plt.subplot(9, 2, sub)\\n sub = sub + 1\\n a = df_all[dataPoints[i]].plot.line(color=\\\"gold\\\")\\n a.axes.get_xaxis().set_visible(False)\\n a.yaxis.set_label_position(\\\"left\\\")\\n plt.ylabel(dataPoints[i], fontsize=75, bbox=dict(facecolor=\\\"whitesmoke\\\"))\\n # print(df['pm25'].max())\\n\\n plt.subplot(9, 2, sub)\\n sub = sub + 1\\n plt.hist(df_all[dataPoints[i]], bins=50, color=\\\"green\\\")\\n i = i + 1\\n i = 0\\n while i < 9:\\n # Calculates statistics\\n nDf = df[dataPoints[i]]\\n missing = nDf.isna().sum() + sum(n < 0 for n in nDf)\\n minVal = nDf.min()\\n maxVal = nDf.max()\\n meanVal = nDf.mean()\\n medianVal = nDf.median()\\n percentile = nDf.quantile(0.95)\\n print(\\\"---------------\\\")\\n print(color.BOLD + color.UNDERLINE + dataPoints[i] + color.END)\\n print(\\\"min = \\\" + str(0))\\n print(\\\"max = \\\" + str(maxVal))\\n print(\\\"missing = \\\" + str(missing))\\n print(\\\"mean = \\\" + str(meanVal))\\n print(\\\"median = \\\" + str(medianVal))\\n print(\\\"95th percentile = \\\" + str(percentile))\\n i = i + 1\",\n \"def plot_PCA():\\n X, languages = prepare_data_matrix()\\n #print(X)\\n eigenvectors, eigenvalues=power_iteration_two_components(X)\\n explain = explained_variance_ratio(X, eigenvectors, eigenvalues)\\n X=project_to_eigenvectors(X,eigenvectors)\\n\\n #print(X)\\n plt.title('Explained variance: %.3f' % explain)\\n plt.scatter(X[:,0], X[:,1])\\n for i in range(len(X)):\\n plt.text(X[i,0], X[i,1], languages[i][:3])\\n plt.show()\",\n \"def plot_results(infer_images, inference_predicted_class, inference_predictions, class_names=['plants', 'water']):\\n plt.style.use(['dark_background', 'bmh'])\\n rc('figure', figsize=(8, 8), max_open_warning=False)\\n rc('axes', facecolor='none')\\n plt.figure(figsize=(15, 15))\\n\\n for i, (infer_img, _) in enumerate(infer_images.take(10)):\\n ax = plt.subplot(5, 2, i + 1)\\n plt.imshow(infer_img.numpy()/255)\\n\\n # Find the predicted class from predictions\\n m = \\\"Predicted: {}, {:.2f}%\\\".format(\\n class_names[inference_predicted_class[i]], inference_predictions[i]*100)\\n plt.title(m)\\n plt.axis(\\\"off\\\")\\n plt.show()\",\n \"def plot_prec_value1(self):\\n# self.query_dict={'code':code.value,'exchange':exchange.value,\\\\\\n# 'structure':struct.value,'element':element.value,'properties':prop.value}\\n# print ('POSTING', self.query_dict)\\n# self.query_api(endpoint='evk')\\n\\n #layout_doc.children[4].children[0] = self.plot_pade_figure()\\n\\n\\n self.query_dict={'code':code.value,'exchange':exchange.value,\\\\\\n 'structure':struct.value,'element':element.value,'properties':prop.value}\\n print ('POSTING', self.query_dict)\\n self.query_api(endpoint='evk')\\n\\n layout_doc.children[4].children[0] = self.plot_pade_figure()\",\n \"def plotPaperFigures(folder='results/'):\\n #model Example\\n _AiryDisc(amplitude=1e6, center_x=10.0, center_y=10.0, radius=0.5, focus=0.4, size=21)\\n _CCDkernel(CCDx=10, CCDy=10, width_x=0.35, width_y=0.4, size=21)\\n plotModelExample()\\n\\n #simulation figures\\n theta1 = (2.e5, 9.9, 10.03, 0.41, 0.51, 10., 10., 0.291, 0.335)\\n try:\\n _plotDifferenceIndividualVsJoined(individuals='simulatedResults/RunI*.pkl',\\n joined='results/simulated800nmJoint.pkl',\\n title='Simulated Data: CCD PSF Recovery', truthx=theta1[7], truthy=theta1[8],\\n requirementE=None, requirementFWHM=None, requirementR2=None)\\n _plotModelResiduals(id='RunI1', folder='simulatedResults/', out='Residual1.pdf', individual=True)\\n except:\\n print 'No simulated data to plot...'\\n\\n #real data\\n _plotDifferenceIndividualVsJoined(individuals=folder+'I800nm?.pkl', joined=folder+'J800nm.pkl', title='800nm',\\n FWHMlims=(7.3, 11.8))\\n\\n _plotModelResiduals(id='I800nm2', folder=folder, out='ResidualData.pdf', individual=True)\\n _plotModelResiduals(id='RunI2', folder='simulatedResults/', out='Residual2.pdf', individual=True)\\n\\n #_plotModelResiduals(id='G600nm0', folder=folder, out='ResidualG600.pdf', individual=True)\\n #_plotModelResiduals(id='G700nm0', folder=folder, out='ResidualG700.pdf', individual=True)\\n _plotModelResiduals(id='G800nm0', folder=folder, out='ResidualG800.pdf', individual=True)\\n #_plotModelResiduals(id='G890nm0', folder=folder, out='ResidualG890.pdf', individual=True)\\n\\n #wavelength dependency\\n plotLambdaDependency()\\n\\n #brighter fatter\\n plotBrighterFatter()\",\n \"def run_and_plot(self):\\n self.raw_processing()\\n self.data_averaging_and_cleaning()\\n\\n print(self.organized_names)\\n print(self.organized_film)\\n print(self.organized_plank)\\n\\n height = self.organized_film\\n bars = tuple(self.organized_names.copy())\\n y_pos = np.arange(len(bars))\\n\\n plt.bar(y_pos, height)\\n plt.xticks(y_pos, bars)\\n plt.xlabel('TH% in 100ul water/TH mixture')\\n plt.ylabel('CFU/mL count')\\n plt.title('Experiment 2.5 (Sucrose Concentration) 7 Aug 2018')\\n\\n plt.show()\\n\\n height2 = self.organized_plank\\n\\n plt.bar(y_pos, height2)\\n plt.xticks(y_pos, bars)\\n plt.xlabel('TH% in 100ul water/TH mixture')\\n plt.ylabel('Proportion of Biofilm CFUs to Planktonic CFUs')\\n plt.title('Experiment 2.5 (Sucrose Concentration) 7 Aug 2018')\\n\\n plt.show()\",\n \"def _plotModelResiduals(id='simulated800nmJoint1', folder='results/', out='Residual.pdf', individual=False):\\n #data\\n if individual:\\n data = pf.getdata(folder+id+'small.fits')\\n data[data < 1] = 1.\\n data = np.log10(data)\\n else:\\n data = pf.getdata(folder+id+'datafit.fits')\\n data[data < 1] = 1.\\n data = np.log10(data)\\n #model\\n model = pf.getdata(folder+id+'model.fits ')\\n model[model < 1] = 1.\\n model = np.log10(model)\\n #residual\\n residual = pf.getdata(folder+id+'residual.fits')\\n #squared residual\\n residualSQ = pf.getdata(folder+id+'residualSQ.fits')\\n\\n max = np.max((data.max(), model.max()))\\n\\n #figure\\n fig = plt.figure(figsize=(12, 12))\\n ax1 = fig.add_subplot(221)\\n ax2 = fig.add_subplot(222)\\n ax3 = fig.add_subplot(223)\\n ax4 = fig.add_subplot(224)\\n ax = [ax1, ax2, ax3, ax4]\\n fig.subplots_adjust(hspace=0.05, wspace=0.3, top=0.95, bottom=0.02, left=0.02, right=0.9)\\n ax1.set_title('Data')\\n ax2.set_title('Model')\\n ax3.set_title('Residual')\\n ax4.set_title('$L^{2}$ Residual')\\n\\n im1 = ax1.imshow(data, interpolation='none', vmax=max, origin='lower', vmin=0.1)\\n im2 = ax2.imshow(model, interpolation='none', vmax=max, origin='lower', vmin=0.1)\\n im3 = ax3.imshow(residual, interpolation='none', origin='lower', vmin=-100, vmax=100)\\n im4 = ax4.imshow(residualSQ, interpolation='none', origin='lower', vmin=0., vmax=10)\\n\\n divider = make_axes_locatable(ax1)\\n cax1 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n divider = make_axes_locatable(ax2)\\n cax2 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n divider = make_axes_locatable(ax3)\\n cax3 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n divider = make_axes_locatable(ax4)\\n cax4 = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n cbar1 = plt.colorbar(im1, cax=cax1)\\n cbar1.set_label(r'$\\\\log_{10}(D_{i, j} \\\\quad [e^{-}]$)')\\n cbar2 = plt.colorbar(im2, cax=cax2)\\n cbar2.set_label(r'$\\\\log_{10}(M_{i, j} \\\\quad [e^{-}]$)')\\n cbar3 = plt.colorbar(im3, cax=cax3)\\n cbar3.set_label(r'$M_{i, j} - D_{i, j} \\\\quad [e^{-}]$')\\n cbar4 = plt.colorbar(im4, cax=cax4)\\n cbar4.set_label(r'$\\\\frac{(M_{i, j} - D_{i, j})^{2}}{\\\\sigma_{CCD}^{2}}$')\\n\\n for tmp in ax:\\n plt.sca(tmp)\\n plt.xticks(visible=False)\\n plt.yticks(visible=False)\\n\\n plt.savefig(out)\\n plt.close()\",\n \"def PlotEnsemblePredictions(self,ens, sampling_freq=10, fontSizeLabels=24, fontSizeLegend=20,rescale=1.,pow10first=False):\\n pylab.figure()\\n\\tpylab.axes([0.15,0.35,0.95-0.15,0.95-0.35])\\n\\tdata_experiments = self.data.experiments\\n for independentValues in data_experiments:\\n Xdata = self.data.X[independentValues]\\n Ydata = self.data.Y[independentValues]\\n\\t Xtheory = scipy.logspace(scipy.log10(min(Xdata)),scipy.log10(max(Xdata)),num=100)\\n\\t pointType = self.data.pointType[independentValues]\\n\\t errorBar = self.data.errorBar[independentValues]\\n\\t mean_theory = scipy.zeros(len(Xtheory))\\n\\t std_theory = scipy.zeros(len(Xtheory))\\n for i in range(0, num_to_count):\\n\\t\\t ens_theory = self.theory.Y(Xtheory,ens[i*sampling_freq],independentValues)\\n\\t\\t mean_theory += ens_theory\\n\\t\\t std_theory += (ens_theory)**2\\n\\t mean_theory = mean_theory/(1.0*num_to_count)\\n\\t std_theory = scipy.sqrt((std_theory-num_to_count*mean_theory**2)/(num_to_count-1.))\\n\\t pylab.loglog(Xdata,Ydata,pointType[1])\\n\\t lb = self.getLabel(self.theory.independentNames,independentValues,pow10first=pow10first)\\n\\t pylab.errorbar(Xdata,Ydata, yerr=errorBar, fmt=pointType,label=lb)\\n\\t pylab.loglog(Xtheory,mean_theory,pointType[0])\\n\\t axis_dep=self.getAxis(Xdata,Ydata)\\n\\t #upper_bound = mean_theory+std_theory\\n\\t #lower_bound = mean_theory-std_theory\\n\\t upper_bound = scipy.exp(scipy.log(mean_theory) + scipy.log(1.+std_theory/mean_theory)*rescale)\\n\\t lower_bound = scipy.exp(scipy.log(mean_theory)+scipy.log(1.-std_theory/mean_theory)*rescale)\\n\\t for i in range(0, len(lower_bound)):\\n\\t\\t if lower_bound[i]<=0:\\n\\t\\t\\t lower_bound[i]=10.**(-16)\\n\\t pylab.fill_between(Xtheory,lower_bound,y2=upper_bound,color=pointType[0],alpha=0.2)\\n\\n\\t for i, Ax in enumerate(axis_dep):\\n\\t\\t ax0[i] =i%2 and max(ax0[i],Ax) or min(ax0[i],Ax)\\n\\tpylab.axis(tuple(ax0))\\n\\tpylab.legend(loc=(-0.15,-0.52),ncol=3)\",\n \"def setup_plot(self):\\n\\n # Get all the healthy, immune, infected, and dead people seperately \\n healthy_x = self.putil.population.get_all_healthy()[:, index.x_axis]\\n healthy_y = self.putil.population.get_all_healthy()[:, index.y_axis]\\n infected_x = self.putil.population.get_all_infected()[:, index.x_axis]\\n infected_y = self.putil.population.get_all_infected()[:, index.y_axis]\\n immune_x = self.putil.population.get_all_recovered()[:, index.x_axis]\\n immune_y = self.putil.population.get_all_recovered()[:, index.y_axis]\\n dead_x = self.putil.population.get_all_dead()[:, index.x_axis]\\n dead_y = self.putil.population.get_all_dead()[:, index.y_axis]\\n total_infected = self.putil.size - len(healthy_x)\\n total_hospitalized = len(self.putil.persons[self.putil.persons[:,index.hospitalized] == 3])\\n \\n # Current healthcare status\\n self.healthcare_status = \\\"Normal\\\"\\n \\n # Scatter plots to plot people\\n self.scat = self.ax.scatter(healthy_x,\\n healthy_y, vmin=0, vmax=1,\\n cmap=\\\"jet\\\", c=\\\"lightsteelblue\\\", s=10)\\n self.scat2 = self.ax.scatter(infected_x,\\n infected_y, vmin=0, vmax=1,\\n cmap=\\\"jet\\\", c=\\\"indianred\\\", s=10)\\n self.scat3 = self.ax.scatter(immune_x,\\n immune_y, vmin=0, vmax=1,\\n cmap=\\\"jet\\\", c=\\\"mediumseagreen\\\", s=10)\\n self.scat4 = self.ax.scatter(dead_x,\\n dead_y, vmin=0, vmax=1,\\n cmap=\\\"jet\\\", c=\\\"indigo\\\", s=10)\\n # Lists for line graph\\n self.infected = []\\n self.infected_total = []\\n self.deaths = []\\n self.frames = []\\n self.immunes = []\\n self.infected.append(len(infected_x))\\n self.deaths.append(len(dead_x))\\n self.infected_total.append(self.putil.size - len(healthy_x))\\n self.immunes.append(len(immune_x))\\n self.frames.append(0)\\n\\n # Line graph plotting number\\n self.total_infected, = self.ax1.plot(self.frames, self.infected_total)\\n self.currently_infected, = self.ax1.plot(self.frames, self.infected, c=\\\"indianred\\\", label='Currently Infected')\\n self.total_deaths, = self.ax1.plot(self.frames, self.deaths, c=\\\"indigo\\\", label='Total Dead')\\n self.total_immune, = self.ax1.plot(self.frames, self.immunes, c=\\\"mediumseagreen\\\", label='Total Immune')\\n\\n # Code below prints statistics \\n if(self.putil.enforce_social_distance_at > 0):\\n self.ax1.plot([self.putil.enforce_social_distance_at]*2, [0,self.putil.size],c=\\\"gold\\\", label=\\\"Social Distancing\\\")\\n self.social_distancing_info = (\\\"At frame \\\" + str(self.putil.enforce_social_distance_at))\\n self.social_distancing_num = str(int(self.putil.social_distance_per * self.putil.size)) + \\\" or \\\" + str(self.putil.social_distance_per*100)+\\\"%\\\"\\n else:\\n self.social_distancing_info = (\\\"Disabled\\\")\\n self.social_distancing_num = \\\"0 or 0%\\\"\\n\\n if(self.putil.enforce_mask_wearing_at > 0):\\n self.ax1.plot([self.putil.enforce_mask_wearing_at]*2, [0,self.putil.size],c=\\\"hotpink\\\", label=\\\"Mask Mandate\\\")\\n self.mask_wearing_info = \\\"At frame \\\" + str(self.putil.enforce_mask_wearing_at) \\n else:\\n self.mask_wearing_info = \\\"Disabled\\\"\\n\\n self.ax1.tick_params(axis=\\\"y\\\",direction=\\\"in\\\", pad=3)\\n self.ax1.plot([0,1000],[self.putil.virus.total_healthcare_capacity]*2, c=\\\"silver\\\")\\n self.ax1.get_xaxis().set_visible(False)\\n self.ax1.legend(prop={'size': 8},loc='upper right')\\n self.ax2.text(0,1,\\\"Statistics\\\", fontsize='large' , fontweight='bold')\\n self.ax2.text(0,-0.5, \\\"Frame:\\\\nCurrently Infected:\\\\nHealthy People:\\\\nImmune People:\\\\nTotal Deaths:\\\\nHealthcare Conditions:\\\")\\n self.ax2.text(0.54,-0.5, \\\"Population:\\\\nMasks Wearing:\\\\nSocial Distancing:\\\\nPeople Distancing:\\\\nTotal Infected:\\\\n\\\")\\n self.ax.text(0,1.06, \\\"Simulation\\\", fontsize='xx-large' , fontweight='bold')\\n self.text = self.ax2.text(0.33, -0.5, \\\"%i \\\\n%i \\\\n%s \\\\n%s \\\\n%s \\\\n%s\\\" %(0,len(infected_x),str(len(healthy_x)) + \\\" or 0%\\\", str(len(immune_x)) + \\\" or 0%\\\",str(len(dead_x)) + \\\" or 0%\\\",self.healthcare_status))\\n self.text2 = self.ax2.text(0.81,-0.5,\\\"%d \\\\n%s \\\\n%s \\\\n%s \\\\n%s\\\\n\\\" % (self.putil.size, self.mask_wearing_info, self.social_distancing_info, self.social_distancing_num , total_infected))\\n\\n return self.scat, self.scat2, self.scat3, self.scat4, self.currently_infected, self.total_infected,\",\n \"def vaccination_population_plot(r_zero,\\n data_file_name='vaccination_data_solution2020-06-16.pkl',\\n fig_file_name='model_solution.pdf'):\\n df = pd.read_pickle(data_file_name)\\n fig, ((ax_s, ax_e, ax_cl), (ax_i_s, ax_i_a, ax_r), (ax_d, ax_v, ax_t)) = plt.subplots(nrows=3, ncols=3)\\n #\\n n_whole = 1.0 # without rescaling population\\n t = df['time']\\n ax_s = plt.subplot(331)\\n ax_s.plot(t, n_whole * df['s'], label=\\\"s\\\")\\n ax_s.legend(loc=0)\\n #\\n ax_e = plt.subplot(332)\\n ax_e.plot(t, n_whole * df['e'], label=\\\"e\\\")\\n ax_e.legend(loc=0)\\n #\\n cl = n_whole * (df['s'] + df['e'] + df['i_s'] +\\n df['i_a'] + df['r'] + df['d'] +\\n df['v'] + df['treat'])\\n #\\n ax_cl = plt.subplot(333)\\n ax_cl.plot(t, cl, label=\\\"cl\\\")\\n ax_cl.legend(loc=0)\\n #\\n ax_i_s = plt.subplot(334)\\n ax_i_s.plot(t, n_whole * df['i_s'], label=\\\"i_s\\\")\\n ax_i_s.legend(loc=0)\\n #\\n ax_i_a = plt.subplot(335)\\n ax_i_a.plot(t, n_whole * df['i_a'], label=\\\"i_a\\\")\\n ax_i_a.legend(loc=0)\\n #\\n ax_r = plt.subplot(336)\\n ax_r.plot(t, n_whole * df['r'], label=\\\"r\\\")\\n ax_r.legend(loc=0)\\n #\\n ax_d = plt.subplot(337)\\n ax_d.plot(t, n_whole * df['d'], label=\\\"d\\\")\\n ax_d.legend(loc=0)\\n #\\n ax_v = plt.subplot(338)\\n ax_v.plot(t, n_whole * df['v'], label=\\\"v\\\")\\n ax_v.legend(loc=0)\\n #\\n ax_t = plt.subplot(339)\\n ax_t.plot(t, n_whole * df['treat'], label=\\\"treat\\\")\\n ax_t.legend(loc=0)\\n #\\n plt.tight_layout()\\n fig.suptitle(\\\"R0: \\\" + str(r_zero))\\n plt.savefig(fig_file_name)\\n plt.show()\\n return\",\n \"def plot_for_pop(exp,correctp,data_crop,IV,DV,bins,xticklabels,xlabel,ylabel,robust=0):\\n \\n\\n \\n if not os.path.exists('plots'):\\n os.makedirs('plots')\\n \\n if exp == 'POP1':\\n logLossmedian=1.386294\\n colors = [\\\"orangered\\\", \\\"silver\\\"]\\n else:\\n logLossmedian=1.098612\\n colors = [\\\"steelblue\\\", \\\"silver\\\"]\\n \\n customPalette = sns.set_palette(sns.color_palette(colors))\\n plt.rc('font', size=15) \\n \\n if IV=='Final.balance':\\n correctp.loc[correctp['logLoss.streak'] == 0]\\n if IV=='logLoss.streak':\\n correctp.loc[correctp['Final.balance'] == 0]\\n if IV == 'logResult':\\n correctp.loc[correctp['Final.balance'] == 0]\\n\\n # Predicted\\n IV_p=correctp.groupby(['group',IV]).mean().loc[:,'predicted']\\n IV_p=IV_p.unstack(level=0)\\n IV_p=pd.DataFrame(IV_p)\\n if exp == 'POP1':\\n #IV_p.reset_index(inplace=True)\\n IV_p=IV_p.loc[:,['Voucher predicted','Cash predicted']]\\n print(IV_p)\\n # Raw Data\\n # Create arbitrary bins and check N in each\\n data_crop.loc[:,'IV.binned']=pd.cut(data_crop.loc[:,IV], \\n bins=bins)\\n IV_nbins=pd.cut(data_crop.loc[:,IV], \\n bins=bins).value_counts()\\n IV_data=data_crop.groupby(['Participant','IV.binned','group']).mean().loc[:,DV]\\n IV_data=pd.DataFrame(IV_data)\\n\\n IV_data = IV_data.stack(level=0).reset_index(level=0) \\\\\\n .reset_index().drop('level_2',axis=1) \\\\\\n .rename(columns={0:'DV'})\\n print(IV_nbins)\\n IV_nbins.to_csv('plots/'+IV+'binN.csv',header=False )\\n # Build figure\\n fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,4),sharey=True)\\n #boxplot ax1\\n if exp=='POP1':\\n sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',\\n ax=ax1,palette=customPalette,linewidth=1,fliersize=5, hue_order=['Voucher data','Cash data'])\\n else:\\n sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',\\n ax=ax1,palette=customPalette,linewidth=1,fliersize=5)\\n if IV == 'Trial.type.binary':\\n IV_p.plot.bar(ax=ax2)\\n ax1.xaxis.set_ticklabels(['Win','Loss'])\\n ax2.xaxis.set_ticklabels(['Win','Loss'],rotation=0)\\n else:\\n IV_p.plot(ax=ax2)\\n ax1.set_ylabel(ylabel)\\n ax2.set_ylabel(ylabel)\\n ax1.set_xlabel(xlabel)\\n ax2.set_xlabel(xlabel)\\n fig.legend(title=None,loc=1,frameon=False)\\n ax1.legend().set_visible(False)\\n ax2.legend().set_visible(False)\\n if DV == 'logISI':\\n ax1.set_ylim(bottom=5,top=9.5)\\n # Change yaxis labels for boxplot - use minor ticks to define edges and turn off major\\n xbins=[]\\n for i in np.arange(-0.5, 100,1):\\n xbins.append(i)\\n if len(xbins)==len(xticklabels):\\n break\\n if IV != 'Trial.type.binary':\\n ax1.xaxis.set_ticks(xbins,minor=True)\\n ax1.xaxis.set_ticklabels(xticklabels,minor=True)\\n ax1.xaxis.set_ticks([])\\n ax1.xaxis.set_tick_params(which='minor',length=4)\\n sns.despine(top=True,right=True)\\n plt.tight_layout()\\n if robust==1:\\n fig.savefig(fname='plots/robust_'+IV +'.png',format='png',transparent=True)\\n fig.savefig(fname='plots/robust_'+IV + '.eps',format='eps',transparent=True)\\n else:\\n fig.savefig(fname='plots/'+IV +'.png',format='png',transparent=True)\\n fig.savefig(fname='plots/'+IV + '.eps',format='eps',transparent=True)\",\n \"def plot_ave_profile(f,y):\\n favex = np.mean(f, axis=1) # Horizontal average\\n\\n # Plot\\n fig, ax = plt.subplots(1,1, figsize=(15,9))\\n ax.plot(favex, y)\\n\\n # Label\\n ax.set_xlabel(r'$\\\\overline{T}$')\\n ax.set_ylabel(r'$y$')\\n fig.tight_layout()\\n\\n # Save\\n fig.savefig('Tave.pdf')\\n \\n plt.show()\",\n \"def p_plot(data,pv_index=0,alpha=0.05):\\n ####if it's a pd.dataframe, rename to col header\\n if isinstance(data, pd.DataFrame):\\n if isinstance(pv_index, int):\\n pv_index = data.columns.get_values()[pv_index]\\n data =data.rename(columns ={pv_index: \\\"p_value\\\"})\\n if not (np.issubdtype(data['p_value'].dtypes, np.number)):\\n raise TypeError(\\\"Please ensure you have specified the column index of numeric p-values.\\\")\\n ###or make a vector a pd.dataframe\\n else:\\n data = pd.DataFrame({\\\"p_value\\\": data})\\n \\n if (data[\\\"p_value\\\"].max()> 1) or (data[\\\"p_value\\\"].max()< 0):\\n raise ProbabilityError(\\\"One or more p-values is not between 0 and 1!\\\")\\n \\n m = len(data['p_value'])\\n\\n data = data.sort_values('p_value',ascending=True)\\n data['rank'] = np.arange(1,len(data['p_value'])+1)\\n data['critical_value'] = data['rank']*alpha/m\\n\\n fig = plt.clf()\\n plt.scatter(data['rank'],data['p_value'],color='black')\\n plt.axhline(y=alpha,label='Bonferroni')\\n plt.plot(data['rank'],data['critical_value'],label='BH',color='red')\\n plt.legend()\\n plt.title(\\\"Bonferroni vs BH\\\")\\n plt.xlabel(\\\"Rank\\\")\\n plt.ylabel(\\\"p(k)\\\")\\n return fig\",\n \"def main(logy):\\n filep = sys.stdin\\n dataf = pd.read_csv(filep, sep=\\\" \\\", index_col=0, header=None)\\n dataf.plot(logy=logy)\\n print(dataf)\\n plt.savefig(\\\"scaling.pdf\\\")\",\n \"def show_results(self):\\n\\n N = split_list(self.N)\\n # create subplot\\n fig = make_subplots(rows=1,cols=2,\\n subplot_titles=('Fish population', 'Harvested fish'),\\n specs=[[{'type': 'xy'}, {'type': 'pie'}]])\\n #Add population line graph\\n fig.add_trace(go.Scatter(y=N['odds'], x=np.linspace(1, 11, 6), name='odd year population',\\n hovertemplate =\\n 'Year: %{x}'+ '<br>Pop: %{y}'),\\n row=1, col=1)\\n fig.add_trace(go.Scatter(y=N['evens'], x=np.linspace(2, 12, 6), name='even year population',\\n hovertemplate =\\n 'Year: %{x}'+ '<br>Pop: %{y}'),\\n row=1, col=1)\\n fig.update_xaxes(title_text=\\\"year\\\", row=1, col=1)\\n fig.update_yaxes(title_text=\\\"population\\\", row=1, col=1)\\n\\n # cannot use 'paper' as yref due to bug in sublplot.\\n fig.add_shape(type='line',\\n xref='x', yref='y',\\n x0=2.5, y0=-10, x1=2.5, y1=1000,\\n line=dict(color='Black', width=3),\\n row=1, col=1)\\n\\n # create pie chart\\n colors = ['#636EFA', '#EF553B'] \\n labels = ['total odd year harvest', 'total even year harvest']\\n M = split_list(self.harvest_record)\\n values = [sum(M['odds']), sum(M['evens'])]\\n fig.add_trace(go.Pie(labels=labels, values=values, hoverinfo='label', textinfo='value', marker=dict(colors=colors)), \\n row=1, col=2)\\n\\n # add title\\n fig.update_layout(title_text='Results') \\n fig.write_html(\\\"fish_trap_simulation.html\\\")\\n\\n \\n return fig\",\n \"def plot_spatial_res(data_obj, test_num):\\n\\n #---------------------------------------------------------------------------------------------#\\n # Go through each file [image] and plot the spatial resolution ROI images and MTF data graphs #\\n #---------------------------------------------------------------------------------------------#\\n for i in range(len(data_obj.files)):\\n\\n fig = new_pdf_page(data_obj.pdf_obj) #Create a new pdf page\\n plt.axis('off')\\n\\n # Create and plot the (centered) title of the page - that also states the MTF20 values\\n str1 = 'Test ' + str(test_num) + ': Spatial Resolution \\\\n' + \\\\\\n 'MTF20x = ' + \\\"{0:.2f}\\\".format(data_obj.MTF_data.MTF20_x) + \\\\\\n '\\\\n MTF20y = ' + \\\"{0:.2f}\\\".format(data_obj.MTF_data.MTF20_y)\\n plt.suptitle(str1)\\n\\n #-----------------------------------------------#\\n # Plot the main image in the middle of the page #\\n #-----------------------------------------------#\\n ax1 = fig.add_subplot(312)\\n plt.imshow(data_obj.img_data[i].lead_foil_ROI.img) #Display the ROI image of the lead foil\\n\\n # Display the title of the ROI image - the orientation\\n plt.title('Orientation ' + str(data_obj.img_data[i].orientation))\\n plt.xticks([]) # labels\\n plt.yticks([])\\n ax = plt.gca()\\n ax.xaxis.set_ticks_position('none') # tick markers\\n ax.yaxis.set_ticks_position('none')\\n\\n if data_obj.img_data[i].orientation % 2 == 1:\\n plt.xlabel('x axis')\\n plt.ylabel('y axis')\\n else:\\n plt.xlabel('y axis')\\n plt.ylabel('x axis')\\n\\n #-----------------------------#\\n # Display the Horizontal plot #\\n #-----------------------------#\\n ax2 = fig.add_subplot(325)\\n plt.ylabel('MTFx')\\n plt.xlabel('Spatial frequency (cycles/mm)')\\n plt.plot(data_obj.img_data[i].MTF_obj.MTF_x_f, data_obj.img_data[i].MTF_obj.MTF_x)\\n text = 'MTF20x = ' + \\\"{0:.2f}\\\".format(data_obj.img_data[i].MTF_obj.MTF20_x)\\n\\n ax2.annotate(text,\\n xy=(data_obj.img_data[i].MTF_obj.MTF20_x, 0.2),\\n xytext=(np.max(data_obj.img_data[i].MTF_obj.MTF_x_f), 0.7),\\n arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth=4),\\n horizontalalignment='right')\\n\\n #---------------#\\n # Vertical plot #\\n #---------------#\\n ax3 = fig.add_subplot(326)\\n plt.ylabel('MTFy')\\n plt.xlabel('Spatial frequency (cycles/mm)')\\n plt.plot(data_obj.img_data[i].MTF_obj.MTF_y_f, data_obj.img_data[i].MTF_obj.MTF_y)\\n text = 'MTF20y = ' + \\\"{0:.2f}\\\".format(data_obj.img_data[i].MTF_obj.MTF20_y)\\n\\n ax3.annotate(text,\\n xy=(data_obj.img_data[i].MTF_obj.MTF20_y, 0.2),\\n xytext=(np.max(data_obj.img_data[i].MTF_obj.MTF_y_f), 0.7),\\n arrowprops=dict(facecolor='black', shrink=0.05, width=0.5, headwidth=4),\\n horizontalalignment='right')\",\n \"def plot(self, dtables, figs, **kwargs):\\n self.safe_update(**kwargs)\\n sumtable = dtables['biasoscorr_stats']\\n figs.plot_stat_color('mean-s', sumtable['s_correl_mean'].reshape(9, 16))\\n figs.plot_stat_color('mean-p', sumtable['p_correl_mean'].reshape(9, 16))\",\n \"def dofigure5():\\n required = {'Plastocyanin (Phormidium)':[61,92],\\n 'Plastocyanin (Anabaena variabilis)':[62,92],\\n 'Bovine Beta-Lactoglobulin':[64,114]}\\n \\n for p in DB.getRecs():\\n prot = DB[p]['name']\\n if prot in required:\\n Eh = data=DB[p]['1H NMR']\\n En = data=DB[p]['15N NMR']\\n for d in Eh.datasets:\\n hdata = Eh.getDataset(d)\\n num = int(t.getResidueFields(hdata)['res_num'])\\n #print d, type(num), required[prot]\\n if num in required[prot]:\\n print num\\n ndata = En.getDataset(d)\\n Eh.plotDatasets(d, filename=d+'.png')\\n return\",\n \"def analyse():\\n health = db.execute(\\\"SELECT * FROM health WHERE user_ID = :id\\\", id=session[\\\"user_id\\\"])\\n phone = db.execute(\\\"SELECT * FROM usage WHERE user_ID = :id\\\", id=session[\\\"user_id\\\"])\\n hdata, pdata = []\\n # get relevant values for health data then put values into two array\\n for i in range(3):\\n health1 = [health[i][h] for h in health[i]]\\n hdata.append(health1[2:6])\\n hdata = np.array(hdata)\\n hdata[[0, 2]] = hdata[[2, 0]]\\n print(np.mean(hdata, axis = 0))\\n print(np.std(hdata, axis = 0))\\n print(np.median(hdata, axis = 0))\\n\\n # get relevant values for phone data then put values into 2D array\\n for i in range(3):\\n phone1 = [phone[i][h] for h in phone[i]]\\n pdata.append(phone1[2:7] + phone1[9:14])\\n pdata = np.array(pdata)\\n red = pdata[...,2]\\n dep = []\\n print(red)\\n for i in hdata[...,0]:\\n dep.append(int(i))\\n dep = np.array(dep)\\n print(dep)\\n # pearson\\n print (np.corrcoef(red, dep ))\\n\\n\\n\\n return render_template(\\\"stats.html\\\",)\",\n \"def show_stats(self):\\n if len(self.keypoints) == 0:\\n logger.warning(\\\"No keypoints yet: running process before display\\\")\\n self.process()\\n import pylab\\n f = pylab.figure()\\n ax = f.add_subplot(1, 1, 1)\\n ax.plot(self.keypoints.sigma, self.keypoints.I, '.r')\\n ax.set_xlabel(\\\"Sigma\\\")\\n ax.set_ylabel(\\\"Intensity\\\")\\n ax.set_title(\\\"Peak repartition\\\")\\n f.show()\",\n \"def plot_precision_figure(self):\\n\\n data_analysis = DatabaseData(dataframe=self.plot_data)\\n prop_data, energy_data, M, C, pred_energy, pred_property = \\\\\\n data_analysis.create_precision_bokeh_compat(self.prop_data, self.energy_data, properties=self.properties)\\n p = figure(plot_height=400, plot_width=400,tools=\\\"pan,wheel_zoom,box_zoom,reset,previewsave\\\",\\\\\\n x_axis_type=\\\"log\\\", y_axis_type=\\\"log\\\", x_axis_label='Energy Convergence (meV/atom)', title='Slope M is {0}'.format(str(M)) )\\n p.line(pred_energy, pred_property, color='red')\\n p.circle(self.energy_data, self.prop_data, color='blue',size=5, line_alpha=0)\\n #p.multi_line(xs_err, ys_err, color='black')\\n if self.properties == 'B':\\n p.yaxis.axis_label = 'Bulk Modulus B (%)'\\n elif self.properties == 'dB':\\n p.yaxis.axis_label = 'Bulk Modulus Pressure Derivative (%)'\\n elif self.properties == 'Multiple':\\n p.yaxis.axis_label = \\\"V0, B, B' (%)\\\"\\n elif self.properties == 'V0':\\n p.yaxis.axis_label = 'Volume (%)'\\n\\n return p\",\n \"def gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total):\\n with PdfPages(name) as pdf:\\n total_xuf = []\\n total_yuf = []\\n total_xf = []\\n total_yf = []\\n for entry in uf_dict:\\n print 'Making plot for ' + entry\\n xuf, yuf = zip(*uf_dict[entry])\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ax1.scatter(xuf, yuf, c='#ad4851', marker='o', label='initial structures')\\n try:\\n xf, yf = zip(*f_dict[entry])\\n ax1.scatter(xf, yf, c='orange', marker='x', label='selected structures')\\n except ValueError:\\n xf = []\\n yf = []\\n plt.legend(loc='upper right')\\n plt.title(entry, fontsize=30)\\n plt.xlim(min_x, max_x)\\n plt.ylim(min_y, max_y)\\n plt.xlabel(axes[0], fontsize=20)\\n plt.ylabel(axes[1], fontsize=20)\\n pdf.savefig(fig)\\n plt.close()\\n\\n if total:\\n total_xuf.extend(xuf)\\n total_yuf.extend(yuf)\\n total_xf.extend(xf)\\n total_yf.extend(yf)\\n\\n if histogram:\\n bins = np.linspace(min_y, max_y, num=10)\\n plt.hist(yuf, bins, alpha=0.5, color='b', label='initial structures')\\n try:\\n plt.hist(yf, bins, alpha=0.5, color='orange', label='selected structures')\\n except ValueError:\\n pass\\n plt.legend(loc='upper right')\\n plt.title(entry, fontsize=30)\\n plt.xlabel(axes[1], fontsize=20)\\n plt.ylabel('Frequency', fontsize=20)\\n pdf.savefig()\\n plt.close()\\n\\n if total:\\n print 'Making composite plot'\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ax1.scatter(total_xuf, total_yuf, c='#ad4851', marker='o', label='initial structures')\\n ax1.scatter(total_xf, total_yf, c='orange', marker='x', label='selected structures')\\n plt.legend(loc='upper right')\\n plt.title('Composite Plot', fontsize=30)\\n plt.xlim(min_x, max_x)\\n plt.ylim(min_y, max_y)\\n plt.xlabel(axes[0], fontsize=20)\\n plt.ylabel(axes[1], fontsize=20)\\n pdf.savefig(fig)\\n plt.close()\",\n \"def plot(self,experiment_id = None,image_path = None):\\n c = self.cursor()\\n where_experiment_id = ''\\n if not experiment_id is None:\\n if isinstance(experiment_id, list):\\n exp_ids = ','.join([ str(f) for f in experiment_id ])\\n where_experiment_id = ' WHERE id in ({})'.format(exp_ids)\\n else:\\n where_experiment_id = ' WHERE id = {}'.format(experiment_id)\\n c.execute(\\n 'SELECT exp_id,exp_name,exp_description,var_name FROM experiment'\\n + where_experiment_id\\n )\\n experiments = c.fetchall()\\n exp_count = len(experiments)\\n fig, axs = plt.subplots(exp_count)\\n if exp_count == 1:\\n axs = [axs]\\n trend = lambda a,b: np.poly1d(np.polyfit(a, b, 1))(a)\\n for i in range(exp_count):\\n axs[i].set_title(experiments[i]['exp_name'])\\n axs[i].set_xlabel(experiments[i]['exp_description'])\\n # build x-axis \\n x_axis = []\\n c.execute(\\n '''\\n SELECT val FROM fact\\n WHERE var_name = ?\\n AND exp_id = ?\\n ORDER BY step_id ASC\\n ''',\\n (\\n experiments[i]['var_name'],\\n experiments[i]['exp_id']\\n )\\n )\\n x_axis = [r['val'] for r in c.fetchall()]\\n c.execute(\\n '''\\n SELECT DISTINCT var_name FROM fact \\n WHERE exp_id = ? AND var_name != ?\\n ORDER BY var_name ASC\\n ''',\\n (experiments[i]['exp_id'],experiments[i]['var_name'])\\n )\\n variables = [r['var_name'] for r in c.fetchall()]\\n for variable in variables:\\n c.execute(\\n '''\\n SELECT val FROM fact\\n WHERE exp_id = ? AND var_name = ?\\n ORDER BY step_id ASC \\n ''',\\n (experiments[i]['exp_id'], variable)\\n )\\n y_axis = [r['val'] for r in c.fetchall()]\\n axs[i].scatter(x_axis, y_axis)\\n axs[i].plot(x_axis,trend(x_axis, y_axis),label=variable)\\n axs[i].legend()\\n fig.tight_layout()\\n # save into image on headless machine\\n if not image_path is None:\\n plt.savefig(image_path)\\n else:\\n try:\\n plt.show()\\n except:\\n plt.savefig(\\\"plot.png\\\") \\n self.commit()\",\n \"def plot_data(df, title=\\\"normalized Stock prices\\\", ylabel=\\\"Price\\\", xlabel=\\\"Date\\\" ):\\n plt.clf()\\n ax = df.plot(title=title, fontsize=12)\\n ax.set_xlabel(xlabel)\\n ax.set_ylabel(ylabel)\\n plt.savefig('files/output/'+title+'.png')\",\n \"def plotting(dataframe, prod_num):\\n fig, axs = plt.subplots(2, sharex=True)\\n axs[0].plot(dataframe['STU'])\\n axs[1].plot(dataframe['STU'].diff().dropna())\\n axs[0].set_title(\\\"Time Series of Product\\\" + f\\\"_{prod_num}\\\")\\n axs[1].set_title(\\\"Differenced Time Series of Product\\\" + f\\\"_{prod_num}\\\")\\n plt.savefig(\\\"Time Series of Product\\\" + f\\\"_{prod_num}\\\" + \\\".pdf\\\")\",\n \"def plot(plot_count, density_map, padding, name):\\n density_map = density_map[padding:-1 - padding, padding:-1 - padding]\\n print(\\\"max density = %g\\\" % (np.amax(density_map)))\\n print(\\\"mean density = %g\\\" % (np.mean(density_map)))\\n\\n fig = plt.figure()\\n ax = fig.gca(projection='3d')\\n\\n x = np.arange(density_map.shape[0])\\n y = np.arange(density_map.shape[1])\\n\\n x, y = np.meshgrid(x, y)\\n ax.plot_surface(x, y, density_map, alpha=0.8)\\n\\n ax.set_xlabel('x')\\n ax.set_ylabel('y')\\n ax.set_zlabel('density')\\n\\n #plt.tight_layout()\\n plt.savefig(name + \\\".3d.%d.png\\\" % (plot_count))\\n plt.close()\\n\\n #plt.clf()\\n\\n #fig, ax = plt.subplots()\\n\\n #ax.pcolor(density_map)\\n\\n ## Loop over data dimensions and create text annotations.\\n ##for i in range(density_map.shape[0]):\\n ## for j in range(density_map.shape[1]):\\n ## text = ax.text(j, i, density_map[i, j],\\n ## ha=\\\"center\\\", va=\\\"center\\\", color=\\\"w\\\")\\n #fig.tight_layout()\\n #plt.savefig(name+\\\".2d.%d.png\\\" % (plot_count))\\n #plt.close()\",\n \"def generate_results(self, test_no, test_dict):\\n g_s = gridspec.GridSpec(4, 2, wspace=0.2, hspace=1.5)\\n fig = plt.figure(figsize=(20, 6))\\n fig.suptitle('Experiment Results', y=0.93)\\n\\n x_val = np.arange(1, self.iters+1)\\n\\n ax1 = plt.subplot(g_s[0:3, :1], label = 'Mean Rewards')\\n ax1.set_title('Mean Rewards')\\n ax1.scatter(x_val, self.mean_rewards, s=5)\\n ax1.set(xlabel='Iteration', ylabel='Mean Reward')\\n\\n ax2 = plt.subplot(g_s[0:3, 1:])\\n ax2.scatter(x_val, self.sub_goals, s=5, label='Sub-optimal Goal')\\n ax2.scatter(x_val, self.opt_goals, s=5, label='Optimal Goal')\\n ax2.set_title('Goal Success Percentage by Type')\\n ax2.set(xlabel='Iteration', ylabel='Success Percentage (%)')\\n ax2.legend(loc=0)\\n\\n cells = list(test_dict.values())\\n cells = [str(i) for i in cells]\\n columns = list(test_dict.keys())\\n ax3 = plt.subplot(g_s[3:, :])\\n ax3.axis('off')\\n ax3.table(cellText=[cells], colLabels=columns, loc='center', cellLoc='center')\\n\\n plt.savefig(f'results/charts/Test_{test_no}.png', bbox_inches='tight')\",\n \"def plot_collective(xdict, ydict, xprop, yprop, documents):\\n x_ion = {\\\"Mg\\\": [], \\\"Ca\\\": [], \\\"Zn\\\": [], \\\"Li\\\": [], \\\"Na\\\": []}\\n y_ion = {\\\"Mg\\\": [], \\\"Ca\\\": [], \\\"Zn\\\": [], \\\"Li\\\": [], \\\"Na\\\": []}\\n for item in documents:\\n if item[\\\"path_id\\\"][-3:] == \\\"001\\\":\\n x_ion[item[\\\"cation_type\\\"]].append(xdict[item[\\\"path_id\\\"]])\\n y_ion[item[\\\"cation_type\\\"]].append(ydict[item[\\\"path_id\\\"]])\\n fig = plt.figure(figsize=(6,6), dpi=plotting_dpi)\\n ax = fig.add_subplot(111)\\n for ion in [\\\"Mg\\\", \\\"Ca\\\", \\\"Zn\\\", \\\"Li\\\", \\\"Na\\\"]:\\n ax.scatter(x_ion[ion], y_ion[ion], s=70, zorder=2, color=color_dict[ion], linewidths=2.5, edgecolors='black',\\n label=ion)\\n xlabel = \\\"\\\\\\\\textbf{\\\" + label_dict[xprop] + \\\"}\\\"\\n ylabel = \\\"\\\\\\\\textbf{\\\" + label_dict[yprop] + \\\"}\\\"\\n \\n # # Plot lines for fitting, if useful\\n # x2 = np.arange(-700, 3300, 1)\\n # ax.plot(x2, x2)\\n \\n # # For setting axis boundaries\\n # ax.set_xlim([-700, 3500])\\n # ax.set_ylim([0,100])\\n \\n # Plot display settings\\n ax.set_xlabel(xlabel, fontsize=24)\\n ax.set_ylabel(ylabel, fontsize=24)\\n ax.tick_params(axis='x', labelsize=22)\\n ax.tick_params(axis='y', labelsize=22)\\n border_width = 2\\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\\n plt.tight_layout()\\n plt.legend(loc='best', prop={'size':10})\\n # plt.legend(loc='best')\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='sans-serif')\\n plt.show()\",\n \"def plotObsExpSigEnrichment(f):\\n mat = pd.read_csv(f, sep=\\\"\\\\t\\\", index_col=0)\\n fig, ax = pylab.subplots()\\n ax.plot(mat.index,\\n mat[\\\"observed\\\"] * 100,\\n color=colors[0],\\n label=\\\"observed\\\")\\n ax.plot(mat.index,\\n mat[\\\"expected\\\"] * 100,\\n color=colors[1],\\n label=\\\"expected\\\")\\n ax.set_xlabel(\\\"Percentage of Bins\\\")\\n ax.set_ylabel(\\\"Percetange of PETs\\\")\\n ax.legend(loc=\\\"upper left\\\")\\n #ax2 = ax.twinx()\\n #ax2.plot( mat.index, mat[\\\"Obs/Exp\\\"],color=colors[2],label=\\\"Obs/Exp\\\")\\n #ax2.set_ylabel(\\\"Obs/Exp\\\")\\n #for t in ax2.get_yticklabels():\\n # t.set_color(colors[2])\\n pylab.savefig(\\\"%s.pdf\\\" % (f.replace(\\\".txt\\\", \\\"\\\")))\",\n \"def plot_modelparametercollections(plotname, parametercollection_SF, parametercollection_AGN,\\n stat_SF, stat_AGN, AGNcol='blue',SFcol='red', constraintsstr=None,\\n fluxratiodictionarylist=None, verbose=True):\\n\\n Nobj = len(parametercollection_SF)\\n if verbose: print(' - Will generate plots of NEOGAL \\\"PDFs\\\" for all '+str(Nobj)+' objects in parameter collections')\\n for oo in np.arange(Nobj):\\n objid = parametercollection_SF[oo]['id']\\n if verbose:\\n infostr = ' plotting info for '+str(objid)+' ('+str(\\\"%.5d\\\" % (oo+1))+' / '+str(\\\"%.5d\\\" % Nobj)+') '\\n sys.stdout.write(\\\"%s\\\\r\\\" % infostr)\\n sys.stdout.flush()\\n plotname_obj = plotname.replace('.pdf','_id'+str(objid)+'.pdf')\\n # if verbose: print(' - Generating the figure '+plotname_obj)\\n figuresize_x = 6\\n figuresize_y = 5\\n fig = plt.figure(figsize=(figuresize_x,figuresize_y))\\n Fsize = 9\\n LW = 2\\n plt.rc('text', usetex=True) # enabling LaTex rendering of text\\n plt.rc('font', family='serif',size=Fsize) # setting text font\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n\\n left = 0.10 # the left side of the subplots of the figure\\n right = 0.95 # the right side of the subplots of the figure\\n bottom = 0.10 # the bottom of the subplots of the figure\\n top = 0.90 # the top of the subplots of the figure\\n wspace = 1.50 # the amount of width reserved for blank space between subplots\\n hspace = 0.50 # the amount of height reserved for white space between subplots\\n plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)\\n\\n Nrows, Ncols = 3, 6\\n ylabel = 'Number of NEOGAL SF ('+str(SFcol)+') and AGN ('+str(AGNcol)+') models'\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n Nmodels_SF = float(len(parametercollection_SF[oo]['Zgas']))\\n Nmodels_AGN = float(len(parametercollection_AGN[oo]['Zgas']))\\n\\n titlestr = 'Models satisfying ID='+str(objid)+' cuts: SF='+str(Nmodels_SF)+'; AGN='+str(Nmodels_AGN)\\n if (Nmodels_AGN > 0) & (Nmodels_SF > 0):\\n Nmodels_ratio = Nmodels_SF/Nmodels_AGN\\n titlestr_addition = '; SF/AGN='+str(\\\"%.4f\\\" % Nmodels_ratio)\\n titlestr = titlestr+titlestr_addition\\n\\n if fluxratiodictionarylist is not None:\\n constraints = fluxratiodictionarylist[oo]\\n constraintslist = [key+':['+str(\\\"%.2f\\\" % constraints[key][0])+','+str(\\\"%.2f\\\" % constraints[key][1])+']'\\n for key in constraints.keys() if key not in ['id']]\\n\\n if len(constraintslist) < 4:\\n constraintsstr = '; '.join(constraintslist)\\n elif (len(constraintslist) > 3) & (len(constraintslist) < 7):\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])\\n elif (len(constraintslist) > 6) & (len(constraintslist) < 10):\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])+\\\\\\n '\\\\n'+'; '.join(constraintslist[6:])\\n else:\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])+\\\\\\n '\\\\n'+'; '.join(constraintslist[6:9])+'\\\\n'+'; '.join(constraintslist[9:])\\n\\n constraintsstr = constraintsstr.replace('10000000000.00','1e10')\\n\\n titlestr = titlestr+'\\\\n'+constraintsstr\\n # titlestr = r'{\\\\fontsize{'+str(Fsize)+'pt}{3em}\\\\selectfont{}{'+titlestr+'\\\\r}{\\\\fontsize{'+str((Fsize-2.))+'pt}{3em}\\\\selectfont{}('+constraintsstr+'}'\\n\\n # plt.text(x=0.5, y=0.94, s=titlestr, fontsize=Fsize, ha=\\\"center\\\", transform=fig.transFigure)\\n # plt.text(x=0.5, y=0.88, s=constraintsstr, fontsize=Fsize-2, ha=\\\"center\\\", transform=fig.transFigure)\\n # fig.title(titlestr,fontsize=Fsize)\\n fig.suptitle(titlestr,fontsize=Fsize-2)\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # Zgas\\n plt.subplot(Nrows, Ncols, (1,3))\\n\\n bindefs = np.array([0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.014,\\n 0.017, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07])-0.00001\\n\\n plotkey = 'Zgas'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xscale('log')\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([0.00001,0.1])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # logUs\\n plt.subplot(Nrows, Ncols, (4,6))\\n\\n bindefs = np.arange(-4.75, -0.25, 0.5)\\n\\n plotkey = 'logUs'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-5,-0.5])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # xid\\n plt.subplot(Nrows, Ncols, (7,9))\\n\\n bindefs = np.array([0.0, 0.2, 0.4, 0.6])\\n\\n plotkey = 'xid'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-0.05,0.65])\\n plt.ylabel(ylabel)\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # nh\\n plt.subplot(Nrows, Ncols, (10,12))\\n\\n bindefs = 10**np.array([1.5, 2.5, 3.5, 4.5])\\n\\n plotkey = 'nh'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n plt.xscale('log')\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([10,1e5])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # COCOsol\\n plt.subplot(Nrows, Ncols, (13,14))\\n\\n #bindefs = np.array([0.10, 0.14, 0.20, 0.27, 0.38, 0.52, 0.72, 1.00, 1.40])\\n bindefs = np.arange(0.05,1.5,0.06)\\n\\n plotkey = 'COCOsol'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\\n stat_SF[oo][plotkey],None,\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([0.00,1.55])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # mup\\n plt.subplot(Nrows, Ncols, (15,16))\\n\\n bindefs = np.array([0,200,400])\\n\\n plotkey = 'mup'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\\n stat_SF[oo][plotkey],None,\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-10,410])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # alpha\\n plt.subplot(Nrows, Ncols, (17,18))\\n\\n bindefs = np.array([-2.15,-1.85,-1.55,-1.25,-0.95])\\n\\n plotkey = 'alpha'\\n Nbins = 10\\n nm.plot_modelparametercollections_addhist(None,parametercollection_AGN[oo][plotkey],\\n None,stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=None,Nbins=Nbins)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-2.2,-0.9])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n plt.savefig(plotname_obj)\\n plt.clf()\\n plt.close('all')\\n # if verbose: print(' - Successfully saved figure to file')\\n if verbose: print('\\\\n done...')\",\n \"def plot_PDs(output_path,M_max,max_dim,normalized=False,output_name=None):\\n plots_folder = os.path.join(output_path,'plots')\\n if output_name is None: \\n aux = ['PDs']\\n input_file = os.path.join(output_path,'outputs_PDS.csv')\\n else:\\n aux = [output_name,'PDs']\\n input_file = os.path.join(output_path,'%s_PDS.csv' % output_name)\\n if normalized is True:\\n aux.append('normalized')\\n plot_file_name = '-'.join(aux)+'.png'\\n plot_file_name = os.path.join(plots_folder,plot_file_name)\\n\\n \\n data = pd.read_csv(input_file,index_col = 0)\\n \\n\\n fig_size_aux = 15/(3-max_dim)\\n plt.figure(figsize=(fig_size_aux,5))\\n if(normalized):\\n for i in range(1,max_dim+2):\\n plt.subplot(1,max_dim+1,i, aspect='equal')\\n plt.plot(data[data.dimH==i-1].birth.values/np.float(M_max),data[data.dimH==i-1].death.values/np.float(M_max),'bo',alpha=.3,label='dim %i'%(i-1))\\n plt.xlim((0,1)); plt.ylim((0,1))\\n frame = plt.legend(loc=4,frameon=True,title='normalized')\\n frame = frame.get_frame()\\n frame.set_edgecolor('black')\\n plt.xlabel('birth'); plt.ylabel('death')\\n plt.plot([0, 1], [0, 1], ls=\\\"--\\\", c=\\\".3\\\")\\n plt.suptitle('Persistence Diagrams')\\n plt.savefig(plot_file_name)\\n print 'Saved PDs plots in %s'%plot_file_name\\n else:\\n for i in range(1,max_dim+2):\\n plt.subplot(1,max_dim+1,i, aspect='equal')\\n plt.plot(data[data.dimH==i-1].birth.values,data[data.dimH==i-1].death.values,'bo',alpha=.3,label='dim %i'%(i-1))\\n plt.xlim((0,M_max)); plt.ylim((0,M_max))\\n frame = plt.legend(loc=4,frameon=True)\\n frame = frame.get_frame()\\n frame.set_edgecolor('black')\\n plt.xlabel('birth'); plt.ylabel('death')\\n plt.plot([0, M_max], [0, M_max], ls=\\\"--\\\", c=\\\".3\\\")\\n plt.suptitle('Persistence Diagrams')\\n plt.savefig(plot_file_name)\\n print 'Saved PDs plots in %s'%plot_file_name\\n return()\",\n \"def PlotMA(df, show_plot=True):\\n # Ensure 'Study' is in header.\\n # TODO: Add code.\\n # Obtain monthly means.\\n df_ave = stats.MonthlyMean(df)\\n # Get list of variables.\\n var_list = set(df_ave.columns.get_level_values('Part B'))\\n # Determine number of axes in the figure.\\n n_var = len(var_list)\\n n_row = np.int(np.around(np.sqrt(n_var)))\\n if n_row**2 >= n_var:\\n n_col = n_row\\n else:\\n n_col = n_row + 1\\n fig, ax = plt.subplots(nrows=n_row, ncols=n_col, figsize=(16, 9))\\n # Remove extra axes, if necessary.\\n if n_row * n_col != n_var:\\n n_empty = n_row * n_col - n_var\\n for k in range(n_empty):\\n ax[-1, -(1+k)].remove()\\n # Initialize variables for updating axes.\\n cur_row = 0\\n cur_col = 0\\n var_width = 0.9\\n idx = np.arange(12)\\n x_min = 0\\n x_max = 12\\n # Plot each variable.\\n for var in var_list:\\n df_var = df_ave.xs(var, level='Part B', axis=1)\\n # Initialize parameters for current axis.\\n y_min = np.min(df_var.values) * 0.95\\n y_max = np.max(df_var.values) * 1.05\\n col_var = df_var.columns\\n bar_width = var_width / len(col_var)\\n # Select current axis.\\n if n_row == 1 and n_col == 1:\\n ax_cur = ax\\n elif n_row == 1:\\n ax_cur = ax[cur_col]\\n else:\\n ax_cur = ax[cur_row, cur_col]\\n # Plot study results for each variable.\\n for c in col_var:\\n idx_c = idx + 0.05 + col_var.get_loc(c) * bar_width\\n val_c = df_var[c].values\\n lab_c = '{} {}'.format(*c[:2])\\n ax_cur.bar(idx_c, val_c, bar_width, label=lab_c, align='edge')\\n # Set plot title.\\n ax_cur.set_title('Monthly Averages of {}'.format(var))\\n # Modify x-axis and y-axis.\\n ax_cur.set_xticks(idx + 0.5)\\n tick_labels = list(df_ave.index)\\n ax_cur.set_xticklabels(tick_labels)\\n label_c = set(df_var.columns.get_level_values('Part C'))\\n _unit = df_var.columns.get_level_values('Units')\\n _dtyp = df_var.columns.get_level_values('Data Type')\\n unit_d = list(zip(_unit, _dtyp))\\n combo_unit = [' '.join(i) for i in unit_d]\\n label_u = set(combo_unit)\\n ax_cur.set_ylabel('{} ({})'.format(r'/'.join(label_c),\\n r'/'.join(label_u)))\\n ax_cur.set_ylim(bottom=y_min, top=y_max)\\n ax_cur.set_xlim(left=x_min, right=x_max)\\n ax_cur.set_xlabel('Month')\\n ax_cur.spines['right'].set_visible(False)\\n ax_cur.spines['top'].set_visible(False)\\n # Set legend.\\n ax_cur.legend(title='Study, Part A')\\n # Update current axis.\\n cur_col += 1\\n if cur_col >= n_col:\\n cur_col = 0\\n cur_row += 1\\n # Adjust layout.\\n plt.tight_layout()\\n # Add figure notes.\\n t = PlotChartNotes()\\n if n_row * n_col != n_var:\\n x_pos = (1 - n_empty / n_col) * 1.05\\n y_pos = (1 / n_row) * 0.95\\n plt.figtext(x_pos, y_pos, t, ha='left', va='top', wrap=True)\\n else:\\n plt.figtext(0.05, 0, t, ha='left', va='bottom', wrap=True)\\n plt.subplots_adjust(bottom=0.2)\\n # Show plot, if requested.\\n if show_plot:\\n plt.show()\\n # Return figure and axes.\\n return fig, ax\",\n \"def logit_model_plots(ds,Population = 'Population_%',Event_rate ='Event_rate',decile ='Band',Cumulative_Non_Event = 'Cumulative_Non_Event_%',Cumulative_Event= 'Cumulative_Event_%',sample_type ='Development'):\\n \\n import matplotlib.pyplot as plt\\n fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(15, 4))\\n _= ax1.plot(plot_df[Cumulative_Non_Event],plot_df[Cumulative_Event])\\n _= ax1.set_ylabel(Cumulative_Non_Event)\\n _= ax1.set_title('Gini Curve : '+str(sample_type) +' sample')\\n _= ax1.set_xlabel(Cumulative_Event)\\n\\n _= plot_df[Population].plot(kind='bar', color='b', width = 0.35,legend=True , label = Population)\\n _= plot_df[Event_rate].plot(kind='line',color ='r', secondary_y=True,legend=True, label = Event_rate)\\n _= ax2.set_xticklabels(plot_df[decile])\\n _= ax2.set_ylim(0,plot_df[Event_rate].max()*0.15)\\n _= ax2.right_ax.set_ylim(0,plot_df[Event_rate].max()*1.5)\\n _= ax2.right_ax.set_ylabel(Event_rate)\\n _= ax2.set_ylabel(Population)\\n _= ax2.set_title('Decile Wise Event Rate : ' +str(sample_type) +' sample')\\n _= ax2.set_xlabel(decile)\\n plt.show()\",\n \"def ploter(self):\\n if len(self.dataset[self.first_title]) != 2:\\n print('plot is only avilable for two features')\\n return\\n x_axis = []\\n y_axis = []\\n for title in self.dataset:\\n x_axis.append(self.dataset[title][0])\\n y_axis.append(self.dataset[title][1])\\n plt.plot(x_axis, y_axis, 'o')\\n plt.show()\",\n \"def hotspot_fields_plot(self, results_dict, tas_bound=None, pr_bound=None):\\n sorted_keys = [(f\\\"{period}_{season}_{variable}\\\"\\n f\\\"_{project}_{results_dict['scenario']}\\\")\\n for variable in self.variables\\n for period in self.cfg[\\\"future_periods\\\"]\\n for project in self.projects for season in self.seasons]\\n sorted_keys = [\\n sorted_keys[:len(sorted_keys) // 2],\\n sorted_keys[len(sorted_keys) // 2:]\\n ]\\n ancestor_files_var = [[\\n ancestor_file for ancestor_file in results_dict[\\\"ancestors\\\"]\\n if f\\\"/{var}_\\\" in ancestor_file\\n ] for var in self.variables]\\n for ancestor_files, keys, variable in zip(ancestor_files_var,\\n sorted_keys, self.variables):\\n fig = plt.figure(figsize=(14.4, 3.4),\\n constrained_layout=True,\\n dpi=300)\\n plt.gcf().subplots_adjust()\\n # bound colorbar to abs(max) value on the map\\n style = self.cb_bounds(variable, results_dict, keys,\\n [tas_bound, pr_bound])\\n # plot each panel\\n fill, frame = self._hotspot_fields_plot_panels(\\n results_dict, fig, keys, style)\\n # plot figtexts\\n self._hotspot_fields_plot_figtexts(results_dict['scenario'], frame)\\n # plot line\\n self._hotspot_fields_plot_line(fig, frame)\\n # plot colorbar\\n cbar = plt.colorbar(fill,\\n plt.gcf().add_axes([0.25, 0.125, 0.5, 0.04]),\\n orientation=\\\"horizontal\\\",\\n extend=\\\"both\\\")\\n if variable == \\\"pr\\\":\\n cbar.set_label(\\\"%\\\")\\n against_region = (\\n f\\\"{self.cfg['region'][2]}$^o$ N-\\\"\\n f\\\"{self.cfg['region'][3]}$^o$ N latitudinal belt\\\")\\n else:\\n cbar.set_label(\\n self.formatter(str(results_dict[keys[-1]].units)))\\n against_region = \\\"global\\\"\\n\\n # plot title and save\\n self._hotspot_fields_plot_save(against_region, variable,\\n results_dict['scenario'],\\n ancestor_files)\",\n \"def make_N4255_plots(data_obj, aspect_corr=1.0, title_pages=False):\\n\\n print(\\\"Generating plots...\\\")\\n\\n #Create color maps\\n cmap = plt.get_cmap('jet')\\n cmap = plt.get_cmap('gray')\\n\\n #Call the function to create the title page of the pdf document\\n plot_front_title(data_obj)\\n\\n #-----------------------------------------------------------------------#\\n # Initialize the position variables for the text and graphs on the pdf. #\\n #-----------------------------------------------------------------------#\\n y0 = 0.9\\n dy = [0.03, 0.025]\\n\\n ha = 'left'\\n va = 'center'\\n fs = 10\\n dfs = 2\\n\\n # metric name value unc min\\n xpos = [0.0, 0.4, 0.5, 0.75]\\n yi = y0 - 0.1 # The position of the text on the y access, which is constantly updated as more text is added\\n\\n #-----------------------------------------------------------------------------------#\\n # Plot the 'summary' page listing all the tests and the overall results - TEXT ONLY #\\n #-----------------------------------------------------------------------------------#\\n\\n #Create the title of the page\\n plot_overall_text(data_obj, yi, xpos, ha, va, fs)\\n\\n #Plot the overall results text of the first test, Penetration\\n yi = yi - dy[0]\\n plot_pen_text(data_obj, 1, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the first test, Organic Material Detection\\n yi = yi - dy[0]\\n plot_BSNR_text(data_obj, 2, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the third test, Spatial Resolution\\n yi = yi - dy[0]\\n plot_spatial_text(data_obj, 3, yi, yi - dy[1], xpos, ha, va, fs, dfs)\\n yi = yi - dy[1] #Make sure the local yi is updated\\n\\n #Plot the overall results text of the fourth test, Dynamic Range\\n yi = yi - dy[0]\\n plot_dyn_text(data_obj, 4, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the fifth test, NEQ Noise\\n yi = yi - dy[0]\\n plot_noise_text(data_obj, 5, yi, dy, xpos, ha, va, fs, dfs)\\n yi = yi - (dy[1] * 2) #Make sure to update yi, as it was only locally changed in 'plot_noise_text()'\\n\\n #Plot the overall results text of the sixth test, Flatness of field\\n yi = yi - dy[0]\\n plot_ff_text(data_obj, 6, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the seventh test, Image Extent\\n yi = yi - dy[0]\\n plot_extent_text(data_obj, 7, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the eighth test, Image Area\\n yi = yi - dy[0]\\n plot_area_text(data_obj, 8, yi, xpos, ha, va, fs, dfs)\\n\\n #Plot the overall results text of the ninth test, Aspect Ratio\\n yi = yi - dy[0]\\n plot_a_ratio_text(data_obj, 9, yi, xpos, ha, va, fs, dfs)\\n\\n #--------------------------------------------------#\\n # Plot the footnotes for the overall results page. #\\n #--------------------------------------------------#\\n plot_overall_footnotes(xpos, ha, va, fs, dfs)\\n\\n\\n #-----------------#\\n # Plot the images #\\n #-----------------#\\n plot_images(data_obj, fs) #Plot the images to the pdf\\n\\n plot_image_footnotes(data_obj, xpos, ha, va, fs, dfs) #Add in the footnotes to the pdf\\n\\n\\n #-------------------#\\n # Penetration plots #\\n #-------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 1: Penetration\\\")\\n\\n #Call the function to plot the Steel Penetration results to the pdf\\n plot_steel_pen_N4255(data_obj, 1)\\n\\n\\n #------------#\\n # BSNR plots #\\n #------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 2: Organic Material Detection\\\")\\n\\n # Call the function to plot the Organic Material Detection results to the pdf\\n plot_BSNR(data_obj, 2, cmap)\\n\\n\\n #--------------------#\\n # Spatial Resolution #\\n #--------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 3: Spatial Resolution\\\")\\n\\n # Call the function to plot the Spatial Resolution results to the pdf\\n plot_spatial_res(data_obj, 3)\\n\\n #---------------#\\n # Dynamic Range #\\n #---------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 4: Dynamic Range\\\")\\n\\n # Call the function to plot the Dynamic Range results to the pdf\\n plot_dynamic_range(data_obj, 4)\\n\\n #-------#\\n # Noise #\\n #-------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 5: Noise (NEQ)\\\")\\n\\n # Call the function to plot the Noise (NEQ) results to the pdf\\n plot_noise(data_obj, 5)\\n\\n #-------------------#\\n # Flatness of field #\\n #-------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 6: Flatness of Field\\\")\\n\\n # Call the function to plot the Flatness of Field results to the pdf\\n plot_field_flatness(data_obj, 6)\\n\\n #--------------#\\n # Image extent #\\n #--------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 7: Image Extent\\\")\\n\\n # Call the function to plot the Image Extent results to the pdf\\n plot_image_extent(data_obj, 7)\\n\\n\\n #------------#\\n # Image Area #\\n #------------#\\n if title_pages:\\n fig = new_pdf_page(data_obj.pdf_obj)\\n plt.axis('off')\\n plt.text(0.5, 0.5, 'Test 8: Image Area', ha='center', va='center', fontsize=20)\\n str1 = str(data_obj.image_area[0]) + ' by ' + str(data_obj.image_area[1]) + ' pixels'\\n plt.text(0.5, 0.4, str1, ha='center', va='center', fontsize=12)\\n\\n #--------------#\\n # Aspect Ratio #\\n #--------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 9: Aspect Ratio\\\")\\n\\n #Call the function to plot the Aspect Ratio results to the pdf\\n plot_aspect_ratio(data_obj, 9, cmap, aspect_corr)\\n\\n fig = new_pdf_page(data_obj.pdf_obj, open_fig=False)\",\n \"def covariance_plot(catl_pd, param_dict, proj_dict, plot_only_feat=False,\\n fig_fmt='pdf', figsize=(10, 8), fig_number=2):\\n file_msg = param_dict['Prog_msg']\\n plot_dict = param_dict['plot_dict']\\n ## Filename\\n fname = os.path.join( proj_dict['figure_dir'],\\n 'Fig_{0}_{1}_feature_covariance.{2}'.format(\\n fig_number,\\n param_dict['catl_str_fig'],\\n fig_fmt))\\n ##\\n ## Paper Figure\\n fname_paper = os.path.join( proj_dict['paper_fig_dir'],\\n 'Figure_02.{0}'.format(fig_fmt))\\n ## Renaming properties\\n catl_pd_copy = catl_pd.copy()\\n ## Dropping columns\\n cols_drop = ['GG_pointing', 'GG_haloid_point', 'GG_mhalo_point', \\n 'g_brightest', 'GG_r_rms']\\n catl_pd_copy.drop(cols_drop, axis=1, inplace=True)\\n ## Reordering columns\\n mhalo_key = 'M_h'\\n # Saving data from Halo mass\\n gal_mhalo_arr = catl_pd_copy[mhalo_key].values\\n # Removing Halo mass\\n catl_pd_copy.drop(mhalo_key, axis=1, inplace=True)\\n # Inserting it back again to the DataFrame\\n catl_pd_copy.insert(0, mhalo_key, gal_mhalo_arr)\\n ##\\n ## Rearranging columns\\n df_cols_new = [ 'M_h','dist_centre_group', 'M_r', 'logssfr', 'g_galtype',\\n 'g_r', 'GG_mr_brightest', 'GG_mr_ratio',\\n 'GG_M_r', 'GG_logssfr', 'GG_shape', 'GG_ngals',\\n 'GG_rproj', 'GG_r_tot', 'GG_r_med', 'GG_sigma_v',\\n 'GG_sigma_v_rmed', 'GG_M_group', 'GG_mdyn_rproj',\\n 'GG_dist_cluster']\\n catl_pd_copy = catl_pd_copy.loc[:, df_cols_new]\\n # Plotting only features if applicable\\n if plot_only_feat:\\n feat_cols = param_dict['ml_args']._feature_cols()\\n catl_pd_copy = catl_pd_copy.loc[:,feat_cols]\\n ## Renaming\\n catl_pd_copy.rename(columns=param_dict['feat_cols_dict'], inplace=True)\\n ## Selecting certain columns only\\n ## Figure details\\n plt.clf()\\n plt.close()\\n fig = plt.figure(figsize=figsize)\\n ax1 = fig.add_subplot(111, facecolor='white')\\n fig.subplots_adjust(bottom=0.1, top=0.9, left=0.1, right=0.8,\\n wspace=0.02, hspace=0.02)\\n cax = fig.add_axes([0.30, 0.85, 0.30, 0.05])\\n ## Correlation\\n corr = catl_pd_copy.corr()\\n # Generate a mask for the upper triangle\\n mask = num.zeros_like(corr, dtype=num.bool)\\n mask[num.triu_indices_from(mask)] = True\\n # Generate a custom diverging colormap\\n cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n # Draw the heatmat with the mask and correct aspect ratio\\n g = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1.0, vmin=-1., center=0,\\n square=True, linewidths=0.5, cbar=False, ax=ax1)\\n cbar = fig.colorbar(g.get_children()[0], cax=cax, orientation='horizontal')\\n cbar.set_label(r'$\\\\Leftarrow$ Correlation $\\\\Rightarrow$',\\n fontsize=plot_dict['size_label'])\\n cbar.ax.tick_params(labelsize=20)\\n g.yaxis.set_tick_params(labelsize=25)\\n g.xaxis.set_tick_params(labelsize=25)\\n ##\\n ## Saving figure\\n if fig_fmt=='pdf':\\n plt.savefig(fname, bbox_inches='tight')\\n plt.savefig(fname_paper, bbox_inches='tight')\\n else:\\n plt.savefig(fname, bbox_inches='tight', dpi=400)\\n plt.savefig(fname_paper, bbox_inches='tight', dpi=400)\\n ##\\n ##\\n print('{0} Figure saved as: {1}'.format(file_msg, fname))\\n print('{0} Paper Figure saved as: {1}'.format(file_msg, fname_paper))\\n plt.clf()\\n plt.close()\",\n \"def output_results(self, filename):\\n\\n self.data.plot(title='Result of applying {} onto data set'.format(self.transformations[-1]))\\n plt.savefig(\\\"results/{}.png\\\".format(filename))\\n plt.close()\",\n \"def visualizePredictions(testData,knn_predictions):\\r\\n testData.visualize.scatterPlot('Petal length','Petal width')\\r\\n testData.dataDict[testData.reference] = knn_predictions\\r\\n testData.visualize.scatterPlot('Petal length','Petal width')\\r\\n\\r\\n pass\",\n \"def make_F792_plots(data_obj, title_pages=False):\\n\\n print(\\\"Generating plots...\\\")\\n\\n # Create color maps\\n cmap = plt.get_cmap('jet')\\n cmap = plt.get_cmap('gray')\\n\\n # Call the\\n plot_front_title(data_obj)\\n\\n # -----------------------------------------------------------------------#\\n # Initialize the position variables for the text and graphs on the pdf. #\\n # -----------------------------------------------------------------------#\\n y0 = 0.9\\n dy = [0.03, 0.025]\\n\\n ha = 'left'\\n va = 'center'\\n fs = 10\\n dfs = 2\\n\\n # metric name value unc min\\n xpos = [0.0, 0.4, 0.5, 0.75]\\n yi = y0 - 0.1 # The position of the text on the y access, which is constantly updated as more text is added\\n\\n # -----------------------------------------------------------------------------------#\\n # Plot the 'summary' page listing all the tests and the overall results - TEXT ONLY #\\n # -----------------------------------------------------------------------------------#\\n\\n # Create the title of the page\\n plot_overall_text(data_obj, yi, xpos, ha, va, fs)\\n\\n #Plot the overall results text of the first test, Steel Differentiation\\n\\n\\n # Plot the overall results text of the second test, Penetration\\n yi = yi - dy[0]\\n plot_pen_text(data_obj, 2, yi, xpos, ha, va, fs, dfs)\\n\\n # Plot the overall results text of the third test, Organic Material Detection\\n yi = yi - dy[0]\\n plot_BSNR_text(data_obj, 3, yi, xpos, ha, va, fs, dfs)\\n\\n # Plot the overall results text of the fourth test, Spatial Resolution\\n yi = yi - dy[0]\\n plot_spatial_text(data_obj, 4, yi, yi - dy[1], xpos, ha, va, fs, dfs)\\n yi = yi - dy[1] # Make sure the local yi is updated\\n\\n # Plot the overall results text of the fifth test, Dynamic Range\\n yi = yi - dy[0]\\n plot_dyn_text(data_obj, 5, yi, xpos, ha, va, fs, dfs)\\n\\n # Plot the overall results text of the sixth test, Noise\\n yi = yi - dy[0]\\n plot_noise_text(data_obj, 6, yi, dy, xpos, ha, va, fs, dfs)\\n yi = yi - (dy[1] * 2) # Make sure to update yi, as it was only locally changed in 'plot_noise_text()'\\n\\n # --------------------------------------------------#\\n # Plot the footnotes for the overall results page. #\\n # --------------------------------------------------#\\n plot_overall_footnotes(xpos, ha, va, fs, dfs, standard=\\\"ASTM F792\\\")\\n\\n\\n #---------------------------------------------------------#\\n # Plot the cropped and rotated images from the processing #\\n #---------------------------------------------------------#\\n plot_images(data_obj, fs) # Plot the images to the pdf\\n\\n plot_image_footnotes(data_obj, xpos, ha, va, fs, dfs) # Add in the footnotes to the pdf\\n\\n # NOTE: Above image plotting the same, with the same footnotes, for F792???\\n\\n #-----------------------------#\\n # Steel differentiation plots #\\n #-----------------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 1: Steel Differentiation\\\")\\n\\n #Call the function to plot the Steel Differentiation results to the pdf\\n\\n\\n #-------------------#\\n # Penetration plots #\\n #-------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 2: Penetration\\\")\\n\\n # Call the function to plot the Steel Penetration results to the pdf\\n #plot_steel_pen(data_obj, 2)\\n\\n #------------#\\n # BSNR plots #\\n #------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 3: Organic Material Detection\\\")\\n\\n # Call the function to plot the Organic Material Detection results to the pdf\\n plot_BSNR(data_obj, 3, cmap)\\n\\n #--------------------#\\n # Spatial Resolution #\\n #--------------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 4: Spatial Resolution\\\")\\n\\n # Call the function to plot the Spatial Resolution results to the pdf\\n plot_spatial_res(data_obj, 4)\\n\\n #---------------#\\n # Dynamic Range #\\n #---------------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 5: Dynamic Range\\\")\\n\\n # Call the function to plot the Dynamic Range results to the pdf\\n plot_dynamic_range(data_obj, 5)\\n\\n #-------#\\n # Noise #\\n #-------#\\n if title_pages:\\n new_title_page(data_obj, \\\"Test 6: Noise (NEQ)\\\")\\n\\n # Call the function to plot the Noise (NEQ) results to the pdf\\n plot_noise(data_obj, 6)\\n\\n fig = new_pdf_page(data_obj.pdf_obj, open_fig=False)\",\n \"def analyze_data(self, bandwith=1):\\n data = {\\\"Topple Count\\\": self.topple_count, \\\"Fallen mass\\\": self.mass_fallen_count}\\n print(data)\\n\\n self.plot()\\n self.plot(type='mass')\\n self.plot(type='topple')\\n self.plot(type='histogram',bandwith=bandwith)\\n self.plot(type='pdf',bandwith=bandwith)\",\n \"def plot(var):\\n # MISSCHIEN KUNNEN WE HIER NOG IETS MEE\\n # total_dead = len(train_data[\\\"Survived\\\"] == 0)\\n # total_survived = len(train_data[\\\"Survived\\\"] == 1)\\n # died = train_data[train_data[\\\"Survived\\\"] == 0][var].value_counts() / total_dead\\n # survived = train_data[train_data[\\\"Survived\\\"] == 1][var].value_counts() / total_survived\\n sns.set()\\n sns.set_color_codes(\\\"pastel\\\")\\n\\n # order bars for family size variable\\n if var == \\\"FamSize\\\":\\n sns.barplot(x=var, y=\\\"Survived\\\", data=train_data, color=\\\"b\\\",\\\\\\n capsize=.1, errwidth=.7, order=[\\\"alone\\\", 1, 2, 3, \\\"4 or more\\\"]).\\\\\\n tick_params(labelsize=18)\\n else:\\n sns.barplot(x=var, y=\\\"Survived\\\", data=train_data, color=\\\"b\\\",\\\\\\n capsize=.1, errwidth=1.1).tick_params(labelsize=18)\\n\\n # plot style properties\\n ax = plt.gca()\\n\\n for ax in plt.gcf().axes:\\n x = ax.get_xlabel()\\n y = ax.get_ylabel()\\n ax.set_xlabel(x, fontsize=20)\\n ax.set_ylabel(y, fontsize=20)\\n\\n plt.title(\\\"Ratio of survivors for variable \\\" + str(var), fontsize=22)\\n t = ax.title\\n t.set_position([.5, 1.05])\\n plt.ylim([0, 1])\\n plt.subplots_adjust(bottom=.15, left=.15)\\n plt.savefig(\\\"results/survived_\\\" + str(var) + \\\".png\\\", bbox_inches=\\\"tight\\\")\\n\\n plt.show()\",\n \"def plot_ppplot(obj1,sheet1,variable1,obj2,sheet2,variable2,title,opath):\\n p1 = np.percentile(obj1.me[sheet1][variable1],range(0,101,1))\\n p2 = np.percentile(obj2.me[sheet2][variable2],range(0,101,1))\\n p1c = np.cumsum(np.array(p1))/np.cumsum(np.array(p1)).max()\\n p2c = np.cumsum(np.array(p2))/np.cumsum(np.array(p2)).max()\\n fig = plt.figure(figsize=(8,8),dpi=120)\\n plt.scatter(p1c,p2c,color='#566c73',s=30)\\n plt.plot([0,1],[0,1],color='red',alpha=0.3)\\n plt.xlim(0,1)\\n plt.ylim(0,1)\\n plt.grid()\\n plt.xlabel(sheet1+'_'+variable1)\\n plt.ylabel(sheet2+'_'+variable2)\\n plt.title(title)\\n plt.savefig(opath+'.png')\\n plt.close()\",\n \"def plot_observed_predictions(self):\\n \\n # Plot of X vs Y\\n fig = plt.figure(figsize=(15,5))\\n plt.subplot(1,3,1) \\n for k in self.phd_filter['estimated_positions'].keys():\\n plt.plot(self.phd_filter['estimated_positions'][k][0], self.phd_filter['estimated_positions'][k][1], 'bx')\\n plt.xlabel(\\\"X\\\",fontsize=20)\\n plt.ylabel(\\\"Y\\\",fontsize=20)\\n \\n # Plot of time vs X\\n plt.subplot(1,3,2)\\n for k in self.phd_filter['estimated_positions'].keys(): \\n plt.plot(k*np.ones(self.phd_filter['estimated_positions'][k].shape[1]), self.phd_filter['estimated_positions'][k][0], 'bx')\\n plt.xlabel(\\\"time\\\",fontsize=20)\\n plt.ylabel(\\\"X\\\",fontsize=20)\\n plt.xlim(0,self.n_time_steps+1)\\n\\n # Plot of time vs Y\\n plt.subplot(1,3,3)\\n for k in self.phd_filter['estimated_positions'].keys():\\n plt.plot(k*np.ones(self.phd_filter['estimated_positions'][k].shape[1]), self.phd_filter['estimated_positions'][k][1], 'bx')\\n plt.xlabel(\\\"time\\\",fontsize=20)\\n plt.ylabel(\\\"Y\\\",fontsize=20)\\n plt.xlim(0,self.n_time_steps+1)\\n plt.show();\",\n \"def plot_gmm_preds(x, z, with_supervision, plot_id):\\n plt.figure(figsize=(12, 8))\\n plt.title('{} GMM Predictions'.format('Semi-supervised' if with_supervision else 'Unsupervised'))\\n plt.xlabel('x_1')\\n plt.ylabel('x_2')\\n\\n for x_1, x_2, z_ in zip(x[:, 0], x[:, 1], z):\\n color = 'gray' if z_ < 0 else PLOT_COLORS[int(z_)]\\n alpha = 0.25 if z_ < 0 else 0.75\\n plt.scatter(x_1, x_2, marker='.', c=color, alpha=alpha)\\n\\n file_name = 'pred{}_{}.pdf'.format('_ss' if with_supervision else '', plot_id)\\n save_path = os.path.join('.', file_name)\\n plt.savefig(save_path)\",\n \"def plot_gmm_preds(x, z, with_supervision, plot_id):\\n plt.figure(figsize=(12, 8))\\n plt.title('{} GMM Predictions'.format('Semi-supervised' if with_supervision else 'Unsupervised'))\\n plt.xlabel('x_1')\\n plt.ylabel('x_2')\\n\\n for x_1, x_2, z_ in zip(x[:, 0], x[:, 1], z):\\n color = 'gray' if z_ < 0 else PLOT_COLORS[int(z_)]\\n alpha = 0.25 if z_ < 0 else 0.75\\n plt.scatter(x_1, x_2, marker='.', c=color, alpha=alpha)\\n\\n file_name = 'pred{}_{}.pdf'.format('_ss' if with_supervision else '', plot_id)\\n save_path = os.path.join('.', file_name)\\n plt.savefig(save_path)\",\n \"def plot4(self, plog=False):\\n\\n probs = pd.read_csv(self.probfile)\\n\\n plt.rc('font', size=14)\\n fig, ax = plt.subplots()\\n plt.plot(self.ds.freq, self.snr, 'k-', alpha=0.5, zorder=1)\\n\\n # plot the SNR range to search across when finding snr_modes\\n for idx, line in enumerate(self.ds.mode_id['f0']):\\n w = np.exp(self.ds.mode_id['w0'][idx])\\n plt.axvline(x=line-w, color='b', linestyle='-', alpha=0.4)\\n plt.axvline(x=line+w, color='b', linestyle='-', alpha=0.4)\\n\\n # overplot the predicted SNR values at the modes\\n plt.scatter(probs['f0'], probs['SNR_Kepler'], label='Kepler - 4yrs', alpha=1, zorder=2)\\n plt.scatter(probs['f0'], probs['SNR_TESS365'], label='TESS - 1 yr', alpha=1, zorder=3)\\n plt.scatter(probs['f0'], probs['SNR_TESS27'], label='TESS - 27 days', alpha=1, zorder=4)\\n\\n if plog:\\n plt.xscale('log')\\n plt.yscale('log')\\n plt.xlabel(r'$\\\\nu$ / $\\\\rm \\\\mu Hz$')\\n plt.ylabel(r'SNR')\\n\\n mn = min(star.ds.mode_id['f0']) -\\\\\\n (max(star.ds.mode_id['f0'])-min(star.ds.mode_id['f0']))/7.\\n mx = max(star.ds.mode_id['f0']) +\\\\\\n (max(star.ds.mode_id['f0'])-min(star.ds.mode_id['f0']))/7.\\n plt.xlim([mn,mx])\\n\\n plt.legend()\\n plt.title('KIC ' + str(self.ds.epic))\\n plt.show()\\n fig.savefig(os.getcwd() + os.sep + 'DetTest1_plots' + os.sep +\\\\\\n 'plot4_SNR' + self.ds.epic + '.pdf')\",\n \"def plot_data(indf, prefix='html'):\\n list_of_plots = []\\n# scatter_matrix(indf)\\n# pl.savefig('scatter_matrix.png')\\n# list_of_plots.append('scatter_matrix.png')\\n\\n for col in indf:\\n pl.clf()\\n# cond = indf[col].notnull()\\n# v = indf[cond][col]\\n v = indf[col]\\n# nent = len(v)\\n# hmin, hmax = v.min(), v.max()\\n# xbins = np.linspace(hmin,hmax,nent)\\n# hmin, hmax, nbin = BOUNDS[col]\\n# xbins = np.linspace(hmin, hmax, nbin)\\n v.hist(bins=20, histtype='step', normed=True, log=True)\\n pl.title(col)\\n pl.savefig('%s_hist.png' % col)\\n list_of_plots.append('%s_hist.png' % col)\\n\\n create_html_page_of_plots(list_of_plots, prefix)\\n return\",\n \"def display(self):\\r\\n \\r\\n plt.rcParams['font.size'] = 14\\r\\n plt.rcParams['axes.linewidth'] = 1.2 # 1.2 for single plot, 0.5 for all 6\\r\\n plt.rcParams['lines.linewidth'] = 20.0 # Aah, this doesn't work because line width is changed later on\\r\\n\\r\\n cwd = os.getcwd() # Gets current working directory.\\r\\n cwd = cwd.replace('\\\\\\\\', '/')\\r\\n path = cwd + directory # This is the folder all the results are stored in.\\r\\n \\r\\n if type(array_element) == str:\\r\\n dataframes = [file + array_element] # This is to pass a single csv file\\r\\n else:\\r\\n dataframes = [file + i for i in array_element] # This is a list so you can pass multiple csv files to be overlayed on the same plot.\\r\\n\\r\\n colours = ['black', 'darkred', 'darkmagenta', 'darkturquoise', 'saddlebrown'] # Array of colours for the lines.\\r\\n\\r\\n dfE = pd.read_csv(cwd + \\\"/experimental_data.csv\\\") # Reads in the experimental data as a pandas dataframe.\\r\\n\\r\\n # Rescale the x-axis of the experimental data.\\r\\n ratio_of_capacities = 272.4 / 338.313338 # experimental maximum capacity / theoretical maximum capacity\\r\\n dfE[\\\"x_theo\\\"] = ratio_of_capacities * dfE[\\\"x\\\"]\\r\\n # 'x' is the experimental x and 'x_theo' is the theoretical x.\\r\\n\\r\\n # Second derivative of enthalpy for experimental data. One w/ respect to the experimental x and one w/ respect to theoretical x.\\r\\n secder_enthalpy_experimental_x = np.gradient(np.array(dfE['Enthalpy dH/dx']), np.array(dfE['x']))\\r\\n secder_enthalpy_experimental_x_theo = np.gradient(np.array(dfE['Enthalpy dH/dx']), np.array(dfE['x_theo']))\\r\\n dfE['secder enthalpy x'] = secder_enthalpy_experimental_x\\r\\n dfE['secder enthalpy x theo'] = secder_enthalpy_experimental_x_theo\\r\\n\\r\\n # vertical shift on p.m. entropy for vibrational effect\\r\\n vibrational_shift = 0.0108 # eV K this includes being multiplied by the ratio of capacities.\\r\\n dfE[\\\"Entropy dS/dx\\\"] = (dfE[\\\"Entropy dS/dx\\\"]) - vibrational_shift\\r\\n\\r\\n # Integrates the p.m. entropy\\r\\n entropy_list_experimental = integrate.cumtrapz(dfE['Entropy dS/dx'], dfE['x'],\\r\\n initial=0) # Contains the entropy values\\r\\n dfE['Entropy'] = entropy_list_experimental\\r\\n\\r\\n dfE['x_new'] = ((dfE['x_theo'] - dfE['x_theo'].iloc[0]) * dfE['x_theo'][73]) / (dfE['x_theo'][73] - dfE['x_theo'].iloc[0]) # Rescales the line so that the experimental data starts at 0.\\r\\n dfE['x'] = ((dfE['x'] - dfE['x'].iloc[0]) * dfE['x'][73]) / (dfE['x'][73] - dfE['x'].iloc[0]) # Same as above but for experimental x axis.\\r\\n\\r\\n # Calculates the analytical solution\\r\\n points = 1000\\r\\n x_pos = np.linspace(0, 1, points) # x for p.m. entropy\\r\\n y_pos = np.linspace(0, 1, points) # y for p.m. etropy\\r\\n s_x = np.linspace(0, 1, points) # x for entropy\\r\\n s_y = np.linspace(0, 1, points) # y for entropy\\r\\n l = 0.329217689 # This must be the same as what was used in the main script\\r\\n R = -0.0000862 # eV/K.Site\\r\\n T = 288 # K\\r\\n for index, x in enumerate(x_pos):\\r\\n if x < l:\\r\\n s_y[index] = (R * (x * np.log(x / l) - (x - l) * np.log((l - x) / l))) * T\\r\\n y_pos[index] = T * R * (np.log(x / l) - np.log((l - x) / l))\\r\\n else:\\r\\n s_y[index] = (R * l * (\\r\\n (x / l - 1) * np.log(x / l - 1) + (1 - x) / l * np.log((1 - x) / l) - (1 - l) / l * np.log(\\r\\n (1 - l) / l))) * T\\r\\n y_pos[index] = T * R * (np.log(x / l - 1) - np.log(1 / l - x / l))\\r\\n\\r\\n # Calculates the single solid state entropy\\r\\n x_ent = np.linspace(0, 1, points)\\r\\n y_ent = np.linspace(0, 1, points)\\r\\n for index, x in enumerate(x_ent):\\r\\n y_ent[index] = T * R * (x * np.log(x) + (1-x) * np.log(1-x))\\r\\n \\r\\n \\\"\\\"\\\"\\r\\n #\\r\\n #\\r\\n # Create plot and formats\\r\\n #\\r\\n #\\r\\n \\\"\\\"\\\"\\r\\n \\r\\n fig, axes = plt.subplots(nrows=num_row, ncols=num_col, constrained_layout=True, squeeze=False)\\r\\n # squeeze=False is needed to prevent errors when plotting a single subplot\\r\\n plt.rc('legend', fontsize=13, handlelength=1)\\r\\n plt.rc('tick')\\r\\n lw = 1.5 # Line width\\r\\n \\r\\n plt.tick_params(bottom=True, top=True, left=True, right=True)\\r\\n plt.tick_params(labelbottom=True, labeltop=False, labelleft=True, labelright=False)\\r\\n plt.tick_params(direction='in', width=1.2, length=4.5, pad=3) # For single plot\\r\\n # plt.tick_params(direction='in', width=1, length=4.5, pad=3) # For multiple plots\\r\\n\\r\\n marker_list = ['v', '^', 'p', 'o']\\r\\n mark_size = 3 #0.7 for 6 plots\\r\\n \\r\\n colours = ['#176ba0', '#af4bce', 'orangered', '#48a11b', '#3caea3'] #'#af4bce'\\r\\n common_legend = ['400 Averaging Steps', '800 Averaging Steps', '2000 Averaging Steps']\\r\\n \\r\\n if num_col==2 and num_row==3: # This will work when using the original axes dimensions (3 rows, 2 columns)\\r\\n placement = dict([\\r\\n ('voltage', axes[0, 0]),\\r\\n ('dS/dx', axes[0, 1]),\\r\\n ('dQ/dV', axes[1, 0]),\\r\\n ('dH/dx', axes[1, 1]),\\r\\n ('S', axes[2, 0]),\\r\\n ('d/dx(dH/dx)', axes[2, 1])\\r\\n ])\\r\\n else: # If axes dimensions are different, I'm probably trying to plot one graph\\r\\n \\\"\\\"\\\"\\r\\n If plotting more than one graph, the position on the plot in the subplot can be adjusted\\r\\n by appropriately altering the axes[] parameter. For the graphs that are not being plotted, \\r\\n leave their position as axes[0, 0].\\r\\n \\\"\\\"\\\"\\r\\n placement = dict([\\r\\n ('voltage', axes[0, 0]),\\r\\n ('dS/dx', axes[0, 0]),\\r\\n ('dQ/dV', axes[0, 0]),\\r\\n ('dH/dx', axes[0, 0]),\\r\\n ('S', axes[0, 0]),\\r\\n ('d/dx(dH/dx)', axes[0, 0])\\r\\n ])\\r\\n \\r\\n # Plots all of the experimental data\\r\\n if experimental_plot == True:\\r\\n if pick_plot['voltage'] == True:\\r\\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['voltage'], x='x_new', y='OCV')\\r\\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['voltage'], x='x', y='OCV')\\r\\n \\r\\n if pick_plot['dS/dx'] == True:\\r\\n ax2 = dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dS/dx'], x='x_new', y='Entropy dS/dx')\\r\\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['dS/dx'], x='x', y='Entropy dS/dx')\\r\\n \\r\\n if pick_plot['dQ/dV'] == True:\\r\\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dQ/dV'], x='OCV', y='dQdV') \\r\\n \\r\\n if pick_plot['dH/dx'] == True:\\r\\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['dH/dx'], x='x_new', y='Enthalpy dH/dx')\\r\\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['dH/dx'], x='x', y='Enthalpy dH/dx')\\r\\n \\r\\n if pick_plot['S'] == True:\\r\\n ax5 = dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['S'], x='x_new', y='Entropy')\\r\\n \\r\\n if pick_plot['d/dx(dH/dx)'] == True:\\r\\n dfE.plot(linestyle='-', color='darkgreen', lw=lw, ax=placement['d/dx(dH/dx)'], x='x_new', y='secder enthalpy x theo')\\r\\n dfE.plot(linestyle='-', color='darkblue', lw=lw, ax=placement['d/dx(dH/dx)'], x='x', y='secder enthalpy x')\\r\\n\\r\\n # Iterate through all the data to be plotted\\r\\n if simulation_plot == True:\\r\\n for count, df in enumerate(dataframes):\\r\\n df1 = pd.read_csv(path + df) # reads file into a dataframe.\\r\\n \\r\\n df1 = df1.replace(0, np.nan).dropna(axis=0, how='all') # For the rows with all '0' entries they are replaced with 'nan' and then these rows are dropped.\\r\\n df1 = df1.replace(np.nan, 0) # As some legitimate 0 entries such as 0 volts we flip back the remaining from 'nan' to 0.\\r\\n \\r\\n # Integrates the p.m. entropy\\r\\n entropy_list = integrate.cumtrapz(df1['Partial molar entropy'], df1['Total mole fraction'],\\r\\n initial=0) # Contains the entropy values\\r\\n df1['Entropy'] = entropy_list\\r\\n \\r\\n # Rescale voltage profile and p.m. enthalpy by the chain rule.\\r\\n df1[\\\"adjusted voltage\\\"] = df1[\\\"Chemical potential\\\"] * ratio_of_capacities\\r\\n df1[\\\"adjusted enthalpy\\\"] = df1[\\\"Partial molar enthalpy\\\"] * ratio_of_capacities\\r\\n df1[\\\"adjusted entropy\\\"] = df1[\\\"Partial molar entropy\\\"] * ratio_of_capacities\\r\\n df1[\\\"adjusted dq/de\\\"] = df1[\\\"dq/de\\\"] * (1/ratio_of_capacities)**2\\r\\n \\r\\n # Differentiate the p.m. enthalpy to get the second derivative.\\r\\n pm_enthalpy = np.array(df1['adjusted enthalpy'])\\r\\n mole_fraction = np.array(df1['Total mole fraction'])\\r\\n secder_enthalpy = np.gradient(pm_enthalpy, mole_fraction)\\r\\n df1['secder enthalpy'] = secder_enthalpy\\r\\n \\r\\n if pick_plot['voltage'] == True:\\r\\n ax1 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['voltage'], x='Total mole fraction', y='adjusted voltage')\\r\\n ax1.set_xlim([0, 1])\\r\\n ax1.set_xlabel('Na content $[x]$')\\r\\n ax1.set_ylabel('Voltage $[V]$')\\r\\n ax1.legend(common_legend) \\r\\n # ax1.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\\r\\n \\r\\n if pick_plot['dS/dx'] == True:\\r\\n ax2 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['dS/dx'], x='Total mole fraction', y='adjusted entropy')\\r\\n # ax2.plot(x_pos, y_pos, linewidth=lw, color='red') # Plots the ideal p.m. entropy\\r\\n ax2.set_xlim([0, 1])\\r\\n ax2.set_xlabel('Na content $[x]$')\\r\\n ax2.set_ylabel('$\\\\\\\\frac{dS}{dx}$ $[eV K/site]$')\\r\\n ax2.legend(common_legend) \\r\\n # ax2.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data', 'Analytical solution'])\\r\\n \\r\\n if pick_plot['dQ/dV'] == True:\\r\\n ax3 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['dQ/dV'], x='Chemical potential', y='adjusted dq/de') \\r\\n ax3.set_xlim([-0.1, 1])\\r\\n ax3.set_xlabel('Voltage $[V]$')\\r\\n ax3.set_ylabel('$\\\\\\\\frac{dQ}{dV}$ [$\\\\mathregular{eV^{-1}}$]')\\r\\n ax3.legend(common_legend)\\r\\n # ax3.legend(['Experimental data', 'Monte Carlo Data'])\\r\\n \\r\\n if pick_plot['dH/dx'] == True:\\r\\n ax4 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['dH/dx'], x='Total mole fraction', y='adjusted enthalpy')\\r\\n ax4.set_xlim([0, 1])\\r\\n ax4.set_xlabel('Na content $[x]$')\\r\\n ax4.set_ylabel('$\\\\\\\\frac{dH}{dx}$ $[eV/site]$')\\r\\n ax4.legend(common_legend) \\r\\n # ax4.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\\r\\n \\r\\n if pick_plot['d/dx(dH/dx)'] == True:\\r\\n ax5 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['d/dx(dH/dx)'], x='Total mole fraction', y='secder enthalpy')\\r\\n ax5.set_xlim([0, 1])\\r\\n ax5.set_ylim([0, 6])\\r\\n ax5.set_xlabel('Na content $[x]$')\\r\\n ax5.set_ylabel('$\\\\\\\\frac{d^2H}{dx^2}$ $[eV/site]$')\\r\\n ax5.legend(common_legend)\\r\\n \\r\\n # ax5.legend(['Experimental data (Adjusted x)', 'Raw experimental data', 'Monte Carlo data'])\\r\\n \\r\\n if pick_plot['S'] == True:\\r\\n ax6 = df1.plot(linestyle='-', color=colours[count], lw=lw, marker=marker_list[count], markeredgecolor=colours[count],\\r\\n markersize=mark_size, ax=placement['S'], x='Total mole fraction', y='Entropy')\\r\\n \\r\\n # ax6.plot(s_x, s_y, linewidth=lw, color='red') # Plots the entropy for l=0.32...\\r\\n # ax6.plot(x_ent, y_ent, linewidth=lw, color='grey') # Plots the entropy for solid state solution.\\r\\n ax6.set_xlim([0, 1])\\r\\n ax6.set_xlabel('Na content $[x]$')\\r\\n ax6.set_ylabel('S $[eV K/site]$')\\r\\n ax6.legend(common_legend)\\r\\n # ax6.legend(['Experimental data', 'Monte Carlo data', 'Analytical solution', 'Solid state solution'], loc='upper right', bbox_to_anchor=(0.75, 0.5))\\r\\n \\r\\n \\r\\n\\r\\n # parameter_file = open(path + \\\"/Input_arguments_\\\" + uid + \\\".txt\\\", \\\"w\\\")\\r\\n # parameter_file.write(str(self.args))\\r\\n # parameter_file.close()\\r\\n\\r\\n # manager = plt.get_current_fig_manager()\\r\\n # # manager.resize(*manager.window.maxsize())\\r\\n # # fig_path = cwd + \\\"/Na_plot_results.png\\\"\\r\\n # # plt.savefig(path + \\\"/Na_monte_carlo_plot_\\\" + uid + \\\".png\\\")\\r\\n # plt.show()\\r\\n \\r\\n plt.savefig(\\\"Varying sps Overlaid Plots - dQ_dV\\\", dpi = 300)\\r\\n\\r\\n plt.show()\",\n \"def grafCasosAtivos(tipPDF):\\n\\n\\t\\tdf = pd.read_csv('dadosPaises.csv') #dataFrame que recebe a tabela de dados\\n\\t\\tdf = df.sort_values(by=['Casos Ativos'], ascending=False) #ordena pela coluna 'casos ativos' de forma descendente\\n\\t\\tdf2 = df[:5] # Separa em um novo dataframe as 5 primeiras linhas\\n\\n\\t\\tfig, ax = plt.subplots()\\n\\n\\t\\tindex = np.arange(len(df2['Casos Ativos'])) #indice para definir as posicoes dos labels\\n\\t\\tbar_width = 0.45 #grossura da barra\\n\\t\\tscore_label = np.arange(0,50000,5000) #array com volares a serem apresentados no eixo y\\n\\t\\tbar = ax.bar(index, df2['Casos Ativos'], bar_width, label='Casos Ativos',color='#ff4500')\\n\\t\\t\\n\\t\\t#setting axis labels\\n\\t\\tax.set_xticks(index) #distancia entre barras\\n\\t\\tax.set_xticklabels(df2['Dados do País']) #label das barras\\n\\t\\tax.set_yticks(score_label) #distancia entre marcas do eixo y\\n\\t\\tax.set_yticklabels(score_label) #labels das marcas do eixo y\\n\\t\\tax.legend() #legenda\\n\\n\\t\\t#ciclo para plotar os labels\\n\\t\\tfor i in index:\\n\\t\\t\\tax.annotate('{}'.format(bar[i].get_height()), #string a ser plotada\\n\\t\\t\\t\\t\\t\\txy=(bar[i].get_x(),bar[i].get_height()), #local da plotagem do label\\n\\t\\t\\t\\t\\t\\txytext=(0,2), #movimentacao adicional do label\\n\\t\\t\\t\\t\\t\\ttextcoords='offset points',\\n\\t\\t\\t\\t\\t\\tva='bottom'\\n\\t\\t\\t\\t\\t\\t)\\n\\n\\t\\tplt.title('Paises com mais casos ativos')\\n\\n\\t\\t#Se true, o tipo selecionado sera pdf, senao png\\n\\t\\tif tipPDF:\\n\\t\\t\\tnomFig = 'grafico.pdf'\\n\\t\\telse :\\n\\t\\t\\tnomFig = 'grafico.png'\\t\\n\\t\\tplt.savefig(nomFig) #salva a imagem (poder ser salvo como pdf mudando o nome do arquivo\",\n \"def plot_data_pca(data_dict):\\n f = plt.figure()\\n ndata, ntime, nhidden = data_dict['hiddens'].shape\\n\\n print('Number of data examples: ', ndata)\\n print('Number of timesteps: ', ntime)\\n print('Number of data dimensions: ', nhidden)\\n pca = PCA(n_components=100)\\n pca.fit(onp.reshape(data_dict['hiddens'], [ndata * ntime, nhidden]))\\n\\n plt.plot(onp.arange(1, 16), onp.cumsum(pca.explained_variance_ratio_)[0:15],\\n '-o');\\n plt.plot([1, 15], [0.95, 0.95])\\n plt.xlabel('PC #')\\n plt.ylabel('Cumulative Variance')\\n plt.xlim([1, 15])\\n plt.ylim([0.3, 1]);\\n return f\",\n \"def figure_2(df):\\n\\n sns.set(style=\\\"white\\\", palette=sns.color_palette(\\\"cubehelix\\\", 6))\\n f, axes = plt.subplots(4, 1, figsize=(6, 9)) # , sharex=True)\\n sns.despine(top=True, bottom=True)\\n # f.suptitle(\\\"Etiology of Central Events, Grouped by %Central Events\\\")\\n\\n OSA_pure_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \\\"Mainly OSA\\\"], return_df=True).sort_values(\\\"Dx\\\"))\\n OSA_predom_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \\\"Combined OSA/CSA\\\"], return_df=True).sort_values(\\\"Dx\\\"))\\n CSA_predom_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \\\"Predominantly CSA\\\"], return_df=True).sort_values(\\\"Dx\\\"))\\n CSA_pure_hist = replace_etiology_labels(histo_dx_includes(df.loc[df['BaseDx'] == \\\"Pure CSA\\\"], return_df=True).sort_values(\\\"Dx\\\"))\\n\\n # Create count plot for each #CSA on the left, then a Pie Chart with proportion on the right\\n\\n hatches = ['///', 'xxx', '---', '', '']\\n face_color = ['white', 'white', 'white', 'white', 'dimgrey']\\n\\n # Pure OSA\\n bar = sns.barplot(x=\\\"Count\\\", y=\\\"Dx\\\", data=OSA_pure_hist, ax=axes[3])\\n for i, this_bar in enumerate(bar.patches):\\n # Set a different hatch for each bar\\n this_bar.set_edgecolor('black')\\n this_bar.set_facecolor(face_color[i])\\n this_bar.set_hatch(hatches[i])\\n axes[3].set(xlabel=\\\"Number of Patients\\\", ylabel=\\\"<10% CSA\\\")\\n\\n # Predom OSA\\n bar = sns.barplot(x=\\\"Count\\\", y=\\\"Dx\\\", data=OSA_predom_hist, ax=axes[2])\\n for i, this_bar in enumerate(bar.patches):\\n # Set a different hatch for each bar\\n this_bar.set_edgecolor('black')\\n this_bar.set_facecolor(face_color[i])\\n this_bar.set_hatch(hatches[i])\\n axes[2].set(xlabel=\\\"\\\", ylabel=\\\"10-49.9% CSA\\\")\\n\\n # Predom CSA\\n bar = sns.barplot(x=\\\"Count\\\", y=\\\"Dx\\\", data=CSA_predom_hist, ax=axes[1])\\n for i, this_bar in enumerate(bar.patches):\\n # Set a different hatch for each bar\\n this_bar.set_edgecolor('black')\\n this_bar.set_facecolor(face_color[i])\\n this_bar.set_hatch(hatches[i])\\n axes[1].set(xlabel=\\\"\\\", ylabel=\\\"50-90% CSA\\\")\\n\\n # Pure CSA\\n bar = sns.barplot(x=\\\"Count\\\", y=\\\"Dx\\\", data=CSA_pure_hist, ax=axes[0])\\n for i, this_bar in enumerate(bar.patches):\\n # Set a different hatch for each bar\\n this_bar.set_edgecolor('black')\\n this_bar.set_facecolor(face_color[i])\\n this_bar.set_hatch(hatches[i])\\n axes[0].set(xlabel=\\\"\\\", ylabel=\\\">90% CSA\\\")\\n\\n # Combined X axis for L side\\n axes[3].get_shared_x_axes().join(axes[3], axes[2], axes[1], axes[0])\\n axes[0].set_xticklabels(\\\"\\\")\\n axes[1].set_xticklabels(\\\"\\\")\\n axes[2].set_xticklabels(\\\"\\\")\\n # Leave bottom aka [3,0] labels in\\n\\n # Resize all\\n axes[0].autoscale()\\n axes[1].autoscale()\\n axes[2].autoscale()\\n axes[3].autoscale()\\n\\n f.tight_layout(rect=[0, 0, 1, 1]) # .95 to leave space for title\\n f.savefig('Figure 2 - etio by perc csa', dpi=100)\\n # plt.show()\",\n \"def plot_word_class_pr_genre(df):\\n df['nouns'] = df['nouns'] * 100\\n df['verbs'] = df['verbs'] * 100\\n df['adverbs'] = df['adverbs'] * 100\\n # plotting nouns\\n plotting_helper_method('nouns', 'genre', df)\\n plt.title('Amount of nouns pr song pr. genre')\\n plt.xlabel(\\\"Amount of nouns in each song\\\")\\n plt.ylabel('Genre')\\n plt.legend()\\n plt.show()\\n # plt.savefig('src/visualization/feature_plots/nouns_pr_genre_plot')\\n\\n # plotting verbs\\n plotting_helper_method('verbs', 'genre', df)\\n plt.title('Amount of verbs pr song pr. genre')\\n plt.xlabel('Amount of verbs in each song')\\n plt.ylabel('Genre')\\n plt.legend()\\n plt.show()\\n # plt.savefig('src/visualization/feature_plots/verbs_pr_genre_plot')\\n\\n # plotting adverbs\\n plotting_helper_method('adverbs', 'genre', df)\\n plt.title('Amount of adverbs pr song pr. genre')\\n plt.xlabel('Amount of adverbs in each song')\\n plt.ylabel('Genre')\\n plt.legend()\\n plt.show()\\n # plt.savefig('src/visualization/feature_plots/adverbs_pr_genre_plot')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7190156","0.6753629","0.60996735","0.6064925","0.6016448","0.6008773","0.60022396","0.60006684","0.5999985","0.59736115","0.5944627","0.59434086","0.5941799","0.5931249","0.5897575","0.5877158","0.5876371","0.5840248","0.58335423","0.58302295","0.58285284","0.58272463","0.5807024","0.5799569","0.57845443","0.5781996","0.5779757","0.5770024","0.575117","0.5739089","0.57363075","0.5735732","0.5713691","0.5693648","0.5693578","0.56858295","0.56805944","0.56687164","0.5658263","0.5648382","0.564504","0.5644673","0.56430984","0.56416816","0.5638266","0.5636274","0.56346494","0.56346226","0.5629099","0.5628261","0.5614559","0.56079715","0.5606076","0.5605636","0.55997616","0.5580656","0.5579532","0.556964","0.5567809","0.55675817","0.5566145","0.5564619","0.5561161","0.55587965","0.55553675","0.5551361","0.5542913","0.5542413","0.5533799","0.55314314","0.5527168","0.5526548","0.5526236","0.5522208","0.55158806","0.55117553","0.5511285","0.5507037","0.5503371","0.54993296","0.5497811","0.549305","0.5489936","0.5488964","0.5487613","0.5484082","0.54818964","0.5474708","0.54729474","0.5472021","0.54715574","0.54663956","0.54663956","0.5466015","0.5461071","0.54608077","0.54574543","0.5455361","0.5455204","0.54476535"],"string":"[\n \"0.7190156\",\n \"0.6753629\",\n \"0.60996735\",\n \"0.6064925\",\n \"0.6016448\",\n \"0.6008773\",\n \"0.60022396\",\n \"0.60006684\",\n \"0.5999985\",\n \"0.59736115\",\n \"0.5944627\",\n \"0.59434086\",\n \"0.5941799\",\n \"0.5931249\",\n \"0.5897575\",\n \"0.5877158\",\n \"0.5876371\",\n \"0.5840248\",\n \"0.58335423\",\n \"0.58302295\",\n \"0.58285284\",\n \"0.58272463\",\n \"0.5807024\",\n \"0.5799569\",\n \"0.57845443\",\n \"0.5781996\",\n \"0.5779757\",\n \"0.5770024\",\n \"0.575117\",\n \"0.5739089\",\n \"0.57363075\",\n \"0.5735732\",\n \"0.5713691\",\n \"0.5693648\",\n \"0.5693578\",\n \"0.56858295\",\n \"0.56805944\",\n \"0.56687164\",\n \"0.5658263\",\n \"0.5648382\",\n \"0.564504\",\n \"0.5644673\",\n \"0.56430984\",\n \"0.56416816\",\n \"0.5638266\",\n \"0.5636274\",\n \"0.56346494\",\n \"0.56346226\",\n \"0.5629099\",\n \"0.5628261\",\n \"0.5614559\",\n \"0.56079715\",\n \"0.5606076\",\n \"0.5605636\",\n \"0.55997616\",\n \"0.5580656\",\n \"0.5579532\",\n \"0.556964\",\n \"0.5567809\",\n \"0.55675817\",\n \"0.5566145\",\n \"0.5564619\",\n \"0.5561161\",\n \"0.55587965\",\n \"0.55553675\",\n \"0.5551361\",\n \"0.5542913\",\n \"0.5542413\",\n \"0.5533799\",\n \"0.55314314\",\n \"0.5527168\",\n \"0.5526548\",\n \"0.5526236\",\n \"0.5522208\",\n \"0.55158806\",\n \"0.55117553\",\n \"0.5511285\",\n \"0.5507037\",\n \"0.5503371\",\n \"0.54993296\",\n \"0.5497811\",\n \"0.549305\",\n \"0.5489936\",\n \"0.5488964\",\n \"0.5487613\",\n \"0.5484082\",\n \"0.54818964\",\n \"0.5474708\",\n \"0.54729474\",\n \"0.5472021\",\n \"0.54715574\",\n \"0.54663956\",\n \"0.54663956\",\n \"0.5466015\",\n \"0.5461071\",\n \"0.54608077\",\n \"0.54574543\",\n \"0.5455361\",\n \"0.5455204\",\n \"0.54476535\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7230312"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95682,"cells":{"query":{"kind":"string","value":"Create weights as defined in Section 5.1 of our paper."},"document":{"kind":"string","value":"def create_weights(N, weights_type):\n if weights_type == \"uniform\":\n weights = np.array([1 / N,] * N)\n elif weights_type == \"decreasing\":\n normaliser = sum([1 / i for i in range(1, N + 1)])\n weights = np.array([1 / (i * normaliser) for i in range(1, N + 1)])\n elif weights_type == \"increasing\":\n normaliser = sum([1 / i for i in range(1, N + 1)])\n weights = np.array([1 / ((N + 1 - i) * normaliser) for i in range(1, N + 1)])\n elif weights_type == \"centred\":\n if N % 2 == 1:\n normaliser = sum([1 / (abs((N + 1) / 2 - i) + 1) for i in range(1, N + 1)])\n weights = np.array(\n [1 / ((abs((N + 1) / 2 - i) + 1) * normaliser) for i in range(1, N + 1)]\n )\n else:\n normaliser = sum(\n [1 / (abs((N + 1) / 2 - i) + 0.5) for i in range(1, N + 1)]\n )\n weights = np.array(\n [\n 1 / ((abs((N + 1) / 2 - i) + 0.5) * normaliser)\n for i in range(1, N + 1)\n ]\n )\n else:\n raise ValueError(\n 'The value of weights_type should be \"uniform\" or'\n '\"decreasing\" or \"increasing\" or \"centred\".'\n )\n return weights"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _create_weights(self):\n gate_size = self._hidden_size * self._num_gates\n # Compute the shape of weight and bias.\n matrix_shapes, bias_shapes = [], []\n for layer in range(self._num_layers):\n for direction in range(self._num_directions):\n layer_input_size = self._input_size if layer == 0 \\\n else self._hidden_size * self._num_directions\n w_ih_shape = [gate_size, layer_input_size]\n w_hh_shape = [gate_size, self._hidden_size]\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\n bias_shapes.extend([b_ih_shape, b_hh_shape])\n # Create single float32 weights.\n weights_count = 0\n self._weights_shapes = matrix_shapes + bias_shapes\n for shape in self._weights_shapes:\n weights_count += math_util.prod(shape)\n self._weights = Tensor([weights_count])\n self._weights.requires_grad = True","def generate_weights(sizes):\n weights = {}\n weights[\"w\"] = []\n weights[\"b\"] = []\n for i in range(len(sizes)-2):\n weights[\"w\"].append(np.random.randn(sizes[i], sizes[i+1]))\n weights[\"b\"].append(np.random.randn(sizes[i+1]))\n weights[\"w_final\"] = np.random.randn(sizes[-2], sizes[-1])/np.sqrt(sizes[-1])\n weights[\"b_final\"] = np.random.randn(sizes[-1])\n return weights","def weightGenerate(self):\n\t\tfor i in range(0, self.numberOfInput):\n\t\t\tself.weight.append(random.random()-0.5)","def _generate_weights(self):\n weights = [random.uniform(0, 1) for x in range(self.num_weights)]\n return self._normalize_weights(weights)","def init_weights_(self):\n raise NotImplementedError","def get_weights(self):","def weights_initializer(self):\n self.weights = [np.random.normal(0, 1 / np.sqrt(x), (x, y)) for x, y in list(zip(self.structure[1:], self.structure[:-1]))]","def InitWeights(self):\n self.w = -1 + 2 * np.random.rand(self.num_of_inputs,)\n self.w0 = -1 + 2 * np.random.rand()","def gen_W(users, items, ratings):\n\n # initiate graph\n user = users.tolist()\n item = items.tolist()\n rating = ratings.tolist()\n B = nx.Graph()\n B.add_nodes_from(user, bipartite=0)\n B.add_nodes_from(item, bipartite=1)\n\n # create edges\n for i in range(len(user)):\n B.add_edges_from([(user[i], item[i])])\n B[user[i]][item[i]]['weight'] = rating[i]\n\n users_unique = users.unique() \n items_unique = items.unique()\n\n # BiAdjacency matrix - for bipartite network\n W = biadjacency_matrix(B, users_unique,items_unique).toarray()\n\n # sparce form of Biadjacency matrix\n W = spa.csr_matrix(W)\n print('Shape of W: '+str(W.shape))\n\n return W, users_unique, items_unique","def _create_weights(self):\n\n self.mu_W = tf.get_variable(\n name=\"mu_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.z_dim])\n\n self.mu_b = tf.get_variable(\n name=\"mu_b\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim])\n\n self.log_sig_sq_W = tf.get_variable(\n name=\"log_sig_sq_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.z_dim])\n\n self.log_sig_sq_b = tf.get_variable(\n name=\"log_sig_sq_b\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim])\n \n self.y_W = tf.get_variable(\n name=\"y_W\", initializer=tf.random_normal_initializer(),\n shape=[self.z_dim, self.rnn_dim])\n\n self.y_b = tf.get_variable(\n name=\"y_b\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim])\n \n self.softmax_W = tf.get_variable(\n name=\"softmax_W\", initializer=tf.random_normal_initializer(),\n shape=[self.rnn_dim, self.vocabulary_size])\n \n self.softmax_b = tf.get_variable(\n name=\"softmax_b\", initializer=tf.random_normal_initializer(),\n shape=[self.vocabulary_size])","def init_weight(w):\n shape = w.shape\n if len(shape) == 4:\n i, o, u, v = shape\n k = np.sqrt(6 / (i * u * v + o * u * v))\n w.data.uniform_(-k, k)\n elif len(shape) == 2:\n k = np.sqrt(6 / sum(shape))\n w.data.uniform_(-k, k)\n elif len(shape) == 1:\n w.data.zero_()","def _initialize_weights(self):\n pass","def gen_tb_tb_weights(weight=1.):\r\n W = np.zeros([8, 8])\r\n sinusoid = -(np.cos(np.linspace(0, 2 * np.pi, 8, endpoint=False)) - 1) / 2\r\n for i in range(8):\r\n values = np.roll(sinusoid, i)\r\n W[i, :] = values\r\n return weight * W","def _generate_weights(self):\n weights = []\n for i in range(1, len(self.layers) - 1):\n weights.append(2 * np.random.random(\n (self.layers[i - 1] + 1, self.layers[i] + 1)) - 1)\n weights.append(2 * np.random.random(\n (self.layers[i] + 1, self.layers[i + 1])) - 1)\n return weights","def init_weights(self):\n \n self.w = np.random.randn(self.D) / np.sqrt(self.D)","def init_weights(self, dims):\n self.W = np.random.normal(size=dims) * 0.0001","def initializeWeights(n_in,n_out):\r\n \r\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\r\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\r\n return W","def initializeWeights(n_in,n_out):\r\n \r\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\r\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\r\n return W","def weights(self):\r\n\t\treturn None","def generate_weight(N, J, delta, sigma, z):\n J = numpy.asarray(J)\n delta = numpy.asarray(delta)\n sigma = numpy.asarray(sigma)\n x = numpy.linspace(-0.5, 0.5, N).reshape((1, -1))\n W = numpy.empty((2 * N, 2 * N))\n W[:N, :N] = weight(x, J[0, 0], delta[0, 0], sigma[0, 0], z[:N, :N])\n W[N:, :N] = weight(x, J[1, 0], delta[1, 0], sigma[1, 0], z[N:, :N])\n W[:N, N:] = weight(x, -J[0, 1], -delta[0, 1], sigma[0, 1], z[:N, N:])\n W[N:, N:] = weight(x, -J[1, 1], -delta[1, 1], sigma[1, 1], z[N:, N:])\n return W","def gen_in_weights(self):\n\n gen = Generator(device = self.device).manual_seed(self.random_seed)\n n, m = self.n_nodes_, self.n_inputs_\n in_w_shape_ = (n, m)\n print('m,n', m,n)\n\n #at the moment all input weight matrices use uniform bias.\n self.bias = rand( n, 1, generator = gen, device = self.device) * 2 - 1\n\n #weights\n if self.input_weight_type_ == \"uniform\":\n self.in_weights = rand((n,m), generator = gen, device = self.device)\n self.in_weights = self.in_weights * 2 - 1\n print('in_weights', self.in_weights.shape)\n\n elif self.input_weight_type_ == \"exponential\":\n printc(\"BUILDING SIGN_\", 'fail')\n sign1 = random_state.choice([-1, 1], size= (in_w_shape_[0], in_w_shape_[1]//2))\n sign2 = random_state.choice([-1, 1], size= (in_w_shape_[0], in_w_shape_[1]//2))\n\n self.sign_dual = (sign1, sign2)\n self.sign = np.concatenate((sign1, sign2), axis = 1)\n\n #regularization\n self.feedback_weights = rand(n, 1, **self.tensorArgs, generator = gen) * 2 - 1\n\n #regularization\n self.noise_z = normal(0, 1, size = (n, m), **self.tensorArgs, generator = gen)","def create_weight_matrices(self):\n rad = 1 / np.sqrt(self.no_of_in_nodes)\n X = truncated_normal(mean=0, \n sd=1, \n low=-rad, \n upp=rad)\n self.wih = X.rvs((self.no_of_hidden_nodes, \n self.no_of_in_nodes))\n rad = 1 / np.sqrt(self.no_of_hidden_nodes)\n X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad)\n self.who = X.rvs((self.no_of_out_nodes, \n self.no_of_hidden_nodes))","def weights(self):\n \n n = self.n\n lambda_ = self.alpha**2 * (n +self.kappa) - n\n \n c = .5 / (n + lambda_)\n Wc = np.full(2*n + 1, c)\n Wm = np.full(2*n + 1, c)\n Wc[0] = lambda_ / (n + lambda_) + (1 - self.alpha**2 + self.beta)\n Wm[0] = lambda_ / (n + lambda_)\n \n return Wm, Wc","def initializeWeights(n_in,n_out):\n \n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\n return W","def initWeights(self):\n self.weights = []\n self.bias = []\n for i, dim in enumerate(self.dimensions[1:]):\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\n self.bias.append(np.random.uniform(-1,1,dim))","def initializeWeights(n_in,n_out):\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\n return W","def initializeWeights(n_in,n_out):\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\n return W","def initializeWeights(n_in,n_out):\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\n return W","def initialize_weights(self):\n # compute lmda.\n self.lmda = float(self.c)/float(self.N)\n # bias term. This should not get a regularization penalty.\n self.bias = {}\n # weight vector\n self.w = {}\n # lastW[j] = k, indicates that feature j was last updated at time k.\n self.lastW = {}\n for lbl in self.labels:\n self.bias[lbl] = 0\n self.w[lbl] = {}\n self.lastW[lbl] = {}\n pass","def default_weight_initializer(self):\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]","def init_weights(model):\n ...","def initialize_weights_xavier(self):\n\t\tself.weights = [np.random.uniform(-1/sqrt(size1), 1/sqrt(size1)) for size1, size2 in zip(self.sizes[:-1], self.sizes[1:])]\n\t\tself.biases = [np.zeros([size, ]) for size in self.sizes[1:]]","def my_assign_weights(context, data):\n pass","def initializeWeights(n_hidden_units, n_inputs, n_outputs):\n W1 = np.random.randn(n_hidden_units, n_inputs) * np.sqrt(1/(n_inputs*n_hidden_units))\n W2 = np.random.randn(n_outputs, n_hidden_units) * np.sqrt(1/(n_hidden_units*n_outputs))\n b1 = np.random.randn(n_hidden_units, 1) * np.sqrt(1/n_hidden_units)\n b2 = np.random.randn(n_outputs, 1) * np.sqrt(1/n_outputs)\n return W1, b1, W2, b2","def initialize_weights(training_points):\n N = len(training_points)\n ans = {}\n for p in training_points:\n ans[p] = make_fraction(1, N)\n return ans","def update_weights(self):\n\t\tpass","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)\n # Tie weights if needed\n self.tie_weights()","def initialize_weights(self, weights_initializer, bias_initializer):\n wshapes = [\n [self.input_size, self.hidden_size[0]],\n [self.hidden_size[0], self.hidden_size[1]],\n [self.hidden_size[1], self.output_size]\n ]\n\n bshapes = [\n [1, self.hidden_size[0]],\n [1, self.hidden_size[1]],\n [1, self.output_size]\n ]\n\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\n\n self.trainable_variables = self.weights + self.biases","def setWeights(self, w):\n raise NotImplementedError","def initialize_weights(self):\n w1 = np.random.uniform(-1.0, 1.0, size = self.n_hidden * (self.n_features + 1)).reshape(self.n_hidden, (self.n_features + 1))/(self.n_features + 1)\n w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden+1)).reshape(self.n_output, self.n_hidden+ 1)/(self.n_hidden + 1)\n return w1, w2","def instantiate_weights(self):\n self.product_embeddings = tf.get_variable(\n name='product_embeddings',\n shape=[50000, 300],\n dtype=tf.float32\n )\n self.aisle_embeddings = tf.get_variable(\n name='aisle_embeddings',\n shape=[250, 50],\n dtype=tf.float32\n )\n self.department_embeddings = tf.get_variable(\n name='department_embeddings',\n shape=[50, 10],\n dtype=tf.float32\n )\n self.W_relu = tf.get_variable(\"W_relu\",shape=[670, 30]) #这个参数后续需要自适应\n self.b_relu = tf.get_variable(\"bias_relu\",shape=[30]) \n self.W_projection = tf.get_variable(\"W_projection\",shape=[30, 1]) \n self.b_projection = tf.get_variable(\"bias_projection\",shape=[1])","def make_weights_for_balanced_classes(self):\n\n count = [0] * self.get_num_classes()\n\n # label = self.class_map_dict[self.meta_data.loc[image_id]['dx']]\n # labels = [self.class_map_dict[l] for l in self.get_labels()]\n\n labels = self.get_labels()\n\n # Count how many instances there are for each class\n for l in labels:\n count[l] += 1\n\n weight_per_class = [0.] * self.get_num_classes()\n\n N = float(sum(count))\n\n # Assign a weight which is inversely proportional to class frequency\n for i in range(self.get_num_classes()):\n weight_per_class[i] = N/float(count[i])\n\n # Save results for debugging purposes\n self._weight_per_class = weight_per_class\n\n # Now assign a weight to each data point\n weight = [0] * len(labels)\n\n for idx, val in enumerate(labels):\n weight[idx] = weight_per_class[val]\n\n return weight","def _mutate_weights(self, weights):\n return weights + normal(loc=0, scale=self.standard_deviation, size=weights.shape[0])","def weights(self) -> List[float]:","def __initialize_weights__(num_neurons: int) -> np.ndarray:\n\n weights = np.zeros((num_neurons, num_neurons))\n\n return weights","def init_weights(num_tilings, tiles_per_dim, num_dims, num_actions):\n weights = np.zeros((num_tilings*tiles_per_dim**num_dims*num_actions))\n return weights","def set_weights(self, weights):\r\n self.weights = weights","def _generate_weighted_matrices(self):\n self.degree_weighted_matrices = dict()\n mes = []\n args = []\n for metaedge, matrix in self.adj_matrices.items():\n mes.append(metaedge)\n args.append({'matrix': matrix, 'w': self.w, 'degree_fwd': self.out_degree[metaedge],\n 'degree_rev': self.in_degree[metaedge]})\n res = parallel_process(array=args, function=mt.weight_by_degree, use_kwargs=True, n_jobs=self.n_jobs,\n front_num=0)\n for metaedge, matrix in zip(mes, res):\n self.degree_weighted_matrices[metaedge] = matrix","def ComputeWeights(self, p_float=..., p_float=..., p_float=..., *args, **kwargs):\n ...","def _build_multinomial_weights(self) -> None:\n weights_obs = ramp_up_weights(\n len(self.obs), self.tpe.full_weight_num, self.tpe.equal_weight\n )\n counts_obs = numpy.bincount(\n self.obs, minlength=len(self.choices), weights=weights_obs\n )\n counts_obs = counts_obs + self.tpe.prior_weight\n self.weights = counts_obs / counts_obs.sum()","def _set_weights(self, weights):\r\n self.weights = weights.reshape(self.output_size, self.input_size+1)","def _set_initial_weights(self):\n weights = {'h0': None, 'y': None}\n weights['h0'] = np.array([[-0.6, 0.4, 0.5], [-0.2, 0.8, 0.8]])\n weights['y'] = np.array([[-0.3, -0.4, 0.9]])\n \n return weights","def init_weights(self, size):\n\n w = np.random.uniform(-0.5, 0.5, size + 1) # creates array with length size\n return w","def __create_conv_weights(self, conv_weights):\n\n conv_xform_weights = []\n curr_n = 32\n k = 5\n for idx, conv_w in enumerate(conv_weights):\n\n curr_n = self.n_values[idx]\n W = self.__create_W_matrix(curr_n, conv_w)\n conv_xform_weights.append(W)\n\n return conv_xform_weights","def init_weights(self):\n for i in range(5):\n default_init_weights(getattr(self, f'conv{i+1}'), 0.1)","def _write_weights(self, write_gates, usage):\n\n write_allocation_weights = self._allocation_weights(write_gates, self.num_write_heads, usage)\n write_gate = write_gates.unsqueeze(-1)\n write_weights = write_gate*write_allocation_weights\n return write_weights","def default_weights(n):\n return np.array([1/n for _ in range(n)])","def init_weight(self):\n init_bn(self.norm0)","def GetTrainWeights(Labels,Weights):\n Weights = np.where(Weights > 0, Weights, 0) #Setting negative weights to zero for training\n ReferenceLength = len(Labels[Labels == 0])\n for Class in np.unique(Labels):\n CWeight = np.sum(Weights[Labels == Class])\n RenormFactor = ReferenceLength/CWeight\n Weights = np.where(Labels != Class,Weights,Weights*RenormFactor)\n \n return Weights","def init_weights(self):\n # Initialize weights\n self.apply(self._init_weights)","def init_weights(no_input, no_hidden, no_output):\r\n weight_lst = []\r\n no_of_weights = (no_input * no_hidden) + (no_hidden * no_output)\r\n\r\n for weight in range(no_of_weights):\r\n weight = 0.01 * random.random()\r\n weight_lst.append(weight)\r\n\r\n return weight_lst","def numWeights(self):\r\n\t\treturn None","def init_weights(self, input_size=None, min_w=WEIGHT_MIN,\n max_w=WEIGHT_MAX):\n if input_size is None:\n input_size = self.INPUT_SIZE\n\n # Add a bias weight to each neuron\n weights_per_neuron = input_size + 1\n\n self.weights = np.random.rand(self.size, weights_per_neuron) \\\n * (max_w - min_w) + min_w","def weight(self):","def init_weights(self, num_features):\n for each_label in self.valid_labels:\n self.weights[each_label] = np.zeros(num_features)","def init_weights(self) -> None:\n nn.init.kaiming_normal_(self._U)\n nn.init.kaiming_normal_(self._W)\n nn.init.kaiming_normal_(self._V)\n\n nn.init.normal_(self._b)","def init_weights(self):\n self.embedding.weight.data.uniform_(-0.1, 0.1)\n self.word.bias.data.fill_(0)\n self.word.weight.data.uniform_(-0.1, 0.1)","def __init__(self, weights):\n self._weights = weights","def get_weights(self):\n return [self.w, self.b]","def init_weights(w_shape, layer_index, weight_initializer):\n\n return tf.Variable(weight_initializer(w_shape), name=\"weight{}\".format(layer_index))","def setupWeightComputation(self, file, weight, p, wordsize, ignoreMSBs = 0):\n file.write(\"weight: BITVECTOR(16);\\n\")\n file.write(self.getWeightString(p, wordsize, ignoreMSBs) + \"\\n\")\n file.write(\"ASSERT(weight = {0:#018b});\\n\".format(weight))\n return","def gen_weights(self, f_target):\n\n # calculate x and psi\n x_track = self.cs.rollout()\n psi_track = self.gen_psi(x_track)\n\n # efficiently calculate BF weights using weighted linear regression\n self.w = jnp.zeros((self.n_dmps, self.n_bfs))\n for d in range(self.n_dmps):\n # spatial scaling term\n k = self.goal[d] - self.y0[d]\n for b in range(self.n_bfs):\n numer = jnp.sum(x_track * psi_track[:, b] * f_target[:, d])\n denom = jnp.sum(x_track ** 2 * psi_track[:, b])\n self.w[d, b] = numer / denom\n if abs(k) > 1e-5:\n self.w[d, b] /= k\n\n self.w = jnp.nan_to_num(self.w)","def init_weights(self):\n\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\n self.fc3.out_features)\n self.fc3.weight.data.uniform_(-r, r)\n self.fc3.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)\n r = np.sqrt(1.) / np.sqrt(self.fc1.in_features +\n self.fc1.out_features)\n self.fc1.weight.data.uniform_(-r, r)\n self.fc1.bias.data.fill_(0)","def _make_sample_weights(n_repetitions, n_samples, random_state):\n sample_weights = np.zeros((n_repetitions, n_samples), dtype=np.float64)\n\n for repetition in range(n_repetitions):\n sample_weights[repetition] = random_state.rand(n_samples) + 1\n\n return sample_weights","def generate_weights(n_features, hidden_layer_sizes, n_out):\n\n weights = []\n biases = []\n\n # Weights from input layer to first hidden layer\n weights.append(init_weight(hidden_layer_sizes[0], n_features))\n biases.append(init_bias(hidden_layer_sizes[0]))\n\n # Weights from one hidden layer to the next\n for i in range(1, hidden_layer_sizes.size):\n weights.append(\n init_weight(hidden_layer_sizes[i],hidden_layer_sizes[i - 1]))\n biases.append(init_bias(hidden_layer_sizes[i]))\n\n # Weights from last hidden layer to output layer\n weights.append(init_weight(n_out, hidden_layer_sizes[-1]))\n biases.append(init_bias(n_out))\n\n return weights, biases","def weights(self, weights):\n\n self._weights = weights","def build(self,input_shape):\n\n self.w = self.add_weight(shape=(input_shape[-1],self.units),\n initializer='random_normal',\n trainable=True)\n self.b = self.add_weight(shape=(self.units,),\n initializer='random_normal',\n trainable=True)","def _init_weights(n_rows, n_cols, std_dev=0.5):\n weights = np.random.randn(n_rows, n_cols) * std_dev\n return weights","def generate_weighted_graph():\n \n Adj_Matrix = np.array([\n [0.0, 0.2, 0.2, 0.3, 0.2, 0.1],\n [0.1, 0.0, 0.3, 0.3, 0.1, 0.2],\n [0.3, 0.2, 0.0, 0.1, 0.2, 0.2],\n [0.1, 0.4, 0.2, 0.0, 0.2, 0.1],\n [0.2, 0.2, 0.2, 0.2, 0.0, 0.2],\n [0.2, 0.1, 0.1, 0.3, 0.3, 0.0]\n ])\n\n return Adj_Matrix","def setWeightInitializer(self,weights):\n self.init_w = weights","def init_weights(self, load_weights=None):\n if load_weights:\n # TODO\n pass\n else:\n # x: lower layer nodes n\n # y: current layer nodes n\n x = self.weights_shape[1]\n y = self.weights_shape[0]\n self.weights = np.random.randn(y, x) / np.sqrt(x) # pylint: disable=no-member\n self.biases = np.random.randn(y, 1) # pylint: disable=no-member","def _buildWeights(self):\r\n # Compute the spatial tree\r\n kd = spatial.cKDTree(self.XYin)\r\n \r\n # Perform query on all of the points in the grid\r\n dist,self.ind=kd.query(self.XYout,distance_upper_bound=self.maxdist,k=self.NNear)\r\n \r\n self.Nc = np.size(self.ind,axis=0)\r\n print '%d interpolation points.'%self.Nc\r\n # Now loop through and get the weights for each point\r\n self.W = np.zeros((self.NNear,self.Nc))\r\n\r\n # Print percentages\r\n p0=0\r\n pstep=5\r\n for ii in range(0,self.Nc):\r\n \r\n if self.verbose:\r\n pfinish = float(ii)/float(self.Nc)*100.0\r\n if pfinish> p0:\r\n print '%3.1f %% complete...'%pfinish\r\n p0+=pstep\r\n \r\n W = self.getWeights(dist[ii,:],self.XYin[self.ind[ii,:],0],self.XYin[self.ind[ii,:],1])\r\n self.W[:,ii] = W.T","def init_sg_weights(self):\n n = self.weights_shape[0] # size of current layer\n # pylint: disable=no-member\n A = np.random.randn(n, n) / np.sqrt(n)\n B = np.random.randn(10, n) / np.sqrt(n)\n C = np.random.randn(1, n) / np.sqrt(n)\n # pylint: enable=no-member\n self.sg_weights = [A, B, C]","def initialize_weights(self, seed=None):\r\n if seed!=None:\r\n np.random.seed(seed)\r\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions)","def WeightInitializer():\n return np.random.uniform(-1, 1)","def _allocation_weights(self, write_gates, num_writes, usage):\n write_gates = write_gates.unsqueeze(-1)\n allocation_weights = []\n for i in range(num_writes):\n allocation = self._allocate(usage)\n allocation_weights.append(allocation)\n usage = usage + ((1-usage)*write_gates[:, i, :]*allocation_weights[i])\n allocation_weights = torch.stack(allocation_weights, 1)\n return allocation_weights","def init_weights(self):\n r = np.sqrt(6.) / np.sqrt(self.fc1.in_features +\n self.fc1.out_features)\n self.fc1.weight.data.uniform_(-r, r)\n self.fc1.bias.data.fill_(0)\n r = np.sqrt(6.) / np.sqrt(self.fc2.in_features +\n self.fc2.out_features)\n self.fc2.weight.data.uniform_(-r, r)\n self.fc2.bias.data.fill_(0)","def calc_assn_weights():\n\t\n\t\t\t#\n\t\t\t#\n\ttext(\"\"\"INSERT INTO assignments (mentor_id, course_id, cost)\n\t\t\tSELECT M.mentor_id, C.course_id, SUM(COALESCE(PW.weight_value,PT.def_weight_val))\n\t\t\tFROM mentors M, courses C\n\t\t\tJOIN course2pref C2P ON C2P.course_id = C.course_id\n\t\t\tJOIN prefs P ON P.pref_id = C2P.pref_id\n\t\t\tJOIN pref_types PT ON PT.pref_type_id = P.pref_type_id\n\t\t\tJOIN pref_weights PW ON PW.pref_type_id = P.pref_type_id\n\t\t\tLEFT JOIN choices Ch ON Ch.mentor_id = M.mentor_id AND Ch.weight_id = PW.pref_id","def get_data_with_weights(self):\n weights = np.zeros((self.contexts.shape[0], self.num_actions))\n a_ind = np.array([(i, val) for i, val in enumerate(self.actions)])\n weights[a_ind[:, 0], a_ind[:, 1]] = 1.0\n return self.contexts, self.rewards, weights","def apply_weights(self):\n w0_array = np.ones(self.N)*self.w0\n return w0_array + self.X.dot(self.w)","def get_weights(self, nn_weights, rov_id): # Get weights from CCEA population\n\n for w in range(self.n_weights):\n self.weights[rov_id, w] = nn_weights[w]","def _degree_weight_weighted_matrices(self):\n for meta_edge, matrix in self.degree_weighted_matrices.items():\n self.degree_weighted_matrices[meta_edge] = matrix.multiply(self.weighted_adj_matrices[meta_edge])","def initialize_weights(self):\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\n self._batch)","def fama2(self, n, weight, nums):\n ws = {0}\n for i in range(0, n):\n ts = set()\n for j in range(0, nums[i]):\n for w in ws:\n ts.add(weight[i] * (j + 1) + w)\n ws = ws.union(ts)\n for j in range(0, nums[i]):\n ws.add(weight[i] * (j + 1))\n return len(ws)","def weight_setup(self, weighting):\n if weighting == \"overlap\":\n self.weights = overlap_generator(overlap, self.graph)\n elif weighting == \"unit\":\n self.weights = overlap_generator(unit, self.graph)\n elif weighting == \"min_norm\":\n self.weights = overlap_generator(min_norm, self.graph)\n else:\n self.weights = overlap_generator(normalized_overlap, self.graph)","def weights(self, pad=0, feather=0, apply=False, mode='raised cosine'):\n # find dimensions of matrix\n sh = getattr(self, self.fields[0]).shape\n if len(sh)==3:\n ny, nx, nband = sh\n else:\n nband =1\n ny, nx = sh\n # allocate for weights matrix\n self.weight = np.ones((ny,nx), dtype=float)\n # feathering the weight matrix\n if feather:\n if mode == 'raised cosine':\n self.raised_cosine_weights(pad, feather)\n elif mode == 'gaussian':\n self.gaussian_weights(pad, feather)\n if pad:\n self.pad_edges(pad)\n # if applying the weights to the original z data\n if apply:\n for field in self.fields:\n if nband > 1:\n for band in range(nband):\n getattr(self, field)[:,:,band] *= self.weight\n else:\n getattr(self, field)[:,:] *= self.weight\n return self","def get_weights(self, size, name='W'):\n\t\treturn self._initW(size, name)","def set_weights(self, weights):\n self._weights = weights\n self.normalize_weights() ########################added\n #self.get_weights()","def get_weights(self):\n return []","def __init__(self, weights:np.ndarray):\n self.w = weights.copy()"],"string":"[\n \"def _create_weights(self):\\n gate_size = self._hidden_size * self._num_gates\\n # Compute the shape of weight and bias.\\n matrix_shapes, bias_shapes = [], []\\n for layer in range(self._num_layers):\\n for direction in range(self._num_directions):\\n layer_input_size = self._input_size if layer == 0 \\\\\\n else self._hidden_size * self._num_directions\\n w_ih_shape = [gate_size, layer_input_size]\\n w_hh_shape = [gate_size, self._hidden_size]\\n b_ih_shape, b_hh_shape = [gate_size], [gate_size]\\n matrix_shapes.extend([w_ih_shape, w_hh_shape])\\n bias_shapes.extend([b_ih_shape, b_hh_shape])\\n # Create single float32 weights.\\n weights_count = 0\\n self._weights_shapes = matrix_shapes + bias_shapes\\n for shape in self._weights_shapes:\\n weights_count += math_util.prod(shape)\\n self._weights = Tensor([weights_count])\\n self._weights.requires_grad = True\",\n \"def generate_weights(sizes):\\n weights = {}\\n weights[\\\"w\\\"] = []\\n weights[\\\"b\\\"] = []\\n for i in range(len(sizes)-2):\\n weights[\\\"w\\\"].append(np.random.randn(sizes[i], sizes[i+1]))\\n weights[\\\"b\\\"].append(np.random.randn(sizes[i+1]))\\n weights[\\\"w_final\\\"] = np.random.randn(sizes[-2], sizes[-1])/np.sqrt(sizes[-1])\\n weights[\\\"b_final\\\"] = np.random.randn(sizes[-1])\\n return weights\",\n \"def weightGenerate(self):\\n\\t\\tfor i in range(0, self.numberOfInput):\\n\\t\\t\\tself.weight.append(random.random()-0.5)\",\n \"def _generate_weights(self):\\n weights = [random.uniform(0, 1) for x in range(self.num_weights)]\\n return self._normalize_weights(weights)\",\n \"def init_weights_(self):\\n raise NotImplementedError\",\n \"def get_weights(self):\",\n \"def weights_initializer(self):\\n self.weights = [np.random.normal(0, 1 / np.sqrt(x), (x, y)) for x, y in list(zip(self.structure[1:], self.structure[:-1]))]\",\n \"def InitWeights(self):\\n self.w = -1 + 2 * np.random.rand(self.num_of_inputs,)\\n self.w0 = -1 + 2 * np.random.rand()\",\n \"def gen_W(users, items, ratings):\\n\\n # initiate graph\\n user = users.tolist()\\n item = items.tolist()\\n rating = ratings.tolist()\\n B = nx.Graph()\\n B.add_nodes_from(user, bipartite=0)\\n B.add_nodes_from(item, bipartite=1)\\n\\n # create edges\\n for i in range(len(user)):\\n B.add_edges_from([(user[i], item[i])])\\n B[user[i]][item[i]]['weight'] = rating[i]\\n\\n users_unique = users.unique() \\n items_unique = items.unique()\\n\\n # BiAdjacency matrix - for bipartite network\\n W = biadjacency_matrix(B, users_unique,items_unique).toarray()\\n\\n # sparce form of Biadjacency matrix\\n W = spa.csr_matrix(W)\\n print('Shape of W: '+str(W.shape))\\n\\n return W, users_unique, items_unique\",\n \"def _create_weights(self):\\n\\n self.mu_W = tf.get_variable(\\n name=\\\"mu_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.z_dim])\\n\\n self.mu_b = tf.get_variable(\\n name=\\\"mu_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim])\\n\\n self.log_sig_sq_W = tf.get_variable(\\n name=\\\"log_sig_sq_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.z_dim])\\n\\n self.log_sig_sq_b = tf.get_variable(\\n name=\\\"log_sig_sq_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim])\\n \\n self.y_W = tf.get_variable(\\n name=\\\"y_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.z_dim, self.rnn_dim])\\n\\n self.y_b = tf.get_variable(\\n name=\\\"y_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim])\\n \\n self.softmax_W = tf.get_variable(\\n name=\\\"softmax_W\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.rnn_dim, self.vocabulary_size])\\n \\n self.softmax_b = tf.get_variable(\\n name=\\\"softmax_b\\\", initializer=tf.random_normal_initializer(),\\n shape=[self.vocabulary_size])\",\n \"def init_weight(w):\\n shape = w.shape\\n if len(shape) == 4:\\n i, o, u, v = shape\\n k = np.sqrt(6 / (i * u * v + o * u * v))\\n w.data.uniform_(-k, k)\\n elif len(shape) == 2:\\n k = np.sqrt(6 / sum(shape))\\n w.data.uniform_(-k, k)\\n elif len(shape) == 1:\\n w.data.zero_()\",\n \"def _initialize_weights(self):\\n pass\",\n \"def gen_tb_tb_weights(weight=1.):\\r\\n W = np.zeros([8, 8])\\r\\n sinusoid = -(np.cos(np.linspace(0, 2 * np.pi, 8, endpoint=False)) - 1) / 2\\r\\n for i in range(8):\\r\\n values = np.roll(sinusoid, i)\\r\\n W[i, :] = values\\r\\n return weight * W\",\n \"def _generate_weights(self):\\n weights = []\\n for i in range(1, len(self.layers) - 1):\\n weights.append(2 * np.random.random(\\n (self.layers[i - 1] + 1, self.layers[i] + 1)) - 1)\\n weights.append(2 * np.random.random(\\n (self.layers[i] + 1, self.layers[i + 1])) - 1)\\n return weights\",\n \"def init_weights(self):\\n \\n self.w = np.random.randn(self.D) / np.sqrt(self.D)\",\n \"def init_weights(self, dims):\\n self.W = np.random.normal(size=dims) * 0.0001\",\n \"def initializeWeights(n_in,n_out):\\r\\n \\r\\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\r\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\r\\n return W\",\n \"def initializeWeights(n_in,n_out):\\r\\n \\r\\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\r\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\r\\n return W\",\n \"def weights(self):\\r\\n\\t\\treturn None\",\n \"def generate_weight(N, J, delta, sigma, z):\\n J = numpy.asarray(J)\\n delta = numpy.asarray(delta)\\n sigma = numpy.asarray(sigma)\\n x = numpy.linspace(-0.5, 0.5, N).reshape((1, -1))\\n W = numpy.empty((2 * N, 2 * N))\\n W[:N, :N] = weight(x, J[0, 0], delta[0, 0], sigma[0, 0], z[:N, :N])\\n W[N:, :N] = weight(x, J[1, 0], delta[1, 0], sigma[1, 0], z[N:, :N])\\n W[:N, N:] = weight(x, -J[0, 1], -delta[0, 1], sigma[0, 1], z[:N, N:])\\n W[N:, N:] = weight(x, -J[1, 1], -delta[1, 1], sigma[1, 1], z[N:, N:])\\n return W\",\n \"def gen_in_weights(self):\\n\\n gen = Generator(device = self.device).manual_seed(self.random_seed)\\n n, m = self.n_nodes_, self.n_inputs_\\n in_w_shape_ = (n, m)\\n print('m,n', m,n)\\n\\n #at the moment all input weight matrices use uniform bias.\\n self.bias = rand( n, 1, generator = gen, device = self.device) * 2 - 1\\n\\n #weights\\n if self.input_weight_type_ == \\\"uniform\\\":\\n self.in_weights = rand((n,m), generator = gen, device = self.device)\\n self.in_weights = self.in_weights * 2 - 1\\n print('in_weights', self.in_weights.shape)\\n\\n elif self.input_weight_type_ == \\\"exponential\\\":\\n printc(\\\"BUILDING SIGN_\\\", 'fail')\\n sign1 = random_state.choice([-1, 1], size= (in_w_shape_[0], in_w_shape_[1]//2))\\n sign2 = random_state.choice([-1, 1], size= (in_w_shape_[0], in_w_shape_[1]//2))\\n\\n self.sign_dual = (sign1, sign2)\\n self.sign = np.concatenate((sign1, sign2), axis = 1)\\n\\n #regularization\\n self.feedback_weights = rand(n, 1, **self.tensorArgs, generator = gen) * 2 - 1\\n\\n #regularization\\n self.noise_z = normal(0, 1, size = (n, m), **self.tensorArgs, generator = gen)\",\n \"def create_weight_matrices(self):\\n rad = 1 / np.sqrt(self.no_of_in_nodes)\\n X = truncated_normal(mean=0, \\n sd=1, \\n low=-rad, \\n upp=rad)\\n self.wih = X.rvs((self.no_of_hidden_nodes, \\n self.no_of_in_nodes))\\n rad = 1 / np.sqrt(self.no_of_hidden_nodes)\\n X = truncated_normal(mean=0, sd=1, low=-rad, upp=rad)\\n self.who = X.rvs((self.no_of_out_nodes, \\n self.no_of_hidden_nodes))\",\n \"def weights(self):\\n \\n n = self.n\\n lambda_ = self.alpha**2 * (n +self.kappa) - n\\n \\n c = .5 / (n + lambda_)\\n Wc = np.full(2*n + 1, c)\\n Wm = np.full(2*n + 1, c)\\n Wc[0] = lambda_ / (n + lambda_) + (1 - self.alpha**2 + self.beta)\\n Wm[0] = lambda_ / (n + lambda_)\\n \\n return Wm, Wc\",\n \"def initializeWeights(n_in,n_out):\\n \\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\n return W\",\n \"def initWeights(self):\\n self.weights = []\\n self.bias = []\\n for i, dim in enumerate(self.dimensions[1:]):\\n self.weights.append(np.random.uniform(-1,1,(self.dimensions[i],dim)))\\n self.bias.append(np.random.uniform(-1,1,dim))\",\n \"def initializeWeights(n_in,n_out):\\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\n return W\",\n \"def initializeWeights(n_in,n_out):\\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\n return W\",\n \"def initializeWeights(n_in,n_out):\\n epsilon = sqrt(6) / sqrt(n_in + n_out + 1);\\n W = (np.random.rand(n_out, n_in + 1)*2* epsilon) - epsilon;\\n return W\",\n \"def initialize_weights(self):\\n # compute lmda.\\n self.lmda = float(self.c)/float(self.N)\\n # bias term. This should not get a regularization penalty.\\n self.bias = {}\\n # weight vector\\n self.w = {}\\n # lastW[j] = k, indicates that feature j was last updated at time k.\\n self.lastW = {}\\n for lbl in self.labels:\\n self.bias[lbl] = 0\\n self.w[lbl] = {}\\n self.lastW[lbl] = {}\\n pass\",\n \"def default_weight_initializer(self):\\n self.biases = [np.random.randn(y, 1) for y in self.sizes[1:]]\\n self.weights = [np.random.randn(y, x)/np.sqrt(x)\\n for x, y in zip(self.sizes[:-1], self.sizes[1:])]\",\n \"def init_weights(model):\\n ...\",\n \"def initialize_weights_xavier(self):\\n\\t\\tself.weights = [np.random.uniform(-1/sqrt(size1), 1/sqrt(size1)) for size1, size2 in zip(self.sizes[:-1], self.sizes[1:])]\\n\\t\\tself.biases = [np.zeros([size, ]) for size in self.sizes[1:]]\",\n \"def my_assign_weights(context, data):\\n pass\",\n \"def initializeWeights(n_hidden_units, n_inputs, n_outputs):\\n W1 = np.random.randn(n_hidden_units, n_inputs) * np.sqrt(1/(n_inputs*n_hidden_units))\\n W2 = np.random.randn(n_outputs, n_hidden_units) * np.sqrt(1/(n_hidden_units*n_outputs))\\n b1 = np.random.randn(n_hidden_units, 1) * np.sqrt(1/n_hidden_units)\\n b2 = np.random.randn(n_outputs, 1) * np.sqrt(1/n_outputs)\\n return W1, b1, W2, b2\",\n \"def initialize_weights(training_points):\\n N = len(training_points)\\n ans = {}\\n for p in training_points:\\n ans[p] = make_fraction(1, N)\\n return ans\",\n \"def update_weights(self):\\n\\t\\tpass\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\\n # Tie weights if needed\\n self.tie_weights()\",\n \"def initialize_weights(self, weights_initializer, bias_initializer):\\n wshapes = [\\n [self.input_size, self.hidden_size[0]],\\n [self.hidden_size[0], self.hidden_size[1]],\\n [self.hidden_size[1], self.output_size]\\n ]\\n\\n bshapes = [\\n [1, self.hidden_size[0]],\\n [1, self.hidden_size[1]],\\n [1, self.output_size]\\n ]\\n\\n self.weights = [init_weights(s, weights_initializer) for s in wshapes]\\n self.biases = [init_weights(s, bias_initializer) for s in bshapes]\\n\\n self.trainable_variables = self.weights + self.biases\",\n \"def setWeights(self, w):\\n raise NotImplementedError\",\n \"def initialize_weights(self):\\n w1 = np.random.uniform(-1.0, 1.0, size = self.n_hidden * (self.n_features + 1)).reshape(self.n_hidden, (self.n_features + 1))/(self.n_features + 1)\\n w2 = np.random.uniform(-1.0, 1.0, size=self.n_output*(self.n_hidden+1)).reshape(self.n_output, self.n_hidden+ 1)/(self.n_hidden + 1)\\n return w1, w2\",\n \"def instantiate_weights(self):\\n self.product_embeddings = tf.get_variable(\\n name='product_embeddings',\\n shape=[50000, 300],\\n dtype=tf.float32\\n )\\n self.aisle_embeddings = tf.get_variable(\\n name='aisle_embeddings',\\n shape=[250, 50],\\n dtype=tf.float32\\n )\\n self.department_embeddings = tf.get_variable(\\n name='department_embeddings',\\n shape=[50, 10],\\n dtype=tf.float32\\n )\\n self.W_relu = tf.get_variable(\\\"W_relu\\\",shape=[670, 30]) #这个参数后续需要自适应\\n self.b_relu = tf.get_variable(\\\"bias_relu\\\",shape=[30]) \\n self.W_projection = tf.get_variable(\\\"W_projection\\\",shape=[30, 1]) \\n self.b_projection = tf.get_variable(\\\"bias_projection\\\",shape=[1])\",\n \"def make_weights_for_balanced_classes(self):\\n\\n count = [0] * self.get_num_classes()\\n\\n # label = self.class_map_dict[self.meta_data.loc[image_id]['dx']]\\n # labels = [self.class_map_dict[l] for l in self.get_labels()]\\n\\n labels = self.get_labels()\\n\\n # Count how many instances there are for each class\\n for l in labels:\\n count[l] += 1\\n\\n weight_per_class = [0.] * self.get_num_classes()\\n\\n N = float(sum(count))\\n\\n # Assign a weight which is inversely proportional to class frequency\\n for i in range(self.get_num_classes()):\\n weight_per_class[i] = N/float(count[i])\\n\\n # Save results for debugging purposes\\n self._weight_per_class = weight_per_class\\n\\n # Now assign a weight to each data point\\n weight = [0] * len(labels)\\n\\n for idx, val in enumerate(labels):\\n weight[idx] = weight_per_class[val]\\n\\n return weight\",\n \"def _mutate_weights(self, weights):\\n return weights + normal(loc=0, scale=self.standard_deviation, size=weights.shape[0])\",\n \"def weights(self) -> List[float]:\",\n \"def __initialize_weights__(num_neurons: int) -> np.ndarray:\\n\\n weights = np.zeros((num_neurons, num_neurons))\\n\\n return weights\",\n \"def init_weights(num_tilings, tiles_per_dim, num_dims, num_actions):\\n weights = np.zeros((num_tilings*tiles_per_dim**num_dims*num_actions))\\n return weights\",\n \"def set_weights(self, weights):\\r\\n self.weights = weights\",\n \"def _generate_weighted_matrices(self):\\n self.degree_weighted_matrices = dict()\\n mes = []\\n args = []\\n for metaedge, matrix in self.adj_matrices.items():\\n mes.append(metaedge)\\n args.append({'matrix': matrix, 'w': self.w, 'degree_fwd': self.out_degree[metaedge],\\n 'degree_rev': self.in_degree[metaedge]})\\n res = parallel_process(array=args, function=mt.weight_by_degree, use_kwargs=True, n_jobs=self.n_jobs,\\n front_num=0)\\n for metaedge, matrix in zip(mes, res):\\n self.degree_weighted_matrices[metaedge] = matrix\",\n \"def ComputeWeights(self, p_float=..., p_float=..., p_float=..., *args, **kwargs):\\n ...\",\n \"def _build_multinomial_weights(self) -> None:\\n weights_obs = ramp_up_weights(\\n len(self.obs), self.tpe.full_weight_num, self.tpe.equal_weight\\n )\\n counts_obs = numpy.bincount(\\n self.obs, minlength=len(self.choices), weights=weights_obs\\n )\\n counts_obs = counts_obs + self.tpe.prior_weight\\n self.weights = counts_obs / counts_obs.sum()\",\n \"def _set_weights(self, weights):\\r\\n self.weights = weights.reshape(self.output_size, self.input_size+1)\",\n \"def _set_initial_weights(self):\\n weights = {'h0': None, 'y': None}\\n weights['h0'] = np.array([[-0.6, 0.4, 0.5], [-0.2, 0.8, 0.8]])\\n weights['y'] = np.array([[-0.3, -0.4, 0.9]])\\n \\n return weights\",\n \"def init_weights(self, size):\\n\\n w = np.random.uniform(-0.5, 0.5, size + 1) # creates array with length size\\n return w\",\n \"def __create_conv_weights(self, conv_weights):\\n\\n conv_xform_weights = []\\n curr_n = 32\\n k = 5\\n for idx, conv_w in enumerate(conv_weights):\\n\\n curr_n = self.n_values[idx]\\n W = self.__create_W_matrix(curr_n, conv_w)\\n conv_xform_weights.append(W)\\n\\n return conv_xform_weights\",\n \"def init_weights(self):\\n for i in range(5):\\n default_init_weights(getattr(self, f'conv{i+1}'), 0.1)\",\n \"def _write_weights(self, write_gates, usage):\\n\\n write_allocation_weights = self._allocation_weights(write_gates, self.num_write_heads, usage)\\n write_gate = write_gates.unsqueeze(-1)\\n write_weights = write_gate*write_allocation_weights\\n return write_weights\",\n \"def default_weights(n):\\n return np.array([1/n for _ in range(n)])\",\n \"def init_weight(self):\\n init_bn(self.norm0)\",\n \"def GetTrainWeights(Labels,Weights):\\n Weights = np.where(Weights > 0, Weights, 0) #Setting negative weights to zero for training\\n ReferenceLength = len(Labels[Labels == 0])\\n for Class in np.unique(Labels):\\n CWeight = np.sum(Weights[Labels == Class])\\n RenormFactor = ReferenceLength/CWeight\\n Weights = np.where(Labels != Class,Weights,Weights*RenormFactor)\\n \\n return Weights\",\n \"def init_weights(self):\\n # Initialize weights\\n self.apply(self._init_weights)\",\n \"def init_weights(no_input, no_hidden, no_output):\\r\\n weight_lst = []\\r\\n no_of_weights = (no_input * no_hidden) + (no_hidden * no_output)\\r\\n\\r\\n for weight in range(no_of_weights):\\r\\n weight = 0.01 * random.random()\\r\\n weight_lst.append(weight)\\r\\n\\r\\n return weight_lst\",\n \"def numWeights(self):\\r\\n\\t\\treturn None\",\n \"def init_weights(self, input_size=None, min_w=WEIGHT_MIN,\\n max_w=WEIGHT_MAX):\\n if input_size is None:\\n input_size = self.INPUT_SIZE\\n\\n # Add a bias weight to each neuron\\n weights_per_neuron = input_size + 1\\n\\n self.weights = np.random.rand(self.size, weights_per_neuron) \\\\\\n * (max_w - min_w) + min_w\",\n \"def weight(self):\",\n \"def init_weights(self, num_features):\\n for each_label in self.valid_labels:\\n self.weights[each_label] = np.zeros(num_features)\",\n \"def init_weights(self) -> None:\\n nn.init.kaiming_normal_(self._U)\\n nn.init.kaiming_normal_(self._W)\\n nn.init.kaiming_normal_(self._V)\\n\\n nn.init.normal_(self._b)\",\n \"def init_weights(self):\\n self.embedding.weight.data.uniform_(-0.1, 0.1)\\n self.word.bias.data.fill_(0)\\n self.word.weight.data.uniform_(-0.1, 0.1)\",\n \"def __init__(self, weights):\\n self._weights = weights\",\n \"def get_weights(self):\\n return [self.w, self.b]\",\n \"def init_weights(w_shape, layer_index, weight_initializer):\\n\\n return tf.Variable(weight_initializer(w_shape), name=\\\"weight{}\\\".format(layer_index))\",\n \"def setupWeightComputation(self, file, weight, p, wordsize, ignoreMSBs = 0):\\n file.write(\\\"weight: BITVECTOR(16);\\\\n\\\")\\n file.write(self.getWeightString(p, wordsize, ignoreMSBs) + \\\"\\\\n\\\")\\n file.write(\\\"ASSERT(weight = {0:#018b});\\\\n\\\".format(weight))\\n return\",\n \"def gen_weights(self, f_target):\\n\\n # calculate x and psi\\n x_track = self.cs.rollout()\\n psi_track = self.gen_psi(x_track)\\n\\n # efficiently calculate BF weights using weighted linear regression\\n self.w = jnp.zeros((self.n_dmps, self.n_bfs))\\n for d in range(self.n_dmps):\\n # spatial scaling term\\n k = self.goal[d] - self.y0[d]\\n for b in range(self.n_bfs):\\n numer = jnp.sum(x_track * psi_track[:, b] * f_target[:, d])\\n denom = jnp.sum(x_track ** 2 * psi_track[:, b])\\n self.w[d, b] = numer / denom\\n if abs(k) > 1e-5:\\n self.w[d, b] /= k\\n\\n self.w = jnp.nan_to_num(self.w)\",\n \"def init_weights(self):\\n\\n r = np.sqrt(1.) / np.sqrt(self.fc3.in_features +\\n self.fc3.out_features)\\n self.fc3.weight.data.uniform_(-r, r)\\n self.fc3.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\\n r = np.sqrt(1.) / np.sqrt(self.fc1.in_features +\\n self.fc1.out_features)\\n self.fc1.weight.data.uniform_(-r, r)\\n self.fc1.bias.data.fill_(0)\",\n \"def _make_sample_weights(n_repetitions, n_samples, random_state):\\n sample_weights = np.zeros((n_repetitions, n_samples), dtype=np.float64)\\n\\n for repetition in range(n_repetitions):\\n sample_weights[repetition] = random_state.rand(n_samples) + 1\\n\\n return sample_weights\",\n \"def generate_weights(n_features, hidden_layer_sizes, n_out):\\n\\n weights = []\\n biases = []\\n\\n # Weights from input layer to first hidden layer\\n weights.append(init_weight(hidden_layer_sizes[0], n_features))\\n biases.append(init_bias(hidden_layer_sizes[0]))\\n\\n # Weights from one hidden layer to the next\\n for i in range(1, hidden_layer_sizes.size):\\n weights.append(\\n init_weight(hidden_layer_sizes[i],hidden_layer_sizes[i - 1]))\\n biases.append(init_bias(hidden_layer_sizes[i]))\\n\\n # Weights from last hidden layer to output layer\\n weights.append(init_weight(n_out, hidden_layer_sizes[-1]))\\n biases.append(init_bias(n_out))\\n\\n return weights, biases\",\n \"def weights(self, weights):\\n\\n self._weights = weights\",\n \"def build(self,input_shape):\\n\\n self.w = self.add_weight(shape=(input_shape[-1],self.units),\\n initializer='random_normal',\\n trainable=True)\\n self.b = self.add_weight(shape=(self.units,),\\n initializer='random_normal',\\n trainable=True)\",\n \"def _init_weights(n_rows, n_cols, std_dev=0.5):\\n weights = np.random.randn(n_rows, n_cols) * std_dev\\n return weights\",\n \"def generate_weighted_graph():\\n \\n Adj_Matrix = np.array([\\n [0.0, 0.2, 0.2, 0.3, 0.2, 0.1],\\n [0.1, 0.0, 0.3, 0.3, 0.1, 0.2],\\n [0.3, 0.2, 0.0, 0.1, 0.2, 0.2],\\n [0.1, 0.4, 0.2, 0.0, 0.2, 0.1],\\n [0.2, 0.2, 0.2, 0.2, 0.0, 0.2],\\n [0.2, 0.1, 0.1, 0.3, 0.3, 0.0]\\n ])\\n\\n return Adj_Matrix\",\n \"def setWeightInitializer(self,weights):\\n self.init_w = weights\",\n \"def init_weights(self, load_weights=None):\\n if load_weights:\\n # TODO\\n pass\\n else:\\n # x: lower layer nodes n\\n # y: current layer nodes n\\n x = self.weights_shape[1]\\n y = self.weights_shape[0]\\n self.weights = np.random.randn(y, x) / np.sqrt(x) # pylint: disable=no-member\\n self.biases = np.random.randn(y, 1) # pylint: disable=no-member\",\n \"def _buildWeights(self):\\r\\n # Compute the spatial tree\\r\\n kd = spatial.cKDTree(self.XYin)\\r\\n \\r\\n # Perform query on all of the points in the grid\\r\\n dist,self.ind=kd.query(self.XYout,distance_upper_bound=self.maxdist,k=self.NNear)\\r\\n \\r\\n self.Nc = np.size(self.ind,axis=0)\\r\\n print '%d interpolation points.'%self.Nc\\r\\n # Now loop through and get the weights for each point\\r\\n self.W = np.zeros((self.NNear,self.Nc))\\r\\n\\r\\n # Print percentages\\r\\n p0=0\\r\\n pstep=5\\r\\n for ii in range(0,self.Nc):\\r\\n \\r\\n if self.verbose:\\r\\n pfinish = float(ii)/float(self.Nc)*100.0\\r\\n if pfinish> p0:\\r\\n print '%3.1f %% complete...'%pfinish\\r\\n p0+=pstep\\r\\n \\r\\n W = self.getWeights(dist[ii,:],self.XYin[self.ind[ii,:],0],self.XYin[self.ind[ii,:],1])\\r\\n self.W[:,ii] = W.T\",\n \"def init_sg_weights(self):\\n n = self.weights_shape[0] # size of current layer\\n # pylint: disable=no-member\\n A = np.random.randn(n, n) / np.sqrt(n)\\n B = np.random.randn(10, n) / np.sqrt(n)\\n C = np.random.randn(1, n) / np.sqrt(n)\\n # pylint: enable=no-member\\n self.sg_weights = [A, B, C]\",\n \"def initialize_weights(self, seed=None):\\r\\n if seed!=None:\\r\\n np.random.seed(seed)\\r\\n self.weights = np.random.randn(self.number_of_nodes,self.input_dimensions)\",\n \"def WeightInitializer():\\n return np.random.uniform(-1, 1)\",\n \"def _allocation_weights(self, write_gates, num_writes, usage):\\n write_gates = write_gates.unsqueeze(-1)\\n allocation_weights = []\\n for i in range(num_writes):\\n allocation = self._allocate(usage)\\n allocation_weights.append(allocation)\\n usage = usage + ((1-usage)*write_gates[:, i, :]*allocation_weights[i])\\n allocation_weights = torch.stack(allocation_weights, 1)\\n return allocation_weights\",\n \"def init_weights(self):\\n r = np.sqrt(6.) / np.sqrt(self.fc1.in_features +\\n self.fc1.out_features)\\n self.fc1.weight.data.uniform_(-r, r)\\n self.fc1.bias.data.fill_(0)\\n r = np.sqrt(6.) / np.sqrt(self.fc2.in_features +\\n self.fc2.out_features)\\n self.fc2.weight.data.uniform_(-r, r)\\n self.fc2.bias.data.fill_(0)\",\n \"def calc_assn_weights():\\n\\t\\n\\t\\t\\t#\\n\\t\\t\\t#\\n\\ttext(\\\"\\\"\\\"INSERT INTO assignments (mentor_id, course_id, cost)\\n\\t\\t\\tSELECT M.mentor_id, C.course_id, SUM(COALESCE(PW.weight_value,PT.def_weight_val))\\n\\t\\t\\tFROM mentors M, courses C\\n\\t\\t\\tJOIN course2pref C2P ON C2P.course_id = C.course_id\\n\\t\\t\\tJOIN prefs P ON P.pref_id = C2P.pref_id\\n\\t\\t\\tJOIN pref_types PT ON PT.pref_type_id = P.pref_type_id\\n\\t\\t\\tJOIN pref_weights PW ON PW.pref_type_id = P.pref_type_id\\n\\t\\t\\tLEFT JOIN choices Ch ON Ch.mentor_id = M.mentor_id AND Ch.weight_id = PW.pref_id\",\n \"def get_data_with_weights(self):\\n weights = np.zeros((self.contexts.shape[0], self.num_actions))\\n a_ind = np.array([(i, val) for i, val in enumerate(self.actions)])\\n weights[a_ind[:, 0], a_ind[:, 1]] = 1.0\\n return self.contexts, self.rewards, weights\",\n \"def apply_weights(self):\\n w0_array = np.ones(self.N)*self.w0\\n return w0_array + self.X.dot(self.w)\",\n \"def get_weights(self, nn_weights, rov_id): # Get weights from CCEA population\\n\\n for w in range(self.n_weights):\\n self.weights[rov_id, w] = nn_weights[w]\",\n \"def _degree_weight_weighted_matrices(self):\\n for meta_edge, matrix in self.degree_weighted_matrices.items():\\n self.degree_weighted_matrices[meta_edge] = matrix.multiply(self.weighted_adj_matrices[meta_edge])\",\n \"def initialize_weights(self):\\n weights_initializer.WeightsInitializer.initialize_layer_or_model(\\n self._batch)\",\n \"def fama2(self, n, weight, nums):\\n ws = {0}\\n for i in range(0, n):\\n ts = set()\\n for j in range(0, nums[i]):\\n for w in ws:\\n ts.add(weight[i] * (j + 1) + w)\\n ws = ws.union(ts)\\n for j in range(0, nums[i]):\\n ws.add(weight[i] * (j + 1))\\n return len(ws)\",\n \"def weight_setup(self, weighting):\\n if weighting == \\\"overlap\\\":\\n self.weights = overlap_generator(overlap, self.graph)\\n elif weighting == \\\"unit\\\":\\n self.weights = overlap_generator(unit, self.graph)\\n elif weighting == \\\"min_norm\\\":\\n self.weights = overlap_generator(min_norm, self.graph)\\n else:\\n self.weights = overlap_generator(normalized_overlap, self.graph)\",\n \"def weights(self, pad=0, feather=0, apply=False, mode='raised cosine'):\\n # find dimensions of matrix\\n sh = getattr(self, self.fields[0]).shape\\n if len(sh)==3:\\n ny, nx, nband = sh\\n else:\\n nband =1\\n ny, nx = sh\\n # allocate for weights matrix\\n self.weight = np.ones((ny,nx), dtype=float)\\n # feathering the weight matrix\\n if feather:\\n if mode == 'raised cosine':\\n self.raised_cosine_weights(pad, feather)\\n elif mode == 'gaussian':\\n self.gaussian_weights(pad, feather)\\n if pad:\\n self.pad_edges(pad)\\n # if applying the weights to the original z data\\n if apply:\\n for field in self.fields:\\n if nband > 1:\\n for band in range(nband):\\n getattr(self, field)[:,:,band] *= self.weight\\n else:\\n getattr(self, field)[:,:] *= self.weight\\n return self\",\n \"def get_weights(self, size, name='W'):\\n\\t\\treturn self._initW(size, name)\",\n \"def set_weights(self, weights):\\n self._weights = weights\\n self.normalize_weights() ########################added\\n #self.get_weights()\",\n \"def get_weights(self):\\n return []\",\n \"def __init__(self, weights:np.ndarray):\\n self.w = weights.copy()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7466049","0.7392558","0.7357619","0.72760403","0.71676654","0.7089966","0.7077687","0.70380753","0.7031394","0.7028164","0.7026333","0.70108604","0.69093025","0.69080865","0.68695116","0.68506765","0.6768174","0.6768174","0.6765752","0.67634475","0.67488414","0.6747982","0.6745061","0.6725261","0.67054415","0.669429","0.669429","0.669429","0.66904694","0.66880125","0.6683625","0.66819024","0.66602176","0.66510737","0.66196394","0.66082466","0.658591","0.6557125","0.6536917","0.6532068","0.65275294","0.6516978","0.65090597","0.64876497","0.6464693","0.6462223","0.6461195","0.6449746","0.64389765","0.64385223","0.6432014","0.6425508","0.641518","0.6407862","0.6401697","0.63959134","0.6385981","0.63849574","0.63814014","0.63799906","0.6375351","0.6368261","0.6366396","0.6360322","0.63579106","0.63566613","0.6348568","0.6339457","0.6321952","0.63057154","0.6292362","0.62876654","0.628676","0.62816834","0.62716204","0.6270287","0.62692904","0.62685794","0.62471855","0.62447095","0.6237987","0.6237306","0.6224132","0.62220436","0.6208041","0.6205188","0.62011504","0.6199975","0.61994743","0.61925566","0.61908275","0.61783296","0.61746085","0.6172446","0.6167409","0.6158438","0.6148874","0.61458814","0.61452067","0.613003"],"string":"[\n \"0.7466049\",\n \"0.7392558\",\n \"0.7357619\",\n \"0.72760403\",\n \"0.71676654\",\n \"0.7089966\",\n \"0.7077687\",\n \"0.70380753\",\n \"0.7031394\",\n \"0.7028164\",\n \"0.7026333\",\n \"0.70108604\",\n \"0.69093025\",\n \"0.69080865\",\n \"0.68695116\",\n \"0.68506765\",\n \"0.6768174\",\n \"0.6768174\",\n \"0.6765752\",\n \"0.67634475\",\n \"0.67488414\",\n \"0.6747982\",\n \"0.6745061\",\n \"0.6725261\",\n \"0.67054415\",\n \"0.669429\",\n \"0.669429\",\n \"0.669429\",\n \"0.66904694\",\n \"0.66880125\",\n \"0.6683625\",\n \"0.66819024\",\n \"0.66602176\",\n \"0.66510737\",\n \"0.66196394\",\n \"0.66082466\",\n \"0.658591\",\n \"0.6557125\",\n \"0.6536917\",\n \"0.6532068\",\n \"0.65275294\",\n \"0.6516978\",\n \"0.65090597\",\n \"0.64876497\",\n \"0.6464693\",\n \"0.6462223\",\n \"0.6461195\",\n \"0.6449746\",\n \"0.64389765\",\n \"0.64385223\",\n \"0.6432014\",\n \"0.6425508\",\n \"0.641518\",\n \"0.6407862\",\n \"0.6401697\",\n \"0.63959134\",\n \"0.6385981\",\n \"0.63849574\",\n \"0.63814014\",\n \"0.63799906\",\n \"0.6375351\",\n \"0.6368261\",\n \"0.6366396\",\n \"0.6360322\",\n \"0.63579106\",\n \"0.63566613\",\n \"0.6348568\",\n \"0.6339457\",\n \"0.6321952\",\n \"0.63057154\",\n \"0.6292362\",\n \"0.62876654\",\n \"0.628676\",\n \"0.62816834\",\n \"0.62716204\",\n \"0.6270287\",\n \"0.62692904\",\n \"0.62685794\",\n \"0.62471855\",\n \"0.62447095\",\n \"0.6237987\",\n \"0.6237306\",\n \"0.6224132\",\n \"0.62220436\",\n \"0.6208041\",\n \"0.6205188\",\n \"0.62011504\",\n \"0.6199975\",\n \"0.61994743\",\n \"0.61925566\",\n \"0.61908275\",\n \"0.61783296\",\n \"0.61746085\",\n \"0.6172446\",\n \"0.6167409\",\n \"0.6158438\",\n \"0.6148874\",\n \"0.61458814\",\n \"0.61452067\",\n \"0.613003\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6555952"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":95683,"cells":{"query":{"kind":"string","value":"Runs evaluate on the provided data and generates a detailed error report"},"document":{"kind":"string","value":"def make_report(self, report_name, id_test, x_test, y_test, country_test, frame_test):\n if not os.path.exists('Reports/' + report_name):\n os.mkdir('Reports/' + report_name)\n results = self.predict(x_test)\n\n # Generate detailied evaluation report\n header = 'Country,Child,Frame'\n for output_layer in self.get_config()['output_layers']:\n header += ',{}_Actual'.format(output_layer[0])\n for output_layer in self.get_config()['output_layers']:\n header += ',{}_Prediction'.format(output_layer[0]) \n header += '\\n'\n\n with open('Reports/{}/evaluation_report.txt'.format(report_name), 'a') as f:\n if os.stat('Reports/{}/evaluation_report.txt'.format(report_name)).st_size == 0:\n f.write(header)\n for row in range(len(results)):\n entry = ','.join([str(i) for i in country_test[row]]) + ','\n entry += ','.join([str(i) for i in id_test[row]]) + ','\n entry += ','.join([str(i) for i in frame_test[row]]) + ','\n entry += ','.join([str(i) for i in y_test[row]]) + ','\n entry += ','.join([str(i) for i in results[row]]) + '\\n'\n f.write(entry)\n\n # Generate report of summary statistics\n cultures = np.unique(country_test)\n for c in cultures:\n culture_rows = np.where(country_test == c)[0] # get row numbers for culture c \n culture_ids = id_test[culture_rows] # get ID rows for culture c \n unique_ids = np.unique(culture_ids) # get unique IDs for culture c \n\n for u in unique_ids: \n all_id_rows = np.where(id_test == u)[0]\n id_rows = np.intersect1d(all_id_rows, culture_rows) # get ID rows for child u \n\n id_icc = icc(results[id_rows], y_test[id_rows])[0] # compute ICC for child u \n id_pcc = pcc(results[id_rows], y_test[id_rows])[0][0] # compute PCC for child u \n id_ccc = ccc(results[id_rows], y_test[id_rows]) # compute CCC for child u \n id_mae = mae(results[id_rows], y_test[id_rows]) # compute MAE for child u \n\n icc_entry = '{},{},{}\\n'.format(c, u, id_icc)\n pcc_entry = '{},{},{}\\n'.format(c, u, id_pcc)\n ccc_entry = '{},{},{}\\n'.format(c, u, id_ccc)\n mae_entry = '{},{},{}\\n'.format(c, u, id_mae)\n \n with open('Reports/{}/icc_report.txt'.format(report_name), 'a') as f:\n f.write(icc_entry)\n\n with open('Reports/{}/pcc_report.txt'.format(report_name), 'a') as f:\n f.write(pcc_entry)\n\n with open('Reports/{}/ccc_report.txt'.format(report_name), 'a') as f:\n f.write(ccc_entry)\n\n with open('Reports/{}/mae_report.txt'.format(report_name), 'a') as f:\n f.write(mae_entry)\n\n return results"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def evaluate(self, test_data):\n result = self.model.run(test_data)\n self._save_result(result)","def evaluate(self, dataset):\n\t\tpass","def evaluate(\n self,\n test_data=None,\n print_report=True,\n save_path=\"ktrain_classification_report.csv\",\n class_names=[],\n ):\n return self.validate(\n val_data=test_data,\n print_report=print_report,\n save_path=save_path,\n class_names=class_names,\n )","def evaluate():\n click.echo(\"Not implemented yet. In the future, this command will be used for evaluation.\")\n sys.exit(-2)","def test_evaluate(self):\n # Check build does not raise errors\n dataset = KDDCupDataset()\n dataset.create_fixed_samples(\n *self.data, samples_num=1, partition_sizes=self.partition_sizes)\n dataset.set_current_sample(0)\n model = self.MODEL(dataset, **self.model_arguments)\n model.fit(training_epochs=50)\n metric = model.evaluate('test')\n self.assertLessEqual(0, metric)\n self.assertGreaterEqual(1, metric)","def evaluate(self, test_data, test_labels):\n raise NotImplementedError","def evaluate(parser):\n required_args = (\n 'train_tfrecord',\n 'valid_tfrecord',\n 'predicted_data',\n 'actual_data',\n )\n cli_args = add_all_args(parser, EVALUATION, *required_args)\n evaluator = Evaluator(\n input_shape=cli_args.input_shape,\n model_configuration=cli_args.model_cfg,\n train_tf_record=cli_args.train_tfrecord,\n valid_tf_record=cli_args.valid_tfrecord,\n classes_file=cli_args.classes,\n max_boxes=cli_args.max_boxes,\n iou_threshold=cli_args.iou_threshold,\n score_threshold=cli_args.score_threshold,\n )\n predicted = pd.read_csv(cli_args.predicted_data)\n actual = pd.read_csv(cli_args.actual_data)\n evaluator.calculate_map(\n prediction_data=predicted,\n actual_data=actual,\n min_overlaps=cli_args.min_overlaps,\n display_stats=cli_args.display_stats,\n save_figs=cli_args.save_figs,\n plot_results=cli_args.plot_stats,\n )","def evaluate(model, datagen, X_test, Y_test, batch_size, save_folder_path=None):\n\n print(\"[INFO] Evaluating model...\")\n\n scores = model.evaluate_generator(\n datagen.flow(X_test, Y_test, batch_size=batch_size),\n verbose=1)\n \n print(\"[INFO] Evaluation results:\\n{0}: {1:.2f}\\n{2}: {3:.2f}\".format(model.metrics_names[0], scores[0]*100, model.metrics_names[1], scores[1]*100))\n \n if save_folder_path is not None:\n # Write results to path\n assert os.path.isdir(save_folder_path) == True, \"Unable to save evaluation results, save_folder_path is not a folder\"\n eval_results_path = save_folder_path + \"/eval_results.txt\"\n eval_handle = open(eval_results_path, 'w')\n eval_handle.write(\"Model name: {}\\n\\n\".format(MODEL_NAME))\n eval_handle.write(\"Evaluation results:\\n{0}: {1:.2f}\\n{2}: {3:.2f}\".format(model.metrics_names[0], scores[0]*100, model.metrics_names[1], scores[1]*100))\n eval_handle.close()","def _evaluate_model(\n run_id: str, dataset_filename: str, dataset_sampling_column: str = None\n):\n fix_multiprocessing_with_keras_on_macos()\n\n run = _get_run(run_id)\n hyperparameters = run.config\n\n # no need to run this on a gpu since it's 1 epoch\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n\n with ModelBestH5File(run) as model_h5_filepath:\n model = _load_untrainable_model(hyperparameters, model_h5_filepath)\n\n model_name = run.config[\"model_name\"]\n x, y = _get_prepared_dataset(\n model_name, hyperparameters, dataset_filename, dataset_sampling_column\n )\n\n wandb.init(\n config={\n \"run_id\": run_id,\n \"dataset_filename\": dataset_filename,\n \"dataset_sampling_column\": dataset_sampling_column,\n },\n tags=[\"model-evaluation\"],\n )\n\n batch_size = hyperparameters[\"batch_size\"]\n label_scale_factor_mmhg = hyperparameters[\"label_scale_factor_mmhg\"]\n acceptable_error_mg_l = hyperparameters[\"acceptable_error_mg_l\"]\n acceptable_fraction_outside_error = hyperparameters[\n \"acceptable_fraction_outside_error\"\n ]\n\n # we're using fit() instead of evaluate() to get the functionality of these callbacks\n # training performance in the results should be ignored, as it can be affected by some\n # training-only layers such as dropout\n model.fit(\n x,\n y,\n batch_size=batch_size,\n epochs=1,\n verbose=2,\n validation_data=(x, y),\n callbacks=[\n ThresholdValMeanAbsoluteErrorOnCustomMetric(\n acceptable_fraction_outside_error=acceptable_fraction_outside_error,\n acceptable_error_mg_l=acceptable_error_mg_l,\n ),\n WandbCallback(verbose=1, monitor=\"val_adjusted_mean_absolute_error\"),\n LogPredictionsAndWeights(\n metric=\"val_adjusted_mean_absolute_error\",\n dataset=([], [], x, y),\n label_scale_factor_mmhg=label_scale_factor_mmhg,\n ),\n ],\n )\n\n # returning model and dataset for use in jupyter notebooks\n return model, (x, y)","def evaluate_data(df_train, w_train, df_test, w_test,\n evaluation_setups=None, preprocessing=None):\n if evaluation_setups is None:\n evaluation_setups = standard_evaluation_setups\n if preprocessing is None:\n preprocessing = preprocess\n if (\"weights\" in df_train) and df_train[\"weights\"].size > 0:\n weights = df_train[\"weights\"].as_matrix().squeeze()\n else:\n weights = np.ones(df_train.shape[0])\n\n # create a new dataframe which will hold all the generated errors\n df = pd.DataFrame()\n for one_w_train, one_w_test in zip(w_train, w_test):\n # setup testing function\n X_test, y_test = preprocessing(df_test, snr=one_w_test)\n # extract noisy data\n X_train, y_train = preprocessing(df_train, snr=one_w_train)\n for e in evaluation_setups:\n regressor = e.regressor\n regressor.fit(X_train, y_train, weights)\n y_pred = regressor.predict(X_test)\n # save results to a dataframe\n errors = np.abs(y_pred - y_test)\n errors = errors.reshape(len(errors), 1)\n current_df = DataFrame(errors * 100,\n columns=[\"Errors\"])\n current_df[\"Method\"] = e.name\n current_df[\"SNR\"] = int(one_w_test)\n df = pd.concat([df, current_df], ignore_index=True)\n\n return df","def evaluate(self) :\n pass","def evaluate(self):\n pass","def evaluate(self):\n pass","def evaluated_data(self) -> Dict:\n if not self.eval_data:\n raise Exception(\"Evaluation Failed\")\n\n statistics = [stat['statistics'] for stat in self.eval_data]\n for count, stat in enumerate(statistics):\n if stat:\n eval_data = stat.split(\"=\")\n char_error = re.findall(r\"\\d+\\.\\d+\", eval_data[-2])[0]\n word_error = eval_data[-1]\n\n self.eval_data[count]['character_error'] = float(char_error)\n self.eval_data[count]['word_error'] = float(word_error)\n del self.eval_data[count]['statistics']\n\n return self.eval_data","def Evaluate():\n global reg\n reg = ModelRegression()\n \n # Retrieve dataset files\n data_fls, ref_fls = LoadTroikaDataset()\n errs, confs = [], []\n for data_fl, ref_fl in zip(data_fls, ref_fls):\n # Run the pulse rate algorithm on each trial in the dataset\n errors, confidence = RunPulseRateAlgorithm(data_fl, ref_fl)\n errs.append(errors)\n confs.append(confidence)\n # Compute aggregate error metric\n errs = np.hstack(errs)\n confs = np.hstack(confs)\n return AggregateErrorMetric(errs, confs)","def validate(self):\n self.set_model_mode('eval')\n self.evaluator.reset()\n losses = MetricMeter()\n\n print('Do evaluation on {} set'.format('valid set'))\n data_loader = self.val_loader\n assert data_loader is not None\n for batch_idx, batch in enumerate(data_loader):\n input, label = self.parse_batch_test(batch)\n loss = self.forward_backward(batch, backprob=False)\n losses.update(loss)\n # total_loss += loss['loss']\n output = self.model_inference(input)\n self.evaluator.process(output, label)\n\n results = self.evaluator.evaluate()\n total_loss = losses.meters['loss_x'].avg\n\n for k, v in results.items():\n tag = '{}/{}'.format('validation', k)\n self.write_scalar(tag, v, self.epoch)\n # if full_results:\n return [total_loss,losses.dict_results(),results]\n # return total_loss","def evaluate(self, dataset):\n logging.info('Start evaluation')\n\n loss, predictions, labels = self.run_one_epoch(dataset, RunnerPhase.VALIDATE)\n\n metrics_dict = self.metric_class.get_metrics_dict(predictions, labels)\n\n eval_info = self.metric_class.metrics_dict_to_str(metrics_dict)\n\n logging.info(eval_info)\n\n logging.info('Evaluation finished')\n\n return metrics_dict","def evaluate(cfg: DictConfig):\n\n experiments = cfg.get('experiment_type', f'{cfg.model.name}_only')\n fixed_t0 = cfg.get('fixed_t0', False)\n ext = '_fixedT0' if fixed_t0 else ''\n\n base_dir = cfg.device.root\n datasource = cfg.datasource.name\n\n if experiments == 'ablations':\n models = {\n 'FluxRGNN': ['final',\n 'final_without_encoder',\n 'final_without_boundary'],\n 'LocalLSTM': ['final']\n }\n elif experiments == 'final':\n models = {\n 'FluxRGNN': ['final'],\n 'GAM': ['final'],\n 'HA': ['final'],\n 'GBT': ['final']\n }\n else:\n m = cfg.model.name\n year = cfg.datasource.test_year\n\n # find all experiments available for this model, datasource and test year\n result_dir = osp.join(base_dir, 'results', datasource, m, f'test_{year}')\n models = {\n m : [ f.name for f in os.scandir(result_dir) if f.is_dir() ]\n }\n\n\n # thresholds for binary classification metrics\n if cfg.datasource.name == 'abm':\n thresholds = [0.0019, 0.0207]\n else:\n thresholds = [0, 10, 20]\n\n rmse_per_hour = []\n mae_per_hour = []\n pcc_per_hour = []\n bin_per_hour = []\n\n rmse_per_night = []\n mae_per_night = []\n\n output_dir = osp.join(base_dir, 'results', datasource, f'performance_evaluation{ext}', experiments)\n os.makedirs(output_dir, exist_ok=True)\n\n counter = 0\n\n for m, dirs in models.items():\n print(f'evaluate {m}')\n\n for d in dirs:\n result_dir = osp.join(base_dir, 'results', datasource, m, f'test_{cfg.datasource.test_year}', d)\n\n # check if directory exists\n if os.path.isdir(result_dir):\n results, model_cfg = load_cv_results(result_dir, trials=cfg.task.repeats, ext=ext)\n\n df_prep = pd.read_csv(osp.join(base_dir, 'data', 'preprocessed',\n f'{model_cfg[\"t_unit\"]}_{model_cfg[\"model\"][\"edge_type\"]}_ndummy={model_cfg[\"datasource\"][\"n_dummy_radars\"]}',\n datasource, cfg.season, str(cfg.datasource.test_year), 'dynamic_features.csv'))\n tidx2night = dict(zip(df_prep.tidx, df_prep.nightID))\n\n rmse_per_hour.append(compute_rmse(m, d, results, tidx2night, groupby=['horizon', 'trial'],\n threshold=0, km2=True, fixed_t0=fixed_t0))\n mae_per_hour.append(compute_mae(m, d, results, tidx2night, groupby=['horizon', 'trial'],\n threshold=0, km2=True, fixed_t0=fixed_t0))\n pcc_per_hour.append(compute_pcc(m, d, results, tidx2night, groupby=['horizon', 'trial'],\n threshold=0, km2=True, fixed_t0=fixed_t0))\n\n if fixed_t0:\n rmse_per_night.append(compute_rmse_per_night(m, d, results, tidx2night, groupby=['night_horizon', 'trial']))\n mae_per_night.append(compute_mae_per_night(m, d, results, tidx2night, groupby=['night_horizon', 'trial']))\n\n # compute binary classification measures\n for thr in thresholds:\n bin_per_hour.append(compute_bin(m, d, results, groupby=['horizon', 'trial'], threshold=thr, km2=True))\n\n counter += 1\n\n else:\n print(f'Experiment \"{d}\" for model \"{m}\" and datasource \"{datasource}\" is not available. '\n f'Use \"run_experiments.py model={m} datasource={datasource} +experiment={d}\" to run this experiment.')\n\n if counter > 0:\n rmse_per_hour = pd.concat(rmse_per_hour)\n rmse_per_hour.to_csv(osp.join(output_dir, f'rmse_per_hour.csv'))\n\n mae_per_hour = pd.concat(mae_per_hour)\n mae_per_hour.to_csv(osp.join(output_dir, f'mae_per_hour.csv'))\n\n pcc_per_hour = pd.concat(pcc_per_hour)\n pcc_per_hour.to_csv(osp.join(output_dir, f'pcc_per_hour.csv'))\n\n bin_per_hour = pd.concat(bin_per_hour)\n bin_per_hour.to_csv(osp.join(output_dir, f'bin_per_hour.csv'))\n\n if fixed_t0:\n rmse_per_night = pd.concat(rmse_per_night)\n rmse_per_night.to_csv(osp.join(output_dir, f'rmse_per_night.csv'))\n\n mae_per_night = pd.concat(mae_per_night)\n mae_per_night.to_csv(osp.join(output_dir, f'mae_per_night.csv'))","def regression_evaluation(self, test_set, predicted_values):\r\n\r\n MAE = self.mean_absolute_error(test_set, predicted_values)\r\n MSE = self.mean_square_error(test_set, predicted_values)\r\n print(f\"Mean Percent Error:\\t{MAE:.2f}\")\r\n print(f\"Mean Square Error:\\t{MSE:.2f}\")","def _evaluate(model):\n _recompile(model)\n if isinstance(eval_dataset, tuple):\n eval_images, eval_labels = eval_dataset\n return model.evaluate(\n eval_images, eval_labels, verbose=verbose, return_dict=True)\n else:\n return model.evaluate(eval_dataset, verbose=verbose, return_dict=True)","def _check_evaluate_implementation(self) -> None:\n logging.debug(f\"Evaluate_batch_defined: {self._evaluate_batch_defined()}.\")\n logging.debug(f\"Evaluate full dataset defined: {self._evaluate_full_dataset_defined()}.\")\n check.not_eq(\n self._evaluate_batch_defined(),\n self._evaluate_full_dataset_defined(),\n \"Please define exactly one of: `evaluate_batch()` or `evaluate_full_dataset()`. \"\n \"For most use cases `evaluate_batch()` is recommended is recommended because \"\n \"it can be parallelized across all devices.\",\n )","def evaluate(self, data, metric, classes=None):\n func_dict = {\n 'mutual_information': sklearn.metrics.mutual_info_score,\n 'normed_mutual_information': sklearn.metrics.normalized_mutual_info_score,\n 'square_error': sklearn.metrics.mean_squared_error,\n 't-test': scipy.stats.ttest_ind,\n 'wilcoxon': scipy.stats.wilcoxon,\n 'correlation': np.corrcoef\n }\n self.make_signature(data, classes)\n try:\n if metric in {'mutual_information', 'normed_mutual_information'}:\n self.score = func_dict[metric](classes, self.digit_signature()) \n elif metric == 'square_error':\n self.score = func_dict[metric](classes, self.signatures)\n elif metric in {'t-test', 'wilcoxon'} :\n self.score = np.abs(func_dict[metric](self.signatures[classes==1], \\\n self.signatures[classes==0])[0])\n \n elif metric == 'correlation':\n self.score = func_dict[metric](classes, self.signatures)[1,0]\n \n except: KeyError(\"no such a function\") \n \n return self.score","def evaluate(self):\n raise Exception(\"Not implemented.\")","def validate(\n self,\n val_data=None,\n print_report=True,\n save_path=\"ktrain_classification_report.csv\",\n class_names=[],\n ):\n if val_data is not None:\n val = val_data\n else:\n val = self.val_data\n\n classification, multilabel = U.is_classifier(self.model)\n if not classification:\n # warnings.warn('learner.validate is only for classification problems. '\n #'For regression, etc., use learner.predict and learner.ground_truth '\n #'to manually validate.')\n # return\n pass\n is_multilabel = U.is_multilabel(val) or multilabel\n y_pred = self.predict(val_data=val)\n y_true = self.ground_truth(val_data=val)\n y_pred = np.squeeze(y_pred)\n y_true = np.squeeze(y_true)\n\n # regression evaluation\n if not classification:\n from sklearn.metrics import mean_absolute_error, mean_squared_error\n\n regout = []\n metrics = U.metrics_from_model(self.model)\n for m in metrics:\n if m in [\"mae\", \"mean_absolute_error\"]:\n regout.append((m, mean_absolute_error(y_true, y_pred)))\n elif m in [\"mse\", \"mean_squared_error\"]:\n regout.append((m, mean_squared_error(y_true, y_pred)))\n if not regout:\n warnings.warn(\n \"%s is not supported by validate/evaluate - falling back to MAE\"\n )\n regout.append((\"mae\", mean_absolute_error(y_true, y_pred)))\n return regout\n\n if len(y_pred.shape) == 1:\n y_pred = np.where(y_pred > 0.5, 1, 0)\n y_true = np.where(y_true > 0.5, 1, 0)\n elif is_multilabel:\n from sklearn.preprocessing import binarize\n\n y_pred = binarize(y_pred, threshold=0.5)\n else:\n y_pred = np.argmax(y_pred, axis=1)\n y_true = np.argmax(y_true, axis=1)\n\n if print_report or save_path is not None:\n if class_names:\n try:\n class_names = [str(s) for s in class_names]\n except:\n pass\n report = classification_report(\n y_true,\n y_pred,\n target_names=class_names,\n output_dict=not print_report,\n )\n else:\n report = classification_report(\n y_true,\n y_pred,\n output_dict=not print_report,\n zero_division=0,\n )\n if print_report:\n print(report)\n else:\n df = pd.DataFrame(report).transpose()\n df.to_csv(save_path)\n print(\"classification report saved to: %s\" % (save_path))\n cm_func = confusion_matrix\n if is_multilabel:\n warnings.warn(\n \"Confusion matrices do not currently support multilabel classification, so returning None\"\n )\n return\n\n cm = confusion_matrix(y_true, y_pred)\n return cm","def test_evaluate():\n X_train, X_test, y_train, y_test = src.load()\n clf, score = src.train(X_train, y_train)\n test_score = src.evaluate(clf, X_test, y_test)\n assert isinstance(test_score, float)","def evaluate_print(self, test_data):\n test_results = [(np.argmax(self.feedforward(x)), np.argmax(y)) #argmax 返回最大数的索引\n for (x, y) in test_data]\n #统计预测错误的数据特征\n error = []\n for i, (x, y) in enumerate(test_results):\n if (x!=y):\n error.append(test_data[i][0])\n error = np.insert(error, 0, values=y, axis=1) #将正确答案插入第一行\n right = sum(int(x == y) for (x, y) in test_results)\n #打印出用全部测试集进行测试得到的结果\n print( \"TrainTest : {0} / {1} = {2}\".format(\n right, len(test_data), round(right/len(test_data),6) ))\n return error","def evaluate(self):\r\n raise Exception(\"Not implemented.\")","def eval_n_verify(yaml_data, dry_run, fail_eval):\n print('='*10, 'Verifying Results', '='*10)\n try:\n for model in yaml_data['models']:\n for i, topic in enumerate(yaml_data['topics']):\n for eval in yaml_data['evals']:\n eval_cmd = [\n os.path.join(yaml_data['root'], eval['command']),\n ' '.join(eval['params']) if eval['params'] else '',\n os.path.join(yaml_data['root'], yaml_data['qrels_root'], topic['qrel']),\n 'run.{0}.{1}.{2}'.format(yaml_data['name'], model['name'], topic['path'])\n ]\n if dry_run:\n print(' '.join(eval_cmd))\n continue\n\n out = [line for line in check_output(' '.join(eval_cmd)).decode('utf-8').split('\\n') if line.strip()][-1]\n if not out.strip():\n continue\n eval_out = out.strip().split(eval['separator'])[eval['parse_index']]\n expected = round(model['results'][eval['metric']][i], eval['metric_precision'])\n real = round(float(eval_out), eval['metric_precision'])\n if isclose(expected, real):\n print(OKBLUE, '[OK]', yaml_data['name'], model['name'], topic['name'], eval['metric'], expected, real, ENDC)\n else:\n print(FAIL, ['ERROR'], yaml_data['name'], model['name'], topic['name'], eval['metric'], expected, real, '!!!!', ENDC)\n if fail_eval:\n assert False\n finally:\n print(ENDC)","def evaluate():\n log.info('Loading dev data...')\n if args.version_2:\n dev_data = SQuAD('dev', version='2.0')\n else:\n dev_data = SQuAD('dev', version='1.1')\n (_, _), (data_file_name, _) \\\n = dev_data._data_file[dev_data._version][dev_data._segment]\n dev_data_path = os.path.join(dev_data._root, data_file_name)\n\n if args.debug:\n sampled_data = [dev_data[0], dev_data[1], dev_data[2]]\n dev_data = mx.gluon.data.SimpleDataset(sampled_data)\n log.info('Number of records in dev data: %d', len(dev_data))\n\n dev_data_features = preprocess_dataset(\n tokenizer, dev_data, vocab=vocab, max_seq_length=args.max_seq_length,\n doc_stride=args.doc_stride, num_workers=args.num_workers,\n max_query_length=args.max_query_length, load_from_pickle=args.load_pickle,\n feature_file=args.dev_dataset_file)\n\n dev_data_input = convert_full_features_to_input_features(dev_data_features)\n log.info('The number of examples after preprocessing: %d', len(dev_data_input))\n\n dev_dataloader = mx.gluon.data.DataLoader(dev_data_input, batchify_fn=batchify_fn,\n num_workers=4, batch_size=args.test_batch_size,\n shuffle=False, last_batch='keep')\n\n log.info('start prediction')\n\n all_results = collections.defaultdict(list)\n\n epoch_tic = time.time()\n total_num = 0\n for (batch_id, data) in enumerate(dev_dataloader):\n data_list = list(split_and_load(data, ctx))\n for splited_data in data_list:\n example_ids, inputs, token_types, valid_length, p_mask, _, _, _ = splited_data\n total_num += len(inputs)\n outputs = net_eval(inputs, token_types, valid_length, p_mask=p_mask)\n example_ids = example_ids.asnumpy().tolist()\n for c, example_ids in enumerate(example_ids):\n result = RawResultExtended(start_top_log_probs=outputs[0][c].asnumpy().tolist(),\n start_top_index=outputs[1][c].asnumpy().tolist(),\n end_top_log_probs=outputs[2][c].asnumpy().tolist(),\n end_top_index=outputs[3][c].asnumpy().tolist(),\n cls_logits=outputs[4][c].asnumpy().tolist())\n all_results[example_ids].append(result)\n if batch_id % args.log_interval == 0:\n log.info('Batch: %d/%d', batch_id + 1, len(dev_dataloader))\n\n epoch_toc = time.time()\n log.info('Time cost=%2f s, Thoughput=%.2f samples/s', epoch_toc - epoch_tic,\n total_num / (epoch_toc - epoch_tic))\n\n log.info('Get prediction results...')\n\n all_predictions = collections.OrderedDict()\n all_nbest_json = collections.OrderedDict()\n scores_diff_json = collections.OrderedDict()\n for features in dev_data_features:\n results = all_results[features[0].example_id]\n example_qas_id = features[0].qas_id\n score_diff, best_non_null_entry, nbest_json = predict_extended(\n features=features, results=results, n_best_size=args.n_best_size,\n max_answer_length=args.max_answer_length, start_n_top=args.start_top_n,\n end_n_top=args.end_top_n)\n scores_diff_json[example_qas_id] = score_diff\n all_predictions[example_qas_id] = best_non_null_entry\n all_nbest_json[example_qas_id] = nbest_json\n\n output_prediction_file = os.path.join(args.output_dir, 'predictions.json')\n output_nbest_file = os.path.join(args.output_dir, 'nbest_predictions.json')\n output_null_log_odds_file = os.path.join(args.output_dir, 'null_odds.json')\n\n with open(output_prediction_file, 'w') as writer:\n writer.write(json.dumps(all_predictions, indent=4) + '\\n')\n with open(output_nbest_file, 'w') as writer:\n writer.write(json.dumps(all_nbest_json, indent=4) + '\\n')\n with open(output_null_log_odds_file, 'w') as writer:\n writer.write(json.dumps(scores_diff_json, indent=4) + '\\n')\n\n if os.path.exists(sys.path[0] + '/evaluate-v2.0.py'):\n arguments = [\n dev_data_path, output_prediction_file, '--na-prob-thresh',\n str(args.null_score_diff_threshold)\n ]\n if args.version_2:\n arguments += ['--na-prob-file', output_null_log_odds_file]\n subprocess.call([sys.executable, sys.path[0] + '/evaluate-v2.0.py'] + arguments)\n else:\n log.info('Please download evaluate-v2.0.py to get evaluation results for SQuAD. '\n 'Check index.rst for the detail.')","def evaluate(model, eval_data, num_labels): \n # Turn on the evaluation state to ignore dropouts\n model.eval()\n results = [predict(model, x) for x, y in eval_data]\n f1_score, accuracy = get_metrics(np.array([y for x, y in eval_data]), results, num_labels)\n return f1_score, accuracy","def evaluate(self):\n\n\t\t## We should be evaluating on dev dataset as well, so commenting x_test\n\t\t#self.model_score = self.model.evaluate(self.x_test, self.y_test_oh, batch_size=2048)\n\t\tself.model_score = self.model.evaluate(self.x_dev, self.y_dev_oh, batch_size=2048)\n\t\tprint(\"%s score = %f\\n\" %(self.modelName, self.model_score[1]))\n\n\t\t##Saving atucal vs predicted predictions\n\t\t##np.argmax returns the index where it see's 1 in the row\n\t\t#y_pred = np.argmax(self.model.predict(self.x_test, batch_size=2048), axis=1)\n\t\ty_pred = np.argmax(self.model.predict(self.x_dev, batch_size=2048), axis=1)\n\n\t\t## vstack will stack them in 2 rows, so we use Trasnpose to get them in column stack\n\t\t#output_predict = np.vstack((np.argmax(self.y_test_oh, axis=1), y_pred)).T\n\t\toutput_predict = np.vstack((np.argmax(self.y_dev_oh, axis=1), y_pred)).T\n\t\toutputFile = self.resultDir + \"/outputPredict.csv\" \n\t\tnp.savetxt(outputFile, output_predict, fmt=\"%5.0f\", delimiter=\",\")\n\n\t\t##Error Analysis of the prediction\n\t\terrorAnalysis(outputFile)\n\n\t\treturn self.model_score","def _evaluate(self):\n logging.warning('-> evaluate EMPTY experiment...')","def model_evaluate(model,x_train,n_y_array,x_val, vald_array):\n\n scores = model.evaluate(x_train, n_y_array, verbose=1)\n\n scores2 = model.evaluate(x_val, vald_array, verbose=1)\n\n\n print(\"for traininf set\")\n\n print(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100))\n\n print(\"%s: %.2f%%\" % (model.metrics_names[0], scores[0]))\n\n\n\n print(\"for validation set : \") \n\n print(\"%s: %.2f%%\" % (model.metrics_names[1], scores2[1]*100))\n\n print(\"%s: %.2f%%\" % (model.metrics_names[0], scores2[0]))","def test_evaluate_model(sequential_model, model_data):\n _, _, _, _, x_test, y_test = model_data\n compile_model(sequential_model)\n output = evaluate_model(sequential_model, x_test, y_test, 64)\n assert len(output) == 2","def evaluate(self, dataset):\n return self.model.evaluate(dataset.X_val, dataset.y_val)","def evaluate(self):\n\n rew_rl = []\n for ckpt_path in self._config.eval_ckpt_paths:\n self._load_ckpt(ckpt_path, self._config.ckpt_num)\n\n logger.info(\n \"Run %d evaluations for %d environments\",\n self._config.num_eval,\n len(self._envs),\n )\n rollouts, info = self._evaluate(record_video=self._config.record_video)\n\n info_stat = info.get_stat()\n\n rew_rl.append(np.mean(info[\"rew_rl\"]))\n\n logger.info(\"All Eval Rew Values: %s\", rew_rl)\n logger.info(\"Eval Rew RL Average: %f, Std: %f\", np.mean(rew_rl), np.std(rew_rl))\n\n os.makedirs(\"result\", exist_ok=True)\n with h5py.File(\"result/{}.hdf5\".format(self._config.run_name), \"w\") as hf:\n for k, v in info.items():\n hf.create_dataset(k, data=info[k])\n with open(\"result/{}.txt\".format(self._config.run_name), \"w\") as f:\n for k, v in info_stat.items():\n f.write(\"{}\\t{:.03f} $\\\\pm$ {:.03f}\\n\".format(k, v[0], v[1]))","def evaluate(env, X_data, y_data, batch_size=1):\n print('\\nEvaluating')\n n_sample = X_data.shape[0]\n n_batch = int((n_sample+batch_size-1) / batch_size)\n loss, acc = 0, 0\n for batch in range(n_batch):\n print(' batch {0}/{1}'.format(batch + 1, n_batch))\n print('\\r')\n start = batch * batch_size\n end = min(n_sample, start + batch_size)\n cnt = end - start\n batch_loss, batch_acc = env.sess.run(\n [env.loss, env.acc],\n feed_dict={env.x: X_data[start:end],\n env.y: y_data[start:end]})\n loss += batch_loss * cnt\n acc += batch_acc * cnt\n loss /= n_sample\n acc /= n_sample\n print(' loss: {0:.4f} acc: {1:.4f}'.format(loss, acc))\n return loss, acc","def error_analyze(\n self,\n data_dir: Path,\n processed_data_dir: Path,\n result_dir: Path,\n output_report_dir: Path,\n ) -> NoReturn:\n pass","def evaluate_model(model, X_train, y_train, X_test, y_test):\n model = model\n model.fit(X_train, y_train)\n\n y_pred = model.predict(X_test)\n\n report = classificationreport(y_test, y_pred, target_names= [\"0\", \"1\"], output_dict=True)\n\n return report","def evaluate(self):\n # Method variables definition\n X_train, X_test, y_train, y_test = dm.reshape_y_set_split_data(self.datasetManager)\n featureScaleDependentVariables = self.datasetManager.params.featureScaleDependentVariables\n\n # Feature Scaling\n X_scaler, X_train = dm.do_feature_scaling(X_train)\n if featureScaleDependentVariables:\n y_scaler, y_train = dm.do_feature_scaling(y_train)\n else:\n y_scaler = None\n y_train = self.datasetManager.y_train\n \n self.X_scaler = X_scaler\n self.y_scaler = y_scaler\n\n # Training the SVR model on the training set\n regressor = SVR(kernel = 'rbf')\n regressor.fit(X_train, y_train.ravel())\n self.regressor = regressor\n\n # Predicting the Test set results\n self.y_pred = y_scaler.inverse_transform(regressor.predict(X_scaler.transform(X_test))) if featureScaleDependentVariables else regressor.predict(X_test)\n \n # Returning the process result : the regression type and the predicted dependent variables set\n return [\"Support Vector Regression\", self.get_r2_score(y_test, self.y_pred)]","def evaluate(self):\n raise NotImplementedError()","def evaluate(self, eval_data, eval_labels, eval_input_fn):\n raise NotImplementedError(\"Method must be implemented by subclass\")","def evaluate(self):\n predictions = self.model.predict(self.test[0])\n accuracy = accuracy_score(self.test[1], predictions)\n print(\"Accuracy:\", str(accuracy * 100) + \"%\")\n self.plot_results(predictions)","def test_evaluate_error_score(error_score, return_data, strategy, backend):\n # skip test for dask backend if dask is not installed\n if backend == \"dask\" and not _check_soft_dependencies(\"dask\", severity=\"none\"):\n return None\n\n forecaster = ExponentialSmoothing(sp=12)\n y = load_airline()\n # add NaN to make ExponentialSmoothing fail\n y.iloc[1] = np.nan\n fh = [1, 2, 3]\n cv = ExpandingWindowSplitter(step_length=48, initial_window=12, fh=fh)\n if error_score in [np.nan, 1000]:\n with pytest.warns(FitFailedWarning):\n results = evaluate(\n forecaster=forecaster,\n y=y,\n cv=cv,\n return_data=return_data,\n error_score=error_score,\n strategy=strategy,\n backend=backend,\n )\n if isinstance(error_score, type(np.nan)):\n assert results[\"test_MeanAbsolutePercentageError\"].isna().sum() > 0\n if error_score == 1000:\n assert results[\"test_MeanAbsolutePercentageError\"].max() == 1000\n if error_score == \"raise\":\n with pytest.raises(Exception): # noqa: B017\n evaluate(\n forecaster=forecaster,\n y=y,\n cv=cv,\n return_data=return_data,\n error_score=error_score,\n strategy=strategy,\n )","def error_analysis(predictions, gold, result_collector):\n # scores = defaultdict(list)\n for iteration_id, texts in predictions.items():\n # map iteration id to fold\n fold = str(int(iteration_id) / 5)\n for tid, pred_tree in texts.items():\n gold_tree = gold[tid]\n print(iteration_id, fold, tid)\n print(gold_tree.get_triples())\n print(pred_tree.get_triples())\n for level, scores in eval_prediction([gold_tree], [pred_tree]):\n result_collector.add_result(tid, fold, level, scores)\n print(\"Done.\")","def evaluate_model(self, predictions, expected, bypass_data_to_eval):\n\n result = []\n for i, unique_id in enumerate(np.squeeze(expected[\"unique_ids\"])):\n start_logits = predictions['tf_electra_for_question_answering'][i]\n start_top_index = predictions['tf_electra_for_question_answering_1'\n ][i]\n end_logits = predictions['tf_electra_for_question_answering_2'][i]\n end_top_index = predictions['tf_electra_for_question_answering_3'][i\n ]\n cls_logits = predictions['tf_electra_for_question_answering_4'][i]\n\n result.append(\n SquadResult(\n unique_id,\n start_logits.tolist(),\n end_logits.tolist(),\n start_top_index=start_top_index.tolist(),\n end_top_index=end_top_index.tolist(),\n cls_logits=cls_logits.tolist(),\n )\n )\n\n dev_features = bypass_data_to_eval[\"dev_features\"]\n dev_examples = bypass_data_to_eval[\"dev_examples\"]\n\n answers, nbest_answers = get_answers(\n dev_examples, dev_features, result, self._args\n )\n\n output_prediction_file = os.path.join(\n self._args.output_dir, \"predictions.json\"\n )\n output_nbest_file = os.path.join(\n self._args.output_dir, \"nbest_predictions.json\"\n )\n\n with open(output_prediction_file, \"w\") as f:\n f.write(json.dumps(answers, indent=4) + \"\\n\")\n with open(output_nbest_file, \"w\") as f:\n f.write(json.dumps(nbest_answers, indent=4) + \"\\n\")\n\n if self._args.version_2_with_negative:\n dev_file = \"dev-v2.0.json\"\n eval_file = \"evaluate-v2.0.py\"\n else:\n dev_file = \"dev-v1.1.json\"\n eval_file = \"evaluate-v1.1.py\"\n\n command_str = (\n f\"{sys.executable} {os.path.join(self._args.data_dir, eval_file)} \"\n f\"{os.path.join(self._args.data_dir, dev_file)} \"\n f\"{output_prediction_file}\"\n )\n\n logging.debug(f\"\\nExecuting: `{command_str}`\\n\")\n\n eval_out = subprocess.check_output(shlex.split(command_str))\n\n # scores: {'exact_match': 87.06717123935667, 'f1': 92.78048326711645}\n scores = json.loads(eval_out.decode(\"UTF-8\").strip())\n\n logging.debug(\"scores:\", scores)\n\n metric_units = \"f1\"\n\n return scores[metric_units], metric_units","def evaluate(model, val_data, epoch):\n print('validating')\n\n # 设置为评估模式 \n model.eval() \n\n val_loss = []\n with torch.no_grad():\n DEVICE = config.DEVICE\n\n val_dataloader = DataLoader(dataset=val_data,\n batch_size=config.batch_size,\n shuffle=True,\n pin_memory=True, drop_last=True,\n collate_fn=collate_fn)\n\n for batch, data in enumerate(tqdm(val_dataloader)):\n\n x, y, x_len, y_len, oov, len_oovs = data\n\n if config.is_cuda:\n x = x.to(DEVICE)\n y = y.to(DEVICE)\n x_len = x_len.to(DEVICE)\n len_oovs = len_oovs.to(DEVICE)\n\n loss = model(x, x_len, y, len_oovs, batch=batch, \n num_batches=len(val_dataloader),\n teacher_forcing=True)\n\n val_loss.append(loss.item())\n\n return np.mean(val_loss)","def eval(self):\n\n # parameters initialize\n torch = import_optional_dependency(\"torch\")\n eval_total = 0\n eval_correct = 0\n eval_loss = 0\n self._set_eval()\n\n # display the information\n if self.info:\n print(f\"\\rEvaluating...\", end=\"\")\n\n # start eval part\n for i, (source, target) in enumerate(self.eval_dataset):\n # send data to device\n source = source.to(self.device)\n target = target.to(self.device)\n\n result = self.model(source)\n eval_loss += self.criterion(result, target).item()\n _, predicted = torch.max(result.data, 1)\n eval_total += target.size(0)\n eval_correct += (predicted == target).sum().item()\n\n accuracy = eval_correct / eval_total\n eval_loss = eval_loss / eval_total\n\n if self.info:\n print(f\"\\rEvaluation loss: { eval_loss } | Accuracy: { accuracy }\")\n\n return eval_loss, accuracy","def test_epe_evaluate(self):\n epe_metric = EPE()\n epe_metric.process(self.data_batch, self.data_samples)\n epe = epe_metric.evaluate(1)\n self.assertAlmostEqual(epe['EPE'], 11.5355339)","def evaluate(self, epoch, exploration_paths):\n logger.log(\"Collecting samples for evaluation\")\n paths = self._sample_eval_paths(epoch)\n statistics = OrderedDict()\n\n statistics.update(self._statistics_from_paths(paths, \"Test\"))\n statistics.update(self._get_other_statistics())\n statistics.update(self._statistics_from_paths(exploration_paths,\n \"Exploration\"))\n\n statistics['AverageReturn'] = get_average_returns(paths)\n statistics['Epoch'] = epoch\n\n for key, value in statistics.items():\n logger.record_tabular(key, value)\n\n self.log_diagnostics(paths)","def evaluate(model, data):\n n_targets = 0\n n_correct_predictions = 0\n\n # Set the model on evaluatio mode.\n model.eval()\n\n # Create progress bar.\n progress_bar = tqdm.tqdm(total=len(data),\n unit='batch',\n desc='[evaluate] batch accuracy: 0.000',\n leave=False)\n\n # Loop through validation batches.\n for inputs, targets in data:\n\n # Send data to GPU if CUDA is enabled.\n if next(model.parameters()).is_cuda:\n inputs = inputs.cuda()\n targets = targets.cuda()\n\n # Feed forward.\n with torch.set_grad_enabled(False):\n outputs = model(inputs)\n\n # Choose the class with maximum probability.\n _, predictions = torch.max(outputs, 1)\n\n accuracy = (predictions == targets).sum().item() / len(targets)\n progress_bar.update(1)\n progress_bar.set_description(\n '[evaluate] batch accuracy: {accuracy:.3f}'.format(\n accuracy=accuracy))\n\n # Accumulate targets and correct predictions count.\n n_targets += len(targets)\n n_correct_predictions += (predictions == targets).sum().item()\n\n # Close progress bar.\n progress_bar.close()\n\n return n_correct_predictions / n_targets","def test_evaluation_error():\n folding = Folding(DATASET,\n reset=True)\n\n for fold in folding.fold():\n sjob = SamplingJob(fold,\n OVERSAMPLER,\n OVERSAMPLER_PARAMS)\n\n result = sjob.do_oversampling()\n\n ejob = EvaluationJob(result,\n [('smote_variants.classifiers',\n 'ErrorWarningClassifier',\n {'raise_value_error': True})])\n\n result_eval = ejob.do_evaluation()\n\n assert len(result_eval[0]['error']) > 3\n\n ejob = EvaluationJob(result,\n [('smote_variants.classifiers',\n 'ErrorWarningClassifier',\n {'raise_runtime_error': True})])\n\n result_eval = ejob.do_evaluation()\n\n assert len(result_eval[0]['error']) > 3","def evaluate(self, data, category):\n n_data = len(data)\n eval_scores = [{} for _ in range(n_data)]\n\n # Calculate average sentence-level scores for factual consistency\n src_list, output_list = [], []\n n_sents = [] # the number of sentences in the claim\n for i in range(n_data):\n source = data[i]['source']\n system_outputs = sent_tokenize(data[i]['system_output'])\n n_sents.append(len(system_outputs))\n for j in range(len(system_outputs)):\n src_list.append(source)\n output_list.append(system_outputs[j])\n input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task)\n sent_score = self.scorer.score(input_list, self.task, category, self.dim)\n\n # Get average score for each sample\n start_idx = 0\n score = []\n for cur_n_sent in n_sents:\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent)\n start_idx += cur_n_sent\n\n for i in range(n_data):\n eval_scores[i][self.dim] = score[i]\n\n return eval_scores","def evaluate(self, session, *args, evaluate_data_iterator=None, **kwargs):\n\n raise NotImplementedError(\"Implement evaluate() method\")","def evaluate(exe, metric, loss, correct, dev_program, data_loader,\n phase=\"eval\"):\n metric.reset()\n returns = [loss]\n if isinstance(correct, list) or isinstance(correct, tuple):\n returns.extend(list(correct))\n else:\n returns.append(correct)\n for batch in data_loader:\n exe.run(dev_program, feed=batch, \\\n fetch_list=returns)\n return_numpys = exe.run(dev_program, feed=batch, \\\n fetch_list=returns)\n metric_numpy = return_numpys[1] if len(return_numpys[\n 1:]) == 1 else return_numpys[1:]\n metric.update(metric_numpy)\n res = metric.accumulate()\n if isinstance(metric, Mcc):\n print(\"%s loss: %f, mcc: %s\" % (phase, return_numpys[0], res[0]))\n elif isinstance(metric, PearsonAndSpearman):\n print(\"%s loss: %f, pearson: %s, spearman: %s, pearson and spearman: %s\"\n % (phase, return_numpys[0], res[0], res[1], res[2]))\n else:\n print(\"%s loss: %f, acc: %s, \" % (phase, return_numpys[0], res))","def evaluate(self):\n try:\n self._evaluate()\n except Exception as e:\n if str(e) == \"assignment destination is read-only\":\n log.exception(\n \"Encountered error during scenario evaluation. Be sure \"\n + \"that the classifier's predict() isn't directly modifying the \"\n + \"input variable itself, as this can cause unexpected behavior in ART.\"\n )\n else:\n log.exception(\"Encountered error during scenario evaluation.\")\n sys.exit(1)\n\n if self.results is None:\n log.warning(f\"{self._evaluate} did not set self.results to a dict\")\n\n self.save()","def evaluate(self, X_test, y_test):\n y_pred_train = self.pipeline.predict(self.X)\n mse_train = mean_squared_error(self.y, y_pred_train)\n rmse_train = np.sqrt(mse_train)\n \n self.mlflow_log_metric('rmse_train', rmse_train)\n \n y_pred_test = self.pipeline.predict(X_test)\n mse_test = mean_squared_error(y_test, y_pred_test)\n rmse_test = np.sqrt(mse_test)\n self.mlflow_log_metric('rmse_test', rmse_test)\n \n return (round(rmse_train, 3) ,round(rmse_test, 3))","def evaluate(self, data, label_indices, evaluator=np.argmax):\n\n assert(len(data) == len(label_indices))\n\n assert(len(data) == len(label_indices))\n assert(data[0].shape == (self.sizes[0], 1))\n for idx in label_indices:\n assert(idx >= 0 and idx < self.sizes[-1])\n\n results = [(evaluator(self.feedforward(x)), y)\n for (x, y) in zip(data, label_indices)]\n\n num = len(data)\n num_correct = sum(int(x == y) for (x, y) in results)\n num_incorrect = num - num_correct\n accuracy = num_correct / num\n\n return {'num_testing': len(data),\n 'num_correct': num_correct,\n 'num_incorrect': num_incorrect,\n 'accuracy': accuracy}","def _evaluate(self,\n logits,\n predictions,\n data, evaluation_fn,\n max_eval_batches=None,\n calculate_scores=True,\n write_results=False):\n # counting the evaluation batches\n num_eval_batches = 0\n # logits and predictions from the model\n all_logits = []\n all_predictions = []\n # fetched data that led to the predictions\n # dictionary of {seq_1: [], seq_2: [], target: []}\n all_fetched_data = collections.defaultdict(list)\n try:\n while True:\n # sample predictions\n (fetched_logits,\n fetched_predictions,\n fetched_data) = self._fetch_data_batch(\n logits=logits, predictions=predictions, data=data)\n\n # Cache the data\n all_logits += fetched_logits\n all_predictions += fetched_predictions\n all_fetched_data[\"target\"] += fetched_data[\"target\"]\n\n # break the loop if max_eval_batches is set\n num_eval_batches += 1\n if (max_eval_batches and\n num_eval_batches >= max_eval_batches):\n break\n\n except tf.errors.OutOfRangeError:\n pass\n\n # Evaluate\n scores = None\n if calculate_scores:\n scores = evaluation_fn(\n all_predictions,\n all_fetched_data[\"seq_1\"], # Should be empty\n all_fetched_data[\"seq_2\"], # Should be empty\n all_fetched_data[\"target\"])\n\n if write_results:\n _write_results_to_csv(\n all_logits,\n all_predictions,\n all_fetched_data,\n output_dir=os.path.join(\n self._logdir, RESULTS_CSV_FNAME))\n\n return len(all_predictions), scores","def evaluate_model(model_name, y_true, y_pred):\n\n # Calculate performance metrics\n rmse_eval = evaluate_rmse(y_true, y_pred)\n mae_eval = evaluate_mae(y_true, y_pred) \n r2_eval = evaluate_r2(y_true, y_pred)\n\n # Print results\n print_evaluation(model_name, mae_eval, rmse_eval, r2_eval)","def main(cfg: DictConfig):\n result, evaluation_metric = evaluate(cfg)\n print(result)\n print(f\"Validation dice coefficient: {result[evaluation_metric]}\")","def evaluate(self, X_test, y_test):\n self.run(self)\n self.y_pred = self.pipeline.predict(X_test)\n self.rmse = compute_rmse(self.y_pred, y_test)","def mgcEval(self):\n import numpy as np\n def report_to_df(report):\n\n \"\"\"\n function to convert classification report to dataframe (for visualisation plot)\n \"\"\"\n\n report = re.sub(r\" +\", \" \", report).replace(\"avg / total\", \"avg/total\").replace(\"\\n \", \"\\n\")\n # update this due to sklearn classification report output change\n report = re.sub(r\" +\", \" \", report).replace(\"micro avg\", \"micro_avg\").replace(\"macro avg\", \"macro_avg\").replace(\"weighted avg\", \"weighted_avg\").replace(\"\\n \", \"\\n\")\n report_df = pd.read_csv(StringIO(\"Classes\" + report), sep=' ', index_col=0) \n return(report_df)\n \n #txt report to df\n class_rpttop1 = classification_report(self.y_true, self.y_pred)\n df_report = report_to_df(class_rpttop1)\n\n df_report = df_report.iloc[:self.nb_classes, :].copy()\n df_report.index = df_report.index.astype(int)\n \n\n # classifier prediction metrics\n def classMetrics(averagex):\n precision, recall, fscore, support = score(self.y_true, self.y_pred, average=averagex)\n \n return(\n print(''), \n print('-------------{0:}--------------------'.format(averagex)), \n print('precision: {0:.4f}'.format(precision)),\n print('recall: {0:.4f}'.format(recall)),\n print('fscore: {0:.4f}'.format(fscore)),\n print(''),\n print('kappa score: {0:.4f}'.format(cohen_kappa_score(self.y_true, self.y_pred))),\n print('accuracy score: {0:.4f}'.format(accuracy_score(self.y_true, self.y_pred))))\n \n def predSamp():\n\n correct = np.nonzero(self.y_pred==self.y_true)[0]\n incorrect = np.nonzero(self.y_pred!=self.y_true)[0]\n\n # quick check of the number of correct prediction from validation set\n print(\"\")\n print(\"correct/total = {0: .4f}\".format(len(correct)/(len(correct)+len(incorrect))))\n print(\"total correct sample = {0: .0f}\".format(len(correct)))\n print('------------------------------------------------------------------')\n \n def classReport():\n print('----------------------------- Classfication Report -------------------------------')\n print(classification_report(pd.Series(self.y_true).map(self.dict_label), pd.Series(self.y_pred).map(self.dict_label)))\n \n self.class_rpt = pd.concat([pd.DataFrame(pd.Series(df_report.index.tolist()).map(self.dict_label), columns = ['label']), df_report], axis = 1)\n \n self.classMetricsMac = classMetrics(\"macro\")\n self.classMetricsMic = classMetrics(\"micro\")\n self.predSample = predSamp()\n self.class_rptTop1 = classReport()\n \n return self","def train_and_evaluate(name, model, train, test, evaluation, final_eval, output_dir):\n\n print(\"---\" * 5)\n print(\"Running pipeline for {}\".format(name))\n\n plot_dir = os.path.join(output_dir, \"plots\")\n\n pipeline = make_pipeline(model)\n\n X_train, y_train = train.drop(\n [\"PM10\"], axis=1).values, train[\"PM10\"].values\n X_test, y_test = test.drop([\"PM10\"], axis=1).values, test[\"PM10\"].values\n X_eval, y_eval = evaluation.drop(\n [\"PM10\"], axis=1).values, evaluation[\"PM10\"].values\n X_final, y_final = final_eval.drop(\n [\"PM10\"], axis=1), final_eval[\"PM10\"].values\n\n # first round - fit on train, predict on test\n print(\"Fitting pipeline on train data\")\n pipeline.fit(X_train, y_train)\n yhat = pipeline.predict(X_test)\n mae = mean_absolute_error(y_test, yhat)\n print(\"MAE: {}\".format(mae))\n plot_predictions(\n y_test, yhat, title=\"{} - Predicted vs. Actual on Test\".format(name), output_dir=plot_dir)\n\n # second round - fit on train + test, predict on evaluation\n X_train = np.concatenate([X_train, X_test])\n y_train = np.concatenate([y_train, y_test])\n print(\"Fitting pipeline on train + test data\")\n pipeline.fit(X_train,y_train)\n yhat = pipeline.predict(X_eval)\n mae = mean_absolute_error(y_eval,yhat)\n print(\"MAE: {}\".format(mae))\n plot_predictions(y_eval,yhat,title=\"{} - Predicted vs. Actual on Evaluation\".format(name),output_dir=plot_dir)\n\n # final round - fit on last X hours, by which the actual score will be measured\n X_train = np.concatenate([X_train, X_eval])\n y_train = np.concatenate([y_train, y_eval])\n print(\"Fitting pipeline on all \\\"all available data\\\"\")\n pipeline.fit(X_train, y_train)\n yhat = pipeline.predict(X_final)\n mae = mean_absolute_error(y_final, yhat)\n print(\"MAE: {}\".format(mae))\n plot_predictions(\n y_final, yhat, title=\"{} - Predicted vs. Actual\".format(name), output_dir=plot_dir)\n\n # save the model\n joblib.dump(model, os.path.join(\n output_dir, \"models\", \"{}.joblib\".format(name)))\n\n return yhat, mae","def evaluate(network: nn.Module, data: DataLoader, metric: callable) -> list:\n error_list = []\n with torch.no_grad():\n for x, y in data:\n \n prediction = network.forward(x)\n error_list.append(metric(prediction, y))\n \n return torch.tensor(error_list)","def evaluate(self,\n data: Union[\"SparkXShards\",\n \"SparkDataFrame\",\n \"TFDataset\",\n \"ray.data.Dataset\",\n Callable],\n batch_size: int=32,\n num_steps: Optional[int]=None,\n verbose: Union[str, int]=1,\n sample_weight: Optional[\"np.ndarray\"]=None,\n callbacks: Optional[List[\"Callback\"]]=None,\n data_config: Optional[Dict]=None,\n feature_cols: Optional[List[str]]=None,\n label_cols: Optional[List[str]]=None) -> Dict:\n if not isinstance(data, types.FunctionType):\n invalidInputError(isinstance(batch_size, int) and batch_size > 0,\n \"batch_size should be a positive integer\")\n else:\n # batch_size can be None if the return of data_creator already generates batches\n if batch_size:\n invalidInputError(isinstance(batch_size, int) and batch_size > 0,\n \"batch_size should be a positive integer\")\n # Use the local batch size for each worker to convert to XShards\n if batch_size:\n local_batch_size = batch_size // self.num_workers\n if local_batch_size <= 0:\n local_batch_size = 1\n else:\n local_batch_size = None\n logger.info(\"Starting validation step.\")\n params = dict(\n batch_size=batch_size,\n verbose=verbose,\n sample_weight=sample_weight,\n steps=num_steps,\n callbacks=callbacks,\n data_config=data_config,\n )\n from bigdl.orca.data import SparkXShards\n from bigdl.orca.data.tf.data import Dataset\n from bigdl.orca.data.tf.tf2_data import TF2Dataset\n\n data, _ = maybe_dataframe_to_xshards(data,\n validation_data=None,\n feature_cols=feature_cols,\n label_cols=label_cols,\n mode=\"evaluate\",\n num_workers=self.num_workers,\n accept_str_col=True,\n shard_size=local_batch_size)\n\n if isinstance(data, SparkXShards):\n # Make sure each worker can get at least one data partition\n if data.num_partitions() < self.num_workers:\n data = data.repartition(self.num_workers)\n if data._get_class_name() == 'pandas.core.frame.DataFrame':\n data = process_xshards_of_pandas_dataframe(data, feature_cols, label_cols)\n ray_xshards = RayXShards.from_spark_xshards(data) # type:ignore\n worker_stats = self._evaluate_ray_xshards(ray_xshards, params)\n elif isinstance(data, Dataset):\n ray_xshards = TF2Dataset(data).get_ray_xshards(self.num_workers)\n worker_stats = self._evaluate_ray_xshards(ray_xshards, params)\n elif isinstance(data, ray.data.Dataset):\n shards = data.split(n=self.num_workers, locality_hints=self.remote_workers)\n\n remote_worker_stats = []\n for shard, worker in zip(shards, self.remote_workers):\n params[\"data_creator\"] = self.process_ray_dataset(shard,\n label_cols,\n feature_cols,\n data_config)\n remote_worker_stats.append(worker.validate.remote(**params))\n worker_stats = ray.get(remote_worker_stats)\n else: # data_creator functions; should return Iter or DataLoader\n params[\"data_creator\"] = data # type:ignore\n params_list = [params] * self.num_workers\n\n worker_stats = ray.get([w.validate.remote(**params_list[i])\n for i, w in enumerate(self.remote_workers)])\n stats = worker_stats[0].copy()\n return stats","def post_eval_callout(outdir, X_data, Y_pred, E_pred, N_SP_pred, Y_data=None , tail=None) -> None:\n \n error = np.sum((Y_pred[:,0:2] - Y_data[:, 0:2])**2, axis = 1)\n error = np.reshape(error, (error.shape[0],1))\n I1, _, E = compute_l2_norm(error, X_data[:,1:3], outdim=2)\n\n data = {}\n data['E_init'] = E_pred[0]\n data['E_PI'] = E_pred[1]\n data['N_SP'] = int(N_SP_pred)\n data['E_ref'] = np.sqrt(I1+ E)\n\n print(os.path.join(outdir, \"run_\" + get_prefix() +tail[:-4] + \"_error.json\"))\n out_file = open( os.path.join(outdir,\"..\", \"run_\"+get_prefix()+\"values\", \"run_\" + get_prefix() +tail[:-4] + \"_error.json\"), \"w\")\n json.dump(data, out_file, indent=4)\n out_file.close()\n \n return","def evaluate(self) -> Dict[str, float]:\n eval_dataloader = self.get_eval_dataloader()\n\n output = self._prediction_loop(eval_dataloader, description=\"Evaluation\")\n return output.metrics","def evaluate_model():\n\n print '\\n\\tevaluate result'\n os.system('./conlleval.pl -d \\'\\t\\' < ' + encoded_test + ' >> ' + result_file)\n print '\\t--done\\n'","def test_run_experiment_lr_eval_with_object(self):\n source = \"lr-eval-object\"\n experiment_id = \"lr_eval_object\"\n\n configdir = join(rsmtool_test_dir, \"data\", \"experiments\", source)\n\n config_dict = {\n \"predictions_file\": \"../../files/predictions_scaled_with_subgroups.csv\",\n \"system_score_column\": \"score\",\n \"description\": \"An evaluation of LinearRegression predictions.\",\n \"human_score_column\": \"h1\",\n \"id_column\": \"id\",\n \"experiment_id\": \"lr_eval_object\",\n \"subgroups\": \"QUESTION\",\n \"scale_with\": \"asis\",\n \"trim_min\": 1,\n \"trim_max\": 6,\n }\n\n config_obj = Configuration(config_dict, context=\"rsmeval\", configdir=configdir)\n\n check_run_evaluation(source, experiment_id, config_obj_or_dict=config_obj)","def evaluate_model():\n\n # Get the processed data (in proper format to evaluate the NER model)\n data = get_json_from_file_path(PROCESSED_DATA_PATH)\n # Split the dataset for training and test as we did for training\n train_data, test_data = train_test_split(data, train_size=0.7, \n random_state=4)\n\n # Load the model trained\n try:\n ner_model = spacy.load(OUTPUT_MODEL_PATH)\n except Exception as err:\n msg = f'Could not load the model. Error: {err}'\n raise Exception(msg)\n\n # Compute evaluation scores\n print('Computing metrics...')\n scores = evaluate(ner_model, test_data)\n # General metrics of the model\n f_score = scores.get('ents_f')\n precision = scores.get('ents_p')\n recall = scores.get('ents_r')\n print('\\nScoring:')\n print(f'F-score: {f_score}')\n print(f'Precision: {precision}')\n print(f'Recall: {recall}')\n\n # Get the specific scores for each entity \n scores_per_entity = scores.get('ents_per_type')\n # Get the F-score of the entities\n f_scores_of_entities = []\n for entity_scores in scores_per_entity.values():\n f_scores_of_entities.append(entity_scores['f'])\n # Compute the macro averaged F-score\n macro_avg_f_score = sum(f_scores_of_entities)/len(f_scores_of_entities)\n print(f'Macro averaged F-score: {macro_avg_f_score}')\n \n print('\\nScores per entity;')\n print('{:<15} {:<10} {:<10} {:<10}'.format('Entity','F-score','Precision','Recall'))\n for key, value in scores_per_entity.items():\n entity = key\n f, p, r = value['f'], value['p'], value['r']\n print('{:<15} {:<10.2f} {:<10.2f} {:<10.2f}'.format(entity, f, p, r))","def runEval(self, cmdText, varName, rhs):\n\n _globals= self._globals\n\n self.logPyCode= '# Error: ' + str(varName) + '=' + str(rhs)\n try:\n rhs= self.pyFromVec(rhs)\n except:\n print(\"Error: runEval at pyFromVec: \" + rhs)\n\n try:\n rhs= self.pyFromEng(rhs)\n except:\n print(\"Error: runEval at pyFromEng: \" + rhs)\n\n saved_handler = np.seterrcall(self.err_handler)\n save_err = np.seterr(divide='call', over='call', under='call',\n invalid='call')\n\n if varName:\n self.logPyCode= str(varName) + '=' + str(rhs)\n else:\n self.logPyCode= str(rhs)\n\n try:\n result= eval(str(rhs), _globals, _globals)\n _globals[str(varName)]= result\n success= True\n except:\n self.logCommandRHS= '# Error: ' + self.logPyCode\n result= 0\n success= False\n np.seterrcall(saved_handler)\n return result, success","def do_system_evaluation(dataset, result_path, dataset_evaluation_mode='folds'):\n\n # Set warnings off, sklearn metrics will trigger warning for classes without\n # predicted samples in F1-scoring. This is just to keep printing clean.\n warnings.simplefilter(\"ignore\")\n\n overall_metrics_per_scene = {}\n\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n if scene_label not in overall_metrics_per_scene:\n overall_metrics_per_scene[scene_label] = {}\n\n dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\n dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\n\n for fold in dataset.folds(mode=dataset_evaluation_mode):\n results = []\n result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)\n\n if os.path.isfile(result_filename):\n with open(result_filename, 'rt') as f:\n for row in csv.reader(f, delimiter='\\t'):\n results.append(row)\n else:\n raise IOError(\"Result file not found [%s]\" % result_filename)\n\n for file_id, item in enumerate(dataset.test(fold, scene_label=scene_label)):\n current_file_results = []\n for result_line in results:\n if len(result_line) != 0 and result_line[0] == dataset.absolute_to_relative(item['file']):\n current_file_results.append(\n {'file': result_line[0],\n 'event_onset': float(result_line[1]),\n 'event_offset': float(result_line[2]),\n 'event_label': result_line[3].rstrip()\n }\n )\n meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))\n\n dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\n dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\n\n overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()\n overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()\n\n print \" Evaluation over %d folds\" % dataset.fold_count\n print \" \"\n print \" Results per scene \"\n print \" {:18s} | {:5s} | | {:39s} \".format('', 'Main', 'Secondary metrics')\n print \" {:18s} | {:5s} | | {:38s} | {:14s} | {:14s} | {:14s} \".format('', '', 'Seg/Overall','Seg/Class', 'Event/Overall','Event/Class')\n print \" {:18s} | {:5s} | | {:6s} : {:5s} : {:5s} : {:5s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} |\".format('Scene', 'ER', 'F1', 'ER', 'ER/S', 'ER/D', 'ER/I', 'F1', 'ER', 'F1', 'ER', 'F1', 'ER')\n print \" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\"\n averages = {\n 'segment_based_metrics': {\n 'overall': {\n 'ER': [],\n 'F': [],\n },\n 'class_wise_average': {\n 'ER': [],\n 'F': [],\n }\n },\n 'event_based_metrics': {\n 'overall': {\n 'ER': [],\n 'F': [],\n },\n 'class_wise_average': {\n 'ER': [],\n 'F': [],\n }\n },\n }\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n print \" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:5.2f} : {:5.2f} : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\".format(scene_label,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'] * 100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['S'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['D'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['I'],\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'],\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'],\n )\n averages['segment_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'])\n averages['segment_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'])\n averages['segment_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'])\n averages['segment_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F'])\n averages['event_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'])\n averages['event_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F'])\n averages['event_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\n averages['event_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F'])\n\n print \" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\"\n print \" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:21s} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\".format('Average',\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\n numpy.mean(averages['segment_based_metrics']['overall']['F'])*100,\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\n ' ',\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['F'])*100,\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['ER']),\n numpy.mean(averages['event_based_metrics']['overall']['F'])*100,\n numpy.mean(averages['event_based_metrics']['overall']['ER']),\n numpy.mean(averages['event_based_metrics']['class_wise_average']['F'])*100,\n numpy.mean(averages['event_based_metrics']['class_wise_average']['ER']),\n )\n\n print \" \"\n # Restore warnings to default settings\n warnings.simplefilter(\"default\")\n print \" Results per events \"\n\n for scene_id, scene_label in enumerate(dataset.scene_labels):\n print \" \"\n print \" \"+scene_label.upper()\n print \" {:20s} | {:30s} | | {:15s} \".format('', 'Segment-based', 'Event-based')\n print \" {:20s} | {:5s} : {:5s} : {:6s} : {:5s} | | {:5s} : {:5s} : {:6s} : {:5s} |\".format('Event', 'Nref', 'Nsys', 'F1', 'ER', 'Nref', 'Nsys', 'F1', 'ER')\n print \" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\"\n seg_Nref = 0\n seg_Nsys = 0\n\n event_Nref = 0\n event_Nsys = 0\n for event_label in sorted(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise']):\n print \" {:20s} | {:5d} : {:5d} : {:4.1f} % : {:5.2f} | | {:5d} : {:5d} : {:4.1f} % : {:5.2f} |\".format(event_label,\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref']),\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys']),\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['ER'],\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref']),\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys']),\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['ER'])\n seg_Nref += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref'])\n seg_Nsys += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys'])\n\n event_Nref += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref'])\n event_Nsys += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys'])\n print \" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\"\n print \" {:20s} | {:5d} : {:5d} : {:14s} | | {:5d} : {:5d} : {:14s} |\".format('Sum',\n seg_Nref,\n seg_Nsys,\n '',\n event_Nref,\n event_Nsys,\n '')\n print \" {:20s} | {:5s} {:5s} : {:4.1f} % : {:5.2f} | | {:5s} {:5s} : {:4.1f} % : {:5.2f} |\".format('Average',\n '', '',\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\n '', '',\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\n print \" \"","def eval(approach, datapath, incremental):\n param_grid = approaches[approach] if not incremental else params[approach]\n report_path = evaluate(datapath,\n approach=approach,\n param_grid=param_grid,\n incremental=incremental)\n c.echo('Report compiled at {0}.'.format(report_path))","def evaluate(pred, ground_truth, target, count_to_level = False, Y_name = None, thresholds = None, print_metrics=True):\n \n if target == 'count':\n \n # fill NaNs with zeroes\n pred = pred.fillna(method = \"ffill\")\n pred = pred.fillna(method = \"bfill\")\n ground_truth = ground_truth.fillna(method = \"ffill\")\n ground_truth = ground_truth.fillna(method = \"bfill\")\n \n # Set negative predictions to zero\n pred[pred < 0] = 0\n ground_truth[ground_truth < 0] = 0\n \n # Calculate error metrics\n rmse = mean_squared_error(ground_truth, pred, squared=False)\n mae = mean_absolute_error(ground_truth, pred)\n \n # Calculate error metrics only for crowded moments (p75) \n busy = np.percentile(ground_truth, 75)\n ground_truth_busy = ground_truth[ground_truth > busy].dropna()\n pred_busy = pred[ground_truth > busy].dropna()\n rmse_busy = mean_squared_error(ground_truth_busy, pred_busy, squared=False)\n mae_busy = mean_absolute_error(ground_truth_busy, pred_busy)\n \n # Store error metrics in dict\n error_metrics = dict({'rmse': rmse, 'rmse_busy': rmse_busy, 'mae': mae, 'mae_busy': mae_busy})\n \n if print_metrics:\n print(f\"Root mean squared error: {rmse.round(1)}\")\n print(f\"Root mean squared error (crowded): {rmse_busy.round(1)}\")\n print(f\"Mean absolute error: {mae.round(1)}\")\n print(f\"Mean absolute error (crowded): {mae_busy.round(1)}\")\n \n if count_to_level:\n pred = get_crowd_levels(pred, Y_name, thresholds)\n ground_truth = get_crowd_levels(ground_truth, Y_name, thresholds)\n \n # Confusion matrix\n conf_mat = confusion_matrix(ground_truth, pred)\n \n error_metrics['conf_mat'] = conf_mat\n \n elif target == \"level\":\n \n # Set dtype to category\n pred = pred.astype('category')\n ground_truth = ground_truth.astype('category')\n \n # Forward fill NaNs\n pred = pred.fillna(method = \"ffill\")\n ground_truth = ground_truth.fillna(method = \"ffill\")\n \n # Confusion matrix\n conf_mat = confusion_matrix(ground_truth, pred)\n \n # Classification report (recall, precision, F1)\n class_report = classification_report(ground_truth, pred, output_dict = True)\n class_report = pd.DataFrame(class_report).transpose()\n \n error_metrics = dict({\"conf_mat\": conf_mat, \"class_report\": class_report})\n \n if print_metrics:\n print(f\"Confusion matrix: {conf_mat}\")\n print(f\"Classification report: {class_report}\")\n \n return error_metrics","def evaluate(self, X_test, y_test):\n pipeline = run()\n y_pred = pipeline.predict(X_test)\n rmse = compute_rmse(y_pred, y_test)\n print(rmse)\n return rmse","def evaluator(model, config, test_dir=None):\n shottype = config.shottype\n dataset = config.data_set\n seed = config.seed\n if test_dir is None:\n test_data_gen_dir, _, _ = _generator_dir(\n config=config, target_gen=\"test\", data_dir=None\n )\n if test_dir is not None:\n print(\"Evaluating directory: '{}'.\".format(test_dir))\n test_data_gen_dir, _, _ = _generator_dir(\n config=config, target_gen=\"test\", data_dir=test_dir\n )\n score = model.evaluate_generator(test_data_gen_dir)\n print(\n \"Test metrics: \"\n \"Loss: {:.4f}, \"\n \"Accuracy: {:.4f}, \"\n \"Top 3 accuracy: {:.4f}\".format(score[0], score[1], score[2])\n )\n return score","def evaluate(self, params):\n model = self.train(params)\n Y_pred = model.predict(self.X_test)\n\n if self.dataset_type == PROBLEM.CLASSIFICATION:\n return {\n 'score': accuracy_score(self.Y_test, Y_pred),\n 'matrix': confusion_matrix(self.Y_test, Y_pred).tolist(),\n 'report': classification_report(self.Y_test, Y_pred,\n target_names=self.labels,\n zero_division=1)\n }\n else:\n return {\n 'max_error': max_error(self.Y_test, Y_pred),\n 'mae': mean_absolute_error(self.Y_test, Y_pred),\n 'mse': mean_squared_error(self.Y_test, Y_pred)\n }","def run(self):\n logging.info('running experiment...')\n self._prepare()\n self._load_data()\n self._run()\n self._evaluate()\n self._summarise()\n return True","def run_analyses(y_predict_train, y_train, y_predict, y_test):\n # calculate metrics\n _, training_error = output_error(y_predict_train, y_train)\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\n \n # print out metrics\n print 'Average Precision:', np.average(precision)\n print 'Average Recall:', np.average(recall)\n print 'Average F1:', np.average(f1)\n print 'Training Error:', training_error\n print 'Testing Error:', testing_error","def print_evaluation(model_name, mae_eval, rmse_eval, r2_eval):\n\n print(model_name, \"rmse (Eval):\", rmse_eval)\n print(model_name, \"mae (Eval):\", mae_eval)\n print(model_name, \"r2 (Eval):\", r2_eval)","def evaluate(dataset, predictions, nms_thresh, recall_metrics=(1,5), iou_metrics=(0.3,0.5,0.7)):\n dataset_name = dataset.__class__.__name__\n logger = logging.getLogger(\"tcn-vmr.inference\")\n logger.info(\"Performing {} evaluation (Size: {}).\".format(dataset_name, len(dataset)))\n \n num_recall_metrics, num_iou_metrics = len(recall_metrics), len(iou_metrics)\n table = [['Rank@{},mIoU@{}'.format(i,j) \\\n for i in recall_metrics for j in iou_metrics]]\n \n recall_metrics = torch.tensor(recall_metrics)\n iou_metrics = torch.tensor(iou_metrics)\n recall_x_iou = torch.zeros(num_recall_metrics, num_iou_metrics)\n\n num_clips = predictions[0].shape[-1]\n\n\n for idx, score2d in tqdm(enumerate(predictions)): \n duration = dataset.get_duration(idx)\n moment = dataset.get_moment(idx) \n\n candidates, scores = score2d_to_moments_scores(score2d, num_clips, duration)\n moments = nms(candidates, scores, topk=recall_metrics[-1], thresh=nms_thresh)\n\n for i, r in enumerate(recall_metrics):\n mious = iou(moments[:r], dataset.get_moment(idx))\n bools = mious[:,None].expand(r, num_iou_metrics) > iou_metrics\n recall_x_iou[i] += bools.any(dim=0)\n\n recall_x_iou /= len(predictions)\n\n table.append(['{:.02f}'.format(recall_x_iou[i][j]*100) \\\n for i in range(num_recall_metrics) for j in range(num_iou_metrics)])\n\n \n table = AsciiTable(table)\n for i in range(num_recall_metrics*num_iou_metrics):\n table.justify_columns[i] = 'center'\n\n logger.info('\\n' + table.table)","def dependent_error_exp(data, weak_signal_data, num_weak_signal):\n\n w_model = train_weak_signals(data, weak_signal_data, num_weak_signal)\n\n training_data = data['training_data'][0].T\n training_labels = data['training_data'][1]\n val_data, val_labels = data['validation_data']\n val_data = val_data.T\n test_data = data['test_data'][0].T\n test_labels = data['test_data'][1]\n\n num_features, num_data_points = training_data.shape\n\n weak_signal_ub = w_model['error_bounds']\n weak_signal_probabilities = w_model['probabilities']\n weak_test_accuracy = w_model['test_accuracy']\n\n weights = np.zeros(num_features)\n\n print(\"Running tests...\")\n\n optimized_weights, ineq_constraint = train_all(val_data, weights, weak_signal_probabilities, weak_signal_ub, max_iter=5000)\n\n # calculate test probabilities\n test_probabilities = probability(test_data, optimized_weights)\n # calculate test accuracy\n test_accuracy = getModelAccuracy(test_probabilities, test_labels)\n\n print(\"\")\n print(\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n print(\"Experiment %d\"%num_weak_signal)\n print(\"We trained %d learnable classifiers with %d weak signals\" %(1, num_weak_signal))\n print(\"The accuracy of the model on the test data is\", test_accuracy)\n print(\"The accuracy of weak signal(s) on the test data is\", weak_test_accuracy)\n print(\"\")\n\n # calculate ge criteria\n print(\"Running tests on ge criteria...\")\n model = ge_criterion_train(val_data.T, val_labels, weak_signal_probabilities, num_weak_signal)\n ge_test_accuracy = accuracy_score(test_labels, np.round(probability(test_data, model)))\n print(\"The accuracy of ge criteria on test data is\", ge_test_accuracy)\n print(\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n\n # calculate baseline\n print(\"Running tests on the baselines...\")\n baselines = runBaselineTests(val_data, weak_signal_probabilities)\n b_test_accuracy = getWeakSignalAccuracy(test_data, test_labels, baselines)\n print(\"The accuracy of the baseline models on test data is\", b_test_accuracy)\n print(\"\")\n\n output = {}\n output['ALL'] = test_accuracy\n output['WS'] = w_model['test_accuracy'][-1]\n output['GE'] = ge_test_accuracy\n output['AVG'] = b_test_accuracy[-1]\n\n return output","def evaluate(model, test_files):\n print(\"Running predictions.\")\n models = load_model(model)\n predictions = predict(models, test_files)\n\n # # write predictions to file\n # write_predictions(\"evaluate_out.json\",predictions)\n evaluate_individual(predictions, test_files, models)\n evaluate_overall(predictions)","def print_eval(trainset, testset, exptypes=EXPTYPES, semantic=False, savemodels=False, loadmodels=False, deprep=False, externals=True, predict=True):\n system_pairs = []\n print \"== cleaning lsts ==\"\n cleanupnonespanexpressions(testset)\n cleanholdercandidates(testset)\n cleanholders(testset)\n cleanupnonespanexpressions(trainset)\n cleanholdercandidates(trainset)\n cleanholders(trainset)\n \n print \"== train ==\"\n ev = evaluate()\n features, labels, stats = getfeaturesandlabels(trainset, semantic=semantic, predict=False)\n print counters, '\\n'\n\n print \"== test ==\"\n counters.clear()\n ftest, ltest, stest = getfeaturesandlabels(testset, semantic=semantic, predict=predict)\n print counters\n for exp in exptypes:\n vec, X, y = create_matrix(features[exp], labels[exp])\n if externals:\n vecw, Xw, yw = create_matrix(features[exp + 'w'], labels[exp + 'w'])\n vecimp, Ximp, yimp = create_matrix(features[exp + 'w'], labels[exp + 'implicit'])\n if loadmodels:\n clf = read_model(loadmodels + exp)\n else:\n clf = create_model(X, y)\n if externals:\n clfw = create_model(Xw, yw)\n clfimp = create_model(Ximp, yimp)\n if savemodels:\n write_model(clf, savemodels + exp)\n print \"== eval ==\"\n if deprep:\n print \"== {} ==\".format(deprep)\n Xt, yt = transform_to_matrix(ftest[exp], ltest[exp], vec)\n if externals:\n Xtw, ytw = transform_to_matrix(ftest[exp + 'w'], ltest[exp + 'w'], vecw)\n Xtimp, ytimp = transform_to_matrix(ftest[exp + 'w'], ltest[exp + 'implicit'], vecimp)\n results = clf.predict_proba(Xt)\n s_p_w = False\n s_p_imp = False\n gold_p1 = ev.get_unique_exp(copy.deepcopy(stest['positions'][exp + 'w']), exp, count=False)\n gold_p2 = copy.deepcopy(gold_p1)\n gold_p3 = copy.deepcopy(gold_p1)\n if clfw:\n resultsw = clfw.predict_proba(Xtw)\n s_p_w=ev.get_system_pairs_prob(stest['positions'][exp + 'w'], resultsw, gold_p1)\n counters['s_p_w' + exp] = len(s_p_w)\n if DEBUG:\n print \"RESULTSW\"\n print resultsw\n if clfimp:\n resultsimp = clfimp.predict_proba(Xtimp)\n s_p_imp=ev.get_system_pairs_prob(stest['positions'][exp + 'implicit'], resultsimp, gold_p2)\n counters['s_p_imp' + exp] = len(s_p_imp)\n if DEBUG:\n print \"RESULTSIMP\"\n print resultsimp\n s_p_int=ev.get_system_pairs_prob(stest['positions'][exp], results, gold_p3)\n counters['s_p_int' + exp] = len(s_p_int)\n system_pairs_exp = ev.merge_system_pairs(s_p_int, s_p_imp=s_p_imp, s_p_w=s_p_w)\n counters['system_pairs_all' + exp] = len(system_pairs_exp)\n for pair in system_pairs_exp:\n if 'confidence' in pair and pair['confidence'] > 0:\n counters['system_pairs' + exp] += 1\n if predict:\n ssc_exp = ev.spansetcoverage_o_p(system_pairs_exp, exptype=exp)\n print \"system exp - {}:\\n{}\".format(exp, prf_prettystring(ssc_exp))\n else:\n ssc_exp = ev.spansetcoverage_o_p(system_pairs_exp, exptype=exp)\n print \"gold exp - {}:\\n{}\".format(exp, prf_prettystring(ssc_exp))\n system_pairs.extend(system_pairs_exp)\n if predict:\n ssc = ev.spansetcoverage_o_p(system_pairs)\n print \"system exp - all:\\n\", prf_prettystring(ssc)\n else:\n ssc = ev.spansetcoverage_o_p(system_pairs)\n print \"gold exp - all: \\n\", prf_prettystring(ssc)\n \n for k,v in sorted(counters.items(), key=lambda x: x[0]):\n print k, v\n if isinstance(deprep, basestring):\n dump_jsonfile(system_pairs, 'system_pairs-' + deprep + '.json')\n return {'stats': stest, 'system_pairs': system_pairs}","def _evaluate_loss(self, batch_data, evaluate_settings=None, inference_mode=None):\n\n if not (type(inference_mode) is bool):\n raise TrainerStateInvalidException(\"Inference mode is not set\")\n\n if not inference_mode:\n # @tf.function on the train step level\n return self._call_model(batch_data, evaluate_settings, inference_mode)\n else:\n return self._call_model_tf_func(batch_data, evaluate_settings, tf.constant(inference_mode, dtype=tf.bool))","def test(self, test_data_path):\n test_data = read_datafile(test_data_path)\n test_data = self.preprocessor.preprocess(test_data)\n self.run(test_data)\n if check_format(self.get_result_path()):\n thresholds, precisions, avg_precision, reciprocal_rank, num_relevant = evaluate(test_data_path,\n self.get_result_path())\n return avg_precision","def evaluate(input_path, model_path, metrics_path):\n\n logger = logging.getLogger(__name__)\n\n logger.info(\"Loading input dataset\")\n dataset = pd.read_csv(input_path)\n\n X_eval = dataset.drop(\"Survived\", axis=1)\n y_eval = dataset[\"Survived\"]\n\n logger.info(\"Loading model\")\n model = joblib.load(model_path)\n\n logger.info(\"Calculating metrics\")\n scorer = metrics.make_scorer(metrics.mean_squared_error)\n cv_results = cross_validate(model, X=X_eval, y=y_eval, scoring=scorer, cv=5)\n\n metric_values = {\"mse\": cv_results[\"test_score\"].mean()}\n\n logger.info(f\"Writing output to {metrics_path}\")\n with open(metrics_path, \"w\") as file_:\n json.dump(metric_values, file_)","def evaluate(self, data_collection):\n # map inputs\n mapped_input_dict = self._dict_from_data_collection(self.input_mapping,\n data_collection)\n\n # combine auxiliary inputs with mapped inputs\n all_inputs = mapped_input_dict.copy()\n all_inputs.update(self.auxiliary_inputs)\n\n # evaluate meter\n outputs_dict = self.evaluate_raw(**all_inputs)\n\n # combine auxiliary outputs with raw outputs\n all_outputs = outputs_dict.copy()\n all_outputs.update(self.auxiliary_outputs)\n\n\n # map meter evaluations back to data_collection form\n mapped_output_data_collection = self._data_collection_from_dict(\n self.output_mapping, all_outputs)\n\n # combine with original data, add tags as necessary\n mapped_output_data_collection.add_tags(self.tagspace)\n\n return mapped_output_data_collection","def run(self):\n self.evaluate()\n self.accumulate()\n self.summarize()","def evaluate_raw(self):\n message = \"Use the meter.evaluate(data_collection) method for\" \\\n \"YamlDefinedMeters.\"\n raise NotImplementedError(message)","def evaluate(data_folder):\n seq_length=150\n g = tf.Graph()\n with g.as_default():\n # Load dataset.\n provider = data_provider_kush.get_provider(FLAGS.task)(data_folder)\n num_classes = 6\n audio, ground_truth, num_examples = provider.get_split(FLAGS.portion, FLAGS.batch_size)\n\n # Define model graph.\n with slim.arg_scope([slim.batch_norm],\n is_training=False):\n predictions = models_kush.get_model(FLAGS.model,ground_truth)(audio, num_lstm_modules=FLAGS.num_lstm_modules)\n #pred_argmax = tf.argmax(predictions, 1) \n #lab_argmax = tf.argmax(labels, 1)\n\t\n metrics = {\n \"eval/accuracy\": slim.metrics.streaming_mean_squared_error(predictions, ground_truth[:,seq_length-1,:])\n }\n\n total_error = tf.reduce_sum(tf.square(tf.subtract(ground_truth[:,seq_length-1,:], tf.reduce_mean(ground_truth[:,seq_length-1,:]))))\n unexplained_error = tf.reduce_sum(tf.square(tf.subtract(ground_truth[:,seq_length-1,:], predictions)))\n R_squared = tf.subtract(tf.cast(1, tf.float32), tf.divide(total_error, unexplained_error))\n print('R_squared value: ',R_squared)\n for i in range(num_classes):\n name ='eval/mse_{}'.format(i)\n recall = slim.metrics.streaming_mean_squared_error(predictions[:,i],ground_truth[:,seq_length-1,i])\n metrics[name] = recall\n\n metrics['R_squared']=(R_squared,tf.subtract(tf.cast(1, tf.float32), tf.div(total_error, unexplained_error)))\n #print(zip(metrics.values()))\n #metric_names = metrics.keys()\n #value_ops, update_ops = zip(*metrics.values())\n #names_to_values, names_to_updates = dict(zip(metric_names, value_ops)), dict(zip(metric_names, update_ops))\n names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(metrics)\n \n summary_ops = []\n metrics = dict()\n for name, value in names_to_values.items():\n op = tf.summary.scalar(name, value)\n op = tf.Print(op, [value], name)\n summary_ops.append(op)\n metrics[name] = value\n\n # Computing the unweighted average recall and add it into the summaries.\n uar = sum([metrics['eval/mse_{}'.format(i)] for i in range(num_classes)]) / num_classes\n op = tf.summary.scalar('eval/mse', uar)\n op = tf.Print(op, [uar], 'eval/mse')\n summary_ops.append(op)\n\n num_examples = FLAGS.num_examples or num_examples\n num_batches = math.ceil(num_examples / float(FLAGS.batch_size))\n logging.set_verbosity(1)\n\n # Setup the global step.\n slim.get_or_create_global_step()\n\n # How often to run the evaluation.\n eval_interval_secs = FLAGS.eval_interval_secs \n\n slim.evaluation.evaluation_loop(\n '',\n FLAGS.checkpoint_dir,\n FLAGS.log_dir,\n num_evals=num_batches,\n eval_op=list(names_to_updates.values()),\n summary_op=tf.summary.merge(summary_ops),\n eval_interval_secs=eval_interval_secs)","def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:\n ds = data.gen_tf_dataset(\n batch_size, is_training=False, preprocess=self._preprocess)\n return self._model.evaluate(ds)","def evaluate(pred_file, ref_file):\n ref_dict, pred_dict, query_dict, id_dict = build_pred_ref_dict(ref_file, pred_file, ref_file)\n total, acc, scores = res_eval_with_type_acc(query_dict, pred_dict, ref_dict, id_dict, save=False)\n em = calculate_exact_match(pred_dict, ref_dict)\n print('Comp Acc: {:.3f}%\\tBleu-4: {:.3f}\\tRouge-L: {:.3f}'.format(acc, scores['Bleu-4'], scores['Rouge-L']))\n print('EM: {:.3f}%'.format(em))\n # calculate_sketch_type_acc(ref_file, pred_file)\n # calculate_exact_match_for_each_q_type(ref_file, pred_file)\n return total, acc, scores, em","def validate(self):\n stats = {}\n evaluate_config = {\"verbose\": self.verbose}\n evaluate_config.update(self.config.get(\"evaluate_config\", {}))\n\n results = self.model.evaluate(self.test_dataset, **evaluate_config)\n if results is None:\n # Using local Model since model.evaluate() returns None\n # for MultiWorkerMirroredStrategy\n logger.warning(\"Running a local model to get validation score.\")\n self.local_model = self.model_creator(self.config)\n self.local_model.set_weights(self.model.get_weights())\n results = self.local_model.evaluate(self.test_dataset,\n **evaluate_config)\n\n if isinstance(results, list):\n stats = {\n \"validation_\" + k: v\n for k, v in zip(self.model.metrics_names, results)\n }\n else:\n stats = {\"loss\": results}\n\n return stats","def evaluate(hparams, summary_dir, num_gpus, model_type, eval_set, eval_size,\n eval_shard, data_dir, num_targets, dataset, validate, seed,\n shuffled, shift, pad, batch_size=100, checkpoint=None):\n output_dir = summary_dir\n load_dir = summary_dir + '/train/'\n summary_dir += '/eval/' + FLAGS.dataset + '/' + eval_set\n with tf.Graph().as_default():\n features = get_features(eval_set, batch_size, num_gpus, data_dir,\n num_targets, dataset, validate, evaluate=True,\n seed=seed, shuffled=shuffled, shift=shift,\n pad=pad, eval_shard=eval_shard)\n model = models[model_type](hparams)\n result, _ = model.multi_gpu(features, num_gpus)\n test_writer = tf.summary.FileWriter(summary_dir)\n seen_step = -1\n paused = 0\n while paused < 360:\n print('start evaluation, model defined')\n if checkpoint:\n step = extract_step(checkpoint)\n last_checkpoint = checkpoint\n else:\n step, last_checkpoint = find_checkpoint(load_dir, seen_step)\n if step == -1:\n time.sleep(60)\n paused += 1\n else:\n paused = 0\n seen_step = step\n run_experiment(load_eval, last_checkpoint, test_writer,\n eval_experiment, model, result,\n eval_size // batch_size, features=features,\n eval_set=eval_set, output_dir=output_dir,\n unsupervised=hparams.unsupervised,\n num_gpus=num_gpus)\n if checkpoint:\n break\n\n test_writer.close()","def _evaluate_fn(model, dataset):\n # Reset the local variables so that the returned metrics are computed using\n # the given data. Similar to the `reset_states` method of `tf.metrics.Metric`.\n for var in model.local_variables:\n if var.initial_value is not None:\n var.assign(var.initial_value)\n else:\n var.assign(tf.zeros_like(var))\n\n def eval_fn(dummy_state, batch):\n \"\"\"Evaluates the model on a batch.\"\"\"\n model.forward_pass(batch, training=False)\n return dummy_state\n\n # Evaluate on the dataset.\n dataset.reduce(initial_state=0, reduce_func=eval_fn)\n\n # Obtain the metrics.\n results = collections.OrderedDict()\n local_outputs = model.report_local_outputs()\n for name, metric in local_outputs.items():\n if isinstance(metric, list) and (len(metric) == 2):\n # Some metrics returned by `report_local_outputs()` can have two scalars:\n # one represents `sum`, and the other represents `count`. Ideally, we want\n # to return a single scalar for each metric.\n results[name] = metric[0] / metric[1]\n else:\n results[name] = metric[0] if isinstance(metric, list) else metric\n return results","def eval_perf_total(model, X_train, y_train, X_test, y_test):\n\n y_hat_train = model.predict(X_train)\n y_hat_test = model.predict(X_test)\n \n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\n train_r = metrics.r2_score(y_train, y_hat_train)\n\n print('Evaluating Performance on Training Data:\\n')\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\n')\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\n print(f'Train R-Square Value: {round(train_r,2)}')\n\n print('\\n'+'---'*25+'\\n')\n\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\n test_r = metrics.r2_score(y_test, y_hat_test)\n\n print('Evaluating Performance on Testing Data:\\n')\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\n')\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\n print(f'Test R-Square Value: {round(test_r,2)}')","def evaluate(\n config,\n _,\n pstate,\n eval_ds,\n rng,\n unused_num_eval_steps = -1,\n):\n logging.info(\"Starting evaluation.\")\n eval_metrics = None\n state = flax_utils.unreplicate(pstate)\n\n render_loop = instant_ngp_utils.make_render_loop(state.params, config)\n with utils.StepTraceContextHelper(\"eval\", 0) as trace_context:\n for step, batch in enumerate(eval_ds): # pytype: disable=wrong-arg-types\n data = jax.tree_map(jnp.asarray, batch)\n render_poses = data[\"c2w\"]\n hwf = data[\"hwf\"]\n rng = jax.random.fold_in(rng, step)\n\n frames = []\n for pose in render_poses:\n frames.append(\n render_loop(instant_ngp_utils.camera_ray_batch(pose, hwf), rng)[0]\n )\n psnrs_test = [\n -10 * jnp.log10(jnp.mean(jnp.square(rgb - gt)))\n for (rgb, gt) in zip(frames, data[\"images\"])\n ]\n psnr_test = np.array(psnrs_test).mean()\n eval_metrics = EvalMetrics.single_from_model_output(psnr=psnr_test)\n trace_context.next_step()\n eval_info = {\n \"out\": jnp.concatenate([x[None, Ellipsis] for x in frames], axis=0),\n \"gtr\": data[\"images\"],\n }\n return eval_metrics, eval_info","def _evaluate(self, x):\n raise NotImplementedError()","def __call__(self, test_data, verbose=True):\n\n self.model.eval()\n with torch.no_grad():\n loss, rho, nmse = self._iteration(test_data)\n if verbose:\n print(f'\\n=> Test result: \\nloss: {loss:.3e}'\n f' rho: {rho:.3e} NMSE: {nmse:.3e}\\n')\n return loss, rho, nmse"],"string":"[\n \"def evaluate(self, test_data):\\n result = self.model.run(test_data)\\n self._save_result(result)\",\n \"def evaluate(self, dataset):\\n\\t\\tpass\",\n \"def evaluate(\\n self,\\n test_data=None,\\n print_report=True,\\n save_path=\\\"ktrain_classification_report.csv\\\",\\n class_names=[],\\n ):\\n return self.validate(\\n val_data=test_data,\\n print_report=print_report,\\n save_path=save_path,\\n class_names=class_names,\\n )\",\n \"def evaluate():\\n click.echo(\\\"Not implemented yet. In the future, this command will be used for evaluation.\\\")\\n sys.exit(-2)\",\n \"def test_evaluate(self):\\n # Check build does not raise errors\\n dataset = KDDCupDataset()\\n dataset.create_fixed_samples(\\n *self.data, samples_num=1, partition_sizes=self.partition_sizes)\\n dataset.set_current_sample(0)\\n model = self.MODEL(dataset, **self.model_arguments)\\n model.fit(training_epochs=50)\\n metric = model.evaluate('test')\\n self.assertLessEqual(0, metric)\\n self.assertGreaterEqual(1, metric)\",\n \"def evaluate(self, test_data, test_labels):\\n raise NotImplementedError\",\n \"def evaluate(parser):\\n required_args = (\\n 'train_tfrecord',\\n 'valid_tfrecord',\\n 'predicted_data',\\n 'actual_data',\\n )\\n cli_args = add_all_args(parser, EVALUATION, *required_args)\\n evaluator = Evaluator(\\n input_shape=cli_args.input_shape,\\n model_configuration=cli_args.model_cfg,\\n train_tf_record=cli_args.train_tfrecord,\\n valid_tf_record=cli_args.valid_tfrecord,\\n classes_file=cli_args.classes,\\n max_boxes=cli_args.max_boxes,\\n iou_threshold=cli_args.iou_threshold,\\n score_threshold=cli_args.score_threshold,\\n )\\n predicted = pd.read_csv(cli_args.predicted_data)\\n actual = pd.read_csv(cli_args.actual_data)\\n evaluator.calculate_map(\\n prediction_data=predicted,\\n actual_data=actual,\\n min_overlaps=cli_args.min_overlaps,\\n display_stats=cli_args.display_stats,\\n save_figs=cli_args.save_figs,\\n plot_results=cli_args.plot_stats,\\n )\",\n \"def evaluate(model, datagen, X_test, Y_test, batch_size, save_folder_path=None):\\n\\n print(\\\"[INFO] Evaluating model...\\\")\\n\\n scores = model.evaluate_generator(\\n datagen.flow(X_test, Y_test, batch_size=batch_size),\\n verbose=1)\\n \\n print(\\\"[INFO] Evaluation results:\\\\n{0}: {1:.2f}\\\\n{2}: {3:.2f}\\\".format(model.metrics_names[0], scores[0]*100, model.metrics_names[1], scores[1]*100))\\n \\n if save_folder_path is not None:\\n # Write results to path\\n assert os.path.isdir(save_folder_path) == True, \\\"Unable to save evaluation results, save_folder_path is not a folder\\\"\\n eval_results_path = save_folder_path + \\\"/eval_results.txt\\\"\\n eval_handle = open(eval_results_path, 'w')\\n eval_handle.write(\\\"Model name: {}\\\\n\\\\n\\\".format(MODEL_NAME))\\n eval_handle.write(\\\"Evaluation results:\\\\n{0}: {1:.2f}\\\\n{2}: {3:.2f}\\\".format(model.metrics_names[0], scores[0]*100, model.metrics_names[1], scores[1]*100))\\n eval_handle.close()\",\n \"def _evaluate_model(\\n run_id: str, dataset_filename: str, dataset_sampling_column: str = None\\n):\\n fix_multiprocessing_with_keras_on_macos()\\n\\n run = _get_run(run_id)\\n hyperparameters = run.config\\n\\n # no need to run this on a gpu since it's 1 epoch\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\"-1\\\"\\n\\n with ModelBestH5File(run) as model_h5_filepath:\\n model = _load_untrainable_model(hyperparameters, model_h5_filepath)\\n\\n model_name = run.config[\\\"model_name\\\"]\\n x, y = _get_prepared_dataset(\\n model_name, hyperparameters, dataset_filename, dataset_sampling_column\\n )\\n\\n wandb.init(\\n config={\\n \\\"run_id\\\": run_id,\\n \\\"dataset_filename\\\": dataset_filename,\\n \\\"dataset_sampling_column\\\": dataset_sampling_column,\\n },\\n tags=[\\\"model-evaluation\\\"],\\n )\\n\\n batch_size = hyperparameters[\\\"batch_size\\\"]\\n label_scale_factor_mmhg = hyperparameters[\\\"label_scale_factor_mmhg\\\"]\\n acceptable_error_mg_l = hyperparameters[\\\"acceptable_error_mg_l\\\"]\\n acceptable_fraction_outside_error = hyperparameters[\\n \\\"acceptable_fraction_outside_error\\\"\\n ]\\n\\n # we're using fit() instead of evaluate() to get the functionality of these callbacks\\n # training performance in the results should be ignored, as it can be affected by some\\n # training-only layers such as dropout\\n model.fit(\\n x,\\n y,\\n batch_size=batch_size,\\n epochs=1,\\n verbose=2,\\n validation_data=(x, y),\\n callbacks=[\\n ThresholdValMeanAbsoluteErrorOnCustomMetric(\\n acceptable_fraction_outside_error=acceptable_fraction_outside_error,\\n acceptable_error_mg_l=acceptable_error_mg_l,\\n ),\\n WandbCallback(verbose=1, monitor=\\\"val_adjusted_mean_absolute_error\\\"),\\n LogPredictionsAndWeights(\\n metric=\\\"val_adjusted_mean_absolute_error\\\",\\n dataset=([], [], x, y),\\n label_scale_factor_mmhg=label_scale_factor_mmhg,\\n ),\\n ],\\n )\\n\\n # returning model and dataset for use in jupyter notebooks\\n return model, (x, y)\",\n \"def evaluate_data(df_train, w_train, df_test, w_test,\\n evaluation_setups=None, preprocessing=None):\\n if evaluation_setups is None:\\n evaluation_setups = standard_evaluation_setups\\n if preprocessing is None:\\n preprocessing = preprocess\\n if (\\\"weights\\\" in df_train) and df_train[\\\"weights\\\"].size > 0:\\n weights = df_train[\\\"weights\\\"].as_matrix().squeeze()\\n else:\\n weights = np.ones(df_train.shape[0])\\n\\n # create a new dataframe which will hold all the generated errors\\n df = pd.DataFrame()\\n for one_w_train, one_w_test in zip(w_train, w_test):\\n # setup testing function\\n X_test, y_test = preprocessing(df_test, snr=one_w_test)\\n # extract noisy data\\n X_train, y_train = preprocessing(df_train, snr=one_w_train)\\n for e in evaluation_setups:\\n regressor = e.regressor\\n regressor.fit(X_train, y_train, weights)\\n y_pred = regressor.predict(X_test)\\n # save results to a dataframe\\n errors = np.abs(y_pred - y_test)\\n errors = errors.reshape(len(errors), 1)\\n current_df = DataFrame(errors * 100,\\n columns=[\\\"Errors\\\"])\\n current_df[\\\"Method\\\"] = e.name\\n current_df[\\\"SNR\\\"] = int(one_w_test)\\n df = pd.concat([df, current_df], ignore_index=True)\\n\\n return df\",\n \"def evaluate(self) :\\n pass\",\n \"def evaluate(self):\\n pass\",\n \"def evaluate(self):\\n pass\",\n \"def evaluated_data(self) -> Dict:\\n if not self.eval_data:\\n raise Exception(\\\"Evaluation Failed\\\")\\n\\n statistics = [stat['statistics'] for stat in self.eval_data]\\n for count, stat in enumerate(statistics):\\n if stat:\\n eval_data = stat.split(\\\"=\\\")\\n char_error = re.findall(r\\\"\\\\d+\\\\.\\\\d+\\\", eval_data[-2])[0]\\n word_error = eval_data[-1]\\n\\n self.eval_data[count]['character_error'] = float(char_error)\\n self.eval_data[count]['word_error'] = float(word_error)\\n del self.eval_data[count]['statistics']\\n\\n return self.eval_data\",\n \"def Evaluate():\\n global reg\\n reg = ModelRegression()\\n \\n # Retrieve dataset files\\n data_fls, ref_fls = LoadTroikaDataset()\\n errs, confs = [], []\\n for data_fl, ref_fl in zip(data_fls, ref_fls):\\n # Run the pulse rate algorithm on each trial in the dataset\\n errors, confidence = RunPulseRateAlgorithm(data_fl, ref_fl)\\n errs.append(errors)\\n confs.append(confidence)\\n # Compute aggregate error metric\\n errs = np.hstack(errs)\\n confs = np.hstack(confs)\\n return AggregateErrorMetric(errs, confs)\",\n \"def validate(self):\\n self.set_model_mode('eval')\\n self.evaluator.reset()\\n losses = MetricMeter()\\n\\n print('Do evaluation on {} set'.format('valid set'))\\n data_loader = self.val_loader\\n assert data_loader is not None\\n for batch_idx, batch in enumerate(data_loader):\\n input, label = self.parse_batch_test(batch)\\n loss = self.forward_backward(batch, backprob=False)\\n losses.update(loss)\\n # total_loss += loss['loss']\\n output = self.model_inference(input)\\n self.evaluator.process(output, label)\\n\\n results = self.evaluator.evaluate()\\n total_loss = losses.meters['loss_x'].avg\\n\\n for k, v in results.items():\\n tag = '{}/{}'.format('validation', k)\\n self.write_scalar(tag, v, self.epoch)\\n # if full_results:\\n return [total_loss,losses.dict_results(),results]\\n # return total_loss\",\n \"def evaluate(self, dataset):\\n logging.info('Start evaluation')\\n\\n loss, predictions, labels = self.run_one_epoch(dataset, RunnerPhase.VALIDATE)\\n\\n metrics_dict = self.metric_class.get_metrics_dict(predictions, labels)\\n\\n eval_info = self.metric_class.metrics_dict_to_str(metrics_dict)\\n\\n logging.info(eval_info)\\n\\n logging.info('Evaluation finished')\\n\\n return metrics_dict\",\n \"def evaluate(cfg: DictConfig):\\n\\n experiments = cfg.get('experiment_type', f'{cfg.model.name}_only')\\n fixed_t0 = cfg.get('fixed_t0', False)\\n ext = '_fixedT0' if fixed_t0 else ''\\n\\n base_dir = cfg.device.root\\n datasource = cfg.datasource.name\\n\\n if experiments == 'ablations':\\n models = {\\n 'FluxRGNN': ['final',\\n 'final_without_encoder',\\n 'final_without_boundary'],\\n 'LocalLSTM': ['final']\\n }\\n elif experiments == 'final':\\n models = {\\n 'FluxRGNN': ['final'],\\n 'GAM': ['final'],\\n 'HA': ['final'],\\n 'GBT': ['final']\\n }\\n else:\\n m = cfg.model.name\\n year = cfg.datasource.test_year\\n\\n # find all experiments available for this model, datasource and test year\\n result_dir = osp.join(base_dir, 'results', datasource, m, f'test_{year}')\\n models = {\\n m : [ f.name for f in os.scandir(result_dir) if f.is_dir() ]\\n }\\n\\n\\n # thresholds for binary classification metrics\\n if cfg.datasource.name == 'abm':\\n thresholds = [0.0019, 0.0207]\\n else:\\n thresholds = [0, 10, 20]\\n\\n rmse_per_hour = []\\n mae_per_hour = []\\n pcc_per_hour = []\\n bin_per_hour = []\\n\\n rmse_per_night = []\\n mae_per_night = []\\n\\n output_dir = osp.join(base_dir, 'results', datasource, f'performance_evaluation{ext}', experiments)\\n os.makedirs(output_dir, exist_ok=True)\\n\\n counter = 0\\n\\n for m, dirs in models.items():\\n print(f'evaluate {m}')\\n\\n for d in dirs:\\n result_dir = osp.join(base_dir, 'results', datasource, m, f'test_{cfg.datasource.test_year}', d)\\n\\n # check if directory exists\\n if os.path.isdir(result_dir):\\n results, model_cfg = load_cv_results(result_dir, trials=cfg.task.repeats, ext=ext)\\n\\n df_prep = pd.read_csv(osp.join(base_dir, 'data', 'preprocessed',\\n f'{model_cfg[\\\"t_unit\\\"]}_{model_cfg[\\\"model\\\"][\\\"edge_type\\\"]}_ndummy={model_cfg[\\\"datasource\\\"][\\\"n_dummy_radars\\\"]}',\\n datasource, cfg.season, str(cfg.datasource.test_year), 'dynamic_features.csv'))\\n tidx2night = dict(zip(df_prep.tidx, df_prep.nightID))\\n\\n rmse_per_hour.append(compute_rmse(m, d, results, tidx2night, groupby=['horizon', 'trial'],\\n threshold=0, km2=True, fixed_t0=fixed_t0))\\n mae_per_hour.append(compute_mae(m, d, results, tidx2night, groupby=['horizon', 'trial'],\\n threshold=0, km2=True, fixed_t0=fixed_t0))\\n pcc_per_hour.append(compute_pcc(m, d, results, tidx2night, groupby=['horizon', 'trial'],\\n threshold=0, km2=True, fixed_t0=fixed_t0))\\n\\n if fixed_t0:\\n rmse_per_night.append(compute_rmse_per_night(m, d, results, tidx2night, groupby=['night_horizon', 'trial']))\\n mae_per_night.append(compute_mae_per_night(m, d, results, tidx2night, groupby=['night_horizon', 'trial']))\\n\\n # compute binary classification measures\\n for thr in thresholds:\\n bin_per_hour.append(compute_bin(m, d, results, groupby=['horizon', 'trial'], threshold=thr, km2=True))\\n\\n counter += 1\\n\\n else:\\n print(f'Experiment \\\"{d}\\\" for model \\\"{m}\\\" and datasource \\\"{datasource}\\\" is not available. '\\n f'Use \\\"run_experiments.py model={m} datasource={datasource} +experiment={d}\\\" to run this experiment.')\\n\\n if counter > 0:\\n rmse_per_hour = pd.concat(rmse_per_hour)\\n rmse_per_hour.to_csv(osp.join(output_dir, f'rmse_per_hour.csv'))\\n\\n mae_per_hour = pd.concat(mae_per_hour)\\n mae_per_hour.to_csv(osp.join(output_dir, f'mae_per_hour.csv'))\\n\\n pcc_per_hour = pd.concat(pcc_per_hour)\\n pcc_per_hour.to_csv(osp.join(output_dir, f'pcc_per_hour.csv'))\\n\\n bin_per_hour = pd.concat(bin_per_hour)\\n bin_per_hour.to_csv(osp.join(output_dir, f'bin_per_hour.csv'))\\n\\n if fixed_t0:\\n rmse_per_night = pd.concat(rmse_per_night)\\n rmse_per_night.to_csv(osp.join(output_dir, f'rmse_per_night.csv'))\\n\\n mae_per_night = pd.concat(mae_per_night)\\n mae_per_night.to_csv(osp.join(output_dir, f'mae_per_night.csv'))\",\n \"def regression_evaluation(self, test_set, predicted_values):\\r\\n\\r\\n MAE = self.mean_absolute_error(test_set, predicted_values)\\r\\n MSE = self.mean_square_error(test_set, predicted_values)\\r\\n print(f\\\"Mean Percent Error:\\\\t{MAE:.2f}\\\")\\r\\n print(f\\\"Mean Square Error:\\\\t{MSE:.2f}\\\")\",\n \"def _evaluate(model):\\n _recompile(model)\\n if isinstance(eval_dataset, tuple):\\n eval_images, eval_labels = eval_dataset\\n return model.evaluate(\\n eval_images, eval_labels, verbose=verbose, return_dict=True)\\n else:\\n return model.evaluate(eval_dataset, verbose=verbose, return_dict=True)\",\n \"def _check_evaluate_implementation(self) -> None:\\n logging.debug(f\\\"Evaluate_batch_defined: {self._evaluate_batch_defined()}.\\\")\\n logging.debug(f\\\"Evaluate full dataset defined: {self._evaluate_full_dataset_defined()}.\\\")\\n check.not_eq(\\n self._evaluate_batch_defined(),\\n self._evaluate_full_dataset_defined(),\\n \\\"Please define exactly one of: `evaluate_batch()` or `evaluate_full_dataset()`. \\\"\\n \\\"For most use cases `evaluate_batch()` is recommended is recommended because \\\"\\n \\\"it can be parallelized across all devices.\\\",\\n )\",\n \"def evaluate(self, data, metric, classes=None):\\n func_dict = {\\n 'mutual_information': sklearn.metrics.mutual_info_score,\\n 'normed_mutual_information': sklearn.metrics.normalized_mutual_info_score,\\n 'square_error': sklearn.metrics.mean_squared_error,\\n 't-test': scipy.stats.ttest_ind,\\n 'wilcoxon': scipy.stats.wilcoxon,\\n 'correlation': np.corrcoef\\n }\\n self.make_signature(data, classes)\\n try:\\n if metric in {'mutual_information', 'normed_mutual_information'}:\\n self.score = func_dict[metric](classes, self.digit_signature()) \\n elif metric == 'square_error':\\n self.score = func_dict[metric](classes, self.signatures)\\n elif metric in {'t-test', 'wilcoxon'} :\\n self.score = np.abs(func_dict[metric](self.signatures[classes==1], \\\\\\n self.signatures[classes==0])[0])\\n \\n elif metric == 'correlation':\\n self.score = func_dict[metric](classes, self.signatures)[1,0]\\n \\n except: KeyError(\\\"no such a function\\\") \\n \\n return self.score\",\n \"def evaluate(self):\\n raise Exception(\\\"Not implemented.\\\")\",\n \"def validate(\\n self,\\n val_data=None,\\n print_report=True,\\n save_path=\\\"ktrain_classification_report.csv\\\",\\n class_names=[],\\n ):\\n if val_data is not None:\\n val = val_data\\n else:\\n val = self.val_data\\n\\n classification, multilabel = U.is_classifier(self.model)\\n if not classification:\\n # warnings.warn('learner.validate is only for classification problems. '\\n #'For regression, etc., use learner.predict and learner.ground_truth '\\n #'to manually validate.')\\n # return\\n pass\\n is_multilabel = U.is_multilabel(val) or multilabel\\n y_pred = self.predict(val_data=val)\\n y_true = self.ground_truth(val_data=val)\\n y_pred = np.squeeze(y_pred)\\n y_true = np.squeeze(y_true)\\n\\n # regression evaluation\\n if not classification:\\n from sklearn.metrics import mean_absolute_error, mean_squared_error\\n\\n regout = []\\n metrics = U.metrics_from_model(self.model)\\n for m in metrics:\\n if m in [\\\"mae\\\", \\\"mean_absolute_error\\\"]:\\n regout.append((m, mean_absolute_error(y_true, y_pred)))\\n elif m in [\\\"mse\\\", \\\"mean_squared_error\\\"]:\\n regout.append((m, mean_squared_error(y_true, y_pred)))\\n if not regout:\\n warnings.warn(\\n \\\"%s is not supported by validate/evaluate - falling back to MAE\\\"\\n )\\n regout.append((\\\"mae\\\", mean_absolute_error(y_true, y_pred)))\\n return regout\\n\\n if len(y_pred.shape) == 1:\\n y_pred = np.where(y_pred > 0.5, 1, 0)\\n y_true = np.where(y_true > 0.5, 1, 0)\\n elif is_multilabel:\\n from sklearn.preprocessing import binarize\\n\\n y_pred = binarize(y_pred, threshold=0.5)\\n else:\\n y_pred = np.argmax(y_pred, axis=1)\\n y_true = np.argmax(y_true, axis=1)\\n\\n if print_report or save_path is not None:\\n if class_names:\\n try:\\n class_names = [str(s) for s in class_names]\\n except:\\n pass\\n report = classification_report(\\n y_true,\\n y_pred,\\n target_names=class_names,\\n output_dict=not print_report,\\n )\\n else:\\n report = classification_report(\\n y_true,\\n y_pred,\\n output_dict=not print_report,\\n zero_division=0,\\n )\\n if print_report:\\n print(report)\\n else:\\n df = pd.DataFrame(report).transpose()\\n df.to_csv(save_path)\\n print(\\\"classification report saved to: %s\\\" % (save_path))\\n cm_func = confusion_matrix\\n if is_multilabel:\\n warnings.warn(\\n \\\"Confusion matrices do not currently support multilabel classification, so returning None\\\"\\n )\\n return\\n\\n cm = confusion_matrix(y_true, y_pred)\\n return cm\",\n \"def test_evaluate():\\n X_train, X_test, y_train, y_test = src.load()\\n clf, score = src.train(X_train, y_train)\\n test_score = src.evaluate(clf, X_test, y_test)\\n assert isinstance(test_score, float)\",\n \"def evaluate_print(self, test_data):\\n test_results = [(np.argmax(self.feedforward(x)), np.argmax(y)) #argmax 返回最大数的索引\\n for (x, y) in test_data]\\n #统计预测错误的数据特征\\n error = []\\n for i, (x, y) in enumerate(test_results):\\n if (x!=y):\\n error.append(test_data[i][0])\\n error = np.insert(error, 0, values=y, axis=1) #将正确答案插入第一行\\n right = sum(int(x == y) for (x, y) in test_results)\\n #打印出用全部测试集进行测试得到的结果\\n print( \\\"TrainTest : {0} / {1} = {2}\\\".format(\\n right, len(test_data), round(right/len(test_data),6) ))\\n return error\",\n \"def evaluate(self):\\r\\n raise Exception(\\\"Not implemented.\\\")\",\n \"def eval_n_verify(yaml_data, dry_run, fail_eval):\\n print('='*10, 'Verifying Results', '='*10)\\n try:\\n for model in yaml_data['models']:\\n for i, topic in enumerate(yaml_data['topics']):\\n for eval in yaml_data['evals']:\\n eval_cmd = [\\n os.path.join(yaml_data['root'], eval['command']),\\n ' '.join(eval['params']) if eval['params'] else '',\\n os.path.join(yaml_data['root'], yaml_data['qrels_root'], topic['qrel']),\\n 'run.{0}.{1}.{2}'.format(yaml_data['name'], model['name'], topic['path'])\\n ]\\n if dry_run:\\n print(' '.join(eval_cmd))\\n continue\\n\\n out = [line for line in check_output(' '.join(eval_cmd)).decode('utf-8').split('\\\\n') if line.strip()][-1]\\n if not out.strip():\\n continue\\n eval_out = out.strip().split(eval['separator'])[eval['parse_index']]\\n expected = round(model['results'][eval['metric']][i], eval['metric_precision'])\\n real = round(float(eval_out), eval['metric_precision'])\\n if isclose(expected, real):\\n print(OKBLUE, '[OK]', yaml_data['name'], model['name'], topic['name'], eval['metric'], expected, real, ENDC)\\n else:\\n print(FAIL, ['ERROR'], yaml_data['name'], model['name'], topic['name'], eval['metric'], expected, real, '!!!!', ENDC)\\n if fail_eval:\\n assert False\\n finally:\\n print(ENDC)\",\n \"def evaluate():\\n log.info('Loading dev data...')\\n if args.version_2:\\n dev_data = SQuAD('dev', version='2.0')\\n else:\\n dev_data = SQuAD('dev', version='1.1')\\n (_, _), (data_file_name, _) \\\\\\n = dev_data._data_file[dev_data._version][dev_data._segment]\\n dev_data_path = os.path.join(dev_data._root, data_file_name)\\n\\n if args.debug:\\n sampled_data = [dev_data[0], dev_data[1], dev_data[2]]\\n dev_data = mx.gluon.data.SimpleDataset(sampled_data)\\n log.info('Number of records in dev data: %d', len(dev_data))\\n\\n dev_data_features = preprocess_dataset(\\n tokenizer, dev_data, vocab=vocab, max_seq_length=args.max_seq_length,\\n doc_stride=args.doc_stride, num_workers=args.num_workers,\\n max_query_length=args.max_query_length, load_from_pickle=args.load_pickle,\\n feature_file=args.dev_dataset_file)\\n\\n dev_data_input = convert_full_features_to_input_features(dev_data_features)\\n log.info('The number of examples after preprocessing: %d', len(dev_data_input))\\n\\n dev_dataloader = mx.gluon.data.DataLoader(dev_data_input, batchify_fn=batchify_fn,\\n num_workers=4, batch_size=args.test_batch_size,\\n shuffle=False, last_batch='keep')\\n\\n log.info('start prediction')\\n\\n all_results = collections.defaultdict(list)\\n\\n epoch_tic = time.time()\\n total_num = 0\\n for (batch_id, data) in enumerate(dev_dataloader):\\n data_list = list(split_and_load(data, ctx))\\n for splited_data in data_list:\\n example_ids, inputs, token_types, valid_length, p_mask, _, _, _ = splited_data\\n total_num += len(inputs)\\n outputs = net_eval(inputs, token_types, valid_length, p_mask=p_mask)\\n example_ids = example_ids.asnumpy().tolist()\\n for c, example_ids in enumerate(example_ids):\\n result = RawResultExtended(start_top_log_probs=outputs[0][c].asnumpy().tolist(),\\n start_top_index=outputs[1][c].asnumpy().tolist(),\\n end_top_log_probs=outputs[2][c].asnumpy().tolist(),\\n end_top_index=outputs[3][c].asnumpy().tolist(),\\n cls_logits=outputs[4][c].asnumpy().tolist())\\n all_results[example_ids].append(result)\\n if batch_id % args.log_interval == 0:\\n log.info('Batch: %d/%d', batch_id + 1, len(dev_dataloader))\\n\\n epoch_toc = time.time()\\n log.info('Time cost=%2f s, Thoughput=%.2f samples/s', epoch_toc - epoch_tic,\\n total_num / (epoch_toc - epoch_tic))\\n\\n log.info('Get prediction results...')\\n\\n all_predictions = collections.OrderedDict()\\n all_nbest_json = collections.OrderedDict()\\n scores_diff_json = collections.OrderedDict()\\n for features in dev_data_features:\\n results = all_results[features[0].example_id]\\n example_qas_id = features[0].qas_id\\n score_diff, best_non_null_entry, nbest_json = predict_extended(\\n features=features, results=results, n_best_size=args.n_best_size,\\n max_answer_length=args.max_answer_length, start_n_top=args.start_top_n,\\n end_n_top=args.end_top_n)\\n scores_diff_json[example_qas_id] = score_diff\\n all_predictions[example_qas_id] = best_non_null_entry\\n all_nbest_json[example_qas_id] = nbest_json\\n\\n output_prediction_file = os.path.join(args.output_dir, 'predictions.json')\\n output_nbest_file = os.path.join(args.output_dir, 'nbest_predictions.json')\\n output_null_log_odds_file = os.path.join(args.output_dir, 'null_odds.json')\\n\\n with open(output_prediction_file, 'w') as writer:\\n writer.write(json.dumps(all_predictions, indent=4) + '\\\\n')\\n with open(output_nbest_file, 'w') as writer:\\n writer.write(json.dumps(all_nbest_json, indent=4) + '\\\\n')\\n with open(output_null_log_odds_file, 'w') as writer:\\n writer.write(json.dumps(scores_diff_json, indent=4) + '\\\\n')\\n\\n if os.path.exists(sys.path[0] + '/evaluate-v2.0.py'):\\n arguments = [\\n dev_data_path, output_prediction_file, '--na-prob-thresh',\\n str(args.null_score_diff_threshold)\\n ]\\n if args.version_2:\\n arguments += ['--na-prob-file', output_null_log_odds_file]\\n subprocess.call([sys.executable, sys.path[0] + '/evaluate-v2.0.py'] + arguments)\\n else:\\n log.info('Please download evaluate-v2.0.py to get evaluation results for SQuAD. '\\n 'Check index.rst for the detail.')\",\n \"def evaluate(model, eval_data, num_labels): \\n # Turn on the evaluation state to ignore dropouts\\n model.eval()\\n results = [predict(model, x) for x, y in eval_data]\\n f1_score, accuracy = get_metrics(np.array([y for x, y in eval_data]), results, num_labels)\\n return f1_score, accuracy\",\n \"def evaluate(self):\\n\\n\\t\\t## We should be evaluating on dev dataset as well, so commenting x_test\\n\\t\\t#self.model_score = self.model.evaluate(self.x_test, self.y_test_oh, batch_size=2048)\\n\\t\\tself.model_score = self.model.evaluate(self.x_dev, self.y_dev_oh, batch_size=2048)\\n\\t\\tprint(\\\"%s score = %f\\\\n\\\" %(self.modelName, self.model_score[1]))\\n\\n\\t\\t##Saving atucal vs predicted predictions\\n\\t\\t##np.argmax returns the index where it see's 1 in the row\\n\\t\\t#y_pred = np.argmax(self.model.predict(self.x_test, batch_size=2048), axis=1)\\n\\t\\ty_pred = np.argmax(self.model.predict(self.x_dev, batch_size=2048), axis=1)\\n\\n\\t\\t## vstack will stack them in 2 rows, so we use Trasnpose to get them in column stack\\n\\t\\t#output_predict = np.vstack((np.argmax(self.y_test_oh, axis=1), y_pred)).T\\n\\t\\toutput_predict = np.vstack((np.argmax(self.y_dev_oh, axis=1), y_pred)).T\\n\\t\\toutputFile = self.resultDir + \\\"/outputPredict.csv\\\" \\n\\t\\tnp.savetxt(outputFile, output_predict, fmt=\\\"%5.0f\\\", delimiter=\\\",\\\")\\n\\n\\t\\t##Error Analysis of the prediction\\n\\t\\terrorAnalysis(outputFile)\\n\\n\\t\\treturn self.model_score\",\n \"def _evaluate(self):\\n logging.warning('-> evaluate EMPTY experiment...')\",\n \"def model_evaluate(model,x_train,n_y_array,x_val, vald_array):\\n\\n scores = model.evaluate(x_train, n_y_array, verbose=1)\\n\\n scores2 = model.evaluate(x_val, vald_array, verbose=1)\\n\\n\\n print(\\\"for traininf set\\\")\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[1], scores[1]*100))\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[0], scores[0]))\\n\\n\\n\\n print(\\\"for validation set : \\\") \\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[1], scores2[1]*100))\\n\\n print(\\\"%s: %.2f%%\\\" % (model.metrics_names[0], scores2[0]))\",\n \"def test_evaluate_model(sequential_model, model_data):\\n _, _, _, _, x_test, y_test = model_data\\n compile_model(sequential_model)\\n output = evaluate_model(sequential_model, x_test, y_test, 64)\\n assert len(output) == 2\",\n \"def evaluate(self, dataset):\\n return self.model.evaluate(dataset.X_val, dataset.y_val)\",\n \"def evaluate(self):\\n\\n rew_rl = []\\n for ckpt_path in self._config.eval_ckpt_paths:\\n self._load_ckpt(ckpt_path, self._config.ckpt_num)\\n\\n logger.info(\\n \\\"Run %d evaluations for %d environments\\\",\\n self._config.num_eval,\\n len(self._envs),\\n )\\n rollouts, info = self._evaluate(record_video=self._config.record_video)\\n\\n info_stat = info.get_stat()\\n\\n rew_rl.append(np.mean(info[\\\"rew_rl\\\"]))\\n\\n logger.info(\\\"All Eval Rew Values: %s\\\", rew_rl)\\n logger.info(\\\"Eval Rew RL Average: %f, Std: %f\\\", np.mean(rew_rl), np.std(rew_rl))\\n\\n os.makedirs(\\\"result\\\", exist_ok=True)\\n with h5py.File(\\\"result/{}.hdf5\\\".format(self._config.run_name), \\\"w\\\") as hf:\\n for k, v in info.items():\\n hf.create_dataset(k, data=info[k])\\n with open(\\\"result/{}.txt\\\".format(self._config.run_name), \\\"w\\\") as f:\\n for k, v in info_stat.items():\\n f.write(\\\"{}\\\\t{:.03f} $\\\\\\\\pm$ {:.03f}\\\\n\\\".format(k, v[0], v[1]))\",\n \"def evaluate(env, X_data, y_data, batch_size=1):\\n print('\\\\nEvaluating')\\n n_sample = X_data.shape[0]\\n n_batch = int((n_sample+batch_size-1) / batch_size)\\n loss, acc = 0, 0\\n for batch in range(n_batch):\\n print(' batch {0}/{1}'.format(batch + 1, n_batch))\\n print('\\\\r')\\n start = batch * batch_size\\n end = min(n_sample, start + batch_size)\\n cnt = end - start\\n batch_loss, batch_acc = env.sess.run(\\n [env.loss, env.acc],\\n feed_dict={env.x: X_data[start:end],\\n env.y: y_data[start:end]})\\n loss += batch_loss * cnt\\n acc += batch_acc * cnt\\n loss /= n_sample\\n acc /= n_sample\\n print(' loss: {0:.4f} acc: {1:.4f}'.format(loss, acc))\\n return loss, acc\",\n \"def error_analyze(\\n self,\\n data_dir: Path,\\n processed_data_dir: Path,\\n result_dir: Path,\\n output_report_dir: Path,\\n ) -> NoReturn:\\n pass\",\n \"def evaluate_model(model, X_train, y_train, X_test, y_test):\\n model = model\\n model.fit(X_train, y_train)\\n\\n y_pred = model.predict(X_test)\\n\\n report = classificationreport(y_test, y_pred, target_names= [\\\"0\\\", \\\"1\\\"], output_dict=True)\\n\\n return report\",\n \"def evaluate(self):\\n # Method variables definition\\n X_train, X_test, y_train, y_test = dm.reshape_y_set_split_data(self.datasetManager)\\n featureScaleDependentVariables = self.datasetManager.params.featureScaleDependentVariables\\n\\n # Feature Scaling\\n X_scaler, X_train = dm.do_feature_scaling(X_train)\\n if featureScaleDependentVariables:\\n y_scaler, y_train = dm.do_feature_scaling(y_train)\\n else:\\n y_scaler = None\\n y_train = self.datasetManager.y_train\\n \\n self.X_scaler = X_scaler\\n self.y_scaler = y_scaler\\n\\n # Training the SVR model on the training set\\n regressor = SVR(kernel = 'rbf')\\n regressor.fit(X_train, y_train.ravel())\\n self.regressor = regressor\\n\\n # Predicting the Test set results\\n self.y_pred = y_scaler.inverse_transform(regressor.predict(X_scaler.transform(X_test))) if featureScaleDependentVariables else regressor.predict(X_test)\\n \\n # Returning the process result : the regression type and the predicted dependent variables set\\n return [\\\"Support Vector Regression\\\", self.get_r2_score(y_test, self.y_pred)]\",\n \"def evaluate(self):\\n raise NotImplementedError()\",\n \"def evaluate(self, eval_data, eval_labels, eval_input_fn):\\n raise NotImplementedError(\\\"Method must be implemented by subclass\\\")\",\n \"def evaluate(self):\\n predictions = self.model.predict(self.test[0])\\n accuracy = accuracy_score(self.test[1], predictions)\\n print(\\\"Accuracy:\\\", str(accuracy * 100) + \\\"%\\\")\\n self.plot_results(predictions)\",\n \"def test_evaluate_error_score(error_score, return_data, strategy, backend):\\n # skip test for dask backend if dask is not installed\\n if backend == \\\"dask\\\" and not _check_soft_dependencies(\\\"dask\\\", severity=\\\"none\\\"):\\n return None\\n\\n forecaster = ExponentialSmoothing(sp=12)\\n y = load_airline()\\n # add NaN to make ExponentialSmoothing fail\\n y.iloc[1] = np.nan\\n fh = [1, 2, 3]\\n cv = ExpandingWindowSplitter(step_length=48, initial_window=12, fh=fh)\\n if error_score in [np.nan, 1000]:\\n with pytest.warns(FitFailedWarning):\\n results = evaluate(\\n forecaster=forecaster,\\n y=y,\\n cv=cv,\\n return_data=return_data,\\n error_score=error_score,\\n strategy=strategy,\\n backend=backend,\\n )\\n if isinstance(error_score, type(np.nan)):\\n assert results[\\\"test_MeanAbsolutePercentageError\\\"].isna().sum() > 0\\n if error_score == 1000:\\n assert results[\\\"test_MeanAbsolutePercentageError\\\"].max() == 1000\\n if error_score == \\\"raise\\\":\\n with pytest.raises(Exception): # noqa: B017\\n evaluate(\\n forecaster=forecaster,\\n y=y,\\n cv=cv,\\n return_data=return_data,\\n error_score=error_score,\\n strategy=strategy,\\n )\",\n \"def error_analysis(predictions, gold, result_collector):\\n # scores = defaultdict(list)\\n for iteration_id, texts in predictions.items():\\n # map iteration id to fold\\n fold = str(int(iteration_id) / 5)\\n for tid, pred_tree in texts.items():\\n gold_tree = gold[tid]\\n print(iteration_id, fold, tid)\\n print(gold_tree.get_triples())\\n print(pred_tree.get_triples())\\n for level, scores in eval_prediction([gold_tree], [pred_tree]):\\n result_collector.add_result(tid, fold, level, scores)\\n print(\\\"Done.\\\")\",\n \"def evaluate_model(self, predictions, expected, bypass_data_to_eval):\\n\\n result = []\\n for i, unique_id in enumerate(np.squeeze(expected[\\\"unique_ids\\\"])):\\n start_logits = predictions['tf_electra_for_question_answering'][i]\\n start_top_index = predictions['tf_electra_for_question_answering_1'\\n ][i]\\n end_logits = predictions['tf_electra_for_question_answering_2'][i]\\n end_top_index = predictions['tf_electra_for_question_answering_3'][i\\n ]\\n cls_logits = predictions['tf_electra_for_question_answering_4'][i]\\n\\n result.append(\\n SquadResult(\\n unique_id,\\n start_logits.tolist(),\\n end_logits.tolist(),\\n start_top_index=start_top_index.tolist(),\\n end_top_index=end_top_index.tolist(),\\n cls_logits=cls_logits.tolist(),\\n )\\n )\\n\\n dev_features = bypass_data_to_eval[\\\"dev_features\\\"]\\n dev_examples = bypass_data_to_eval[\\\"dev_examples\\\"]\\n\\n answers, nbest_answers = get_answers(\\n dev_examples, dev_features, result, self._args\\n )\\n\\n output_prediction_file = os.path.join(\\n self._args.output_dir, \\\"predictions.json\\\"\\n )\\n output_nbest_file = os.path.join(\\n self._args.output_dir, \\\"nbest_predictions.json\\\"\\n )\\n\\n with open(output_prediction_file, \\\"w\\\") as f:\\n f.write(json.dumps(answers, indent=4) + \\\"\\\\n\\\")\\n with open(output_nbest_file, \\\"w\\\") as f:\\n f.write(json.dumps(nbest_answers, indent=4) + \\\"\\\\n\\\")\\n\\n if self._args.version_2_with_negative:\\n dev_file = \\\"dev-v2.0.json\\\"\\n eval_file = \\\"evaluate-v2.0.py\\\"\\n else:\\n dev_file = \\\"dev-v1.1.json\\\"\\n eval_file = \\\"evaluate-v1.1.py\\\"\\n\\n command_str = (\\n f\\\"{sys.executable} {os.path.join(self._args.data_dir, eval_file)} \\\"\\n f\\\"{os.path.join(self._args.data_dir, dev_file)} \\\"\\n f\\\"{output_prediction_file}\\\"\\n )\\n\\n logging.debug(f\\\"\\\\nExecuting: `{command_str}`\\\\n\\\")\\n\\n eval_out = subprocess.check_output(shlex.split(command_str))\\n\\n # scores: {'exact_match': 87.06717123935667, 'f1': 92.78048326711645}\\n scores = json.loads(eval_out.decode(\\\"UTF-8\\\").strip())\\n\\n logging.debug(\\\"scores:\\\", scores)\\n\\n metric_units = \\\"f1\\\"\\n\\n return scores[metric_units], metric_units\",\n \"def evaluate(model, val_data, epoch):\\n print('validating')\\n\\n # 设置为评估模式 \\n model.eval() \\n\\n val_loss = []\\n with torch.no_grad():\\n DEVICE = config.DEVICE\\n\\n val_dataloader = DataLoader(dataset=val_data,\\n batch_size=config.batch_size,\\n shuffle=True,\\n pin_memory=True, drop_last=True,\\n collate_fn=collate_fn)\\n\\n for batch, data in enumerate(tqdm(val_dataloader)):\\n\\n x, y, x_len, y_len, oov, len_oovs = data\\n\\n if config.is_cuda:\\n x = x.to(DEVICE)\\n y = y.to(DEVICE)\\n x_len = x_len.to(DEVICE)\\n len_oovs = len_oovs.to(DEVICE)\\n\\n loss = model(x, x_len, y, len_oovs, batch=batch, \\n num_batches=len(val_dataloader),\\n teacher_forcing=True)\\n\\n val_loss.append(loss.item())\\n\\n return np.mean(val_loss)\",\n \"def eval(self):\\n\\n # parameters initialize\\n torch = import_optional_dependency(\\\"torch\\\")\\n eval_total = 0\\n eval_correct = 0\\n eval_loss = 0\\n self._set_eval()\\n\\n # display the information\\n if self.info:\\n print(f\\\"\\\\rEvaluating...\\\", end=\\\"\\\")\\n\\n # start eval part\\n for i, (source, target) in enumerate(self.eval_dataset):\\n # send data to device\\n source = source.to(self.device)\\n target = target.to(self.device)\\n\\n result = self.model(source)\\n eval_loss += self.criterion(result, target).item()\\n _, predicted = torch.max(result.data, 1)\\n eval_total += target.size(0)\\n eval_correct += (predicted == target).sum().item()\\n\\n accuracy = eval_correct / eval_total\\n eval_loss = eval_loss / eval_total\\n\\n if self.info:\\n print(f\\\"\\\\rEvaluation loss: { eval_loss } | Accuracy: { accuracy }\\\")\\n\\n return eval_loss, accuracy\",\n \"def test_epe_evaluate(self):\\n epe_metric = EPE()\\n epe_metric.process(self.data_batch, self.data_samples)\\n epe = epe_metric.evaluate(1)\\n self.assertAlmostEqual(epe['EPE'], 11.5355339)\",\n \"def evaluate(self, epoch, exploration_paths):\\n logger.log(\\\"Collecting samples for evaluation\\\")\\n paths = self._sample_eval_paths(epoch)\\n statistics = OrderedDict()\\n\\n statistics.update(self._statistics_from_paths(paths, \\\"Test\\\"))\\n statistics.update(self._get_other_statistics())\\n statistics.update(self._statistics_from_paths(exploration_paths,\\n \\\"Exploration\\\"))\\n\\n statistics['AverageReturn'] = get_average_returns(paths)\\n statistics['Epoch'] = epoch\\n\\n for key, value in statistics.items():\\n logger.record_tabular(key, value)\\n\\n self.log_diagnostics(paths)\",\n \"def evaluate(model, data):\\n n_targets = 0\\n n_correct_predictions = 0\\n\\n # Set the model on evaluatio mode.\\n model.eval()\\n\\n # Create progress bar.\\n progress_bar = tqdm.tqdm(total=len(data),\\n unit='batch',\\n desc='[evaluate] batch accuracy: 0.000',\\n leave=False)\\n\\n # Loop through validation batches.\\n for inputs, targets in data:\\n\\n # Send data to GPU if CUDA is enabled.\\n if next(model.parameters()).is_cuda:\\n inputs = inputs.cuda()\\n targets = targets.cuda()\\n\\n # Feed forward.\\n with torch.set_grad_enabled(False):\\n outputs = model(inputs)\\n\\n # Choose the class with maximum probability.\\n _, predictions = torch.max(outputs, 1)\\n\\n accuracy = (predictions == targets).sum().item() / len(targets)\\n progress_bar.update(1)\\n progress_bar.set_description(\\n '[evaluate] batch accuracy: {accuracy:.3f}'.format(\\n accuracy=accuracy))\\n\\n # Accumulate targets and correct predictions count.\\n n_targets += len(targets)\\n n_correct_predictions += (predictions == targets).sum().item()\\n\\n # Close progress bar.\\n progress_bar.close()\\n\\n return n_correct_predictions / n_targets\",\n \"def test_evaluation_error():\\n folding = Folding(DATASET,\\n reset=True)\\n\\n for fold in folding.fold():\\n sjob = SamplingJob(fold,\\n OVERSAMPLER,\\n OVERSAMPLER_PARAMS)\\n\\n result = sjob.do_oversampling()\\n\\n ejob = EvaluationJob(result,\\n [('smote_variants.classifiers',\\n 'ErrorWarningClassifier',\\n {'raise_value_error': True})])\\n\\n result_eval = ejob.do_evaluation()\\n\\n assert len(result_eval[0]['error']) > 3\\n\\n ejob = EvaluationJob(result,\\n [('smote_variants.classifiers',\\n 'ErrorWarningClassifier',\\n {'raise_runtime_error': True})])\\n\\n result_eval = ejob.do_evaluation()\\n\\n assert len(result_eval[0]['error']) > 3\",\n \"def evaluate(self, data, category):\\n n_data = len(data)\\n eval_scores = [{} for _ in range(n_data)]\\n\\n # Calculate average sentence-level scores for factual consistency\\n src_list, output_list = [], []\\n n_sents = [] # the number of sentences in the claim\\n for i in range(n_data):\\n source = data[i]['source']\\n system_outputs = sent_tokenize(data[i]['system_output'])\\n n_sents.append(len(system_outputs))\\n for j in range(len(system_outputs)):\\n src_list.append(source)\\n output_list.append(system_outputs[j])\\n input_list = add_question(dimension=self.dim, output=output_list, src=src_list, task=self.task)\\n sent_score = self.scorer.score(input_list, self.task, category, self.dim)\\n\\n # Get average score for each sample\\n start_idx = 0\\n score = []\\n for cur_n_sent in n_sents:\\n score.append(sum(sent_score[start_idx:start_idx + cur_n_sent]) / cur_n_sent)\\n start_idx += cur_n_sent\\n\\n for i in range(n_data):\\n eval_scores[i][self.dim] = score[i]\\n\\n return eval_scores\",\n \"def evaluate(self, session, *args, evaluate_data_iterator=None, **kwargs):\\n\\n raise NotImplementedError(\\\"Implement evaluate() method\\\")\",\n \"def evaluate(exe, metric, loss, correct, dev_program, data_loader,\\n phase=\\\"eval\\\"):\\n metric.reset()\\n returns = [loss]\\n if isinstance(correct, list) or isinstance(correct, tuple):\\n returns.extend(list(correct))\\n else:\\n returns.append(correct)\\n for batch in data_loader:\\n exe.run(dev_program, feed=batch, \\\\\\n fetch_list=returns)\\n return_numpys = exe.run(dev_program, feed=batch, \\\\\\n fetch_list=returns)\\n metric_numpy = return_numpys[1] if len(return_numpys[\\n 1:]) == 1 else return_numpys[1:]\\n metric.update(metric_numpy)\\n res = metric.accumulate()\\n if isinstance(metric, Mcc):\\n print(\\\"%s loss: %f, mcc: %s\\\" % (phase, return_numpys[0], res[0]))\\n elif isinstance(metric, PearsonAndSpearman):\\n print(\\\"%s loss: %f, pearson: %s, spearman: %s, pearson and spearman: %s\\\"\\n % (phase, return_numpys[0], res[0], res[1], res[2]))\\n else:\\n print(\\\"%s loss: %f, acc: %s, \\\" % (phase, return_numpys[0], res))\",\n \"def evaluate(self):\\n try:\\n self._evaluate()\\n except Exception as e:\\n if str(e) == \\\"assignment destination is read-only\\\":\\n log.exception(\\n \\\"Encountered error during scenario evaluation. Be sure \\\"\\n + \\\"that the classifier's predict() isn't directly modifying the \\\"\\n + \\\"input variable itself, as this can cause unexpected behavior in ART.\\\"\\n )\\n else:\\n log.exception(\\\"Encountered error during scenario evaluation.\\\")\\n sys.exit(1)\\n\\n if self.results is None:\\n log.warning(f\\\"{self._evaluate} did not set self.results to a dict\\\")\\n\\n self.save()\",\n \"def evaluate(self, X_test, y_test):\\n y_pred_train = self.pipeline.predict(self.X)\\n mse_train = mean_squared_error(self.y, y_pred_train)\\n rmse_train = np.sqrt(mse_train)\\n \\n self.mlflow_log_metric('rmse_train', rmse_train)\\n \\n y_pred_test = self.pipeline.predict(X_test)\\n mse_test = mean_squared_error(y_test, y_pred_test)\\n rmse_test = np.sqrt(mse_test)\\n self.mlflow_log_metric('rmse_test', rmse_test)\\n \\n return (round(rmse_train, 3) ,round(rmse_test, 3))\",\n \"def evaluate(self, data, label_indices, evaluator=np.argmax):\\n\\n assert(len(data) == len(label_indices))\\n\\n assert(len(data) == len(label_indices))\\n assert(data[0].shape == (self.sizes[0], 1))\\n for idx in label_indices:\\n assert(idx >= 0 and idx < self.sizes[-1])\\n\\n results = [(evaluator(self.feedforward(x)), y)\\n for (x, y) in zip(data, label_indices)]\\n\\n num = len(data)\\n num_correct = sum(int(x == y) for (x, y) in results)\\n num_incorrect = num - num_correct\\n accuracy = num_correct / num\\n\\n return {'num_testing': len(data),\\n 'num_correct': num_correct,\\n 'num_incorrect': num_incorrect,\\n 'accuracy': accuracy}\",\n \"def _evaluate(self,\\n logits,\\n predictions,\\n data, evaluation_fn,\\n max_eval_batches=None,\\n calculate_scores=True,\\n write_results=False):\\n # counting the evaluation batches\\n num_eval_batches = 0\\n # logits and predictions from the model\\n all_logits = []\\n all_predictions = []\\n # fetched data that led to the predictions\\n # dictionary of {seq_1: [], seq_2: [], target: []}\\n all_fetched_data = collections.defaultdict(list)\\n try:\\n while True:\\n # sample predictions\\n (fetched_logits,\\n fetched_predictions,\\n fetched_data) = self._fetch_data_batch(\\n logits=logits, predictions=predictions, data=data)\\n\\n # Cache the data\\n all_logits += fetched_logits\\n all_predictions += fetched_predictions\\n all_fetched_data[\\\"target\\\"] += fetched_data[\\\"target\\\"]\\n\\n # break the loop if max_eval_batches is set\\n num_eval_batches += 1\\n if (max_eval_batches and\\n num_eval_batches >= max_eval_batches):\\n break\\n\\n except tf.errors.OutOfRangeError:\\n pass\\n\\n # Evaluate\\n scores = None\\n if calculate_scores:\\n scores = evaluation_fn(\\n all_predictions,\\n all_fetched_data[\\\"seq_1\\\"], # Should be empty\\n all_fetched_data[\\\"seq_2\\\"], # Should be empty\\n all_fetched_data[\\\"target\\\"])\\n\\n if write_results:\\n _write_results_to_csv(\\n all_logits,\\n all_predictions,\\n all_fetched_data,\\n output_dir=os.path.join(\\n self._logdir, RESULTS_CSV_FNAME))\\n\\n return len(all_predictions), scores\",\n \"def evaluate_model(model_name, y_true, y_pred):\\n\\n # Calculate performance metrics\\n rmse_eval = evaluate_rmse(y_true, y_pred)\\n mae_eval = evaluate_mae(y_true, y_pred) \\n r2_eval = evaluate_r2(y_true, y_pred)\\n\\n # Print results\\n print_evaluation(model_name, mae_eval, rmse_eval, r2_eval)\",\n \"def main(cfg: DictConfig):\\n result, evaluation_metric = evaluate(cfg)\\n print(result)\\n print(f\\\"Validation dice coefficient: {result[evaluation_metric]}\\\")\",\n \"def evaluate(self, X_test, y_test):\\n self.run(self)\\n self.y_pred = self.pipeline.predict(X_test)\\n self.rmse = compute_rmse(self.y_pred, y_test)\",\n \"def mgcEval(self):\\n import numpy as np\\n def report_to_df(report):\\n\\n \\\"\\\"\\\"\\n function to convert classification report to dataframe (for visualisation plot)\\n \\\"\\\"\\\"\\n\\n report = re.sub(r\\\" +\\\", \\\" \\\", report).replace(\\\"avg / total\\\", \\\"avg/total\\\").replace(\\\"\\\\n \\\", \\\"\\\\n\\\")\\n # update this due to sklearn classification report output change\\n report = re.sub(r\\\" +\\\", \\\" \\\", report).replace(\\\"micro avg\\\", \\\"micro_avg\\\").replace(\\\"macro avg\\\", \\\"macro_avg\\\").replace(\\\"weighted avg\\\", \\\"weighted_avg\\\").replace(\\\"\\\\n \\\", \\\"\\\\n\\\")\\n report_df = pd.read_csv(StringIO(\\\"Classes\\\" + report), sep=' ', index_col=0) \\n return(report_df)\\n \\n #txt report to df\\n class_rpttop1 = classification_report(self.y_true, self.y_pred)\\n df_report = report_to_df(class_rpttop1)\\n\\n df_report = df_report.iloc[:self.nb_classes, :].copy()\\n df_report.index = df_report.index.astype(int)\\n \\n\\n # classifier prediction metrics\\n def classMetrics(averagex):\\n precision, recall, fscore, support = score(self.y_true, self.y_pred, average=averagex)\\n \\n return(\\n print(''), \\n print('-------------{0:}--------------------'.format(averagex)), \\n print('precision: {0:.4f}'.format(precision)),\\n print('recall: {0:.4f}'.format(recall)),\\n print('fscore: {0:.4f}'.format(fscore)),\\n print(''),\\n print('kappa score: {0:.4f}'.format(cohen_kappa_score(self.y_true, self.y_pred))),\\n print('accuracy score: {0:.4f}'.format(accuracy_score(self.y_true, self.y_pred))))\\n \\n def predSamp():\\n\\n correct = np.nonzero(self.y_pred==self.y_true)[0]\\n incorrect = np.nonzero(self.y_pred!=self.y_true)[0]\\n\\n # quick check of the number of correct prediction from validation set\\n print(\\\"\\\")\\n print(\\\"correct/total = {0: .4f}\\\".format(len(correct)/(len(correct)+len(incorrect))))\\n print(\\\"total correct sample = {0: .0f}\\\".format(len(correct)))\\n print('------------------------------------------------------------------')\\n \\n def classReport():\\n print('----------------------------- Classfication Report -------------------------------')\\n print(classification_report(pd.Series(self.y_true).map(self.dict_label), pd.Series(self.y_pred).map(self.dict_label)))\\n \\n self.class_rpt = pd.concat([pd.DataFrame(pd.Series(df_report.index.tolist()).map(self.dict_label), columns = ['label']), df_report], axis = 1)\\n \\n self.classMetricsMac = classMetrics(\\\"macro\\\")\\n self.classMetricsMic = classMetrics(\\\"micro\\\")\\n self.predSample = predSamp()\\n self.class_rptTop1 = classReport()\\n \\n return self\",\n \"def train_and_evaluate(name, model, train, test, evaluation, final_eval, output_dir):\\n\\n print(\\\"---\\\" * 5)\\n print(\\\"Running pipeline for {}\\\".format(name))\\n\\n plot_dir = os.path.join(output_dir, \\\"plots\\\")\\n\\n pipeline = make_pipeline(model)\\n\\n X_train, y_train = train.drop(\\n [\\\"PM10\\\"], axis=1).values, train[\\\"PM10\\\"].values\\n X_test, y_test = test.drop([\\\"PM10\\\"], axis=1).values, test[\\\"PM10\\\"].values\\n X_eval, y_eval = evaluation.drop(\\n [\\\"PM10\\\"], axis=1).values, evaluation[\\\"PM10\\\"].values\\n X_final, y_final = final_eval.drop(\\n [\\\"PM10\\\"], axis=1), final_eval[\\\"PM10\\\"].values\\n\\n # first round - fit on train, predict on test\\n print(\\\"Fitting pipeline on train data\\\")\\n pipeline.fit(X_train, y_train)\\n yhat = pipeline.predict(X_test)\\n mae = mean_absolute_error(y_test, yhat)\\n print(\\\"MAE: {}\\\".format(mae))\\n plot_predictions(\\n y_test, yhat, title=\\\"{} - Predicted vs. Actual on Test\\\".format(name), output_dir=plot_dir)\\n\\n # second round - fit on train + test, predict on evaluation\\n X_train = np.concatenate([X_train, X_test])\\n y_train = np.concatenate([y_train, y_test])\\n print(\\\"Fitting pipeline on train + test data\\\")\\n pipeline.fit(X_train,y_train)\\n yhat = pipeline.predict(X_eval)\\n mae = mean_absolute_error(y_eval,yhat)\\n print(\\\"MAE: {}\\\".format(mae))\\n plot_predictions(y_eval,yhat,title=\\\"{} - Predicted vs. Actual on Evaluation\\\".format(name),output_dir=plot_dir)\\n\\n # final round - fit on last X hours, by which the actual score will be measured\\n X_train = np.concatenate([X_train, X_eval])\\n y_train = np.concatenate([y_train, y_eval])\\n print(\\\"Fitting pipeline on all \\\\\\\"all available data\\\\\\\"\\\")\\n pipeline.fit(X_train, y_train)\\n yhat = pipeline.predict(X_final)\\n mae = mean_absolute_error(y_final, yhat)\\n print(\\\"MAE: {}\\\".format(mae))\\n plot_predictions(\\n y_final, yhat, title=\\\"{} - Predicted vs. Actual\\\".format(name), output_dir=plot_dir)\\n\\n # save the model\\n joblib.dump(model, os.path.join(\\n output_dir, \\\"models\\\", \\\"{}.joblib\\\".format(name)))\\n\\n return yhat, mae\",\n \"def evaluate(network: nn.Module, data: DataLoader, metric: callable) -> list:\\n error_list = []\\n with torch.no_grad():\\n for x, y in data:\\n \\n prediction = network.forward(x)\\n error_list.append(metric(prediction, y))\\n \\n return torch.tensor(error_list)\",\n \"def evaluate(self,\\n data: Union[\\\"SparkXShards\\\",\\n \\\"SparkDataFrame\\\",\\n \\\"TFDataset\\\",\\n \\\"ray.data.Dataset\\\",\\n Callable],\\n batch_size: int=32,\\n num_steps: Optional[int]=None,\\n verbose: Union[str, int]=1,\\n sample_weight: Optional[\\\"np.ndarray\\\"]=None,\\n callbacks: Optional[List[\\\"Callback\\\"]]=None,\\n data_config: Optional[Dict]=None,\\n feature_cols: Optional[List[str]]=None,\\n label_cols: Optional[List[str]]=None) -> Dict:\\n if not isinstance(data, types.FunctionType):\\n invalidInputError(isinstance(batch_size, int) and batch_size > 0,\\n \\\"batch_size should be a positive integer\\\")\\n else:\\n # batch_size can be None if the return of data_creator already generates batches\\n if batch_size:\\n invalidInputError(isinstance(batch_size, int) and batch_size > 0,\\n \\\"batch_size should be a positive integer\\\")\\n # Use the local batch size for each worker to convert to XShards\\n if batch_size:\\n local_batch_size = batch_size // self.num_workers\\n if local_batch_size <= 0:\\n local_batch_size = 1\\n else:\\n local_batch_size = None\\n logger.info(\\\"Starting validation step.\\\")\\n params = dict(\\n batch_size=batch_size,\\n verbose=verbose,\\n sample_weight=sample_weight,\\n steps=num_steps,\\n callbacks=callbacks,\\n data_config=data_config,\\n )\\n from bigdl.orca.data import SparkXShards\\n from bigdl.orca.data.tf.data import Dataset\\n from bigdl.orca.data.tf.tf2_data import TF2Dataset\\n\\n data, _ = maybe_dataframe_to_xshards(data,\\n validation_data=None,\\n feature_cols=feature_cols,\\n label_cols=label_cols,\\n mode=\\\"evaluate\\\",\\n num_workers=self.num_workers,\\n accept_str_col=True,\\n shard_size=local_batch_size)\\n\\n if isinstance(data, SparkXShards):\\n # Make sure each worker can get at least one data partition\\n if data.num_partitions() < self.num_workers:\\n data = data.repartition(self.num_workers)\\n if data._get_class_name() == 'pandas.core.frame.DataFrame':\\n data = process_xshards_of_pandas_dataframe(data, feature_cols, label_cols)\\n ray_xshards = RayXShards.from_spark_xshards(data) # type:ignore\\n worker_stats = self._evaluate_ray_xshards(ray_xshards, params)\\n elif isinstance(data, Dataset):\\n ray_xshards = TF2Dataset(data).get_ray_xshards(self.num_workers)\\n worker_stats = self._evaluate_ray_xshards(ray_xshards, params)\\n elif isinstance(data, ray.data.Dataset):\\n shards = data.split(n=self.num_workers, locality_hints=self.remote_workers)\\n\\n remote_worker_stats = []\\n for shard, worker in zip(shards, self.remote_workers):\\n params[\\\"data_creator\\\"] = self.process_ray_dataset(shard,\\n label_cols,\\n feature_cols,\\n data_config)\\n remote_worker_stats.append(worker.validate.remote(**params))\\n worker_stats = ray.get(remote_worker_stats)\\n else: # data_creator functions; should return Iter or DataLoader\\n params[\\\"data_creator\\\"] = data # type:ignore\\n params_list = [params] * self.num_workers\\n\\n worker_stats = ray.get([w.validate.remote(**params_list[i])\\n for i, w in enumerate(self.remote_workers)])\\n stats = worker_stats[0].copy()\\n return stats\",\n \"def post_eval_callout(outdir, X_data, Y_pred, E_pred, N_SP_pred, Y_data=None , tail=None) -> None:\\n \\n error = np.sum((Y_pred[:,0:2] - Y_data[:, 0:2])**2, axis = 1)\\n error = np.reshape(error, (error.shape[0],1))\\n I1, _, E = compute_l2_norm(error, X_data[:,1:3], outdim=2)\\n\\n data = {}\\n data['E_init'] = E_pred[0]\\n data['E_PI'] = E_pred[1]\\n data['N_SP'] = int(N_SP_pred)\\n data['E_ref'] = np.sqrt(I1+ E)\\n\\n print(os.path.join(outdir, \\\"run_\\\" + get_prefix() +tail[:-4] + \\\"_error.json\\\"))\\n out_file = open( os.path.join(outdir,\\\"..\\\", \\\"run_\\\"+get_prefix()+\\\"values\\\", \\\"run_\\\" + get_prefix() +tail[:-4] + \\\"_error.json\\\"), \\\"w\\\")\\n json.dump(data, out_file, indent=4)\\n out_file.close()\\n \\n return\",\n \"def evaluate(self) -> Dict[str, float]:\\n eval_dataloader = self.get_eval_dataloader()\\n\\n output = self._prediction_loop(eval_dataloader, description=\\\"Evaluation\\\")\\n return output.metrics\",\n \"def evaluate_model():\\n\\n print '\\\\n\\\\tevaluate result'\\n os.system('./conlleval.pl -d \\\\'\\\\t\\\\' < ' + encoded_test + ' >> ' + result_file)\\n print '\\\\t--done\\\\n'\",\n \"def test_run_experiment_lr_eval_with_object(self):\\n source = \\\"lr-eval-object\\\"\\n experiment_id = \\\"lr_eval_object\\\"\\n\\n configdir = join(rsmtool_test_dir, \\\"data\\\", \\\"experiments\\\", source)\\n\\n config_dict = {\\n \\\"predictions_file\\\": \\\"../../files/predictions_scaled_with_subgroups.csv\\\",\\n \\\"system_score_column\\\": \\\"score\\\",\\n \\\"description\\\": \\\"An evaluation of LinearRegression predictions.\\\",\\n \\\"human_score_column\\\": \\\"h1\\\",\\n \\\"id_column\\\": \\\"id\\\",\\n \\\"experiment_id\\\": \\\"lr_eval_object\\\",\\n \\\"subgroups\\\": \\\"QUESTION\\\",\\n \\\"scale_with\\\": \\\"asis\\\",\\n \\\"trim_min\\\": 1,\\n \\\"trim_max\\\": 6,\\n }\\n\\n config_obj = Configuration(config_dict, context=\\\"rsmeval\\\", configdir=configdir)\\n\\n check_run_evaluation(source, experiment_id, config_obj_or_dict=config_obj)\",\n \"def evaluate_model():\\n\\n # Get the processed data (in proper format to evaluate the NER model)\\n data = get_json_from_file_path(PROCESSED_DATA_PATH)\\n # Split the dataset for training and test as we did for training\\n train_data, test_data = train_test_split(data, train_size=0.7, \\n random_state=4)\\n\\n # Load the model trained\\n try:\\n ner_model = spacy.load(OUTPUT_MODEL_PATH)\\n except Exception as err:\\n msg = f'Could not load the model. Error: {err}'\\n raise Exception(msg)\\n\\n # Compute evaluation scores\\n print('Computing metrics...')\\n scores = evaluate(ner_model, test_data)\\n # General metrics of the model\\n f_score = scores.get('ents_f')\\n precision = scores.get('ents_p')\\n recall = scores.get('ents_r')\\n print('\\\\nScoring:')\\n print(f'F-score: {f_score}')\\n print(f'Precision: {precision}')\\n print(f'Recall: {recall}')\\n\\n # Get the specific scores for each entity \\n scores_per_entity = scores.get('ents_per_type')\\n # Get the F-score of the entities\\n f_scores_of_entities = []\\n for entity_scores in scores_per_entity.values():\\n f_scores_of_entities.append(entity_scores['f'])\\n # Compute the macro averaged F-score\\n macro_avg_f_score = sum(f_scores_of_entities)/len(f_scores_of_entities)\\n print(f'Macro averaged F-score: {macro_avg_f_score}')\\n \\n print('\\\\nScores per entity;')\\n print('{:<15} {:<10} {:<10} {:<10}'.format('Entity','F-score','Precision','Recall'))\\n for key, value in scores_per_entity.items():\\n entity = key\\n f, p, r = value['f'], value['p'], value['r']\\n print('{:<15} {:<10.2f} {:<10.2f} {:<10.2f}'.format(entity, f, p, r))\",\n \"def runEval(self, cmdText, varName, rhs):\\n\\n _globals= self._globals\\n\\n self.logPyCode= '# Error: ' + str(varName) + '=' + str(rhs)\\n try:\\n rhs= self.pyFromVec(rhs)\\n except:\\n print(\\\"Error: runEval at pyFromVec: \\\" + rhs)\\n\\n try:\\n rhs= self.pyFromEng(rhs)\\n except:\\n print(\\\"Error: runEval at pyFromEng: \\\" + rhs)\\n\\n saved_handler = np.seterrcall(self.err_handler)\\n save_err = np.seterr(divide='call', over='call', under='call',\\n invalid='call')\\n\\n if varName:\\n self.logPyCode= str(varName) + '=' + str(rhs)\\n else:\\n self.logPyCode= str(rhs)\\n\\n try:\\n result= eval(str(rhs), _globals, _globals)\\n _globals[str(varName)]= result\\n success= True\\n except:\\n self.logCommandRHS= '# Error: ' + self.logPyCode\\n result= 0\\n success= False\\n np.seterrcall(saved_handler)\\n return result, success\",\n \"def do_system_evaluation(dataset, result_path, dataset_evaluation_mode='folds'):\\n\\n # Set warnings off, sklearn metrics will trigger warning for classes without\\n # predicted samples in F1-scoring. This is just to keep printing clean.\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n overall_metrics_per_scene = {}\\n\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n if scene_label not in overall_metrics_per_scene:\\n overall_metrics_per_scene[scene_label] = {}\\n\\n dcase2016_segment_based_metric = DCASE2016_EventDetection_SegmentBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\\n dcase2016_event_based_metric = DCASE2016_EventDetection_EventBasedMetrics(class_list=dataset.event_labels(scene_label=scene_label))\\n\\n for fold in dataset.folds(mode=dataset_evaluation_mode):\\n results = []\\n result_filename = get_result_filename(fold=fold, scene_label=scene_label, path=result_path)\\n\\n if os.path.isfile(result_filename):\\n with open(result_filename, 'rt') as f:\\n for row in csv.reader(f, delimiter='\\\\t'):\\n results.append(row)\\n else:\\n raise IOError(\\\"Result file not found [%s]\\\" % result_filename)\\n\\n for file_id, item in enumerate(dataset.test(fold, scene_label=scene_label)):\\n current_file_results = []\\n for result_line in results:\\n if len(result_line) != 0 and result_line[0] == dataset.absolute_to_relative(item['file']):\\n current_file_results.append(\\n {'file': result_line[0],\\n 'event_onset': float(result_line[1]),\\n 'event_offset': float(result_line[2]),\\n 'event_label': result_line[3].rstrip()\\n }\\n )\\n meta = dataset.file_meta(dataset.absolute_to_relative(item['file']))\\n\\n dcase2016_segment_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\\n dcase2016_event_based_metric.evaluate(system_output=current_file_results, annotated_ground_truth=meta)\\n\\n overall_metrics_per_scene[scene_label]['segment_based_metrics'] = dcase2016_segment_based_metric.results()\\n overall_metrics_per_scene[scene_label]['event_based_metrics'] = dcase2016_event_based_metric.results()\\n\\n print \\\" Evaluation over %d folds\\\" % dataset.fold_count\\n print \\\" \\\"\\n print \\\" Results per scene \\\"\\n print \\\" {:18s} | {:5s} | | {:39s} \\\".format('', 'Main', 'Secondary metrics')\\n print \\\" {:18s} | {:5s} | | {:38s} | {:14s} | {:14s} | {:14s} \\\".format('', '', 'Seg/Overall','Seg/Class', 'Event/Overall','Event/Class')\\n print \\\" {:18s} | {:5s} | | {:6s} : {:5s} : {:5s} : {:5s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} | {:6s} : {:5s} |\\\".format('Scene', 'ER', 'F1', 'ER', 'ER/S', 'ER/D', 'ER/I', 'F1', 'ER', 'F1', 'ER', 'F1', 'ER')\\n print \\\" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\\\"\\n averages = {\\n 'segment_based_metrics': {\\n 'overall': {\\n 'ER': [],\\n 'F': [],\\n },\\n 'class_wise_average': {\\n 'ER': [],\\n 'F': [],\\n }\\n },\\n 'event_based_metrics': {\\n 'overall': {\\n 'ER': [],\\n 'F': [],\\n },\\n 'class_wise_average': {\\n 'ER': [],\\n 'F': [],\\n }\\n },\\n }\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n print \\\" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:5.2f} : {:5.2f} : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\\\".format(scene_label,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'] * 100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['S'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['D'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['I'],\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'],\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'],\\n )\\n averages['segment_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['ER'])\\n averages['segment_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['overall']['F'])\\n averages['segment_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'])\\n averages['segment_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F'])\\n averages['event_based_metrics']['overall']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['ER'])\\n averages['event_based_metrics']['overall']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['overall']['F'])\\n averages['event_based_metrics']['class_wise_average']['ER'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\\n averages['event_based_metrics']['class_wise_average']['F'].append(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F'])\\n\\n print \\\" -------------------+-------+ +--------+-------+-------+-------+-------+--------+-------+--------+-------+--------+-------+\\\"\\n print \\\" {:18s} | {:5.2f} | | {:4.1f} % : {:5.2f} : {:21s} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} | {:4.1f} % : {:5.2f} |\\\".format('Average',\\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\\n numpy.mean(averages['segment_based_metrics']['overall']['F'])*100,\\n numpy.mean(averages['segment_based_metrics']['overall']['ER']),\\n ' ',\\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['F'])*100,\\n numpy.mean(averages['segment_based_metrics']['class_wise_average']['ER']),\\n numpy.mean(averages['event_based_metrics']['overall']['F'])*100,\\n numpy.mean(averages['event_based_metrics']['overall']['ER']),\\n numpy.mean(averages['event_based_metrics']['class_wise_average']['F'])*100,\\n numpy.mean(averages['event_based_metrics']['class_wise_average']['ER']),\\n )\\n\\n print \\\" \\\"\\n # Restore warnings to default settings\\n warnings.simplefilter(\\\"default\\\")\\n print \\\" Results per events \\\"\\n\\n for scene_id, scene_label in enumerate(dataset.scene_labels):\\n print \\\" \\\"\\n print \\\" \\\"+scene_label.upper()\\n print \\\" {:20s} | {:30s} | | {:15s} \\\".format('', 'Segment-based', 'Event-based')\\n print \\\" {:20s} | {:5s} : {:5s} : {:6s} : {:5s} | | {:5s} : {:5s} : {:6s} : {:5s} |\\\".format('Event', 'Nref', 'Nsys', 'F1', 'ER', 'Nref', 'Nsys', 'F1', 'ER')\\n print \\\" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\\\"\\n seg_Nref = 0\\n seg_Nsys = 0\\n\\n event_Nref = 0\\n event_Nsys = 0\\n for event_label in sorted(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise']):\\n print \\\" {:20s} | {:5d} : {:5d} : {:4.1f} % : {:5.2f} | | {:5d} : {:5d} : {:4.1f} % : {:5.2f} |\\\".format(event_label,\\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref']),\\n int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys']),\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['ER'],\\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref']),\\n int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys']),\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['ER'])\\n seg_Nref += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nref'])\\n seg_Nsys += int(overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise'][event_label]['Nsys'])\\n\\n event_Nref += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nref'])\\n event_Nsys += int(overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise'][event_label]['Nsys'])\\n print \\\" ---------------------+-------+-------+--------+-------+ +-------+-------+--------+-------+\\\"\\n print \\\" {:20s} | {:5d} : {:5d} : {:14s} | | {:5d} : {:5d} : {:14s} |\\\".format('Sum',\\n seg_Nref,\\n seg_Nsys,\\n '',\\n event_Nref,\\n event_Nsys,\\n '')\\n print \\\" {:20s} | {:5s} {:5s} : {:4.1f} % : {:5.2f} | | {:5s} {:5s} : {:4.1f} % : {:5.2f} |\\\".format('Average',\\n '', '',\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['segment_based_metrics']['class_wise_average']['ER'],\\n '', '',\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['F']*100,\\n overall_metrics_per_scene[scene_label]['event_based_metrics']['class_wise_average']['ER'])\\n print \\\" \\\"\",\n \"def eval(approach, datapath, incremental):\\n param_grid = approaches[approach] if not incremental else params[approach]\\n report_path = evaluate(datapath,\\n approach=approach,\\n param_grid=param_grid,\\n incremental=incremental)\\n c.echo('Report compiled at {0}.'.format(report_path))\",\n \"def evaluate(pred, ground_truth, target, count_to_level = False, Y_name = None, thresholds = None, print_metrics=True):\\n \\n if target == 'count':\\n \\n # fill NaNs with zeroes\\n pred = pred.fillna(method = \\\"ffill\\\")\\n pred = pred.fillna(method = \\\"bfill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"ffill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"bfill\\\")\\n \\n # Set negative predictions to zero\\n pred[pred < 0] = 0\\n ground_truth[ground_truth < 0] = 0\\n \\n # Calculate error metrics\\n rmse = mean_squared_error(ground_truth, pred, squared=False)\\n mae = mean_absolute_error(ground_truth, pred)\\n \\n # Calculate error metrics only for crowded moments (p75) \\n busy = np.percentile(ground_truth, 75)\\n ground_truth_busy = ground_truth[ground_truth > busy].dropna()\\n pred_busy = pred[ground_truth > busy].dropna()\\n rmse_busy = mean_squared_error(ground_truth_busy, pred_busy, squared=False)\\n mae_busy = mean_absolute_error(ground_truth_busy, pred_busy)\\n \\n # Store error metrics in dict\\n error_metrics = dict({'rmse': rmse, 'rmse_busy': rmse_busy, 'mae': mae, 'mae_busy': mae_busy})\\n \\n if print_metrics:\\n print(f\\\"Root mean squared error: {rmse.round(1)}\\\")\\n print(f\\\"Root mean squared error (crowded): {rmse_busy.round(1)}\\\")\\n print(f\\\"Mean absolute error: {mae.round(1)}\\\")\\n print(f\\\"Mean absolute error (crowded): {mae_busy.round(1)}\\\")\\n \\n if count_to_level:\\n pred = get_crowd_levels(pred, Y_name, thresholds)\\n ground_truth = get_crowd_levels(ground_truth, Y_name, thresholds)\\n \\n # Confusion matrix\\n conf_mat = confusion_matrix(ground_truth, pred)\\n \\n error_metrics['conf_mat'] = conf_mat\\n \\n elif target == \\\"level\\\":\\n \\n # Set dtype to category\\n pred = pred.astype('category')\\n ground_truth = ground_truth.astype('category')\\n \\n # Forward fill NaNs\\n pred = pred.fillna(method = \\\"ffill\\\")\\n ground_truth = ground_truth.fillna(method = \\\"ffill\\\")\\n \\n # Confusion matrix\\n conf_mat = confusion_matrix(ground_truth, pred)\\n \\n # Classification report (recall, precision, F1)\\n class_report = classification_report(ground_truth, pred, output_dict = True)\\n class_report = pd.DataFrame(class_report).transpose()\\n \\n error_metrics = dict({\\\"conf_mat\\\": conf_mat, \\\"class_report\\\": class_report})\\n \\n if print_metrics:\\n print(f\\\"Confusion matrix: {conf_mat}\\\")\\n print(f\\\"Classification report: {class_report}\\\")\\n \\n return error_metrics\",\n \"def evaluate(self, X_test, y_test):\\n pipeline = run()\\n y_pred = pipeline.predict(X_test)\\n rmse = compute_rmse(y_pred, y_test)\\n print(rmse)\\n return rmse\",\n \"def evaluator(model, config, test_dir=None):\\n shottype = config.shottype\\n dataset = config.data_set\\n seed = config.seed\\n if test_dir is None:\\n test_data_gen_dir, _, _ = _generator_dir(\\n config=config, target_gen=\\\"test\\\", data_dir=None\\n )\\n if test_dir is not None:\\n print(\\\"Evaluating directory: '{}'.\\\".format(test_dir))\\n test_data_gen_dir, _, _ = _generator_dir(\\n config=config, target_gen=\\\"test\\\", data_dir=test_dir\\n )\\n score = model.evaluate_generator(test_data_gen_dir)\\n print(\\n \\\"Test metrics: \\\"\\n \\\"Loss: {:.4f}, \\\"\\n \\\"Accuracy: {:.4f}, \\\"\\n \\\"Top 3 accuracy: {:.4f}\\\".format(score[0], score[1], score[2])\\n )\\n return score\",\n \"def evaluate(self, params):\\n model = self.train(params)\\n Y_pred = model.predict(self.X_test)\\n\\n if self.dataset_type == PROBLEM.CLASSIFICATION:\\n return {\\n 'score': accuracy_score(self.Y_test, Y_pred),\\n 'matrix': confusion_matrix(self.Y_test, Y_pred).tolist(),\\n 'report': classification_report(self.Y_test, Y_pred,\\n target_names=self.labels,\\n zero_division=1)\\n }\\n else:\\n return {\\n 'max_error': max_error(self.Y_test, Y_pred),\\n 'mae': mean_absolute_error(self.Y_test, Y_pred),\\n 'mse': mean_squared_error(self.Y_test, Y_pred)\\n }\",\n \"def run(self):\\n logging.info('running experiment...')\\n self._prepare()\\n self._load_data()\\n self._run()\\n self._evaluate()\\n self._summarise()\\n return True\",\n \"def run_analyses(y_predict_train, y_train, y_predict, y_test):\\n # calculate metrics\\n _, training_error = output_error(y_predict_train, y_train)\\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\\n \\n # print out metrics\\n print 'Average Precision:', np.average(precision)\\n print 'Average Recall:', np.average(recall)\\n print 'Average F1:', np.average(f1)\\n print 'Training Error:', training_error\\n print 'Testing Error:', testing_error\",\n \"def print_evaluation(model_name, mae_eval, rmse_eval, r2_eval):\\n\\n print(model_name, \\\"rmse (Eval):\\\", rmse_eval)\\n print(model_name, \\\"mae (Eval):\\\", mae_eval)\\n print(model_name, \\\"r2 (Eval):\\\", r2_eval)\",\n \"def evaluate(dataset, predictions, nms_thresh, recall_metrics=(1,5), iou_metrics=(0.3,0.5,0.7)):\\n dataset_name = dataset.__class__.__name__\\n logger = logging.getLogger(\\\"tcn-vmr.inference\\\")\\n logger.info(\\\"Performing {} evaluation (Size: {}).\\\".format(dataset_name, len(dataset)))\\n \\n num_recall_metrics, num_iou_metrics = len(recall_metrics), len(iou_metrics)\\n table = [['Rank@{},mIoU@{}'.format(i,j) \\\\\\n for i in recall_metrics for j in iou_metrics]]\\n \\n recall_metrics = torch.tensor(recall_metrics)\\n iou_metrics = torch.tensor(iou_metrics)\\n recall_x_iou = torch.zeros(num_recall_metrics, num_iou_metrics)\\n\\n num_clips = predictions[0].shape[-1]\\n\\n\\n for idx, score2d in tqdm(enumerate(predictions)): \\n duration = dataset.get_duration(idx)\\n moment = dataset.get_moment(idx) \\n\\n candidates, scores = score2d_to_moments_scores(score2d, num_clips, duration)\\n moments = nms(candidates, scores, topk=recall_metrics[-1], thresh=nms_thresh)\\n\\n for i, r in enumerate(recall_metrics):\\n mious = iou(moments[:r], dataset.get_moment(idx))\\n bools = mious[:,None].expand(r, num_iou_metrics) > iou_metrics\\n recall_x_iou[i] += bools.any(dim=0)\\n\\n recall_x_iou /= len(predictions)\\n\\n table.append(['{:.02f}'.format(recall_x_iou[i][j]*100) \\\\\\n for i in range(num_recall_metrics) for j in range(num_iou_metrics)])\\n\\n \\n table = AsciiTable(table)\\n for i in range(num_recall_metrics*num_iou_metrics):\\n table.justify_columns[i] = 'center'\\n\\n logger.info('\\\\n' + table.table)\",\n \"def dependent_error_exp(data, weak_signal_data, num_weak_signal):\\n\\n w_model = train_weak_signals(data, weak_signal_data, num_weak_signal)\\n\\n training_data = data['training_data'][0].T\\n training_labels = data['training_data'][1]\\n val_data, val_labels = data['validation_data']\\n val_data = val_data.T\\n test_data = data['test_data'][0].T\\n test_labels = data['test_data'][1]\\n\\n num_features, num_data_points = training_data.shape\\n\\n weak_signal_ub = w_model['error_bounds']\\n weak_signal_probabilities = w_model['probabilities']\\n weak_test_accuracy = w_model['test_accuracy']\\n\\n weights = np.zeros(num_features)\\n\\n print(\\\"Running tests...\\\")\\n\\n optimized_weights, ineq_constraint = train_all(val_data, weights, weak_signal_probabilities, weak_signal_ub, max_iter=5000)\\n\\n # calculate test probabilities\\n test_probabilities = probability(test_data, optimized_weights)\\n # calculate test accuracy\\n test_accuracy = getModelAccuracy(test_probabilities, test_labels)\\n\\n print(\\\"\\\")\\n print(\\\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\\\")\\n print(\\\"Experiment %d\\\"%num_weak_signal)\\n print(\\\"We trained %d learnable classifiers with %d weak signals\\\" %(1, num_weak_signal))\\n print(\\\"The accuracy of the model on the test data is\\\", test_accuracy)\\n print(\\\"The accuracy of weak signal(s) on the test data is\\\", weak_test_accuracy)\\n print(\\\"\\\")\\n\\n # calculate ge criteria\\n print(\\\"Running tests on ge criteria...\\\")\\n model = ge_criterion_train(val_data.T, val_labels, weak_signal_probabilities, num_weak_signal)\\n ge_test_accuracy = accuracy_score(test_labels, np.round(probability(test_data, model)))\\n print(\\\"The accuracy of ge criteria on test data is\\\", ge_test_accuracy)\\n print(\\\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\\\")\\n\\n # calculate baseline\\n print(\\\"Running tests on the baselines...\\\")\\n baselines = runBaselineTests(val_data, weak_signal_probabilities)\\n b_test_accuracy = getWeakSignalAccuracy(test_data, test_labels, baselines)\\n print(\\\"The accuracy of the baseline models on test data is\\\", b_test_accuracy)\\n print(\\\"\\\")\\n\\n output = {}\\n output['ALL'] = test_accuracy\\n output['WS'] = w_model['test_accuracy'][-1]\\n output['GE'] = ge_test_accuracy\\n output['AVG'] = b_test_accuracy[-1]\\n\\n return output\",\n \"def evaluate(model, test_files):\\n print(\\\"Running predictions.\\\")\\n models = load_model(model)\\n predictions = predict(models, test_files)\\n\\n # # write predictions to file\\n # write_predictions(\\\"evaluate_out.json\\\",predictions)\\n evaluate_individual(predictions, test_files, models)\\n evaluate_overall(predictions)\",\n \"def print_eval(trainset, testset, exptypes=EXPTYPES, semantic=False, savemodels=False, loadmodels=False, deprep=False, externals=True, predict=True):\\n system_pairs = []\\n print \\\"== cleaning lsts ==\\\"\\n cleanupnonespanexpressions(testset)\\n cleanholdercandidates(testset)\\n cleanholders(testset)\\n cleanupnonespanexpressions(trainset)\\n cleanholdercandidates(trainset)\\n cleanholders(trainset)\\n \\n print \\\"== train ==\\\"\\n ev = evaluate()\\n features, labels, stats = getfeaturesandlabels(trainset, semantic=semantic, predict=False)\\n print counters, '\\\\n'\\n\\n print \\\"== test ==\\\"\\n counters.clear()\\n ftest, ltest, stest = getfeaturesandlabels(testset, semantic=semantic, predict=predict)\\n print counters\\n for exp in exptypes:\\n vec, X, y = create_matrix(features[exp], labels[exp])\\n if externals:\\n vecw, Xw, yw = create_matrix(features[exp + 'w'], labels[exp + 'w'])\\n vecimp, Ximp, yimp = create_matrix(features[exp + 'w'], labels[exp + 'implicit'])\\n if loadmodels:\\n clf = read_model(loadmodels + exp)\\n else:\\n clf = create_model(X, y)\\n if externals:\\n clfw = create_model(Xw, yw)\\n clfimp = create_model(Ximp, yimp)\\n if savemodels:\\n write_model(clf, savemodels + exp)\\n print \\\"== eval ==\\\"\\n if deprep:\\n print \\\"== {} ==\\\".format(deprep)\\n Xt, yt = transform_to_matrix(ftest[exp], ltest[exp], vec)\\n if externals:\\n Xtw, ytw = transform_to_matrix(ftest[exp + 'w'], ltest[exp + 'w'], vecw)\\n Xtimp, ytimp = transform_to_matrix(ftest[exp + 'w'], ltest[exp + 'implicit'], vecimp)\\n results = clf.predict_proba(Xt)\\n s_p_w = False\\n s_p_imp = False\\n gold_p1 = ev.get_unique_exp(copy.deepcopy(stest['positions'][exp + 'w']), exp, count=False)\\n gold_p2 = copy.deepcopy(gold_p1)\\n gold_p3 = copy.deepcopy(gold_p1)\\n if clfw:\\n resultsw = clfw.predict_proba(Xtw)\\n s_p_w=ev.get_system_pairs_prob(stest['positions'][exp + 'w'], resultsw, gold_p1)\\n counters['s_p_w' + exp] = len(s_p_w)\\n if DEBUG:\\n print \\\"RESULTSW\\\"\\n print resultsw\\n if clfimp:\\n resultsimp = clfimp.predict_proba(Xtimp)\\n s_p_imp=ev.get_system_pairs_prob(stest['positions'][exp + 'implicit'], resultsimp, gold_p2)\\n counters['s_p_imp' + exp] = len(s_p_imp)\\n if DEBUG:\\n print \\\"RESULTSIMP\\\"\\n print resultsimp\\n s_p_int=ev.get_system_pairs_prob(stest['positions'][exp], results, gold_p3)\\n counters['s_p_int' + exp] = len(s_p_int)\\n system_pairs_exp = ev.merge_system_pairs(s_p_int, s_p_imp=s_p_imp, s_p_w=s_p_w)\\n counters['system_pairs_all' + exp] = len(system_pairs_exp)\\n for pair in system_pairs_exp:\\n if 'confidence' in pair and pair['confidence'] > 0:\\n counters['system_pairs' + exp] += 1\\n if predict:\\n ssc_exp = ev.spansetcoverage_o_p(system_pairs_exp, exptype=exp)\\n print \\\"system exp - {}:\\\\n{}\\\".format(exp, prf_prettystring(ssc_exp))\\n else:\\n ssc_exp = ev.spansetcoverage_o_p(system_pairs_exp, exptype=exp)\\n print \\\"gold exp - {}:\\\\n{}\\\".format(exp, prf_prettystring(ssc_exp))\\n system_pairs.extend(system_pairs_exp)\\n if predict:\\n ssc = ev.spansetcoverage_o_p(system_pairs)\\n print \\\"system exp - all:\\\\n\\\", prf_prettystring(ssc)\\n else:\\n ssc = ev.spansetcoverage_o_p(system_pairs)\\n print \\\"gold exp - all: \\\\n\\\", prf_prettystring(ssc)\\n \\n for k,v in sorted(counters.items(), key=lambda x: x[0]):\\n print k, v\\n if isinstance(deprep, basestring):\\n dump_jsonfile(system_pairs, 'system_pairs-' + deprep + '.json')\\n return {'stats': stest, 'system_pairs': system_pairs}\",\n \"def _evaluate_loss(self, batch_data, evaluate_settings=None, inference_mode=None):\\n\\n if not (type(inference_mode) is bool):\\n raise TrainerStateInvalidException(\\\"Inference mode is not set\\\")\\n\\n if not inference_mode:\\n # @tf.function on the train step level\\n return self._call_model(batch_data, evaluate_settings, inference_mode)\\n else:\\n return self._call_model_tf_func(batch_data, evaluate_settings, tf.constant(inference_mode, dtype=tf.bool))\",\n \"def test(self, test_data_path):\\n test_data = read_datafile(test_data_path)\\n test_data = self.preprocessor.preprocess(test_data)\\n self.run(test_data)\\n if check_format(self.get_result_path()):\\n thresholds, precisions, avg_precision, reciprocal_rank, num_relevant = evaluate(test_data_path,\\n self.get_result_path())\\n return avg_precision\",\n \"def evaluate(input_path, model_path, metrics_path):\\n\\n logger = logging.getLogger(__name__)\\n\\n logger.info(\\\"Loading input dataset\\\")\\n dataset = pd.read_csv(input_path)\\n\\n X_eval = dataset.drop(\\\"Survived\\\", axis=1)\\n y_eval = dataset[\\\"Survived\\\"]\\n\\n logger.info(\\\"Loading model\\\")\\n model = joblib.load(model_path)\\n\\n logger.info(\\\"Calculating metrics\\\")\\n scorer = metrics.make_scorer(metrics.mean_squared_error)\\n cv_results = cross_validate(model, X=X_eval, y=y_eval, scoring=scorer, cv=5)\\n\\n metric_values = {\\\"mse\\\": cv_results[\\\"test_score\\\"].mean()}\\n\\n logger.info(f\\\"Writing output to {metrics_path}\\\")\\n with open(metrics_path, \\\"w\\\") as file_:\\n json.dump(metric_values, file_)\",\n \"def evaluate(self, data_collection):\\n # map inputs\\n mapped_input_dict = self._dict_from_data_collection(self.input_mapping,\\n data_collection)\\n\\n # combine auxiliary inputs with mapped inputs\\n all_inputs = mapped_input_dict.copy()\\n all_inputs.update(self.auxiliary_inputs)\\n\\n # evaluate meter\\n outputs_dict = self.evaluate_raw(**all_inputs)\\n\\n # combine auxiliary outputs with raw outputs\\n all_outputs = outputs_dict.copy()\\n all_outputs.update(self.auxiliary_outputs)\\n\\n\\n # map meter evaluations back to data_collection form\\n mapped_output_data_collection = self._data_collection_from_dict(\\n self.output_mapping, all_outputs)\\n\\n # combine with original data, add tags as necessary\\n mapped_output_data_collection.add_tags(self.tagspace)\\n\\n return mapped_output_data_collection\",\n \"def run(self):\\n self.evaluate()\\n self.accumulate()\\n self.summarize()\",\n \"def evaluate_raw(self):\\n message = \\\"Use the meter.evaluate(data_collection) method for\\\" \\\\\\n \\\"YamlDefinedMeters.\\\"\\n raise NotImplementedError(message)\",\n \"def evaluate(data_folder):\\n seq_length=150\\n g = tf.Graph()\\n with g.as_default():\\n # Load dataset.\\n provider = data_provider_kush.get_provider(FLAGS.task)(data_folder)\\n num_classes = 6\\n audio, ground_truth, num_examples = provider.get_split(FLAGS.portion, FLAGS.batch_size)\\n\\n # Define model graph.\\n with slim.arg_scope([slim.batch_norm],\\n is_training=False):\\n predictions = models_kush.get_model(FLAGS.model,ground_truth)(audio, num_lstm_modules=FLAGS.num_lstm_modules)\\n #pred_argmax = tf.argmax(predictions, 1) \\n #lab_argmax = tf.argmax(labels, 1)\\n\\t\\n metrics = {\\n \\\"eval/accuracy\\\": slim.metrics.streaming_mean_squared_error(predictions, ground_truth[:,seq_length-1,:])\\n }\\n\\n total_error = tf.reduce_sum(tf.square(tf.subtract(ground_truth[:,seq_length-1,:], tf.reduce_mean(ground_truth[:,seq_length-1,:]))))\\n unexplained_error = tf.reduce_sum(tf.square(tf.subtract(ground_truth[:,seq_length-1,:], predictions)))\\n R_squared = tf.subtract(tf.cast(1, tf.float32), tf.divide(total_error, unexplained_error))\\n print('R_squared value: ',R_squared)\\n for i in range(num_classes):\\n name ='eval/mse_{}'.format(i)\\n recall = slim.metrics.streaming_mean_squared_error(predictions[:,i],ground_truth[:,seq_length-1,i])\\n metrics[name] = recall\\n\\n metrics['R_squared']=(R_squared,tf.subtract(tf.cast(1, tf.float32), tf.div(total_error, unexplained_error)))\\n #print(zip(metrics.values()))\\n #metric_names = metrics.keys()\\n #value_ops, update_ops = zip(*metrics.values())\\n #names_to_values, names_to_updates = dict(zip(metric_names, value_ops)), dict(zip(metric_names, update_ops))\\n names_to_values, names_to_updates = slim.metrics.aggregate_metric_map(metrics)\\n \\n summary_ops = []\\n metrics = dict()\\n for name, value in names_to_values.items():\\n op = tf.summary.scalar(name, value)\\n op = tf.Print(op, [value], name)\\n summary_ops.append(op)\\n metrics[name] = value\\n\\n # Computing the unweighted average recall and add it into the summaries.\\n uar = sum([metrics['eval/mse_{}'.format(i)] for i in range(num_classes)]) / num_classes\\n op = tf.summary.scalar('eval/mse', uar)\\n op = tf.Print(op, [uar], 'eval/mse')\\n summary_ops.append(op)\\n\\n num_examples = FLAGS.num_examples or num_examples\\n num_batches = math.ceil(num_examples / float(FLAGS.batch_size))\\n logging.set_verbosity(1)\\n\\n # Setup the global step.\\n slim.get_or_create_global_step()\\n\\n # How often to run the evaluation.\\n eval_interval_secs = FLAGS.eval_interval_secs \\n\\n slim.evaluation.evaluation_loop(\\n '',\\n FLAGS.checkpoint_dir,\\n FLAGS.log_dir,\\n num_evals=num_batches,\\n eval_op=list(names_to_updates.values()),\\n summary_op=tf.summary.merge(summary_ops),\\n eval_interval_secs=eval_interval_secs)\",\n \"def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:\\n ds = data.gen_tf_dataset(\\n batch_size, is_training=False, preprocess=self._preprocess)\\n return self._model.evaluate(ds)\",\n \"def evaluate(pred_file, ref_file):\\n ref_dict, pred_dict, query_dict, id_dict = build_pred_ref_dict(ref_file, pred_file, ref_file)\\n total, acc, scores = res_eval_with_type_acc(query_dict, pred_dict, ref_dict, id_dict, save=False)\\n em = calculate_exact_match(pred_dict, ref_dict)\\n print('Comp Acc: {:.3f}%\\\\tBleu-4: {:.3f}\\\\tRouge-L: {:.3f}'.format(acc, scores['Bleu-4'], scores['Rouge-L']))\\n print('EM: {:.3f}%'.format(em))\\n # calculate_sketch_type_acc(ref_file, pred_file)\\n # calculate_exact_match_for_each_q_type(ref_file, pred_file)\\n return total, acc, scores, em\",\n \"def validate(self):\\n stats = {}\\n evaluate_config = {\\\"verbose\\\": self.verbose}\\n evaluate_config.update(self.config.get(\\\"evaluate_config\\\", {}))\\n\\n results = self.model.evaluate(self.test_dataset, **evaluate_config)\\n if results is None:\\n # Using local Model since model.evaluate() returns None\\n # for MultiWorkerMirroredStrategy\\n logger.warning(\\\"Running a local model to get validation score.\\\")\\n self.local_model = self.model_creator(self.config)\\n self.local_model.set_weights(self.model.get_weights())\\n results = self.local_model.evaluate(self.test_dataset,\\n **evaluate_config)\\n\\n if isinstance(results, list):\\n stats = {\\n \\\"validation_\\\" + k: v\\n for k, v in zip(self.model.metrics_names, results)\\n }\\n else:\\n stats = {\\\"loss\\\": results}\\n\\n return stats\",\n \"def evaluate(hparams, summary_dir, num_gpus, model_type, eval_set, eval_size,\\n eval_shard, data_dir, num_targets, dataset, validate, seed,\\n shuffled, shift, pad, batch_size=100, checkpoint=None):\\n output_dir = summary_dir\\n load_dir = summary_dir + '/train/'\\n summary_dir += '/eval/' + FLAGS.dataset + '/' + eval_set\\n with tf.Graph().as_default():\\n features = get_features(eval_set, batch_size, num_gpus, data_dir,\\n num_targets, dataset, validate, evaluate=True,\\n seed=seed, shuffled=shuffled, shift=shift,\\n pad=pad, eval_shard=eval_shard)\\n model = models[model_type](hparams)\\n result, _ = model.multi_gpu(features, num_gpus)\\n test_writer = tf.summary.FileWriter(summary_dir)\\n seen_step = -1\\n paused = 0\\n while paused < 360:\\n print('start evaluation, model defined')\\n if checkpoint:\\n step = extract_step(checkpoint)\\n last_checkpoint = checkpoint\\n else:\\n step, last_checkpoint = find_checkpoint(load_dir, seen_step)\\n if step == -1:\\n time.sleep(60)\\n paused += 1\\n else:\\n paused = 0\\n seen_step = step\\n run_experiment(load_eval, last_checkpoint, test_writer,\\n eval_experiment, model, result,\\n eval_size // batch_size, features=features,\\n eval_set=eval_set, output_dir=output_dir,\\n unsupervised=hparams.unsupervised,\\n num_gpus=num_gpus)\\n if checkpoint:\\n break\\n\\n test_writer.close()\",\n \"def _evaluate_fn(model, dataset):\\n # Reset the local variables so that the returned metrics are computed using\\n # the given data. Similar to the `reset_states` method of `tf.metrics.Metric`.\\n for var in model.local_variables:\\n if var.initial_value is not None:\\n var.assign(var.initial_value)\\n else:\\n var.assign(tf.zeros_like(var))\\n\\n def eval_fn(dummy_state, batch):\\n \\\"\\\"\\\"Evaluates the model on a batch.\\\"\\\"\\\"\\n model.forward_pass(batch, training=False)\\n return dummy_state\\n\\n # Evaluate on the dataset.\\n dataset.reduce(initial_state=0, reduce_func=eval_fn)\\n\\n # Obtain the metrics.\\n results = collections.OrderedDict()\\n local_outputs = model.report_local_outputs()\\n for name, metric in local_outputs.items():\\n if isinstance(metric, list) and (len(metric) == 2):\\n # Some metrics returned by `report_local_outputs()` can have two scalars:\\n # one represents `sum`, and the other represents `count`. Ideally, we want\\n # to return a single scalar for each metric.\\n results[name] = metric[0] / metric[1]\\n else:\\n results[name] = metric[0] if isinstance(metric, list) else metric\\n return results\",\n \"def eval_perf_total(model, X_train, y_train, X_test, y_test):\\n\\n y_hat_train = model.predict(X_train)\\n y_hat_test = model.predict(X_test)\\n \\n train_mae = metrics.mean_absolute_error(y_train, y_hat_train)\\n train_mse = metrics.mean_squared_error(y_train, y_hat_train)\\n train_rmse = np.sqrt(metrics.mean_squared_error(y_train, y_hat_train))\\n train_r = metrics.r2_score(y_train, y_hat_train)\\n\\n print('Evaluating Performance on Training Data:\\\\n')\\n print(f' Train Mean Absolute Error: {train_mae:,.2f}')\\n print(f' Train Mean Squared Error: {train_mse:,.2f}\\\\n')\\n print(f'Train Root Mean Squared Error: {train_rmse:,.2f}')\\n print(f'Train R-Square Value: {round(train_r,2)}')\\n\\n print('\\\\n'+'---'*25+'\\\\n')\\n\\n test_mae = metrics.mean_absolute_error(y_test, y_hat_test)\\n test_mse = metrics.mean_squared_error(y_test, y_hat_test)\\n test_rmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat_test))\\n test_r = metrics.r2_score(y_test, y_hat_test)\\n\\n print('Evaluating Performance on Testing Data:\\\\n')\\n print(f' Test Mean Absolute Error: {test_mae:,.2f}')\\n print(f' Test Mean Squared Error: {test_mse:,.2f}\\\\n')\\n print(f'Test Root Mean Squared Error: {test_rmse:,.2f}')\\n print(f'Test R-Square Value: {round(test_r,2)}')\",\n \"def evaluate(\\n config,\\n _,\\n pstate,\\n eval_ds,\\n rng,\\n unused_num_eval_steps = -1,\\n):\\n logging.info(\\\"Starting evaluation.\\\")\\n eval_metrics = None\\n state = flax_utils.unreplicate(pstate)\\n\\n render_loop = instant_ngp_utils.make_render_loop(state.params, config)\\n with utils.StepTraceContextHelper(\\\"eval\\\", 0) as trace_context:\\n for step, batch in enumerate(eval_ds): # pytype: disable=wrong-arg-types\\n data = jax.tree_map(jnp.asarray, batch)\\n render_poses = data[\\\"c2w\\\"]\\n hwf = data[\\\"hwf\\\"]\\n rng = jax.random.fold_in(rng, step)\\n\\n frames = []\\n for pose in render_poses:\\n frames.append(\\n render_loop(instant_ngp_utils.camera_ray_batch(pose, hwf), rng)[0]\\n )\\n psnrs_test = [\\n -10 * jnp.log10(jnp.mean(jnp.square(rgb - gt)))\\n for (rgb, gt) in zip(frames, data[\\\"images\\\"])\\n ]\\n psnr_test = np.array(psnrs_test).mean()\\n eval_metrics = EvalMetrics.single_from_model_output(psnr=psnr_test)\\n trace_context.next_step()\\n eval_info = {\\n \\\"out\\\": jnp.concatenate([x[None, Ellipsis] for x in frames], axis=0),\\n \\\"gtr\\\": data[\\\"images\\\"],\\n }\\n return eval_metrics, eval_info\",\n \"def _evaluate(self, x):\\n raise NotImplementedError()\",\n \"def __call__(self, test_data, verbose=True):\\n\\n self.model.eval()\\n with torch.no_grad():\\n loss, rho, nmse = self._iteration(test_data)\\n if verbose:\\n print(f'\\\\n=> Test result: \\\\nloss: {loss:.3e}'\\n f' rho: {rho:.3e} NMSE: {nmse:.3e}\\\\n')\\n return loss, rho, nmse\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6641769","0.66378486","0.62046313","0.6194362","0.6182727","0.61543846","0.6143863","0.61434543","0.6132906","0.61051583","0.6086003","0.60806614","0.60806614","0.6062375","0.60618806","0.6046971","0.604346","0.603338","0.60103774","0.60077417","0.60042995","0.600045","0.599409","0.59850633","0.59844273","0.597519","0.59740853","0.5954438","0.59376717","0.59191453","0.58973527","0.58740664","0.5866037","0.5865239","0.58331716","0.5809086","0.580573","0.58054024","0.57872725","0.5780027","0.57743084","0.5768616","0.5767731","0.5764745","0.5760096","0.5758343","0.57427204","0.57421696","0.5737063","0.5736322","0.57342345","0.5733124","0.57322717","0.57206833","0.57161224","0.57148075","0.57081324","0.57063794","0.5699823","0.56965196","0.5689107","0.5684422","0.5667683","0.56664205","0.56570786","0.5655871","0.5652238","0.56500983","0.5647138","0.5642938","0.5637388","0.56364655","0.56318176","0.5630196","0.5625515","0.5615498","0.5615437","0.5600947","0.55988467","0.55903345","0.5590074","0.55858266","0.5583357","0.5580276","0.5575671","0.5571724","0.55711406","0.55701953","0.5563676","0.5561428","0.5560663","0.5557713","0.5557201","0.55563456","0.5550451","0.5545374","0.5539956","0.55391276","0.5537199","0.5530665","0.55126756"],"string":"[\n \"0.6641769\",\n \"0.66378486\",\n \"0.62046313\",\n \"0.6194362\",\n \"0.6182727\",\n \"0.61543846\",\n \"0.6143863\",\n \"0.61434543\",\n \"0.6132906\",\n \"0.61051583\",\n \"0.6086003\",\n \"0.60806614\",\n \"0.60806614\",\n \"0.6062375\",\n \"0.60618806\",\n \"0.6046971\",\n \"0.604346\",\n \"0.603338\",\n \"0.60103774\",\n \"0.60077417\",\n \"0.60042995\",\n \"0.600045\",\n \"0.599409\",\n \"0.59850633\",\n \"0.59844273\",\n \"0.597519\",\n \"0.59740853\",\n \"0.5954438\",\n \"0.59376717\",\n \"0.59191453\",\n \"0.58973527\",\n \"0.58740664\",\n \"0.5866037\",\n \"0.5865239\",\n \"0.58331716\",\n \"0.5809086\",\n \"0.580573\",\n \"0.58054024\",\n \"0.57872725\",\n \"0.5780027\",\n \"0.57743084\",\n \"0.5768616\",\n \"0.5767731\",\n \"0.5764745\",\n \"0.5760096\",\n \"0.5758343\",\n \"0.57427204\",\n \"0.57421696\",\n \"0.5737063\",\n \"0.5736322\",\n \"0.57342345\",\n \"0.5733124\",\n \"0.57322717\",\n \"0.57206833\",\n \"0.57161224\",\n \"0.57148075\",\n \"0.57081324\",\n \"0.57063794\",\n \"0.5699823\",\n \"0.56965196\",\n \"0.5689107\",\n \"0.5684422\",\n \"0.5667683\",\n \"0.56664205\",\n \"0.56570786\",\n \"0.5655871\",\n \"0.5652238\",\n \"0.56500983\",\n \"0.5647138\",\n \"0.5642938\",\n \"0.5637388\",\n \"0.56364655\",\n \"0.56318176\",\n \"0.5630196\",\n \"0.5625515\",\n \"0.5615498\",\n \"0.5615437\",\n \"0.5600947\",\n \"0.55988467\",\n \"0.55903345\",\n \"0.5590074\",\n \"0.55858266\",\n \"0.5583357\",\n \"0.5580276\",\n \"0.5575671\",\n \"0.5571724\",\n \"0.55711406\",\n \"0.55701953\",\n \"0.5563676\",\n \"0.5561428\",\n \"0.5560663\",\n \"0.5557713\",\n \"0.5557201\",\n \"0.55563456\",\n \"0.5550451\",\n \"0.5545374\",\n \"0.5539956\",\n \"0.55391276\",\n \"0.5537199\",\n \"0.5530665\",\n \"0.55126756\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95684,"cells":{"query":{"kind":"string","value":"Applies VRTfunctions to a GDALreadable inputfile, rendering outputfile. Functions must be an iterable of singleparameter functions that take a filename as input."},"document":{"kind":"string","value":"def pipeline(inputfile, outputfile, functions, **kwargs):\n if not functions:\n raise ValueError('Must have at least one function')\n\n tmpfiles = []\n try:\n previous = inputfile\n for name, f in functions:\n logging.debug(name)\n vrt = f(previous)\n current = vrt.get_tempfile(suffix='.vrt', prefix='gdal')\n tmpfiles.append(current)\n previous = current.name\n logging.info('Rendering reprojected image')\n return vrt.render(outputfile=outputfile, **kwargs)\n finally:\n for f in tmpfiles:\n f.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main(fn_input, fn_output):\n # read file\n inter = Interpolator()\n inter.read_file(fn_input)\n inter.write_interpolated(fn_output)","def merge(files: List[str], output_file: str, resample: str = \"average\") -> None:\n\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\n\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\n\n if os.path.isfile(constants.TEMP_VRT_FILE):\n os.remove(constants.TEMP_VRT_FILE)","def run(self):\n\t\tif not self.is_valid:\n\t\t\traise ValueError('Paths are not valid')\n\t\t\n\t\tfrom manipulators.run import all_functions\n\t\tinput_file = open(self.input_path, 'rb')\n\t\tinput_csv = csv.reader(input_file, delimiter=',')\n\t\toutput_file = open(self.output_path, 'wb')\n\t\toutput_csv = csv.writer(output_file, delimiter=',')\n\t\tall_functions(input_csv, output_csv)","def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\n\n options = gdal.BuildVRTOptions(srcNodata=0)\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\n add_pixel_fn(vrt, resample_name)","def run_transform(i_fn, o_fn):\n if not (all(is_bed(fn) for fn in [i_fn, o_fn]) or all(is_vcf(fn) for fn in [i_fn, o_fn])):\n raise ValueError(\"Input and output must be both BED or VCF\")\n with get_reader(i_fn) as reader:\n with get_writer(o_fn, reader.samples) as writer:\n for r in reader:\n writer.writeRecord(transform_coordinate_of_sv(r))","def convert_vrt(fname, out_fname, dataset_name='dataset',\n compression=H5CompressionFilter.LZF, filter_opts=None,\n attrs=None):\n with h5py.File(out_fname) as fid:\n with rasterio.open(fname) as rds:\n # set default chunks and set dimensions\n if rds.count == 3:\n chunks = (3, 256, 256)\n dims = (3, rds.height, rds.width)\n else:\n chunks = (256, 256)\n dims = (rds.height, rds.width)\n\n # create empty or copy the user supplied filter options\n if not filter_opts:\n filter_opts = dict()\n filter_opts['chunks'] = chunks\n else:\n filter_opts = filter_opts.copy()\n\n\n if 'chunks' not in filter_opts:\n filter_opts['chunks'] = chunks\n\n # modify to have 3D chunks if we have a multiband vrt\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\n # copy the users original 2D chunk and insert the third\n chunks = list(filter_opts['chunks'])\n chunks.insert(0, 3)\n filter_opts['chunks'] = chunks\n\n # dataset attributes\n if attrs:\n attrs = attrs.copy()\n else:\n attrs = {}\n\n attrs['geotransform'] = rds.transform.to_gdal()\n attrs['crs_wkt'] = rds.crs.wkt\n\n # dataset creation options\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\n kwargs['shape'] = dims\n kwargs['dtype'] = rds.dtypes[0]\n\n dataset = fid.create_dataset(dataset_name, **kwargs)\n attach_image_attributes(dataset, attrs)\n\n # tiled processing (all cols by chunked rows)\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\n\n for tile in tiles:\n # numpy index\n if rds.count == 3:\n idx = (\n slice(None),\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n else:\n idx = (\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n\n # ensure single band rds is read as 2D not 3D\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\n\n # write\n dataset[idx] = data","def create_user_functions():\n\n # user_function.lgi useing PARAM_A and PARAM_B for slope and intercept\n lagrit_input = \"\"\"\ncmo/DELATT/mo_pts/dfield\ncompute / distance_field / mo_pts / mo_line_work / dfield\nmath/multiply/mo_pts/x_four/1,0,0/mo_pts/dfield/PARAM_A/\nmath/add/mo_pts/x_four/1,0,0/mo_pts/x_four/PARAM_B/\ncmo/copyatt/mo_pts/mo_pts/fac_n/x_four\nfinish\n\"\"\"\n f = open('user_function.lgi', 'w')\n f.write(lagrit_input)\n f.close()\n\n # user_function2.lgi uses PARAM_A2 and PARAM_B2 for slope and intercept\n lagrit_input = \"\"\"\ncmo/DELATT/mo_pts/dfield\ncompute / distance_field / mo_pts / mo_line_work / dfield\nmath/multiply/mo_pts/x_four/1,0,0/mo_pts/dfield/PARAM_A2/\nmath/add/mo_pts/x_four/1,0,0/mo_pts/x_four/PARAM_B2/\ncmo/copyatt/mo_pts/mo_pts/fac_n/x_four\nfinish\n\"\"\"\n f = open('user_function2.lgi', 'w')\n f.write(lagrit_input)\n f.close()","def __call__(self, format, filename):\n # turn the filename into something suitable for use in #define's\n prettyname = filename.replace(\".\", \"_\").upper()\n prettyname = prettyname.replace(\"/\", \"__\")\n prettyname = prettyname.replace(\":\", \"__\")\n prettyname = prettyname.replace(\"-\", \"__\")\n\n # try and open the file\n with open(filename, \"w\") as output:\n self.writeFuncsLut[format]( output, prettyname )","def analyze(file,process):\n readin(file)\n # inspecting(file, functions)\n process(file, functions)","def compute_functions(file_name,E_max,E_min,gamma,gamma_k):\n\n file = open(file_name,'r')\n data = file.readlines()\n file.close()\n\n print \"Computing functions\"\n all_lor = []\n for fila in data[2:]:\n vals = map(eval,fila.split())\n if(len(vals)>1):\n if(vals[1]>E_min and vals[1] < E_max):\n ek = vals[1]\n weight = 0\n for j in range(2,len(vals)):\n weight += vals[j]\n A = get_func(vals[0],vals[1],weight,gamma,gamma_k)\n all_lor.append(A)\n\n return all_lor","def process_function(regexDict: typing.Dict[str, str], fileContent: str, flags: int = re.IGNORECASE) -> str:\n\t\tfor pattern, replace in sorted(regexDict.items()):\n\t\t\tfileContent = re.sub(pattern, replace, fileContent, flags=flags)\n\t\treturn fileContent","def make_vrt(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n txt_file = i.joinpath('subnational/tiffs.txt')\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\n if not outfile.exists():\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\n subprocess.call(gdal_cmd, shell=True)","def cli(source_f, raster_f, output, verbose):\n with fiona.open(source_f, 'r') as source:\n source_driver = source.driver\n source_crs = source.crs\n sink_schema = source.schema.copy()\n\n source_geom = source.schema['geometry']\n if source_geom == 'Point':\n sink_schema['geometry'] = '3D Point'\n elif source_geom == 'LineString':\n sink_schema['geometry'] = '3D LineString'\n elif source_geom == '3D Point' or source_geom == '3D LineString':\n pass\n else:\n click.BadParameter(\"Source geometry type {} not implemented\".format(source_geom))\n\n with rasterio.open(raster_f) as raster:\n if source_crs != raster.crs:\n click.BadParameter(\"Features and raster have different CRS.\")\n if raster.count > 1:\n warnings.warn(\"Found {0} bands in {1}, expected a single band raster\".format(raster.bands, raster_f))\n supported = ['int16', 'int32', 'float32', 'float64']\n if raster.dtypes[0] not in supported:\n warnings.warn(\"Found {0} type in {1}, expected one of {2}\".format(raster.dtypes[0], raster_f, supported))\n with fiona.open(\n output, 'w',\n driver=source_driver,\n crs=source_crs,\n schema=sink_schema) as sink:\n\n for feature in source:\n try:\n feature_z = drapery.drape(raster, feature)\n sink.write({\n 'geometry': mapping(feature_z),\n 'properties': feature['properties'],\n })\n except Exception:\n logging.exception(\"Error processing feature %s:\", feature['id'])\n #print(sink.closed)\n #print(raster.closed)\n #print(source.closed)","def process_data(*args, **kwargs):\n\n filepath = kwargs[\"filepath\"]\n func = kwargs[\"func\"]\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(datafile)\n print('{}/{} files processed.'.format(i, num_files))","def parse_flt_files(files=[], info=None, uniquename=False, use_visit=False,\n get_footprint = False, \n translate = {'AEGIS-':'aegis-', \n 'COSMOS-':'cosmos-', \n 'GNGRISM':'goodsn-', \n 'GOODS-SOUTH-':'goodss-', \n 'UDS-':'uds-'}): \n \n if info is None:\n if not files:\n files=glob.glob('*flt.fits')\n \n if len(files) == 0:\n return False\n \n info = get_flt_info(files)\n else:\n info = info.copy()\n \n for c in info.colnames:\n if not c.islower(): \n info.rename_column(c, c.lower())\n\n if 'expstart' not in info.colnames:\n info['expstart'] = info['exptime']*0.\n\n so = np.argsort(info['expstart'])\n info = info[so]\n\n #pa_v3 = np.round(info['pa_v3']*10)/10 % 360.\n pa_v3 = np.round(info['pa_v3']) % 360.\n \n target_list = []\n for i in range(len(info)):\n #### Replace ANY targets with JRhRmRs-DdDmDs\n if info['targname'][i] == 'ANY': \n if use_visit:\n new_targname=info['file'][i][:6]\n else:\n new_targname = 'par-'+radec_to_targname(ra=info['ra_targ'][i],\n dec=info['dec_targ'][i])\n \n target_list.append(new_targname.lower())\n else:\n target_list.append(info['targname'][i])\n \n target_list = np.array(target_list)\n\n info['progIDs'] = [file[1:4] for file in info['file']]\n\n progIDs = np.unique(info['progIDs'])\n visits = np.array([os.path.basename(file)[4:6] for file in info['file']])\n dates = np.array([''.join(date.split('-')[1:]) for date in info['date-obs']])\n \n targets = np.unique(target_list)\n \n output_list = [] #OrderedDict()\n filter_list = OrderedDict()\n \n for filter in np.unique(info['filter']):\n filter_list[filter] = OrderedDict()\n \n angles = np.unique(pa_v3[(info['filter'] == filter)]) \n for angle in angles:\n filter_list[filter][angle] = []\n \n for target in targets:\n #### 3D-HST targname translations\n target_use = target\n for key in translate.keys():\n target_use = target_use.replace(key, translate[key])\n \n ## pad i < 10 with zero\n for key in translate.keys():\n if translate[key] in target_use:\n spl = target_use.split('-')\n try:\n if (int(spl[-1]) < 10) & (len(spl[-1]) == 1):\n spl[-1] = '{0:02d}'.format(int(spl[-1]))\n target_use = '-'.join(spl)\n except:\n pass\n\n for filter in np.unique(info['filter'][(target_list == target)]):\n angles = np.unique(pa_v3[(info['filter'] == filter) & \n (target_list == target)])\n \n for angle in angles:\n exposure_list = []\n exposure_start = []\n product='{0}-{1:05.1f}-{2}'.format(target_use, angle, filter) \n\n visit_match = np.unique(visits[(target_list == target) &\n (info['filter'] == filter)])\n \n this_progs = []\n this_visits = []\n \n for visit in visit_match:\n ix = (visits == visit) & (target_list == target) & (info['filter'] == filter)\n #this_progs.append(info['progIDs'][ix][0])\n #print visit, ix.sum(), np.unique(info['progIDs'][ix])\n new_progs = list(np.unique(info['progIDs'][ix]))\n this_visits.extend([visit]*len(new_progs))\n this_progs.extend(new_progs)\n \n for visit, prog in zip(this_visits, this_progs):\n visit_list = []\n visit_start = []\n visit_product = '{0}-{1}-{2}-{3:05.1f}-{4}'.format(target_use, prog, visit, angle, filter) \n \n use = ((target_list == target) & \n (info['filter'] == filter) & \n (visits == visit) & (pa_v3 == angle) &\n (info['progIDs'] == prog))\n \n if use.sum() == 0:\n continue\n\n for tstart, file in zip(info['expstart'][use],\n info['file'][use]):\n \n f = file.split('.gz')[0]\n if f not in exposure_list:\n visit_list.append(str(f))\n visit_start.append(tstart)\n \n exposure_list = np.append(exposure_list, visit_list)\n exposure_start.extend(visit_start)\n \n filter_list[filter][angle].extend(visit_list)\n \n if uniquename:\n print(visit_product, len(visit_list))\n so = np.argsort(visit_start)\n exposure_list = np.array(visit_list)[so]\n #output_list[visit_product.lower()] = visit_list\n \n d = OrderedDict(product=str(visit_product.lower()),\n files=list(np.array(visit_list)[so]))\n output_list.append(d)\n \n if not uniquename:\n print(product, len(exposure_list))\n so = np.argsort(exposure_start)\n exposure_list = np.array(exposure_list)[so]\n #output_list[product.lower()] = exposure_list\n d = OrderedDict(product=str(product.lower()),\n files=list(np.array(exposure_list)[so]))\n output_list.append(d)\n \n ### Get visit footprint from FLT WCS\n if get_footprint:\n from shapely.geometry import Polygon\n \n N = len(output_list)\n for i in range(N):\n for j in range(len(output_list[i]['files'])):\n flt_file = output_list[i]['files'][j]\n if (not os.path.exists(flt_file)) & os.path.exists('../RAW/'+flt_file):\n flt_file = '../RAW/'+flt_file\n \n flt_j = pyfits.open(flt_file)\n h = flt_j[0].header\n if (h['INSTRUME'] == 'WFC3') & (h['DETECTOR'] == 'IR'):\n wcs_j = pywcs.WCS(flt_j['SCI',1])\n else:\n wcs_j = pywcs.WCS(flt_j['SCI',1], fobj=flt_j)\n \n fp_j = Polygon(wcs_j.calc_footprint())\n if j == 0:\n fp_i = fp_j\n else:\n fp_i = fp_i.union(fp_j)\n \n output_list[i]['footprint'] = fp_i\n \n return output_list, filter_list","def load_from_function(function, path_to_file, **kwargs):\n return function(path_to_file, **kwargs)","def apply(input, output, fields, delimiter, encoding, verbose, format_in, zipfile, script, filter):\n if verbose:\n enableVerbose()\n options = {}\n options['delimiter'] = delimiter\n options['fields'] = fields\n options['output'] = output\n options['encoding'] = encoding\n options['format_in'] = format_in\n options['zipfile'] = zipfile\n options['filter'] = filter\n options['script'] = script\n acmd = Transformer()\n acmd.script(input, options)\n pass","def write_func(functions, filename=def_func_name):\n file = open(filename + \".txt\", \"w\")\n for i in range(3):\n line = str(functions[i])\n file.write(line+'\\n')\n file.close()","def translate_files(input_file, output_file, translate_dict, delete_symbols):\n\n for line in input_file:\n result = translate(line, translate_dict, delete_symbols)\n output_file.write(result)","def process_input_files(inputs):\n for ifile in inputs:\n with open(ifile) as fin:\n exec(compile(fin.read(), ifile, 'exec'))","def add_pixel_fn(filename: str, resample_name: str) -> None:\n\n header = \"\"\" <VRTRasterBand dataType=\"Byte\" band=\"1\" subClass=\"VRTDerivedRasterBand\">\"\"\"\n contents = \"\"\"\n <PixelFunctionType>{0}</PixelFunctionType>\n <PixelFunctionLanguage>Python</PixelFunctionLanguage>\n <PixelFunctionCode><![CDATA[{1}]]>\n </PixelFunctionCode>\"\"\"\n\n lines = open(filename, 'r').readlines()\n lines[3] = header # FIX ME: 3 is a hand constant\n lines.insert(4, contents.format(resample_name,\n get_resample(resample_name)))\n open(filename, 'w').write(\"\".join(lines))","def HandleFiles(variables):\n\n # The template file is the html file into which we will write the\n # data from the stats file, formatted correctly for the gviz_api.\n template_file = open(variables[1], \"r\")\n page_template = template_file.read()\n template_file.close()\n\n # This is the path match pattern for finding stats files amongst\n # all the other files it could be. eg: *.stt\n file_pattern = variables[2]\n\n # This is the directory with files that we will use to do the comparison\n # against.\n baseline_dir = variables[3]\n snrs = ''\n filestable = {}\n filestable['dsnr'] = ''\n filestable['drate'] = ''\n filestable['avg'] = ''\n\n # Go through each metric in the list.\n for column in range(1,2):\n\n # Dirs is directories after the baseline to compare to the base.\n dirs = variables[4:len(variables)]\n\n # Find the metric files in the baseline directory.\n dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))\n\n for metric in ['avg','dsnr','drate']:\n description = {\"file\": (\"string\", \"File\")}\n\n # Go through each directory and add a column header to our description.\n countoverall = {}\n sumoverall = {}\n\n for directory in dirs:\n description[directory] = (\"number\", directory)\n countoverall[directory] = 0\n sumoverall[directory] = 0\n\n # Data holds the data for the visualization, name given comes from\n # gviz_api sample code.\n data = []\n for filename in dir_list:\n row = {'file': splitext(basename(filename))[0] }\n baseline_file_name = baseline_dir + \"/\" + filename\n\n # Read the metric file from each of the directories in our list.\n for directory in dirs:\n metric_file_name = directory + \"/\" + filename\n\n # If there is a metric file in the current directory, open it\n # and calculate its overall difference between it and the baseline\n # directory's metric file.\n if os.path.isfile(metric_file_name):\n overall = FileBetter(baseline_file_name, metric_file_name,\n column, metric)\n row[directory] = overall\n\n sumoverall[directory] += overall\n countoverall[directory] += 1\n\n data.append(row)\n\n # Add the overall numbers.\n row = {\"file\": \"OVERALL\" }\n if countoverall[directory]:\n for directory in dirs:\n row[directory] = sumoverall[directory] / countoverall[directory]\n data.append(row)\n\n # write the tables out\n data_table = gviz_api.DataTable(description)\n data_table.LoadData(data)\n\n filestable[metric] = ( filestable[metric] + \"filestable_\" + metric +\n \"[\" + str(column) + \"]=\" + data_table.ToJSon()\n + \"\\n\" )\n\n filestable_avg = filestable['avg']\n filestable_dpsnr = filestable['dsnr']\n filestable_drate = filestable['drate']\n\n # Now we collect all the data for all the graphs. First the column\n # headers which will be Datarate and then each directory.\n columns = (\"datarate\",baseline_dir)\n description = {\"datarate\":(\"number\", \"Datarate\")}\n for directory in dirs:\n description[directory] = (\"number\", directory)\n\n description[baseline_dir] = (\"number\", baseline_dir)\n\n snrs = snrs + \"snrs[\" + str(column) + \"] = [\"\n\n # Now collect the data for the graphs, file by file.\n for filename in dir_list:\n\n data = []\n\n # Collect the file in each directory and store all of its metrics\n # in the associated gviz metrics table.\n all_dirs = dirs + [baseline_dir]\n for directory in all_dirs:\n\n metric_file_name = directory + \"/\" + filename\n if not os.path.isfile(metric_file_name):\n continue\n\n # Read and parse the metrics file storing it to the data we'll\n # use for the gviz_api.Datatable.\n metrics = ParseMetricFile(metric_file_name, column)\n for bitrate, metric in metrics:\n data.append({\"datarate\": bitrate, directory: metric})\n\n data_table = gviz_api.DataTable(description)\n data_table.LoadData(data)\n snrs = snrs + \"'\" + data_table.ToJSon(\n columns_order=tuple([\"datarate\",baseline_dir]+dirs)) + \"',\"\n\n snrs = snrs + \"]\\n\"\n\n formatters = \"\"\n for i in range(len(dirs)):\n formatters = \"%s formatter.format(better, %d);\" % (formatters, i+1)\n\n print FillForm(page_template, vars())\n return","def run(fn, *input_values, **kwds):\n \n ee = kwds.get('ee', shared_exec_engine)\n input_types = [arg.type for arg in fn.args]\n gv_inputs = [gv_from_python(x, t) \n for (x,t) in \n zip(input_values, input_types)]\n \n return run_with_generic_values(fn, gv_inputs, ee)","def parsedFile(self, *args, **kwargs):\n\t\tif not self._parsed:\n\t\t\tself.parse()\n\n\t\treturn function(self, *args, **kwargs)","def filter_f(fns, ltaper, lowerp, upperp, utaper, eqband, eqltaper, equtaper, npow, bindir):\n # filtercmd = bindir+\"/filter4\"\n filtercmd = bindir + \"/filter4 1>/dev/null\"\n for src, tar, eqtar in fns:\n p = sp.Popen(filtercmd, shell=True, bufsize=0, stdin=sp.PIPE, stdout=None)\n child = p.stdin\n print >> child, ltaper, lowerp, upperp, utaper, npow, src, tar + '_tmp'\n err = child.close()\n ret = p.wait()\n if err or ret != 0:\n raise RuntimeError, '%r failed with exit code %d' % (filtercmd, err)\n p = sp.Popen(filtercmd, shell=True, bufsize=0, stdin=sp.PIPE, stdout=None)\n child = p.stdin\n print >> child, eqltaper, eqband[0], eqband[1], equtaper, npow, tar + '_tmp', eqtar + '_tmp'\n err = child.close()\n ret = p.wait()\n if err or ret != 0:\n raise RuntimeError, '%r failed with exit code %d' % (filtercmd, err)\n return 1","def io_files(self, iterable, ext=None, func=None):\n for input_path in iterable:\n output_path, temp_file = self.check_output_path(input_path, ext)\n\n try:\n func(input_path, temp_file)\n except Exception as e:\n if self._force_continue is True:\n self.handle_error(e, input_path)\n else:\n raise e\n\n self.overwrite_output_path(input_path, output_path, temp_file)","def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\n outputs = []\n npy_outputs = []\n if resamplemethod == 'nearest':\n rs = Resampling.nearest\n else:\n print('only nearest neighbor resampling is supported at this time')\n sys.exit(0)\n\n for i, warpfile in enumerate(inputs):\n print('warpfile', warpfile)\n with rasterio.open(warpfile) as src:\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\n with WarpedVRT(src, resampling=rs,\n crs=self.crs,\n transform=self.transform,\n height=self.rows,\n width=self.cols) as vrt:\n data = vrt.read()\n print(type(vrt))\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\n outputs.append(outwarp)\n\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\n # from this method for us in the rest of the code.\n for ow in outputs:\n with rasterio.open(ow, 'r') as src:\n arr = src.read(1)\n npy_outputs.append(arr)\n\n return npy_outputs","def render_functions(r2, covered_bbs, output_dir):\n for func_addr in function_addrs(r2):\n # Get the function name\n func_name = r2.cmdj('agj 0x%x' % func_addr)[0]['name']\n\n dot_str = r2.cmd('ag 0x%x' % func_addr)\n dot = pydot.graph_from_dot_data(dot_str)\n if not dot:\n continue\n else:\n dot = dot[0]\n\n for node in dot.get_nodes():\n node_name = node.get_name()\n try:\n # XXX This is very hacky - need something more robust\n if node_name.startswith('\"'):\n node_name = node_name[1:-1]\n node_addr = int(node_name, 16)\n except ValueError:\n # Node name is not a hex string\n continue\n\n if node_addr in covered_bbs:\n node.set_fillcolor('darkolivegreen2')\n\n svg_path = os.path.join(output_dir, '%s_0x%x.svg' % (func_name, func_addr))\n with open(svg_path, 'wb') as f:\n svg = dot.create_svg()\n f.write(svg)","def file_func(self,):\n return self._file_func","def process_data(db_cursor, filepath, func):\n\n # Get all files matching extension from directory\n all_files = []\n for root, _, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print(f'{num_files} files found in {filepath}')\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(db_cursor, datafile)\n print(f'{i}/{num_files} files processed.')","def gen_code(self, filename, func_name):\n\n assert self.bits is not None\n\n vd_list = []\n n_vars = 0\n for tree in self.trees:\n vd_list.append(tree.gen_code(n_vars))\n n_vars += len(vd_list[-1])\n\n # checks the type by the suffix\n\n is_v = filename.split(\".\")[-1] == \"v\"\n\n assert self.inputs\n\n f = open(filename, \"w\")\n\n i_bits = np.sum(self.bits[:-1])\n o_bits = self.bits[-1]\n o_sign = self.is_neg[-1]\n\n if is_v:\n f.write(\"module {}(input [{}:0] i, output [{}:0] o);\\n\".format(\n func_name, i_bits-1, o_bits-1))\n else:\n f.write(\"#include<ac_int.h>\\n\\n\")\n f.write(\"void {}(ac_int<{},false> i, ac_int<{},{}> &o)\\n\".format(\n func_name, i_bits, o_bits, o_sign))\n f.write(\"{\\n\")\n\n\n # write function headline\n s_in_line = []\n\n i_bits = self.bits[0]\n i_sign = self.is_neg[0]\n\n if is_v:\n i_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if i_sign else \"\", i_bits-1)\n else:\n i_datatype = \" ac_int<{},{}> \".format(i_bits, i_sign)\n\n len_s = len(i_datatype)\n\n for i in range(self.inputs):\n if is_v:\n s = (\n \"i_\" + str(i) + \" = \" + \"i[\" + str(i_bits*(i+1)-1) + \":\" +\n str(i_bits*i) + \"]\"\n )\n else:\n s = (\n \"i_\" + str(i) + \" = \" + \"i.slc<\" + str(i_bits) + \">(\" +\n str(i_bits*i) + \")\"\n )\n if (\n len_s + len(s) + 2 > 70 or i_bits != self.bits[i] or\n i_sign != self.is_neg[i]\n ):\n f.write(i_datatype + \", \".join(s_in_line) + \";\\n\")\n\n s_in_line = []\n if is_v:\n i_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if i_sign else \"\", i_bits-1)\n else:\n i_datatype = \" ac_int<{},{}> \".format(i_bits, i_sign)\n\n len_s = len(i_datatype)\n\n s_in_line.append(s)\n len_s += len(s) + 2\n\n if s_in_line:\n f.write(i_datatype + \", \".join(s_in_line) + \";\\n\")\n\n if is_v:\n o_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if o_sign else \"\", o_bits)\n else:\n o_datatype = \" ac_int<{},{}> \".format(o_bits, o_sign)\n\n o_list = []\n for i in range(len(vd_list)):\n for v in vd_list[i]:\n if is_v:\n f.write(o_datatype + v + \" = \" + vd_list[i][v] + \";\\n\")\n else:\n f.write(o_datatype + v + \" = \" + vd_list[i][v] + \";\\n\")\n f.write(\"\\n\")\n o_list.append(v)\n\n assert len(o_list) <= 3\n\n if is_v:\n f.write(\" assign \")\n else:\n f.write(\" \")\n\n if len(o_list) == 1:\n f.write(\"o = \" + o_list[0] + \";\")\n elif len(o_list) == 2:\n cond = \"( \" + o_list[0] + \" == \" + o_list[1] + \" ) \"\n n1 = o_list[0]\n n0 = \"( ( \" + \" + \".join(o_list) + \" ) >> 1 )\"\n f.write(\"o = \" + cond + \"? \" + n1 + \": \" + n0)\n elif len(o_list) == 3:\n cond = (\n \"( \" +\n \"( \" + \" == \".join(o_list[0:2]) + \" )?\" + o_list[0] + \":\" +\n \"( \" + \" == \".join(o_list[1:]) + \" )?\" + o_list[1] + \":\" +\n \"( \" + \" == \".join([o_list[0], o_list[2]]) + \" )?\" + o_list[0] +\n \":\" + \"( \" + \" < \".join(o_list[0:2]) + \" ) ?\" +\n \"( ( \" + \" < \".join(o_list[1:]) + \" ) ?\" + o_list[1] + \":\" +\n o_list[2] + \" ) : \" +\n \"( ( \" + \" < \".join([o_list[0], o_list[2]]) + \" ) ?\" + o_list[0] +\n \":\" + o_list[2] + \" )\"\n )\n f.write(\"o = \" + cond + \";\\n\")\n if is_v:\n f.write(\"endmodule\")\n else:\n f.write(\"}\")\n\n f.close()","def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\n args = [\n \"gdal_calc.py\",\n '-A', infile,\n '--outfile={}'.format(tmpfile_1),\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\n '--type=Byte', '--NoDataValue=0',\n '--co=SPARSE_OK=YES',\n '--co=NBITS=1',\n '--quiet'\n # Could enable compression\n # --co=\"COMPRESS=LZW\"\n ]\n subprocess.run(args)\n\n subprocess.run([\n \"gdal_polygonize.py\",\n tmpfile_1,\n '-q',\n '-f', 'ESRI Shapefile',\n tmpfile_2\n ])\n\n subprocess.run([\n \"ogr2ogr\",\n '-a_srs', 'EPSG:4326',\n outfile,\n tmpfile_2\n ])\n\n subprocess.run([\"rm\", tmpfile_1])\n subprocess.run([\"rm\", tmpfile_2])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'shx')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'dbf')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'prj')])","def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\n outputs = []\n npy_outputs = []\n if resamplemethod == 'nearest':\n rs = Resampling.nearest\n else:\n print('only nearest neighbor resampling is supported at this time')\n sys.exit(0)\n\n for i, warpfile in enumerate(inputs):\n # print('warpfile', warpfile)\n with rasterio.open(warpfile) as src:\n # TODO - make the default configurable.\n# if src.crs == None:\n# src.crs = CRS.from_epsg(4326)\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\n with WarpedVRT(src, resampling=rs,\n crs=self.crs,\n transform=self.transform,\n height=self.rows,\n width=self.cols) as vrt:\n data = vrt.read()\n # print(type(vrt))\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\n outputs.append(outwarp)\n\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\n # from this method for us in the rest of the code.\n for ow in outputs:\n with rasterio.open(ow, 'r') as src:\n arr = src.read(1)\n npy_outputs.append(arr)\n\n return npy_outputs","def process_data(cursor, connection, filepath, function):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n function(cursor, datafile)\n connection.commit()\n print('{}/{} files processed.'.format(i, num_files))","def postprocess_cga(lines, outfile):\n pattern = re.compile(\"^\\s*([0-9,]+)\\s+\\([ 0-9.]+%\\)\\s+Source/(\\S+):(\\S+)\\(.*\\).*$\")\n\n totalCost = 0.0\n functionTable = []\n functionMap = {}\n\n for line in lines:\n line = line.strip()\n match = pattern.match(line)\n if not match:\n continue\n\n cost = float(match.group(1).replace(\",\", \"\"))\n sourceFile = match.group(2)\n function = match.group(3)\n\n # Filter out library code we don't want to change\n if function.startswith(\"stbi__\"):\n continue\n\n totalCost += cost\n\n # Accumulate the scores from functions in multiple call chains\n if function in functionMap:\n index = functionMap[function]\n functionTable[index][1] += cost\n functionTable[index][2] += cost\n # Else add new functions to the end of the table\n else:\n functionMap[function] = len(functionTable)\n functionTable.append([function, cost, cost])\n\n # Sort the table by accumulated cost\n functionTable.sort(key=lambda x: 101.0 - x[2])\n\n for function in functionTable:\n function[2] /= totalCost\n function[2] *= 100.0\n\n with open(outfile, \"w\") as fileHandle:\n\n totals = 0.0\n for function in functionTable:\n # Omit entries less than 1% load\n if function[2] < 1:\n break\n\n totals += function[2]\n fileHandle.write(\"%5.2f%% %s\\n\" % (function[2], function[0]))\n\n fileHandle.write(\"======\\n\")\n fileHandle.write(f\"{totals:5.2f}%\\n\")","def run(self, verbose=False):\n from utils import write_to_file # function to write json to file\n self.read_json()\n graph = self.parse_jsons()\n json = self.pipe_vl2vg(graph)\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)","def CallF_DirFile(dir_path,function,file_filter_=None,regular=False,**kw):\n file_list = Get_dir_file_list(dir_path=dir_path,file_filter_=file_filter_,regular=regular,distinguish=True)\n file_list = file_list[0]\n save_list = []\n for file_name in file_list:\n file_path = os.path.join(dir_path,file_name)\n save_list.append(function(file_path=file_path,**kw))\n return save_list","def py_SurfStatAvSurf(filenames, fun = np.add, output_surfstat=False):\n \n if filenames.ndim is not 2:\n raise ValueError('Filenames must be a 2-dimensional array.')\n \n for i in range(0, filenames.shape[0]):\n surfaces = np.empty(filenames.shape[1], dtype=np.object)\n for j in range(0, filenames.shape[1]):\n \n # Check whether input is BSPolyData or a filename. \n if isinstance(filenames[i,j], BSPolyData):\n surfaces[j] = filenames[i,j] \n else:\n surfaces[j] = read_surface(filenames[i,j])\n \n # Concatenate second dimension of filenames. \n if j is 0:\n tri = get_cells(surfaces[j]) \n coord = get_points(surfaces[j])\n else:\n tri = np.concatenate((tri, get_cells(surfaces[j]) + coord.shape[0]), axis=0)\n coord = np.concatenate((coord, get_points(surfaces[j])), axis=0)\n \n if i is 0:\n m = 1\n coord_all = coord\n else:\n coord_all = fun(coord_all,coord)\n m = fun(m,1)\n \n coord_all = coord_all / m \n \n if output_surfstat:\n surface = {'tri': np.array(tri) + 1, 'coord': np.array(coord_all).T}\n else:\n surface = build_polydata(coord_all, tri)\n \n return surface","def vjp_assemble_eval(\n fenics_function: Callable, fenics_templates: FenicsVariable, *args: np.array\n) -> Tuple[np.array, Callable]:\n\n numpy_output, ufl_form, fenics_inputs = assemble_eval(\n fenics_function, fenics_templates, *args\n )\n\n def vjp_fun(g):\n return tuple(\n vjp if vjp is not None else jax.ad_util.zeros_like_jaxval(args[i])\n for i, vjp in enumerate(vjp_assemble_impl(g, ufl_form, fenics_inputs))\n )\n\n return numpy_output, vjp_fun","def InterpolateFunctions(self, , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def process(self,fileobj_out,fileobj_in):\n pass","def run_functions( functions, dir ):\n res = []\n for i in range(0, len(functions)):\n res.append(functions[i].execute(dir))\n return summarize_as_html(res)","def bind(self, func: Callable[[Any], IO]) -> IO:\n\n filename, g = self._value\n return ReadFile(filename, lambda s: g(s).bind(func))","def read_func(filename=def_func_name, index=def_index):\n file = open(filename+str(index)+\".txt\", \"r\")\n functions = []\n for i in range(3):\n line = file.readline()\n line = line.replace('\\'', '').strip()\n functions.append(parse_line(line))\n return functions","def transform(infile, output, insrs, format_name):\n\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \n in_srs = osr.SpatialReference()\n in_srs.ImportFromEPSG(insrs)\n out_srs = osr.SpatialReference()\n out_srs.ImportFromEPSG(4324)\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\n\n in_dsn = ogr.Open(infile)\n in_layer = in_dsn.GetLayer()\n in_feature_definition = in_layer.GetLayerDefn()\n\n out_driver = ogr.GetDriverByName(format_name)\n out_dsn = out_driver.CreateDataSource(output)\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\n geom_type=in_layer.GetGeomType())\n\n # add fields\n for i in range(0, in_feature_definition.GetFieldCount()):\n fieldDefn = in_feature_definition.GetFieldDefn(i)\n out_layer.CreateField(fieldDefn)\n\n # get the output layer's feature definition\n out_feature_definition = out_layer.GetLayerDefn()\n\n # loop through the input features\n inFeature = in_layer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef().Clone()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(out_feature_definition)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, out_feature_definition.GetFieldCount()):\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n out_layer.CreateFeature(outFeature)\n # destroy the features and get the next input feature\n outFeature.Destroy()\n inFeature.Destroy()\n inFeature = in_layer.GetNextFeature()\n\n # close the shapefiles\n in_dsn.Destroy()\n out_dsn.Destroy()","def Map(context, funcname, *nodesets):\n (prefix, local) = ExpandQName(funcname, namespaces=context.processorNss)\n func = (g_extFunctions.get(expanded) or\n CoreFunctions.CoreFunctions.get(expanded, None))\n if not func:\n raise Exception('Dynamically invoked function %s not found.'%funcname)\n flist = [f]*len(nodesets)\n lf = lambda x, f, *args: apply(f, args)\n retlist = apply(map, (lf, flist) + nodesets)\n\n proc = context.processor\n result_nodeset = []\n for ret in retlist:\n proc.pushResult()\n proc.writers[-1].text(Conversions.StringValue(ret))\n frag = proc.popResult()\n context.rtfs.append(frag)\n result_nodeset.append(frag.childNodes[0])\n return result_nodeset","def buildFunc(self, parFiles):\n imageMatrix = None\n affine = None\n TR = None\n\n ### Loop over all of the par files\n nVols = len(parFiles)\n for par_fname in parFiles:\n # build full path to this par file\n par_fname = join(self.seriesDir, par_fname)\n\n # make sure there is a corresponding .rec file\n if not os.path.isfile(par_fname.replace('.par', '.rec')):\n print('No REC file found to match par: {}', par_fname)\n\n ### Build the 3d voxel array for this volume and reorder to RAS+\n # nibabel will load the par/rec, but there can be multiple images\n # (mag, phase, etc...) concatenated into the 4th dimension. Loading\n # with the strict_sort option (I think) will make sure the first\n # image is the data we want. Extract this data, then reorder the\n # voxel array to RAS+\n thisVol = nib.load(par_fname, strict_sort=True)\n\n # get the vol index (0-based index) from the acq_nr field of the\n # header (1-based index)\n volIdx = int(thisVol.header.general_info['acq_nr']) - 1\n\n # set TR\n if TR is None:\n TR = thisVol.header.general_info['repetition_time'][0]\n\n # convert to RAS+\n thisVol_RAS = nib.as_closest_canonical(thisVol)\n\n # construct the imageMatrix if it hasn't been made yet\n if imageMatrix is None:\n imageMatrix = np.zeros(shape=(thisVol_RAS.shape[0],\n thisVol_RAS.shape[1],\n thisVol_RAS.shape[2],\n nVols), dtype=np.uint16)\n\n # construct the affine if it isn't made yet\n if affine is None:\n affine = thisVol_RAS.affine\n\n # Add this data to the image matrix\n imageMatrix[:, :, :, volIdx] = thisVol_RAS.get_fdata()[:, :, :, 0].astype('uint16')\n\n ### Build a Nifti object\n funcImage = nib.Nifti1Image(imageMatrix, affine=affine)\n\n # add the correct TR to the header\n pixDims = np.array(funcImage.header.get_zooms())\n pixDims[3] = TR\n funcImage.header.set_zooms(pixDims)\n\n return funcImage","def map_func(h, configs, args):\n\tif args.verbose:\n\t\tcmd = \"python {} -i {}/threshold{}.tif -o {}/threshold{}.shp -v\".format(\n\t\t\tconfigs[\"path\"][\"polygons\"],\n\t\t\tconfigs[\"path\"][\"output\"],\n\t\t\th,\n\t\t\tconfigs[\"path\"][\"output\"],\n\t\t\th\n\t\t)\n\t\tprint cmd\n\telse:\n\t\tcmd = \"python {} -i {}/threshold{}.tif -o {}/threshold{}.shp\".format(\n\t\t\tconfigs[\"path\"][\"polygons\"],\n\t\t\tconfigs[\"path\"][\"output\"],\n\t\t\th,\n\t\t\tconfigs[\"path\"][\"output\"],\n\t\t\th\n\t\t)\n\tcmd_args = shlex.split(cmd)\n\tstdout,stderr = sp.Popen(\n\t\tcmd_args,\n\t\tstdin = sp.PIPE,\n\t\tstdout = sp.PIPE,\n\t\tstderr = sp.PIPE\n\t).communicate()\n\tif args.verbose:\n\t\tprint stdout, stderr\n\treturn True","def main(path):\n vm_files = []\n if not os.path.exists(path):\n print(\"Error: File or directory does not exist: %s\"\n % path)\n return\n\n elif os.path.isdir(path): # Directory of files\n vm_files = filter_paths(path)\n dir_path = path\n file_name = os.path.basename(path) + FILE_EXTENSION_ASM\n if not vm_files: # no vm files found\n print(\"Error: No files matching %s found in supplied \"\n \"directory: %s\" % (FILE_EXTENSION_VM, path))\n return\n\n elif os.path.isfile(path): # Single file\n if not path.endswith(FILE_EXTENSION_VM):\n print(\"Error: Mismatched file type.\\n\\\"%s\\\"suffix is not a valid \"\n \"file type. Please supply .vm filename or dir.\" % path)\n return\n vm_files.append(path)\n dir_path = os.path.dirname(path)\n file_name = os.path.splitext(os.path.basename(path))[0] + \\\n FILE_EXTENSION_ASM\n\n else:\n print(\"Error: Unrecognized path: \\\"%s\\\"\\n\"\n \"Please supply dir or path/filename.vm\")\n return\n\n try:\n # Initilizes write based, using a condition for multiple file reading.\n # Multiple files have a special initlization\n writer = CodeWriter(os.path.join(dir_path, file_name),\n len(vm_files) > 1)\n for vm_file in vm_files:\n translate_file(vm_file, writer)\n writer.close()\n\n except OSError:\n print(\"Could not open some file.\\n \"\n \"If file exists, check spelling of file path.\")\n return\n\n except Exception as e:\n print(\"Some exception occurred while parsing.\", e)\n traceback.print_exc()\n return","def addfunctions(dtls, bunchdt):\n snames = [\n \"BuildingSurface:Detailed\",\n \"Wall:Detailed\",\n \"RoofCeiling:Detailed\",\n \"Floor:Detailed\",\n \"FenestrationSurface:Detailed\",\n \"Shading:Site:Detailed\",\n \"Shading:Building:Detailed\",\n \"Shading:Zone:Detailed\",\n ]\n for sname in snames:\n if sname.upper() in bunchdt:\n surfaces = bunchdt[sname.upper()]\n for surface in surfaces:\n func_dict = {\n \"area\": fh.area,\n \"height\": fh.height, # not working correctly\n \"width\": fh.width, # not working correctly\n \"azimuth\": fh.azimuth,\n \"tilt\": fh.tilt,\n \"coords\": fh.getcoords, # needed for debugging\n }\n try:\n surface.__functions.update(func_dict)\n except KeyError as e:\n surface.__functions = func_dict\n # add common functions\n # for name in dtls:\n # for idfobject in bunchdt[name]:\n # idfobject.__functions\n # idfobject['__functions']['fieldnames'] = fieldnames\n # idfobject['__functions']['fieldvalues'] = fieldvalues\n # idfobject['__functions']['getrange'] = GetRange(idfobject)\n # idfobject['__functions']['checkrange'] = CheckRange(idfobject)","def main():\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--input', help='Input .py file', nargs='+')\n args = parser.parse_args()\n\n mod_func = []\n\n for pyfile in args.input:\n tree = ast.parse(open(pyfile).read())\n\n methods = sorted({node.name for node in ast.walk(tree)\n if isinstance(node, ast.FunctionDef)})\n mod_func.extend([[pyfile, methods[i]] for i in range(len(methods))])\n\n write_csv(\"meth_func.csv\", mod_func)","def transform(self, *fs):\n return transform(self, *fs)","def get_vulnerability_functions_04(fname):\n categories = dict(assetCategory=set(), lossCategory=set(),\n vulnerabilitySetID=set())\n imts = set()\n taxonomies = set()\n vf_dict = {} # imt, taxonomy -> vulnerability function\n for vset in nrml.read(fname).vulnerabilityModel:\n categories['assetCategory'].add(vset['assetCategory'])\n categories['lossCategory'].add(vset['lossCategory'])\n categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])\n IML = vset.IML\n imt_str = IML['IMT']\n imls = ~IML\n imts.add(imt_str)\n for vfun in vset.getnodes('discreteVulnerability'):\n taxonomy = vfun['vulnerabilityFunctionID']\n if taxonomy in taxonomies:\n raise InvalidFile(\n 'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %\n (taxonomy, fname, vfun.lineno))\n taxonomies.add(taxonomy)\n with context(fname, vfun):\n loss_ratios = ~vfun.lossRatio\n coefficients = ~vfun.coefficientsVariation\n if len(loss_ratios) != len(imls):\n raise InvalidFile(\n 'There are %d loss ratios, but %d imls: %s, line %d' %\n (len(loss_ratios), len(imls), fname,\n vfun.lossRatio.lineno))\n if len(coefficients) != len(imls):\n raise InvalidFile(\n 'There are %d coefficients, but %d imls: %s, line %d' %\n (len(coefficients), len(imls), fname,\n vfun.coefficientsVariation.lineno))\n with context(fname, vfun):\n vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(\n taxonomy, imt_str, imls, loss_ratios, coefficients,\n vfun['probabilisticDistribution'])\n categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))\n del categories['vulnerabilitySetID']\n return vf_dict, categories","def define_functions(self):\n\n self.define_vector_functions()\n self.define_scalar_functions()\n\n return None","def vl2img(vl_json_in, fileformat):\n\n # TODO would prefer to do this properly with pipes\n # using | and shell=True is safe though given no arguments\n executables = {\"svg\": \"vg2svg\", \"png\": \"vg2png\", \"pdf\": \"vg2pdf\"}\n try:\n exe = executables[fileformat]\n except KeyError as e:\n print(e.output)\n try:\n return subprocess.check_output(\"vl2vg | %s\" % exe, shell=True, input=vl_json_in)\n except subprocess.CalledProcessError as e:\n print(e.output)","def render_function(children):\r\n fname = children[0].latex\r\n if casify(fname) not in functions:\r\n pass # TODO turn unknown function red or give some kind of error\r\n\r\n # Wrap the input of the function with parens or braces.\r\n inner = children[1].latex\r\n if fname == \"sqrt\":\r\n inner = u\"{{{expr}}}\".format(expr=inner)\r\n else:\r\n if children[1].tall:\r\n inner = ur\"\\left({expr}\\right)\".format(expr=inner)\r\n else:\r\n inner = u\"({expr})\".format(expr=inner)\r\n\r\n # Correctly format the name of the function.\r\n if fname == \"sqrt\":\r\n fname = ur\"\\sqrt\"\r\n elif fname == \"log10\":\r\n fname = ur\"\\log_{10}\"\r\n elif fname == \"log2\":\r\n fname = ur\"\\log_2\"\r\n else:\r\n fname = ur\"\\text{{{fname}}}\".format(fname=fname)\r\n\r\n # Put it together.\r\n latex = fname + inner\r\n return LatexRendered(latex, tall=children[1].tall)","def _zipper(self, func):\n if func not in self.resmap:\n return []\n return itertools.chain(*[f(self.blob.get(name, None), self.filepath, self.tree, self.conn, self.dbpath)\n for name, f in self.resmap[func]])","def process(self, file_list, shapefiles_dir, output_dir):\n for datatype in self.data_types:\n for data_file in file_list:\n data_file = str(data_file)\n original_filename = os.path.basename(data_file)\n cut_fname = original_filename[:len(self.date_format)+2]\n observation_date = datetime.strptime(cut_fname, self.date_format).strftime('%Y%m%d')\n try:\n input_file = HE5(self.data_directory, original_filename)\n if ('nc4' in data_file):\n input_file = NC4(self.data_directory, original_filename)\n data = input_file.read(datatype.ds_name)\n except Exception as e:\n print(e)\n continue\n\n rasterdata = input_file.genTif(data, datatype.resolution)\n\n for shpfile in Path(shapefiles_dir).rglob('*.shp'):\n shapefile = Shapefile(shapefiles_dir, shpfile.name)\n csvfile = shapefile.read_shape_file(datatype, rasterdata, data, self.minVal)\n csvfile = np.array(csvfile)\n\n output = OutputFile(output_dir, shapefile.getDirName(), self.name, datatype, observation_date)\n output.save(csvfile)\n\n del rasterdata","def create_test_function(source, output, lang):\n with open(source) as f:\n snippet = f.read()\n with open(output) as f:\n res = f.read()\n\n def tst_func(slf):\n slf.do(snippet, res, lang=lang)\n\n return tst_func","def main(config_file, rows, cols):\n # setup paths\n _, _, params = cf.get_ifg_paths(config_file)\n _postprocess_linrate(rows, cols, params)\n if params[cf.TIME_SERIES_CAL]:\n _postprocess_timeseries(rows, cols, params)","def convert(self):\n \n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\n splitAt = len(self.fullPath) + 1\n \n if len(vrtlist)!=0:\n for i in range(0,len(vrtlist)):\n prefix = str(vrtlist[i].split(\".vrt\")[0])\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \n prefix = prefix, subset = self.subset, res = self.resolution, \n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\n ct.run()\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\n for f in mosdel:\n os.remove(f)\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \n for f in xmldel:\n os.remove(f)\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\n for f in vrtdel:\n os.remove(f)\n tifCount = len(glob.glob(self.fullPath + '/*.tif'))\n dataCount = self.subset.count('1')\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d mosaicked images were successfully converted to %d %s files.' % (dataCount, len(vrtlist), tifCount, str(self.outformat)))\n \n \n if len(vrtlist)==0: \n \n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\n for i in range(len(hdflist)):\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i]], subset = self.subset, outformat = 'GTiff')\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\n\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\n splitAt = len(self.fullPath) + 1\n \n for i in range(0,len(vrtlist)):\n prefix = str(vrtlist[i].split(\".vrt\")[0])\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \n prefix = prefix, subset = self.subset, res = self.resolution, \n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\n ct.run()\n \n mosdel = glob.glob(self.fullPath + '/*mos.tif')\n for f in mosdel:\n os.remove(f)\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \n for f in xmldel:\n os.remove(f)\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\n for f in vrtdel:\n os.remove(f)\n tifCount = len(glob.glob(self.fullPath + '/full*.tif'))\n dataCount = self.subset.count('1')\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d HDF files were successfully converted to %d %s files.' % (dataCount, len(hdflist), tifCount, str(self.outformat)))","def function_closure(functions, casify):\r\n def render_function(children):\r\n \"\"\"\r\n Escape function names and give proper formatting to exceptions.\r\n\r\n The exceptions being 'sqrt', 'log2', and 'log10' as of now.\r\n \"\"\"\r\n fname = children[0].latex\r\n if casify(fname) not in functions:\r\n pass # TODO turn unknown function red or give some kind of error\r\n\r\n # Wrap the input of the function with parens or braces.\r\n inner = children[1].latex\r\n if fname == \"sqrt\":\r\n inner = u\"{{{expr}}}\".format(expr=inner)\r\n else:\r\n if children[1].tall:\r\n inner = ur\"\\left({expr}\\right)\".format(expr=inner)\r\n else:\r\n inner = u\"({expr})\".format(expr=inner)\r\n\r\n # Correctly format the name of the function.\r\n if fname == \"sqrt\":\r\n fname = ur\"\\sqrt\"\r\n elif fname == \"log10\":\r\n fname = ur\"\\log_{10}\"\r\n elif fname == \"log2\":\r\n fname = ur\"\\log_2\"\r\n else:\r\n fname = ur\"\\text{{{fname}}}\".format(fname=fname)\r\n\r\n # Put it together.\r\n latex = fname + inner\r\n return LatexRendered(latex, tall=children[1].tall)\r\n # Return the function within the closure.\r\n return render_function","def generic_function(self, node, ordered_functions):\n for generic in node.fortran_generic:\n new = node.clone()\n ordered_functions.append(new)\n self.append_function_index(new)\n new._generated = \"fortran_generic\"\n fmt = new.fmtdict\n # XXX append to existing suffix\n if generic.fmtdict:\n fmt.update(generic.fmtdict)\n fmt.function_suffix = fmt.function_suffix + generic.function_suffix\n new.fortran_generic = {}\n new.wrap.assign(fortran=True)\n new.ast.declarator.params = generic.decls\n\n # Try to call original C function if possible.\n # All arguments are native scalar.\n need_wrapper = False\n if new.ast.declarator.is_indirect():\n need_wrapper = True\n \n for arg in new.ast.declarator.params:\n if arg.declarator.is_indirect():\n need_wrapper = True\n break\n elif arg.typemap.sgroup == \"native\":\n pass\n else:\n need_wrapper = True\n break\n\n if need_wrapper:\n # The C wrapper is required to cast constants.\n # generic.yaml: GenericReal\n new.C_force_wrapper = True\n new.wrap.c = True\n new._PTR_C_CXX_index = node._function_index\n else:\n new._PTR_F_C_index = node._function_index\n \n # Do not process templated node, instead process\n # generated functions above.\n # node.wrap.c = False\n node.wrap.fortran = False","def _run_file_contents(contents: dict, strict_fn, debug_print):\n\n default_fns_args = {\n \"adjust_focus\": {\"on\": \"hexbin\"},\n \"adjust_positioning\": True,\n \"adjust_opacity\": True\n }\n\n input_fns = contents.pop(\"functions\", {})\n for k, v in default_fns_args.items():\n if k not in input_fns:\n input_fns[k] = v\n\n output = contents.pop(\"output\", False)\n display = contents.pop(\"display\", True)\n builder = PlotBuilder.builder_from_dict(**contents)\n debug_print(\"* all layers loaded\")\n\n for k, v in input_fns.items():\n if v:\n args, kwargs = parse_args_kwargs(v)\n try:\n _fn_map[k](builder, *args, **kwargs)\n debug_print(f\"* invoked function '{_fn_map[k].__name__}'.\\nargs: {args}\\nkwargs: {kwargs}\")\n except Exception as e:\n try:\n debug_print(f\"* error while performing '{_fn_map[k].__name__}'.\\nerror: {e}\")\n strict_fn(e)\n except KeyError as f:\n debug_print(f\"* no such function as '{k}'.\")\n strict_fn(f)\n\n builder.finalize(raise_errors=False)\n\n if output:\n args, kwargs = parse_args_kwargs(output)\n builder.output(*args, **kwargs)\n debug_print(\"* figure output.\")\n\n if display:\n args, kwargs = parse_args_kwargs(display)\n builder.display(*args, **kwargs)\n debug_print(\"* figure displayed.\")\n\n return builder.get_plot_status()","def main():\r\n\r\n parser = argparse.ArgumentParser()\r\n parser.add_argument(dest='INFILE')\r\n parser.add_argument(dest='OUTFILE')\r\n\r\n args = parser.parse_args()\r\n function1(args.INFILE, args.OUTFILE)","def apply_function_vector(function_vector, x_vector):\n function_index = 0\n element_index = 1\n \n def d():\n for e in zip(function_vector, x_vector):\n print(e[1])\n d()\n \n return list(map(lambda fx_set: fx_set[function_index](fx_set[element_index]), zip(function_vector, x_vector)))","def use_vectors(*funcs):\n\n global GLOBAL_STATUS\n if funcs:\n GLOBAL_STATUS.discard('ARRAYS')\n GLOBAL_STATUS.add('VECTORS')\n GLOBAL_STATUS.discard('SCALARS')\n else:\n GLOBAL_STATUS.discard('ARRAYS')\n GLOBAL_STATUS.discard('VECTORS')\n GLOBAL_STATUS.discard('SCALARS')\n\n for name in _get_func_names(funcs):\n if ('scalar' not in name and 'vector' not in name\n and 'array' not in name):\n globals()[name] = globals()[name].vector","def convert(func, overload_module, transformers):\n source, _ = parsing.parse_entity(func)\n entity_info = transformer.EntityInfo(\n source_code=source,\n source_file='<fragment>',\n namespace={},\n arg_values=None,\n arg_types={},\n owner_type=None)\n\n overload_module.PyctrReturnException = PyctrReturnException\n\n namer = naming.Namer(entity_info.namespace)\n ctx = transformer.EntityContext(namer, entity_info)\n overload_name = ctx.namer.new_symbol('overload', set())\n overload = config.VirtualizationConfig(overload_module, overload_name)\n\n source = _transform(source, ctx, overload, transformers)\n gen_func = _wrap_in_generator(func, source, namer, overload)\n gen_func = _attach_closure(func, gen_func)\n return gen_func","def load_raw_wignetting_function():\n global rawvignfun\n if rawvignfun is None:\n vignfile = get_vigneting_by_urd(28)\n x = 23.5 + np.tan(vignfile[\"Offset angles\"].data[\"X\"]*pi/180/60.)*F/DL\n y = 23.5 + np.tan(vignfile[\"Offset angles\"].data[\"Y\"]*pi/180/60.)*F/DL\n rawvignfun = RegularGridInterpolator((vignfile[\"5 arcmin PSF\"].data[\"E\"], x, y), vignfile[\"5 arcmin PSF\"].data[\"EFFAREA\"])\n return rawvignfun","def main():\r\n\r\n #Create a list of all files that have the GPX file format\r\n fileList = glob.glob(os.path.join(inFolder,\"*.{0}\".format(inFormat)))\r\n\r\n #Create a connection to PostGIS database\r\n pgConn = createPostgisConnection(dbFormat, dbHost, dbName, dbSchema, dbUser, dbPWD)\r\n\r\n #Process each *listed* layer type from a GPS file\r\n for f in fileList:\r\n importGPX(f, gpxImportLayers, pgConn)","def main(file):\n\n # Get the current working directory.\n here = os.getcwd()\n #Need the file_name to set globe, so that other functions can access to it.\n global file_name\n # Spite the Input into file_path and file_name.\n file_path = spilt_path(file)[0]\n file_name = spilt_path(file)[1]\n\n # Try to get into the file_path, if exist\n try:\n os.chdir(file_path)\n except IOError, e:\n print e\n\n # Now convert it\n convertFile(file_name)\n # going back to orgin folder\n os.chdir(here)\n return os.path.join(output_dir, file_name)","def input(self, *args, **kwargs):\n return lambda wildcards: self.samples.map(*args, file=\"samples/all/runs/{sample_run}/samples.csv\", **wildcards, **kwargs)","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n # use the func to insert data from these files to database's fact and dim tables\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def assemble_eval(\n fenics_function: Callable,\n fenics_templates: Iterable[FenicsVariable],\n *args: np.array,\n) -> Tuple[np.array, ufl.Form, Tuple[FenicsVariable]]:\n\n check_input(fenics_templates, *args)\n fenics_inputs = convert_all_to_fenics(fenics_templates, *args)\n\n out = fenics_function(*fenics_inputs)\n if not isinstance(out, tuple):\n raise ValueError(\n \"FEniCS function output should be in the form (assembly_output, ufl_form).\"\n )\n\n assembly_output, ufl_form = out\n\n if isinstance(assembly_output, tuple):\n raise ValueError(\n \"Only single solution output from FEniCS function is supported.\"\n )\n\n if not isinstance(assembly_output, float):\n raise ValueError(\n f\"FEniCS function output should be in the form (assembly_output, ufl_form). Got {type(assembly_output)} instead of float\"\n )\n\n if not isinstance(ufl_form, ufl.Form):\n raise ValueError(\n f\"FEniCS function output should be in the form (assembly_output, ufl_form). Got {type(ufl_form)} instead of ufl.Form\"\n )\n\n numpy_output = np.asarray(assembly_output)\n return numpy_output, ufl_form, fenics_inputs","def load_viewpoints(viewpoint_file_list):\n\n if isinstance(viewpoint_file_list, str):\n vp_file_list = [viewpoint_file_list]\n\n try:\n vp_file_list = iter(viewpoint_file_list)\n except TypeError:\n print(\"viewpoint_file_list is not an iterable object\")\n\n for vp_file in vp_file_list:\n yield load_viewpoint(vp_file)","def apply_funcs(self, src_dir, pkl_name, dest_dir, funclist):\n if not os.path.exists(dest_dir):\n os.makedirs(dest_dir)\n pkl_files = os.listdir(src_dir)\n data_in = open(os.path.join(src_dir, pkl_name), 'rb')\n data_array = pickle.load(data_in)\n dump_array = []\n for i, data_dict in enumerate(data_array):\n new_data_dict = self.dutils.apply_flist(data_dict, funclist, w_rosdict=True)\n if new_data_dict.get(\"flag\", True):\n dump_array.append(new_data_dict)\n new_pkl_name = pkl_name\n dump_path = os.path.join(dest_dir, new_pkl_name)\n self.pickledump(dump_array, dump_path)\n self.seen_pkls.append(new_pkl_name)\n return new_pkl_name","def write_triggers_to_file(triggerfile, functionfile,\n generators, entityname, table, path, select,\n indextable, index):\n for generator in generators:\n gen = generator(entityname, table, path, select, indextable, index)\n functionfile.write(gen.function)\n triggerfile.write(gen.trigger)","def jvp_assemble_eval(\n fenics_function: Callable,\n fenics_templates: Iterable[FenicsVariable],\n primals: Tuple[np.array],\n tangents: Tuple[np.array],\n) -> Tuple[np.array]:\n\n numpy_output_primal, output_primal_form, fenics_primals = assemble_eval(\n fenics_function, fenics_templates, *primals\n )\n\n # Now tangent evaluation!\n fenics_tangents = convert_all_to_fenics(fenics_primals, *tangents)\n output_tangent_form = 0.0\n for fp, ft in zip(fenics_primals, fenics_tangents):\n output_tangent_form += fenics.derivative(output_primal_form, fp, ft)\n\n if not isinstance(output_tangent_form, float):\n output_tangent_form = ufl.algorithms.expand_derivatives(output_tangent_form)\n output_tangent = fenics.assemble(output_tangent_form)\n\n jax_output_tangent = output_tangent\n\n return numpy_output_primal, jax_output_tangent","def process_svgs(model_info, directory, output, allow_missing=False):\n r2dt.write(model_info, directory, output, allow_missing=allow_missing)","def transform_regions(region_list, wcs_in, wcs_out, preroot,\n reg_format='fits', clobber=False):\n\n if reg_format == 'ascii':\n func = transform_region_ascii\n elif reg_format == 'fits':\n func = transform_region_fits\n else:\n raise ValueError(\"Unsupported reg_format=\" + reg_format)\n\n outfiles = []\n for region_file in region_list:\n\n # Do not do anything if the output file already exists and\n # clobber is not set.\n #\n outfile = preroot + region_file\n outfiles.append(outfile)\n if not clobber and os.path.exists(outfile):\n continue\n\n func(region_file, outfile, wcs_in, wcs_out)\n\n return outfiles","def process(sources, output, force):\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s [%(levelname)s] - %(message)s', datefmt=\"%H:%M:%S\")\n\n logging.getLogger('shapely.geos').setLevel(logging.WARNING)\n logging.getLogger('Fiona').setLevel(logging.WARNING)\n logging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.WARNING)\n requests.packages.urllib3.disable_warnings()\n # logging.getLogger('processing').setLevel(logging.DEBUG)\n\n catalog_features = []\n failures = []\n path_parts_to_skip = utils.get_path_parts(sources).index(\"sources\") + 1\n success = True\n for path in utils.get_files(sources):\n try:\n logging.info(\"Processing \" + path)\n pathparts = utils.get_path_parts(path)[path_parts_to_skip:]\n pathparts[-1] = pathparts[-1].replace('.json', '.geojson')\n\n outdir = os.path.join(output, *pathparts[:-1], pathparts[-1].replace('.geojson', ''))\n outfile = os.path.join(output, *pathparts)\n\n source = utils.read_json(path)\n urlfile = urlparse(source['url']).path.split('/')[-1]\n \n if not hasattr(adapters, source['filetype']):\n logging.error('Unknown filetype ' + source['filetype'])\n failures.append(path)\n continue\n \n read_existing = False\n if os.path.isfile(outfile):\n logging.info(\"Output file exists\")\n if os.path.getmtime(outfile) > os.path.getmtime(path):\n logging.info(\"Output file is up to date\")\n if not force:\n read_existing = True\n logging.warning('Skipping ' + path + ' since generated file exists. Use --force to regenerate.') \n else:\n logging.info(\"Output is outdated, {} < {}\".format(\n datetime.datetime.fromtimestamp(os.path.getmtime(outfile)),\n datetime.datetime.fromtimestamp(os.path.getmtime(path))))\n\n if read_existing:\n with open(outfile, \"rb\") as f:\n geojson = json.load(f)\n properties = geojson['properties']\n else:\n logging.info('Downloading ' + source['url'])\n \n try:\n fp = utils.download(source['url'])\n except IOError:\n logging.error('Failed to download ' + source['url'])\n failures.append(path)\n continue\n \n logging.info('Reading ' + urlfile)\n \n if 'filter' in source:\n filterer = BasicFilterer(source['filter'], source.get('filterOperator', 'and'))\n else:\n filterer = None\n \n try:\n geojson = getattr(adapters, source['filetype'])\\\n .read(fp, source['properties'],\n filterer=filterer,\n layer_name=source.get(\"layerName\", None),\n source_filename=source.get(\"filenameInZip\", None))\n except IOError as e:\n logging.error('Failed to read ' + urlfile + \" \" + str(e))\n failures.append(path)\n continue\n except zipfile.BadZipfile as e:\n logging.error('Unable to open zip file ' + source['url'])\n failures.append(path)\n continue\n finally:\n os.remove(fp.name)\n if(len(geojson['features'])) == 0:\n logging.error(\"Result contained no features for \" + path)\n continue\n excluded_keys = ['filetype', 'url', 'properties', 'filter', 'filenameInZip']\n properties = {k:v for k,v in list(source.items()) if k not in excluded_keys}\n properties['source_url'] = source['url']\n properties['feature_count'] = len(geojson['features'])\n logging.info(\"Generating demo point\")\n properties['demo'] = geoutils.get_demo_point(geojson)\n \n geojson['properties'] = properties\n \n utils.make_sure_path_exists(os.path.dirname(outfile))\n\n #cleanup existing generated files\n if os.path.exists(outdir):\n rmtree(outdir)\n filename_to_match, ext = os.path.splitext(pathparts[-1])\n output_file_dir = os.sep.join(utils.get_path_parts(outfile)[:-1])\n logging.info(\"looking for generated files to delete in \" + output_file_dir)\n for name in os.listdir(output_file_dir):\n base, ext = os.path.splitext(name)\n if base == filename_to_match:\n to_remove = os.path.join(output_file_dir, name)\n logging.info(\"Removing generated file \" + to_remove)\n os.remove(to_remove)\n\n utils.write_json(outfile, geojson)\n\n logging.info(\"Generating label points\")\n label_geojson = geoutils.get_label_points(geojson)\n label_path = outfile.replace('.geojson', '.labels.geojson')\n utils.write_json(label_path, label_geojson)\n\n logging.info('Done. Processed to ' + outfile)\n \n if not \"demo\" in properties:\n properties['demo'] = geoutils.get_demo_point(geojson)\n\n properties['path'] = \"/\".join(pathparts)\n catalog_entry = {\n 'type': 'Feature',\n 'properties': properties,\n 'geometry': geoutils.get_union(geojson)\n }\n catalog_features.append(catalog_entry)\n\n if not os.path.exists(outdir) or not os.path.exists(os.path.join(outdir, \"units.json\")):\n logging.info(\"Generated exploded GeoJSON to \" + outdir)\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n # .json instead of .geojson, incase there is a unit named \"source\"\n utils.write_json(os.path.join(outdir, \"source.json\"), catalog_entry) \n units = []\n for feature in geojson['features']:\n feature_id = str(feature['properties']['id'])\n feature_id = feature_id.replace('/', '')\n feature_filename = os.path.join(outdir, feature_id + \".geojson\")\n utils.write_json(feature_filename, feature)\n units.append(feature['properties'])\n utils.write_json(os.path.join(outdir, \"units.json\"), units)\n else:\n logging.debug(\"exploded GeoJSON already exists, not generating\")\n\n except Exception as e:\n logging.error(str(e))\n logging.exception(\"Error processing file \" + path)\n failures.append(path)\n success = False\n\n catalog = {\n 'type': 'FeatureCollection',\n 'features': catalog_features\n }\n utils.write_json(os.path.join(output,'catalog.geojson'), catalog)\n\n if not success:\n logging.error(\"Failed sources: \" + \", \".join(failures))\n sys.exit(-1)","def _generate_and_save_function(self, filename, expression, parameters):\n\n # check for / create the save folder for this expression\n folder = self.config_folder + '/' + filename\n abr_control.utils.os_utils.makedirs(folder)\n\n if self.use_cython is True:\n # binaries saved by specifying tempdir parameter\n function = autowrap(expression, backend=\"cython\",\n args=parameters, tempdir=folder)\n function = sp.lambdify(parameters, expression, \"numpy\")\n\n return function","def _add_function(self, alias, func):\n # Construct a function that will call the user supplied function with\n # the proper arguments. We prepend 'self' so the user supplied function\n # has easy access to all the filepaths.\n def fname(**kwargs):\n return func(self, **kwargs)\n\n # Bind the fname function to this instance of FileNames\n self.__dict__[alias] = fname","def get_verts(ulist, vlist, func):\n verts = []\n for u in ulist:\n for v in vlist:\n verts.append(func(u, v))\n return verts","def ramlwrap(file_path, function_map):\n\n try:\n # Check if file is RAML (.raml)\n if file_path.endswith(\".raml\"):\n patterns = raml_url_patterns(file_path, function_map)\n else:\n error_msg = \"The file: '{}' does not have a .raml extension!\".format(file_path)\n logger.error(error_msg)\n raise FatalException(error_msg)\n\n except AttributeError as error:\n error_msg = \"An error occurred reading '{}': {}\".format(file_path, error)\n logger.error(error_msg)\n raise FatalException(error_msg)\n\n return patterns","def file_converter(self, **kwds):\n if (self.reformat == 'zarr'):\n # output zarr file\n self.HDF5_to_zarr(**kwds)\n elif (self.reformat == 'HDF5'):\n # output rechunked HDF5 file\n self.HDF5_to_HDF5(**kwds)\n # elif (reformat == 'JPL'):\n # # output JPL captoolkit formatted HDF5 files\n # self.HDF5_to_JPL_HDF5(**kwds)\n elif self.reformat in ('csv','txt'):\n # output reduced files to ascii formats\n self.HDF5_to_ascii(**kwds)\n elif self.reformat in ('dataframe',):\n # output reduced files to pandas dataframe\n return self.HDF5_to_dataframe(**kwds)\n else:\n raise ValueError(f'Unknown format {self.reformat}')","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def get_vit_fn(model, num_classes, spatial_res):\n model = model.lower()\n assert model in VIT_FNS\n vit_fn = VIT_FNS[model]\n\n def wrapped_vit_model_fn(*args, **kwargs):\n return vit_fn(*args, num_classes=num_classes,\n image_size=spatial_res, **kwargs)\n return wrapped_vit_model_fn","def _rasterize_vector_onto_base(\n base_raster_path, base_vector_path, attribute_id,\n target_raster_path, filter_string=None):\n base_raster = gdal.OpenEx(base_raster_path, gdal.OF_RASTER)\n raster_driver = gdal.GetDriverByName('GTiff')\n target_raster = raster_driver.CreateCopy(target_raster_path, base_raster)\n base_raster = None\n\n vector = gdal.OpenEx(base_vector_path)\n layer = vector.GetLayer()\n\n if filter_string is not None:\n layer.SetAttributeFilter(str(filter_string))\n gdal.RasterizeLayer(\n target_raster, [1], layer,\n options=['ATTRIBUTE=%s' % attribute_id])\n target_raster.FlushCache()\n target_raster = None\n layer = None\n vector = None","def get_transfer_functioin(self, color_file=None, volume_opacity=0.25):\n if color_file: # Color file is given\n\n import csv\n fid = open(color_file, \"r\")\n reader_color = csv.reader(fid)\n\n dict_RGB = {}\n for line in reader_color:\n dict_RGB[int(line[0])] = [float(line[2]) / 255.0,\n float(line[3]) / 255.0,\n float(line[4]) / 255.0]\n fid.close()\n\n # Define colour transfer function\n color_transfor = vtk.vtkColorTransferFunction()\n\n for idx in dict_RGB.keys():\n color_transfor.AddRGBPoint(idx,\n dict_RGB[idx][0],\n dict_RGB[idx][1],\n dict_RGB[idx][2])\n\n # Opacity transfer function\n opacity_scalar = vtk.vtkPiecewiseFunction()\n\n for idx in dict_RGB.keys():\n opacity_scalar.AddPoint(\n idx, volume_opacity if idx != 0 else 0.0)\n\n # Opacity Gradient Transfer function\n opacity_gradient = vtk.vtkPiecewiseFunction()\n opacity_gradient.AddPoint(1, 0.0)\n opacity_gradient.AddPoint(5, 0.1)\n opacity_gradient.AddPoint(100, 1.0)\n\n return color_transfor, opacity_scalar, opacity_gradient\n\n else: # Default color transfer functions\n\n # min, max = self.get_value_range()\n color_transfor = vtk.vtkColorTransferFunction()\n color_transfor.AddRGBPoint(0, 0.0, 0.0, 0.0)\n color_transfor.AddRGBPoint(500, 1.0, 0.5, 0.3)\n color_transfor.AddRGBPoint(1000, 1.0, 0.5, 0.3)\n color_transfor.AddRGBPoint(1150, 1.0, 1.0, 0.9)\n\n # The opacity transfer function is used to control the opacity\n # of different tissue types.\n opacity_scalar = vtk.vtkPiecewiseFunction()\n opacity_scalar.AddPoint(0, 0.00)\n opacity_scalar.AddPoint(500, 0.15)\n opacity_scalar.AddPoint(1000, 0.15)\n opacity_scalar.AddPoint(1150, 0.85)\n\n # The gradient opacity function is used to decrease the opacity\n # in the \"flat\" regions of the volume while maintaining the opacity\n # at the boundaries between tissue types. The gradient is measured\n # as the amount by which the intensity changes over unit distance.\n # For most medical data, the unit distance is 1mm.\n opacity_gradient = vtk.vtkPiecewiseFunction()\n opacity_gradient.AddPoint(0, 0.0)\n opacity_gradient.AddPoint(90, 0.5)\n opacity_gradient.AddPoint(100, 1.0)\n\n return color_transfor, opacity_scalar, opacity_gradient","def input_fn():\n files = tf.data.Dataset.list_files(os.path.join(\n tft_working_dir, filebase + '*'))\n dataset = files.interleave(\n tf.data.TFRecordDataset, cycle_length=4, block_length=16)\n dataset = dataset.map(parser)\n\n if shuffle:\n dataset = dataset.shuffle(buffer_size)\n\n dataset = dataset.repeat(num_epochs)\n dataset = dataset.batch(batch_size)\n\n dataset = dataset.prefetch(prefetch_buffer_size)\n iterator = dataset.make_one_shot_iterator()\n transformed_features, transformed_labels = iterator.get_next()\n\n return transformed_features, transformed_labels","def process_data(cur, conn, filepath: str, func: Callable) -> None:\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, \"*.json\"))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print(\"{} files found in {}\".format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print(\"{}/{} files processed.\".format(i, num_files))","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def list_functions(filename, output_file):\n file_pointer = open(filename)\n file_split = filename.replace(\"/\",\".\")\n file_split = file_split.split(\".\")\n\n file_text = file_pointer.read()\n my_re = re.compile(\"\\ndef ([a-z][^\\(]*)\")\n functions = my_re.findall(file_text)\n functions.sort()\n first = True\n\n cr_re = re.compile(r\"\\n *\")\n for function in functions:\n function = cr_re.sub(\" \", function)\n if first:\n first = False\n output_file.write(\"Functions\\n\")\n output_file.write(\"^^^^^^^^^\\n\")\n output_file.write(\"- \")\n module = file_split[4]\n output_file.write(f\":func:`~arcade.{module}.{function}`\")\n output_file.write(\"\\n\")\n if not first:\n output_file.write(\"\\n\")","def rasterize(\n ctx,\n files,\n output,\n driver,\n like,\n bounds,\n dimensions,\n res,\n src_crs,\n all_touched,\n default_value,\n fill,\n prop,\n force_overwrite,\n creation_options):\n from rasterio.crs import CRS\n from rasterio.features import rasterize\n from rasterio.features import bounds as calculate_bounds\n\n verbosity = (ctx.obj and ctx.obj.get('verbosity')) or 1\n\n output, files = resolve_inout(\n files=files, output=output, force_overwrite=force_overwrite)\n\n bad_param = click.BadParameter('invalid CRS. Must be an EPSG code.',\n ctx, param=src_crs, param_hint='--src_crs')\n has_src_crs = src_crs is not None\n try:\n src_crs = CRS.from_string(src_crs) if has_src_crs else CRS.from_string('EPSG:4326')\n except CRSError:\n raise bad_param\n\n # If values are actually meant to be integers, we need to cast them\n # as such or rasterize creates floating point outputs\n if default_value == int(default_value):\n default_value = int(default_value)\n if fill == int(fill):\n fill = int(fill)\n\n with rasterio.Env(CPL_DEBUG=verbosity > 2):\n\n def feature_value(feature):\n if prop and 'properties' in feature:\n return feature['properties'].get(prop, default_value)\n return default_value\n\n with click.open_file(files.pop(0) if files else '-') as gj_f:\n geojson = json.loads(gj_f.read())\n if 'features' in geojson:\n geometries = []\n for f in geojson['features']:\n geometries.append((f['geometry'], feature_value(f)))\n elif 'geometry' in geojson:\n geometries = ((geojson['geometry'], feature_value(geojson)), )\n else:\n raise click.BadParameter('Invalid GeoJSON', param=input,\n param_hint='input')\n\n geojson_bounds = geojson.get('bbox', calculate_bounds(geojson))\n\n if os.path.exists(output):\n with rasterio.open(output, 'r+') as out:\n if has_src_crs and src_crs != out.crs:\n raise click.BadParameter('GeoJSON does not match crs of '\n 'existing output raster',\n param='input', param_hint='input')\n\n if disjoint_bounds(geojson_bounds, out.bounds):\n click.echo(\"GeoJSON outside bounds of existing output \"\n \"raster. Are they in different coordinate \"\n \"reference systems?\",\n err=True)\n\n meta = out.meta.copy()\n\n result = rasterize(\n geometries,\n out_shape=(meta['height'], meta['width']),\n transform=meta.get('affine', meta['transform']),\n all_touched=all_touched,\n dtype=meta.get('dtype', None),\n default_value=default_value,\n fill=fill)\n\n for bidx in range(1, meta['count'] + 1):\n data = out.read(bidx, masked=True)\n # Burn in any non-fill pixels, and update mask accordingly\n ne = result != fill\n data[ne] = result[ne]\n data.mask[ne] = False\n out.write(data, indexes=bidx)\n\n else:\n if like is not None:\n template_ds = rasterio.open(like)\n\n if has_src_crs and src_crs != template_ds.crs:\n raise click.BadParameter('GeoJSON does not match crs of '\n '--like raster',\n param='input', param_hint='input')\n\n if disjoint_bounds(geojson_bounds, template_ds.bounds):\n click.echo(\"GeoJSON outside bounds of --like raster. \"\n \"Are they in different coordinate reference \"\n \"systems?\",\n err=True)\n\n kwargs = template_ds.meta.copy()\n kwargs['count'] = 1\n\n # DEPRECATED\n # upgrade transform to affine object or we may get an invalid\n # transform set on output\n kwargs['transform'] = template_ds.affine\n\n template_ds.close()\n\n else:\n bounds = bounds or geojson_bounds\n\n if src_crs.is_geographic:\n if (bounds[0] < -180 or bounds[2] > 180 or\n bounds[1] < -80 or bounds[3] > 80):\n raise click.BadParameter(\n \"Bounds are beyond the valid extent for \"\n \"EPSG:4326.\",\n ctx, param=bounds, param_hint='--bounds')\n\n if dimensions:\n width, height = dimensions\n res = (\n (bounds[2] - bounds[0]) / float(width),\n (bounds[3] - bounds[1]) / float(height)\n )\n\n else:\n if not res:\n raise click.BadParameter(\n 'pixel dimensions are required',\n ctx, param=res, param_hint='--res')\n\n elif len(res) == 1:\n res = (res[0], res[0])\n\n width = max(int(ceil((bounds[2] - bounds[0]) /\n float(res[0]))), 1)\n height = max(int(ceil((bounds[3] - bounds[1]) /\n float(res[1]))), 1)\n\n kwargs = {\n 'count': 1,\n 'crs': src_crs,\n 'width': width,\n 'height': height,\n 'transform': Affine(res[0], 0, bounds[0], 0, -res[1],\n bounds[3]),\n 'driver': driver\n }\n kwargs.update(**creation_options)\n\n result = rasterize(\n geometries,\n out_shape=(kwargs['height'], kwargs['width']),\n transform=kwargs.get('affine', kwargs['transform']),\n all_touched=all_touched,\n dtype=kwargs.get('dtype', None),\n default_value=default_value,\n fill=fill)\n\n if 'dtype' not in kwargs:\n kwargs['dtype'] = result.dtype\n\n kwargs['nodata'] = fill\n\n with rasterio.open(output, 'w', **kwargs) as out:\n out.write(result, indexes=1)","def vtp(self, f_vtu, f_vtp):\r\n reader = vtk.vtkXMLUnstructuredGridReader()\r\n reader.SetFileName(f_vtu)\r\n reader.Update()\r\n ugrid = reader.GetOutput()\r\n geometryFilter = vtk.vtkGeometryFilter()\r\n geometryFilter.SetInputData(ugrid)\r\n geometryFilter.Update()\r\n polydata = geometryFilter.GetOutput()\r\n writer =vtk.vtkXMLPolyDataWriter()\r\n writer.SetFileName(f_vtp)\r\n writer.SetInputData(polydata)\r\n writer.Write()\r\n print(\"vtp file created.\")","def func(self, name, vecs):\n arity = len(vecs)\n funcobj = self.get_ground_vector('!Func:{}:{}'.format(arity, name))\n\n argobjs = [\n associate_comp(\n self.get_ground_vector('!Arg:{}:{}:{}'.format(arity, name, i)),\n vec)\n for i, vec in enumerate(vecs) ]\n\n result = normalize_comp(\n self.func_weights @\n torch.cat([\n funcobj,\n merge(argobjs),\n merge(vecs),\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\n\n return result","def func(self, name, vecs):\n arity = len(vecs)\n funcobj = self.get_ground_vector('!Func:{}:{}'.format(arity, name))\n\n argobjs = [\n associate_comp(\n self.get_ground_vector('!Arg:{}:{}:{}'.format(arity, name, i)),\n vec)\n for i, vec in enumerate(vecs) ]\n\n result = normalize_comp(\n self.func_weights @\n torch.cat([\n funcobj,\n merge(argobjs),\n merge(vecs),\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\n\n return result","def map(self, function):\n pass"],"string":"[\n \"def main(fn_input, fn_output):\\n # read file\\n inter = Interpolator()\\n inter.read_file(fn_input)\\n inter.write_interpolated(fn_output)\",\n \"def merge(files: List[str], output_file: str, resample: str = \\\"average\\\") -> None:\\n\\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\\n\\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\\n\\n if os.path.isfile(constants.TEMP_VRT_FILE):\\n os.remove(constants.TEMP_VRT_FILE)\",\n \"def run(self):\\n\\t\\tif not self.is_valid:\\n\\t\\t\\traise ValueError('Paths are not valid')\\n\\t\\t\\n\\t\\tfrom manipulators.run import all_functions\\n\\t\\tinput_file = open(self.input_path, 'rb')\\n\\t\\tinput_csv = csv.reader(input_file, delimiter=',')\\n\\t\\toutput_file = open(self.output_path, 'wb')\\n\\t\\toutput_csv = csv.writer(output_file, delimiter=',')\\n\\t\\tall_functions(input_csv, output_csv)\",\n \"def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\\n\\n options = gdal.BuildVRTOptions(srcNodata=0)\\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\\n add_pixel_fn(vrt, resample_name)\",\n \"def run_transform(i_fn, o_fn):\\n if not (all(is_bed(fn) for fn in [i_fn, o_fn]) or all(is_vcf(fn) for fn in [i_fn, o_fn])):\\n raise ValueError(\\\"Input and output must be both BED or VCF\\\")\\n with get_reader(i_fn) as reader:\\n with get_writer(o_fn, reader.samples) as writer:\\n for r in reader:\\n writer.writeRecord(transform_coordinate_of_sv(r))\",\n \"def convert_vrt(fname, out_fname, dataset_name='dataset',\\n compression=H5CompressionFilter.LZF, filter_opts=None,\\n attrs=None):\\n with h5py.File(out_fname) as fid:\\n with rasterio.open(fname) as rds:\\n # set default chunks and set dimensions\\n if rds.count == 3:\\n chunks = (3, 256, 256)\\n dims = (3, rds.height, rds.width)\\n else:\\n chunks = (256, 256)\\n dims = (rds.height, rds.width)\\n\\n # create empty or copy the user supplied filter options\\n if not filter_opts:\\n filter_opts = dict()\\n filter_opts['chunks'] = chunks\\n else:\\n filter_opts = filter_opts.copy()\\n\\n\\n if 'chunks' not in filter_opts:\\n filter_opts['chunks'] = chunks\\n\\n # modify to have 3D chunks if we have a multiband vrt\\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\\n # copy the users original 2D chunk and insert the third\\n chunks = list(filter_opts['chunks'])\\n chunks.insert(0, 3)\\n filter_opts['chunks'] = chunks\\n\\n # dataset attributes\\n if attrs:\\n attrs = attrs.copy()\\n else:\\n attrs = {}\\n\\n attrs['geotransform'] = rds.transform.to_gdal()\\n attrs['crs_wkt'] = rds.crs.wkt\\n\\n # dataset creation options\\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\\n kwargs['shape'] = dims\\n kwargs['dtype'] = rds.dtypes[0]\\n\\n dataset = fid.create_dataset(dataset_name, **kwargs)\\n attach_image_attributes(dataset, attrs)\\n\\n # tiled processing (all cols by chunked rows)\\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\\n\\n for tile in tiles:\\n # numpy index\\n if rds.count == 3:\\n idx = (\\n slice(None),\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n else:\\n idx = (\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n\\n # ensure single band rds is read as 2D not 3D\\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\\n\\n # write\\n dataset[idx] = data\",\n \"def create_user_functions():\\n\\n # user_function.lgi useing PARAM_A and PARAM_B for slope and intercept\\n lagrit_input = \\\"\\\"\\\"\\ncmo/DELATT/mo_pts/dfield\\ncompute / distance_field / mo_pts / mo_line_work / dfield\\nmath/multiply/mo_pts/x_four/1,0,0/mo_pts/dfield/PARAM_A/\\nmath/add/mo_pts/x_four/1,0,0/mo_pts/x_four/PARAM_B/\\ncmo/copyatt/mo_pts/mo_pts/fac_n/x_four\\nfinish\\n\\\"\\\"\\\"\\n f = open('user_function.lgi', 'w')\\n f.write(lagrit_input)\\n f.close()\\n\\n # user_function2.lgi uses PARAM_A2 and PARAM_B2 for slope and intercept\\n lagrit_input = \\\"\\\"\\\"\\ncmo/DELATT/mo_pts/dfield\\ncompute / distance_field / mo_pts / mo_line_work / dfield\\nmath/multiply/mo_pts/x_four/1,0,0/mo_pts/dfield/PARAM_A2/\\nmath/add/mo_pts/x_four/1,0,0/mo_pts/x_four/PARAM_B2/\\ncmo/copyatt/mo_pts/mo_pts/fac_n/x_four\\nfinish\\n\\\"\\\"\\\"\\n f = open('user_function2.lgi', 'w')\\n f.write(lagrit_input)\\n f.close()\",\n \"def __call__(self, format, filename):\\n # turn the filename into something suitable for use in #define's\\n prettyname = filename.replace(\\\".\\\", \\\"_\\\").upper()\\n prettyname = prettyname.replace(\\\"/\\\", \\\"__\\\")\\n prettyname = prettyname.replace(\\\":\\\", \\\"__\\\")\\n prettyname = prettyname.replace(\\\"-\\\", \\\"__\\\")\\n\\n # try and open the file\\n with open(filename, \\\"w\\\") as output:\\n self.writeFuncsLut[format]( output, prettyname )\",\n \"def analyze(file,process):\\n readin(file)\\n # inspecting(file, functions)\\n process(file, functions)\",\n \"def compute_functions(file_name,E_max,E_min,gamma,gamma_k):\\n\\n file = open(file_name,'r')\\n data = file.readlines()\\n file.close()\\n\\n print \\\"Computing functions\\\"\\n all_lor = []\\n for fila in data[2:]:\\n vals = map(eval,fila.split())\\n if(len(vals)>1):\\n if(vals[1]>E_min and vals[1] < E_max):\\n ek = vals[1]\\n weight = 0\\n for j in range(2,len(vals)):\\n weight += vals[j]\\n A = get_func(vals[0],vals[1],weight,gamma,gamma_k)\\n all_lor.append(A)\\n\\n return all_lor\",\n \"def process_function(regexDict: typing.Dict[str, str], fileContent: str, flags: int = re.IGNORECASE) -> str:\\n\\t\\tfor pattern, replace in sorted(regexDict.items()):\\n\\t\\t\\tfileContent = re.sub(pattern, replace, fileContent, flags=flags)\\n\\t\\treturn fileContent\",\n \"def make_vrt(self):\\n for index, i in enumerate(self.months):\\n month = str(index + 1)\\n if len(month) < 2:\\n month = '0' + month\\n txt_file = i.joinpath('subnational/tiffs.txt')\\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\\n if not outfile.exists():\\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\\n subprocess.call(gdal_cmd, shell=True)\",\n \"def cli(source_f, raster_f, output, verbose):\\n with fiona.open(source_f, 'r') as source:\\n source_driver = source.driver\\n source_crs = source.crs\\n sink_schema = source.schema.copy()\\n\\n source_geom = source.schema['geometry']\\n if source_geom == 'Point':\\n sink_schema['geometry'] = '3D Point'\\n elif source_geom == 'LineString':\\n sink_schema['geometry'] = '3D LineString'\\n elif source_geom == '3D Point' or source_geom == '3D LineString':\\n pass\\n else:\\n click.BadParameter(\\\"Source geometry type {} not implemented\\\".format(source_geom))\\n\\n with rasterio.open(raster_f) as raster:\\n if source_crs != raster.crs:\\n click.BadParameter(\\\"Features and raster have different CRS.\\\")\\n if raster.count > 1:\\n warnings.warn(\\\"Found {0} bands in {1}, expected a single band raster\\\".format(raster.bands, raster_f))\\n supported = ['int16', 'int32', 'float32', 'float64']\\n if raster.dtypes[0] not in supported:\\n warnings.warn(\\\"Found {0} type in {1}, expected one of {2}\\\".format(raster.dtypes[0], raster_f, supported))\\n with fiona.open(\\n output, 'w',\\n driver=source_driver,\\n crs=source_crs,\\n schema=sink_schema) as sink:\\n\\n for feature in source:\\n try:\\n feature_z = drapery.drape(raster, feature)\\n sink.write({\\n 'geometry': mapping(feature_z),\\n 'properties': feature['properties'],\\n })\\n except Exception:\\n logging.exception(\\\"Error processing feature %s:\\\", feature['id'])\\n #print(sink.closed)\\n #print(raster.closed)\\n #print(source.closed)\",\n \"def process_data(*args, **kwargs):\\n\\n filepath = kwargs[\\\"filepath\\\"]\\n func = kwargs[\\\"func\\\"]\\n \\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(datafile)\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def parse_flt_files(files=[], info=None, uniquename=False, use_visit=False,\\n get_footprint = False, \\n translate = {'AEGIS-':'aegis-', \\n 'COSMOS-':'cosmos-', \\n 'GNGRISM':'goodsn-', \\n 'GOODS-SOUTH-':'goodss-', \\n 'UDS-':'uds-'}): \\n \\n if info is None:\\n if not files:\\n files=glob.glob('*flt.fits')\\n \\n if len(files) == 0:\\n return False\\n \\n info = get_flt_info(files)\\n else:\\n info = info.copy()\\n \\n for c in info.colnames:\\n if not c.islower(): \\n info.rename_column(c, c.lower())\\n\\n if 'expstart' not in info.colnames:\\n info['expstart'] = info['exptime']*0.\\n\\n so = np.argsort(info['expstart'])\\n info = info[so]\\n\\n #pa_v3 = np.round(info['pa_v3']*10)/10 % 360.\\n pa_v3 = np.round(info['pa_v3']) % 360.\\n \\n target_list = []\\n for i in range(len(info)):\\n #### Replace ANY targets with JRhRmRs-DdDmDs\\n if info['targname'][i] == 'ANY': \\n if use_visit:\\n new_targname=info['file'][i][:6]\\n else:\\n new_targname = 'par-'+radec_to_targname(ra=info['ra_targ'][i],\\n dec=info['dec_targ'][i])\\n \\n target_list.append(new_targname.lower())\\n else:\\n target_list.append(info['targname'][i])\\n \\n target_list = np.array(target_list)\\n\\n info['progIDs'] = [file[1:4] for file in info['file']]\\n\\n progIDs = np.unique(info['progIDs'])\\n visits = np.array([os.path.basename(file)[4:6] for file in info['file']])\\n dates = np.array([''.join(date.split('-')[1:]) for date in info['date-obs']])\\n \\n targets = np.unique(target_list)\\n \\n output_list = [] #OrderedDict()\\n filter_list = OrderedDict()\\n \\n for filter in np.unique(info['filter']):\\n filter_list[filter] = OrderedDict()\\n \\n angles = np.unique(pa_v3[(info['filter'] == filter)]) \\n for angle in angles:\\n filter_list[filter][angle] = []\\n \\n for target in targets:\\n #### 3D-HST targname translations\\n target_use = target\\n for key in translate.keys():\\n target_use = target_use.replace(key, translate[key])\\n \\n ## pad i < 10 with zero\\n for key in translate.keys():\\n if translate[key] in target_use:\\n spl = target_use.split('-')\\n try:\\n if (int(spl[-1]) < 10) & (len(spl[-1]) == 1):\\n spl[-1] = '{0:02d}'.format(int(spl[-1]))\\n target_use = '-'.join(spl)\\n except:\\n pass\\n\\n for filter in np.unique(info['filter'][(target_list == target)]):\\n angles = np.unique(pa_v3[(info['filter'] == filter) & \\n (target_list == target)])\\n \\n for angle in angles:\\n exposure_list = []\\n exposure_start = []\\n product='{0}-{1:05.1f}-{2}'.format(target_use, angle, filter) \\n\\n visit_match = np.unique(visits[(target_list == target) &\\n (info['filter'] == filter)])\\n \\n this_progs = []\\n this_visits = []\\n \\n for visit in visit_match:\\n ix = (visits == visit) & (target_list == target) & (info['filter'] == filter)\\n #this_progs.append(info['progIDs'][ix][0])\\n #print visit, ix.sum(), np.unique(info['progIDs'][ix])\\n new_progs = list(np.unique(info['progIDs'][ix]))\\n this_visits.extend([visit]*len(new_progs))\\n this_progs.extend(new_progs)\\n \\n for visit, prog in zip(this_visits, this_progs):\\n visit_list = []\\n visit_start = []\\n visit_product = '{0}-{1}-{2}-{3:05.1f}-{4}'.format(target_use, prog, visit, angle, filter) \\n \\n use = ((target_list == target) & \\n (info['filter'] == filter) & \\n (visits == visit) & (pa_v3 == angle) &\\n (info['progIDs'] == prog))\\n \\n if use.sum() == 0:\\n continue\\n\\n for tstart, file in zip(info['expstart'][use],\\n info['file'][use]):\\n \\n f = file.split('.gz')[0]\\n if f not in exposure_list:\\n visit_list.append(str(f))\\n visit_start.append(tstart)\\n \\n exposure_list = np.append(exposure_list, visit_list)\\n exposure_start.extend(visit_start)\\n \\n filter_list[filter][angle].extend(visit_list)\\n \\n if uniquename:\\n print(visit_product, len(visit_list))\\n so = np.argsort(visit_start)\\n exposure_list = np.array(visit_list)[so]\\n #output_list[visit_product.lower()] = visit_list\\n \\n d = OrderedDict(product=str(visit_product.lower()),\\n files=list(np.array(visit_list)[so]))\\n output_list.append(d)\\n \\n if not uniquename:\\n print(product, len(exposure_list))\\n so = np.argsort(exposure_start)\\n exposure_list = np.array(exposure_list)[so]\\n #output_list[product.lower()] = exposure_list\\n d = OrderedDict(product=str(product.lower()),\\n files=list(np.array(exposure_list)[so]))\\n output_list.append(d)\\n \\n ### Get visit footprint from FLT WCS\\n if get_footprint:\\n from shapely.geometry import Polygon\\n \\n N = len(output_list)\\n for i in range(N):\\n for j in range(len(output_list[i]['files'])):\\n flt_file = output_list[i]['files'][j]\\n if (not os.path.exists(flt_file)) & os.path.exists('../RAW/'+flt_file):\\n flt_file = '../RAW/'+flt_file\\n \\n flt_j = pyfits.open(flt_file)\\n h = flt_j[0].header\\n if (h['INSTRUME'] == 'WFC3') & (h['DETECTOR'] == 'IR'):\\n wcs_j = pywcs.WCS(flt_j['SCI',1])\\n else:\\n wcs_j = pywcs.WCS(flt_j['SCI',1], fobj=flt_j)\\n \\n fp_j = Polygon(wcs_j.calc_footprint())\\n if j == 0:\\n fp_i = fp_j\\n else:\\n fp_i = fp_i.union(fp_j)\\n \\n output_list[i]['footprint'] = fp_i\\n \\n return output_list, filter_list\",\n \"def load_from_function(function, path_to_file, **kwargs):\\n return function(path_to_file, **kwargs)\",\n \"def apply(input, output, fields, delimiter, encoding, verbose, format_in, zipfile, script, filter):\\n if verbose:\\n enableVerbose()\\n options = {}\\n options['delimiter'] = delimiter\\n options['fields'] = fields\\n options['output'] = output\\n options['encoding'] = encoding\\n options['format_in'] = format_in\\n options['zipfile'] = zipfile\\n options['filter'] = filter\\n options['script'] = script\\n acmd = Transformer()\\n acmd.script(input, options)\\n pass\",\n \"def write_func(functions, filename=def_func_name):\\n file = open(filename + \\\".txt\\\", \\\"w\\\")\\n for i in range(3):\\n line = str(functions[i])\\n file.write(line+'\\\\n')\\n file.close()\",\n \"def translate_files(input_file, output_file, translate_dict, delete_symbols):\\n\\n for line in input_file:\\n result = translate(line, translate_dict, delete_symbols)\\n output_file.write(result)\",\n \"def process_input_files(inputs):\\n for ifile in inputs:\\n with open(ifile) as fin:\\n exec(compile(fin.read(), ifile, 'exec'))\",\n \"def add_pixel_fn(filename: str, resample_name: str) -> None:\\n\\n header = \\\"\\\"\\\" <VRTRasterBand dataType=\\\"Byte\\\" band=\\\"1\\\" subClass=\\\"VRTDerivedRasterBand\\\">\\\"\\\"\\\"\\n contents = \\\"\\\"\\\"\\n <PixelFunctionType>{0}</PixelFunctionType>\\n <PixelFunctionLanguage>Python</PixelFunctionLanguage>\\n <PixelFunctionCode><![CDATA[{1}]]>\\n </PixelFunctionCode>\\\"\\\"\\\"\\n\\n lines = open(filename, 'r').readlines()\\n lines[3] = header # FIX ME: 3 is a hand constant\\n lines.insert(4, contents.format(resample_name,\\n get_resample(resample_name)))\\n open(filename, 'w').write(\\\"\\\".join(lines))\",\n \"def HandleFiles(variables):\\n\\n # The template file is the html file into which we will write the\\n # data from the stats file, formatted correctly for the gviz_api.\\n template_file = open(variables[1], \\\"r\\\")\\n page_template = template_file.read()\\n template_file.close()\\n\\n # This is the path match pattern for finding stats files amongst\\n # all the other files it could be. eg: *.stt\\n file_pattern = variables[2]\\n\\n # This is the directory with files that we will use to do the comparison\\n # against.\\n baseline_dir = variables[3]\\n snrs = ''\\n filestable = {}\\n filestable['dsnr'] = ''\\n filestable['drate'] = ''\\n filestable['avg'] = ''\\n\\n # Go through each metric in the list.\\n for column in range(1,2):\\n\\n # Dirs is directories after the baseline to compare to the base.\\n dirs = variables[4:len(variables)]\\n\\n # Find the metric files in the baseline directory.\\n dir_list = sorted(fnmatch.filter(os.listdir(baseline_dir), file_pattern))\\n\\n for metric in ['avg','dsnr','drate']:\\n description = {\\\"file\\\": (\\\"string\\\", \\\"File\\\")}\\n\\n # Go through each directory and add a column header to our description.\\n countoverall = {}\\n sumoverall = {}\\n\\n for directory in dirs:\\n description[directory] = (\\\"number\\\", directory)\\n countoverall[directory] = 0\\n sumoverall[directory] = 0\\n\\n # Data holds the data for the visualization, name given comes from\\n # gviz_api sample code.\\n data = []\\n for filename in dir_list:\\n row = {'file': splitext(basename(filename))[0] }\\n baseline_file_name = baseline_dir + \\\"/\\\" + filename\\n\\n # Read the metric file from each of the directories in our list.\\n for directory in dirs:\\n metric_file_name = directory + \\\"/\\\" + filename\\n\\n # If there is a metric file in the current directory, open it\\n # and calculate its overall difference between it and the baseline\\n # directory's metric file.\\n if os.path.isfile(metric_file_name):\\n overall = FileBetter(baseline_file_name, metric_file_name,\\n column, metric)\\n row[directory] = overall\\n\\n sumoverall[directory] += overall\\n countoverall[directory] += 1\\n\\n data.append(row)\\n\\n # Add the overall numbers.\\n row = {\\\"file\\\": \\\"OVERALL\\\" }\\n if countoverall[directory]:\\n for directory in dirs:\\n row[directory] = sumoverall[directory] / countoverall[directory]\\n data.append(row)\\n\\n # write the tables out\\n data_table = gviz_api.DataTable(description)\\n data_table.LoadData(data)\\n\\n filestable[metric] = ( filestable[metric] + \\\"filestable_\\\" + metric +\\n \\\"[\\\" + str(column) + \\\"]=\\\" + data_table.ToJSon()\\n + \\\"\\\\n\\\" )\\n\\n filestable_avg = filestable['avg']\\n filestable_dpsnr = filestable['dsnr']\\n filestable_drate = filestable['drate']\\n\\n # Now we collect all the data for all the graphs. First the column\\n # headers which will be Datarate and then each directory.\\n columns = (\\\"datarate\\\",baseline_dir)\\n description = {\\\"datarate\\\":(\\\"number\\\", \\\"Datarate\\\")}\\n for directory in dirs:\\n description[directory] = (\\\"number\\\", directory)\\n\\n description[baseline_dir] = (\\\"number\\\", baseline_dir)\\n\\n snrs = snrs + \\\"snrs[\\\" + str(column) + \\\"] = [\\\"\\n\\n # Now collect the data for the graphs, file by file.\\n for filename in dir_list:\\n\\n data = []\\n\\n # Collect the file in each directory and store all of its metrics\\n # in the associated gviz metrics table.\\n all_dirs = dirs + [baseline_dir]\\n for directory in all_dirs:\\n\\n metric_file_name = directory + \\\"/\\\" + filename\\n if not os.path.isfile(metric_file_name):\\n continue\\n\\n # Read and parse the metrics file storing it to the data we'll\\n # use for the gviz_api.Datatable.\\n metrics = ParseMetricFile(metric_file_name, column)\\n for bitrate, metric in metrics:\\n data.append({\\\"datarate\\\": bitrate, directory: metric})\\n\\n data_table = gviz_api.DataTable(description)\\n data_table.LoadData(data)\\n snrs = snrs + \\\"'\\\" + data_table.ToJSon(\\n columns_order=tuple([\\\"datarate\\\",baseline_dir]+dirs)) + \\\"',\\\"\\n\\n snrs = snrs + \\\"]\\\\n\\\"\\n\\n formatters = \\\"\\\"\\n for i in range(len(dirs)):\\n formatters = \\\"%s formatter.format(better, %d);\\\" % (formatters, i+1)\\n\\n print FillForm(page_template, vars())\\n return\",\n \"def run(fn, *input_values, **kwds):\\n \\n ee = kwds.get('ee', shared_exec_engine)\\n input_types = [arg.type for arg in fn.args]\\n gv_inputs = [gv_from_python(x, t) \\n for (x,t) in \\n zip(input_values, input_types)]\\n \\n return run_with_generic_values(fn, gv_inputs, ee)\",\n \"def parsedFile(self, *args, **kwargs):\\n\\t\\tif not self._parsed:\\n\\t\\t\\tself.parse()\\n\\n\\t\\treturn function(self, *args, **kwargs)\",\n \"def filter_f(fns, ltaper, lowerp, upperp, utaper, eqband, eqltaper, equtaper, npow, bindir):\\n # filtercmd = bindir+\\\"/filter4\\\"\\n filtercmd = bindir + \\\"/filter4 1>/dev/null\\\"\\n for src, tar, eqtar in fns:\\n p = sp.Popen(filtercmd, shell=True, bufsize=0, stdin=sp.PIPE, stdout=None)\\n child = p.stdin\\n print >> child, ltaper, lowerp, upperp, utaper, npow, src, tar + '_tmp'\\n err = child.close()\\n ret = p.wait()\\n if err or ret != 0:\\n raise RuntimeError, '%r failed with exit code %d' % (filtercmd, err)\\n p = sp.Popen(filtercmd, shell=True, bufsize=0, stdin=sp.PIPE, stdout=None)\\n child = p.stdin\\n print >> child, eqltaper, eqband[0], eqband[1], equtaper, npow, tar + '_tmp', eqtar + '_tmp'\\n err = child.close()\\n ret = p.wait()\\n if err or ret != 0:\\n raise RuntimeError, '%r failed with exit code %d' % (filtercmd, err)\\n return 1\",\n \"def io_files(self, iterable, ext=None, func=None):\\n for input_path in iterable:\\n output_path, temp_file = self.check_output_path(input_path, ext)\\n\\n try:\\n func(input_path, temp_file)\\n except Exception as e:\\n if self._force_continue is True:\\n self.handle_error(e, input_path)\\n else:\\n raise e\\n\\n self.overwrite_output_path(input_path, output_path, temp_file)\",\n \"def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\\n outputs = []\\n npy_outputs = []\\n if resamplemethod == 'nearest':\\n rs = Resampling.nearest\\n else:\\n print('only nearest neighbor resampling is supported at this time')\\n sys.exit(0)\\n\\n for i, warpfile in enumerate(inputs):\\n print('warpfile', warpfile)\\n with rasterio.open(warpfile) as src:\\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\\n with WarpedVRT(src, resampling=rs,\\n crs=self.crs,\\n transform=self.transform,\\n height=self.rows,\\n width=self.cols) as vrt:\\n data = vrt.read()\\n print(type(vrt))\\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\\n outputs.append(outwarp)\\n\\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\\n # from this method for us in the rest of the code.\\n for ow in outputs:\\n with rasterio.open(ow, 'r') as src:\\n arr = src.read(1)\\n npy_outputs.append(arr)\\n\\n return npy_outputs\",\n \"def render_functions(r2, covered_bbs, output_dir):\\n for func_addr in function_addrs(r2):\\n # Get the function name\\n func_name = r2.cmdj('agj 0x%x' % func_addr)[0]['name']\\n\\n dot_str = r2.cmd('ag 0x%x' % func_addr)\\n dot = pydot.graph_from_dot_data(dot_str)\\n if not dot:\\n continue\\n else:\\n dot = dot[0]\\n\\n for node in dot.get_nodes():\\n node_name = node.get_name()\\n try:\\n # XXX This is very hacky - need something more robust\\n if node_name.startswith('\\\"'):\\n node_name = node_name[1:-1]\\n node_addr = int(node_name, 16)\\n except ValueError:\\n # Node name is not a hex string\\n continue\\n\\n if node_addr in covered_bbs:\\n node.set_fillcolor('darkolivegreen2')\\n\\n svg_path = os.path.join(output_dir, '%s_0x%x.svg' % (func_name, func_addr))\\n with open(svg_path, 'wb') as f:\\n svg = dot.create_svg()\\n f.write(svg)\",\n \"def file_func(self,):\\n return self._file_func\",\n \"def process_data(db_cursor, filepath, func):\\n\\n # Get all files matching extension from directory\\n all_files = []\\n for root, _, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print(f'{num_files} files found in {filepath}')\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(db_cursor, datafile)\\n print(f'{i}/{num_files} files processed.')\",\n \"def gen_code(self, filename, func_name):\\n\\n assert self.bits is not None\\n\\n vd_list = []\\n n_vars = 0\\n for tree in self.trees:\\n vd_list.append(tree.gen_code(n_vars))\\n n_vars += len(vd_list[-1])\\n\\n # checks the type by the suffix\\n\\n is_v = filename.split(\\\".\\\")[-1] == \\\"v\\\"\\n\\n assert self.inputs\\n\\n f = open(filename, \\\"w\\\")\\n\\n i_bits = np.sum(self.bits[:-1])\\n o_bits = self.bits[-1]\\n o_sign = self.is_neg[-1]\\n\\n if is_v:\\n f.write(\\\"module {}(input [{}:0] i, output [{}:0] o);\\\\n\\\".format(\\n func_name, i_bits-1, o_bits-1))\\n else:\\n f.write(\\\"#include<ac_int.h>\\\\n\\\\n\\\")\\n f.write(\\\"void {}(ac_int<{},false> i, ac_int<{},{}> &o)\\\\n\\\".format(\\n func_name, i_bits, o_bits, o_sign))\\n f.write(\\\"{\\\\n\\\")\\n\\n\\n # write function headline\\n s_in_line = []\\n\\n i_bits = self.bits[0]\\n i_sign = self.is_neg[0]\\n\\n if is_v:\\n i_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if i_sign else \\\"\\\", i_bits-1)\\n else:\\n i_datatype = \\\" ac_int<{},{}> \\\".format(i_bits, i_sign)\\n\\n len_s = len(i_datatype)\\n\\n for i in range(self.inputs):\\n if is_v:\\n s = (\\n \\\"i_\\\" + str(i) + \\\" = \\\" + \\\"i[\\\" + str(i_bits*(i+1)-1) + \\\":\\\" +\\n str(i_bits*i) + \\\"]\\\"\\n )\\n else:\\n s = (\\n \\\"i_\\\" + str(i) + \\\" = \\\" + \\\"i.slc<\\\" + str(i_bits) + \\\">(\\\" +\\n str(i_bits*i) + \\\")\\\"\\n )\\n if (\\n len_s + len(s) + 2 > 70 or i_bits != self.bits[i] or\\n i_sign != self.is_neg[i]\\n ):\\n f.write(i_datatype + \\\", \\\".join(s_in_line) + \\\";\\\\n\\\")\\n\\n s_in_line = []\\n if is_v:\\n i_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if i_sign else \\\"\\\", i_bits-1)\\n else:\\n i_datatype = \\\" ac_int<{},{}> \\\".format(i_bits, i_sign)\\n\\n len_s = len(i_datatype)\\n\\n s_in_line.append(s)\\n len_s += len(s) + 2\\n\\n if s_in_line:\\n f.write(i_datatype + \\\", \\\".join(s_in_line) + \\\";\\\\n\\\")\\n\\n if is_v:\\n o_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if o_sign else \\\"\\\", o_bits)\\n else:\\n o_datatype = \\\" ac_int<{},{}> \\\".format(o_bits, o_sign)\\n\\n o_list = []\\n for i in range(len(vd_list)):\\n for v in vd_list[i]:\\n if is_v:\\n f.write(o_datatype + v + \\\" = \\\" + vd_list[i][v] + \\\";\\\\n\\\")\\n else:\\n f.write(o_datatype + v + \\\" = \\\" + vd_list[i][v] + \\\";\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n o_list.append(v)\\n\\n assert len(o_list) <= 3\\n\\n if is_v:\\n f.write(\\\" assign \\\")\\n else:\\n f.write(\\\" \\\")\\n\\n if len(o_list) == 1:\\n f.write(\\\"o = \\\" + o_list[0] + \\\";\\\")\\n elif len(o_list) == 2:\\n cond = \\\"( \\\" + o_list[0] + \\\" == \\\" + o_list[1] + \\\" ) \\\"\\n n1 = o_list[0]\\n n0 = \\\"( ( \\\" + \\\" + \\\".join(o_list) + \\\" ) >> 1 )\\\"\\n f.write(\\\"o = \\\" + cond + \\\"? \\\" + n1 + \\\": \\\" + n0)\\n elif len(o_list) == 3:\\n cond = (\\n \\\"( \\\" +\\n \\\"( \\\" + \\\" == \\\".join(o_list[0:2]) + \\\" )?\\\" + o_list[0] + \\\":\\\" +\\n \\\"( \\\" + \\\" == \\\".join(o_list[1:]) + \\\" )?\\\" + o_list[1] + \\\":\\\" +\\n \\\"( \\\" + \\\" == \\\".join([o_list[0], o_list[2]]) + \\\" )?\\\" + o_list[0] +\\n \\\":\\\" + \\\"( \\\" + \\\" < \\\".join(o_list[0:2]) + \\\" ) ?\\\" +\\n \\\"( ( \\\" + \\\" < \\\".join(o_list[1:]) + \\\" ) ?\\\" + o_list[1] + \\\":\\\" +\\n o_list[2] + \\\" ) : \\\" +\\n \\\"( ( \\\" + \\\" < \\\".join([o_list[0], o_list[2]]) + \\\" ) ?\\\" + o_list[0] +\\n \\\":\\\" + o_list[2] + \\\" )\\\"\\n )\\n f.write(\\\"o = \\\" + cond + \\\";\\\\n\\\")\\n if is_v:\\n f.write(\\\"endmodule\\\")\\n else:\\n f.write(\\\"}\\\")\\n\\n f.close()\",\n \"def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\\n args = [\\n \\\"gdal_calc.py\\\",\\n '-A', infile,\\n '--outfile={}'.format(tmpfile_1),\\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\\n '--type=Byte', '--NoDataValue=0',\\n '--co=SPARSE_OK=YES',\\n '--co=NBITS=1',\\n '--quiet'\\n # Could enable compression\\n # --co=\\\"COMPRESS=LZW\\\"\\n ]\\n subprocess.run(args)\\n\\n subprocess.run([\\n \\\"gdal_polygonize.py\\\",\\n tmpfile_1,\\n '-q',\\n '-f', 'ESRI Shapefile',\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\n \\\"ogr2ogr\\\",\\n '-a_srs', 'EPSG:4326',\\n outfile,\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\\"rm\\\", tmpfile_1])\\n subprocess.run([\\\"rm\\\", tmpfile_2])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'shx')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'dbf')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'prj')])\",\n \"def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\\n outputs = []\\n npy_outputs = []\\n if resamplemethod == 'nearest':\\n rs = Resampling.nearest\\n else:\\n print('only nearest neighbor resampling is supported at this time')\\n sys.exit(0)\\n\\n for i, warpfile in enumerate(inputs):\\n # print('warpfile', warpfile)\\n with rasterio.open(warpfile) as src:\\n # TODO - make the default configurable.\\n# if src.crs == None:\\n# src.crs = CRS.from_epsg(4326)\\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\\n with WarpedVRT(src, resampling=rs,\\n crs=self.crs,\\n transform=self.transform,\\n height=self.rows,\\n width=self.cols) as vrt:\\n data = vrt.read()\\n # print(type(vrt))\\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\\n outputs.append(outwarp)\\n\\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\\n # from this method for us in the rest of the code.\\n for ow in outputs:\\n with rasterio.open(ow, 'r') as src:\\n arr = src.read(1)\\n npy_outputs.append(arr)\\n\\n return npy_outputs\",\n \"def process_data(cursor, connection, filepath, function):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n function(cursor, datafile)\\n connection.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def postprocess_cga(lines, outfile):\\n pattern = re.compile(\\\"^\\\\s*([0-9,]+)\\\\s+\\\\([ 0-9.]+%\\\\)\\\\s+Source/(\\\\S+):(\\\\S+)\\\\(.*\\\\).*$\\\")\\n\\n totalCost = 0.0\\n functionTable = []\\n functionMap = {}\\n\\n for line in lines:\\n line = line.strip()\\n match = pattern.match(line)\\n if not match:\\n continue\\n\\n cost = float(match.group(1).replace(\\\",\\\", \\\"\\\"))\\n sourceFile = match.group(2)\\n function = match.group(3)\\n\\n # Filter out library code we don't want to change\\n if function.startswith(\\\"stbi__\\\"):\\n continue\\n\\n totalCost += cost\\n\\n # Accumulate the scores from functions in multiple call chains\\n if function in functionMap:\\n index = functionMap[function]\\n functionTable[index][1] += cost\\n functionTable[index][2] += cost\\n # Else add new functions to the end of the table\\n else:\\n functionMap[function] = len(functionTable)\\n functionTable.append([function, cost, cost])\\n\\n # Sort the table by accumulated cost\\n functionTable.sort(key=lambda x: 101.0 - x[2])\\n\\n for function in functionTable:\\n function[2] /= totalCost\\n function[2] *= 100.0\\n\\n with open(outfile, \\\"w\\\") as fileHandle:\\n\\n totals = 0.0\\n for function in functionTable:\\n # Omit entries less than 1% load\\n if function[2] < 1:\\n break\\n\\n totals += function[2]\\n fileHandle.write(\\\"%5.2f%% %s\\\\n\\\" % (function[2], function[0]))\\n\\n fileHandle.write(\\\"======\\\\n\\\")\\n fileHandle.write(f\\\"{totals:5.2f}%\\\\n\\\")\",\n \"def run(self, verbose=False):\\n from utils import write_to_file # function to write json to file\\n self.read_json()\\n graph = self.parse_jsons()\\n json = self.pipe_vl2vg(graph)\\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)\",\n \"def CallF_DirFile(dir_path,function,file_filter_=None,regular=False,**kw):\\n file_list = Get_dir_file_list(dir_path=dir_path,file_filter_=file_filter_,regular=regular,distinguish=True)\\n file_list = file_list[0]\\n save_list = []\\n for file_name in file_list:\\n file_path = os.path.join(dir_path,file_name)\\n save_list.append(function(file_path=file_path,**kw))\\n return save_list\",\n \"def py_SurfStatAvSurf(filenames, fun = np.add, output_surfstat=False):\\n \\n if filenames.ndim is not 2:\\n raise ValueError('Filenames must be a 2-dimensional array.')\\n \\n for i in range(0, filenames.shape[0]):\\n surfaces = np.empty(filenames.shape[1], dtype=np.object)\\n for j in range(0, filenames.shape[1]):\\n \\n # Check whether input is BSPolyData or a filename. \\n if isinstance(filenames[i,j], BSPolyData):\\n surfaces[j] = filenames[i,j] \\n else:\\n surfaces[j] = read_surface(filenames[i,j])\\n \\n # Concatenate second dimension of filenames. \\n if j is 0:\\n tri = get_cells(surfaces[j]) \\n coord = get_points(surfaces[j])\\n else:\\n tri = np.concatenate((tri, get_cells(surfaces[j]) + coord.shape[0]), axis=0)\\n coord = np.concatenate((coord, get_points(surfaces[j])), axis=0)\\n \\n if i is 0:\\n m = 1\\n coord_all = coord\\n else:\\n coord_all = fun(coord_all,coord)\\n m = fun(m,1)\\n \\n coord_all = coord_all / m \\n \\n if output_surfstat:\\n surface = {'tri': np.array(tri) + 1, 'coord': np.array(coord_all).T}\\n else:\\n surface = build_polydata(coord_all, tri)\\n \\n return surface\",\n \"def vjp_assemble_eval(\\n fenics_function: Callable, fenics_templates: FenicsVariable, *args: np.array\\n) -> Tuple[np.array, Callable]:\\n\\n numpy_output, ufl_form, fenics_inputs = assemble_eval(\\n fenics_function, fenics_templates, *args\\n )\\n\\n def vjp_fun(g):\\n return tuple(\\n vjp if vjp is not None else jax.ad_util.zeros_like_jaxval(args[i])\\n for i, vjp in enumerate(vjp_assemble_impl(g, ufl_form, fenics_inputs))\\n )\\n\\n return numpy_output, vjp_fun\",\n \"def InterpolateFunctions(self, , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def process(self,fileobj_out,fileobj_in):\\n pass\",\n \"def run_functions( functions, dir ):\\n res = []\\n for i in range(0, len(functions)):\\n res.append(functions[i].execute(dir))\\n return summarize_as_html(res)\",\n \"def bind(self, func: Callable[[Any], IO]) -> IO:\\n\\n filename, g = self._value\\n return ReadFile(filename, lambda s: g(s).bind(func))\",\n \"def read_func(filename=def_func_name, index=def_index):\\n file = open(filename+str(index)+\\\".txt\\\", \\\"r\\\")\\n functions = []\\n for i in range(3):\\n line = file.readline()\\n line = line.replace('\\\\'', '').strip()\\n functions.append(parse_line(line))\\n return functions\",\n \"def transform(infile, output, insrs, format_name):\\n\\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \\n in_srs = osr.SpatialReference()\\n in_srs.ImportFromEPSG(insrs)\\n out_srs = osr.SpatialReference()\\n out_srs.ImportFromEPSG(4324)\\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\\n\\n in_dsn = ogr.Open(infile)\\n in_layer = in_dsn.GetLayer()\\n in_feature_definition = in_layer.GetLayerDefn()\\n\\n out_driver = ogr.GetDriverByName(format_name)\\n out_dsn = out_driver.CreateDataSource(output)\\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\\n geom_type=in_layer.GetGeomType())\\n\\n # add fields\\n for i in range(0, in_feature_definition.GetFieldCount()):\\n fieldDefn = in_feature_definition.GetFieldDefn(i)\\n out_layer.CreateField(fieldDefn)\\n\\n # get the output layer's feature definition\\n out_feature_definition = out_layer.GetLayerDefn()\\n\\n # loop through the input features\\n inFeature = in_layer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef().Clone()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(out_feature_definition)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, out_feature_definition.GetFieldCount()):\\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n out_layer.CreateFeature(outFeature)\\n # destroy the features and get the next input feature\\n outFeature.Destroy()\\n inFeature.Destroy()\\n inFeature = in_layer.GetNextFeature()\\n\\n # close the shapefiles\\n in_dsn.Destroy()\\n out_dsn.Destroy()\",\n \"def Map(context, funcname, *nodesets):\\n (prefix, local) = ExpandQName(funcname, namespaces=context.processorNss)\\n func = (g_extFunctions.get(expanded) or\\n CoreFunctions.CoreFunctions.get(expanded, None))\\n if not func:\\n raise Exception('Dynamically invoked function %s not found.'%funcname)\\n flist = [f]*len(nodesets)\\n lf = lambda x, f, *args: apply(f, args)\\n retlist = apply(map, (lf, flist) + nodesets)\\n\\n proc = context.processor\\n result_nodeset = []\\n for ret in retlist:\\n proc.pushResult()\\n proc.writers[-1].text(Conversions.StringValue(ret))\\n frag = proc.popResult()\\n context.rtfs.append(frag)\\n result_nodeset.append(frag.childNodes[0])\\n return result_nodeset\",\n \"def buildFunc(self, parFiles):\\n imageMatrix = None\\n affine = None\\n TR = None\\n\\n ### Loop over all of the par files\\n nVols = len(parFiles)\\n for par_fname in parFiles:\\n # build full path to this par file\\n par_fname = join(self.seriesDir, par_fname)\\n\\n # make sure there is a corresponding .rec file\\n if not os.path.isfile(par_fname.replace('.par', '.rec')):\\n print('No REC file found to match par: {}', par_fname)\\n\\n ### Build the 3d voxel array for this volume and reorder to RAS+\\n # nibabel will load the par/rec, but there can be multiple images\\n # (mag, phase, etc...) concatenated into the 4th dimension. Loading\\n # with the strict_sort option (I think) will make sure the first\\n # image is the data we want. Extract this data, then reorder the\\n # voxel array to RAS+\\n thisVol = nib.load(par_fname, strict_sort=True)\\n\\n # get the vol index (0-based index) from the acq_nr field of the\\n # header (1-based index)\\n volIdx = int(thisVol.header.general_info['acq_nr']) - 1\\n\\n # set TR\\n if TR is None:\\n TR = thisVol.header.general_info['repetition_time'][0]\\n\\n # convert to RAS+\\n thisVol_RAS = nib.as_closest_canonical(thisVol)\\n\\n # construct the imageMatrix if it hasn't been made yet\\n if imageMatrix is None:\\n imageMatrix = np.zeros(shape=(thisVol_RAS.shape[0],\\n thisVol_RAS.shape[1],\\n thisVol_RAS.shape[2],\\n nVols), dtype=np.uint16)\\n\\n # construct the affine if it isn't made yet\\n if affine is None:\\n affine = thisVol_RAS.affine\\n\\n # Add this data to the image matrix\\n imageMatrix[:, :, :, volIdx] = thisVol_RAS.get_fdata()[:, :, :, 0].astype('uint16')\\n\\n ### Build a Nifti object\\n funcImage = nib.Nifti1Image(imageMatrix, affine=affine)\\n\\n # add the correct TR to the header\\n pixDims = np.array(funcImage.header.get_zooms())\\n pixDims[3] = TR\\n funcImage.header.set_zooms(pixDims)\\n\\n return funcImage\",\n \"def map_func(h, configs, args):\\n\\tif args.verbose:\\n\\t\\tcmd = \\\"python {} -i {}/threshold{}.tif -o {}/threshold{}.shp -v\\\".format(\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"polygons\\\"],\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"output\\\"],\\n\\t\\t\\th,\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"output\\\"],\\n\\t\\t\\th\\n\\t\\t)\\n\\t\\tprint cmd\\n\\telse:\\n\\t\\tcmd = \\\"python {} -i {}/threshold{}.tif -o {}/threshold{}.shp\\\".format(\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"polygons\\\"],\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"output\\\"],\\n\\t\\t\\th,\\n\\t\\t\\tconfigs[\\\"path\\\"][\\\"output\\\"],\\n\\t\\t\\th\\n\\t\\t)\\n\\tcmd_args = shlex.split(cmd)\\n\\tstdout,stderr = sp.Popen(\\n\\t\\tcmd_args,\\n\\t\\tstdin = sp.PIPE,\\n\\t\\tstdout = sp.PIPE,\\n\\t\\tstderr = sp.PIPE\\n\\t).communicate()\\n\\tif args.verbose:\\n\\t\\tprint stdout, stderr\\n\\treturn True\",\n \"def main(path):\\n vm_files = []\\n if not os.path.exists(path):\\n print(\\\"Error: File or directory does not exist: %s\\\"\\n % path)\\n return\\n\\n elif os.path.isdir(path): # Directory of files\\n vm_files = filter_paths(path)\\n dir_path = path\\n file_name = os.path.basename(path) + FILE_EXTENSION_ASM\\n if not vm_files: # no vm files found\\n print(\\\"Error: No files matching %s found in supplied \\\"\\n \\\"directory: %s\\\" % (FILE_EXTENSION_VM, path))\\n return\\n\\n elif os.path.isfile(path): # Single file\\n if not path.endswith(FILE_EXTENSION_VM):\\n print(\\\"Error: Mismatched file type.\\\\n\\\\\\\"%s\\\\\\\"suffix is not a valid \\\"\\n \\\"file type. Please supply .vm filename or dir.\\\" % path)\\n return\\n vm_files.append(path)\\n dir_path = os.path.dirname(path)\\n file_name = os.path.splitext(os.path.basename(path))[0] + \\\\\\n FILE_EXTENSION_ASM\\n\\n else:\\n print(\\\"Error: Unrecognized path: \\\\\\\"%s\\\\\\\"\\\\n\\\"\\n \\\"Please supply dir or path/filename.vm\\\")\\n return\\n\\n try:\\n # Initilizes write based, using a condition for multiple file reading.\\n # Multiple files have a special initlization\\n writer = CodeWriter(os.path.join(dir_path, file_name),\\n len(vm_files) > 1)\\n for vm_file in vm_files:\\n translate_file(vm_file, writer)\\n writer.close()\\n\\n except OSError:\\n print(\\\"Could not open some file.\\\\n \\\"\\n \\\"If file exists, check spelling of file path.\\\")\\n return\\n\\n except Exception as e:\\n print(\\\"Some exception occurred while parsing.\\\", e)\\n traceback.print_exc()\\n return\",\n \"def addfunctions(dtls, bunchdt):\\n snames = [\\n \\\"BuildingSurface:Detailed\\\",\\n \\\"Wall:Detailed\\\",\\n \\\"RoofCeiling:Detailed\\\",\\n \\\"Floor:Detailed\\\",\\n \\\"FenestrationSurface:Detailed\\\",\\n \\\"Shading:Site:Detailed\\\",\\n \\\"Shading:Building:Detailed\\\",\\n \\\"Shading:Zone:Detailed\\\",\\n ]\\n for sname in snames:\\n if sname.upper() in bunchdt:\\n surfaces = bunchdt[sname.upper()]\\n for surface in surfaces:\\n func_dict = {\\n \\\"area\\\": fh.area,\\n \\\"height\\\": fh.height, # not working correctly\\n \\\"width\\\": fh.width, # not working correctly\\n \\\"azimuth\\\": fh.azimuth,\\n \\\"tilt\\\": fh.tilt,\\n \\\"coords\\\": fh.getcoords, # needed for debugging\\n }\\n try:\\n surface.__functions.update(func_dict)\\n except KeyError as e:\\n surface.__functions = func_dict\\n # add common functions\\n # for name in dtls:\\n # for idfobject in bunchdt[name]:\\n # idfobject.__functions\\n # idfobject['__functions']['fieldnames'] = fieldnames\\n # idfobject['__functions']['fieldvalues'] = fieldvalues\\n # idfobject['__functions']['getrange'] = GetRange(idfobject)\\n # idfobject['__functions']['checkrange'] = CheckRange(idfobject)\",\n \"def main():\\n import argparse\\n parser = argparse.ArgumentParser()\\n parser.add_argument('-i', '--input', help='Input .py file', nargs='+')\\n args = parser.parse_args()\\n\\n mod_func = []\\n\\n for pyfile in args.input:\\n tree = ast.parse(open(pyfile).read())\\n\\n methods = sorted({node.name for node in ast.walk(tree)\\n if isinstance(node, ast.FunctionDef)})\\n mod_func.extend([[pyfile, methods[i]] for i in range(len(methods))])\\n\\n write_csv(\\\"meth_func.csv\\\", mod_func)\",\n \"def transform(self, *fs):\\n return transform(self, *fs)\",\n \"def get_vulnerability_functions_04(fname):\\n categories = dict(assetCategory=set(), lossCategory=set(),\\n vulnerabilitySetID=set())\\n imts = set()\\n taxonomies = set()\\n vf_dict = {} # imt, taxonomy -> vulnerability function\\n for vset in nrml.read(fname).vulnerabilityModel:\\n categories['assetCategory'].add(vset['assetCategory'])\\n categories['lossCategory'].add(vset['lossCategory'])\\n categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])\\n IML = vset.IML\\n imt_str = IML['IMT']\\n imls = ~IML\\n imts.add(imt_str)\\n for vfun in vset.getnodes('discreteVulnerability'):\\n taxonomy = vfun['vulnerabilityFunctionID']\\n if taxonomy in taxonomies:\\n raise InvalidFile(\\n 'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %\\n (taxonomy, fname, vfun.lineno))\\n taxonomies.add(taxonomy)\\n with context(fname, vfun):\\n loss_ratios = ~vfun.lossRatio\\n coefficients = ~vfun.coefficientsVariation\\n if len(loss_ratios) != len(imls):\\n raise InvalidFile(\\n 'There are %d loss ratios, but %d imls: %s, line %d' %\\n (len(loss_ratios), len(imls), fname,\\n vfun.lossRatio.lineno))\\n if len(coefficients) != len(imls):\\n raise InvalidFile(\\n 'There are %d coefficients, but %d imls: %s, line %d' %\\n (len(coefficients), len(imls), fname,\\n vfun.coefficientsVariation.lineno))\\n with context(fname, vfun):\\n vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(\\n taxonomy, imt_str, imls, loss_ratios, coefficients,\\n vfun['probabilisticDistribution'])\\n categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))\\n del categories['vulnerabilitySetID']\\n return vf_dict, categories\",\n \"def define_functions(self):\\n\\n self.define_vector_functions()\\n self.define_scalar_functions()\\n\\n return None\",\n \"def vl2img(vl_json_in, fileformat):\\n\\n # TODO would prefer to do this properly with pipes\\n # using | and shell=True is safe though given no arguments\\n executables = {\\\"svg\\\": \\\"vg2svg\\\", \\\"png\\\": \\\"vg2png\\\", \\\"pdf\\\": \\\"vg2pdf\\\"}\\n try:\\n exe = executables[fileformat]\\n except KeyError as e:\\n print(e.output)\\n try:\\n return subprocess.check_output(\\\"vl2vg | %s\\\" % exe, shell=True, input=vl_json_in)\\n except subprocess.CalledProcessError as e:\\n print(e.output)\",\n \"def render_function(children):\\r\\n fname = children[0].latex\\r\\n if casify(fname) not in functions:\\r\\n pass # TODO turn unknown function red or give some kind of error\\r\\n\\r\\n # Wrap the input of the function with parens or braces.\\r\\n inner = children[1].latex\\r\\n if fname == \\\"sqrt\\\":\\r\\n inner = u\\\"{{{expr}}}\\\".format(expr=inner)\\r\\n else:\\r\\n if children[1].tall:\\r\\n inner = ur\\\"\\\\left({expr}\\\\right)\\\".format(expr=inner)\\r\\n else:\\r\\n inner = u\\\"({expr})\\\".format(expr=inner)\\r\\n\\r\\n # Correctly format the name of the function.\\r\\n if fname == \\\"sqrt\\\":\\r\\n fname = ur\\\"\\\\sqrt\\\"\\r\\n elif fname == \\\"log10\\\":\\r\\n fname = ur\\\"\\\\log_{10}\\\"\\r\\n elif fname == \\\"log2\\\":\\r\\n fname = ur\\\"\\\\log_2\\\"\\r\\n else:\\r\\n fname = ur\\\"\\\\text{{{fname}}}\\\".format(fname=fname)\\r\\n\\r\\n # Put it together.\\r\\n latex = fname + inner\\r\\n return LatexRendered(latex, tall=children[1].tall)\",\n \"def _zipper(self, func):\\n if func not in self.resmap:\\n return []\\n return itertools.chain(*[f(self.blob.get(name, None), self.filepath, self.tree, self.conn, self.dbpath)\\n for name, f in self.resmap[func]])\",\n \"def process(self, file_list, shapefiles_dir, output_dir):\\n for datatype in self.data_types:\\n for data_file in file_list:\\n data_file = str(data_file)\\n original_filename = os.path.basename(data_file)\\n cut_fname = original_filename[:len(self.date_format)+2]\\n observation_date = datetime.strptime(cut_fname, self.date_format).strftime('%Y%m%d')\\n try:\\n input_file = HE5(self.data_directory, original_filename)\\n if ('nc4' in data_file):\\n input_file = NC4(self.data_directory, original_filename)\\n data = input_file.read(datatype.ds_name)\\n except Exception as e:\\n print(e)\\n continue\\n\\n rasterdata = input_file.genTif(data, datatype.resolution)\\n\\n for shpfile in Path(shapefiles_dir).rglob('*.shp'):\\n shapefile = Shapefile(shapefiles_dir, shpfile.name)\\n csvfile = shapefile.read_shape_file(datatype, rasterdata, data, self.minVal)\\n csvfile = np.array(csvfile)\\n\\n output = OutputFile(output_dir, shapefile.getDirName(), self.name, datatype, observation_date)\\n output.save(csvfile)\\n\\n del rasterdata\",\n \"def create_test_function(source, output, lang):\\n with open(source) as f:\\n snippet = f.read()\\n with open(output) as f:\\n res = f.read()\\n\\n def tst_func(slf):\\n slf.do(snippet, res, lang=lang)\\n\\n return tst_func\",\n \"def main(config_file, rows, cols):\\n # setup paths\\n _, _, params = cf.get_ifg_paths(config_file)\\n _postprocess_linrate(rows, cols, params)\\n if params[cf.TIME_SERIES_CAL]:\\n _postprocess_timeseries(rows, cols, params)\",\n \"def convert(self):\\n \\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\\n splitAt = len(self.fullPath) + 1\\n \\n if len(vrtlist)!=0:\\n for i in range(0,len(vrtlist)):\\n prefix = str(vrtlist[i].split(\\\".vrt\\\")[0])\\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \\n prefix = prefix, subset = self.subset, res = self.resolution, \\n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\\n ct.run()\\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\\n for f in mosdel:\\n os.remove(f)\\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \\n for f in xmldel:\\n os.remove(f)\\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\\n for f in vrtdel:\\n os.remove(f)\\n tifCount = len(glob.glob(self.fullPath + '/*.tif'))\\n dataCount = self.subset.count('1')\\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d mosaicked images were successfully converted to %d %s files.' % (dataCount, len(vrtlist), tifCount, str(self.outformat)))\\n \\n \\n if len(vrtlist)==0: \\n \\n hdflist = sorted(glob.glob(self.fullPath + '/*.hdf'))\\n for i in range(len(hdflist)):\\n ms = pymodis.convertmodis_gdal.createMosaicGDAL(hdfnames = [hdflist[i]], subset = self.subset, outformat = 'GTiff')\\n ms.run(str(hdflist[i].split('.h')[0]) + 'mos.tif')\\n ms.write_vrt(output = str(hdflist[i].split('.h')[0]), separate = True)\\n\\n vrtlist = sorted(glob.glob(self.fullPath + '/*vrt'))\\n splitAt = len(self.fullPath) + 1\\n \\n for i in range(0,len(vrtlist)):\\n prefix = str(vrtlist[i].split(\\\".vrt\\\")[0])\\n prefix = prefix[:splitAt] + 'full' + prefix[splitAt:]\\n ct = pymodis.convertmodis_gdal.convertModisGDAL(hdfname = vrtlist[i], \\n prefix = prefix, subset = self.subset, res = self.resolution, \\n outformat = self.outformat, wkt = self.projection, resampl = 'NEAREST_NEIGHBOR', vrt = True)\\n ct.run()\\n \\n mosdel = glob.glob(self.fullPath + '/*mos.tif')\\n for f in mosdel:\\n os.remove(f)\\n xmldel = glob.glob(self.fullPath + '/*mos.tif.xml') \\n for f in xmldel:\\n os.remove(f)\\n vrtdel = glob.glob(self.fullPath + '/*.vrt')\\n for f in vrtdel:\\n os.remove(f)\\n tifCount = len(glob.glob(self.fullPath + '/full*.tif'))\\n dataCount = self.subset.count('1')\\n logger.log('SUCCESS', 'Conversion complete! The %d bands of %d HDF files were successfully converted to %d %s files.' % (dataCount, len(hdflist), tifCount, str(self.outformat)))\",\n \"def function_closure(functions, casify):\\r\\n def render_function(children):\\r\\n \\\"\\\"\\\"\\r\\n Escape function names and give proper formatting to exceptions.\\r\\n\\r\\n The exceptions being 'sqrt', 'log2', and 'log10' as of now.\\r\\n \\\"\\\"\\\"\\r\\n fname = children[0].latex\\r\\n if casify(fname) not in functions:\\r\\n pass # TODO turn unknown function red or give some kind of error\\r\\n\\r\\n # Wrap the input of the function with parens or braces.\\r\\n inner = children[1].latex\\r\\n if fname == \\\"sqrt\\\":\\r\\n inner = u\\\"{{{expr}}}\\\".format(expr=inner)\\r\\n else:\\r\\n if children[1].tall:\\r\\n inner = ur\\\"\\\\left({expr}\\\\right)\\\".format(expr=inner)\\r\\n else:\\r\\n inner = u\\\"({expr})\\\".format(expr=inner)\\r\\n\\r\\n # Correctly format the name of the function.\\r\\n if fname == \\\"sqrt\\\":\\r\\n fname = ur\\\"\\\\sqrt\\\"\\r\\n elif fname == \\\"log10\\\":\\r\\n fname = ur\\\"\\\\log_{10}\\\"\\r\\n elif fname == \\\"log2\\\":\\r\\n fname = ur\\\"\\\\log_2\\\"\\r\\n else:\\r\\n fname = ur\\\"\\\\text{{{fname}}}\\\".format(fname=fname)\\r\\n\\r\\n # Put it together.\\r\\n latex = fname + inner\\r\\n return LatexRendered(latex, tall=children[1].tall)\\r\\n # Return the function within the closure.\\r\\n return render_function\",\n \"def generic_function(self, node, ordered_functions):\\n for generic in node.fortran_generic:\\n new = node.clone()\\n ordered_functions.append(new)\\n self.append_function_index(new)\\n new._generated = \\\"fortran_generic\\\"\\n fmt = new.fmtdict\\n # XXX append to existing suffix\\n if generic.fmtdict:\\n fmt.update(generic.fmtdict)\\n fmt.function_suffix = fmt.function_suffix + generic.function_suffix\\n new.fortran_generic = {}\\n new.wrap.assign(fortran=True)\\n new.ast.declarator.params = generic.decls\\n\\n # Try to call original C function if possible.\\n # All arguments are native scalar.\\n need_wrapper = False\\n if new.ast.declarator.is_indirect():\\n need_wrapper = True\\n \\n for arg in new.ast.declarator.params:\\n if arg.declarator.is_indirect():\\n need_wrapper = True\\n break\\n elif arg.typemap.sgroup == \\\"native\\\":\\n pass\\n else:\\n need_wrapper = True\\n break\\n\\n if need_wrapper:\\n # The C wrapper is required to cast constants.\\n # generic.yaml: GenericReal\\n new.C_force_wrapper = True\\n new.wrap.c = True\\n new._PTR_C_CXX_index = node._function_index\\n else:\\n new._PTR_F_C_index = node._function_index\\n \\n # Do not process templated node, instead process\\n # generated functions above.\\n # node.wrap.c = False\\n node.wrap.fortran = False\",\n \"def _run_file_contents(contents: dict, strict_fn, debug_print):\\n\\n default_fns_args = {\\n \\\"adjust_focus\\\": {\\\"on\\\": \\\"hexbin\\\"},\\n \\\"adjust_positioning\\\": True,\\n \\\"adjust_opacity\\\": True\\n }\\n\\n input_fns = contents.pop(\\\"functions\\\", {})\\n for k, v in default_fns_args.items():\\n if k not in input_fns:\\n input_fns[k] = v\\n\\n output = contents.pop(\\\"output\\\", False)\\n display = contents.pop(\\\"display\\\", True)\\n builder = PlotBuilder.builder_from_dict(**contents)\\n debug_print(\\\"* all layers loaded\\\")\\n\\n for k, v in input_fns.items():\\n if v:\\n args, kwargs = parse_args_kwargs(v)\\n try:\\n _fn_map[k](builder, *args, **kwargs)\\n debug_print(f\\\"* invoked function '{_fn_map[k].__name__}'.\\\\nargs: {args}\\\\nkwargs: {kwargs}\\\")\\n except Exception as e:\\n try:\\n debug_print(f\\\"* error while performing '{_fn_map[k].__name__}'.\\\\nerror: {e}\\\")\\n strict_fn(e)\\n except KeyError as f:\\n debug_print(f\\\"* no such function as '{k}'.\\\")\\n strict_fn(f)\\n\\n builder.finalize(raise_errors=False)\\n\\n if output:\\n args, kwargs = parse_args_kwargs(output)\\n builder.output(*args, **kwargs)\\n debug_print(\\\"* figure output.\\\")\\n\\n if display:\\n args, kwargs = parse_args_kwargs(display)\\n builder.display(*args, **kwargs)\\n debug_print(\\\"* figure displayed.\\\")\\n\\n return builder.get_plot_status()\",\n \"def main():\\r\\n\\r\\n parser = argparse.ArgumentParser()\\r\\n parser.add_argument(dest='INFILE')\\r\\n parser.add_argument(dest='OUTFILE')\\r\\n\\r\\n args = parser.parse_args()\\r\\n function1(args.INFILE, args.OUTFILE)\",\n \"def apply_function_vector(function_vector, x_vector):\\n function_index = 0\\n element_index = 1\\n \\n def d():\\n for e in zip(function_vector, x_vector):\\n print(e[1])\\n d()\\n \\n return list(map(lambda fx_set: fx_set[function_index](fx_set[element_index]), zip(function_vector, x_vector)))\",\n \"def use_vectors(*funcs):\\n\\n global GLOBAL_STATUS\\n if funcs:\\n GLOBAL_STATUS.discard('ARRAYS')\\n GLOBAL_STATUS.add('VECTORS')\\n GLOBAL_STATUS.discard('SCALARS')\\n else:\\n GLOBAL_STATUS.discard('ARRAYS')\\n GLOBAL_STATUS.discard('VECTORS')\\n GLOBAL_STATUS.discard('SCALARS')\\n\\n for name in _get_func_names(funcs):\\n if ('scalar' not in name and 'vector' not in name\\n and 'array' not in name):\\n globals()[name] = globals()[name].vector\",\n \"def convert(func, overload_module, transformers):\\n source, _ = parsing.parse_entity(func)\\n entity_info = transformer.EntityInfo(\\n source_code=source,\\n source_file='<fragment>',\\n namespace={},\\n arg_values=None,\\n arg_types={},\\n owner_type=None)\\n\\n overload_module.PyctrReturnException = PyctrReturnException\\n\\n namer = naming.Namer(entity_info.namespace)\\n ctx = transformer.EntityContext(namer, entity_info)\\n overload_name = ctx.namer.new_symbol('overload', set())\\n overload = config.VirtualizationConfig(overload_module, overload_name)\\n\\n source = _transform(source, ctx, overload, transformers)\\n gen_func = _wrap_in_generator(func, source, namer, overload)\\n gen_func = _attach_closure(func, gen_func)\\n return gen_func\",\n \"def load_raw_wignetting_function():\\n global rawvignfun\\n if rawvignfun is None:\\n vignfile = get_vigneting_by_urd(28)\\n x = 23.5 + np.tan(vignfile[\\\"Offset angles\\\"].data[\\\"X\\\"]*pi/180/60.)*F/DL\\n y = 23.5 + np.tan(vignfile[\\\"Offset angles\\\"].data[\\\"Y\\\"]*pi/180/60.)*F/DL\\n rawvignfun = RegularGridInterpolator((vignfile[\\\"5 arcmin PSF\\\"].data[\\\"E\\\"], x, y), vignfile[\\\"5 arcmin PSF\\\"].data[\\\"EFFAREA\\\"])\\n return rawvignfun\",\n \"def main():\\r\\n\\r\\n #Create a list of all files that have the GPX file format\\r\\n fileList = glob.glob(os.path.join(inFolder,\\\"*.{0}\\\".format(inFormat)))\\r\\n\\r\\n #Create a connection to PostGIS database\\r\\n pgConn = createPostgisConnection(dbFormat, dbHost, dbName, dbSchema, dbUser, dbPWD)\\r\\n\\r\\n #Process each *listed* layer type from a GPS file\\r\\n for f in fileList:\\r\\n importGPX(f, gpxImportLayers, pgConn)\",\n \"def main(file):\\n\\n # Get the current working directory.\\n here = os.getcwd()\\n #Need the file_name to set globe, so that other functions can access to it.\\n global file_name\\n # Spite the Input into file_path and file_name.\\n file_path = spilt_path(file)[0]\\n file_name = spilt_path(file)[1]\\n\\n # Try to get into the file_path, if exist\\n try:\\n os.chdir(file_path)\\n except IOError, e:\\n print e\\n\\n # Now convert it\\n convertFile(file_name)\\n # going back to orgin folder\\n os.chdir(here)\\n return os.path.join(output_dir, file_name)\",\n \"def input(self, *args, **kwargs):\\n return lambda wildcards: self.samples.map(*args, file=\\\"samples/all/runs/{sample_run}/samples.csv\\\", **wildcards, **kwargs)\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n # use the func to insert data from these files to database's fact and dim tables\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def assemble_eval(\\n fenics_function: Callable,\\n fenics_templates: Iterable[FenicsVariable],\\n *args: np.array,\\n) -> Tuple[np.array, ufl.Form, Tuple[FenicsVariable]]:\\n\\n check_input(fenics_templates, *args)\\n fenics_inputs = convert_all_to_fenics(fenics_templates, *args)\\n\\n out = fenics_function(*fenics_inputs)\\n if not isinstance(out, tuple):\\n raise ValueError(\\n \\\"FEniCS function output should be in the form (assembly_output, ufl_form).\\\"\\n )\\n\\n assembly_output, ufl_form = out\\n\\n if isinstance(assembly_output, tuple):\\n raise ValueError(\\n \\\"Only single solution output from FEniCS function is supported.\\\"\\n )\\n\\n if not isinstance(assembly_output, float):\\n raise ValueError(\\n f\\\"FEniCS function output should be in the form (assembly_output, ufl_form). Got {type(assembly_output)} instead of float\\\"\\n )\\n\\n if not isinstance(ufl_form, ufl.Form):\\n raise ValueError(\\n f\\\"FEniCS function output should be in the form (assembly_output, ufl_form). Got {type(ufl_form)} instead of ufl.Form\\\"\\n )\\n\\n numpy_output = np.asarray(assembly_output)\\n return numpy_output, ufl_form, fenics_inputs\",\n \"def load_viewpoints(viewpoint_file_list):\\n\\n if isinstance(viewpoint_file_list, str):\\n vp_file_list = [viewpoint_file_list]\\n\\n try:\\n vp_file_list = iter(viewpoint_file_list)\\n except TypeError:\\n print(\\\"viewpoint_file_list is not an iterable object\\\")\\n\\n for vp_file in vp_file_list:\\n yield load_viewpoint(vp_file)\",\n \"def apply_funcs(self, src_dir, pkl_name, dest_dir, funclist):\\n if not os.path.exists(dest_dir):\\n os.makedirs(dest_dir)\\n pkl_files = os.listdir(src_dir)\\n data_in = open(os.path.join(src_dir, pkl_name), 'rb')\\n data_array = pickle.load(data_in)\\n dump_array = []\\n for i, data_dict in enumerate(data_array):\\n new_data_dict = self.dutils.apply_flist(data_dict, funclist, w_rosdict=True)\\n if new_data_dict.get(\\\"flag\\\", True):\\n dump_array.append(new_data_dict)\\n new_pkl_name = pkl_name\\n dump_path = os.path.join(dest_dir, new_pkl_name)\\n self.pickledump(dump_array, dump_path)\\n self.seen_pkls.append(new_pkl_name)\\n return new_pkl_name\",\n \"def write_triggers_to_file(triggerfile, functionfile,\\n generators, entityname, table, path, select,\\n indextable, index):\\n for generator in generators:\\n gen = generator(entityname, table, path, select, indextable, index)\\n functionfile.write(gen.function)\\n triggerfile.write(gen.trigger)\",\n \"def jvp_assemble_eval(\\n fenics_function: Callable,\\n fenics_templates: Iterable[FenicsVariable],\\n primals: Tuple[np.array],\\n tangents: Tuple[np.array],\\n) -> Tuple[np.array]:\\n\\n numpy_output_primal, output_primal_form, fenics_primals = assemble_eval(\\n fenics_function, fenics_templates, *primals\\n )\\n\\n # Now tangent evaluation!\\n fenics_tangents = convert_all_to_fenics(fenics_primals, *tangents)\\n output_tangent_form = 0.0\\n for fp, ft in zip(fenics_primals, fenics_tangents):\\n output_tangent_form += fenics.derivative(output_primal_form, fp, ft)\\n\\n if not isinstance(output_tangent_form, float):\\n output_tangent_form = ufl.algorithms.expand_derivatives(output_tangent_form)\\n output_tangent = fenics.assemble(output_tangent_form)\\n\\n jax_output_tangent = output_tangent\\n\\n return numpy_output_primal, jax_output_tangent\",\n \"def process_svgs(model_info, directory, output, allow_missing=False):\\n r2dt.write(model_info, directory, output, allow_missing=allow_missing)\",\n \"def transform_regions(region_list, wcs_in, wcs_out, preroot,\\n reg_format='fits', clobber=False):\\n\\n if reg_format == 'ascii':\\n func = transform_region_ascii\\n elif reg_format == 'fits':\\n func = transform_region_fits\\n else:\\n raise ValueError(\\\"Unsupported reg_format=\\\" + reg_format)\\n\\n outfiles = []\\n for region_file in region_list:\\n\\n # Do not do anything if the output file already exists and\\n # clobber is not set.\\n #\\n outfile = preroot + region_file\\n outfiles.append(outfile)\\n if not clobber and os.path.exists(outfile):\\n continue\\n\\n func(region_file, outfile, wcs_in, wcs_out)\\n\\n return outfiles\",\n \"def process(sources, output, force):\\n logging.basicConfig(level=logging.INFO,\\n format='%(asctime)s [%(levelname)s] - %(message)s', datefmt=\\\"%H:%M:%S\\\")\\n\\n logging.getLogger('shapely.geos').setLevel(logging.WARNING)\\n logging.getLogger('Fiona').setLevel(logging.WARNING)\\n logging.getLogger('requests.packages.urllib3.connectionpool').setLevel(logging.WARNING)\\n requests.packages.urllib3.disable_warnings()\\n # logging.getLogger('processing').setLevel(logging.DEBUG)\\n\\n catalog_features = []\\n failures = []\\n path_parts_to_skip = utils.get_path_parts(sources).index(\\\"sources\\\") + 1\\n success = True\\n for path in utils.get_files(sources):\\n try:\\n logging.info(\\\"Processing \\\" + path)\\n pathparts = utils.get_path_parts(path)[path_parts_to_skip:]\\n pathparts[-1] = pathparts[-1].replace('.json', '.geojson')\\n\\n outdir = os.path.join(output, *pathparts[:-1], pathparts[-1].replace('.geojson', ''))\\n outfile = os.path.join(output, *pathparts)\\n\\n source = utils.read_json(path)\\n urlfile = urlparse(source['url']).path.split('/')[-1]\\n \\n if not hasattr(adapters, source['filetype']):\\n logging.error('Unknown filetype ' + source['filetype'])\\n failures.append(path)\\n continue\\n \\n read_existing = False\\n if os.path.isfile(outfile):\\n logging.info(\\\"Output file exists\\\")\\n if os.path.getmtime(outfile) > os.path.getmtime(path):\\n logging.info(\\\"Output file is up to date\\\")\\n if not force:\\n read_existing = True\\n logging.warning('Skipping ' + path + ' since generated file exists. Use --force to regenerate.') \\n else:\\n logging.info(\\\"Output is outdated, {} < {}\\\".format(\\n datetime.datetime.fromtimestamp(os.path.getmtime(outfile)),\\n datetime.datetime.fromtimestamp(os.path.getmtime(path))))\\n\\n if read_existing:\\n with open(outfile, \\\"rb\\\") as f:\\n geojson = json.load(f)\\n properties = geojson['properties']\\n else:\\n logging.info('Downloading ' + source['url'])\\n \\n try:\\n fp = utils.download(source['url'])\\n except IOError:\\n logging.error('Failed to download ' + source['url'])\\n failures.append(path)\\n continue\\n \\n logging.info('Reading ' + urlfile)\\n \\n if 'filter' in source:\\n filterer = BasicFilterer(source['filter'], source.get('filterOperator', 'and'))\\n else:\\n filterer = None\\n \\n try:\\n geojson = getattr(adapters, source['filetype'])\\\\\\n .read(fp, source['properties'],\\n filterer=filterer,\\n layer_name=source.get(\\\"layerName\\\", None),\\n source_filename=source.get(\\\"filenameInZip\\\", None))\\n except IOError as e:\\n logging.error('Failed to read ' + urlfile + \\\" \\\" + str(e))\\n failures.append(path)\\n continue\\n except zipfile.BadZipfile as e:\\n logging.error('Unable to open zip file ' + source['url'])\\n failures.append(path)\\n continue\\n finally:\\n os.remove(fp.name)\\n if(len(geojson['features'])) == 0:\\n logging.error(\\\"Result contained no features for \\\" + path)\\n continue\\n excluded_keys = ['filetype', 'url', 'properties', 'filter', 'filenameInZip']\\n properties = {k:v for k,v in list(source.items()) if k not in excluded_keys}\\n properties['source_url'] = source['url']\\n properties['feature_count'] = len(geojson['features'])\\n logging.info(\\\"Generating demo point\\\")\\n properties['demo'] = geoutils.get_demo_point(geojson)\\n \\n geojson['properties'] = properties\\n \\n utils.make_sure_path_exists(os.path.dirname(outfile))\\n\\n #cleanup existing generated files\\n if os.path.exists(outdir):\\n rmtree(outdir)\\n filename_to_match, ext = os.path.splitext(pathparts[-1])\\n output_file_dir = os.sep.join(utils.get_path_parts(outfile)[:-1])\\n logging.info(\\\"looking for generated files to delete in \\\" + output_file_dir)\\n for name in os.listdir(output_file_dir):\\n base, ext = os.path.splitext(name)\\n if base == filename_to_match:\\n to_remove = os.path.join(output_file_dir, name)\\n logging.info(\\\"Removing generated file \\\" + to_remove)\\n os.remove(to_remove)\\n\\n utils.write_json(outfile, geojson)\\n\\n logging.info(\\\"Generating label points\\\")\\n label_geojson = geoutils.get_label_points(geojson)\\n label_path = outfile.replace('.geojson', '.labels.geojson')\\n utils.write_json(label_path, label_geojson)\\n\\n logging.info('Done. Processed to ' + outfile)\\n \\n if not \\\"demo\\\" in properties:\\n properties['demo'] = geoutils.get_demo_point(geojson)\\n\\n properties['path'] = \\\"/\\\".join(pathparts)\\n catalog_entry = {\\n 'type': 'Feature',\\n 'properties': properties,\\n 'geometry': geoutils.get_union(geojson)\\n }\\n catalog_features.append(catalog_entry)\\n\\n if not os.path.exists(outdir) or not os.path.exists(os.path.join(outdir, \\\"units.json\\\")):\\n logging.info(\\\"Generated exploded GeoJSON to \\\" + outdir)\\n if not os.path.exists(outdir):\\n os.makedirs(outdir)\\n # .json instead of .geojson, incase there is a unit named \\\"source\\\"\\n utils.write_json(os.path.join(outdir, \\\"source.json\\\"), catalog_entry) \\n units = []\\n for feature in geojson['features']:\\n feature_id = str(feature['properties']['id'])\\n feature_id = feature_id.replace('/', '')\\n feature_filename = os.path.join(outdir, feature_id + \\\".geojson\\\")\\n utils.write_json(feature_filename, feature)\\n units.append(feature['properties'])\\n utils.write_json(os.path.join(outdir, \\\"units.json\\\"), units)\\n else:\\n logging.debug(\\\"exploded GeoJSON already exists, not generating\\\")\\n\\n except Exception as e:\\n logging.error(str(e))\\n logging.exception(\\\"Error processing file \\\" + path)\\n failures.append(path)\\n success = False\\n\\n catalog = {\\n 'type': 'FeatureCollection',\\n 'features': catalog_features\\n }\\n utils.write_json(os.path.join(output,'catalog.geojson'), catalog)\\n\\n if not success:\\n logging.error(\\\"Failed sources: \\\" + \\\", \\\".join(failures))\\n sys.exit(-1)\",\n \"def _generate_and_save_function(self, filename, expression, parameters):\\n\\n # check for / create the save folder for this expression\\n folder = self.config_folder + '/' + filename\\n abr_control.utils.os_utils.makedirs(folder)\\n\\n if self.use_cython is True:\\n # binaries saved by specifying tempdir parameter\\n function = autowrap(expression, backend=\\\"cython\\\",\\n args=parameters, tempdir=folder)\\n function = sp.lambdify(parameters, expression, \\\"numpy\\\")\\n\\n return function\",\n \"def _add_function(self, alias, func):\\n # Construct a function that will call the user supplied function with\\n # the proper arguments. We prepend 'self' so the user supplied function\\n # has easy access to all the filepaths.\\n def fname(**kwargs):\\n return func(self, **kwargs)\\n\\n # Bind the fname function to this instance of FileNames\\n self.__dict__[alias] = fname\",\n \"def get_verts(ulist, vlist, func):\\n verts = []\\n for u in ulist:\\n for v in vlist:\\n verts.append(func(u, v))\\n return verts\",\n \"def ramlwrap(file_path, function_map):\\n\\n try:\\n # Check if file is RAML (.raml)\\n if file_path.endswith(\\\".raml\\\"):\\n patterns = raml_url_patterns(file_path, function_map)\\n else:\\n error_msg = \\\"The file: '{}' does not have a .raml extension!\\\".format(file_path)\\n logger.error(error_msg)\\n raise FatalException(error_msg)\\n\\n except AttributeError as error:\\n error_msg = \\\"An error occurred reading '{}': {}\\\".format(file_path, error)\\n logger.error(error_msg)\\n raise FatalException(error_msg)\\n\\n return patterns\",\n \"def file_converter(self, **kwds):\\n if (self.reformat == 'zarr'):\\n # output zarr file\\n self.HDF5_to_zarr(**kwds)\\n elif (self.reformat == 'HDF5'):\\n # output rechunked HDF5 file\\n self.HDF5_to_HDF5(**kwds)\\n # elif (reformat == 'JPL'):\\n # # output JPL captoolkit formatted HDF5 files\\n # self.HDF5_to_JPL_HDF5(**kwds)\\n elif self.reformat in ('csv','txt'):\\n # output reduced files to ascii formats\\n self.HDF5_to_ascii(**kwds)\\n elif self.reformat in ('dataframe',):\\n # output reduced files to pandas dataframe\\n return self.HDF5_to_dataframe(**kwds)\\n else:\\n raise ValueError(f'Unknown format {self.reformat}')\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def get_vit_fn(model, num_classes, spatial_res):\\n model = model.lower()\\n assert model in VIT_FNS\\n vit_fn = VIT_FNS[model]\\n\\n def wrapped_vit_model_fn(*args, **kwargs):\\n return vit_fn(*args, num_classes=num_classes,\\n image_size=spatial_res, **kwargs)\\n return wrapped_vit_model_fn\",\n \"def _rasterize_vector_onto_base(\\n base_raster_path, base_vector_path, attribute_id,\\n target_raster_path, filter_string=None):\\n base_raster = gdal.OpenEx(base_raster_path, gdal.OF_RASTER)\\n raster_driver = gdal.GetDriverByName('GTiff')\\n target_raster = raster_driver.CreateCopy(target_raster_path, base_raster)\\n base_raster = None\\n\\n vector = gdal.OpenEx(base_vector_path)\\n layer = vector.GetLayer()\\n\\n if filter_string is not None:\\n layer.SetAttributeFilter(str(filter_string))\\n gdal.RasterizeLayer(\\n target_raster, [1], layer,\\n options=['ATTRIBUTE=%s' % attribute_id])\\n target_raster.FlushCache()\\n target_raster = None\\n layer = None\\n vector = None\",\n \"def get_transfer_functioin(self, color_file=None, volume_opacity=0.25):\\n if color_file: # Color file is given\\n\\n import csv\\n fid = open(color_file, \\\"r\\\")\\n reader_color = csv.reader(fid)\\n\\n dict_RGB = {}\\n for line in reader_color:\\n dict_RGB[int(line[0])] = [float(line[2]) / 255.0,\\n float(line[3]) / 255.0,\\n float(line[4]) / 255.0]\\n fid.close()\\n\\n # Define colour transfer function\\n color_transfor = vtk.vtkColorTransferFunction()\\n\\n for idx in dict_RGB.keys():\\n color_transfor.AddRGBPoint(idx,\\n dict_RGB[idx][0],\\n dict_RGB[idx][1],\\n dict_RGB[idx][2])\\n\\n # Opacity transfer function\\n opacity_scalar = vtk.vtkPiecewiseFunction()\\n\\n for idx in dict_RGB.keys():\\n opacity_scalar.AddPoint(\\n idx, volume_opacity if idx != 0 else 0.0)\\n\\n # Opacity Gradient Transfer function\\n opacity_gradient = vtk.vtkPiecewiseFunction()\\n opacity_gradient.AddPoint(1, 0.0)\\n opacity_gradient.AddPoint(5, 0.1)\\n opacity_gradient.AddPoint(100, 1.0)\\n\\n return color_transfor, opacity_scalar, opacity_gradient\\n\\n else: # Default color transfer functions\\n\\n # min, max = self.get_value_range()\\n color_transfor = vtk.vtkColorTransferFunction()\\n color_transfor.AddRGBPoint(0, 0.0, 0.0, 0.0)\\n color_transfor.AddRGBPoint(500, 1.0, 0.5, 0.3)\\n color_transfor.AddRGBPoint(1000, 1.0, 0.5, 0.3)\\n color_transfor.AddRGBPoint(1150, 1.0, 1.0, 0.9)\\n\\n # The opacity transfer function is used to control the opacity\\n # of different tissue types.\\n opacity_scalar = vtk.vtkPiecewiseFunction()\\n opacity_scalar.AddPoint(0, 0.00)\\n opacity_scalar.AddPoint(500, 0.15)\\n opacity_scalar.AddPoint(1000, 0.15)\\n opacity_scalar.AddPoint(1150, 0.85)\\n\\n # The gradient opacity function is used to decrease the opacity\\n # in the \\\"flat\\\" regions of the volume while maintaining the opacity\\n # at the boundaries between tissue types. The gradient is measured\\n # as the amount by which the intensity changes over unit distance.\\n # For most medical data, the unit distance is 1mm.\\n opacity_gradient = vtk.vtkPiecewiseFunction()\\n opacity_gradient.AddPoint(0, 0.0)\\n opacity_gradient.AddPoint(90, 0.5)\\n opacity_gradient.AddPoint(100, 1.0)\\n\\n return color_transfor, opacity_scalar, opacity_gradient\",\n \"def input_fn():\\n files = tf.data.Dataset.list_files(os.path.join(\\n tft_working_dir, filebase + '*'))\\n dataset = files.interleave(\\n tf.data.TFRecordDataset, cycle_length=4, block_length=16)\\n dataset = dataset.map(parser)\\n\\n if shuffle:\\n dataset = dataset.shuffle(buffer_size)\\n\\n dataset = dataset.repeat(num_epochs)\\n dataset = dataset.batch(batch_size)\\n\\n dataset = dataset.prefetch(prefetch_buffer_size)\\n iterator = dataset.make_one_shot_iterator()\\n transformed_features, transformed_labels = iterator.get_next()\\n\\n return transformed_features, transformed_labels\",\n \"def process_data(cur, conn, filepath: str, func: Callable) -> None:\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, \\\"*.json\\\"))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print(\\\"{} files found in {}\\\".format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print(\\\"{}/{} files processed.\\\".format(i, num_files))\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def list_functions(filename, output_file):\\n file_pointer = open(filename)\\n file_split = filename.replace(\\\"/\\\",\\\".\\\")\\n file_split = file_split.split(\\\".\\\")\\n\\n file_text = file_pointer.read()\\n my_re = re.compile(\\\"\\\\ndef ([a-z][^\\\\(]*)\\\")\\n functions = my_re.findall(file_text)\\n functions.sort()\\n first = True\\n\\n cr_re = re.compile(r\\\"\\\\n *\\\")\\n for function in functions:\\n function = cr_re.sub(\\\" \\\", function)\\n if first:\\n first = False\\n output_file.write(\\\"Functions\\\\n\\\")\\n output_file.write(\\\"^^^^^^^^^\\\\n\\\")\\n output_file.write(\\\"- \\\")\\n module = file_split[4]\\n output_file.write(f\\\":func:`~arcade.{module}.{function}`\\\")\\n output_file.write(\\\"\\\\n\\\")\\n if not first:\\n output_file.write(\\\"\\\\n\\\")\",\n \"def rasterize(\\n ctx,\\n files,\\n output,\\n driver,\\n like,\\n bounds,\\n dimensions,\\n res,\\n src_crs,\\n all_touched,\\n default_value,\\n fill,\\n prop,\\n force_overwrite,\\n creation_options):\\n from rasterio.crs import CRS\\n from rasterio.features import rasterize\\n from rasterio.features import bounds as calculate_bounds\\n\\n verbosity = (ctx.obj and ctx.obj.get('verbosity')) or 1\\n\\n output, files = resolve_inout(\\n files=files, output=output, force_overwrite=force_overwrite)\\n\\n bad_param = click.BadParameter('invalid CRS. Must be an EPSG code.',\\n ctx, param=src_crs, param_hint='--src_crs')\\n has_src_crs = src_crs is not None\\n try:\\n src_crs = CRS.from_string(src_crs) if has_src_crs else CRS.from_string('EPSG:4326')\\n except CRSError:\\n raise bad_param\\n\\n # If values are actually meant to be integers, we need to cast them\\n # as such or rasterize creates floating point outputs\\n if default_value == int(default_value):\\n default_value = int(default_value)\\n if fill == int(fill):\\n fill = int(fill)\\n\\n with rasterio.Env(CPL_DEBUG=verbosity > 2):\\n\\n def feature_value(feature):\\n if prop and 'properties' in feature:\\n return feature['properties'].get(prop, default_value)\\n return default_value\\n\\n with click.open_file(files.pop(0) if files else '-') as gj_f:\\n geojson = json.loads(gj_f.read())\\n if 'features' in geojson:\\n geometries = []\\n for f in geojson['features']:\\n geometries.append((f['geometry'], feature_value(f)))\\n elif 'geometry' in geojson:\\n geometries = ((geojson['geometry'], feature_value(geojson)), )\\n else:\\n raise click.BadParameter('Invalid GeoJSON', param=input,\\n param_hint='input')\\n\\n geojson_bounds = geojson.get('bbox', calculate_bounds(geojson))\\n\\n if os.path.exists(output):\\n with rasterio.open(output, 'r+') as out:\\n if has_src_crs and src_crs != out.crs:\\n raise click.BadParameter('GeoJSON does not match crs of '\\n 'existing output raster',\\n param='input', param_hint='input')\\n\\n if disjoint_bounds(geojson_bounds, out.bounds):\\n click.echo(\\\"GeoJSON outside bounds of existing output \\\"\\n \\\"raster. Are they in different coordinate \\\"\\n \\\"reference systems?\\\",\\n err=True)\\n\\n meta = out.meta.copy()\\n\\n result = rasterize(\\n geometries,\\n out_shape=(meta['height'], meta['width']),\\n transform=meta.get('affine', meta['transform']),\\n all_touched=all_touched,\\n dtype=meta.get('dtype', None),\\n default_value=default_value,\\n fill=fill)\\n\\n for bidx in range(1, meta['count'] + 1):\\n data = out.read(bidx, masked=True)\\n # Burn in any non-fill pixels, and update mask accordingly\\n ne = result != fill\\n data[ne] = result[ne]\\n data.mask[ne] = False\\n out.write(data, indexes=bidx)\\n\\n else:\\n if like is not None:\\n template_ds = rasterio.open(like)\\n\\n if has_src_crs and src_crs != template_ds.crs:\\n raise click.BadParameter('GeoJSON does not match crs of '\\n '--like raster',\\n param='input', param_hint='input')\\n\\n if disjoint_bounds(geojson_bounds, template_ds.bounds):\\n click.echo(\\\"GeoJSON outside bounds of --like raster. \\\"\\n \\\"Are they in different coordinate reference \\\"\\n \\\"systems?\\\",\\n err=True)\\n\\n kwargs = template_ds.meta.copy()\\n kwargs['count'] = 1\\n\\n # DEPRECATED\\n # upgrade transform to affine object or we may get an invalid\\n # transform set on output\\n kwargs['transform'] = template_ds.affine\\n\\n template_ds.close()\\n\\n else:\\n bounds = bounds or geojson_bounds\\n\\n if src_crs.is_geographic:\\n if (bounds[0] < -180 or bounds[2] > 180 or\\n bounds[1] < -80 or bounds[3] > 80):\\n raise click.BadParameter(\\n \\\"Bounds are beyond the valid extent for \\\"\\n \\\"EPSG:4326.\\\",\\n ctx, param=bounds, param_hint='--bounds')\\n\\n if dimensions:\\n width, height = dimensions\\n res = (\\n (bounds[2] - bounds[0]) / float(width),\\n (bounds[3] - bounds[1]) / float(height)\\n )\\n\\n else:\\n if not res:\\n raise click.BadParameter(\\n 'pixel dimensions are required',\\n ctx, param=res, param_hint='--res')\\n\\n elif len(res) == 1:\\n res = (res[0], res[0])\\n\\n width = max(int(ceil((bounds[2] - bounds[0]) /\\n float(res[0]))), 1)\\n height = max(int(ceil((bounds[3] - bounds[1]) /\\n float(res[1]))), 1)\\n\\n kwargs = {\\n 'count': 1,\\n 'crs': src_crs,\\n 'width': width,\\n 'height': height,\\n 'transform': Affine(res[0], 0, bounds[0], 0, -res[1],\\n bounds[3]),\\n 'driver': driver\\n }\\n kwargs.update(**creation_options)\\n\\n result = rasterize(\\n geometries,\\n out_shape=(kwargs['height'], kwargs['width']),\\n transform=kwargs.get('affine', kwargs['transform']),\\n all_touched=all_touched,\\n dtype=kwargs.get('dtype', None),\\n default_value=default_value,\\n fill=fill)\\n\\n if 'dtype' not in kwargs:\\n kwargs['dtype'] = result.dtype\\n\\n kwargs['nodata'] = fill\\n\\n with rasterio.open(output, 'w', **kwargs) as out:\\n out.write(result, indexes=1)\",\n \"def vtp(self, f_vtu, f_vtp):\\r\\n reader = vtk.vtkXMLUnstructuredGridReader()\\r\\n reader.SetFileName(f_vtu)\\r\\n reader.Update()\\r\\n ugrid = reader.GetOutput()\\r\\n geometryFilter = vtk.vtkGeometryFilter()\\r\\n geometryFilter.SetInputData(ugrid)\\r\\n geometryFilter.Update()\\r\\n polydata = geometryFilter.GetOutput()\\r\\n writer =vtk.vtkXMLPolyDataWriter()\\r\\n writer.SetFileName(f_vtp)\\r\\n writer.SetInputData(polydata)\\r\\n writer.Write()\\r\\n print(\\\"vtp file created.\\\")\",\n \"def func(self, name, vecs):\\n arity = len(vecs)\\n funcobj = self.get_ground_vector('!Func:{}:{}'.format(arity, name))\\n\\n argobjs = [\\n associate_comp(\\n self.get_ground_vector('!Arg:{}:{}:{}'.format(arity, name, i)),\\n vec)\\n for i, vec in enumerate(vecs) ]\\n\\n result = normalize_comp(\\n self.func_weights @\\n torch.cat([\\n funcobj,\\n merge(argobjs),\\n merge(vecs),\\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\\n\\n return result\",\n \"def func(self, name, vecs):\\n arity = len(vecs)\\n funcobj = self.get_ground_vector('!Func:{}:{}'.format(arity, name))\\n\\n argobjs = [\\n associate_comp(\\n self.get_ground_vector('!Arg:{}:{}:{}'.format(arity, name, i)),\\n vec)\\n for i, vec in enumerate(vecs) ]\\n\\n result = normalize_comp(\\n self.func_weights @\\n torch.cat([\\n funcobj,\\n merge(argobjs),\\n merge(vecs),\\n ]).reshape(-1, 2 * self.hrr_size)).reshape(2, self.hrr_size)\\n\\n return result\",\n \"def map(self, function):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5867644","0.5854852","0.55360836","0.52705306","0.52492493","0.52203393","0.50498486","0.50492585","0.49810517","0.49677193","0.4958387","0.49372962","0.4903304","0.4866099","0.4855312","0.4854982","0.4847482","0.48274714","0.48194093","0.48180053","0.48166987","0.48053375","0.47922784","0.47851402","0.47790444","0.4766691","0.47532347","0.47505015","0.47469786","0.4730921","0.47222206","0.47211903","0.47167945","0.47099823","0.47059247","0.47042257","0.46952733","0.46944457","0.46904847","0.46774563","0.46732005","0.46714556","0.46680468","0.46638173","0.46617842","0.46470067","0.46463394","0.46454737","0.4642319","0.46407366","0.46318436","0.46218064","0.46205938","0.4619611","0.46186405","0.4612022","0.4607727","0.45800278","0.45761594","0.457386","0.45732576","0.4561411","0.45595834","0.45570475","0.45559648","0.4553753","0.45448166","0.45423642","0.45397243","0.4535832","0.45291469","0.4528213","0.45054793","0.44969362","0.44949448","0.44895923","0.44854906","0.44788384","0.44703394","0.4469909","0.44595578","0.44576818","0.44538018","0.4452553","0.4450711","0.4447997","0.4446187","0.4445656","0.44442564","0.44420236","0.44394374","0.44335666","0.4427762","0.4427762","0.44242752","0.44210765","0.44186485","0.4418442","0.4418442","0.44167835"],"string":"[\n \"0.5867644\",\n \"0.5854852\",\n \"0.55360836\",\n \"0.52705306\",\n \"0.52492493\",\n \"0.52203393\",\n \"0.50498486\",\n \"0.50492585\",\n \"0.49810517\",\n \"0.49677193\",\n \"0.4958387\",\n \"0.49372962\",\n \"0.4903304\",\n \"0.4866099\",\n \"0.4855312\",\n \"0.4854982\",\n \"0.4847482\",\n \"0.48274714\",\n \"0.48194093\",\n \"0.48180053\",\n \"0.48166987\",\n \"0.48053375\",\n \"0.47922784\",\n \"0.47851402\",\n \"0.47790444\",\n \"0.4766691\",\n \"0.47532347\",\n \"0.47505015\",\n \"0.47469786\",\n \"0.4730921\",\n \"0.47222206\",\n \"0.47211903\",\n \"0.47167945\",\n \"0.47099823\",\n \"0.47059247\",\n \"0.47042257\",\n \"0.46952733\",\n \"0.46944457\",\n \"0.46904847\",\n \"0.46774563\",\n \"0.46732005\",\n \"0.46714556\",\n \"0.46680468\",\n \"0.46638173\",\n \"0.46617842\",\n \"0.46470067\",\n \"0.46463394\",\n \"0.46454737\",\n \"0.4642319\",\n \"0.46407366\",\n \"0.46318436\",\n \"0.46218064\",\n \"0.46205938\",\n \"0.4619611\",\n \"0.46186405\",\n \"0.4612022\",\n \"0.4607727\",\n \"0.45800278\",\n \"0.45761594\",\n \"0.457386\",\n \"0.45732576\",\n \"0.4561411\",\n \"0.45595834\",\n \"0.45570475\",\n \"0.45559648\",\n \"0.4553753\",\n \"0.45448166\",\n \"0.45423642\",\n \"0.45397243\",\n \"0.4535832\",\n \"0.45291469\",\n \"0.4528213\",\n \"0.45054793\",\n \"0.44969362\",\n \"0.44949448\",\n \"0.44895923\",\n \"0.44854906\",\n \"0.44788384\",\n \"0.44703394\",\n \"0.4469909\",\n \"0.44595578\",\n \"0.44576818\",\n \"0.44538018\",\n \"0.4452553\",\n \"0.4450711\",\n \"0.4447997\",\n \"0.4446187\",\n \"0.4445656\",\n \"0.44442564\",\n \"0.44420236\",\n \"0.44394374\",\n \"0.44335666\",\n \"0.4427762\",\n \"0.4427762\",\n \"0.44242752\",\n \"0.44210765\",\n \"0.44186485\",\n \"0.4418442\",\n \"0.4418442\",\n \"0.44167835\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7520964"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95685,"cells":{"query":{"kind":"string","value":"Takes an inputfile (probably a VRT) and generates a singleband VRT."},"document":{"kind":"string","value":"def extract_color_band(inputfile, band):\n dataset = Dataset(inputfile)\n if not 1 <= band <= dataset.RasterCount:\n raise ValueError(\n \"band must be between 1 and {0}\".format(dataset.RasterCount)\n )\n\n command = [\n GDALTRANSLATE,\n '-q', # Quiet\n '-of', 'VRT', # Output to VRT\n '-b', band, # Single band\n inputfile,\n '/vsistdout'\n ]\n try:\n return VRT(check_output_gdal([str(e) for e in command]))\n except CalledGdalError as e:\n if e.error == (\"ERROR 6: Read or update mode not supported on /vsistdout\"):\n # HACK: WTF?!?\n return VRT(e.output)\n raise"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\n\n options = gdal.BuildVRTOptions(srcNodata=0)\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\n add_pixel_fn(vrt, resample_name)","def run_vorpaline(self, input_file, *options):\n\n self._input_file = input_file\n args = list(options) + [self._input_file, 'out.meshb']\n self._run_command(\"vorpalite\", args)","def convert_vrt(fname, out_fname, dataset_name='dataset',\n compression=H5CompressionFilter.LZF, filter_opts=None,\n attrs=None):\n with h5py.File(out_fname) as fid:\n with rasterio.open(fname) as rds:\n # set default chunks and set dimensions\n if rds.count == 3:\n chunks = (3, 256, 256)\n dims = (3, rds.height, rds.width)\n else:\n chunks = (256, 256)\n dims = (rds.height, rds.width)\n\n # create empty or copy the user supplied filter options\n if not filter_opts:\n filter_opts = dict()\n filter_opts['chunks'] = chunks\n else:\n filter_opts = filter_opts.copy()\n\n\n if 'chunks' not in filter_opts:\n filter_opts['chunks'] = chunks\n\n # modify to have 3D chunks if we have a multiband vrt\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\n # copy the users original 2D chunk and insert the third\n chunks = list(filter_opts['chunks'])\n chunks.insert(0, 3)\n filter_opts['chunks'] = chunks\n\n # dataset attributes\n if attrs:\n attrs = attrs.copy()\n else:\n attrs = {}\n\n attrs['geotransform'] = rds.transform.to_gdal()\n attrs['crs_wkt'] = rds.crs.wkt\n\n # dataset creation options\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\n kwargs['shape'] = dims\n kwargs['dtype'] = rds.dtypes[0]\n\n dataset = fid.create_dataset(dataset_name, **kwargs)\n attach_image_attributes(dataset, attrs)\n\n # tiled processing (all cols by chunked rows)\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\n\n for tile in tiles:\n # numpy index\n if rds.count == 3:\n idx = (\n slice(None),\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n else:\n idx = (\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n\n # ensure single band rds is read as 2D not 3D\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\n\n # write\n dataset[idx] = data","def create_video(input_file, output_file):\n input_video = VideoFileClip(input_file)\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\n output_video.write_videofile(output_file, audio=False)","def readFT(self,file=\"out__1.ft\"):","def _write_raw_brainvision(raw, bids_fname, events, overwrite):\n if not check_version(\"pybv\", PYBV_VERSION): # pragma: no cover\n raise ImportError(\n f\"pybv >= {PYBV_VERSION} is required for converting\"\n \" file to BrainVision format\"\n )\n from pybv import write_brainvision\n\n # Subtract raw.first_samp because brainvision marks events starting from\n # the first available data point and ignores the raw.first_samp\n if events is not None:\n events[:, 0] -= raw.first_samp\n events = events[:, [0, 2]] # reorder for pybv required order\n meas_date = raw.info[\"meas_date\"]\n if meas_date is not None:\n meas_date = _stamp_to_dt(meas_date)\n\n # pybv needs to know the units of the data for appropriate scaling\n # get voltage units as micro-volts and all other units \"as is\"\n unit = []\n for chs in raw.info[\"chs\"]:\n if chs[\"unit\"] == FIFF.FIFF_UNIT_V:\n unit.append(\"µV\")\n else:\n unit.append(_unit2human.get(chs[\"unit\"], \"n/a\"))\n unit = [u if u not in [\"NA\"] else \"n/a\" for u in unit]\n\n # We enforce conversion to float32 format\n # XXX: pybv can also write to int16, to do that, we need to get\n # original units of data prior to conversion, and add an optimization\n # function to pybv that maximizes the resolution parameter while\n # ensuring that int16 can represent the data in original units.\n if raw.orig_format != \"single\":\n warn(\n f'Encountered data in \"{raw.orig_format}\" format. '\n \"Converting to float32.\",\n RuntimeWarning,\n )\n\n # Writing to float32 µV with 0.1 resolution are the pybv defaults,\n # which guarantees accurate roundtrip for values >= 1e-7 µV\n fmt = \"binary_float32\"\n resolution = 1e-1\n write_brainvision(\n data=raw.get_data(),\n sfreq=raw.info[\"sfreq\"],\n ch_names=raw.ch_names,\n ref_ch_names=None,\n fname_base=op.splitext(op.basename(bids_fname))[0],\n folder_out=op.dirname(bids_fname),\n overwrite=overwrite,\n events=events,\n resolution=resolution,\n unit=unit,\n fmt=fmt,\n meas_date=None,\n )","def simulate_UVSPEC(file, config):\n\n wavelength = config['wavelength']\n\n # Coordenates from position of the station Hannover\n latitude = 52.39 # positive in the northern hemisphere\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\n\n # Read name of the file (correct time)\n name = os.path.split(file)\n time_n = name[1][0:15]\n print(\"Time name\", time_n)\n # convert time to datetime format\n time = datetime.datetime.strptime(time_n,\n '%Y%m%d_%H%M%S')\n # Calculate the azimuth and zenith angles in function of the date\n elev = ps.GetAltitude(latitude, longitud, time)\n azi = ps.GetAzimuth(latitude, longitud, time)\n zenith = 90 - elev\n\n # Correction between the sign and real azimuth for plot of radiance\n if -180 <= azi < 0:\n azi = 180 - azi\n elif -360 <= azi < -180:\n azi = -azi - 180\n else:\n pass\n\n print(\"Azimuth: {:5.1f}\".format(azi),\n \"\\nZenith: {:5.1f}\".format(zenith))\n\n # Change the value of zenith and azimuth angles in function of the time and\n # position in the UVSPEC file\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\n data = file.readlines()\n\n data[14] = \"day_of_year \" + str(time.timetuple().tm_yday) + \" \" + \"\\n\"\n data[15] = \"wavelength \" + str(\"{}\".format(wavelength)) + \" \" + \\\n str(\"{}\".format(wavelength)) + \\\n \" # wavelength to calcule [nm] \\n\"\n data[17] = \"sza \" + str(\"{:2.3f}\".format(zenith)) + \\\n \" # Solar zenith angle \\n\"\n data[18] = \"phi0 \" + str(\"{:2.3f}\".format(azi)) + \\\n \" #Azimuth angle with zenith position \\n\"\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\n file.writelines(data)\n\n # Create the directory to save the results\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\n wavelength)),\n exist_ok=True)\n\n # Run the program UVSPEC in the terminal\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\n '.txt')","def make_big_vdwradii( targetpath ):\n\n file = open( os.path.join( targetpath, 'vdwradii.dat'), 'w')\n text=\"\"\"; Very approximate VanderWaals radii\n; only used for drawing atoms as balls or for calculating atomic overlap.\n; longest matches are used\n; '???' or '*' matches any residue name\n; 'AAA' matches any protein residue name\n; MODIFIED TO USE BIG VDW RADII TO PREVENT WATERS BEING PUT IN THE PROTEIN WHERE WE DON'T WANT THEM. DLM\n??? C 0.3\n??? F 0.3\n??? H 0.3\n??? N 0.3\n??? O 0.3\n??? P 0.3\n??? S 0.3\n??? LP1 0\n??? LP2 0\nSOL H 0.04\nSOL O 0.105\nWAT H 0.04\nWAT O 0.105\nGLY MN1 0\nGLY MN2 0\nALA MCB1 0\nALA MCB2 0 \nVAL MCG1 0 \nVAL MCG2 0 \nILE MCG1 0 \nILE MCG2 0 \nILE MCD1 0 \nILE MCD2 0 \nLEU MCD1 0 \nLEU MCD2 0 \nMET MCE1 0 \nMET MCE2 0 \nTRP MTRP1 0 \nTRP MTRP2 0\nTHR MCG1 0\nTHR MCG2 0\nLYSH MNZ1 0 \nLYSH MNZ2 0 \n\"\"\" \n file.writelines(text)\n file.close()","def make_vrt(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n txt_file = i.joinpath('subnational/tiffs.txt')\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\n if not outfile.exists():\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\n subprocess.call(gdal_cmd, shell=True)","def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\n from gdal import Translate\n\n if out_fname is None:\n out_fname = in_fname + \".vrt\"\n\n # Using Translate... but would use Warp if every reprojecting\n if bbox:\n left, bottom, right, top = bbox\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\n else:\n projwin = None\n if verbose:\n logger.info(f\"Creating {out_fname}, subset bbox: {bbox}\")\n Translate(out_fname, in_fname, projWin=projwin)","def genChirpVC(Vclamp=-70,Rm=100,amp=10,graphToo=False):\n xs,ys,zi=genSine()\n ys=ys*amp+Vclamp\n genATF(xs,ys,'stimulus-VC.atf')\n graphData(xs,ys,zi,\"Voltage Clamp Stimulus\",'stimulus-VC.png')","def subset_multiband_vrt(src: Union[str, Path], band_request: Sequence = []):\n if not isinstance(src, (str, Path)) and not Path(src).is_file():\n raise ValueError(f\"Invalid source multiband raster.\\n\"\n f\"Got {src}\")\n with rasterio.open(src) as ras, MemoryFile() as mem:\n riocopy(ras, mem.name, driver='VRT')\n vrt_xml = mem.read().decode('utf-8')\n vrt_dataset = ET.fromstring(vrt_xml)\n vrt_dataset_dict = {int(band.get('band')): band for band in vrt_dataset.iter(\"VRTRasterBand\")}\n for band in vrt_dataset_dict.values():\n vrt_dataset.remove(band)\n\n for dest_band_idx, src_band_idx in enumerate(band_request, start=1):\n vrt_band = vrt_dataset_dict[src_band_idx]\n vrt_band.set('band', str(dest_band_idx))\n vrt_dataset.append(vrt_band)\n\n return ET.tostring(vrt_dataset).decode('UTF-8')","def stack_singlebands_vrt(srcs: List, band: int = 1):\n vrt_bands = []\n for srcnum, src in enumerate(srcs, start=1):\n with check_rasterio_im_load(src) as ras, MemoryFile() as mem:\n riocopy(ras, mem.name, driver='VRT')\n vrt_xml = mem.read().decode('utf-8')\n vrt_dataset = ET.fromstring(vrt_xml)\n for bandnum, vrt_band in enumerate(vrt_dataset.iter('VRTRasterBand'), start=1):\n if bandnum == band:\n vrt_band.set('band', str(srcnum))\n vrt_bands.append(vrt_band)\n vrt_dataset.remove(vrt_band)\n for vrt_band in vrt_bands:\n vrt_dataset.append(vrt_band)\n\n return ET.tostring(vrt_dataset).decode('UTF-8')","def vtt_to_srt(str_name_file: str):\n file_contents: str = read_text_file(str_name_file)\n str_data: str = \"\"\n str_data = str_data + convert_content(file_contents)\n str_name_file: str = str_name_file.replace(\".vtt\", \".srt\")\n print(str_name_file)\n file_create(str_name_file, str_data)","def change_input(filename, frac_l=0.019994, force=25.0, time=200_000, seed=None):\r\n\r\n frac_sl = round(1.5 /(32**3 *3), 6)\r\n frac_l = round(frac_l -frac_sl, 6)\r\n frac_w = round(1 -frac_l -frac_sl, 6)\r\n \r\n params = {'Box': \"32 32 32\\t1 1 1\", 'RNGSeed': seed if seed is not None else -4073, 'Step': 0.02, 'Time': time, \r\n 'SamplePeriod': 100, 'AnalysisPeriod': 1000, 'DensityPeriod': time, 'DisplayPeriod': time //10, 'RestartPeriod': time,\r\n }\r\n\r\n with open(filename, 'rt') as rf:\r\n with open(filename+'_sim', 'wt') as wf:\r\n\r\n for line in rf:\r\n \r\n if line.startswith('Polymer\\tWater') or line.startswith('Polymer Water'):\r\n line = line.strip().split()\r\n line[2] = f\"{frac_w:.6f}\"\r\n \r\n # Converts list to list[str]\r\n line = list(map(str, line))\r\n wf.write('\\t'.join(line) + '\\n')\r\n\r\n elif line.startswith('Polymer\\tLipid') or line.startswith('Polymer Lipid'):\r\n line = line.strip().split()\r\n line[2] = f\"{frac_l:.6f}\"\r\n\r\n # Converts list to list[str]\r\n line = list(map(str, line))\r\n wf.write('\\t'.join(line) + '\\n')\r\n \r\n elif line.startswith('Polymer\\tSingleLipid') or line.startswith('Polymer SingleLipid'):\r\n line = line.strip().split()\r\n line[2] = f\"{frac_sl:.6f}\"\r\n\r\n # Converts list to list[str]\r\n line = list(map(str, line))\r\n wf.write('\\t'.join(line) + '\\n')\r\n\r\n elif line.startswith('Command\\tConstantForceOnTarget') or line.startswith('Command ConstantForceOnTarget'):\r\n line = line.strip().split()\r\n\r\n if line[3] == \"singleLipidHead\":\r\n line[-1] = f\"{force /3:.6f}\" \r\n\r\n elif line[3] == \"lowerHeads\":\r\n line[-1] = f\"{force /3 /(1636):.6f}\"\r\n\r\n # Converts list to list[str]\r\n line = list(map(str, line))\r\n wf.write('\\t'.join(line) + '\\n')\r\n \r\n # if line.startswith('\tTimes\t0 1000'):\r\n # line = line.strip().split()\r\n # line[2] = f\"{time}\"\r\n \r\n # # Converts list to list[str]\r\n # line = list(map(str, line))\r\n # wf.write('\\t'.join(line) + '\\n') \r\n # line = next(rf) \r\n \r\n # if line.strip().split() and line.strip().split()[0] in params.keys():\r\n # key = line.strip().split()[0]\r\n # wf.write(f\"{key:<12}\\t{str(params[key])}\\n\")\r\n\r\n else:\r\n wf.write(line)","def __init__(self, filename):\r\n self.__output__ = open(format(filename, '08X') + '.gen', 'wb')","def write_input_file(y,z,fname):\n file = open('c:/4nec2/out/' + fname + '.nec', 'w')\n file.write('CM Seeddesign \\n')\n file.write('CM Zigzag Antenna \\n')\n file.write('CE File generated by python \\n')\n seg = 1\n\n #write the antenna\n for i in range(0,len(y)-1):\n file.write('GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\\n' % (1,seg,0,y[i],z[i],0,y[i+1],z[i+1],1))\n\n file.write('GE 0 \\n')\n file.write('EK \\n')\n file.write('EX %3i %3i %3i %3i %3i %3i %3i\\n' % (0,1,1,1,1,0,0))\n file.write('GN -1 \\n')\n \n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\n' % (0,1,0,0,900,0))\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\n' % (0,11,0,0,850,10))\n\n file.write('LD %3i %3i %3i %3i %8.4f %8.4f\\n' % (5,1,0,0,58000000,2))\n file.write('RP %3i %3i %3i %3i %8.4f %8.4f %8.4f %8.4f\\n' % (0,1,1,1000,90,0,0,0))\n\n file.write('EN \\n')\n file.close()","def get_rad_file(self, input_file, out_dir, overwrite_rad):\n # name of the rad file - replace psv with rad (or PSV with RAD)\n self.rad_file = self.get_rad_filename(input_file)\n\n if \"rad\" in self.rad_file.lower() and self.rad_file.lower().endswith(\".tab\"):\n if self.rad_file == input_file:\n return True\n if os.path.isfile(self.rad_file) and not overwrite_rad:\n # valid rad file already exists, just return\n return True\n\n # input is psv and rad file does not yet exist - let's create it first.\n # create rad file and change path to where it will end up in out_dir\n if out_dir is not None:\n (path, filename) = os.path.split(self.rad_file)\n self.rad_file = os.path.join(out_dir, filename)\n else:\n (out_dir, filename) = os.path.split(input_file)\n radiance_cal = RadianceCalibration(self.logfile, self.main_app)\n return radiance_cal.calibrate_to_radiance(InputType.FILE, input_file, out_dir, overwrite_rad)","def which_band_is_file(filename):\n if not is_galex_file(filename):\n return None\n return \"fuv\" if \"-fd-\" in filename else \"nuv\" if \"-nd-\" in filename \\\n else \"unknown\"","def outputSingleFrame(self, frame=None):\n if frame is None:\n frame = 1\n\n self.loadFringe(frame=frame)\n\n outputName = self.inputFilenames['ofd'][:-4] + '_single_f' + str(frame) + '.ofd'\n\n with open(outputName, 'wb') as f:\n self.rawBScan.astype('uint16').tofile(f)","def merge(files: List[str], output_file: str, resample: str = \"average\") -> None:\n\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\n\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\n\n if os.path.isfile(constants.TEMP_VRT_FILE):\n os.remove(constants.TEMP_VRT_FILE)","def buildvrt(indir, outdir):\n indir = Path(indir)\n outdir = Path(outdir)\n\n # loop over each day directory\n for day in indir.iterdir():\n # expecting 20 subdatasets in each hdf4 file (hopefully the order gdal lists them in is consistent)\n subdataset_fnames = {i: [] for i in range(20)}\n\n # mosaic each MODIS tile for the current day directory\n for h4_fname in day.rglob('*.hdf'):\n with rasterio.open(str(h4_fname.absolute())) as h4_ds:\n\n # each subdataset will form a separate mosaic\n for i, sds_name in enumerate(h4_ds.subdatasets):\n subdataset_fnames[i].append(sds_name)\n\n # loop over each subdataset and mosaic from all supporting MODIS tiles\n for _, file_list in subdataset_fnames.items():\n\n # temp file for the input file list\n with tempfile.NamedTemporaryFile('w') as tmpf:\n tmpf.writelines(\"\\n\".join(file_list))\n tmpf.flush()\n\n # mimic the 'day' directory partition\n base_name = Path(file_list[0].replace(':', '/')).name\n out_fname = outdir.joinpath(day.name, '{}.vrt'.format(base_name))\n\n if not out_fname.parent.exists():\n out_fname.parent.mkdir(parents=True)\n\n # buildvrt\n cmd = [\n 'gdalbuildvrt',\n '-input_file_list',\n tmpf.name,\n str(out_fname)\n ]\n\n check_call(cmd)","def nirspec_spectrum1d_reader(file_name):\n\n hdulist = fits.open(file_name)\n\n # make wavelength a seperate component in addition to coordinate\n # so you can plot it on the x axis\n wavelength = np.linspace(hdulist['DATA'].header['CRVAL1'],\n hdulist['DATA'].header['CRVAL1']*hdulist['DATA'].header['CDELT1'],\n hdulist['DATA'].header['NAXIS1'])[::-1]\n\n data = Data(label='1D Spectrum')\n data.header = hdulist['DATA'].header\n data.add_component(wavelength, 'Wavelength')\n data.add_component(hdulist['DATA'].data, 'Flux')\n data.add_component(np.sqrt(hdulist['VAR'].data), 'Uncertainty')\n\n return data","def make_spectra(directory,frame):\n oober = st.short_oober(directory, frame=frame)\n #st.MakeVelocitySpectra(oober,frame)\n #st.MakeAccelSpectra(oober,frame)\n #st.MakeMagneticSpectra(oober,frame)\n st.MakeDensitySpectra(oober,frame)","def open_raw(path):\r\n raw_file_reader = RawFileReader.RawFileReaderAdapter.FileFactory(path)\r\n raw_file_reader.SelectInstrument(Business.Device.UV, 1)\r\n return raw_file_reader","def make_spec(fi, bw, n, rms, v0, transitions, bandpass, baseline, order,\n Te, ne, Tr, W, dD, EM, cont, Tc, nu, plot, plot_out,\n n_max=1500, verbose=False):\n \n ff = fi + bw\n df = bw/n\n \n # Make the arrays to store the spectrum\n freq = np.arange(fi, ff, df)\n tau = np.zeros(n)\n \n # Create Gaussian noise and add it to the spectrum.\n noise = synth.make_noise(0, rms, n)\n tau_n = tau + noise\n \n # Create a baseline that mimics a standing wave \n # and add it to the spectrum.\n # This values produce a nice variation.\n if bandpass:\n tau_n += synth.make_ripples(4*n, n/2., n, rms)\n \n if baseline:\n tau_n += synth.make_offset(rms, freq, order=order)\n \n z = v0/3e5\n print \"Will use a redshift of: {0}\".format(z)\n print \"Subband edges: {0}--{1}\".format(fi, ff)\n \n for i,trans in enumerate(transitions.split(',')):\n if trans != '':\n n_l, f_l = crrls.find_lines_sb(freq, trans, z, verbose)\n \n # Generate the line properties as a function of n\n dL_r = crrls.radiation_broad_salgado(n_l, W, Tr)/1e6\n dL_p = crrls.pressure_broad_salgado(n_l, Te, ne)/1e6\n dL = dL_r + dL_p\n \n if Tr != 0:\n other = 'case_diffuse_{0}'.format(rrlmod.val2str(Tr))\n else:\n other = ''\n \n n_itau, a_itau = rrlmod.itau(rrlmod.val2str(Te), \n ne, \n trans, n_max=n_max, \n other=other,\n verbose=True)\n \n dD_f = crrls.dv2df(f_l, dD*1e3)\n \n for j,f in enumerate(f_l):\n itau = a_itau[np.where(n_itau==n_l[j])[0]][0]/1e6\n line = crrls.voigt(freq, dD_f[j]/2., dL[j]/2., f, itau*EM)\n if verbose:\n print \"Line properties:\"\n print(\"f: {0}, A: {1}, dD: {2}, dD_f/2: {3}, \" \\\n \"dL/2: {4}\".format(f, itau*EM, dD, dD_f[j]/2., dL[j]/2.))\n tau_n += line\n \n if cont:\n tau_n = (1 + tau_n)*power_law(freq, Tc, nu)\n \n if plot:\n fig = plt.figure(frameon=False)\n ax = fig.add_subplot(1, 1, 1)\n ax.plot(freq, tau_n, 'k-', label='generated spectrum')\n ax.plot(freq, power_law(freq, Tc, nu), 'r-', label='continuum')\n plt.legend(loc=0, numpoints=1, frameon=False)\n plt.savefig('{0}'.format(plot_out), \n bbox_inches='tight', pad_inches=0.3)\n plt.close()\n \n \n #np.savetxt(spec, np.c_[freq, tau_n])\n \n return freq, tau_n","def get_rad_filename(input_file):\n # replace PSV with RAD\n (path, filename) = os.path.split(input_file)\n rad_filename = filename.replace('psv', 'rad')\n rad_filename = rad_filename.replace('PSV', 'RAD')\n rad_file = os.path.join(path, rad_filename)\n\n # rename to .TAB from .TXT in case of raw input\n rad_file = rad_file.replace('.TXT', '.tab')\n rad_file = rad_file.replace('.txt', '.tab')\n\n return rad_file","def test_simple_single_file():\n\n out_data = run_tvnamer(\n with_files = ['S01E02 - Some File.avi'],\n with_flags = [\"--batch\"])\n\n expected_files = ['S01E02 - Some File.avi']\n\n verify_out_data(out_data, expected_files, expected_returncode = 2)","def convert_vtt_to_str(file):\n if \".vtt\" in file:\n vtt_to_srt(file)","def test_RV():\n\n spec = IGRINSSpectrum(file=file)\n\n assert spec.uncertainty is not None\n assert hasattr(spec, \"barycentric_correct\")\n\n correction_velocity = spec.estimate_barycorr()\n\n assert isinstance(spec.RA, astropy.units.quantity.Quantity)\n assert isinstance(spec.DEC, astropy.units.quantity.Quantity)\n assert correction_velocity is not None\n assert isinstance(correction_velocity, astropy.units.quantity.Quantity)\n\n new_spec = spec.barycentric_correct()\n assert new_spec is not None\n assert isinstance(new_spec, Spectrum1D)","def create_training_file(D_RAT):\r\n return create_arff_file(D_RAT, 0)","def createInput(dirPath,gSettings):\n \n with open(os.path.join('../in','input.txt')) as f:\n inpFile = f.readlines()\n \n\n # Model settings\n model = gSettings[\"Model\"]\n inpFile[13] = \"insgrav: {:1d}\\n\".format(int(model[\"NS gravity\"][\"Flag\"]))\n inpFile[14] = \"isun: {:1d}\\n\".format(int(model[\"Lunisolar\"][\"Sun\"]))\n inpFile[15] = \"imoon: {:1d}\\n\".format(int(model[\"Lunisolar\"][\"Moon\"]))\n\n if model[\"Drag\"][\"Flag\"] == False:\n inpFile[16] = \"idrag: 0\\n\"\n else:\n dm = model[\"Drag\"][\"Model\"].lower()\n if dm == \"wertz\":\n idrag = 1\n elif dm == \"us76\":\n idrag = 2\n elif dm == \"j77\":\n idrag = 3\n elif dm == \"msis00\":\n idrag = 4\n else:\n raise ValueError('Value \"' + model[\"Drag\"][\"Model\"] + '\" invalid.')\n inpFile[16] = \"idrag: {:1d}\\n\".format(idrag)\n if model[\"Drag\"][\"Solar flux\"].lower() == \"constant\":\n inpFile[17] = \"iF107: 0\\n\"\n elif model[\"Drag\"][\"Solar flux\"].lower() == \"variable\":\n inpFile[17] = \"iF107: 1\\n\"\n else:\n raise ValueError('Value \"' + model[\"Drag\"][\"Solar flux\"] + '\" invalid.')\n\n if model[\"SRP\"][\"Flag\"] == False:\n inpFile[18] = \"iSRP: {:1d}\\n\".format(int(model[\"SRP\"][\"Flag\"]))\n else:\n inpFile[18] = \"iSRP: {:1d}\\n\".format(int(model[\"SRP\"][\"Flag\"]))\n if model[\"SRP\"][\"Eclipses\"]:\n inpFile[18] = \"iSRP: 2\\n\"\n \n if model[\"Lunisolar\"][\"Ephemerides\"] == \"DE431\":\n inpFile[19] = \"iephem: 1\\n\"\n elif model[\"Lunisolar\"][\"Ephemerides\"] == \"Meeus\":\n inpFile[19] = \"iephem: 2\\n\"\n else:\n raise ValueError('Value \"' + model[\"Lunisolar\"][\"Ephemerides\"] + '\" invalid.')\n \n inpFile[20] = \"gdeg: {:3d}\\n\".format(model[\"NS gravity\"][\"Degree\"])\n if model[\"NS gravity\"][\"Order\"] <= model[\"NS gravity\"][\"Degree\"]:\n inpFile[21] = \"gord: {:3d}\\n\".format(model[\"NS gravity\"][\"Order\"])\n else:\n raise ValueError(\"Order {0:d} of the gravity field is greater than degree {1:d}\".format(model[\"NS gravity\"][\"Order\"],model[\"NS gravity\"][\"Degree\"]))\n \n\n\n # Integration settings\n integ = gSettings[\"Integration\"]\n inpFile[29] = \"tol: {:22.15E}\\n\".format(integ[\"Tolerance\"])\n inpFile[30] = \"tspan: {:22.15E}\\n\".format(integ[\"Duration\"] * 365.25)\n inpFile[31] = \"tstep: {:22.15E}\\n\".format(integ[\"Step\"])\n inpFile[39] = \"eqs: {:2d}\\n\".format(integ[\"Equations\"])\n\n\n\n # Output settings\n inpFile[44] = \"verb: 0\\n\"\n inpFile[45] = \"out: \" + os.path.abspath(os.path.join(dirPath, ' '))\n\n\n with open(os.path.join(dirPath,'input.txt'),'w') as f:\n f.writelines(inpFile)","def get_san(infp, outfp):\n\n return ...","def deimos_spectrum1D_reader(file_name):\n\n hdulist = fits.open(file_name)\n data = Data(label='1D Spectrum')\n data.header = hdulist[1].header\n\n full_wl = np.append(hdulist[1].data['LAMBDA'][0], hdulist[2].data['LAMBDA'][0])\n full_spec = np.append(hdulist[1].data['SPEC'][0], hdulist[2].data['SPEC'][0])\n full_ivar = np.append(hdulist[1].data['IVAR'][0], hdulist[2].data['IVAR'][0])\n\n data.add_component(full_wl, 'Wavelength')\n data.add_component(full_spec, 'Flux')\n data.add_component(1/np.sqrt(full_ivar), 'Uncertainty')\n\n return data","def process_rom_file(name):\n\n # Link the instruction rom file to the tb directory\n SRC_FILE = REPO_ROOT + '/tests/riscv-tests-simple/generated/' + name + \".verilog\"\n ROM_FILE = os.getcwd() + '/verilog.rom' # need to link the instruction ram file the the current directory\n if os.path.isfile(ROM_FILE):\n os.remove(ROM_FILE)\n os.symlink(SRC_FILE, ROM_FILE)\n\n FP = open('verilog.rom', \"r\")\n IRAM_FP = open('instr_ram.rom', \"w\")\n DRAM_FP = open('data_ram.rom', \"w\")\n\n iram = True\n for line in FP.readlines():\n if line.rstrip() == \"@01000000\":\n iram = False\n continue\n if iram:\n IRAM_FP.write(line)\n else:\n DRAM_FP.write(line)\n\n\n FP.close()\n IRAM_FP.close()\n DRAM_FP.close()","def test_process_mono_file(self):\n test_path = pathlib.Path(__file__).parent.absolute() / 'data/mono.wav'\n self.default_kwargs['input_file'] = test_path\n self.default_kwargs['output_file'] = pathlib.Path(self.temp_file.name)\n self.encoder = FileEncoder(**self.default_kwargs)\n self.encoder.process()","def main():\r\n\r\n ### Choose and Import File\r\n\r\n inSound = Sound()\r\n\r\n rate = inSound.rate\r\n data = inSound.data\r\n dataLength = len(data)\r\n \r\n info = inSound.get_info()\r\n head, filename = os.path.split(info[0]) # get filename of input\r\n \r\n # Decide output directory and filename\r\n outDir = r'out'\r\n outFile = os.path.join(outDir, 'out_'+filename)\r\n\r\n # Check if data has multiple channels, if yes use only one\r\n if(len(data.shape) > 1):\r\n data = data[:,0]\r\n\r\n\r\n ### Set All Parameters\r\n\r\n #get parameters from user dialogue\r\n params = getParameters()\r\n\r\n numChannels = params['numChannels'][0] # number of Channels\r\n loFreq = params['loFreq'][0] # lower bound on frequencies\r\n hiFreq = params['hiFreq'][0] # upper bound on frequencies\r\n plotChannels = params['plotChannels'][0] # if it should plot the Gammatone channels\r\n block_time = params['block_time'][0] # in ms\r\n block_shift = params['block_shift'][0] # in ms\r\n selectChannels = params['selectChannels'][0] # number of channels to activate at a single time\r\n\r\n\r\n ### Filter input file\r\n\r\n filtered, channel_fs = filterDataGamaTone(data, rate, numChannels, loFreq, hiFreq, plotChannels)\r\n\r\n\r\n ### Gammatones -> Stimulation Amplitude for time block\r\n\r\n samples_in_block = np.floor(block_time * rate / 1000).astype('int')\r\n samples_in_shift = np.floor(block_shift * rate / 1000).astype('int')\r\n\r\n summed = gammatoneToAmplitude(filtered, samples_in_block, samples_in_shift)\r\n\r\n # only activate the n electrodes that have the largest stimulation\r\n amps = n_largest_channels(summed, n=selectChannels)\r\n\r\n \r\n #### Sound reconstruction\r\n\r\n # for each timeblock we need to duplicate enough samples to fill it at sample rate\r\n amps_samples = np.repeat(amps, samples_in_shift, axis=1)\r\n #trim end to get same length as input\r\n amps_samples = amps_samples[:,:dataLength] \r\n\r\n # from amplitude samples and frequencies, reconstruct sound\r\n res_data = generateSound(amps_samples, channel_fs, rate)\r\n\r\n\r\n ### Write to output file\r\n write(outFile, rate, res_data)\r\n print('Wrote file to: \\n' + outFile)","def generateNHDRHeader(self, inputFile):\r\n\r\n logging.info('Processing started')\r\n #initialize PCR object\r\n imagePCRFile = PCRDataObject()\r\n #import image parameters of PCR object\r\n imagePCRFile.ImportFromFile(inputFile)\r\n\r\n filePathName, fileExtension = os.path.splitext(inputFile)\r\n #The directory of the .nhdr file\r\n nhdrPathName = filePathName + \".nhdr\"\r\n\r\n if fileExtension == \".pcr\":\r\n if imagePCRFile.form == 1 or imagePCRFile.form == 5 or imagePCRFile.form == 10:\r\n with open(nhdrPathName, \"w\") as headerFile:\r\n headerFile.write(\"NRRD0004\\n\")\r\n headerFile.write(\"# Complete NRRD file format specification at:\\n\")\r\n headerFile.write(\"# http://teem.sourceforge.net/nrrd/format.html\\n\")\r\n if imagePCRFile.form == 5:\r\n headerFile.write(\"type: ushort\\n\")\r\n elif imagePCRFile.form == 10:\r\n headerFile.write(\"type: float\\n\")\r\n elif imagePCRFile.form == 1:\r\n headerFile.write(\"type: uchar\\n\")\r\n headerFile.write(\"dimension: 3\\n\")\r\n headerFile.write(\"space: left-posterior-superior\\n\")\r\n sizeX = imagePCRFile.X\r\n sizeY = imagePCRFile.Y\r\n sizeZ = imagePCRFile.Z\r\n headerFile.write(f\"sizes: {sizeX} {sizeY} {sizeZ}\\n\")\r\n volSpace = imagePCRFile.voxelSize\r\n headerFile.write(f\"space directions: ({volSpace}, 0.0, 0.0) (0.0, {volSpace}, 0.0) (0.0, 0.0, {volSpace})\\n\")\r\n headerFile.write(\"kinds: domain domain domain\\n\")\r\n headerFile.write(\"endian: little\\n\")\r\n headerFile.write(\"encoding: raw\\n\")\r\n headerFile.write(\"space origin: (0.0, 0.0, 0.0)\\n\")\r\n volPathName = filePathName + \".vol\"\r\n volPathSplit = []\r\n volPathSplit = volPathName.split('/')\r\n volFileName = volPathSplit[len(volPathSplit)-1]\r\n headerFile.write(f\"data file: {volFileName}\\n\")\r\n # print(imagePCRFile.form)\r\n print(f\".nhdr file path is: {nhdrPathName}\")\r\n #Automatically loading .vol file using the generated .nhdr file.\r\n if os.path.exists(volPathName):\r\n slicer.util.loadVolume(nhdrPathName)\r\n print(f\"{volFileName} loaded\\n\")\r\n else:\r\n print(f\"{volFileName} is not in the same directory\\n\")\r\n else:\r\n print(\"The format of this dataset is currently not supported by this module. Currently only float (format=10), unsigned 16 bit integer (format=5) and unsigned 8 bit integer (format=1) data types are supported. Please contact us with this dataset to enable this data type.\")\r\n else:\r\n print(\"This is not a PCR file, please re-select a PCR file\")","def processfile(file, out, me):\n if out.endswith('.npz'):\n out = out[:-4]\n out = out + ('-%03d' % me) + '.npz'\n if os.path.exists(out):\n return\n f = QPM.QPMSubsampleFile(file)\n xis = []\n mybf = QPM.LogWindowBiasFunction(edges[me], edges[me + 1])\n mymock = QPM.halomock(f, 1e6, mybf, continuous=False)\n for i in range(me + 1):\n bf = QPM.LogWindowBiasFunction(edges[i], edges[i + 1])\n mock = QPM.halomock(f, 1e6, bf, continuous=False)\n # the error is nonsense since we have just one sample\n # deal with it in combine\n xi = QPM.xi([mock], mocks2=[mymock])\n r = xi[0]\n xis.append(xi[1])\n print edges[i], edges[i + 1], len(mock[0]), mock[1].sum()\n numpy.savez(out, r=r, xi=xis, edges=edges, N=1e6, me=me)","def readDriverFile(self, input_file):\n\n\n fid = open(self.basePath + input_file,'r')\n\n # Line 1\n line = fid.readline()\n l_input = line.split('!')\n mshfile = l_input[0].rstrip()\n\n # Line 2\n line = fid.readline()\n l_input = line.split('!')\n obsfile = l_input[0].rstrip()\n\n # Line 3\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='null':\n topofile = []\n\n else:\n topofile = l_input[0].rstrip()\n\n\n # Line 4\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n mstart = float(l_input[1])\n\n else:\n mstart = l_input[0].rstrip()\n\n # Line 5\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n mref = float(l_input[1])\n\n else:\n mref = l_input[0].rstrip()\n\n # Line 6\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n staticInput = float(l_input[1])\n\n elif l_input[0]=='DEFAULT':\n staticInput = None\n\n else:\n staticInput = l_input[0].rstrip()\n\n\n # Line 7\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='DEFAULT':\n magfile = []\n\n else:\n magfile = l_input[0].rstrip()\n\n # Line 8\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='DEFAULT':\n wgtfile = []\n\n else:\n wgtfile = l_input[0].rstrip()\n\n # Line 9\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n chi = float(l_input[0])\n\n # Line 10\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n val = np.array(l_input[0:4])\n alphas = val.astype(np.float)\n\n # Line 11\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:3])\n bounds = val.astype(np.float)\n\n else:\n bounds = l_input[0].rstrip()\n\n # Line 12\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:6])\n lpnorms = val.astype(np.float)\n\n else:\n lpnorms = l_input[0].rstrip()\n\n # Line 13\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:3])\n eps = val.astype(np.float)\n\n else:\n eps = [None,None]\n\n self.mshfile = mshfile\n self.obsfile = obsfile\n self.topofile = topofile\n self.mstart = mstart\n self._mrefInput = mref\n self._staticInput = staticInput\n self.magfile = magfile\n self.wgtfile = wgtfile\n self.chi = chi\n self.alphas = alphas\n self.bounds = bounds\n self.lpnorms = lpnorms\n self.eps = eps","def run_vorpastat(self, *options):\n\n args = list(options) + [self._input_file, 'out.meshb']\n self._run_command(\"vorpastat\", args)","def calc_out_voltage(self, input_photocurrent_file):\n pass","def vt_send(filename, key):\n url = \"https://www.virustotal.com/vtapi/v2/file/scan\"\n attr = {\"apikey\": key}\n files = {\"file\": open(filename, 'rb')}\n res = requests.post(url, data=attr, files=files)\n\n if res.status_code == 200:\n return res.text\n else:\n return res.text","def __init__(self, input_file_path, convert_to_bgr=False):\n self.__yuv_video = YuvDecoder(input_file_path, convert_to_bgr=True)\n print('After INSTANTIATION')\n self.__yuv_video.start()","def generate(inputFilename, outputFilename = defaultFileName, \n sizeOfReducedSample = DEFSIZEOFREDUCEDSAMPLE, \n centerEta = DEFCENTERETA, centerPhi = DEFCENTERPHI): \n listOfSignals = convert(inputFilename)\n arrayOfSignals = np.array(listOfSignals)\n arrayOfSignals.shape\n np.save(outputFilename, arrayOfSignals, allow_pickle=False)\n print(\"npy array name: \",outputFilename)","def get_vrt_band_list():\n logger.debug('get_vrt_band_list() called')\n vrt_band_list = []\n#===============================================================================\n# sensor_dict = self.bands[tile_type_id][(dataset_info['satellite_tag'], dataset_info['sensor_name'])]\n# # log_multiline(logger.debug, sensor, 'Sensor', '\\t')\n# for file_number in sorted(sensor_dict.keys()):\n# band_info = sensor_dict[file_number]\n# if band_info['level_name'] == 'NBAR':\n# dataset_dir = dataset_info['nbar_dataset_path']\n# dataset_id = dataset_info['nbar_dataset_id']\n# processing_level = dataset_info['nbar_level_name']\n# nodata_value = dataset_info['nbar_nodata_value']\n# resampling_method = dataset_info['nbar_resampling_method']\n# elif band_info['level_name'] == 'ORTHO':\n# dataset_dir = dataset_info['l1t_dataset_path']\n# dataset_id = dataset_info['l1t_dataset_id']\n# processing_level = dataset_info['l1t_level_name']\n# nodata_value = dataset_info['l1t_nodata_value']\n# resampling_method = dataset_info['l1t_resampling_method']\n# else:\n# continue # Ignore any pan-chromatic and derived bands\n# \n# dataset_dir = os.path.join(dataset_dir, 'scene01')\n# filename = find_file(dataset_dir, band_info['file_pattern'])\n# vrt_band_list.append({'file_number': band_info['file_number'], \n# 'filename': filename, \n# 'name': band_info['band_name'],\n# 'dataset_id': dataset_id,\n# 'band_id': band_info['band_id'],\n# 'processing_level': processing_level,\n# 'nodata_value': nodata_value,\n# 'resampling_method': resampling_method,\n# 'tile_layer': band_info['tile_layer']})\n#===============================================================================\n \n #TODO: Make this able to handle multiple derived layers\n for band_level in ['FC']:\n derived_bands = self.bands[tile_type_id][('DERIVED', band_level)]\n for file_number in sorted(derived_bands.keys()):\n band_info = derived_bands[file_number]\n file_pattern = band_info['file_pattern']\n dataset_dir = os.path.join(dataset_info['fc_dataset_path'], 'scene01')\n dataset_id = dataset_info['fc_dataset_id']\n filename = find_file(dataset_dir, file_pattern) \n processing_level = dataset_info['fc_level_name']\n nodata_value = dataset_info['fc_nodata_value'] # Should be None for FC\n resampling_method = dataset_info['fc_resampling_method']\n vrt_band_list.append({'file_number': None, \n 'filename': filename, \n 'name': band_info['band_name'],\n 'dataset_id': dataset_id,\n 'band_id': band_info['band_id'],\n 'processing_level': processing_level,\n 'nodata_value': nodata_value,\n 'resampling_method': resampling_method,\n 'tile_layer': 1})\n \n log_multiline(logger.debug, vrt_band_list, 'vrt_band_list = %s', '\\t')\n return vrt_band_list","def verb(filename,l,t,d,wout=True): #l = predelay d= decay smaller = less decay, t= number of delays\n#low l turns into chorus\n start=time.time()\n n, data, data_dB,sr,ch=inputwav(filename)\n data_ex=np.zeros(((n+l*t),ch))\n data_ex[0:n,:]=data\n data_Rex=np.zeros((len(data_ex),t,ch))\n print('Applying reverb...')\n for k in range (ch):\n for i in range (len(data)):\n for j in range(t):\n data_Rex[i+l*(j+1),j,k]=data_ex[i,k]*np.exp(-d*(j+1))\n data_F=data_ex\n print('Mixing...')\n for i in range (t):\n data_F=data_F+1*data_Rex[:,i,:]\n data_F=1*data_F\n data_verb=data_F+data_ex\n if wout==True:\n print('Exporting...')\n sf.write(filename[0:len(filename)-4]+'_verbed.wav',data_verb,sr,'PCM_16')\n print('Done!')\n end=time.time()\n elapsed=int(1000*(end-start))\n print('...............................')\n print('Completed in '+str(elapsed)+' milliseconds.')\n return data_verb","def flow_write(filename,uv,v=None):\n nBands = 2\n\n if v is None:\n assert(uv.ndim == 3)\n assert(uv.shape[2] == 2)\n u = uv[:,:,0]\n v = uv[:,:,1]\n else:\n u = uv\n\n assert(u.shape == v.shape)\n height,width = u.shape\n f = open(filename,'wb')\n # write the header\n f.write(TAG_CHAR)\n np.array(width).astype(np.int32).tofile(f)\n np.array(height).astype(np.int32).tofile(f)\n # arrange into matrix form\n tmp = np.zeros((height, width*nBands))\n tmp[:,np.arange(width)*2] = u\n tmp[:,np.arange(width)*2 + 1] = v\n tmp.astype(np.float32).tofile(f)\n f.close()","def forwardModel(file, out='Data', wavelength=None, gain=3.1, size=10, burn=500, spotx=2888, spoty=3514, run=700,\n simulation=False, truths=None, blurred=False):\n print '\\n\\n\\n'\n print '_'*120\n print 'Processing:', file\n #get data and convert to electrons\n o = pf.getdata(file)*gain\n\n if simulation:\n data = o\n else:\n #roughly the correct location - to avoid identifying e.g. cosmic rays\n data = o[spoty-(size*3):spoty+(size*3)+1, spotx-(size*3):spotx+(size*3)+1].copy()\n\n #maximum position within the cutout\n y, x = m.maximum_position(data)\n\n #spot and the peak pixel within the spot, this is also the CCD kernel position\n spot = data[y-size:y+size+1, x-size:x+size+1].copy()\n CCDy, CCDx = m.maximum_position(spot)\n print 'CCD Kernel Position (within the postage stamp):', CCDx, CCDy\n\n #bias estimate\n if simulation:\n bias = 9000.\n rn = 4.5\n else:\n bias = np.median(o[spoty-size: spoty+size, spotx-220:spotx-20]) #works for read o\n rn = np.std(o[spoty-size: spoty+size, spotx-220:spotx-20])\n\n print 'Readnoise (e):', rn\n if rn < 2. or rn > 6.:\n print 'NOTE: suspicious readout noise estimate...'\n print 'ADC offset (e):', bias\n\n #remove bias\n spot -= bias\n\n #save to file\n fileIO.writeFITS(spot, out+'small.fits', int=False)\n\n #make a copy ot generate error array\n data = spot.copy().flatten()\n #assume that uncertanties scale as sqrt of the values + readnoise\n #sigma = np.sqrt(data/gain + rn**2)\n tmp = data.copy()\n tmp[tmp + rn**2 < 0.] = 0. #set highly negative values to zero\n var = tmp.copy() + rn**2\n #Gary B. said that actually this should be from the model or is biased,\n #so I only pass the readout noise part now\n\n #fit a simple model\n print 'Least Squares Fitting...'\n gaus = models.Gaussian2D(spot.max(), size, size, x_stddev=0.5, y_stddev=0.5)\n gaus.theta.fixed = True #fix angle\n p_init = gaus\n fit_p = fitting.LevMarLSQFitter()\n stopy, stopx = spot.shape\n X, Y = np.meshgrid(np.arange(0, stopx, 1), np.arange(0, stopy, 1))\n p = fit_p(p_init, X, Y, spot)\n print p\n model = p(X, Y)\n fileIO.writeFITS(model, out+'BasicModel.fits', int=False)\n fileIO.writeFITS(model - spot, out+'BasicModelResidual.fits', int=False)\n\n #goodness of fit\n gof = (1./(np.size(data) - 5.)) * np.sum((model.flatten() - data)**2 / var)\n print 'GoF:', gof\n print 'Done\\n\\n'\n\n #maximum value\n max = np.max(spot)\n peakrange = (0.9*max, 1.7*max)\n sum = np.sum(spot)\n\n print 'Maximum Value:', max\n print 'Sum of the values:', sum\n print 'Peak Range:', peakrange\n\n #MCMC based fitting\n print 'Bayesian Model Fitting...'\n nwalkers = 1000\n\n # Initialize the sampler with the chosen specs.\n #Create the coordinates x and y\n x = np.arange(0, spot.shape[1])\n y = np.arange(0, spot.shape[0])\n #Put the coordinates in a mesh\n xx, yy = np.meshgrid(x, y)\n\n #Flatten the arrays\n xx = xx.flatten()\n yy = yy.flatten()\n\n print 'Fitting full model...'\n ndim = 7\n\n #Choose an initial set of positions for the walkers - fairly large area not to bias the results\n p0 = np.zeros((nwalkers, ndim))\n #peak, center_x, center_y, radius, focus, width_x, width_y = theta\n p0[:, 0] = np.random.normal(max, max/100., size=nwalkers) # peak value\n p0[:, 1] = np.random.normal(p.x_mean.value, 0.1, size=nwalkers) # x\n p0[:, 2] = np.random.normal(p.y_mean.value, 0.1, size=nwalkers) # y\n\n if wavelength is None:\n if blurred:\n print 'Using initial guess [radius, focus, width_x, width_y]:', [0.5, 1., 0.3, 0.3]\n p0[:, 3] = np.random.normal(0.5, 0.01, size=nwalkers) # radius\n p0[:, 4] = np.random.normal(1., 0.01, size=nwalkers) # focus\n p0[:, 5] = np.random.normal(0.3, 0.01, size=nwalkers) # width_x\n p0[:, 6] = np.random.normal(0.3, 0.01, size=nwalkers) # width_y\n else:\n p0[:, 3] = np.random.uniform(.45, 0.55, size=nwalkers) # radius\n p0[:, 4] = np.random.uniform(.40, 0.45, size=nwalkers) # focus\n p0[:, 5] = np.random.uniform(.35, 0.45, size=nwalkers) # width_x\n p0[:, 6] = np.random.uniform(.35, 0.45, size=nwalkers) # width_y\n else:\n tmp = _expectedValues()[wavelength]\n if blurred:\n print 'Using initial guess [radius, focus, width_x, width_y]:', [tmp[0], 0.9, tmp[2], tmp[3]]\n p0[:, 3] = np.random.normal(tmp[0], 0.01, size=nwalkers) # radius\n p0[:, 4] = np.random.normal(0.9, 0.01, size=nwalkers) # focus\n p0[:, 5] = np.random.normal(tmp[2], 0.01, size=nwalkers) # width_x\n p0[:, 6] = np.random.normal(tmp[3], 0.01, size=nwalkers) # width_y\n else:\n print 'Using initial guess [radius, focus, width_x, width_y]:', tmp\n p0[:, 3] = np.random.normal(tmp[0], 0.01, size=nwalkers) # radius\n p0[:, 4] = np.random.normal(tmp[1], 0.01, size=nwalkers) # focus\n p0[:, 5] = np.random.normal(tmp[2], 0.01, size=nwalkers) # width_x\n p0[:, 6] = np.random.normal(tmp[3], 0.01, size=nwalkers) # width_y\n\n #initiate sampler\n pool = Pool(cores) #A hack Dan gave me to not have ghost processes running as with threads keyword\n #sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[xx, yy, data, var, peakrange, spot.shape],\n sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior,\n args=[xx, yy, data, rn**2, peakrange, spot.shape, blurred],\n pool=pool)\n\n # Run a burn-in and set new starting position\n print \"Burning-in...\"\n pos, prob, state = sampler.run_mcmc(p0, burn)\n best_pos = sampler.flatchain[sampler.flatlnprobability.argmax()]\n print best_pos\n print \"Mean acceptance fraction:\", np.mean(sampler.acceptance_fraction)\n pos = emcee.utils.sample_ball(best_pos, best_pos/100., size=nwalkers)\n sampler.reset()\n\n print \"Running an improved estimate...\"\n pos, prob, state = sampler.run_mcmc(pos, burn)\n print \"Mean acceptance fraction:\", np.mean(sampler.acceptance_fraction)\n sampler.reset()\n print \"Running MCMC...\"\n pos, prob, state = sampler.run_mcmc(pos, run, rstate0=state)\n print \"Mean acceptance fraction:\", np.mean(sampler.acceptance_fraction)\n\n #Get the index with the highest probability\n maxprob_index = np.argmax(prob)\n\n #Get the best parameters and their respective errors and print best fits\n params_fit = pos[maxprob_index]\n errors_fit = [sampler.flatchain[:,i].std() for i in xrange(ndim)]\n amplitudeE, center_xE, center_yE, radiusE, focusE, width_xE, width_yE = errors_fit\n _printResults(params_fit, errors_fit)\n\n #Best fit model\n peak, center_x, center_y, radius, focus, width_x, width_y = params_fit\n amplitude = _amplitudeFromPeak(peak, center_x, center_y, radius, x_0=CCDx, y_0=CCDy)\n airy = models.AiryDisk2D(amplitude, center_x, center_y, radius)\n adata = airy.eval(xx, yy, amplitude, center_x, center_y, radius).reshape(spot.shape)\n f = models.Gaussian2D(1., center_x, center_y, focus, focus, 0.)\n focusdata = f.eval(xx, yy, 1., center_x, center_y, focus, focus, 0.).reshape(spot.shape)\n foc = signal.convolve2d(adata, focusdata, mode='same')\n CCD = models.Gaussian2D(1., CCDx, CCDy, width_x, width_y, 0.)\n CCDdata = CCD.eval(xx, yy, 1., CCDx, CCDy, width_x, width_y, 0.).reshape(spot.shape)\n model = signal.convolve2d(foc, CCDdata, mode='same')\n #save model\n fileIO.writeFITS(model, out+'model.fits', int=False)\n\n #residuals\n fileIO.writeFITS(model - spot, out+'residual.fits', int=False)\n fileIO.writeFITS(((model - spot)**2 / var.reshape(spot.shape)), out+'residualSQ.fits', int=False)\n\n # a simple goodness of fit\n gof = (1./(np.size(data) - ndim)) * np.sum((model.flatten() - data)**2 / var)\n maxdiff = np.max(np.abs(model - spot))\n print 'GoF:', gof, ' Maximum difference:', maxdiff\n if maxdiff > 2e3 or gof > 4.:\n print '\\nFIT UNLIKELY TO BE GOOD...\\n'\n print 'Amplitude estimate:', amplitude\n\n #results and save results\n _printFWHM(width_x, width_y, errors_fit[5], errors_fit[6])\n res = dict(wx=width_x, wy=width_y, wxerr=width_xE, wyerr=width_yE, out=out,\n peakvalue=max, CCDmodel=CCD, CCDmodeldata=CCDdata, GoF=gof,\n maximumdiff=maxdiff, fit=params_fit)\n fileIO.cPickleDumpDictionary(res, out+'.pkl')\n\n #plot\n samples = sampler.chain.reshape((-1, ndim))\n extents = None\n if simulation:\n extents = [(0.91*truth, 1.09*truth) for truth in truths]\n extents[1] = (truths[1]*0.995, truths[1]*1.005)\n extents[2] = (truths[2]*0.995, truths[2]*1.005)\n extents[3] = (0.395, 0.425)\n extents[4] = (0.503, 0.517)\n truths[0] = _peakFromTruth(truths)\n print truths\n fig = triangle.corner(samples,\n labels=['peak', 'x', 'y', 'radius', 'focus', 'width_x', 'width_y'],\n truths=truths)#, extents=extents)\n fig.savefig(out+'Triangle.png')\n plt.close()\n pool.close()","def generate_siaf_pre_flight_reference_files_nircam():\n\n instrument = 'NIRCam'\n overwrite_wedge_file = False\n overwrite_grism_file = False\n\n\n # wedge definitions\n wedge_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_wedge_offsets.txt'.format(instrument.lower()))\n\n if (not os.path.isfile(wedge_file) or (overwrite_wedge_file)):\n\n wedge_offsets = Table.read(os.path.join(JWST_SOURCE_DATA_ROOT, instrument, 'wedge_offsets.txt'), format='ascii.basic', delimiter=' ', guess=False)\n\n comments = []\n comments.append('{} detector parameter definition file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the wedge offsets.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n wedge_offsets.meta['comments'] = comments\n wedge_offsets.write(wedge_file, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False)\n\n # grism definitions\n grism_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_grism_parameters.txt'.format(instrument.lower()))\n\n if (not os.path.isfile(wedge_file) or (overwrite_grism_file)):\n # grism parameters, see WFSS worksheet in EXCEL SIAF\n grism_parameters = Table.read(grism_file, format='ascii.basic', delimiter=',', guess=False)\n\n # Save a backup copy of the grism file\n cmd = 'cp {} {}'.format(grism_file,os.path.join(JWST_TEMPORARY_DATA_ROOT, instrument, 'nircam_siaf_grism_parameters_backup.txt'))\n os.system(cmd)\n\n # different sign in Y for NRCB apertures\n factor = np.array(\n [1. if 'NRCA' in grism_parameters['aperture_name'][i] else -1. for i in range(len(grism_parameters))])\n\n for col in grism_parameters.colnames[1:]:\n # these are Sci coordinates\n if col[0] != 'D':\n if 'X' in col:\n grism_parameters['D{}'.format(col)] = grism_parameters[col].data - 1024.5\n elif 'Y' in col:\n grism_parameters['D{}'.format(col)] = factor * (grism_parameters[col].data - 1024.5)\n\n\n\n comments = []\n comments.append('{} grism parameter definition file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the grism parameters.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n grism_parameters.meta['comments'] = comments\n grism_parameters.write(grism_file, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False)\n\n # Transformation parameters, mapping used to select rows in cold_fit_[] file\n coldfit_name_mapping = {\n 'NRCA1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RT_20170307121022',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALWToOTESKY_RT_20170307121022',\n },\n 'NRCB1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_1_20161025081604',\n 'pixels_to_mm':'NIRCAMBSW_1ToNIRCAMBSW_20161025081604',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_2_20161025081912',\n 'pixels_to_mm':'NIRCAMBSW_2ToNIRCAMBSW_20161025081912',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_3_20161025082300',\n 'pixels_to_mm':'NIRCAMBSW_3ToNIRCAMBSW_20161025082300',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_4_20161025082647',\n 'pixels_to_mm':'NIRCAMBSW_4ToNIRCAMBSW_20161025082647',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMBLW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBLWToNIRCAMBLW_1_20161227162336',\n 'pixels_to_mm':'NIRCAMBLW_1ToNIRCAMBLW_20161227162336',\n 'mm_to_degrees':'NIRCAMBLWToOTESKY_RT_20170307121023',\n },\n 'NRCA1_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\n },\n 'NRCA2_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\n },\n 'NRCA3_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\n },\n 'NRCA4_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\n },\n 'NRCA1_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\n },\n 'NRCA2_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\n },\n 'NRCA3_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\n },\n 'NRCA4_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\n },\n 'NRCA5_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALW_RNDToOTESKY_202005150434',\n },\n 'NRCA5_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMALW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALW_BARToOTESKY_202005150434',\n }\n }\n\n # coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('cold_fit_201703071210.csv'))\n coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('nircam_cold_fit.txt'))\n print('NIRCam coldfit data from', coldfit_source_data_file)\n t = open(coldfit_source_data_file)\n coldfit_source_data = t.readlines()\n t.close()\n # remove comments from read content\n coldfit_source_data = [line for line in coldfit_source_data if line[0] != '#']\n\n siaf_detector_layout = iando.read.read_siaf_detector_layout()\n # siaf_alignment_parameters = iando.read.read_siaf_alignment_parameters(instrument)\n siaf_aperture_definitions = iando.read.read_siaf_aperture_definitions(instrument)\n # aperture_dict = {}\n aperture_name_list = siaf_aperture_definitions['AperName'].tolist()\n\n # generate alignment reference file, one file for all master apertures\n outfile = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_alignment.txt'.format(instrument.lower()))\n siaf_alignment = Table()\n\n for AperName in aperture_name_list:\n\n # process the master apertures of NIRCam\n if AperName in siaf_detector_layout['AperName']:\n (A, B, C, D, betaX, betaY, V2Ref, V3Ref) = nircam_get_polynomial_both(AperName, siaf_aperture_definitions, coldfit_name_mapping, coldfit_source_data)\n\n #generate distortion reference file\n number_of_coefficients = len(A)\n polynomial_degree = int((np.sqrt(8 * number_of_coefficients + 1) - 3) / 2)\n siaf_index = []\n exponent_x = []\n exponent_y = []\n for i in range(polynomial_degree + 1):\n for j in np.arange(i + 1):\n siaf_index.append('{:d}{:d}'.format(i, j))\n exponent_x.append(i - j)\n exponent_y.append(j)\n\n distortion_reference_table = Table((siaf_index, exponent_x, exponent_y, A, B, C, D), names=(\n 'siaf_index', 'exponent_x', 'exponent_y', 'Sci2IdlX', 'Sci2IdlY', 'Idl2SciX', 'Idl2SciY'))\n distortion_reference_table.add_column(\n Column([AperName] * len(distortion_reference_table), name='AperName'), index=0)\n distortion_reference_file_name = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_distortion_{}.txt'.format(instrument.lower(),\n AperName.lower()))\n # distortion_reference_table.pprint()\n comments = []\n comments.append('{} distortion reference file for SIAF\\n'.format(instrument))\n comments.append('Aperture: {}\\n'.format(AperName))\n comments.append('Based on coefficients given in {},'.format(os.path.basename(coldfit_source_data_file)))\n # comments.append('that were rescaled, shifted for a different reference pixel location, and rotated:')\n # comments.append('Rotation of {:2.3f} deg was removed and is carried separately in V3IdlYangle.'.format(\n # np.rad2deg(V3angle))) # *units.deg.to(units.arcsecond)\n # if 'may_2015' in distortion_file_name:\n # comments.append(\n # 'These parameters are stored in the currently (January 2018) active SIAF (PRDOPSSOC-G-012). ')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n distortion_reference_table.meta['comments'] = comments\n distortion_reference_table.write(distortion_reference_file_name, format='ascii.fixed_width',\n delimiter=',', delimiter_pad=' ', bookend=False, overwrite=True)\n\n V3SciYAngle = betaY\n V3SciXAngle = betaX\n if np.abs(V3SciYAngle) < 90.:\n V3IdlYAngle = V3SciYAngle\n else:\n V3IdlYAngle = V3SciYAngle - np.sign(V3SciYAngle) * 180.\n\n if len(siaf_alignment) == 0: # first entry\n siaf_alignment['AperName'] = ['{:>30}'.format(AperName)]\n siaf_alignment['V3IdlYAngle'] = [V3IdlYAngle]\n siaf_alignment['V3SciXAngle'] = V3SciXAngle #[np.rad2deg(betaX)]\n siaf_alignment['V3SciYAngle'] = V3SciYAngle #[np.rad2deg(betaY)]\n siaf_alignment['V2Ref'] = [V2Ref]\n siaf_alignment['V3Ref'] = [V3Ref]\n else:\n siaf_alignment.add_row(['{:>30}'.format(AperName), V3IdlYAngle, V3SciXAngle, V3SciYAngle, V2Ref,V3Ref])\n comments = []\n comments.append('{} alignment parameter reference file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the focal plane alignment parameters of master apertures calibrated')\n comments.append('during FGS-SI alignment.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n siaf_alignment.meta['comments'] = comments\n siaf_alignment.write(outfile, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False, overwrite=True)","def writeFlow(filename,uv,v=None):\n nBands = 2\n\n if v is None:\n assert(uv.ndim == 3)\n assert(uv.shape[2] == 2)\n u = uv[:,:,0]\n v = uv[:,:,1]\n else:\n u = uv\n\n assert(u.shape == v.shape)\n height,width = u.shape\n f = open(filename,'wb')\n # write the header\n f.write(TAG_CHAR)\n np.array(width).astype(np.int32).tofile(f)\n np.array(height).astype(np.int32).tofile(f)\n # arrange into matrix form\n tmp = np.zeros((height, width*nBands))\n tmp[:,np.arange(width)*2] = u\n tmp[:,np.arange(width)*2 + 1] = v\n tmp.astype(np.float32).tofile(f)\n f.close()","def write_to_vcf(self):\n\n # 1. Generate header info\n date_for_vcf = datetime.now().strftime('%Y%m%d')\n header_info = [\n '##fileformat=VCFv4.2',\n '##fileDate=%s' % date_for_vcf,\n '##source=%s' % self.get_analyser_name(),\n '##reference=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz',\n '##contig=<ID=chr1,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr1.fa.gz>',\n '##contig=<ID=chr2,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr2.fa.gz>',\n '##contig=<ID=chr3,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr3.fa.gz>',\n '##contig=<ID=chr4,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr4.fa.gz>',\n '##contig=<ID=chr5,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr5.fa.gz>',\n '##contig=<ID=chr6,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr6.fa.gz>',\n '##contig=<ID=chr7,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr7.fa.gz>',\n '##contig=<ID=chr8,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr8.fa.gz>',\n '##contig=<ID=chr9,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr9.fa.gz>',\n '##contig=<ID=chr10,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr10.fa.gz>',\n '##contig=<ID=chr11,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr11.fa.gz>',\n '##contig=<ID=chr12,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr12.fa.gz>',\n '##contig=<ID=chr13,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr13.fa.gz>',\n '##contig=<ID=chr14,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr14.fa.gz>',\n '##contig=<ID=chr15,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr15.fa.gz>',\n '##contig=<ID=chr16,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr16.fa.gz>',\n '##contig=<ID=chr17,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr17.fa.gz>',\n '##contig=<ID=chr18,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr18.fa.gz>',\n '##contig=<ID=chr19,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr19.fa.gz>',\n '##contig=<ID=chr20,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr20.fa.gz>',\n '##contig=<ID=chr21,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr21.fa.gz>',\n '##contig=<ID=chr22,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr22.fa.gz>',\n '##contig=<ID=chrM,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrM.fa.gz>',\n '##contig=<ID=chrX,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrX.fa.gz>',\n '##contig=<ID=chrY,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrY.fa.gz>',\n ]\n header_parameters = [\n '##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">',\n '##FORMAT=<ID=MTQ,Number=1,Type=String,Description=\"MassArray Typer quality value for SNP call. '\n 'A=Conservative, B=Moderate, C=Aggressive, D=Low Probability, E=User Call, i=Low Intensity. A and B are considered high '\n 'quality scores.\">',\n '##INFO=<ID=PCR,Number=2,Type=String,Description=\"PCR sequences used in assay.\">',\n '##INFO=<ID=AF,Number=A,Type=Float,Description=\"Minor allele frequency from population data.\">',\n '##INFO=<ID=Gene,Number=A,Type=String,Description=\"HGNC Gene Name for gene containing SNP.\">',\n '##INFO=<ID=Build,Number=A,Type=String,Description=\"Genome build used to determine SNP position for assay.\">',\n '##FILTER=<ID=LowCallRate,Description=\"SNP not called in at least 30% of samples in assay.\">',\n ]\n\n # 2. Extract info from XML file\n results = self.get_results()\n snps = self.get_snps()\n pcr_sequences = self.get_pcr_sequences()\n call_rates = self.get_snp_call_rate()\n\n # 3. For each sample, create VCF, add headers, determine genotype of each SNP and write to file.\n for sample, variants in results.items():\n\n with open(os.path.join(self.output, '%s.vcf' % sample), 'w+') as outfile:\n\n header_fields = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', str(sample)]\n\n outfile.write('%s\\n' % '\\n'.join(header_info))\n outfile.write('%s\\n' % '\\n'.join(header_parameters))\n outfile.write('#%s\\n' % '\\t'.join(header_fields))\n\n # for each variant, make a line to add to the file which will\n # then be sorted\n lines_to_write = []\n for snp, info in variants.items():\n\n ref_allele = snps[snp]['ref']\n alt_alleles = snps[snp]['alt']\n alt_list = alt_alleles.split(',')\n\n # Genotype formatting matches VCF v4.0 spec where ./. is no call.\n gt_list = []\n called_genotype = info['genotype']\n if not called_genotype:\n gt_list = ['.', '.']\n elif len(called_genotype) == 1:\n called_genotype += called_genotype\n for allele in list(called_genotype):\n if allele == ref_allele:\n gt_list.append(0)\n else:\n if allele in alt_list:\n idx = alt_list.index(allele)\n gt_list.append(idx + 1)\n else:\n raise ValueError(\n 'Called genotype %s not provided as possible alt in bed file. Sample %s and SNP '\n '%s %s.' % (called_genotype, sample, snp, alt_alleles)\n )\n gt = '/'.join([str(x) for x in gt_list])\n\n # Threshold currently set to 0.3 (70% results have a call).\n snp_call_rate = call_rates[snp]\n if snp_call_rate >= 0.3:\n vcf_filter = 'LowCallRate'\n else:\n vcf_filter = 'PASS'\n\n snp_pcr_seqs = pcr_sequences[snp]\n\n lines_to_write.append(\n '{chr}\\t{pos}\\t{id}\\t{ref}\\t{alt}\\t.\\t{filter}\\tAF={af};PCR={pcr};Gene={gene};Build={build}\\t'\n 'GT:MTQ\\t{gt}:{qual}\\n'.format(\n chr=snps[snp]['chrom'],\n pos=snps[snp]['pos'],\n id=snp,\n ref=ref_allele,\n alt=alt_alleles,\n filter=vcf_filter,\n af=snps[snp]['maf'],\n pcr=','.join(snp_pcr_seqs),\n gene=snps[snp]['gene'],\n build=snps[snp]['genome_build'],\n gt=gt,\n qual=','.join(info['quality'])\n )\n )\n\n sorted_lines_to_write = sorted(\n lines_to_write,\n key=lambda x: (\n # first key for sorting is the int value of chr\n int(x.split('\\t')[0][3:]),\n # second key for sorting is the position of the variant\n int(x.split('\\t')[1])\n )\n )\n\n for line in sorted_lines_to_write:\n outfile.write(line)","def main(input_file, dtb_file):\n\n LOGGER.info(\"Reading: %s\" % dtb_file)\n with open(dtb_file, \"rb\") as max_table_file:\n LOGGER.info(\"max10_device_table.bin size: %x\" %\n os.path.getsize(max_table_file.name))\n LOGGER.info(\"Max max10 table size: %x\" % MAX10_TABLE_SIZE)\n if (os.path.getsize(max_table_file.name) > MAX10_TABLE_SIZE):\n raise Exception(LOGGER.error(\"max10_device_table.bin is too big\"))\n max10_table = max_table_file.read()\n\n LOGGER.info(\"Writing file: %s\" % input_file)\n with open(input_file, \"rb+\") as rpd_file:\n rpd_file.seek(MAX10_TABLE_START)\n rpd_file.write(bytearray(max10_table))\n LOGGER.info(\"Done merging Max10 device table\")","def sample_vcf():\n file_content = b\"\"\"##fileformat=VCFv4.2\n##hailversion=0.2.100-2ea2615a797a\n##INFO=<ID=QUALapprox,Number=1,Type=Integer,Description=\"\">\n##INFO=<ID=SB,Number=.,Type=Integer,Description=\"\">\n##INFO=<ID=MQ,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=MQRankSum,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=VarDP,Number=1,Type=Integer,Description=\"\">\n##INFO=<ID=AS_ReadPosRankSum,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_pab_max,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_QD,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_MQ,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=QD,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_MQRankSum,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=FS,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_FS,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=ReadPosRankSum,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=AS_QUALapprox,Number=1,Type=Integer,Description=\"\">\n##INFO=<ID=AS_SB_TABLE,Number=.,Type=Integer,Description=\"\">\n##INFO=<ID=AS_VarDP,Number=1,Type=Integer,Description=\"\">\n##INFO=<ID=AS_SOR,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=SOR,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=singleton,Number=0,Type=Flag,Description=\"\">\n##INFO=<ID=transmitted_singleton,Number=0,Type=Flag,Description=\"\">\n##INFO=<ID=omni,Number=0,Type=Flag,Description=\"\">\n##INFO=<ID=mills,Number=0,Type=Flag,Description=\"\">\n##INFO=<ID=monoallelic,Number=0,Type=Flag,Description=\"\">\n##INFO=<ID=AS_VQSLOD,Number=1,Type=Float,Description=\"\">\n##INFO=<ID=InbreedingCoeff,Number=1,Type=Float,Description=\"\">\n##FILTER=<ID=AC0,Description=\"Allele count is zero after filtering out low-confidence genotypes (GQ < 20; DP < 10; and AB < 0.2 for het calls)\">\n##FILTER=<ID=AS_VQSR,Description=\"Failed VQSR filtering thresholds of -2.7739 for SNPs and -1.0606 for indels\">\n##contig=<ID=chr1,length=248956422,assembly=GRCh38>\n#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\nchr1\t10330\t.\tCCCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAA\tC\t.\tPASS\tQUALapprox=21493;SB=325,1077,113,694;MQ=32.1327;MQRankSum=0.720000;VarDP=2236;AS_ReadPosRankSum=-0.736000;AS_pab_max=1.00000;AS_QD=5.17857;AS_MQ=29.5449;QD=9.61225;AS_MQRankSum=0.00000;FS=8.55065;AS_FS=.;ReadPosRankSum=0.727000;AS_QUALapprox=145;AS_SB_TABLE=325,1077,2,5;AS_VarDP=28;AS_SOR=0.311749;SOR=1.48100;singleton;AS_VQSLOD=13.4641;InbreedingCoeff=-0.000517845\"\"\"\n file = io.BytesIO(file_content)\n return file","def read(file):\n\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\n nol = [1, 1, 1, 1, 1, 4, 1,\n 0, 0, 0, 0, 0, 1,\n 0, 1, 0, 0, 0, 0]\n rawd = re.compile('rawd\\d\\d')\n\n retval = True\n version = 0\n b = 0 # current block index\n raw = {}\n for item in blocks:\n raw[item] = []\n\n data = []\n mdata = [] # multi-line data\n mline = 0 # line counter for multi-line models\n\n # parse file into raw with to_number conversions\n fid = open(file, 'r')\n for num, line in enumerate(fid.readlines()):\n line = line.strip()\n if num == 0: # get basemva and frequency\n data = line.split('/')[0]\n data = data.split(',')\n\n mva = float(data[1])\n freq = float(data[5])\n version = int(data[2])\n\n if not version:\n version = int(rawd.search(line).group(0).strip('rawd'))\n if version < 32 or version > 33:\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\n continue\n elif num == 1: # store the case info line\n logging.info(line)\n continue\n elif num == 2:\n continue\n elif num >= 3:\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\n b += 1\n continue\n elif line[0] is 'Q': # end of file\n break\n data = line.split(',')\n\n data = [to_number(item) for item in data]\n mdata.append(data)\n mline += 1\n if mline == nol[b]:\n if nol[b] == 1:\n mdata = mdata[0]\n raw[blocks[b]].append(mdata)\n mdata = []\n mline = 0\n fid.close()\n\n # add device elements params and add to PSAT formatted dictionary\n\n for data in raw['bus']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\n \"\"\"\n idx = data[0]\n ty = data[3]\n angle = data[8]\n try:\n lat = data[9]\n except:\n # logging.warning('<No Coordinates in .raw file>')\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\n else:\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n 'latitude': data[9],\n 'longitude': data[10]\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\n Settings.Bus.append(psatlist)\n Settings.BusNames.append(data[1])\n # Add BusSTORE Dictionary For Later Reference\n Settings.BusStore[idx] = param\n\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\n 38.656721]\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\n\n #for idx, line in enumerate(Settings.Bus):\n # line.extend([xcoord[idx], ycoord[idx]])\n\n maxV = 1.1\n minV = 0.9\n maxQ = 1\n minQ = 0\n convimp = 0\n status = 1\n loss = 1\n\n for data in raw['load']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\n \"\"\"\n\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n voltage = Settings.BusStore[busidx]['voltage']\n param = {'bus': busidx,\n 'Vn': vn,\n 'Sn': mva,\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\n 'owner': data[11],\n 'type': Settings.BusStore[busidx]['type'],\n 'voltage': voltage\n }\n\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\n Settings.PQ.append(psatlist)\n \"\"\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\"\"\"\n\n for data in raw['fshunt']:\n \"\"\"\n 0, 1, 2, 3, 4\n Bus, name, Status, g (MW), b (Mvar)\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n param = {'bus': busidx,\n 'Vn': vn,\n 'status': data[2],\n 'Sn': mva,\n 'g': data[3] / mva,\n 'b': data[4] / mva,\n }\n\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\n Settings.Shunt.append(psatlist)\n\n gen_idx = 0\n type = 6\n\n for data in raw['gen']:\n \"\"\"\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n gen_mva = data[8]\n gen_idx += 1\n status = data[14]\n leak = 0\n param = {'Sn': gen_mva,\n 'Vn': vn,\n 'u': status,\n 'idx': gen_idx,\n 'bus': busidx,\n 'pg': status * data[2] / mva,\n 'qg': status * data[3] / mva,\n 'qmax': data[4] / mva,\n 'qmin': data[5] / mva,\n 'v0': data[6],\n 'ra': data[9], # ra armature resistance\n 'xs': data[10], # xs synchronous reactance\n 'pmax': data[16] / mva,\n 'pmin': data[17] / mva,\n }\n\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\n refangle = 0\n refBus = 1\n PGuess = 1\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\n maxV, minV, PGuess, loss, refBus, status]\n SW = swlist\n Settings.SW.append(swlist)\n Settings.SWStore[busidx] = param\n Settings.SynStore[busidx] = param\n continue\n\n if busidx not in Settings.BusStore.keys():\n \"\"\" Need data from .dyr file. Create initial list, then append data from .dyr\"\"\"\n else:\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\n # Syn.append(psatlist)\n Settings.SynStore[busidx] = param\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\n param['qmax'], param['qmin'], maxV, minV, loss, status]\n Settings.PV.append(pvlist)\n\n\n for data in raw['branch']:\n \"\"\"\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\n \"\"\"\n param = {'bus1': data[0],\n 'bus2': data[1],\n 'id' : data[2],\n 'r': data[3],\n 'x': data[4],\n 'b': data[5],\n 'rate_a': data[6],\n 'rate_b': data[7],\n 'rate_c': data[8],\n 'Vn': Settings.BusStore[data[0]]['Vn'],\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\n 'length': data[14],\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n 'status': data[13]\n }\n\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['status']]\n Settings.Lineij.append([data[0], data[1], data[2]])\n Settings.Lineji.append([data[1], data[0], data[2]])\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.branches += 1\n Settings.linecount += 1\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\n\n for data in raw['transf']:\n \"\"\"\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\n R1-2,X1-2,SBASE1-2\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\n WINDV2,NOMV2\n \"\"\"\n if len(data[1]) < 5:\n ty = 2\n else:\n ty = 3\n if ty == 3:\n continue\n # raise NotImplementedError('Three-winding transformer not implemented')\n\n tap = data[2][0]\n phi = data[2][2]\n\n if tap == 1 and phi == 0:\n trasf = False\n else:\n trasf = True\n param = {'trasf': trasf,\n 'bus1': data[0][0],\n 'bus2': data[0][1],\n 'u': data[0][11],\n 'b': data[0][8],\n 'r': data[1][0],\n 'x': data[1][1],\n 'tap': tap,\n 'phi': phi,\n 'rate_a': data[2][3],\n 'Vn': Settings.BusStore[busidx]['Vn'],\n 'Vn2': Settings.BusStore[busidx]['Vn'],\n # 'length': data[?][?], FIND CORRECT INDEX\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n }\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['u']]\n\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.linecount += 1\n Settings.transformers += 1\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\n # Re-Order Line Data for correct sequence\n for key in Settings.LineOrd:\n for item in Settings.LineOrd[key]:\n Settings.Line.append(item)\n\n for data in raw['area']:\n Settings.Areas.append(data[4])\n\n for data in raw['zone']:\n Settings.Regions.append(data[1])\n\n return retval","def main(file):\n\n # Get the current working directory.\n here = os.getcwd()\n #Need the file_name to set globe, so that other functions can access to it.\n global file_name\n # Spite the Input into file_path and file_name.\n file_path = spilt_path(file)[0]\n file_name = spilt_path(file)[1]\n\n # Try to get into the file_path, if exist\n try:\n os.chdir(file_path)\n except IOError, e:\n print e\n\n # Now convert it\n convertFile(file_name)\n # going back to orgin folder\n os.chdir(here)\n return os.path.join(output_dir, file_name)","def load_spectrum(inputfile):\n if inputfile.endswith(\"fits\"):\n wav, flux = spectrum_sdss_fits(inputfile)\n imodel = False\n inu = False\n\n else:\n f = open(inputfile, \"r\")\n # Read header\n try:\n nn = int(f.tell())\n f.readline()\n except BaseException:\n pass\n\n # Read first line\n f.readline()\n # Check format of second line\n test = f.readline()\n f.seek(0) # rewind to begining\n\n # Read data\n if (len(test.split()) == 10) or (len(test.split()) == 6): # test62\n wav, flux = spectrum_test62(f)\n imodel = True\n inu = True\n\n elif len(test.split(\",\")) == 2 or len(test.split(\",\")) == 4: # csv\n wav, flux = spectrum_csv(f)\n imodel = False\n inu = False\n\n elif len(test.split()) == 2: # tsv\n wav, flux = spectrum_tsv(f)\n imodel = False\n inu = False\n\n elif len(test.split()) == 3: # tsv with uncertainties\n wav, flux = spectrum_tsv3(f)\n imodel = False\n inu = False\n\n elif len(test.split()) == 5 or len(test.split()) == 7: # mics format\n wav, flux = spectrum_misc(f)\n imodel = False\n inu = False\n\n else:\n\n raise ValueError(f\"Unknown format for {inputfile}.\")\n\n f.close()\n\n return Spectrum(wav, flux, (imodel, inu))","def view(filename):\n n, data, data_dB,sr,ch=inputwav(filename)\n t=np.linspace(0,n/sr,n)\n py.close()\n fig, (ax1) = py.subplots(nrows=1) \n ax1.plot(t[0:n:100],data[0:n:100],'k-',linewidth=1,label=filename)\n ax1.legend(loc=1)\n ax1.set_ylabel('Amplitude (Rel. Bit)')\n ax1.set_xlabel('Time (s)')","def wave_create():\n return _u2i(_pigpio_command(_control, _PI_CMD_WVCRE, 0, 0))","def generate_data(path=resource_filename('locals', 'data/fake/'), mag_range=(11.13,18)):\n # Get some random spectra\n try:\n files = glob.glob('/user/jfilippazzo/Models/ACES/default/*.fits')[::50]\n except:\n files = glob.glob('/Users/jfilippazzo/Documents/Modules/_DEPRECATED/limb_dark_jeff/limb/specint/*.fits')[::20]\n \n # Make a fake source catalog (with only essential columns for now)\n catpath = os.path.join(path,'fake_source_catalog.ecsv')\n ids = list(range(len(files)))\n coords = SkyCoord([89.7455]*len(ids), [-29.05744]*len(ids), unit='deg', frame='icrs')\n cat = at.QTable([ids,coords], names=('id','icrs_centroid'))\n cat.write(catpath)\n \n # Open the x1d file\n header = fits.getheader(resource_filename('locals', 'data/template_x1d.fits'))\n \n # Make Spectrum objects from models at R=150\n wavelength = np.arange(0.05,2.6,0.0001)[::66]*q.um\n \n # Normalize the spectra to a random F200W magnitude\n spectra = []\n f200w = Bandpass('NIRISS.F200W')\n f200w.wave_units = q.um\n for file in files:\n \n # Create Spectrum\n flux = fits.getdata(file)[-1][::66]*q.erg/q.s/q.cm**2/q.AA\n unc = flux/50.\n spec = Spectrum(wavelength, flux, unc)\n \n # Normalize to F200W\n mag = np.random.uniform(*mag_range)\n norm_spec = spec.renormalize(mag, f200w)\n spectra.append(norm_spec)\n \n # Make a separate x1d file and photometry file for each bandpass\n # containing data for each source\n for band in NIRISS_bands:\n \n try:\n \n # Get the Bandpass object\n bp = Bandpass(band)\n bp.wave_units = q.um\n \n # Make x1d file for spectra\n x1d_file = os.path.join(path,'{}_x1d.fits'.format(band))\n x1d_hdu = fits.HDUList(fits.PrimaryHDU(header=header))\n \n # Make csv file for photometry\n phot_file = os.path.join(path,'{}_phot.csv'.format(band))\n phot_data = at.Table(names=('id','band','magnitude','magnitude_unc'), dtype=(int,'S20',float,float))\n \n # Iterate over spectra\n for id,(f,spec) in enumerate(zip(files,spectra)):\n \n # Trim spectrum to bandpass for x1d file\n spec = Spectrum(*spec.spectrum, trim=[(0*q.um,bp.WavelengthMin*1E-4*q.um),(bp.WavelengthMax*1E-4*q.um,10*q.um)])\n \n # Calculate magnitude and add to photometry table\n mag, mag_unc = spec.synthetic_magnitude(bp, force=True)\n phot_data.add_row([id, band, mag, mag_unc])\n \n # Add source spectrum params for verification\n params = f.split('/')[-1].split('-')\n header['TEFF'] = int(params[0].replace('lte',''))\n header['LOGG'] = float(params[1][:4])\n header['FEH'] = float(params[-6][:-8].split('+')[-1])\n header['FILEPATH'] = f\n header['PUPIL'] = band\n\n # Put spectrum in x1d fits file\n data = fits.BinTableHDU(data=np.rec.array(list(zip(*spec.data)),\n formats='float32,float32,float32',\n names='WAVELENGTH,FLUX,ERROR'),\n header=header)\n data.name = 'EXTRACT1D'\n \n x1d_hdu.append(data)\n \n # Write the photometry file\n phot_data.write(phot_file, format='ascii.csv')\n del phot_data\n \n # Write the x1d file\n x1d_hdu.writeto(x1d_file, overwrite=True)\n del x1d_hdu\n \n except IOError:\n pass","def convert(infile,arcsec_per_pixel=0.2,sigma_conv=1.,expansion_factor=5,writeout=None,overwrite=False,keep_units=False):\n \n PLATESCALE = 1.2120 # arcsec / mm\n rss = fits.open( infile )\n phdr = rss[1].header\n dhdr = rss[0].header\n data = rss[0].data\n \n conff=dm.read_fibers_extension(phdr)\n bundles_values=conff.bundles.keys()\n sky_bundles=[]\n for bundlei in bundles_values:\n if phdr[\"BUN%03d_T\" % bundlei]=='SKY':\n sky_bundles.append(bundlei)\n \n w0 = dhdr['CRVAL1'] # reference wavelength\n try : dw = dhdr['CRDELT1'] # wavelength step\n except : dw = dhdr['CDELT1'] # wavelength step\n wunit = dhdr['CUNIT1'] # wavelength unit\n wtype = 'WAVE' # type spectra\n\n # define the dimensions of the spaxel array \n Nx, Ny, x0, y0, dx, dy = getspaxdim( data,phdr,sky_bundles,expansion_factor=expansion_factor)\n\n nbin=int(round(float(arcsec_per_pixel)/float(dx)))\n\n\n Nw = dhdr['NAXIS1'] # number of wave. steps\n \n\n \n # initialize an empty 3-d cube (zero everywhere)\n cube = fits.PrimaryHDU()\n #cube.header=rss[0].header \n #cube.header.remove('CRPIX1') \n #cube.header.remove('CRVAL1') \n #cube.header.remove('CUNIT1') \n #cube.header.remove('CTYPE1') \n #cube.header.remove('CRPIX2') \n #cube.header.remove('CRVAL2') \n #cube.header.remove('CDELT2') \n #cube.header.remove('CTYPE2') \n cube.header.update(NAXIS=3)\n cube.header.update(NAXIS1=Nx)\n cube.header.update(NAXIS2=Ny)\n cube.header.update(NAXIS3=Nw)\n cube.header.update(CD1_1=-dx/3600.)\n cube.header.update(CD2_2=dy/3600.)\n cube.header.update(CD3_3=dw)\n cube.header.update(CRPIX1=0)\n cube.header.update(CRPIX2=0)\n cube.header.update(CRPIX3=0)\n cube.header.update(CRVAL1=x0)\n cube.header.update(CRVAL2=y0)\n cube.header.update(CRVAL3=w0)\n\n cube.header.update(CTYPE1='RA---DEG')\n cube.header.update(CTYPE2='DEC--DEG')\n cube.header.update(CTYPE3=wtype)\n cube.header.update(CUNIT3=wunit)\n\n cube.header.update(CD1_2=0)\n cube.header.update(CD1_3=0)\n cube.header.update(CD2_1=0)\n cube.header.update(CD2_3=0)\n cube.header.update(CD3_1=0)\n cube.header.update(CD3_2=0)\n\n\n cube.data = numpy.zeros( (Nw,Ny,Nx) )\n\n # extract each spectrum and place it\n # into the 3-d cube\n for ispec in range(len(data)): \n fib_str='{:3d}'.format(ispec+1)\n fib_str=fib_str.replace(' ','0') \n if not(phdr['FIB'+fib_str+'_B'] in sky_bundles):\n try:\n end_sp=phdr['FIB'+fib_str+'W2'] \n start_sp=phdr['FIB'+fib_str+'W1']\n except:\n if ('start_sp' in locals()):\n print('Warning! FIB'+fib_str+'W1 and W2 information missing in header. Assuming previous fiber wavelength coverage.') \n else: \n end_sp=Nw\n start_sp=1 \n print('Warning! FIB'+fib_str+'W1 and W2 information missing in header. Assuming default wavelength coverage.') \n \n if end_sp!=start_sp:\n spec = data[ispec][:]\n Nwspec = Nw \n \n xpos = (phdr['FIB'+fib_str+'_x']+5.)*PLATESCALE \n ypos = (phdr['FIB'+fib_str+'_y']+5.)*PLATESCALE\n ix = int( round((xpos - x0),3) / dx )\n iy = int( round((ypos - y0),3) / dy )\n \n lambda_arr=w0+dw*numpy.arange(0,Nwspec,1)\n \n if keep_units==True:\n for i in range( start_sp, min(end_sp,Nwspec) ):\n cube.data[i][iy][ix] = spec[i]##same units \n else:\n for i in range( start_sp, min(end_sp,Nwspec) ):\n cube.data[i][iy][ix] = spec[i]*3.00e-5/lambda_arr[i]**2 ## Jy to erg/s/cm**2/A \n else:\n end_sp=Nwspec \n print('1st step') \n sigma_conv_pix=sigma_conv/((dx*nbin)/expansion_factor) \n for i in range( start_sp, min(end_sp,Nwspec)):\n print(str(i)+'/'+str(Nwspec)+' spectral channels',end=\"\\r\")\n cube.data[i]=scipy.ndimage.filters.gaussian_filter(cube.data[i], sigma=sigma_conv_pix)\n \n \n cube_rebin = fits.PrimaryHDU()\n cube_rebin.header=rss[0].header \n cube_rebin.header.remove('CRPIX1') \n cube_rebin.header.remove('CRVAL1') \n cube_rebin.header.remove('CUNIT1') \n cube_rebin.header.remove('CTYPE1') \n cube_rebin.header.remove('CDELT1')\n cube_rebin.header.remove('CRPIX2') \n cube_rebin.header.remove('CRVAL2') \n #cube_rebin.header.remove('CUNIT2') \n cube_rebin.header.remove('CDELT2') \n cube_rebin.header.remove('CTYPE2') \n cube_rebin.header.update(NAXIS=3)\n cube_rebin.header.update(NAXIS1=Nx//nbin)\n cube_rebin.header.update(NAXIS2=Ny//nbin)\n cube_rebin.header.update(NAXIS3=Nw)\n cube_rebin.header.update(CD1_1=-dx*nbin/3600.)\n cube_rebin.header.update(CD2_2=dy*nbin/3600.)\n cube_rebin.header.update(CD3_3=dw)\n cube_rebin.header.update(CRPIX1=0)\n cube_rebin.header.update(CRPIX2=0)\n cube_rebin.header.update(CRPIX3=0)\n cube_rebin.header.update(CRVAL1=x0)\n cube_rebin.header.update(CRVAL2=y0)\n cube_rebin.header.update(CRVAL3=w0)\n \n cube_rebin.header.update(CTYPE1='RA---SIN')\n cube_rebin.header.update(CTYPE2='DEC--SIN')\n cube_rebin.header.update(CTYPE3=wtype)\n cube_rebin.header.update(CUNIT3=wunit)\n cube_rebin.header.update(CUNIT1='deg')\n cube_rebin.header.update(CUNIT2='deg')\n \n cube_rebin.header.update(CD1_2=0)\n cube_rebin.header.update(CD1_3=0)\n cube_rebin.header.update(CD2_1=0)\n cube_rebin.header.update(CD2_3=0)\n cube_rebin.header.update(CD3_1=0)\n cube_rebin.header.update(CD3_2=0)\n cube_rebin.verify('fix')\n if keep_units:\n cube_rebin.header.update(BUNIT= dhdr['BUNIT']) ##the rss one!!\n else:\n cube_rebin.header.update(BUNIT= 'erg/s/cm**2/Angstrom') \n\n\n\n \n cube_rebin.data = numpy.zeros( (Nw,Ny//nbin,Nx//nbin) )\n print('')\n print('2nd step')\n for i in range( 0, Nwspec) : \n shape=cube.data[i].shape \n print(str(i)+'/'+str(Nwspec)+' spectral channels',end=\"\\r\")\n for xi in numpy.arange(0,shape[0],nbin)[:-1]:\n for yj in numpy.arange(0,shape[1],nbin)[:-1]:\n pixel_ij=numpy.sum(cube.data[i][xi:xi+nbin,yj:yj+nbin]) \n cube_rebin.data[i][xi//nbin,yj//nbin]=pixel_ij \n if writeout !=None:\n cube_rebin.writeto(writeout,overwrite=overwrite)\n return( cube_rebin)","def generate_vec_file(args, path_list, file_name):\n if args.vec != None:\n #If the user wants to create a vec file, so calls opencv to create it.\n command = \" \".join([\"opencv_createsamples -vec\", args.vec,\n \"-info\", os.path.join(path_list, file_name),\n \"-num\", str(args.num), \"-h\", str(args.height),\n \"-w\", str(args.width)])\n execute_commands([command])\n if args.out_img_folder == None:\n \"\"\"If the user don't want to save created samples,\n so deletes the entire folder\"\"\"\n rmtree(os.path.abspath(path_list))","def from_srf_file(self, filename, normalize=False):\n with open(filename, \"rt\") as f:\n # go to POINTS block\n line = f.readline()\n while 'POINTS' not in line:\n line = f.readline()\n\n npoints = int(line.split()[1])\n sources = []\n\n for _ in np.arange(npoints):\n lon, lat, dep, stk, dip, area, tinit, dt = \\\n map(float, f.readline().split())\n rake, slip1, nt1, slip2, nt2, slip3, nt3 = \\\n map(float, f.readline().split())\n\n dep *= 1e3 # km > m\n area *= 1e-4 # cm^2 > m^2\n slip1 *= 1e-2 # cm > m\n slip2 *= 1e-2 # cm > m\n # slip3 *= 1e-2 # cm > m\n\n nt1, nt2, nt3 = map(int, (nt1, nt2, nt3))\n\n if nt1 > 0:\n line = f.readline()\n while len(line.split()) < nt1:\n line = line + f.readline()\n stf = np.array(line.split(), dtype=float)\n if normalize:\n stf /= np.trapz(stf, dx=dt)\n\n M0 = area * DEFAULT_MU * slip1\n\n sources.append(\n Source.from_strike_dip_rake(\n lat, lon, dep, stk, dip, rake, M0,\n time_shift=tinit, sliprate=stf, dt=dt))\n\n if nt2 > 0:\n line = f.readline()\n while len(line.split()) < nt2:\n line = line + f.readline()\n stf = np.array(line.split(), dtype=float)\n if normalize:\n stf /= np.trapz(stf, dx=dt)\n\n M0 = area * DEFAULT_MU * slip2\n\n sources.append(\n Source.from_strike_dip_rake(\n lat, lon, dep, stk, dip, rake, M0,\n time_shift=tinit, sliprate=stf, dt=dt))\n\n if nt3 > 0:\n raise NotImplementedError('Slip along u3 axis')\n\n return self(pointsources=sources)","def SingleQubitRBT(qubit: qreg, seqFileDir, analyzedPulse: pulse, add_cals=True):\n\n # Original:\n # # Setup a pulse library\n # pulseLib = [AC(qubit, cliffNum) for cliffNum in range(24)]\n # pulseLib.append(analyzedPulse)\n # measBlock = MEAS(qubit)\n\n # seqs = []\n # for ct in range(10):\n # fileName = 'RBT_Seqs_fast_{0}_F1.txt'.format(ct+1)\n # tmpSeqs = []\n # with open(os.path.join(seqFileDir, fileName),'r') as FID:\n # fileReader = reader(FID)\n # for pulseSeqStr in fileReader:\n # seq = []\n # for pulseStr in pulseSeqStr:\n # seq.append(pulseLib[int(pulseStr)-1])\n # seq.append(measBlock)\n # tmpSeqs.append(seq)\n # seqs += tmpSeqs[:12]*12 + tmpSeqs[12:-12] + tmpSeqs[-12:]*12\n\n # seqsPerFile = 100\n # numFiles = len(seqs)//seqsPerFile\n\n # for ct in range(numFiles):\n # chunk = seqs[ct*seqsPerFile:(ct+1)*seqsPerFile]\n # # Tack on the calibration scalings\n # if add_cals:\n # numCals = 4\n # chunk += [[Id(qubit), measBlock]]*numCals + [[X(qubit), measBlock]]*numCals\n # fileNames = compile_to_hardware(chunk, 'RBT/RBT', suffix='_{0}'.format(ct+1))\n\n pulseSeqStrs = []\n for ct in range(10):\n fileName = 'RBT_Seqs_fast_{0}_F1.txt'.format(ct+1)\n tmpSeqs = []\n with open(os.path.join(seqFileDir, fileName),'r') as FID:\n fileReader = reader(FID)\n for pulseSeqStr in fileReader:\n tmpSeqs.append(pulseSeqStr)\n pulseSeqStrs = tmpSeqs[:12]*12 + tmpSeqs[12:-12] + tmpSeqs[-12:]*12\n\n numSeqs = len(pulseSeqStrs)\n seqsPerFile = 100\n numFiles = numSeqs//seqsPerFile\n numCals = 4\n\n for ct in range(numFiles):\n for s in range(seqsPerFile):\n init(qubit)\n seqStr = pulseSeqStrs[ct*seqsPerFile+s]\n getPulseSeq(qubit, seqStr)\n if add_cals:\n # Add numCals calibration scalings\n for _ in range(numCals):\n init(qubit)\n Id(qubit)\n MEAS(qubit)\n\n init(qubit)\n X(qubit)\n MEAS(qubit)","def get_input(inputfile, ndim, radii):\n\n d = readdump(inputfile, ndim)\n d.read_onefile()\n results = []\n for n in range(d.SnapshotNumber):\n ParticleRadii = np.array(pd.Series(d.ParticleType[n]).map(radii))\n PositionRadii = np.column_stack((d.Positions[n], ParticleRadii))\n voroinput = np.column_stack((np.arange(d.ParticleNumber[n]) + 1, PositionRadii))\n results.append(voroinput)\n\n return (results, d.Boxbounds)","def signalroisample(filename,obs):\n from samplingdist import readworkspace,readfile\n #f,w,obsdict,modeldict,databkgdict,datasigdict = readworkspace(filename)\n f,obsdict,modeldict,databkgdict,datasigdict = readfile(filename)\n if not obsdict.has_key(obs):\n raise RuntimeError(\"Observable '%s' not defined\" % obs)\n sd = ObservableSamplingProb(obsdict[obs])\n sd.setupmodel('bkg','negative_binomial_pdf')\n sd.setupmodel('sig','negative_binomial_sum_pdf')\n\n datasig = datasigdict['dvsig_'+obs]\n databkg = databkgdict['dvbkg_'+obs]\n sd.fitTo(datasig,'sig')\n sd.fitTo(databkg,'bkg')\n\n samplename = filename.split('_')[1]\n sd.plot(samplename,datasig,'sig',sample=samplename+'_sig')\n sd.plot(samplename,databkg,'bkg',sample=samplename+'_bkg')\n\n nfile = filename.split('_')[1]+'_bkgsig_'+obs+'_ws.root'\n sd.update('w',nfile,[datasig,databkg])","def add_pixel_fn(filename: str, resample_name: str) -> None:\n\n header = \"\"\" <VRTRasterBand dataType=\"Byte\" band=\"1\" subClass=\"VRTDerivedRasterBand\">\"\"\"\n contents = \"\"\"\n <PixelFunctionType>{0}</PixelFunctionType>\n <PixelFunctionLanguage>Python</PixelFunctionLanguage>\n <PixelFunctionCode><![CDATA[{1}]]>\n </PixelFunctionCode>\"\"\"\n\n lines = open(filename, 'r').readlines()\n lines[3] = header # FIX ME: 3 is a hand constant\n lines.insert(4, contents.format(resample_name,\n get_resample(resample_name)))\n open(filename, 'w').write(\"\".join(lines))","def dorv(visitfiles) :\n # last list elements has configuration variables in a tuple\n allvisit = visitfiles[0]\n load = visitfiles[1]\n field=visitfiles[-1][0]\n obj=visitfiles[-1][1].decode('UTF-8')\n clobber=visitfiles[-1][2]\n verbose=visitfiles[-1][3]\n tweak=visitfiles[-1][4]\n plot=visitfiles[-1][5]\n windows=visitfiles[-1][6]\n #rvrange=visitfiles[-1][7]\n if tweak: suffix='_tweak'\n else : suffix='_out'\n outdir = os.path.dirname(load.filename('Star',field=field,obj=obj))\n outdir = outdir.replace('/stars/','/rv/')\n\n if os.path.exists(outdir+'/'+obj+suffix+'.pkl') and not clobber:\n print(obj,' already done')\n fp=open(outdir+'/'+obj+suffix+'.pkl','rb')\n try: \n out=pickle.load(fp)\n fp.close()\n return out\n except: \n print('error loading: ', obj+suffix+'.pkl')\n pass\n\n speclist=[]\n pixelmask=bitmask.PixelBitMask()\n badval=pixelmask.badval()|pixelmask.getval('SIG_SKYLINE')|pixelmask.getval('LITTROW_GHOST')\n \n # if we have a significant number of low S/N visits, combine first using\n # barycentric correction only, use that to get an estimate of systemic\n # velocity, then do RV determination restricting RVs to within 50 km/s\n # of estimate. This seems to help significant for faint visits\n lowsnr_visits=np.where(allvisit['SNR']<10)[0]\n if (len(lowsnr_visits) > 1) & (len(lowsnr_visits)/len(allvisit) > 0.1) :\n try :\n apstar_bc=visitcomb(allvisit,bconly=True,load=load,write=False,dorvfit=False) \n apstar_bc.setmask(badval)\n spec=doppler.Spec1D(apstar_bc.flux[0,:],err=apstar_bc.err[0,:],bitmask=apstar_bc.bitmask[0,:],\n mask=apstar_bc.mask[0,:],wave=apstar_bc.wave,lsfpars=np.array([0]),\n lsfsigma=apstar_bc.wave/22500/2.354,instrument='APOGEE',\n filename=apstar_bc.filename)\n print('running BC jointfit for :',obj)\n out= doppler.rv.jointfit([spec],verbose=verbose,plot=plot,tweak=tweak,maxvel=[-500,500])\n rvrange=[out[1][0]['vrel']-50,out[1][0]['vrel']+50]\n except :\n print(' BC jointfit failed')\n rvrange=[-500,500]\n elif allvisit['H'].max() > 13.5 : \n # if it's faint, restrict to +/- 500 km/s\n rvrange=[-500,500]\n else :\n # otherwise, restrict to +/ 1000 km/s\n rvrange=[-1000,1000]\n\n for i in range(len(allvisit)) :\n\n # load all of the visits into doppler Spec1D objects\n if load.telescope == 'apo1m' :\n visitfile= load.allfile('Visit',plate=allvisit['PLATE'][i],\n mjd=allvisit['MJD'][i],reduction=allvisit['APOGEE_ID'][i])\n else :\n visitfile= load.allfile('Visit',plate=int(allvisit['PLATE'][i]),\n mjd=allvisit['MJD'][i],fiber=allvisit['FIBERID'][i])\n spec=doppler.read(visitfile,badval=badval)\n\n if windows is not None :\n # if we have spectral windows to mask, do so here\n for ichip in range(3) :\n mask = np.full_like(spec.mask[:,ichip],True)\n gd = []\n for window in windows :\n gd.extend(np.where((spec.wave[:,ichip] > window[0]) & (spec.wave[:,ichip] < window[1]))[0])\n mask[gd] = False\n spec.mask[:,ichip] |= mask\n \n if spec is not None : speclist.append(spec)\n\n # now do the doppler jointfit to get RVs\n # dump empty pickle to stand in case of failure (to prevent redo if not clobber)\n try:\n # dump empty pickle to stand in case of failure (to prevent redo if not clobber)\n fp=open(outdir+'/'+obj+suffix+'.pkl','wb')\n pickle.dump(None,fp)\n fp.close()\n print('running jointfit for : {:s} rvrange:[{:.1f},{:.1f}] nvisits: {:d}'.format(obj,*rvrange,len(speclist)))\n out= doppler.rv.jointfit(speclist,maxvel=rvrange,verbose=verbose,\n plot=plot,saveplot=plot,outdir=outdir+'/',tweak=tweak)\n print('running decomp for :',obj)\n gout = gauss_decomp(out[1],phase='two',filt=True)\n fp=open(outdir+'/'+obj+suffix+'.pkl','wb')\n pickle.dump([out,gout],fp)\n fp.close()\n print('running plots for :',obj,outdir)\n try : os.makedirs(outdir+'/plots/')\n except : pass\n dop_plot(outdir+'/plots/',obj,out,decomp=gout)\n except KeyboardInterrupt : \n raise\n except ValueError as err:\n print('Exception raised in dorv for: ', field, obj)\n print(\"ValueError: {0}\".format(err))\n return\n except RuntimeError as err:\n print('Exception raised in dorv for: ', field, obj)\n print(\"Runtime error: {0}\".format(err))\n return\n except :\n raise\n print('Exception raised in dorv for: ', field, obj)\n return\n\n # return summary RV info, visit RV info, decomp info \n return [out[0:2],gout]","def dirToRAD(directory, toFile):\n #Open Directory\n trainFiles = [file for file in os.listdir(directory) if file.endswith(\".txt\")]\n fOut = open(toFile, 'w')\n for file in trainFiles:\n print(file)\n rawData = getRawData(directory+file)\n if 'rad' in toFile:\n data = rawToRAD(rawData)\n elif 'cust' in toFile:\n data = rawToCust(rawData)\n else:\n print('ERROR. Transformation to ' + toFile + ' failed. Please enter a string containing cust for custom representation or rad for RAD format.')\n #print('Raw:', rawData)\n #print('RAD:', RADdata)\n label = int(file[1:3])\n if len(sys.argv) > 1:\n enumData = enumerate(getHistogram(data, file, int(sys.argv[1])))\n else:\n enumData = enumerate(getHistogram(data, file))\n fOut.write(str(label) + ' ')\n fOut.writelines([\" %d:%s\" % (index+1, elem) for index,elem in enumData])\n fOut.write('\\n')\n fOut.close()","def rebin_spectra(source):\n # Unpack arguments using the 'source' label; all are assumed to be in km/s\n v_lo, v_hi, new_dv = velocity_ranges[source]\n # Load data cube\n data_filename = os.path.abspath(data_filepaths[source])\n cube = SpectralCube.read(data_filename)\n # Check units\n try:\n # See if the cube can be converted to Kelvins easily\n cube = cube.to(u.K)\n except:\n # Check if it looks like a temperature\n old_bunit = cube.header['BUNIT']\n if \"K (Ta*)\" in old_bunit:\n cube._unit = u.K\n print(f\"Data unit {cube.unit} assigned, based on the header BUNIT {old_bunit}.\")\n else:\n # Don't bother trying to fix it, leave it alone\n print(f\"Data units <{cube._unit}> aren't equivalent to Kelvins, leaving them alone\")\n # Get current channel width (np.diff should return an array of all the same value, so np.mean is overkill but it doesn't matter)\n old_dv = np.mean(np.diff(cube.spectral_axis))\n # Construct a box filter to average the channels\n # Filter width is number of channels; if rebinning from 0.1 km/s to 1 km/s, filter is 10 channels\n # Need to add km/s units to new_dv (old_dv already has units)\n filter_width = np.abs(((new_dv*u.km/u.s) / old_dv).decompose().to_value())\n # Round to nearest integer\n filter_width = np.around(filter_width, 0)\n # Make filter using astropy.convolution.Box1DKernel\n filter = Box1DKernel(filter_width)\n # Define the new spectral axis using the inclusive limits and the new channel width\n new_spectral_axis = np.arange(v_lo, v_hi+new_dv, new_dv) * u.km/u.s\n\n # Do the computationally intensive work\n print(\"Starting spectral smooth\")\n cube = cube.spectral_smooth(filter)\n print(\"Finished spectral smooth. Starting spectral rebin.\")\n cube = cube.spectral_interpolate(new_spectral_axis)\n print(\"Finished spectral rebin.\")\n # Create savename with \"rebin\" and the channel width inserted before the filetype suffix\n save_filename = data_filename.replace(\".fits\", f\".rebin{new_dv:d}kms.fits\")\n cube.write(save_filename, format='fits')","def make(input_filepath, output_filepath) -> None:\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def flow_write(filename,uv,v=None):\n TAG_CHAR = 'PIEH'\n nBands = 2\n\n if v is None:\n assert(uv.ndim == 3)\n assert(uv.shape[2] == 2)\n u = uv[:,:,0]\n v = uv[:,:,1]\n else:\n u = uv\n\n assert(u.shape == v.shape)\n height,width = u.shape\n f = open(filename,'wb')\n # write the header\n f.write(TAG_CHAR)\n np.array(width).astype(np.int32).tofile(f)\n np.array(height).astype(np.int32).tofile(f)\n # arrange into matrix form\n tmp = np.zeros((height, width*nBands))\n tmp[:,np.arange(width)*2] = u\n tmp[:,np.arange(width)*2 + 1] = v\n tmp.astype(np.float32).tofile(f)\n f.close()","def flow_write(filename,uv,v=None):\n nBands = 2\n\n if v is None:\n assert(uv.ndim == 3)\n assert(uv.shape[2] == 2)\n u = uv[:,:,0]\n v = uv[:,:,1]\n else:\n u = uv\n\n assert(u.shape == v.shape)\n height,width = u.shape\n f = open(filename,'wb')\n TAG_CHAR = b'PIEH'\n f.write(TAG_CHAR)\n np.array(width).astype(np.int32).tofile(f)\n np.array(height).astype(np.int32).tofile(f)\n # arrange into matrix form\n tmp = np.zeros((height, width*nBands))\n tmp[:,np.arange(width)*2] = u\n tmp[:,np.arange(width)*2 + 1] = v\n tmp.astype(np.float32).tofile(f)\n f.close()","def modconvert(infile, outfile, scale_factor=1.0):\n index, freq, brightness_temp, flux, rj_temp = read_infile(infile)\n\n wave, flux, temp = sort_spectrum(freq, flux, brightness_temp)\n\n scaled_flux = scale_factor * flux\n\n plot_scaled_spectrum(wave, scaled_flux, scale_factor, infile)\n\n write_scaled_spectrum(wave, scaled_flux, scale_factor, temp,\n infile, outfile)\n\n model = pd.DataFrame({'wavelength': wave, 'flux': scaled_flux,\n 't_br': temp})\n return model","def spectrum_parser():\n from tools import file_importer, file_outporter\n from random import random\n # from math import log10\n \n print(\"this is spectrum parser\")\n \n relPath = \"bob/processed/OST-24-05-2017_combined.csv\"\n outPath = \"bob/processed/OST-24-05-2017_combined_no0_spectrum.csv\"\n inpF = file_importer(relPath)\n outF = file_outporter(outPath) \n headerFlag = True\n rowCount = 0\n for inpLine in inpF:\n if headerFlag: \n headerFlag = False\n spColCount = 0\n inpList = inpLine.split(\"\\t\")\n for headerI in inpList:\n if \"Peptides ST-1|Peptides ST-2|Peptides ST-3\" == headerI:\n break\n else: spColCount += 1\n outF.write(\"ID,Protein ID, Gene name,\") # write header into output file\n for headI in inpList[spColCount].split(\"|\"):\n outF.write(headI + \",\")\n for headI in inpList[spColCount + 1].split(\"|\")[:-1]:\n outF.write(headI + \",\")\n outF.write(inpList[spColCount + 1].split(\"|\")[-1] + \"\\n\")\n rowCount += 1\n continue\n \n outF.write(str(rowCount) + \",\")\n \n inpLine = inpLine.strip()\n inpItems = inpLine.split(\"\\t\")\n inpName = max(inpItems[0].split(\"|\"), key=len) # get unique protein ID\n inpGeneName = max(inpItems[6].split(\"|\"), key=len) # and gene name\n outF.write(inpName + \",\" + inpGeneName + \",\")\n \n inpSP = inpItems[spColCount].split(\"|\") + inpItems[spColCount + 1].split(\"|\") # get lfq intensity scores\n # print inpSP\n for lfqI in inpSP[:-1]: # write lfq values\n if lfqI == \"_\" or lfqI == \"0\":\n outF.write(str(random()) + \",\") ################## try with log10 values this time\n else:\n try:\n outF.write(str(lfqI) + \",\")\n except ValueError:\n print(inpItems)\n raise\n \n if inpSP[-1] == \"_\" or inpSP[-1] == \"0\": outF.write(str(random()) + \"\\n\")\n else: outF.write(inpSP[-1] + \"\\n\")\n \n \n \n rowCount += 1","def read_raytomo_dbase(self, inh5fname, runid, dtype='ph', wtype='ray', create_header=True, Tmin=-999, Tmax=999, verbose=False):\n if dtype is not 'ph' and dtype is not 'gr':\n raise ValueError('data type can only be ph or gr!')\n if wtype is not 'ray' and wtype is not 'lov':\n raise ValueError('wave type can only be ray or lov!')\n stalst = self.waveforms.list()\n if len(stalst) == 0:\n print 'Inversion with surface wave datasets only, not added yet!'\n return\n indset = h5py.File(inh5fname)\n #--------------------------------------------\n # header information from input hdf5 file\n #--------------------------------------------\n dataid = 'reshaped_qc_run_'+str(runid)\n pers = indset.attrs['period_array']\n grp = indset[dataid]\n isotropic = grp.attrs['isotropic']\n org_grp = indset['qc_run_'+str(runid)]\n minlon = indset.attrs['minlon']\n maxlon = indset.attrs['maxlon']\n minlat = indset.attrs['minlat']\n maxlat = indset.attrs['maxlat']\n if isotropic:\n print 'isotropic inversion results do not output gaussian std!'\n return\n dlon_HD = org_grp.attrs['dlon_HD']\n dlat_HD = org_grp.attrs['dlat_HD']\n dlon = org_grp.attrs['dlon']\n dlat = org_grp.attrs['dlat']\n if create_header:\n inv_header = {'minlon': minlon, 'maxlon': maxlon, 'minlat': minlat, 'maxlat': maxlat,\n 'dlon': dlon, 'dlat': dlat, 'dlon_HD': dlon_HD, 'dlat_HD': dlat_HD}\n self.add_auxiliary_data(data=np.array([]), data_type='Header', path='raytomo', parameters=inv_header)\n self._get_lon_lat_arr(path='raytomo', hd=True)\n for staid in stalst:\n netcode, stacode = staid.split('.')\n staid_aux = netcode+'_'+stacode\n stla, elev, stlo = self.waveforms[staid].coordinates.values()\n if stlo < 0.:\n stlo += 360.\n if stla > maxlat or stla < minlat or stlo > maxlon or stlo < minlon:\n print 'WARNING: station: '+ staid+', lat = '+str(stla)+' lon = '+str(stlo)+', out of the range of tomograpic maps!'\n continue\n disp_v = np.array([])\n disp_un = np.array([])\n T = np.array([])\n #-----------------------------\n # determine the indices\n #-----------------------------\n ind_lon = np.where(stlo<=self.lons)[0][0]\n find_lon = ind_lon \n ind_lat = np.where(stla<=self.lats)[0][0]\n find_lat = ind_lat\n # point 1\n distmin, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon]) # distance is in m\n # point 2\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon-1]) # distance is in m\n if dist < distmin:\n find_lon = ind_lon-1\n distmin = dist\n # point 3\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon]) # distance is in m\n if dist < distmin:\n find_lat = ind_lat-1\n distmin = dist\n # point 4\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon-1]) # distance is in m\n if dist < distmin:\n find_lat = ind_lat-1\n find_lon = ind_lon-1\n distmin = dist\n for per in pers:\n if per < Tmin or per > Tmax:\n continue\n try:\n pergrp = grp['%g_sec'%( per )]\n vel = pergrp['vel_iso_HD'].value\n vel_sem = pergrp['vel_sem_HD'].value\n except KeyError:\n if verbose:\n print 'No data for T = '+str(per)+' sec'\n continue\n T = np.append(T, per)\n disp_v = np.append(disp_v, vel[find_lat, find_lon])\n disp_un = np.append(disp_un, vel_sem[find_lat, find_lon])\n data = np.zeros((3, T.size))\n data[0, :] = T[:]\n data[1, :] = disp_v[:]\n data[2, :] = disp_un[:]\n disp_header = {'Np': T.size}\n self.add_auxiliary_data(data=data, data_type='RayDISPcurve', path=wtype+'/'+dtype+'/'+staid_aux, parameters=disp_header)\n indset.close()\n return","def preprocess_sample(file, params):\n\n videoFile = file + \".mp4\"\n audioFile = file + \".wav\"\n roiFile = file + \".png\"\n visualFeaturesFile = file + \".npy\"\n\n roiSize = params[\"roiSize\"]\n normMean = params[\"normMean\"]\n normStd = params[\"normStd\"]\n vf = params[\"vf\"]\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\n #Extract the audio from the video file using the FFmpeg utility and save it to a wav file.\n v2aCommand = \"ffmpeg -y -v quiet -i \" + videoFile + \" -ac 1 -ar 16000 -vn \" + audioFile\n os.system(v2aCommand)\n\n\n #for each frame, resize to 224x224 and crop the central 112x112 region\n captureObj = cv.VideoCapture(videoFile)\n roiSequence = list()\n while (captureObj.isOpened()):\n ret, frame = captureObj.read()\n if ret == True:\n grayed = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n grayed = grayed/255\n grayed = cv.resize(grayed, (224,224))\n roi = grayed[int(112-(roiSize/2)):int(112+(roiSize/2)), int(112-(roiSize/2)):int(112+(roiSize/2))]\n roiSequence.append(roi)\n else:\n break\n captureObj.release()\n cv.imwrite(roiFile, np.floor(255*np.concatenate(roiSequence, axis=1)).astype(np.int))\n\n\n #normalise the frames and extract features for each frame using the visual frontend\n #save the visual features to a .npy file\n inp = np.stack(roiSequence, axis=0)\n inp = np.expand_dims(inp, axis=[1,2])\n inp = (inp - normMean)/normStd\n inputBatch = torch.from_numpy(inp)\n inputBatch = (inputBatch.float()).to(device)\n vf.eval()\n with torch.no_grad():\n outputBatch = vf(inputBatch)\n out = torch.squeeze(outputBatch, dim=1)\n out = out.cpu().numpy()\n np.save(visualFeaturesFile, out)\n return","def preprocess(self, name, input_file, observation):\n\n lba = LBAFile(input_file, self.sample_rate)\n\n if self.max_samples > 0:\n # User specified max number of samples\n max_samples = min(self.max_samples, lba.max_samples)\n else:\n max_samples = lba.max_samples\n\n # Length of the observation for the number of samples we're reading\n obs_length = lba.obs_length(max_samples)\n start = lba.obs_start\n end = start + datetime.timedelta(seconds=obs_length)\n\n LOG.info(\"LBA obs time: start {0} end {1} duration {2} sec\".format(start, end, obs_length))\n\n observation.observation_name = os.path.basename(name)\n observation.additional_metadata = json.dumps(lba.header)\n observation.antenna_name = self.antenna_name if self.antenna_name is not None else lba.header.get('ANTENNANAME', '')\n observation.sample_rate = self.sample_rate\n observation.length_seconds = obs_length\n observation.start_time = start.timestamp()\n observation.num_channels = lba.num_channels\n observation.num_samples = max_samples\n\n channel_map = None\n if self.obs_filename is not None:\n try:\n vex = pyvex.Vex(self.obs_filename)\n self._fill_source_array(observation, vex, start, end, max_samples)\n\n # Get channel info from the VEX file.\n # Pick the appropriate mode from the vex file. We run with the assumption for now that there's only one\n # mode, and get everything from it. If there are multiple modes, then error out\n if len(vex.modes) == 0:\n LOG.warning(\"No modes in vex file to get channel info from\")\n elif len(vex.modes) > 1:\n LOG.error(\"Cannot get channel information from vex file because multiple modes are present. This is currently unsupported\")\n else:\n # Get antenna info\n antenna = next((a for a in vex.antennas if a.def_name == self.antenna_name), None)\n if antenna is None:\n LOG.error(\"Specified antenna def name {0} is not present in the vex file\".format(self.antenna_name))\n else:\n LOG.info(\"Found antenna def name {0}. Name {1}\".format(self.antenna_name, antenna.name))\n mode = vex.modes[0]\n setup = mode.setups[antenna.name]\n channel_map = [[channel, mode.subbands[channel.subband_id], setup.ifs[\"IF_{0}\".format(channel.if_name)]] for channel in setup.channels]\n # TODO: I'm unsure if this is correct or what order we should use here\n # - Ordering by subband_id will match the CH0(0-8) ordering present in the vex file.\n # - Ordering by record_chan seems to make some logic sense as it orders them from low to\n # high frequency across pol R then pol L. It also orders the BBC values ascending.\n # - Neither of these seem to match the observation_details.md table.\n\n # TODO: For now, I'll assume sorting by record chan\n channel_map.sort(key=lambda c: c[0].record_chan)\n\n except Exception as e:\n LOG.error(\"Failed to parse vex file {0}\".format(e))\n\n samples_read = 0\n while samples_read < max_samples:\n remaining_samples = max_samples - samples_read\n samples_to_read = min(remaining_samples, self.chunk_size)\n samples = lba.read(samples_read, samples_to_read)\n\n for channel_index in range(samples.shape[1]):\n # Loop over each channel, then either output the channel directly\n # into the HDF5 file, or get the channel info to get the appropriate metadata\n # for the channel, then output that.\n\n channel_name = \"channel_{0}\".format(channel_index)\n\n out_channel = observation[channel_name]\n if out_channel is None:\n out_channel = observation.create_channel(channel_name, shape=(max_samples,), dtype=np.int8)\n if channel_map is not None:\n vex_channel, vex_subband, vex_if = channel_map[channel_index]\n # Add info from the vex file to the channel\n out_channel.freq_start = vex_channel.bbc_freq\n out_channel.freq_end = vex_channel.bbc_freq + vex_channel.bbc_bandwidth\n out_channel.additional_metadata = json.dumps({\n 'channel_name': vex_channel.name,\n 'polarisation': vex_subband.pol.decode('utf-8'),\n 'bbc_name': vex_channel.bbc_name,\n 'record_chan': vex_channel.record_chan,\n 'subband_id': vex_channel.subband_id,\n 'vlba_band_name': vex_if.vlba_band_name,\n 'if_sslo': vex_if.if_sslo,\n 'lower_edge_freq': vex_if.lower_edge_freq\n }, indent=4)\n data = samples[:, channel_index]\n out_channel.write_data(samples_read, data)\n\n samples_read += samples_to_read\n\n if (samples_read // self.chunk_size) % 10 == 0:\n LOG.info(\"{0:.2%} {1}/{2}\".format(samples_read / max_samples, samples_read, max_samples))\n\n LOG.info(\"LBA preprocessor complete\")","def createOutput():\n\n firstPeriod = True\n # get edge No\n edgesNo = 0\n edgesSet = set()\n for timestep, taxiList in vtypeDict.iteritems():\n for tup in taxiList:\n edgesSet.add(tup[1])\n edgesNo = len(edgesSet)\n\n outputFile = open(path.FQoutput, 'w')\n outputFile.write('<?xml version=\"1.0\"?>\\n')\n outputFile.write('<paramEffects aggregationInterval=\"%d\" vehicles=\"%d\" edges=\"%d\">\\n' % (\n aggInterval, vehSum, edgesNo))\n for period, quota, vtypeDictR, taxiSum in generatePeriodQuotaSets(True):\n if quota is None:\n if not firstPeriod:\n outputFile.write(\"\\t</periods>\\n\")\n else:\n firstPeriod = False\n outputFile.write('\\t<periods period=\"%d\">\\n' % (period))\n else:\n simpleTaxiMeanVList = [0, 1]\n simpleEdgeMeanVList = [0, 1]\n drivenEdgesSet = set()\n\n if len(vtypeDictR) == 0: # if the processed FCD returns no Values\n print(\"noData p\", period, \" q\", quota)\n drivenEdgesSet.add(0)\n else: # create mean from all taxi speed values\n for timestep, taxiList in vtypeDictR.iteritems():\n for tup in taxiList: # all elements in this timestep\n simpleTaxiMeanVList[0] += tup[2]\n simpleTaxiMeanVList[1] += 1\n drivenEdgesSet.add(tup[1])\n # create mean from all edge speed values which are driven by the\n # chosen taxis\n drivenEdgesList = list(drivenEdgesSet)\n drivenEdgesList.sort()\n # print \"dataSets \",simpleTaxiMeanVList[1]\n\n # --edgeDump-- #\n \"\"\"\n for i in edgeDumpDict.keys(): #all intervals\n for edge,v in edgeDumpDict[i]:\n if BinarySearch.isElmInList(drivenEdgesList,edge):\n simpleEdgeMeanVList[0]+=v\n simpleEdgeMeanVList[1]+=1\n \"\"\"\n # --vtype-- #\n\n for timestep, taxiList in vtypeDict.iteritems():\n for tup in taxiList:\n if BinarySearch.isElmInList(drivenEdgesList, tup[1]):\n simpleEdgeMeanVList[0] += tup[2]\n simpleEdgeMeanVList[1] += 1\n\n # calc values for output\n detectedEdges = len(drivenEdgesSet)\n relDetectedEdges = detectedEdges * 100.0 / edgesNo\n vSim = simpleEdgeMeanVList[0] / simpleEdgeMeanVList[1]\n vSimFCD = simpleTaxiMeanVList[0] / simpleTaxiMeanVList[1]\n vAbsDiff = vSimFCD - vSim\n if vSim != 0:\n vRelDiff = vAbsDiff / vSim * 100\n else:\n vRelDiff = 100\n if vRelDiff < -40:\n vRelDiff = -35\n\n outputFile.write('\\t\\t<values taxiQuota=\"%f\" taxis=\"%d\" simMeanSpeed=\"%f\" simFcdMeanSpeed=\"%f\" ' % (\n quota, taxiSum, vSim, vSimFCD,))\n outputFile.write('detectedEdges=\"%d\" notDetectedEdges=\"%d\" ' % (\n detectedEdges, edgesNo - detectedEdges))\n outputFile.write('absSpeedDiff=\"%f\" relSpeedDiff=\"%f\" relDetectedEdges=\"%f\" relNotDetectedEdges=\"%f\"/>\\n' %\n (vAbsDiff, vRelDiff, relDetectedEdges, 100 - relDetectedEdges))\n outputFile.write(\"\\t</periods>\\n</paramEffects>\")\n outputFile.close()","def build_spectrum(spectrum_filename):\n hdulist = fits.open(spectrum_filename)\n data = hdulist[1].data\n \n spec = Spectrum(data['wave'], data['flux'], data['error'])\n \n return spec","def t8_loadFile(self):\n print \"spectrumFile\"\n filename = tkFileDialog.askopenfilename()\n filename = self.filenameparser(filename)\n self.t8_filename = filename","def register(\n sample_file,\n select,\n src=dstain.config.DATA_RAW,\n output=dstain.config.REGISTRATION,\n window=512,\n downsample=1,\n patches=0,\n zip_patches=False,\n verbose=True,\n thumbnail_downsample=128,\n nfeatures=5000,\n ransacreprojthreshold=25,\n affine=True):\n\n # Reading list of files in each sample\n # samples[key] = value\n # key is the name of the sample\n # value is a list of filenames\n samples = dstain.utils.read_sample_file(sample_file, use_split=False)\n\n # Allow subselection of samples to register\n if select == ():\n select = sorted(samples.keys())\n\n # Loop through samples\n for (i, sample) in enumerate(select):\n print(\"Sample #{} / {}: {}\".format(i + 1, len(select), sample), flush=True)\n dstain.utils.register.register_sample(\n src, os.path.join(output, sample), samples[sample], window, downsample,\n patches, zip_patches, verbose, thumbnail_downsample, nfeatures, ransacreprojthreshold, affine)","def test_process_5_1_surround_file(self):\n test_path = pathlib.Path(__file__).parent.absolute() / 'data/surround.wav'\n self.default_kwargs['input_file'] = test_path\n self.encoder = FileEncoder(**self.default_kwargs)\n self.encoder.process()","def fromFile(self,fn = None):\n\n while True:\n\n if fn == None:\n fn = getFilename(\"Spectrometer file\",\"spec\")\n else:\n fn = getExpandedFilename(fn) # Sort out logicals\n if not fn.endswith(\"spec\"): # Append \".spec\" if not given\n fn += \".spec\"\n\n try:\n sfile= open(fn,\"r\") # open file\n lines = sfile.readlines()\n sfile.close()\n break\n except FileNotFoundError:\n getLogger().error(\"Failed to find spectrometer file : \" + str(fn))\n fn = None\n\n\n # read file and process one line at a time\n #\n\n # Read through line at a time\n for line in lines:\n\n line = line.strip()\n if not line.startswith(\"#\") and len(line) > 0: # Kill comments and blanks\n token = line.split()\n\n if token[0].startswith(\"point\"):\n v = eval(token[1])\n self.setPoint(v)\n\n elif token[0].startswith(\"index\"):\n self.setIndex(token[1])\n\n elif token[0].startswith(\"angle\"):\n self.angle = math.radians(float(token[1]))\n self.setTilt(self.tilt) # Reset surfaces\n\n elif token[0].startswith(\"height\"):\n self.height = float(token[1])\n\n elif token[0].startswith(\"beam\"):\n self.beam = float(token[1])\n\n elif token[0].startswith(\"tilt\"):\n self.setTilt(math.radians(token[1]))\n\n elif token[0].startswith(\"setup\"):\n self.setUpWavelength(float(token[1]))\n\n else:\n raise ValueError(\"Sprectometer: illegal key : {0:s}\".format(token[0]))\n\n return self","def mono(filename,wout=True):\n n, data, data_dB,sr,ch=inputwav(filename)\n if ch==2:\n print('Converting to mono...')\n L=data[:,0]\n R=data[:,1]\n n=len(data)\n data_m=np.zeros((n,1))\n data_m=L/2.0+R/2.0\n if wout==True:\n print('Exporting...')\n sf.write(filename[0:len(filename)-4]+'_mono.wav',data_m,sr,'PCM_16')\n print('Done!')\n return data_m\n else:\n print( \"Error: input is already mono stoooooooooopid!\")","def load(filename):\n root,ext = _os_path.splitext(filename)\n loader = LOADER[ext]\n frequency,raw_signal = loader(filename)\n iinfo = _numpy.iinfo(raw_signal.dtype)\n raw_signal_midpoint = (iinfo.max + iinfo.min)/2.\n raw_signal_range = iinfo.max - raw_signal_midpoint\n unit_output_signal = (raw_signal - raw_signal_midpoint)/raw_signal_range\n return (frequency, unit_output_signal)","def __convert_single_only_au_AutoVC_format_to_dataset__(self, filename, build_train_dataset=True):\n\n global_clip_index, video_name = filename\n\n # audio_file = os.path.join(self.src_dir, 'raw_wav', '{}.wav'.\n # format(video_name[:-4]))\n audio_file = os.path.join(self.src_dir, 'raw_wav', '{:05d}_{}_audio.wav'.\n format(global_clip_index, video_name[:-4]))\n if(not build_train_dataset):\n import shutil\n audio_file = os.path.join(self.src_dir, 'raw_wav', '{:05d}_{}_audio.wav'.\n format(global_clip_index, video_name[:-4]))\n shutil.copy(os.path.join(self.src_dir, 'test_wav_files', video_name), audio_file)\n\n sound = AudioSegment.from_file(audio_file, \"wav\")\n normalized_sound = match_target_amplitude(sound, -20.0)\n normalized_sound.export(audio_file, format='wav')\n\n from third_party.autovc.retrain_version.vocoder_spec.extract_f0_func import extract_f0_func_audiofile\n S, f0_norm = extract_f0_func_audiofile(audio_file, 'M')\n\n from third_party.autovc.utils import quantize_f0_interp\n f0_onehot = quantize_f0_interp(f0_norm)\n\n from third_party.resemblyer_util.speaker_emb import get_spk_emb\n mean_emb, _ = get_spk_emb(audio_file)\n\n return S, mean_emb, f0_onehot","def fix_file(file_path: str, output_path_fixed: str, target_rate: int=16000,\n channels: int=1, output_path: str=None, min_duration: int=None,\n verbose_level: int=0):\n file_name = file_path.split(os.sep)[-1]\n new_file_path = output_path_fixed + os.sep + file_name\n\n try:\n # Get the audio length\n out = syscommand.system('soxi -D ' + file_path)\n duration = float(out)\n # Get the number of channels\n out = syscommand.system('soxi -c ' + file_path)\n current_n_channels = int(out)\n # Get the current rate\n out = syscommand.system('soxi -r ' + file_path)\n current_rate = int(out)\n if min_duration is not None and duration < min_duration:\n raise Exception(\"Minimum length not satisfied\")\n except Exception as err:\n if str(verbose_level) != '0':\n print(err)\n return None, file_path\n\n if current_rate != target_rate and current_n_channels != channels:\n speed = float(current_rate) / float(target_rate)\n cmd = 'sox -V{} -r 16k {} {} channels 1 ' \\\n 'speed {}'.format(verbose_level, file_path, new_file_path,\n speed)\n if str(verbose_level) == '2':\n print(cmd)\n os.system(cmd)\n return new_file_path, file_path\n elif current_rate != target_rate:\n speed = float(current_rate) / float(target_rate)\n cmd = 'sox -V{} -r 16k {} {} ' \\\n 'speed {}'.format(verbose_level, file_path, new_file_path,\n speed)\n if str(verbose_level) == '2':\n print(cmd)\n os.system(cmd)\n return new_file_path, file_path\n elif current_n_channels != channels:\n cmd = 'sox -V{} {} {} channels 1'.format(verbose_level, file_path,\n new_file_path)\n if str(verbose_level) == '2':\n print(cmd)\n os.system(cmd)\n return new_file_path, file_path\n\n # Copy file if output path were provided and the file is not there\n if output_path is not None:\n copyfile(file_path, new_file_path)\n return file_path, file_path","def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\n outputs = []\n npy_outputs = []\n if resamplemethod == 'nearest':\n rs = Resampling.nearest\n else:\n print('only nearest neighbor resampling is supported at this time')\n sys.exit(0)\n\n for i, warpfile in enumerate(inputs):\n print('warpfile', warpfile)\n with rasterio.open(warpfile) as src:\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\n with WarpedVRT(src, resampling=rs,\n crs=self.crs,\n transform=self.transform,\n height=self.rows,\n width=self.cols) as vrt:\n data = vrt.read()\n print(type(vrt))\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\n outputs.append(outwarp)\n\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\n # from this method for us in the rest of the code.\n for ow in outputs:\n with rasterio.open(ow, 'r') as src:\n arr = src.read(1)\n npy_outputs.append(arr)\n\n return npy_outputs","def read_ultrasound_file(ult_file):\n\n return np.fromfile(open(ult_file, \"rb\"), dtype=np.uint8)","def generate_rsat_bg_file(input_file_path, output_file_path, strand_type):\n cmd = [\"sh\", \"generate_rsat_bg_file.sh\",\n \"-i\", input_file_path,\n \"-o\", output_file_path,\n \"-s\", strand_type]\n process = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n process.wait()","def single_file(filename, exposure_times, osc_start, osc_width, epoch):\n index = scan_helper_image_files.image_to_index(os.path.split(filename)[-1])\n if epoch is None:\n epoch = 0.0\n\n # if the oscillation width is negative at this stage it is almost\n # certainly an artefact of the omega end being 0 when the omega start\n # angle was ~ 360 so it would be ~ -360 - see dxtbx#378\n if osc_width < -180:\n osc_width += 360\n\n return ScanFactory.make_scan(\n (index, index), exposure_times, (osc_start, osc_width), {index: epoch}\n )","def t4_loadFile(self):\n print \"subspectrumFile\"\n filename = tkFileDialog.askopenfilename()\n filename = \"SubSpectrumData/\" + self.filenameparser(filename)\n self.t4_filename = filename","def __init__(self, in_file: str, out_file: str, sample_rate: float,\n max_time_gap: timedelta):\n\n self.in_file = in_file\n self.out_file = out_file\n\n # Set up the input/output file objects:\n self.in_data = MobileData(in_file, 'r')\n self.out_data = MobileData(out_file, 'w')\n\n # Hold information about the different fields to use:\n # All fields in the input file:\n self.all_fields = None # type: Optional[Dict[str, str]]\n\n # Stamp field name:\n self.stamp_field = default_stamp_field\n\n # Only sensor fields:\n self.sensor_fields = None # type: Optional[Dict[str, str]]\n\n # List of label fields:\n self.label_fields = default_label_fields\n\n # Determine the output sample interval in seconds:\n self.sample_interval = timedelta(seconds=1.0 / sample_rate)\n\n # Maximum gap between input events - longer than this and we will restart resampling:\n self.max_time_gap = max_time_gap\n\n # The previous and next output stamps to use:\n self.prev_out_stamp = None # type: Optional[datetime]\n self.next_out_stamp = None # type: Optional[datetime]\n\n # The next event from the input file:\n self.next_input_event = None # type: Optional[Dict[str, Union[float, str, datetime, None]]]\n\n # The last-seen input event:\n self.last_seen_input_event = None # type: Optional[Dict[str, Union[float, str, datetime, None]]]\n\n # Information about input events seen in a sample interval:\n self.num_events_in_interval = 0\n self.interval_sensor_values = None # type: Optional[Dict[str, List[float, str]]]\n self.interval_labels = None # type: Optional[Dict[str, List[str]]]\n\n # Status update info:\n self.status_num_events_interval = status_num_events_interval\n self.num_input_events_processed = 0\n self.num_events_since_last_status = 0\n self.first_event_stamp = None # type: Optional[datetime]","def rdmb_povray(file_base,\n time_point=2000,\n width=800, height=600,\n angle=14):\n\n file_prms = file_base + \"_prms.npz\"\n vs, *_ = load_rd_prms(file_prms)\n file_uv = file_base + \"_{:05}.npz\".format(time_point)\n\n file_png = file_base + \"_{:05}.png\".format(time_point)\n ucs, vcs = load_rd_uv(file_uv)\n\n rdmb_povray_save(file_png,\n vs,\n ucs, vcs,\n width=width, height=height,\n rotx=0, roty=0, rotz=0,\n angle=angle)\n\n return file_png","def t1_loadFile(self):\n print \"subspectrumFile\"\n filename = tkFileDialog.askopenfilename()\n filename = self.filenameparser(filename)\n self.t1_filename = filename","def convert_file(self, file_path: str) -> None:\n print(\n f\"Converting {os.path.split(file_path)[1]} to {self.output_format}\")\n\n output = AudioSegment.from_file(file_path, format=self.output_format)\n output.export(file_path.replace(self.input_format,\n f'{self.output_format}'), format=self.output_format)","def test_input_flux_file():\n # Generate an input file\n flux_input_file = tstutils.data_path('test.flux')\n if os.path.isfile(flux_input_file):\n os.remove(flux_input_file)\n\n cfg_lines = ['[fluxcalib]']\n cfg_lines += [' extinct_correct = False # Set to True if your SENSFUNC derived with the UVIS algorithm\\n']\n cfg_lines += ['# Please add your SENSFUNC file name below before running pypeit_flux_calib']\n\n # These files need to be in tests/files/\n data = Table()\n data['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data['sensfile'] = 'sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits'\n # \n paths = [tstutils.data_path('')]\n\n fluxFile = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data)\n # Write\n fluxFile.write(flux_input_file)\n\n # Read\n fluxFile2 = inputfiles.FluxFile.from_file(flux_input_file)\n assert np.all(fluxFile2.data['filename'] == data['filename'])\n\n # Test path\n assert fluxFile2.file_paths[0] == paths[0]\n assert fluxFile2.filenames[0] == os.path.join(paths[0], data['filename'][0])\n\n # #################\n # Tickle the other ways to do sensfiles\n data3 = Table()\n data3['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data3['sensfile'] = ['sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits',\n '']\n\n fluxFile3 = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data3)\n assert fluxFile3.sensfiles[1] == os.path.join(paths[0], data['sensfile'][0])\n \n data4 = Table()\n data4['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data4['sensfile'] = ''\n\n fluxFile4 = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data4)\n assert len(fluxFile4.sensfiles) == 0\n\n # Clean up\n os.remove(flux_input_file)","def from_file(cls, infilename):\n\n spect_file_obj = _get_file_object(infilename)\n\n kwargs = {'counts': spect_file_obj.data,\n 'input_file_object': spect_file_obj,\n 'bin_edges_kev': spect_file_obj.bin_edges_kev}\n\n # TODO Get more attributes from self.infileobj\n\n return cls(**kwargs)","def readNextGenSpectrum(fname=''):\n\n print('Reading : ', fname)\n\n with open(fname, 'r') as rfile:\n dum = rfile.readline()\n sdum = dum.split()\n teff = float(sdum[0])\n logg = float(sdum[1])\n mph = float(sdum[2])\n dum = rfile.readline()\n nwav = float(dum.split()[0])\n\n bigline = []\n dum = rfile.readline()\n while dum.strip() != '':\n sdum = dum.split()\n for i in range(len(sdum)):\n bigline.append(float(sdum[i]))\n dum = rfile.readline()\n\n bigline = np.array(bigline)\n # Convert wavelength from angstrom to micron\n wav = bigline[:nwav] / 1e4\n inu = bigline[nwav:2 * nwav]\n bnu = bigline[nwav * 2:nwav * 3]\n\n ii = wav.argsort()\n wav = wav[ii]\n inu = inu[ii] * 1e-8 * wav * 1e4 / np.pi / (29979245800.0 / wav * 1e4)\n bnu = bnu[ii] * 1e-8 * wav * 1e4 / np.pi / (29979245800.0 / wav * 1e4)\n\n #\n # The unit is now erg/s/cm/Hz/ster\n #\n\n return {'teff': teff, 'logg': logg, 'mph': mph, 'nwav': nwav, 'wav': wav, 'inu': inu, 'bnu': bnu}"],"string":"[\n \"def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\\n\\n options = gdal.BuildVRTOptions(srcNodata=0)\\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\\n add_pixel_fn(vrt, resample_name)\",\n \"def run_vorpaline(self, input_file, *options):\\n\\n self._input_file = input_file\\n args = list(options) + [self._input_file, 'out.meshb']\\n self._run_command(\\\"vorpalite\\\", args)\",\n \"def convert_vrt(fname, out_fname, dataset_name='dataset',\\n compression=H5CompressionFilter.LZF, filter_opts=None,\\n attrs=None):\\n with h5py.File(out_fname) as fid:\\n with rasterio.open(fname) as rds:\\n # set default chunks and set dimensions\\n if rds.count == 3:\\n chunks = (3, 256, 256)\\n dims = (3, rds.height, rds.width)\\n else:\\n chunks = (256, 256)\\n dims = (rds.height, rds.width)\\n\\n # create empty or copy the user supplied filter options\\n if not filter_opts:\\n filter_opts = dict()\\n filter_opts['chunks'] = chunks\\n else:\\n filter_opts = filter_opts.copy()\\n\\n\\n if 'chunks' not in filter_opts:\\n filter_opts['chunks'] = chunks\\n\\n # modify to have 3D chunks if we have a multiband vrt\\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\\n # copy the users original 2D chunk and insert the third\\n chunks = list(filter_opts['chunks'])\\n chunks.insert(0, 3)\\n filter_opts['chunks'] = chunks\\n\\n # dataset attributes\\n if attrs:\\n attrs = attrs.copy()\\n else:\\n attrs = {}\\n\\n attrs['geotransform'] = rds.transform.to_gdal()\\n attrs['crs_wkt'] = rds.crs.wkt\\n\\n # dataset creation options\\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\\n kwargs['shape'] = dims\\n kwargs['dtype'] = rds.dtypes[0]\\n\\n dataset = fid.create_dataset(dataset_name, **kwargs)\\n attach_image_attributes(dataset, attrs)\\n\\n # tiled processing (all cols by chunked rows)\\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\\n\\n for tile in tiles:\\n # numpy index\\n if rds.count == 3:\\n idx = (\\n slice(None),\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n else:\\n idx = (\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n\\n # ensure single band rds is read as 2D not 3D\\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\\n\\n # write\\n dataset[idx] = data\",\n \"def create_video(input_file, output_file):\\n input_video = VideoFileClip(input_file)\\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\\n output_video.write_videofile(output_file, audio=False)\",\n \"def readFT(self,file=\\\"out__1.ft\\\"):\",\n \"def _write_raw_brainvision(raw, bids_fname, events, overwrite):\\n if not check_version(\\\"pybv\\\", PYBV_VERSION): # pragma: no cover\\n raise ImportError(\\n f\\\"pybv >= {PYBV_VERSION} is required for converting\\\"\\n \\\" file to BrainVision format\\\"\\n )\\n from pybv import write_brainvision\\n\\n # Subtract raw.first_samp because brainvision marks events starting from\\n # the first available data point and ignores the raw.first_samp\\n if events is not None:\\n events[:, 0] -= raw.first_samp\\n events = events[:, [0, 2]] # reorder for pybv required order\\n meas_date = raw.info[\\\"meas_date\\\"]\\n if meas_date is not None:\\n meas_date = _stamp_to_dt(meas_date)\\n\\n # pybv needs to know the units of the data for appropriate scaling\\n # get voltage units as micro-volts and all other units \\\"as is\\\"\\n unit = []\\n for chs in raw.info[\\\"chs\\\"]:\\n if chs[\\\"unit\\\"] == FIFF.FIFF_UNIT_V:\\n unit.append(\\\"µV\\\")\\n else:\\n unit.append(_unit2human.get(chs[\\\"unit\\\"], \\\"n/a\\\"))\\n unit = [u if u not in [\\\"NA\\\"] else \\\"n/a\\\" for u in unit]\\n\\n # We enforce conversion to float32 format\\n # XXX: pybv can also write to int16, to do that, we need to get\\n # original units of data prior to conversion, and add an optimization\\n # function to pybv that maximizes the resolution parameter while\\n # ensuring that int16 can represent the data in original units.\\n if raw.orig_format != \\\"single\\\":\\n warn(\\n f'Encountered data in \\\"{raw.orig_format}\\\" format. '\\n \\\"Converting to float32.\\\",\\n RuntimeWarning,\\n )\\n\\n # Writing to float32 µV with 0.1 resolution are the pybv defaults,\\n # which guarantees accurate roundtrip for values >= 1e-7 µV\\n fmt = \\\"binary_float32\\\"\\n resolution = 1e-1\\n write_brainvision(\\n data=raw.get_data(),\\n sfreq=raw.info[\\\"sfreq\\\"],\\n ch_names=raw.ch_names,\\n ref_ch_names=None,\\n fname_base=op.splitext(op.basename(bids_fname))[0],\\n folder_out=op.dirname(bids_fname),\\n overwrite=overwrite,\\n events=events,\\n resolution=resolution,\\n unit=unit,\\n fmt=fmt,\\n meas_date=None,\\n )\",\n \"def simulate_UVSPEC(file, config):\\n\\n wavelength = config['wavelength']\\n\\n # Coordenates from position of the station Hannover\\n latitude = 52.39 # positive in the northern hemisphere\\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\\n\\n # Read name of the file (correct time)\\n name = os.path.split(file)\\n time_n = name[1][0:15]\\n print(\\\"Time name\\\", time_n)\\n # convert time to datetime format\\n time = datetime.datetime.strptime(time_n,\\n '%Y%m%d_%H%M%S')\\n # Calculate the azimuth and zenith angles in function of the date\\n elev = ps.GetAltitude(latitude, longitud, time)\\n azi = ps.GetAzimuth(latitude, longitud, time)\\n zenith = 90 - elev\\n\\n # Correction between the sign and real azimuth for plot of radiance\\n if -180 <= azi < 0:\\n azi = 180 - azi\\n elif -360 <= azi < -180:\\n azi = -azi - 180\\n else:\\n pass\\n\\n print(\\\"Azimuth: {:5.1f}\\\".format(azi),\\n \\\"\\\\nZenith: {:5.1f}\\\".format(zenith))\\n\\n # Change the value of zenith and azimuth angles in function of the time and\\n # position in the UVSPEC file\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\\n data = file.readlines()\\n\\n data[14] = \\\"day_of_year \\\" + str(time.timetuple().tm_yday) + \\\" \\\" + \\\"\\\\n\\\"\\n data[15] = \\\"wavelength \\\" + str(\\\"{}\\\".format(wavelength)) + \\\" \\\" + \\\\\\n str(\\\"{}\\\".format(wavelength)) + \\\\\\n \\\" # wavelength to calcule [nm] \\\\n\\\"\\n data[17] = \\\"sza \\\" + str(\\\"{:2.3f}\\\".format(zenith)) + \\\\\\n \\\" # Solar zenith angle \\\\n\\\"\\n data[18] = \\\"phi0 \\\" + str(\\\"{:2.3f}\\\".format(azi)) + \\\\\\n \\\" #Azimuth angle with zenith position \\\\n\\\"\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\\n file.writelines(data)\\n\\n # Create the directory to save the results\\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\\n wavelength)),\\n exist_ok=True)\\n\\n # Run the program UVSPEC in the terminal\\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\\n '.txt')\",\n \"def make_big_vdwradii( targetpath ):\\n\\n file = open( os.path.join( targetpath, 'vdwradii.dat'), 'w')\\n text=\\\"\\\"\\\"; Very approximate VanderWaals radii\\n; only used for drawing atoms as balls or for calculating atomic overlap.\\n; longest matches are used\\n; '???' or '*' matches any residue name\\n; 'AAA' matches any protein residue name\\n; MODIFIED TO USE BIG VDW RADII TO PREVENT WATERS BEING PUT IN THE PROTEIN WHERE WE DON'T WANT THEM. DLM\\n??? C 0.3\\n??? F 0.3\\n??? H 0.3\\n??? N 0.3\\n??? O 0.3\\n??? P 0.3\\n??? S 0.3\\n??? LP1 0\\n??? LP2 0\\nSOL H 0.04\\nSOL O 0.105\\nWAT H 0.04\\nWAT O 0.105\\nGLY MN1 0\\nGLY MN2 0\\nALA MCB1 0\\nALA MCB2 0 \\nVAL MCG1 0 \\nVAL MCG2 0 \\nILE MCG1 0 \\nILE MCG2 0 \\nILE MCD1 0 \\nILE MCD2 0 \\nLEU MCD1 0 \\nLEU MCD2 0 \\nMET MCE1 0 \\nMET MCE2 0 \\nTRP MTRP1 0 \\nTRP MTRP2 0\\nTHR MCG1 0\\nTHR MCG2 0\\nLYSH MNZ1 0 \\nLYSH MNZ2 0 \\n\\\"\\\"\\\" \\n file.writelines(text)\\n file.close()\",\n \"def make_vrt(self):\\n for index, i in enumerate(self.months):\\n month = str(index + 1)\\n if len(month) < 2:\\n month = '0' + month\\n txt_file = i.joinpath('subnational/tiffs.txt')\\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\\n if not outfile.exists():\\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\\n subprocess.call(gdal_cmd, shell=True)\",\n \"def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\\n from gdal import Translate\\n\\n if out_fname is None:\\n out_fname = in_fname + \\\".vrt\\\"\\n\\n # Using Translate... but would use Warp if every reprojecting\\n if bbox:\\n left, bottom, right, top = bbox\\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\\n else:\\n projwin = None\\n if verbose:\\n logger.info(f\\\"Creating {out_fname}, subset bbox: {bbox}\\\")\\n Translate(out_fname, in_fname, projWin=projwin)\",\n \"def genChirpVC(Vclamp=-70,Rm=100,amp=10,graphToo=False):\\n xs,ys,zi=genSine()\\n ys=ys*amp+Vclamp\\n genATF(xs,ys,'stimulus-VC.atf')\\n graphData(xs,ys,zi,\\\"Voltage Clamp Stimulus\\\",'stimulus-VC.png')\",\n \"def subset_multiband_vrt(src: Union[str, Path], band_request: Sequence = []):\\n if not isinstance(src, (str, Path)) and not Path(src).is_file():\\n raise ValueError(f\\\"Invalid source multiband raster.\\\\n\\\"\\n f\\\"Got {src}\\\")\\n with rasterio.open(src) as ras, MemoryFile() as mem:\\n riocopy(ras, mem.name, driver='VRT')\\n vrt_xml = mem.read().decode('utf-8')\\n vrt_dataset = ET.fromstring(vrt_xml)\\n vrt_dataset_dict = {int(band.get('band')): band for band in vrt_dataset.iter(\\\"VRTRasterBand\\\")}\\n for band in vrt_dataset_dict.values():\\n vrt_dataset.remove(band)\\n\\n for dest_band_idx, src_band_idx in enumerate(band_request, start=1):\\n vrt_band = vrt_dataset_dict[src_band_idx]\\n vrt_band.set('band', str(dest_band_idx))\\n vrt_dataset.append(vrt_band)\\n\\n return ET.tostring(vrt_dataset).decode('UTF-8')\",\n \"def stack_singlebands_vrt(srcs: List, band: int = 1):\\n vrt_bands = []\\n for srcnum, src in enumerate(srcs, start=1):\\n with check_rasterio_im_load(src) as ras, MemoryFile() as mem:\\n riocopy(ras, mem.name, driver='VRT')\\n vrt_xml = mem.read().decode('utf-8')\\n vrt_dataset = ET.fromstring(vrt_xml)\\n for bandnum, vrt_band in enumerate(vrt_dataset.iter('VRTRasterBand'), start=1):\\n if bandnum == band:\\n vrt_band.set('band', str(srcnum))\\n vrt_bands.append(vrt_band)\\n vrt_dataset.remove(vrt_band)\\n for vrt_band in vrt_bands:\\n vrt_dataset.append(vrt_band)\\n\\n return ET.tostring(vrt_dataset).decode('UTF-8')\",\n \"def vtt_to_srt(str_name_file: str):\\n file_contents: str = read_text_file(str_name_file)\\n str_data: str = \\\"\\\"\\n str_data = str_data + convert_content(file_contents)\\n str_name_file: str = str_name_file.replace(\\\".vtt\\\", \\\".srt\\\")\\n print(str_name_file)\\n file_create(str_name_file, str_data)\",\n \"def change_input(filename, frac_l=0.019994, force=25.0, time=200_000, seed=None):\\r\\n\\r\\n frac_sl = round(1.5 /(32**3 *3), 6)\\r\\n frac_l = round(frac_l -frac_sl, 6)\\r\\n frac_w = round(1 -frac_l -frac_sl, 6)\\r\\n \\r\\n params = {'Box': \\\"32 32 32\\\\t1 1 1\\\", 'RNGSeed': seed if seed is not None else -4073, 'Step': 0.02, 'Time': time, \\r\\n 'SamplePeriod': 100, 'AnalysisPeriod': 1000, 'DensityPeriod': time, 'DisplayPeriod': time //10, 'RestartPeriod': time,\\r\\n }\\r\\n\\r\\n with open(filename, 'rt') as rf:\\r\\n with open(filename+'_sim', 'wt') as wf:\\r\\n\\r\\n for line in rf:\\r\\n \\r\\n if line.startswith('Polymer\\\\tWater') or line.startswith('Polymer Water'):\\r\\n line = line.strip().split()\\r\\n line[2] = f\\\"{frac_w:.6f}\\\"\\r\\n \\r\\n # Converts list to list[str]\\r\\n line = list(map(str, line))\\r\\n wf.write('\\\\t'.join(line) + '\\\\n')\\r\\n\\r\\n elif line.startswith('Polymer\\\\tLipid') or line.startswith('Polymer Lipid'):\\r\\n line = line.strip().split()\\r\\n line[2] = f\\\"{frac_l:.6f}\\\"\\r\\n\\r\\n # Converts list to list[str]\\r\\n line = list(map(str, line))\\r\\n wf.write('\\\\t'.join(line) + '\\\\n')\\r\\n \\r\\n elif line.startswith('Polymer\\\\tSingleLipid') or line.startswith('Polymer SingleLipid'):\\r\\n line = line.strip().split()\\r\\n line[2] = f\\\"{frac_sl:.6f}\\\"\\r\\n\\r\\n # Converts list to list[str]\\r\\n line = list(map(str, line))\\r\\n wf.write('\\\\t'.join(line) + '\\\\n')\\r\\n\\r\\n elif line.startswith('Command\\\\tConstantForceOnTarget') or line.startswith('Command ConstantForceOnTarget'):\\r\\n line = line.strip().split()\\r\\n\\r\\n if line[3] == \\\"singleLipidHead\\\":\\r\\n line[-1] = f\\\"{force /3:.6f}\\\" \\r\\n\\r\\n elif line[3] == \\\"lowerHeads\\\":\\r\\n line[-1] = f\\\"{force /3 /(1636):.6f}\\\"\\r\\n\\r\\n # Converts list to list[str]\\r\\n line = list(map(str, line))\\r\\n wf.write('\\\\t'.join(line) + '\\\\n')\\r\\n \\r\\n # if line.startswith('\\tTimes\\t0 1000'):\\r\\n # line = line.strip().split()\\r\\n # line[2] = f\\\"{time}\\\"\\r\\n \\r\\n # # Converts list to list[str]\\r\\n # line = list(map(str, line))\\r\\n # wf.write('\\\\t'.join(line) + '\\\\n') \\r\\n # line = next(rf) \\r\\n \\r\\n # if line.strip().split() and line.strip().split()[0] in params.keys():\\r\\n # key = line.strip().split()[0]\\r\\n # wf.write(f\\\"{key:<12}\\\\t{str(params[key])}\\\\n\\\")\\r\\n\\r\\n else:\\r\\n wf.write(line)\",\n \"def __init__(self, filename):\\r\\n self.__output__ = open(format(filename, '08X') + '.gen', 'wb')\",\n \"def write_input_file(y,z,fname):\\n file = open('c:/4nec2/out/' + fname + '.nec', 'w')\\n file.write('CM Seeddesign \\\\n')\\n file.write('CM Zigzag Antenna \\\\n')\\n file.write('CE File generated by python \\\\n')\\n seg = 1\\n\\n #write the antenna\\n for i in range(0,len(y)-1):\\n file.write('GW %3i %3i %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f %8.4f\\\\n' % (1,seg,0,y[i],z[i],0,y[i+1],z[i+1],1))\\n\\n file.write('GE 0 \\\\n')\\n file.write('EK \\\\n')\\n file.write('EX %3i %3i %3i %3i %3i %3i %3i\\\\n' % (0,1,1,1,1,0,0))\\n file.write('GN -1 \\\\n')\\n \\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (0,1,0,0,900,0))\\n file.write('FR %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (0,11,0,0,850,10))\\n\\n file.write('LD %3i %3i %3i %3i %8.4f %8.4f\\\\n' % (5,1,0,0,58000000,2))\\n file.write('RP %3i %3i %3i %3i %8.4f %8.4f %8.4f %8.4f\\\\n' % (0,1,1,1000,90,0,0,0))\\n\\n file.write('EN \\\\n')\\n file.close()\",\n \"def get_rad_file(self, input_file, out_dir, overwrite_rad):\\n # name of the rad file - replace psv with rad (or PSV with RAD)\\n self.rad_file = self.get_rad_filename(input_file)\\n\\n if \\\"rad\\\" in self.rad_file.lower() and self.rad_file.lower().endswith(\\\".tab\\\"):\\n if self.rad_file == input_file:\\n return True\\n if os.path.isfile(self.rad_file) and not overwrite_rad:\\n # valid rad file already exists, just return\\n return True\\n\\n # input is psv and rad file does not yet exist - let's create it first.\\n # create rad file and change path to where it will end up in out_dir\\n if out_dir is not None:\\n (path, filename) = os.path.split(self.rad_file)\\n self.rad_file = os.path.join(out_dir, filename)\\n else:\\n (out_dir, filename) = os.path.split(input_file)\\n radiance_cal = RadianceCalibration(self.logfile, self.main_app)\\n return radiance_cal.calibrate_to_radiance(InputType.FILE, input_file, out_dir, overwrite_rad)\",\n \"def which_band_is_file(filename):\\n if not is_galex_file(filename):\\n return None\\n return \\\"fuv\\\" if \\\"-fd-\\\" in filename else \\\"nuv\\\" if \\\"-nd-\\\" in filename \\\\\\n else \\\"unknown\\\"\",\n \"def outputSingleFrame(self, frame=None):\\n if frame is None:\\n frame = 1\\n\\n self.loadFringe(frame=frame)\\n\\n outputName = self.inputFilenames['ofd'][:-4] + '_single_f' + str(frame) + '.ofd'\\n\\n with open(outputName, 'wb') as f:\\n self.rawBScan.astype('uint16').tofile(f)\",\n \"def merge(files: List[str], output_file: str, resample: str = \\\"average\\\") -> None:\\n\\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\\n\\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\\n\\n if os.path.isfile(constants.TEMP_VRT_FILE):\\n os.remove(constants.TEMP_VRT_FILE)\",\n \"def buildvrt(indir, outdir):\\n indir = Path(indir)\\n outdir = Path(outdir)\\n\\n # loop over each day directory\\n for day in indir.iterdir():\\n # expecting 20 subdatasets in each hdf4 file (hopefully the order gdal lists them in is consistent)\\n subdataset_fnames = {i: [] for i in range(20)}\\n\\n # mosaic each MODIS tile for the current day directory\\n for h4_fname in day.rglob('*.hdf'):\\n with rasterio.open(str(h4_fname.absolute())) as h4_ds:\\n\\n # each subdataset will form a separate mosaic\\n for i, sds_name in enumerate(h4_ds.subdatasets):\\n subdataset_fnames[i].append(sds_name)\\n\\n # loop over each subdataset and mosaic from all supporting MODIS tiles\\n for _, file_list in subdataset_fnames.items():\\n\\n # temp file for the input file list\\n with tempfile.NamedTemporaryFile('w') as tmpf:\\n tmpf.writelines(\\\"\\\\n\\\".join(file_list))\\n tmpf.flush()\\n\\n # mimic the 'day' directory partition\\n base_name = Path(file_list[0].replace(':', '/')).name\\n out_fname = outdir.joinpath(day.name, '{}.vrt'.format(base_name))\\n\\n if not out_fname.parent.exists():\\n out_fname.parent.mkdir(parents=True)\\n\\n # buildvrt\\n cmd = [\\n 'gdalbuildvrt',\\n '-input_file_list',\\n tmpf.name,\\n str(out_fname)\\n ]\\n\\n check_call(cmd)\",\n \"def nirspec_spectrum1d_reader(file_name):\\n\\n hdulist = fits.open(file_name)\\n\\n # make wavelength a seperate component in addition to coordinate\\n # so you can plot it on the x axis\\n wavelength = np.linspace(hdulist['DATA'].header['CRVAL1'],\\n hdulist['DATA'].header['CRVAL1']*hdulist['DATA'].header['CDELT1'],\\n hdulist['DATA'].header['NAXIS1'])[::-1]\\n\\n data = Data(label='1D Spectrum')\\n data.header = hdulist['DATA'].header\\n data.add_component(wavelength, 'Wavelength')\\n data.add_component(hdulist['DATA'].data, 'Flux')\\n data.add_component(np.sqrt(hdulist['VAR'].data), 'Uncertainty')\\n\\n return data\",\n \"def make_spectra(directory,frame):\\n oober = st.short_oober(directory, frame=frame)\\n #st.MakeVelocitySpectra(oober,frame)\\n #st.MakeAccelSpectra(oober,frame)\\n #st.MakeMagneticSpectra(oober,frame)\\n st.MakeDensitySpectra(oober,frame)\",\n \"def open_raw(path):\\r\\n raw_file_reader = RawFileReader.RawFileReaderAdapter.FileFactory(path)\\r\\n raw_file_reader.SelectInstrument(Business.Device.UV, 1)\\r\\n return raw_file_reader\",\n \"def make_spec(fi, bw, n, rms, v0, transitions, bandpass, baseline, order,\\n Te, ne, Tr, W, dD, EM, cont, Tc, nu, plot, plot_out,\\n n_max=1500, verbose=False):\\n \\n ff = fi + bw\\n df = bw/n\\n \\n # Make the arrays to store the spectrum\\n freq = np.arange(fi, ff, df)\\n tau = np.zeros(n)\\n \\n # Create Gaussian noise and add it to the spectrum.\\n noise = synth.make_noise(0, rms, n)\\n tau_n = tau + noise\\n \\n # Create a baseline that mimics a standing wave \\n # and add it to the spectrum.\\n # This values produce a nice variation.\\n if bandpass:\\n tau_n += synth.make_ripples(4*n, n/2., n, rms)\\n \\n if baseline:\\n tau_n += synth.make_offset(rms, freq, order=order)\\n \\n z = v0/3e5\\n print \\\"Will use a redshift of: {0}\\\".format(z)\\n print \\\"Subband edges: {0}--{1}\\\".format(fi, ff)\\n \\n for i,trans in enumerate(transitions.split(',')):\\n if trans != '':\\n n_l, f_l = crrls.find_lines_sb(freq, trans, z, verbose)\\n \\n # Generate the line properties as a function of n\\n dL_r = crrls.radiation_broad_salgado(n_l, W, Tr)/1e6\\n dL_p = crrls.pressure_broad_salgado(n_l, Te, ne)/1e6\\n dL = dL_r + dL_p\\n \\n if Tr != 0:\\n other = 'case_diffuse_{0}'.format(rrlmod.val2str(Tr))\\n else:\\n other = ''\\n \\n n_itau, a_itau = rrlmod.itau(rrlmod.val2str(Te), \\n ne, \\n trans, n_max=n_max, \\n other=other,\\n verbose=True)\\n \\n dD_f = crrls.dv2df(f_l, dD*1e3)\\n \\n for j,f in enumerate(f_l):\\n itau = a_itau[np.where(n_itau==n_l[j])[0]][0]/1e6\\n line = crrls.voigt(freq, dD_f[j]/2., dL[j]/2., f, itau*EM)\\n if verbose:\\n print \\\"Line properties:\\\"\\n print(\\\"f: {0}, A: {1}, dD: {2}, dD_f/2: {3}, \\\" \\\\\\n \\\"dL/2: {4}\\\".format(f, itau*EM, dD, dD_f[j]/2., dL[j]/2.))\\n tau_n += line\\n \\n if cont:\\n tau_n = (1 + tau_n)*power_law(freq, Tc, nu)\\n \\n if plot:\\n fig = plt.figure(frameon=False)\\n ax = fig.add_subplot(1, 1, 1)\\n ax.plot(freq, tau_n, 'k-', label='generated spectrum')\\n ax.plot(freq, power_law(freq, Tc, nu), 'r-', label='continuum')\\n plt.legend(loc=0, numpoints=1, frameon=False)\\n plt.savefig('{0}'.format(plot_out), \\n bbox_inches='tight', pad_inches=0.3)\\n plt.close()\\n \\n \\n #np.savetxt(spec, np.c_[freq, tau_n])\\n \\n return freq, tau_n\",\n \"def get_rad_filename(input_file):\\n # replace PSV with RAD\\n (path, filename) = os.path.split(input_file)\\n rad_filename = filename.replace('psv', 'rad')\\n rad_filename = rad_filename.replace('PSV', 'RAD')\\n rad_file = os.path.join(path, rad_filename)\\n\\n # rename to .TAB from .TXT in case of raw input\\n rad_file = rad_file.replace('.TXT', '.tab')\\n rad_file = rad_file.replace('.txt', '.tab')\\n\\n return rad_file\",\n \"def test_simple_single_file():\\n\\n out_data = run_tvnamer(\\n with_files = ['S01E02 - Some File.avi'],\\n with_flags = [\\\"--batch\\\"])\\n\\n expected_files = ['S01E02 - Some File.avi']\\n\\n verify_out_data(out_data, expected_files, expected_returncode = 2)\",\n \"def convert_vtt_to_str(file):\\n if \\\".vtt\\\" in file:\\n vtt_to_srt(file)\",\n \"def test_RV():\\n\\n spec = IGRINSSpectrum(file=file)\\n\\n assert spec.uncertainty is not None\\n assert hasattr(spec, \\\"barycentric_correct\\\")\\n\\n correction_velocity = spec.estimate_barycorr()\\n\\n assert isinstance(spec.RA, astropy.units.quantity.Quantity)\\n assert isinstance(spec.DEC, astropy.units.quantity.Quantity)\\n assert correction_velocity is not None\\n assert isinstance(correction_velocity, astropy.units.quantity.Quantity)\\n\\n new_spec = spec.barycentric_correct()\\n assert new_spec is not None\\n assert isinstance(new_spec, Spectrum1D)\",\n \"def create_training_file(D_RAT):\\r\\n return create_arff_file(D_RAT, 0)\",\n \"def createInput(dirPath,gSettings):\\n \\n with open(os.path.join('../in','input.txt')) as f:\\n inpFile = f.readlines()\\n \\n\\n # Model settings\\n model = gSettings[\\\"Model\\\"]\\n inpFile[13] = \\\"insgrav: {:1d}\\\\n\\\".format(int(model[\\\"NS gravity\\\"][\\\"Flag\\\"]))\\n inpFile[14] = \\\"isun: {:1d}\\\\n\\\".format(int(model[\\\"Lunisolar\\\"][\\\"Sun\\\"]))\\n inpFile[15] = \\\"imoon: {:1d}\\\\n\\\".format(int(model[\\\"Lunisolar\\\"][\\\"Moon\\\"]))\\n\\n if model[\\\"Drag\\\"][\\\"Flag\\\"] == False:\\n inpFile[16] = \\\"idrag: 0\\\\n\\\"\\n else:\\n dm = model[\\\"Drag\\\"][\\\"Model\\\"].lower()\\n if dm == \\\"wertz\\\":\\n idrag = 1\\n elif dm == \\\"us76\\\":\\n idrag = 2\\n elif dm == \\\"j77\\\":\\n idrag = 3\\n elif dm == \\\"msis00\\\":\\n idrag = 4\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Drag\\\"][\\\"Model\\\"] + '\\\" invalid.')\\n inpFile[16] = \\\"idrag: {:1d}\\\\n\\\".format(idrag)\\n if model[\\\"Drag\\\"][\\\"Solar flux\\\"].lower() == \\\"constant\\\":\\n inpFile[17] = \\\"iF107: 0\\\\n\\\"\\n elif model[\\\"Drag\\\"][\\\"Solar flux\\\"].lower() == \\\"variable\\\":\\n inpFile[17] = \\\"iF107: 1\\\\n\\\"\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Drag\\\"][\\\"Solar flux\\\"] + '\\\" invalid.')\\n\\n if model[\\\"SRP\\\"][\\\"Flag\\\"] == False:\\n inpFile[18] = \\\"iSRP: {:1d}\\\\n\\\".format(int(model[\\\"SRP\\\"][\\\"Flag\\\"]))\\n else:\\n inpFile[18] = \\\"iSRP: {:1d}\\\\n\\\".format(int(model[\\\"SRP\\\"][\\\"Flag\\\"]))\\n if model[\\\"SRP\\\"][\\\"Eclipses\\\"]:\\n inpFile[18] = \\\"iSRP: 2\\\\n\\\"\\n \\n if model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] == \\\"DE431\\\":\\n inpFile[19] = \\\"iephem: 1\\\\n\\\"\\n elif model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] == \\\"Meeus\\\":\\n inpFile[19] = \\\"iephem: 2\\\\n\\\"\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] + '\\\" invalid.')\\n \\n inpFile[20] = \\\"gdeg: {:3d}\\\\n\\\".format(model[\\\"NS gravity\\\"][\\\"Degree\\\"])\\n if model[\\\"NS gravity\\\"][\\\"Order\\\"] <= model[\\\"NS gravity\\\"][\\\"Degree\\\"]:\\n inpFile[21] = \\\"gord: {:3d}\\\\n\\\".format(model[\\\"NS gravity\\\"][\\\"Order\\\"])\\n else:\\n raise ValueError(\\\"Order {0:d} of the gravity field is greater than degree {1:d}\\\".format(model[\\\"NS gravity\\\"][\\\"Order\\\"],model[\\\"NS gravity\\\"][\\\"Degree\\\"]))\\n \\n\\n\\n # Integration settings\\n integ = gSettings[\\\"Integration\\\"]\\n inpFile[29] = \\\"tol: {:22.15E}\\\\n\\\".format(integ[\\\"Tolerance\\\"])\\n inpFile[30] = \\\"tspan: {:22.15E}\\\\n\\\".format(integ[\\\"Duration\\\"] * 365.25)\\n inpFile[31] = \\\"tstep: {:22.15E}\\\\n\\\".format(integ[\\\"Step\\\"])\\n inpFile[39] = \\\"eqs: {:2d}\\\\n\\\".format(integ[\\\"Equations\\\"])\\n\\n\\n\\n # Output settings\\n inpFile[44] = \\\"verb: 0\\\\n\\\"\\n inpFile[45] = \\\"out: \\\" + os.path.abspath(os.path.join(dirPath, ' '))\\n\\n\\n with open(os.path.join(dirPath,'input.txt'),'w') as f:\\n f.writelines(inpFile)\",\n \"def get_san(infp, outfp):\\n\\n return ...\",\n \"def deimos_spectrum1D_reader(file_name):\\n\\n hdulist = fits.open(file_name)\\n data = Data(label='1D Spectrum')\\n data.header = hdulist[1].header\\n\\n full_wl = np.append(hdulist[1].data['LAMBDA'][0], hdulist[2].data['LAMBDA'][0])\\n full_spec = np.append(hdulist[1].data['SPEC'][0], hdulist[2].data['SPEC'][0])\\n full_ivar = np.append(hdulist[1].data['IVAR'][0], hdulist[2].data['IVAR'][0])\\n\\n data.add_component(full_wl, 'Wavelength')\\n data.add_component(full_spec, 'Flux')\\n data.add_component(1/np.sqrt(full_ivar), 'Uncertainty')\\n\\n return data\",\n \"def process_rom_file(name):\\n\\n # Link the instruction rom file to the tb directory\\n SRC_FILE = REPO_ROOT + '/tests/riscv-tests-simple/generated/' + name + \\\".verilog\\\"\\n ROM_FILE = os.getcwd() + '/verilog.rom' # need to link the instruction ram file the the current directory\\n if os.path.isfile(ROM_FILE):\\n os.remove(ROM_FILE)\\n os.symlink(SRC_FILE, ROM_FILE)\\n\\n FP = open('verilog.rom', \\\"r\\\")\\n IRAM_FP = open('instr_ram.rom', \\\"w\\\")\\n DRAM_FP = open('data_ram.rom', \\\"w\\\")\\n\\n iram = True\\n for line in FP.readlines():\\n if line.rstrip() == \\\"@01000000\\\":\\n iram = False\\n continue\\n if iram:\\n IRAM_FP.write(line)\\n else:\\n DRAM_FP.write(line)\\n\\n\\n FP.close()\\n IRAM_FP.close()\\n DRAM_FP.close()\",\n \"def test_process_mono_file(self):\\n test_path = pathlib.Path(__file__).parent.absolute() / 'data/mono.wav'\\n self.default_kwargs['input_file'] = test_path\\n self.default_kwargs['output_file'] = pathlib.Path(self.temp_file.name)\\n self.encoder = FileEncoder(**self.default_kwargs)\\n self.encoder.process()\",\n \"def main():\\r\\n\\r\\n ### Choose and Import File\\r\\n\\r\\n inSound = Sound()\\r\\n\\r\\n rate = inSound.rate\\r\\n data = inSound.data\\r\\n dataLength = len(data)\\r\\n \\r\\n info = inSound.get_info()\\r\\n head, filename = os.path.split(info[0]) # get filename of input\\r\\n \\r\\n # Decide output directory and filename\\r\\n outDir = r'out'\\r\\n outFile = os.path.join(outDir, 'out_'+filename)\\r\\n\\r\\n # Check if data has multiple channels, if yes use only one\\r\\n if(len(data.shape) > 1):\\r\\n data = data[:,0]\\r\\n\\r\\n\\r\\n ### Set All Parameters\\r\\n\\r\\n #get parameters from user dialogue\\r\\n params = getParameters()\\r\\n\\r\\n numChannels = params['numChannels'][0] # number of Channels\\r\\n loFreq = params['loFreq'][0] # lower bound on frequencies\\r\\n hiFreq = params['hiFreq'][0] # upper bound on frequencies\\r\\n plotChannels = params['plotChannels'][0] # if it should plot the Gammatone channels\\r\\n block_time = params['block_time'][0] # in ms\\r\\n block_shift = params['block_shift'][0] # in ms\\r\\n selectChannels = params['selectChannels'][0] # number of channels to activate at a single time\\r\\n\\r\\n\\r\\n ### Filter input file\\r\\n\\r\\n filtered, channel_fs = filterDataGamaTone(data, rate, numChannels, loFreq, hiFreq, plotChannels)\\r\\n\\r\\n\\r\\n ### Gammatones -> Stimulation Amplitude for time block\\r\\n\\r\\n samples_in_block = np.floor(block_time * rate / 1000).astype('int')\\r\\n samples_in_shift = np.floor(block_shift * rate / 1000).astype('int')\\r\\n\\r\\n summed = gammatoneToAmplitude(filtered, samples_in_block, samples_in_shift)\\r\\n\\r\\n # only activate the n electrodes that have the largest stimulation\\r\\n amps = n_largest_channels(summed, n=selectChannels)\\r\\n\\r\\n \\r\\n #### Sound reconstruction\\r\\n\\r\\n # for each timeblock we need to duplicate enough samples to fill it at sample rate\\r\\n amps_samples = np.repeat(amps, samples_in_shift, axis=1)\\r\\n #trim end to get same length as input\\r\\n amps_samples = amps_samples[:,:dataLength] \\r\\n\\r\\n # from amplitude samples and frequencies, reconstruct sound\\r\\n res_data = generateSound(amps_samples, channel_fs, rate)\\r\\n\\r\\n\\r\\n ### Write to output file\\r\\n write(outFile, rate, res_data)\\r\\n print('Wrote file to: \\\\n' + outFile)\",\n \"def generateNHDRHeader(self, inputFile):\\r\\n\\r\\n logging.info('Processing started')\\r\\n #initialize PCR object\\r\\n imagePCRFile = PCRDataObject()\\r\\n #import image parameters of PCR object\\r\\n imagePCRFile.ImportFromFile(inputFile)\\r\\n\\r\\n filePathName, fileExtension = os.path.splitext(inputFile)\\r\\n #The directory of the .nhdr file\\r\\n nhdrPathName = filePathName + \\\".nhdr\\\"\\r\\n\\r\\n if fileExtension == \\\".pcr\\\":\\r\\n if imagePCRFile.form == 1 or imagePCRFile.form == 5 or imagePCRFile.form == 10:\\r\\n with open(nhdrPathName, \\\"w\\\") as headerFile:\\r\\n headerFile.write(\\\"NRRD0004\\\\n\\\")\\r\\n headerFile.write(\\\"# Complete NRRD file format specification at:\\\\n\\\")\\r\\n headerFile.write(\\\"# http://teem.sourceforge.net/nrrd/format.html\\\\n\\\")\\r\\n if imagePCRFile.form == 5:\\r\\n headerFile.write(\\\"type: ushort\\\\n\\\")\\r\\n elif imagePCRFile.form == 10:\\r\\n headerFile.write(\\\"type: float\\\\n\\\")\\r\\n elif imagePCRFile.form == 1:\\r\\n headerFile.write(\\\"type: uchar\\\\n\\\")\\r\\n headerFile.write(\\\"dimension: 3\\\\n\\\")\\r\\n headerFile.write(\\\"space: left-posterior-superior\\\\n\\\")\\r\\n sizeX = imagePCRFile.X\\r\\n sizeY = imagePCRFile.Y\\r\\n sizeZ = imagePCRFile.Z\\r\\n headerFile.write(f\\\"sizes: {sizeX} {sizeY} {sizeZ}\\\\n\\\")\\r\\n volSpace = imagePCRFile.voxelSize\\r\\n headerFile.write(f\\\"space directions: ({volSpace}, 0.0, 0.0) (0.0, {volSpace}, 0.0) (0.0, 0.0, {volSpace})\\\\n\\\")\\r\\n headerFile.write(\\\"kinds: domain domain domain\\\\n\\\")\\r\\n headerFile.write(\\\"endian: little\\\\n\\\")\\r\\n headerFile.write(\\\"encoding: raw\\\\n\\\")\\r\\n headerFile.write(\\\"space origin: (0.0, 0.0, 0.0)\\\\n\\\")\\r\\n volPathName = filePathName + \\\".vol\\\"\\r\\n volPathSplit = []\\r\\n volPathSplit = volPathName.split('/')\\r\\n volFileName = volPathSplit[len(volPathSplit)-1]\\r\\n headerFile.write(f\\\"data file: {volFileName}\\\\n\\\")\\r\\n # print(imagePCRFile.form)\\r\\n print(f\\\".nhdr file path is: {nhdrPathName}\\\")\\r\\n #Automatically loading .vol file using the generated .nhdr file.\\r\\n if os.path.exists(volPathName):\\r\\n slicer.util.loadVolume(nhdrPathName)\\r\\n print(f\\\"{volFileName} loaded\\\\n\\\")\\r\\n else:\\r\\n print(f\\\"{volFileName} is not in the same directory\\\\n\\\")\\r\\n else:\\r\\n print(\\\"The format of this dataset is currently not supported by this module. Currently only float (format=10), unsigned 16 bit integer (format=5) and unsigned 8 bit integer (format=1) data types are supported. Please contact us with this dataset to enable this data type.\\\")\\r\\n else:\\r\\n print(\\\"This is not a PCR file, please re-select a PCR file\\\")\",\n \"def processfile(file, out, me):\\n if out.endswith('.npz'):\\n out = out[:-4]\\n out = out + ('-%03d' % me) + '.npz'\\n if os.path.exists(out):\\n return\\n f = QPM.QPMSubsampleFile(file)\\n xis = []\\n mybf = QPM.LogWindowBiasFunction(edges[me], edges[me + 1])\\n mymock = QPM.halomock(f, 1e6, mybf, continuous=False)\\n for i in range(me + 1):\\n bf = QPM.LogWindowBiasFunction(edges[i], edges[i + 1])\\n mock = QPM.halomock(f, 1e6, bf, continuous=False)\\n # the error is nonsense since we have just one sample\\n # deal with it in combine\\n xi = QPM.xi([mock], mocks2=[mymock])\\n r = xi[0]\\n xis.append(xi[1])\\n print edges[i], edges[i + 1], len(mock[0]), mock[1].sum()\\n numpy.savez(out, r=r, xi=xis, edges=edges, N=1e6, me=me)\",\n \"def readDriverFile(self, input_file):\\n\\n\\n fid = open(self.basePath + input_file,'r')\\n\\n # Line 1\\n line = fid.readline()\\n l_input = line.split('!')\\n mshfile = l_input[0].rstrip()\\n\\n # Line 2\\n line = fid.readline()\\n l_input = line.split('!')\\n obsfile = l_input[0].rstrip()\\n\\n # Line 3\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='null':\\n topofile = []\\n\\n else:\\n topofile = l_input[0].rstrip()\\n\\n\\n # Line 4\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n mstart = float(l_input[1])\\n\\n else:\\n mstart = l_input[0].rstrip()\\n\\n # Line 5\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n mref = float(l_input[1])\\n\\n else:\\n mref = l_input[0].rstrip()\\n\\n # Line 6\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n staticInput = float(l_input[1])\\n\\n elif l_input[0]=='DEFAULT':\\n staticInput = None\\n\\n else:\\n staticInput = l_input[0].rstrip()\\n\\n\\n # Line 7\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='DEFAULT':\\n magfile = []\\n\\n else:\\n magfile = l_input[0].rstrip()\\n\\n # Line 8\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='DEFAULT':\\n wgtfile = []\\n\\n else:\\n wgtfile = l_input[0].rstrip()\\n\\n # Line 9\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n chi = float(l_input[0])\\n\\n # Line 10\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n val = np.array(l_input[0:4])\\n alphas = val.astype(np.float)\\n\\n # Line 11\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:3])\\n bounds = val.astype(np.float)\\n\\n else:\\n bounds = l_input[0].rstrip()\\n\\n # Line 12\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:6])\\n lpnorms = val.astype(np.float)\\n\\n else:\\n lpnorms = l_input[0].rstrip()\\n\\n # Line 13\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:3])\\n eps = val.astype(np.float)\\n\\n else:\\n eps = [None,None]\\n\\n self.mshfile = mshfile\\n self.obsfile = obsfile\\n self.topofile = topofile\\n self.mstart = mstart\\n self._mrefInput = mref\\n self._staticInput = staticInput\\n self.magfile = magfile\\n self.wgtfile = wgtfile\\n self.chi = chi\\n self.alphas = alphas\\n self.bounds = bounds\\n self.lpnorms = lpnorms\\n self.eps = eps\",\n \"def run_vorpastat(self, *options):\\n\\n args = list(options) + [self._input_file, 'out.meshb']\\n self._run_command(\\\"vorpastat\\\", args)\",\n \"def calc_out_voltage(self, input_photocurrent_file):\\n pass\",\n \"def vt_send(filename, key):\\n url = \\\"https://www.virustotal.com/vtapi/v2/file/scan\\\"\\n attr = {\\\"apikey\\\": key}\\n files = {\\\"file\\\": open(filename, 'rb')}\\n res = requests.post(url, data=attr, files=files)\\n\\n if res.status_code == 200:\\n return res.text\\n else:\\n return res.text\",\n \"def __init__(self, input_file_path, convert_to_bgr=False):\\n self.__yuv_video = YuvDecoder(input_file_path, convert_to_bgr=True)\\n print('After INSTANTIATION')\\n self.__yuv_video.start()\",\n \"def generate(inputFilename, outputFilename = defaultFileName, \\n sizeOfReducedSample = DEFSIZEOFREDUCEDSAMPLE, \\n centerEta = DEFCENTERETA, centerPhi = DEFCENTERPHI): \\n listOfSignals = convert(inputFilename)\\n arrayOfSignals = np.array(listOfSignals)\\n arrayOfSignals.shape\\n np.save(outputFilename, arrayOfSignals, allow_pickle=False)\\n print(\\\"npy array name: \\\",outputFilename)\",\n \"def get_vrt_band_list():\\n logger.debug('get_vrt_band_list() called')\\n vrt_band_list = []\\n#===============================================================================\\n# sensor_dict = self.bands[tile_type_id][(dataset_info['satellite_tag'], dataset_info['sensor_name'])]\\n# # log_multiline(logger.debug, sensor, 'Sensor', '\\\\t')\\n# for file_number in sorted(sensor_dict.keys()):\\n# band_info = sensor_dict[file_number]\\n# if band_info['level_name'] == 'NBAR':\\n# dataset_dir = dataset_info['nbar_dataset_path']\\n# dataset_id = dataset_info['nbar_dataset_id']\\n# processing_level = dataset_info['nbar_level_name']\\n# nodata_value = dataset_info['nbar_nodata_value']\\n# resampling_method = dataset_info['nbar_resampling_method']\\n# elif band_info['level_name'] == 'ORTHO':\\n# dataset_dir = dataset_info['l1t_dataset_path']\\n# dataset_id = dataset_info['l1t_dataset_id']\\n# processing_level = dataset_info['l1t_level_name']\\n# nodata_value = dataset_info['l1t_nodata_value']\\n# resampling_method = dataset_info['l1t_resampling_method']\\n# else:\\n# continue # Ignore any pan-chromatic and derived bands\\n# \\n# dataset_dir = os.path.join(dataset_dir, 'scene01')\\n# filename = find_file(dataset_dir, band_info['file_pattern'])\\n# vrt_band_list.append({'file_number': band_info['file_number'], \\n# 'filename': filename, \\n# 'name': band_info['band_name'],\\n# 'dataset_id': dataset_id,\\n# 'band_id': band_info['band_id'],\\n# 'processing_level': processing_level,\\n# 'nodata_value': nodata_value,\\n# 'resampling_method': resampling_method,\\n# 'tile_layer': band_info['tile_layer']})\\n#===============================================================================\\n \\n #TODO: Make this able to handle multiple derived layers\\n for band_level in ['FC']:\\n derived_bands = self.bands[tile_type_id][('DERIVED', band_level)]\\n for file_number in sorted(derived_bands.keys()):\\n band_info = derived_bands[file_number]\\n file_pattern = band_info['file_pattern']\\n dataset_dir = os.path.join(dataset_info['fc_dataset_path'], 'scene01')\\n dataset_id = dataset_info['fc_dataset_id']\\n filename = find_file(dataset_dir, file_pattern) \\n processing_level = dataset_info['fc_level_name']\\n nodata_value = dataset_info['fc_nodata_value'] # Should be None for FC\\n resampling_method = dataset_info['fc_resampling_method']\\n vrt_band_list.append({'file_number': None, \\n 'filename': filename, \\n 'name': band_info['band_name'],\\n 'dataset_id': dataset_id,\\n 'band_id': band_info['band_id'],\\n 'processing_level': processing_level,\\n 'nodata_value': nodata_value,\\n 'resampling_method': resampling_method,\\n 'tile_layer': 1})\\n \\n log_multiline(logger.debug, vrt_band_list, 'vrt_band_list = %s', '\\\\t')\\n return vrt_band_list\",\n \"def verb(filename,l,t,d,wout=True): #l = predelay d= decay smaller = less decay, t= number of delays\\n#low l turns into chorus\\n start=time.time()\\n n, data, data_dB,sr,ch=inputwav(filename)\\n data_ex=np.zeros(((n+l*t),ch))\\n data_ex[0:n,:]=data\\n data_Rex=np.zeros((len(data_ex),t,ch))\\n print('Applying reverb...')\\n for k in range (ch):\\n for i in range (len(data)):\\n for j in range(t):\\n data_Rex[i+l*(j+1),j,k]=data_ex[i,k]*np.exp(-d*(j+1))\\n data_F=data_ex\\n print('Mixing...')\\n for i in range (t):\\n data_F=data_F+1*data_Rex[:,i,:]\\n data_F=1*data_F\\n data_verb=data_F+data_ex\\n if wout==True:\\n print('Exporting...')\\n sf.write(filename[0:len(filename)-4]+'_verbed.wav',data_verb,sr,'PCM_16')\\n print('Done!')\\n end=time.time()\\n elapsed=int(1000*(end-start))\\n print('...............................')\\n print('Completed in '+str(elapsed)+' milliseconds.')\\n return data_verb\",\n \"def flow_write(filename,uv,v=None):\\n nBands = 2\\n\\n if v is None:\\n assert(uv.ndim == 3)\\n assert(uv.shape[2] == 2)\\n u = uv[:,:,0]\\n v = uv[:,:,1]\\n else:\\n u = uv\\n\\n assert(u.shape == v.shape)\\n height,width = u.shape\\n f = open(filename,'wb')\\n # write the header\\n f.write(TAG_CHAR)\\n np.array(width).astype(np.int32).tofile(f)\\n np.array(height).astype(np.int32).tofile(f)\\n # arrange into matrix form\\n tmp = np.zeros((height, width*nBands))\\n tmp[:,np.arange(width)*2] = u\\n tmp[:,np.arange(width)*2 + 1] = v\\n tmp.astype(np.float32).tofile(f)\\n f.close()\",\n \"def forwardModel(file, out='Data', wavelength=None, gain=3.1, size=10, burn=500, spotx=2888, spoty=3514, run=700,\\n simulation=False, truths=None, blurred=False):\\n print '\\\\n\\\\n\\\\n'\\n print '_'*120\\n print 'Processing:', file\\n #get data and convert to electrons\\n o = pf.getdata(file)*gain\\n\\n if simulation:\\n data = o\\n else:\\n #roughly the correct location - to avoid identifying e.g. cosmic rays\\n data = o[spoty-(size*3):spoty+(size*3)+1, spotx-(size*3):spotx+(size*3)+1].copy()\\n\\n #maximum position within the cutout\\n y, x = m.maximum_position(data)\\n\\n #spot and the peak pixel within the spot, this is also the CCD kernel position\\n spot = data[y-size:y+size+1, x-size:x+size+1].copy()\\n CCDy, CCDx = m.maximum_position(spot)\\n print 'CCD Kernel Position (within the postage stamp):', CCDx, CCDy\\n\\n #bias estimate\\n if simulation:\\n bias = 9000.\\n rn = 4.5\\n else:\\n bias = np.median(o[spoty-size: spoty+size, spotx-220:spotx-20]) #works for read o\\n rn = np.std(o[spoty-size: spoty+size, spotx-220:spotx-20])\\n\\n print 'Readnoise (e):', rn\\n if rn < 2. or rn > 6.:\\n print 'NOTE: suspicious readout noise estimate...'\\n print 'ADC offset (e):', bias\\n\\n #remove bias\\n spot -= bias\\n\\n #save to file\\n fileIO.writeFITS(spot, out+'small.fits', int=False)\\n\\n #make a copy ot generate error array\\n data = spot.copy().flatten()\\n #assume that uncertanties scale as sqrt of the values + readnoise\\n #sigma = np.sqrt(data/gain + rn**2)\\n tmp = data.copy()\\n tmp[tmp + rn**2 < 0.] = 0. #set highly negative values to zero\\n var = tmp.copy() + rn**2\\n #Gary B. said that actually this should be from the model or is biased,\\n #so I only pass the readout noise part now\\n\\n #fit a simple model\\n print 'Least Squares Fitting...'\\n gaus = models.Gaussian2D(spot.max(), size, size, x_stddev=0.5, y_stddev=0.5)\\n gaus.theta.fixed = True #fix angle\\n p_init = gaus\\n fit_p = fitting.LevMarLSQFitter()\\n stopy, stopx = spot.shape\\n X, Y = np.meshgrid(np.arange(0, stopx, 1), np.arange(0, stopy, 1))\\n p = fit_p(p_init, X, Y, spot)\\n print p\\n model = p(X, Y)\\n fileIO.writeFITS(model, out+'BasicModel.fits', int=False)\\n fileIO.writeFITS(model - spot, out+'BasicModelResidual.fits', int=False)\\n\\n #goodness of fit\\n gof = (1./(np.size(data) - 5.)) * np.sum((model.flatten() - data)**2 / var)\\n print 'GoF:', gof\\n print 'Done\\\\n\\\\n'\\n\\n #maximum value\\n max = np.max(spot)\\n peakrange = (0.9*max, 1.7*max)\\n sum = np.sum(spot)\\n\\n print 'Maximum Value:', max\\n print 'Sum of the values:', sum\\n print 'Peak Range:', peakrange\\n\\n #MCMC based fitting\\n print 'Bayesian Model Fitting...'\\n nwalkers = 1000\\n\\n # Initialize the sampler with the chosen specs.\\n #Create the coordinates x and y\\n x = np.arange(0, spot.shape[1])\\n y = np.arange(0, spot.shape[0])\\n #Put the coordinates in a mesh\\n xx, yy = np.meshgrid(x, y)\\n\\n #Flatten the arrays\\n xx = xx.flatten()\\n yy = yy.flatten()\\n\\n print 'Fitting full model...'\\n ndim = 7\\n\\n #Choose an initial set of positions for the walkers - fairly large area not to bias the results\\n p0 = np.zeros((nwalkers, ndim))\\n #peak, center_x, center_y, radius, focus, width_x, width_y = theta\\n p0[:, 0] = np.random.normal(max, max/100., size=nwalkers) # peak value\\n p0[:, 1] = np.random.normal(p.x_mean.value, 0.1, size=nwalkers) # x\\n p0[:, 2] = np.random.normal(p.y_mean.value, 0.1, size=nwalkers) # y\\n\\n if wavelength is None:\\n if blurred:\\n print 'Using initial guess [radius, focus, width_x, width_y]:', [0.5, 1., 0.3, 0.3]\\n p0[:, 3] = np.random.normal(0.5, 0.01, size=nwalkers) # radius\\n p0[:, 4] = np.random.normal(1., 0.01, size=nwalkers) # focus\\n p0[:, 5] = np.random.normal(0.3, 0.01, size=nwalkers) # width_x\\n p0[:, 6] = np.random.normal(0.3, 0.01, size=nwalkers) # width_y\\n else:\\n p0[:, 3] = np.random.uniform(.45, 0.55, size=nwalkers) # radius\\n p0[:, 4] = np.random.uniform(.40, 0.45, size=nwalkers) # focus\\n p0[:, 5] = np.random.uniform(.35, 0.45, size=nwalkers) # width_x\\n p0[:, 6] = np.random.uniform(.35, 0.45, size=nwalkers) # width_y\\n else:\\n tmp = _expectedValues()[wavelength]\\n if blurred:\\n print 'Using initial guess [radius, focus, width_x, width_y]:', [tmp[0], 0.9, tmp[2], tmp[3]]\\n p0[:, 3] = np.random.normal(tmp[0], 0.01, size=nwalkers) # radius\\n p0[:, 4] = np.random.normal(0.9, 0.01, size=nwalkers) # focus\\n p0[:, 5] = np.random.normal(tmp[2], 0.01, size=nwalkers) # width_x\\n p0[:, 6] = np.random.normal(tmp[3], 0.01, size=nwalkers) # width_y\\n else:\\n print 'Using initial guess [radius, focus, width_x, width_y]:', tmp\\n p0[:, 3] = np.random.normal(tmp[0], 0.01, size=nwalkers) # radius\\n p0[:, 4] = np.random.normal(tmp[1], 0.01, size=nwalkers) # focus\\n p0[:, 5] = np.random.normal(tmp[2], 0.01, size=nwalkers) # width_x\\n p0[:, 6] = np.random.normal(tmp[3], 0.01, size=nwalkers) # width_y\\n\\n #initiate sampler\\n pool = Pool(cores) #A hack Dan gave me to not have ghost processes running as with threads keyword\\n #sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[xx, yy, data, var, peakrange, spot.shape],\\n sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior,\\n args=[xx, yy, data, rn**2, peakrange, spot.shape, blurred],\\n pool=pool)\\n\\n # Run a burn-in and set new starting position\\n print \\\"Burning-in...\\\"\\n pos, prob, state = sampler.run_mcmc(p0, burn)\\n best_pos = sampler.flatchain[sampler.flatlnprobability.argmax()]\\n print best_pos\\n print \\\"Mean acceptance fraction:\\\", np.mean(sampler.acceptance_fraction)\\n pos = emcee.utils.sample_ball(best_pos, best_pos/100., size=nwalkers)\\n sampler.reset()\\n\\n print \\\"Running an improved estimate...\\\"\\n pos, prob, state = sampler.run_mcmc(pos, burn)\\n print \\\"Mean acceptance fraction:\\\", np.mean(sampler.acceptance_fraction)\\n sampler.reset()\\n print \\\"Running MCMC...\\\"\\n pos, prob, state = sampler.run_mcmc(pos, run, rstate0=state)\\n print \\\"Mean acceptance fraction:\\\", np.mean(sampler.acceptance_fraction)\\n\\n #Get the index with the highest probability\\n maxprob_index = np.argmax(prob)\\n\\n #Get the best parameters and their respective errors and print best fits\\n params_fit = pos[maxprob_index]\\n errors_fit = [sampler.flatchain[:,i].std() for i in xrange(ndim)]\\n amplitudeE, center_xE, center_yE, radiusE, focusE, width_xE, width_yE = errors_fit\\n _printResults(params_fit, errors_fit)\\n\\n #Best fit model\\n peak, center_x, center_y, radius, focus, width_x, width_y = params_fit\\n amplitude = _amplitudeFromPeak(peak, center_x, center_y, radius, x_0=CCDx, y_0=CCDy)\\n airy = models.AiryDisk2D(amplitude, center_x, center_y, radius)\\n adata = airy.eval(xx, yy, amplitude, center_x, center_y, radius).reshape(spot.shape)\\n f = models.Gaussian2D(1., center_x, center_y, focus, focus, 0.)\\n focusdata = f.eval(xx, yy, 1., center_x, center_y, focus, focus, 0.).reshape(spot.shape)\\n foc = signal.convolve2d(adata, focusdata, mode='same')\\n CCD = models.Gaussian2D(1., CCDx, CCDy, width_x, width_y, 0.)\\n CCDdata = CCD.eval(xx, yy, 1., CCDx, CCDy, width_x, width_y, 0.).reshape(spot.shape)\\n model = signal.convolve2d(foc, CCDdata, mode='same')\\n #save model\\n fileIO.writeFITS(model, out+'model.fits', int=False)\\n\\n #residuals\\n fileIO.writeFITS(model - spot, out+'residual.fits', int=False)\\n fileIO.writeFITS(((model - spot)**2 / var.reshape(spot.shape)), out+'residualSQ.fits', int=False)\\n\\n # a simple goodness of fit\\n gof = (1./(np.size(data) - ndim)) * np.sum((model.flatten() - data)**2 / var)\\n maxdiff = np.max(np.abs(model - spot))\\n print 'GoF:', gof, ' Maximum difference:', maxdiff\\n if maxdiff > 2e3 or gof > 4.:\\n print '\\\\nFIT UNLIKELY TO BE GOOD...\\\\n'\\n print 'Amplitude estimate:', amplitude\\n\\n #results and save results\\n _printFWHM(width_x, width_y, errors_fit[5], errors_fit[6])\\n res = dict(wx=width_x, wy=width_y, wxerr=width_xE, wyerr=width_yE, out=out,\\n peakvalue=max, CCDmodel=CCD, CCDmodeldata=CCDdata, GoF=gof,\\n maximumdiff=maxdiff, fit=params_fit)\\n fileIO.cPickleDumpDictionary(res, out+'.pkl')\\n\\n #plot\\n samples = sampler.chain.reshape((-1, ndim))\\n extents = None\\n if simulation:\\n extents = [(0.91*truth, 1.09*truth) for truth in truths]\\n extents[1] = (truths[1]*0.995, truths[1]*1.005)\\n extents[2] = (truths[2]*0.995, truths[2]*1.005)\\n extents[3] = (0.395, 0.425)\\n extents[4] = (0.503, 0.517)\\n truths[0] = _peakFromTruth(truths)\\n print truths\\n fig = triangle.corner(samples,\\n labels=['peak', 'x', 'y', 'radius', 'focus', 'width_x', 'width_y'],\\n truths=truths)#, extents=extents)\\n fig.savefig(out+'Triangle.png')\\n plt.close()\\n pool.close()\",\n \"def generate_siaf_pre_flight_reference_files_nircam():\\n\\n instrument = 'NIRCam'\\n overwrite_wedge_file = False\\n overwrite_grism_file = False\\n\\n\\n # wedge definitions\\n wedge_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_wedge_offsets.txt'.format(instrument.lower()))\\n\\n if (not os.path.isfile(wedge_file) or (overwrite_wedge_file)):\\n\\n wedge_offsets = Table.read(os.path.join(JWST_SOURCE_DATA_ROOT, instrument, 'wedge_offsets.txt'), format='ascii.basic', delimiter=' ', guess=False)\\n\\n comments = []\\n comments.append('{} detector parameter definition file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the wedge offsets.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n wedge_offsets.meta['comments'] = comments\\n wedge_offsets.write(wedge_file, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False)\\n\\n # grism definitions\\n grism_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_grism_parameters.txt'.format(instrument.lower()))\\n\\n if (not os.path.isfile(wedge_file) or (overwrite_grism_file)):\\n # grism parameters, see WFSS worksheet in EXCEL SIAF\\n grism_parameters = Table.read(grism_file, format='ascii.basic', delimiter=',', guess=False)\\n\\n # Save a backup copy of the grism file\\n cmd = 'cp {} {}'.format(grism_file,os.path.join(JWST_TEMPORARY_DATA_ROOT, instrument, 'nircam_siaf_grism_parameters_backup.txt'))\\n os.system(cmd)\\n\\n # different sign in Y for NRCB apertures\\n factor = np.array(\\n [1. if 'NRCA' in grism_parameters['aperture_name'][i] else -1. for i in range(len(grism_parameters))])\\n\\n for col in grism_parameters.colnames[1:]:\\n # these are Sci coordinates\\n if col[0] != 'D':\\n if 'X' in col:\\n grism_parameters['D{}'.format(col)] = grism_parameters[col].data - 1024.5\\n elif 'Y' in col:\\n grism_parameters['D{}'.format(col)] = factor * (grism_parameters[col].data - 1024.5)\\n\\n\\n\\n comments = []\\n comments.append('{} grism parameter definition file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the grism parameters.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n grism_parameters.meta['comments'] = comments\\n grism_parameters.write(grism_file, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False)\\n\\n # Transformation parameters, mapping used to select rows in cold_fit_[] file\\n coldfit_name_mapping = {\\n 'NRCA1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RT_20170307121022',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALWToOTESKY_RT_20170307121022',\\n },\\n 'NRCB1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_1_20161025081604',\\n 'pixels_to_mm':'NIRCAMBSW_1ToNIRCAMBSW_20161025081604',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_2_20161025081912',\\n 'pixels_to_mm':'NIRCAMBSW_2ToNIRCAMBSW_20161025081912',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_3_20161025082300',\\n 'pixels_to_mm':'NIRCAMBSW_3ToNIRCAMBSW_20161025082300',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_4_20161025082647',\\n 'pixels_to_mm':'NIRCAMBSW_4ToNIRCAMBSW_20161025082647',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMBLW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBLWToNIRCAMBLW_1_20161227162336',\\n 'pixels_to_mm':'NIRCAMBLW_1ToNIRCAMBLW_20161227162336',\\n 'mm_to_degrees':'NIRCAMBLWToOTESKY_RT_20170307121023',\\n },\\n 'NRCA1_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\\n },\\n 'NRCA2_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA3_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\\n },\\n 'NRCA4_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA1_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\\n },\\n 'NRCA2_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\\n },\\n 'NRCA3_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\\n },\\n 'NRCA4_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\\n },\\n 'NRCA5_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA5_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMALW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALW_BARToOTESKY_202005150434',\\n }\\n }\\n\\n # coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('cold_fit_201703071210.csv'))\\n coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('nircam_cold_fit.txt'))\\n print('NIRCam coldfit data from', coldfit_source_data_file)\\n t = open(coldfit_source_data_file)\\n coldfit_source_data = t.readlines()\\n t.close()\\n # remove comments from read content\\n coldfit_source_data = [line for line in coldfit_source_data if line[0] != '#']\\n\\n siaf_detector_layout = iando.read.read_siaf_detector_layout()\\n # siaf_alignment_parameters = iando.read.read_siaf_alignment_parameters(instrument)\\n siaf_aperture_definitions = iando.read.read_siaf_aperture_definitions(instrument)\\n # aperture_dict = {}\\n aperture_name_list = siaf_aperture_definitions['AperName'].tolist()\\n\\n # generate alignment reference file, one file for all master apertures\\n outfile = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_alignment.txt'.format(instrument.lower()))\\n siaf_alignment = Table()\\n\\n for AperName in aperture_name_list:\\n\\n # process the master apertures of NIRCam\\n if AperName in siaf_detector_layout['AperName']:\\n (A, B, C, D, betaX, betaY, V2Ref, V3Ref) = nircam_get_polynomial_both(AperName, siaf_aperture_definitions, coldfit_name_mapping, coldfit_source_data)\\n\\n #generate distortion reference file\\n number_of_coefficients = len(A)\\n polynomial_degree = int((np.sqrt(8 * number_of_coefficients + 1) - 3) / 2)\\n siaf_index = []\\n exponent_x = []\\n exponent_y = []\\n for i in range(polynomial_degree + 1):\\n for j in np.arange(i + 1):\\n siaf_index.append('{:d}{:d}'.format(i, j))\\n exponent_x.append(i - j)\\n exponent_y.append(j)\\n\\n distortion_reference_table = Table((siaf_index, exponent_x, exponent_y, A, B, C, D), names=(\\n 'siaf_index', 'exponent_x', 'exponent_y', 'Sci2IdlX', 'Sci2IdlY', 'Idl2SciX', 'Idl2SciY'))\\n distortion_reference_table.add_column(\\n Column([AperName] * len(distortion_reference_table), name='AperName'), index=0)\\n distortion_reference_file_name = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_distortion_{}.txt'.format(instrument.lower(),\\n AperName.lower()))\\n # distortion_reference_table.pprint()\\n comments = []\\n comments.append('{} distortion reference file for SIAF\\\\n'.format(instrument))\\n comments.append('Aperture: {}\\\\n'.format(AperName))\\n comments.append('Based on coefficients given in {},'.format(os.path.basename(coldfit_source_data_file)))\\n # comments.append('that were rescaled, shifted for a different reference pixel location, and rotated:')\\n # comments.append('Rotation of {:2.3f} deg was removed and is carried separately in V3IdlYangle.'.format(\\n # np.rad2deg(V3angle))) # *units.deg.to(units.arcsecond)\\n # if 'may_2015' in distortion_file_name:\\n # comments.append(\\n # 'These parameters are stored in the currently (January 2018) active SIAF (PRDOPSSOC-G-012). ')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n distortion_reference_table.meta['comments'] = comments\\n distortion_reference_table.write(distortion_reference_file_name, format='ascii.fixed_width',\\n delimiter=',', delimiter_pad=' ', bookend=False, overwrite=True)\\n\\n V3SciYAngle = betaY\\n V3SciXAngle = betaX\\n if np.abs(V3SciYAngle) < 90.:\\n V3IdlYAngle = V3SciYAngle\\n else:\\n V3IdlYAngle = V3SciYAngle - np.sign(V3SciYAngle) * 180.\\n\\n if len(siaf_alignment) == 0: # first entry\\n siaf_alignment['AperName'] = ['{:>30}'.format(AperName)]\\n siaf_alignment['V3IdlYAngle'] = [V3IdlYAngle]\\n siaf_alignment['V3SciXAngle'] = V3SciXAngle #[np.rad2deg(betaX)]\\n siaf_alignment['V3SciYAngle'] = V3SciYAngle #[np.rad2deg(betaY)]\\n siaf_alignment['V2Ref'] = [V2Ref]\\n siaf_alignment['V3Ref'] = [V3Ref]\\n else:\\n siaf_alignment.add_row(['{:>30}'.format(AperName), V3IdlYAngle, V3SciXAngle, V3SciYAngle, V2Ref,V3Ref])\\n comments = []\\n comments.append('{} alignment parameter reference file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the focal plane alignment parameters of master apertures calibrated')\\n comments.append('during FGS-SI alignment.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n siaf_alignment.meta['comments'] = comments\\n siaf_alignment.write(outfile, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False, overwrite=True)\",\n \"def writeFlow(filename,uv,v=None):\\n nBands = 2\\n\\n if v is None:\\n assert(uv.ndim == 3)\\n assert(uv.shape[2] == 2)\\n u = uv[:,:,0]\\n v = uv[:,:,1]\\n else:\\n u = uv\\n\\n assert(u.shape == v.shape)\\n height,width = u.shape\\n f = open(filename,'wb')\\n # write the header\\n f.write(TAG_CHAR)\\n np.array(width).astype(np.int32).tofile(f)\\n np.array(height).astype(np.int32).tofile(f)\\n # arrange into matrix form\\n tmp = np.zeros((height, width*nBands))\\n tmp[:,np.arange(width)*2] = u\\n tmp[:,np.arange(width)*2 + 1] = v\\n tmp.astype(np.float32).tofile(f)\\n f.close()\",\n \"def write_to_vcf(self):\\n\\n # 1. Generate header info\\n date_for_vcf = datetime.now().strftime('%Y%m%d')\\n header_info = [\\n '##fileformat=VCFv4.2',\\n '##fileDate=%s' % date_for_vcf,\\n '##source=%s' % self.get_analyser_name(),\\n '##reference=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz',\\n '##contig=<ID=chr1,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr1.fa.gz>',\\n '##contig=<ID=chr2,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr2.fa.gz>',\\n '##contig=<ID=chr3,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr3.fa.gz>',\\n '##contig=<ID=chr4,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr4.fa.gz>',\\n '##contig=<ID=chr5,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr5.fa.gz>',\\n '##contig=<ID=chr6,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr6.fa.gz>',\\n '##contig=<ID=chr7,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr7.fa.gz>',\\n '##contig=<ID=chr8,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr8.fa.gz>',\\n '##contig=<ID=chr9,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr9.fa.gz>',\\n '##contig=<ID=chr10,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr10.fa.gz>',\\n '##contig=<ID=chr11,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr11.fa.gz>',\\n '##contig=<ID=chr12,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr12.fa.gz>',\\n '##contig=<ID=chr13,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr13.fa.gz>',\\n '##contig=<ID=chr14,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr14.fa.gz>',\\n '##contig=<ID=chr15,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr15.fa.gz>',\\n '##contig=<ID=chr16,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr16.fa.gz>',\\n '##contig=<ID=chr17,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr17.fa.gz>',\\n '##contig=<ID=chr18,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr18.fa.gz>',\\n '##contig=<ID=chr19,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr19.fa.gz>',\\n '##contig=<ID=chr20,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr20.fa.gz>',\\n '##contig=<ID=chr21,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr21.fa.gz>',\\n '##contig=<ID=chr22,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chr22.fa.gz>',\\n '##contig=<ID=chrM,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrM.fa.gz>',\\n '##contig=<ID=chrX,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrX.fa.gz>',\\n '##contig=<ID=chrY,URL=https://hgdownload.soe.ucsc.edu/goldenPath/hg38/chromosomes/chrY.fa.gz>',\\n ]\\n header_parameters = [\\n '##FORMAT=<ID=GT,Number=1,Type=String,Description=\\\"Genotype\\\">',\\n '##FORMAT=<ID=MTQ,Number=1,Type=String,Description=\\\"MassArray Typer quality value for SNP call. '\\n 'A=Conservative, B=Moderate, C=Aggressive, D=Low Probability, E=User Call, i=Low Intensity. A and B are considered high '\\n 'quality scores.\\\">',\\n '##INFO=<ID=PCR,Number=2,Type=String,Description=\\\"PCR sequences used in assay.\\\">',\\n '##INFO=<ID=AF,Number=A,Type=Float,Description=\\\"Minor allele frequency from population data.\\\">',\\n '##INFO=<ID=Gene,Number=A,Type=String,Description=\\\"HGNC Gene Name for gene containing SNP.\\\">',\\n '##INFO=<ID=Build,Number=A,Type=String,Description=\\\"Genome build used to determine SNP position for assay.\\\">',\\n '##FILTER=<ID=LowCallRate,Description=\\\"SNP not called in at least 30% of samples in assay.\\\">',\\n ]\\n\\n # 2. Extract info from XML file\\n results = self.get_results()\\n snps = self.get_snps()\\n pcr_sequences = self.get_pcr_sequences()\\n call_rates = self.get_snp_call_rate()\\n\\n # 3. For each sample, create VCF, add headers, determine genotype of each SNP and write to file.\\n for sample, variants in results.items():\\n\\n with open(os.path.join(self.output, '%s.vcf' % sample), 'w+') as outfile:\\n\\n header_fields = ['CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', str(sample)]\\n\\n outfile.write('%s\\\\n' % '\\\\n'.join(header_info))\\n outfile.write('%s\\\\n' % '\\\\n'.join(header_parameters))\\n outfile.write('#%s\\\\n' % '\\\\t'.join(header_fields))\\n\\n # for each variant, make a line to add to the file which will\\n # then be sorted\\n lines_to_write = []\\n for snp, info in variants.items():\\n\\n ref_allele = snps[snp]['ref']\\n alt_alleles = snps[snp]['alt']\\n alt_list = alt_alleles.split(',')\\n\\n # Genotype formatting matches VCF v4.0 spec where ./. is no call.\\n gt_list = []\\n called_genotype = info['genotype']\\n if not called_genotype:\\n gt_list = ['.', '.']\\n elif len(called_genotype) == 1:\\n called_genotype += called_genotype\\n for allele in list(called_genotype):\\n if allele == ref_allele:\\n gt_list.append(0)\\n else:\\n if allele in alt_list:\\n idx = alt_list.index(allele)\\n gt_list.append(idx + 1)\\n else:\\n raise ValueError(\\n 'Called genotype %s not provided as possible alt in bed file. Sample %s and SNP '\\n '%s %s.' % (called_genotype, sample, snp, alt_alleles)\\n )\\n gt = '/'.join([str(x) for x in gt_list])\\n\\n # Threshold currently set to 0.3 (70% results have a call).\\n snp_call_rate = call_rates[snp]\\n if snp_call_rate >= 0.3:\\n vcf_filter = 'LowCallRate'\\n else:\\n vcf_filter = 'PASS'\\n\\n snp_pcr_seqs = pcr_sequences[snp]\\n\\n lines_to_write.append(\\n '{chr}\\\\t{pos}\\\\t{id}\\\\t{ref}\\\\t{alt}\\\\t.\\\\t{filter}\\\\tAF={af};PCR={pcr};Gene={gene};Build={build}\\\\t'\\n 'GT:MTQ\\\\t{gt}:{qual}\\\\n'.format(\\n chr=snps[snp]['chrom'],\\n pos=snps[snp]['pos'],\\n id=snp,\\n ref=ref_allele,\\n alt=alt_alleles,\\n filter=vcf_filter,\\n af=snps[snp]['maf'],\\n pcr=','.join(snp_pcr_seqs),\\n gene=snps[snp]['gene'],\\n build=snps[snp]['genome_build'],\\n gt=gt,\\n qual=','.join(info['quality'])\\n )\\n )\\n\\n sorted_lines_to_write = sorted(\\n lines_to_write,\\n key=lambda x: (\\n # first key for sorting is the int value of chr\\n int(x.split('\\\\t')[0][3:]),\\n # second key for sorting is the position of the variant\\n int(x.split('\\\\t')[1])\\n )\\n )\\n\\n for line in sorted_lines_to_write:\\n outfile.write(line)\",\n \"def main(input_file, dtb_file):\\n\\n LOGGER.info(\\\"Reading: %s\\\" % dtb_file)\\n with open(dtb_file, \\\"rb\\\") as max_table_file:\\n LOGGER.info(\\\"max10_device_table.bin size: %x\\\" %\\n os.path.getsize(max_table_file.name))\\n LOGGER.info(\\\"Max max10 table size: %x\\\" % MAX10_TABLE_SIZE)\\n if (os.path.getsize(max_table_file.name) > MAX10_TABLE_SIZE):\\n raise Exception(LOGGER.error(\\\"max10_device_table.bin is too big\\\"))\\n max10_table = max_table_file.read()\\n\\n LOGGER.info(\\\"Writing file: %s\\\" % input_file)\\n with open(input_file, \\\"rb+\\\") as rpd_file:\\n rpd_file.seek(MAX10_TABLE_START)\\n rpd_file.write(bytearray(max10_table))\\n LOGGER.info(\\\"Done merging Max10 device table\\\")\",\n \"def sample_vcf():\\n file_content = b\\\"\\\"\\\"##fileformat=VCFv4.2\\n##hailversion=0.2.100-2ea2615a797a\\n##INFO=<ID=QUALapprox,Number=1,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=SB,Number=.,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=MQ,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=MQRankSum,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=VarDP,Number=1,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=AS_ReadPosRankSum,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_pab_max,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_QD,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_MQ,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=QD,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_MQRankSum,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=FS,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_FS,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=ReadPosRankSum,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=AS_QUALapprox,Number=1,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=AS_SB_TABLE,Number=.,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=AS_VarDP,Number=1,Type=Integer,Description=\\\"\\\">\\n##INFO=<ID=AS_SOR,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=SOR,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=singleton,Number=0,Type=Flag,Description=\\\"\\\">\\n##INFO=<ID=transmitted_singleton,Number=0,Type=Flag,Description=\\\"\\\">\\n##INFO=<ID=omni,Number=0,Type=Flag,Description=\\\"\\\">\\n##INFO=<ID=mills,Number=0,Type=Flag,Description=\\\"\\\">\\n##INFO=<ID=monoallelic,Number=0,Type=Flag,Description=\\\"\\\">\\n##INFO=<ID=AS_VQSLOD,Number=1,Type=Float,Description=\\\"\\\">\\n##INFO=<ID=InbreedingCoeff,Number=1,Type=Float,Description=\\\"\\\">\\n##FILTER=<ID=AC0,Description=\\\"Allele count is zero after filtering out low-confidence genotypes (GQ < 20; DP < 10; and AB < 0.2 for het calls)\\\">\\n##FILTER=<ID=AS_VQSR,Description=\\\"Failed VQSR filtering thresholds of -2.7739 for SNPs and -1.0606 for indels\\\">\\n##contig=<ID=chr1,length=248956422,assembly=GRCh38>\\n#CHROM\\tPOS\\tID\\tREF\\tALT\\tQUAL\\tFILTER\\tINFO\\nchr1\\t10330\\t.\\tCCCCTAACCCTAACCCTAACCCTACCCTAACCCTAACCCTAACCCTAACCCTAA\\tC\\t.\\tPASS\\tQUALapprox=21493;SB=325,1077,113,694;MQ=32.1327;MQRankSum=0.720000;VarDP=2236;AS_ReadPosRankSum=-0.736000;AS_pab_max=1.00000;AS_QD=5.17857;AS_MQ=29.5449;QD=9.61225;AS_MQRankSum=0.00000;FS=8.55065;AS_FS=.;ReadPosRankSum=0.727000;AS_QUALapprox=145;AS_SB_TABLE=325,1077,2,5;AS_VarDP=28;AS_SOR=0.311749;SOR=1.48100;singleton;AS_VQSLOD=13.4641;InbreedingCoeff=-0.000517845\\\"\\\"\\\"\\n file = io.BytesIO(file_content)\\n return file\",\n \"def read(file):\\n\\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\\n nol = [1, 1, 1, 1, 1, 4, 1,\\n 0, 0, 0, 0, 0, 1,\\n 0, 1, 0, 0, 0, 0]\\n rawd = re.compile('rawd\\\\d\\\\d')\\n\\n retval = True\\n version = 0\\n b = 0 # current block index\\n raw = {}\\n for item in blocks:\\n raw[item] = []\\n\\n data = []\\n mdata = [] # multi-line data\\n mline = 0 # line counter for multi-line models\\n\\n # parse file into raw with to_number conversions\\n fid = open(file, 'r')\\n for num, line in enumerate(fid.readlines()):\\n line = line.strip()\\n if num == 0: # get basemva and frequency\\n data = line.split('/')[0]\\n data = data.split(',')\\n\\n mva = float(data[1])\\n freq = float(data[5])\\n version = int(data[2])\\n\\n if not version:\\n version = int(rawd.search(line).group(0).strip('rawd'))\\n if version < 32 or version > 33:\\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\\n continue\\n elif num == 1: # store the case info line\\n logging.info(line)\\n continue\\n elif num == 2:\\n continue\\n elif num >= 3:\\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\\n b += 1\\n continue\\n elif line[0] is 'Q': # end of file\\n break\\n data = line.split(',')\\n\\n data = [to_number(item) for item in data]\\n mdata.append(data)\\n mline += 1\\n if mline == nol[b]:\\n if nol[b] == 1:\\n mdata = mdata[0]\\n raw[blocks[b]].append(mdata)\\n mdata = []\\n mline = 0\\n fid.close()\\n\\n # add device elements params and add to PSAT formatted dictionary\\n\\n for data in raw['bus']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\\n \\\"\\\"\\\"\\n idx = data[0]\\n ty = data[3]\\n angle = data[8]\\n try:\\n lat = data[9]\\n except:\\n # logging.warning('<No Coordinates in .raw file>')\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\\n else:\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n 'latitude': data[9],\\n 'longitude': data[10]\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\\n Settings.Bus.append(psatlist)\\n Settings.BusNames.append(data[1])\\n # Add BusSTORE Dictionary For Later Reference\\n Settings.BusStore[idx] = param\\n\\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\\n 38.656721]\\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\\n\\n #for idx, line in enumerate(Settings.Bus):\\n # line.extend([xcoord[idx], ycoord[idx]])\\n\\n maxV = 1.1\\n minV = 0.9\\n maxQ = 1\\n minQ = 0\\n convimp = 0\\n status = 1\\n loss = 1\\n\\n for data in raw['load']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\\n \\\"\\\"\\\"\\n\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n voltage = Settings.BusStore[busidx]['voltage']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'Sn': mva,\\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\\n 'owner': data[11],\\n 'type': Settings.BusStore[busidx]['type'],\\n 'voltage': voltage\\n }\\n\\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\\n Settings.PQ.append(psatlist)\\n \\\"\\\"\\\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\\\"\\\"\\\"\\n\\n for data in raw['fshunt']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4\\n Bus, name, Status, g (MW), b (Mvar)\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'status': data[2],\\n 'Sn': mva,\\n 'g': data[3] / mva,\\n 'b': data[4] / mva,\\n }\\n\\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\\n Settings.Shunt.append(psatlist)\\n\\n gen_idx = 0\\n type = 6\\n\\n for data in raw['gen']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n gen_mva = data[8]\\n gen_idx += 1\\n status = data[14]\\n leak = 0\\n param = {'Sn': gen_mva,\\n 'Vn': vn,\\n 'u': status,\\n 'idx': gen_idx,\\n 'bus': busidx,\\n 'pg': status * data[2] / mva,\\n 'qg': status * data[3] / mva,\\n 'qmax': data[4] / mva,\\n 'qmin': data[5] / mva,\\n 'v0': data[6],\\n 'ra': data[9], # ra armature resistance\\n 'xs': data[10], # xs synchronous reactance\\n 'pmax': data[16] / mva,\\n 'pmin': data[17] / mva,\\n }\\n\\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\\n refangle = 0\\n refBus = 1\\n PGuess = 1\\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\\n maxV, minV, PGuess, loss, refBus, status]\\n SW = swlist\\n Settings.SW.append(swlist)\\n Settings.SWStore[busidx] = param\\n Settings.SynStore[busidx] = param\\n continue\\n\\n if busidx not in Settings.BusStore.keys():\\n \\\"\\\"\\\" Need data from .dyr file. Create initial list, then append data from .dyr\\\"\\\"\\\"\\n else:\\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\\n # Syn.append(psatlist)\\n Settings.SynStore[busidx] = param\\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\\n param['qmax'], param['qmin'], maxV, minV, loss, status]\\n Settings.PV.append(pvlist)\\n\\n\\n for data in raw['branch']:\\n \\\"\\\"\\\"\\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\\n \\\"\\\"\\\"\\n param = {'bus1': data[0],\\n 'bus2': data[1],\\n 'id' : data[2],\\n 'r': data[3],\\n 'x': data[4],\\n 'b': data[5],\\n 'rate_a': data[6],\\n 'rate_b': data[7],\\n 'rate_c': data[8],\\n 'Vn': Settings.BusStore[data[0]]['Vn'],\\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\\n 'length': data[14],\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n 'status': data[13]\\n }\\n\\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['status']]\\n Settings.Lineij.append([data[0], data[1], data[2]])\\n Settings.Lineji.append([data[1], data[0], data[2]])\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.branches += 1\\n Settings.linecount += 1\\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\\n\\n for data in raw['transf']:\\n \\\"\\\"\\\"\\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\\n R1-2,X1-2,SBASE1-2\\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\\n WINDV2,NOMV2\\n \\\"\\\"\\\"\\n if len(data[1]) < 5:\\n ty = 2\\n else:\\n ty = 3\\n if ty == 3:\\n continue\\n # raise NotImplementedError('Three-winding transformer not implemented')\\n\\n tap = data[2][0]\\n phi = data[2][2]\\n\\n if tap == 1 and phi == 0:\\n trasf = False\\n else:\\n trasf = True\\n param = {'trasf': trasf,\\n 'bus1': data[0][0],\\n 'bus2': data[0][1],\\n 'u': data[0][11],\\n 'b': data[0][8],\\n 'r': data[1][0],\\n 'x': data[1][1],\\n 'tap': tap,\\n 'phi': phi,\\n 'rate_a': data[2][3],\\n 'Vn': Settings.BusStore[busidx]['Vn'],\\n 'Vn2': Settings.BusStore[busidx]['Vn'],\\n # 'length': data[?][?], FIND CORRECT INDEX\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n }\\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['u']]\\n\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.linecount += 1\\n Settings.transformers += 1\\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\\n # Re-Order Line Data for correct sequence\\n for key in Settings.LineOrd:\\n for item in Settings.LineOrd[key]:\\n Settings.Line.append(item)\\n\\n for data in raw['area']:\\n Settings.Areas.append(data[4])\\n\\n for data in raw['zone']:\\n Settings.Regions.append(data[1])\\n\\n return retval\",\n \"def main(file):\\n\\n # Get the current working directory.\\n here = os.getcwd()\\n #Need the file_name to set globe, so that other functions can access to it.\\n global file_name\\n # Spite the Input into file_path and file_name.\\n file_path = spilt_path(file)[0]\\n file_name = spilt_path(file)[1]\\n\\n # Try to get into the file_path, if exist\\n try:\\n os.chdir(file_path)\\n except IOError, e:\\n print e\\n\\n # Now convert it\\n convertFile(file_name)\\n # going back to orgin folder\\n os.chdir(here)\\n return os.path.join(output_dir, file_name)\",\n \"def load_spectrum(inputfile):\\n if inputfile.endswith(\\\"fits\\\"):\\n wav, flux = spectrum_sdss_fits(inputfile)\\n imodel = False\\n inu = False\\n\\n else:\\n f = open(inputfile, \\\"r\\\")\\n # Read header\\n try:\\n nn = int(f.tell())\\n f.readline()\\n except BaseException:\\n pass\\n\\n # Read first line\\n f.readline()\\n # Check format of second line\\n test = f.readline()\\n f.seek(0) # rewind to begining\\n\\n # Read data\\n if (len(test.split()) == 10) or (len(test.split()) == 6): # test62\\n wav, flux = spectrum_test62(f)\\n imodel = True\\n inu = True\\n\\n elif len(test.split(\\\",\\\")) == 2 or len(test.split(\\\",\\\")) == 4: # csv\\n wav, flux = spectrum_csv(f)\\n imodel = False\\n inu = False\\n\\n elif len(test.split()) == 2: # tsv\\n wav, flux = spectrum_tsv(f)\\n imodel = False\\n inu = False\\n\\n elif len(test.split()) == 3: # tsv with uncertainties\\n wav, flux = spectrum_tsv3(f)\\n imodel = False\\n inu = False\\n\\n elif len(test.split()) == 5 or len(test.split()) == 7: # mics format\\n wav, flux = spectrum_misc(f)\\n imodel = False\\n inu = False\\n\\n else:\\n\\n raise ValueError(f\\\"Unknown format for {inputfile}.\\\")\\n\\n f.close()\\n\\n return Spectrum(wav, flux, (imodel, inu))\",\n \"def view(filename):\\n n, data, data_dB,sr,ch=inputwav(filename)\\n t=np.linspace(0,n/sr,n)\\n py.close()\\n fig, (ax1) = py.subplots(nrows=1) \\n ax1.plot(t[0:n:100],data[0:n:100],'k-',linewidth=1,label=filename)\\n ax1.legend(loc=1)\\n ax1.set_ylabel('Amplitude (Rel. Bit)')\\n ax1.set_xlabel('Time (s)')\",\n \"def wave_create():\\n return _u2i(_pigpio_command(_control, _PI_CMD_WVCRE, 0, 0))\",\n \"def generate_data(path=resource_filename('locals', 'data/fake/'), mag_range=(11.13,18)):\\n # Get some random spectra\\n try:\\n files = glob.glob('/user/jfilippazzo/Models/ACES/default/*.fits')[::50]\\n except:\\n files = glob.glob('/Users/jfilippazzo/Documents/Modules/_DEPRECATED/limb_dark_jeff/limb/specint/*.fits')[::20]\\n \\n # Make a fake source catalog (with only essential columns for now)\\n catpath = os.path.join(path,'fake_source_catalog.ecsv')\\n ids = list(range(len(files)))\\n coords = SkyCoord([89.7455]*len(ids), [-29.05744]*len(ids), unit='deg', frame='icrs')\\n cat = at.QTable([ids,coords], names=('id','icrs_centroid'))\\n cat.write(catpath)\\n \\n # Open the x1d file\\n header = fits.getheader(resource_filename('locals', 'data/template_x1d.fits'))\\n \\n # Make Spectrum objects from models at R=150\\n wavelength = np.arange(0.05,2.6,0.0001)[::66]*q.um\\n \\n # Normalize the spectra to a random F200W magnitude\\n spectra = []\\n f200w = Bandpass('NIRISS.F200W')\\n f200w.wave_units = q.um\\n for file in files:\\n \\n # Create Spectrum\\n flux = fits.getdata(file)[-1][::66]*q.erg/q.s/q.cm**2/q.AA\\n unc = flux/50.\\n spec = Spectrum(wavelength, flux, unc)\\n \\n # Normalize to F200W\\n mag = np.random.uniform(*mag_range)\\n norm_spec = spec.renormalize(mag, f200w)\\n spectra.append(norm_spec)\\n \\n # Make a separate x1d file and photometry file for each bandpass\\n # containing data for each source\\n for band in NIRISS_bands:\\n \\n try:\\n \\n # Get the Bandpass object\\n bp = Bandpass(band)\\n bp.wave_units = q.um\\n \\n # Make x1d file for spectra\\n x1d_file = os.path.join(path,'{}_x1d.fits'.format(band))\\n x1d_hdu = fits.HDUList(fits.PrimaryHDU(header=header))\\n \\n # Make csv file for photometry\\n phot_file = os.path.join(path,'{}_phot.csv'.format(band))\\n phot_data = at.Table(names=('id','band','magnitude','magnitude_unc'), dtype=(int,'S20',float,float))\\n \\n # Iterate over spectra\\n for id,(f,spec) in enumerate(zip(files,spectra)):\\n \\n # Trim spectrum to bandpass for x1d file\\n spec = Spectrum(*spec.spectrum, trim=[(0*q.um,bp.WavelengthMin*1E-4*q.um),(bp.WavelengthMax*1E-4*q.um,10*q.um)])\\n \\n # Calculate magnitude and add to photometry table\\n mag, mag_unc = spec.synthetic_magnitude(bp, force=True)\\n phot_data.add_row([id, band, mag, mag_unc])\\n \\n # Add source spectrum params for verification\\n params = f.split('/')[-1].split('-')\\n header['TEFF'] = int(params[0].replace('lte',''))\\n header['LOGG'] = float(params[1][:4])\\n header['FEH'] = float(params[-6][:-8].split('+')[-1])\\n header['FILEPATH'] = f\\n header['PUPIL'] = band\\n\\n # Put spectrum in x1d fits file\\n data = fits.BinTableHDU(data=np.rec.array(list(zip(*spec.data)),\\n formats='float32,float32,float32',\\n names='WAVELENGTH,FLUX,ERROR'),\\n header=header)\\n data.name = 'EXTRACT1D'\\n \\n x1d_hdu.append(data)\\n \\n # Write the photometry file\\n phot_data.write(phot_file, format='ascii.csv')\\n del phot_data\\n \\n # Write the x1d file\\n x1d_hdu.writeto(x1d_file, overwrite=True)\\n del x1d_hdu\\n \\n except IOError:\\n pass\",\n \"def convert(infile,arcsec_per_pixel=0.2,sigma_conv=1.,expansion_factor=5,writeout=None,overwrite=False,keep_units=False):\\n \\n PLATESCALE = 1.2120 # arcsec / mm\\n rss = fits.open( infile )\\n phdr = rss[1].header\\n dhdr = rss[0].header\\n data = rss[0].data\\n \\n conff=dm.read_fibers_extension(phdr)\\n bundles_values=conff.bundles.keys()\\n sky_bundles=[]\\n for bundlei in bundles_values:\\n if phdr[\\\"BUN%03d_T\\\" % bundlei]=='SKY':\\n sky_bundles.append(bundlei)\\n \\n w0 = dhdr['CRVAL1'] # reference wavelength\\n try : dw = dhdr['CRDELT1'] # wavelength step\\n except : dw = dhdr['CDELT1'] # wavelength step\\n wunit = dhdr['CUNIT1'] # wavelength unit\\n wtype = 'WAVE' # type spectra\\n\\n # define the dimensions of the spaxel array \\n Nx, Ny, x0, y0, dx, dy = getspaxdim( data,phdr,sky_bundles,expansion_factor=expansion_factor)\\n\\n nbin=int(round(float(arcsec_per_pixel)/float(dx)))\\n\\n\\n Nw = dhdr['NAXIS1'] # number of wave. steps\\n \\n\\n \\n # initialize an empty 3-d cube (zero everywhere)\\n cube = fits.PrimaryHDU()\\n #cube.header=rss[0].header \\n #cube.header.remove('CRPIX1') \\n #cube.header.remove('CRVAL1') \\n #cube.header.remove('CUNIT1') \\n #cube.header.remove('CTYPE1') \\n #cube.header.remove('CRPIX2') \\n #cube.header.remove('CRVAL2') \\n #cube.header.remove('CDELT2') \\n #cube.header.remove('CTYPE2') \\n cube.header.update(NAXIS=3)\\n cube.header.update(NAXIS1=Nx)\\n cube.header.update(NAXIS2=Ny)\\n cube.header.update(NAXIS3=Nw)\\n cube.header.update(CD1_1=-dx/3600.)\\n cube.header.update(CD2_2=dy/3600.)\\n cube.header.update(CD3_3=dw)\\n cube.header.update(CRPIX1=0)\\n cube.header.update(CRPIX2=0)\\n cube.header.update(CRPIX3=0)\\n cube.header.update(CRVAL1=x0)\\n cube.header.update(CRVAL2=y0)\\n cube.header.update(CRVAL3=w0)\\n\\n cube.header.update(CTYPE1='RA---DEG')\\n cube.header.update(CTYPE2='DEC--DEG')\\n cube.header.update(CTYPE3=wtype)\\n cube.header.update(CUNIT3=wunit)\\n\\n cube.header.update(CD1_2=0)\\n cube.header.update(CD1_3=0)\\n cube.header.update(CD2_1=0)\\n cube.header.update(CD2_3=0)\\n cube.header.update(CD3_1=0)\\n cube.header.update(CD3_2=0)\\n\\n\\n cube.data = numpy.zeros( (Nw,Ny,Nx) )\\n\\n # extract each spectrum and place it\\n # into the 3-d cube\\n for ispec in range(len(data)): \\n fib_str='{:3d}'.format(ispec+1)\\n fib_str=fib_str.replace(' ','0') \\n if not(phdr['FIB'+fib_str+'_B'] in sky_bundles):\\n try:\\n end_sp=phdr['FIB'+fib_str+'W2'] \\n start_sp=phdr['FIB'+fib_str+'W1']\\n except:\\n if ('start_sp' in locals()):\\n print('Warning! FIB'+fib_str+'W1 and W2 information missing in header. Assuming previous fiber wavelength coverage.') \\n else: \\n end_sp=Nw\\n start_sp=1 \\n print('Warning! FIB'+fib_str+'W1 and W2 information missing in header. Assuming default wavelength coverage.') \\n \\n if end_sp!=start_sp:\\n spec = data[ispec][:]\\n Nwspec = Nw \\n \\n xpos = (phdr['FIB'+fib_str+'_x']+5.)*PLATESCALE \\n ypos = (phdr['FIB'+fib_str+'_y']+5.)*PLATESCALE\\n ix = int( round((xpos - x0),3) / dx )\\n iy = int( round((ypos - y0),3) / dy )\\n \\n lambda_arr=w0+dw*numpy.arange(0,Nwspec,1)\\n \\n if keep_units==True:\\n for i in range( start_sp, min(end_sp,Nwspec) ):\\n cube.data[i][iy][ix] = spec[i]##same units \\n else:\\n for i in range( start_sp, min(end_sp,Nwspec) ):\\n cube.data[i][iy][ix] = spec[i]*3.00e-5/lambda_arr[i]**2 ## Jy to erg/s/cm**2/A \\n else:\\n end_sp=Nwspec \\n print('1st step') \\n sigma_conv_pix=sigma_conv/((dx*nbin)/expansion_factor) \\n for i in range( start_sp, min(end_sp,Nwspec)):\\n print(str(i)+'/'+str(Nwspec)+' spectral channels',end=\\\"\\\\r\\\")\\n cube.data[i]=scipy.ndimage.filters.gaussian_filter(cube.data[i], sigma=sigma_conv_pix)\\n \\n \\n cube_rebin = fits.PrimaryHDU()\\n cube_rebin.header=rss[0].header \\n cube_rebin.header.remove('CRPIX1') \\n cube_rebin.header.remove('CRVAL1') \\n cube_rebin.header.remove('CUNIT1') \\n cube_rebin.header.remove('CTYPE1') \\n cube_rebin.header.remove('CDELT1')\\n cube_rebin.header.remove('CRPIX2') \\n cube_rebin.header.remove('CRVAL2') \\n #cube_rebin.header.remove('CUNIT2') \\n cube_rebin.header.remove('CDELT2') \\n cube_rebin.header.remove('CTYPE2') \\n cube_rebin.header.update(NAXIS=3)\\n cube_rebin.header.update(NAXIS1=Nx//nbin)\\n cube_rebin.header.update(NAXIS2=Ny//nbin)\\n cube_rebin.header.update(NAXIS3=Nw)\\n cube_rebin.header.update(CD1_1=-dx*nbin/3600.)\\n cube_rebin.header.update(CD2_2=dy*nbin/3600.)\\n cube_rebin.header.update(CD3_3=dw)\\n cube_rebin.header.update(CRPIX1=0)\\n cube_rebin.header.update(CRPIX2=0)\\n cube_rebin.header.update(CRPIX3=0)\\n cube_rebin.header.update(CRVAL1=x0)\\n cube_rebin.header.update(CRVAL2=y0)\\n cube_rebin.header.update(CRVAL3=w0)\\n \\n cube_rebin.header.update(CTYPE1='RA---SIN')\\n cube_rebin.header.update(CTYPE2='DEC--SIN')\\n cube_rebin.header.update(CTYPE3=wtype)\\n cube_rebin.header.update(CUNIT3=wunit)\\n cube_rebin.header.update(CUNIT1='deg')\\n cube_rebin.header.update(CUNIT2='deg')\\n \\n cube_rebin.header.update(CD1_2=0)\\n cube_rebin.header.update(CD1_3=0)\\n cube_rebin.header.update(CD2_1=0)\\n cube_rebin.header.update(CD2_3=0)\\n cube_rebin.header.update(CD3_1=0)\\n cube_rebin.header.update(CD3_2=0)\\n cube_rebin.verify('fix')\\n if keep_units:\\n cube_rebin.header.update(BUNIT= dhdr['BUNIT']) ##the rss one!!\\n else:\\n cube_rebin.header.update(BUNIT= 'erg/s/cm**2/Angstrom') \\n\\n\\n\\n \\n cube_rebin.data = numpy.zeros( (Nw,Ny//nbin,Nx//nbin) )\\n print('')\\n print('2nd step')\\n for i in range( 0, Nwspec) : \\n shape=cube.data[i].shape \\n print(str(i)+'/'+str(Nwspec)+' spectral channels',end=\\\"\\\\r\\\")\\n for xi in numpy.arange(0,shape[0],nbin)[:-1]:\\n for yj in numpy.arange(0,shape[1],nbin)[:-1]:\\n pixel_ij=numpy.sum(cube.data[i][xi:xi+nbin,yj:yj+nbin]) \\n cube_rebin.data[i][xi//nbin,yj//nbin]=pixel_ij \\n if writeout !=None:\\n cube_rebin.writeto(writeout,overwrite=overwrite)\\n return( cube_rebin)\",\n \"def generate_vec_file(args, path_list, file_name):\\n if args.vec != None:\\n #If the user wants to create a vec file, so calls opencv to create it.\\n command = \\\" \\\".join([\\\"opencv_createsamples -vec\\\", args.vec,\\n \\\"-info\\\", os.path.join(path_list, file_name),\\n \\\"-num\\\", str(args.num), \\\"-h\\\", str(args.height),\\n \\\"-w\\\", str(args.width)])\\n execute_commands([command])\\n if args.out_img_folder == None:\\n \\\"\\\"\\\"If the user don't want to save created samples,\\n so deletes the entire folder\\\"\\\"\\\"\\n rmtree(os.path.abspath(path_list))\",\n \"def from_srf_file(self, filename, normalize=False):\\n with open(filename, \\\"rt\\\") as f:\\n # go to POINTS block\\n line = f.readline()\\n while 'POINTS' not in line:\\n line = f.readline()\\n\\n npoints = int(line.split()[1])\\n sources = []\\n\\n for _ in np.arange(npoints):\\n lon, lat, dep, stk, dip, area, tinit, dt = \\\\\\n map(float, f.readline().split())\\n rake, slip1, nt1, slip2, nt2, slip3, nt3 = \\\\\\n map(float, f.readline().split())\\n\\n dep *= 1e3 # km > m\\n area *= 1e-4 # cm^2 > m^2\\n slip1 *= 1e-2 # cm > m\\n slip2 *= 1e-2 # cm > m\\n # slip3 *= 1e-2 # cm > m\\n\\n nt1, nt2, nt3 = map(int, (nt1, nt2, nt3))\\n\\n if nt1 > 0:\\n line = f.readline()\\n while len(line.split()) < nt1:\\n line = line + f.readline()\\n stf = np.array(line.split(), dtype=float)\\n if normalize:\\n stf /= np.trapz(stf, dx=dt)\\n\\n M0 = area * DEFAULT_MU * slip1\\n\\n sources.append(\\n Source.from_strike_dip_rake(\\n lat, lon, dep, stk, dip, rake, M0,\\n time_shift=tinit, sliprate=stf, dt=dt))\\n\\n if nt2 > 0:\\n line = f.readline()\\n while len(line.split()) < nt2:\\n line = line + f.readline()\\n stf = np.array(line.split(), dtype=float)\\n if normalize:\\n stf /= np.trapz(stf, dx=dt)\\n\\n M0 = area * DEFAULT_MU * slip2\\n\\n sources.append(\\n Source.from_strike_dip_rake(\\n lat, lon, dep, stk, dip, rake, M0,\\n time_shift=tinit, sliprate=stf, dt=dt))\\n\\n if nt3 > 0:\\n raise NotImplementedError('Slip along u3 axis')\\n\\n return self(pointsources=sources)\",\n \"def SingleQubitRBT(qubit: qreg, seqFileDir, analyzedPulse: pulse, add_cals=True):\\n\\n # Original:\\n # # Setup a pulse library\\n # pulseLib = [AC(qubit, cliffNum) for cliffNum in range(24)]\\n # pulseLib.append(analyzedPulse)\\n # measBlock = MEAS(qubit)\\n\\n # seqs = []\\n # for ct in range(10):\\n # fileName = 'RBT_Seqs_fast_{0}_F1.txt'.format(ct+1)\\n # tmpSeqs = []\\n # with open(os.path.join(seqFileDir, fileName),'r') as FID:\\n # fileReader = reader(FID)\\n # for pulseSeqStr in fileReader:\\n # seq = []\\n # for pulseStr in pulseSeqStr:\\n # seq.append(pulseLib[int(pulseStr)-1])\\n # seq.append(measBlock)\\n # tmpSeqs.append(seq)\\n # seqs += tmpSeqs[:12]*12 + tmpSeqs[12:-12] + tmpSeqs[-12:]*12\\n\\n # seqsPerFile = 100\\n # numFiles = len(seqs)//seqsPerFile\\n\\n # for ct in range(numFiles):\\n # chunk = seqs[ct*seqsPerFile:(ct+1)*seqsPerFile]\\n # # Tack on the calibration scalings\\n # if add_cals:\\n # numCals = 4\\n # chunk += [[Id(qubit), measBlock]]*numCals + [[X(qubit), measBlock]]*numCals\\n # fileNames = compile_to_hardware(chunk, 'RBT/RBT', suffix='_{0}'.format(ct+1))\\n\\n pulseSeqStrs = []\\n for ct in range(10):\\n fileName = 'RBT_Seqs_fast_{0}_F1.txt'.format(ct+1)\\n tmpSeqs = []\\n with open(os.path.join(seqFileDir, fileName),'r') as FID:\\n fileReader = reader(FID)\\n for pulseSeqStr in fileReader:\\n tmpSeqs.append(pulseSeqStr)\\n pulseSeqStrs = tmpSeqs[:12]*12 + tmpSeqs[12:-12] + tmpSeqs[-12:]*12\\n\\n numSeqs = len(pulseSeqStrs)\\n seqsPerFile = 100\\n numFiles = numSeqs//seqsPerFile\\n numCals = 4\\n\\n for ct in range(numFiles):\\n for s in range(seqsPerFile):\\n init(qubit)\\n seqStr = pulseSeqStrs[ct*seqsPerFile+s]\\n getPulseSeq(qubit, seqStr)\\n if add_cals:\\n # Add numCals calibration scalings\\n for _ in range(numCals):\\n init(qubit)\\n Id(qubit)\\n MEAS(qubit)\\n\\n init(qubit)\\n X(qubit)\\n MEAS(qubit)\",\n \"def get_input(inputfile, ndim, radii):\\n\\n d = readdump(inputfile, ndim)\\n d.read_onefile()\\n results = []\\n for n in range(d.SnapshotNumber):\\n ParticleRadii = np.array(pd.Series(d.ParticleType[n]).map(radii))\\n PositionRadii = np.column_stack((d.Positions[n], ParticleRadii))\\n voroinput = np.column_stack((np.arange(d.ParticleNumber[n]) + 1, PositionRadii))\\n results.append(voroinput)\\n\\n return (results, d.Boxbounds)\",\n \"def signalroisample(filename,obs):\\n from samplingdist import readworkspace,readfile\\n #f,w,obsdict,modeldict,databkgdict,datasigdict = readworkspace(filename)\\n f,obsdict,modeldict,databkgdict,datasigdict = readfile(filename)\\n if not obsdict.has_key(obs):\\n raise RuntimeError(\\\"Observable '%s' not defined\\\" % obs)\\n sd = ObservableSamplingProb(obsdict[obs])\\n sd.setupmodel('bkg','negative_binomial_pdf')\\n sd.setupmodel('sig','negative_binomial_sum_pdf')\\n\\n datasig = datasigdict['dvsig_'+obs]\\n databkg = databkgdict['dvbkg_'+obs]\\n sd.fitTo(datasig,'sig')\\n sd.fitTo(databkg,'bkg')\\n\\n samplename = filename.split('_')[1]\\n sd.plot(samplename,datasig,'sig',sample=samplename+'_sig')\\n sd.plot(samplename,databkg,'bkg',sample=samplename+'_bkg')\\n\\n nfile = filename.split('_')[1]+'_bkgsig_'+obs+'_ws.root'\\n sd.update('w',nfile,[datasig,databkg])\",\n \"def add_pixel_fn(filename: str, resample_name: str) -> None:\\n\\n header = \\\"\\\"\\\" <VRTRasterBand dataType=\\\"Byte\\\" band=\\\"1\\\" subClass=\\\"VRTDerivedRasterBand\\\">\\\"\\\"\\\"\\n contents = \\\"\\\"\\\"\\n <PixelFunctionType>{0}</PixelFunctionType>\\n <PixelFunctionLanguage>Python</PixelFunctionLanguage>\\n <PixelFunctionCode><![CDATA[{1}]]>\\n </PixelFunctionCode>\\\"\\\"\\\"\\n\\n lines = open(filename, 'r').readlines()\\n lines[3] = header # FIX ME: 3 is a hand constant\\n lines.insert(4, contents.format(resample_name,\\n get_resample(resample_name)))\\n open(filename, 'w').write(\\\"\\\".join(lines))\",\n \"def dorv(visitfiles) :\\n # last list elements has configuration variables in a tuple\\n allvisit = visitfiles[0]\\n load = visitfiles[1]\\n field=visitfiles[-1][0]\\n obj=visitfiles[-1][1].decode('UTF-8')\\n clobber=visitfiles[-1][2]\\n verbose=visitfiles[-1][3]\\n tweak=visitfiles[-1][4]\\n plot=visitfiles[-1][5]\\n windows=visitfiles[-1][6]\\n #rvrange=visitfiles[-1][7]\\n if tweak: suffix='_tweak'\\n else : suffix='_out'\\n outdir = os.path.dirname(load.filename('Star',field=field,obj=obj))\\n outdir = outdir.replace('/stars/','/rv/')\\n\\n if os.path.exists(outdir+'/'+obj+suffix+'.pkl') and not clobber:\\n print(obj,' already done')\\n fp=open(outdir+'/'+obj+suffix+'.pkl','rb')\\n try: \\n out=pickle.load(fp)\\n fp.close()\\n return out\\n except: \\n print('error loading: ', obj+suffix+'.pkl')\\n pass\\n\\n speclist=[]\\n pixelmask=bitmask.PixelBitMask()\\n badval=pixelmask.badval()|pixelmask.getval('SIG_SKYLINE')|pixelmask.getval('LITTROW_GHOST')\\n \\n # if we have a significant number of low S/N visits, combine first using\\n # barycentric correction only, use that to get an estimate of systemic\\n # velocity, then do RV determination restricting RVs to within 50 km/s\\n # of estimate. This seems to help significant for faint visits\\n lowsnr_visits=np.where(allvisit['SNR']<10)[0]\\n if (len(lowsnr_visits) > 1) & (len(lowsnr_visits)/len(allvisit) > 0.1) :\\n try :\\n apstar_bc=visitcomb(allvisit,bconly=True,load=load,write=False,dorvfit=False) \\n apstar_bc.setmask(badval)\\n spec=doppler.Spec1D(apstar_bc.flux[0,:],err=apstar_bc.err[0,:],bitmask=apstar_bc.bitmask[0,:],\\n mask=apstar_bc.mask[0,:],wave=apstar_bc.wave,lsfpars=np.array([0]),\\n lsfsigma=apstar_bc.wave/22500/2.354,instrument='APOGEE',\\n filename=apstar_bc.filename)\\n print('running BC jointfit for :',obj)\\n out= doppler.rv.jointfit([spec],verbose=verbose,plot=plot,tweak=tweak,maxvel=[-500,500])\\n rvrange=[out[1][0]['vrel']-50,out[1][0]['vrel']+50]\\n except :\\n print(' BC jointfit failed')\\n rvrange=[-500,500]\\n elif allvisit['H'].max() > 13.5 : \\n # if it's faint, restrict to +/- 500 km/s\\n rvrange=[-500,500]\\n else :\\n # otherwise, restrict to +/ 1000 km/s\\n rvrange=[-1000,1000]\\n\\n for i in range(len(allvisit)) :\\n\\n # load all of the visits into doppler Spec1D objects\\n if load.telescope == 'apo1m' :\\n visitfile= load.allfile('Visit',plate=allvisit['PLATE'][i],\\n mjd=allvisit['MJD'][i],reduction=allvisit['APOGEE_ID'][i])\\n else :\\n visitfile= load.allfile('Visit',plate=int(allvisit['PLATE'][i]),\\n mjd=allvisit['MJD'][i],fiber=allvisit['FIBERID'][i])\\n spec=doppler.read(visitfile,badval=badval)\\n\\n if windows is not None :\\n # if we have spectral windows to mask, do so here\\n for ichip in range(3) :\\n mask = np.full_like(spec.mask[:,ichip],True)\\n gd = []\\n for window in windows :\\n gd.extend(np.where((spec.wave[:,ichip] > window[0]) & (spec.wave[:,ichip] < window[1]))[0])\\n mask[gd] = False\\n spec.mask[:,ichip] |= mask\\n \\n if spec is not None : speclist.append(spec)\\n\\n # now do the doppler jointfit to get RVs\\n # dump empty pickle to stand in case of failure (to prevent redo if not clobber)\\n try:\\n # dump empty pickle to stand in case of failure (to prevent redo if not clobber)\\n fp=open(outdir+'/'+obj+suffix+'.pkl','wb')\\n pickle.dump(None,fp)\\n fp.close()\\n print('running jointfit for : {:s} rvrange:[{:.1f},{:.1f}] nvisits: {:d}'.format(obj,*rvrange,len(speclist)))\\n out= doppler.rv.jointfit(speclist,maxvel=rvrange,verbose=verbose,\\n plot=plot,saveplot=plot,outdir=outdir+'/',tweak=tweak)\\n print('running decomp for :',obj)\\n gout = gauss_decomp(out[1],phase='two',filt=True)\\n fp=open(outdir+'/'+obj+suffix+'.pkl','wb')\\n pickle.dump([out,gout],fp)\\n fp.close()\\n print('running plots for :',obj,outdir)\\n try : os.makedirs(outdir+'/plots/')\\n except : pass\\n dop_plot(outdir+'/plots/',obj,out,decomp=gout)\\n except KeyboardInterrupt : \\n raise\\n except ValueError as err:\\n print('Exception raised in dorv for: ', field, obj)\\n print(\\\"ValueError: {0}\\\".format(err))\\n return\\n except RuntimeError as err:\\n print('Exception raised in dorv for: ', field, obj)\\n print(\\\"Runtime error: {0}\\\".format(err))\\n return\\n except :\\n raise\\n print('Exception raised in dorv for: ', field, obj)\\n return\\n\\n # return summary RV info, visit RV info, decomp info \\n return [out[0:2],gout]\",\n \"def dirToRAD(directory, toFile):\\n #Open Directory\\n trainFiles = [file for file in os.listdir(directory) if file.endswith(\\\".txt\\\")]\\n fOut = open(toFile, 'w')\\n for file in trainFiles:\\n print(file)\\n rawData = getRawData(directory+file)\\n if 'rad' in toFile:\\n data = rawToRAD(rawData)\\n elif 'cust' in toFile:\\n data = rawToCust(rawData)\\n else:\\n print('ERROR. Transformation to ' + toFile + ' failed. Please enter a string containing cust for custom representation or rad for RAD format.')\\n #print('Raw:', rawData)\\n #print('RAD:', RADdata)\\n label = int(file[1:3])\\n if len(sys.argv) > 1:\\n enumData = enumerate(getHistogram(data, file, int(sys.argv[1])))\\n else:\\n enumData = enumerate(getHistogram(data, file))\\n fOut.write(str(label) + ' ')\\n fOut.writelines([\\\" %d:%s\\\" % (index+1, elem) for index,elem in enumData])\\n fOut.write('\\\\n')\\n fOut.close()\",\n \"def rebin_spectra(source):\\n # Unpack arguments using the 'source' label; all are assumed to be in km/s\\n v_lo, v_hi, new_dv = velocity_ranges[source]\\n # Load data cube\\n data_filename = os.path.abspath(data_filepaths[source])\\n cube = SpectralCube.read(data_filename)\\n # Check units\\n try:\\n # See if the cube can be converted to Kelvins easily\\n cube = cube.to(u.K)\\n except:\\n # Check if it looks like a temperature\\n old_bunit = cube.header['BUNIT']\\n if \\\"K (Ta*)\\\" in old_bunit:\\n cube._unit = u.K\\n print(f\\\"Data unit {cube.unit} assigned, based on the header BUNIT {old_bunit}.\\\")\\n else:\\n # Don't bother trying to fix it, leave it alone\\n print(f\\\"Data units <{cube._unit}> aren't equivalent to Kelvins, leaving them alone\\\")\\n # Get current channel width (np.diff should return an array of all the same value, so np.mean is overkill but it doesn't matter)\\n old_dv = np.mean(np.diff(cube.spectral_axis))\\n # Construct a box filter to average the channels\\n # Filter width is number of channels; if rebinning from 0.1 km/s to 1 km/s, filter is 10 channels\\n # Need to add km/s units to new_dv (old_dv already has units)\\n filter_width = np.abs(((new_dv*u.km/u.s) / old_dv).decompose().to_value())\\n # Round to nearest integer\\n filter_width = np.around(filter_width, 0)\\n # Make filter using astropy.convolution.Box1DKernel\\n filter = Box1DKernel(filter_width)\\n # Define the new spectral axis using the inclusive limits and the new channel width\\n new_spectral_axis = np.arange(v_lo, v_hi+new_dv, new_dv) * u.km/u.s\\n\\n # Do the computationally intensive work\\n print(\\\"Starting spectral smooth\\\")\\n cube = cube.spectral_smooth(filter)\\n print(\\\"Finished spectral smooth. Starting spectral rebin.\\\")\\n cube = cube.spectral_interpolate(new_spectral_axis)\\n print(\\\"Finished spectral rebin.\\\")\\n # Create savename with \\\"rebin\\\" and the channel width inserted before the filetype suffix\\n save_filename = data_filename.replace(\\\".fits\\\", f\\\".rebin{new_dv:d}kms.fits\\\")\\n cube.write(save_filename, format='fits')\",\n \"def make(input_filepath, output_filepath) -> None:\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def flow_write(filename,uv,v=None):\\n TAG_CHAR = 'PIEH'\\n nBands = 2\\n\\n if v is None:\\n assert(uv.ndim == 3)\\n assert(uv.shape[2] == 2)\\n u = uv[:,:,0]\\n v = uv[:,:,1]\\n else:\\n u = uv\\n\\n assert(u.shape == v.shape)\\n height,width = u.shape\\n f = open(filename,'wb')\\n # write the header\\n f.write(TAG_CHAR)\\n np.array(width).astype(np.int32).tofile(f)\\n np.array(height).astype(np.int32).tofile(f)\\n # arrange into matrix form\\n tmp = np.zeros((height, width*nBands))\\n tmp[:,np.arange(width)*2] = u\\n tmp[:,np.arange(width)*2 + 1] = v\\n tmp.astype(np.float32).tofile(f)\\n f.close()\",\n \"def flow_write(filename,uv,v=None):\\n nBands = 2\\n\\n if v is None:\\n assert(uv.ndim == 3)\\n assert(uv.shape[2] == 2)\\n u = uv[:,:,0]\\n v = uv[:,:,1]\\n else:\\n u = uv\\n\\n assert(u.shape == v.shape)\\n height,width = u.shape\\n f = open(filename,'wb')\\n TAG_CHAR = b'PIEH'\\n f.write(TAG_CHAR)\\n np.array(width).astype(np.int32).tofile(f)\\n np.array(height).astype(np.int32).tofile(f)\\n # arrange into matrix form\\n tmp = np.zeros((height, width*nBands))\\n tmp[:,np.arange(width)*2] = u\\n tmp[:,np.arange(width)*2 + 1] = v\\n tmp.astype(np.float32).tofile(f)\\n f.close()\",\n \"def modconvert(infile, outfile, scale_factor=1.0):\\n index, freq, brightness_temp, flux, rj_temp = read_infile(infile)\\n\\n wave, flux, temp = sort_spectrum(freq, flux, brightness_temp)\\n\\n scaled_flux = scale_factor * flux\\n\\n plot_scaled_spectrum(wave, scaled_flux, scale_factor, infile)\\n\\n write_scaled_spectrum(wave, scaled_flux, scale_factor, temp,\\n infile, outfile)\\n\\n model = pd.DataFrame({'wavelength': wave, 'flux': scaled_flux,\\n 't_br': temp})\\n return model\",\n \"def spectrum_parser():\\n from tools import file_importer, file_outporter\\n from random import random\\n # from math import log10\\n \\n print(\\\"this is spectrum parser\\\")\\n \\n relPath = \\\"bob/processed/OST-24-05-2017_combined.csv\\\"\\n outPath = \\\"bob/processed/OST-24-05-2017_combined_no0_spectrum.csv\\\"\\n inpF = file_importer(relPath)\\n outF = file_outporter(outPath) \\n headerFlag = True\\n rowCount = 0\\n for inpLine in inpF:\\n if headerFlag: \\n headerFlag = False\\n spColCount = 0\\n inpList = inpLine.split(\\\"\\\\t\\\")\\n for headerI in inpList:\\n if \\\"Peptides ST-1|Peptides ST-2|Peptides ST-3\\\" == headerI:\\n break\\n else: spColCount += 1\\n outF.write(\\\"ID,Protein ID, Gene name,\\\") # write header into output file\\n for headI in inpList[spColCount].split(\\\"|\\\"):\\n outF.write(headI + \\\",\\\")\\n for headI in inpList[spColCount + 1].split(\\\"|\\\")[:-1]:\\n outF.write(headI + \\\",\\\")\\n outF.write(inpList[spColCount + 1].split(\\\"|\\\")[-1] + \\\"\\\\n\\\")\\n rowCount += 1\\n continue\\n \\n outF.write(str(rowCount) + \\\",\\\")\\n \\n inpLine = inpLine.strip()\\n inpItems = inpLine.split(\\\"\\\\t\\\")\\n inpName = max(inpItems[0].split(\\\"|\\\"), key=len) # get unique protein ID\\n inpGeneName = max(inpItems[6].split(\\\"|\\\"), key=len) # and gene name\\n outF.write(inpName + \\\",\\\" + inpGeneName + \\\",\\\")\\n \\n inpSP = inpItems[spColCount].split(\\\"|\\\") + inpItems[spColCount + 1].split(\\\"|\\\") # get lfq intensity scores\\n # print inpSP\\n for lfqI in inpSP[:-1]: # write lfq values\\n if lfqI == \\\"_\\\" or lfqI == \\\"0\\\":\\n outF.write(str(random()) + \\\",\\\") ################## try with log10 values this time\\n else:\\n try:\\n outF.write(str(lfqI) + \\\",\\\")\\n except ValueError:\\n print(inpItems)\\n raise\\n \\n if inpSP[-1] == \\\"_\\\" or inpSP[-1] == \\\"0\\\": outF.write(str(random()) + \\\"\\\\n\\\")\\n else: outF.write(inpSP[-1] + \\\"\\\\n\\\")\\n \\n \\n \\n rowCount += 1\",\n \"def read_raytomo_dbase(self, inh5fname, runid, dtype='ph', wtype='ray', create_header=True, Tmin=-999, Tmax=999, verbose=False):\\n if dtype is not 'ph' and dtype is not 'gr':\\n raise ValueError('data type can only be ph or gr!')\\n if wtype is not 'ray' and wtype is not 'lov':\\n raise ValueError('wave type can only be ray or lov!')\\n stalst = self.waveforms.list()\\n if len(stalst) == 0:\\n print 'Inversion with surface wave datasets only, not added yet!'\\n return\\n indset = h5py.File(inh5fname)\\n #--------------------------------------------\\n # header information from input hdf5 file\\n #--------------------------------------------\\n dataid = 'reshaped_qc_run_'+str(runid)\\n pers = indset.attrs['period_array']\\n grp = indset[dataid]\\n isotropic = grp.attrs['isotropic']\\n org_grp = indset['qc_run_'+str(runid)]\\n minlon = indset.attrs['minlon']\\n maxlon = indset.attrs['maxlon']\\n minlat = indset.attrs['minlat']\\n maxlat = indset.attrs['maxlat']\\n if isotropic:\\n print 'isotropic inversion results do not output gaussian std!'\\n return\\n dlon_HD = org_grp.attrs['dlon_HD']\\n dlat_HD = org_grp.attrs['dlat_HD']\\n dlon = org_grp.attrs['dlon']\\n dlat = org_grp.attrs['dlat']\\n if create_header:\\n inv_header = {'minlon': minlon, 'maxlon': maxlon, 'minlat': minlat, 'maxlat': maxlat,\\n 'dlon': dlon, 'dlat': dlat, 'dlon_HD': dlon_HD, 'dlat_HD': dlat_HD}\\n self.add_auxiliary_data(data=np.array([]), data_type='Header', path='raytomo', parameters=inv_header)\\n self._get_lon_lat_arr(path='raytomo', hd=True)\\n for staid in stalst:\\n netcode, stacode = staid.split('.')\\n staid_aux = netcode+'_'+stacode\\n stla, elev, stlo = self.waveforms[staid].coordinates.values()\\n if stlo < 0.:\\n stlo += 360.\\n if stla > maxlat or stla < minlat or stlo > maxlon or stlo < minlon:\\n print 'WARNING: station: '+ staid+', lat = '+str(stla)+' lon = '+str(stlo)+', out of the range of tomograpic maps!'\\n continue\\n disp_v = np.array([])\\n disp_un = np.array([])\\n T = np.array([])\\n #-----------------------------\\n # determine the indices\\n #-----------------------------\\n ind_lon = np.where(stlo<=self.lons)[0][0]\\n find_lon = ind_lon \\n ind_lat = np.where(stla<=self.lats)[0][0]\\n find_lat = ind_lat\\n # point 1\\n distmin, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon]) # distance is in m\\n # point 2\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon-1]) # distance is in m\\n if dist < distmin:\\n find_lon = ind_lon-1\\n distmin = dist\\n # point 3\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon]) # distance is in m\\n if dist < distmin:\\n find_lat = ind_lat-1\\n distmin = dist\\n # point 4\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon-1]) # distance is in m\\n if dist < distmin:\\n find_lat = ind_lat-1\\n find_lon = ind_lon-1\\n distmin = dist\\n for per in pers:\\n if per < Tmin or per > Tmax:\\n continue\\n try:\\n pergrp = grp['%g_sec'%( per )]\\n vel = pergrp['vel_iso_HD'].value\\n vel_sem = pergrp['vel_sem_HD'].value\\n except KeyError:\\n if verbose:\\n print 'No data for T = '+str(per)+' sec'\\n continue\\n T = np.append(T, per)\\n disp_v = np.append(disp_v, vel[find_lat, find_lon])\\n disp_un = np.append(disp_un, vel_sem[find_lat, find_lon])\\n data = np.zeros((3, T.size))\\n data[0, :] = T[:]\\n data[1, :] = disp_v[:]\\n data[2, :] = disp_un[:]\\n disp_header = {'Np': T.size}\\n self.add_auxiliary_data(data=data, data_type='RayDISPcurve', path=wtype+'/'+dtype+'/'+staid_aux, parameters=disp_header)\\n indset.close()\\n return\",\n \"def preprocess_sample(file, params):\\n\\n videoFile = file + \\\".mp4\\\"\\n audioFile = file + \\\".wav\\\"\\n roiFile = file + \\\".png\\\"\\n visualFeaturesFile = file + \\\".npy\\\"\\n\\n roiSize = params[\\\"roiSize\\\"]\\n normMean = params[\\\"normMean\\\"]\\n normStd = params[\\\"normStd\\\"]\\n vf = params[\\\"vf\\\"]\\n device = torch.device(\\\"cuda\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\\n\\n #Extract the audio from the video file using the FFmpeg utility and save it to a wav file.\\n v2aCommand = \\\"ffmpeg -y -v quiet -i \\\" + videoFile + \\\" -ac 1 -ar 16000 -vn \\\" + audioFile\\n os.system(v2aCommand)\\n\\n\\n #for each frame, resize to 224x224 and crop the central 112x112 region\\n captureObj = cv.VideoCapture(videoFile)\\n roiSequence = list()\\n while (captureObj.isOpened()):\\n ret, frame = captureObj.read()\\n if ret == True:\\n grayed = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\\n grayed = grayed/255\\n grayed = cv.resize(grayed, (224,224))\\n roi = grayed[int(112-(roiSize/2)):int(112+(roiSize/2)), int(112-(roiSize/2)):int(112+(roiSize/2))]\\n roiSequence.append(roi)\\n else:\\n break\\n captureObj.release()\\n cv.imwrite(roiFile, np.floor(255*np.concatenate(roiSequence, axis=1)).astype(np.int))\\n\\n\\n #normalise the frames and extract features for each frame using the visual frontend\\n #save the visual features to a .npy file\\n inp = np.stack(roiSequence, axis=0)\\n inp = np.expand_dims(inp, axis=[1,2])\\n inp = (inp - normMean)/normStd\\n inputBatch = torch.from_numpy(inp)\\n inputBatch = (inputBatch.float()).to(device)\\n vf.eval()\\n with torch.no_grad():\\n outputBatch = vf(inputBatch)\\n out = torch.squeeze(outputBatch, dim=1)\\n out = out.cpu().numpy()\\n np.save(visualFeaturesFile, out)\\n return\",\n \"def preprocess(self, name, input_file, observation):\\n\\n lba = LBAFile(input_file, self.sample_rate)\\n\\n if self.max_samples > 0:\\n # User specified max number of samples\\n max_samples = min(self.max_samples, lba.max_samples)\\n else:\\n max_samples = lba.max_samples\\n\\n # Length of the observation for the number of samples we're reading\\n obs_length = lba.obs_length(max_samples)\\n start = lba.obs_start\\n end = start + datetime.timedelta(seconds=obs_length)\\n\\n LOG.info(\\\"LBA obs time: start {0} end {1} duration {2} sec\\\".format(start, end, obs_length))\\n\\n observation.observation_name = os.path.basename(name)\\n observation.additional_metadata = json.dumps(lba.header)\\n observation.antenna_name = self.antenna_name if self.antenna_name is not None else lba.header.get('ANTENNANAME', '')\\n observation.sample_rate = self.sample_rate\\n observation.length_seconds = obs_length\\n observation.start_time = start.timestamp()\\n observation.num_channels = lba.num_channels\\n observation.num_samples = max_samples\\n\\n channel_map = None\\n if self.obs_filename is not None:\\n try:\\n vex = pyvex.Vex(self.obs_filename)\\n self._fill_source_array(observation, vex, start, end, max_samples)\\n\\n # Get channel info from the VEX file.\\n # Pick the appropriate mode from the vex file. We run with the assumption for now that there's only one\\n # mode, and get everything from it. If there are multiple modes, then error out\\n if len(vex.modes) == 0:\\n LOG.warning(\\\"No modes in vex file to get channel info from\\\")\\n elif len(vex.modes) > 1:\\n LOG.error(\\\"Cannot get channel information from vex file because multiple modes are present. This is currently unsupported\\\")\\n else:\\n # Get antenna info\\n antenna = next((a for a in vex.antennas if a.def_name == self.antenna_name), None)\\n if antenna is None:\\n LOG.error(\\\"Specified antenna def name {0} is not present in the vex file\\\".format(self.antenna_name))\\n else:\\n LOG.info(\\\"Found antenna def name {0}. Name {1}\\\".format(self.antenna_name, antenna.name))\\n mode = vex.modes[0]\\n setup = mode.setups[antenna.name]\\n channel_map = [[channel, mode.subbands[channel.subband_id], setup.ifs[\\\"IF_{0}\\\".format(channel.if_name)]] for channel in setup.channels]\\n # TODO: I'm unsure if this is correct or what order we should use here\\n # - Ordering by subband_id will match the CH0(0-8) ordering present in the vex file.\\n # - Ordering by record_chan seems to make some logic sense as it orders them from low to\\n # high frequency across pol R then pol L. It also orders the BBC values ascending.\\n # - Neither of these seem to match the observation_details.md table.\\n\\n # TODO: For now, I'll assume sorting by record chan\\n channel_map.sort(key=lambda c: c[0].record_chan)\\n\\n except Exception as e:\\n LOG.error(\\\"Failed to parse vex file {0}\\\".format(e))\\n\\n samples_read = 0\\n while samples_read < max_samples:\\n remaining_samples = max_samples - samples_read\\n samples_to_read = min(remaining_samples, self.chunk_size)\\n samples = lba.read(samples_read, samples_to_read)\\n\\n for channel_index in range(samples.shape[1]):\\n # Loop over each channel, then either output the channel directly\\n # into the HDF5 file, or get the channel info to get the appropriate metadata\\n # for the channel, then output that.\\n\\n channel_name = \\\"channel_{0}\\\".format(channel_index)\\n\\n out_channel = observation[channel_name]\\n if out_channel is None:\\n out_channel = observation.create_channel(channel_name, shape=(max_samples,), dtype=np.int8)\\n if channel_map is not None:\\n vex_channel, vex_subband, vex_if = channel_map[channel_index]\\n # Add info from the vex file to the channel\\n out_channel.freq_start = vex_channel.bbc_freq\\n out_channel.freq_end = vex_channel.bbc_freq + vex_channel.bbc_bandwidth\\n out_channel.additional_metadata = json.dumps({\\n 'channel_name': vex_channel.name,\\n 'polarisation': vex_subband.pol.decode('utf-8'),\\n 'bbc_name': vex_channel.bbc_name,\\n 'record_chan': vex_channel.record_chan,\\n 'subband_id': vex_channel.subband_id,\\n 'vlba_band_name': vex_if.vlba_band_name,\\n 'if_sslo': vex_if.if_sslo,\\n 'lower_edge_freq': vex_if.lower_edge_freq\\n }, indent=4)\\n data = samples[:, channel_index]\\n out_channel.write_data(samples_read, data)\\n\\n samples_read += samples_to_read\\n\\n if (samples_read // self.chunk_size) % 10 == 0:\\n LOG.info(\\\"{0:.2%} {1}/{2}\\\".format(samples_read / max_samples, samples_read, max_samples))\\n\\n LOG.info(\\\"LBA preprocessor complete\\\")\",\n \"def createOutput():\\n\\n firstPeriod = True\\n # get edge No\\n edgesNo = 0\\n edgesSet = set()\\n for timestep, taxiList in vtypeDict.iteritems():\\n for tup in taxiList:\\n edgesSet.add(tup[1])\\n edgesNo = len(edgesSet)\\n\\n outputFile = open(path.FQoutput, 'w')\\n outputFile.write('<?xml version=\\\"1.0\\\"?>\\\\n')\\n outputFile.write('<paramEffects aggregationInterval=\\\"%d\\\" vehicles=\\\"%d\\\" edges=\\\"%d\\\">\\\\n' % (\\n aggInterval, vehSum, edgesNo))\\n for period, quota, vtypeDictR, taxiSum in generatePeriodQuotaSets(True):\\n if quota is None:\\n if not firstPeriod:\\n outputFile.write(\\\"\\\\t</periods>\\\\n\\\")\\n else:\\n firstPeriod = False\\n outputFile.write('\\\\t<periods period=\\\"%d\\\">\\\\n' % (period))\\n else:\\n simpleTaxiMeanVList = [0, 1]\\n simpleEdgeMeanVList = [0, 1]\\n drivenEdgesSet = set()\\n\\n if len(vtypeDictR) == 0: # if the processed FCD returns no Values\\n print(\\\"noData p\\\", period, \\\" q\\\", quota)\\n drivenEdgesSet.add(0)\\n else: # create mean from all taxi speed values\\n for timestep, taxiList in vtypeDictR.iteritems():\\n for tup in taxiList: # all elements in this timestep\\n simpleTaxiMeanVList[0] += tup[2]\\n simpleTaxiMeanVList[1] += 1\\n drivenEdgesSet.add(tup[1])\\n # create mean from all edge speed values which are driven by the\\n # chosen taxis\\n drivenEdgesList = list(drivenEdgesSet)\\n drivenEdgesList.sort()\\n # print \\\"dataSets \\\",simpleTaxiMeanVList[1]\\n\\n # --edgeDump-- #\\n \\\"\\\"\\\"\\n for i in edgeDumpDict.keys(): #all intervals\\n for edge,v in edgeDumpDict[i]:\\n if BinarySearch.isElmInList(drivenEdgesList,edge):\\n simpleEdgeMeanVList[0]+=v\\n simpleEdgeMeanVList[1]+=1\\n \\\"\\\"\\\"\\n # --vtype-- #\\n\\n for timestep, taxiList in vtypeDict.iteritems():\\n for tup in taxiList:\\n if BinarySearch.isElmInList(drivenEdgesList, tup[1]):\\n simpleEdgeMeanVList[0] += tup[2]\\n simpleEdgeMeanVList[1] += 1\\n\\n # calc values for output\\n detectedEdges = len(drivenEdgesSet)\\n relDetectedEdges = detectedEdges * 100.0 / edgesNo\\n vSim = simpleEdgeMeanVList[0] / simpleEdgeMeanVList[1]\\n vSimFCD = simpleTaxiMeanVList[0] / simpleTaxiMeanVList[1]\\n vAbsDiff = vSimFCD - vSim\\n if vSim != 0:\\n vRelDiff = vAbsDiff / vSim * 100\\n else:\\n vRelDiff = 100\\n if vRelDiff < -40:\\n vRelDiff = -35\\n\\n outputFile.write('\\\\t\\\\t<values taxiQuota=\\\"%f\\\" taxis=\\\"%d\\\" simMeanSpeed=\\\"%f\\\" simFcdMeanSpeed=\\\"%f\\\" ' % (\\n quota, taxiSum, vSim, vSimFCD,))\\n outputFile.write('detectedEdges=\\\"%d\\\" notDetectedEdges=\\\"%d\\\" ' % (\\n detectedEdges, edgesNo - detectedEdges))\\n outputFile.write('absSpeedDiff=\\\"%f\\\" relSpeedDiff=\\\"%f\\\" relDetectedEdges=\\\"%f\\\" relNotDetectedEdges=\\\"%f\\\"/>\\\\n' %\\n (vAbsDiff, vRelDiff, relDetectedEdges, 100 - relDetectedEdges))\\n outputFile.write(\\\"\\\\t</periods>\\\\n</paramEffects>\\\")\\n outputFile.close()\",\n \"def build_spectrum(spectrum_filename):\\n hdulist = fits.open(spectrum_filename)\\n data = hdulist[1].data\\n \\n spec = Spectrum(data['wave'], data['flux'], data['error'])\\n \\n return spec\",\n \"def t8_loadFile(self):\\n print \\\"spectrumFile\\\"\\n filename = tkFileDialog.askopenfilename()\\n filename = self.filenameparser(filename)\\n self.t8_filename = filename\",\n \"def register(\\n sample_file,\\n select,\\n src=dstain.config.DATA_RAW,\\n output=dstain.config.REGISTRATION,\\n window=512,\\n downsample=1,\\n patches=0,\\n zip_patches=False,\\n verbose=True,\\n thumbnail_downsample=128,\\n nfeatures=5000,\\n ransacreprojthreshold=25,\\n affine=True):\\n\\n # Reading list of files in each sample\\n # samples[key] = value\\n # key is the name of the sample\\n # value is a list of filenames\\n samples = dstain.utils.read_sample_file(sample_file, use_split=False)\\n\\n # Allow subselection of samples to register\\n if select == ():\\n select = sorted(samples.keys())\\n\\n # Loop through samples\\n for (i, sample) in enumerate(select):\\n print(\\\"Sample #{} / {}: {}\\\".format(i + 1, len(select), sample), flush=True)\\n dstain.utils.register.register_sample(\\n src, os.path.join(output, sample), samples[sample], window, downsample,\\n patches, zip_patches, verbose, thumbnail_downsample, nfeatures, ransacreprojthreshold, affine)\",\n \"def test_process_5_1_surround_file(self):\\n test_path = pathlib.Path(__file__).parent.absolute() / 'data/surround.wav'\\n self.default_kwargs['input_file'] = test_path\\n self.encoder = FileEncoder(**self.default_kwargs)\\n self.encoder.process()\",\n \"def fromFile(self,fn = None):\\n\\n while True:\\n\\n if fn == None:\\n fn = getFilename(\\\"Spectrometer file\\\",\\\"spec\\\")\\n else:\\n fn = getExpandedFilename(fn) # Sort out logicals\\n if not fn.endswith(\\\"spec\\\"): # Append \\\".spec\\\" if not given\\n fn += \\\".spec\\\"\\n\\n try:\\n sfile= open(fn,\\\"r\\\") # open file\\n lines = sfile.readlines()\\n sfile.close()\\n break\\n except FileNotFoundError:\\n getLogger().error(\\\"Failed to find spectrometer file : \\\" + str(fn))\\n fn = None\\n\\n\\n # read file and process one line at a time\\n #\\n\\n # Read through line at a time\\n for line in lines:\\n\\n line = line.strip()\\n if not line.startswith(\\\"#\\\") and len(line) > 0: # Kill comments and blanks\\n token = line.split()\\n\\n if token[0].startswith(\\\"point\\\"):\\n v = eval(token[1])\\n self.setPoint(v)\\n\\n elif token[0].startswith(\\\"index\\\"):\\n self.setIndex(token[1])\\n\\n elif token[0].startswith(\\\"angle\\\"):\\n self.angle = math.radians(float(token[1]))\\n self.setTilt(self.tilt) # Reset surfaces\\n\\n elif token[0].startswith(\\\"height\\\"):\\n self.height = float(token[1])\\n\\n elif token[0].startswith(\\\"beam\\\"):\\n self.beam = float(token[1])\\n\\n elif token[0].startswith(\\\"tilt\\\"):\\n self.setTilt(math.radians(token[1]))\\n\\n elif token[0].startswith(\\\"setup\\\"):\\n self.setUpWavelength(float(token[1]))\\n\\n else:\\n raise ValueError(\\\"Sprectometer: illegal key : {0:s}\\\".format(token[0]))\\n\\n return self\",\n \"def mono(filename,wout=True):\\n n, data, data_dB,sr,ch=inputwav(filename)\\n if ch==2:\\n print('Converting to mono...')\\n L=data[:,0]\\n R=data[:,1]\\n n=len(data)\\n data_m=np.zeros((n,1))\\n data_m=L/2.0+R/2.0\\n if wout==True:\\n print('Exporting...')\\n sf.write(filename[0:len(filename)-4]+'_mono.wav',data_m,sr,'PCM_16')\\n print('Done!')\\n return data_m\\n else:\\n print( \\\"Error: input is already mono stoooooooooopid!\\\")\",\n \"def load(filename):\\n root,ext = _os_path.splitext(filename)\\n loader = LOADER[ext]\\n frequency,raw_signal = loader(filename)\\n iinfo = _numpy.iinfo(raw_signal.dtype)\\n raw_signal_midpoint = (iinfo.max + iinfo.min)/2.\\n raw_signal_range = iinfo.max - raw_signal_midpoint\\n unit_output_signal = (raw_signal - raw_signal_midpoint)/raw_signal_range\\n return (frequency, unit_output_signal)\",\n \"def __convert_single_only_au_AutoVC_format_to_dataset__(self, filename, build_train_dataset=True):\\n\\n global_clip_index, video_name = filename\\n\\n # audio_file = os.path.join(self.src_dir, 'raw_wav', '{}.wav'.\\n # format(video_name[:-4]))\\n audio_file = os.path.join(self.src_dir, 'raw_wav', '{:05d}_{}_audio.wav'.\\n format(global_clip_index, video_name[:-4]))\\n if(not build_train_dataset):\\n import shutil\\n audio_file = os.path.join(self.src_dir, 'raw_wav', '{:05d}_{}_audio.wav'.\\n format(global_clip_index, video_name[:-4]))\\n shutil.copy(os.path.join(self.src_dir, 'test_wav_files', video_name), audio_file)\\n\\n sound = AudioSegment.from_file(audio_file, \\\"wav\\\")\\n normalized_sound = match_target_amplitude(sound, -20.0)\\n normalized_sound.export(audio_file, format='wav')\\n\\n from third_party.autovc.retrain_version.vocoder_spec.extract_f0_func import extract_f0_func_audiofile\\n S, f0_norm = extract_f0_func_audiofile(audio_file, 'M')\\n\\n from third_party.autovc.utils import quantize_f0_interp\\n f0_onehot = quantize_f0_interp(f0_norm)\\n\\n from third_party.resemblyer_util.speaker_emb import get_spk_emb\\n mean_emb, _ = get_spk_emb(audio_file)\\n\\n return S, mean_emb, f0_onehot\",\n \"def fix_file(file_path: str, output_path_fixed: str, target_rate: int=16000,\\n channels: int=1, output_path: str=None, min_duration: int=None,\\n verbose_level: int=0):\\n file_name = file_path.split(os.sep)[-1]\\n new_file_path = output_path_fixed + os.sep + file_name\\n\\n try:\\n # Get the audio length\\n out = syscommand.system('soxi -D ' + file_path)\\n duration = float(out)\\n # Get the number of channels\\n out = syscommand.system('soxi -c ' + file_path)\\n current_n_channels = int(out)\\n # Get the current rate\\n out = syscommand.system('soxi -r ' + file_path)\\n current_rate = int(out)\\n if min_duration is not None and duration < min_duration:\\n raise Exception(\\\"Minimum length not satisfied\\\")\\n except Exception as err:\\n if str(verbose_level) != '0':\\n print(err)\\n return None, file_path\\n\\n if current_rate != target_rate and current_n_channels != channels:\\n speed = float(current_rate) / float(target_rate)\\n cmd = 'sox -V{} -r 16k {} {} channels 1 ' \\\\\\n 'speed {}'.format(verbose_level, file_path, new_file_path,\\n speed)\\n if str(verbose_level) == '2':\\n print(cmd)\\n os.system(cmd)\\n return new_file_path, file_path\\n elif current_rate != target_rate:\\n speed = float(current_rate) / float(target_rate)\\n cmd = 'sox -V{} -r 16k {} {} ' \\\\\\n 'speed {}'.format(verbose_level, file_path, new_file_path,\\n speed)\\n if str(verbose_level) == '2':\\n print(cmd)\\n os.system(cmd)\\n return new_file_path, file_path\\n elif current_n_channels != channels:\\n cmd = 'sox -V{} {} {} channels 1'.format(verbose_level, file_path,\\n new_file_path)\\n if str(verbose_level) == '2':\\n print(cmd)\\n os.system(cmd)\\n return new_file_path, file_path\\n\\n # Copy file if output path were provided and the file is not there\\n if output_path is not None:\\n copyfile(file_path, new_file_path)\\n return file_path, file_path\",\n \"def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\\n outputs = []\\n npy_outputs = []\\n if resamplemethod == 'nearest':\\n rs = Resampling.nearest\\n else:\\n print('only nearest neighbor resampling is supported at this time')\\n sys.exit(0)\\n\\n for i, warpfile in enumerate(inputs):\\n print('warpfile', warpfile)\\n with rasterio.open(warpfile) as src:\\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\\n with WarpedVRT(src, resampling=rs,\\n crs=self.crs,\\n transform=self.transform,\\n height=self.rows,\\n width=self.cols) as vrt:\\n data = vrt.read()\\n print(type(vrt))\\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\\n outputs.append(outwarp)\\n\\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\\n # from this method for us in the rest of the code.\\n for ow in outputs:\\n with rasterio.open(ow, 'r') as src:\\n arr = src.read(1)\\n npy_outputs.append(arr)\\n\\n return npy_outputs\",\n \"def read_ultrasound_file(ult_file):\\n\\n return np.fromfile(open(ult_file, \\\"rb\\\"), dtype=np.uint8)\",\n \"def generate_rsat_bg_file(input_file_path, output_file_path, strand_type):\\n cmd = [\\\"sh\\\", \\\"generate_rsat_bg_file.sh\\\",\\n \\\"-i\\\", input_file_path,\\n \\\"-o\\\", output_file_path,\\n \\\"-s\\\", strand_type]\\n process = subprocess.Popen(cmd, stdout=subprocess.PIPE)\\n process.wait()\",\n \"def single_file(filename, exposure_times, osc_start, osc_width, epoch):\\n index = scan_helper_image_files.image_to_index(os.path.split(filename)[-1])\\n if epoch is None:\\n epoch = 0.0\\n\\n # if the oscillation width is negative at this stage it is almost\\n # certainly an artefact of the omega end being 0 when the omega start\\n # angle was ~ 360 so it would be ~ -360 - see dxtbx#378\\n if osc_width < -180:\\n osc_width += 360\\n\\n return ScanFactory.make_scan(\\n (index, index), exposure_times, (osc_start, osc_width), {index: epoch}\\n )\",\n \"def t4_loadFile(self):\\n print \\\"subspectrumFile\\\"\\n filename = tkFileDialog.askopenfilename()\\n filename = \\\"SubSpectrumData/\\\" + self.filenameparser(filename)\\n self.t4_filename = filename\",\n \"def __init__(self, in_file: str, out_file: str, sample_rate: float,\\n max_time_gap: timedelta):\\n\\n self.in_file = in_file\\n self.out_file = out_file\\n\\n # Set up the input/output file objects:\\n self.in_data = MobileData(in_file, 'r')\\n self.out_data = MobileData(out_file, 'w')\\n\\n # Hold information about the different fields to use:\\n # All fields in the input file:\\n self.all_fields = None # type: Optional[Dict[str, str]]\\n\\n # Stamp field name:\\n self.stamp_field = default_stamp_field\\n\\n # Only sensor fields:\\n self.sensor_fields = None # type: Optional[Dict[str, str]]\\n\\n # List of label fields:\\n self.label_fields = default_label_fields\\n\\n # Determine the output sample interval in seconds:\\n self.sample_interval = timedelta(seconds=1.0 / sample_rate)\\n\\n # Maximum gap between input events - longer than this and we will restart resampling:\\n self.max_time_gap = max_time_gap\\n\\n # The previous and next output stamps to use:\\n self.prev_out_stamp = None # type: Optional[datetime]\\n self.next_out_stamp = None # type: Optional[datetime]\\n\\n # The next event from the input file:\\n self.next_input_event = None # type: Optional[Dict[str, Union[float, str, datetime, None]]]\\n\\n # The last-seen input event:\\n self.last_seen_input_event = None # type: Optional[Dict[str, Union[float, str, datetime, None]]]\\n\\n # Information about input events seen in a sample interval:\\n self.num_events_in_interval = 0\\n self.interval_sensor_values = None # type: Optional[Dict[str, List[float, str]]]\\n self.interval_labels = None # type: Optional[Dict[str, List[str]]]\\n\\n # Status update info:\\n self.status_num_events_interval = status_num_events_interval\\n self.num_input_events_processed = 0\\n self.num_events_since_last_status = 0\\n self.first_event_stamp = None # type: Optional[datetime]\",\n \"def rdmb_povray(file_base,\\n time_point=2000,\\n width=800, height=600,\\n angle=14):\\n\\n file_prms = file_base + \\\"_prms.npz\\\"\\n vs, *_ = load_rd_prms(file_prms)\\n file_uv = file_base + \\\"_{:05}.npz\\\".format(time_point)\\n\\n file_png = file_base + \\\"_{:05}.png\\\".format(time_point)\\n ucs, vcs = load_rd_uv(file_uv)\\n\\n rdmb_povray_save(file_png,\\n vs,\\n ucs, vcs,\\n width=width, height=height,\\n rotx=0, roty=0, rotz=0,\\n angle=angle)\\n\\n return file_png\",\n \"def t1_loadFile(self):\\n print \\\"subspectrumFile\\\"\\n filename = tkFileDialog.askopenfilename()\\n filename = self.filenameparser(filename)\\n self.t1_filename = filename\",\n \"def convert_file(self, file_path: str) -> None:\\n print(\\n f\\\"Converting {os.path.split(file_path)[1]} to {self.output_format}\\\")\\n\\n output = AudioSegment.from_file(file_path, format=self.output_format)\\n output.export(file_path.replace(self.input_format,\\n f'{self.output_format}'), format=self.output_format)\",\n \"def test_input_flux_file():\\n # Generate an input file\\n flux_input_file = tstutils.data_path('test.flux')\\n if os.path.isfile(flux_input_file):\\n os.remove(flux_input_file)\\n\\n cfg_lines = ['[fluxcalib]']\\n cfg_lines += [' extinct_correct = False # Set to True if your SENSFUNC derived with the UVIS algorithm\\\\n']\\n cfg_lines += ['# Please add your SENSFUNC file name below before running pypeit_flux_calib']\\n\\n # These files need to be in tests/files/\\n data = Table()\\n data['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data['sensfile'] = 'sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits'\\n # \\n paths = [tstutils.data_path('')]\\n\\n fluxFile = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data)\\n # Write\\n fluxFile.write(flux_input_file)\\n\\n # Read\\n fluxFile2 = inputfiles.FluxFile.from_file(flux_input_file)\\n assert np.all(fluxFile2.data['filename'] == data['filename'])\\n\\n # Test path\\n assert fluxFile2.file_paths[0] == paths[0]\\n assert fluxFile2.filenames[0] == os.path.join(paths[0], data['filename'][0])\\n\\n # #################\\n # Tickle the other ways to do sensfiles\\n data3 = Table()\\n data3['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data3['sensfile'] = ['sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits',\\n '']\\n\\n fluxFile3 = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data3)\\n assert fluxFile3.sensfiles[1] == os.path.join(paths[0], data['sensfile'][0])\\n \\n data4 = Table()\\n data4['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data4['sensfile'] = ''\\n\\n fluxFile4 = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data4)\\n assert len(fluxFile4.sensfiles) == 0\\n\\n # Clean up\\n os.remove(flux_input_file)\",\n \"def from_file(cls, infilename):\\n\\n spect_file_obj = _get_file_object(infilename)\\n\\n kwargs = {'counts': spect_file_obj.data,\\n 'input_file_object': spect_file_obj,\\n 'bin_edges_kev': spect_file_obj.bin_edges_kev}\\n\\n # TODO Get more attributes from self.infileobj\\n\\n return cls(**kwargs)\",\n \"def readNextGenSpectrum(fname=''):\\n\\n print('Reading : ', fname)\\n\\n with open(fname, 'r') as rfile:\\n dum = rfile.readline()\\n sdum = dum.split()\\n teff = float(sdum[0])\\n logg = float(sdum[1])\\n mph = float(sdum[2])\\n dum = rfile.readline()\\n nwav = float(dum.split()[0])\\n\\n bigline = []\\n dum = rfile.readline()\\n while dum.strip() != '':\\n sdum = dum.split()\\n for i in range(len(sdum)):\\n bigline.append(float(sdum[i]))\\n dum = rfile.readline()\\n\\n bigline = np.array(bigline)\\n # Convert wavelength from angstrom to micron\\n wav = bigline[:nwav] / 1e4\\n inu = bigline[nwav:2 * nwav]\\n bnu = bigline[nwav * 2:nwav * 3]\\n\\n ii = wav.argsort()\\n wav = wav[ii]\\n inu = inu[ii] * 1e-8 * wav * 1e4 / np.pi / (29979245800.0 / wav * 1e4)\\n bnu = bnu[ii] * 1e-8 * wav * 1e4 / np.pi / (29979245800.0 / wav * 1e4)\\n\\n #\\n # The unit is now erg/s/cm/Hz/ster\\n #\\n\\n return {'teff': teff, 'logg': logg, 'mph': mph, 'nwav': nwav, 'wav': wav, 'inu': inu, 'bnu': bnu}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5761271","0.539191","0.53234494","0.5275649","0.52752906","0.527388","0.5264173","0.5234479","0.52175415","0.52002573","0.51625293","0.50720745","0.50369114","0.50217336","0.4970289","0.4957576","0.4887836","0.48874044","0.48784587","0.48682615","0.48431507","0.4821813","0.4798794","0.47955054","0.47944763","0.47788334","0.4760828","0.47491097","0.47395235","0.473859","0.4728464","0.47205108","0.4718296","0.4699509","0.46907875","0.46900317","0.46839598","0.46676472","0.46654132","0.4662987","0.4661223","0.46526825","0.46471262","0.46460265","0.46451494","0.46400797","0.46325704","0.46313334","0.462308","0.46189407","0.46108603","0.46087196","0.46037698","0.4601273","0.4597162","0.4594524","0.45913455","0.45891723","0.4584942","0.45793033","0.45779246","0.45749986","0.45504475","0.45463043","0.4543516","0.45414495","0.452874","0.45271194","0.45237294","0.45118976","0.4500996","0.450067","0.44967198","0.4493465","0.44839185","0.4480523","0.4479649","0.44785467","0.44770253","0.44769156","0.44735265","0.447134","0.44712424","0.44710973","0.4469421","0.44648623","0.44594908","0.44590545","0.44494408","0.4441082","0.44405836","0.4437614","0.44366112","0.4431811","0.4430243","0.44246772","0.44173437","0.44167867","0.4416742","0.44151467"],"string":"[\n \"0.5761271\",\n \"0.539191\",\n \"0.53234494\",\n \"0.5275649\",\n \"0.52752906\",\n \"0.527388\",\n \"0.5264173\",\n \"0.5234479\",\n \"0.52175415\",\n \"0.52002573\",\n \"0.51625293\",\n \"0.50720745\",\n \"0.50369114\",\n \"0.50217336\",\n \"0.4970289\",\n \"0.4957576\",\n \"0.4887836\",\n \"0.48874044\",\n \"0.48784587\",\n \"0.48682615\",\n \"0.48431507\",\n \"0.4821813\",\n \"0.4798794\",\n \"0.47955054\",\n \"0.47944763\",\n \"0.47788334\",\n \"0.4760828\",\n \"0.47491097\",\n \"0.47395235\",\n \"0.473859\",\n \"0.4728464\",\n \"0.47205108\",\n \"0.4718296\",\n \"0.4699509\",\n \"0.46907875\",\n \"0.46900317\",\n \"0.46839598\",\n \"0.46676472\",\n \"0.46654132\",\n \"0.4662987\",\n \"0.4661223\",\n \"0.46526825\",\n \"0.46471262\",\n \"0.46460265\",\n \"0.46451494\",\n \"0.46400797\",\n \"0.46325704\",\n \"0.46313334\",\n \"0.462308\",\n \"0.46189407\",\n \"0.46108603\",\n \"0.46087196\",\n \"0.46037698\",\n \"0.4601273\",\n \"0.4597162\",\n \"0.4594524\",\n \"0.45913455\",\n \"0.45891723\",\n \"0.4584942\",\n \"0.45793033\",\n \"0.45779246\",\n \"0.45749986\",\n \"0.45504475\",\n \"0.45463043\",\n \"0.4543516\",\n \"0.45414495\",\n \"0.452874\",\n \"0.45271194\",\n \"0.45237294\",\n \"0.45118976\",\n \"0.4500996\",\n \"0.450067\",\n \"0.44967198\",\n \"0.4493465\",\n \"0.44839185\",\n \"0.4480523\",\n \"0.4479649\",\n \"0.44785467\",\n \"0.44770253\",\n \"0.44769156\",\n \"0.44735265\",\n \"0.447134\",\n \"0.44712424\",\n \"0.44710973\",\n \"0.4469421\",\n \"0.44648623\",\n \"0.44594908\",\n \"0.44590545\",\n \"0.44494408\",\n \"0.4441082\",\n \"0.44405836\",\n \"0.4437614\",\n \"0.44366112\",\n \"0.4431811\",\n \"0.4430243\",\n \"0.44246772\",\n \"0.44173437\",\n \"0.44167867\",\n \"0.4416742\",\n \"0.44151467\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.546153"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":95686,"cells":{"query":{"kind":"string","value":"Takes an GDALreadable inputfile and generates the VRT to warp it."},"document":{"kind":"string","value":"def warp(inputfile, spatial_ref=None, cmd=GDALWARP, resampling=None,\n maximum_resolution=None):\n dataset = Dataset(inputfile)\n\n warp_cmd = [\n cmd,\n '-q', # Quiet - FIXME: Use logging\n '-of', 'VRT', # Output to VRT\n ]\n\n # Warping to Mercator.\n if spatial_ref is None:\n spatial_ref = SpatialReference.FromEPSG(EPSG_WEB_MERCATOR)\n warp_cmd.extend(['-t_srs', spatial_ref.GetEPSGString()])\n\n # Resampling method\n if resampling is not None:\n if not isinstance(resampling, basestring):\n try:\n resampling = RESAMPLING_METHODS[resampling]\n except KeyError:\n raise UnknownResamplingMethodError(resampling)\n elif resampling not in list(RESAMPLING_METHODS.values()):\n raise UnknownResamplingMethodError(resampling)\n warp_cmd.extend(['-r', resampling])\n\n # Propagate No Data Value\n nodata_values = [dataset.GetRasterBand(i).GetNoDataValue()\n for i in range(1, dataset.RasterCount + 1)]\n if any(nodata_values):\n nodata_values = [str(v).lower() for v in nodata_values]\n warp_cmd.extend(['-dstnodata', ' '.join(nodata_values)])\n\n # Call gdalwarp\n warp_cmd.extend([inputfile, '/vsistdout'])\n\n try:\n return VRT(check_output_gdal([str(e) for e in warp_cmd]))\n except CalledGdalError as e:\n if e.error == (\"ERROR 6: Read or update mode not supported on /vsistdout\"):\n return VRT(e.output)\n raise"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_vrt(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n txt_file = i.joinpath('subnational/tiffs.txt')\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\n if not outfile.exists():\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\n subprocess.call(gdal_cmd, shell=True)","def convert_vrt(fname, out_fname, dataset_name='dataset',\n compression=H5CompressionFilter.LZF, filter_opts=None,\n attrs=None):\n with h5py.File(out_fname) as fid:\n with rasterio.open(fname) as rds:\n # set default chunks and set dimensions\n if rds.count == 3:\n chunks = (3, 256, 256)\n dims = (3, rds.height, rds.width)\n else:\n chunks = (256, 256)\n dims = (rds.height, rds.width)\n\n # create empty or copy the user supplied filter options\n if not filter_opts:\n filter_opts = dict()\n filter_opts['chunks'] = chunks\n else:\n filter_opts = filter_opts.copy()\n\n\n if 'chunks' not in filter_opts:\n filter_opts['chunks'] = chunks\n\n # modify to have 3D chunks if we have a multiband vrt\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\n # copy the users original 2D chunk and insert the third\n chunks = list(filter_opts['chunks'])\n chunks.insert(0, 3)\n filter_opts['chunks'] = chunks\n\n # dataset attributes\n if attrs:\n attrs = attrs.copy()\n else:\n attrs = {}\n\n attrs['geotransform'] = rds.transform.to_gdal()\n attrs['crs_wkt'] = rds.crs.wkt\n\n # dataset creation options\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\n kwargs['shape'] = dims\n kwargs['dtype'] = rds.dtypes[0]\n\n dataset = fid.create_dataset(dataset_name, **kwargs)\n attach_image_attributes(dataset, attrs)\n\n # tiled processing (all cols by chunked rows)\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\n\n for tile in tiles:\n # numpy index\n if rds.count == 3:\n idx = (\n slice(None),\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n else:\n idx = (\n slice(tile[0][0], tile[0][1]),\n slice(tile[1][0], tile[1][1])\n )\n\n # ensure single band rds is read as 2D not 3D\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\n\n # write\n dataset[idx] = data","def run_vorpaline(self, input_file, *options):\n\n self._input_file = input_file\n args = list(options) + [self._input_file, 'out.meshb']\n self._run_command(\"vorpalite\", args)","def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\n\n options = gdal.BuildVRTOptions(srcNodata=0)\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\n add_pixel_fn(vrt, resample_name)","def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\n from gdal import Translate\n\n if out_fname is None:\n out_fname = in_fname + \".vrt\"\n\n # Using Translate... but would use Warp if every reprojecting\n if bbox:\n left, bottom, right, top = bbox\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\n else:\n projwin = None\n if verbose:\n logger.info(f\"Creating {out_fname}, subset bbox: {bbox}\")\n Translate(out_fname, in_fname, projWin=projwin)","def buildvrt(indir, outdir):\n indir = Path(indir)\n outdir = Path(outdir)\n\n # loop over each day directory\n for day in indir.iterdir():\n # expecting 20 subdatasets in each hdf4 file (hopefully the order gdal lists them in is consistent)\n subdataset_fnames = {i: [] for i in range(20)}\n\n # mosaic each MODIS tile for the current day directory\n for h4_fname in day.rglob('*.hdf'):\n with rasterio.open(str(h4_fname.absolute())) as h4_ds:\n\n # each subdataset will form a separate mosaic\n for i, sds_name in enumerate(h4_ds.subdatasets):\n subdataset_fnames[i].append(sds_name)\n\n # loop over each subdataset and mosaic from all supporting MODIS tiles\n for _, file_list in subdataset_fnames.items():\n\n # temp file for the input file list\n with tempfile.NamedTemporaryFile('w') as tmpf:\n tmpf.writelines(\"\\n\".join(file_list))\n tmpf.flush()\n\n # mimic the 'day' directory partition\n base_name = Path(file_list[0].replace(':', '/')).name\n out_fname = outdir.joinpath(day.name, '{}.vrt'.format(base_name))\n\n if not out_fname.parent.exists():\n out_fname.parent.mkdir(parents=True)\n\n # buildvrt\n cmd = [\n 'gdalbuildvrt',\n '-input_file_list',\n tmpf.name,\n str(out_fname)\n ]\n\n check_call(cmd)","def merge(files: List[str], output_file: str, resample: str = \"average\") -> None:\n\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\n\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\n\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\n\n if os.path.isfile(constants.TEMP_VRT_FILE):\n os.remove(constants.TEMP_VRT_FILE)","def open_input(self):\n gdal.SetConfigOption(\"GDAL_PAM_ENABLED\", \"YES\")\n gdal.AllRegister()\n # self.options.verbose=True\n if self.options.tms_osm:\n self.s_y_type=\"osm\"\n else:\n self.s_y_type=\"tms\"\n if self.options.verbose:\n print \"open_input :\", self.input,\" osm[\",self.options.tms_osm,\",\",self.s_y_type,\"] mbtiles[\",self.options.mbtiles,\"] mbtiles_todisk[\",self.options.mbtiles_todisk,\"] mbtiles_fromdisk[\",self.options.mbtiles_fromdisk,\"]\";\n # Open the input file\n if self.input:\n self.in_ds = gdal.Open(self.input, gdal.GA_ReadOnly)\n else:\n raise Exception(\"No input file was specified\")\n\n if self.options.verbose:\n print \"Input file:\", \"( %sP x %sL - %s bands)\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\n\n if not self.in_ds:\n # Note: GDAL prints the ERROR message too\n self.error(\"It is not possible to open the input file '%s'.\" % self.input )\n\n # Read metadata from the input file\n if self.in_ds.RasterCount == 0:\n self.error( \"Input file '%s' has no raster band\" % self.input )\n\n if self.in_ds.GetRasterBand(1).GetRasterColorTable():\n # TODO: Process directly paletted dataset by generating VRT in memory\n self.error( \"Please convert this file to RGB/RGBA and run gdal2mbtiles on the result.\",\n \"\"\"From paletted file you can create RGBA file (temp.vrt) by:\ngdal_translate -of vrt -expand rgba %s temp.vrt\nthen run:\ngdal2mbtiles temp.vrt\"\"\" % self.input )\n\n # Get NODATA value\n # User supplied values overwrite everything else.\n if self.options.srcnodata is not None:\n nds = map(float, self.options.srcnodata.split(','))\n if len(nds) < self.in_ds.RasterCount:\n self.in_nodata = (nds * self.in_ds.RasterCount)[:self.in_ds.RasterCount]\n else:\n self.in_nodata = nds\n else:\n # If the source dataset has NODATA, use it.\n self.in_nodata = []\n for i in range(1, self.in_ds.RasterCount+1):\n if self.in_ds.GetRasterBand(i).GetNoDataValue() != None:\n self.in_nodata.append( self.in_ds.GetRasterBand(i).GetNoDataValue() )\n\n if self.options.verbose:\n print \"NODATA: %s\" % self.in_nodata\n\n # INIT DEST\n if self.options.init_dest is not None:\n if self.options.tile_format == \"jpeg\":\n if self.in_ds.RasterCount == 4:\n nbands = 3\n else:\n nbands = self.in_ds.RasterCount\n\n nds = map(float, self.options.init_dest.split(','))\n\n if len(nds) == 1:\n init_dest = nds * nbands\n elif len(nds) == nbands:\n init_dest = nds\n else:\n print \"WARNING: you suplied %d '--init-dest' values but the dataset has %d data bands\" % (len(nds), nbands)\n init_dest = None\n else:\n init_dest = None\n print \"WARNING: --init-dest can be used only with 'jpeg' tile format\"\n else:\n if self.options.tile_format == \"jpeg\":\n init_dest = [255,255,255]\n else:\n init_dest = None\n\n #\n # Here we should have RGBA input dataset opened in self.in_ds\n #\n\n if self.options.verbose:\n print \"Preprocessed file:\", \"( %sP x %sL - %s bands)\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\n\n # Spatial Reference System of the input raster\n\n\n self.in_srs = None\n\n if self.options.s_srs:\n self.in_srs = osr.SpatialReference()\n self.in_srs.SetFromUserInput(self.options.s_srs)\n self.in_srs_wkt = self.in_srs.ExportToWkt()\n else:\n self.in_srs_wkt = self.in_ds.GetProjection()\n if not self.in_srs_wkt and self.in_ds.GetGCPCount() != 0:\n self.in_srs_wkt = self.in_ds.GetGCPProjection()\n if self.in_srs_wkt:\n self.in_srs = osr.SpatialReference()\n self.in_srs.ImportFromWkt(self.in_srs_wkt)\n #elif self.options.profile != 'raster':\n # self.error(\"There is no spatial reference system info included in the input file.\",\"You should run gdal2mbtiles with --s_srs EPSG:XXXX or similar.\")\n\n # Spatial Reference System of tiles\n\n self.out_srs = osr.SpatialReference()\n\n if self.options.profile == 'mercator':\n self.out_srs.ImportFromEPSG(900913)\n elif self.options.profile in ('geodetic', 'gearth', 'garmin'):\n self.out_srs.ImportFromEPSG(4326)\n else:\n self.out_srs = self.in_srs\n\n # Are the reference systems the same? Reproject if necessary.\n\n self.out_ds = None\n\n if self.options.profile in ('mercator', 'geodetic', 'gearth', 'garmin'):\n\n if (self.in_ds.GetGeoTransform() == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) and (self.in_ds.GetGCPCount() == 0):\n self.error(\"There is no georeference - neither affine transformation (worldfile) nor GCPs. You can generate only 'raster' profile tiles.\",\n \"Either gdal2mbtiles with parameter -p 'raster' or use another GIS software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\")\n\n if self.in_srs:\n\n if (self.in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or (self.in_ds.GetGCPCount() != 0):\n\n # Generation of VRT dataset in tile projection, default 'nearest neighbour' warping\n self.out_ds = gdal.AutoCreateWarpedVRT( self.in_ds, self.in_srs_wkt, self.out_srs.ExportToWkt() )\n\n # TODO: HIGH PRIORITY: Correction of AutoCreateWarpedVRT according the max zoomlevel for correct direct warping!!!\n\n if self.options.verbose:\n print \"Warping of the raster by AutoCreateWarpedVRT (result saved into 'tiles.vrt')\"\n self.out_ds.GetDriver().CreateCopy(\"tiles.vrt\", self.out_ds)\n\n # Note: self.in_srs and self.in_srs_wkt contain still the non-warped reference system!!!\n\n # Correction of AutoCreateWarpedVRT for NODATA values\n if self.in_nodata != []:\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"<GDALWarpOptions>\"\"\",\"\"\"<GDALWarpOptions>\n <Option name=\"UNIFIED_SRC_NODATA\">YES</Option>\n <Option name=\"INIT_DEST\">NO_DATA</Option>\"\"\")\n # replace BandMapping tag for NODATA bands....\n if init_dest is None:\n dstnodata = self.in_nodata\n else:\n dstnodata = init_dest\n for i in range(len(self.in_nodata)):\n s = s.replace(\"\"\"<BandMapping src=\"%i\" dst=\"%i\"/>\"\"\" % ((i+1),(i+1)),\"\"\"<BandMapping src=\"%i\" dst=\"%i\">\n <SrcNoDataReal>%i</SrcNoDataReal>\n <SrcNoDataImag>0</SrcNoDataImag>\n <DstNoDataReal>%i</DstNoDataReal>\n <DstNoDataImag>0</DstNoDataImag>\n </BandMapping>\"\"\" % ((i+1), (i+1), self.in_nodata[i], dstnodata[i]))\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n # set NODATA_VALUE metadata\n self.out_ds.SetMetadataItem('NODATA_VALUES','%s' % \" \".join(str(int(f)) for f in self.in_nodata))\n\n if self.options.verbose:\n print \"Modified warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n # -----------------------------------\n # Correction of AutoCreateWarpedVRT for Mono (1 band) and RGB (3 bands) files without NODATA:\n # equivalent of gdalwarp -dstalpha\n elif self.in_nodata == [] and self.out_ds.RasterCount in (1,3):\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"<BlockXSize>\"\"\",\"\"\"<VRTRasterBand dataType=\"Byte\" band=\"%i\" subClass=\"VRTWarpedRasterBand\">\n <ColorInterp>Alpha</ColorInterp>\n </VRTRasterBand>\n <BlockXSize>\"\"\" % (self.out_ds.RasterCount + 1))\n s = s.replace(\"\"\"</GDALWarpOptions>\"\"\", \"\"\"<DstAlphaBand>%i</DstAlphaBand>\n </GDALWarpOptions>\"\"\" % (self.out_ds.RasterCount + 1))\n if init_dest is None:\n init_dest_str = \"0\"\n else:\n init_dest_str = \",\".join(str(f) for f in init_dest)\n s = s.replace(\"\"\"</WorkingDataType>\"\"\", \"\"\"</WorkingDataType>\n <Option name=\"INIT_DEST\">%s</Option>\"\"\" % init_dest_str)\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n if self.options.verbose:\n print \"Modified -dstalpha warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n elif init_dest is not None:\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"</WorkingDataType>\"\"\", \"\"\"</WorkingDataType>\n <Option name=\"INIT_DEST\">%s</Option>\"\"\" % \",\".join(str(f) for f in init_dest))\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n if self.options.verbose:\n print \"Modified warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n # For raster with 4-bands: 4th unknown band set to alpha\n if (self.out_ds.RasterCount == 4\n and self.out_ds.GetRasterBand(4).GetRasterColorInterpretation() == gdal.GCI_Undefined):\n self.out_ds.GetRasterBand(4).SetRasterColorInterpretation(gdal.GCI_AlphaBand)\n\n s = '''\n '''\n\n else:\n self.error(\"Input file has unknown SRS.\", \"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\" )\n\n if self.out_ds and self.options.verbose:\n print \"Projected file:\", \"tiles.vrt\", \"( %sP x %sL - %s bands)\" % (self.out_ds.RasterXSize, self.out_ds.RasterYSize, self.out_ds.RasterCount)\n\n if not self.out_ds:\n self.out_ds = self.in_ds\n\n #\n # Here we should have a raster (out_ds) in the correct Spatial Reference system\n #\n\n # KML test\n self.isepsg4326 = False\n srs4326 = osr.SpatialReference()\n srs4326.ImportFromEPSG(4326)\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\n self.kml = True\n self.isepsg4326 = True\n if self.options.verbose:\n print \"KML autotest OK!\"\n\n # Read the georeference\n\n self.out_gt = self.out_ds.GetGeoTransform()\n\n #originX, originY = self.out_gt[0], self.out_gt[3]\n #pixelSize = self.out_gt[1] # = self.out_gt[5]\n\n # Test the size of the pixel\n\n # MAPTILER - COMMENTED\n #if self.out_gt[1] != (-1 * self.out_gt[5]) and self.options.profile != 'raster':\n # TODO: Process corectly coordinates with are have swichted Y axis (display in OpenLayers too)\n #self.error(\"Size of the pixel in the output differ for X and Y axes.\")\n\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\n if (self.out_gt[2], self.out_gt[4]) != (0,0):\n self.error(\"Georeference of the raster contains rotation or skew. Such raster is not supported. Please use gdalwarp first.\")\n # TODO: Do the warping in this case automaticaly\n\n #\n # Here we expect: pixel is square, no rotation on the raster\n #\n\n # Output Bounds - coordinates in the output SRS\n self.ominx = self.out_gt[0]\n self.omaxx = self.out_gt[0]+self.out_ds.RasterXSize*self.out_gt[1]\n self.omaxy = self.out_gt[3]\n self.ominy = self.out_gt[3]-self.out_ds.RasterYSize*self.out_gt[1]\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\n # user defined bounds to extract - coordinates in the output SRS\n if self.options.te_bounds != '':\n if self.te_minx >= self.ominx and self.te_minx <= self.omaxx:\n if self.te_maxx >= self.ominx and self.te_maxx <= self.omaxx:\n if self.te_miny >= self.ominy and self.te_miny <= self.omaxy:\n if self.te_maxy >= self.ominy and self.te_maxy <= self.omaxy:\n # replace only if inside the read bounds\n self.ominx = self.te_minx\n self.omaxx = self.te_maxx\n self.ominy = self.te_miny\n self.omaxy = self.te_maxy\n if self.options.verbose:\n print \"User defined Bounds (output srs) have been set:\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\n\n if self.options.verbose:\n print \"Bounds (output srs):\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\n\n if self.options.mbtiles:\n self.options.profile = 'mercator'\n if self.options.profile == 'mercator':\n self.mercator = GlobalMercator(self.options.tms_osm) # from globalmaptiles.py\n\n #\n # Calculating ranges for tiles in different zoom levels\n #\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.mercator.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0,32)\n for tz in range(0, 32):\n tminx, tminy = self.mercator.MetersToTile( self.ominx, self.ominy, tz )\n tmaxx, tmaxy = self.mercator.MetersToTile( self.omaxx, self.omaxy, tz )\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the minimal zoom level (map covers area equivalent to one tile)\n if self.tminz == None:\n self.tminz = self.mercator.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tmaxz == None:\n self.tmaxz = self.mercator.ZoomForPixelSize( self.out_gt[1] )\n\n if self.options.verbose:\n print \"Bounds (latlong):\", self.mercator.MetersToLatLon( self.ominx, self.ominy), self.mercator.MetersToLatLon( self.omaxx, self.omaxy)\n print 'MinZoomLevel:', self.tminz\n print \"MaxZoomLevel:\", self.tmaxz, \"(\", self.mercator.Resolution( self.tmaxz ),\")\"\n\n # this must be call befor ImageOutput is called (self.output may be changed)\n if self.options.mbtiles:\n if not self.mbtiles_db:\n self.mbtiles_setup(1);\n\n # Instantiate image output.\n self.image_output = ImageOutput(self.options.tile_format, self.out_ds, self.tilesize,\n self.options.resampling, init_dest, self.output,\n self.options.verbose,self.options.mbtiles)\n if self.options.profile == 'geodetic':\n\n self.geodetic = GlobalGeodetic() # from globalmaptiles.py\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.geodetic.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0,32)\n for tz in range(0, 32):\n tminx, tminy = self.geodetic.LatLonToTile( self.ominx, self.ominy, tz )\n tmaxx, tmaxy = self.geodetic.LatLonToTile( self.omaxx, self.omaxy, tz )\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tminz == None:\n self.tminz = self.geodetic.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tmaxz == None:\n self.tmaxz = self.geodetic.ZoomForPixelSize( self.out_gt[1] )\n\n if self.options.verbose:\n print \"Bounds (latlong):\", self.ominx, self.ominy, self.omaxx, self.omaxy\n\n if self.options.profile in ('raster', 'gearth', 'garmin'):\n\n log2 = lambda x: math.log10(x) / math.log10(2) # log2 (base 2 logarithm)\n\n self.nativezoom = int(max( math.ceil(log2(self.out_ds.RasterXSize/float(self.tilesize))),\n math.ceil(log2(self.out_ds.RasterYSize/float(self.tilesize)))))\n\n if self.options.verbose:\n print \"Native zoom of the raster:\", self.nativezoom\n\n # Get the minimal zoom level (whole raster in one tile)\n if self.tminz == None:\n self.tminz = 0\n\n # Get the maximal zoom level (native resolution of the raster)\n if self.tmaxz == None:\n self.tmaxz = self.nativezoom\n\n # Garmin has maximally 100 tiles - lower the tmaxz if necessary\n if self.options.profile == 'garmin':\n tno = math.ceil(self.out_ds.RasterXSize / self.tilesize) * math.ceil(self.out_ds.RasterYSize / self.tilesize)\n for tz in range(self.tmaxz, 1, -1):\n if tno > 100:\n tno /= 4\n self.tmaxz -= 1\n print \"Warning: GARMIN has a limit 100 tiles per device: lowering the max zoom level to:\", self.tmaxz\n else:\n continue\n\n # Force only one zoom level for the 'garmin' tile profile\n if self.options.profile == 'garmin':\n self.tminz = self.tmaxz\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0, self.tmaxz+1)\n self.tsize = range(0, self.tmaxz+1)\n for tz in range(0, self.tmaxz+1):\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\n tminx, tminy = 0, 0\n tmaxx = int(math.ceil( self.out_ds.RasterXSize / tsize )) - 1\n tmaxy = int(math.ceil( self.out_ds.RasterYSize / tsize )) - 1\n self.tsize[tz] = math.ceil(tsize)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # Function which generates SWNE in LatLong for given tile\n if self.kml and self.in_srs_wkt:\n self.ct = osr.CoordinateTransformation(self.in_srs, srs4326)\n def rastertileswne(x,y,z):\n pixelsizex = (2**(self.nativezoom-z) * self.out_gt[1]) # X-pixel size in level\n pixelsizey = (2**(self.nativezoom-z) * self.out_gt[5]) # Y-pixel size in level (usually -1*pixelsizex)\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\n east = west + self.tilesize*pixelsizex\n south = self.ominy + y*self.tilesize*pixelsizex\n north = south + self.tilesize*pixelsizex\n if not self.isepsg4326:\n # Transformation to EPSG:4326 (WGS84 datum)\n west, south = self.ct.TransformPoint(west, south)[:2]\n east, north = self.ct.TransformPoint(east, north)[:2]\n return south, west, north, east\n\n self.tileswne = rastertileswne\n else:\n self.tileswne = lambda x, y, z: (0,0,0,0)","def pipeline(inputfile, outputfile, functions, **kwargs):\n if not functions:\n raise ValueError('Must have at least one function')\n\n tmpfiles = []\n try:\n previous = inputfile\n for name, f in functions:\n logging.debug(name)\n vrt = f(previous)\n current = vrt.get_tempfile(suffix='.vrt', prefix='gdal')\n tmpfiles.append(current)\n previous = current.name\n logging.info('Rendering reprojected image')\n return vrt.render(outputfile=outputfile, **kwargs)\n finally:\n for f in tmpfiles:\n f.close()","def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\n outputs = []\n npy_outputs = []\n if resamplemethod == 'nearest':\n rs = Resampling.nearest\n else:\n print('only nearest neighbor resampling is supported at this time')\n sys.exit(0)\n\n for i, warpfile in enumerate(inputs):\n print('warpfile', warpfile)\n with rasterio.open(warpfile) as src:\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\n with WarpedVRT(src, resampling=rs,\n crs=self.crs,\n transform=self.transform,\n height=self.rows,\n width=self.cols) as vrt:\n data = vrt.read()\n print(type(vrt))\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\n outputs.append(outwarp)\n\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\n # from this method for us in the rest of the code.\n for ow in outputs:\n with rasterio.open(ow, 'r') as src:\n arr = src.read(1)\n npy_outputs.append(arr)\n\n return npy_outputs","def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\n outputs = []\n npy_outputs = []\n if resamplemethod == 'nearest':\n rs = Resampling.nearest\n else:\n print('only nearest neighbor resampling is supported at this time')\n sys.exit(0)\n\n for i, warpfile in enumerate(inputs):\n # print('warpfile', warpfile)\n with rasterio.open(warpfile) as src:\n # TODO - make the default configurable.\n# if src.crs == None:\n# src.crs = CRS.from_epsg(4326)\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\n with WarpedVRT(src, resampling=rs,\n crs=self.crs,\n transform=self.transform,\n height=self.rows,\n width=self.cols) as vrt:\n data = vrt.read()\n # print(type(vrt))\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\n outputs.append(outwarp)\n\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\n # from this method for us in the rest of the code.\n for ow in outputs:\n with rasterio.open(ow, 'r') as src:\n arr = src.read(1)\n npy_outputs.append(arr)\n\n return npy_outputs","def transform(infile, output, insrs, format_name):\n\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \n in_srs = osr.SpatialReference()\n in_srs.ImportFromEPSG(insrs)\n out_srs = osr.SpatialReference()\n out_srs.ImportFromEPSG(4324)\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\n\n in_dsn = ogr.Open(infile)\n in_layer = in_dsn.GetLayer()\n in_feature_definition = in_layer.GetLayerDefn()\n\n out_driver = ogr.GetDriverByName(format_name)\n out_dsn = out_driver.CreateDataSource(output)\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\n geom_type=in_layer.GetGeomType())\n\n # add fields\n for i in range(0, in_feature_definition.GetFieldCount()):\n fieldDefn = in_feature_definition.GetFieldDefn(i)\n out_layer.CreateField(fieldDefn)\n\n # get the output layer's feature definition\n out_feature_definition = out_layer.GetLayerDefn()\n\n # loop through the input features\n inFeature = in_layer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef().Clone()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(out_feature_definition)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, out_feature_definition.GetFieldCount()):\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n out_layer.CreateFeature(outFeature)\n # destroy the features and get the next input feature\n outFeature.Destroy()\n inFeature.Destroy()\n inFeature = in_layer.GetNextFeature()\n\n # close the shapefiles\n in_dsn.Destroy()\n out_dsn.Destroy()","def vtp(self, f_vtu, f_vtp):\r\n reader = vtk.vtkXMLUnstructuredGridReader()\r\n reader.SetFileName(f_vtu)\r\n reader.Update()\r\n ugrid = reader.GetOutput()\r\n geometryFilter = vtk.vtkGeometryFilter()\r\n geometryFilter.SetInputData(ugrid)\r\n geometryFilter.Update()\r\n polydata = geometryFilter.GetOutput()\r\n writer =vtk.vtkXMLPolyDataWriter()\r\n writer.SetFileName(f_vtp)\r\n writer.SetInputData(polydata)\r\n writer.Write()\r\n print(\"vtp file created.\")","def pan_corr(file):\n\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\flatfield\\\\NHDBflat_1D'\n # # infile = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Jure_naloga_banje_raw_pyt\\\\NHDRGoreMorje_3D'\n #\n # # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\n # in_ref = in_path + in_pan_ref_file\n #\n # inreffil = gdal.Open(in_ref)\n # image_ref = inreffil.ReadAsArray()\n # # size_ref = image_ref.shape\n # # pix_count = size_ref[0]*size_ref[1]\n #\n # image_ref = image_ref[800:930, 1420:1640]\n # size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n #\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # raz_g1 = 1\n # raz_g2 = g1_avg/g2_avg\n # raz_r1 = g1_avg/r1_avg\n # raz_b1 = g1_avg/b1_avg\n #\n # avg = (g1+g2+r1+b1)/pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n raz_g1 = 1\n raz_g2 = 1.0245196396115988\n raz_r1 = 1.0131841989689434\n raz_b1 = 1.0517113199247086\n\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Slo_PAN\\_26_30\\\\'\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\"nhd\") and filename.lower().endswith(\"tif\")]\n\n \n\n \n\n # print('image', i)\n in_ref=file\n inreffil = gdal.Open(in_ref)\n image_ref = inreffil.ReadAsArray()\n size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n # pix_count = np.count_nonzero(image_ref)\n # pix_count = 3664*650\n\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # avg = (g1 + g2 + r1 + b1) / pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n # popravek\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\n\n\n for i in range(size_ref[0]):\n for j in range(size_ref[1]):\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\n \n _,_,_,_,P=return_flatfield_set_path(2)\n P_flat=gdal_array.LoadFile(P)\n \n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \n \n # outout\n \n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\n \n out=os.path.abspath(file)+\"/corr/\"+os.path.basename(file)[:-4] + \"_pop_flat_corr.tif\"\n\n \n # out = in_ref[:-4] + \"_pop_flat_corr.tif\"\n\n driver = gdal.GetDriverByName('GTiff')\n\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\n\n outband = outRaster.GetRasterBand(1)\n outband.WriteArray(im_p_pop)\n outband.FlushCache()","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def main(file):\n\n # Get the current working directory.\n here = os.getcwd()\n #Need the file_name to set globe, so that other functions can access to it.\n global file_name\n # Spite the Input into file_path and file_name.\n file_path = spilt_path(file)[0]\n file_name = spilt_path(file)[1]\n\n # Try to get into the file_path, if exist\n try:\n os.chdir(file_path)\n except IOError, e:\n print e\n\n # Now convert it\n convertFile(file_name)\n # going back to orgin folder\n os.chdir(here)\n return os.path.join(output_dir, file_name)","def open_input(self):\n gdal.AllRegister()\n\n self.out_drv = gdal.GetDriverByName(self.tiledriver)\n self.mem_drv = gdal.GetDriverByName('MEM')\n\n if not self.out_drv:\n raise Exception(\"The '%s' driver was not found, is it available in this GDAL build?\",\n self.tiledriver)\n if not self.mem_drv:\n raise Exception(\"The 'MEM' driver was not found, is it available in this GDAL build?\")\n\n # Open the input file\n\n if self.input_file:\n input_dataset = gdal.Open(self.input_file, gdal.GA_ReadOnly)\n else:\n raise Exception(\"No input file was specified\")\n\n if self.options.verbose:\n print(\"Input file:\",\n \"( %sP x %sL - %s bands)\" % (input_dataset.RasterXSize,\n input_dataset.RasterYSize,\n input_dataset.RasterCount))\n\n if not input_dataset:\n # Note: GDAL prints the ERROR message too\n exit_with_error(\"It is not possible to open the input file '%s'.\" % self.input_file)\n\n # Read metadata from the input file\n if input_dataset.RasterCount == 0:\n exit_with_error(\"Input file '%s' has no raster band\" % self.input_file)\n\n if input_dataset.GetRasterBand(1).GetRasterColorTable():\n exit_with_error(\n \"Please convert this file to RGB/RGBA and run gdal2tiles on the result.\",\n \"From paletted file you can create RGBA file (temp.vrt) by:\\n\"\n \"gdal_translate -of vrt -expand rgba %s temp.vrt\\n\"\n \"then run:\\n\"\n \"gdal2tiles temp.vrt\" % self.input_file\n )\n\n in_nodata = setup_no_data_values(input_dataset, self.options)\n\n if self.options.verbose:\n print(\"Preprocessed file:\",\n \"( %sP x %sL - %s bands)\" % (input_dataset.RasterXSize,\n input_dataset.RasterYSize,\n input_dataset.RasterCount))\n\n in_srs, self.in_srs_wkt = setup_input_srs(input_dataset, self.options)\n\n self.out_srs = setup_output_srs(in_srs, self.options)\n\n # If input and output reference systems are different, we reproject the input dataset into\n # the output reference system for easier manipulation\n\n self.warped_input_dataset = None\n\n if self.options.profile in ('mercator', 'geodetic'):\n\n if not in_srs:\n exit_with_error(\n \"Input file has unknown SRS.\",\n \"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\")\n\n if not has_georeference(input_dataset):\n exit_with_error(\n \"There is no georeference - neither affine transformation (worldfile) \"\n \"nor GCPs. You can generate only 'raster' profile tiles.\",\n \"Either gdal2tiles with parameter -p 'raster' or use another GIS \"\n \"software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\"\n )\n\n if ((in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or\n (input_dataset.GetGCPCount() != 0)):\n self.warped_input_dataset = reproject_dataset(\n input_dataset, in_srs, self.out_srs)\n\n if in_nodata:\n self.warped_input_dataset = update_no_data_values(\n self.warped_input_dataset, in_nodata, options=self.options)\n else:\n self.warped_input_dataset = update_alpha_value_for_non_alpha_inputs(\n self.warped_input_dataset, options=self.options)\n\n if self.warped_input_dataset and self.options.verbose:\n print(\"Projected file:\", \"tiles.vrt\", \"( %sP x %sL - %s bands)\" % (\n self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize,\n self.warped_input_dataset.RasterCount))\n\n if not self.warped_input_dataset:\n self.warped_input_dataset = input_dataset\n\n #Salva o arquivo reprojetado\n self.warped_input_dataset.GetDriver().CreateCopy(self.tmp_vrt_filename,\n self.warped_input_dataset)\n\n # Get alpha band (either directly or from NODATA value)\n self.alphaband = self.warped_input_dataset.GetRasterBand(1).GetMaskBand()\n self.dataBandsCount = nb_data_bands(self.warped_input_dataset)\n\n # KML test\n self.isepsg4326 = False\n srs4326 = osr.SpatialReference()\n srs4326.ImportFromEPSG(4326)\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\n self.kml = True\n self.isepsg4326 = True\n if self.options.verbose:\n print(\"KML autotest OK!\")\n\n # Read the georeference\n self.out_gt = self.warped_input_dataset.GetGeoTransform()\n\n # Test the size of the pixel\n\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\n if (self.out_gt[2], self.out_gt[4]) != (0, 0):\n exit_with_error(\"Georeference of the raster contains rotation or skew. \"\n \"Such raster is not supported. Please use gdalwarp first.\")\n\n # Here we expect: pixel is square, no rotation on the raster\n\n # Output Bounds - coordinates in the output SRS\n self.ominx = self.out_gt[0]\n self.omaxx = self.out_gt[0] + self.warped_input_dataset.RasterXSize * self.out_gt[1]\n self.omaxy = self.out_gt[3]\n self.ominy = self.out_gt[3] - self.warped_input_dataset.RasterYSize * self.out_gt[1]\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\n\n if self.options.verbose:\n print(\"Bounds (output srs):\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy)\n\n # Calculating ranges for tiles in different zoom levels\n if self.options.profile == 'mercator':\n\n self.mercator = GlobalMercator()\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.mercator.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, 32))\n for tz in range(0, 32):\n tminx, tminy = self.mercator.MetersToTile(self.ominx, self.ominy, tz)\n tmaxx, tmaxy = self.mercator.MetersToTile(self.omaxx, self.omaxy, tz)\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the minimal zoom level (map covers area equivalent to one tile)\n if self.tminz is None:\n self.tminz = self.mercator.ZoomForPixelSize(\n self.out_gt[1] *\n max(self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize) /\n float(self.tilesize))\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tmaxz is None:\n self.tmaxz = self.mercator.ZoomForPixelSize(self.out_gt[1])\n\n if self.options.verbose:\n print(\"Bounds (latlong):\",\n self.mercator.MetersToLatLon(self.ominx, self.ominy),\n self.mercator.MetersToLatLon(self.omaxx, self.omaxy))\n print('MinZoomLevel:', self.tminz)\n print(\"MaxZoomLevel:\",\n self.tmaxz,\n \"(\",\n self.mercator.Resolution(self.tmaxz),\n \")\")\n\n if self.options.profile == 'geodetic':\n\n self.geodetic = GlobalGeodetic(self.options.tmscompatible)\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.geodetic.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, 32))\n for tz in range(0, 32):\n tminx, tminy = self.geodetic.LonLatToTile(self.ominx, self.ominy, tz)\n tmaxx, tmaxy = self.geodetic.LonLatToTile(self.omaxx, self.omaxy, tz)\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tminz is None:\n self.tminz = self.geodetic.ZoomForPixelSize(\n self.out_gt[1] *\n max(self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize) /\n float(self.tilesize))\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tmaxz is None:\n self.tmaxz = self.geodetic.ZoomForPixelSize(self.out_gt[1])\n\n if self.options.verbose:\n print(\"Bounds (latlong):\", self.ominx, self.ominy, self.omaxx, self.omaxy)\n\n if self.options.profile == 'raster':\n\n def log2(x):\n return math.log10(x) / math.log10(2)\n\n self.nativezoom = int(\n max(math.ceil(log2(self.warped_input_dataset.RasterXSize/float(self.tilesize))),\n math.ceil(log2(self.warped_input_dataset.RasterYSize/float(self.tilesize)))))\n\n if self.options.verbose:\n print(\"Native zoom of the raster:\", self.nativezoom)\n\n # Get the minimal zoom level (whole raster in one tile)\n if self.tminz is None:\n self.tminz = 0\n\n # Get the maximal zoom level (native resolution of the raster)\n if self.tmaxz is None:\n self.tmaxz = self.nativezoom\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, self.tmaxz+1))\n self.tsize = list(range(0, self.tmaxz+1))\n for tz in range(0, self.tmaxz+1):\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\n tminx, tminy = 0, 0\n tmaxx = int(math.ceil(self.warped_input_dataset.RasterXSize / tsize)) - 1\n tmaxy = int(math.ceil(self.warped_input_dataset.RasterYSize / tsize)) - 1\n self.tsize[tz] = math.ceil(tsize)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # Function which generates SWNE in LatLong for given tile\n if self.kml and self.in_srs_wkt:\n ct = osr.CoordinateTransformation(in_srs, srs4326)\n\n def rastertileswne(x, y, z):\n pixelsizex = (2**(self.tmaxz-z) * self.out_gt[1]) # X-pixel size in level\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\n east = west + self.tilesize*pixelsizex\n south = self.ominy + y*self.tilesize*pixelsizex\n north = south + self.tilesize*pixelsizex\n if not self.isepsg4326:\n # Transformation to EPSG:4326 (WGS84 datum)\n west, south = ct.TransformPoint(west, south)[:2]\n east, north = ct.TransformPoint(east, north)[:2]\n return south, west, north, east\n\n self.tileswne = rastertileswne\n else:\n self.tileswne = lambda x, y, z: (0, 0, 0, 0) # noqa","def read(cls, file_name=None, lexclude=[], lonly=[], verbose=False):\n###################################################################\n\n # import\n import numpy as np\n \n # init\n \n vf = Velocity_Field()\n\n # fake 4-letters code generation using hexadecimal\n def __gen_fake_code__(n):\n \n FAKE = []\n for i in np.arange(n):\n fake_code = (\"%4s\" % hex(i).split('x')[-1].replace('L', '')).replace(' ', '0')\n FAKE.append(fake_code.upper())\n \n return(np.array(FAKE))\n\n # reads psvelo file\n\n if verbose:\n print(\"-- Reading GMT psvelo file: %s \" % file_name)\n \n try:\n np_vel = np.array(np.mat(np.genfromtxt(file_name, comments='#')))\n except:\n raise IOError(\"!!! Could not read file: %s\" % file_name)\n \n # empty psvelo file\n if np_vel.size == 0:\n return( vf )\n \n if (np_vel.shape[1] == 8):\n if verbose:\n print(\"-- file %s has 8 columns\" % file_name)\n\n np_vel = np.delete(np_vel, -1, axis=1)\n np_code = np.array(np.mat(np.genfromtxt(file_name, comments='#', usecols=(7), dtype=str))).flatten()\n \n elif (np_vel.shape[1] == 3):\n\n if verbose:\n print(\"-- file %s has 3 columns\" % file_name)\n\n np_vel = np.delete(np_vel, -1, axis=1)\n np_code = np.array(np.mat(np.genfromtxt(file_name, comments='#', usecols=(2)))).flatten()\n\n elif (np_vel.shape[1] not in [3, 8]):\n np_code = __gen_fake_code__(np_vel.shape[0])\n else:\n raise IOError(\"!!! Could not decipher file content: %s\", file_name)\n\n # populates velocity field\n \n from pyacs.lib.gmtpoint import GMT_Point\n\n lgmt_points = []\n\n for i in np.arange(np_vel.shape[0]):\n\n code = np_code[i]\n \n if np_vel.shape[1] >= 7:\n lon, lat, Ve, Vn, SVe, SVn, SVen = np_vel[i, :]\n M = GMT_Point(lon=lon, lat=lat, Ve=Ve, Vn=Vn, SVe=SVe, SVn=SVn, SVen=SVen, code=code)\n else:\n lon, lat = np_vel[i, :]\n M = GMT_Point(lon=lon, lat=lat, code=code)\n\n if verbose:\n M.get_info(display=True)\n \n # tests whether site will be added\n \n if lonly != []:\n if M.code in lonly:\n lgmt_points.append(M)\n \n else:\n if lexclude != []:\n if M.code not in lexclude:\n lgmt_points.append(M)\n else:\n lgmt_points.append(M)\n \n vf.file_name = file_name\n vf.sites = lgmt_points\n \n return vf","def make(input_filepath, output_filepath) -> None:\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')","def createInput(dirPath,gSettings):\n \n with open(os.path.join('../in','input.txt')) as f:\n inpFile = f.readlines()\n \n\n # Model settings\n model = gSettings[\"Model\"]\n inpFile[13] = \"insgrav: {:1d}\\n\".format(int(model[\"NS gravity\"][\"Flag\"]))\n inpFile[14] = \"isun: {:1d}\\n\".format(int(model[\"Lunisolar\"][\"Sun\"]))\n inpFile[15] = \"imoon: {:1d}\\n\".format(int(model[\"Lunisolar\"][\"Moon\"]))\n\n if model[\"Drag\"][\"Flag\"] == False:\n inpFile[16] = \"idrag: 0\\n\"\n else:\n dm = model[\"Drag\"][\"Model\"].lower()\n if dm == \"wertz\":\n idrag = 1\n elif dm == \"us76\":\n idrag = 2\n elif dm == \"j77\":\n idrag = 3\n elif dm == \"msis00\":\n idrag = 4\n else:\n raise ValueError('Value \"' + model[\"Drag\"][\"Model\"] + '\" invalid.')\n inpFile[16] = \"idrag: {:1d}\\n\".format(idrag)\n if model[\"Drag\"][\"Solar flux\"].lower() == \"constant\":\n inpFile[17] = \"iF107: 0\\n\"\n elif model[\"Drag\"][\"Solar flux\"].lower() == \"variable\":\n inpFile[17] = \"iF107: 1\\n\"\n else:\n raise ValueError('Value \"' + model[\"Drag\"][\"Solar flux\"] + '\" invalid.')\n\n if model[\"SRP\"][\"Flag\"] == False:\n inpFile[18] = \"iSRP: {:1d}\\n\".format(int(model[\"SRP\"][\"Flag\"]))\n else:\n inpFile[18] = \"iSRP: {:1d}\\n\".format(int(model[\"SRP\"][\"Flag\"]))\n if model[\"SRP\"][\"Eclipses\"]:\n inpFile[18] = \"iSRP: 2\\n\"\n \n if model[\"Lunisolar\"][\"Ephemerides\"] == \"DE431\":\n inpFile[19] = \"iephem: 1\\n\"\n elif model[\"Lunisolar\"][\"Ephemerides\"] == \"Meeus\":\n inpFile[19] = \"iephem: 2\\n\"\n else:\n raise ValueError('Value \"' + model[\"Lunisolar\"][\"Ephemerides\"] + '\" invalid.')\n \n inpFile[20] = \"gdeg: {:3d}\\n\".format(model[\"NS gravity\"][\"Degree\"])\n if model[\"NS gravity\"][\"Order\"] <= model[\"NS gravity\"][\"Degree\"]:\n inpFile[21] = \"gord: {:3d}\\n\".format(model[\"NS gravity\"][\"Order\"])\n else:\n raise ValueError(\"Order {0:d} of the gravity field is greater than degree {1:d}\".format(model[\"NS gravity\"][\"Order\"],model[\"NS gravity\"][\"Degree\"]))\n \n\n\n # Integration settings\n integ = gSettings[\"Integration\"]\n inpFile[29] = \"tol: {:22.15E}\\n\".format(integ[\"Tolerance\"])\n inpFile[30] = \"tspan: {:22.15E}\\n\".format(integ[\"Duration\"] * 365.25)\n inpFile[31] = \"tstep: {:22.15E}\\n\".format(integ[\"Step\"])\n inpFile[39] = \"eqs: {:2d}\\n\".format(integ[\"Equations\"])\n\n\n\n # Output settings\n inpFile[44] = \"verb: 0\\n\"\n inpFile[45] = \"out: \" + os.path.abspath(os.path.join(dirPath, ' '))\n\n\n with open(os.path.join(dirPath,'input.txt'),'w') as f:\n f.writelines(inpFile)","def create_vector_datapackage(pk_type, path, file_flag, out_path):\n process_source(pk_type, path, file_flag, out_path)","def vl2img(vl_json_in, fileformat):\n\n # TODO would prefer to do this properly with pipes\n # using | and shell=True is safe though given no arguments\n executables = {\"svg\": \"vg2svg\", \"png\": \"vg2png\", \"pdf\": \"vg2pdf\"}\n try:\n exe = executables[fileformat]\n except KeyError as e:\n print(e.output)\n try:\n return subprocess.check_output(\"vl2vg | %s\" % exe, shell=True, input=vl_json_in)\n except subprocess.CalledProcessError as e:\n print(e.output)","def read_raytomo_dbase(self, inh5fname, runid, dtype='ph', wtype='ray', create_header=True, Tmin=-999, Tmax=999, verbose=False):\n if dtype is not 'ph' and dtype is not 'gr':\n raise ValueError('data type can only be ph or gr!')\n if wtype is not 'ray' and wtype is not 'lov':\n raise ValueError('wave type can only be ray or lov!')\n stalst = self.waveforms.list()\n if len(stalst) == 0:\n print 'Inversion with surface wave datasets only, not added yet!'\n return\n indset = h5py.File(inh5fname)\n #--------------------------------------------\n # header information from input hdf5 file\n #--------------------------------------------\n dataid = 'reshaped_qc_run_'+str(runid)\n pers = indset.attrs['period_array']\n grp = indset[dataid]\n isotropic = grp.attrs['isotropic']\n org_grp = indset['qc_run_'+str(runid)]\n minlon = indset.attrs['minlon']\n maxlon = indset.attrs['maxlon']\n minlat = indset.attrs['minlat']\n maxlat = indset.attrs['maxlat']\n if isotropic:\n print 'isotropic inversion results do not output gaussian std!'\n return\n dlon_HD = org_grp.attrs['dlon_HD']\n dlat_HD = org_grp.attrs['dlat_HD']\n dlon = org_grp.attrs['dlon']\n dlat = org_grp.attrs['dlat']\n if create_header:\n inv_header = {'minlon': minlon, 'maxlon': maxlon, 'minlat': minlat, 'maxlat': maxlat,\n 'dlon': dlon, 'dlat': dlat, 'dlon_HD': dlon_HD, 'dlat_HD': dlat_HD}\n self.add_auxiliary_data(data=np.array([]), data_type='Header', path='raytomo', parameters=inv_header)\n self._get_lon_lat_arr(path='raytomo', hd=True)\n for staid in stalst:\n netcode, stacode = staid.split('.')\n staid_aux = netcode+'_'+stacode\n stla, elev, stlo = self.waveforms[staid].coordinates.values()\n if stlo < 0.:\n stlo += 360.\n if stla > maxlat or stla < minlat or stlo > maxlon or stlo < minlon:\n print 'WARNING: station: '+ staid+', lat = '+str(stla)+' lon = '+str(stlo)+', out of the range of tomograpic maps!'\n continue\n disp_v = np.array([])\n disp_un = np.array([])\n T = np.array([])\n #-----------------------------\n # determine the indices\n #-----------------------------\n ind_lon = np.where(stlo<=self.lons)[0][0]\n find_lon = ind_lon \n ind_lat = np.where(stla<=self.lats)[0][0]\n find_lat = ind_lat\n # point 1\n distmin, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon]) # distance is in m\n # point 2\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon-1]) # distance is in m\n if dist < distmin:\n find_lon = ind_lon-1\n distmin = dist\n # point 3\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon]) # distance is in m\n if dist < distmin:\n find_lat = ind_lat-1\n distmin = dist\n # point 4\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon-1]) # distance is in m\n if dist < distmin:\n find_lat = ind_lat-1\n find_lon = ind_lon-1\n distmin = dist\n for per in pers:\n if per < Tmin or per > Tmax:\n continue\n try:\n pergrp = grp['%g_sec'%( per )]\n vel = pergrp['vel_iso_HD'].value\n vel_sem = pergrp['vel_sem_HD'].value\n except KeyError:\n if verbose:\n print 'No data for T = '+str(per)+' sec'\n continue\n T = np.append(T, per)\n disp_v = np.append(disp_v, vel[find_lat, find_lon])\n disp_un = np.append(disp_un, vel_sem[find_lat, find_lon])\n data = np.zeros((3, T.size))\n data[0, :] = T[:]\n data[1, :] = disp_v[:]\n data[2, :] = disp_un[:]\n disp_header = {'Np': T.size}\n self.add_auxiliary_data(data=data, data_type='RayDISPcurve', path=wtype+'/'+dtype+'/'+staid_aux, parameters=disp_header)\n indset.close()\n return","def make_svm_input_file(input_filename, output_custom_pars_file='custom_svm_params.json', clobber=False,\n log_level=logutil.logging.INFO):\n log.setLevel(log_level)\n if not clobber:\n if os.path.exists(output_custom_pars_file):\n msg = \"A file named '{}' already exists. Please choose a unique name for the custom SVM parameter file.\".format(output_custom_pars_file)\n log.critical(msg)\n sys.exit()\n # Define trailer file (log file) that will contain the log entries for all processing\n if isinstance(input_filename, str): # input file is a poller file -- easy case\n logname = input_filename.replace('.out', '_svm_partam_gen.log')\n\n else:\n logname = 'svm_param_gen.log'\n\n # Initialize total trailer filename as temp logname\n logging.basicConfig(filename=logname, format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)\n # start processing\n starting_dt = datetime.datetime.now()\n log.info(\"Run start time: {}\".format(str(starting_dt)))\n\n try:\n # Parse the poller file and generate the the obs_info_dict, as well as the total detection\n # product lists which contain the ExposureProduct, FilterProduct, and TotalProduct objects\n # A poller file contains visit data for a single instrument. The TotalProduct discriminant\n # is the detector. A TotalProduct object is comprised of FilterProducts and ExposureProducts\n # where its FilterProduct is distinguished by the filter in use, and the ExposureProduct\n # is the atomic exposure data.\n log.info(\"Parse the poller and determine what exposures need to be combined into separate products.\\n\")\n obs_info_dict, total_obj_list = poller_utils.interpret_obset_input(input_filename, log_level)\n\n # Update all of the product objects with their associated configuration information.\n for total_item in total_obj_list:\n log.info(\"Preparing configuration parameter values for total product {}\".format(total_item.drizzle_filename))\n total_item.configobj_pars = config_utils.HapConfig(total_item,\n log_level=log_level,\n output_custom_pars_file=output_custom_pars_file)\n for filter_item in total_item.fdp_list:\n log.info(\"Preparing configuration parameter values for filter product {}\".format(filter_item.drizzle_filename))\n filter_item.configobj_pars = config_utils.HapConfig(filter_item,\n log_level=log_level,\n output_custom_pars_file=output_custom_pars_file)\n update_ci_values(filter_item, output_custom_pars_file, log_level)\n\n for expo_item in total_item.edp_list:\n log.info(\"Preparing configuration parameter values for exposure product {}\".format(expo_item.drizzle_filename))\n expo_item.configobj_pars = config_utils.HapConfig(expo_item,\n log_level=log_level,\n output_custom_pars_file=output_custom_pars_file)\n # Housekeeping: remove those pesky renamed copies of the input flc.fits/flt.fits files\n # generated by drizzlepac.haputils.product()\n if expo_item.drizzle_filename.endswith(\"_drc.fits\"):\n file_to_remove = expo_item.drizzle_filename.replace(\"_drc.fits\", \"_flc.fits\")\n if expo_item.drizzle_filename.endswith(\"_drz.fits\"):\n file_to_remove = expo_item.drizzle_filename.replace(\"_drz.fits\", \"_flt.fits\")\n if os.path.exists(file_to_remove):\n os.remove(file_to_remove)\n except Exception:\n exc_type, exc_value, exc_tb = sys.exc_info()\n traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)\n err_msg = \"Something went wrong!\"\n log.error(err_msg)\n raise Exception(err_msg)","def from_srf_file(self, filename, normalize=False):\n with open(filename, \"rt\") as f:\n # go to POINTS block\n line = f.readline()\n while 'POINTS' not in line:\n line = f.readline()\n\n npoints = int(line.split()[1])\n sources = []\n\n for _ in np.arange(npoints):\n lon, lat, dep, stk, dip, area, tinit, dt = \\\n map(float, f.readline().split())\n rake, slip1, nt1, slip2, nt2, slip3, nt3 = \\\n map(float, f.readline().split())\n\n dep *= 1e3 # km > m\n area *= 1e-4 # cm^2 > m^2\n slip1 *= 1e-2 # cm > m\n slip2 *= 1e-2 # cm > m\n # slip3 *= 1e-2 # cm > m\n\n nt1, nt2, nt3 = map(int, (nt1, nt2, nt3))\n\n if nt1 > 0:\n line = f.readline()\n while len(line.split()) < nt1:\n line = line + f.readline()\n stf = np.array(line.split(), dtype=float)\n if normalize:\n stf /= np.trapz(stf, dx=dt)\n\n M0 = area * DEFAULT_MU * slip1\n\n sources.append(\n Source.from_strike_dip_rake(\n lat, lon, dep, stk, dip, rake, M0,\n time_shift=tinit, sliprate=stf, dt=dt))\n\n if nt2 > 0:\n line = f.readline()\n while len(line.split()) < nt2:\n line = line + f.readline()\n stf = np.array(line.split(), dtype=float)\n if normalize:\n stf /= np.trapz(stf, dx=dt)\n\n M0 = area * DEFAULT_MU * slip2\n\n sources.append(\n Source.from_strike_dip_rake(\n lat, lon, dep, stk, dip, rake, M0,\n time_shift=tinit, sliprate=stf, dt=dt))\n\n if nt3 > 0:\n raise NotImplementedError('Slip along u3 axis')\n\n return self(pointsources=sources)","def vaex_vertices_from_plyfile(filename):\n xyz = vertex_dict_from_plyfile(filename)\n return vx.from_dict(xyz)","def convert_rows_to_wv(direct_file, grism_file, rows):\n\n # Collect data from FITS headers\n with fits.open(grism_file) as hdu:\n hdr = hdu[0].header\n hdr1 = hdu[1].header\n sci_postarg_1 = hdr['POSTARG1']\n sci_postarg_2 = hdr['POSTARG2']\n sci_crpix_1 = hdr1['CRPIX1'] # this isn't a real keyword...\n sci_crpix_2 = hdr1['CRPIX2'] \n\n with fits.open(direct_file) as hdu:\n hdr = hdu[0].header\n hdr1 = hdu[1].header\n data = hdu[1].data\n cal_postarg_1 = hdr['POSTARG1']\n cal_postarg_2 = hdr['POSTARG2']\n cal_crpix_1 = hdr1['CRPIX1']\n cal_crpix_2 = hdr1['CRPIX2']\n\n\n # Find the central source\n mean, med, std = sigma_clipped_stats(data, sigma=3.0, iters=5)\n sources = daofind(data-med, fwhm=3.0, threshold=5.*std)\n \n source = sources[np.where(sources['flux'] == np.max(sources['flux']))]\n x_cen, y_cen = source['xcentroid'], source['ycentroid']\n\n\n # Calculate the offset\n x_offset = sci_crpix_1 - cal_crpix_1 + (sci_postarg_1 - cal_postarg_1)/0.135\n y_offset = sci_crpix_2 - cal_crpix_2 + (sci_postarg_2 - cal_postarg_2)/0.121\n\n pos_x, pos_y = x_cen + x_offset, y_cen + y_offset\n\n constants_0 = [8.95E3, 9.35925E-2, 0.0, 0.0, 0.0, 0.0]\n constants_1 = [4.51423E1, 3.17239E-4, 2.17055E-3, -7.42504E-7, 3.4863E-7, 3.09213E-7]\n\n coords_0 = constants_0[0] + constants_0[1]*pos_x + constants_0[2]*pos_y\n coords_1 = constants_1[0] + constants_1[1]*pos_x + constants_1[2]*pos_y + constants_1[3]*pos_x**2 + constants_1[4]*pos_x*pos_y + constants_1[5]*pos_y**2\n \n wv = coords_0 + coords_1*(rows-pos_x) + pos_y\n\n return wv","def cloud_optimize_inPlace(in_file:str,compress=\"LZW\") -> None:\n\t## add overviews to file\n\tcloudOpArgs = [\"gdaladdo\",in_file]\n\tsubprocess.call(cloudOpArgs)\n\n\t## copy file\n\tintermediate_file = in_file.replace(\".tif\",\".TEMP.tif\")\n\twith open(intermediate_file,'wb') as a:\n\t\twith open(in_file,'rb') as b:\n\t\t\tshutil.copyfileobj(b,a)\n\n\t## add tiling to file\n\tcloudOpArgs = [\"gdal_translate\",intermediate_file,in_file,'-q','-co', \"TILED=YES\",'-co',\"COPY_SRC_OVERVIEWS=YES\",'-co', f\"COMPRESS={compress}\"]#, \"-co\", \"PREDICTOR=2\"]\n\tsubprocess.call(cloudOpArgs)\n\n\t## remove intermediate\n\tos.remove(intermediate_file)","def vtkReformat(inPath, outPath):\n # Get size of map\n inFile = open(inPath,\"rb\")\n lineList = inFile.readlines()\n for line in lineList:\n if line.lower().strip().startswith(\"dimensions\"):\n size = map(int,line.split(\" \")[1:dimension+1])\n break\n inFile.close()\n\n if dimension == 2: size += [0]\n\n outFile = open(outPath,\"wb\")\n for (i,line) in enumerate(lineList):\n if i == 1:\n newline = line.lstrip(line.rstrip(\"\\n\"))\n line = \"lddmm 8 0 0 {0} {0} 0 0 {1} {1} 0 0 {2} {2}\".format(size[2]-1, size[1]-1, size[0]-1) + newline\n outFile.write(line)","def __init__(self, inputfile, mode=GA_ReadOnly):\n # Open the input file and read some metadata\n open(inputfile, 'r').close() # HACK: GDAL gives a useless exception\n if not isinstance(inputfile, bytes):\n inputfile = inputfile.encode('utf-8')\n try:\n # Since this is a SWIG object, clone the ``this`` pointer\n self.this = gdal.Open(inputfile, mode).this\n except RuntimeError as e:\n raise GdalError(str(e))\n\n # Shadow for metadata so we can overwrite it without saving\n # it to the original file.\n self._geotransform = None\n self._rastersizes = None","def read_velocities(filename, return_grid=True, return_proj=False):\n ds = gdal.Open(filename)\n #Get dimensions\n nc = ds.RasterXSize\n nr = ds.RasterYSize\n \n geotransform = ds.GetGeoTransform()\n xOrigin = geotransform[0]\n xPix = geotransform[1] #pixel width in x-direction\n yOrigin = geotransform[3]\n yPix = geotransform[5] #pixel height in y-direction\n \n lons = xOrigin + np.arange(0, nc)*xPix\n lats = yOrigin + np.arange(0, nr)*yPix\n \n x, y = np.meshgrid(lons, lats)\n \n vband = ds.GetRasterBand(1)\n varr = vband.ReadAsArray()\n \n if return_grid and return_proj:\n return x, y, varr, ds.GetProjection()\n elif return_grid:\n return x, y, varr\n else: \n return varr","def reproject_vector( path, epsg_from=None, epsg_to=None):\n\n if not epsg_to: raise Exception(\"please, specify the output EPSG codes\")\n\n inDataSet = None\n outDataSet = None\n inFeature = None\n outFeature = None\n outLayer = None\n\n try:\n\n driver = ogr.GetDriverByName('ESRI Shapefile')\n inDataSet = driver.Open(path, 0) # 0 means read-only\n\n # define input SpatialReference\n if not epsg_from:\n layer = inDataSet.GetLayer()\n inSpatialRef = layer.GetSpatialRef()\n else:\n inSpatialRef = osr.SpatialReference()\n inSpatialRef.ImportFromEPSG(epsg_from)\n\n # define output SpatialReference\n outSpatialRef = osr.SpatialReference()\n outSpatialRef.ImportFromEPSG(epsg_to)\n\n # create the CoordinateTransformation\n coordTrans = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)\n\n # get the first input layer and the geometry type\n inLayer = inDataSet.GetLayer()\n geotype = inLayer.GetGeomType()\n lname = inLayer.GetName()\n\n drv = ogr.GetDriverByName(\"ESRI Shapefile\")\n outDataSet = drv.CreateDataSource(\"/vsimem/memory.shp\")\n\n outLayer = outDataSet.CreateLayer(lname, srs=outSpatialRef, geom_type=geotype)\n\n # add fields\n inLayerDefn = inLayer.GetLayerDefn()\n\n for i in range(0, inLayerDefn.GetFieldCount()):\n fieldDefn = inLayerDefn.GetFieldDefn(i)\n outLayer.CreateField(fieldDefn)\n\n # get the output layer\"s feature definition\n outLayerDefn = outLayer.GetLayerDefn()\n\n counter = 1\n\n # loop through the input features\n inFeature = inLayer.GetNextFeature()\n while inFeature:\n # get the input geometry\n geom = inFeature.GetGeometryRef()\n # reproject the geometry\n geom.Transform(coordTrans)\n # create a new feature\n outFeature = ogr.Feature(outLayerDefn)\n # set the geometry and attribute\n outFeature.SetGeometry(geom)\n for i in range(0, outLayerDefn.GetFieldCount()):\n outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\n # add the feature to the shapefile\n outLayer.CreateFeature(outFeature)\n\n # destroy the features and get the next input feature\n if outFeature: outFeature = None\n inFeature = inLayer.GetNextFeature()\n\n counter += 1\n #print(counter)\n\n return outDataSet\n\n except RuntimeError as err:\n raise err\n except Exception as e:\n raise e\n\n finally:\n if inDataSet: outDataSet == None # give back control to C++\n if outDataSet: outDataSet == None\n if outLayer: outLayer == None\n if inFeature: inFeature == None\n if outFeature: outFeature = None","def saveVelocityAndPressureVTK_binary(pressure,u,v,w,x,y,z,filename,dims):\n numEl_size = u.size; numEl = np.prod(numEl_size);\n # open the file and write the ASCII header:\n file = open(filename,'w')\n file.write('# vtk DataFile Version 3.0\\n')\n file.write('VTK file for data post-processed with Python\\n')\n file.write('Binary\\n\\n')\n file.write('DATASET STRUCTURED_GRID\\n')\n file.write('DIMENSIONS %d %d %d \\n'%(dims[0],dims[1],dims[2]))\n file.write('POINTS %d float\\n'%(numEl))\n file.close()\n \n # append binary x,y,z data\n file = open(filename,'ab')\n for i in range(len(x)): # there really needs to be a better way.\n pt = [x[i],y[i],z[i]]\n pt_buf = array('f',pt)\n pt_buf.byteswap()\n file.write(pt_buf)\n \n \n file.close()\n \n # append an ASCII sub header\n file = open(filename,'a')\n file.write('POINT_DATA %d \\n'%numEl)\n file.write('VECTORS velocity_vectors float\\n')\n file.close()\n \n # append binary u,v,w data\n file = open(filename,'ab')\n for i in range(len(u)):\n pt = [u[i],v[i],w[i]]\n pt_buf = array('f',pt)\n pt_buf.byteswap()\n file.write(pt_buf)\n \n file.close()\n \n # append ASCII sub header for scalar velocity magnitude data\n file = open(filename,'a')\n file.write('SCALARS VelocityMagnitude float\\n')\n file.write('LOOKUP_TABLE default\\n')\n \n file.close()\n \n file = open(filename,'ab')\n v_mag = np.sqrt(u**2+v**2+w**2)\n file = open(filename,'ab')\n p_buf = array('f',v_mag); p_buf.byteswap()\n file.write(p_buf)\n file.close()\n \n \n # append another ASCII sub header for the scalar pressure data\n file = open(filename,'a')\n file.write('SCALARS Pressure float\\n')\n file.write('LOOKUP_TABLE default\\n')\n file.close()\n \n # append binary pressure data\n file = open(filename,'ab')\n p_buf = array('f',pressure); p_buf.byteswap()\n file.write(p_buf)\n file.close()","def cli(source_f, raster_f, output, verbose):\n with fiona.open(source_f, 'r') as source:\n source_driver = source.driver\n source_crs = source.crs\n sink_schema = source.schema.copy()\n\n source_geom = source.schema['geometry']\n if source_geom == 'Point':\n sink_schema['geometry'] = '3D Point'\n elif source_geom == 'LineString':\n sink_schema['geometry'] = '3D LineString'\n elif source_geom == '3D Point' or source_geom == '3D LineString':\n pass\n else:\n click.BadParameter(\"Source geometry type {} not implemented\".format(source_geom))\n\n with rasterio.open(raster_f) as raster:\n if source_crs != raster.crs:\n click.BadParameter(\"Features and raster have different CRS.\")\n if raster.count > 1:\n warnings.warn(\"Found {0} bands in {1}, expected a single band raster\".format(raster.bands, raster_f))\n supported = ['int16', 'int32', 'float32', 'float64']\n if raster.dtypes[0] not in supported:\n warnings.warn(\"Found {0} type in {1}, expected one of {2}\".format(raster.dtypes[0], raster_f, supported))\n with fiona.open(\n output, 'w',\n driver=source_driver,\n crs=source_crs,\n schema=sink_schema) as sink:\n\n for feature in source:\n try:\n feature_z = drapery.drape(raster, feature)\n sink.write({\n 'geometry': mapping(feature_z),\n 'properties': feature['properties'],\n })\n except Exception:\n logging.exception(\"Error processing feature %s:\", feature['id'])\n #print(sink.closed)\n #print(raster.closed)\n #print(source.closed)","def run(self, verbose=False):\n from utils import write_to_file # function to write json to file\n self.read_json()\n graph = self.parse_jsons()\n json = self.pipe_vl2vg(graph)\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)","def compute_templates(filename, TDUR, filt, ratios, dt, ncor, window, \\\n winlength, nattempts, waittime, method='RMS'):\n # To transform latitude and longitude into kilometers\n a = 6378.136\n e = 0.006694470\n lat0 = 41.0\n lon0 = -123.0\n dx = (pi / 180.0) * a * cos(lat0 * pi / 180.0) / sqrt(1.0 - e * e * \\\n sin(lat0 * pi / 180.0) * sin(lat0 * pi / 180.0))\n dy = (3.6 * pi / 648.0) * a * (1.0 - e * e) / ((1.0 - e * e * sin(lat0 * \\\n pi / 180.0) * sin(lat0 * pi / 180.0)) ** 1.5)\n\n # Get the names of the stations which have a waveform for this LFE family\n file = open('../data/Plourde_2015/detections/' + filename + \\\n '_detect5_cull.txt')\n first_line = file.readline().strip()\n staNames = first_line.split()\n file.close()\n\n # Get the time of LFE detections\n LFEtime = np.loadtxt('../data/Plourde_2015/detections/' + filename + \\\n '_detect5_cull.txt', \\\n dtype={'names': ('unknown', 'day', 'hour', 'second', 'threshold'), \\\n 'formats': (np.float, '|S6', np.int, np.float, np.float)}, \\\n skiprows=2)\n\n # Get the network, channels, and location of the stations\n staloc = pd.read_csv('../data/Plourde_2015/station_locations.txt', \\\n sep=r'\\s{1,}', header=None)\n staloc.columns = ['station', 'network', 'channels', 'location', \\\n 'server', 'latitude', 'longitude']\n\n # Get the location of the source of the LFE\n LFEloc = np.loadtxt('../data/Plourde_2015/templates_list.txt', \\\n dtype={'names': ('name', 'family', 'lat', 'lon', 'depth', 'eH', \\\n 'eZ', 'nb'), \\\n 'formats': ('S13', 'S3', np.float, np.float, np.float, \\\n np.float, np.float, np.int)}, \\\n skiprows=1)\n for ie in range(0, len(LFEloc)):\n if (filename == LFEloc[ie][0].decode('utf-8')):\n lats = LFEloc[ie][2]\n lons = LFEloc[ie][3]\n xs = dx * (lons - lon0)\n ys = dy * (lats - lat0)\n\n # Create directory to store the waveforms\n namedir = 'templates/' + filename\n if not os.path.exists(namedir):\n os.makedirs(namedir)\n\n # Read origin time and station slowness files\n origintime = pickle.load(open('timearrival/origintime.pkl', 'rb'))\n slowness = pickle.load(open('timearrival/slowness.pkl', 'rb'))\n\n # File to write error messages\n errorfile = 'error/' + filename + '.txt'\n\n # Loop over stations\n for station in staNames:\n # Create streams\n EW = Stream()\n NS = Stream()\n UD = Stream()\n # Get station metadata for downloading\n for ir in range(0, len(staloc)):\n if (station == staloc['station'][ir]):\n network = staloc['network'][ir]\n channels = staloc['channels'][ir]\n location = staloc['location'][ir]\n server = staloc['server'][ir]\n # Compute source-receiver distance\n latitude = staloc['latitude'][ir]\n longitude = staloc['longitude'][ir]\n xr = dx * (longitude - lon0)\n yr = dy * (latitude - lat0)\n distance = sqrt((xr - xs) ** 2.0 + (yr - ys) ** 2.0)\n # Loop on LFEs\n for i in range(0, np.shape(LFEtime)[0]):\n YMD = LFEtime[i][1]\n myYear = 2000 + int(YMD[0 : 2])\n myMonth = int(YMD[2 : 4])\n myDay = int(YMD[4 : 6])\n myHour = LFEtime[i][2] - 1\n myMinute = int(LFEtime[i][3] / 60.0)\n mySecond = int(LFEtime[i][3] - 60.0 * myMinute)\n myMicrosecond = int(1000000.0 * \\\n (LFEtime[i][3] - 60.0 * myMinute - mySecond))\n Tori = UTCDateTime(year=myYear, month=myMonth, day=myDay, \\\n hour=myHour, minute=myMinute, second=mySecond, \\\n microsecond=myMicrosecond)\n Tstart = Tori - TDUR\n Tend = Tori + 60.0 + TDUR\n # First case: we can get the data from IRIS\n if (server == 'IRIS'):\n (D, orientation) = get_from_IRIS(station, network, channels, \\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\n errorfile)\n # Second case: we get the data from NCEDC\n elif (server == 'NCEDC'):\n (D, orientation) = get_from_NCEDC(station, network, channels, \\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\n errorfile)\n else:\n raise ValueError( \\\n 'You can only download data from IRIS and NCEDC')\n if (type(D) == obspy.core.stream.Stream):\n # Add to stream\n if (channels == 'EH1,EH2,EHZ'):\n EW.append(D.select(channel='EH1').slice(Tori, \\\n Tori + 60.0)[0])\n NS.append(D.select(channel='EH2').slice(Tori, \\\n Tori + 60.0)[0])\n UD.append(D.select(channel='EHZ').slice(Tori, \\\n Tori + 60.0)[0])\n else:\n EW.append(D.select(component='E').slice(Tori, \\\n Tori + 60.0)[0])\n NS.append(D.select(component='N').slice(Tori, \\\n Tori + 60.0)[0])\n UD.append(D.select(component='Z').slice(Tori, \\\n Tori + 60.0)[0])\n else:\n print('Failed at downloading data')\n # Stack\n if (len(EW) > 0 and len(NS) > 0 and len(UD) > 0):\n # Stack waveforms\n EWstack = linstack([EW], normalize=True, method=method) \n NSstack = linstack([NS], normalize=True, method=method)\n UDstack = linstack([UD], normalize=True, method=method)\n # Initializations\n maxCC = np.zeros(len(EW))\n cc0EW = np.zeros(len(EW))\n cc0NS = np.zeros(len(EW))\n cc0UD = np.zeros(len(EW))\n if (window == True):\n # Get time arrival\n arrivaltime = origintime[filename] + \\\n slowness[station] * distance\n Tmin = arrivaltime - winlength / 2.0\n Tmax = arrivaltime + winlength / 2.0\n if Tmin < 0.0:\n Tmin = 0.0\n if Tmax > EWstack[0].stats.delta * (EWstack[0].stats.npts - 1):\n Tmax = EWstack[0].stats.delta * (EWstack[0].stats.npts - 1)\n ibegin = int(Tmin / EWstack[0].stats.delta)\n iend = int(Tmax / EWstack[0].stats.delta) + 1\n # Cross correlation\n for i in range(0, len(EW)):\n ccEW = correlate(EWstack[0].data[ibegin : iend], \\\n EW[i].data[ibegin : iend], ncor)\n ccNS = correlate(NSstack[0].data[ibegin : iend], \\\n NS[i].data[ibegin : iend], ncor)\n ccUD = correlate(UDstack[0].data[ibegin : iend], \\\n UD[i].data[ibegin : iend], ncor)\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\n cc0EW[i] = ccEW[ncor]\n cc0NS[i] = ccNS[ncor]\n cc0UD[i] = ccUD[ncor]\n else:\n # Cross correlation\n for i in range(0, len(EW)):\n ccEW = correlate(EWstack[0].data, EW[i].data, ncor)\n ccNS = correlate(NSstack[0].data, NS[i].data, ncor)\n ccUD = correlate(UDstack[0].data, UD[i].data, ncor)\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\n cc0EW[i] = ccEW[ncor]\n cc0NS[i] = ccNS[ncor]\n cc0UD[i] = ccUD[ncor]\n # Sort cross correlations\n index = np.flip(np.argsort(maxCC), axis=0)\n EWbest = Stream()\n NSbest = Stream()\n UDbest = Stream()\n # Compute stack of best LFEs\n for j in range(0, len(ratios)):\n nLFE = int(ratios[j] * len(EW) / 100.0)\n EWselect = Stream()\n NSselect = Stream()\n UDselect = Stream()\n for i in range(0, nLFE):\n EWselect.append(EW[index[i]])\n NSselect.append(NS[index[i]])\n UDselect.append(UD[index[i]])\n # Stack best LFEs\n EWbest.append(linstack([EWselect], normalize=True, \\\n method=method)[0])\n NSbest.append(linstack([NSselect], normalize=True, \\\n method=method)[0])\n UDbest.append(linstack([UDselect], normalize=True, \\\n method=method)[0])\n # Plot figure\n plt.figure(1, figsize=(20, 15))\n params = {'xtick.labelsize':16,\n 'ytick.labelsize':16}\n pylab.rcParams.update(params) \n colors = cm.rainbow(np.linspace(0, 1, len(ratios)))\n # East - West component\n ax1 = plt.subplot(311)\n dt = EWstack[0].stats.delta\n nt = EWstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = EWbest[j].data / np.sqrt(np.mean(np.square( \\\n EWbest[j].data)))\n elif (method == 'MAD'):\n norm = EWbest[j].data / np.median(np.abs(EWbest[j].data - \\\n np.median(EWbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = EWstack[0].data / np.sqrt(np.mean(np.square( \\\n EWstack[0].data)))\n elif (method == 'MAD'):\n norm = EWstack[0].data / np.median(np.abs(EWstack[0].data - \\\n np.median(EWstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('East - West component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # North - South component\n ax2 = plt.subplot(312)\n dt = NSstack[0].stats.delta\n nt = NSstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = NSbest[j].data / np.sqrt(np.mean(np.square( \\\n NSbest[j].data)))\n elif (method == 'MAD'):\n norm = NSbest[j].data / np.median(np.abs(NSbest[j].data - \\\n np.median(NSbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = NSstack[0].data / np.sqrt(np.mean(np.square( \\\n NSstack[0].data)))\n elif (method == 'MAD'):\n norm = NSstack[0].data / np.median(np.abs(NSstack[0].data - \\\n np.median(NSstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('North - South component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # Vertical component\n ax3 = plt.subplot(313)\n dt = UDstack[0].stats.delta\n nt = UDstack[0].stats.npts\n t = dt * np.arange(0, nt)\n for j in range(0, len(ratios)):\n if (method == 'RMS'):\n norm = UDbest[j].data / np.sqrt(np.mean(np.square( \\\n UDbest[j].data)))\n elif (method == 'MAD'):\n norm = UDbest[j].data / np.median(np.abs(UDbest[j].data - \\\n np.median(UDbest[j].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, color = colors[j], \\\n label = str(int(ratios[j])) + '%')\n if (method == 'RMS'):\n norm = UDstack[0].data / np.sqrt(np.mean(np.square( \\\n UDstack[0].data)))\n elif (method == 'MAD'):\n norm = UDstack[0].data / np.median(np.abs(UDstack[0].data - \\\n np.median(UDstack[0].data)))\n else:\n raise ValueError('Method must be RMS or MAD')\n norm = np.nan_to_num(norm)\n plt.plot(t, norm, 'k', label='All')\n if (window == True):\n plt.axvline(Tmin, linewidth=2, color='grey')\n plt.axvline(Tmax, linewidth=2, color='grey')\n plt.xlim([np.min(t), np.max(t)])\n plt.title('Vertical component', fontsize=24)\n plt.xlabel('Time (s)', fontsize=24)\n plt.legend(loc=1)\n # End figure\n plt.suptitle(station, fontsize=24)\n plt.savefig(namedir + '/' + station + '.eps', format='eps')\n ax1.clear()\n ax2.clear()\n ax3.clear()\n plt.close(1)\n # Save stacks into files\n savename = namedir + '/' + station +'.pkl'\n pickle.dump([EWstack[0], NSstack[0], UDstack[0]], \\\n open(savename, 'wb'))\n for j in range(0, len(ratios)):\n savename = namedir + '/' + station + '_' + \\\n str(int(ratios[j])) + '.pkl'\n pickle.dump([EWbest[j], NSbest[j], UDbest[j]], \\\n open(savename, 'wb'))\n # Save cross correlations into files\n savename = namedir + '/' + station + '_cc.pkl'\n pickle.dump([cc0EW, cc0NS, cc0UD], \\\n open(savename, 'wb'))","def create_dat_from_shapefile(windgrid_file, DAT_header, output_file, wind_field='Vg_mph'):\n t0 = time()\n output_file = output_file + '.dat'\n print('reading windgrid')\n t1 = time()\n gdf = gpd.read_file(windgrid_file)\n create_dat_from_geodataframe(\n gdf, DAT_header, output_file, wind_field=wind_field)","def load_viewpoint(viewpoint_file):\n with open(viewpoint_file) as viewpoints:\n for line in viewpoints.readlines():\n yield VP(*line.strip().split())","def _get_std_vmr_file(std_vmr_file):\n if std_vmr_file is None:\n gggpath = os.getenv('GGGPATH')\n if gggpath is None:\n raise GGGPathError('GGGPATH environmental variable is not defined. Either define it, explicitly pass a '\n 'path to a .vmr file with northern midlat profiles for all gases, or pass False to '\n 'only write the primary gases to the .vmr file')\n std_vmr_file = os.path.join(gggpath, 'vmrs', 'gnd', 'summer_35N.vmr')\n if not os.path.isfile(std_vmr_file):\n raise GGGPathError('The standard .vmr file is not present in the expected location ({}). Your GGGPATH '\n 'environmental variable may be incorrect, or the structure of the GGG directory has '\n 'changed. Either correct your GGGPATH value, explicitly pass a path to a .vmr file with '\n 'northern midlat profiles for all gases, or pass False to only write the primary gases '\n 'to the .vmr file'.format(std_vmr_file))\n return std_vmr_file\n else:\n return std_vmr_file","def _create_farm_result_vector(\n base_vector_path, target_vector_path):\n base_vector = gdal.OpenEx(base_vector_path, gdal.OF_VECTOR)\n\n driver = gdal.GetDriverByName('ESRI Shapefile')\n target_vector = driver.CreateCopy(\n target_vector_path, base_vector)\n target_layer = target_vector.GetLayer()\n\n farm_pollinator_abundance_defn = ogr.FieldDefn(\n _POLLINATOR_ABUNDANCE_FARM_FIELD_ID, ogr.OFTReal)\n farm_pollinator_abundance_defn.SetWidth(25)\n farm_pollinator_abundance_defn.SetPrecision(11)\n target_layer.CreateField(farm_pollinator_abundance_defn)\n\n total_farm_yield_field_defn = ogr.FieldDefn(\n _TOTAL_FARM_YIELD_FIELD_ID, ogr.OFTReal)\n total_farm_yield_field_defn.SetWidth(25)\n total_farm_yield_field_defn.SetPrecision(11)\n target_layer.CreateField(total_farm_yield_field_defn)\n\n pol_proportion_farm_yield_field_defn = ogr.FieldDefn(\n _POLLINATOR_PROPORTION_FARM_YIELD_FIELD_ID, ogr.OFTReal)\n pol_proportion_farm_yield_field_defn.SetWidth(25)\n pol_proportion_farm_yield_field_defn.SetPrecision(11)\n target_layer.CreateField(pol_proportion_farm_yield_field_defn)\n\n wild_pol_farm_yield_field_defn = ogr.FieldDefn(\n _WILD_POLLINATOR_FARM_YIELD_FIELD_ID, ogr.OFTReal)\n wild_pol_farm_yield_field_defn.SetWidth(25)\n wild_pol_farm_yield_field_defn.SetPrecision(11)\n target_layer.CreateField(wild_pol_farm_yield_field_defn)\n\n target_layer = None\n target_vector.FlushCache()\n target_vector = None","def read_vector_file(fname):\n return np.genfromtxt(fname)","def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\n args = [\n \"gdal_calc.py\",\n '-A', infile,\n '--outfile={}'.format(tmpfile_1),\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\n '--type=Byte', '--NoDataValue=0',\n '--co=SPARSE_OK=YES',\n '--co=NBITS=1',\n '--quiet'\n # Could enable compression\n # --co=\"COMPRESS=LZW\"\n ]\n subprocess.run(args)\n\n subprocess.run([\n \"gdal_polygonize.py\",\n tmpfile_1,\n '-q',\n '-f', 'ESRI Shapefile',\n tmpfile_2\n ])\n\n subprocess.run([\n \"ogr2ogr\",\n '-a_srs', 'EPSG:4326',\n outfile,\n tmpfile_2\n ])\n\n subprocess.run([\"rm\", tmpfile_1])\n subprocess.run([\"rm\", tmpfile_2])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'shx')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'dbf')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'prj')])","def generate_ROI_file(FreeSurfer_ROI_file):\n\tfrom nipype.interfaces.freesurfer import MRIConvert\n\tmc = MRIConvert()\n\tmc.inputs.in_file = FreeSurfer_ROI_file\n\tmc.inputs.out_type = 'niigz'\n\tmc.run()\n\n\timport nipype.interfaces.cmtk as cmtk\n\trg = cmtk.ROIGen()\n\trg.inputs.aparc_aseg_file = FreeSurfer_ROI_file.split('.')[0] + '_out.nii.gz'\n\trg.inputs.use_freesurfer_LUT = True\n\tout_file = rg.run()\n\n\treturn out_file","def read_DEM(fn=None, fjord=None):\n # intake.open_rasterio accepts a list of input files and may effectively do what this function does!\n # try using cropped versions of the input files. Doesn't seem to make a difference r.e. crashing\n '''\n cropped_fn = fn.rpartition(\".tif\")[0] + \"_cropped.tif\"\n print(cropped_fn)\n if os._exists(cropped_fn):\n fn = cropped_fn\n elif fjord != None:\n bbox = fjord_props.get_fjord_bounds(fjord)\n ds = rioxarray.open_rasterio(fn)\n trimmed_ds = ds.rio.slice_xy(*bbox)\n trimmed_ds.rio.to_raster(fn.rpartition(\".tif\")[0] + \"_cropped.tif\")\n del ds\n del trimmed_ds\n fn = cropped_fn \n '''\n\n # try bringing in the rasters as virtual rasters (i.e. lazily loading)\n with rasterio.open(fn) as src:\n # print('Source CRS:' +str(src.crs))\n # print(src.is_tiled)\n # print(src.block_shapes)\n with WarpedVRT(src,src_crs=src.crs,crs=src.crs) as vrt:\n # warp_mem_limit=12000,warp_extras={'NUM_THREADS':2}) as vrt:\n # print('Destination CRS:' +str(vrt.crs))\n darr = xr.open_rasterio(vrt)\n # ds = rioxarray.open_rasterio(vrt).chunk({'x':1500,'y':1500,'band':1}).to_dataset(name='HLS_Red')\n\n\n # Rasterio automatically checks that the file exists\n # ultimately switch to using rioxarray, but it causes issues down the pipeline so it will need to be debugged through\n # with rioxarray.open_rasterio(fn) as src:\n # with xr.open_rasterio(fn) as darr:\n # darr = src\n\n # open_rasterio automatically brings the geotiff in as a DataArray with 'band' as a dimensional coordinate\n # we rename it and remove the band as a coordinate, since our DEM only has one dimension\n # squeeze removes dimensions of length 0 or 1, in this case our 'band'\n # Then, drop('band') actually removes the 'band' dimension from the Dataset\n darr = darr.rename('elevation').squeeze().drop_vars('band')\n # darr = darr.rename({'band':'dtime'})\n \n # if we wanted to instead convert it to a dataset\n # attr = darr.attrs\n # darr = darr.to_dataset(name='elevation').squeeze().drop('band')\n # darr.attrs = attr\n # attr=None\n # newest version of xarray (0.16) has promote_attrs=True kwarg. Earlier versions don't...\n # darr = darr.to_dataset(name='elevation', promote_attrs=True).squeeze().drop('band')\n\n # mask out the nodata values, since the nodatavals attribute is wrong\n darr = darr.where(darr != -9999.)\n\n # the gdalwarp geoid files have this extra attribute in the geoTiff, which when brought in\n # ultimately causes a \"__module__\" related error when trying to plot with hvplot\n try:\n del darr.attrs[\"units\"] \n except KeyError:\n pass\n\n if fjord != None:\n # USE RIOXARRAY - specifically, slicexy() which can be fed the bounding box\n # darr = darr.rio.slice_xy(fjord_props.get_fjord_bounds(fjord))\n bbox = fjord_props.get_fjord_bounds(fjord)\n if pd.Series(darr.y).is_monotonic_increasing:\n darr = darr.sel(x=slice(bbox[0], bbox[2]), y=slice(bbox[1], bbox[3]))\n else:\n darr = darr.sel(x=slice(bbox[0], bbox[2]), y=slice(bbox[3], bbox[1]))\n \n return darr","def write_input(self):\n # load template, substitute parameters and write input file\n self.homog_core()\n self.write_mat_string()\n input_tmpl = open('base_input.txt')\n templ = Template(input_tmpl.read())\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\n r_core = self.r,\n core_z = self.z,\n r_refl = self.r + self.refl_t,\n refl_min = -self.refl_t,\n refl_max = self.z + self.refl_t,\n fuel_string = self.fuel_string,\n fuel_rho = self.rho,\n fuel_vol = self.core_vol,\n refl_vol = self.core_vol,\n thermal_power = self.Q_therm)\n # write the file\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \n round(self.r, 3))\n ifile = open(filename, 'w')\n ifile.write(file_string)\n ifile.close()\n\n return filename","def align_rasters(ref_raster, tar_raster, output_suffix):\n command = [\"gdalbuildvrt\", \"-te\"]\n hDataset = gdal.Open(ref_raster, gdal.GA_ReadOnly)\n if hDataset is None:\n return False\n adfGeoTransform = hDataset.GetGeoTransform(can_return_null=True)\n\n tif_file=tar_raster\n vrt_file = tif_file.replace('.tif', '.vrt')\n\n if adfGeoTransform is not None:\n dfGeoXUL = adfGeoTransform[0]\n dfGeoYUL = adfGeoTransform[3]\n dfGeoXLR = adfGeoTransform[0] + adfGeoTransform[1] * hDataset.RasterXSize + \\\n adfGeoTransform[2] * hDataset.RasterYSize\n dfGeoYLR = adfGeoTransform[3] + adfGeoTransform[4] * hDataset.RasterXSize + \\\n adfGeoTransform[5] * hDataset.RasterYSize\n xres = str(abs(adfGeoTransform[1]))\n yres = str(abs(adfGeoTransform[5]))\n\n subprocess.call(command + [str(dfGeoXUL), str(dfGeoYLR), str(dfGeoXLR),\n str(dfGeoYUL), \"-q\", \"-tr\", xres, yres,\n vrt_file, tif_file])\n\n output_file = tif_file.replace('.tif', output_suffix)\n\n print('gdal_translate -q {} {}'.format(vrt_file, output_file))\n\n cmd = 'gdal_translate -q {} {}'.format(vrt_file, output_file)\n\n #print(dfGeoXUL, dfGeoYLR, dfGeoXLR, dfGeoYUL, xres, yres)\n\n subprocess.call(cmd, shell=True)\n os.remove(vrt_file)\n\n return True\n\n else:\n\n return False","def exec_ripser(data_path,output_path,max_dim,input_file='input.txt',format_file = 'lower-distance',threshold=None):\n ############# RIPSER ####################\n # high dimension\n ## execfile ripser (OUTPUT from ripser)\n start = timeit.default_timer() \n print 'input_file ',input_file\n input_file_full = os.path.join(data_path,input_file)\n output_file_full = os.path.join(output_path,'output_ripser.txt')\n\n if threshold is None:\n ripser_arguments = 'ripser --format %s --dim %i %s'%(format_file,max_dim,input_file_full)\n else:\n ripser_arguments = 'ripser --format %s --dim %i --threshold %f %s'%(format_file,max_dim,threshold,input_file_full)\n \n ripser_call(ripser_arguments.split(' '),output_file_full)\n #os.system(ripser_call) # OLD CALL BASED ON executable\n\n stop = timeit.default_timer()\n print 'Ripser execution time '\n print stop - start \n input_file_path = os.path.join(data_path,'input.txt')\n if(os.path.isfile(input_file_path)):\n os.remove(input_file_path) ## remove auxiliar file with lower matrix used as input for Ripser\n return()","def process_input_file(filename):\n f = open(filename, 'r')\n\n rows = []\n i = 0\n for line in f:\n # skip optimal steps and time limit\n if i > 1 and len(line.strip()) > 0:\n rows.append(list(line.strip()))\n i += 1\n\n f.close()\n\n row_len = len(rows[0])\n num_rows = len(rows)\n\n return LaserTankMap(row_len, num_rows, rows)","def main(fn_input, fn_output):\n # read file\n inter = Interpolator()\n inter.read_file(fn_input)\n inter.write_interpolated(fn_output)","def convert(input, output, point_format_id, file_version, force):\n if (\n point_format_id is not None\n and point_format_id not in pylas.supported_point_formats()\n ):\n click.echo(\n click.style(\n \"Point format {} is not supported\".format(point_format_id), fg=\"red\"\n )\n )\n raise click.Abort()\n\n if file_version is not None and file_version not in pylas.supported_versions():\n click.echo(\n click.style(\n \"LAS version {} is not supported\".format(file_version), fg=\"red\"\n )\n )\n raise click.Abort()\n\n las = pylas.read(openbin_file(input))\n if point_format_id is not None and not force:\n lost_dimensions = pylas.lost_dimensions(\n las.points_data.point_format.id, point_format_id\n )\n if lost_dimensions:\n click.echo(\"Converting will lose: {}\".format(lost_dimensions))\n click.confirm(\"Continue ?\", abort=True)\n\n try:\n las = pylas.convert(\n las, point_format_id=point_format_id, file_version=file_version\n )\n except pylas.errors.PylasError as e:\n click.echo(click.style(\"{}: {}\".format(e.__class__.__name__, e), fg=\"red\"))\n raise click.Abort()\n except Exception as e:\n click.echo(click.style(str(e), fg=\"red\"))\n raise click.Abort()\n else:\n las.write(openbin_file(output, mode='w'), do_compress=output.endswith('.laz'))","def build_r_map(input_file: str, output_file: str, threshold: float):\n\n DataSiPM = db.DataSiPMsim_only('petalo', 0) # full body PET\n DataSiPM_idx = DataSiPM.set_index('SensorID')\n\n try:\n sns_response = pd.read_hdf(input_file, 'MC/sns_response')\n except ValueError:\n print(f'File {input_file} not found')\n exit()\n except OSError:\n print(f'File {input_file} not found')\n exit()\n except KeyError:\n print(f'No object named MC/sns_response in file {input_file}')\n exit()\n print(f'Analyzing file {input_file}')\n\n sel_df = rf.find_SiPMs_over_threshold(sns_response, threshold)\n\n particles = pd.read_hdf(input_file, 'MC/particles')\n hits = pd.read_hdf(input_file, 'MC/hits')\n events = particles.event_id.unique()\n\n true_r1, true_r2 = [], []\n var_phi1, var_phi2 = [], []\n var_z1, var_z2 = [], []\n\n touched_sipms1, touched_sipms2 = [], []\n\n for evt in events:\n\n ### Select photoelectric events only\n evt_parts = particles[particles.event_id == evt]\n evt_hits = hits [hits .event_id == evt]\n select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)\n if not select: continue\n\n sns_resp = sel_df[sel_df.event_id == evt]\n if len(sns_resp) == 0: continue\n\n _, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(sns_resp, true_pos, DataSiPM_idx)\n\n if len(pos1) > 0:\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:,1]\n _, var_phi = rf.phi_mean_var(pos_phi, q1)\n\n pos_z = np.array(pos1)[:,2]\n mean_z = np.average(pos_z, weights=q1)\n var_z = np.average((pos_z-mean_z)**2, weights=q1)\n r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)\n\n var_phi1 .append(var_phi)\n var_z1 .append(var_z)\n touched_sipms1.append(len(pos1))\n true_r1 .append(r)\n\n else:\n var_phi1 .append(1.e9)\n var_z1 .append(1.e9)\n touched_sipms1.append(1.e9)\n true_r1 .append(1.e9)\n\n if len(pos2) > 0:\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos2))[:,1]\n _, var_phi = rf.phi_mean_var(pos_phi, q2)\n\n pos_z = np.array(pos2)[:,2]\n mean_z = np.average(pos_z, weights=q2)\n var_z = np.average((pos_z-mean_z)**2, weights=q2)\n r = np.sqrt(true_pos[1][0]**2 + true_pos[1][1]**2)\n\n var_phi2 .append(var_phi)\n var_z2 .append(var_z)\n touched_sipms2.append(len(pos2))\n true_r2 .append(r)\n\n else:\n var_phi2 .append(1.e9)\n var_z2 .append(1.e9)\n touched_sipms2.append(1.e9)\n true_r2 .append(1.e9)\n\n a_true_r1 = np.array(true_r1)\n a_true_r2 = np.array(true_r2)\n a_var_phi1 = np.array(var_phi1)\n a_var_phi2 = np.array(var_phi2)\n a_var_z1 = np.array(var_z1)\n a_var_z2 = np.array(var_z2)\n\n a_touched_sipms1 = np.array(touched_sipms1)\n a_touched_sipms2 = np.array(touched_sipms2)\n\n\n np.savez(output_file, a_true_r1=a_true_r1, a_true_r2=a_true_r2, a_var_phi1=a_var_phi1, a_var_phi2=a_var_phi2, a_var_z1=a_var_z1, a_var_z2=a_var_z2, a_touched_sipms1=a_touched_sipms1, a_touched_sipms2=a_touched_sipms2)","def make_big_vdwradii( targetpath ):\n\n file = open( os.path.join( targetpath, 'vdwradii.dat'), 'w')\n text=\"\"\"; Very approximate VanderWaals radii\n; only used for drawing atoms as balls or for calculating atomic overlap.\n; longest matches are used\n; '???' or '*' matches any residue name\n; 'AAA' matches any protein residue name\n; MODIFIED TO USE BIG VDW RADII TO PREVENT WATERS BEING PUT IN THE PROTEIN WHERE WE DON'T WANT THEM. DLM\n??? C 0.3\n??? F 0.3\n??? H 0.3\n??? N 0.3\n??? O 0.3\n??? P 0.3\n??? S 0.3\n??? LP1 0\n??? LP2 0\nSOL H 0.04\nSOL O 0.105\nWAT H 0.04\nWAT O 0.105\nGLY MN1 0\nGLY MN2 0\nALA MCB1 0\nALA MCB2 0 \nVAL MCG1 0 \nVAL MCG2 0 \nILE MCG1 0 \nILE MCG2 0 \nILE MCD1 0 \nILE MCD2 0 \nLEU MCD1 0 \nLEU MCD2 0 \nMET MCE1 0 \nMET MCE2 0 \nTRP MTRP1 0 \nTRP MTRP2 0\nTHR MCG1 0\nTHR MCG2 0\nLYSH MNZ1 0 \nLYSH MNZ2 0 \n\"\"\" \n file.writelines(text)\n file.close()","def filter_ground(jparams):\n\n # load las file and relevant parameters\n point_cloud = File(jparams['input-las'], mode='r')\n scale = point_cloud.header.scale[0]\n print(point_cloud.header.min)\n print('- Flattening point cloud')\n gridded_pc = point_cloud_to_grid(point_cloud=point_cloud, tf=jparams['thinning-factor'],\n cell_size=int(jparams['gf-cellsize'] / scale))\n\n ground_points, unprocessed_points, ll_origin = gridded_pc[0], gridded_pc[1], gridded_pc[2]\n\n print('- Growing terrain')\n dt = startin.DT()\n dt.insert(list(ground_points))\n dt = grow_terrain(tin=dt, p=unprocessed_points, gp=ground_points,\n max_distance=int(jparams['gf-distance'] / scale),\n max_angle=jparams['gf-angle'])\n\n print('- Writing point cloud')\n with File(jparams['output-las'], mode='w', header=point_cloud.header) as out_file:\n gp = dt.all_vertices()[1:]\n out_file.X = [p[0] for p in gp]\n out_file.Y = [p[1] for p in gp]\n out_file.Z = [p[2] for p in gp]\n\n print('- Creating raster (TIN)\\n\\t- Interpolating (TIN)')\n dg = tin_interp(tin=dt, cell_size=int(jparams['grid-cellsize'] / scale))\n\n print('\\t- Writing Esri Ascii (TIN)')\n write_asc(grid=np.rot90(dg[0]) * scale + point_cloud.header.min[2],\n cell_size=jparams['grid-cellsize'],\n fn=jparams['output-grid-tin'],\n origin=(point_cloud.header.min[0]+dg[1][0]*scale, point_cloud.header.min[1] + dg[1][1]*scale),\n depth=2)\n\n print('- Creating raster (IDW)\\n\\t- Interpolating (IDW)')\n ig = idw_interp(tin=dt, cell_size=int(jparams['grid-cellsize'] / scale),\n radius=jparams['idw-radius'] / scale, \n power=jparams['idw-power'])\n\n print('\\t- Writing Esri Ascii (IDW)')\n write_asc(grid=np.rot90(ig[0]) * scale + point_cloud.header.min[2],\n cell_size=jparams['grid-cellsize'],\n fn=jparams['output-grid-idw'],\n origin=(point_cloud.header.min[0]+ig[1][0]*scale, point_cloud.header.min[1]+ig[1][1]*scale),\n depth=2)\n\n return","def process_radia_vcf(job, radia_vcf, work_dir, univ_options):\n radia_vcf = job.fileStore.readGlobalFile(radia_vcf)\n with open(radia_vcf, 'r') as infile, open(radia_vcf + 'radia_parsed.tmp', 'w') as outfile:\n # The columns in INFILE are\n # [0] CHROM\n # [1] POS\n # [2] ID\n # [3] REF\n # [4] ALT\n # [5] QUAL\n # [6] FILTER\n # [7] INFO\n # [8] FORMAT\n # [9] DNA_NORMAL\n # [10] DNA_TUMOR\n # [11] RNA_TUMOR - Not always present\n for line in infile:\n # Print header to outfile\n if line.startswith('#'):\n print(line.strip(), file=outfile)\n continue\n line = line.strip().split('\\t')\n # If the call was not PASSing, or if the call was germline: skip\n if line[6] != 'PASS' or 'MT=GERM' in line[7]:\n continue\n # If there is just 1 ALT allele, print and continue\n if len(line[4]) == 1:\n print('\\t'.join(line), file=outfile)\n # If not, process\n else:\n seq_field_indeces = [9, 10]\n alleles = [line[3]] + line[4].split(',') # all alleles, incl. REF\n # collect tumor, normal and (if present) rna AD and AFs\n # AD = Depth of reads supporting each allele\n # AF = Fraction of reads supporting each allele\n # normal_ad = line[9].split(':')[5].split(',')\n normal_af = line[9].split(':')[6].split(',')\n tumor_ad = line[10].split(':')[5].split(',')\n tumor_af = line[10].split(':')[6].split(',')\n if len(line[11]) > 1:\n rna_ad = line[11].split(':')[5].split(',')\n rna_af = line[11].split(':')[6].split(',')\n seq_field_indeces += [11] # append rna since it is present\n else:\n # If rna is missing, set RNA_AD and RNA_AF to null sets for easily\n # integrating into the logic in the following code\n rna_ad = rna_af = [0, 0, 0, 0]\n # Initialise variables to store the probable ALT alleles and the index values of\n # the same wrt AD and AF\n out_alleles = set([])\n out_af_ad_index = {0}\n # parse AD and AF to get most probable ALT alleles\n for i in range(1, len(normal_af)):\n # Criteria for selection = AD > 4 and AF >0.1 in either tumor or RNA, given\n # normal AF < 0.1\n if ((float(tumor_af[i]) >= 0.1 and int(tumor_ad[i]) >= 4) or\n (float(rna_af[i]) >= 0.1 and int(rna_ad[i]) >= 4)) and \\\n (float(normal_af[i]) < 0.1):\n out_alleles.add(alleles[i])\n out_af_ad_index.add(i)\n # If the number of probable alleles is greater than 0 the print to outfile with\n # the modified allele fraction representing reads corrresponding to all alleles\n if len(out_alleles) > 0:\n line[4] = ','.join(out_alleles) # set alt alleles\n # Modify the AD and AF values in the TUMOR/NORMAL/RNA fields\n # one at a time. Seq fields contain\n # [0] GT* - Genotype\n # [1] DP - Read depth at this position in the sample\n # [2] INDEL - Number of indels\n # [3] START - Number of reads starting at this position\n # [4] STOP - Number of reads stopping at this position\n # [5] AD* - Depth of reads supporting alleles\n # [6] AF* - Fraction of reads supporting alleles\n # [7] BQ* - Avg base quality for reads supporting alleles\n # [8] SB* - Strand Bias for reads supporting alleles\n # Fields marked with *s are teh ones that contain info for each seq field\n # and need to be modified\n for seq_field_index in seq_field_indeces:\n # Get the details for seq_field\n deets = line[seq_field_index].split(':')\n # modify fields 5 thu 8 to hold only info for the probable\n # alleles\n for field_index in range(5, 9):\n field = deets[field_index].split(\",\")\n deets[field_index] = \",\".join([x for i, x in enumerate(field)\n if i in out_af_ad_index])\n # Modify DP to hold the new total of reads\n deets[1] = str(sum([int(x) for x in deets[5].split(\",\")]))\n # get the most likely genotypes based on AD and AF\n gt_by_ad = set([i for i, x in enumerate(deets[5].split(\",\"))\n if int(x) >= 4])\n gt_by_af = set([i for i, x in enumerate(deets[6].split(\",\"))\n if float(x) >= 0.1])\n # Get the consensus genotype\n genotype = gt_by_ad.intersection(gt_by_af)\n if len(genotype) == 0:\n deets[0] = \"0/0\"\n elif len(genotype) == 1:\n deets[0] = \"/\".join([str(x) for x in genotype] +\n [str(x) for x in genotype])\n elif len(genotype) == 2:\n deets[0] = \"/\".join([str(x) for x in genotype])\n else:\n print(\"ERROR : triple genotype detected\", file=sys.stderr)\n print(line, file=sys.stdout)\n # Rejoin the details line\n line[seq_field_index] = \":\".join(deets)\n # Print the modified line to output\n print(\"\\t\".join(line), file=outfile)\n # Else do nothing\n else:\n pass\n return outfile.name","def loadGeoTransform(filepath):\n \n from osgeo import gdal\n \n ds = gdal.Open(filepath, 0)\n \n return ds.GetGeoTransform()","def generate_resfile_from_pdb(pdbfilename, resfilename, input_sc = True ):\n\tp = rosetta.core.import_pose.pose_from_file(pdbfilename)\n\tgenerate_resfile_from_pose(p, resfilename, input_sc)","def postTTUWRFanalysis(inpath, outpath,\n refpath='/lustre/research/bancell/aucolema/HWT2016runs/2016050800/wrfoutREF'):\n # Pull analysis variables\n og_analysis = Dataset(inpath)\n anlvars = og_analysis.variables['T'][0]\n gph300 = anlvars[0,:,:]\n gph500 = anlvars[1,:,:]\n gph700 = anlvars[2,:,:]\n gph850 = anlvars[3,:,:]\n gph925 = anlvars[4,:,:]\n temp300 = anlvars[5,:,:]\n temp500 = anlvars[6,:,:]\n temp700 = anlvars[7,:,:]\n temp850 = anlvars[8,:,:]\n temp925 = anlvars[9,:,:]\n u300 = anlvars[10,:,:]\n u500 = anlvars[11,:,:]\n u700 = anlvars[12,:,:]\n u850 = anlvars[13,:,:]\n u925 = anlvars[14,:,:]\n v300 = anlvars[15,:,:]\n v500 = anlvars[16,:,:]\n v700 = anlvars[17,:,:]\n v850 = anlvars[18,:,:]\n v925 = anlvars[19,:,:]\n td300 = anlvars[20,:,:]\n td500 = anlvars[21,:,:]\n td700 = anlvars[22,:,:]\n td850 = anlvars[23,:,:]\n td925 = anlvars[24,:,:]\n q300 = anlvars[25,:,:]\n q500 = anlvars[26,:,:]\n q700 = anlvars[27,:,:]\n q850 = anlvars[28,:,:]\n q925 = anlvars[29,:,:]\n slp = anlvars[30,:,:]\n t2 = anlvars[31,:,:]\n td2 = anlvars[32,:,:]\n u10 = anlvars[33,:,:]\n v10 = anlvars[34,:,:]\n\n wrf_d1 = Dataset(refpath)\n lons, lats = wrf_d1.variables['XLONG'][0], wrf_d1.variables['XLAT'][0]\n wrf_idim = len(lons[0,:])\n wrf_jdim = len(lats[:,0])\n\n sensvarlist = [gph300,gph500,gph700,gph850,gph925,temp300,temp500,temp700,\n temp850,temp925,u300,u500,u700,u850,u925,v300,\n v500,v700,v850,v925,td300,td500,td700,td850,\n td925,q300,q500,q700,q850,q925,slp,t2,td2,u10,v10]\n sensstringslist = [\"300 hPa GPH\",\"500 hPa GPH\",\"700 hPa GPH\",\n \"850 hPa GPH\",\"925 hPa GPH\",\"300 hPa T\",\"500 hPa T\",\n \"700 hPa T\",\"850 hPa T\",\"925 hPa T\",\"300 hPa U-Wind\",\n \"500 hPa U-Wind\",\"700 hPa U-Wind\",\"850 hPa U-Wind\",\n \"925 hPa U-Wind\",\"300 hPa V-Wind\",\"500 hPa V-Wind\",\n \"700 hPa V-Wind\",\"850 hPa V-Wind\",\"925 hPa V-Wind\",\n \"300 hPa Dewpt\", \"500 hPa Dewpt\", \"700 hPa Dewpt\",\n \"850 hPa Dewpt\", \"925 hPa Dewpt\", \"300 hPa Q\",\n \"500 hPa Q\", \"700 hPa Q\", \"850 hPa Q\", \"925 hPa Q\",\n \"SLP\",\"2m Temp\",\"2m Dewpt\",\n \"10m U-Wind\",\"10m V-Wind\"]\n\n\n # Write interpolated variables to netCDF\n new_analysis = Dataset(outpath, \"w\", format=\"NETCDF4\")\n new_analysis.createDimension('lat', wrf_jdim)\n new_analysis.createDimension('lon', wrf_idim)\n new_analysis.createDimension('time', None)\n xlat = new_analysis.createVariable(\"XLAT\", float, dimensions=('lat','lon'))\n xlat[:,:] = lats\n xlon = new_analysis.createVariable(\"XLONG\", float, dimensions=('lat','lon'))\n xlon[:,:] = lons\n\n # Interpolate and save!!\n for i in range(len(sensvarlist)):\n var = new_analysis.createVariable(sensstringslist[i].replace(\" \",\"_\"),\n sensvarlist[i].dtype,\n dimensions=('lat','lon'))\n var[:,:] = sensvarlist[i]\n new_analysis.close()\n return","def postTTUWRFanalysis(inpath, outpath,\n refpath='/lustre/research/bancell/aucolema/HWT2016runs/2016050800/wrfoutREF'):\n # Pull analysis variables\n og_analysis = Dataset(inpath)\n anlvars = og_analysis.variables['T'][0]\n gph300 = anlvars[0,:,:]\n gph500 = anlvars[1,:,:]\n gph700 = anlvars[2,:,:]\n gph850 = anlvars[3,:,:]\n gph925 = anlvars[4,:,:]\n temp300 = anlvars[5,:,:]\n temp500 = anlvars[6,:,:]\n temp700 = anlvars[7,:,:]\n temp850 = anlvars[8,:,:]\n temp925 = anlvars[9,:,:]\n u300 = anlvars[10,:,:]\n u500 = anlvars[11,:,:]\n u700 = anlvars[12,:,:]\n u850 = anlvars[13,:,:]\n u925 = anlvars[14,:,:]\n v300 = anlvars[15,:,:]\n v500 = anlvars[16,:,:]\n v700 = anlvars[17,:,:]\n v850 = anlvars[18,:,:]\n v925 = anlvars[19,:,:]\n td300 = anlvars[20,:,:]\n td500 = anlvars[21,:,:]\n td700 = anlvars[22,:,:]\n td850 = anlvars[23,:,:]\n td925 = anlvars[24,:,:]\n q300 = anlvars[25,:,:]\n q500 = anlvars[26,:,:]\n q700 = anlvars[27,:,:]\n q850 = anlvars[28,:,:]\n q925 = anlvars[29,:,:]\n slp = anlvars[30,:,:]\n t2 = anlvars[31,:,:]\n td2 = anlvars[32,:,:]\n u10 = anlvars[33,:,:]\n v10 = anlvars[34,:,:]\n\n wrf_d1 = Dataset(refpath)\n lons, lats = wrf_d1.variables['XLONG'][0], wrf_d1.variables['XLAT'][0]\n wrf_idim = len(lons[0,:])\n wrf_jdim = len(lats[:,0])\n\n sensvarlist = [gph300,gph500,gph700,gph850,gph925,temp300,temp500,temp700,\n temp850,temp925,u300,u500,u700,u850,u925,v300,\n v500,v700,v850,v925,td300,td500,td700,td850,\n td925,q300,q500,q700,q850,q925,slp,t2,td2,u10,v10]\n sensstringslist = [\"300 hPa GPH\",\"500 hPa GPH\",\"700 hPa GPH\",\n \"850 hPa GPH\",\"925 hPa GPH\",\"300 hPa T\",\"500 hPa T\",\n \"700 hPa T\",\"850 hPa T\",\"925 hPa T\",\"300 hPa U-Wind\",\n \"500 hPa U-Wind\",\"700 hPa U-Wind\",\"850 hPa U-Wind\",\n \"925 hPa U-Wind\",\"300 hPa V-Wind\",\"500 hPa V-Wind\",\n \"700 hPa V-Wind\",\"850 hPa V-Wind\",\"925 hPa V-Wind\",\n \"300 hPa Dewpt\", \"500 hPa Dewpt\", \"700 hPa Dewpt\",\n \"850 hPa Dewpt\", \"925 hPa Dewpt\", \"300 hPa Q\",\n \"500 hPa Q\", \"700 hPa Q\", \"850 hPa Q\", \"925 hPa Q\",\n \"SLP\",\"2m Temp\",\"2m Dewpt\",\n \"10m U-Wind\",\"10m V-Wind\"]\n\n\n # Write interpolated variables to netCDF\n new_analysis = Dataset(outpath, \"w\", format=\"NETCDF4\")\n new_analysis.createDimension('lat', wrf_jdim)\n new_analysis.createDimension('lon', wrf_idim)\n new_analysis.createDimension('time', None)\n xlat = new_analysis.createVariable(\"XLAT\", float, dimensions=('lat','lon'))\n xlat[:,:] = lats\n xlon = new_analysis.createVariable(\"XLONG\", float, dimensions=('lat','lon'))\n xlon[:,:] = lons\n\n # Interpolate and save!!\n for i in range(len(sensvarlist)):\n var = new_analysis.createVariable(sensstringslist[i].replace(\" \",\"_\"),\n sensvarlist[i].dtype,\n dimensions=('lat','lon'))\n var[:,:] = sensvarlist[i]\n new_analysis.close()\n return","def inputfile(filename):\n infile = open(filename, 'r')\n lines = infile.readlines()\n\n # --------------------------------------------------------------------------\n # Domain specifications\n\n Nx = eval(lines[15][lines[15].find('=')+1:].strip())\n ax = eval(lines[16][lines[16].find('=')+1:].strip())\n bx = eval(lines[17][lines[17].find('=')+1:].strip())\n\n Ny = eval(lines[19][lines[19].find('=')+1:].strip())\n ay = eval(lines[20][lines[20].find('=')+1:].strip())\n by = eval(lines[21][lines[21].find('=')+1:].strip())\n\n Nz = eval(lines[23][lines[23].find('=')+1:].strip())\n az = eval(lines[24][lines[24].find('=')+1:].strip())\n bz = eval(lines[25][lines[25].find('=')+1:].strip())\n\n Nvx = eval(lines[27][lines[27].find('=')+1:].strip())\n avx = eval(lines[28][lines[28].find('=')+1:].strip())\n bvx = eval(lines[29][lines[29].find('=')+1:].strip())\n\n Nvy = eval(lines[31][lines[31].find('=')+1:].strip())\n avy = eval(lines[32][lines[32].find('=')+1:].strip())\n bvy = eval(lines[33][lines[33].find('=')+1:].strip())\n\n Nvz = eval(lines[35][lines[35].find('=')+1:].strip())\n avz = eval(lines[36][lines[36].find('=')+1:].strip())\n bvz = eval(lines[37][lines[37].find('=')+1:].strip())\n\n Nt = eval(lines[39][lines[39].find('=')+1:].strip())\n T = eval(lines[40][lines[40].find('=')+1:].strip())\n\n N = eval(lines[46][lines[46].find('=')+1:].strip())\n\n # --------------------------------------------------------------------------\n # list of phase space variables used, in etc/params.dat must set unused\n # vars to have Nz as None, z = x, vx, y, ...\n # e.g. in 1D1V, phasespace_vars = ['x', 'vx']\n phasespace_vars = []\n if Nx is not None:\n phasespace_vars.append('x')\n if Ny is not None:\n phasespace_vars.append('y')\n if Nz is not None:\n phasespace_vars.append('z')\n if Nvx is not None:\n phasespace_vars.append('vx')\n if Nvy is not None:\n phasespace_vars.append('vy')\n if Nvz is not None:\n phasespace_vars.append('vz')\n\n # ==========================================================================\n # Boundary conditions dictionary -- contains dist. function BCs as well as phi\n\n BC = {}\n BC['f'] = {}\n BC['phi'] = {}\n\n # BC['f'] = BC dict on distribution function f\n\n # BC['f']['x'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['f']['y'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['f']['z'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['f']['vx'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['f']['vy'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['f']['vz'] = {'lower' : lower_value, 'upper' : upper_value}\n\n # BC['phi'] = BC dict on electric potential phi\n\n # BC['phi']['x'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['phi']['y'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['phi']['z'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['phi']['vx'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['phi']['vy'] = {'lower' : lower_value, 'upper' : upper_value}\n # BC['phi']['vz'] = {'lower' : lower_value, 'upper' : upper_value}\n #\n # subdict objects that give keyword descriptions that match method names in lib.boundaryconditions and lib.fieldsolvers\n # include, for var in phasespace_vars:\n #\n # BC['f'][var]['type'] and BC['phi'][var]['type']\n #\n # these are used to assemble function handle strings that select the corresponding routine needed for the specified BCs\n\n\n BC_infilename = './etc/' + lines[106][lines[106].find(':')+1:].strip()\n BC_infile = open(BC_infilename, 'r')\n BC_infile_lines = BC_infile.readlines()\n\n # DECSKS will throw an error if numbers are inputted as BCs in etc/params.dat\n\n # strings are stored as lowercase as they are used in an eval statement to access\n # the relevant method in lib.boundaryconditions. e.g. 'absorbing' is accessed as\n # either eval('lib.boundaryconditions.absorbing_lower_boundary') or\n # eval('lib.boundaryconditions.absorbing_upper_boundary') in lib.convect.remap_step\n\n BC['f']['x'] = {}\n BC['f']['x']['lower'] = safe_eval(BC_infile_lines[40][BC_infile_lines[40].find('=')+1:].strip())\n BC['f']['x']['upper'] = safe_eval(BC_infile_lines[41][BC_infile_lines[41].find('=')+1:].strip())\n\n BC['f']['y'] = {}\n BC['f']['y']['lower'] = safe_eval(BC_infile_lines[43][BC_infile_lines[43].find('=')+1:].strip())\n BC['f']['y']['upper'] = safe_eval(BC_infile_lines[44][BC_infile_lines[44].find('=')+1:].strip())\n\n BC['f']['z'] = {}\n BC['f']['z']['lower'] = safe_eval(BC_infile_lines[46][BC_infile_lines[46].find('=')+1:].strip())\n BC['f']['z']['upper'] = safe_eval(BC_infile_lines[47][BC_infile_lines[47].find('=')+1:].strip())\n\n BC['f']['vx'] = {}\n BC['f']['vx']['lower'] = safe_eval(BC_infile_lines[55][BC_infile_lines[55].find('=')+1:].strip())\n BC['f']['vx']['upper'] = safe_eval(BC_infile_lines[56][BC_infile_lines[56].find('=')+1:].strip())\n\n BC['f']['vy'] = {}\n BC['f']['vy']['lower'] = safe_eval(BC_infile_lines[58][BC_infile_lines[58].find('=')+1:].strip())\n BC['f']['vy']['upper'] = safe_eval(BC_infile_lines[59][BC_infile_lines[59].find('=')+1:].strip())\n\n BC['f']['vz'] = {}\n BC['f']['vz']['lower'] = safe_eval(BC_infile_lines[61][BC_infile_lines[61].find('=')+1:].strip())\n BC['f']['vz']['upper'] = safe_eval(BC_infile_lines[62][BC_infile_lines[62].find('=')+1:].strip())\n\n # make all BCs lowercase strings so they can be used to construct the function strings in lib.boundaryconditions module\n # whose names are all lowercase\n\n # if an accepted boundary condition synonym as been used, change value to the name it goes by in lib.boundaryconditions\n # check that all inputs for evolved phase space variables are recognized keywords and are compatible with the\n # boundary at which they are indicated\n for var in phasespace_vars:\n for boundary in ['lower', 'upper']:\n BC['f'][var][boundary] = BC['f'][var][boundary].lower()\n if BC['f'][var][boundary] == 'open' or BC['f'][var][boundary] == 'cutoff':\n print \"\\nCourtesy notice to user: the boundary condition %s was selected for the distribution function on %s at the %s boundary in params_boundaryconditions.dat; \" % (BC['f'][var][boundary].upper(), var, boundary)\n print \"this is a recognized input synonym for a '%s' condition. Changing value stored to BC['f']['%s']['%s'] = '%s'\\n\" % ('ABSORBING', var, boundary, 'ABSORBING')\n print \"Please regard any warnings/error messages that cite the keyword '%s' with this change in mind\\n\" % ('ABSORBING')\n BC['f'][var][boundary] = 'absorbing'\n\n elif BC['f'][var][boundary] == 'collector':\n pass\n\n elif BC['f'][var][boundary] == 'absorbing':\n pass\n\n elif BC['f'][var][boundary] == 'symmetry':\n if boundary == 'upper':\n raise NotImplementedError('a symmetric UPPER boundary condition on the distribution function was specified in params_boundaryconditions.dat; however, DECSKS only has functionality to permit lower boundary symmetry.')\n elif boundary == 'lower':\n print \"\\nCourtesy notice to user: the boundary condition %s was selected for the distribution function on %s at the %s boundary in params_boundaryconditions.dat; \" % (BC['f'][var][boundary].upper(), var, boundary)\n print \"this is a recognized input synonym for a '%s' condition. Changing value stored to BC['f']['%s']['%s'] = '%s'\\n\" % ('SYMMETRIC', var, boundary, 'SYMMETRIC')\n print \"Please regard any warnings/error messages that cite the keyword '%s' with this change in mind\\n\" % ('SYMMETRIC')\n BC['f'][var][boundary] = 'symmetric'\n\n elif BC['f'][var][boundary] == 'symmetric':\n if boundary == 'lower':\n pass\n elif boundary == 'upper':\n raise NotImplementedError('a symmetric UPPER boundary condition on the distribution function was specified in params_boundaryconditions.dat; however, DECSKS only has functionality to permit lower boundary symmetry.')\n\n elif BC['f'][var][boundary] == 'periodic':\n pass\n\n else: # inputs do not match any options\n print '\\nThe invalid keyword %s was specified in params_boundaryconditions.dat on the variable %s at the %s boundary\\n' % (BC['f'][var][boundary].upper(), var, boundary)\n raise InputError('inputs are restricted to those listed as options in params_boundaryconditions.dat')\n\n # above we have checked for valid input. Next, check for compatible inputs (if 'periodic' is selected, it must be selected for both\n # upper and lower bounds) and store a descriptor that toggles the correct orchestrator\n # function in lib.boundaryconditions module ('periodic' vs. 'nonperiodic')\n for var in phasespace_vars:\n if BC['f'][var]['lower'] == 'periodic' and BC['f'][var]['upper'] == 'periodic':\n BC['f'][var]['type'] = 'periodic'\n\n elif BC['f'][var]['lower'] == 'symmetric' and BC['f'][var]['upper'] != 'periodic':\n BC['f'][var]['type'] = 'nonperiodic'\n \n # check for invalid inputs\n elif BC['f'][var]['lower'] == 'symmetric' and BC['f'][var]['upper'] == 'periodic':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat:\"\n print \"\\nlower boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['lower'].upper())\n print \"upper boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['upper'].upper())\n\n print \"\\nare inconsistent. Cannot combine a symmetric lower boundary with a periodic upper boundary condition. Periodic boundary conditions involve both boundaries (both boundaries would have to be set to PERIODIC)\\n\"\n\n raise InputError('cannot combine a symmetric lower boundary condition with a periodic upper boundary condition for the distribution function. Check inputs in boundaryconditions.dat and change the upper bound to be of non-periodic type')\n\n elif BC['f'][var]['lower'] == 'periodic' and BC['f'][var]['upper'] != 'periodic':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat:\"\n print \"\\nlower boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['lower'].upper())\n print \"upper boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['upper'].upper())\n\n print \"\\nare inconsistent. Cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\"\n\n raise InputError('cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\n elif BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] == 'periodic':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat:\"\n print \"\\nlower boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['lower'].upper())\n print \"upper boundary condition on f for the variable %s: %s\" % (var, BC['f'][var]['upper'].upper())\n\n print \"\\nare inconsistent. Cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\"\n\n raise InputError('cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\n else: # boundary conditions are combination of only: symmetric (lower), collector (lower or upper), absorbing (lower or upper)\n BC['f'][var]['type'] = 'nonperiodic'\n\n distribution_function_boundarycondition_orchestrator_prefix = 'DECSKS.lib.boundaryconditions'\n\n # create a dictionary of function handles that call either\n # the 'periodic', 'nonperiodic', or 'symmetric' orchestrator in lib.boundaryconditions\n #\n # i.e. we form the string handle for each active variable var:\n #\n # distribution_function_boundarycondition_orchestrator_handle[var] =\n #\n # DECSKS.lib.boundaryconditions.periodic\n # DECSKS.lib.boundaryconditions.nonperiodic\n # DECSKS.lib.boundaryconditions.symmetric\n\n distribution_function_boundarycondition_orchestrator_handle = {}\n\n for var in phasespace_vars:\n distribution_function_boundarycondition_orchestrator_handle[var] = \".\".join(\n (distribution_function_boundarycondition_orchestrator_prefix, BC['f'][var]['type']))\n\n # --------------------------------------------------------------------------\n # Store number of active gridpoints for every phase space variable\n #\n # Note: for periodic BCs: Nz_active = Nz - 1, we evolve Nz_active nodes and assign by periodicity the f[Nz-1] = f[0]\n # for all other BCs: Nz_active = Nz\n\n # active_dims vs. total_dims\n # note a generalized loop cannot be used as assignments cannot be made under an assembled string with eval\n if BC['f']['x']['lower'] == 'periodic' and BC['f']['x']['upper'] == 'periodic' and Nx is not None:\n Nx_active = Nx - 1\n else:\n Nx_active = Nx\n\n if BC['f']['y']['lower'] == 'periodic' and BC['f']['y']['upper'] == 'periodic' and Ny is not None:\n Ny_active = Ny - 1\n else:\n Ny_active = Ny\n\n if BC['f']['z']['lower'] == 'periodic' and BC['f']['z']['upper'] == 'periodic' and Nz is not None:\n Nz_active = Nz - 1\n else:\n Nz_active = Nz\n\n if BC['f']['vx']['lower'] == 'periodic' and BC['f']['vx']['upper'] == 'periodic' and Nvx is not None:\n Nvx_active = Nvx - 1\n else:\n Nvx_active = Nvx\n\n if BC['f']['vy']['lower'] == 'periodic' and BC['f']['vy']['upper'] == 'periodic' and Nvy is not None:\n Nvy_active = Nvy - 1\n else:\n Nvy_active = Nvy\n\n if BC['f']['vz']['lower'] == 'periodic' and BC['f']['vz']['upper'] == 'periodic' and Nvz is not None:\n Nvz_active = Nvz - 1\n else:\n Nvz_active = Nvz\n\n # --------------------------------------------------------------------------\n # High order correction (HOC) method applied to each phase space variable\n\n # store as uppercase\n\n HOC = {}\n HOC['x'] = safe_eval(lines[56][lines[56].find(':')+1:].strip())\n HOC['y'] = safe_eval(lines[57][lines[57].find(':')+1:].strip())\n HOC['z'] = safe_eval(lines[58][lines[58].find(':')+1:].strip())\n\n HOC['vx'] = safe_eval(lines[60][lines[60].find(':')+1:].strip())\n HOC['vy'] = safe_eval(lines[61][lines[61].find(':')+1:].strip())\n HOC['vz'] = safe_eval(lines[62][lines[62].find(':')+1:].strip())\n\n # make all non-None inputs capitalized\n for key in HOC.keys():\n if HOC[key] is not None:\n HOC[key] = HOC[key].upper()\n else:\n pass\n\n # check for valid inputs\n for key in HOC.keys():\n if HOC[key] is not None:\n if type(HOC[key]) != str:\n raise InputError('A non-string entry was found as a high order correction specification. Only FD or FOURIER are accepted')\n elif HOC[key] != 'FD' and HOC[key] != 'FOURIER':\n print \"\\nThe following high order correction was specified in params.dat, but is not recognized:\"\n print \"\\nHigh order correction on %s: %s\\n\" % (key, HOC[key].upper())\n print \"only FD and FOURIER are accepted keywords\\n\"\n raise InputError('An unrecognized high order correction was specified. Only FD or FOURIER are accepted')\n\n elif HOC[key] == 'FOURIER' and BC['f'][key]['type'] != 'periodic': # Fourier corrections use trigonometric derivatives, which rely on periodicity of the underlying functions\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat:\"\n print \"\\nlower boundary condition on f for the variable %s: %s\" % (key, BC['f'][key]['lower'].upper())\n print \"upper boundary condition on f fore the variable %s: %s\\n\\n\" % (key, BC['f'][key]['upper'].upper())\n\n print \"are inconsistent with the high order correction specified in params.dat:\"\n print \"\\nhigh order correction on %s: %s\\n\\n\" % (key, HOC[var].upper())\n\n print \"FOURIER high order corrections only make sense for periodic systems (if this is the intention, the BCs on f and phi must be set to PERIODIC in params_boundaryconditions.dat)\\n\"\n\n raise InputError('Fourier corrections on a variable only make sense for periodic systems. The boundary conditions on the distribution function were read-in as not periodic for this variable.')\n elif eval('N' + key) is None:\n raise InputError('a variable not involved in the simulation (its number of grid points was specified as None) must also have its high order correction method specified as None. While reading in the input deck, the aforementioned expectation was not met. Please revisit the entries (number of grid points) and high order correction specification.')\n\n # store lists containing number of total and active gridpoints\n # this is acknowledged as redundant given the above storing as Nx_active, Ny_active,\n # etc., but these objects are used in legacy methods inside DECSKS\n\n # initialize lists\n total_dims = [] # e.g. in 1D1V this could contain [Nx, Nvx]\n active_dims = [] # e.g. in 1D1V this could contain [Nx_active, Nvx_active]\n\n for var in phasespace_vars:\n total_dims.append(eval('N' + var))\n active_dims.append(eval('N' + var + '_active'))\n\n numdims = len(phasespace_vars)\n # --------------------------------------------------------------------------\n # Initial density specification (2 species)\n\n mu = safe_eval(lines[68][lines[68].find(':')+1:].strip())\n\n densities_list = lines[69][lines[69].find(':')+1:].strip().split(', ')\n for i in range(len(densities_list)):\n densities_list[i] = densities_list[i].lower()\n\n if len(densities_list) == 2: # if two species return dictionary of strings\n density = {}\n density['electrons'] = densities_list[0]\n density['electrons'] = density['electrons'].lower()\n density['ions'] = densities_list[1]\n density['ions'] = density['ions'].lower()\n print \"\\ntwo species simulation with initial densities:\\n\"\n print \"electrons: %s\" % density['electrons']\n print \"ions: %s\\n\" % density['ions']\n\n # --------------------------------------------------------------------------\n # split scheme specification\n\n split_scheme = lines[81][lines[81].find('=')+1:].strip()\n split_scheme = split_scheme.upper()\n print \"split scheme: %s\\n\" % split_scheme\n\n # filepath to splitting coefficient tables\n filename = lines[82][lines[82].find(':')+1:].strip()\n filepath = './etc/' + filename\n\n # get splitting coefficients for chosen scheme\n if split_scheme is not None:\n splitting = splitting_coefficients(filepath, split_scheme)\n else:\n splitting = None\n\n\n\n # --------------------------------------------------------------------------\n # check for validity on split scheme vs. boundary conditions\n #\n # i.e. check that if the problem is bounded, the user cannot use a split scheme that has negative time substeps\n #\n # Schemes with only positive time substeps: LF2\n # Schemes that contain negative time substeps: Y4, O6-4, O11-6, O14-6\n #\n\n for var in phasespace_vars:\n if BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] != 'periodic':\n if split_scheme in ['LF2']:\n pass\n else: # a split scheme that involves negative time substeps has been selected\n print \"\\nThe following set of user specified information is not accepted by DECSKS:\\n\"\n print \"\\nin params.dat, the following was specified:\"\n print \"split scheme = %s:\" % split_scheme\n print \"\\nand the boundary data was specified in params_boundaryconditions.dat:\\n\"\n print \"distribution function lower boundary condition on %s: %s\" % (BC['f'][var]['lower'],var)\n print \"distribution function upper boundary condition on %s: %s\" % (BC['f'][var]['upper'], var)\n print \"\\nThe split scheme involves negative time substeps, while the boundary conditions are non-periodic. The BOUNDED Vlasov-Poisson problem is irreversible. A split scheme with negative time substeps can only be used in periodic systems, which correspond to systems of infinite extent\\n\"\n raise InputError('The split scheme involves negative time substeps, while the boundary conditions are non-periodic. The BOUNDED Vlasov-Poisson problem is irreversible. A split scheme with negative time substeps can only be used in periodic systems, which correspond to systems of infinite extent. To rectify this, the user may wish to select periodic boundary conditions on the distribution function (hence phi).')\n\n # --------------------------------------------------------------------------\n # Plot window specification (used in lib.plots.Setup)\n\n xmin = eval(lines[96][lines[96].find('=')+1:].strip())\n xmax = eval(lines[97][lines[97].find('=')+1:].strip())\n ymin = eval(lines[99][lines[99].find('=')+1:].strip())\n ymax = eval(lines[100][lines[100].find('=')+1:].strip())\n\n plot_params = dict(xmin = xmin, xmax = xmax,\n ymin = ymin, ymax = ymax)\n\n record_outputs = lines[103][lines[103].find(':')+1:].strip()\n record_outputs = record_outputs.lower()\n\n if record_outputs == 'yes':\n # output filepath setup\n filename = lines[104][lines[104].find(':')+1:].strip()\n filepath = './etc/' + filename\n outfiles = output_files(filepath) # dictionary of opened files\n else:\n outfiles = None\n\n # --------------------------------------------------------------------------\n # DICTIONARIES AND MATRICES RELEVANT FOR HIGH ORDER CORRECTION APPLICATIONS\n #\n\n # Constructing the finite different weight matrices, W.\n #-------------------------------------------------------\n # requires: (dict) FD_schemes\n #\n # Note: FD_schemes is only needed to construct W. W is what is used in\n # the simulation. Hence, the building routine for FD_schemes\n # is not optimized, since it happens before the simulation starts\n # and is not a source of repeated computational cost.\n #\n # FD_schemes is a dictionary containing the families of every order derivative\n # needed for the indicated global error N in etc/params.dat, i.e. all schemes\n # of various degrees of asymmetry and handedness. For large N, this can be a\n # large dictionary, cf. the function routine read_FD_schemes to see all\n # that gets stored inside. It is used to construct the difference coefficient\n # matrices W (for applying high order corrections). The other scheme\n # FD_scheme_dn1 is used to construct the matrix W_dn1 which is a difference\n # coefficient matrix for the first derivative (dn = 1) at LTE = 6, and used\n # to compute the electric field E = \"-dphi\" = W_dn1.dot(phi),\n # where dphi is the first derivative# of the electric potential, as calculated by\n # the methods in lib.fieldsolvers package\n #---------------------------------------------------------------------------\n #\n # initialize all dictionaries whose keys correspond to phase space vars\n # and whose values contain the relevant ndarrays\n\n Xi = {}\n xi = {}\n W = {}\n\n # top level check: if any var has FD corrections, store FD_schemes and init FD weight matrix W\n # for 6th order first derivative\n if 'FD' in HOC.values():\n # store finite difference schemes\n FD_schemes = read_FD_schemes(N)\n\n # if FD on a configuration variable, need to differentiate phi to obtain the acceleration a ~ E = -dphi\n if HOC['x'] == 'FD' or HOC['y'] == 'FD' or HOC['z'] == 'FD':\n # first derivative with LTE = 6, used to find dphi = -E after phi is\n # found from a 6th order Poisson solve\n FD_scheme_dn1 = read_FD_scheme(1,6)\n W_dn1_LTE6 = assemble_finite_difference_weight_matrix_const_dn_const_LTE(Nx_active,\n FD_scheme_dn1,\n dn = 1,\n LTE = 6\n )\n\n else:\n # else, Fourier Gauss solver is used, no need for this matrix\n W_dn1_LTE6 = None\n\n # variable-by-variable checks: assemble consistent objects needed\n # for the specified means of HOC from etc/params.dat\n\n # Note: the following is organized with the expectation that\n # higher dimensional implementations would be stepped through\n # as sets of 2D advection problems, always paired as z and vz\n # i.e. not as mixed stepthroughs with x paired with vy for example\n\n for var in phasespace_vars:\n if HOC[var] == 'FD':\n W[var] = assemble_finite_difference_weight_matrix(\n eval('N' + var + '_active'),\n N,\n FD_schemes\n )\n elif HOC[var] == 'FOURIER':\n # ensure the correct number of grid points\n # is passed for the generalized velocity Nvz_active\n # for x,y,z, 'vz' = vx, vy, vz\n # for vx, vy, vz, 'vz' = ax, ay, az, which have\n # the same number of dims as x, y, z, respectively\n # this is needed in the routine assemble_spectral_derivative_operator\n # so that the correctly dimensioned 2D arrays are returned\n\n if var[0] == 'v':\n # if a velocity variable, the velocity of this velocity is an acceleration\n # which has the same dimensions as the corresponding configuration variable\n # e.g. vx has velocity(vx) = ax which has the same dimensions as x\n Nvz_active = eval('N' + var[1] + '_active')\n else:\n # if a configuration variable, the velocity is the physical velocity, which\n # must be a coresponding active variable\n # e.g. x has a velocity vx\n Nvz_active = eval('Nv' + var + '_active')\n\n\n # The 3D tensor Xi is used to compute trigonometric derivatives\n # by operating on a 2D array of Fourier wave components (transformed\n # row-wise for each column, where as usual the objects have been\n # transpoed if needed so that the variation (x or vx) is along\n # rows, not columns)\n #\n # Fourier transform (derivatives) = Xi * Fourier transform (f)\n # derivatives = inverse transform (Xi * Fourier(f))\n #\n #\n # the object xi is used in legacy methods in DECSKS (pre-DECSKSv2.0)\n\n Xi, xi = assemble_spectral_derivative_operator(Xi, xi,\n var,\n eval('a' + var),\n eval('b' + var),\n eval('N' + var),\n eval('N' + var + '_active'),\n Nvz_active,\n N)\n\n # ---------------------------------------------------------------------\n # \"Alternating\" identity matrix\n\n # in lib.HOC.correctors, require an diagonal matrix with shape = (Nz_active, Nz_active)\n # with entries as (-1)^i, where i is the row number, for details see on github\n #\n # dsirajud/IPython-notebooks/\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\n #\n # section \"2D casting of correction coefficients c (vector) -> c (tensor)\"\n\n I_alternating = np.diag( (-np.ones(N)) ** np.arange(N) )\n\n # ---------------------------------------------------------------------\n # Bernoulli number storage, and forming the matrices A_pos, A_neg\n\n # obtain Bernoulli numbers (note: only 23 numbers are entered into the dat file ->\n # max global error is 23 - 1 = 22) for a correction up to global error order\n # N, N-1 Bernoulli numbers are needed. If higher than global error order 22 is\n # desired, additional Bernoulli numbes need to be entered in\n #\n # etc/Table_of_Bernoulli_numbers.dat\n #\n\n # Store Bernoulli numbers from dat file etc/Table_of_Bernoulli_numbers.dat\n filename = 'Table_of_Bernoulli_numbers.dat'\n filepath = './etc/' + filename\n Bernoulli_numbers = Bernoulli(filepath)\n\n # \"A\" matrices for Bernoulli number storage and matrix HOC application\n # in lib.HOC.Beta_matrix, see notebook on github at\n # dsirajud/IPython-notebooks/\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\n #\n # the A matrices are matrices containing scaled Bernoulli numbers (normalized by factorials)\n # that also factor in the sign (direction) information of the advecting density packets\n # (the different amounts to all odd coefficients having opposite sign)\n\n # The A matrices are used in the method lib.HOC.Beta_matrix (used to construct the array of the *magnitudes*\n # of the Nvz sets of N beta coefficients; note that the high order flux is further computed as a sum of\n # products that alternating with sign according to the parity of the derivative number, i.e. alternates signs\n # among odds and evens. These prefactors are applied at the end of the method lib.HOC.correctors by matrix\n # pre-multiplication of the matrix B with the alternating (in sight) identity matrix I formed above)\n\n # the method lib.HOC.Beta_matrix is called from inside lib.HOC.correctors (used to assemble the 2D array c of correctors)\n\n A_pos, A_neg = np.zeros([N,N]), np.zeros([N,N])\n for i in range(N):\n for j in range(i+1):\n A_pos[i,j] = Bernoulli_numbers[i-j] / scipy.misc.factorial(i-j)\n if (i - j) == 1:\n A_neg[i,j] = -A_pos[i,j]\n else:\n A_neg[i,j] = A_pos[i,j]\n\n A_matrix = {}\n # dictionary container\n # allow dictionary access to relevant matrix of Bernoulli numbers\n # by operating with str(int(np.sign(CFL.frac)))\n\n A_matrix['1'] = A_pos\n A_matrix['0'] = A_pos\n A_matrix['-1'] = A_neg\n\n #--------------------------------------------------------------------------------------------#\n # ELECTRIC POTENTIAL PHI\n #--------------------------------------------------------------------------------------------#\n\n #--------------------------------------------------------------------------------------------#\n # Boundary conditions BC['phi'] dictionary and dictionary of boundary values, phi_BC\n #\n # BC['phi']['x', 'y', or 'z']['lower' or 'upper'] = string keyword that describes the BC\n # phi_BC['x', 'y', or 'z'] = boundary value vector phi_BC that appears in a Poisson solver\n #--------------------------------------------------------------------------------------------#\n\n phi_BC = {}\n # keys: 'x', 'y', 'z'\n # values: ndarrays of size eval('N' + var + '_active)\n\n BC['phi'] = {}\n # keys: 'x', 'y', 'z'\n # values / keys for subdict: 'lower', 'upper'\n # values for subdict: string keyword that describes the BC at the key specification\n\n # --------------------------------------------------------------------------\n # PHI BOUNDARY CONDITIONS AND PHI BOUNDARY VALUES VECTORS FOR SOLVER Phi_BC['x', 'y', or 'z']\n\n # lines read in from boundaryconditions dat file were stored above in BC_infile_lines\n if HOC['x'] == 'FD':\n BC['phi']['x'] = {}\n BC['phi']['x']['lower'] = safe_eval(BC_infile_lines[196][BC_infile_lines[196].find('=')+1:].strip())\n BC['phi']['x']['upper'] = safe_eval(BC_infile_lines[197][BC_infile_lines[197].find('=')+1:].strip())\n phi_BC['x'] = np.zeros(Nx_active)\n elif HOC['x'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\n phi_BC['x'] = None\n\n if HOC['y'] == 'FD':\n BC['phi']['y'] = {}\n BC['phi']['y']['lower'] = safe_eval(BC_infile_lines[199][BC_infile_lines[199].find('=')+1:].strip())\n BC['phi']['y']['upper'] = safe_eval(BC_infile_lines[200][BC_infile_lines[200].find('=')+1:].strip())\n phi_BC['y'] = np.zeros(Ny_active)\n elif HOC['y'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\n phi_BC['y'] = None\n\n if HOC['z'] == 'FD':\n BC['phi']['z'] = {}\n BC['phi']['z']['lower'] = safe_eval(BC_infile_lines[202][BC_infile_lines[202].find('=')+1:].strip())\n BC['phi']['z']['upper'] = safe_eval(BC_infile_lines[203][BC_infile_lines[203].find('=')+1:].strip())\n phi_BC['z'] = np.zeros(Nz_active)\n elif HOC['z'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\n phi_BC['z'] = None\n\n # ensure all inputs stored above in BC['phi'] dict objects are uppercase and recognized\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n if HOC[var] == 'FOURIER':\n pass\n else: # HOC is FD which computes the Lorentz term through a potential phi (Fourier uses the electric field E)\n\n # LOWER BOUNDARY CHECKS\n if BC['phi'][var]['lower'] is None:\n raise InputError('a NoneType was specified as a LOWER boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not meant to be evolved, set its number of grid points to None')\n\n elif type(BC['phi'][var]['lower']) != str:\n raise InputError('a non-string type as a LOWER boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not intended to be active, set its number of grid points to None. Otherwise, a recognized string keyword must be specified on the boundary condition on phi for this variable.')\n\n else:\n BC['phi'][var]['lower'] = BC['phi'][var]['lower'].upper()\n\n if BC['phi'][var]['lower'] not in ['PERIODIC', 'SELF-CONSISTENT', 'SYMMETRIC', 'SYMMETRY', 'BIAS']:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat is not a recognized keyword:\\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['phi'][var]['lower'].upper())\n\n raise InputError('boundary condition indicated on phi is not an accepted keyword option')\n\n elif (BC['phi'][var]['lower'] == 'SYMMETRIC' or BC['phi'][var]['lower'] == 'SYMMETRY') and BC['f'][var]['lower'] != 'symmetric':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat is:\\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\\n\" % (var, BC['phi'][var]['lower'].upper())\n print \"lower boundary condition on f for variable %s: %s\" % (var, BC['f'][var]['lower'].upper())\n print \"upper boundary condition on f for variable %s: %s\\n\" % (var, BC['f'][var]['upper'].upper())\n\n print \"a SYMMETRIC boundary condition must be specified on both phi and f\"\n # by this point all synonyms have been normalized on BC['f'][var], 'symmetric' corresponds to the symmetry condition\n raise InputError('a SYMMETRY boundary condition on phi was specified, but a symmetry boundary was not specified on the distribution function f at this same (lower) boundary. A symmetric domain requires a lower boundary condition to be SYMMETRIC on both phi and f.')\n\n else:\n pass\n\n # UPPER BOUNDARY CHECKS\n if BC['phi'][var]['upper'] is None:\n raise InputError('a NoneType was specified as an upper boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not meant to be evolved, set its number of grid points to None')\n\n elif type(BC['phi'][var]['upper']) != str:\n raise InputError('a non-string type as an upper boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not intended to be active, set its number of grid points to None. Otherwise, a recognized string keyword must be specified on the boundary condition on phi for this variable.')\n\n else:\n BC['phi'][var]['upper'] = BC['phi'][var]['upper'].upper()\n\n if BC['phi'][var]['upper'] not in ['PERIODIC', 'SELF-CONSISTENT', 'SYMMETRIC', 'SYMMETRY', 'BIAS']:\n print \"\\nThe following boundary condition specified in params_boundaryconditions.dat is not a recognized boundary condition keyword:\\n\\n\"\n print \"upper boundary condition on phi for variable %s: %s\\n\" % (var, BC['phi'][var]['upper'].upper())\n\n raise InputError('boundary condition indicated on phi is not an accepted keyword option')\n\n elif BC['phi'][var]['upper'] == 'SYMMETRIC' or BC['phi'][var]['upper'] == 'SYMMETRY':\n print \"\\nThe following boundary condition specified in params_boundaryconditions.dat is not available:\\n\\n\"\n print \"upper boundary condition on phi: %s\\n\" % BC['phi'][var]['upper'].upper()\n\n raise NotImplementedError('a SYMMETRY boundary condition on phi as an UPPER boundary is specified in params_boundaryconditions.dat; only lower boundaries can support a symmetry boundary condition.')\n\n\n # CHECK FOR CONSISTENCY IN BOUNDARY CONDITIONS BETWEEN BOTH LOWER AND UPPER SPECIFICATIONS\n if BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] != 'PERIODIC':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['phi'][var]['lower'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\\n\" % (var, BC['phi'][var]['upper'].upper())\n\n raise InputError('PERIODIC boundary conditions on phi involve both lower and upper boundaries. The read-in of params_boundaryconditions.dat has the lower boundary condition as PERIODIC but the upper boundary condition is NOT. Both boundary conditions on phi must be set to PERIODIC if a periodic plasma is to be simulated.')\n\n elif BC['phi'][var]['lower'] != 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['phi'][var]['lower'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\\n\" % (var, BC['phi'][var]['upper'].upper())\n\n raise InputError('PERIODIC boundary conditions on phi involve both lower and upper boundaries. The read-in of params_boundaryconditions.dat has the upper boundary condition as PERIODIC but the lower boundary condition is NOT. Both boundary conditions on phi must be set to PERIODIC if a periodic plasma is to be simulated.')\n\n elif BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC':\n\n if BC['f'][var]['type'] != 'periodic': # note that validity and consistency checks on inputs for the distribution function have already been done above\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['phi'][var]['lower'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\" % (var, BC['phi'][var]['upper'].upper())\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['f'][var]['lower'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\" % (var, BC['f'][var]['upper'].upper())\n print \"e.g. periodic boundaries on phi require periodic boundaries on f for the same variable\\n\"\n raise InputError('PERIODIC boundary conditions on were specifed consistently for phi in params_boundaryconditions.dat; however, periodic boundary conditions must also be consistently specified on the distribution function. Revisit params_boundaryconditions.dat and ensure that both lower and upper boundaries on the distribution function f and the potential phi are set to PERIODIC if a periodic plasma is intended to be simulated.')\n elif BC['f'][var]['type'] == 'periodic': # note that validity and consistency checks on inputs for the distribution function have already been done above\n pass\n\n\n # CHECK FOR CONSISTENCY ON PHI BCS WITH HIGH ORDER CORRECTION METHOD SPECIFIED (note we have already checked this against the distribution function BCs)\n # here, we are only checking to see if that BCs on phi aren't periodic, to ensure that HOC is NOT set to fourier (relies on periodicity))\n # the following conditional check asks: \"if (BCs on phi are not periodic) AND (HOC is FOURIER)\"\n if ((BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] != 'PERIODIC') or (BC['phi'][var]['lower'] != 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC')) and HOC[var] == 'fourier':\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent with the specified high order correction method in params.dat: \\n\\n\"\n print \"lower boundary condition on phi for variable %s: %s\" % (var, BC['phi'][var]['lower'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\\n\" % (var, BC['phi'][var]['upper'].upper())\n print \"upper boundary condition on phi for variable %s: %s\\n\\n\" % (var, HOC[var].upper())\n print \"\\n\\nFourier high order corrections require periodic boundary conditions on both phi and the distribution function f\\n\"\n\n raise InputError('the high order correction is specified as FOURIER; however, the BCs on the electric potential phi are not periodic. FOURIER corrections require PERIODIC BCs on phi and the distribution function as the methods rely on periodicity')\n\n #--------------------------------------------------------------------------------------------#\n # BIAS values\n #--------------------------------------------------------------------------------------------#\n\n Bias = {} # this dictionary is created for reading in the bias values, it is not returned\n # in sim_params dict. If a bias condition is set on any boundary, this dictionary\n # assigns its value at that boundary in the vector phi_BC[var], phi_BC[var] is\n # returned (as usual, var = ['x', 'y', 'z'])\n\n Bias['x'] = {}\n Bias['y'] = {}\n Bias['z'] = {}\n\n Bias['x']['lower'] = safe_eval(BC_infile_lines[214][BC_infile_lines[214].find('=')+1:].strip())\n Bias['x']['upper'] = safe_eval(BC_infile_lines[215][BC_infile_lines[215].find('=')+1:].strip())\n Bias['y']['lower'] = safe_eval(BC_infile_lines[217][BC_infile_lines[217].find('=')+1:].strip())\n Bias['y']['upper'] = safe_eval(BC_infile_lines[218][BC_infile_lines[218].find('=')+1:].strip())\n Bias['z']['lower'] = safe_eval(BC_infile_lines[220][BC_infile_lines[220].find('=')+1:].strip())\n Bias['z']['upper'] = safe_eval(BC_infile_lines[221][BC_infile_lines[221].find('=')+1:].strip())\n\n # check for valid inputs on active variables for any boundary that is specified as BIAS\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n if HOC[var] == 'FOURIER':\n pass\n else:\n for boundary in ['lower', 'upper']:\n if var in phasespace_vars:\n if BC['phi'][var][boundary] == 'BIAS':\n if Bias[var][boundary] is None: # if the BC is BIAS but the value input for the BIAS value is None\n print \"\\nThe following specifications in params_boundaryconditions.dat are inconsistent:\\n\"\n print \"%s boundary condition on phi for variable %s: %s\" % (boundary, var, BC['phi'][var][boundary].upper())\n print \"%s BIAS value on phi for variable %s: %s\\n\" % (boundary, var, Bias[var][boundary])\n print \"e.g. if a boundary condition on phi is set to BIAS for a variable, a number must be specifed under BIAS value\\n\"\n raise InputError('A phi boundary condition on an active variable (number of grid points on this variable has been set as non-None) has been specified as BIAS; however, the corresponding BIAS value is NoneType. Must be a number.')\n elif type(Bias[var][boundary]) == str:\n print \"\\nThe following specifications in params_boundaryconditions.dat are inconsistent:\\n\"\n print \"%s boundary condition on phi for variable %s: %s\" % (boundary, var, BC['phi'][var][boundary].upper())\n print \"%s BIAS value on phi for variable %s: %s\\n\" % (boundary, var, Bias[var][boundary])\n print \"e.g. if a boundary condition on phi is set to BIAS for a variable, a number must be specifed under BIAS value\\n\"\n\n raise InputError('A phi boundary condition on an active variable (number of grid points on this variable has been set as non-None) has been specified as BIAS; however, the corresponding BIAS value is str type. Must be a number.')\n else:\n pass\n\n # E is calculated by the following call flow, first an ORCHESTRATOR is called:\n #\n # E = lib.fieldsolvers.compute_electric_field_fourier <--- solves with a Gauss' law solver directly\n #\n # or\n #\n # E = lib.fieldsolvers.compute_electric_field_fd <--- solves a Poisson solver for phi, then differentiate to get E\n #\n # which can generally be called by eval operating on string handles that are themselves constructed\n # per 'lib.fieldsolvers.compute_electric_field_' + HOC[var].lower()\n #\n # If a finite difference routine is specified, a Poisson solve must be performed to obtain phi.\n # We call the relevant Poisson solver among the following options (L = lower boundary, U = upper boundary, DBC = Dirichlet BC, NBC = Neumann BC):\n #\n # Poisson_6th_PBC\n # Poisson_6th_LDBC_UDBC\n # Poisson_6th_LDBC_UNBC\n # Poisson_6th_LNBC_UDBC\n # Poisson_6th_LDBC_LDBC\n # Poisson_6th_UDBC_UNBC\n #\n\n # which are selected based on the boundary conditions the user has supplied in params_boundaryconditions.dat.\n #\n # finally, we compute and return:\n #\n # E = - 1 / config_var.width * W_dn1_LTE6.dot(phi)\n #\n\n # --------------------------------------------------------------------------\n # fieldsolver orchestator handle string for electric field (periodic or non-periodic)\n #\n # currently only 1D1V, only one handle needed. When this will be generalized, can make a dict object with keys corresponding\n # to each active configuration variable\n\n compute_electric_field_orchestrator_handle = {}\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n # dictionary key labels the component of the electric field: 'x', 'y', 'z'\n compute_electric_field_orchestrator_handle[var] = \"DECSKS.lib.fieldsolvers.compute_electric_field_\" + HOC[var].lower()\n\n\n # ---------------------------------------------------------------------\n # initialize dictionaries for wall charge objects\n\n sigma = {}\n sigma_n = {}\n\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n sigma_n[var] = {}\n sigma[var] = {}\n\n # --------------------------------------------------------------------------\n # Dictionary for the specific electric potential phi function solver needed\n # according to the specified boundary conditions on phi\n\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n\n if HOC[var] == 'FOURIER':\n pass # uses electric field E, periodic boundary conditions only\n\n else: # is FD corrections, and electric potential phi in a Poisson solver, can be periodic or other BCs\n BC['phi'][var]['type'] = BC['phi'][var]['lower'] + '_' + BC['phi'][var]['upper']\n if BC['phi'][var]['type'] == 'PERIODIC_PERIODIC':\n BC['phi'][var]['type'] = 'PBC'\n\n if BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] != 'periodic':\n raise InputError('A boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\n\n\n if BC['phi'][var]['type'] == 'BIAS_BIAS':\n BC['phi'][var]['type'] = 'LDBC_UDBC'\n\n # Dirichlet condition, phi = BIAS value\n phi_BC[var][0] = float(Bias[var]['lower'])\n # Dirichlet condition, phi = BIAS value\n phi_BC[var][-1] = float(Bias[var]['upper'])\n\n if BC['f'][var]['lower'] != 'absorbing' or BC['f'][var]['upper'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n raise InputError('A boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\n\n elif BC['phi'][var]['type'] == 'BIAS_SELF-CONSISTENT':\n BC['phi'][var]['type'] = 'LDBC_UNBC'\n\n # Dirichlet condition, phi = BIAS value\n phi_BC[var][0] = float(Bias[var]['lower'])\n # Neumann condition, dphi = sigma_upper, translates to phi_BC[-1] = -6 var.width * sigma_upper (see https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb for details)\n # phi_BC[-1] = - 6 * var.width * sim_params['sigma'][var]['upper'], changes with time step\n\n if BC['f'][var]['lower'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n raise InputError('A lower boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\n\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"upper boundary condition on phi for variable %s: %s\\n\" % (var, BC['phi'][var]['upper'].upper())\n print \"upper boundary condition on f for variable %s: %s\\n\" % (var, BC['f'][var]['upper'].upper())\n print \"\\ne.g. an upper boundary condition on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\"\n print \"\\ne.g. an upper boundary condition on f as ASBORBING must have the upper boundary condition on phi as BIAS\\n\"\n\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\n\n elif BC['phi'][var]['type'] == 'SELF-CONSISTENT_BIAS':\n BC['phi'][var]['type'] = 'LNBC_UDBC'\n\n # Neumann condition, dphi = -sigma_lower, translates to phi_BC[0] = -6 var.width * sigma_lower (see https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb for details)\n #phi_BC[var][0] = - 6 * var.width * sim_params['sigma'][var]['lower'], changes with time step\n # Dirichlet condition, phi = BIAS value\n phi_BC[var][-1] = float(Bias[var]['upper'])\n\n # check upper boundary\n if BC['f'][var]['upper'] == 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n pass\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"upper boundary condition on phi for variable %s: %s\\n\" % (var, BC['phi'][var]['upper'].upper())\n print \"upper boundary condition on f for variable %s: %s\\n\\n\" % (var, BC['f'][var]['upper'].upper())\n print \"\\ne.g. an upper boundary condition set on phi as BIAS must have the upper boundary condition on f as ABSORBING\\n\"\n\n raise InputError('An upper boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\n\n # check lower boundary\n if BC['f'][var]['lower'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\n sigma[var]['lower'] = 0 # initialize to zero charge at time zero\n sigma_n[var]['lower'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"lower boundary condition on phi: %s\" % BC['phi'][var]['lower'].upper()\n print \"lower boundary condition on f: %s\\n\" % BC['f'][var]['lower'].upper()\n print \"\\ne.g. an lower boundary condition set on phi as SELF-CONSISTENT must have the lower boundary condition on f as COLLECTOR\"\n print \"e.g. an lower boundary condition set on f as ABSORBING must have the lower boundary condition on phi as BIAS\"\n print \"e.g. an lower boundary condition set on f as PERIODIC requires the upper boundary on f to be PERIODIC as well as both lower and upper boundary conditions on phi to be set to PERIODIC\\n\"\n raise InputError('A lower boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector if self-consistent boundary potentials are desired). Equivalently, phi is not compatible with f (e.g. if periodic boundaries on f were desired, the potential must also be periodic)')\n\n elif BC['phi'][var]['type'] == 'SYMMETRIC_BIAS' or BC['phi'][var]['type'] == 'SYMMETRY_BIAS':\n BC['phi'][var]['type'] = 'LNBC_UDBC'\n\n # Neumann condition, dphi = 0 for symmetry\n phi_BC[var][0] = 0.\n # Dirichlet condition, phi = BIAS value\n phi_BC[var][-1] = float(Bias[var]['upper'])\n\n if BC['f'][var]['upper'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"upper boundary condition on phi: %s\" % BC['phi'][var]['upper'].upper()\n print \"upper boundary condition on f: %s\\n\\n\" % BC['f'][var]['upper'].upper()\n print \"\\ne.g. an upper boundary condition set on phi as BIAS must have the upper boundary condition on f as ABSORBING\\n \"\n raise InputError('An upper boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\n\n\n elif BC['phi'][var]['type'] == 'SYMMETRIC_SELF-CONSISTENT' or BC['phi'][var]['type'] == 'SYMMETRY_SELF-CONSISTENT':\n BC['phi'][var]['type'] = 'LDBC_LNBC'\n\n # We default to a LDBC_LNBC solver, both boundary conditions on left edge, entries 0 (Dirichlet) and 1 (Neumann)\n # cf. DECSKS-04 notebook for more details:\n #\n # https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb\n #\n # Dirichlet condition, set reference potential phi = 0\n phi_BC[var][0] = 0. # reference potential set to zero\n # Neumann condition, dphi = 0 for symmetry\n phi_BC[var][1] = 0.\n\n\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\n # By virtue of the setup, the above enforcements on the lower boundary ensures this unenforced upper Neumann BC is\n # satisfied automatically given the relationship that Neumann BCs are fixed by due to the Poisson equation\n #\n # see github.com/dsirajud/IPython-Notebooks/DECSKS-04 for more information (final few sections of the notebook)\n #\n # Thus, we do not need to actually enforce the wall potential directly in terms of the charge accumulated for this boundary; however,\n # we initialize and track the objects here so that the data can be accessed, analyzed or otherwise plotted, should the user wish\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"upper boundary condition on phi: %s\" % BC['phi'][var]['upper'].upper()\n print \"upper boundary condition on f: %s\\n\\n\" % BC['f'][var]['upper'].upper()\n print \"\\ne.g. an upper boundary condition set on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\\n \"\n\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\n\n elif BC['phi'][var]['type'] == 'SELF-CONSISTENT_SELF-CONSISTENT':\n BC['phi'][var]['type'] = 'LDBC_LNBC'\n\n # We default to a LDBC_LNBC solver, both boundary conditions on left edge, entries 0 (Dirichlet) and 1 (Neumann)\n # cf. DECSKS-04 notebook for more details:\n #\n # https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb\n #\n # Dirichlet condition, set reference potential phi = 0\n phi_BC[var][0] = 0. # reference potential set to zero\n # Neumann condition, dphi = 0 for symmetry\n #phi_BC[var][1] = - 6 * var.width * sim_params['sigma'][var]['lower'], changes with time step\n\n\n if BC['f'][var]['lower'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n # initialize wall charge densities\n sigma[var]['lower'] = 0 # initialize to zero charge at time zero\n sigma_n[var]['lower'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"lower boundary condition on phi on variable %s: SELF-CONSISTENT\" % var\n print \"lower boundary condition on f on variable %s: %s\\n\\n\" % (var, BC['f'][var]['lower'].upper())\n print \"\\ne.g. a lower boundary condition set on phi as SELF-CONSISTENT must have the lower boundary condition on f as COLLECTOR\\n \"\n\n raise InputError('A lower boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\n\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\n # By virtue of the setup, the above enforcements on the lower boundary ensures this unenforced upper Neumann BC is\n # satisfied automatically given the relationship that Neumann BCs are fixed by due to the Poisson equation\n #\n # see github.com/dsirajud/IPython-Notebooks/DECSKS-04 for more information (final few sections of the notebook)\n #\n # Thus, we do not need to actually enforce the wall potential directly in terms of the charge accumulated for this boundary; however,\n # we initialize and track the objects here so that the data can be accessed, analyzed or otherwise plotted, should the user wish\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\n else:\n print \"\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\n\\n\"\n print \"upper boundary condition on phi: SELF-CONSISTENT\"\n print \"upper boundary condition on f: %s\\n\\n\" % BC['f'][var]['upper'].upper()\n print \"\\ne.g an upper boundary condition set on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\\n \"\n\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\n\n # else: boundary conditions have already been checked for valid inputs, no invalid input will be encountered\n\n # --------------------------------------------------------------------------\n # ELECTRIC POTENTIAL PHI FUNCTION HANDLE STRING and BOUNDARY CONDITION TYPE FUNCTION HANDLE STRING\n #\n # currently only 1D1V, only one handle needed. When this will be generalized, can make a dict objects with keys corresponding\n # to each active configuration variable\n #\n # The forms of each string call their associated method per the boundary conditions specified by the user in params_boundaryconditions.dat,\n # based on the boundary conditions specified by the user, one of the following will be created:\n #\n # compute_electric_potential_phi_handle[var] =\n #\n # DECSKS.lib.fieldsolvers.Poisson_6th_PBC\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_UDBC\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_UNBC\n # DECSKS.lib.fieldsolvers.Poisson_6th_LNBC_UDBC\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_LNBC\n # DECSKS.lib.fieldsolvers.Poisson_6th_UDBC_UNBC (<-- available, but not used in any current combination of BCs)\n #\n #\n # and, one of the following\n #\n # distribution_function_boundarycondition_handle[var]['lower'] =\n #\n # DECSKS.lib.boundaryconditions.absorbing_lower_boundary\n # DECSKS.lib.boundaryconditions.collector_lower_boundary\n # DECSKS.lib.boundaryconditions.symmetric_lower_boundary\n #\n # NOTE: if 'periodic' has been specified, everything is\n # handled in the orchestrator, distribution_function_boundarycondition_orchestrator\n # which would take on the string value = 'DECSKS.lib.boundaryconditions.periodic\n\n\n distribution_function_boundarycondition_prefix = 'DECSKS.lib.boundaryconditions'\n distribution_function_boundarycondition_handle = {}\n for var in phasespace_vars:\n if BC['f'][var]['type'] == 'periodic':\n pass\n else:\n distribution_function_boundarycondition_handle[var] = {}\n\n distribution_function_boundarycondition_handle[var]['lower'] = \".\".join((distribution_function_boundarycondition_prefix, BC['f'][var]['lower']))\n distribution_function_boundarycondition_handle[var]['lower'] = \"_\".join((distribution_function_boundarycondition_handle[var]['lower'], 'lower_boundary'))\n\n distribution_function_boundarycondition_handle[var]['upper'] = \".\".join((distribution_function_boundarycondition_prefix, BC['f'][var]['upper']))\n distribution_function_boundarycondition_handle[var]['upper'] = \"_\".join((distribution_function_boundarycondition_handle[var]['upper'], 'upper_boundary'))\n\n\n compute_electric_potential_phi_handle = {}\n compute_electric_potential_phi_prefix = \"DECSKS.lib.fieldsolvers.Poisson_6th_\"\n for var in ['x', 'y', 'z']:\n if var in phasespace_vars:\n if HOC[var] == 'FOURIER': # uses a Gauss law solver to find E directly, which is called by the orchestrator on the fieldsolver\n pass\n else: # computes the electric field E by differentiating phi in an orchestrator fieldsolver function (string handle constructed above)\n # inside the orchestrator, a particular Poisson solver is called according with the boundary conditions indicated in params_boundaryconditions.dat\n compute_electric_potential_phi_handle[var] = compute_electric_potential_phi_prefix + BC['phi'][var]['type']\n else:\n pass\n\n # in the future, can generalize this to multiple dimensions by making this a dict with keys ['x', 'y', 'z']\n # currently just on 1D1V and expecting an 'x' variable to be evolved in configuration\n\n if 'x' not in phasespace_vars:\n raise NotImplementedError('Current 1D1V version of DECSKS is expecting x to be the active configuration variable. Please revise the intended simulation so that x is the symbol chosen in params.dat.')\n else:\n if HOC['x'] == 'FOURIER': # uses a Gauss solver to find E directly\n Poisson_6th_order_FD_solver_matrices = None\n\n else: # uses a Poisson solver to find phi, then differentiates to obtain E\n Poisson_6th_order_FD_solver_matrices = assemble_Poisson_6th_order_FD_solver_matrices(Nx_active, BC)\n\n derivative_method = {}\n derivative_method_prefix = 'DECSKS.lib.derivatives'\n for var in phasespace_vars:\n derivative_method[var] = \".\".join((derivative_method_prefix, HOC[var].lower()))\n\n sim_params = dict(\n N = N, HOC = HOC,\n derivative_method = derivative_method,\n Nx = Nx, ax = ax, bx = bx,\n Ny = Ny, ay = ay, by = by,\n Nz = Nz, az = az, bz = bz,\n Nvx = Nvx, avx = avx, bvx = bvx,\n Nvy = Nvy, avy = avy, bvy = bvy,\n Nvz = Nvz, avz = avz, bvz = bvz,\n Nt = Nt, T = T,\n phasespace_vars = phasespace_vars,\n numdims = numdims,\n active_dims = active_dims,\n total_dims = total_dims,\n density = density,\n mu = mu,\n split_scheme = split_scheme,\n splitting = splitting,\n plot_params = plot_params,\n record_outputs = record_outputs,\n outfiles = outfiles,\n BC = BC, # boundary condition types on all phase space variables on distribution function f and phi\n phi_BC = phi_BC, # dictionary containing boundary value vector for electric potential used in Poisson solve, e.g. phi_BC['x']\n sigma = sigma,\n sigma_n = sigma_n, # this was put in for charge history plots\n distribution_function_boundarycondition_handle = distribution_function_boundarycondition_handle, # dictionary with keys (var in phasespace_vars), which are keys to a subdict with keys 'lower', 'upper'\n distribution_function_boundarycondition_orchestrator_handle = distribution_function_boundarycondition_orchestrator_handle, # dictionary with keys (var in phasespace_vars)\n compute_electric_potential_phi_handle = compute_electric_potential_phi_handle,\n compute_electric_field_orchestrator_handle = compute_electric_field_orchestrator_handle,\n I_alternating = I_alternating, # identity matrix with alternating signs according to row, used in computing correctors c\n A_matrix = A_matrix, # Matrices of Bernoulli numbers for HOC\n W = W,\n W_dn1_LTE6 = W_dn1_LTE6,\n Xi = Xi, # spectral differentiation operator matrix (1j*xi[i,j]) ** q\n xi = xi, # wave number vector\n Poisson_6th_order_FD_solver_matrices = Poisson_6th_order_FD_solver_matrices\n )\n\n infile.close()\n\n # --------------------------------------------------------------------------\n # Before return, broadcast notification\n # regarding start of simulation and order of solver\n\n print \"\\nStarting 1D1V Vlasov-Poisson simulation\"\n print \"\\nadvection solver: LTE order %d\" % (N+1)\n print \"\\nwill step through %d-dimensional solution in variables: %s\\n\" % (len(phasespace_vars), phasespace_vars)\n for var in phasespace_vars:\n print \"high order correction method on %s: %s\" % (var, HOC[var])\n\n print \"\\n\"\n return sim_params","def shapefileToRaster(in_shapefile, model_raster, out_dir, name_override=None, zone_field:str = None, dtype = None, *args, **kwargs) -> str:\n\t# correct variable names\n\tshapefile_path = in_shapefile\n\t# get out_path\n\tif name_override:\n\t\tout_path = os.path.join(out_dir,name_override)\n\telse:\n\t\tin_base = os.path.splitext(os.path.basename(in_shapefile))[0]\n\t\tmodel_ext = os.path.splitext(model_raster)[1]\n\t\tout_path= os.path.join(out_dir,in_base+\"_RASTER\"+model_ext)\n\t# read file\n\tshp = gpd.read_file(shapefile_path)\n\twith rasterio.open(model_raster,'r') as rst:\n\t\tmeta = rst.meta.copy()\n\n\t# this is where we create a generator of geom, value pairs to use in rasterizing\n\tif zone_field is not None:\n\t\tzone_vals = []\n\t\tfor i in range(len(shp)):\n\t\t\tzone_vals.append(shp.at[i,zone_field])\n\t\tzone_codes = [i for i, val in enumerate(zone_vals)]\n\t\tshapes = ((geom,val) for geom, val in zip(shp.geometry,zone_codes))\n\telse:\n\t\tshapes = ((geom,1) for geom in shp.geometry)\n\n\t# set data type\n\tif dtype:\n\t\tmeta.update(dtype=dtype)\n\telif zone_field:\n\t\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype(zone_codes))\n\telse:\n\t\tmeta.update(dtype=\"int16\")\n\n\ttry:\n\t\tout = rasterio.open(out_path, 'w+', **meta)\n\t# merra-2 files have a very high nodata value, beyond the range of int32.\n\t# This block catches the resulting ValueError and swaps in the minimum\n\t# allowable data type. Nice of rasterio to have such a function.\n\texcept ValueError:\n\t\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype([meta['nodata']]))\n\t\tout = rasterio.open(out_path, 'w+', **meta)\n\t\tout_arr = out.read(1)\n\t\tburned = features.rasterize(shapes=shapes, fill=0, out=out_arr, transform=out.transform)\n\t\tout.write_band(1, burned)\n\tout.close()\n\n\treturn out_path","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n\n conn = sqlite3.connect('../raw/td_V2.db')\n git_commits = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS\",conn)\n szz_fault_inducing_commits = pd.read_sql_query(\"SELECT * FROM szz_fault_inducing_commits\",conn)\n refactoring_miner = pd.read_sql_query(\"SELECT * FROM refactoring_miner\",conn)\n refactoring_miner = refactoring_miner[refactoring_miner[\"COMMIT_HASH\"].isin(git_commits[\"COMMIT_HASH\"])]\n git_commits_changes = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS_CHANGES\", conn)\n git_commits_changes = git_commits_changes[git_commits_changes[\"COMMIT_HASH\"].isin(refactoring_miner[\"COMMIT_HASH\"])]\n\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)","def create_sample_vectors(cleaned_data_directory, out_vectors_path):\n vectors = []\n\n for filename in os.listdir(cleaned_data_directory):\n if not filename.endswith(\".txt\"):\n continue\n\n path = os.path.join(cleaned_data_directory, filename)\n f = open(path, mode='r', encoding='utf8')\n\n print(\"Processing\", path)\n\n lang = filename[:2]\n lang_number = language_codes.index(lang)\n\n print(f\"\\tLanguage: {lang} ({lang_number})\")\n print(\"\\tReading...\", end=' ')\n\n file_content = f.read()\n content_length = len(file_content)\n\n print(\"done.\")\n print(\"\\tExtracting vectors...\", end=' ')\n\n sample_start_index = 0\n count = 0\n\n while sample_start_index + text_sample_size < content_length:\n sample = get_sample(file_content, sample_start_index, text_sample_size)\n input_vector = build_input_vector(sample)\n vector = input_vector + [lang_number]\n vectors.append(vector)\n sample_start_index += text_sample_size\n count += 1\n\n print(\"done.\")\n print(f\"\\tExtracted {count} vectors.\")\n\n del file_content\n\n print(f\"Total {len(vectors)} vectors.\")\n\n np_vectors = np.array(vectors, dtype=np.uint16)\n np.random.shuffle(np_vectors)\n\n print(f\"Converted to NumPy array, shape: {np_vectors.shape}.\")\n\n np.savez_compressed(out_vectors_path, data=np_vectors)\n\n print(f\"Saved to {out_vectors_path}.\")","def vtt_to_srt(str_name_file: str):\n file_contents: str = read_text_file(str_name_file)\n str_data: str = \"\"\n str_data = str_data + convert_content(file_contents)\n str_name_file: str = str_name_file.replace(\".vtt\", \".srt\")\n print(str_name_file)\n file_create(str_name_file, str_data)","def make_voxels_isometric(input_file):\n import os\n import nibabel as nb\n from mindboggle.utils.utils import execute\n\n # Load image volume\n img = nb.load(input_file)\n lenx, leny, lenz = img.shape\n if lenx != leny or lenx != lenz or leny != lenz:\n\n # Pad smaller dimensions\n max_dim = max([lenx, leny, lenz])\n padx = max_dim - lenx\n pady = max_dim - leny\n padz = max_dim - lenz\n\n # Save padded output\n out_file = 'isometric_' + os.path.basename(input_file)\n pad_dims = '{0}x{1}x{2}vox'.format(padx, pady, padz)\n cmd = ['c3d', input_file, '-pad 0x0x0vox', pad_dims, '-o ', out_file]\n execute(cmd)\n\n # Resample output\n max_dims = '{0}x{1}x{2}'.format(max_dim, max_dim, max_dim)\n cmd = ['c3d', out_file, '-resample', max_dims, '-o ', out_file]\n execute(cmd)\n\n else:\n out_file = input_file\n\n return out_file","def drr(input_filename,output_directory,calibration_files,output_extension,threshold,min_out,max_out,default_pixel_value,rot,t,cor,n_cam,res,size,transformed_vol,verbose):\n \n click.echo(' inputFileName : {}'.format(input_filename))\n click.echo(' out_directory : {}'.format(output_directory))\n click.echo(' outputExtension : {}'.format(output_extension))\n click.echo(' Verbose Status : {}'.format(verbose))\n click.echo(' Pixel Size : {}'.format(res))\n click.echo(' Output image Size : {}'.format(size))\n click.echo(' Translation : {}'.format(t))\n click.echo(' Rotation : {}'.format(rot))\n click.echo(' Centre of Rotation : {}'.format(cor))\n click.echo(' Threshold : {}'.format(threshold))\n click.echo(' Number of Cameras : {}'.format(n_cam)) \n click.echo(' Minimum Out : {}'.format(min_out))\n click.echo(' Maximum Out : {}'.format(max_out))\n click.echo(' Calibration Files : {}'.format(calibration_files)) \n \n if len(calibration_files) != n_cam :\n raise Exception('Number of Calibration files', len(calibration_files),'do not correspond with the number of Cameras',n_cam)\n \n #%%------------------ Starting the main body of the code ---------------- \n # -------------------- Reader -------------------------\n InputPixelType = itk.ctype(\"short\")\n OutputPixelType = itk.ctype(\"short\")\n ScalarType = itk.D\n DimensionIn = 3\n DimensionOut = 3\n \n InputImageType = itk.Image[InputPixelType , DimensionIn ]\n OutputImageType = itk.Image[OutputPixelType, DimensionOut]\n \n \n ReaderType = itk.ImageFileReader[InputImageType]\n reader = ReaderType.New()\n reader.SetFileName(input_filename)\n \n try:\n print(\"Reading image: \" + input_filename)\n reader.Update()\n print(\"Image Read Successfully\")\n except ValueError: \n print(\"ERROR: ExceptionObject cauth! \\n\")\n print(ValueError)\n sys.exit()\n \n inputImage = reader.GetOutput()\n \n if verbose :\n print(inputImage)\n \n \n #%% ------------------ Transformation \n # This part is inevitable since the interpolator (Ray-cast) and resample Image\n # image filter uses a Transformation -- Here we set it to identity. \n TransformType = itk.CenteredEuler3DTransform[itk.D]\n transform = TransformType.New()\n \n transform.SetRotation(numpy.deg2rad(rot[0]),numpy.deg2rad(rot[1]),numpy.deg2rad(rot[2])) # Setting the rotation of the transform\n transform.SetTranslation(itk.Vector.D3(t)) # Setting the translation of the transform\n transform.SetComputeZYX(True) # The order of rotation will be ZYX. \n \n imOrigin = inputImage.GetOrigin() # Get the origin of the image.\n inRes = inputImage.GetSpacing() # Get the resolution of the input image.\n inSiz = inputImage.GetBufferedRegion().GetSize() # Get the size of the input image.\n \n center = itk.Point.D3(imOrigin) + numpy.multiply(inRes,inSiz)/2. # Setting the center of rotation as center of 3D object + offset determined by cor. \n \n transform.SetCenter(center) # Setting the center of rotation. \n \n if verbose :\n print(transform)\n \n #%% \n for ii in range(n_cam):\n imageCalibrationInfo = CalibrationUsingJointTrack.CalibrationTool() # Setting up the image calibration info class. \n imageCalibrationInfo.SetCalibrationInfo(calibration_files[ii]) # Assign the information from the calibration file to the imageCalibrationInfo class. \n \n spaceOutput= imageCalibrationInfo.GetPixelSize() # The resolution (spacing) along x,y,z directions of output image\n \n imageCalibrationInfo.SetOutputImageSize(size[0],size[1],1) # Setting the size of the output image. \n imageCalibrationInfo.SetGlobalOriginForImagePlane() # Setting the global origin of the output image. \n \n originOutput = imageCalibrationInfo.GetGlobalOriginForImagePlane() # Setting the output origin. \n \n directionOutput = imageCalibrationInfo.GetDirectionMatrix() # Direction of Image plane 3x3 matrix. \n focalPoint = imageCalibrationInfo.GetFocalPoint() # Position of the x-ray source. \n \n #%% ----------------- Ray Cast Interpolator \n # In this part the Ray Cast interpolator is defined and applied to the input\n # image data. \n \n InterpolatorType = itk.RayCastInterpolateImageFunction[InputImageType,ScalarType] # Defining the interpolator type from the template. \n interpolator = InterpolatorType.New() # Pointer to the interpolator\n \n interpolator.SetInputImage(inputImage) # Setting the input image data\n interpolator.SetThreshold(threshold) # Setting the output threshold\n interpolator.SetFocalPoint(itk.Point.D3(focalPoint)) # Setting the focal point (x-ray source location)\n interpolator.SetTransform(transform) # Setting the transform (here identity)\n \n if verbose:\n print(interpolator)\n #%%----------------- Resample Image Filter ------------------------\n # In this part the resample image filter to map a 3D image to 2D image plane with desired specs is designed\n \n FilterType = itk.ResampleImageFilter[InputImageType,OutputImageType] # Defining the resample image filter type. \n resamplefilter = FilterType.New() # Pointer to the filter\n resamplefilter.SetInput(inputImage) # Setting the input image data \n resamplefilter.SetDefaultPixelValue(default_pixel_value) # Setting the default Pixel value\n resamplefilter.SetInterpolator(interpolator) # Setting the interpolator\n resamplefilter.SetTransform(transform) # Setting the transform\n resamplefilter.SetSize([size[0],size[1],1]) # Setting the size of the output image. \n resamplefilter.SetOutputSpacing(itk.Vector.D3([spaceOutput[0],spaceOutput[1],1])) # Setting the spacing(resolution) of the output image. \n resamplefilter.SetOutputOrigin(originOutput) # Setting the output origin of the image\n Functions.ChangeImageDirection(oldDirection=resamplefilter.GetOutputDirection(),newDirection=directionOutput,DimensionOut=3) # Setting the output direction of the image --- resamplefilter.SetImageDirection(args) was not working properly\n \n resamplefilter.Update() # Updating the resample image filter.\n \n if verbose:\n print(resamplefilter)\n #%%---------------- Rescaler Image Filter --------------------------\n RescalerFilterType = itk.RescaleIntensityImageFilter[InputImageType,OutputImageType] # Defining the rescale image filter. \n rescaler = RescalerFilterType.New() # Pointer to the rescale filter\n rescaler.SetOutputMinimum(min_out) # Minimum output\n rescaler.SetOutputMaximum(max_out) # Maximum output \n rescaler.SetInput(resamplefilter.GetOutput()) # Setting the input to the image filter. \n rescaler.Update() \n \n if verbose:\n print(rescaler)\n \n #%% ------------------ Writer ------------------------------------\n # The output of the resample filter can then be passed to a writer to\n # save the DRR image to a file.\n WriterType = itk.ImageFileWriter[OutputImageType]\n writer = WriterType.New()\n \n outputPath = os.path.join(output_directory,'Cam')+str(ii+1)\n \n if not os.path.exists(outputPath):\n os.mkdir(outputPath)\n \n if ii == 0:\n time = datetime.datetime.now() \n dummy = ('rx'+str(int(rot[0]))+'ry'+str(int(rot[1]))+'rz'+str(int(rot[2]))+'tx'\n + str(int(t[0]))+'ty'+str(int(t[1]))+'tz'+str(int(t[2]))+'y'+str(time.year)+'m'+str(time.month)\n +'d'+str(time.day)+'hr'+str(time.hour)+'m'+str(time.minute)+'s'+str(time.second)+ output_extension)\n \n outputName = 'Cam'+str(ii+1)+dummy\n output_filename = str(os.path.join(outputPath,outputName))\n \n writer.SetFileName(output_filename)\n # writer.SetFileName('/Volumes/Storage/Payam/Desktop/output.nii') \n writer.SetInput(rescaler.GetOutput())\n \n try:\n print(\"Writing image: \" + output_filename)\n writer.Update()\n print(\"Image Printed Successfully\")\n except ValueError: \n print(\"ERROR: ExceptionObject cauth! \\n\")\n print(ValueError)\n sys.exit()\n \n \n # Writing the transformed volume\n if transformed_vol:\n WriterType=itk.ImageFileWriter[InputImageType]\n writer3d=WriterType.New()\n \n output_filename3d = os.path.join(output_directory,'TransformedVolume'+output_extension)\n writer3d.SetFileName(output_filename3d)\n writer3d.SetInput(resamplefilter.GetOutput())\n \n try:\n print(\"Writing the transformed Volume at : \" + output_filename3d)\n writer.Update()\n print(\"Volume Printed Successfully\")\n except ValueError: \n print(\"ERROR: ExceptionObject cauth! \\n\")\n print(ValueError)\n sys.exit()","def vpsc_in(\r\n ## # elem, ph and vol fraction of phases.\r\n ## and ishape for flag for morphology updates\r\n nelem=1, nph=1, wph =[1.0, 0.0], ishape=[0,],\r\n\r\n #framgmentation options and critical value limit \r\n # in distortion of the inclusion \r\n # as well as ellipsoid description: orientation and \r\n # principal lengths of'em\r\n fragmentn = [0,0], crit = [25,25],\r\n ellipangl = [[0., 0., 0.],[0.,0.,0.]],\r\n ellipaxes = [[1., 1., 1.],[1.,1.,1.]],\r\n \r\n # vpsc7.in file name (fixed)\r\n # texture, singlecrystal and shape file names in list format\r\n vin_name = 'VPSC7.in',\r\n tfile= ['texture/500.tex',],\r\n sxfile=['sx/hijhiihb.sx',],\r\n fileaxes=['shape1.100', 'shape1.100'],\r\n\r\n ### precision settings for convergence prodcedure\r\n ##\r\n err=0.001, errd=0.001, errm=0.001, errso=0.01,\r\n nmxiter=100, exiter=100, initer=100,\r\n irsvar=0, xrsini=2, xrsfin=10, xrstep=2,\r\n\r\n ### post-mortem flags, texture component calc. flag.\r\n ## \r\n irecover = 0, isave=0, icubcomp = 0, nwrite = 0, \r\n\r\n ## Single crystal hardening rule (-2: dislocation, 0:Voce, 1: MTS, 2: composite)\r\n ihardlaw = 0, \r\n\r\n ## fluctuation flag\r\n iflu = 0,\r\n\r\n ## rate insensitive, interaction :(FC,aff,2:sec,3:neff,4:tan,5:SO)\r\n ## update flags for orient, grain shape, hardening, itran(youngung)\r\n ## nneigh (0 for no neighbors, 1 for pairs, etc.)\r\n iratesens = 0, interaction = 3, iupdate = [1,1,1,0], nneigh = 0, \r\n\r\n ## proce number and processes\r\n npro = None,\r\n prcs = [], #processes\r\n \r\n ishow=False\r\n ):\r\n try: os\r\n except: import os\r\n else: pass\r\n cwd = os.getcwd()\r\n\r\n # READ EXISITING 'vpsc7.in'\r\n if os.path.isfile(vin_name): #if there already exists the file!\r\n f_vin = file(vin_name, 'r')\r\n lines = f_vin.read()\r\n lines = lines.split('\\n')\r\n f_temp = file('%s.tmp'%vin_name, 'w')\r\n for i in lines:\r\n f_temp.writelines(i)\r\n f_temp.writelines('\\n')\r\n pass\r\n f_temp.close()\r\n f_vin.close()\r\n \"\"\"\r\n # DEL EXISITING 'vpsc7.in'\r\n f_vin.close()\r\n os.remove(vin_name)\r\n \"\"\"\r\n # MAKE NEW 'vpsc7.in'\r\n f_vin = file(vin_name, 'w')\r\n f_vin.writelines('%i nelem\\n'%(nelem))\r\n f_vin.writelines('%i nph\\n'%(nph))\r\n for i in range(nph):\r\n f_vin.writelines('%f '%(wph[i]))\r\n f_vin.writelines(' wph[i]\\n')\r\n for i in range(nph): \r\n f_vin.writelines('*** INFORMATION ABOUT PHASE #%i\\n'%(i+1))\r\n f_vin.writelines('%i %i %i'%(ishape[i], fragmentn[i], crit[i]))\r\n f_vin.writelines(' ishape, fragmentn, crit aspect\\n')\r\n f_vin.writelines('%f %f %f'%(ellipaxes[i][0], \r\n ellipaxes[i][1], \r\n ellipaxes[i][2]))\r\n f_vin.writelines(' initial ellipsoid ratios (dummy if ishape=4)\\n')\r\n f_vin.writelines('%f %f %f'%(ellipangl[i][0], \r\n ellipangl[i][1], \r\n ellipangl[i][2]))\r\n f_vin.writelines(' init Eul ang ellips axes(dummy if ishape=3,4)\\n')\r\n # WRITES TEXTURE FILE\r\n f_vin.writelines('----------- filetext\\n')\r\n f_vin.writelines(tfile[i]+'\\n')\r\n f_vin.writelines('----------- sxfile\\n')\r\n # WRITES SINGLE CRYSTAL FILE\r\n f_vin.writelines(sxfile[i]+'\\n')\r\n # WRITES SHAPE FILE\r\n f_vin.writelines('----------- fileaxes (dummy if ishape=0) \\n')\r\n f_vin.writelines('%s \\n'%(fileaxes[i]))\r\n\r\n\r\n #### precision control block ###\r\n f_vin.writelines('*** PRECISION SETTINGS FOR')\r\n f_vin.writelines(' CONVERGENCE PROCEDURES (default values)\\n')\r\n f_vin.writelines('%f %f %f %f'%(err, #err\r\n errd, \r\n errm,\r\n errso))\r\n f_vin.writelines(' errs,errd,errm,errso\\n')\r\n f_vin.writelines('%i %i %i %25s'%( nmxiter, exiter, initer,' '))\r\n f_vin.writelines('itmax: max # of iter, external, internal and SO loops\\n')\r\n f_vin.writelines('%i %i %i %i %30s'%(irsvar, xrsini, xrsfin, xrstep,' '))\r\n f_vin.writelines('irsvar & xrsini, xrsfin, xrstep (dummy if irsvar = 0) \\n')\r\n f_vin.writelines(\"%i ibcinv (0: don't use <Bc>**-1, 1: use \\n\"%(1))\r\n f_vin.writelines('*** INPUT/OUTPUT SETTINGS FOR THE RUN(default is zero)\\n')\r\n f_vin.writelines('%i irecover:read grain stats from POSTMORT.IN(1) OR NOT(0)\\n'%(irecover))\r\n f_vin.writelines(\"%i isave: write grain states in postmor.out at step 'isave'?\\n\"%(isave))\r\n f_vin.writelines(\"%i icubcompL calcuate fcc rolling components?\\n\"%(icubcomp))\r\n f_vin.writelines(\"%i nwrite (frequency of texture downloads)\\n\"%(nwrite))\r\n f_vin.writelines(\"*** MODELING CONDITIONS FOR THE RUN \\n\")\r\n f_vin.writelines(\"%i ihardlaw(0:VOCE, 1: MTS, 2: composite grain\\n\"%(ihardlaw))\r\n f_vin.writelines('%1i' %(iratesens))\r\n f_vin.writelines(' iratesens (0:rate insensitive, 1:rate sensitive) \\n')\r\n f_vin.writelines('%1i' %(interaction))\r\n f_vin.writelines(' interaction (0:FC,1:affine,2:secant,3:neff=10,4:tangent,5:SO) \\n')\r\n f_vin.writelines('%1i%3i%3i%3i'%(iupdate[0],iupdate[1],iupdate[2],iupdate[3]))\r\n f_vin.writelines(' iupdate: update ori, grain shape, hardening, itran \\n')\r\n f_vin.writelines('%1i'%(nneigh))\r\n f_vin.writelines(' nneigh (0 for no neighbors, 1 for pairs, etc.)\\n')\r\n f_vin.writelines('%i'%(iflu))\r\n f_vin.writelines(\" iflu(0:don't calc, 1: calc fluctuations\\n\")\r\n\r\n f_vin.writelines(\"*NUMBER OF PROCESSES\\n\")\r\n f_vin.writelines(\"%i \\n\"%(npro))\r\n f_vin.writelines(\"** (0,1: loadings, 2:pcys, 3:lankf, 4: RBR,\")\r\n f_vin.writelines(\"5:pcys_pl, 6: pysc_pl 6d(ix,iy, nprob, ang\")\r\n f_vin.writelines(\"( or if negative behave like pcys))) \\n\")\r\n for i in range(len(prcs)):\r\n f_vin.writelines(prcs[i]+'\\n')\r\n \r\n # Closure of the file\r\n f_vin.close() \r\n # -------------------\r\n\r\n # Printing-out the just-made-vpsc7.in file\r\n if ishow==True:\r\n if os.name=='nt' or os.name=='posix':\r\n print \"****** VPSC input file ******\"\r\n print \"* VPSC7 input parameters \"\r\n print \"* has been written down to \"\r\n print \"* '%8s' as below\"%vin_name\r\n print \"*****************************\\n\"\r\n if os.name=='nt':\r\n os.system('%s %s'%('type',vin_name))\r\n elif os.name=='posix':\r\n os.system('%s %s'%('cat',vin_name))\r\n else: pass","def transfer_rast_to_vect(poly_cstr, lyrname, out_field, rast_cstr, srs, method, where=None, geom_field=\"geometry\",\n id_field=\"ogc_fid\", buffer_rad=0, restrict_to_tile=True):\n ds = gdal.Open(rast_cstr)\n georef = ds.GetGeoTransform()\n raster_array = ds.ReadAsArray()\n img_shape = (ds.RasterYSize, ds.RasterXSize)\n LOG.info(\"Done reading raster, shape is: %s\", img_shape)\n ctx = {\n 'lyrname': lyrname,\n 'out_field': out_field,\n 'where': where,\n 'geom_field': geom_field,\n 'id_field': id_field,\n \"srs\": srs\n }\n if buffer_rad:\n ctx['geom_field'] = 'st_buffer({}, {})'.format(geom_field, buffer_rad)\n layer_sql = \"\"\"select {geom_field}, {out_field}, {id_field} as the_id from {lyrname}\"\"\".format(**ctx)\n if restrict_to_tile:\n # Weird geoms could be skipped by this, so add as an optione\n layer_sql += \" where st_intersects({geom_field}, st_geomfromtext(WKT_EXT, {srs}))\".format(**ctx)\n\n if where:\n if restrict_to_tile:\n layer_sql += \" and \" + where\n else:\n layer_sql += \" where \" + where\n LOG.info(\"Layersql: %s\", layer_sql)\n extent = get_extent(georef, img_shape)\n LOG.info(\"Extent: %s\", extent)\n vec_ds, lyr = open(poly_cstr, layersql=layer_sql, extent=extent, open_for_update=True)\n mask = just_burn_layer(lyr, georef, img_shape, attr='the_id', dtype=np.int32, all_touched=False)\n LOG.info(\"Done burning - setting attr in %d features\", lyr.GetFeatureCount())\n LOG.debug(\"%s\", np.unique(mask))\n n_ok = 0\n for n, feat in enumerate(lyr):\n if n % 100 == 0:\n LOG.info(\"Done: %d, ok: %d\", n, n_ok)\n daid = feat['the_id']\n ctx['the_id'] = daid\n area = feat.GetGeometryRef().GetArea()\n I, J = np.where(mask == daid)\n # At least 30% covered if already set - todo: provide this as argument\n if I.size > 0 and (feat[out_field] is None or I.size * (georef[1] ** 2) > area * 0.3):\n is_ok, val = method(raster_array, I, J)\n if is_ok:\n n_ok += 1\n ctx['_value_'] = val\n updatesql = \"update {lyrname} set {out_field}={_value_} where {id_field}={the_id}\".format(**ctx)\n LOG.debug(\"Executing: %s\", updatesql)\n vec_ds.ExecuteSQL(updatesql)\n else:\n LOG.debug(\"Nothing found for %s - mask size: %s, valid: %s, area: %s\",\n daid, I.size, feat.GetGeometryRef().IsValid(), area)","def readGR3File(inputFilename):\n print 'Reading ' + inputFilename + ' ...'\n infile = open(inputFilename, 'r')\n description = infile.readline().strip() # remove leading/trailing whitespace\n tmpStr = infile.readline()\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\n\n # nodes\n nodeArray = readNodeBlock(infile, nNodes)\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\n nodexyz = np.zeros((nNodes, 3))\n nodexyz[:, :2] = nodeArray[:, 1:3]\n nodalValues = nodeArray[:, 3]\n\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\n\n # triangular elements\n triArray = readElemBlock(infile, nTriangles)\n\n trinum = triArray[:, 0].flatten()\n tritype = triArray[0, 1]\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\n #triangles = meshElements(trinodes,trinum,tritype)\n\n x = nodexyz[:, 0]\n y = nodexyz[:, 1]\n\n tmpStr = infile.readline()\n boundaries = []\n if len(tmpStr) > 0:\n # boundary information, if not end of file\n nOpenBndSegments = int(tmpStr.split()[0])\n nOpenBndNodesTot = int(infile.readline().split()[0])\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\n for iBnd in range(nOpenBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n tag = bndHeader[-1]\n if tag.isdigit():\n tag = 'open' + tag\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\n nLandBndSegments = int(infile.readline().split()[0])\n nLandBndNodesTot = int(infile.readline().split()[0])\n landBndTags = range(\n nOpenBndSegments + 1,\n nOpenBndSegments + nLandBndSegments + 1)\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\n for iBnd in range(nLandBndSegments):\n bndHeader = infile.readline().split()\n nBndNodes = int(bndHeader[0])\n try:\n landType = int(bndHeader[1])\n except:\n print \"\"\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\n 1002 = Total number of closed boundary nodes\n 501 0 = Number of nodes in closed boundary 1\"\"\"\n raise Exception(\n 'Could not parse land boundary type (0/1 - land/island)\\n')\n landType = 'island' if landType == 1 else 'land'\n tag = landType + bndHeader[-1]\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\n tmpList = []\n for iN in range(nBndNodes):\n tmpList.append(int(infile.readline()))\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\n nodes = np.array(tmpList, dtype=int) - 1\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\n\n infile.close()\n\n # for better interpolation, round coordinates to 1e-4\n nDig = 4\n x = np.round(x, nDig)\n y = np.round(y, nDig)\n\n return x, y, nodalValues, trinodes, boundaries, description","def mrtrix_mesh2vox(surface_path, template_path, temp_dir, output_prefix):\n # Adapt affine translation using metadata\n template = nib.load(template_path)\n _, _, meta = read_geometry(surface_path, read_metadata=True)\n\n template = nib.as_closest_canonical(template)\n affine = template.affine.copy()\n affine[:-1, -1] = template.affine[:-1, -1] - meta['cras']\n\n new_template = nib.Nifti1Image(template.dataobj, affine)\n new_template_path = temp_dir / 'template.mgz'\n nib.save(new_template, new_template_path)\n\n # Reconstruct volume from mesh\n subprocess.run(['mesh2voxel', surface_path, new_template_path, temp_dir / f'{output_prefix}_output.mgz'])\n\n # Save the reconstructed volume with the right affine\n output = nib.load(temp_dir / f'{output_prefix}_output.mgz')\n new_output = nib.Nifti1Image(output.dataobj, template.affine)\n # nib.save(new_output, output_path)\n\n return new_output","async def transform(self, file):\n\t\tpass","def load_rd_uv(in_file):\n uv_data = np.load(in_file)\n return uv_data['ucs'], uv_data['vcs']","def inputfile(filename):\n infile = open(filename, 'r')\n lines = infile.readlines()\n\n # --------------------------------------------------------------------------\n # Domain specifications\n\n Nx = eval(lines[15][lines[15].find('=')+1:].strip())\n ax = eval(lines[16][lines[16].find('=')+1:].strip())\n bx = eval(lines[17][lines[17].find('=')+1:].strip())\n\n Ny = eval(lines[21][lines[21].find('=')+1:].strip())\n ay = eval(lines[22][lines[22].find('=')+1:].strip())\n by = eval(lines[23][lines[23].find('=')+1:].strip())\n\n Nz = eval(lines[27][lines[27].find('=')+1:].strip())\n az = eval(lines[28][lines[28].find('=')+1:].strip())\n bz = eval(lines[29][lines[29].find('=')+1:].strip())\n\n Nvx = eval(lines[33][lines[33].find('=')+1:].strip())\n avx = eval(lines[34][lines[34].find('=')+1:].strip())\n bvx = eval(lines[35][lines[35].find('=')+1:].strip())\n\n Nvy = eval(lines[39][lines[39].find('=')+1:].strip())\n avy = eval(lines[40][lines[40].find('=')+1:].strip())\n bvy = eval(lines[41][lines[41].find('=')+1:].strip())\n\n Nvz = eval(lines[45][lines[45].find('=')+1:].strip())\n avz = eval(lines[46][lines[46].find('=')+1:].strip())\n bvz = eval(lines[47][lines[47].find('=')+1:].strip())\n\n Nt = eval(lines[51][lines[51].find('=')+1:].strip())\n T = eval(lines[52][lines[52].find('=')+1:].strip())\n\n N = eval(lines[58][lines[58].find('=')+1:].strip())\n\n # --------------------------------------------------------------------------\n # Broadcast notification regarding start of simulation and order of solver\n\n print \"\\nStarting 1D1V Vlasov-Poisson simulation\"\n print \"\\nadvection solver: LTE order %d\" % (N+1)\n\n # --------------------------------------------------------------------------\n # Boundary conditions\n\n # stored as a dictionary of dictionaries, access as\n # BC['z']['upper'] and BC['z']['lower'] for z = {x, y, ...}\n\n BC = {}\n # main dictionary with key/values {'x' : {'lower' : value, 'upper : value},\n # {'y' : {'lower' : value, 'upper : value},\n # {'z' : {'lower' : value, 'upper : value},\n # {'vx' : {'lower' : value, 'upper : value},\n # {'vy' : {'lower' : value, 'upper : value},\n # {'vz' : {'lower' : value, 'upper : value},\n\n\n # subdictionaries with key/values {'lower' : BC_value, and 'upper' : BC_value}\n BC['x'] = {}\n BC['x']['lower'] = lines[18][lines[18].find('=')+1:].strip()\n BC['x']['upper'] = lines[19][lines[19].find('=')+1:].strip()\n\n BC['y'] = {}\n BC['y']['lower'] = lines[24][lines[24].find('=')+1:].strip()\n BC['y']['upper'] = lines[25][lines[25].find('=')+1:].strip()\n\n BC['z'] = {}\n BC['z']['lower'] = lines[30][lines[30].find('=')+1:].strip()\n BC['z']['upper'] = lines[31][lines[31].find('=')+1:].strip()\n\n BC['vx'] = {}\n BC['vx']['lower'] = lines[36][lines[36].find('=')+1:].strip()\n BC['vx']['upper'] = lines[37][lines[37].find('=')+1:].strip()\n\n BC['vy'] = {}\n BC['vy']['lower'] = lines[42][lines[42].find('=')+1:].strip()\n BC['vy']['upper'] = lines[43][lines[43].find('=')+1:].strip()\n\n BC['vz'] = {}\n BC['vz']['lower'] = lines[48][lines[48].find('=')+1:].strip()\n BC['vz']['upper'] = lines[49][lines[49].find('=')+1:].strip()\n\n # --------------------------------------------------------------------------\n # Store number of active gridpoints for every phase space variable\n #\n # Note: for periodic BCs: Nz_active = Nz - 1\n # for all other BCs: Nz_active = Nz\n\n # TODO this is acknowledged as being redundant, but more specific than the lists\n # active_dims vs. total_dims\n if BC['x']['lower'] == 'periodic' and BC['x']['upper'] == 'periodic' and Nx is not None:\n Nx_active = Nx - 1\n else:\n Nx_active = Nx\n\n if BC['y']['lower'] == 'periodic' and BC['y']['upper'] == 'periodic' and Ny is not None:\n Ny_active = Ny - 1\n else:\n Ny_active = Ny\n\n if BC['z']['lower'] == 'periodic' and BC['z']['upper'] == 'periodic' and Nz is not None:\n Nz_active = Nz - 1\n else:\n Nz_active = Nz\n\n if BC['vx']['lower'] == 'periodic' and BC['vx']['upper'] == 'periodic' and Nvx is not None:\n Nvx_active = Nvx - 1\n else:\n Nvx_active = Nvx\n\n if BC['vy']['lower'] == 'periodic' and BC['vy']['upper'] == 'periodic' and Nvy is not None:\n Nvy_active = Nvy - 1\n else:\n Nvy_active = Nvy\n\n if BC['vz']['lower'] == 'periodic' and BC['vz']['upper'] == 'periodic' and Nvz is not None:\n Nvz_active = Nvz - 1\n else:\n Nvz_active = Nvz\n\n # --------------------------------------------------------------------------\n # High order correction (HOC) method applied to each phase space variable\n\n HOC = {}\n HOC['x'] = lines[68][lines[68].find(':')+1:].strip().upper()\n HOC['y'] = lines[69][lines[69].find(':')+1:].strip().upper()\n HOC['z'] = lines[70][lines[70].find(':')+1:].strip().upper()\n\n HOC['vx'] = lines[72][lines[72].find(':')+1:].strip().upper()\n HOC['vy'] = lines[73][lines[73].find(':')+1:].strip().upper()\n HOC['vz'] = lines[74][lines[74].find(':')+1:].strip().upper()\n\n\n # list of phase space variables used, in etc/params.dat must set unused\n # vars to have Nz as None, z = x, vx, y, ...\n # e.g. in 1D1V, phasespace_vars = ['x', 'vx']\n phasespace_vars = []\n if Nx is not None:\n phasespace_vars.append('x')\n if Ny is not None:\n phasespace_vars.append('y')\n if Nz is not None:\n phasespace_vars.append('z')\n if Nvx is not None:\n phasespace_vars.append('vx')\n if Nvy is not None:\n phasespace_vars.append('vy')\n if Nvz is not None:\n phasespace_vars.append('vz')\n\n print \"will step through %d-dimensional solution in variables: %s\" % (len(phasespace_vars), phasespace_vars)\n for var in phasespace_vars:\n print \"high order correction method on %s: %s\" % (var, HOC[var])\n\n # for periodic BCs, the number of active dims is not equal to the\n # total number of dims, we evolve \"Nz-1\" gridpoints, then assign\n # the Nth point by periodicity as equal to the 0th point. Hence,\n # a distinction is needed between active dims and total dims\n # where we note they are identical in all cases but periodic BCs.\n\n # TODO as mentioned above, this is now a redundant set of total grid points\n # as compared to active grid points. At some point, need to trace where\n # this is actually used in the code and replace or remove it\n\n # initialize lists\n total_dims = []\n active_dims = []\n\n # strip all whitespace in each entry\n for var in phasespace_vars:\n total_dims.append(eval('N' + var))\n\n if ( (BC[var]['lower'] == 'periodic') and (BC[var]['upper'] == 'periodic') ):\n active_dims.append(eval('N' + var) - 1)\n else:\n active_dims.append(eval('N' + var))\n\n # TODO this is a misleading name, should be numvars\n numdims = len(phasespace_vars)\n\n # --------------------------------------------------------------------------\n # Initial density specification\n #\n # the following establishes a difference between the number of densities\n # specified in etc/params.dat. Should there be two, the solver is a two\n # species Vlasov solver. If only one, then a cold background will be\n # automatically computed (TODO)\n\n\n densities_list = lines[79][lines[79].find(':')+1:].strip().split(', ')\n for i in range(len(densities_list)):\n densities_list[i] = densities_list[i].lower()\n\n if len(densities_list) == 2: # if two species return dictionary of strings\n density = {}\n density['electrons'] = densities_list[0]\n density['electrons'] = density['electrons'].lower()\n density['ions'] = densities_list[1]\n density['ions'] = density['ions'].lower()\n print \"\\ntwo species simulation with initial densities:\\n\"\n print \"electrons: %s\" % density['electrons']\n print \"ions: %s\\n\" % density['ions']\n\n elif len(densities_list) == 1: # if one species return a string\n density = densities_list[0]\n print \"one species (electron) simulation with initial density: %s\" % density\n # TODO compute cold background, store both this and the above\n # in a common dictionary as above for two species.\n\n # --------------------------------------------------------------------------\n # Split scheme specification\n\n split_scheme = lines[98][lines[98].find('=')+1:].strip()\n split_scheme = split_scheme.upper()\n print \"split scheme: %s\\n\\n\" % split_scheme\n\n # filepath to splitting coefficient tables\n filename = lines[99][lines[99].find(':')+1:].strip()\n filepath = './etc/' + filename\n\n # get splitting coefficients for chosen scheme\n if split_scheme is not None:\n splitting = splitting_coefficients(filepath, split_scheme)\n else:\n splitting = None\n\n # --------------------------------------------------------------------------\n # Plot window specification (used in lib.plots.Setup)\n\n xmin = eval(lines[113][lines[113].find('=')+1:].strip())\n xmax = eval(lines[114][lines[114].find('=')+1:].strip())\n ymin = eval(lines[116][lines[116].find('=')+1:].strip())\n ymax = eval(lines[117][lines[117].find('=')+1:].strip())\n\n plot_params = dict(xmin = xmin, xmax = xmax,\n ymin = ymin, ymax = ymax)\n\n record_outputs = lines[120][lines[120].find(':')+1:].strip()\n record_outputs = record_outputs.lower()\n\n if record_outputs == 'yes':\n # output filepath setup\n filename = lines[121][lines[121].find(':')+1:].strip()\n filepath = './etc/' + filename\n outfiles = output_files(filepath) # dictionary of opened files\n else:\n outfiles = None\n\n # --------------------------------------------------------------------------\n # MISC STORAGE (e.g. stored matrices that are used routinely)\n #\n # dictionaries and matrices relevant for high order correction applications\n #\n # Constructing the finite different weight matricies, W.\n #-------------------------------------------------------\n # requires: (dict) FD_schemes\n #\n # Note: FD_schemes is only needed to construct W. W is what is used in\n # the simulation. Hence, the building routine for FD_schemes\n # is not optimized, since it happens before the simulation starts\n # and hence is not a source of repeated computational cost.\n #\n # FD_schemes is a dictionary containing the families of every order derivative\n # needed for the indicated global error N in etc/params.dat, i.e. all schemes\n # of various degrees of asymmetry and handedness. For large N, this can be a\n # very large dictionary, see the function routine read_FD_schemes to see all\n # that gets stored inside. It is used to construct the difference coefficient\n # matrices W (for applying high order corrections). The other scheme\n # FD_scheme_dn1 is used to construct the matrix W_dn1 which is a difference\n # coefficient matrix for the first derivative (dn = 1) at LTE = 6, and used\n # in the finite difference 6th order Poisson solver (PBCs currently only).\n #---------------------------------------------------------------------------\n #\n # initialize all dictionaries whose keys correspond to phase space vars\n # and whose values contain the relevant ndarrays\n\n Xi = {}\n xi = {}\n W = {}\n\n # top level check: if any var has FD corrections, store FD_schemes and init W\n if 'FD' in HOC.values():\n # store finite difference schemes\n FD_schemes = read_FD_schemes(N)\n\n if HOC['x'] == 'FD':\n # first derivative with LTE = 6, used to find dphi = -E after phi is\n # found from a 6th order Poisson solve\n FD_scheme_dn1 = read_FD_scheme(1,6)\n W_dn1_LTE6 = assemble_finite_difference_weight_matrix_const_dn_const_LTE(Nx_active,\n FD_scheme_dn1,\n dn = 1,\n LTE = 6\n )\n\n # TODO if more than one or different spatial dimension\n # TODO than 'x' with FD corrections need to permit access to this\n # TODO dictionary W_dn1_LTE6 and have it be assembled.\n\n else:\n # else, Fourier Gauss solver is used, no need for this matrix\n W_dn1_LTE6 = None\n\n # variable-by-variable checks: assemble consistent objects needed\n # for the specified means of HOC from etc/params.dat\n\n # Note: the following is organized with the expectation that\n # higher dimensional implementations would be stepped through\n # as sets of 2D advection problems, always paired as z and vz\n # i.e. not as mixed stepthroughs with x paired with vy for example\n\n for var in phasespace_vars:\n if HOC[var] == 'FD':\n W[var] = assemble_finite_difference_weight_matrix(\n eval('N' + var + '_active'),\n N,\n FD_schemes\n )\n elif HOC[var] == 'FOURIER':\n # ensure the correct number of grid points\n # is passed for the generalized velocity Nvz_active\n # for x,y,z, 'vz' = vx, vy, vz\n # for vx, vy, vz, 'vz' = ax, ay, az, which have\n # the same number of dims as x, y, z, respectively\n\n if var[0] == 'v':\n Nvz_active = eval('N' + var[1] + '_active')\n else:\n Nvz_active = eval('Nv' + var + '_active')\n\n Xi, xi = assemble_spectral_derivative_operator(Xi, xi,\n var,\n eval('a' + var),\n eval('b' + var),\n eval('N' + var),\n eval('N' + var + '_active'),\n Nvz_active,\n N)\n\n # ---------------------------------------------------------------------\n # \"Alternating\" identity matrix\n\n\n # in lib.HOC.correctors, require an N x N diagonal matrix with entries\n # (-1)^i, where i is the row number, for details see on github\n #\n # dsirajud/IPython-notebooks/\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\n #\n # section \"2D casting of correction coefficients c (vector) -> c (tensor)\"\n\n I_alternating = np.diag( (-np.ones(N)) ** np.arange(N) )\n\n # obtain Bernoulli numbers (note: list only 23 numbers are listed)\n # for a correction up to global error order N, N-1 Bernoulli numbers\n # are needed. If higher than global error order 22 is desired, additional\n # Bernoulli numbes need to be entered in\n #\n # etc/Table_of_Bernoulli_numbers.dat\n #\n\n # Store Bernoulli numbers from dat file etc/Table_of_Bernoulli_numbers.dat\n filename = 'Table_of_Bernoulli_numbers.dat'\n filepath = './etc/' + filename\n Bernoulli_numbers = Bernoulli(filepath)\n\n # \"A\" matrices for Bernoulli number storage and matrix HOC application\n # in lib.HOC.Beta_matrix, see notebook on github at\n # dsirajud/IPython-notebooks/\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\n A_pos, A_neg = np.zeros([N,N]), np.zeros([N,N])\n for i in range(N):\n for j in range(i+1):\n A_pos[i,j] = Bernoulli_numbers[i-j] / scipy.misc.factorial(i-j)\n if (i - j) == 1:\n A_neg[i,j] = -A_pos[i,j]\n else:\n A_neg[i,j] = A_pos[i,j]\n\n A_matrix = {}\n # dictionary container\n # allow dictionary access to relevant matrix of Bernoulli numbers\n # by operating with str(int(np.sign(CFL.frac)))\n\n A_matrix['1'] = A_pos\n A_matrix['0'] = A_pos\n A_matrix['-1'] = A_neg\n\n\n # ---------------------------------------------------------------------\n # 6th order finite difference Poisson solver for periodic BCs\n # (stored as keys 'D' [difference matrix] and 'B' [inhomogeneity])\n\n Poisson_6th_order_PBC_FD_solver_matrices = assemble_Poisson_6th_order_PBC_FD_solver_matrices(Nx, BC)\n\n # TODO specialize right now to just be x, vx. Figure out how to generalize later with higher dimensions\n compute_electric_field_function_handle_prefix = \"DECSKS.lib.fieldsolvers.compute_electric_field_\"\n compute_electric_field_function_handle = \"\".join((compute_electric_field_function_handle_prefix, HOC['x'].lower()))\n\n derivative_method = {}\n derivative_method_prefix = 'DECSKS.lib.derivatives'\n for var in phasespace_vars:\n derivative_method[var] = \".\".join((derivative_method_prefix, HOC[var].lower()))\n\n sim_params = dict(\n N = N, HOC = HOC,\n derivative_method = derivative_method,\n Nx = Nx, ax = ax, bx = bx,\n Ny = Ny, ay = ay, by = by,\n Nz = Nz, az = az, bz = bz,\n Nvx = Nvx, avx = avx, bvx = bvx,\n Nvy = Nvy, avy = avy, bvy = bvy,\n Nvz = Nvz, avz = avz, bvz = bvz,\n Nt = Nt, T = T,\n phasespace_vars = phasespace_vars,\n numdims = numdims,\n active_dims = active_dims,\n total_dims = total_dims,\n density = density,\n split_scheme = split_scheme,\n splitting = splitting,\n plot_params = plot_params,\n record_outputs = record_outputs,\n outfiles = outfiles,\n BC = BC, # boundary conditions on all phase space variables\n I_alternating = I_alternating, # identity matrix with alternating signs according to row, used in computing correctors c\n A_matrix = A_matrix, # Matrices of Bernoulli numbers for HOC\n W = W,\n W_dn1_LTE6 = W_dn1_LTE6,\n Xi = Xi, # spectral differentiation operator matrix (1j*xi[i,j]) ** q\n xi = xi, # wave number vector\n Poisson_6th_order_PBC_FD_solver_matrices = Poisson_6th_order_PBC_FD_solver_matrices,\n compute_electric_field_function_handle = compute_electric_field_function_handle # determines if solver is FD or fourier based\n )\n\n infile.close()\n\n return sim_params","def create_raster_datapackage(pk_type, path, file_flag, out_path):\n process_source(pk_type, path, file_flag, out_path)","def readtempfilt(MAIN_OUTPUT_FILE='photz', OUTPUT_DIRECTORY='./OUTPUT'):\n\n root = os.path.join(OUTPUT_DIRECTORY, MAIN_OUTPUT_FILE)\n \n ###### .tempfilt\n file_path = root+'.tempfilt'\n \n if os.path.exists(file_path) is False:\n raise ValueError('File, %s, not found.' %(file_path))\n\n with open(file_path,'rb') as f:\n # summary data\n s = np.fromfile(file=f,dtype=np.int32, count=4)\n NFILT=s[0] # number of filters\n NTEMP=s[1] # number of templates\n NZ=s[2] # number points on the redshift grid\n NOBJ=s[3] # number of objects\n # (?) template SED convolved with filter transmission at each redshift\n tempfilt = np.fromfile(file=f,dtype=np.double,count=NFILT*NTEMP*NZ).reshape((NZ,NTEMP,NFILT)).transpose()\n # filter pivot wavelengths\n lc = np.fromfile(file=f,dtype=np.double,count=NFILT)\n # redshift grid\n zgrid = np.fromfile(file=f,dtype=np.double,count=NZ)\n # observed flux\n fnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\n # (?) error in observed flux\n efnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\n \n tempfilt = {'NFILT':NFILT,'NTEMP':NTEMP,'NZ':NZ,'NOBJ':NOBJ,\\\n 'tempfilt':tempfilt,'lc':lc,'zgrid':zgrid,'fnu':fnu,'efnu':efnu}\n\n return tempfilt","def trimesh_to_vrml(mesh, output_file):\n\n with open(output_file, 'w') as f:\n f.write('#VRML V2.0 utf8\\n')\n f.write('Transform {\\n'\n ' children [\\n'\n ' Shape {\\n'\n ' geometry IndexedFaceSet {\\n'\n ' coord Coordinate {\\n'\n ' point[\\n')\n for v in mesh.vertices:\n f.write(' {:0.6f} {:0.6f} {:0.6f},\\n'.format(*v.tolist()))\n f.write(' ] # end of points\\n'\n ' } # end of Coordinate\\n'\n ' coordIndex [\\n')\n\n for face in mesh.faces:\n f.write(' {:d}, {:d}, {:d}, -1,\\n'.format(*face.tolist()))\n\n f.write(' ] # end of coordIndex\\n'\n ' } # end of geometry\\n'\n ' appearance Appearance {\\n'\n ' material Material {\\n'\n ' diffuseColor 0.7 0.7 0.7\\n'\n ' emissiveColor 0.05 0.05 0.05\\n'\n ' specularColor 1.0 1.0 1.0\\n'\n ' ambientIntensity 0.2\\n'\n ' shininess 0.2\\n'\n ' transparency 0.0\\n'\n ' } #end of material\\n'\n ' } #end of appearance\\n'\n ' } # end of Shape\\n'\n ' ] # end of children\\n'\n '}\\n')","def cal_voro(inputfile, ndim, radii, ppp = '-p', results_path = '../../analysis/voro/'): \n if not os.path.exists(results_path):\n os.makedirs(results_path)\n\n basename = os.path.splitext(os.path.basename(inputfile))[0]\n fneighbor = open(results_path + basename + '.neighbor.dat', 'w')\n ffacearea = open(results_path + basename + '.facearea.dat', 'w')\n findex = open(results_path + basename + '.voroindex.dat', 'w') \n foverall = open(results_path + basename + '.overall.dat', 'w')\n\n position, bounds = get_input(inputfile, ndim, radii)\n for n in range(len(position)):\n fileformat = '%d ' + '%.6f ' * ndim + '%.2f'\n np.savetxt('dumpused', position[n], fmt = fileformat)\n \n #use box boundaries from snapshot\n Boxbounds = bounds[n].ravel()\n #use box boundaries from particle coordinates \n # boundsmin = position[n][:, 1: ndim + 1].min(axis = 0) - 0.1\n # boundsmax = position[n][:, 1: ndim + 1].max(axis = 0) + 0.1\n # Boxbounds = (np.column_stack((boundsmin, boundsmax))).ravel()\n\n cmdline = 'voro++ ' + ppp + ' -r -c \"%i %s %v %F @%i %A @%i %s %n @%i %s %f\" '\\\n + ('%f %f ' * ndim % tuple(Boxbounds)) + 'dumpused'\n if n == 0: print (cmdline)\n subprocess.run(cmdline, shell = True)\n\n fneighbor.write('id cn neighborlist\\n')\n ffacearea.write('id cn facearealist\\n')\n findex.write('id voro_index 0_to_7_faces\\n')\n foverall.write('id cn volume facearea\\n')\n f = open('dumpused.vol', 'r')\n for i in range(len(position[n][:, 0])):\n item = f.readline().split('@')\n foverall.write(item[0] + '\\n')\n findex.write(item[1] + '\\n')\n fneighbor.write(item[2] + '\\n')\n ffacearea.write(item[3])\n f.close()\n\n os.remove('dumpused') #delete temporary files\n os.remove('dumpused.vol')\n fneighbor.close()\n ffacearea.close()\n foverall.close()\n findex.close()\n print ('---------- Voronoi Analysis Done ------------')","def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\r\n from osgeo import gdal\r\n input_raster = gdal.Open(inFilepath)\r\n EPSG_string = \"EPSG:\"+str(to_EPSG)\r\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\r\n del ras","def voxelization(infile_path, outfile_path, voxel_size):\n\n infile = laspy.file.File(infile_path, mode=\"rw\")\n\n # 计算每个点的voxel码\n scaled_x = np.vectorize(int)((1 / voxel_size) * (infile.x - infile.header.min[0]))\n scaled_y = np.vectorize(int)((1 / voxel_size) * (infile.y - infile.header.min[1]))\n scaled_z = np.vectorize(int)((1 / voxel_size) * (infile.z - infile.header.min[2]))\n indices = np.lexsort((scaled_z, scaled_y, scaled_x))\n voxel_count = 0\n point_count = 0\n point_lengh = len(infile.x)\n\n # the array to store the code of the voxel, this is actually the row, columm and height number of the voxel\n code_array = []\n\n # the array to store the point number in each voxel\n points_in_one_voxel_array = []\n\n # the array to store the average intensity of points in a voxel\n intensity_in_one_voxel_array = []\n\n while point_count < point_lengh - 1:\n\n # the counter of points number in one voxel\n points_in_one_voxel_count = 1\n intensity_in_one_voxel_count = 0\n # loop of finding points with same code\n while point_count < point_lengh - 1 and \\\n scaled_x[indices[point_count + 1]] == scaled_x[indices[point_count]] and \\\n scaled_y[indices[point_count + 1]] == scaled_y[indices[point_count]] and \\\n scaled_z[indices[point_count + 1]] == scaled_z[indices[point_count]]:\n # add a voxel index label to the point\n infile.voxel_index[indices[point_count]] = voxel_count\n intensity_in_one_voxel_count += infile.intensity[indices[point_count]]\n point_count += 1\n points_in_one_voxel_count += 1\n\n infile.voxel_index[indices[point_count]] = voxel_count\n intensity_in_one_voxel_count += infile.intensity[indices[point_count]]\n intensity_in_one_voxel_array.append(intensity_in_one_voxel_count / points_in_one_voxel_count)\n points_in_one_voxel_array.append(points_in_one_voxel_count)\n # save the code to an array which later will be stored in the csv file\n code = \"{:0>4d}\".format(scaled_x[indices[point_count]]) + \\\n \"{:0>4d}\".format(scaled_y[indices[point_count]]) + \\\n \"{:0>4d}\".format(scaled_z[indices[point_count]])\n code_array.append(code)\n point_count += 1\n voxel_count += 1\n\n # save the code to the csv file sequentially\n code_array_length = len(code_array)\n with open(outfile_path, 'wb') as csvfile:\n writer = csv.writer(csvfile)\n count = 0\n while count < code_array_length:\n writer.writerow([code_array[count], points_in_one_voxel_array[count], intensity_in_one_voxel_array[count]])\n count += 1","def _rasterize_vector_onto_base(\n base_raster_path, base_vector_path, attribute_id,\n target_raster_path, filter_string=None):\n base_raster = gdal.OpenEx(base_raster_path, gdal.OF_RASTER)\n raster_driver = gdal.GetDriverByName('GTiff')\n target_raster = raster_driver.CreateCopy(target_raster_path, base_raster)\n base_raster = None\n\n vector = gdal.OpenEx(base_vector_path)\n layer = vector.GetLayer()\n\n if filter_string is not None:\n layer.SetAttributeFilter(str(filter_string))\n gdal.RasterizeLayer(\n target_raster, [1], layer,\n options=['ATTRIBUTE=%s' % attribute_id])\n target_raster.FlushCache()\n target_raster = None\n layer = None\n vector = None","def compilation(inputfile):\r\n\r\n # create empty dictionary to hold DataFrames\r\n f = {}\r\n\r\n # generate list of relevant files\r\n filelist = glob.glob(inputfile)\r\n\r\n # iterate through list of relevant files\r\n for infile in filelist:\r\n # run computations using lev files\r\n f[getfilename(infile)] = new_trans_imp(infile)\r\n # concatenate all of the DataFrames in dictionary f to one DataFrame: g\r\n g = pd.concat(f)\r\n # remove multiindex and replace with index=Datetime\r\n g = g.reset_index()\r\n g = g.set_index(['DateTime'])\r\n # drop old indexes\r\n g = g.drop(['level_0'], axis=1)\r\n # remove duplicates based on index then sort by index\r\n g['ind'] = g.index\r\n g.drop_duplicates(subset='ind', inplace=True)\r\n g.drop('ind', axis=1, inplace=True)\r\n g = g.sort_index()\r\n outfile = g\r\n return g","def _read_dx(self, FN):\n if FN.endswith('.dx'):\n F = open(FN, 'r')\n else:\n import gzip\n F = gzip.open(FN, 'r')\n\n # Read the header\n line = F.readline()\n while line.find('object') == -1:\n line = F.readline()\n header = {}\n header['counts'] = [int(x) for x in line.split(' ')[-3:]]\n for name in ['origin', 'd0', 'd1', 'd2']:\n header[name] = [float(x) for x in F.readline().split(' ')[-3:]]\n F.readline()\n header['npts'] = int(F.readline().split(' ')[-3])\n\n # Test to make sure the grid type is okay.\n # These conditions are not absolultely essential,\n # but they reduce the number of subtraction operations.\n if not (header['d0'][1] == 0 and header['d0'][2] == 0\n and header['d1'][0] == 0 and header['d1'][2] == 0\n and header['d2'][0] == 0 and header['d2'][1] == 0):\n raise Exception('Trilinear grid must be in original basis')\n if not (header['d0'][0] > 0 and header['d1'][1] > 0\n and header['d2'][2] > 0):\n raise Exception('Trilinear grid must have positive coordinates')\n\n # Read the data\n vals = np.ndarray(shape=header['npts'], dtype=float)\n index = 0\n while index < header['npts']:\n line = F.readline()[:-1]\n items = [float(item) for item in line.split()]\n vals[index:index + len(items)] = items\n index = index + len(items)\n F.close()\n\n data = {\n 'origin':np.array(header['origin']), \\\n 'spacing':np.array([header['d0'][0],header['d1'][1],header['d2'][2]]), \\\n 'counts':np.array(header['counts']), \\\n 'vals':vals}\n return data","def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\n \n #assign which driver to use for the file format - default is .tif\n driver = gdal.GetDriverByName(drv)\n \n #source raster from which to use the geospatial metadata\n src_gt = gdal.Open(src_file)\n \n #data shape - detects whether to produce multiple bands or not\n if len(data.shape) == 2:\n [cols, rows] = data.shape\n n = 1\n else:\n [n, cols, rows] = data.shape\n\n #create the destination file\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\n \n #get the geotransform from the source file\n gt = src_gt.GetGeoTransform()\n \n #coordinate system in which to create the raster, in wkt form\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(epsg)\n dest_wkt = srs.ExportToWkt()\n \n #set the projection in the desired reference system\n dst_gt.SetGeoTransform(gt)\n dst_gt.SetProjection(dest_wkt)\n \n #set the data\n if n==1:\n dst_gt.GetRasterBand(1).WriteArray(data)\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\n #close and write to file\n dst_gt.FlushCache()\n #used if there are multiple bands\n else:\n for i in range(n):\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\n dst_gt.FlushCache()","def preparehspiceidvgGEO4(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,Ach_UFCMparam,Cins_UFCMparam,W_UFCMparam,NBODYparam,NFINparam):\n#L=Lparam Ach_UFCM=Ach_UFCMparam Cins_UFCM=Cins_UFCMparam W_UFCM=W_UFCMparam NBODY=NBODYparam NFIN=NFINparam\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Ach_UFCMparam',Ach_UFCMparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Cins_UFCMparam', Cins_UFCMparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'W_UFCMparam',W_UFCMparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)","def preparehspiceidvgGEO1(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam):\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)","def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\n #make an aux copy of hspice file to simulate\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\n #make an aux copy of modelcard file to simulate\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\n\n #update path of model and modelcard\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\"modelcardaux.nmos\\\"')\n\n #bias update\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\n\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)","def render_vtk(file_name):\n import vtk\n\n # Read the source file.\n reader = vtk.vtkUnstructuredGridReader()\n reader.SetFileName(file_name)\n reader.Update() # Needed because of GetScalarRange\n output = reader.GetOutput()\n scalar_range = output.GetScalarRange()\n\n # Create the mapper that corresponds the objects of the vtk.vtk file\n # into graphics elements\n mapper = vtk.vtkDataSetMapper()\n mapper.SetInputData(output)\n mapper.SetScalarRange(scalar_range)\n\n # Create the Actor\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n\n # Create the Renderer\n renderer = vtk.vtkRenderer()\n renderer.AddActor(actor)\n renderer.SetBackground(1, 1, 1) # Set background to white\n\n # Create the RendererWindow\n renderer_window = vtk.vtkRenderWindow()\n renderer_window.AddRenderer(renderer)\n\n # Create the RendererWindowInteractor and display the vtk_file\n interactor = vtk.vtkRenderWindowInteractor()\n interactor.SetRenderWindow(renderer_window)\n interactor.Initialize()\n interactor.Start()","def fgm2iaga(path,\n fgm_fname,\n ftype='v',\n output_template='{stn}{date:%Y%m%d}{ftype}{interval}.{interval}'):\n df = parse(fgm_fname)\n delta = (df.index[1] - df.index[0]).total_seconds()\n if delta == 1.0:\n interval = 'sec'\n elif delta == 60.0:\n interval = 'min'\n else:\n raise ValueError('unknown data interval found in {}'.format(fgm_fname))\n stn = df.siteid[:3].upper()\n out_fname = os.path.join(path,\n output_template.format(stn=stn.lower(),\n date=df.date,\n ftype=ftype,\n interval=interval))\n with open(out_fname, 'w') as fid:\n fid.write(HEADER_TEMPLATE.format(stn=stn.upper(),\n lat=df.lat,\n lon=df.lon,\n el=0))\n for row in df.itertuples():\n dt = row.Index\n if row.flag:\n X = Y = Z = F = 99999\n else:\n X = row.x\n Y = row.y\n Z = row.z\n F = np.linalg.norm([X, Y, Z])\n fid.write('{date:%Y-%m-%d %H:%M:%S.000} {date:%j}'\n ' {X:>9.2f} {Y:>9.2f} {Z:>9.2f} {F:>9.2f}\\n'.format(date=dt,\n X=X,\n Y=Y,\n Z=Z,\n F=F))\n return out_fname","def render(self, outputfile, cmd=GDALTRANSLATE, working_memory=1024,\n compress=None, tempdir=None):\n tmpfile = NamedTemporaryFile(\n suffix='.tif', prefix='gdalrender',\n dir=os.path.dirname(outputfile), delete=False\n )\n\n try:\n with self.get_tempfile(dir=tempdir) as inputfile:\n warp_cmd = [\n cmd,\n '-q', # Quiet - FIXME: Use logging\n '-of', 'GTiff', # Output to GeoTIFF\n '-co', 'BIGTIFF=IF_SAFER', # Use BigTIFF if >2GB\n '-co', 'NUM_THREADS=ALL_CPUS', # multithreaded compression for GeoTiff\n # gdal_translate does not support the following\n # '-multi', # Use multiple processes\n # '-overwrite', # Overwrite outputfile\n ]\n\n # Set the working memory so that gdalwarp doesn't stall of disk\n # I/O\n warp_cmd.extend([\n # gdal_translate does not support -wm\n # '-wm', working_memory,\n '--config', 'GDAL_CACHEMAX', working_memory\n ])\n\n # Use compression\n compress = str(compress).upper()\n if compress and compress != 'NONE':\n warp_cmd.extend(['-co', 'COMPRESS=%s' % compress])\n if compress in ('LZW', 'DEFLATE'):\n warp_cmd.extend(['-co', 'PREDICTOR=2'])\n\n # Run gdalwarp and output to tmpfile.name\n warp_cmd.extend([inputfile.name, tmpfile.name])\n check_output_gdal([str(e) for e in warp_cmd])\n\n # If it succeeds, then we move it to overwrite the actual\n # output\n os.rename(tmpfile.name, outputfile)\n return outputfile\n finally:\n rmfile(tmpfile.name, ignore_missing=True)\n rmfile(tmpfile.name + '.aux.xml', ignore_missing=True)","def transform_from_file(self,\n file,\n format):\n\n # Prepare query URL\n _query_builder = Configuration.base_uri\n _query_builder += '/transform'\n _query_parameters = {\n 'format': format\n }\n _query_builder = APIHelper.append_url_with_query_parameters(_query_builder,\n _query_parameters, Configuration.array_serialization)\n _query_url = APIHelper.clean_url(_query_builder)\n\n # Prepare files\n _files = {\n 'file': file\n }\n\n # Prepare and execute request\n _request = self.http_client.post(_query_url, files=_files)\n CustomHeaderAuth.apply(_request)\n _context = self.execute_request(_request)\n self.validate_response(_context)\n\n # Return appropriate type\n return _context.response.raw_body","def main(input_file_path, layer='all', out=None, grid_id_name='GRIDMET_ID',\n buffer=25, scale_factor=0.1, function='invdist', smooth=0, params=None,\n grid_res=None, z_stats=True, res_plot=True, overwrite=False, \n options=None, grid_meta_path=None):\n # build fishnet for interpolation\n make_grid(input_file_path, \n grid_id_name=grid_id_name,\n grid_meta_path=grid_meta_path, \n buffer=buffer, \n overwrite=overwrite,\n grid_res=grid_res)\n \n # run spatial interpolation depending on options\n interpolate(\n input_file_path, \n layer=layer, \n out=out,\n scale_factor=scale_factor, \n function=function, \n smooth=smooth,\n params=params,\n buffer=buffer,\n z_stats=z_stats,\n res_plot=res_plot,\n grid_id_name=grid_id_name,\n grid_res=grid_res,\n options=options,\n grid_meta_path=grid_meta_path)","def create_video(input_file, output_file):\n input_video = VideoFileClip(input_file)\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\n output_video.write_videofile(output_file, audio=False)","def populate(infile):\n main(infile)","def read_vmdas(self,):\n fd = self.f\n # The raw files produced by VMDAS contain a binary navigation data\n # block.\n self.cfg['sourceprog'] = 'VMDAS'\n ens = self.ensemble\n k = ens.k\n if self._source != 1 and self._debug_level >= 1:\n print(' \\n***** Apparently a VMDAS file \\n\\n')\n self._source = 1\n self.vars_read += ['time_gps',\n 'latitude_gps',\n 'longitude_gps',\n 'etime_gps',\n 'elatitude_gps',\n 'elongitude_gps',\n 'flags',\n 'ntime', ]\n utim = fd.read_ui8(4)\n date = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])\n # This byte is in hundredths of seconds (10s of milliseconds):\n time = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) / 10)))\n fd.seek(4, 1) # \"PC clock offset from UTC\" - clock drift in ms?\n ens.time_gps[k] = tmlib.date2epoch(date + time)[0]\n ens.latitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.longitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.etime_gps[k] = tmlib.date2epoch(date + tmlib.timedelta(\n milliseconds=int(fd.read_ui32(1) * 10)))[0]\n ens.elatitude_gps[k] = fd.read_i32(1) * self._cfac\n ens.elongitude_gps[k] = fd.read_i32(1) * self._cfac\n fd.seek(12, 1)\n ens.flags[k] = fd.read_ui16(1)\n fd.seek(6, 1)\n utim = fd.read_ui8(4)\n date = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])\n ens.ntime[k] = tmlib.date2epoch(date + tmlib.timedelta(\n milliseconds=int(fd.read_ui32(1) / 10)))[0]\n fd.seek(16, 1)\n self._nbyte = 2 + 76","def init_from_file(self,file_name):\n with open(file_name, mode='rb') as file: # b is important -> binary\n file_content = file.read(1)\n x = file_content\n ct = int.from_bytes(x, byteorder='little', signed=False)\n file_content = file.read(ct)\n header = file_content.decode().split(\" \")\n vindex = header.index('-vectortype')\n vectortype = header[vindex + 1]\n\n if vectortype != 'REAL':\n print('Can\\'t initialize real vector store from ',vectortype,' vectors.')\n return\n\n #read in vectors and wrap in RealVectors\n incoming_terms, incoming_vectors = svu.readfile(file_name)\n self.init_from_lists(incoming_terms,incoming_vectors)","def readDriverFile(self, input_file):\n\n\n fid = open(self.basePath + input_file,'r')\n\n # Line 1\n line = fid.readline()\n l_input = line.split('!')\n mshfile = l_input[0].rstrip()\n\n # Line 2\n line = fid.readline()\n l_input = line.split('!')\n obsfile = l_input[0].rstrip()\n\n # Line 3\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='null':\n topofile = []\n\n else:\n topofile = l_input[0].rstrip()\n\n\n # Line 4\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n mstart = float(l_input[1])\n\n else:\n mstart = l_input[0].rstrip()\n\n # Line 5\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n mref = float(l_input[1])\n\n else:\n mref = l_input[0].rstrip()\n\n # Line 6\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n staticInput = float(l_input[1])\n\n elif l_input[0]=='DEFAULT':\n staticInput = None\n\n else:\n staticInput = l_input[0].rstrip()\n\n\n # Line 7\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='DEFAULT':\n magfile = []\n\n else:\n magfile = l_input[0].rstrip()\n\n # Line 8\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input=='DEFAULT':\n wgtfile = []\n\n else:\n wgtfile = l_input[0].rstrip()\n\n # Line 9\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n chi = float(l_input[0])\n\n # Line 10\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n val = np.array(l_input[0:4])\n alphas = val.astype(np.float)\n\n # Line 11\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:3])\n bounds = val.astype(np.float)\n\n else:\n bounds = l_input[0].rstrip()\n\n # Line 12\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:6])\n lpnorms = val.astype(np.float)\n\n else:\n lpnorms = l_input[0].rstrip()\n\n # Line 13\n line = fid.readline()\n l_input = re.split('[!\\s]',line)\n if l_input[0]=='VALUE':\n val = np.array(l_input[1:3])\n eps = val.astype(np.float)\n\n else:\n eps = [None,None]\n\n self.mshfile = mshfile\n self.obsfile = obsfile\n self.topofile = topofile\n self.mstart = mstart\n self._mrefInput = mref\n self._staticInput = staticInput\n self.magfile = magfile\n self.wgtfile = wgtfile\n self.chi = chi\n self.alphas = alphas\n self.bounds = bounds\n self.lpnorms = lpnorms\n self.eps = eps","def create_verde_rules_lp(schema_file, input_file, mapping_file,\n query_fields, trial_id, directory, rule_config, baseline_lp,\n verde_rule_template_dir):\n\n logging.info(f'creating verde rules lp based on {schema_file} and {mapping_file}')\n\n context = Dict()\n context.id = trial_id\n context.directory = directory\n context.rule_config = rule_config\n context.verde_rule_template_dir = verde_rule_template_dir\n\n # Load the input mapping json\n with open(mapping_file) as f:\n mapping_json = json.load(f)\n\n lp_str = \"\"\n # Apply each verde rule to extend the lp\n if rule_config.rule_01_causal_relationships.do:\n lp_str += vrule01.rule_01_causal_relationships(context, schema_file, mapping_json, query_fields)\n else:\n logging.warning('verde rule_01_causal_relationships is disabled in config')\n\n if rule_config.rule_02_data_precision.do:\n lp_str += vrule02.rule_02_data_precision(context, mapping_json, query_fields)\n else:\n logging.warning('verde rule_02_data_precision is disabled in config')\n\n if rule_config.rule_03_ordinal_sort.do:\n lp_str += vrule03.rule_03_ordinal_sort(context, schema_file, input_file, mapping_json, query_fields)\n else:\n logging.warning('verde rule_03_ordinal_sort is disabled in config')\n\n if rule_config.rule_04_entity_colours.do:\n lp_str += vrule04.rule_04_colour(context, schema_file, mapping_json)\n else:\n logging.warning('verde rule_04_entity_colours is disabled in config')\n\n lp = lp_str.split('\\n') # bit messy, we used lists elsewhere then moved to jinja templates\n\n # Write out the partial lp containing the data schema and our verde soft rules\n if rule_config.write_lp:\n lp_file = os.path.join(directory, f'{context.id}_verde_schema_query.lp')\n vutils.write_list_to_file(baseline_lp + lp, lp_file, 'verde full schema and query lp')\n\n return baseline_lp + lp","def create_true_vectors(filename, reference_file):\n\n all_cases = np.array([], dtype=np.int64).reshape(0, cs.NN_VECTOR_LENGTH)\n alignment_file = open(filename, 'r')\n\n for line in alignment_file:\n line = line.split('\\t')\n if len(line) >= 9:\n vector_nn = np.array(reference_to_signal_partial_mapping(line[9], reference_file, line[0][3:], line[1],\n int(line[2]), int(line[4])))\n all_cases = np.append(all_cases, vector_nn, axis=0)\n if len(all_cases) > cs.NUMBER_OF_EXAMPLES:\n break\n\n return all_cases","def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old\")\n h_nom.Rebin(num_rebin)\n fout = ROOT.TFile.Open(\"hists_input_\"+tag_name+\".root\", \"recreate\")\n\n h_purity_sys = fin.Get(\"bkg_purity_syst_gg_full\").Clone(\"bkg_purity_syst_gg\")\n h_reducible_sys = fin.Get(\"bkg_reducible_syst_gg_full\").Clone(\"bkg_reducible_syst_gg\")\n h_irreducible_sys = fin.Get(\"bkg_irreducible_syst_gg_full\").Clone(\"bkg_irreducible_syst_gg\")\n h_iso_sys = fin.Get(\"bkg_iso_syst_gg_full\").Clone(\"bkg_iso_syst_gg\")\n\n #file_iso = \"isolation_sys/hist.root\"\n #fin2 = ROOT.TFile.Open(file_iso, \"read\")\n #h_iso_sys = fin2.Get(\"bkg_isolation_syst_gg\")\n ## inflat irreducible uncertainty by factor of 10\n # so that it closes to stats uncertainty in data\n sf = 1\n if INFLATE_SYS:\n sf = 10\n\n # after rebinning systematic uncertainties, need to scale down,\n # otherwise the uncertainties are inflated.\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n\n ## truncate the histograms to [200, 2000] GeV\n h_nom_new = truncate_hist(h_nom, \"bkg_nominal\")\n h_purity_sys_new = truncate_hist(h_purity_sys, \"h_purity_sys_new\")\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \"h_irreducible_sys_new\")\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \"h_reducible_sys_new\")\n h_iso_sys_new = truncate_hist(h_iso_sys, \"h_iso_sys_new\")\n\n #write down sys and nominal\n fout.cd()\n h_nom_new.Write()\n h_purity_sys_new.Write()\n h_reducible_sys_new.Write()\n h_irreducible_sys_new.Write()\n h_iso_sys_new.Write()\n\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \"purity_sys\")\n h_purity_up.Write()\n h_purity_down.Write()\n\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \"reducible_sys\")\n h_red_up.Write()\n h_red_down.Write()\n\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \"irreducible_sys\")\n h_irred_up.Write()\n h_irred_down.Write()\n\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \"isolation_sys\")\n h_iso_up.Write()\n h_iso_down.Write()\n\n fin.Close()\n fout.Close()","def process_svgs(model_info, directory, output, allow_missing=False):\n r2dt.write(model_info, directory, output, allow_missing=allow_missing)"],"string":"[\n \"def make_vrt(self):\\n for index, i in enumerate(self.months):\\n month = str(index + 1)\\n if len(month) < 2:\\n month = '0' + month\\n txt_file = i.joinpath('subnational/tiffs.txt')\\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\\n if not outfile.exists():\\n gdal_cmd = f'gdalbuildvrt -input_file_list {str(txt_file)} {str(outfile)}'\\n subprocess.call(gdal_cmd, shell=True)\",\n \"def convert_vrt(fname, out_fname, dataset_name='dataset',\\n compression=H5CompressionFilter.LZF, filter_opts=None,\\n attrs=None):\\n with h5py.File(out_fname) as fid:\\n with rasterio.open(fname) as rds:\\n # set default chunks and set dimensions\\n if rds.count == 3:\\n chunks = (3, 256, 256)\\n dims = (3, rds.height, rds.width)\\n else:\\n chunks = (256, 256)\\n dims = (rds.height, rds.width)\\n\\n # create empty or copy the user supplied filter options\\n if not filter_opts:\\n filter_opts = dict()\\n filter_opts['chunks'] = chunks\\n else:\\n filter_opts = filter_opts.copy()\\n\\n\\n if 'chunks' not in filter_opts:\\n filter_opts['chunks'] = chunks\\n\\n # modify to have 3D chunks if we have a multiband vrt\\n if rds.count == 3 and len(filter_opts['chunks']) != 3:\\n # copy the users original 2D chunk and insert the third\\n chunks = list(filter_opts['chunks'])\\n chunks.insert(0, 3)\\n filter_opts['chunks'] = chunks\\n\\n # dataset attributes\\n if attrs:\\n attrs = attrs.copy()\\n else:\\n attrs = {}\\n\\n attrs['geotransform'] = rds.transform.to_gdal()\\n attrs['crs_wkt'] = rds.crs.wkt\\n\\n # dataset creation options\\n kwargs = compression.config(**filter_opts).dataset_compression_kwargs()\\n kwargs['shape'] = dims\\n kwargs['dtype'] = rds.dtypes[0]\\n\\n dataset = fid.create_dataset(dataset_name, **kwargs)\\n attach_image_attributes(dataset, attrs)\\n\\n # tiled processing (all cols by chunked rows)\\n ytile = filter_opts['chunks'][1] if rds.count == 3 else filter_opts['chunks'][0]\\n tiles = generate_tiles(rds.width, rds.height, rds.width, ytile)\\n\\n for tile in tiles:\\n # numpy index\\n if rds.count == 3:\\n idx = (\\n slice(None),\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n else:\\n idx = (\\n slice(tile[0][0], tile[0][1]),\\n slice(tile[1][0], tile[1][1])\\n )\\n\\n # ensure single band rds is read as 2D not 3D\\n data = rds.read(window=tile) if rds.count == 3 else rds.read(1, window=tile)\\n\\n # write\\n dataset[idx] = data\",\n \"def run_vorpaline(self, input_file, *options):\\n\\n self._input_file = input_file\\n args = list(options) + [self._input_file, 'out.meshb']\\n self._run_command(\\\"vorpalite\\\", args)\",\n \"def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\\n\\n options = gdal.BuildVRTOptions(srcNodata=0)\\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\\n add_pixel_fn(vrt, resample_name)\",\n \"def copy_vrt(in_fname, out_fname=None, bbox=None, verbose=True):\\n from gdal import Translate\\n\\n if out_fname is None:\\n out_fname = in_fname + \\\".vrt\\\"\\n\\n # Using Translate... but would use Warp if every reprojecting\\n if bbox:\\n left, bottom, right, top = bbox\\n projwin = (left, top, right, bottom) # unclear why Translate does UL LR\\n else:\\n projwin = None\\n if verbose:\\n logger.info(f\\\"Creating {out_fname}, subset bbox: {bbox}\\\")\\n Translate(out_fname, in_fname, projWin=projwin)\",\n \"def buildvrt(indir, outdir):\\n indir = Path(indir)\\n outdir = Path(outdir)\\n\\n # loop over each day directory\\n for day in indir.iterdir():\\n # expecting 20 subdatasets in each hdf4 file (hopefully the order gdal lists them in is consistent)\\n subdataset_fnames = {i: [] for i in range(20)}\\n\\n # mosaic each MODIS tile for the current day directory\\n for h4_fname in day.rglob('*.hdf'):\\n with rasterio.open(str(h4_fname.absolute())) as h4_ds:\\n\\n # each subdataset will form a separate mosaic\\n for i, sds_name in enumerate(h4_ds.subdatasets):\\n subdataset_fnames[i].append(sds_name)\\n\\n # loop over each subdataset and mosaic from all supporting MODIS tiles\\n for _, file_list in subdataset_fnames.items():\\n\\n # temp file for the input file list\\n with tempfile.NamedTemporaryFile('w') as tmpf:\\n tmpf.writelines(\\\"\\\\n\\\".join(file_list))\\n tmpf.flush()\\n\\n # mimic the 'day' directory partition\\n base_name = Path(file_list[0].replace(':', '/')).name\\n out_fname = outdir.joinpath(day.name, '{}.vrt'.format(base_name))\\n\\n if not out_fname.parent.exists():\\n out_fname.parent.mkdir(parents=True)\\n\\n # buildvrt\\n cmd = [\\n 'gdalbuildvrt',\\n '-input_file_list',\\n tmpf.name,\\n str(out_fname)\\n ]\\n\\n check_call(cmd)\",\n \"def merge(files: List[str], output_file: str, resample: str = \\\"average\\\") -> None:\\n\\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', 'YES')\\n\\n gdal.Translate(destName=output_file, srcDS=constants.TEMP_VRT_FILE)\\n\\n gdal.SetConfigOption('GDAL_VRT_ENABLE_PYTHON', None)\\n\\n if os.path.isfile(constants.TEMP_VRT_FILE):\\n os.remove(constants.TEMP_VRT_FILE)\",\n \"def open_input(self):\\n gdal.SetConfigOption(\\\"GDAL_PAM_ENABLED\\\", \\\"YES\\\")\\n gdal.AllRegister()\\n # self.options.verbose=True\\n if self.options.tms_osm:\\n self.s_y_type=\\\"osm\\\"\\n else:\\n self.s_y_type=\\\"tms\\\"\\n if self.options.verbose:\\n print \\\"open_input :\\\", self.input,\\\" osm[\\\",self.options.tms_osm,\\\",\\\",self.s_y_type,\\\"] mbtiles[\\\",self.options.mbtiles,\\\"] mbtiles_todisk[\\\",self.options.mbtiles_todisk,\\\"] mbtiles_fromdisk[\\\",self.options.mbtiles_fromdisk,\\\"]\\\";\\n # Open the input file\\n if self.input:\\n self.in_ds = gdal.Open(self.input, gdal.GA_ReadOnly)\\n else:\\n raise Exception(\\\"No input file was specified\\\")\\n\\n if self.options.verbose:\\n print \\\"Input file:\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\\n\\n if not self.in_ds:\\n # Note: GDAL prints the ERROR message too\\n self.error(\\\"It is not possible to open the input file '%s'.\\\" % self.input )\\n\\n # Read metadata from the input file\\n if self.in_ds.RasterCount == 0:\\n self.error( \\\"Input file '%s' has no raster band\\\" % self.input )\\n\\n if self.in_ds.GetRasterBand(1).GetRasterColorTable():\\n # TODO: Process directly paletted dataset by generating VRT in memory\\n self.error( \\\"Please convert this file to RGB/RGBA and run gdal2mbtiles on the result.\\\",\\n \\\"\\\"\\\"From paletted file you can create RGBA file (temp.vrt) by:\\ngdal_translate -of vrt -expand rgba %s temp.vrt\\nthen run:\\ngdal2mbtiles temp.vrt\\\"\\\"\\\" % self.input )\\n\\n # Get NODATA value\\n # User supplied values overwrite everything else.\\n if self.options.srcnodata is not None:\\n nds = map(float, self.options.srcnodata.split(','))\\n if len(nds) < self.in_ds.RasterCount:\\n self.in_nodata = (nds * self.in_ds.RasterCount)[:self.in_ds.RasterCount]\\n else:\\n self.in_nodata = nds\\n else:\\n # If the source dataset has NODATA, use it.\\n self.in_nodata = []\\n for i in range(1, self.in_ds.RasterCount+1):\\n if self.in_ds.GetRasterBand(i).GetNoDataValue() != None:\\n self.in_nodata.append( self.in_ds.GetRasterBand(i).GetNoDataValue() )\\n\\n if self.options.verbose:\\n print \\\"NODATA: %s\\\" % self.in_nodata\\n\\n # INIT DEST\\n if self.options.init_dest is not None:\\n if self.options.tile_format == \\\"jpeg\\\":\\n if self.in_ds.RasterCount == 4:\\n nbands = 3\\n else:\\n nbands = self.in_ds.RasterCount\\n\\n nds = map(float, self.options.init_dest.split(','))\\n\\n if len(nds) == 1:\\n init_dest = nds * nbands\\n elif len(nds) == nbands:\\n init_dest = nds\\n else:\\n print \\\"WARNING: you suplied %d '--init-dest' values but the dataset has %d data bands\\\" % (len(nds), nbands)\\n init_dest = None\\n else:\\n init_dest = None\\n print \\\"WARNING: --init-dest can be used only with 'jpeg' tile format\\\"\\n else:\\n if self.options.tile_format == \\\"jpeg\\\":\\n init_dest = [255,255,255]\\n else:\\n init_dest = None\\n\\n #\\n # Here we should have RGBA input dataset opened in self.in_ds\\n #\\n\\n if self.options.verbose:\\n print \\\"Preprocessed file:\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\\n\\n # Spatial Reference System of the input raster\\n\\n\\n self.in_srs = None\\n\\n if self.options.s_srs:\\n self.in_srs = osr.SpatialReference()\\n self.in_srs.SetFromUserInput(self.options.s_srs)\\n self.in_srs_wkt = self.in_srs.ExportToWkt()\\n else:\\n self.in_srs_wkt = self.in_ds.GetProjection()\\n if not self.in_srs_wkt and self.in_ds.GetGCPCount() != 0:\\n self.in_srs_wkt = self.in_ds.GetGCPProjection()\\n if self.in_srs_wkt:\\n self.in_srs = osr.SpatialReference()\\n self.in_srs.ImportFromWkt(self.in_srs_wkt)\\n #elif self.options.profile != 'raster':\\n # self.error(\\\"There is no spatial reference system info included in the input file.\\\",\\\"You should run gdal2mbtiles with --s_srs EPSG:XXXX or similar.\\\")\\n\\n # Spatial Reference System of tiles\\n\\n self.out_srs = osr.SpatialReference()\\n\\n if self.options.profile == 'mercator':\\n self.out_srs.ImportFromEPSG(900913)\\n elif self.options.profile in ('geodetic', 'gearth', 'garmin'):\\n self.out_srs.ImportFromEPSG(4326)\\n else:\\n self.out_srs = self.in_srs\\n\\n # Are the reference systems the same? Reproject if necessary.\\n\\n self.out_ds = None\\n\\n if self.options.profile in ('mercator', 'geodetic', 'gearth', 'garmin'):\\n\\n if (self.in_ds.GetGeoTransform() == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) and (self.in_ds.GetGCPCount() == 0):\\n self.error(\\\"There is no georeference - neither affine transformation (worldfile) nor GCPs. You can generate only 'raster' profile tiles.\\\",\\n \\\"Either gdal2mbtiles with parameter -p 'raster' or use another GIS software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\\\")\\n\\n if self.in_srs:\\n\\n if (self.in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or (self.in_ds.GetGCPCount() != 0):\\n\\n # Generation of VRT dataset in tile projection, default 'nearest neighbour' warping\\n self.out_ds = gdal.AutoCreateWarpedVRT( self.in_ds, self.in_srs_wkt, self.out_srs.ExportToWkt() )\\n\\n # TODO: HIGH PRIORITY: Correction of AutoCreateWarpedVRT according the max zoomlevel for correct direct warping!!!\\n\\n if self.options.verbose:\\n print \\\"Warping of the raster by AutoCreateWarpedVRT (result saved into 'tiles.vrt')\\\"\\n self.out_ds.GetDriver().CreateCopy(\\\"tiles.vrt\\\", self.out_ds)\\n\\n # Note: self.in_srs and self.in_srs_wkt contain still the non-warped reference system!!!\\n\\n # Correction of AutoCreateWarpedVRT for NODATA values\\n if self.in_nodata != []:\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"<GDALWarpOptions>\\\"\\\"\\\",\\\"\\\"\\\"<GDALWarpOptions>\\n <Option name=\\\"UNIFIED_SRC_NODATA\\\">YES</Option>\\n <Option name=\\\"INIT_DEST\\\">NO_DATA</Option>\\\"\\\"\\\")\\n # replace BandMapping tag for NODATA bands....\\n if init_dest is None:\\n dstnodata = self.in_nodata\\n else:\\n dstnodata = init_dest\\n for i in range(len(self.in_nodata)):\\n s = s.replace(\\\"\\\"\\\"<BandMapping src=\\\"%i\\\" dst=\\\"%i\\\"/>\\\"\\\"\\\" % ((i+1),(i+1)),\\\"\\\"\\\"<BandMapping src=\\\"%i\\\" dst=\\\"%i\\\">\\n <SrcNoDataReal>%i</SrcNoDataReal>\\n <SrcNoDataImag>0</SrcNoDataImag>\\n <DstNoDataReal>%i</DstNoDataReal>\\n <DstNoDataImag>0</DstNoDataImag>\\n </BandMapping>\\\"\\\"\\\" % ((i+1), (i+1), self.in_nodata[i], dstnodata[i]))\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n # set NODATA_VALUE metadata\\n self.out_ds.SetMetadataItem('NODATA_VALUES','%s' % \\\" \\\".join(str(int(f)) for f in self.in_nodata))\\n\\n if self.options.verbose:\\n print \\\"Modified warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n # -----------------------------------\\n # Correction of AutoCreateWarpedVRT for Mono (1 band) and RGB (3 bands) files without NODATA:\\n # equivalent of gdalwarp -dstalpha\\n elif self.in_nodata == [] and self.out_ds.RasterCount in (1,3):\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"<BlockXSize>\\\"\\\"\\\",\\\"\\\"\\\"<VRTRasterBand dataType=\\\"Byte\\\" band=\\\"%i\\\" subClass=\\\"VRTWarpedRasterBand\\\">\\n <ColorInterp>Alpha</ColorInterp>\\n </VRTRasterBand>\\n <BlockXSize>\\\"\\\"\\\" % (self.out_ds.RasterCount + 1))\\n s = s.replace(\\\"\\\"\\\"</GDALWarpOptions>\\\"\\\"\\\", \\\"\\\"\\\"<DstAlphaBand>%i</DstAlphaBand>\\n </GDALWarpOptions>\\\"\\\"\\\" % (self.out_ds.RasterCount + 1))\\n if init_dest is None:\\n init_dest_str = \\\"0\\\"\\n else:\\n init_dest_str = \\\",\\\".join(str(f) for f in init_dest)\\n s = s.replace(\\\"\\\"\\\"</WorkingDataType>\\\"\\\"\\\", \\\"\\\"\\\"</WorkingDataType>\\n <Option name=\\\"INIT_DEST\\\">%s</Option>\\\"\\\"\\\" % init_dest_str)\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n if self.options.verbose:\\n print \\\"Modified -dstalpha warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n elif init_dest is not None:\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"</WorkingDataType>\\\"\\\"\\\", \\\"\\\"\\\"</WorkingDataType>\\n <Option name=\\\"INIT_DEST\\\">%s</Option>\\\"\\\"\\\" % \\\",\\\".join(str(f) for f in init_dest))\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n if self.options.verbose:\\n print \\\"Modified warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n # For raster with 4-bands: 4th unknown band set to alpha\\n if (self.out_ds.RasterCount == 4\\n and self.out_ds.GetRasterBand(4).GetRasterColorInterpretation() == gdal.GCI_Undefined):\\n self.out_ds.GetRasterBand(4).SetRasterColorInterpretation(gdal.GCI_AlphaBand)\\n\\n s = '''\\n '''\\n\\n else:\\n self.error(\\\"Input file has unknown SRS.\\\", \\\"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\\\" )\\n\\n if self.out_ds and self.options.verbose:\\n print \\\"Projected file:\\\", \\\"tiles.vrt\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.out_ds.RasterXSize, self.out_ds.RasterYSize, self.out_ds.RasterCount)\\n\\n if not self.out_ds:\\n self.out_ds = self.in_ds\\n\\n #\\n # Here we should have a raster (out_ds) in the correct Spatial Reference system\\n #\\n\\n # KML test\\n self.isepsg4326 = False\\n srs4326 = osr.SpatialReference()\\n srs4326.ImportFromEPSG(4326)\\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\\n self.kml = True\\n self.isepsg4326 = True\\n if self.options.verbose:\\n print \\\"KML autotest OK!\\\"\\n\\n # Read the georeference\\n\\n self.out_gt = self.out_ds.GetGeoTransform()\\n\\n #originX, originY = self.out_gt[0], self.out_gt[3]\\n #pixelSize = self.out_gt[1] # = self.out_gt[5]\\n\\n # Test the size of the pixel\\n\\n # MAPTILER - COMMENTED\\n #if self.out_gt[1] != (-1 * self.out_gt[5]) and self.options.profile != 'raster':\\n # TODO: Process corectly coordinates with are have swichted Y axis (display in OpenLayers too)\\n #self.error(\\\"Size of the pixel in the output differ for X and Y axes.\\\")\\n\\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\\n if (self.out_gt[2], self.out_gt[4]) != (0,0):\\n self.error(\\\"Georeference of the raster contains rotation or skew. Such raster is not supported. Please use gdalwarp first.\\\")\\n # TODO: Do the warping in this case automaticaly\\n\\n #\\n # Here we expect: pixel is square, no rotation on the raster\\n #\\n\\n # Output Bounds - coordinates in the output SRS\\n self.ominx = self.out_gt[0]\\n self.omaxx = self.out_gt[0]+self.out_ds.RasterXSize*self.out_gt[1]\\n self.omaxy = self.out_gt[3]\\n self.ominy = self.out_gt[3]-self.out_ds.RasterYSize*self.out_gt[1]\\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\\n # user defined bounds to extract - coordinates in the output SRS\\n if self.options.te_bounds != '':\\n if self.te_minx >= self.ominx and self.te_minx <= self.omaxx:\\n if self.te_maxx >= self.ominx and self.te_maxx <= self.omaxx:\\n if self.te_miny >= self.ominy and self.te_miny <= self.omaxy:\\n if self.te_maxy >= self.ominy and self.te_maxy <= self.omaxy:\\n # replace only if inside the read bounds\\n self.ominx = self.te_minx\\n self.omaxx = self.te_maxx\\n self.ominy = self.te_miny\\n self.omaxy = self.te_maxy\\n if self.options.verbose:\\n print \\\"User defined Bounds (output srs) have been set:\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.verbose:\\n print \\\"Bounds (output srs):\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.mbtiles:\\n self.options.profile = 'mercator'\\n if self.options.profile == 'mercator':\\n self.mercator = GlobalMercator(self.options.tms_osm) # from globalmaptiles.py\\n\\n #\\n # Calculating ranges for tiles in different zoom levels\\n #\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.mercator.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0,32)\\n for tz in range(0, 32):\\n tminx, tminy = self.mercator.MetersToTile( self.ominx, self.ominy, tz )\\n tmaxx, tmaxy = self.mercator.MetersToTile( self.omaxx, self.omaxy, tz )\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the minimal zoom level (map covers area equivalent to one tile)\\n if self.tminz == None:\\n self.tminz = self.mercator.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.mercator.ZoomForPixelSize( self.out_gt[1] )\\n\\n if self.options.verbose:\\n print \\\"Bounds (latlong):\\\", self.mercator.MetersToLatLon( self.ominx, self.ominy), self.mercator.MetersToLatLon( self.omaxx, self.omaxy)\\n print 'MinZoomLevel:', self.tminz\\n print \\\"MaxZoomLevel:\\\", self.tmaxz, \\\"(\\\", self.mercator.Resolution( self.tmaxz ),\\\")\\\"\\n\\n # this must be call befor ImageOutput is called (self.output may be changed)\\n if self.options.mbtiles:\\n if not self.mbtiles_db:\\n self.mbtiles_setup(1);\\n\\n # Instantiate image output.\\n self.image_output = ImageOutput(self.options.tile_format, self.out_ds, self.tilesize,\\n self.options.resampling, init_dest, self.output,\\n self.options.verbose,self.options.mbtiles)\\n if self.options.profile == 'geodetic':\\n\\n self.geodetic = GlobalGeodetic() # from globalmaptiles.py\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.geodetic.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0,32)\\n for tz in range(0, 32):\\n tminx, tminy = self.geodetic.LatLonToTile( self.ominx, self.ominy, tz )\\n tmaxx, tmaxy = self.geodetic.LatLonToTile( self.omaxx, self.omaxy, tz )\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tminz == None:\\n self.tminz = self.geodetic.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.geodetic.ZoomForPixelSize( self.out_gt[1] )\\n\\n if self.options.verbose:\\n print \\\"Bounds (latlong):\\\", self.ominx, self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.profile in ('raster', 'gearth', 'garmin'):\\n\\n log2 = lambda x: math.log10(x) / math.log10(2) # log2 (base 2 logarithm)\\n\\n self.nativezoom = int(max( math.ceil(log2(self.out_ds.RasterXSize/float(self.tilesize))),\\n math.ceil(log2(self.out_ds.RasterYSize/float(self.tilesize)))))\\n\\n if self.options.verbose:\\n print \\\"Native zoom of the raster:\\\", self.nativezoom\\n\\n # Get the minimal zoom level (whole raster in one tile)\\n if self.tminz == None:\\n self.tminz = 0\\n\\n # Get the maximal zoom level (native resolution of the raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.nativezoom\\n\\n # Garmin has maximally 100 tiles - lower the tmaxz if necessary\\n if self.options.profile == 'garmin':\\n tno = math.ceil(self.out_ds.RasterXSize / self.tilesize) * math.ceil(self.out_ds.RasterYSize / self.tilesize)\\n for tz in range(self.tmaxz, 1, -1):\\n if tno > 100:\\n tno /= 4\\n self.tmaxz -= 1\\n print \\\"Warning: GARMIN has a limit 100 tiles per device: lowering the max zoom level to:\\\", self.tmaxz\\n else:\\n continue\\n\\n # Force only one zoom level for the 'garmin' tile profile\\n if self.options.profile == 'garmin':\\n self.tminz = self.tmaxz\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0, self.tmaxz+1)\\n self.tsize = range(0, self.tmaxz+1)\\n for tz in range(0, self.tmaxz+1):\\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\\n tminx, tminy = 0, 0\\n tmaxx = int(math.ceil( self.out_ds.RasterXSize / tsize )) - 1\\n tmaxy = int(math.ceil( self.out_ds.RasterYSize / tsize )) - 1\\n self.tsize[tz] = math.ceil(tsize)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # Function which generates SWNE in LatLong for given tile\\n if self.kml and self.in_srs_wkt:\\n self.ct = osr.CoordinateTransformation(self.in_srs, srs4326)\\n def rastertileswne(x,y,z):\\n pixelsizex = (2**(self.nativezoom-z) * self.out_gt[1]) # X-pixel size in level\\n pixelsizey = (2**(self.nativezoom-z) * self.out_gt[5]) # Y-pixel size in level (usually -1*pixelsizex)\\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\\n east = west + self.tilesize*pixelsizex\\n south = self.ominy + y*self.tilesize*pixelsizex\\n north = south + self.tilesize*pixelsizex\\n if not self.isepsg4326:\\n # Transformation to EPSG:4326 (WGS84 datum)\\n west, south = self.ct.TransformPoint(west, south)[:2]\\n east, north = self.ct.TransformPoint(east, north)[:2]\\n return south, west, north, east\\n\\n self.tileswne = rastertileswne\\n else:\\n self.tileswne = lambda x, y, z: (0,0,0,0)\",\n \"def pipeline(inputfile, outputfile, functions, **kwargs):\\n if not functions:\\n raise ValueError('Must have at least one function')\\n\\n tmpfiles = []\\n try:\\n previous = inputfile\\n for name, f in functions:\\n logging.debug(name)\\n vrt = f(previous)\\n current = vrt.get_tempfile(suffix='.vrt', prefix='gdal')\\n tmpfiles.append(current)\\n previous = current.name\\n logging.info('Rendering reprojected image')\\n return vrt.render(outputfile=outputfile, **kwargs)\\n finally:\\n for f in tmpfiles:\\n f.close()\",\n \"def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\\n outputs = []\\n npy_outputs = []\\n if resamplemethod == 'nearest':\\n rs = Resampling.nearest\\n else:\\n print('only nearest neighbor resampling is supported at this time')\\n sys.exit(0)\\n\\n for i, warpfile in enumerate(inputs):\\n print('warpfile', warpfile)\\n with rasterio.open(warpfile) as src:\\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\\n with WarpedVRT(src, resampling=rs,\\n crs=self.crs,\\n transform=self.transform,\\n height=self.rows,\\n width=self.cols) as vrt:\\n data = vrt.read()\\n print(type(vrt))\\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\\n outputs.append(outwarp)\\n\\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\\n # from this method for us in the rest of the code.\\n for ow in outputs:\\n with rasterio.open(ow, 'r') as src:\\n arr = src.read(1)\\n npy_outputs.append(arr)\\n\\n return npy_outputs\",\n \"def normalize_to_std_grid(self, inputs, resamplemethod = 'nearest'):\\n outputs = []\\n npy_outputs = []\\n if resamplemethod == 'nearest':\\n rs = Resampling.nearest\\n else:\\n print('only nearest neighbor resampling is supported at this time')\\n sys.exit(0)\\n\\n for i, warpfile in enumerate(inputs):\\n # print('warpfile', warpfile)\\n with rasterio.open(warpfile) as src:\\n # TODO - make the default configurable.\\n# if src.crs == None:\\n# src.crs = CRS.from_epsg(4326)\\n # create the virtual raster based on the standard rasterio attributes from the sample tiff and shapefile feature.\\n with WarpedVRT(src, resampling=rs,\\n crs=self.crs,\\n transform=self.transform,\\n height=self.rows,\\n width=self.cols) as vrt:\\n data = vrt.read()\\n # print(type(vrt))\\n # save the file as an enumerated tiff. reopen outside this loop with the outputs list\\n outwarp = os.path.join(self.temp_folder, 'temp_{}.tif'.format(i))\\n rio_shutil.copy(vrt, outwarp, driver='GTiff')\\n outputs.append(outwarp)\\n\\n # output each virtual file as a temporary .tif file in a temp folder somewhere in the outputs directory.\\n # for each file in the temp directory read in the raster as a numpy array and return the list of numpy arrays\\n # from this method for us in the rest of the code.\\n for ow in outputs:\\n with rasterio.open(ow, 'r') as src:\\n arr = src.read(1)\\n npy_outputs.append(arr)\\n\\n return npy_outputs\",\n \"def transform(infile, output, insrs, format_name):\\n\\n logging.info('Transforming %s from %s to %s' % (infile, insrs, output)) \\n in_srs = osr.SpatialReference()\\n in_srs.ImportFromEPSG(insrs)\\n out_srs = osr.SpatialReference()\\n out_srs.ImportFromEPSG(4324)\\n coordTrans = osr.CoordinateTransformation(in_srs, out_srs)\\n\\n in_dsn = ogr.Open(infile)\\n in_layer = in_dsn.GetLayer()\\n in_feature_definition = in_layer.GetLayerDefn()\\n\\n out_driver = ogr.GetDriverByName(format_name)\\n out_dsn = out_driver.CreateDataSource(output)\\n out_layer = out_dsn.CreateLayer(in_layer.GetName(),\\n geom_type=in_layer.GetGeomType())\\n\\n # add fields\\n for i in range(0, in_feature_definition.GetFieldCount()):\\n fieldDefn = in_feature_definition.GetFieldDefn(i)\\n out_layer.CreateField(fieldDefn)\\n\\n # get the output layer's feature definition\\n out_feature_definition = out_layer.GetLayerDefn()\\n\\n # loop through the input features\\n inFeature = in_layer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef().Clone()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(out_feature_definition)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, out_feature_definition.GetFieldCount()):\\n outFeature.SetField(out_feature_definition.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n out_layer.CreateFeature(outFeature)\\n # destroy the features and get the next input feature\\n outFeature.Destroy()\\n inFeature.Destroy()\\n inFeature = in_layer.GetNextFeature()\\n\\n # close the shapefiles\\n in_dsn.Destroy()\\n out_dsn.Destroy()\",\n \"def vtp(self, f_vtu, f_vtp):\\r\\n reader = vtk.vtkXMLUnstructuredGridReader()\\r\\n reader.SetFileName(f_vtu)\\r\\n reader.Update()\\r\\n ugrid = reader.GetOutput()\\r\\n geometryFilter = vtk.vtkGeometryFilter()\\r\\n geometryFilter.SetInputData(ugrid)\\r\\n geometryFilter.Update()\\r\\n polydata = geometryFilter.GetOutput()\\r\\n writer =vtk.vtkXMLPolyDataWriter()\\r\\n writer.SetFileName(f_vtp)\\r\\n writer.SetInputData(polydata)\\r\\n writer.Write()\\r\\n print(\\\"vtp file created.\\\")\",\n \"def pan_corr(file):\\n\\n # # infile = 'd:\\\\\\\\Projekti\\\\\\\\Satelit\\\\\\\\CO\\\\\\\\Razpis\\\\\\\\Flat field images_new2020\\\\\\\\flatfield\\\\\\\\NHDBflat_1D'\\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Jure_naloga_banje_raw_pyt\\\\\\\\NHDRGoreMorje_3D'\\n #\\n # # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\\n # in_ref = in_path + in_pan_ref_file\\n #\\n # inreffil = gdal.Open(in_ref)\\n # image_ref = inreffil.ReadAsArray()\\n # # size_ref = image_ref.shape\\n # # pix_count = size_ref[0]*size_ref[1]\\n #\\n # image_ref = image_ref[800:930, 1420:1640]\\n # size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n #\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # raz_g1 = 1\\n # raz_g2 = g1_avg/g2_avg\\n # raz_r1 = g1_avg/r1_avg\\n # raz_b1 = g1_avg/b1_avg\\n #\\n # avg = (g1+g2+r1+b1)/pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n raz_g1 = 1\\n raz_g2 = 1.0245196396115988\\n raz_r1 = 1.0131841989689434\\n raz_b1 = 1.0517113199247086\\n\\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Slo_PAN\\\\_26_30\\\\\\\\'\\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\\\"nhd\\\") and filename.lower().endswith(\\\"tif\\\")]\\n\\n \\n\\n \\n\\n # print('image', i)\\n in_ref=file\\n inreffil = gdal.Open(in_ref)\\n image_ref = inreffil.ReadAsArray()\\n size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n # pix_count = np.count_nonzero(image_ref)\\n # pix_count = 3664*650\\n\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # avg = (g1 + g2 + r1 + b1) / pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n # popravek\\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\\n\\n\\n for i in range(size_ref[0]):\\n for j in range(size_ref[1]):\\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\\n \\n _,_,_,_,P=return_flatfield_set_path(2)\\n P_flat=gdal_array.LoadFile(P)\\n \\n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \\n \\n # outout\\n \\n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\\n \\n out=os.path.abspath(file)+\\\"/corr/\\\"+os.path.basename(file)[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n \\n # out = in_ref[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\\n\\n outband = outRaster.GetRasterBand(1)\\n outband.WriteArray(im_p_pop)\\n outband.FlushCache()\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def main(file):\\n\\n # Get the current working directory.\\n here = os.getcwd()\\n #Need the file_name to set globe, so that other functions can access to it.\\n global file_name\\n # Spite the Input into file_path and file_name.\\n file_path = spilt_path(file)[0]\\n file_name = spilt_path(file)[1]\\n\\n # Try to get into the file_path, if exist\\n try:\\n os.chdir(file_path)\\n except IOError, e:\\n print e\\n\\n # Now convert it\\n convertFile(file_name)\\n # going back to orgin folder\\n os.chdir(here)\\n return os.path.join(output_dir, file_name)\",\n \"def open_input(self):\\n gdal.AllRegister()\\n\\n self.out_drv = gdal.GetDriverByName(self.tiledriver)\\n self.mem_drv = gdal.GetDriverByName('MEM')\\n\\n if not self.out_drv:\\n raise Exception(\\\"The '%s' driver was not found, is it available in this GDAL build?\\\",\\n self.tiledriver)\\n if not self.mem_drv:\\n raise Exception(\\\"The 'MEM' driver was not found, is it available in this GDAL build?\\\")\\n\\n # Open the input file\\n\\n if self.input_file:\\n input_dataset = gdal.Open(self.input_file, gdal.GA_ReadOnly)\\n else:\\n raise Exception(\\\"No input file was specified\\\")\\n\\n if self.options.verbose:\\n print(\\\"Input file:\\\",\\n \\\"( %sP x %sL - %s bands)\\\" % (input_dataset.RasterXSize,\\n input_dataset.RasterYSize,\\n input_dataset.RasterCount))\\n\\n if not input_dataset:\\n # Note: GDAL prints the ERROR message too\\n exit_with_error(\\\"It is not possible to open the input file '%s'.\\\" % self.input_file)\\n\\n # Read metadata from the input file\\n if input_dataset.RasterCount == 0:\\n exit_with_error(\\\"Input file '%s' has no raster band\\\" % self.input_file)\\n\\n if input_dataset.GetRasterBand(1).GetRasterColorTable():\\n exit_with_error(\\n \\\"Please convert this file to RGB/RGBA and run gdal2tiles on the result.\\\",\\n \\\"From paletted file you can create RGBA file (temp.vrt) by:\\\\n\\\"\\n \\\"gdal_translate -of vrt -expand rgba %s temp.vrt\\\\n\\\"\\n \\\"then run:\\\\n\\\"\\n \\\"gdal2tiles temp.vrt\\\" % self.input_file\\n )\\n\\n in_nodata = setup_no_data_values(input_dataset, self.options)\\n\\n if self.options.verbose:\\n print(\\\"Preprocessed file:\\\",\\n \\\"( %sP x %sL - %s bands)\\\" % (input_dataset.RasterXSize,\\n input_dataset.RasterYSize,\\n input_dataset.RasterCount))\\n\\n in_srs, self.in_srs_wkt = setup_input_srs(input_dataset, self.options)\\n\\n self.out_srs = setup_output_srs(in_srs, self.options)\\n\\n # If input and output reference systems are different, we reproject the input dataset into\\n # the output reference system for easier manipulation\\n\\n self.warped_input_dataset = None\\n\\n if self.options.profile in ('mercator', 'geodetic'):\\n\\n if not in_srs:\\n exit_with_error(\\n \\\"Input file has unknown SRS.\\\",\\n \\\"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\\\")\\n\\n if not has_georeference(input_dataset):\\n exit_with_error(\\n \\\"There is no georeference - neither affine transformation (worldfile) \\\"\\n \\\"nor GCPs. You can generate only 'raster' profile tiles.\\\",\\n \\\"Either gdal2tiles with parameter -p 'raster' or use another GIS \\\"\\n \\\"software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\\\"\\n )\\n\\n if ((in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or\\n (input_dataset.GetGCPCount() != 0)):\\n self.warped_input_dataset = reproject_dataset(\\n input_dataset, in_srs, self.out_srs)\\n\\n if in_nodata:\\n self.warped_input_dataset = update_no_data_values(\\n self.warped_input_dataset, in_nodata, options=self.options)\\n else:\\n self.warped_input_dataset = update_alpha_value_for_non_alpha_inputs(\\n self.warped_input_dataset, options=self.options)\\n\\n if self.warped_input_dataset and self.options.verbose:\\n print(\\\"Projected file:\\\", \\\"tiles.vrt\\\", \\\"( %sP x %sL - %s bands)\\\" % (\\n self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize,\\n self.warped_input_dataset.RasterCount))\\n\\n if not self.warped_input_dataset:\\n self.warped_input_dataset = input_dataset\\n\\n #Salva o arquivo reprojetado\\n self.warped_input_dataset.GetDriver().CreateCopy(self.tmp_vrt_filename,\\n self.warped_input_dataset)\\n\\n # Get alpha band (either directly or from NODATA value)\\n self.alphaband = self.warped_input_dataset.GetRasterBand(1).GetMaskBand()\\n self.dataBandsCount = nb_data_bands(self.warped_input_dataset)\\n\\n # KML test\\n self.isepsg4326 = False\\n srs4326 = osr.SpatialReference()\\n srs4326.ImportFromEPSG(4326)\\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\\n self.kml = True\\n self.isepsg4326 = True\\n if self.options.verbose:\\n print(\\\"KML autotest OK!\\\")\\n\\n # Read the georeference\\n self.out_gt = self.warped_input_dataset.GetGeoTransform()\\n\\n # Test the size of the pixel\\n\\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\\n if (self.out_gt[2], self.out_gt[4]) != (0, 0):\\n exit_with_error(\\\"Georeference of the raster contains rotation or skew. \\\"\\n \\\"Such raster is not supported. Please use gdalwarp first.\\\")\\n\\n # Here we expect: pixel is square, no rotation on the raster\\n\\n # Output Bounds - coordinates in the output SRS\\n self.ominx = self.out_gt[0]\\n self.omaxx = self.out_gt[0] + self.warped_input_dataset.RasterXSize * self.out_gt[1]\\n self.omaxy = self.out_gt[3]\\n self.ominy = self.out_gt[3] - self.warped_input_dataset.RasterYSize * self.out_gt[1]\\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\\n\\n if self.options.verbose:\\n print(\\\"Bounds (output srs):\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy)\\n\\n # Calculating ranges for tiles in different zoom levels\\n if self.options.profile == 'mercator':\\n\\n self.mercator = GlobalMercator()\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.mercator.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, 32))\\n for tz in range(0, 32):\\n tminx, tminy = self.mercator.MetersToTile(self.ominx, self.ominy, tz)\\n tmaxx, tmaxy = self.mercator.MetersToTile(self.omaxx, self.omaxy, tz)\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the minimal zoom level (map covers area equivalent to one tile)\\n if self.tminz is None:\\n self.tminz = self.mercator.ZoomForPixelSize(\\n self.out_gt[1] *\\n max(self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize) /\\n float(self.tilesize))\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.mercator.ZoomForPixelSize(self.out_gt[1])\\n\\n if self.options.verbose:\\n print(\\\"Bounds (latlong):\\\",\\n self.mercator.MetersToLatLon(self.ominx, self.ominy),\\n self.mercator.MetersToLatLon(self.omaxx, self.omaxy))\\n print('MinZoomLevel:', self.tminz)\\n print(\\\"MaxZoomLevel:\\\",\\n self.tmaxz,\\n \\\"(\\\",\\n self.mercator.Resolution(self.tmaxz),\\n \\\")\\\")\\n\\n if self.options.profile == 'geodetic':\\n\\n self.geodetic = GlobalGeodetic(self.options.tmscompatible)\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.geodetic.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, 32))\\n for tz in range(0, 32):\\n tminx, tminy = self.geodetic.LonLatToTile(self.ominx, self.ominy, tz)\\n tmaxx, tmaxy = self.geodetic.LonLatToTile(self.omaxx, self.omaxy, tz)\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tminz is None:\\n self.tminz = self.geodetic.ZoomForPixelSize(\\n self.out_gt[1] *\\n max(self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize) /\\n float(self.tilesize))\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.geodetic.ZoomForPixelSize(self.out_gt[1])\\n\\n if self.options.verbose:\\n print(\\\"Bounds (latlong):\\\", self.ominx, self.ominy, self.omaxx, self.omaxy)\\n\\n if self.options.profile == 'raster':\\n\\n def log2(x):\\n return math.log10(x) / math.log10(2)\\n\\n self.nativezoom = int(\\n max(math.ceil(log2(self.warped_input_dataset.RasterXSize/float(self.tilesize))),\\n math.ceil(log2(self.warped_input_dataset.RasterYSize/float(self.tilesize)))))\\n\\n if self.options.verbose:\\n print(\\\"Native zoom of the raster:\\\", self.nativezoom)\\n\\n # Get the minimal zoom level (whole raster in one tile)\\n if self.tminz is None:\\n self.tminz = 0\\n\\n # Get the maximal zoom level (native resolution of the raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.nativezoom\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, self.tmaxz+1))\\n self.tsize = list(range(0, self.tmaxz+1))\\n for tz in range(0, self.tmaxz+1):\\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\\n tminx, tminy = 0, 0\\n tmaxx = int(math.ceil(self.warped_input_dataset.RasterXSize / tsize)) - 1\\n tmaxy = int(math.ceil(self.warped_input_dataset.RasterYSize / tsize)) - 1\\n self.tsize[tz] = math.ceil(tsize)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # Function which generates SWNE in LatLong for given tile\\n if self.kml and self.in_srs_wkt:\\n ct = osr.CoordinateTransformation(in_srs, srs4326)\\n\\n def rastertileswne(x, y, z):\\n pixelsizex = (2**(self.tmaxz-z) * self.out_gt[1]) # X-pixel size in level\\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\\n east = west + self.tilesize*pixelsizex\\n south = self.ominy + y*self.tilesize*pixelsizex\\n north = south + self.tilesize*pixelsizex\\n if not self.isepsg4326:\\n # Transformation to EPSG:4326 (WGS84 datum)\\n west, south = ct.TransformPoint(west, south)[:2]\\n east, north = ct.TransformPoint(east, north)[:2]\\n return south, west, north, east\\n\\n self.tileswne = rastertileswne\\n else:\\n self.tileswne = lambda x, y, z: (0, 0, 0, 0) # noqa\",\n \"def read(cls, file_name=None, lexclude=[], lonly=[], verbose=False):\\n###################################################################\\n\\n # import\\n import numpy as np\\n \\n # init\\n \\n vf = Velocity_Field()\\n\\n # fake 4-letters code generation using hexadecimal\\n def __gen_fake_code__(n):\\n \\n FAKE = []\\n for i in np.arange(n):\\n fake_code = (\\\"%4s\\\" % hex(i).split('x')[-1].replace('L', '')).replace(' ', '0')\\n FAKE.append(fake_code.upper())\\n \\n return(np.array(FAKE))\\n\\n # reads psvelo file\\n\\n if verbose:\\n print(\\\"-- Reading GMT psvelo file: %s \\\" % file_name)\\n \\n try:\\n np_vel = np.array(np.mat(np.genfromtxt(file_name, comments='#')))\\n except:\\n raise IOError(\\\"!!! Could not read file: %s\\\" % file_name)\\n \\n # empty psvelo file\\n if np_vel.size == 0:\\n return( vf )\\n \\n if (np_vel.shape[1] == 8):\\n if verbose:\\n print(\\\"-- file %s has 8 columns\\\" % file_name)\\n\\n np_vel = np.delete(np_vel, -1, axis=1)\\n np_code = np.array(np.mat(np.genfromtxt(file_name, comments='#', usecols=(7), dtype=str))).flatten()\\n \\n elif (np_vel.shape[1] == 3):\\n\\n if verbose:\\n print(\\\"-- file %s has 3 columns\\\" % file_name)\\n\\n np_vel = np.delete(np_vel, -1, axis=1)\\n np_code = np.array(np.mat(np.genfromtxt(file_name, comments='#', usecols=(2)))).flatten()\\n\\n elif (np_vel.shape[1] not in [3, 8]):\\n np_code = __gen_fake_code__(np_vel.shape[0])\\n else:\\n raise IOError(\\\"!!! Could not decipher file content: %s\\\", file_name)\\n\\n # populates velocity field\\n \\n from pyacs.lib.gmtpoint import GMT_Point\\n\\n lgmt_points = []\\n\\n for i in np.arange(np_vel.shape[0]):\\n\\n code = np_code[i]\\n \\n if np_vel.shape[1] >= 7:\\n lon, lat, Ve, Vn, SVe, SVn, SVen = np_vel[i, :]\\n M = GMT_Point(lon=lon, lat=lat, Ve=Ve, Vn=Vn, SVe=SVe, SVn=SVn, SVen=SVen, code=code)\\n else:\\n lon, lat = np_vel[i, :]\\n M = GMT_Point(lon=lon, lat=lat, code=code)\\n\\n if verbose:\\n M.get_info(display=True)\\n \\n # tests whether site will be added\\n \\n if lonly != []:\\n if M.code in lonly:\\n lgmt_points.append(M)\\n \\n else:\\n if lexclude != []:\\n if M.code not in lexclude:\\n lgmt_points.append(M)\\n else:\\n lgmt_points.append(M)\\n \\n vf.file_name = file_name\\n vf.sites = lgmt_points\\n \\n return vf\",\n \"def make(input_filepath, output_filepath) -> None:\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\",\n \"def createInput(dirPath,gSettings):\\n \\n with open(os.path.join('../in','input.txt')) as f:\\n inpFile = f.readlines()\\n \\n\\n # Model settings\\n model = gSettings[\\\"Model\\\"]\\n inpFile[13] = \\\"insgrav: {:1d}\\\\n\\\".format(int(model[\\\"NS gravity\\\"][\\\"Flag\\\"]))\\n inpFile[14] = \\\"isun: {:1d}\\\\n\\\".format(int(model[\\\"Lunisolar\\\"][\\\"Sun\\\"]))\\n inpFile[15] = \\\"imoon: {:1d}\\\\n\\\".format(int(model[\\\"Lunisolar\\\"][\\\"Moon\\\"]))\\n\\n if model[\\\"Drag\\\"][\\\"Flag\\\"] == False:\\n inpFile[16] = \\\"idrag: 0\\\\n\\\"\\n else:\\n dm = model[\\\"Drag\\\"][\\\"Model\\\"].lower()\\n if dm == \\\"wertz\\\":\\n idrag = 1\\n elif dm == \\\"us76\\\":\\n idrag = 2\\n elif dm == \\\"j77\\\":\\n idrag = 3\\n elif dm == \\\"msis00\\\":\\n idrag = 4\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Drag\\\"][\\\"Model\\\"] + '\\\" invalid.')\\n inpFile[16] = \\\"idrag: {:1d}\\\\n\\\".format(idrag)\\n if model[\\\"Drag\\\"][\\\"Solar flux\\\"].lower() == \\\"constant\\\":\\n inpFile[17] = \\\"iF107: 0\\\\n\\\"\\n elif model[\\\"Drag\\\"][\\\"Solar flux\\\"].lower() == \\\"variable\\\":\\n inpFile[17] = \\\"iF107: 1\\\\n\\\"\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Drag\\\"][\\\"Solar flux\\\"] + '\\\" invalid.')\\n\\n if model[\\\"SRP\\\"][\\\"Flag\\\"] == False:\\n inpFile[18] = \\\"iSRP: {:1d}\\\\n\\\".format(int(model[\\\"SRP\\\"][\\\"Flag\\\"]))\\n else:\\n inpFile[18] = \\\"iSRP: {:1d}\\\\n\\\".format(int(model[\\\"SRP\\\"][\\\"Flag\\\"]))\\n if model[\\\"SRP\\\"][\\\"Eclipses\\\"]:\\n inpFile[18] = \\\"iSRP: 2\\\\n\\\"\\n \\n if model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] == \\\"DE431\\\":\\n inpFile[19] = \\\"iephem: 1\\\\n\\\"\\n elif model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] == \\\"Meeus\\\":\\n inpFile[19] = \\\"iephem: 2\\\\n\\\"\\n else:\\n raise ValueError('Value \\\"' + model[\\\"Lunisolar\\\"][\\\"Ephemerides\\\"] + '\\\" invalid.')\\n \\n inpFile[20] = \\\"gdeg: {:3d}\\\\n\\\".format(model[\\\"NS gravity\\\"][\\\"Degree\\\"])\\n if model[\\\"NS gravity\\\"][\\\"Order\\\"] <= model[\\\"NS gravity\\\"][\\\"Degree\\\"]:\\n inpFile[21] = \\\"gord: {:3d}\\\\n\\\".format(model[\\\"NS gravity\\\"][\\\"Order\\\"])\\n else:\\n raise ValueError(\\\"Order {0:d} of the gravity field is greater than degree {1:d}\\\".format(model[\\\"NS gravity\\\"][\\\"Order\\\"],model[\\\"NS gravity\\\"][\\\"Degree\\\"]))\\n \\n\\n\\n # Integration settings\\n integ = gSettings[\\\"Integration\\\"]\\n inpFile[29] = \\\"tol: {:22.15E}\\\\n\\\".format(integ[\\\"Tolerance\\\"])\\n inpFile[30] = \\\"tspan: {:22.15E}\\\\n\\\".format(integ[\\\"Duration\\\"] * 365.25)\\n inpFile[31] = \\\"tstep: {:22.15E}\\\\n\\\".format(integ[\\\"Step\\\"])\\n inpFile[39] = \\\"eqs: {:2d}\\\\n\\\".format(integ[\\\"Equations\\\"])\\n\\n\\n\\n # Output settings\\n inpFile[44] = \\\"verb: 0\\\\n\\\"\\n inpFile[45] = \\\"out: \\\" + os.path.abspath(os.path.join(dirPath, ' '))\\n\\n\\n with open(os.path.join(dirPath,'input.txt'),'w') as f:\\n f.writelines(inpFile)\",\n \"def create_vector_datapackage(pk_type, path, file_flag, out_path):\\n process_source(pk_type, path, file_flag, out_path)\",\n \"def vl2img(vl_json_in, fileformat):\\n\\n # TODO would prefer to do this properly with pipes\\n # using | and shell=True is safe though given no arguments\\n executables = {\\\"svg\\\": \\\"vg2svg\\\", \\\"png\\\": \\\"vg2png\\\", \\\"pdf\\\": \\\"vg2pdf\\\"}\\n try:\\n exe = executables[fileformat]\\n except KeyError as e:\\n print(e.output)\\n try:\\n return subprocess.check_output(\\\"vl2vg | %s\\\" % exe, shell=True, input=vl_json_in)\\n except subprocess.CalledProcessError as e:\\n print(e.output)\",\n \"def read_raytomo_dbase(self, inh5fname, runid, dtype='ph', wtype='ray', create_header=True, Tmin=-999, Tmax=999, verbose=False):\\n if dtype is not 'ph' and dtype is not 'gr':\\n raise ValueError('data type can only be ph or gr!')\\n if wtype is not 'ray' and wtype is not 'lov':\\n raise ValueError('wave type can only be ray or lov!')\\n stalst = self.waveforms.list()\\n if len(stalst) == 0:\\n print 'Inversion with surface wave datasets only, not added yet!'\\n return\\n indset = h5py.File(inh5fname)\\n #--------------------------------------------\\n # header information from input hdf5 file\\n #--------------------------------------------\\n dataid = 'reshaped_qc_run_'+str(runid)\\n pers = indset.attrs['period_array']\\n grp = indset[dataid]\\n isotropic = grp.attrs['isotropic']\\n org_grp = indset['qc_run_'+str(runid)]\\n minlon = indset.attrs['minlon']\\n maxlon = indset.attrs['maxlon']\\n minlat = indset.attrs['minlat']\\n maxlat = indset.attrs['maxlat']\\n if isotropic:\\n print 'isotropic inversion results do not output gaussian std!'\\n return\\n dlon_HD = org_grp.attrs['dlon_HD']\\n dlat_HD = org_grp.attrs['dlat_HD']\\n dlon = org_grp.attrs['dlon']\\n dlat = org_grp.attrs['dlat']\\n if create_header:\\n inv_header = {'minlon': minlon, 'maxlon': maxlon, 'minlat': minlat, 'maxlat': maxlat,\\n 'dlon': dlon, 'dlat': dlat, 'dlon_HD': dlon_HD, 'dlat_HD': dlat_HD}\\n self.add_auxiliary_data(data=np.array([]), data_type='Header', path='raytomo', parameters=inv_header)\\n self._get_lon_lat_arr(path='raytomo', hd=True)\\n for staid in stalst:\\n netcode, stacode = staid.split('.')\\n staid_aux = netcode+'_'+stacode\\n stla, elev, stlo = self.waveforms[staid].coordinates.values()\\n if stlo < 0.:\\n stlo += 360.\\n if stla > maxlat or stla < minlat or stlo > maxlon or stlo < minlon:\\n print 'WARNING: station: '+ staid+', lat = '+str(stla)+' lon = '+str(stlo)+', out of the range of tomograpic maps!'\\n continue\\n disp_v = np.array([])\\n disp_un = np.array([])\\n T = np.array([])\\n #-----------------------------\\n # determine the indices\\n #-----------------------------\\n ind_lon = np.where(stlo<=self.lons)[0][0]\\n find_lon = ind_lon \\n ind_lat = np.where(stla<=self.lats)[0][0]\\n find_lat = ind_lat\\n # point 1\\n distmin, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon]) # distance is in m\\n # point 2\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat], self.lons[ind_lon-1]) # distance is in m\\n if dist < distmin:\\n find_lon = ind_lon-1\\n distmin = dist\\n # point 3\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon]) # distance is in m\\n if dist < distmin:\\n find_lat = ind_lat-1\\n distmin = dist\\n # point 4\\n dist, az, baz = obspy.geodetics.gps2dist_azimuth(stla, stlo, self.lats[ind_lat-1], self.lons[ind_lon-1]) # distance is in m\\n if dist < distmin:\\n find_lat = ind_lat-1\\n find_lon = ind_lon-1\\n distmin = dist\\n for per in pers:\\n if per < Tmin or per > Tmax:\\n continue\\n try:\\n pergrp = grp['%g_sec'%( per )]\\n vel = pergrp['vel_iso_HD'].value\\n vel_sem = pergrp['vel_sem_HD'].value\\n except KeyError:\\n if verbose:\\n print 'No data for T = '+str(per)+' sec'\\n continue\\n T = np.append(T, per)\\n disp_v = np.append(disp_v, vel[find_lat, find_lon])\\n disp_un = np.append(disp_un, vel_sem[find_lat, find_lon])\\n data = np.zeros((3, T.size))\\n data[0, :] = T[:]\\n data[1, :] = disp_v[:]\\n data[2, :] = disp_un[:]\\n disp_header = {'Np': T.size}\\n self.add_auxiliary_data(data=data, data_type='RayDISPcurve', path=wtype+'/'+dtype+'/'+staid_aux, parameters=disp_header)\\n indset.close()\\n return\",\n \"def make_svm_input_file(input_filename, output_custom_pars_file='custom_svm_params.json', clobber=False,\\n log_level=logutil.logging.INFO):\\n log.setLevel(log_level)\\n if not clobber:\\n if os.path.exists(output_custom_pars_file):\\n msg = \\\"A file named '{}' already exists. Please choose a unique name for the custom SVM parameter file.\\\".format(output_custom_pars_file)\\n log.critical(msg)\\n sys.exit()\\n # Define trailer file (log file) that will contain the log entries for all processing\\n if isinstance(input_filename, str): # input file is a poller file -- easy case\\n logname = input_filename.replace('.out', '_svm_partam_gen.log')\\n\\n else:\\n logname = 'svm_param_gen.log'\\n\\n # Initialize total trailer filename as temp logname\\n logging.basicConfig(filename=logname, format=SPLUNK_MSG_FORMAT, datefmt=MSG_DATEFMT)\\n # start processing\\n starting_dt = datetime.datetime.now()\\n log.info(\\\"Run start time: {}\\\".format(str(starting_dt)))\\n\\n try:\\n # Parse the poller file and generate the the obs_info_dict, as well as the total detection\\n # product lists which contain the ExposureProduct, FilterProduct, and TotalProduct objects\\n # A poller file contains visit data for a single instrument. The TotalProduct discriminant\\n # is the detector. A TotalProduct object is comprised of FilterProducts and ExposureProducts\\n # where its FilterProduct is distinguished by the filter in use, and the ExposureProduct\\n # is the atomic exposure data.\\n log.info(\\\"Parse the poller and determine what exposures need to be combined into separate products.\\\\n\\\")\\n obs_info_dict, total_obj_list = poller_utils.interpret_obset_input(input_filename, log_level)\\n\\n # Update all of the product objects with their associated configuration information.\\n for total_item in total_obj_list:\\n log.info(\\\"Preparing configuration parameter values for total product {}\\\".format(total_item.drizzle_filename))\\n total_item.configobj_pars = config_utils.HapConfig(total_item,\\n log_level=log_level,\\n output_custom_pars_file=output_custom_pars_file)\\n for filter_item in total_item.fdp_list:\\n log.info(\\\"Preparing configuration parameter values for filter product {}\\\".format(filter_item.drizzle_filename))\\n filter_item.configobj_pars = config_utils.HapConfig(filter_item,\\n log_level=log_level,\\n output_custom_pars_file=output_custom_pars_file)\\n update_ci_values(filter_item, output_custom_pars_file, log_level)\\n\\n for expo_item in total_item.edp_list:\\n log.info(\\\"Preparing configuration parameter values for exposure product {}\\\".format(expo_item.drizzle_filename))\\n expo_item.configobj_pars = config_utils.HapConfig(expo_item,\\n log_level=log_level,\\n output_custom_pars_file=output_custom_pars_file)\\n # Housekeeping: remove those pesky renamed copies of the input flc.fits/flt.fits files\\n # generated by drizzlepac.haputils.product()\\n if expo_item.drizzle_filename.endswith(\\\"_drc.fits\\\"):\\n file_to_remove = expo_item.drizzle_filename.replace(\\\"_drc.fits\\\", \\\"_flc.fits\\\")\\n if expo_item.drizzle_filename.endswith(\\\"_drz.fits\\\"):\\n file_to_remove = expo_item.drizzle_filename.replace(\\\"_drz.fits\\\", \\\"_flt.fits\\\")\\n if os.path.exists(file_to_remove):\\n os.remove(file_to_remove)\\n except Exception:\\n exc_type, exc_value, exc_tb = sys.exc_info()\\n traceback.print_exception(exc_type, exc_value, exc_tb, file=sys.stdout)\\n err_msg = \\\"Something went wrong!\\\"\\n log.error(err_msg)\\n raise Exception(err_msg)\",\n \"def from_srf_file(self, filename, normalize=False):\\n with open(filename, \\\"rt\\\") as f:\\n # go to POINTS block\\n line = f.readline()\\n while 'POINTS' not in line:\\n line = f.readline()\\n\\n npoints = int(line.split()[1])\\n sources = []\\n\\n for _ in np.arange(npoints):\\n lon, lat, dep, stk, dip, area, tinit, dt = \\\\\\n map(float, f.readline().split())\\n rake, slip1, nt1, slip2, nt2, slip3, nt3 = \\\\\\n map(float, f.readline().split())\\n\\n dep *= 1e3 # km > m\\n area *= 1e-4 # cm^2 > m^2\\n slip1 *= 1e-2 # cm > m\\n slip2 *= 1e-2 # cm > m\\n # slip3 *= 1e-2 # cm > m\\n\\n nt1, nt2, nt3 = map(int, (nt1, nt2, nt3))\\n\\n if nt1 > 0:\\n line = f.readline()\\n while len(line.split()) < nt1:\\n line = line + f.readline()\\n stf = np.array(line.split(), dtype=float)\\n if normalize:\\n stf /= np.trapz(stf, dx=dt)\\n\\n M0 = area * DEFAULT_MU * slip1\\n\\n sources.append(\\n Source.from_strike_dip_rake(\\n lat, lon, dep, stk, dip, rake, M0,\\n time_shift=tinit, sliprate=stf, dt=dt))\\n\\n if nt2 > 0:\\n line = f.readline()\\n while len(line.split()) < nt2:\\n line = line + f.readline()\\n stf = np.array(line.split(), dtype=float)\\n if normalize:\\n stf /= np.trapz(stf, dx=dt)\\n\\n M0 = area * DEFAULT_MU * slip2\\n\\n sources.append(\\n Source.from_strike_dip_rake(\\n lat, lon, dep, stk, dip, rake, M0,\\n time_shift=tinit, sliprate=stf, dt=dt))\\n\\n if nt3 > 0:\\n raise NotImplementedError('Slip along u3 axis')\\n\\n return self(pointsources=sources)\",\n \"def vaex_vertices_from_plyfile(filename):\\n xyz = vertex_dict_from_plyfile(filename)\\n return vx.from_dict(xyz)\",\n \"def convert_rows_to_wv(direct_file, grism_file, rows):\\n\\n # Collect data from FITS headers\\n with fits.open(grism_file) as hdu:\\n hdr = hdu[0].header\\n hdr1 = hdu[1].header\\n sci_postarg_1 = hdr['POSTARG1']\\n sci_postarg_2 = hdr['POSTARG2']\\n sci_crpix_1 = hdr1['CRPIX1'] # this isn't a real keyword...\\n sci_crpix_2 = hdr1['CRPIX2'] \\n\\n with fits.open(direct_file) as hdu:\\n hdr = hdu[0].header\\n hdr1 = hdu[1].header\\n data = hdu[1].data\\n cal_postarg_1 = hdr['POSTARG1']\\n cal_postarg_2 = hdr['POSTARG2']\\n cal_crpix_1 = hdr1['CRPIX1']\\n cal_crpix_2 = hdr1['CRPIX2']\\n\\n\\n # Find the central source\\n mean, med, std = sigma_clipped_stats(data, sigma=3.0, iters=5)\\n sources = daofind(data-med, fwhm=3.0, threshold=5.*std)\\n \\n source = sources[np.where(sources['flux'] == np.max(sources['flux']))]\\n x_cen, y_cen = source['xcentroid'], source['ycentroid']\\n\\n\\n # Calculate the offset\\n x_offset = sci_crpix_1 - cal_crpix_1 + (sci_postarg_1 - cal_postarg_1)/0.135\\n y_offset = sci_crpix_2 - cal_crpix_2 + (sci_postarg_2 - cal_postarg_2)/0.121\\n\\n pos_x, pos_y = x_cen + x_offset, y_cen + y_offset\\n\\n constants_0 = [8.95E3, 9.35925E-2, 0.0, 0.0, 0.0, 0.0]\\n constants_1 = [4.51423E1, 3.17239E-4, 2.17055E-3, -7.42504E-7, 3.4863E-7, 3.09213E-7]\\n\\n coords_0 = constants_0[0] + constants_0[1]*pos_x + constants_0[2]*pos_y\\n coords_1 = constants_1[0] + constants_1[1]*pos_x + constants_1[2]*pos_y + constants_1[3]*pos_x**2 + constants_1[4]*pos_x*pos_y + constants_1[5]*pos_y**2\\n \\n wv = coords_0 + coords_1*(rows-pos_x) + pos_y\\n\\n return wv\",\n \"def cloud_optimize_inPlace(in_file:str,compress=\\\"LZW\\\") -> None:\\n\\t## add overviews to file\\n\\tcloudOpArgs = [\\\"gdaladdo\\\",in_file]\\n\\tsubprocess.call(cloudOpArgs)\\n\\n\\t## copy file\\n\\tintermediate_file = in_file.replace(\\\".tif\\\",\\\".TEMP.tif\\\")\\n\\twith open(intermediate_file,'wb') as a:\\n\\t\\twith open(in_file,'rb') as b:\\n\\t\\t\\tshutil.copyfileobj(b,a)\\n\\n\\t## add tiling to file\\n\\tcloudOpArgs = [\\\"gdal_translate\\\",intermediate_file,in_file,'-q','-co', \\\"TILED=YES\\\",'-co',\\\"COPY_SRC_OVERVIEWS=YES\\\",'-co', f\\\"COMPRESS={compress}\\\"]#, \\\"-co\\\", \\\"PREDICTOR=2\\\"]\\n\\tsubprocess.call(cloudOpArgs)\\n\\n\\t## remove intermediate\\n\\tos.remove(intermediate_file)\",\n \"def vtkReformat(inPath, outPath):\\n # Get size of map\\n inFile = open(inPath,\\\"rb\\\")\\n lineList = inFile.readlines()\\n for line in lineList:\\n if line.lower().strip().startswith(\\\"dimensions\\\"):\\n size = map(int,line.split(\\\" \\\")[1:dimension+1])\\n break\\n inFile.close()\\n\\n if dimension == 2: size += [0]\\n\\n outFile = open(outPath,\\\"wb\\\")\\n for (i,line) in enumerate(lineList):\\n if i == 1:\\n newline = line.lstrip(line.rstrip(\\\"\\\\n\\\"))\\n line = \\\"lddmm 8 0 0 {0} {0} 0 0 {1} {1} 0 0 {2} {2}\\\".format(size[2]-1, size[1]-1, size[0]-1) + newline\\n outFile.write(line)\",\n \"def __init__(self, inputfile, mode=GA_ReadOnly):\\n # Open the input file and read some metadata\\n open(inputfile, 'r').close() # HACK: GDAL gives a useless exception\\n if not isinstance(inputfile, bytes):\\n inputfile = inputfile.encode('utf-8')\\n try:\\n # Since this is a SWIG object, clone the ``this`` pointer\\n self.this = gdal.Open(inputfile, mode).this\\n except RuntimeError as e:\\n raise GdalError(str(e))\\n\\n # Shadow for metadata so we can overwrite it without saving\\n # it to the original file.\\n self._geotransform = None\\n self._rastersizes = None\",\n \"def read_velocities(filename, return_grid=True, return_proj=False):\\n ds = gdal.Open(filename)\\n #Get dimensions\\n nc = ds.RasterXSize\\n nr = ds.RasterYSize\\n \\n geotransform = ds.GetGeoTransform()\\n xOrigin = geotransform[0]\\n xPix = geotransform[1] #pixel width in x-direction\\n yOrigin = geotransform[3]\\n yPix = geotransform[5] #pixel height in y-direction\\n \\n lons = xOrigin + np.arange(0, nc)*xPix\\n lats = yOrigin + np.arange(0, nr)*yPix\\n \\n x, y = np.meshgrid(lons, lats)\\n \\n vband = ds.GetRasterBand(1)\\n varr = vband.ReadAsArray()\\n \\n if return_grid and return_proj:\\n return x, y, varr, ds.GetProjection()\\n elif return_grid:\\n return x, y, varr\\n else: \\n return varr\",\n \"def reproject_vector( path, epsg_from=None, epsg_to=None):\\n\\n if not epsg_to: raise Exception(\\\"please, specify the output EPSG codes\\\")\\n\\n inDataSet = None\\n outDataSet = None\\n inFeature = None\\n outFeature = None\\n outLayer = None\\n\\n try:\\n\\n driver = ogr.GetDriverByName('ESRI Shapefile')\\n inDataSet = driver.Open(path, 0) # 0 means read-only\\n\\n # define input SpatialReference\\n if not epsg_from:\\n layer = inDataSet.GetLayer()\\n inSpatialRef = layer.GetSpatialRef()\\n else:\\n inSpatialRef = osr.SpatialReference()\\n inSpatialRef.ImportFromEPSG(epsg_from)\\n\\n # define output SpatialReference\\n outSpatialRef = osr.SpatialReference()\\n outSpatialRef.ImportFromEPSG(epsg_to)\\n\\n # create the CoordinateTransformation\\n coordTrans = osr.CoordinateTransformation(inSpatialRef, outSpatialRef)\\n\\n # get the first input layer and the geometry type\\n inLayer = inDataSet.GetLayer()\\n geotype = inLayer.GetGeomType()\\n lname = inLayer.GetName()\\n\\n drv = ogr.GetDriverByName(\\\"ESRI Shapefile\\\")\\n outDataSet = drv.CreateDataSource(\\\"/vsimem/memory.shp\\\")\\n\\n outLayer = outDataSet.CreateLayer(lname, srs=outSpatialRef, geom_type=geotype)\\n\\n # add fields\\n inLayerDefn = inLayer.GetLayerDefn()\\n\\n for i in range(0, inLayerDefn.GetFieldCount()):\\n fieldDefn = inLayerDefn.GetFieldDefn(i)\\n outLayer.CreateField(fieldDefn)\\n\\n # get the output layer\\\"s feature definition\\n outLayerDefn = outLayer.GetLayerDefn()\\n\\n counter = 1\\n\\n # loop through the input features\\n inFeature = inLayer.GetNextFeature()\\n while inFeature:\\n # get the input geometry\\n geom = inFeature.GetGeometryRef()\\n # reproject the geometry\\n geom.Transform(coordTrans)\\n # create a new feature\\n outFeature = ogr.Feature(outLayerDefn)\\n # set the geometry and attribute\\n outFeature.SetGeometry(geom)\\n for i in range(0, outLayerDefn.GetFieldCount()):\\n outFeature.SetField(outLayerDefn.GetFieldDefn(i).GetNameRef(), inFeature.GetField(i))\\n # add the feature to the shapefile\\n outLayer.CreateFeature(outFeature)\\n\\n # destroy the features and get the next input feature\\n if outFeature: outFeature = None\\n inFeature = inLayer.GetNextFeature()\\n\\n counter += 1\\n #print(counter)\\n\\n return outDataSet\\n\\n except RuntimeError as err:\\n raise err\\n except Exception as e:\\n raise e\\n\\n finally:\\n if inDataSet: outDataSet == None # give back control to C++\\n if outDataSet: outDataSet == None\\n if outLayer: outLayer == None\\n if inFeature: inFeature == None\\n if outFeature: outFeature = None\",\n \"def saveVelocityAndPressureVTK_binary(pressure,u,v,w,x,y,z,filename,dims):\\n numEl_size = u.size; numEl = np.prod(numEl_size);\\n # open the file and write the ASCII header:\\n file = open(filename,'w')\\n file.write('# vtk DataFile Version 3.0\\\\n')\\n file.write('VTK file for data post-processed with Python\\\\n')\\n file.write('Binary\\\\n\\\\n')\\n file.write('DATASET STRUCTURED_GRID\\\\n')\\n file.write('DIMENSIONS %d %d %d \\\\n'%(dims[0],dims[1],dims[2]))\\n file.write('POINTS %d float\\\\n'%(numEl))\\n file.close()\\n \\n # append binary x,y,z data\\n file = open(filename,'ab')\\n for i in range(len(x)): # there really needs to be a better way.\\n pt = [x[i],y[i],z[i]]\\n pt_buf = array('f',pt)\\n pt_buf.byteswap()\\n file.write(pt_buf)\\n \\n \\n file.close()\\n \\n # append an ASCII sub header\\n file = open(filename,'a')\\n file.write('POINT_DATA %d \\\\n'%numEl)\\n file.write('VECTORS velocity_vectors float\\\\n')\\n file.close()\\n \\n # append binary u,v,w data\\n file = open(filename,'ab')\\n for i in range(len(u)):\\n pt = [u[i],v[i],w[i]]\\n pt_buf = array('f',pt)\\n pt_buf.byteswap()\\n file.write(pt_buf)\\n \\n file.close()\\n \\n # append ASCII sub header for scalar velocity magnitude data\\n file = open(filename,'a')\\n file.write('SCALARS VelocityMagnitude float\\\\n')\\n file.write('LOOKUP_TABLE default\\\\n')\\n \\n file.close()\\n \\n file = open(filename,'ab')\\n v_mag = np.sqrt(u**2+v**2+w**2)\\n file = open(filename,'ab')\\n p_buf = array('f',v_mag); p_buf.byteswap()\\n file.write(p_buf)\\n file.close()\\n \\n \\n # append another ASCII sub header for the scalar pressure data\\n file = open(filename,'a')\\n file.write('SCALARS Pressure float\\\\n')\\n file.write('LOOKUP_TABLE default\\\\n')\\n file.close()\\n \\n # append binary pressure data\\n file = open(filename,'ab')\\n p_buf = array('f',pressure); p_buf.byteswap()\\n file.write(p_buf)\\n file.close()\",\n \"def cli(source_f, raster_f, output, verbose):\\n with fiona.open(source_f, 'r') as source:\\n source_driver = source.driver\\n source_crs = source.crs\\n sink_schema = source.schema.copy()\\n\\n source_geom = source.schema['geometry']\\n if source_geom == 'Point':\\n sink_schema['geometry'] = '3D Point'\\n elif source_geom == 'LineString':\\n sink_schema['geometry'] = '3D LineString'\\n elif source_geom == '3D Point' or source_geom == '3D LineString':\\n pass\\n else:\\n click.BadParameter(\\\"Source geometry type {} not implemented\\\".format(source_geom))\\n\\n with rasterio.open(raster_f) as raster:\\n if source_crs != raster.crs:\\n click.BadParameter(\\\"Features and raster have different CRS.\\\")\\n if raster.count > 1:\\n warnings.warn(\\\"Found {0} bands in {1}, expected a single band raster\\\".format(raster.bands, raster_f))\\n supported = ['int16', 'int32', 'float32', 'float64']\\n if raster.dtypes[0] not in supported:\\n warnings.warn(\\\"Found {0} type in {1}, expected one of {2}\\\".format(raster.dtypes[0], raster_f, supported))\\n with fiona.open(\\n output, 'w',\\n driver=source_driver,\\n crs=source_crs,\\n schema=sink_schema) as sink:\\n\\n for feature in source:\\n try:\\n feature_z = drapery.drape(raster, feature)\\n sink.write({\\n 'geometry': mapping(feature_z),\\n 'properties': feature['properties'],\\n })\\n except Exception:\\n logging.exception(\\\"Error processing feature %s:\\\", feature['id'])\\n #print(sink.closed)\\n #print(raster.closed)\\n #print(source.closed)\",\n \"def run(self, verbose=False):\\n from utils import write_to_file # function to write json to file\\n self.read_json()\\n graph = self.parse_jsons()\\n json = self.pipe_vl2vg(graph)\\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)\",\n \"def compute_templates(filename, TDUR, filt, ratios, dt, ncor, window, \\\\\\n winlength, nattempts, waittime, method='RMS'):\\n # To transform latitude and longitude into kilometers\\n a = 6378.136\\n e = 0.006694470\\n lat0 = 41.0\\n lon0 = -123.0\\n dx = (pi / 180.0) * a * cos(lat0 * pi / 180.0) / sqrt(1.0 - e * e * \\\\\\n sin(lat0 * pi / 180.0) * sin(lat0 * pi / 180.0))\\n dy = (3.6 * pi / 648.0) * a * (1.0 - e * e) / ((1.0 - e * e * sin(lat0 * \\\\\\n pi / 180.0) * sin(lat0 * pi / 180.0)) ** 1.5)\\n\\n # Get the names of the stations which have a waveform for this LFE family\\n file = open('../data/Plourde_2015/detections/' + filename + \\\\\\n '_detect5_cull.txt')\\n first_line = file.readline().strip()\\n staNames = first_line.split()\\n file.close()\\n\\n # Get the time of LFE detections\\n LFEtime = np.loadtxt('../data/Plourde_2015/detections/' + filename + \\\\\\n '_detect5_cull.txt', \\\\\\n dtype={'names': ('unknown', 'day', 'hour', 'second', 'threshold'), \\\\\\n 'formats': (np.float, '|S6', np.int, np.float, np.float)}, \\\\\\n skiprows=2)\\n\\n # Get the network, channels, and location of the stations\\n staloc = pd.read_csv('../data/Plourde_2015/station_locations.txt', \\\\\\n sep=r'\\\\s{1,}', header=None)\\n staloc.columns = ['station', 'network', 'channels', 'location', \\\\\\n 'server', 'latitude', 'longitude']\\n\\n # Get the location of the source of the LFE\\n LFEloc = np.loadtxt('../data/Plourde_2015/templates_list.txt', \\\\\\n dtype={'names': ('name', 'family', 'lat', 'lon', 'depth', 'eH', \\\\\\n 'eZ', 'nb'), \\\\\\n 'formats': ('S13', 'S3', np.float, np.float, np.float, \\\\\\n np.float, np.float, np.int)}, \\\\\\n skiprows=1)\\n for ie in range(0, len(LFEloc)):\\n if (filename == LFEloc[ie][0].decode('utf-8')):\\n lats = LFEloc[ie][2]\\n lons = LFEloc[ie][3]\\n xs = dx * (lons - lon0)\\n ys = dy * (lats - lat0)\\n\\n # Create directory to store the waveforms\\n namedir = 'templates/' + filename\\n if not os.path.exists(namedir):\\n os.makedirs(namedir)\\n\\n # Read origin time and station slowness files\\n origintime = pickle.load(open('timearrival/origintime.pkl', 'rb'))\\n slowness = pickle.load(open('timearrival/slowness.pkl', 'rb'))\\n\\n # File to write error messages\\n errorfile = 'error/' + filename + '.txt'\\n\\n # Loop over stations\\n for station in staNames:\\n # Create streams\\n EW = Stream()\\n NS = Stream()\\n UD = Stream()\\n # Get station metadata for downloading\\n for ir in range(0, len(staloc)):\\n if (station == staloc['station'][ir]):\\n network = staloc['network'][ir]\\n channels = staloc['channels'][ir]\\n location = staloc['location'][ir]\\n server = staloc['server'][ir]\\n # Compute source-receiver distance\\n latitude = staloc['latitude'][ir]\\n longitude = staloc['longitude'][ir]\\n xr = dx * (longitude - lon0)\\n yr = dy * (latitude - lat0)\\n distance = sqrt((xr - xs) ** 2.0 + (yr - ys) ** 2.0)\\n # Loop on LFEs\\n for i in range(0, np.shape(LFEtime)[0]):\\n YMD = LFEtime[i][1]\\n myYear = 2000 + int(YMD[0 : 2])\\n myMonth = int(YMD[2 : 4])\\n myDay = int(YMD[4 : 6])\\n myHour = LFEtime[i][2] - 1\\n myMinute = int(LFEtime[i][3] / 60.0)\\n mySecond = int(LFEtime[i][3] - 60.0 * myMinute)\\n myMicrosecond = int(1000000.0 * \\\\\\n (LFEtime[i][3] - 60.0 * myMinute - mySecond))\\n Tori = UTCDateTime(year=myYear, month=myMonth, day=myDay, \\\\\\n hour=myHour, minute=myMinute, second=mySecond, \\\\\\n microsecond=myMicrosecond)\\n Tstart = Tori - TDUR\\n Tend = Tori + 60.0 + TDUR\\n # First case: we can get the data from IRIS\\n if (server == 'IRIS'):\\n (D, orientation) = get_from_IRIS(station, network, channels, \\\\\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\\\\n errorfile)\\n # Second case: we get the data from NCEDC\\n elif (server == 'NCEDC'):\\n (D, orientation) = get_from_NCEDC(station, network, channels, \\\\\\n location, Tstart, Tend, filt, dt, nattempts, waittime, \\\\\\n errorfile)\\n else:\\n raise ValueError( \\\\\\n 'You can only download data from IRIS and NCEDC')\\n if (type(D) == obspy.core.stream.Stream):\\n # Add to stream\\n if (channels == 'EH1,EH2,EHZ'):\\n EW.append(D.select(channel='EH1').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n NS.append(D.select(channel='EH2').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n UD.append(D.select(channel='EHZ').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n else:\\n EW.append(D.select(component='E').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n NS.append(D.select(component='N').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n UD.append(D.select(component='Z').slice(Tori, \\\\\\n Tori + 60.0)[0])\\n else:\\n print('Failed at downloading data')\\n # Stack\\n if (len(EW) > 0 and len(NS) > 0 and len(UD) > 0):\\n # Stack waveforms\\n EWstack = linstack([EW], normalize=True, method=method) \\n NSstack = linstack([NS], normalize=True, method=method)\\n UDstack = linstack([UD], normalize=True, method=method)\\n # Initializations\\n maxCC = np.zeros(len(EW))\\n cc0EW = np.zeros(len(EW))\\n cc0NS = np.zeros(len(EW))\\n cc0UD = np.zeros(len(EW))\\n if (window == True):\\n # Get time arrival\\n arrivaltime = origintime[filename] + \\\\\\n slowness[station] * distance\\n Tmin = arrivaltime - winlength / 2.0\\n Tmax = arrivaltime + winlength / 2.0\\n if Tmin < 0.0:\\n Tmin = 0.0\\n if Tmax > EWstack[0].stats.delta * (EWstack[0].stats.npts - 1):\\n Tmax = EWstack[0].stats.delta * (EWstack[0].stats.npts - 1)\\n ibegin = int(Tmin / EWstack[0].stats.delta)\\n iend = int(Tmax / EWstack[0].stats.delta) + 1\\n # Cross correlation\\n for i in range(0, len(EW)):\\n ccEW = correlate(EWstack[0].data[ibegin : iend], \\\\\\n EW[i].data[ibegin : iend], ncor)\\n ccNS = correlate(NSstack[0].data[ibegin : iend], \\\\\\n NS[i].data[ibegin : iend], ncor)\\n ccUD = correlate(UDstack[0].data[ibegin : iend], \\\\\\n UD[i].data[ibegin : iend], ncor)\\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\\n cc0EW[i] = ccEW[ncor]\\n cc0NS[i] = ccNS[ncor]\\n cc0UD[i] = ccUD[ncor]\\n else:\\n # Cross correlation\\n for i in range(0, len(EW)):\\n ccEW = correlate(EWstack[0].data, EW[i].data, ncor)\\n ccNS = correlate(NSstack[0].data, NS[i].data, ncor)\\n ccUD = correlate(UDstack[0].data, UD[i].data, ncor)\\n maxCC[i] = np.max(ccEW) + np.max(ccNS) + np.max(ccUD)\\n cc0EW[i] = ccEW[ncor]\\n cc0NS[i] = ccNS[ncor]\\n cc0UD[i] = ccUD[ncor]\\n # Sort cross correlations\\n index = np.flip(np.argsort(maxCC), axis=0)\\n EWbest = Stream()\\n NSbest = Stream()\\n UDbest = Stream()\\n # Compute stack of best LFEs\\n for j in range(0, len(ratios)):\\n nLFE = int(ratios[j] * len(EW) / 100.0)\\n EWselect = Stream()\\n NSselect = Stream()\\n UDselect = Stream()\\n for i in range(0, nLFE):\\n EWselect.append(EW[index[i]])\\n NSselect.append(NS[index[i]])\\n UDselect.append(UD[index[i]])\\n # Stack best LFEs\\n EWbest.append(linstack([EWselect], normalize=True, \\\\\\n method=method)[0])\\n NSbest.append(linstack([NSselect], normalize=True, \\\\\\n method=method)[0])\\n UDbest.append(linstack([UDselect], normalize=True, \\\\\\n method=method)[0])\\n # Plot figure\\n plt.figure(1, figsize=(20, 15))\\n params = {'xtick.labelsize':16,\\n 'ytick.labelsize':16}\\n pylab.rcParams.update(params) \\n colors = cm.rainbow(np.linspace(0, 1, len(ratios)))\\n # East - West component\\n ax1 = plt.subplot(311)\\n dt = EWstack[0].stats.delta\\n nt = EWstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = EWbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n EWbest[j].data)))\\n elif (method == 'MAD'):\\n norm = EWbest[j].data / np.median(np.abs(EWbest[j].data - \\\\\\n np.median(EWbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = EWstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n EWstack[0].data)))\\n elif (method == 'MAD'):\\n norm = EWstack[0].data / np.median(np.abs(EWstack[0].data - \\\\\\n np.median(EWstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('East - West component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # North - South component\\n ax2 = plt.subplot(312)\\n dt = NSstack[0].stats.delta\\n nt = NSstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = NSbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n NSbest[j].data)))\\n elif (method == 'MAD'):\\n norm = NSbest[j].data / np.median(np.abs(NSbest[j].data - \\\\\\n np.median(NSbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = NSstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n NSstack[0].data)))\\n elif (method == 'MAD'):\\n norm = NSstack[0].data / np.median(np.abs(NSstack[0].data - \\\\\\n np.median(NSstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('North - South component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # Vertical component\\n ax3 = plt.subplot(313)\\n dt = UDstack[0].stats.delta\\n nt = UDstack[0].stats.npts\\n t = dt * np.arange(0, nt)\\n for j in range(0, len(ratios)):\\n if (method == 'RMS'):\\n norm = UDbest[j].data / np.sqrt(np.mean(np.square( \\\\\\n UDbest[j].data)))\\n elif (method == 'MAD'):\\n norm = UDbest[j].data / np.median(np.abs(UDbest[j].data - \\\\\\n np.median(UDbest[j].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, color = colors[j], \\\\\\n label = str(int(ratios[j])) + '%')\\n if (method == 'RMS'):\\n norm = UDstack[0].data / np.sqrt(np.mean(np.square( \\\\\\n UDstack[0].data)))\\n elif (method == 'MAD'):\\n norm = UDstack[0].data / np.median(np.abs(UDstack[0].data - \\\\\\n np.median(UDstack[0].data)))\\n else:\\n raise ValueError('Method must be RMS or MAD')\\n norm = np.nan_to_num(norm)\\n plt.plot(t, norm, 'k', label='All')\\n if (window == True):\\n plt.axvline(Tmin, linewidth=2, color='grey')\\n plt.axvline(Tmax, linewidth=2, color='grey')\\n plt.xlim([np.min(t), np.max(t)])\\n plt.title('Vertical component', fontsize=24)\\n plt.xlabel('Time (s)', fontsize=24)\\n plt.legend(loc=1)\\n # End figure\\n plt.suptitle(station, fontsize=24)\\n plt.savefig(namedir + '/' + station + '.eps', format='eps')\\n ax1.clear()\\n ax2.clear()\\n ax3.clear()\\n plt.close(1)\\n # Save stacks into files\\n savename = namedir + '/' + station +'.pkl'\\n pickle.dump([EWstack[0], NSstack[0], UDstack[0]], \\\\\\n open(savename, 'wb'))\\n for j in range(0, len(ratios)):\\n savename = namedir + '/' + station + '_' + \\\\\\n str(int(ratios[j])) + '.pkl'\\n pickle.dump([EWbest[j], NSbest[j], UDbest[j]], \\\\\\n open(savename, 'wb'))\\n # Save cross correlations into files\\n savename = namedir + '/' + station + '_cc.pkl'\\n pickle.dump([cc0EW, cc0NS, cc0UD], \\\\\\n open(savename, 'wb'))\",\n \"def create_dat_from_shapefile(windgrid_file, DAT_header, output_file, wind_field='Vg_mph'):\\n t0 = time()\\n output_file = output_file + '.dat'\\n print('reading windgrid')\\n t1 = time()\\n gdf = gpd.read_file(windgrid_file)\\n create_dat_from_geodataframe(\\n gdf, DAT_header, output_file, wind_field=wind_field)\",\n \"def load_viewpoint(viewpoint_file):\\n with open(viewpoint_file) as viewpoints:\\n for line in viewpoints.readlines():\\n yield VP(*line.strip().split())\",\n \"def _get_std_vmr_file(std_vmr_file):\\n if std_vmr_file is None:\\n gggpath = os.getenv('GGGPATH')\\n if gggpath is None:\\n raise GGGPathError('GGGPATH environmental variable is not defined. Either define it, explicitly pass a '\\n 'path to a .vmr file with northern midlat profiles for all gases, or pass False to '\\n 'only write the primary gases to the .vmr file')\\n std_vmr_file = os.path.join(gggpath, 'vmrs', 'gnd', 'summer_35N.vmr')\\n if not os.path.isfile(std_vmr_file):\\n raise GGGPathError('The standard .vmr file is not present in the expected location ({}). Your GGGPATH '\\n 'environmental variable may be incorrect, or the structure of the GGG directory has '\\n 'changed. Either correct your GGGPATH value, explicitly pass a path to a .vmr file with '\\n 'northern midlat profiles for all gases, or pass False to only write the primary gases '\\n 'to the .vmr file'.format(std_vmr_file))\\n return std_vmr_file\\n else:\\n return std_vmr_file\",\n \"def _create_farm_result_vector(\\n base_vector_path, target_vector_path):\\n base_vector = gdal.OpenEx(base_vector_path, gdal.OF_VECTOR)\\n\\n driver = gdal.GetDriverByName('ESRI Shapefile')\\n target_vector = driver.CreateCopy(\\n target_vector_path, base_vector)\\n target_layer = target_vector.GetLayer()\\n\\n farm_pollinator_abundance_defn = ogr.FieldDefn(\\n _POLLINATOR_ABUNDANCE_FARM_FIELD_ID, ogr.OFTReal)\\n farm_pollinator_abundance_defn.SetWidth(25)\\n farm_pollinator_abundance_defn.SetPrecision(11)\\n target_layer.CreateField(farm_pollinator_abundance_defn)\\n\\n total_farm_yield_field_defn = ogr.FieldDefn(\\n _TOTAL_FARM_YIELD_FIELD_ID, ogr.OFTReal)\\n total_farm_yield_field_defn.SetWidth(25)\\n total_farm_yield_field_defn.SetPrecision(11)\\n target_layer.CreateField(total_farm_yield_field_defn)\\n\\n pol_proportion_farm_yield_field_defn = ogr.FieldDefn(\\n _POLLINATOR_PROPORTION_FARM_YIELD_FIELD_ID, ogr.OFTReal)\\n pol_proportion_farm_yield_field_defn.SetWidth(25)\\n pol_proportion_farm_yield_field_defn.SetPrecision(11)\\n target_layer.CreateField(pol_proportion_farm_yield_field_defn)\\n\\n wild_pol_farm_yield_field_defn = ogr.FieldDefn(\\n _WILD_POLLINATOR_FARM_YIELD_FIELD_ID, ogr.OFTReal)\\n wild_pol_farm_yield_field_defn.SetWidth(25)\\n wild_pol_farm_yield_field_defn.SetPrecision(11)\\n target_layer.CreateField(wild_pol_farm_yield_field_defn)\\n\\n target_layer = None\\n target_vector.FlushCache()\\n target_vector = None\",\n \"def read_vector_file(fname):\\n return np.genfromtxt(fname)\",\n \"def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\\n args = [\\n \\\"gdal_calc.py\\\",\\n '-A', infile,\\n '--outfile={}'.format(tmpfile_1),\\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\\n '--type=Byte', '--NoDataValue=0',\\n '--co=SPARSE_OK=YES',\\n '--co=NBITS=1',\\n '--quiet'\\n # Could enable compression\\n # --co=\\\"COMPRESS=LZW\\\"\\n ]\\n subprocess.run(args)\\n\\n subprocess.run([\\n \\\"gdal_polygonize.py\\\",\\n tmpfile_1,\\n '-q',\\n '-f', 'ESRI Shapefile',\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\n \\\"ogr2ogr\\\",\\n '-a_srs', 'EPSG:4326',\\n outfile,\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\\"rm\\\", tmpfile_1])\\n subprocess.run([\\\"rm\\\", tmpfile_2])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'shx')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'dbf')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'prj')])\",\n \"def generate_ROI_file(FreeSurfer_ROI_file):\\n\\tfrom nipype.interfaces.freesurfer import MRIConvert\\n\\tmc = MRIConvert()\\n\\tmc.inputs.in_file = FreeSurfer_ROI_file\\n\\tmc.inputs.out_type = 'niigz'\\n\\tmc.run()\\n\\n\\timport nipype.interfaces.cmtk as cmtk\\n\\trg = cmtk.ROIGen()\\n\\trg.inputs.aparc_aseg_file = FreeSurfer_ROI_file.split('.')[0] + '_out.nii.gz'\\n\\trg.inputs.use_freesurfer_LUT = True\\n\\tout_file = rg.run()\\n\\n\\treturn out_file\",\n \"def read_DEM(fn=None, fjord=None):\\n # intake.open_rasterio accepts a list of input files and may effectively do what this function does!\\n # try using cropped versions of the input files. Doesn't seem to make a difference r.e. crashing\\n '''\\n cropped_fn = fn.rpartition(\\\".tif\\\")[0] + \\\"_cropped.tif\\\"\\n print(cropped_fn)\\n if os._exists(cropped_fn):\\n fn = cropped_fn\\n elif fjord != None:\\n bbox = fjord_props.get_fjord_bounds(fjord)\\n ds = rioxarray.open_rasterio(fn)\\n trimmed_ds = ds.rio.slice_xy(*bbox)\\n trimmed_ds.rio.to_raster(fn.rpartition(\\\".tif\\\")[0] + \\\"_cropped.tif\\\")\\n del ds\\n del trimmed_ds\\n fn = cropped_fn \\n '''\\n\\n # try bringing in the rasters as virtual rasters (i.e. lazily loading)\\n with rasterio.open(fn) as src:\\n # print('Source CRS:' +str(src.crs))\\n # print(src.is_tiled)\\n # print(src.block_shapes)\\n with WarpedVRT(src,src_crs=src.crs,crs=src.crs) as vrt:\\n # warp_mem_limit=12000,warp_extras={'NUM_THREADS':2}) as vrt:\\n # print('Destination CRS:' +str(vrt.crs))\\n darr = xr.open_rasterio(vrt)\\n # ds = rioxarray.open_rasterio(vrt).chunk({'x':1500,'y':1500,'band':1}).to_dataset(name='HLS_Red')\\n\\n\\n # Rasterio automatically checks that the file exists\\n # ultimately switch to using rioxarray, but it causes issues down the pipeline so it will need to be debugged through\\n # with rioxarray.open_rasterio(fn) as src:\\n # with xr.open_rasterio(fn) as darr:\\n # darr = src\\n\\n # open_rasterio automatically brings the geotiff in as a DataArray with 'band' as a dimensional coordinate\\n # we rename it and remove the band as a coordinate, since our DEM only has one dimension\\n # squeeze removes dimensions of length 0 or 1, in this case our 'band'\\n # Then, drop('band') actually removes the 'band' dimension from the Dataset\\n darr = darr.rename('elevation').squeeze().drop_vars('band')\\n # darr = darr.rename({'band':'dtime'})\\n \\n # if we wanted to instead convert it to a dataset\\n # attr = darr.attrs\\n # darr = darr.to_dataset(name='elevation').squeeze().drop('band')\\n # darr.attrs = attr\\n # attr=None\\n # newest version of xarray (0.16) has promote_attrs=True kwarg. Earlier versions don't...\\n # darr = darr.to_dataset(name='elevation', promote_attrs=True).squeeze().drop('band')\\n\\n # mask out the nodata values, since the nodatavals attribute is wrong\\n darr = darr.where(darr != -9999.)\\n\\n # the gdalwarp geoid files have this extra attribute in the geoTiff, which when brought in\\n # ultimately causes a \\\"__module__\\\" related error when trying to plot with hvplot\\n try:\\n del darr.attrs[\\\"units\\\"] \\n except KeyError:\\n pass\\n\\n if fjord != None:\\n # USE RIOXARRAY - specifically, slicexy() which can be fed the bounding box\\n # darr = darr.rio.slice_xy(fjord_props.get_fjord_bounds(fjord))\\n bbox = fjord_props.get_fjord_bounds(fjord)\\n if pd.Series(darr.y).is_monotonic_increasing:\\n darr = darr.sel(x=slice(bbox[0], bbox[2]), y=slice(bbox[1], bbox[3]))\\n else:\\n darr = darr.sel(x=slice(bbox[0], bbox[2]), y=slice(bbox[3], bbox[1]))\\n \\n return darr\",\n \"def write_input(self):\\n # load template, substitute parameters and write input file\\n self.homog_core()\\n self.write_mat_string()\\n input_tmpl = open('base_input.txt')\\n templ = Template(input_tmpl.read())\\n file_string = templ.substitute(cool_frac = self.vfrac_cermet,\\n r_core = self.r,\\n core_z = self.z,\\n r_refl = self.r + self.refl_t,\\n refl_min = -self.refl_t,\\n refl_max = self.z + self.refl_t,\\n fuel_string = self.fuel_string,\\n fuel_rho = self.rho,\\n fuel_vol = self.core_vol,\\n refl_vol = self.core_vol,\\n thermal_power = self.Q_therm)\\n # write the file\\n filename = 'r_{0}_{1}.i'.format(round(self.vfrac_cermet, 3), \\n round(self.r, 3))\\n ifile = open(filename, 'w')\\n ifile.write(file_string)\\n ifile.close()\\n\\n return filename\",\n \"def align_rasters(ref_raster, tar_raster, output_suffix):\\n command = [\\\"gdalbuildvrt\\\", \\\"-te\\\"]\\n hDataset = gdal.Open(ref_raster, gdal.GA_ReadOnly)\\n if hDataset is None:\\n return False\\n adfGeoTransform = hDataset.GetGeoTransform(can_return_null=True)\\n\\n tif_file=tar_raster\\n vrt_file = tif_file.replace('.tif', '.vrt')\\n\\n if adfGeoTransform is not None:\\n dfGeoXUL = adfGeoTransform[0]\\n dfGeoYUL = adfGeoTransform[3]\\n dfGeoXLR = adfGeoTransform[0] + adfGeoTransform[1] * hDataset.RasterXSize + \\\\\\n adfGeoTransform[2] * hDataset.RasterYSize\\n dfGeoYLR = adfGeoTransform[3] + adfGeoTransform[4] * hDataset.RasterXSize + \\\\\\n adfGeoTransform[5] * hDataset.RasterYSize\\n xres = str(abs(adfGeoTransform[1]))\\n yres = str(abs(adfGeoTransform[5]))\\n\\n subprocess.call(command + [str(dfGeoXUL), str(dfGeoYLR), str(dfGeoXLR),\\n str(dfGeoYUL), \\\"-q\\\", \\\"-tr\\\", xres, yres,\\n vrt_file, tif_file])\\n\\n output_file = tif_file.replace('.tif', output_suffix)\\n\\n print('gdal_translate -q {} {}'.format(vrt_file, output_file))\\n\\n cmd = 'gdal_translate -q {} {}'.format(vrt_file, output_file)\\n\\n #print(dfGeoXUL, dfGeoYLR, dfGeoXLR, dfGeoYUL, xres, yres)\\n\\n subprocess.call(cmd, shell=True)\\n os.remove(vrt_file)\\n\\n return True\\n\\n else:\\n\\n return False\",\n \"def exec_ripser(data_path,output_path,max_dim,input_file='input.txt',format_file = 'lower-distance',threshold=None):\\n ############# RIPSER ####################\\n # high dimension\\n ## execfile ripser (OUTPUT from ripser)\\n start = timeit.default_timer() \\n print 'input_file ',input_file\\n input_file_full = os.path.join(data_path,input_file)\\n output_file_full = os.path.join(output_path,'output_ripser.txt')\\n\\n if threshold is None:\\n ripser_arguments = 'ripser --format %s --dim %i %s'%(format_file,max_dim,input_file_full)\\n else:\\n ripser_arguments = 'ripser --format %s --dim %i --threshold %f %s'%(format_file,max_dim,threshold,input_file_full)\\n \\n ripser_call(ripser_arguments.split(' '),output_file_full)\\n #os.system(ripser_call) # OLD CALL BASED ON executable\\n\\n stop = timeit.default_timer()\\n print 'Ripser execution time '\\n print stop - start \\n input_file_path = os.path.join(data_path,'input.txt')\\n if(os.path.isfile(input_file_path)):\\n os.remove(input_file_path) ## remove auxiliar file with lower matrix used as input for Ripser\\n return()\",\n \"def process_input_file(filename):\\n f = open(filename, 'r')\\n\\n rows = []\\n i = 0\\n for line in f:\\n # skip optimal steps and time limit\\n if i > 1 and len(line.strip()) > 0:\\n rows.append(list(line.strip()))\\n i += 1\\n\\n f.close()\\n\\n row_len = len(rows[0])\\n num_rows = len(rows)\\n\\n return LaserTankMap(row_len, num_rows, rows)\",\n \"def main(fn_input, fn_output):\\n # read file\\n inter = Interpolator()\\n inter.read_file(fn_input)\\n inter.write_interpolated(fn_output)\",\n \"def convert(input, output, point_format_id, file_version, force):\\n if (\\n point_format_id is not None\\n and point_format_id not in pylas.supported_point_formats()\\n ):\\n click.echo(\\n click.style(\\n \\\"Point format {} is not supported\\\".format(point_format_id), fg=\\\"red\\\"\\n )\\n )\\n raise click.Abort()\\n\\n if file_version is not None and file_version not in pylas.supported_versions():\\n click.echo(\\n click.style(\\n \\\"LAS version {} is not supported\\\".format(file_version), fg=\\\"red\\\"\\n )\\n )\\n raise click.Abort()\\n\\n las = pylas.read(openbin_file(input))\\n if point_format_id is not None and not force:\\n lost_dimensions = pylas.lost_dimensions(\\n las.points_data.point_format.id, point_format_id\\n )\\n if lost_dimensions:\\n click.echo(\\\"Converting will lose: {}\\\".format(lost_dimensions))\\n click.confirm(\\\"Continue ?\\\", abort=True)\\n\\n try:\\n las = pylas.convert(\\n las, point_format_id=point_format_id, file_version=file_version\\n )\\n except pylas.errors.PylasError as e:\\n click.echo(click.style(\\\"{}: {}\\\".format(e.__class__.__name__, e), fg=\\\"red\\\"))\\n raise click.Abort()\\n except Exception as e:\\n click.echo(click.style(str(e), fg=\\\"red\\\"))\\n raise click.Abort()\\n else:\\n las.write(openbin_file(output, mode='w'), do_compress=output.endswith('.laz'))\",\n \"def build_r_map(input_file: str, output_file: str, threshold: float):\\n\\n DataSiPM = db.DataSiPMsim_only('petalo', 0) # full body PET\\n DataSiPM_idx = DataSiPM.set_index('SensorID')\\n\\n try:\\n sns_response = pd.read_hdf(input_file, 'MC/sns_response')\\n except ValueError:\\n print(f'File {input_file} not found')\\n exit()\\n except OSError:\\n print(f'File {input_file} not found')\\n exit()\\n except KeyError:\\n print(f'No object named MC/sns_response in file {input_file}')\\n exit()\\n print(f'Analyzing file {input_file}')\\n\\n sel_df = rf.find_SiPMs_over_threshold(sns_response, threshold)\\n\\n particles = pd.read_hdf(input_file, 'MC/particles')\\n hits = pd.read_hdf(input_file, 'MC/hits')\\n events = particles.event_id.unique()\\n\\n true_r1, true_r2 = [], []\\n var_phi1, var_phi2 = [], []\\n var_z1, var_z2 = [], []\\n\\n touched_sipms1, touched_sipms2 = [], []\\n\\n for evt in events:\\n\\n ### Select photoelectric events only\\n evt_parts = particles[particles.event_id == evt]\\n evt_hits = hits [hits .event_id == evt]\\n select, true_pos = mcf.select_photoelectric(evt_parts, evt_hits)\\n if not select: continue\\n\\n sns_resp = sel_df[sel_df.event_id == evt]\\n if len(sns_resp) == 0: continue\\n\\n _, _, pos1, pos2, q1, q2 = rf.assign_sipms_to_gammas(sns_resp, true_pos, DataSiPM_idx)\\n\\n if len(pos1) > 0:\\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos1))[:,1]\\n _, var_phi = rf.phi_mean_var(pos_phi, q1)\\n\\n pos_z = np.array(pos1)[:,2]\\n mean_z = np.average(pos_z, weights=q1)\\n var_z = np.average((pos_z-mean_z)**2, weights=q1)\\n r = np.sqrt(true_pos[0][0]**2 + true_pos[0][1]**2)\\n\\n var_phi1 .append(var_phi)\\n var_z1 .append(var_z)\\n touched_sipms1.append(len(pos1))\\n true_r1 .append(r)\\n\\n else:\\n var_phi1 .append(1.e9)\\n var_z1 .append(1.e9)\\n touched_sipms1.append(1.e9)\\n true_r1 .append(1.e9)\\n\\n if len(pos2) > 0:\\n pos_phi = rf.from_cartesian_to_cyl(np.array(pos2))[:,1]\\n _, var_phi = rf.phi_mean_var(pos_phi, q2)\\n\\n pos_z = np.array(pos2)[:,2]\\n mean_z = np.average(pos_z, weights=q2)\\n var_z = np.average((pos_z-mean_z)**2, weights=q2)\\n r = np.sqrt(true_pos[1][0]**2 + true_pos[1][1]**2)\\n\\n var_phi2 .append(var_phi)\\n var_z2 .append(var_z)\\n touched_sipms2.append(len(pos2))\\n true_r2 .append(r)\\n\\n else:\\n var_phi2 .append(1.e9)\\n var_z2 .append(1.e9)\\n touched_sipms2.append(1.e9)\\n true_r2 .append(1.e9)\\n\\n a_true_r1 = np.array(true_r1)\\n a_true_r2 = np.array(true_r2)\\n a_var_phi1 = np.array(var_phi1)\\n a_var_phi2 = np.array(var_phi2)\\n a_var_z1 = np.array(var_z1)\\n a_var_z2 = np.array(var_z2)\\n\\n a_touched_sipms1 = np.array(touched_sipms1)\\n a_touched_sipms2 = np.array(touched_sipms2)\\n\\n\\n np.savez(output_file, a_true_r1=a_true_r1, a_true_r2=a_true_r2, a_var_phi1=a_var_phi1, a_var_phi2=a_var_phi2, a_var_z1=a_var_z1, a_var_z2=a_var_z2, a_touched_sipms1=a_touched_sipms1, a_touched_sipms2=a_touched_sipms2)\",\n \"def make_big_vdwradii( targetpath ):\\n\\n file = open( os.path.join( targetpath, 'vdwradii.dat'), 'w')\\n text=\\\"\\\"\\\"; Very approximate VanderWaals radii\\n; only used for drawing atoms as balls or for calculating atomic overlap.\\n; longest matches are used\\n; '???' or '*' matches any residue name\\n; 'AAA' matches any protein residue name\\n; MODIFIED TO USE BIG VDW RADII TO PREVENT WATERS BEING PUT IN THE PROTEIN WHERE WE DON'T WANT THEM. DLM\\n??? C 0.3\\n??? F 0.3\\n??? H 0.3\\n??? N 0.3\\n??? O 0.3\\n??? P 0.3\\n??? S 0.3\\n??? LP1 0\\n??? LP2 0\\nSOL H 0.04\\nSOL O 0.105\\nWAT H 0.04\\nWAT O 0.105\\nGLY MN1 0\\nGLY MN2 0\\nALA MCB1 0\\nALA MCB2 0 \\nVAL MCG1 0 \\nVAL MCG2 0 \\nILE MCG1 0 \\nILE MCG2 0 \\nILE MCD1 0 \\nILE MCD2 0 \\nLEU MCD1 0 \\nLEU MCD2 0 \\nMET MCE1 0 \\nMET MCE2 0 \\nTRP MTRP1 0 \\nTRP MTRP2 0\\nTHR MCG1 0\\nTHR MCG2 0\\nLYSH MNZ1 0 \\nLYSH MNZ2 0 \\n\\\"\\\"\\\" \\n file.writelines(text)\\n file.close()\",\n \"def filter_ground(jparams):\\n\\n # load las file and relevant parameters\\n point_cloud = File(jparams['input-las'], mode='r')\\n scale = point_cloud.header.scale[0]\\n print(point_cloud.header.min)\\n print('- Flattening point cloud')\\n gridded_pc = point_cloud_to_grid(point_cloud=point_cloud, tf=jparams['thinning-factor'],\\n cell_size=int(jparams['gf-cellsize'] / scale))\\n\\n ground_points, unprocessed_points, ll_origin = gridded_pc[0], gridded_pc[1], gridded_pc[2]\\n\\n print('- Growing terrain')\\n dt = startin.DT()\\n dt.insert(list(ground_points))\\n dt = grow_terrain(tin=dt, p=unprocessed_points, gp=ground_points,\\n max_distance=int(jparams['gf-distance'] / scale),\\n max_angle=jparams['gf-angle'])\\n\\n print('- Writing point cloud')\\n with File(jparams['output-las'], mode='w', header=point_cloud.header) as out_file:\\n gp = dt.all_vertices()[1:]\\n out_file.X = [p[0] for p in gp]\\n out_file.Y = [p[1] for p in gp]\\n out_file.Z = [p[2] for p in gp]\\n\\n print('- Creating raster (TIN)\\\\n\\\\t- Interpolating (TIN)')\\n dg = tin_interp(tin=dt, cell_size=int(jparams['grid-cellsize'] / scale))\\n\\n print('\\\\t- Writing Esri Ascii (TIN)')\\n write_asc(grid=np.rot90(dg[0]) * scale + point_cloud.header.min[2],\\n cell_size=jparams['grid-cellsize'],\\n fn=jparams['output-grid-tin'],\\n origin=(point_cloud.header.min[0]+dg[1][0]*scale, point_cloud.header.min[1] + dg[1][1]*scale),\\n depth=2)\\n\\n print('- Creating raster (IDW)\\\\n\\\\t- Interpolating (IDW)')\\n ig = idw_interp(tin=dt, cell_size=int(jparams['grid-cellsize'] / scale),\\n radius=jparams['idw-radius'] / scale, \\n power=jparams['idw-power'])\\n\\n print('\\\\t- Writing Esri Ascii (IDW)')\\n write_asc(grid=np.rot90(ig[0]) * scale + point_cloud.header.min[2],\\n cell_size=jparams['grid-cellsize'],\\n fn=jparams['output-grid-idw'],\\n origin=(point_cloud.header.min[0]+ig[1][0]*scale, point_cloud.header.min[1]+ig[1][1]*scale),\\n depth=2)\\n\\n return\",\n \"def process_radia_vcf(job, radia_vcf, work_dir, univ_options):\\n radia_vcf = job.fileStore.readGlobalFile(radia_vcf)\\n with open(radia_vcf, 'r') as infile, open(radia_vcf + 'radia_parsed.tmp', 'w') as outfile:\\n # The columns in INFILE are\\n # [0] CHROM\\n # [1] POS\\n # [2] ID\\n # [3] REF\\n # [4] ALT\\n # [5] QUAL\\n # [6] FILTER\\n # [7] INFO\\n # [8] FORMAT\\n # [9] DNA_NORMAL\\n # [10] DNA_TUMOR\\n # [11] RNA_TUMOR - Not always present\\n for line in infile:\\n # Print header to outfile\\n if line.startswith('#'):\\n print(line.strip(), file=outfile)\\n continue\\n line = line.strip().split('\\\\t')\\n # If the call was not PASSing, or if the call was germline: skip\\n if line[6] != 'PASS' or 'MT=GERM' in line[7]:\\n continue\\n # If there is just 1 ALT allele, print and continue\\n if len(line[4]) == 1:\\n print('\\\\t'.join(line), file=outfile)\\n # If not, process\\n else:\\n seq_field_indeces = [9, 10]\\n alleles = [line[3]] + line[4].split(',') # all alleles, incl. REF\\n # collect tumor, normal and (if present) rna AD and AFs\\n # AD = Depth of reads supporting each allele\\n # AF = Fraction of reads supporting each allele\\n # normal_ad = line[9].split(':')[5].split(',')\\n normal_af = line[9].split(':')[6].split(',')\\n tumor_ad = line[10].split(':')[5].split(',')\\n tumor_af = line[10].split(':')[6].split(',')\\n if len(line[11]) > 1:\\n rna_ad = line[11].split(':')[5].split(',')\\n rna_af = line[11].split(':')[6].split(',')\\n seq_field_indeces += [11] # append rna since it is present\\n else:\\n # If rna is missing, set RNA_AD and RNA_AF to null sets for easily\\n # integrating into the logic in the following code\\n rna_ad = rna_af = [0, 0, 0, 0]\\n # Initialise variables to store the probable ALT alleles and the index values of\\n # the same wrt AD and AF\\n out_alleles = set([])\\n out_af_ad_index = {0}\\n # parse AD and AF to get most probable ALT alleles\\n for i in range(1, len(normal_af)):\\n # Criteria for selection = AD > 4 and AF >0.1 in either tumor or RNA, given\\n # normal AF < 0.1\\n if ((float(tumor_af[i]) >= 0.1 and int(tumor_ad[i]) >= 4) or\\n (float(rna_af[i]) >= 0.1 and int(rna_ad[i]) >= 4)) and \\\\\\n (float(normal_af[i]) < 0.1):\\n out_alleles.add(alleles[i])\\n out_af_ad_index.add(i)\\n # If the number of probable alleles is greater than 0 the print to outfile with\\n # the modified allele fraction representing reads corrresponding to all alleles\\n if len(out_alleles) > 0:\\n line[4] = ','.join(out_alleles) # set alt alleles\\n # Modify the AD and AF values in the TUMOR/NORMAL/RNA fields\\n # one at a time. Seq fields contain\\n # [0] GT* - Genotype\\n # [1] DP - Read depth at this position in the sample\\n # [2] INDEL - Number of indels\\n # [3] START - Number of reads starting at this position\\n # [4] STOP - Number of reads stopping at this position\\n # [5] AD* - Depth of reads supporting alleles\\n # [6] AF* - Fraction of reads supporting alleles\\n # [7] BQ* - Avg base quality for reads supporting alleles\\n # [8] SB* - Strand Bias for reads supporting alleles\\n # Fields marked with *s are teh ones that contain info for each seq field\\n # and need to be modified\\n for seq_field_index in seq_field_indeces:\\n # Get the details for seq_field\\n deets = line[seq_field_index].split(':')\\n # modify fields 5 thu 8 to hold only info for the probable\\n # alleles\\n for field_index in range(5, 9):\\n field = deets[field_index].split(\\\",\\\")\\n deets[field_index] = \\\",\\\".join([x for i, x in enumerate(field)\\n if i in out_af_ad_index])\\n # Modify DP to hold the new total of reads\\n deets[1] = str(sum([int(x) for x in deets[5].split(\\\",\\\")]))\\n # get the most likely genotypes based on AD and AF\\n gt_by_ad = set([i for i, x in enumerate(deets[5].split(\\\",\\\"))\\n if int(x) >= 4])\\n gt_by_af = set([i for i, x in enumerate(deets[6].split(\\\",\\\"))\\n if float(x) >= 0.1])\\n # Get the consensus genotype\\n genotype = gt_by_ad.intersection(gt_by_af)\\n if len(genotype) == 0:\\n deets[0] = \\\"0/0\\\"\\n elif len(genotype) == 1:\\n deets[0] = \\\"/\\\".join([str(x) for x in genotype] +\\n [str(x) for x in genotype])\\n elif len(genotype) == 2:\\n deets[0] = \\\"/\\\".join([str(x) for x in genotype])\\n else:\\n print(\\\"ERROR : triple genotype detected\\\", file=sys.stderr)\\n print(line, file=sys.stdout)\\n # Rejoin the details line\\n line[seq_field_index] = \\\":\\\".join(deets)\\n # Print the modified line to output\\n print(\\\"\\\\t\\\".join(line), file=outfile)\\n # Else do nothing\\n else:\\n pass\\n return outfile.name\",\n \"def loadGeoTransform(filepath):\\n \\n from osgeo import gdal\\n \\n ds = gdal.Open(filepath, 0)\\n \\n return ds.GetGeoTransform()\",\n \"def generate_resfile_from_pdb(pdbfilename, resfilename, input_sc = True ):\\n\\tp = rosetta.core.import_pose.pose_from_file(pdbfilename)\\n\\tgenerate_resfile_from_pose(p, resfilename, input_sc)\",\n \"def postTTUWRFanalysis(inpath, outpath,\\n refpath='/lustre/research/bancell/aucolema/HWT2016runs/2016050800/wrfoutREF'):\\n # Pull analysis variables\\n og_analysis = Dataset(inpath)\\n anlvars = og_analysis.variables['T'][0]\\n gph300 = anlvars[0,:,:]\\n gph500 = anlvars[1,:,:]\\n gph700 = anlvars[2,:,:]\\n gph850 = anlvars[3,:,:]\\n gph925 = anlvars[4,:,:]\\n temp300 = anlvars[5,:,:]\\n temp500 = anlvars[6,:,:]\\n temp700 = anlvars[7,:,:]\\n temp850 = anlvars[8,:,:]\\n temp925 = anlvars[9,:,:]\\n u300 = anlvars[10,:,:]\\n u500 = anlvars[11,:,:]\\n u700 = anlvars[12,:,:]\\n u850 = anlvars[13,:,:]\\n u925 = anlvars[14,:,:]\\n v300 = anlvars[15,:,:]\\n v500 = anlvars[16,:,:]\\n v700 = anlvars[17,:,:]\\n v850 = anlvars[18,:,:]\\n v925 = anlvars[19,:,:]\\n td300 = anlvars[20,:,:]\\n td500 = anlvars[21,:,:]\\n td700 = anlvars[22,:,:]\\n td850 = anlvars[23,:,:]\\n td925 = anlvars[24,:,:]\\n q300 = anlvars[25,:,:]\\n q500 = anlvars[26,:,:]\\n q700 = anlvars[27,:,:]\\n q850 = anlvars[28,:,:]\\n q925 = anlvars[29,:,:]\\n slp = anlvars[30,:,:]\\n t2 = anlvars[31,:,:]\\n td2 = anlvars[32,:,:]\\n u10 = anlvars[33,:,:]\\n v10 = anlvars[34,:,:]\\n\\n wrf_d1 = Dataset(refpath)\\n lons, lats = wrf_d1.variables['XLONG'][0], wrf_d1.variables['XLAT'][0]\\n wrf_idim = len(lons[0,:])\\n wrf_jdim = len(lats[:,0])\\n\\n sensvarlist = [gph300,gph500,gph700,gph850,gph925,temp300,temp500,temp700,\\n temp850,temp925,u300,u500,u700,u850,u925,v300,\\n v500,v700,v850,v925,td300,td500,td700,td850,\\n td925,q300,q500,q700,q850,q925,slp,t2,td2,u10,v10]\\n sensstringslist = [\\\"300 hPa GPH\\\",\\\"500 hPa GPH\\\",\\\"700 hPa GPH\\\",\\n \\\"850 hPa GPH\\\",\\\"925 hPa GPH\\\",\\\"300 hPa T\\\",\\\"500 hPa T\\\",\\n \\\"700 hPa T\\\",\\\"850 hPa T\\\",\\\"925 hPa T\\\",\\\"300 hPa U-Wind\\\",\\n \\\"500 hPa U-Wind\\\",\\\"700 hPa U-Wind\\\",\\\"850 hPa U-Wind\\\",\\n \\\"925 hPa U-Wind\\\",\\\"300 hPa V-Wind\\\",\\\"500 hPa V-Wind\\\",\\n \\\"700 hPa V-Wind\\\",\\\"850 hPa V-Wind\\\",\\\"925 hPa V-Wind\\\",\\n \\\"300 hPa Dewpt\\\", \\\"500 hPa Dewpt\\\", \\\"700 hPa Dewpt\\\",\\n \\\"850 hPa Dewpt\\\", \\\"925 hPa Dewpt\\\", \\\"300 hPa Q\\\",\\n \\\"500 hPa Q\\\", \\\"700 hPa Q\\\", \\\"850 hPa Q\\\", \\\"925 hPa Q\\\",\\n \\\"SLP\\\",\\\"2m Temp\\\",\\\"2m Dewpt\\\",\\n \\\"10m U-Wind\\\",\\\"10m V-Wind\\\"]\\n\\n\\n # Write interpolated variables to netCDF\\n new_analysis = Dataset(outpath, \\\"w\\\", format=\\\"NETCDF4\\\")\\n new_analysis.createDimension('lat', wrf_jdim)\\n new_analysis.createDimension('lon', wrf_idim)\\n new_analysis.createDimension('time', None)\\n xlat = new_analysis.createVariable(\\\"XLAT\\\", float, dimensions=('lat','lon'))\\n xlat[:,:] = lats\\n xlon = new_analysis.createVariable(\\\"XLONG\\\", float, dimensions=('lat','lon'))\\n xlon[:,:] = lons\\n\\n # Interpolate and save!!\\n for i in range(len(sensvarlist)):\\n var = new_analysis.createVariable(sensstringslist[i].replace(\\\" \\\",\\\"_\\\"),\\n sensvarlist[i].dtype,\\n dimensions=('lat','lon'))\\n var[:,:] = sensvarlist[i]\\n new_analysis.close()\\n return\",\n \"def postTTUWRFanalysis(inpath, outpath,\\n refpath='/lustre/research/bancell/aucolema/HWT2016runs/2016050800/wrfoutREF'):\\n # Pull analysis variables\\n og_analysis = Dataset(inpath)\\n anlvars = og_analysis.variables['T'][0]\\n gph300 = anlvars[0,:,:]\\n gph500 = anlvars[1,:,:]\\n gph700 = anlvars[2,:,:]\\n gph850 = anlvars[3,:,:]\\n gph925 = anlvars[4,:,:]\\n temp300 = anlvars[5,:,:]\\n temp500 = anlvars[6,:,:]\\n temp700 = anlvars[7,:,:]\\n temp850 = anlvars[8,:,:]\\n temp925 = anlvars[9,:,:]\\n u300 = anlvars[10,:,:]\\n u500 = anlvars[11,:,:]\\n u700 = anlvars[12,:,:]\\n u850 = anlvars[13,:,:]\\n u925 = anlvars[14,:,:]\\n v300 = anlvars[15,:,:]\\n v500 = anlvars[16,:,:]\\n v700 = anlvars[17,:,:]\\n v850 = anlvars[18,:,:]\\n v925 = anlvars[19,:,:]\\n td300 = anlvars[20,:,:]\\n td500 = anlvars[21,:,:]\\n td700 = anlvars[22,:,:]\\n td850 = anlvars[23,:,:]\\n td925 = anlvars[24,:,:]\\n q300 = anlvars[25,:,:]\\n q500 = anlvars[26,:,:]\\n q700 = anlvars[27,:,:]\\n q850 = anlvars[28,:,:]\\n q925 = anlvars[29,:,:]\\n slp = anlvars[30,:,:]\\n t2 = anlvars[31,:,:]\\n td2 = anlvars[32,:,:]\\n u10 = anlvars[33,:,:]\\n v10 = anlvars[34,:,:]\\n\\n wrf_d1 = Dataset(refpath)\\n lons, lats = wrf_d1.variables['XLONG'][0], wrf_d1.variables['XLAT'][0]\\n wrf_idim = len(lons[0,:])\\n wrf_jdim = len(lats[:,0])\\n\\n sensvarlist = [gph300,gph500,gph700,gph850,gph925,temp300,temp500,temp700,\\n temp850,temp925,u300,u500,u700,u850,u925,v300,\\n v500,v700,v850,v925,td300,td500,td700,td850,\\n td925,q300,q500,q700,q850,q925,slp,t2,td2,u10,v10]\\n sensstringslist = [\\\"300 hPa GPH\\\",\\\"500 hPa GPH\\\",\\\"700 hPa GPH\\\",\\n \\\"850 hPa GPH\\\",\\\"925 hPa GPH\\\",\\\"300 hPa T\\\",\\\"500 hPa T\\\",\\n \\\"700 hPa T\\\",\\\"850 hPa T\\\",\\\"925 hPa T\\\",\\\"300 hPa U-Wind\\\",\\n \\\"500 hPa U-Wind\\\",\\\"700 hPa U-Wind\\\",\\\"850 hPa U-Wind\\\",\\n \\\"925 hPa U-Wind\\\",\\\"300 hPa V-Wind\\\",\\\"500 hPa V-Wind\\\",\\n \\\"700 hPa V-Wind\\\",\\\"850 hPa V-Wind\\\",\\\"925 hPa V-Wind\\\",\\n \\\"300 hPa Dewpt\\\", \\\"500 hPa Dewpt\\\", \\\"700 hPa Dewpt\\\",\\n \\\"850 hPa Dewpt\\\", \\\"925 hPa Dewpt\\\", \\\"300 hPa Q\\\",\\n \\\"500 hPa Q\\\", \\\"700 hPa Q\\\", \\\"850 hPa Q\\\", \\\"925 hPa Q\\\",\\n \\\"SLP\\\",\\\"2m Temp\\\",\\\"2m Dewpt\\\",\\n \\\"10m U-Wind\\\",\\\"10m V-Wind\\\"]\\n\\n\\n # Write interpolated variables to netCDF\\n new_analysis = Dataset(outpath, \\\"w\\\", format=\\\"NETCDF4\\\")\\n new_analysis.createDimension('lat', wrf_jdim)\\n new_analysis.createDimension('lon', wrf_idim)\\n new_analysis.createDimension('time', None)\\n xlat = new_analysis.createVariable(\\\"XLAT\\\", float, dimensions=('lat','lon'))\\n xlat[:,:] = lats\\n xlon = new_analysis.createVariable(\\\"XLONG\\\", float, dimensions=('lat','lon'))\\n xlon[:,:] = lons\\n\\n # Interpolate and save!!\\n for i in range(len(sensvarlist)):\\n var = new_analysis.createVariable(sensstringslist[i].replace(\\\" \\\",\\\"_\\\"),\\n sensvarlist[i].dtype,\\n dimensions=('lat','lon'))\\n var[:,:] = sensvarlist[i]\\n new_analysis.close()\\n return\",\n \"def inputfile(filename):\\n infile = open(filename, 'r')\\n lines = infile.readlines()\\n\\n # --------------------------------------------------------------------------\\n # Domain specifications\\n\\n Nx = eval(lines[15][lines[15].find('=')+1:].strip())\\n ax = eval(lines[16][lines[16].find('=')+1:].strip())\\n bx = eval(lines[17][lines[17].find('=')+1:].strip())\\n\\n Ny = eval(lines[19][lines[19].find('=')+1:].strip())\\n ay = eval(lines[20][lines[20].find('=')+1:].strip())\\n by = eval(lines[21][lines[21].find('=')+1:].strip())\\n\\n Nz = eval(lines[23][lines[23].find('=')+1:].strip())\\n az = eval(lines[24][lines[24].find('=')+1:].strip())\\n bz = eval(lines[25][lines[25].find('=')+1:].strip())\\n\\n Nvx = eval(lines[27][lines[27].find('=')+1:].strip())\\n avx = eval(lines[28][lines[28].find('=')+1:].strip())\\n bvx = eval(lines[29][lines[29].find('=')+1:].strip())\\n\\n Nvy = eval(lines[31][lines[31].find('=')+1:].strip())\\n avy = eval(lines[32][lines[32].find('=')+1:].strip())\\n bvy = eval(lines[33][lines[33].find('=')+1:].strip())\\n\\n Nvz = eval(lines[35][lines[35].find('=')+1:].strip())\\n avz = eval(lines[36][lines[36].find('=')+1:].strip())\\n bvz = eval(lines[37][lines[37].find('=')+1:].strip())\\n\\n Nt = eval(lines[39][lines[39].find('=')+1:].strip())\\n T = eval(lines[40][lines[40].find('=')+1:].strip())\\n\\n N = eval(lines[46][lines[46].find('=')+1:].strip())\\n\\n # --------------------------------------------------------------------------\\n # list of phase space variables used, in etc/params.dat must set unused\\n # vars to have Nz as None, z = x, vx, y, ...\\n # e.g. in 1D1V, phasespace_vars = ['x', 'vx']\\n phasespace_vars = []\\n if Nx is not None:\\n phasespace_vars.append('x')\\n if Ny is not None:\\n phasespace_vars.append('y')\\n if Nz is not None:\\n phasespace_vars.append('z')\\n if Nvx is not None:\\n phasespace_vars.append('vx')\\n if Nvy is not None:\\n phasespace_vars.append('vy')\\n if Nvz is not None:\\n phasespace_vars.append('vz')\\n\\n # ==========================================================================\\n # Boundary conditions dictionary -- contains dist. function BCs as well as phi\\n\\n BC = {}\\n BC['f'] = {}\\n BC['phi'] = {}\\n\\n # BC['f'] = BC dict on distribution function f\\n\\n # BC['f']['x'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['f']['y'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['f']['z'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['f']['vx'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['f']['vy'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['f']['vz'] = {'lower' : lower_value, 'upper' : upper_value}\\n\\n # BC['phi'] = BC dict on electric potential phi\\n\\n # BC['phi']['x'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['phi']['y'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['phi']['z'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['phi']['vx'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['phi']['vy'] = {'lower' : lower_value, 'upper' : upper_value}\\n # BC['phi']['vz'] = {'lower' : lower_value, 'upper' : upper_value}\\n #\\n # subdict objects that give keyword descriptions that match method names in lib.boundaryconditions and lib.fieldsolvers\\n # include, for var in phasespace_vars:\\n #\\n # BC['f'][var]['type'] and BC['phi'][var]['type']\\n #\\n # these are used to assemble function handle strings that select the corresponding routine needed for the specified BCs\\n\\n\\n BC_infilename = './etc/' + lines[106][lines[106].find(':')+1:].strip()\\n BC_infile = open(BC_infilename, 'r')\\n BC_infile_lines = BC_infile.readlines()\\n\\n # DECSKS will throw an error if numbers are inputted as BCs in etc/params.dat\\n\\n # strings are stored as lowercase as they are used in an eval statement to access\\n # the relevant method in lib.boundaryconditions. e.g. 'absorbing' is accessed as\\n # either eval('lib.boundaryconditions.absorbing_lower_boundary') or\\n # eval('lib.boundaryconditions.absorbing_upper_boundary') in lib.convect.remap_step\\n\\n BC['f']['x'] = {}\\n BC['f']['x']['lower'] = safe_eval(BC_infile_lines[40][BC_infile_lines[40].find('=')+1:].strip())\\n BC['f']['x']['upper'] = safe_eval(BC_infile_lines[41][BC_infile_lines[41].find('=')+1:].strip())\\n\\n BC['f']['y'] = {}\\n BC['f']['y']['lower'] = safe_eval(BC_infile_lines[43][BC_infile_lines[43].find('=')+1:].strip())\\n BC['f']['y']['upper'] = safe_eval(BC_infile_lines[44][BC_infile_lines[44].find('=')+1:].strip())\\n\\n BC['f']['z'] = {}\\n BC['f']['z']['lower'] = safe_eval(BC_infile_lines[46][BC_infile_lines[46].find('=')+1:].strip())\\n BC['f']['z']['upper'] = safe_eval(BC_infile_lines[47][BC_infile_lines[47].find('=')+1:].strip())\\n\\n BC['f']['vx'] = {}\\n BC['f']['vx']['lower'] = safe_eval(BC_infile_lines[55][BC_infile_lines[55].find('=')+1:].strip())\\n BC['f']['vx']['upper'] = safe_eval(BC_infile_lines[56][BC_infile_lines[56].find('=')+1:].strip())\\n\\n BC['f']['vy'] = {}\\n BC['f']['vy']['lower'] = safe_eval(BC_infile_lines[58][BC_infile_lines[58].find('=')+1:].strip())\\n BC['f']['vy']['upper'] = safe_eval(BC_infile_lines[59][BC_infile_lines[59].find('=')+1:].strip())\\n\\n BC['f']['vz'] = {}\\n BC['f']['vz']['lower'] = safe_eval(BC_infile_lines[61][BC_infile_lines[61].find('=')+1:].strip())\\n BC['f']['vz']['upper'] = safe_eval(BC_infile_lines[62][BC_infile_lines[62].find('=')+1:].strip())\\n\\n # make all BCs lowercase strings so they can be used to construct the function strings in lib.boundaryconditions module\\n # whose names are all lowercase\\n\\n # if an accepted boundary condition synonym as been used, change value to the name it goes by in lib.boundaryconditions\\n # check that all inputs for evolved phase space variables are recognized keywords and are compatible with the\\n # boundary at which they are indicated\\n for var in phasespace_vars:\\n for boundary in ['lower', 'upper']:\\n BC['f'][var][boundary] = BC['f'][var][boundary].lower()\\n if BC['f'][var][boundary] == 'open' or BC['f'][var][boundary] == 'cutoff':\\n print \\\"\\\\nCourtesy notice to user: the boundary condition %s was selected for the distribution function on %s at the %s boundary in params_boundaryconditions.dat; \\\" % (BC['f'][var][boundary].upper(), var, boundary)\\n print \\\"this is a recognized input synonym for a '%s' condition. Changing value stored to BC['f']['%s']['%s'] = '%s'\\\\n\\\" % ('ABSORBING', var, boundary, 'ABSORBING')\\n print \\\"Please regard any warnings/error messages that cite the keyword '%s' with this change in mind\\\\n\\\" % ('ABSORBING')\\n BC['f'][var][boundary] = 'absorbing'\\n\\n elif BC['f'][var][boundary] == 'collector':\\n pass\\n\\n elif BC['f'][var][boundary] == 'absorbing':\\n pass\\n\\n elif BC['f'][var][boundary] == 'symmetry':\\n if boundary == 'upper':\\n raise NotImplementedError('a symmetric UPPER boundary condition on the distribution function was specified in params_boundaryconditions.dat; however, DECSKS only has functionality to permit lower boundary symmetry.')\\n elif boundary == 'lower':\\n print \\\"\\\\nCourtesy notice to user: the boundary condition %s was selected for the distribution function on %s at the %s boundary in params_boundaryconditions.dat; \\\" % (BC['f'][var][boundary].upper(), var, boundary)\\n print \\\"this is a recognized input synonym for a '%s' condition. Changing value stored to BC['f']['%s']['%s'] = '%s'\\\\n\\\" % ('SYMMETRIC', var, boundary, 'SYMMETRIC')\\n print \\\"Please regard any warnings/error messages that cite the keyword '%s' with this change in mind\\\\n\\\" % ('SYMMETRIC')\\n BC['f'][var][boundary] = 'symmetric'\\n\\n elif BC['f'][var][boundary] == 'symmetric':\\n if boundary == 'lower':\\n pass\\n elif boundary == 'upper':\\n raise NotImplementedError('a symmetric UPPER boundary condition on the distribution function was specified in params_boundaryconditions.dat; however, DECSKS only has functionality to permit lower boundary symmetry.')\\n\\n elif BC['f'][var][boundary] == 'periodic':\\n pass\\n\\n else: # inputs do not match any options\\n print '\\\\nThe invalid keyword %s was specified in params_boundaryconditions.dat on the variable %s at the %s boundary\\\\n' % (BC['f'][var][boundary].upper(), var, boundary)\\n raise InputError('inputs are restricted to those listed as options in params_boundaryconditions.dat')\\n\\n # above we have checked for valid input. Next, check for compatible inputs (if 'periodic' is selected, it must be selected for both\\n # upper and lower bounds) and store a descriptor that toggles the correct orchestrator\\n # function in lib.boundaryconditions module ('periodic' vs. 'nonperiodic')\\n for var in phasespace_vars:\\n if BC['f'][var]['lower'] == 'periodic' and BC['f'][var]['upper'] == 'periodic':\\n BC['f'][var]['type'] = 'periodic'\\n\\n elif BC['f'][var]['lower'] == 'symmetric' and BC['f'][var]['upper'] != 'periodic':\\n BC['f'][var]['type'] = 'nonperiodic'\\n \\n # check for invalid inputs\\n elif BC['f'][var]['lower'] == 'symmetric' and BC['f'][var]['upper'] == 'periodic':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat:\\\"\\n print \\\"\\\\nlower boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"upper boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['upper'].upper())\\n\\n print \\\"\\\\nare inconsistent. Cannot combine a symmetric lower boundary with a periodic upper boundary condition. Periodic boundary conditions involve both boundaries (both boundaries would have to be set to PERIODIC)\\\\n\\\"\\n\\n raise InputError('cannot combine a symmetric lower boundary condition with a periodic upper boundary condition for the distribution function. Check inputs in boundaryconditions.dat and change the upper bound to be of non-periodic type')\\n\\n elif BC['f'][var]['lower'] == 'periodic' and BC['f'][var]['upper'] != 'periodic':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat:\\\"\\n print \\\"\\\\nlower boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"upper boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['upper'].upper())\\n\\n print \\\"\\\\nare inconsistent. Cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\\\"\\n\\n raise InputError('cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\\n elif BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] == 'periodic':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat:\\\"\\n print \\\"\\\\nlower boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"upper boundary condition on f for the variable %s: %s\\\" % (var, BC['f'][var]['upper'].upper())\\n\\n print \\\"\\\\nare inconsistent. Cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\\\"\\n\\n raise InputError('cannot combine periodic and non-periodic boundary conditions on same variable for distribution function, check inputs in params_boundaryconditions.dat')\\n else: # boundary conditions are combination of only: symmetric (lower), collector (lower or upper), absorbing (lower or upper)\\n BC['f'][var]['type'] = 'nonperiodic'\\n\\n distribution_function_boundarycondition_orchestrator_prefix = 'DECSKS.lib.boundaryconditions'\\n\\n # create a dictionary of function handles that call either\\n # the 'periodic', 'nonperiodic', or 'symmetric' orchestrator in lib.boundaryconditions\\n #\\n # i.e. we form the string handle for each active variable var:\\n #\\n # distribution_function_boundarycondition_orchestrator_handle[var] =\\n #\\n # DECSKS.lib.boundaryconditions.periodic\\n # DECSKS.lib.boundaryconditions.nonperiodic\\n # DECSKS.lib.boundaryconditions.symmetric\\n\\n distribution_function_boundarycondition_orchestrator_handle = {}\\n\\n for var in phasespace_vars:\\n distribution_function_boundarycondition_orchestrator_handle[var] = \\\".\\\".join(\\n (distribution_function_boundarycondition_orchestrator_prefix, BC['f'][var]['type']))\\n\\n # --------------------------------------------------------------------------\\n # Store number of active gridpoints for every phase space variable\\n #\\n # Note: for periodic BCs: Nz_active = Nz - 1, we evolve Nz_active nodes and assign by periodicity the f[Nz-1] = f[0]\\n # for all other BCs: Nz_active = Nz\\n\\n # active_dims vs. total_dims\\n # note a generalized loop cannot be used as assignments cannot be made under an assembled string with eval\\n if BC['f']['x']['lower'] == 'periodic' and BC['f']['x']['upper'] == 'periodic' and Nx is not None:\\n Nx_active = Nx - 1\\n else:\\n Nx_active = Nx\\n\\n if BC['f']['y']['lower'] == 'periodic' and BC['f']['y']['upper'] == 'periodic' and Ny is not None:\\n Ny_active = Ny - 1\\n else:\\n Ny_active = Ny\\n\\n if BC['f']['z']['lower'] == 'periodic' and BC['f']['z']['upper'] == 'periodic' and Nz is not None:\\n Nz_active = Nz - 1\\n else:\\n Nz_active = Nz\\n\\n if BC['f']['vx']['lower'] == 'periodic' and BC['f']['vx']['upper'] == 'periodic' and Nvx is not None:\\n Nvx_active = Nvx - 1\\n else:\\n Nvx_active = Nvx\\n\\n if BC['f']['vy']['lower'] == 'periodic' and BC['f']['vy']['upper'] == 'periodic' and Nvy is not None:\\n Nvy_active = Nvy - 1\\n else:\\n Nvy_active = Nvy\\n\\n if BC['f']['vz']['lower'] == 'periodic' and BC['f']['vz']['upper'] == 'periodic' and Nvz is not None:\\n Nvz_active = Nvz - 1\\n else:\\n Nvz_active = Nvz\\n\\n # --------------------------------------------------------------------------\\n # High order correction (HOC) method applied to each phase space variable\\n\\n # store as uppercase\\n\\n HOC = {}\\n HOC['x'] = safe_eval(lines[56][lines[56].find(':')+1:].strip())\\n HOC['y'] = safe_eval(lines[57][lines[57].find(':')+1:].strip())\\n HOC['z'] = safe_eval(lines[58][lines[58].find(':')+1:].strip())\\n\\n HOC['vx'] = safe_eval(lines[60][lines[60].find(':')+1:].strip())\\n HOC['vy'] = safe_eval(lines[61][lines[61].find(':')+1:].strip())\\n HOC['vz'] = safe_eval(lines[62][lines[62].find(':')+1:].strip())\\n\\n # make all non-None inputs capitalized\\n for key in HOC.keys():\\n if HOC[key] is not None:\\n HOC[key] = HOC[key].upper()\\n else:\\n pass\\n\\n # check for valid inputs\\n for key in HOC.keys():\\n if HOC[key] is not None:\\n if type(HOC[key]) != str:\\n raise InputError('A non-string entry was found as a high order correction specification. Only FD or FOURIER are accepted')\\n elif HOC[key] != 'FD' and HOC[key] != 'FOURIER':\\n print \\\"\\\\nThe following high order correction was specified in params.dat, but is not recognized:\\\"\\n print \\\"\\\\nHigh order correction on %s: %s\\\\n\\\" % (key, HOC[key].upper())\\n print \\\"only FD and FOURIER are accepted keywords\\\\n\\\"\\n raise InputError('An unrecognized high order correction was specified. Only FD or FOURIER are accepted')\\n\\n elif HOC[key] == 'FOURIER' and BC['f'][key]['type'] != 'periodic': # Fourier corrections use trigonometric derivatives, which rely on periodicity of the underlying functions\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat:\\\"\\n print \\\"\\\\nlower boundary condition on f for the variable %s: %s\\\" % (key, BC['f'][key]['lower'].upper())\\n print \\\"upper boundary condition on f fore the variable %s: %s\\\\n\\\\n\\\" % (key, BC['f'][key]['upper'].upper())\\n\\n print \\\"are inconsistent with the high order correction specified in params.dat:\\\"\\n print \\\"\\\\nhigh order correction on %s: %s\\\\n\\\\n\\\" % (key, HOC[var].upper())\\n\\n print \\\"FOURIER high order corrections only make sense for periodic systems (if this is the intention, the BCs on f and phi must be set to PERIODIC in params_boundaryconditions.dat)\\\\n\\\"\\n\\n raise InputError('Fourier corrections on a variable only make sense for periodic systems. The boundary conditions on the distribution function were read-in as not periodic for this variable.')\\n elif eval('N' + key) is None:\\n raise InputError('a variable not involved in the simulation (its number of grid points was specified as None) must also have its high order correction method specified as None. While reading in the input deck, the aforementioned expectation was not met. Please revisit the entries (number of grid points) and high order correction specification.')\\n\\n # store lists containing number of total and active gridpoints\\n # this is acknowledged as redundant given the above storing as Nx_active, Ny_active,\\n # etc., but these objects are used in legacy methods inside DECSKS\\n\\n # initialize lists\\n total_dims = [] # e.g. in 1D1V this could contain [Nx, Nvx]\\n active_dims = [] # e.g. in 1D1V this could contain [Nx_active, Nvx_active]\\n\\n for var in phasespace_vars:\\n total_dims.append(eval('N' + var))\\n active_dims.append(eval('N' + var + '_active'))\\n\\n numdims = len(phasespace_vars)\\n # --------------------------------------------------------------------------\\n # Initial density specification (2 species)\\n\\n mu = safe_eval(lines[68][lines[68].find(':')+1:].strip())\\n\\n densities_list = lines[69][lines[69].find(':')+1:].strip().split(', ')\\n for i in range(len(densities_list)):\\n densities_list[i] = densities_list[i].lower()\\n\\n if len(densities_list) == 2: # if two species return dictionary of strings\\n density = {}\\n density['electrons'] = densities_list[0]\\n density['electrons'] = density['electrons'].lower()\\n density['ions'] = densities_list[1]\\n density['ions'] = density['ions'].lower()\\n print \\\"\\\\ntwo species simulation with initial densities:\\\\n\\\"\\n print \\\"electrons: %s\\\" % density['electrons']\\n print \\\"ions: %s\\\\n\\\" % density['ions']\\n\\n # --------------------------------------------------------------------------\\n # split scheme specification\\n\\n split_scheme = lines[81][lines[81].find('=')+1:].strip()\\n split_scheme = split_scheme.upper()\\n print \\\"split scheme: %s\\\\n\\\" % split_scheme\\n\\n # filepath to splitting coefficient tables\\n filename = lines[82][lines[82].find(':')+1:].strip()\\n filepath = './etc/' + filename\\n\\n # get splitting coefficients for chosen scheme\\n if split_scheme is not None:\\n splitting = splitting_coefficients(filepath, split_scheme)\\n else:\\n splitting = None\\n\\n\\n\\n # --------------------------------------------------------------------------\\n # check for validity on split scheme vs. boundary conditions\\n #\\n # i.e. check that if the problem is bounded, the user cannot use a split scheme that has negative time substeps\\n #\\n # Schemes with only positive time substeps: LF2\\n # Schemes that contain negative time substeps: Y4, O6-4, O11-6, O14-6\\n #\\n\\n for var in phasespace_vars:\\n if BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] != 'periodic':\\n if split_scheme in ['LF2']:\\n pass\\n else: # a split scheme that involves negative time substeps has been selected\\n print \\\"\\\\nThe following set of user specified information is not accepted by DECSKS:\\\\n\\\"\\n print \\\"\\\\nin params.dat, the following was specified:\\\"\\n print \\\"split scheme = %s:\\\" % split_scheme\\n print \\\"\\\\nand the boundary data was specified in params_boundaryconditions.dat:\\\\n\\\"\\n print \\\"distribution function lower boundary condition on %s: %s\\\" % (BC['f'][var]['lower'],var)\\n print \\\"distribution function upper boundary condition on %s: %s\\\" % (BC['f'][var]['upper'], var)\\n print \\\"\\\\nThe split scheme involves negative time substeps, while the boundary conditions are non-periodic. The BOUNDED Vlasov-Poisson problem is irreversible. A split scheme with negative time substeps can only be used in periodic systems, which correspond to systems of infinite extent\\\\n\\\"\\n raise InputError('The split scheme involves negative time substeps, while the boundary conditions are non-periodic. The BOUNDED Vlasov-Poisson problem is irreversible. A split scheme with negative time substeps can only be used in periodic systems, which correspond to systems of infinite extent. To rectify this, the user may wish to select periodic boundary conditions on the distribution function (hence phi).')\\n\\n # --------------------------------------------------------------------------\\n # Plot window specification (used in lib.plots.Setup)\\n\\n xmin = eval(lines[96][lines[96].find('=')+1:].strip())\\n xmax = eval(lines[97][lines[97].find('=')+1:].strip())\\n ymin = eval(lines[99][lines[99].find('=')+1:].strip())\\n ymax = eval(lines[100][lines[100].find('=')+1:].strip())\\n\\n plot_params = dict(xmin = xmin, xmax = xmax,\\n ymin = ymin, ymax = ymax)\\n\\n record_outputs = lines[103][lines[103].find(':')+1:].strip()\\n record_outputs = record_outputs.lower()\\n\\n if record_outputs == 'yes':\\n # output filepath setup\\n filename = lines[104][lines[104].find(':')+1:].strip()\\n filepath = './etc/' + filename\\n outfiles = output_files(filepath) # dictionary of opened files\\n else:\\n outfiles = None\\n\\n # --------------------------------------------------------------------------\\n # DICTIONARIES AND MATRICES RELEVANT FOR HIGH ORDER CORRECTION APPLICATIONS\\n #\\n\\n # Constructing the finite different weight matrices, W.\\n #-------------------------------------------------------\\n # requires: (dict) FD_schemes\\n #\\n # Note: FD_schemes is only needed to construct W. W is what is used in\\n # the simulation. Hence, the building routine for FD_schemes\\n # is not optimized, since it happens before the simulation starts\\n # and is not a source of repeated computational cost.\\n #\\n # FD_schemes is a dictionary containing the families of every order derivative\\n # needed for the indicated global error N in etc/params.dat, i.e. all schemes\\n # of various degrees of asymmetry and handedness. For large N, this can be a\\n # large dictionary, cf. the function routine read_FD_schemes to see all\\n # that gets stored inside. It is used to construct the difference coefficient\\n # matrices W (for applying high order corrections). The other scheme\\n # FD_scheme_dn1 is used to construct the matrix W_dn1 which is a difference\\n # coefficient matrix for the first derivative (dn = 1) at LTE = 6, and used\\n # to compute the electric field E = \\\"-dphi\\\" = W_dn1.dot(phi),\\n # where dphi is the first derivative# of the electric potential, as calculated by\\n # the methods in lib.fieldsolvers package\\n #---------------------------------------------------------------------------\\n #\\n # initialize all dictionaries whose keys correspond to phase space vars\\n # and whose values contain the relevant ndarrays\\n\\n Xi = {}\\n xi = {}\\n W = {}\\n\\n # top level check: if any var has FD corrections, store FD_schemes and init FD weight matrix W\\n # for 6th order first derivative\\n if 'FD' in HOC.values():\\n # store finite difference schemes\\n FD_schemes = read_FD_schemes(N)\\n\\n # if FD on a configuration variable, need to differentiate phi to obtain the acceleration a ~ E = -dphi\\n if HOC['x'] == 'FD' or HOC['y'] == 'FD' or HOC['z'] == 'FD':\\n # first derivative with LTE = 6, used to find dphi = -E after phi is\\n # found from a 6th order Poisson solve\\n FD_scheme_dn1 = read_FD_scheme(1,6)\\n W_dn1_LTE6 = assemble_finite_difference_weight_matrix_const_dn_const_LTE(Nx_active,\\n FD_scheme_dn1,\\n dn = 1,\\n LTE = 6\\n )\\n\\n else:\\n # else, Fourier Gauss solver is used, no need for this matrix\\n W_dn1_LTE6 = None\\n\\n # variable-by-variable checks: assemble consistent objects needed\\n # for the specified means of HOC from etc/params.dat\\n\\n # Note: the following is organized with the expectation that\\n # higher dimensional implementations would be stepped through\\n # as sets of 2D advection problems, always paired as z and vz\\n # i.e. not as mixed stepthroughs with x paired with vy for example\\n\\n for var in phasespace_vars:\\n if HOC[var] == 'FD':\\n W[var] = assemble_finite_difference_weight_matrix(\\n eval('N' + var + '_active'),\\n N,\\n FD_schemes\\n )\\n elif HOC[var] == 'FOURIER':\\n # ensure the correct number of grid points\\n # is passed for the generalized velocity Nvz_active\\n # for x,y,z, 'vz' = vx, vy, vz\\n # for vx, vy, vz, 'vz' = ax, ay, az, which have\\n # the same number of dims as x, y, z, respectively\\n # this is needed in the routine assemble_spectral_derivative_operator\\n # so that the correctly dimensioned 2D arrays are returned\\n\\n if var[0] == 'v':\\n # if a velocity variable, the velocity of this velocity is an acceleration\\n # which has the same dimensions as the corresponding configuration variable\\n # e.g. vx has velocity(vx) = ax which has the same dimensions as x\\n Nvz_active = eval('N' + var[1] + '_active')\\n else:\\n # if a configuration variable, the velocity is the physical velocity, which\\n # must be a coresponding active variable\\n # e.g. x has a velocity vx\\n Nvz_active = eval('Nv' + var + '_active')\\n\\n\\n # The 3D tensor Xi is used to compute trigonometric derivatives\\n # by operating on a 2D array of Fourier wave components (transformed\\n # row-wise for each column, where as usual the objects have been\\n # transpoed if needed so that the variation (x or vx) is along\\n # rows, not columns)\\n #\\n # Fourier transform (derivatives) = Xi * Fourier transform (f)\\n # derivatives = inverse transform (Xi * Fourier(f))\\n #\\n #\\n # the object xi is used in legacy methods in DECSKS (pre-DECSKSv2.0)\\n\\n Xi, xi = assemble_spectral_derivative_operator(Xi, xi,\\n var,\\n eval('a' + var),\\n eval('b' + var),\\n eval('N' + var),\\n eval('N' + var + '_active'),\\n Nvz_active,\\n N)\\n\\n # ---------------------------------------------------------------------\\n # \\\"Alternating\\\" identity matrix\\n\\n # in lib.HOC.correctors, require an diagonal matrix with shape = (Nz_active, Nz_active)\\n # with entries as (-1)^i, where i is the row number, for details see on github\\n #\\n # dsirajud/IPython-notebooks/\\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\\n #\\n # section \\\"2D casting of correction coefficients c (vector) -> c (tensor)\\\"\\n\\n I_alternating = np.diag( (-np.ones(N)) ** np.arange(N) )\\n\\n # ---------------------------------------------------------------------\\n # Bernoulli number storage, and forming the matrices A_pos, A_neg\\n\\n # obtain Bernoulli numbers (note: only 23 numbers are entered into the dat file ->\\n # max global error is 23 - 1 = 22) for a correction up to global error order\\n # N, N-1 Bernoulli numbers are needed. If higher than global error order 22 is\\n # desired, additional Bernoulli numbes need to be entered in\\n #\\n # etc/Table_of_Bernoulli_numbers.dat\\n #\\n\\n # Store Bernoulli numbers from dat file etc/Table_of_Bernoulli_numbers.dat\\n filename = 'Table_of_Bernoulli_numbers.dat'\\n filepath = './etc/' + filename\\n Bernoulli_numbers = Bernoulli(filepath)\\n\\n # \\\"A\\\" matrices for Bernoulli number storage and matrix HOC application\\n # in lib.HOC.Beta_matrix, see notebook on github at\\n # dsirajud/IPython-notebooks/\\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\\n #\\n # the A matrices are matrices containing scaled Bernoulli numbers (normalized by factorials)\\n # that also factor in the sign (direction) information of the advecting density packets\\n # (the different amounts to all odd coefficients having opposite sign)\\n\\n # The A matrices are used in the method lib.HOC.Beta_matrix (used to construct the array of the *magnitudes*\\n # of the Nvz sets of N beta coefficients; note that the high order flux is further computed as a sum of\\n # products that alternating with sign according to the parity of the derivative number, i.e. alternates signs\\n # among odds and evens. These prefactors are applied at the end of the method lib.HOC.correctors by matrix\\n # pre-multiplication of the matrix B with the alternating (in sight) identity matrix I formed above)\\n\\n # the method lib.HOC.Beta_matrix is called from inside lib.HOC.correctors (used to assemble the 2D array c of correctors)\\n\\n A_pos, A_neg = np.zeros([N,N]), np.zeros([N,N])\\n for i in range(N):\\n for j in range(i+1):\\n A_pos[i,j] = Bernoulli_numbers[i-j] / scipy.misc.factorial(i-j)\\n if (i - j) == 1:\\n A_neg[i,j] = -A_pos[i,j]\\n else:\\n A_neg[i,j] = A_pos[i,j]\\n\\n A_matrix = {}\\n # dictionary container\\n # allow dictionary access to relevant matrix of Bernoulli numbers\\n # by operating with str(int(np.sign(CFL.frac)))\\n\\n A_matrix['1'] = A_pos\\n A_matrix['0'] = A_pos\\n A_matrix['-1'] = A_neg\\n\\n #--------------------------------------------------------------------------------------------#\\n # ELECTRIC POTENTIAL PHI\\n #--------------------------------------------------------------------------------------------#\\n\\n #--------------------------------------------------------------------------------------------#\\n # Boundary conditions BC['phi'] dictionary and dictionary of boundary values, phi_BC\\n #\\n # BC['phi']['x', 'y', or 'z']['lower' or 'upper'] = string keyword that describes the BC\\n # phi_BC['x', 'y', or 'z'] = boundary value vector phi_BC that appears in a Poisson solver\\n #--------------------------------------------------------------------------------------------#\\n\\n phi_BC = {}\\n # keys: 'x', 'y', 'z'\\n # values: ndarrays of size eval('N' + var + '_active)\\n\\n BC['phi'] = {}\\n # keys: 'x', 'y', 'z'\\n # values / keys for subdict: 'lower', 'upper'\\n # values for subdict: string keyword that describes the BC at the key specification\\n\\n # --------------------------------------------------------------------------\\n # PHI BOUNDARY CONDITIONS AND PHI BOUNDARY VALUES VECTORS FOR SOLVER Phi_BC['x', 'y', or 'z']\\n\\n # lines read in from boundaryconditions dat file were stored above in BC_infile_lines\\n if HOC['x'] == 'FD':\\n BC['phi']['x'] = {}\\n BC['phi']['x']['lower'] = safe_eval(BC_infile_lines[196][BC_infile_lines[196].find('=')+1:].strip())\\n BC['phi']['x']['upper'] = safe_eval(BC_infile_lines[197][BC_infile_lines[197].find('=')+1:].strip())\\n phi_BC['x'] = np.zeros(Nx_active)\\n elif HOC['x'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\\n phi_BC['x'] = None\\n\\n if HOC['y'] == 'FD':\\n BC['phi']['y'] = {}\\n BC['phi']['y']['lower'] = safe_eval(BC_infile_lines[199][BC_infile_lines[199].find('=')+1:].strip())\\n BC['phi']['y']['upper'] = safe_eval(BC_infile_lines[200][BC_infile_lines[200].find('=')+1:].strip())\\n phi_BC['y'] = np.zeros(Ny_active)\\n elif HOC['y'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\\n phi_BC['y'] = None\\n\\n if HOC['z'] == 'FD':\\n BC['phi']['z'] = {}\\n BC['phi']['z']['lower'] = safe_eval(BC_infile_lines[202][BC_infile_lines[202].find('=')+1:].strip())\\n BC['phi']['z']['upper'] = safe_eval(BC_infile_lines[203][BC_infile_lines[203].find('=')+1:].strip())\\n phi_BC['z'] = np.zeros(Nz_active)\\n elif HOC['z'] == 'FOURIER': # periodic fourier solver is used, a BC vector is not needed\\n phi_BC['z'] = None\\n\\n # ensure all inputs stored above in BC['phi'] dict objects are uppercase and recognized\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n if HOC[var] == 'FOURIER':\\n pass\\n else: # HOC is FD which computes the Lorentz term through a potential phi (Fourier uses the electric field E)\\n\\n # LOWER BOUNDARY CHECKS\\n if BC['phi'][var]['lower'] is None:\\n raise InputError('a NoneType was specified as a LOWER boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not meant to be evolved, set its number of grid points to None')\\n\\n elif type(BC['phi'][var]['lower']) != str:\\n raise InputError('a non-string type as a LOWER boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not intended to be active, set its number of grid points to None. Otherwise, a recognized string keyword must be specified on the boundary condition on phi for this variable.')\\n\\n else:\\n BC['phi'][var]['lower'] = BC['phi'][var]['lower'].upper()\\n\\n if BC['phi'][var]['lower'] not in ['PERIODIC', 'SELF-CONSISTENT', 'SYMMETRIC', 'SYMMETRY', 'BIAS']:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat is not a recognized keyword:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['phi'][var]['lower'].upper())\\n\\n raise InputError('boundary condition indicated on phi is not an accepted keyword option')\\n\\n elif (BC['phi'][var]['lower'] == 'SYMMETRIC' or BC['phi'][var]['lower'] == 'SYMMETRY') and BC['f'][var]['lower'] != 'symmetric':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat is:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['phi'][var]['lower'].upper())\\n print \\\"lower boundary condition on f for variable %s: %s\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"upper boundary condition on f for variable %s: %s\\\\n\\\" % (var, BC['f'][var]['upper'].upper())\\n\\n print \\\"a SYMMETRIC boundary condition must be specified on both phi and f\\\"\\n # by this point all synonyms have been normalized on BC['f'][var], 'symmetric' corresponds to the symmetry condition\\n raise InputError('a SYMMETRY boundary condition on phi was specified, but a symmetry boundary was not specified on the distribution function f at this same (lower) boundary. A symmetric domain requires a lower boundary condition to be SYMMETRIC on both phi and f.')\\n\\n else:\\n pass\\n\\n # UPPER BOUNDARY CHECKS\\n if BC['phi'][var]['upper'] is None:\\n raise InputError('a NoneType was specified as an upper boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not meant to be evolved, set its number of grid points to None')\\n\\n elif type(BC['phi'][var]['upper']) != str:\\n raise InputError('a non-string type as an upper boundary condition on the electric potential phi for an active variable (a non-NoneType was specified for the number of grid points on this variable). If the variable is not intended to be active, set its number of grid points to None. Otherwise, a recognized string keyword must be specified on the boundary condition on phi for this variable.')\\n\\n else:\\n BC['phi'][var]['upper'] = BC['phi'][var]['upper'].upper()\\n\\n if BC['phi'][var]['upper'] not in ['PERIODIC', 'SELF-CONSISTENT', 'SYMMETRIC', 'SYMMETRY', 'BIAS']:\\n print \\\"\\\\nThe following boundary condition specified in params_boundaryconditions.dat is not a recognized boundary condition keyword:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n\\n raise InputError('boundary condition indicated on phi is not an accepted keyword option')\\n\\n elif BC['phi'][var]['upper'] == 'SYMMETRIC' or BC['phi'][var]['upper'] == 'SYMMETRY':\\n print \\\"\\\\nThe following boundary condition specified in params_boundaryconditions.dat is not available:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi: %s\\\\n\\\" % BC['phi'][var]['upper'].upper()\\n\\n raise NotImplementedError('a SYMMETRY boundary condition on phi as an UPPER boundary is specified in params_boundaryconditions.dat; only lower boundaries can support a symmetry boundary condition.')\\n\\n\\n # CHECK FOR CONSISTENCY IN BOUNDARY CONDITIONS BETWEEN BOTH LOWER AND UPPER SPECIFICATIONS\\n if BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] != 'PERIODIC':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['phi'][var]['lower'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n\\n raise InputError('PERIODIC boundary conditions on phi involve both lower and upper boundaries. The read-in of params_boundaryconditions.dat has the lower boundary condition as PERIODIC but the upper boundary condition is NOT. Both boundary conditions on phi must be set to PERIODIC if a periodic plasma is to be simulated.')\\n\\n elif BC['phi'][var]['lower'] != 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['phi'][var]['lower'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n\\n raise InputError('PERIODIC boundary conditions on phi involve both lower and upper boundaries. The read-in of params_boundaryconditions.dat has the upper boundary condition as PERIODIC but the lower boundary condition is NOT. Both boundary conditions on phi must be set to PERIODIC if a periodic plasma is to be simulated.')\\n\\n elif BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC':\\n\\n if BC['f'][var]['type'] != 'periodic': # note that validity and consistency checks on inputs for the distribution function have already been done above\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['phi'][var]['lower'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['f'][var]['upper'].upper())\\n print \\\"e.g. periodic boundaries on phi require periodic boundaries on f for the same variable\\\\n\\\"\\n raise InputError('PERIODIC boundary conditions on were specifed consistently for phi in params_boundaryconditions.dat; however, periodic boundary conditions must also be consistently specified on the distribution function. Revisit params_boundaryconditions.dat and ensure that both lower and upper boundaries on the distribution function f and the potential phi are set to PERIODIC if a periodic plasma is intended to be simulated.')\\n elif BC['f'][var]['type'] == 'periodic': # note that validity and consistency checks on inputs for the distribution function have already been done above\\n pass\\n\\n\\n # CHECK FOR CONSISTENCY ON PHI BCS WITH HIGH ORDER CORRECTION METHOD SPECIFIED (note we have already checked this against the distribution function BCs)\\n # here, we are only checking to see if that BCs on phi aren't periodic, to ensure that HOC is NOT set to fourier (relies on periodicity))\\n # the following conditional check asks: \\\"if (BCs on phi are not periodic) AND (HOC is FOURIER)\\\"\\n if ((BC['phi'][var]['lower'] == 'PERIODIC' and BC['phi'][var]['upper'] != 'PERIODIC') or (BC['phi'][var]['lower'] != 'PERIODIC' and BC['phi'][var]['upper'] == 'PERIODIC')) and HOC[var] == 'fourier':\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent with the specified high order correction method in params.dat: \\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi for variable %s: %s\\\" % (var, BC['phi'][var]['lower'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\\n\\\" % (var, HOC[var].upper())\\n print \\\"\\\\n\\\\nFourier high order corrections require periodic boundary conditions on both phi and the distribution function f\\\\n\\\"\\n\\n raise InputError('the high order correction is specified as FOURIER; however, the BCs on the electric potential phi are not periodic. FOURIER corrections require PERIODIC BCs on phi and the distribution function as the methods rely on periodicity')\\n\\n #--------------------------------------------------------------------------------------------#\\n # BIAS values\\n #--------------------------------------------------------------------------------------------#\\n\\n Bias = {} # this dictionary is created for reading in the bias values, it is not returned\\n # in sim_params dict. If a bias condition is set on any boundary, this dictionary\\n # assigns its value at that boundary in the vector phi_BC[var], phi_BC[var] is\\n # returned (as usual, var = ['x', 'y', 'z'])\\n\\n Bias['x'] = {}\\n Bias['y'] = {}\\n Bias['z'] = {}\\n\\n Bias['x']['lower'] = safe_eval(BC_infile_lines[214][BC_infile_lines[214].find('=')+1:].strip())\\n Bias['x']['upper'] = safe_eval(BC_infile_lines[215][BC_infile_lines[215].find('=')+1:].strip())\\n Bias['y']['lower'] = safe_eval(BC_infile_lines[217][BC_infile_lines[217].find('=')+1:].strip())\\n Bias['y']['upper'] = safe_eval(BC_infile_lines[218][BC_infile_lines[218].find('=')+1:].strip())\\n Bias['z']['lower'] = safe_eval(BC_infile_lines[220][BC_infile_lines[220].find('=')+1:].strip())\\n Bias['z']['upper'] = safe_eval(BC_infile_lines[221][BC_infile_lines[221].find('=')+1:].strip())\\n\\n # check for valid inputs on active variables for any boundary that is specified as BIAS\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n if HOC[var] == 'FOURIER':\\n pass\\n else:\\n for boundary in ['lower', 'upper']:\\n if var in phasespace_vars:\\n if BC['phi'][var][boundary] == 'BIAS':\\n if Bias[var][boundary] is None: # if the BC is BIAS but the value input for the BIAS value is None\\n print \\\"\\\\nThe following specifications in params_boundaryconditions.dat are inconsistent:\\\\n\\\"\\n print \\\"%s boundary condition on phi for variable %s: %s\\\" % (boundary, var, BC['phi'][var][boundary].upper())\\n print \\\"%s BIAS value on phi for variable %s: %s\\\\n\\\" % (boundary, var, Bias[var][boundary])\\n print \\\"e.g. if a boundary condition on phi is set to BIAS for a variable, a number must be specifed under BIAS value\\\\n\\\"\\n raise InputError('A phi boundary condition on an active variable (number of grid points on this variable has been set as non-None) has been specified as BIAS; however, the corresponding BIAS value is NoneType. Must be a number.')\\n elif type(Bias[var][boundary]) == str:\\n print \\\"\\\\nThe following specifications in params_boundaryconditions.dat are inconsistent:\\\\n\\\"\\n print \\\"%s boundary condition on phi for variable %s: %s\\\" % (boundary, var, BC['phi'][var][boundary].upper())\\n print \\\"%s BIAS value on phi for variable %s: %s\\\\n\\\" % (boundary, var, Bias[var][boundary])\\n print \\\"e.g. if a boundary condition on phi is set to BIAS for a variable, a number must be specifed under BIAS value\\\\n\\\"\\n\\n raise InputError('A phi boundary condition on an active variable (number of grid points on this variable has been set as non-None) has been specified as BIAS; however, the corresponding BIAS value is str type. Must be a number.')\\n else:\\n pass\\n\\n # E is calculated by the following call flow, first an ORCHESTRATOR is called:\\n #\\n # E = lib.fieldsolvers.compute_electric_field_fourier <--- solves with a Gauss' law solver directly\\n #\\n # or\\n #\\n # E = lib.fieldsolvers.compute_electric_field_fd <--- solves a Poisson solver for phi, then differentiate to get E\\n #\\n # which can generally be called by eval operating on string handles that are themselves constructed\\n # per 'lib.fieldsolvers.compute_electric_field_' + HOC[var].lower()\\n #\\n # If a finite difference routine is specified, a Poisson solve must be performed to obtain phi.\\n # We call the relevant Poisson solver among the following options (L = lower boundary, U = upper boundary, DBC = Dirichlet BC, NBC = Neumann BC):\\n #\\n # Poisson_6th_PBC\\n # Poisson_6th_LDBC_UDBC\\n # Poisson_6th_LDBC_UNBC\\n # Poisson_6th_LNBC_UDBC\\n # Poisson_6th_LDBC_LDBC\\n # Poisson_6th_UDBC_UNBC\\n #\\n\\n # which are selected based on the boundary conditions the user has supplied in params_boundaryconditions.dat.\\n #\\n # finally, we compute and return:\\n #\\n # E = - 1 / config_var.width * W_dn1_LTE6.dot(phi)\\n #\\n\\n # --------------------------------------------------------------------------\\n # fieldsolver orchestator handle string for electric field (periodic or non-periodic)\\n #\\n # currently only 1D1V, only one handle needed. When this will be generalized, can make a dict object with keys corresponding\\n # to each active configuration variable\\n\\n compute_electric_field_orchestrator_handle = {}\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n # dictionary key labels the component of the electric field: 'x', 'y', 'z'\\n compute_electric_field_orchestrator_handle[var] = \\\"DECSKS.lib.fieldsolvers.compute_electric_field_\\\" + HOC[var].lower()\\n\\n\\n # ---------------------------------------------------------------------\\n # initialize dictionaries for wall charge objects\\n\\n sigma = {}\\n sigma_n = {}\\n\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n sigma_n[var] = {}\\n sigma[var] = {}\\n\\n # --------------------------------------------------------------------------\\n # Dictionary for the specific electric potential phi function solver needed\\n # according to the specified boundary conditions on phi\\n\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n\\n if HOC[var] == 'FOURIER':\\n pass # uses electric field E, periodic boundary conditions only\\n\\n else: # is FD corrections, and electric potential phi in a Poisson solver, can be periodic or other BCs\\n BC['phi'][var]['type'] = BC['phi'][var]['lower'] + '_' + BC['phi'][var]['upper']\\n if BC['phi'][var]['type'] == 'PERIODIC_PERIODIC':\\n BC['phi'][var]['type'] = 'PBC'\\n\\n if BC['f'][var]['lower'] != 'periodic' and BC['f'][var]['upper'] != 'periodic':\\n raise InputError('A boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\\n\\n\\n if BC['phi'][var]['type'] == 'BIAS_BIAS':\\n BC['phi'][var]['type'] = 'LDBC_UDBC'\\n\\n # Dirichlet condition, phi = BIAS value\\n phi_BC[var][0] = float(Bias[var]['lower'])\\n # Dirichlet condition, phi = BIAS value\\n phi_BC[var][-1] = float(Bias[var]['upper'])\\n\\n if BC['f'][var]['lower'] != 'absorbing' or BC['f'][var]['upper'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n raise InputError('A boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\\n\\n elif BC['phi'][var]['type'] == 'BIAS_SELF-CONSISTENT':\\n BC['phi'][var]['type'] = 'LDBC_UNBC'\\n\\n # Dirichlet condition, phi = BIAS value\\n phi_BC[var][0] = float(Bias[var]['lower'])\\n # Neumann condition, dphi = sigma_upper, translates to phi_BC[-1] = -6 var.width * sigma_upper (see https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb for details)\\n # phi_BC[-1] = - 6 * var.width * sim_params['sigma'][var]['upper'], changes with time step\\n\\n if BC['f'][var]['lower'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n raise InputError('A lower boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\\n\\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n print \\\"upper boundary condition on f for variable %s: %s\\\\n\\\" % (var, BC['f'][var]['upper'].upper())\\n print \\\"\\\\ne.g. an upper boundary condition on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\\\"\\n print \\\"\\\\ne.g. an upper boundary condition on f as ASBORBING must have the upper boundary condition on phi as BIAS\\\\n\\\"\\n\\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\\n\\n elif BC['phi'][var]['type'] == 'SELF-CONSISTENT_BIAS':\\n BC['phi'][var]['type'] = 'LNBC_UDBC'\\n\\n # Neumann condition, dphi = -sigma_lower, translates to phi_BC[0] = -6 var.width * sigma_lower (see https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb for details)\\n #phi_BC[var][0] = - 6 * var.width * sim_params['sigma'][var]['lower'], changes with time step\\n # Dirichlet condition, phi = BIAS value\\n phi_BC[var][-1] = float(Bias[var]['upper'])\\n\\n # check upper boundary\\n if BC['f'][var]['upper'] == 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n pass\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi for variable %s: %s\\\\n\\\" % (var, BC['phi'][var]['upper'].upper())\\n print \\\"upper boundary condition on f for variable %s: %s\\\\n\\\\n\\\" % (var, BC['f'][var]['upper'].upper())\\n print \\\"\\\\ne.g. an upper boundary condition set on phi as BIAS must have the upper boundary condition on f as ABSORBING\\\\n\\\"\\n\\n raise InputError('An upper boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\\n\\n # check lower boundary\\n if BC['f'][var]['lower'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\\n sigma[var]['lower'] = 0 # initialize to zero charge at time zero\\n sigma_n[var]['lower'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi: %s\\\" % BC['phi'][var]['lower'].upper()\\n print \\\"lower boundary condition on f: %s\\\\n\\\" % BC['f'][var]['lower'].upper()\\n print \\\"\\\\ne.g. an lower boundary condition set on phi as SELF-CONSISTENT must have the lower boundary condition on f as COLLECTOR\\\"\\n print \\\"e.g. an lower boundary condition set on f as ABSORBING must have the lower boundary condition on phi as BIAS\\\"\\n print \\\"e.g. an lower boundary condition set on f as PERIODIC requires the upper boundary on f to be PERIODIC as well as both lower and upper boundary conditions on phi to be set to PERIODIC\\\\n\\\"\\n raise InputError('A lower boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector if self-consistent boundary potentials are desired). Equivalently, phi is not compatible with f (e.g. if periodic boundaries on f were desired, the potential must also be periodic)')\\n\\n elif BC['phi'][var]['type'] == 'SYMMETRIC_BIAS' or BC['phi'][var]['type'] == 'SYMMETRY_BIAS':\\n BC['phi'][var]['type'] = 'LNBC_UDBC'\\n\\n # Neumann condition, dphi = 0 for symmetry\\n phi_BC[var][0] = 0.\\n # Dirichlet condition, phi = BIAS value\\n phi_BC[var][-1] = float(Bias[var]['upper'])\\n\\n if BC['f'][var]['upper'] != 'absorbing': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi: %s\\\" % BC['phi'][var]['upper'].upper()\\n print \\\"upper boundary condition on f: %s\\\\n\\\\n\\\" % BC['f'][var]['upper'].upper()\\n print \\\"\\\\ne.g. an upper boundary condition set on phi as BIAS must have the upper boundary condition on f as ABSORBING\\\\n \\\"\\n raise InputError('An upper boundary condition on phi was specified as BIAS; however, the corresponding boundary condition on f is not compatible (must be set to absorbing or equivalent synonym)')\\n\\n\\n elif BC['phi'][var]['type'] == 'SYMMETRIC_SELF-CONSISTENT' or BC['phi'][var]['type'] == 'SYMMETRY_SELF-CONSISTENT':\\n BC['phi'][var]['type'] = 'LDBC_LNBC'\\n\\n # We default to a LDBC_LNBC solver, both boundary conditions on left edge, entries 0 (Dirichlet) and 1 (Neumann)\\n # cf. DECSKS-04 notebook for more details:\\n #\\n # https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb\\n #\\n # Dirichlet condition, set reference potential phi = 0\\n phi_BC[var][0] = 0. # reference potential set to zero\\n # Neumann condition, dphi = 0 for symmetry\\n phi_BC[var][1] = 0.\\n\\n\\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\\n # By virtue of the setup, the above enforcements on the lower boundary ensures this unenforced upper Neumann BC is\\n # satisfied automatically given the relationship that Neumann BCs are fixed by due to the Poisson equation\\n #\\n # see github.com/dsirajud/IPython-Notebooks/DECSKS-04 for more information (final few sections of the notebook)\\n #\\n # Thus, we do not need to actually enforce the wall potential directly in terms of the charge accumulated for this boundary; however,\\n # we initialize and track the objects here so that the data can be accessed, analyzed or otherwise plotted, should the user wish\\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi: %s\\\" % BC['phi'][var]['upper'].upper()\\n print \\\"upper boundary condition on f: %s\\\\n\\\\n\\\" % BC['f'][var]['upper'].upper()\\n print \\\"\\\\ne.g. an upper boundary condition set on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\\\\n \\\"\\n\\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\\n\\n elif BC['phi'][var]['type'] == 'SELF-CONSISTENT_SELF-CONSISTENT':\\n BC['phi'][var]['type'] = 'LDBC_LNBC'\\n\\n # We default to a LDBC_LNBC solver, both boundary conditions on left edge, entries 0 (Dirichlet) and 1 (Neumann)\\n # cf. DECSKS-04 notebook for more details:\\n #\\n # https://github.com/dsirajud/IPython-notebooks/DECSKS-04...ipynb\\n #\\n # Dirichlet condition, set reference potential phi = 0\\n phi_BC[var][0] = 0. # reference potential set to zero\\n # Neumann condition, dphi = 0 for symmetry\\n #phi_BC[var][1] = - 6 * var.width * sim_params['sigma'][var]['lower'], changes with time step\\n\\n\\n if BC['f'][var]['lower'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n # initialize wall charge densities\\n sigma[var]['lower'] = 0 # initialize to zero charge at time zero\\n sigma_n[var]['lower'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"lower boundary condition on phi on variable %s: SELF-CONSISTENT\\\" % var\\n print \\\"lower boundary condition on f on variable %s: %s\\\\n\\\\n\\\" % (var, BC['f'][var]['lower'].upper())\\n print \\\"\\\\ne.g. a lower boundary condition set on phi as SELF-CONSISTENT must have the lower boundary condition on f as COLLECTOR\\\\n \\\"\\n\\n raise InputError('A lower boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\\n\\n if BC['f'][var]['upper'] == 'collector': # all synonyms for 'absorbing' (except 'collector') have been seen by this point, and if encountered changed to 'absorbing'\\n # initialize wall charge densities, sigma for the collector (f) /self-consistent (phi) conditions\\n # By virtue of the setup, the above enforcements on the lower boundary ensures this unenforced upper Neumann BC is\\n # satisfied automatically given the relationship that Neumann BCs are fixed by due to the Poisson equation\\n #\\n # see github.com/dsirajud/IPython-Notebooks/DECSKS-04 for more information (final few sections of the notebook)\\n #\\n # Thus, we do not need to actually enforce the wall potential directly in terms of the charge accumulated for this boundary; however,\\n # we initialize and track the objects here so that the data can be accessed, analyzed or otherwise plotted, should the user wish\\n sigma[var]['upper'] = 0 # initialize to zero charge at time zero\\n sigma_n[var]['upper'] = np.zeros(Nt + 1) # this was put in at one point for plotting wall charge vs. time\\n else:\\n print \\\"\\\\nThe following boundary conditions specified in params_boundaryconditions.dat are inconsistent together:\\\\n\\\\n\\\"\\n print \\\"upper boundary condition on phi: SELF-CONSISTENT\\\"\\n print \\\"upper boundary condition on f: %s\\\\n\\\\n\\\" % BC['f'][var]['upper'].upper()\\n print \\\"\\\\ne.g an upper boundary condition set on phi as SELF-CONSISTENT must have the upper boundary condition on f as COLLECTOR\\\\n \\\"\\n\\n raise InputError('An upper boundary condition on phi was specified as SELF-CONSISTENT; however, the corresponding boundary condition on f is not compatible (must be set to collector)')\\n\\n # else: boundary conditions have already been checked for valid inputs, no invalid input will be encountered\\n\\n # --------------------------------------------------------------------------\\n # ELECTRIC POTENTIAL PHI FUNCTION HANDLE STRING and BOUNDARY CONDITION TYPE FUNCTION HANDLE STRING\\n #\\n # currently only 1D1V, only one handle needed. When this will be generalized, can make a dict objects with keys corresponding\\n # to each active configuration variable\\n #\\n # The forms of each string call their associated method per the boundary conditions specified by the user in params_boundaryconditions.dat,\\n # based on the boundary conditions specified by the user, one of the following will be created:\\n #\\n # compute_electric_potential_phi_handle[var] =\\n #\\n # DECSKS.lib.fieldsolvers.Poisson_6th_PBC\\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_UDBC\\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_UNBC\\n # DECSKS.lib.fieldsolvers.Poisson_6th_LNBC_UDBC\\n # DECSKS.lib.fieldsolvers.Poisson_6th_LDBC_LNBC\\n # DECSKS.lib.fieldsolvers.Poisson_6th_UDBC_UNBC (<-- available, but not used in any current combination of BCs)\\n #\\n #\\n # and, one of the following\\n #\\n # distribution_function_boundarycondition_handle[var]['lower'] =\\n #\\n # DECSKS.lib.boundaryconditions.absorbing_lower_boundary\\n # DECSKS.lib.boundaryconditions.collector_lower_boundary\\n # DECSKS.lib.boundaryconditions.symmetric_lower_boundary\\n #\\n # NOTE: if 'periodic' has been specified, everything is\\n # handled in the orchestrator, distribution_function_boundarycondition_orchestrator\\n # which would take on the string value = 'DECSKS.lib.boundaryconditions.periodic\\n\\n\\n distribution_function_boundarycondition_prefix = 'DECSKS.lib.boundaryconditions'\\n distribution_function_boundarycondition_handle = {}\\n for var in phasespace_vars:\\n if BC['f'][var]['type'] == 'periodic':\\n pass\\n else:\\n distribution_function_boundarycondition_handle[var] = {}\\n\\n distribution_function_boundarycondition_handle[var]['lower'] = \\\".\\\".join((distribution_function_boundarycondition_prefix, BC['f'][var]['lower']))\\n distribution_function_boundarycondition_handle[var]['lower'] = \\\"_\\\".join((distribution_function_boundarycondition_handle[var]['lower'], 'lower_boundary'))\\n\\n distribution_function_boundarycondition_handle[var]['upper'] = \\\".\\\".join((distribution_function_boundarycondition_prefix, BC['f'][var]['upper']))\\n distribution_function_boundarycondition_handle[var]['upper'] = \\\"_\\\".join((distribution_function_boundarycondition_handle[var]['upper'], 'upper_boundary'))\\n\\n\\n compute_electric_potential_phi_handle = {}\\n compute_electric_potential_phi_prefix = \\\"DECSKS.lib.fieldsolvers.Poisson_6th_\\\"\\n for var in ['x', 'y', 'z']:\\n if var in phasespace_vars:\\n if HOC[var] == 'FOURIER': # uses a Gauss law solver to find E directly, which is called by the orchestrator on the fieldsolver\\n pass\\n else: # computes the electric field E by differentiating phi in an orchestrator fieldsolver function (string handle constructed above)\\n # inside the orchestrator, a particular Poisson solver is called according with the boundary conditions indicated in params_boundaryconditions.dat\\n compute_electric_potential_phi_handle[var] = compute_electric_potential_phi_prefix + BC['phi'][var]['type']\\n else:\\n pass\\n\\n # in the future, can generalize this to multiple dimensions by making this a dict with keys ['x', 'y', 'z']\\n # currently just on 1D1V and expecting an 'x' variable to be evolved in configuration\\n\\n if 'x' not in phasespace_vars:\\n raise NotImplementedError('Current 1D1V version of DECSKS is expecting x to be the active configuration variable. Please revise the intended simulation so that x is the symbol chosen in params.dat.')\\n else:\\n if HOC['x'] == 'FOURIER': # uses a Gauss solver to find E directly\\n Poisson_6th_order_FD_solver_matrices = None\\n\\n else: # uses a Poisson solver to find phi, then differentiates to obtain E\\n Poisson_6th_order_FD_solver_matrices = assemble_Poisson_6th_order_FD_solver_matrices(Nx_active, BC)\\n\\n derivative_method = {}\\n derivative_method_prefix = 'DECSKS.lib.derivatives'\\n for var in phasespace_vars:\\n derivative_method[var] = \\\".\\\".join((derivative_method_prefix, HOC[var].lower()))\\n\\n sim_params = dict(\\n N = N, HOC = HOC,\\n derivative_method = derivative_method,\\n Nx = Nx, ax = ax, bx = bx,\\n Ny = Ny, ay = ay, by = by,\\n Nz = Nz, az = az, bz = bz,\\n Nvx = Nvx, avx = avx, bvx = bvx,\\n Nvy = Nvy, avy = avy, bvy = bvy,\\n Nvz = Nvz, avz = avz, bvz = bvz,\\n Nt = Nt, T = T,\\n phasespace_vars = phasespace_vars,\\n numdims = numdims,\\n active_dims = active_dims,\\n total_dims = total_dims,\\n density = density,\\n mu = mu,\\n split_scheme = split_scheme,\\n splitting = splitting,\\n plot_params = plot_params,\\n record_outputs = record_outputs,\\n outfiles = outfiles,\\n BC = BC, # boundary condition types on all phase space variables on distribution function f and phi\\n phi_BC = phi_BC, # dictionary containing boundary value vector for electric potential used in Poisson solve, e.g. phi_BC['x']\\n sigma = sigma,\\n sigma_n = sigma_n, # this was put in for charge history plots\\n distribution_function_boundarycondition_handle = distribution_function_boundarycondition_handle, # dictionary with keys (var in phasespace_vars), which are keys to a subdict with keys 'lower', 'upper'\\n distribution_function_boundarycondition_orchestrator_handle = distribution_function_boundarycondition_orchestrator_handle, # dictionary with keys (var in phasespace_vars)\\n compute_electric_potential_phi_handle = compute_electric_potential_phi_handle,\\n compute_electric_field_orchestrator_handle = compute_electric_field_orchestrator_handle,\\n I_alternating = I_alternating, # identity matrix with alternating signs according to row, used in computing correctors c\\n A_matrix = A_matrix, # Matrices of Bernoulli numbers for HOC\\n W = W,\\n W_dn1_LTE6 = W_dn1_LTE6,\\n Xi = Xi, # spectral differentiation operator matrix (1j*xi[i,j]) ** q\\n xi = xi, # wave number vector\\n Poisson_6th_order_FD_solver_matrices = Poisson_6th_order_FD_solver_matrices\\n )\\n\\n infile.close()\\n\\n # --------------------------------------------------------------------------\\n # Before return, broadcast notification\\n # regarding start of simulation and order of solver\\n\\n print \\\"\\\\nStarting 1D1V Vlasov-Poisson simulation\\\"\\n print \\\"\\\\nadvection solver: LTE order %d\\\" % (N+1)\\n print \\\"\\\\nwill step through %d-dimensional solution in variables: %s\\\\n\\\" % (len(phasespace_vars), phasespace_vars)\\n for var in phasespace_vars:\\n print \\\"high order correction method on %s: %s\\\" % (var, HOC[var])\\n\\n print \\\"\\\\n\\\"\\n return sim_params\",\n \"def shapefileToRaster(in_shapefile, model_raster, out_dir, name_override=None, zone_field:str = None, dtype = None, *args, **kwargs) -> str:\\n\\t# correct variable names\\n\\tshapefile_path = in_shapefile\\n\\t# get out_path\\n\\tif name_override:\\n\\t\\tout_path = os.path.join(out_dir,name_override)\\n\\telse:\\n\\t\\tin_base = os.path.splitext(os.path.basename(in_shapefile))[0]\\n\\t\\tmodel_ext = os.path.splitext(model_raster)[1]\\n\\t\\tout_path= os.path.join(out_dir,in_base+\\\"_RASTER\\\"+model_ext)\\n\\t# read file\\n\\tshp = gpd.read_file(shapefile_path)\\n\\twith rasterio.open(model_raster,'r') as rst:\\n\\t\\tmeta = rst.meta.copy()\\n\\n\\t# this is where we create a generator of geom, value pairs to use in rasterizing\\n\\tif zone_field is not None:\\n\\t\\tzone_vals = []\\n\\t\\tfor i in range(len(shp)):\\n\\t\\t\\tzone_vals.append(shp.at[i,zone_field])\\n\\t\\tzone_codes = [i for i, val in enumerate(zone_vals)]\\n\\t\\tshapes = ((geom,val) for geom, val in zip(shp.geometry,zone_codes))\\n\\telse:\\n\\t\\tshapes = ((geom,1) for geom in shp.geometry)\\n\\n\\t# set data type\\n\\tif dtype:\\n\\t\\tmeta.update(dtype=dtype)\\n\\telif zone_field:\\n\\t\\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype(zone_codes))\\n\\telse:\\n\\t\\tmeta.update(dtype=\\\"int16\\\")\\n\\n\\ttry:\\n\\t\\tout = rasterio.open(out_path, 'w+', **meta)\\n\\t# merra-2 files have a very high nodata value, beyond the range of int32.\\n\\t# This block catches the resulting ValueError and swaps in the minimum\\n\\t# allowable data type. Nice of rasterio to have such a function.\\n\\texcept ValueError:\\n\\t\\tmeta.update(dtype=rasterio.dtypes.get_minimum_dtype([meta['nodata']]))\\n\\t\\tout = rasterio.open(out_path, 'w+', **meta)\\n\\t\\tout_arr = out.read(1)\\n\\t\\tburned = features.rasterize(shapes=shapes, fill=0, out=out_arr, transform=out.transform)\\n\\t\\tout.write_band(1, burned)\\n\\tout.close()\\n\\n\\treturn out_path\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n conn = sqlite3.connect('../raw/td_V2.db')\\n git_commits = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS\\\",conn)\\n szz_fault_inducing_commits = pd.read_sql_query(\\\"SELECT * FROM szz_fault_inducing_commits\\\",conn)\\n refactoring_miner = pd.read_sql_query(\\\"SELECT * FROM refactoring_miner\\\",conn)\\n refactoring_miner = refactoring_miner[refactoring_miner[\\\"COMMIT_HASH\\\"].isin(git_commits[\\\"COMMIT_HASH\\\"])]\\n git_commits_changes = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS_CHANGES\\\", conn)\\n git_commits_changes = git_commits_changes[git_commits_changes[\\\"COMMIT_HASH\\\"].isin(refactoring_miner[\\\"COMMIT_HASH\\\"])]\\n\\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)\",\n \"def create_sample_vectors(cleaned_data_directory, out_vectors_path):\\n vectors = []\\n\\n for filename in os.listdir(cleaned_data_directory):\\n if not filename.endswith(\\\".txt\\\"):\\n continue\\n\\n path = os.path.join(cleaned_data_directory, filename)\\n f = open(path, mode='r', encoding='utf8')\\n\\n print(\\\"Processing\\\", path)\\n\\n lang = filename[:2]\\n lang_number = language_codes.index(lang)\\n\\n print(f\\\"\\\\tLanguage: {lang} ({lang_number})\\\")\\n print(\\\"\\\\tReading...\\\", end=' ')\\n\\n file_content = f.read()\\n content_length = len(file_content)\\n\\n print(\\\"done.\\\")\\n print(\\\"\\\\tExtracting vectors...\\\", end=' ')\\n\\n sample_start_index = 0\\n count = 0\\n\\n while sample_start_index + text_sample_size < content_length:\\n sample = get_sample(file_content, sample_start_index, text_sample_size)\\n input_vector = build_input_vector(sample)\\n vector = input_vector + [lang_number]\\n vectors.append(vector)\\n sample_start_index += text_sample_size\\n count += 1\\n\\n print(\\\"done.\\\")\\n print(f\\\"\\\\tExtracted {count} vectors.\\\")\\n\\n del file_content\\n\\n print(f\\\"Total {len(vectors)} vectors.\\\")\\n\\n np_vectors = np.array(vectors, dtype=np.uint16)\\n np.random.shuffle(np_vectors)\\n\\n print(f\\\"Converted to NumPy array, shape: {np_vectors.shape}.\\\")\\n\\n np.savez_compressed(out_vectors_path, data=np_vectors)\\n\\n print(f\\\"Saved to {out_vectors_path}.\\\")\",\n \"def vtt_to_srt(str_name_file: str):\\n file_contents: str = read_text_file(str_name_file)\\n str_data: str = \\\"\\\"\\n str_data = str_data + convert_content(file_contents)\\n str_name_file: str = str_name_file.replace(\\\".vtt\\\", \\\".srt\\\")\\n print(str_name_file)\\n file_create(str_name_file, str_data)\",\n \"def make_voxels_isometric(input_file):\\n import os\\n import nibabel as nb\\n from mindboggle.utils.utils import execute\\n\\n # Load image volume\\n img = nb.load(input_file)\\n lenx, leny, lenz = img.shape\\n if lenx != leny or lenx != lenz or leny != lenz:\\n\\n # Pad smaller dimensions\\n max_dim = max([lenx, leny, lenz])\\n padx = max_dim - lenx\\n pady = max_dim - leny\\n padz = max_dim - lenz\\n\\n # Save padded output\\n out_file = 'isometric_' + os.path.basename(input_file)\\n pad_dims = '{0}x{1}x{2}vox'.format(padx, pady, padz)\\n cmd = ['c3d', input_file, '-pad 0x0x0vox', pad_dims, '-o ', out_file]\\n execute(cmd)\\n\\n # Resample output\\n max_dims = '{0}x{1}x{2}'.format(max_dim, max_dim, max_dim)\\n cmd = ['c3d', out_file, '-resample', max_dims, '-o ', out_file]\\n execute(cmd)\\n\\n else:\\n out_file = input_file\\n\\n return out_file\",\n \"def drr(input_filename,output_directory,calibration_files,output_extension,threshold,min_out,max_out,default_pixel_value,rot,t,cor,n_cam,res,size,transformed_vol,verbose):\\n \\n click.echo(' inputFileName : {}'.format(input_filename))\\n click.echo(' out_directory : {}'.format(output_directory))\\n click.echo(' outputExtension : {}'.format(output_extension))\\n click.echo(' Verbose Status : {}'.format(verbose))\\n click.echo(' Pixel Size : {}'.format(res))\\n click.echo(' Output image Size : {}'.format(size))\\n click.echo(' Translation : {}'.format(t))\\n click.echo(' Rotation : {}'.format(rot))\\n click.echo(' Centre of Rotation : {}'.format(cor))\\n click.echo(' Threshold : {}'.format(threshold))\\n click.echo(' Number of Cameras : {}'.format(n_cam)) \\n click.echo(' Minimum Out : {}'.format(min_out))\\n click.echo(' Maximum Out : {}'.format(max_out))\\n click.echo(' Calibration Files : {}'.format(calibration_files)) \\n \\n if len(calibration_files) != n_cam :\\n raise Exception('Number of Calibration files', len(calibration_files),'do not correspond with the number of Cameras',n_cam)\\n \\n #%%------------------ Starting the main body of the code ---------------- \\n # -------------------- Reader -------------------------\\n InputPixelType = itk.ctype(\\\"short\\\")\\n OutputPixelType = itk.ctype(\\\"short\\\")\\n ScalarType = itk.D\\n DimensionIn = 3\\n DimensionOut = 3\\n \\n InputImageType = itk.Image[InputPixelType , DimensionIn ]\\n OutputImageType = itk.Image[OutputPixelType, DimensionOut]\\n \\n \\n ReaderType = itk.ImageFileReader[InputImageType]\\n reader = ReaderType.New()\\n reader.SetFileName(input_filename)\\n \\n try:\\n print(\\\"Reading image: \\\" + input_filename)\\n reader.Update()\\n print(\\\"Image Read Successfully\\\")\\n except ValueError: \\n print(\\\"ERROR: ExceptionObject cauth! \\\\n\\\")\\n print(ValueError)\\n sys.exit()\\n \\n inputImage = reader.GetOutput()\\n \\n if verbose :\\n print(inputImage)\\n \\n \\n #%% ------------------ Transformation \\n # This part is inevitable since the interpolator (Ray-cast) and resample Image\\n # image filter uses a Transformation -- Here we set it to identity. \\n TransformType = itk.CenteredEuler3DTransform[itk.D]\\n transform = TransformType.New()\\n \\n transform.SetRotation(numpy.deg2rad(rot[0]),numpy.deg2rad(rot[1]),numpy.deg2rad(rot[2])) # Setting the rotation of the transform\\n transform.SetTranslation(itk.Vector.D3(t)) # Setting the translation of the transform\\n transform.SetComputeZYX(True) # The order of rotation will be ZYX. \\n \\n imOrigin = inputImage.GetOrigin() # Get the origin of the image.\\n inRes = inputImage.GetSpacing() # Get the resolution of the input image.\\n inSiz = inputImage.GetBufferedRegion().GetSize() # Get the size of the input image.\\n \\n center = itk.Point.D3(imOrigin) + numpy.multiply(inRes,inSiz)/2. # Setting the center of rotation as center of 3D object + offset determined by cor. \\n \\n transform.SetCenter(center) # Setting the center of rotation. \\n \\n if verbose :\\n print(transform)\\n \\n #%% \\n for ii in range(n_cam):\\n imageCalibrationInfo = CalibrationUsingJointTrack.CalibrationTool() # Setting up the image calibration info class. \\n imageCalibrationInfo.SetCalibrationInfo(calibration_files[ii]) # Assign the information from the calibration file to the imageCalibrationInfo class. \\n \\n spaceOutput= imageCalibrationInfo.GetPixelSize() # The resolution (spacing) along x,y,z directions of output image\\n \\n imageCalibrationInfo.SetOutputImageSize(size[0],size[1],1) # Setting the size of the output image. \\n imageCalibrationInfo.SetGlobalOriginForImagePlane() # Setting the global origin of the output image. \\n \\n originOutput = imageCalibrationInfo.GetGlobalOriginForImagePlane() # Setting the output origin. \\n \\n directionOutput = imageCalibrationInfo.GetDirectionMatrix() # Direction of Image plane 3x3 matrix. \\n focalPoint = imageCalibrationInfo.GetFocalPoint() # Position of the x-ray source. \\n \\n #%% ----------------- Ray Cast Interpolator \\n # In this part the Ray Cast interpolator is defined and applied to the input\\n # image data. \\n \\n InterpolatorType = itk.RayCastInterpolateImageFunction[InputImageType,ScalarType] # Defining the interpolator type from the template. \\n interpolator = InterpolatorType.New() # Pointer to the interpolator\\n \\n interpolator.SetInputImage(inputImage) # Setting the input image data\\n interpolator.SetThreshold(threshold) # Setting the output threshold\\n interpolator.SetFocalPoint(itk.Point.D3(focalPoint)) # Setting the focal point (x-ray source location)\\n interpolator.SetTransform(transform) # Setting the transform (here identity)\\n \\n if verbose:\\n print(interpolator)\\n #%%----------------- Resample Image Filter ------------------------\\n # In this part the resample image filter to map a 3D image to 2D image plane with desired specs is designed\\n \\n FilterType = itk.ResampleImageFilter[InputImageType,OutputImageType] # Defining the resample image filter type. \\n resamplefilter = FilterType.New() # Pointer to the filter\\n resamplefilter.SetInput(inputImage) # Setting the input image data \\n resamplefilter.SetDefaultPixelValue(default_pixel_value) # Setting the default Pixel value\\n resamplefilter.SetInterpolator(interpolator) # Setting the interpolator\\n resamplefilter.SetTransform(transform) # Setting the transform\\n resamplefilter.SetSize([size[0],size[1],1]) # Setting the size of the output image. \\n resamplefilter.SetOutputSpacing(itk.Vector.D3([spaceOutput[0],spaceOutput[1],1])) # Setting the spacing(resolution) of the output image. \\n resamplefilter.SetOutputOrigin(originOutput) # Setting the output origin of the image\\n Functions.ChangeImageDirection(oldDirection=resamplefilter.GetOutputDirection(),newDirection=directionOutput,DimensionOut=3) # Setting the output direction of the image --- resamplefilter.SetImageDirection(args) was not working properly\\n \\n resamplefilter.Update() # Updating the resample image filter.\\n \\n if verbose:\\n print(resamplefilter)\\n #%%---------------- Rescaler Image Filter --------------------------\\n RescalerFilterType = itk.RescaleIntensityImageFilter[InputImageType,OutputImageType] # Defining the rescale image filter. \\n rescaler = RescalerFilterType.New() # Pointer to the rescale filter\\n rescaler.SetOutputMinimum(min_out) # Minimum output\\n rescaler.SetOutputMaximum(max_out) # Maximum output \\n rescaler.SetInput(resamplefilter.GetOutput()) # Setting the input to the image filter. \\n rescaler.Update() \\n \\n if verbose:\\n print(rescaler)\\n \\n #%% ------------------ Writer ------------------------------------\\n # The output of the resample filter can then be passed to a writer to\\n # save the DRR image to a file.\\n WriterType = itk.ImageFileWriter[OutputImageType]\\n writer = WriterType.New()\\n \\n outputPath = os.path.join(output_directory,'Cam')+str(ii+1)\\n \\n if not os.path.exists(outputPath):\\n os.mkdir(outputPath)\\n \\n if ii == 0:\\n time = datetime.datetime.now() \\n dummy = ('rx'+str(int(rot[0]))+'ry'+str(int(rot[1]))+'rz'+str(int(rot[2]))+'tx'\\n + str(int(t[0]))+'ty'+str(int(t[1]))+'tz'+str(int(t[2]))+'y'+str(time.year)+'m'+str(time.month)\\n +'d'+str(time.day)+'hr'+str(time.hour)+'m'+str(time.minute)+'s'+str(time.second)+ output_extension)\\n \\n outputName = 'Cam'+str(ii+1)+dummy\\n output_filename = str(os.path.join(outputPath,outputName))\\n \\n writer.SetFileName(output_filename)\\n # writer.SetFileName('/Volumes/Storage/Payam/Desktop/output.nii') \\n writer.SetInput(rescaler.GetOutput())\\n \\n try:\\n print(\\\"Writing image: \\\" + output_filename)\\n writer.Update()\\n print(\\\"Image Printed Successfully\\\")\\n except ValueError: \\n print(\\\"ERROR: ExceptionObject cauth! \\\\n\\\")\\n print(ValueError)\\n sys.exit()\\n \\n \\n # Writing the transformed volume\\n if transformed_vol:\\n WriterType=itk.ImageFileWriter[InputImageType]\\n writer3d=WriterType.New()\\n \\n output_filename3d = os.path.join(output_directory,'TransformedVolume'+output_extension)\\n writer3d.SetFileName(output_filename3d)\\n writer3d.SetInput(resamplefilter.GetOutput())\\n \\n try:\\n print(\\\"Writing the transformed Volume at : \\\" + output_filename3d)\\n writer.Update()\\n print(\\\"Volume Printed Successfully\\\")\\n except ValueError: \\n print(\\\"ERROR: ExceptionObject cauth! \\\\n\\\")\\n print(ValueError)\\n sys.exit()\",\n \"def vpsc_in(\\r\\n ## # elem, ph and vol fraction of phases.\\r\\n ## and ishape for flag for morphology updates\\r\\n nelem=1, nph=1, wph =[1.0, 0.0], ishape=[0,],\\r\\n\\r\\n #framgmentation options and critical value limit \\r\\n # in distortion of the inclusion \\r\\n # as well as ellipsoid description: orientation and \\r\\n # principal lengths of'em\\r\\n fragmentn = [0,0], crit = [25,25],\\r\\n ellipangl = [[0., 0., 0.],[0.,0.,0.]],\\r\\n ellipaxes = [[1., 1., 1.],[1.,1.,1.]],\\r\\n \\r\\n # vpsc7.in file name (fixed)\\r\\n # texture, singlecrystal and shape file names in list format\\r\\n vin_name = 'VPSC7.in',\\r\\n tfile= ['texture/500.tex',],\\r\\n sxfile=['sx/hijhiihb.sx',],\\r\\n fileaxes=['shape1.100', 'shape1.100'],\\r\\n\\r\\n ### precision settings for convergence prodcedure\\r\\n ##\\r\\n err=0.001, errd=0.001, errm=0.001, errso=0.01,\\r\\n nmxiter=100, exiter=100, initer=100,\\r\\n irsvar=0, xrsini=2, xrsfin=10, xrstep=2,\\r\\n\\r\\n ### post-mortem flags, texture component calc. flag.\\r\\n ## \\r\\n irecover = 0, isave=0, icubcomp = 0, nwrite = 0, \\r\\n\\r\\n ## Single crystal hardening rule (-2: dislocation, 0:Voce, 1: MTS, 2: composite)\\r\\n ihardlaw = 0, \\r\\n\\r\\n ## fluctuation flag\\r\\n iflu = 0,\\r\\n\\r\\n ## rate insensitive, interaction :(FC,aff,2:sec,3:neff,4:tan,5:SO)\\r\\n ## update flags for orient, grain shape, hardening, itran(youngung)\\r\\n ## nneigh (0 for no neighbors, 1 for pairs, etc.)\\r\\n iratesens = 0, interaction = 3, iupdate = [1,1,1,0], nneigh = 0, \\r\\n\\r\\n ## proce number and processes\\r\\n npro = None,\\r\\n prcs = [], #processes\\r\\n \\r\\n ishow=False\\r\\n ):\\r\\n try: os\\r\\n except: import os\\r\\n else: pass\\r\\n cwd = os.getcwd()\\r\\n\\r\\n # READ EXISITING 'vpsc7.in'\\r\\n if os.path.isfile(vin_name): #if there already exists the file!\\r\\n f_vin = file(vin_name, 'r')\\r\\n lines = f_vin.read()\\r\\n lines = lines.split('\\\\n')\\r\\n f_temp = file('%s.tmp'%vin_name, 'w')\\r\\n for i in lines:\\r\\n f_temp.writelines(i)\\r\\n f_temp.writelines('\\\\n')\\r\\n pass\\r\\n f_temp.close()\\r\\n f_vin.close()\\r\\n \\\"\\\"\\\"\\r\\n # DEL EXISITING 'vpsc7.in'\\r\\n f_vin.close()\\r\\n os.remove(vin_name)\\r\\n \\\"\\\"\\\"\\r\\n # MAKE NEW 'vpsc7.in'\\r\\n f_vin = file(vin_name, 'w')\\r\\n f_vin.writelines('%i nelem\\\\n'%(nelem))\\r\\n f_vin.writelines('%i nph\\\\n'%(nph))\\r\\n for i in range(nph):\\r\\n f_vin.writelines('%f '%(wph[i]))\\r\\n f_vin.writelines(' wph[i]\\\\n')\\r\\n for i in range(nph): \\r\\n f_vin.writelines('*** INFORMATION ABOUT PHASE #%i\\\\n'%(i+1))\\r\\n f_vin.writelines('%i %i %i'%(ishape[i], fragmentn[i], crit[i]))\\r\\n f_vin.writelines(' ishape, fragmentn, crit aspect\\\\n')\\r\\n f_vin.writelines('%f %f %f'%(ellipaxes[i][0], \\r\\n ellipaxes[i][1], \\r\\n ellipaxes[i][2]))\\r\\n f_vin.writelines(' initial ellipsoid ratios (dummy if ishape=4)\\\\n')\\r\\n f_vin.writelines('%f %f %f'%(ellipangl[i][0], \\r\\n ellipangl[i][1], \\r\\n ellipangl[i][2]))\\r\\n f_vin.writelines(' init Eul ang ellips axes(dummy if ishape=3,4)\\\\n')\\r\\n # WRITES TEXTURE FILE\\r\\n f_vin.writelines('----------- filetext\\\\n')\\r\\n f_vin.writelines(tfile[i]+'\\\\n')\\r\\n f_vin.writelines('----------- sxfile\\\\n')\\r\\n # WRITES SINGLE CRYSTAL FILE\\r\\n f_vin.writelines(sxfile[i]+'\\\\n')\\r\\n # WRITES SHAPE FILE\\r\\n f_vin.writelines('----------- fileaxes (dummy if ishape=0) \\\\n')\\r\\n f_vin.writelines('%s \\\\n'%(fileaxes[i]))\\r\\n\\r\\n\\r\\n #### precision control block ###\\r\\n f_vin.writelines('*** PRECISION SETTINGS FOR')\\r\\n f_vin.writelines(' CONVERGENCE PROCEDURES (default values)\\\\n')\\r\\n f_vin.writelines('%f %f %f %f'%(err, #err\\r\\n errd, \\r\\n errm,\\r\\n errso))\\r\\n f_vin.writelines(' errs,errd,errm,errso\\\\n')\\r\\n f_vin.writelines('%i %i %i %25s'%( nmxiter, exiter, initer,' '))\\r\\n f_vin.writelines('itmax: max # of iter, external, internal and SO loops\\\\n')\\r\\n f_vin.writelines('%i %i %i %i %30s'%(irsvar, xrsini, xrsfin, xrstep,' '))\\r\\n f_vin.writelines('irsvar & xrsini, xrsfin, xrstep (dummy if irsvar = 0) \\\\n')\\r\\n f_vin.writelines(\\\"%i ibcinv (0: don't use <Bc>**-1, 1: use \\\\n\\\"%(1))\\r\\n f_vin.writelines('*** INPUT/OUTPUT SETTINGS FOR THE RUN(default is zero)\\\\n')\\r\\n f_vin.writelines('%i irecover:read grain stats from POSTMORT.IN(1) OR NOT(0)\\\\n'%(irecover))\\r\\n f_vin.writelines(\\\"%i isave: write grain states in postmor.out at step 'isave'?\\\\n\\\"%(isave))\\r\\n f_vin.writelines(\\\"%i icubcompL calcuate fcc rolling components?\\\\n\\\"%(icubcomp))\\r\\n f_vin.writelines(\\\"%i nwrite (frequency of texture downloads)\\\\n\\\"%(nwrite))\\r\\n f_vin.writelines(\\\"*** MODELING CONDITIONS FOR THE RUN \\\\n\\\")\\r\\n f_vin.writelines(\\\"%i ihardlaw(0:VOCE, 1: MTS, 2: composite grain\\\\n\\\"%(ihardlaw))\\r\\n f_vin.writelines('%1i' %(iratesens))\\r\\n f_vin.writelines(' iratesens (0:rate insensitive, 1:rate sensitive) \\\\n')\\r\\n f_vin.writelines('%1i' %(interaction))\\r\\n f_vin.writelines(' interaction (0:FC,1:affine,2:secant,3:neff=10,4:tangent,5:SO) \\\\n')\\r\\n f_vin.writelines('%1i%3i%3i%3i'%(iupdate[0],iupdate[1],iupdate[2],iupdate[3]))\\r\\n f_vin.writelines(' iupdate: update ori, grain shape, hardening, itran \\\\n')\\r\\n f_vin.writelines('%1i'%(nneigh))\\r\\n f_vin.writelines(' nneigh (0 for no neighbors, 1 for pairs, etc.)\\\\n')\\r\\n f_vin.writelines('%i'%(iflu))\\r\\n f_vin.writelines(\\\" iflu(0:don't calc, 1: calc fluctuations\\\\n\\\")\\r\\n\\r\\n f_vin.writelines(\\\"*NUMBER OF PROCESSES\\\\n\\\")\\r\\n f_vin.writelines(\\\"%i \\\\n\\\"%(npro))\\r\\n f_vin.writelines(\\\"** (0,1: loadings, 2:pcys, 3:lankf, 4: RBR,\\\")\\r\\n f_vin.writelines(\\\"5:pcys_pl, 6: pysc_pl 6d(ix,iy, nprob, ang\\\")\\r\\n f_vin.writelines(\\\"( or if negative behave like pcys))) \\\\n\\\")\\r\\n for i in range(len(prcs)):\\r\\n f_vin.writelines(prcs[i]+'\\\\n')\\r\\n \\r\\n # Closure of the file\\r\\n f_vin.close() \\r\\n # -------------------\\r\\n\\r\\n # Printing-out the just-made-vpsc7.in file\\r\\n if ishow==True:\\r\\n if os.name=='nt' or os.name=='posix':\\r\\n print \\\"****** VPSC input file ******\\\"\\r\\n print \\\"* VPSC7 input parameters \\\"\\r\\n print \\\"* has been written down to \\\"\\r\\n print \\\"* '%8s' as below\\\"%vin_name\\r\\n print \\\"*****************************\\\\n\\\"\\r\\n if os.name=='nt':\\r\\n os.system('%s %s'%('type',vin_name))\\r\\n elif os.name=='posix':\\r\\n os.system('%s %s'%('cat',vin_name))\\r\\n else: pass\",\n \"def transfer_rast_to_vect(poly_cstr, lyrname, out_field, rast_cstr, srs, method, where=None, geom_field=\\\"geometry\\\",\\n id_field=\\\"ogc_fid\\\", buffer_rad=0, restrict_to_tile=True):\\n ds = gdal.Open(rast_cstr)\\n georef = ds.GetGeoTransform()\\n raster_array = ds.ReadAsArray()\\n img_shape = (ds.RasterYSize, ds.RasterXSize)\\n LOG.info(\\\"Done reading raster, shape is: %s\\\", img_shape)\\n ctx = {\\n 'lyrname': lyrname,\\n 'out_field': out_field,\\n 'where': where,\\n 'geom_field': geom_field,\\n 'id_field': id_field,\\n \\\"srs\\\": srs\\n }\\n if buffer_rad:\\n ctx['geom_field'] = 'st_buffer({}, {})'.format(geom_field, buffer_rad)\\n layer_sql = \\\"\\\"\\\"select {geom_field}, {out_field}, {id_field} as the_id from {lyrname}\\\"\\\"\\\".format(**ctx)\\n if restrict_to_tile:\\n # Weird geoms could be skipped by this, so add as an optione\\n layer_sql += \\\" where st_intersects({geom_field}, st_geomfromtext(WKT_EXT, {srs}))\\\".format(**ctx)\\n\\n if where:\\n if restrict_to_tile:\\n layer_sql += \\\" and \\\" + where\\n else:\\n layer_sql += \\\" where \\\" + where\\n LOG.info(\\\"Layersql: %s\\\", layer_sql)\\n extent = get_extent(georef, img_shape)\\n LOG.info(\\\"Extent: %s\\\", extent)\\n vec_ds, lyr = open(poly_cstr, layersql=layer_sql, extent=extent, open_for_update=True)\\n mask = just_burn_layer(lyr, georef, img_shape, attr='the_id', dtype=np.int32, all_touched=False)\\n LOG.info(\\\"Done burning - setting attr in %d features\\\", lyr.GetFeatureCount())\\n LOG.debug(\\\"%s\\\", np.unique(mask))\\n n_ok = 0\\n for n, feat in enumerate(lyr):\\n if n % 100 == 0:\\n LOG.info(\\\"Done: %d, ok: %d\\\", n, n_ok)\\n daid = feat['the_id']\\n ctx['the_id'] = daid\\n area = feat.GetGeometryRef().GetArea()\\n I, J = np.where(mask == daid)\\n # At least 30% covered if already set - todo: provide this as argument\\n if I.size > 0 and (feat[out_field] is None or I.size * (georef[1] ** 2) > area * 0.3):\\n is_ok, val = method(raster_array, I, J)\\n if is_ok:\\n n_ok += 1\\n ctx['_value_'] = val\\n updatesql = \\\"update {lyrname} set {out_field}={_value_} where {id_field}={the_id}\\\".format(**ctx)\\n LOG.debug(\\\"Executing: %s\\\", updatesql)\\n vec_ds.ExecuteSQL(updatesql)\\n else:\\n LOG.debug(\\\"Nothing found for %s - mask size: %s, valid: %s, area: %s\\\",\\n daid, I.size, feat.GetGeometryRef().IsValid(), area)\",\n \"def readGR3File(inputFilename):\\n print 'Reading ' + inputFilename + ' ...'\\n infile = open(inputFilename, 'r')\\n description = infile.readline().strip() # remove leading/trailing whitespace\\n tmpStr = infile.readline()\\n nTriangles, nNodes = (int(s) for s in tmpStr.split())\\n print ' nTriangles={0:d} nNodes={1:d}'.format(nTriangles, nNodes)\\n\\n # nodes\\n nodeArray = readNodeBlock(infile, nNodes)\\n nodenum = np.array(nodeArray[:, 0].flatten(), dtype=int)\\n nodexyz = np.zeros((nNodes, 3))\\n nodexyz[:, :2] = nodeArray[:, 1:3]\\n nodalValues = nodeArray[:, 3]\\n\\n print ' Nodal values min={0:g} max={1:g}'.format(min(nodalValues), max(nodalValues))\\n\\n # triangular elements\\n triArray = readElemBlock(infile, nTriangles)\\n\\n trinum = triArray[:, 0].flatten()\\n tritype = triArray[0, 1]\\n trinodes = triArray[:, -3:] - 1 # three last columns, 0-based indexing\\n #triangles = meshElements(trinodes,trinum,tritype)\\n\\n x = nodexyz[:, 0]\\n y = nodexyz[:, 1]\\n\\n tmpStr = infile.readline()\\n boundaries = []\\n if len(tmpStr) > 0:\\n # boundary information, if not end of file\\n nOpenBndSegments = int(tmpStr.split()[0])\\n nOpenBndNodesTot = int(infile.readline().split()[0])\\n print ' nOpenBndSegments={0:d} nOpenBndNodesTot={1:d}'.format(nOpenBndSegments, nOpenBndNodesTot)\\n for iBnd in range(nOpenBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n tag = bndHeader[-1]\\n if tag.isdigit():\\n tag = 'open' + tag\\n print ' open bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary('open', tag, nodes))\\n nLandBndSegments = int(infile.readline().split()[0])\\n nLandBndNodesTot = int(infile.readline().split()[0])\\n landBndTags = range(\\n nOpenBndSegments + 1,\\n nOpenBndSegments + nLandBndSegments + 1)\\n print ' nLandBndSegments={0:d} nLandBndNodesTot={1:d}'.format(nLandBndSegments, nLandBndNodesTot)\\n for iBnd in range(nLandBndSegments):\\n bndHeader = infile.readline().split()\\n nBndNodes = int(bndHeader[0])\\n try:\\n landType = int(bndHeader[1])\\n except:\\n print \\\"\\\"\\\"Land boundary type missing in gr3 file. Add 0/1 (land/island) after number of nodes in each land boudary, e.g.\\n 1002 = Total number of closed boundary nodes\\n 501 0 = Number of nodes in closed boundary 1\\\"\\\"\\\"\\n raise Exception(\\n 'Could not parse land boundary type (0/1 - land/island)\\\\n')\\n landType = 'island' if landType == 1 else 'land'\\n tag = landType + bndHeader[-1]\\n print ' land bnd {0:d} {1:s}: {2:d} nodes'.format(iBnd + 1, tag, nBndNodes)\\n tmpList = []\\n for iN in range(nBndNodes):\\n tmpList.append(int(infile.readline()))\\n #tmpList = fromfile(infile,dtype=int,count=nBndNodes,sep=' ')\\n nodes = np.array(tmpList, dtype=int) - 1\\n boundaries.append(meshContainer.meshBoundary(landType, tag, nodes))\\n\\n infile.close()\\n\\n # for better interpolation, round coordinates to 1e-4\\n nDig = 4\\n x = np.round(x, nDig)\\n y = np.round(y, nDig)\\n\\n return x, y, nodalValues, trinodes, boundaries, description\",\n \"def mrtrix_mesh2vox(surface_path, template_path, temp_dir, output_prefix):\\n # Adapt affine translation using metadata\\n template = nib.load(template_path)\\n _, _, meta = read_geometry(surface_path, read_metadata=True)\\n\\n template = nib.as_closest_canonical(template)\\n affine = template.affine.copy()\\n affine[:-1, -1] = template.affine[:-1, -1] - meta['cras']\\n\\n new_template = nib.Nifti1Image(template.dataobj, affine)\\n new_template_path = temp_dir / 'template.mgz'\\n nib.save(new_template, new_template_path)\\n\\n # Reconstruct volume from mesh\\n subprocess.run(['mesh2voxel', surface_path, new_template_path, temp_dir / f'{output_prefix}_output.mgz'])\\n\\n # Save the reconstructed volume with the right affine\\n output = nib.load(temp_dir / f'{output_prefix}_output.mgz')\\n new_output = nib.Nifti1Image(output.dataobj, template.affine)\\n # nib.save(new_output, output_path)\\n\\n return new_output\",\n \"async def transform(self, file):\\n\\t\\tpass\",\n \"def load_rd_uv(in_file):\\n uv_data = np.load(in_file)\\n return uv_data['ucs'], uv_data['vcs']\",\n \"def inputfile(filename):\\n infile = open(filename, 'r')\\n lines = infile.readlines()\\n\\n # --------------------------------------------------------------------------\\n # Domain specifications\\n\\n Nx = eval(lines[15][lines[15].find('=')+1:].strip())\\n ax = eval(lines[16][lines[16].find('=')+1:].strip())\\n bx = eval(lines[17][lines[17].find('=')+1:].strip())\\n\\n Ny = eval(lines[21][lines[21].find('=')+1:].strip())\\n ay = eval(lines[22][lines[22].find('=')+1:].strip())\\n by = eval(lines[23][lines[23].find('=')+1:].strip())\\n\\n Nz = eval(lines[27][lines[27].find('=')+1:].strip())\\n az = eval(lines[28][lines[28].find('=')+1:].strip())\\n bz = eval(lines[29][lines[29].find('=')+1:].strip())\\n\\n Nvx = eval(lines[33][lines[33].find('=')+1:].strip())\\n avx = eval(lines[34][lines[34].find('=')+1:].strip())\\n bvx = eval(lines[35][lines[35].find('=')+1:].strip())\\n\\n Nvy = eval(lines[39][lines[39].find('=')+1:].strip())\\n avy = eval(lines[40][lines[40].find('=')+1:].strip())\\n bvy = eval(lines[41][lines[41].find('=')+1:].strip())\\n\\n Nvz = eval(lines[45][lines[45].find('=')+1:].strip())\\n avz = eval(lines[46][lines[46].find('=')+1:].strip())\\n bvz = eval(lines[47][lines[47].find('=')+1:].strip())\\n\\n Nt = eval(lines[51][lines[51].find('=')+1:].strip())\\n T = eval(lines[52][lines[52].find('=')+1:].strip())\\n\\n N = eval(lines[58][lines[58].find('=')+1:].strip())\\n\\n # --------------------------------------------------------------------------\\n # Broadcast notification regarding start of simulation and order of solver\\n\\n print \\\"\\\\nStarting 1D1V Vlasov-Poisson simulation\\\"\\n print \\\"\\\\nadvection solver: LTE order %d\\\" % (N+1)\\n\\n # --------------------------------------------------------------------------\\n # Boundary conditions\\n\\n # stored as a dictionary of dictionaries, access as\\n # BC['z']['upper'] and BC['z']['lower'] for z = {x, y, ...}\\n\\n BC = {}\\n # main dictionary with key/values {'x' : {'lower' : value, 'upper : value},\\n # {'y' : {'lower' : value, 'upper : value},\\n # {'z' : {'lower' : value, 'upper : value},\\n # {'vx' : {'lower' : value, 'upper : value},\\n # {'vy' : {'lower' : value, 'upper : value},\\n # {'vz' : {'lower' : value, 'upper : value},\\n\\n\\n # subdictionaries with key/values {'lower' : BC_value, and 'upper' : BC_value}\\n BC['x'] = {}\\n BC['x']['lower'] = lines[18][lines[18].find('=')+1:].strip()\\n BC['x']['upper'] = lines[19][lines[19].find('=')+1:].strip()\\n\\n BC['y'] = {}\\n BC['y']['lower'] = lines[24][lines[24].find('=')+1:].strip()\\n BC['y']['upper'] = lines[25][lines[25].find('=')+1:].strip()\\n\\n BC['z'] = {}\\n BC['z']['lower'] = lines[30][lines[30].find('=')+1:].strip()\\n BC['z']['upper'] = lines[31][lines[31].find('=')+1:].strip()\\n\\n BC['vx'] = {}\\n BC['vx']['lower'] = lines[36][lines[36].find('=')+1:].strip()\\n BC['vx']['upper'] = lines[37][lines[37].find('=')+1:].strip()\\n\\n BC['vy'] = {}\\n BC['vy']['lower'] = lines[42][lines[42].find('=')+1:].strip()\\n BC['vy']['upper'] = lines[43][lines[43].find('=')+1:].strip()\\n\\n BC['vz'] = {}\\n BC['vz']['lower'] = lines[48][lines[48].find('=')+1:].strip()\\n BC['vz']['upper'] = lines[49][lines[49].find('=')+1:].strip()\\n\\n # --------------------------------------------------------------------------\\n # Store number of active gridpoints for every phase space variable\\n #\\n # Note: for periodic BCs: Nz_active = Nz - 1\\n # for all other BCs: Nz_active = Nz\\n\\n # TODO this is acknowledged as being redundant, but more specific than the lists\\n # active_dims vs. total_dims\\n if BC['x']['lower'] == 'periodic' and BC['x']['upper'] == 'periodic' and Nx is not None:\\n Nx_active = Nx - 1\\n else:\\n Nx_active = Nx\\n\\n if BC['y']['lower'] == 'periodic' and BC['y']['upper'] == 'periodic' and Ny is not None:\\n Ny_active = Ny - 1\\n else:\\n Ny_active = Ny\\n\\n if BC['z']['lower'] == 'periodic' and BC['z']['upper'] == 'periodic' and Nz is not None:\\n Nz_active = Nz - 1\\n else:\\n Nz_active = Nz\\n\\n if BC['vx']['lower'] == 'periodic' and BC['vx']['upper'] == 'periodic' and Nvx is not None:\\n Nvx_active = Nvx - 1\\n else:\\n Nvx_active = Nvx\\n\\n if BC['vy']['lower'] == 'periodic' and BC['vy']['upper'] == 'periodic' and Nvy is not None:\\n Nvy_active = Nvy - 1\\n else:\\n Nvy_active = Nvy\\n\\n if BC['vz']['lower'] == 'periodic' and BC['vz']['upper'] == 'periodic' and Nvz is not None:\\n Nvz_active = Nvz - 1\\n else:\\n Nvz_active = Nvz\\n\\n # --------------------------------------------------------------------------\\n # High order correction (HOC) method applied to each phase space variable\\n\\n HOC = {}\\n HOC['x'] = lines[68][lines[68].find(':')+1:].strip().upper()\\n HOC['y'] = lines[69][lines[69].find(':')+1:].strip().upper()\\n HOC['z'] = lines[70][lines[70].find(':')+1:].strip().upper()\\n\\n HOC['vx'] = lines[72][lines[72].find(':')+1:].strip().upper()\\n HOC['vy'] = lines[73][lines[73].find(':')+1:].strip().upper()\\n HOC['vz'] = lines[74][lines[74].find(':')+1:].strip().upper()\\n\\n\\n # list of phase space variables used, in etc/params.dat must set unused\\n # vars to have Nz as None, z = x, vx, y, ...\\n # e.g. in 1D1V, phasespace_vars = ['x', 'vx']\\n phasespace_vars = []\\n if Nx is not None:\\n phasespace_vars.append('x')\\n if Ny is not None:\\n phasespace_vars.append('y')\\n if Nz is not None:\\n phasespace_vars.append('z')\\n if Nvx is not None:\\n phasespace_vars.append('vx')\\n if Nvy is not None:\\n phasespace_vars.append('vy')\\n if Nvz is not None:\\n phasespace_vars.append('vz')\\n\\n print \\\"will step through %d-dimensional solution in variables: %s\\\" % (len(phasespace_vars), phasespace_vars)\\n for var in phasespace_vars:\\n print \\\"high order correction method on %s: %s\\\" % (var, HOC[var])\\n\\n # for periodic BCs, the number of active dims is not equal to the\\n # total number of dims, we evolve \\\"Nz-1\\\" gridpoints, then assign\\n # the Nth point by periodicity as equal to the 0th point. Hence,\\n # a distinction is needed between active dims and total dims\\n # where we note they are identical in all cases but periodic BCs.\\n\\n # TODO as mentioned above, this is now a redundant set of total grid points\\n # as compared to active grid points. At some point, need to trace where\\n # this is actually used in the code and replace or remove it\\n\\n # initialize lists\\n total_dims = []\\n active_dims = []\\n\\n # strip all whitespace in each entry\\n for var in phasespace_vars:\\n total_dims.append(eval('N' + var))\\n\\n if ( (BC[var]['lower'] == 'periodic') and (BC[var]['upper'] == 'periodic') ):\\n active_dims.append(eval('N' + var) - 1)\\n else:\\n active_dims.append(eval('N' + var))\\n\\n # TODO this is a misleading name, should be numvars\\n numdims = len(phasespace_vars)\\n\\n # --------------------------------------------------------------------------\\n # Initial density specification\\n #\\n # the following establishes a difference between the number of densities\\n # specified in etc/params.dat. Should there be two, the solver is a two\\n # species Vlasov solver. If only one, then a cold background will be\\n # automatically computed (TODO)\\n\\n\\n densities_list = lines[79][lines[79].find(':')+1:].strip().split(', ')\\n for i in range(len(densities_list)):\\n densities_list[i] = densities_list[i].lower()\\n\\n if len(densities_list) == 2: # if two species return dictionary of strings\\n density = {}\\n density['electrons'] = densities_list[0]\\n density['electrons'] = density['electrons'].lower()\\n density['ions'] = densities_list[1]\\n density['ions'] = density['ions'].lower()\\n print \\\"\\\\ntwo species simulation with initial densities:\\\\n\\\"\\n print \\\"electrons: %s\\\" % density['electrons']\\n print \\\"ions: %s\\\\n\\\" % density['ions']\\n\\n elif len(densities_list) == 1: # if one species return a string\\n density = densities_list[0]\\n print \\\"one species (electron) simulation with initial density: %s\\\" % density\\n # TODO compute cold background, store both this and the above\\n # in a common dictionary as above for two species.\\n\\n # --------------------------------------------------------------------------\\n # Split scheme specification\\n\\n split_scheme = lines[98][lines[98].find('=')+1:].strip()\\n split_scheme = split_scheme.upper()\\n print \\\"split scheme: %s\\\\n\\\\n\\\" % split_scheme\\n\\n # filepath to splitting coefficient tables\\n filename = lines[99][lines[99].find(':')+1:].strip()\\n filepath = './etc/' + filename\\n\\n # get splitting coefficients for chosen scheme\\n if split_scheme is not None:\\n splitting = splitting_coefficients(filepath, split_scheme)\\n else:\\n splitting = None\\n\\n # --------------------------------------------------------------------------\\n # Plot window specification (used in lib.plots.Setup)\\n\\n xmin = eval(lines[113][lines[113].find('=')+1:].strip())\\n xmax = eval(lines[114][lines[114].find('=')+1:].strip())\\n ymin = eval(lines[116][lines[116].find('=')+1:].strip())\\n ymax = eval(lines[117][lines[117].find('=')+1:].strip())\\n\\n plot_params = dict(xmin = xmin, xmax = xmax,\\n ymin = ymin, ymax = ymax)\\n\\n record_outputs = lines[120][lines[120].find(':')+1:].strip()\\n record_outputs = record_outputs.lower()\\n\\n if record_outputs == 'yes':\\n # output filepath setup\\n filename = lines[121][lines[121].find(':')+1:].strip()\\n filepath = './etc/' + filename\\n outfiles = output_files(filepath) # dictionary of opened files\\n else:\\n outfiles = None\\n\\n # --------------------------------------------------------------------------\\n # MISC STORAGE (e.g. stored matrices that are used routinely)\\n #\\n # dictionaries and matrices relevant for high order correction applications\\n #\\n # Constructing the finite different weight matricies, W.\\n #-------------------------------------------------------\\n # requires: (dict) FD_schemes\\n #\\n # Note: FD_schemes is only needed to construct W. W is what is used in\\n # the simulation. Hence, the building routine for FD_schemes\\n # is not optimized, since it happens before the simulation starts\\n # and hence is not a source of repeated computational cost.\\n #\\n # FD_schemes is a dictionary containing the families of every order derivative\\n # needed for the indicated global error N in etc/params.dat, i.e. all schemes\\n # of various degrees of asymmetry and handedness. For large N, this can be a\\n # very large dictionary, see the function routine read_FD_schemes to see all\\n # that gets stored inside. It is used to construct the difference coefficient\\n # matrices W (for applying high order corrections). The other scheme\\n # FD_scheme_dn1 is used to construct the matrix W_dn1 which is a difference\\n # coefficient matrix for the first derivative (dn = 1) at LTE = 6, and used\\n # in the finite difference 6th order Poisson solver (PBCs currently only).\\n #---------------------------------------------------------------------------\\n #\\n # initialize all dictionaries whose keys correspond to phase space vars\\n # and whose values contain the relevant ndarrays\\n\\n Xi = {}\\n xi = {}\\n W = {}\\n\\n # top level check: if any var has FD corrections, store FD_schemes and init W\\n if 'FD' in HOC.values():\\n # store finite difference schemes\\n FD_schemes = read_FD_schemes(N)\\n\\n if HOC['x'] == 'FD':\\n # first derivative with LTE = 6, used to find dphi = -E after phi is\\n # found from a 6th order Poisson solve\\n FD_scheme_dn1 = read_FD_scheme(1,6)\\n W_dn1_LTE6 = assemble_finite_difference_weight_matrix_const_dn_const_LTE(Nx_active,\\n FD_scheme_dn1,\\n dn = 1,\\n LTE = 6\\n )\\n\\n # TODO if more than one or different spatial dimension\\n # TODO than 'x' with FD corrections need to permit access to this\\n # TODO dictionary W_dn1_LTE6 and have it be assembled.\\n\\n else:\\n # else, Fourier Gauss solver is used, no need for this matrix\\n W_dn1_LTE6 = None\\n\\n # variable-by-variable checks: assemble consistent objects needed\\n # for the specified means of HOC from etc/params.dat\\n\\n # Note: the following is organized with the expectation that\\n # higher dimensional implementations would be stepped through\\n # as sets of 2D advection problems, always paired as z and vz\\n # i.e. not as mixed stepthroughs with x paired with vy for example\\n\\n for var in phasespace_vars:\\n if HOC[var] == 'FD':\\n W[var] = assemble_finite_difference_weight_matrix(\\n eval('N' + var + '_active'),\\n N,\\n FD_schemes\\n )\\n elif HOC[var] == 'FOURIER':\\n # ensure the correct number of grid points\\n # is passed for the generalized velocity Nvz_active\\n # for x,y,z, 'vz' = vx, vy, vz\\n # for vx, vy, vz, 'vz' = ax, ay, az, which have\\n # the same number of dims as x, y, z, respectively\\n\\n if var[0] == 'v':\\n Nvz_active = eval('N' + var[1] + '_active')\\n else:\\n Nvz_active = eval('Nv' + var + '_active')\\n\\n Xi, xi = assemble_spectral_derivative_operator(Xi, xi,\\n var,\\n eval('a' + var),\\n eval('b' + var),\\n eval('N' + var),\\n eval('N' + var + '_active'),\\n Nvz_active,\\n N)\\n\\n # ---------------------------------------------------------------------\\n # \\\"Alternating\\\" identity matrix\\n\\n\\n # in lib.HOC.correctors, require an N x N diagonal matrix with entries\\n # (-1)^i, where i is the row number, for details see on github\\n #\\n # dsirajud/IPython-notebooks/\\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\\n #\\n # section \\\"2D casting of correction coefficients c (vector) -> c (tensor)\\\"\\n\\n I_alternating = np.diag( (-np.ones(N)) ** np.arange(N) )\\n\\n # obtain Bernoulli numbers (note: list only 23 numbers are listed)\\n # for a correction up to global error order N, N-1 Bernoulli numbers\\n # are needed. If higher than global error order 22 is desired, additional\\n # Bernoulli numbes need to be entered in\\n #\\n # etc/Table_of_Bernoulli_numbers.dat\\n #\\n\\n # Store Bernoulli numbers from dat file etc/Table_of_Bernoulli_numbers.dat\\n filename = 'Table_of_Bernoulli_numbers.dat'\\n filepath = './etc/' + filename\\n Bernoulli_numbers = Bernoulli(filepath)\\n\\n # \\\"A\\\" matrices for Bernoulli number storage and matrix HOC application\\n # in lib.HOC.Beta_matrix, see notebook on github at\\n # dsirajud/IPython-notebooks/\\n # DECSKS-09 -- array-based implementation recast -- part 1.ipynb\\n A_pos, A_neg = np.zeros([N,N]), np.zeros([N,N])\\n for i in range(N):\\n for j in range(i+1):\\n A_pos[i,j] = Bernoulli_numbers[i-j] / scipy.misc.factorial(i-j)\\n if (i - j) == 1:\\n A_neg[i,j] = -A_pos[i,j]\\n else:\\n A_neg[i,j] = A_pos[i,j]\\n\\n A_matrix = {}\\n # dictionary container\\n # allow dictionary access to relevant matrix of Bernoulli numbers\\n # by operating with str(int(np.sign(CFL.frac)))\\n\\n A_matrix['1'] = A_pos\\n A_matrix['0'] = A_pos\\n A_matrix['-1'] = A_neg\\n\\n\\n # ---------------------------------------------------------------------\\n # 6th order finite difference Poisson solver for periodic BCs\\n # (stored as keys 'D' [difference matrix] and 'B' [inhomogeneity])\\n\\n Poisson_6th_order_PBC_FD_solver_matrices = assemble_Poisson_6th_order_PBC_FD_solver_matrices(Nx, BC)\\n\\n # TODO specialize right now to just be x, vx. Figure out how to generalize later with higher dimensions\\n compute_electric_field_function_handle_prefix = \\\"DECSKS.lib.fieldsolvers.compute_electric_field_\\\"\\n compute_electric_field_function_handle = \\\"\\\".join((compute_electric_field_function_handle_prefix, HOC['x'].lower()))\\n\\n derivative_method = {}\\n derivative_method_prefix = 'DECSKS.lib.derivatives'\\n for var in phasespace_vars:\\n derivative_method[var] = \\\".\\\".join((derivative_method_prefix, HOC[var].lower()))\\n\\n sim_params = dict(\\n N = N, HOC = HOC,\\n derivative_method = derivative_method,\\n Nx = Nx, ax = ax, bx = bx,\\n Ny = Ny, ay = ay, by = by,\\n Nz = Nz, az = az, bz = bz,\\n Nvx = Nvx, avx = avx, bvx = bvx,\\n Nvy = Nvy, avy = avy, bvy = bvy,\\n Nvz = Nvz, avz = avz, bvz = bvz,\\n Nt = Nt, T = T,\\n phasespace_vars = phasespace_vars,\\n numdims = numdims,\\n active_dims = active_dims,\\n total_dims = total_dims,\\n density = density,\\n split_scheme = split_scheme,\\n splitting = splitting,\\n plot_params = plot_params,\\n record_outputs = record_outputs,\\n outfiles = outfiles,\\n BC = BC, # boundary conditions on all phase space variables\\n I_alternating = I_alternating, # identity matrix with alternating signs according to row, used in computing correctors c\\n A_matrix = A_matrix, # Matrices of Bernoulli numbers for HOC\\n W = W,\\n W_dn1_LTE6 = W_dn1_LTE6,\\n Xi = Xi, # spectral differentiation operator matrix (1j*xi[i,j]) ** q\\n xi = xi, # wave number vector\\n Poisson_6th_order_PBC_FD_solver_matrices = Poisson_6th_order_PBC_FD_solver_matrices,\\n compute_electric_field_function_handle = compute_electric_field_function_handle # determines if solver is FD or fourier based\\n )\\n\\n infile.close()\\n\\n return sim_params\",\n \"def create_raster_datapackage(pk_type, path, file_flag, out_path):\\n process_source(pk_type, path, file_flag, out_path)\",\n \"def readtempfilt(MAIN_OUTPUT_FILE='photz', OUTPUT_DIRECTORY='./OUTPUT'):\\n\\n root = os.path.join(OUTPUT_DIRECTORY, MAIN_OUTPUT_FILE)\\n \\n ###### .tempfilt\\n file_path = root+'.tempfilt'\\n \\n if os.path.exists(file_path) is False:\\n raise ValueError('File, %s, not found.' %(file_path))\\n\\n with open(file_path,'rb') as f:\\n # summary data\\n s = np.fromfile(file=f,dtype=np.int32, count=4)\\n NFILT=s[0] # number of filters\\n NTEMP=s[1] # number of templates\\n NZ=s[2] # number points on the redshift grid\\n NOBJ=s[3] # number of objects\\n # (?) template SED convolved with filter transmission at each redshift\\n tempfilt = np.fromfile(file=f,dtype=np.double,count=NFILT*NTEMP*NZ).reshape((NZ,NTEMP,NFILT)).transpose()\\n # filter pivot wavelengths\\n lc = np.fromfile(file=f,dtype=np.double,count=NFILT)\\n # redshift grid\\n zgrid = np.fromfile(file=f,dtype=np.double,count=NZ)\\n # observed flux\\n fnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\\n # (?) error in observed flux\\n efnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\\n \\n tempfilt = {'NFILT':NFILT,'NTEMP':NTEMP,'NZ':NZ,'NOBJ':NOBJ,\\\\\\n 'tempfilt':tempfilt,'lc':lc,'zgrid':zgrid,'fnu':fnu,'efnu':efnu}\\n\\n return tempfilt\",\n \"def trimesh_to_vrml(mesh, output_file):\\n\\n with open(output_file, 'w') as f:\\n f.write('#VRML V2.0 utf8\\\\n')\\n f.write('Transform {\\\\n'\\n ' children [\\\\n'\\n ' Shape {\\\\n'\\n ' geometry IndexedFaceSet {\\\\n'\\n ' coord Coordinate {\\\\n'\\n ' point[\\\\n')\\n for v in mesh.vertices:\\n f.write(' {:0.6f} {:0.6f} {:0.6f},\\\\n'.format(*v.tolist()))\\n f.write(' ] # end of points\\\\n'\\n ' } # end of Coordinate\\\\n'\\n ' coordIndex [\\\\n')\\n\\n for face in mesh.faces:\\n f.write(' {:d}, {:d}, {:d}, -1,\\\\n'.format(*face.tolist()))\\n\\n f.write(' ] # end of coordIndex\\\\n'\\n ' } # end of geometry\\\\n'\\n ' appearance Appearance {\\\\n'\\n ' material Material {\\\\n'\\n ' diffuseColor 0.7 0.7 0.7\\\\n'\\n ' emissiveColor 0.05 0.05 0.05\\\\n'\\n ' specularColor 1.0 1.0 1.0\\\\n'\\n ' ambientIntensity 0.2\\\\n'\\n ' shininess 0.2\\\\n'\\n ' transparency 0.0\\\\n'\\n ' } #end of material\\\\n'\\n ' } #end of appearance\\\\n'\\n ' } # end of Shape\\\\n'\\n ' ] # end of children\\\\n'\\n '}\\\\n')\",\n \"def cal_voro(inputfile, ndim, radii, ppp = '-p', results_path = '../../analysis/voro/'): \\n if not os.path.exists(results_path):\\n os.makedirs(results_path)\\n\\n basename = os.path.splitext(os.path.basename(inputfile))[0]\\n fneighbor = open(results_path + basename + '.neighbor.dat', 'w')\\n ffacearea = open(results_path + basename + '.facearea.dat', 'w')\\n findex = open(results_path + basename + '.voroindex.dat', 'w') \\n foverall = open(results_path + basename + '.overall.dat', 'w')\\n\\n position, bounds = get_input(inputfile, ndim, radii)\\n for n in range(len(position)):\\n fileformat = '%d ' + '%.6f ' * ndim + '%.2f'\\n np.savetxt('dumpused', position[n], fmt = fileformat)\\n \\n #use box boundaries from snapshot\\n Boxbounds = bounds[n].ravel()\\n #use box boundaries from particle coordinates \\n # boundsmin = position[n][:, 1: ndim + 1].min(axis = 0) - 0.1\\n # boundsmax = position[n][:, 1: ndim + 1].max(axis = 0) + 0.1\\n # Boxbounds = (np.column_stack((boundsmin, boundsmax))).ravel()\\n\\n cmdline = 'voro++ ' + ppp + ' -r -c \\\"%i %s %v %F @%i %A @%i %s %n @%i %s %f\\\" '\\\\\\n + ('%f %f ' * ndim % tuple(Boxbounds)) + 'dumpused'\\n if n == 0: print (cmdline)\\n subprocess.run(cmdline, shell = True)\\n\\n fneighbor.write('id cn neighborlist\\\\n')\\n ffacearea.write('id cn facearealist\\\\n')\\n findex.write('id voro_index 0_to_7_faces\\\\n')\\n foverall.write('id cn volume facearea\\\\n')\\n f = open('dumpused.vol', 'r')\\n for i in range(len(position[n][:, 0])):\\n item = f.readline().split('@')\\n foverall.write(item[0] + '\\\\n')\\n findex.write(item[1] + '\\\\n')\\n fneighbor.write(item[2] + '\\\\n')\\n ffacearea.write(item[3])\\n f.close()\\n\\n os.remove('dumpused') #delete temporary files\\n os.remove('dumpused.vol')\\n fneighbor.close()\\n ffacearea.close()\\n foverall.close()\\n findex.close()\\n print ('---------- Voronoi Analysis Done ------------')\",\n \"def reprojectAndSaveNewRaster(inFilepath,outFilepath,to_EPSG):\\r\\n from osgeo import gdal\\r\\n input_raster = gdal.Open(inFilepath)\\r\\n EPSG_string = \\\"EPSG:\\\"+str(to_EPSG)\\r\\n ras = gdal.Warp(outFilepath,input_raster,dstSRS=EPSG_string)\\r\\n del ras\",\n \"def voxelization(infile_path, outfile_path, voxel_size):\\n\\n infile = laspy.file.File(infile_path, mode=\\\"rw\\\")\\n\\n # 计算每个点的voxel码\\n scaled_x = np.vectorize(int)((1 / voxel_size) * (infile.x - infile.header.min[0]))\\n scaled_y = np.vectorize(int)((1 / voxel_size) * (infile.y - infile.header.min[1]))\\n scaled_z = np.vectorize(int)((1 / voxel_size) * (infile.z - infile.header.min[2]))\\n indices = np.lexsort((scaled_z, scaled_y, scaled_x))\\n voxel_count = 0\\n point_count = 0\\n point_lengh = len(infile.x)\\n\\n # the array to store the code of the voxel, this is actually the row, columm and height number of the voxel\\n code_array = []\\n\\n # the array to store the point number in each voxel\\n points_in_one_voxel_array = []\\n\\n # the array to store the average intensity of points in a voxel\\n intensity_in_one_voxel_array = []\\n\\n while point_count < point_lengh - 1:\\n\\n # the counter of points number in one voxel\\n points_in_one_voxel_count = 1\\n intensity_in_one_voxel_count = 0\\n # loop of finding points with same code\\n while point_count < point_lengh - 1 and \\\\\\n scaled_x[indices[point_count + 1]] == scaled_x[indices[point_count]] and \\\\\\n scaled_y[indices[point_count + 1]] == scaled_y[indices[point_count]] and \\\\\\n scaled_z[indices[point_count + 1]] == scaled_z[indices[point_count]]:\\n # add a voxel index label to the point\\n infile.voxel_index[indices[point_count]] = voxel_count\\n intensity_in_one_voxel_count += infile.intensity[indices[point_count]]\\n point_count += 1\\n points_in_one_voxel_count += 1\\n\\n infile.voxel_index[indices[point_count]] = voxel_count\\n intensity_in_one_voxel_count += infile.intensity[indices[point_count]]\\n intensity_in_one_voxel_array.append(intensity_in_one_voxel_count / points_in_one_voxel_count)\\n points_in_one_voxel_array.append(points_in_one_voxel_count)\\n # save the code to an array which later will be stored in the csv file\\n code = \\\"{:0>4d}\\\".format(scaled_x[indices[point_count]]) + \\\\\\n \\\"{:0>4d}\\\".format(scaled_y[indices[point_count]]) + \\\\\\n \\\"{:0>4d}\\\".format(scaled_z[indices[point_count]])\\n code_array.append(code)\\n point_count += 1\\n voxel_count += 1\\n\\n # save the code to the csv file sequentially\\n code_array_length = len(code_array)\\n with open(outfile_path, 'wb') as csvfile:\\n writer = csv.writer(csvfile)\\n count = 0\\n while count < code_array_length:\\n writer.writerow([code_array[count], points_in_one_voxel_array[count], intensity_in_one_voxel_array[count]])\\n count += 1\",\n \"def _rasterize_vector_onto_base(\\n base_raster_path, base_vector_path, attribute_id,\\n target_raster_path, filter_string=None):\\n base_raster = gdal.OpenEx(base_raster_path, gdal.OF_RASTER)\\n raster_driver = gdal.GetDriverByName('GTiff')\\n target_raster = raster_driver.CreateCopy(target_raster_path, base_raster)\\n base_raster = None\\n\\n vector = gdal.OpenEx(base_vector_path)\\n layer = vector.GetLayer()\\n\\n if filter_string is not None:\\n layer.SetAttributeFilter(str(filter_string))\\n gdal.RasterizeLayer(\\n target_raster, [1], layer,\\n options=['ATTRIBUTE=%s' % attribute_id])\\n target_raster.FlushCache()\\n target_raster = None\\n layer = None\\n vector = None\",\n \"def compilation(inputfile):\\r\\n\\r\\n # create empty dictionary to hold DataFrames\\r\\n f = {}\\r\\n\\r\\n # generate list of relevant files\\r\\n filelist = glob.glob(inputfile)\\r\\n\\r\\n # iterate through list of relevant files\\r\\n for infile in filelist:\\r\\n # run computations using lev files\\r\\n f[getfilename(infile)] = new_trans_imp(infile)\\r\\n # concatenate all of the DataFrames in dictionary f to one DataFrame: g\\r\\n g = pd.concat(f)\\r\\n # remove multiindex and replace with index=Datetime\\r\\n g = g.reset_index()\\r\\n g = g.set_index(['DateTime'])\\r\\n # drop old indexes\\r\\n g = g.drop(['level_0'], axis=1)\\r\\n # remove duplicates based on index then sort by index\\r\\n g['ind'] = g.index\\r\\n g.drop_duplicates(subset='ind', inplace=True)\\r\\n g.drop('ind', axis=1, inplace=True)\\r\\n g = g.sort_index()\\r\\n outfile = g\\r\\n return g\",\n \"def _read_dx(self, FN):\\n if FN.endswith('.dx'):\\n F = open(FN, 'r')\\n else:\\n import gzip\\n F = gzip.open(FN, 'r')\\n\\n # Read the header\\n line = F.readline()\\n while line.find('object') == -1:\\n line = F.readline()\\n header = {}\\n header['counts'] = [int(x) for x in line.split(' ')[-3:]]\\n for name in ['origin', 'd0', 'd1', 'd2']:\\n header[name] = [float(x) for x in F.readline().split(' ')[-3:]]\\n F.readline()\\n header['npts'] = int(F.readline().split(' ')[-3])\\n\\n # Test to make sure the grid type is okay.\\n # These conditions are not absolultely essential,\\n # but they reduce the number of subtraction operations.\\n if not (header['d0'][1] == 0 and header['d0'][2] == 0\\n and header['d1'][0] == 0 and header['d1'][2] == 0\\n and header['d2'][0] == 0 and header['d2'][1] == 0):\\n raise Exception('Trilinear grid must be in original basis')\\n if not (header['d0'][0] > 0 and header['d1'][1] > 0\\n and header['d2'][2] > 0):\\n raise Exception('Trilinear grid must have positive coordinates')\\n\\n # Read the data\\n vals = np.ndarray(shape=header['npts'], dtype=float)\\n index = 0\\n while index < header['npts']:\\n line = F.readline()[:-1]\\n items = [float(item) for item in line.split()]\\n vals[index:index + len(items)] = items\\n index = index + len(items)\\n F.close()\\n\\n data = {\\n 'origin':np.array(header['origin']), \\\\\\n 'spacing':np.array([header['d0'][0],header['d1'][1],header['d2'][2]]), \\\\\\n 'counts':np.array(header['counts']), \\\\\\n 'vals':vals}\\n return data\",\n \"def UpdateGT(out_file, data, src_file, epsg = 4326, drv = 'GTiff', datatype = gdal.GDT_Float32, NoData = -999):\\n \\n #assign which driver to use for the file format - default is .tif\\n driver = gdal.GetDriverByName(drv)\\n \\n #source raster from which to use the geospatial metadata\\n src_gt = gdal.Open(src_file)\\n \\n #data shape - detects whether to produce multiple bands or not\\n if len(data.shape) == 2:\\n [cols, rows] = data.shape\\n n = 1\\n else:\\n [n, cols, rows] = data.shape\\n\\n #create the destination file\\n dst_gt = driver.Create(out_file, rows, cols, n, datatype)\\n \\n #get the geotransform from the source file\\n gt = src_gt.GetGeoTransform()\\n \\n #coordinate system in which to create the raster, in wkt form\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(epsg)\\n dest_wkt = srs.ExportToWkt()\\n \\n #set the projection in the desired reference system\\n dst_gt.SetGeoTransform(gt)\\n dst_gt.SetProjection(dest_wkt)\\n \\n #set the data\\n if n==1:\\n dst_gt.GetRasterBand(1).WriteArray(data)\\n dst_gt.GetRasterBand(1).SetNoDataValue(NoData)\\n #close and write to file\\n dst_gt.FlushCache()\\n #used if there are multiple bands\\n else:\\n for i in range(n):\\n dst_gt.GetRasterBand(i+1).WriteArray(data[i])\\n dst_gt.GetRasterBand(i+1).SetNoDataValue(NoData)\\n dst_gt.FlushCache()\",\n \"def preparehspiceidvgGEO4(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,Ach_UFCMparam,Cins_UFCMparam,W_UFCMparam,NBODYparam,NFINparam):\\n#L=Lparam Ach_UFCM=Ach_UFCMparam Cins_UFCM=Cins_UFCMparam W_UFCM=W_UFCMparam NBODY=NBODYparam NFIN=NFINparam\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Ach_UFCMparam',Ach_UFCMparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Cins_UFCMparam', Cins_UFCMparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'W_UFCMparam',W_UFCMparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)\",\n \"def preparehspiceidvgGEO1(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam):\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam)\",\n \"def preparehspiceidvgGEO1v2(wheretosimpath,templatepath,modelverilogpath,modelcardpath,vgs,vds,Lparam,HFINparam,TFIN_TOPparam,TFIN_BASEparam,EOTparam,NBODYparam,NFINparam,PHIGparam,RSHSparam,RSHDparam):\\n #make an aux copy of hspice file to simulate\\n shutil.copyfile(templatepath,wheretosimpath+'idvgaux.sp')\\n #make an aux copy of modelcard file to simulate\\n shutil.copyfile(modelcardpath,wheretosimpath+'modelcardaux.nmos')\\n\\n #update path of model and modelcard\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelverilog', modelverilogpath)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'pathmodelcard', '\\\\\\\"modelcardaux.nmos\\\\\\\"')\\n\\n #bias update\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsi', str(vgs[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsf', str(vgs[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vgsdelta', str(vgs[1]-vgs[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsi', str(vds[0]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsf', str(vds[-1]))\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'vdsdelta', str(vds[1]-vds[0]))\\n\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'Lparam', Lparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'HFINparam',HFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_TOPparam', TFIN_TOPparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'TFIN_BASEparam',TFIN_BASEparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'EOTparam', EOTparam)\\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NBODYparam',NBODYparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'NFINparam', NFINparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'PHIGparam', PHIGparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHSparam', RSHSparam) \\n sf.inplace_change(wheretosimpath+'idvgaux.sp', 'RSHDparam', RSHDparam)\",\n \"def render_vtk(file_name):\\n import vtk\\n\\n # Read the source file.\\n reader = vtk.vtkUnstructuredGridReader()\\n reader.SetFileName(file_name)\\n reader.Update() # Needed because of GetScalarRange\\n output = reader.GetOutput()\\n scalar_range = output.GetScalarRange()\\n\\n # Create the mapper that corresponds the objects of the vtk.vtk file\\n # into graphics elements\\n mapper = vtk.vtkDataSetMapper()\\n mapper.SetInputData(output)\\n mapper.SetScalarRange(scalar_range)\\n\\n # Create the Actor\\n actor = vtk.vtkActor()\\n actor.SetMapper(mapper)\\n\\n # Create the Renderer\\n renderer = vtk.vtkRenderer()\\n renderer.AddActor(actor)\\n renderer.SetBackground(1, 1, 1) # Set background to white\\n\\n # Create the RendererWindow\\n renderer_window = vtk.vtkRenderWindow()\\n renderer_window.AddRenderer(renderer)\\n\\n # Create the RendererWindowInteractor and display the vtk_file\\n interactor = vtk.vtkRenderWindowInteractor()\\n interactor.SetRenderWindow(renderer_window)\\n interactor.Initialize()\\n interactor.Start()\",\n \"def fgm2iaga(path,\\n fgm_fname,\\n ftype='v',\\n output_template='{stn}{date:%Y%m%d}{ftype}{interval}.{interval}'):\\n df = parse(fgm_fname)\\n delta = (df.index[1] - df.index[0]).total_seconds()\\n if delta == 1.0:\\n interval = 'sec'\\n elif delta == 60.0:\\n interval = 'min'\\n else:\\n raise ValueError('unknown data interval found in {}'.format(fgm_fname))\\n stn = df.siteid[:3].upper()\\n out_fname = os.path.join(path,\\n output_template.format(stn=stn.lower(),\\n date=df.date,\\n ftype=ftype,\\n interval=interval))\\n with open(out_fname, 'w') as fid:\\n fid.write(HEADER_TEMPLATE.format(stn=stn.upper(),\\n lat=df.lat,\\n lon=df.lon,\\n el=0))\\n for row in df.itertuples():\\n dt = row.Index\\n if row.flag:\\n X = Y = Z = F = 99999\\n else:\\n X = row.x\\n Y = row.y\\n Z = row.z\\n F = np.linalg.norm([X, Y, Z])\\n fid.write('{date:%Y-%m-%d %H:%M:%S.000} {date:%j}'\\n ' {X:>9.2f} {Y:>9.2f} {Z:>9.2f} {F:>9.2f}\\\\n'.format(date=dt,\\n X=X,\\n Y=Y,\\n Z=Z,\\n F=F))\\n return out_fname\",\n \"def render(self, outputfile, cmd=GDALTRANSLATE, working_memory=1024,\\n compress=None, tempdir=None):\\n tmpfile = NamedTemporaryFile(\\n suffix='.tif', prefix='gdalrender',\\n dir=os.path.dirname(outputfile), delete=False\\n )\\n\\n try:\\n with self.get_tempfile(dir=tempdir) as inputfile:\\n warp_cmd = [\\n cmd,\\n '-q', # Quiet - FIXME: Use logging\\n '-of', 'GTiff', # Output to GeoTIFF\\n '-co', 'BIGTIFF=IF_SAFER', # Use BigTIFF if >2GB\\n '-co', 'NUM_THREADS=ALL_CPUS', # multithreaded compression for GeoTiff\\n # gdal_translate does not support the following\\n # '-multi', # Use multiple processes\\n # '-overwrite', # Overwrite outputfile\\n ]\\n\\n # Set the working memory so that gdalwarp doesn't stall of disk\\n # I/O\\n warp_cmd.extend([\\n # gdal_translate does not support -wm\\n # '-wm', working_memory,\\n '--config', 'GDAL_CACHEMAX', working_memory\\n ])\\n\\n # Use compression\\n compress = str(compress).upper()\\n if compress and compress != 'NONE':\\n warp_cmd.extend(['-co', 'COMPRESS=%s' % compress])\\n if compress in ('LZW', 'DEFLATE'):\\n warp_cmd.extend(['-co', 'PREDICTOR=2'])\\n\\n # Run gdalwarp and output to tmpfile.name\\n warp_cmd.extend([inputfile.name, tmpfile.name])\\n check_output_gdal([str(e) for e in warp_cmd])\\n\\n # If it succeeds, then we move it to overwrite the actual\\n # output\\n os.rename(tmpfile.name, outputfile)\\n return outputfile\\n finally:\\n rmfile(tmpfile.name, ignore_missing=True)\\n rmfile(tmpfile.name + '.aux.xml', ignore_missing=True)\",\n \"def transform_from_file(self,\\n file,\\n format):\\n\\n # Prepare query URL\\n _query_builder = Configuration.base_uri\\n _query_builder += '/transform'\\n _query_parameters = {\\n 'format': format\\n }\\n _query_builder = APIHelper.append_url_with_query_parameters(_query_builder,\\n _query_parameters, Configuration.array_serialization)\\n _query_url = APIHelper.clean_url(_query_builder)\\n\\n # Prepare files\\n _files = {\\n 'file': file\\n }\\n\\n # Prepare and execute request\\n _request = self.http_client.post(_query_url, files=_files)\\n CustomHeaderAuth.apply(_request)\\n _context = self.execute_request(_request)\\n self.validate_response(_context)\\n\\n # Return appropriate type\\n return _context.response.raw_body\",\n \"def main(input_file_path, layer='all', out=None, grid_id_name='GRIDMET_ID',\\n buffer=25, scale_factor=0.1, function='invdist', smooth=0, params=None,\\n grid_res=None, z_stats=True, res_plot=True, overwrite=False, \\n options=None, grid_meta_path=None):\\n # build fishnet for interpolation\\n make_grid(input_file_path, \\n grid_id_name=grid_id_name,\\n grid_meta_path=grid_meta_path, \\n buffer=buffer, \\n overwrite=overwrite,\\n grid_res=grid_res)\\n \\n # run spatial interpolation depending on options\\n interpolate(\\n input_file_path, \\n layer=layer, \\n out=out,\\n scale_factor=scale_factor, \\n function=function, \\n smooth=smooth,\\n params=params,\\n buffer=buffer,\\n z_stats=z_stats,\\n res_plot=res_plot,\\n grid_id_name=grid_id_name,\\n grid_res=grid_res,\\n options=options,\\n grid_meta_path=grid_meta_path)\",\n \"def create_video(input_file, output_file):\\n input_video = VideoFileClip(input_file)\\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\\n output_video.write_videofile(output_file, audio=False)\",\n \"def populate(infile):\\n main(infile)\",\n \"def read_vmdas(self,):\\n fd = self.f\\n # The raw files produced by VMDAS contain a binary navigation data\\n # block.\\n self.cfg['sourceprog'] = 'VMDAS'\\n ens = self.ensemble\\n k = ens.k\\n if self._source != 1 and self._debug_level >= 1:\\n print(' \\\\n***** Apparently a VMDAS file \\\\n\\\\n')\\n self._source = 1\\n self.vars_read += ['time_gps',\\n 'latitude_gps',\\n 'longitude_gps',\\n 'etime_gps',\\n 'elatitude_gps',\\n 'elongitude_gps',\\n 'flags',\\n 'ntime', ]\\n utim = fd.read_ui8(4)\\n date = tmlib.datetime(utim[2] + utim[3] * 256, utim[1], utim[0])\\n # This byte is in hundredths of seconds (10s of milliseconds):\\n time = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) / 10)))\\n fd.seek(4, 1) # \\\"PC clock offset from UTC\\\" - clock drift in ms?\\n ens.time_gps[k] = tmlib.date2epoch(date + time)[0]\\n ens.latitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.longitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.etime_gps[k] = tmlib.date2epoch(date + tmlib.timedelta(\\n milliseconds=int(fd.read_ui32(1) * 10)))[0]\\n ens.elatitude_gps[k] = fd.read_i32(1) * self._cfac\\n ens.elongitude_gps[k] = fd.read_i32(1) * self._cfac\\n fd.seek(12, 1)\\n ens.flags[k] = fd.read_ui16(1)\\n fd.seek(6, 1)\\n utim = fd.read_ui8(4)\\n date = tmlib.datetime(utim[0] + utim[1] * 256, utim[3], utim[2])\\n ens.ntime[k] = tmlib.date2epoch(date + tmlib.timedelta(\\n milliseconds=int(fd.read_ui32(1) / 10)))[0]\\n fd.seek(16, 1)\\n self._nbyte = 2 + 76\",\n \"def init_from_file(self,file_name):\\n with open(file_name, mode='rb') as file: # b is important -> binary\\n file_content = file.read(1)\\n x = file_content\\n ct = int.from_bytes(x, byteorder='little', signed=False)\\n file_content = file.read(ct)\\n header = file_content.decode().split(\\\" \\\")\\n vindex = header.index('-vectortype')\\n vectortype = header[vindex + 1]\\n\\n if vectortype != 'REAL':\\n print('Can\\\\'t initialize real vector store from ',vectortype,' vectors.')\\n return\\n\\n #read in vectors and wrap in RealVectors\\n incoming_terms, incoming_vectors = svu.readfile(file_name)\\n self.init_from_lists(incoming_terms,incoming_vectors)\",\n \"def readDriverFile(self, input_file):\\n\\n\\n fid = open(self.basePath + input_file,'r')\\n\\n # Line 1\\n line = fid.readline()\\n l_input = line.split('!')\\n mshfile = l_input[0].rstrip()\\n\\n # Line 2\\n line = fid.readline()\\n l_input = line.split('!')\\n obsfile = l_input[0].rstrip()\\n\\n # Line 3\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='null':\\n topofile = []\\n\\n else:\\n topofile = l_input[0].rstrip()\\n\\n\\n # Line 4\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n mstart = float(l_input[1])\\n\\n else:\\n mstart = l_input[0].rstrip()\\n\\n # Line 5\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n mref = float(l_input[1])\\n\\n else:\\n mref = l_input[0].rstrip()\\n\\n # Line 6\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n staticInput = float(l_input[1])\\n\\n elif l_input[0]=='DEFAULT':\\n staticInput = None\\n\\n else:\\n staticInput = l_input[0].rstrip()\\n\\n\\n # Line 7\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='DEFAULT':\\n magfile = []\\n\\n else:\\n magfile = l_input[0].rstrip()\\n\\n # Line 8\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input=='DEFAULT':\\n wgtfile = []\\n\\n else:\\n wgtfile = l_input[0].rstrip()\\n\\n # Line 9\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n chi = float(l_input[0])\\n\\n # Line 10\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n val = np.array(l_input[0:4])\\n alphas = val.astype(np.float)\\n\\n # Line 11\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:3])\\n bounds = val.astype(np.float)\\n\\n else:\\n bounds = l_input[0].rstrip()\\n\\n # Line 12\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:6])\\n lpnorms = val.astype(np.float)\\n\\n else:\\n lpnorms = l_input[0].rstrip()\\n\\n # Line 13\\n line = fid.readline()\\n l_input = re.split('[!\\\\s]',line)\\n if l_input[0]=='VALUE':\\n val = np.array(l_input[1:3])\\n eps = val.astype(np.float)\\n\\n else:\\n eps = [None,None]\\n\\n self.mshfile = mshfile\\n self.obsfile = obsfile\\n self.topofile = topofile\\n self.mstart = mstart\\n self._mrefInput = mref\\n self._staticInput = staticInput\\n self.magfile = magfile\\n self.wgtfile = wgtfile\\n self.chi = chi\\n self.alphas = alphas\\n self.bounds = bounds\\n self.lpnorms = lpnorms\\n self.eps = eps\",\n \"def create_verde_rules_lp(schema_file, input_file, mapping_file,\\n query_fields, trial_id, directory, rule_config, baseline_lp,\\n verde_rule_template_dir):\\n\\n logging.info(f'creating verde rules lp based on {schema_file} and {mapping_file}')\\n\\n context = Dict()\\n context.id = trial_id\\n context.directory = directory\\n context.rule_config = rule_config\\n context.verde_rule_template_dir = verde_rule_template_dir\\n\\n # Load the input mapping json\\n with open(mapping_file) as f:\\n mapping_json = json.load(f)\\n\\n lp_str = \\\"\\\"\\n # Apply each verde rule to extend the lp\\n if rule_config.rule_01_causal_relationships.do:\\n lp_str += vrule01.rule_01_causal_relationships(context, schema_file, mapping_json, query_fields)\\n else:\\n logging.warning('verde rule_01_causal_relationships is disabled in config')\\n\\n if rule_config.rule_02_data_precision.do:\\n lp_str += vrule02.rule_02_data_precision(context, mapping_json, query_fields)\\n else:\\n logging.warning('verde rule_02_data_precision is disabled in config')\\n\\n if rule_config.rule_03_ordinal_sort.do:\\n lp_str += vrule03.rule_03_ordinal_sort(context, schema_file, input_file, mapping_json, query_fields)\\n else:\\n logging.warning('verde rule_03_ordinal_sort is disabled in config')\\n\\n if rule_config.rule_04_entity_colours.do:\\n lp_str += vrule04.rule_04_colour(context, schema_file, mapping_json)\\n else:\\n logging.warning('verde rule_04_entity_colours is disabled in config')\\n\\n lp = lp_str.split('\\\\n') # bit messy, we used lists elsewhere then moved to jinja templates\\n\\n # Write out the partial lp containing the data schema and our verde soft rules\\n if rule_config.write_lp:\\n lp_file = os.path.join(directory, f'{context.id}_verde_schema_query.lp')\\n vutils.write_list_to_file(baseline_lp + lp, lp_file, 'verde full schema and query lp')\\n\\n return baseline_lp + lp\",\n \"def create_true_vectors(filename, reference_file):\\n\\n all_cases = np.array([], dtype=np.int64).reshape(0, cs.NN_VECTOR_LENGTH)\\n alignment_file = open(filename, 'r')\\n\\n for line in alignment_file:\\n line = line.split('\\\\t')\\n if len(line) >= 9:\\n vector_nn = np.array(reference_to_signal_partial_mapping(line[9], reference_file, line[0][3:], line[1],\\n int(line[2]), int(line[4])))\\n all_cases = np.append(all_cases, vector_nn, axis=0)\\n if len(all_cases) > cs.NUMBER_OF_EXAMPLES:\\n break\\n\\n return all_cases\",\n \"def produce_13TeV_template(tag_name=\\\"HKHI\\\"):\\n num_rebin = 1\\n file_name = \\\"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\\\"+tag_name+\\\"_Lin.root\\\"\\n print \\\"Input: \\\", file_name\\n fin = ROOT.TFile.Open(file_name, \\\"read\\\")\\n h_nom = fin.Get(\\\"bkg_total_gg_full\\\").Clone(\\\"bkg_nominal_old\\\")\\n h_nom.Rebin(num_rebin)\\n fout = ROOT.TFile.Open(\\\"hists_input_\\\"+tag_name+\\\".root\\\", \\\"recreate\\\")\\n\\n h_purity_sys = fin.Get(\\\"bkg_purity_syst_gg_full\\\").Clone(\\\"bkg_purity_syst_gg\\\")\\n h_reducible_sys = fin.Get(\\\"bkg_reducible_syst_gg_full\\\").Clone(\\\"bkg_reducible_syst_gg\\\")\\n h_irreducible_sys = fin.Get(\\\"bkg_irreducible_syst_gg_full\\\").Clone(\\\"bkg_irreducible_syst_gg\\\")\\n h_iso_sys = fin.Get(\\\"bkg_iso_syst_gg_full\\\").Clone(\\\"bkg_iso_syst_gg\\\")\\n\\n #file_iso = \\\"isolation_sys/hist.root\\\"\\n #fin2 = ROOT.TFile.Open(file_iso, \\\"read\\\")\\n #h_iso_sys = fin2.Get(\\\"bkg_isolation_syst_gg\\\")\\n ## inflat irreducible uncertainty by factor of 10\\n # so that it closes to stats uncertainty in data\\n sf = 1\\n if INFLATE_SYS:\\n sf = 10\\n\\n # after rebinning systematic uncertainties, need to scale down,\\n # otherwise the uncertainties are inflated.\\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n\\n ## truncate the histograms to [200, 2000] GeV\\n h_nom_new = truncate_hist(h_nom, \\\"bkg_nominal\\\")\\n h_purity_sys_new = truncate_hist(h_purity_sys, \\\"h_purity_sys_new\\\")\\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \\\"h_irreducible_sys_new\\\")\\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \\\"h_reducible_sys_new\\\")\\n h_iso_sys_new = truncate_hist(h_iso_sys, \\\"h_iso_sys_new\\\")\\n\\n #write down sys and nominal\\n fout.cd()\\n h_nom_new.Write()\\n h_purity_sys_new.Write()\\n h_reducible_sys_new.Write()\\n h_irreducible_sys_new.Write()\\n h_iso_sys_new.Write()\\n\\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \\\"purity_sys\\\")\\n h_purity_up.Write()\\n h_purity_down.Write()\\n\\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \\\"reducible_sys\\\")\\n h_red_up.Write()\\n h_red_down.Write()\\n\\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \\\"irreducible_sys\\\")\\n h_irred_up.Write()\\n h_irred_down.Write()\\n\\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \\\"isolation_sys\\\")\\n h_iso_up.Write()\\n h_iso_down.Write()\\n\\n fin.Close()\\n fout.Close()\",\n \"def process_svgs(model_info, directory, output, allow_missing=False):\\n r2dt.write(model_info, directory, output, allow_missing=allow_missing)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6332105","0.6148278","0.60546756","0.6035137","0.59595346","0.58744794","0.58276","0.58268255","0.57943213","0.57037807","0.56656307","0.5467358","0.541127","0.5396852","0.53866345","0.53866345","0.53640836","0.5348823","0.52539855","0.5224854","0.51994747","0.5192403","0.51900554","0.5165529","0.5159512","0.515376","0.5151332","0.51434046","0.5141987","0.5133459","0.5118285","0.5111108","0.50902104","0.50852007","0.50811803","0.5078187","0.5068416","0.5063147","0.50577945","0.50099593","0.50085336","0.5002134","0.4996009","0.49745375","0.49737355","0.49719802","0.49691865","0.4965909","0.49294093","0.49206844","0.49177408","0.49156937","0.49134704","0.49087375","0.4901378","0.48920193","0.48857296","0.4869468","0.4869468","0.48694164","0.4854838","0.484103","0.48405656","0.48332667","0.48232844","0.4822603","0.4820913","0.48203066","0.4819152","0.48039868","0.47993007","0.47940227","0.47916836","0.4788897","0.47862664","0.47828332","0.47616982","0.47546104","0.47536212","0.4749284","0.474661","0.47417504","0.47408664","0.47398564","0.47284323","0.4727966","0.47255188","0.47156096","0.47144514","0.47106367","0.4708809","0.47057223","0.47050765","0.47025943","0.47021836","0.47015566","0.46951467","0.4694794","0.46844468","0.46841887"],"string":"[\n \"0.6332105\",\n \"0.6148278\",\n \"0.60546756\",\n \"0.6035137\",\n \"0.59595346\",\n \"0.58744794\",\n \"0.58276\",\n \"0.58268255\",\n \"0.57943213\",\n \"0.57037807\",\n \"0.56656307\",\n \"0.5467358\",\n \"0.541127\",\n \"0.5396852\",\n \"0.53866345\",\n \"0.53866345\",\n \"0.53640836\",\n \"0.5348823\",\n \"0.52539855\",\n \"0.5224854\",\n \"0.51994747\",\n \"0.5192403\",\n \"0.51900554\",\n \"0.5165529\",\n \"0.5159512\",\n \"0.515376\",\n \"0.5151332\",\n \"0.51434046\",\n \"0.5141987\",\n \"0.5133459\",\n \"0.5118285\",\n \"0.5111108\",\n \"0.50902104\",\n \"0.50852007\",\n \"0.50811803\",\n \"0.5078187\",\n \"0.5068416\",\n \"0.5063147\",\n \"0.50577945\",\n \"0.50099593\",\n \"0.50085336\",\n \"0.5002134\",\n \"0.4996009\",\n \"0.49745375\",\n \"0.49737355\",\n \"0.49719802\",\n \"0.49691865\",\n \"0.4965909\",\n \"0.49294093\",\n \"0.49206844\",\n \"0.49177408\",\n \"0.49156937\",\n \"0.49134704\",\n \"0.49087375\",\n \"0.4901378\",\n \"0.48920193\",\n \"0.48857296\",\n \"0.4869468\",\n \"0.4869468\",\n \"0.48694164\",\n \"0.4854838\",\n \"0.484103\",\n \"0.48405656\",\n \"0.48332667\",\n \"0.48232844\",\n \"0.4822603\",\n \"0.4820913\",\n \"0.48203066\",\n \"0.4819152\",\n \"0.48039868\",\n \"0.47993007\",\n \"0.47940227\",\n \"0.47916836\",\n \"0.4788897\",\n \"0.47862664\",\n \"0.47828332\",\n \"0.47616982\",\n \"0.47546104\",\n \"0.47536212\",\n \"0.4749284\",\n \"0.474661\",\n \"0.47417504\",\n \"0.47408664\",\n \"0.47398564\",\n \"0.47284323\",\n \"0.4727966\",\n \"0.47255188\",\n \"0.47156096\",\n \"0.47144514\",\n \"0.47106367\",\n \"0.4708809\",\n \"0.47057223\",\n \"0.47050765\",\n \"0.47025943\",\n \"0.47021836\",\n \"0.47015566\",\n \"0.46951467\",\n \"0.4694794\",\n \"0.46844468\",\n \"0.46841887\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6055037"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":95687,"cells":{"query":{"kind":"string","value":"Returns gdal.Band.GetNoDataValue() as a NumPy type"},"document":{"kind":"string","value":"def GetNoDataValue(self):\n result = super(Band, self).GetNoDataValue()\n if result is not None:\n return self.NumPyDataType(result)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_band_nodata(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n return self.dataset.GetRasterBand(band_idx).GetNoDataValue()","def _nodata_value(self):\n try:\n nodata = float(self._info[\"bands\"][0][\"noDataValue\"])\n except KeyError:\n nodata = None\n return nodata","def get_raster_nodata(self):\n\n nodata = list()\n for i in range(0, self.dataset.RasterCount):\n nodata.append(self.dataset.GetRasterBand(i + 1).GetNoDataValue())\n\n return nodata if len(nodata) >= 0 and not all(\n d is None for d in nodata) else None","def sentinel_to_numpy(ds: xr.Dataset) -> np.ndarray:\n array = ds.transpose(\"lat\", \"lon\", \"band\").to_array().values\n\n return np.squeeze(array, 0)","def data_missing():\n return RaggedArray([[], [-1, 0, 1]], dtype='int16')","def read_raster (self, filename):\n raster = gdal.Open (filename)\n band = raster.GetRasterBand(1)\n x = band.ReadAsArray () \n nodata_val = band.GetNoDataValue () # get the missing data flag\n x [x == nodata_val] = np.nan # set missing data properly\n return (x)","def setup_no_data_values(input_dataset, options):\n in_nodata = []\n if options.srcnodata:\n nds = list(map(float, options.srcnodata.split(',')))\n if len(nds) < input_dataset.RasterCount:\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\n else:\n in_nodata = nds\n else:\n for i in range(1, input_dataset.RasterCount+1):\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\n if raster_no_data is not None:\n in_nodata.append(raster_no_data)\n\n if options.verbose:\n print(\"NODATA: %s\" % in_nodata)\n\n return in_nodata","def getNoData(self):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n return TreeLikelihoodBase.getNoData(self)","def test_nodata(self):\n\n \n filename = 'data/test_grid.asc'\n R = read_coverage(filename)\n \n nan = R.get_nodata_value()\n assert nan == -9999\n \n A = R.get_data(nan=False)\n assert numpy.min(A[:]) == -9999\n assert numpy.allclose(numpy.max(A[:]), 50.9879837036) \n \n A = R.get_data(nan=True)\n assert numpy.allclose(numpy.nanmin(A[:]), -50.60135540866)\n assert numpy.allclose(numpy.nanmax(A[:]), 50.9879837036)","def isna(self):\n # type: () -> np.ndarray\n return extract_isnull_bytemap(self.data)","def isna(self):\n # type: () -> np.ndarray\n return extract_isnull_bytemap(self.data)","def test_scalar_null(self):\n dset = self.f.create_dataset('x', shape=(), dtype='i1')\n out = dset[()]\n self.assertIsInstance(out, np.int8)","def unmasked_data(self):\n return numpy.ma.filled(self.data.astype(numpy.float_),\n fill_value=numpy.nan)","def astype(self, dtype):\n return NoneArray","def getBinnedData(self):\n return self._array.sum(0)","def test_single_null(self):\n dset = self.f.create_dataset('x', (1,), dtype='i1')\n out = dset[()]\n self.assertIsInstance(out, np.ndarray)\n self.assertEqual(out.shape, (1,))","def read_gdal_values(dataset=None, nodata=None):\n nbands = dataset.RasterCount\n\n # data values\n bands = []\n for i in range(nbands):\n band = dataset.GetRasterBand(i + 1)\n nd = band.GetNoDataValue()\n data = band.ReadAsArray()\n if nodata is not None:\n data[data == nd] = nodata\n bands.append(data)\n\n return np.squeeze(np.array(bands))","def none(self, type_name):\n provenance = NQExprProvenance(operation='none', args=(type_name, None))\n np_vec = self.zeros_numpy_array(type_name, as_matrix=True)\n return self.as_nql(np_vec, type_name, provenance)","def nonans(array):\n return array[~np.isnan(array)]","def test_raster_integer_nan_value():\n cvs = ds.Canvas(plot_height=2, plot_width=2, x_range=(0, 1), y_range=(0,1))\n array = np.array([[9999, 1, 2, 3], [4, 9999, 6, 7], [8, 9, 9999, 11]])\n coords = {'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}\n xr_array = xr.DataArray(array, coords=coords, dims=['y', 'x'])\n\n agg = cvs.raster(xr_array, downsample_method='max', nan_value=9999)\n expected = np.array([[4, 7], [9, 11]])\n\n assert np.allclose(agg.data, expected)\n assert agg.data.dtype.kind == 'i'\n assert np.allclose(agg.x.values, np.array([0.25, 0.75]))\n assert np.allclose(agg.y.values, np.array([0.25, 0.75]))","def noSpecificValues(self):\n return self.__noSpecificValues(self.__result)","def mask_nodata(self):\n ds_out = self._obj\n for var in self.vars:\n ds_out[var] = ds_out[var].raster.mask_nodata()\n return ds_out","def zero_to_nodata(base_raster):\n target_raster = base_raster.copy()\n target_raster[target_raster == 0] = _IC_NODATA\n return target_raster","def get_nonzeros(self):\n return self.tape.get_nonzeros(self.machine.eval_symbol,\n self.machine.eval_state(self.state))","def test_qf_dataarray_dtype_for_nan_volume(self):\n tickers = [BloombergTicker(\"FMIM10 Index\")]\n start_date = str_to_date(\"2010-01-14\")\n end_date = str_to_date(\"2010-01-19\")\n\n data_array = self.bbg_provider.get_price(tickers, [PriceField.Close, PriceField.Volume], start_date, end_date,\n Frequency.DAILY)\n self.assertEqual(data_array.dtype, np.float64)","def dropna(self) -> \"Dataset\":\n if not self[0]._has_time_axis: # type: ignore\n raise ValueError(\"Not available if no time axis!\")\n\n all_index: List[int] = []\n for i in range(self.n_items):\n x = self[i].to_numpy()\n\n # this seems overly complicated...\n axes = tuple(range(1, x.ndim))\n idx = list(np.where(~np.isnan(x).all(axis=axes))[0])\n if i == 0:\n all_index = idx\n else:\n all_index = list(np.intersect1d(all_index, idx))\n\n return self.isel(all_index, axis=0)","def test_raster_float_nan_value():\n cvs = ds.Canvas(plot_height=2, plot_width=2, x_range=(0, 1), y_range=(0,1))\n array = np.array([[np.NaN, 1., 2., 3.], [4., np.NaN, 6., 7.], [8., 9., np.NaN, 11.]])\n coords = {'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}\n xr_array = xr.DataArray(array, coords=coords, dims=['y', 'x'])\n\n agg = cvs.raster(xr_array, downsample_method='max')\n expected = np.array([[4, 7], [9, 11]])\n\n assert np.allclose(agg.data, expected)\n assert agg.data.dtype.kind == 'f'\n assert np.allclose(agg.x.values, np.array([0.25, 0.75]))\n assert np.allclose(agg.y.values, np.array([0.25, 0.75]))","def test_nodata_value(self):\n\n # Read files with -9999 as nominated nodata value\n for filename in [os.path.join(TESTDATA, 'Population_2010_clip.tif'),\n os.path.join(HAZDATA,\n 'Lembang_Earthquake_Scenario.asc')]:\n\n R = read_layer(filename)\n A = R.get_data(nan=False)\n\n # Verify nodata value\n Amin = min(A.flat[:])\n msg = ('Raster must have -9999 as its minimum for this test. '\n 'We got %f for file %s' % (Amin, filename))\n assert Amin == -9999, msg\n\n # Verify that GDAL knows about this\n nodata = R.get_nodata_value()\n msg = ('File %s should have registered nodata '\n 'value %i but it was %s' % (filename, Amin, nodata))\n assert nodata == Amin, msg\n\n # Then try using numpy.nan\n A = R.get_data(nan=True)\n\n # Verify nodata value\n Amin = numpy.nanmin(A.flat[:])\n msg = ('True raster minimum must exceed -9999. '\n 'We got %f for file %s' % (Amin, filename))\n assert Amin > -9999, msg\n\n # Then try with a number\n A = R.get_data(nan=-100000)\n\n # Verify nodata value\n Amin = numpy.nanmin(A.flat[:])\n msg = ('Raster must have -100000 as its minimum for this test. '\n 'We got %f for file %s' % (Amin, filename))\n assert Amin == -100000, msg\n\n # Try with illegal nan values\n for illegal in [{}, (), [], None, 'a', 'oeuu']:\n try:\n R.get_data(nan=illegal)\n except InaSAFEError:\n pass\n else:\n msg = ('Illegal nan value %s should have raised '\n 'exception' % illegal)\n raise RuntimeError(msg)","def raster_to_numpy_array(raster_data, as_single_band=True,\n old_nodata=None, new_nodata=None):\n bands = as_single_band + (1 - as_single_band) * raster_data.RasterCount\n nrow = raster_data.RasterYSize\n ncol = raster_data.RasterXSize\n dims = (bands, nrow, ncol)\n\n out_data_array = np.full(dims, np.nan)\n\n for i in range(bands):\n srcband = raster_data.GetRasterBand(i + 1)\n srcband_array = np.array(srcband.ReadAsArray().astype(np.float))\n if old_nodata is None:\n old_nodata = srcband.GetNoDataValue()\n if new_nodata is not None and old_nodata is not None:\n if np.isnan(old_nodata):\n srcband_array[np.isnan(srcband_array)] = new_nodata\n else:\n srcband_array[srcband_array == old_nodata] = new_nodata\n print('NoData: Replaced ' + str(old_nodata) +\n ' with ' + str(new_nodata))\n out_data_array[i, :, :] = srcband_array\n\n if as_single_band:\n return out_data_array[0, :, :]\n else:\n return out_data_array","def isnan(data):\n return _make.isnan(data)","def var_data_unc(self, index):\n data = np.ma.array(np.zeros(self.n_levels()), mask=True)\n if index is not None:\n for i in range(self.n_levels()):\n if self.profile_data[i]['variables'][index]['Missing'] or self.profile_data[i]['variables'][index]['Missing_unc']: continue\n data[i] = self.profile_data[i]['variables'][index]['Value_unc']\n return data","def __array__(self):\n return pa.column(\"dummy\", self.data).to_pandas().values","def getNoLabel(self):\n # TODO: this is a quick hack. We need another way to signify \"the empty label\"\n (itemtype, interval) = self.getLabelTypeAndInterval()\n return pynt.rangeset.RangeSet(None, itemtype=itemtype, interval=interval)","def getNoLabel(self):\n # TODO: this is a quick hack. We need another way to signify \"the empty label\"\n (itemtype, interval) = self.getLabelTypeAndInterval()\n return pynt.rangeset.RangeSet(None, itemtype=itemtype, interval=interval)","def data_missing() -> ExtensionArray:\n data_matrix = np.arange(\n 2 * 10 * 10 * 3,\n dtype=np.float_,\n ).reshape(2, 10, 10, 3)\n data_matrix[0, ...] = np.NaN\n grid_points = [\n np.arange(10),\n np.arange(10) / 10,\n ]\n\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)","def data_missing(basis: Basis) -> FDataBasis:\n coef_matrix = np.arange(\n 2 * basis.n_basis,\n dtype=np.float_,\n ).reshape(2, basis.n_basis)\n coef_matrix[0, :] = np.NaN\n\n return FDataBasis(basis=basis, coefficients=coef_matrix)","def test_scalar_null(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dset = f.create_dataset('x', shape=(), dtype='i1')\n out = dset[()]\n\n assert out.dtype == \"int8\"","def data_missing_for_sorting(allow_in_pandas):\n return PandasArray(\n np.array([(1,), np.nan, (0,)])\n )","def nonull(val):\n return val if not pd.isnull(val) else None","def _nodata_mask(self):\n if self.nodata_value is None:\n return np.ones_like(self.array, dtype=np.bool)\n return self.array != self.nodata_value","def getMask(self):\r\n mask = np.array(self.array, dtype=np.float32)\r\n mask[mask == 0] = np.nan\r\n return mask","def band_count(self):\n return self.dataset.RasterCount if self.dataset else None","def _pure_data(data, missing_mask):\n missing_rows = np.where(missing_mask)[0]\n pure_data = np.delete(data, missing_rows, axis=0)\n\n return pure_data","def getNarrowbandIncandSignalLG(self):\r\n\t\treturn self.lowGainNarrowBandIncandData","def nan_value(data):\n return data.isnull().any()","def isnan(self):\n return self.isAny( (lambda x: np.isnan(x)) )","def no_bin(image, *args, **kwargs):\n return image","def remove_zero_bars(dgm):\r\n inds = dgm[:,0] != dgm[:,1]\r\n return dgm[inds,:]","def get_missing(self):\n missing_values = self.df[self.col_name].isnull().sum()\n return missing_values","def notna(self):\n return DataFrameDefault.register(pandas.DataFrame.notna)(self)","def _get_missing_h5lmt(self, dataset_name, inverse=False):\n dataset = self.__getitem__(dataset_name)\n missing_dataset = self.get('/FSMissingGroup/FSMissingDataSet')\n if len(dataset.shape) == 1:\n result = numpy.zeros((dataset.shape[0], 1), dtype=numpy.int8)\n elif dataset.shape == missing_dataset.shape:\n result = missing_dataset[:, :].astype('i8').T\n else:\n result = numpy.zeros(dataset[:, :].shape, dtype=numpy.int8).T\n\n if inverse:\n return (~result.astype(bool)).astype('i8')\n return result","def type(self):\n # type: () -> type\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]","def no_reg(w):\n return np.zeros_like(w)","def extract_unmasked_data(self,radar, field, bad=-32768):\n return radar.fields[field]['data'].filled(fill_value=bad)","def mask(self, mask, logger=logger):\n if self.nodata is not None:\n da_masked = self._obj.where(mask != 0, self.nodata)\n else:\n logger.warning(\"Nodata value missing, skipping mask\")\n da_masked = self._obj\n return da_masked","def extract_unmasked_data(radar, field, bad=-32768):\n return radar.fields[field]['data'].filled(fill_value=bad)","def data_missing(data):\n return type(data)._from_sequence([None, data[0]])","def getNarrowbandIncandSignal(self):\r\n\t\treturn self.narrowBandIncandData","def test_single_null(setup_teardown_file):\n f = setup_teardown_file[3]\n\n dset = f.create_dataset('x', (1,), dtype='i1')\n out = dset[()]\n assert isinstance(out, np.ndarray)\n assert out.shape == (1,)","def missing_values(dataset, inverse=False):\n zero = numpy.int8(0)\n one = numpy.int8(1)\n if inverse:\n converter = numpy.vectorize(lambda x:\n zero if (x == 0.0 and math.copysign(1, x) < 0.0) else one)\n else:\n converter = numpy.vectorize(lambda x:\n one if (x == 0.0 and math.copysign(1, x) < 0.0) else zero)\n return converter(dataset)","def getNotHidden(self):\n return self.abudance_df[self.abundance_df['masked']==False]","def GetOutputNarrowBand(self) -> \"itkVectorContainerUILSNF3_Pointer\":\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF3_GetOutputNarrowBand(self)","def test_default_signal_nxdata(self, nexus_base):\n assert isinstance(nexus_base.default_signal, np.ndarray)","def trend_none(self):\n raise NotImplementedError()","def nan(klass):\n return RatTerm(RatNum(1, 0), 0)","def __array__(self, dtype=None) -> np.ndarray:\n return self.values","def nocoordinate(self):\n return self.__nocoordinate","def non_gpu_data(self):\n\n return self._non_gpu_data","def zero(self):\n q = pinocchio.neutral(self.model)\n v = np.zeros(self.model.nv)\n return np.concatenate([q.flat, v])","def AB_zero_flux(self):\n return self._get_mean_and_samples_attribute('AB_zero_flux')","def __array__(self, copy=None):\n return self.data.to_pandas().values","def build_signal_dataset(self):\n return np.abs(self.bandpassed).mean(axis=-2)","def GetOutputNarrowBand(self) -> \"itkVectorContainerUILSNF2_Pointer\":\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF2_GetOutputNarrowBand(self)","def null(cls):\n return GXMXD()","def usedbands(self):\n lc = self.lc\n return np.unique(np.asarray(lc['band']))","def nid(x):\n return x.__array_interface__['data'][0]","def _strip_nan(val):\n if isinstance(val, float) and np.isnan(val):\n return '__NaN__'\n elif isinstance(val, dict):\n return {key: Database._strip_nan(item) for key, item in list(val.items())}\n elif isinstance(val, list) or isinstance(val, tuple):\n return [Database._strip_nan(item) for item in val]\n elif isinstance(val, set):\n raise NotImplementedError\n return val","def test_raster_integer_nan_value_padding():\n\n cvs = ds.Canvas(plot_height=3, plot_width=3, x_range=(0, 2), y_range=(0, 2))\n array = np.array([[9999, 1, 2, 3], [4, 9999, 6, 7], [8, 9, 9999, 11]])\n xr_array = xr.DataArray(array, coords={'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}, dims=['y', 'x'])\n\n agg = cvs.raster(xr_array, downsample_method='max', nan_value=9999)\n expected = np.array([[4, 7, 9999], [9, 11, 9999], [9999, 9999, 9999]])\n\n assert np.allclose(agg.data, expected)\n assert agg.data.dtype.kind == 'i'\n assert np.allclose(agg.x.values, np.array([1/3., 1.0, 5/3.]))\n assert np.allclose(agg.y.values, np.array([1/3., 1.0, 5/3.]))","def na_value():\n return pd.NA","def nz(self):\n return self._dim[2]","def remove_nan(self, dataframe):\n return dataframe.dropna()","def ng(nanograms):\n return Unit(nanograms,\"nanogram\")","def zero(self):\n if self._chart.manifold().base_field_type() in ['real', 'complex']:\n elt = SR.zero()\n else:\n elt = self._chart.manifold().base_field().zero()\n return self.element_class(self, elt)","def remove_data(ds, nh_lim, sh_lim, time_max, lat_name='lat', time_name='time'):\n return xr.where((ds[lat_name] < nh_lim) &\n (ds[lat_name] > sh_lim) &\n (ds[time_name] < pd.to_datetime([time_max]).values),\n np.nan,\n ds)","def df_nodal_flux(self):\n df_q = pd.DataFrame(index=self.masking_domain)\n df_q[\"q\"] = self.sdb.getNodalValues()\n df_q.dropna(subset=[\"q\"], inplace=True)\n return df_q.join(self.doc.c.mesh.df.nodes())","def Vega_zero_flux(self):\n return self._get_mean_and_samples_attribute('Vega_zero_flux')","def soleDataPoint(self):\n dps = self.datapoints()\n if dps:\n return dps[0]","def tonumpy(self):\n import numpy\n from numpy import ma\n\n # initialize the return\n narray = None\n\n if None in self._data:\n\n # define a lambda function\n # to create the mask array\n make_mask = lambda x: x == None\n\n # create the numpy array,\n # making on the fly the mask\n narray = numpy.ma.array(self._data, mask=list(map(make_mask, self._data)))\n\n else:\n # convert the list to a numpy object\n narray = numpy.array(self._data)\n\n # return the numpy object\n return narray","def get_tif(self, file='', band=1):\n Data = np.ndarray\n\n if band == '':\n band = 1\n\n f = gdal.Open(file)\n if f is not None:\n try:\n Data = f.GetRasterBand(band).ReadAsArray()\n except AttributeError:\n raise AttributeError('Band {band} not found.'.format(band=band))\n else:\n raise IOError('{} not found.'.format(file))\n\n return Data","def getUndefinedDataAt(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\n ...","def filter_nans(seq):\n return np.array([x for x in seq if not isinstance(x, float)])","def oiDataIsNull(self):\n self.charts_1.getGV().hide()\n self.charts_2.getGV().hide()","def replace_value(cls, data, nodata):\n data = data.astype('float64')\n mask = data != nodata\n if hasattr(data, 'where'):\n return data.where(mask, np.NaN)\n return np.where(mask, data, np.NaN)","def readImage(self, imgfile, band):\n imgds = gdal.Open(imgfile, gdal.GA_ReadOnly)\n imgband = imgds.GetRasterBand(band)\n imgdata = imgband.ReadAsArray(0, 0, imgband.XSize, imgband.YSize)\n imgds = None\n return imgdata","def unsetValue(self):\n return _libsbml.FluxBound_unsetValue(self)","def missing_data(self, by='marker'):\n d = np.copy(self.geno).astype(float)\n d[d == -9] = np.nan\n if by == 'marker' or by == 0:\n return np.isnan(d[:,:,0]).mean(0)\n elif by == 'individual' or by == 1:\n return np.isnan(d[:,:,0]).mean(1)\n else:\n raise ValueError(\"`by` should be either 'marker' or 'individual'.\")","def gdx_isnan(val,gdxf):\n return val in [SPECIAL_VALUES[0], SPECIAL_VALUES[1]]","def get_bin_band_arr(self):\n\n fn = self._lib['cwtObj_getBinBandArr']\n fn.argtypes = [POINTER(OpaqueCWT)]\n fn.restype = c_void_p\n p = fn(self._obj)\n ret = np.frombuffer((c_int * self.num).from_address(p), np.int32).copy()\n return ret","def notna(self):\n return super().notna()","def getNZ(self):\n return self._get_nz( )"],"string":"[\n \"def get_band_nodata(self, band_idx=1):\\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\\n raise IndexError(\\\"band index is out of range\\\")\\n return self.dataset.GetRasterBand(band_idx).GetNoDataValue()\",\n \"def _nodata_value(self):\\n try:\\n nodata = float(self._info[\\\"bands\\\"][0][\\\"noDataValue\\\"])\\n except KeyError:\\n nodata = None\\n return nodata\",\n \"def get_raster_nodata(self):\\n\\n nodata = list()\\n for i in range(0, self.dataset.RasterCount):\\n nodata.append(self.dataset.GetRasterBand(i + 1).GetNoDataValue())\\n\\n return nodata if len(nodata) >= 0 and not all(\\n d is None for d in nodata) else None\",\n \"def sentinel_to_numpy(ds: xr.Dataset) -> np.ndarray:\\n array = ds.transpose(\\\"lat\\\", \\\"lon\\\", \\\"band\\\").to_array().values\\n\\n return np.squeeze(array, 0)\",\n \"def data_missing():\\n return RaggedArray([[], [-1, 0, 1]], dtype='int16')\",\n \"def read_raster (self, filename):\\n raster = gdal.Open (filename)\\n band = raster.GetRasterBand(1)\\n x = band.ReadAsArray () \\n nodata_val = band.GetNoDataValue () # get the missing data flag\\n x [x == nodata_val] = np.nan # set missing data properly\\n return (x)\",\n \"def setup_no_data_values(input_dataset, options):\\n in_nodata = []\\n if options.srcnodata:\\n nds = list(map(float, options.srcnodata.split(',')))\\n if len(nds) < input_dataset.RasterCount:\\n in_nodata = (nds * input_dataset.RasterCount)[:input_dataset.RasterCount]\\n else:\\n in_nodata = nds\\n else:\\n for i in range(1, input_dataset.RasterCount+1):\\n raster_no_data = input_dataset.GetRasterBand(i).GetNoDataValue()\\n if raster_no_data is not None:\\n in_nodata.append(raster_no_data)\\n\\n if options.verbose:\\n print(\\\"NODATA: %s\\\" % in_nodata)\\n\\n return in_nodata\",\n \"def getNoData(self):\\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\\n return TreeLikelihoodBase.getNoData(self)\",\n \"def test_nodata(self):\\n\\n \\n filename = 'data/test_grid.asc'\\n R = read_coverage(filename)\\n \\n nan = R.get_nodata_value()\\n assert nan == -9999\\n \\n A = R.get_data(nan=False)\\n assert numpy.min(A[:]) == -9999\\n assert numpy.allclose(numpy.max(A[:]), 50.9879837036) \\n \\n A = R.get_data(nan=True)\\n assert numpy.allclose(numpy.nanmin(A[:]), -50.60135540866)\\n assert numpy.allclose(numpy.nanmax(A[:]), 50.9879837036)\",\n \"def isna(self):\\n # type: () -> np.ndarray\\n return extract_isnull_bytemap(self.data)\",\n \"def isna(self):\\n # type: () -> np.ndarray\\n return extract_isnull_bytemap(self.data)\",\n \"def test_scalar_null(self):\\n dset = self.f.create_dataset('x', shape=(), dtype='i1')\\n out = dset[()]\\n self.assertIsInstance(out, np.int8)\",\n \"def unmasked_data(self):\\n return numpy.ma.filled(self.data.astype(numpy.float_),\\n fill_value=numpy.nan)\",\n \"def astype(self, dtype):\\n return NoneArray\",\n \"def getBinnedData(self):\\n return self._array.sum(0)\",\n \"def test_single_null(self):\\n dset = self.f.create_dataset('x', (1,), dtype='i1')\\n out = dset[()]\\n self.assertIsInstance(out, np.ndarray)\\n self.assertEqual(out.shape, (1,))\",\n \"def read_gdal_values(dataset=None, nodata=None):\\n nbands = dataset.RasterCount\\n\\n # data values\\n bands = []\\n for i in range(nbands):\\n band = dataset.GetRasterBand(i + 1)\\n nd = band.GetNoDataValue()\\n data = band.ReadAsArray()\\n if nodata is not None:\\n data[data == nd] = nodata\\n bands.append(data)\\n\\n return np.squeeze(np.array(bands))\",\n \"def none(self, type_name):\\n provenance = NQExprProvenance(operation='none', args=(type_name, None))\\n np_vec = self.zeros_numpy_array(type_name, as_matrix=True)\\n return self.as_nql(np_vec, type_name, provenance)\",\n \"def nonans(array):\\n return array[~np.isnan(array)]\",\n \"def test_raster_integer_nan_value():\\n cvs = ds.Canvas(plot_height=2, plot_width=2, x_range=(0, 1), y_range=(0,1))\\n array = np.array([[9999, 1, 2, 3], [4, 9999, 6, 7], [8, 9, 9999, 11]])\\n coords = {'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}\\n xr_array = xr.DataArray(array, coords=coords, dims=['y', 'x'])\\n\\n agg = cvs.raster(xr_array, downsample_method='max', nan_value=9999)\\n expected = np.array([[4, 7], [9, 11]])\\n\\n assert np.allclose(agg.data, expected)\\n assert agg.data.dtype.kind == 'i'\\n assert np.allclose(agg.x.values, np.array([0.25, 0.75]))\\n assert np.allclose(agg.y.values, np.array([0.25, 0.75]))\",\n \"def noSpecificValues(self):\\n return self.__noSpecificValues(self.__result)\",\n \"def mask_nodata(self):\\n ds_out = self._obj\\n for var in self.vars:\\n ds_out[var] = ds_out[var].raster.mask_nodata()\\n return ds_out\",\n \"def zero_to_nodata(base_raster):\\n target_raster = base_raster.copy()\\n target_raster[target_raster == 0] = _IC_NODATA\\n return target_raster\",\n \"def get_nonzeros(self):\\n return self.tape.get_nonzeros(self.machine.eval_symbol,\\n self.machine.eval_state(self.state))\",\n \"def test_qf_dataarray_dtype_for_nan_volume(self):\\n tickers = [BloombergTicker(\\\"FMIM10 Index\\\")]\\n start_date = str_to_date(\\\"2010-01-14\\\")\\n end_date = str_to_date(\\\"2010-01-19\\\")\\n\\n data_array = self.bbg_provider.get_price(tickers, [PriceField.Close, PriceField.Volume], start_date, end_date,\\n Frequency.DAILY)\\n self.assertEqual(data_array.dtype, np.float64)\",\n \"def dropna(self) -> \\\"Dataset\\\":\\n if not self[0]._has_time_axis: # type: ignore\\n raise ValueError(\\\"Not available if no time axis!\\\")\\n\\n all_index: List[int] = []\\n for i in range(self.n_items):\\n x = self[i].to_numpy()\\n\\n # this seems overly complicated...\\n axes = tuple(range(1, x.ndim))\\n idx = list(np.where(~np.isnan(x).all(axis=axes))[0])\\n if i == 0:\\n all_index = idx\\n else:\\n all_index = list(np.intersect1d(all_index, idx))\\n\\n return self.isel(all_index, axis=0)\",\n \"def test_raster_float_nan_value():\\n cvs = ds.Canvas(plot_height=2, plot_width=2, x_range=(0, 1), y_range=(0,1))\\n array = np.array([[np.NaN, 1., 2., 3.], [4., np.NaN, 6., 7.], [8., 9., np.NaN, 11.]])\\n coords = {'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}\\n xr_array = xr.DataArray(array, coords=coords, dims=['y', 'x'])\\n\\n agg = cvs.raster(xr_array, downsample_method='max')\\n expected = np.array([[4, 7], [9, 11]])\\n\\n assert np.allclose(agg.data, expected)\\n assert agg.data.dtype.kind == 'f'\\n assert np.allclose(agg.x.values, np.array([0.25, 0.75]))\\n assert np.allclose(agg.y.values, np.array([0.25, 0.75]))\",\n \"def test_nodata_value(self):\\n\\n # Read files with -9999 as nominated nodata value\\n for filename in [os.path.join(TESTDATA, 'Population_2010_clip.tif'),\\n os.path.join(HAZDATA,\\n 'Lembang_Earthquake_Scenario.asc')]:\\n\\n R = read_layer(filename)\\n A = R.get_data(nan=False)\\n\\n # Verify nodata value\\n Amin = min(A.flat[:])\\n msg = ('Raster must have -9999 as its minimum for this test. '\\n 'We got %f for file %s' % (Amin, filename))\\n assert Amin == -9999, msg\\n\\n # Verify that GDAL knows about this\\n nodata = R.get_nodata_value()\\n msg = ('File %s should have registered nodata '\\n 'value %i but it was %s' % (filename, Amin, nodata))\\n assert nodata == Amin, msg\\n\\n # Then try using numpy.nan\\n A = R.get_data(nan=True)\\n\\n # Verify nodata value\\n Amin = numpy.nanmin(A.flat[:])\\n msg = ('True raster minimum must exceed -9999. '\\n 'We got %f for file %s' % (Amin, filename))\\n assert Amin > -9999, msg\\n\\n # Then try with a number\\n A = R.get_data(nan=-100000)\\n\\n # Verify nodata value\\n Amin = numpy.nanmin(A.flat[:])\\n msg = ('Raster must have -100000 as its minimum for this test. '\\n 'We got %f for file %s' % (Amin, filename))\\n assert Amin == -100000, msg\\n\\n # Try with illegal nan values\\n for illegal in [{}, (), [], None, 'a', 'oeuu']:\\n try:\\n R.get_data(nan=illegal)\\n except InaSAFEError:\\n pass\\n else:\\n msg = ('Illegal nan value %s should have raised '\\n 'exception' % illegal)\\n raise RuntimeError(msg)\",\n \"def raster_to_numpy_array(raster_data, as_single_band=True,\\n old_nodata=None, new_nodata=None):\\n bands = as_single_band + (1 - as_single_band) * raster_data.RasterCount\\n nrow = raster_data.RasterYSize\\n ncol = raster_data.RasterXSize\\n dims = (bands, nrow, ncol)\\n\\n out_data_array = np.full(dims, np.nan)\\n\\n for i in range(bands):\\n srcband = raster_data.GetRasterBand(i + 1)\\n srcband_array = np.array(srcband.ReadAsArray().astype(np.float))\\n if old_nodata is None:\\n old_nodata = srcband.GetNoDataValue()\\n if new_nodata is not None and old_nodata is not None:\\n if np.isnan(old_nodata):\\n srcband_array[np.isnan(srcband_array)] = new_nodata\\n else:\\n srcband_array[srcband_array == old_nodata] = new_nodata\\n print('NoData: Replaced ' + str(old_nodata) +\\n ' with ' + str(new_nodata))\\n out_data_array[i, :, :] = srcband_array\\n\\n if as_single_band:\\n return out_data_array[0, :, :]\\n else:\\n return out_data_array\",\n \"def isnan(data):\\n return _make.isnan(data)\",\n \"def var_data_unc(self, index):\\n data = np.ma.array(np.zeros(self.n_levels()), mask=True)\\n if index is not None:\\n for i in range(self.n_levels()):\\n if self.profile_data[i]['variables'][index]['Missing'] or self.profile_data[i]['variables'][index]['Missing_unc']: continue\\n data[i] = self.profile_data[i]['variables'][index]['Value_unc']\\n return data\",\n \"def __array__(self):\\n return pa.column(\\\"dummy\\\", self.data).to_pandas().values\",\n \"def getNoLabel(self):\\n # TODO: this is a quick hack. We need another way to signify \\\"the empty label\\\"\\n (itemtype, interval) = self.getLabelTypeAndInterval()\\n return pynt.rangeset.RangeSet(None, itemtype=itemtype, interval=interval)\",\n \"def getNoLabel(self):\\n # TODO: this is a quick hack. We need another way to signify \\\"the empty label\\\"\\n (itemtype, interval) = self.getLabelTypeAndInterval()\\n return pynt.rangeset.RangeSet(None, itemtype=itemtype, interval=interval)\",\n \"def data_missing() -> ExtensionArray:\\n data_matrix = np.arange(\\n 2 * 10 * 10 * 3,\\n dtype=np.float_,\\n ).reshape(2, 10, 10, 3)\\n data_matrix[0, ...] = np.NaN\\n grid_points = [\\n np.arange(10),\\n np.arange(10) / 10,\\n ]\\n\\n return skfda.FDataGrid(data_matrix, grid_points=grid_points)\",\n \"def data_missing(basis: Basis) -> FDataBasis:\\n coef_matrix = np.arange(\\n 2 * basis.n_basis,\\n dtype=np.float_,\\n ).reshape(2, basis.n_basis)\\n coef_matrix[0, :] = np.NaN\\n\\n return FDataBasis(basis=basis, coefficients=coef_matrix)\",\n \"def test_scalar_null(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dset = f.create_dataset('x', shape=(), dtype='i1')\\n out = dset[()]\\n\\n assert out.dtype == \\\"int8\\\"\",\n \"def data_missing_for_sorting(allow_in_pandas):\\n return PandasArray(\\n np.array([(1,), np.nan, (0,)])\\n )\",\n \"def nonull(val):\\n return val if not pd.isnull(val) else None\",\n \"def _nodata_mask(self):\\n if self.nodata_value is None:\\n return np.ones_like(self.array, dtype=np.bool)\\n return self.array != self.nodata_value\",\n \"def getMask(self):\\r\\n mask = np.array(self.array, dtype=np.float32)\\r\\n mask[mask == 0] = np.nan\\r\\n return mask\",\n \"def band_count(self):\\n return self.dataset.RasterCount if self.dataset else None\",\n \"def _pure_data(data, missing_mask):\\n missing_rows = np.where(missing_mask)[0]\\n pure_data = np.delete(data, missing_rows, axis=0)\\n\\n return pure_data\",\n \"def getNarrowbandIncandSignalLG(self):\\r\\n\\t\\treturn self.lowGainNarrowBandIncandData\",\n \"def nan_value(data):\\n return data.isnull().any()\",\n \"def isnan(self):\\n return self.isAny( (lambda x: np.isnan(x)) )\",\n \"def no_bin(image, *args, **kwargs):\\n return image\",\n \"def remove_zero_bars(dgm):\\r\\n inds = dgm[:,0] != dgm[:,1]\\r\\n return dgm[inds,:]\",\n \"def get_missing(self):\\n missing_values = self.df[self.col_name].isnull().sum()\\n return missing_values\",\n \"def notna(self):\\n return DataFrameDefault.register(pandas.DataFrame.notna)(self)\",\n \"def _get_missing_h5lmt(self, dataset_name, inverse=False):\\n dataset = self.__getitem__(dataset_name)\\n missing_dataset = self.get('/FSMissingGroup/FSMissingDataSet')\\n if len(dataset.shape) == 1:\\n result = numpy.zeros((dataset.shape[0], 1), dtype=numpy.int8)\\n elif dataset.shape == missing_dataset.shape:\\n result = missing_dataset[:, :].astype('i8').T\\n else:\\n result = numpy.zeros(dataset[:, :].shape, dtype=numpy.int8).T\\n\\n if inverse:\\n return (~result.astype(bool)).astype('i8')\\n return result\",\n \"def type(self):\\n # type: () -> type\\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]\",\n \"def no_reg(w):\\n return np.zeros_like(w)\",\n \"def extract_unmasked_data(self,radar, field, bad=-32768):\\n return radar.fields[field]['data'].filled(fill_value=bad)\",\n \"def mask(self, mask, logger=logger):\\n if self.nodata is not None:\\n da_masked = self._obj.where(mask != 0, self.nodata)\\n else:\\n logger.warning(\\\"Nodata value missing, skipping mask\\\")\\n da_masked = self._obj\\n return da_masked\",\n \"def extract_unmasked_data(radar, field, bad=-32768):\\n return radar.fields[field]['data'].filled(fill_value=bad)\",\n \"def data_missing(data):\\n return type(data)._from_sequence([None, data[0]])\",\n \"def getNarrowbandIncandSignal(self):\\r\\n\\t\\treturn self.narrowBandIncandData\",\n \"def test_single_null(setup_teardown_file):\\n f = setup_teardown_file[3]\\n\\n dset = f.create_dataset('x', (1,), dtype='i1')\\n out = dset[()]\\n assert isinstance(out, np.ndarray)\\n assert out.shape == (1,)\",\n \"def missing_values(dataset, inverse=False):\\n zero = numpy.int8(0)\\n one = numpy.int8(1)\\n if inverse:\\n converter = numpy.vectorize(lambda x:\\n zero if (x == 0.0 and math.copysign(1, x) < 0.0) else one)\\n else:\\n converter = numpy.vectorize(lambda x:\\n one if (x == 0.0 and math.copysign(1, x) < 0.0) else zero)\\n return converter(dataset)\",\n \"def getNotHidden(self):\\n return self.abudance_df[self.abundance_df['masked']==False]\",\n \"def GetOutputNarrowBand(self) -> \\\"itkVectorContainerUILSNF3_Pointer\\\":\\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF3_GetOutputNarrowBand(self)\",\n \"def test_default_signal_nxdata(self, nexus_base):\\n assert isinstance(nexus_base.default_signal, np.ndarray)\",\n \"def trend_none(self):\\n raise NotImplementedError()\",\n \"def nan(klass):\\n return RatTerm(RatNum(1, 0), 0)\",\n \"def __array__(self, dtype=None) -> np.ndarray:\\n return self.values\",\n \"def nocoordinate(self):\\n return self.__nocoordinate\",\n \"def non_gpu_data(self):\\n\\n return self._non_gpu_data\",\n \"def zero(self):\\n q = pinocchio.neutral(self.model)\\n v = np.zeros(self.model.nv)\\n return np.concatenate([q.flat, v])\",\n \"def AB_zero_flux(self):\\n return self._get_mean_and_samples_attribute('AB_zero_flux')\",\n \"def __array__(self, copy=None):\\n return self.data.to_pandas().values\",\n \"def build_signal_dataset(self):\\n return np.abs(self.bandpassed).mean(axis=-2)\",\n \"def GetOutputNarrowBand(self) -> \\\"itkVectorContainerUILSNF2_Pointer\\\":\\n return _itkReinitializeLevelSetImageFilterPython.itkReinitializeLevelSetImageFilterIF2_GetOutputNarrowBand(self)\",\n \"def null(cls):\\n return GXMXD()\",\n \"def usedbands(self):\\n lc = self.lc\\n return np.unique(np.asarray(lc['band']))\",\n \"def nid(x):\\n return x.__array_interface__['data'][0]\",\n \"def _strip_nan(val):\\n if isinstance(val, float) and np.isnan(val):\\n return '__NaN__'\\n elif isinstance(val, dict):\\n return {key: Database._strip_nan(item) for key, item in list(val.items())}\\n elif isinstance(val, list) or isinstance(val, tuple):\\n return [Database._strip_nan(item) for item in val]\\n elif isinstance(val, set):\\n raise NotImplementedError\\n return val\",\n \"def test_raster_integer_nan_value_padding():\\n\\n cvs = ds.Canvas(plot_height=3, plot_width=3, x_range=(0, 2), y_range=(0, 2))\\n array = np.array([[9999, 1, 2, 3], [4, 9999, 6, 7], [8, 9, 9999, 11]])\\n xr_array = xr.DataArray(array, coords={'x': np.linspace(0, 1, 4), 'y': np.linspace(0, 1, 3)}, dims=['y', 'x'])\\n\\n agg = cvs.raster(xr_array, downsample_method='max', nan_value=9999)\\n expected = np.array([[4, 7, 9999], [9, 11, 9999], [9999, 9999, 9999]])\\n\\n assert np.allclose(agg.data, expected)\\n assert agg.data.dtype.kind == 'i'\\n assert np.allclose(agg.x.values, np.array([1/3., 1.0, 5/3.]))\\n assert np.allclose(agg.y.values, np.array([1/3., 1.0, 5/3.]))\",\n \"def na_value():\\n return pd.NA\",\n \"def nz(self):\\n return self._dim[2]\",\n \"def remove_nan(self, dataframe):\\n return dataframe.dropna()\",\n \"def ng(nanograms):\\n return Unit(nanograms,\\\"nanogram\\\")\",\n \"def zero(self):\\n if self._chart.manifold().base_field_type() in ['real', 'complex']:\\n elt = SR.zero()\\n else:\\n elt = self._chart.manifold().base_field().zero()\\n return self.element_class(self, elt)\",\n \"def remove_data(ds, nh_lim, sh_lim, time_max, lat_name='lat', time_name='time'):\\n return xr.where((ds[lat_name] < nh_lim) &\\n (ds[lat_name] > sh_lim) &\\n (ds[time_name] < pd.to_datetime([time_max]).values),\\n np.nan,\\n ds)\",\n \"def df_nodal_flux(self):\\n df_q = pd.DataFrame(index=self.masking_domain)\\n df_q[\\\"q\\\"] = self.sdb.getNodalValues()\\n df_q.dropna(subset=[\\\"q\\\"], inplace=True)\\n return df_q.join(self.doc.c.mesh.df.nodes())\",\n \"def Vega_zero_flux(self):\\n return self._get_mean_and_samples_attribute('Vega_zero_flux')\",\n \"def soleDataPoint(self):\\n dps = self.datapoints()\\n if dps:\\n return dps[0]\",\n \"def tonumpy(self):\\n import numpy\\n from numpy import ma\\n\\n # initialize the return\\n narray = None\\n\\n if None in self._data:\\n\\n # define a lambda function\\n # to create the mask array\\n make_mask = lambda x: x == None\\n\\n # create the numpy array,\\n # making on the fly the mask\\n narray = numpy.ma.array(self._data, mask=list(map(make_mask, self._data)))\\n\\n else:\\n # convert the list to a numpy object\\n narray = numpy.array(self._data)\\n\\n # return the numpy object\\n return narray\",\n \"def get_tif(self, file='', band=1):\\n Data = np.ndarray\\n\\n if band == '':\\n band = 1\\n\\n f = gdal.Open(file)\\n if f is not None:\\n try:\\n Data = f.GetRasterBand(band).ReadAsArray()\\n except AttributeError:\\n raise AttributeError('Band {band} not found.'.format(band=band))\\n else:\\n raise IOError('{} not found.'.format(file))\\n\\n return Data\",\n \"def getUndefinedDataAt(self, address: ghidra.program.model.address.Address) -> ghidra.program.model.listing.Data:\\n ...\",\n \"def filter_nans(seq):\\n return np.array([x for x in seq if not isinstance(x, float)])\",\n \"def oiDataIsNull(self):\\n self.charts_1.getGV().hide()\\n self.charts_2.getGV().hide()\",\n \"def replace_value(cls, data, nodata):\\n data = data.astype('float64')\\n mask = data != nodata\\n if hasattr(data, 'where'):\\n return data.where(mask, np.NaN)\\n return np.where(mask, data, np.NaN)\",\n \"def readImage(self, imgfile, band):\\n imgds = gdal.Open(imgfile, gdal.GA_ReadOnly)\\n imgband = imgds.GetRasterBand(band)\\n imgdata = imgband.ReadAsArray(0, 0, imgband.XSize, imgband.YSize)\\n imgds = None\\n return imgdata\",\n \"def unsetValue(self):\\n return _libsbml.FluxBound_unsetValue(self)\",\n \"def missing_data(self, by='marker'):\\n d = np.copy(self.geno).astype(float)\\n d[d == -9] = np.nan\\n if by == 'marker' or by == 0:\\n return np.isnan(d[:,:,0]).mean(0)\\n elif by == 'individual' or by == 1:\\n return np.isnan(d[:,:,0]).mean(1)\\n else:\\n raise ValueError(\\\"`by` should be either 'marker' or 'individual'.\\\")\",\n \"def gdx_isnan(val,gdxf):\\n return val in [SPECIAL_VALUES[0], SPECIAL_VALUES[1]]\",\n \"def get_bin_band_arr(self):\\n\\n fn = self._lib['cwtObj_getBinBandArr']\\n fn.argtypes = [POINTER(OpaqueCWT)]\\n fn.restype = c_void_p\\n p = fn(self._obj)\\n ret = np.frombuffer((c_int * self.num).from_address(p), np.int32).copy()\\n return ret\",\n \"def notna(self):\\n return super().notna()\",\n \"def getNZ(self):\\n return self._get_nz( )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7544999","0.7434405","0.7288697","0.6567842","0.6520311","0.6363698","0.6319562","0.62501335","0.6163293","0.61502856","0.61502856","0.61240387","0.6110622","0.60905856","0.590359","0.5895869","0.58876556","0.58424926","0.58220965","0.5796688","0.5778561","0.57269347","0.5697276","0.5692186","0.5664606","0.5648056","0.5614599","0.558892","0.5553388","0.5547773","0.55438596","0.55414027","0.5538868","0.5538868","0.55292845","0.5524298","0.5514468","0.551139","0.5499059","0.54568845","0.54413116","0.54289687","0.5420883","0.5406187","0.5395783","0.53955954","0.5383611","0.5383192","0.5375553","0.53724414","0.53643703","0.53610915","0.53538567","0.53507227","0.53458756","0.5337329","0.53130645","0.530204","0.52835155","0.5281505","0.5280815","0.5277185","0.52676123","0.5258759","0.5258387","0.5254711","0.52536994","0.52447087","0.5226507","0.5226414","0.5220528","0.52157587","0.5213573","0.52073765","0.52072257","0.52064055","0.52026284","0.519964","0.5196084","0.51917446","0.518738","0.51872295","0.51862764","0.51839083","0.517904","0.51716214","0.5161016","0.5160043","0.515909","0.5158713","0.51535845","0.5150214","0.5147213","0.51391715","0.51378745","0.51344496","0.51323783","0.5130883","0.5112671","0.5100205"],"string":"[\n \"0.7544999\",\n \"0.7434405\",\n \"0.7288697\",\n \"0.6567842\",\n \"0.6520311\",\n \"0.6363698\",\n \"0.6319562\",\n \"0.62501335\",\n \"0.6163293\",\n \"0.61502856\",\n \"0.61502856\",\n \"0.61240387\",\n \"0.6110622\",\n \"0.60905856\",\n \"0.590359\",\n \"0.5895869\",\n \"0.58876556\",\n \"0.58424926\",\n \"0.58220965\",\n \"0.5796688\",\n \"0.5778561\",\n \"0.57269347\",\n \"0.5697276\",\n \"0.5692186\",\n \"0.5664606\",\n \"0.5648056\",\n \"0.5614599\",\n \"0.558892\",\n \"0.5553388\",\n \"0.5547773\",\n \"0.55438596\",\n \"0.55414027\",\n \"0.5538868\",\n \"0.5538868\",\n \"0.55292845\",\n \"0.5524298\",\n \"0.5514468\",\n \"0.551139\",\n \"0.5499059\",\n \"0.54568845\",\n \"0.54413116\",\n \"0.54289687\",\n \"0.5420883\",\n \"0.5406187\",\n \"0.5395783\",\n \"0.53955954\",\n \"0.5383611\",\n \"0.5383192\",\n \"0.5375553\",\n \"0.53724414\",\n \"0.53643703\",\n \"0.53610915\",\n \"0.53538567\",\n \"0.53507227\",\n \"0.53458756\",\n \"0.5337329\",\n \"0.53130645\",\n \"0.530204\",\n \"0.52835155\",\n \"0.5281505\",\n \"0.5280815\",\n \"0.5277185\",\n \"0.52676123\",\n \"0.5258759\",\n \"0.5258387\",\n \"0.5254711\",\n \"0.52536994\",\n \"0.52447087\",\n \"0.5226507\",\n \"0.5226414\",\n \"0.5220528\",\n \"0.52157587\",\n \"0.5213573\",\n \"0.52073765\",\n \"0.52072257\",\n \"0.52064055\",\n \"0.52026284\",\n \"0.519964\",\n \"0.5196084\",\n \"0.51917446\",\n \"0.518738\",\n \"0.51872295\",\n \"0.51862764\",\n \"0.51839083\",\n \"0.517904\",\n \"0.51716214\",\n \"0.5161016\",\n \"0.5160043\",\n \"0.515909\",\n \"0.5158713\",\n \"0.51535845\",\n \"0.5150214\",\n \"0.5147213\",\n \"0.51391715\",\n \"0.51378745\",\n \"0.51344496\",\n \"0.51323783\",\n \"0.5130883\",\n \"0.5112671\",\n \"0.5100205\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86186093"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95688,"cells":{"query":{"kind":"string","value":"Returns the NumPy type associated with gdal.Band.DataType"},"document":{"kind":"string","value":"def NumPyDataType(self):\n datatype = self.DataType\n if datatype == gdalconst.GDT_Byte:\n pixeltype = self.GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE')\n if pixeltype == 'SIGNEDBYTE':\n return numpy.int8\n return numpy.uint8\n elif datatype == gdalconst.GDT_UInt16:\n return numpy.uint16\n elif datatype == gdalconst.GDT_UInt32:\n return numpy.uint32\n elif datatype == gdalconst.GDT_Int16:\n return numpy.int16\n elif datatype == gdalconst.GDT_Int32:\n return numpy.int32\n elif datatype == gdalconst.GDT_Float32:\n return numpy.float32\n elif datatype == gdalconst.GDT_Float64:\n return numpy.float64\n else:\n raise ValueError(\n \"Cannot handle DataType: {0}\".format(\n gdal.GetDataTypeName(datatype)\n )\n )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_band_dtype(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n return self.dataset.GetRasterBand(band_idx).DataType","def type(self):\n # type: () -> type\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]","def GetDataType(self):\n return _gmat_py.ElementWrapper_GetDataType(self)","def get_data_type(self, idx):\n return(self.data[idx].dtype)","def dtype(self):\n return self.dataset.dtype","def dtype(self):\n return self._vars[0].dtype","def _numpy_datatype_from_nd4j_context():\n nd4j_datatype = data_type()\n if nd4j_datatype == 'double':\n return np.float64\n elif nd4j_datatype == 'float':\n return np.float32\n elif nd4j_datatype == 'half':\n return np.float16","def get_gdal_datatype(in_datatype):\n if in_datatype == 'float64':\n return gdalconst.GDT_Float64\n elif in_datatype == 'float32':\n return gdalconst.GDT_Float32\n elif in_datatype == 'int32':\n return gdalconst.GDT_Int32\n else:\n raise ValueError(\n 'Unrecognized data type in get_gdal_datatype():\\n {}'.format(\n in_datatype))","def getDataType(self, label):\n\n try:\n return self._data[label].dtype\n except KeyError:\n return None","def GetNoDataValue(self):\n result = super(Band, self).GetNoDataValue()\n if result is not None:\n return self.NumPyDataType(result)","def dtype():\n return RaggedDtype()","def dtype(self):\n return self._channel.datatype","def dtype(self):\n return self._fl.raw.dtype","def dtype(self):\n return self.data.dtype","def type(self):\n return _python_type_map[self.arrow_dtype.id]","def datatype_name(self):\n return 'array'","def dtype(self):\n return self._data.dtype","def dtype(self):\n return self._data.dtype","def dtype(self):\n return self.array.dtype","def nptype2gdal(dtype):\n if dtype == np.float32:\n return gdal.GDT_Float32\n elif dtype == np.float64:\n return gdal.GDT_Float64\n elif dtype == np.int32:\n return gdal.GDT_Int32\n elif dtype == np.bool or dtype == np.uint8:\n return gdal.GDT_Byte\n return gdal.GDT_Float64","def kind(self):\n # type () -> str\n return np.dtype(self.type).kind","def data_type(self):\n try:\n return self.attributes.workspace.attributes['library:datatype']['items']\n except Exception as e:\n self._logger.debug(f\"data_category {e}\")\n return None","def data_type():\n return DataTypeUtil.getDTypeForName(DataTypeUtil.getDtypeFromContext())","def dtype(self):\n return self._dtype","def dtype(self) -> DtypeLike:\n\n return self.data.dtype","def datatype(self):\n # datatype is type of first dataarg\n return self[self.dataargs()[0]].typename","def dtype(a):\n return a.dtype","def dtype(self) -> DataType:\n return self._dtype","def dtype(self):\n return self.MJD.dtype","def dtype(self):\n return self.config[\"dtype\"]","def type(self) -> DataType:\n return self._type","def numpy_type(sqltype):\n m = re.match(\"char\\(([0-9]+)\\)\", sqltype.strip())\n if m is not None:\n # It's a string\n return np.dtype(\"|S\"+m.group(1))\n else:\n # It's a numeric type\n if sqltype == \"integer\" or sqltype == \"int\":\n return np.int32\n elif sqltype == \"bigint\":\n return np.int64\n elif sqltype == \"real\":\n return np.float32\n elif sqltype == \"float\":\n return np.float64\n else:\n raise ValueError(\"Unsupported data type \"+sqltype)","def data_type(self):\r\n return self._data_type","def dtype(self):\n return self._dtype","def dtype(self):\n return self._dtype","def dtype(self):\n return self._dtype","def getDataType(self):\n\n return self._dataType","def dtype(self):\n # type: () -> ExtensionDtype\n return self._dtype","def dtype(self) -> str:\n return self._dtype","def get_dtype(arr, backend='autograd'):\n if backend == 'pytorch':\n return pytorch_dtype_query_mapping_dict[arr.dtype]\n elif backend == 'autograd':\n return str(arr.dtype)","def dtype(self):\n if self.num_polygons < 2 ** 8:\n dtype = numpy.uint8\n elif self.num_polygons < 2 ** 16:\n dtype = numpy.uint16\n else:\n dtype = numpy.uint32\n return dtype","def data_type(self):\n return self._data_type","def data_type(self):\n return self._data_type","def data_type(self):\n return self._data_type","def dtype(self) -> np.dtype:\n return self._channel_arrays[0].dtype","def dtype(basis: Basis) -> FDataBasisDType:\n return FDataBasisDType(basis=basis)","def typecode (self) :\r\n return self.numeric_typecode","def type(self):\n return struct.unpack('<B', self.raw_data[0])[0]","def _nd4j_datatype_from_np_array(array):\n return _nd4j_datatype_from_np(array.dtype.name)","def type(self) -> np.dtype:\n return self._tensorInfo.dtype","def GetType(vDataSet):\r\n return imaris_types[str(vDataSet.GetType())]","def type(self) -> 'Data_Type':\n return Data_Type(self._info.htype, self._info.ptype)","def dtype(self):\n return self.__dtype","def getTypeCode(self):\n return _libsbml.Dimensions_getTypeCode(self)","def data_type(self) -> pulumi.Input['AssetModelDataType']:\n return pulumi.get(self, \"data_type\")","def to_numpy(self) -> np.dtype:\n return self._numpy_type","async def get_dtype(self):\r\n pass","def data_type(x):\n return (\n DATA_TYPES.get(file_format(x)) or\n DATA_TYPES.get(genomic_file_ext(x))\n )","def dataType(self, data):\n if isinstance(data,str):\n return STRING\n elif isinstance(data,dict):\n return ASSOC\n elif isinstance(data,int) or isinstance(data,float):\n return STRING\n elif is_python2() and isinstance(data,long):\n return STRING\n elif isinstance(data, SpecArray.SpecArrayData):\n self.rows, self.cols = data.shape\n return data.type","def dtype(self) -> np.dtype:\n ...","def getDataSetType(self):\n return self.__data_set_type__","def np_dtype(dali_dtype):\n return numpy.dtype(dali_dtype)","def datatype(self) -> MetricTypeEnum:\n return self._datatype","def dtype(self) -> tf.dtypes.DType:","def datatype(self):\n return self._datatype","def data_type(self) -> int:\n return self.data[\"args\"][\"dataType\"]","def dtype(self) -> Type[DTypeFloat]:\n\n return self._dtype","def type(self):\n # easy enough\n return self._dataset._pyre_id.type","def dtype(self) -> np.dtype:\n return self._dtype","def datatype(self):\n hcell = self._get_hcell2()\n celltype = hcell[\"celltype\"]\n assert celltype == \"structured\"\n return hcell[\"datatype\"]","def dtype(self):\n return self.initial_value.dtype","def recarrtype(self):\n return str(self.dtype.shape) + self.dtype.base.str[1:]","def type(self):\n return self.data.type","def data_type(self):\n return self.unpack_dword(0xC) & DEVPROP_MASK_TYPE","def _dtype(self):\n if self._dtype_ is not None:\n return self._dtype_\n dtype = None\n for raw_extra, filename in zip(self._raw_extras, self._filenames):\n for ent in raw_extra[\"ent\"]:\n if ent is not None:\n with _fiff_get_fid(filename) as fid:\n fid.seek(ent.pos, 0)\n tag = read_tag_info(fid)\n if tag is not None:\n if tag.type in (\n FIFF.FIFFT_COMPLEX_FLOAT,\n FIFF.FIFFT_COMPLEX_DOUBLE,\n ):\n dtype = np.complex128\n else:\n dtype = np.float64\n if dtype is not None:\n break\n if dtype is not None:\n break\n if dtype is None:\n raise RuntimeError(\"bug in reading\")\n self._dtype_ = dtype\n return dtype","def get_data_type(self, col):\n if ((self.data_df[col].dtype == np.int64) or (self.data_df[col].dtype == np.int32)):\n return 'int'\n elif ((self.data_df[col].dtype == np.float64) or (self.data_df[col].dtype == np.float32)):\n return 'float'\n else:\n raise ValueError(\"Unknown data type of feature %s: must be int or float\" % col)","def basetype(series):\n if not series.dtype == types.ObjectType:\n # Type is specific already; return it without further inspection.\n return series.dtype\n # Now type is Object (string) See if any more specific type can be deduced:","def astype(self, dtype):\n return type(self)(self.data.astype(dtype), self.bset)","def form_datatype(self):\n et = get_lh5_element_type(self)\n return 'array<1>{array<1>{' + et + '}}'","def _determine_dtype(self, b):\n if 64 < b or 1 > b:\n raise ValueError('b-Bit size must be 1 <= b <= 64')\n elif b > 32:\n return np.int64\n elif b > 16:\n return np.int32\n elif b > 8:\n return np.int16\n elif b > 1:\n return np.int8\n return np.bool","def dtype(self) -> np.dtype[np.void]:\n return np.dtype(list(self.items()))","def dtype_type( dtype, name = None ):\n if name:\n for property in dtype.descr:\n if property[ 0 ] == name:\n return property[ 1 ]\n raise ValueError( \"Property not found\" )\n else:\n if len( dtype.descr ) > 1:\n raise ValueError( \"Multiple types present\" )\n\n return dtype.descr[ 0 ][ 1 ]","def get_var_type(self, var_name):\n return str(self.get_value_ref(var_name).dtype)","def ogrtype2npytype(ogrtype):\n if ogrtype == ogr.OFTReal:\n return np.float64\n elif ogrtype == ogr.OFTInteger:\n return np.int32\n raise TypeError(\"OGR type cannot be mapped to a numpy dtype.\")","def kind(self):\n # type () -> str\n if pa.types.is_date(self.arrow_dtype):\n return \"O\"\n else:\n return np.dtype(self.arrow_dtype.to_pandas_dtype()).kind","def datatype(f):\n from numpy import bool, uint8, uint16, int32\n code = f.dtype\n if code == bool: type='binary'\n elif code == uint8: type='uint8'\n elif code == uint16: type='uint16'\n elif code == int32: type='int32'\n else:\n assert 0,'Does not accept this typecode: %s' % code\n return type","def get_dimension_type(self, dim):\n dim = self.get_dimension(dim)\n if dim is None:\n return None\n elif dim.type is not None:\n return dim.type\n elif dim in self.vdims:\n return np.float64\n return self.interface.dimension_type(self, dim)","def test_out_dtype(self):\n byt = bytscl(self.array1)\n dtype = byt.dtype\n self.assertEqual(dtype, 'uint8')","def arrayToGLType( cls, value ):\n typeCode = value.dtype\n constant = ARRAY_TO_GL_TYPE_MAPPING.get( typeCode )\n if constant is None:\n raise TypeError(\n \"\"\"Don't know GL type for array of type %r, known types: %s\\nvalue:%s\"\"\"%(\n typeCode, list(ARRAY_TO_GL_TYPE_MAPPING.keys()), value,\n )\n )\n return constant","def get_sensor_type(self):\n return self.data[1][:-1]","def base_dtype(self):\r\n _base_dtype = [\r\n (ptidCol, '<i8'),\r\n (surveyidCol, 'S25'),\r\n (siteCol, 'S10'),\r\n (tranCol, '<i4'),\r\n (datetimeCol, '<M8[us]'),\r\n (dateCol, 'S12'),\r\n (timeCol, 'S11'),\r\n (latCol, '<f8'),\r\n (lonCol, '<f8'),\r\n (depObsCol, '<f8'),\r\n (depInterpCol, '<f8'),\r\n (videoCol, '<i4'),\r\n ]\r\n return _base_dtype","def data_type_spec(self) -> Optional[pulumi.Input['AssetModelDataTypeSpec']]:\n return pulumi.get(self, \"data_type_spec\")","def pixeltype(self):\n return _image.image_pixeltype(self)","def data_type_id(self) -> str:\n return self._data_type_id","def dtype_to_type(dtype) -> Type:\n if dtype == np.object:\n return str\n else:\n return type(np.zeros(1, dtype).item())","def get_dtype(col):\n dtype = col.dtype\n\n if isinstance(dtype, CategoricalDtype):\n col = col.astype(type(col.values[0]))\n out = get_dtype(col)\n elif np.issubdtype(dtype, np.floating):\n out = 'float32'\n elif np.issubdtype(dtype, np.integer):\n if col.max() < 32767:\n out = 'int16'\n else:\n out = 'int32'\n elif np.issubdtype(dtype, np.object_):\n size = int(col.astype(str).str.len().max())\n out = 'S{:}'.format(size)\n else:\n out = dtype\n\n return out","def convert_numpy_type(cls, dtype):\n\n import numpy as np\n\n m = {\n 'int64': cls.DATATYPE_INTEGER64,\n 'float64': cls.DATATYPE_FLOAT,\n 'object': cls.DATATYPE_TEXT # Hack. Pandas makes strings into object.\n\n }\n\n t = m.get(dtype.name, None)\n\n if not t:\n raise TypeError(\n \"Failed to convert numpy type: '{}' \".format(\n dtype.name))\n\n return t","def geometry_type(number):\n try:\n return GDAL_GEOMETRY_TYPES[number]\n except KeyError:\n return","def datatype(parameter):\n return type(parameter)","def dtype_ref(self):\n return self._dtype_ref"],"string":"[\n \"def get_band_dtype(self, band_idx=1):\\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\\n raise IndexError(\\\"band index is out of range\\\")\\n return self.dataset.GetRasterBand(band_idx).DataType\",\n \"def type(self):\\n # type: () -> type\\n return _python_type_map[str(self.xnd_dtype.hidden_dtype)]\",\n \"def GetDataType(self):\\n return _gmat_py.ElementWrapper_GetDataType(self)\",\n \"def get_data_type(self, idx):\\n return(self.data[idx].dtype)\",\n \"def dtype(self):\\n return self.dataset.dtype\",\n \"def dtype(self):\\n return self._vars[0].dtype\",\n \"def _numpy_datatype_from_nd4j_context():\\n nd4j_datatype = data_type()\\n if nd4j_datatype == 'double':\\n return np.float64\\n elif nd4j_datatype == 'float':\\n return np.float32\\n elif nd4j_datatype == 'half':\\n return np.float16\",\n \"def get_gdal_datatype(in_datatype):\\n if in_datatype == 'float64':\\n return gdalconst.GDT_Float64\\n elif in_datatype == 'float32':\\n return gdalconst.GDT_Float32\\n elif in_datatype == 'int32':\\n return gdalconst.GDT_Int32\\n else:\\n raise ValueError(\\n 'Unrecognized data type in get_gdal_datatype():\\\\n {}'.format(\\n in_datatype))\",\n \"def getDataType(self, label):\\n\\n try:\\n return self._data[label].dtype\\n except KeyError:\\n return None\",\n \"def GetNoDataValue(self):\\n result = super(Band, self).GetNoDataValue()\\n if result is not None:\\n return self.NumPyDataType(result)\",\n \"def dtype():\\n return RaggedDtype()\",\n \"def dtype(self):\\n return self._channel.datatype\",\n \"def dtype(self):\\n return self._fl.raw.dtype\",\n \"def dtype(self):\\n return self.data.dtype\",\n \"def type(self):\\n return _python_type_map[self.arrow_dtype.id]\",\n \"def datatype_name(self):\\n return 'array'\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def dtype(self):\\n return self._data.dtype\",\n \"def dtype(self):\\n return self.array.dtype\",\n \"def nptype2gdal(dtype):\\n if dtype == np.float32:\\n return gdal.GDT_Float32\\n elif dtype == np.float64:\\n return gdal.GDT_Float64\\n elif dtype == np.int32:\\n return gdal.GDT_Int32\\n elif dtype == np.bool or dtype == np.uint8:\\n return gdal.GDT_Byte\\n return gdal.GDT_Float64\",\n \"def kind(self):\\n # type () -> str\\n return np.dtype(self.type).kind\",\n \"def data_type(self):\\n try:\\n return self.attributes.workspace.attributes['library:datatype']['items']\\n except Exception as e:\\n self._logger.debug(f\\\"data_category {e}\\\")\\n return None\",\n \"def data_type():\\n return DataTypeUtil.getDTypeForName(DataTypeUtil.getDtypeFromContext())\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self) -> DtypeLike:\\n\\n return self.data.dtype\",\n \"def datatype(self):\\n # datatype is type of first dataarg\\n return self[self.dataargs()[0]].typename\",\n \"def dtype(a):\\n return a.dtype\",\n \"def dtype(self) -> DataType:\\n return self._dtype\",\n \"def dtype(self):\\n return self.MJD.dtype\",\n \"def dtype(self):\\n return self.config[\\\"dtype\\\"]\",\n \"def type(self) -> DataType:\\n return self._type\",\n \"def numpy_type(sqltype):\\n m = re.match(\\\"char\\\\(([0-9]+)\\\\)\\\", sqltype.strip())\\n if m is not None:\\n # It's a string\\n return np.dtype(\\\"|S\\\"+m.group(1))\\n else:\\n # It's a numeric type\\n if sqltype == \\\"integer\\\" or sqltype == \\\"int\\\":\\n return np.int32\\n elif sqltype == \\\"bigint\\\":\\n return np.int64\\n elif sqltype == \\\"real\\\":\\n return np.float32\\n elif sqltype == \\\"float\\\":\\n return np.float64\\n else:\\n raise ValueError(\\\"Unsupported data type \\\"+sqltype)\",\n \"def data_type(self):\\r\\n return self._data_type\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def dtype(self):\\n return self._dtype\",\n \"def getDataType(self):\\n\\n return self._dataType\",\n \"def dtype(self):\\n # type: () -> ExtensionDtype\\n return self._dtype\",\n \"def dtype(self) -> str:\\n return self._dtype\",\n \"def get_dtype(arr, backend='autograd'):\\n if backend == 'pytorch':\\n return pytorch_dtype_query_mapping_dict[arr.dtype]\\n elif backend == 'autograd':\\n return str(arr.dtype)\",\n \"def dtype(self):\\n if self.num_polygons < 2 ** 8:\\n dtype = numpy.uint8\\n elif self.num_polygons < 2 ** 16:\\n dtype = numpy.uint16\\n else:\\n dtype = numpy.uint32\\n return dtype\",\n \"def data_type(self):\\n return self._data_type\",\n \"def data_type(self):\\n return self._data_type\",\n \"def data_type(self):\\n return self._data_type\",\n \"def dtype(self) -> np.dtype:\\n return self._channel_arrays[0].dtype\",\n \"def dtype(basis: Basis) -> FDataBasisDType:\\n return FDataBasisDType(basis=basis)\",\n \"def typecode (self) :\\r\\n return self.numeric_typecode\",\n \"def type(self):\\n return struct.unpack('<B', self.raw_data[0])[0]\",\n \"def _nd4j_datatype_from_np_array(array):\\n return _nd4j_datatype_from_np(array.dtype.name)\",\n \"def type(self) -> np.dtype:\\n return self._tensorInfo.dtype\",\n \"def GetType(vDataSet):\\r\\n return imaris_types[str(vDataSet.GetType())]\",\n \"def type(self) -> 'Data_Type':\\n return Data_Type(self._info.htype, self._info.ptype)\",\n \"def dtype(self):\\n return self.__dtype\",\n \"def getTypeCode(self):\\n return _libsbml.Dimensions_getTypeCode(self)\",\n \"def data_type(self) -> pulumi.Input['AssetModelDataType']:\\n return pulumi.get(self, \\\"data_type\\\")\",\n \"def to_numpy(self) -> np.dtype:\\n return self._numpy_type\",\n \"async def get_dtype(self):\\r\\n pass\",\n \"def data_type(x):\\n return (\\n DATA_TYPES.get(file_format(x)) or\\n DATA_TYPES.get(genomic_file_ext(x))\\n )\",\n \"def dataType(self, data):\\n if isinstance(data,str):\\n return STRING\\n elif isinstance(data,dict):\\n return ASSOC\\n elif isinstance(data,int) or isinstance(data,float):\\n return STRING\\n elif is_python2() and isinstance(data,long):\\n return STRING\\n elif isinstance(data, SpecArray.SpecArrayData):\\n self.rows, self.cols = data.shape\\n return data.type\",\n \"def dtype(self) -> np.dtype:\\n ...\",\n \"def getDataSetType(self):\\n return self.__data_set_type__\",\n \"def np_dtype(dali_dtype):\\n return numpy.dtype(dali_dtype)\",\n \"def datatype(self) -> MetricTypeEnum:\\n return self._datatype\",\n \"def dtype(self) -> tf.dtypes.DType:\",\n \"def datatype(self):\\n return self._datatype\",\n \"def data_type(self) -> int:\\n return self.data[\\\"args\\\"][\\\"dataType\\\"]\",\n \"def dtype(self) -> Type[DTypeFloat]:\\n\\n return self._dtype\",\n \"def type(self):\\n # easy enough\\n return self._dataset._pyre_id.type\",\n \"def dtype(self) -> np.dtype:\\n return self._dtype\",\n \"def datatype(self):\\n hcell = self._get_hcell2()\\n celltype = hcell[\\\"celltype\\\"]\\n assert celltype == \\\"structured\\\"\\n return hcell[\\\"datatype\\\"]\",\n \"def dtype(self):\\n return self.initial_value.dtype\",\n \"def recarrtype(self):\\n return str(self.dtype.shape) + self.dtype.base.str[1:]\",\n \"def type(self):\\n return self.data.type\",\n \"def data_type(self):\\n return self.unpack_dword(0xC) & DEVPROP_MASK_TYPE\",\n \"def _dtype(self):\\n if self._dtype_ is not None:\\n return self._dtype_\\n dtype = None\\n for raw_extra, filename in zip(self._raw_extras, self._filenames):\\n for ent in raw_extra[\\\"ent\\\"]:\\n if ent is not None:\\n with _fiff_get_fid(filename) as fid:\\n fid.seek(ent.pos, 0)\\n tag = read_tag_info(fid)\\n if tag is not None:\\n if tag.type in (\\n FIFF.FIFFT_COMPLEX_FLOAT,\\n FIFF.FIFFT_COMPLEX_DOUBLE,\\n ):\\n dtype = np.complex128\\n else:\\n dtype = np.float64\\n if dtype is not None:\\n break\\n if dtype is not None:\\n break\\n if dtype is None:\\n raise RuntimeError(\\\"bug in reading\\\")\\n self._dtype_ = dtype\\n return dtype\",\n \"def get_data_type(self, col):\\n if ((self.data_df[col].dtype == np.int64) or (self.data_df[col].dtype == np.int32)):\\n return 'int'\\n elif ((self.data_df[col].dtype == np.float64) or (self.data_df[col].dtype == np.float32)):\\n return 'float'\\n else:\\n raise ValueError(\\\"Unknown data type of feature %s: must be int or float\\\" % col)\",\n \"def basetype(series):\\n if not series.dtype == types.ObjectType:\\n # Type is specific already; return it without further inspection.\\n return series.dtype\\n # Now type is Object (string) See if any more specific type can be deduced:\",\n \"def astype(self, dtype):\\n return type(self)(self.data.astype(dtype), self.bset)\",\n \"def form_datatype(self):\\n et = get_lh5_element_type(self)\\n return 'array<1>{array<1>{' + et + '}}'\",\n \"def _determine_dtype(self, b):\\n if 64 < b or 1 > b:\\n raise ValueError('b-Bit size must be 1 <= b <= 64')\\n elif b > 32:\\n return np.int64\\n elif b > 16:\\n return np.int32\\n elif b > 8:\\n return np.int16\\n elif b > 1:\\n return np.int8\\n return np.bool\",\n \"def dtype(self) -> np.dtype[np.void]:\\n return np.dtype(list(self.items()))\",\n \"def dtype_type( dtype, name = None ):\\n if name:\\n for property in dtype.descr:\\n if property[ 0 ] == name:\\n return property[ 1 ]\\n raise ValueError( \\\"Property not found\\\" )\\n else:\\n if len( dtype.descr ) > 1:\\n raise ValueError( \\\"Multiple types present\\\" )\\n\\n return dtype.descr[ 0 ][ 1 ]\",\n \"def get_var_type(self, var_name):\\n return str(self.get_value_ref(var_name).dtype)\",\n \"def ogrtype2npytype(ogrtype):\\n if ogrtype == ogr.OFTReal:\\n return np.float64\\n elif ogrtype == ogr.OFTInteger:\\n return np.int32\\n raise TypeError(\\\"OGR type cannot be mapped to a numpy dtype.\\\")\",\n \"def kind(self):\\n # type () -> str\\n if pa.types.is_date(self.arrow_dtype):\\n return \\\"O\\\"\\n else:\\n return np.dtype(self.arrow_dtype.to_pandas_dtype()).kind\",\n \"def datatype(f):\\n from numpy import bool, uint8, uint16, int32\\n code = f.dtype\\n if code == bool: type='binary'\\n elif code == uint8: type='uint8'\\n elif code == uint16: type='uint16'\\n elif code == int32: type='int32'\\n else:\\n assert 0,'Does not accept this typecode: %s' % code\\n return type\",\n \"def get_dimension_type(self, dim):\\n dim = self.get_dimension(dim)\\n if dim is None:\\n return None\\n elif dim.type is not None:\\n return dim.type\\n elif dim in self.vdims:\\n return np.float64\\n return self.interface.dimension_type(self, dim)\",\n \"def test_out_dtype(self):\\n byt = bytscl(self.array1)\\n dtype = byt.dtype\\n self.assertEqual(dtype, 'uint8')\",\n \"def arrayToGLType( cls, value ):\\n typeCode = value.dtype\\n constant = ARRAY_TO_GL_TYPE_MAPPING.get( typeCode )\\n if constant is None:\\n raise TypeError(\\n \\\"\\\"\\\"Don't know GL type for array of type %r, known types: %s\\\\nvalue:%s\\\"\\\"\\\"%(\\n typeCode, list(ARRAY_TO_GL_TYPE_MAPPING.keys()), value,\\n )\\n )\\n return constant\",\n \"def get_sensor_type(self):\\n return self.data[1][:-1]\",\n \"def base_dtype(self):\\r\\n _base_dtype = [\\r\\n (ptidCol, '<i8'),\\r\\n (surveyidCol, 'S25'),\\r\\n (siteCol, 'S10'),\\r\\n (tranCol, '<i4'),\\r\\n (datetimeCol, '<M8[us]'),\\r\\n (dateCol, 'S12'),\\r\\n (timeCol, 'S11'),\\r\\n (latCol, '<f8'),\\r\\n (lonCol, '<f8'),\\r\\n (depObsCol, '<f8'),\\r\\n (depInterpCol, '<f8'),\\r\\n (videoCol, '<i4'),\\r\\n ]\\r\\n return _base_dtype\",\n \"def data_type_spec(self) -> Optional[pulumi.Input['AssetModelDataTypeSpec']]:\\n return pulumi.get(self, \\\"data_type_spec\\\")\",\n \"def pixeltype(self):\\n return _image.image_pixeltype(self)\",\n \"def data_type_id(self) -> str:\\n return self._data_type_id\",\n \"def dtype_to_type(dtype) -> Type:\\n if dtype == np.object:\\n return str\\n else:\\n return type(np.zeros(1, dtype).item())\",\n \"def get_dtype(col):\\n dtype = col.dtype\\n\\n if isinstance(dtype, CategoricalDtype):\\n col = col.astype(type(col.values[0]))\\n out = get_dtype(col)\\n elif np.issubdtype(dtype, np.floating):\\n out = 'float32'\\n elif np.issubdtype(dtype, np.integer):\\n if col.max() < 32767:\\n out = 'int16'\\n else:\\n out = 'int32'\\n elif np.issubdtype(dtype, np.object_):\\n size = int(col.astype(str).str.len().max())\\n out = 'S{:}'.format(size)\\n else:\\n out = dtype\\n\\n return out\",\n \"def convert_numpy_type(cls, dtype):\\n\\n import numpy as np\\n\\n m = {\\n 'int64': cls.DATATYPE_INTEGER64,\\n 'float64': cls.DATATYPE_FLOAT,\\n 'object': cls.DATATYPE_TEXT # Hack. Pandas makes strings into object.\\n\\n }\\n\\n t = m.get(dtype.name, None)\\n\\n if not t:\\n raise TypeError(\\n \\\"Failed to convert numpy type: '{}' \\\".format(\\n dtype.name))\\n\\n return t\",\n \"def geometry_type(number):\\n try:\\n return GDAL_GEOMETRY_TYPES[number]\\n except KeyError:\\n return\",\n \"def datatype(parameter):\\n return type(parameter)\",\n \"def dtype_ref(self):\\n return self._dtype_ref\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7380224","0.7158212","0.6904815","0.68386203","0.67266405","0.6708839","0.66951716","0.663477","0.663243","0.6614309","0.6602418","0.6594213","0.65762633","0.65756744","0.65720206","0.657198","0.6555877","0.6555877","0.6546687","0.65154874","0.6515137","0.6498225","0.6493472","0.6466837","0.6455337","0.6454839","0.6453198","0.6451606","0.643996","0.6423948","0.64215165","0.64212596","0.640249","0.6401839","0.6401839","0.6401839","0.6401397","0.6385074","0.63768667","0.6376779","0.6369398","0.63643914","0.63643914","0.63643914","0.63552743","0.63526255","0.6332715","0.6330258","0.62927747","0.627919","0.6273387","0.62716174","0.62577516","0.62504685","0.6245434","0.62114185","0.62076026","0.61964643","0.61881423","0.6175947","0.6168868","0.6151739","0.61351687","0.6133396","0.6118896","0.6083055","0.60808295","0.6067263","0.60625273","0.6040692","0.6025394","0.6008395","0.6007711","0.5972361","0.59317416","0.5924599","0.591985","0.58971864","0.5857541","0.585655","0.5846714","0.58189565","0.5803598","0.57848287","0.5770629","0.57440484","0.57424396","0.57290524","0.57259095","0.5721711","0.5702034","0.568362","0.5680798","0.5676757","0.5650429","0.5647226","0.56313425","0.56152123","0.56072325","0.55883914"],"string":"[\n \"0.7380224\",\n \"0.7158212\",\n \"0.6904815\",\n \"0.68386203\",\n \"0.67266405\",\n \"0.6708839\",\n \"0.66951716\",\n \"0.663477\",\n \"0.663243\",\n \"0.6614309\",\n \"0.6602418\",\n \"0.6594213\",\n \"0.65762633\",\n \"0.65756744\",\n \"0.65720206\",\n \"0.657198\",\n \"0.6555877\",\n \"0.6555877\",\n \"0.6546687\",\n \"0.65154874\",\n \"0.6515137\",\n \"0.6498225\",\n \"0.6493472\",\n \"0.6466837\",\n \"0.6455337\",\n \"0.6454839\",\n \"0.6453198\",\n \"0.6451606\",\n \"0.643996\",\n \"0.6423948\",\n \"0.64215165\",\n \"0.64212596\",\n \"0.640249\",\n \"0.6401839\",\n \"0.6401839\",\n \"0.6401839\",\n \"0.6401397\",\n \"0.6385074\",\n \"0.63768667\",\n \"0.6376779\",\n \"0.6369398\",\n \"0.63643914\",\n \"0.63643914\",\n \"0.63643914\",\n \"0.63552743\",\n \"0.63526255\",\n \"0.6332715\",\n \"0.6330258\",\n \"0.62927747\",\n \"0.627919\",\n \"0.6273387\",\n \"0.62716174\",\n \"0.62577516\",\n \"0.62504685\",\n \"0.6245434\",\n \"0.62114185\",\n \"0.62076026\",\n \"0.61964643\",\n \"0.61881423\",\n \"0.6175947\",\n \"0.6168868\",\n \"0.6151739\",\n \"0.61351687\",\n \"0.6133396\",\n \"0.6118896\",\n \"0.6083055\",\n \"0.60808295\",\n \"0.6067263\",\n \"0.60625273\",\n \"0.6040692\",\n \"0.6025394\",\n \"0.6008395\",\n \"0.6007711\",\n \"0.5972361\",\n \"0.59317416\",\n \"0.5924599\",\n \"0.591985\",\n \"0.58971864\",\n \"0.5857541\",\n \"0.585655\",\n \"0.5846714\",\n \"0.58189565\",\n \"0.5803598\",\n \"0.57848287\",\n \"0.5770629\",\n \"0.57440484\",\n \"0.57424396\",\n \"0.57290524\",\n \"0.57259095\",\n \"0.5721711\",\n \"0.5702034\",\n \"0.568362\",\n \"0.5680798\",\n \"0.5676757\",\n \"0.5650429\",\n \"0.5647226\",\n \"0.56313425\",\n \"0.56152123\",\n \"0.56072325\",\n \"0.55883914\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76264346"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95689,"cells":{"query":{"kind":"string","value":"Returns the minimum value that can be stored in this band"},"document":{"kind":"string","value":"def MinimumValue(self):\n datatype = self.NumPyDataType\n if issubclass(datatype, numpy.integer):\n return numpy.iinfo(datatype).min\n elif issubclass(datatype, numpy.floating):\n return -numpy.inf\n else:\n raise TypeError(\"Cannot handle DataType: {0}\".format(datatype))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val","def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val","def _get_minimum(self):\n return self._minimum","def minimum_value(self):\n return self._fitness[self._minidx]","def MinimumValue(self):\n return self._fitness[self._minIndex]","def _get_minimum_value(self):\n if hasattr(self, '_minimum_value'):\n return self._minimum_value\n return None","def _minimum(self) -> float:\n if self._type == \"power\":\n return 1.0\n elif self._type == \"setpoint\":\n return self._product.get_data_config_json()[\"_value_setpoint_min\"]\n elif self._type == \"fan1\":\n fan = 1\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]\n elif self._type == \"fan2\":\n fan = 2\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]\n elif self._type == \"fan3\":\n fan = 3\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]","def getMinValue(self):\n return self.MIN_VALUE","def min_value(self) -> Union[int, float]:\n return self.left_boundary['value']","def get_min(self):\n return self.serie.min()","def get_min(self):\n\t\tif self.left:\n\t\t\treturn self.left.get_min()\n\t\treturn self.value","def min(self) -> Union[float, int, str]:\n return self._data.min()","def min(self):\n return self._min","def min(self):\n return self._min","def min(self):\n return self.__min","def min(self):\r\n return np.min(self.data_array)","def find_min(self):\n return self.min","def find_min(self):\n return self.min","def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")","def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")","def min(self):\n return numpy.ma.min(self.data)","def min_value(self) -> float:\n return DEFAULT_MIN_VALUE","def minimum(self) -> Union[int, float]:\n return self.range[0]","def min(self):\r\n\t\treturn min(self.sample)","def min(self):\n return min(self)","def min(self):\n least = self.data[0]\n \n for i in range(len(self.data)):\n if self.data[i] < least:\n least = self.data[i]\n return least","def minimum(self):\n return self.properties.get('minimum')","def min(self):\n return self._reduce_for_stat_function(F.min, only_numeric=False)","def native_min_value(self) -> float:\n return TEMP_MINIMUM","def min(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"min\")","def min(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"min\")","def Min(data):\n return data.min()","def min(self):\n return self.get_first()","def getmin(self):\n\n return self.X","def min(self) -> float:\n return stats.min(self)","def native_min_value(self) -> float:\n return -9","def get_min(self):\r\n df = pd.read_csv(\"MonthlyRate.csv\")\r\n df = df[df.CurrencyCode == self.choice]\r\n minimum = df.min(axis=1).values[0]\r\n # Round the value to 4 d.p.\r\n minimum = round(float(minimum), 4)\r\n return minimum","def min_flux(self):\n return np.min(self.flux)","def _minimum(self) -> float:\n return self._config[CONF_MIN]","def getMinValue(self):\n if self.left is None:\n return self.data\n return self.left.getMinValue()","def get_min_value(self, dim):\n return self._min_values[dim]","def get_min(self):\n if not self:\n return None\n return self.left.get_min() if self.left else self.value #Ternarary Operator","def native_min_value(self) -> float:\n return self._device.min_offset","def x_min(self):\n return self.get_min_value(self.X_INDEX)","def get_min(self):\n min_value= self.df[self.col_name].min()\n return min_value","def z_min(self):\n return self.get_min_value(self.Z_INDEX)","def get_min(self):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")","def min():\n return KeeperOfMinOrMax(int.__gt__)","def minimum(self):\n return min(self.numbers)","def min(self):\n a = self.array_form\n min = len(a)\n for i in xrange(len(a)):\n if a[i] != i and a[i] < min:\n min = a[i]\n return min","def min(self):\n if 0 in type(self).flatten_shape(self.shape):\n raise ValueError(\"zero-size array has no minimum\")\n if self.isscalar():\n return self.defval\n # If not all blocks are set, then the tensor has an element of defval\n # somewhere.\n m = np.inf if self.is_full() else self.defval\n for v in self.sects.values():\n try:\n m = min(m, np.min(v))\n except ValueError:\n # This block was zero-size, and has no elements.\n pass\n return m","def state_min(self) -> float:\n raise NotImplementedError","def cmin(self):\n return self[\"cmin\"]","def cmin(self):\n return self['cmin']","def accepted_minimum(self):\n\n return self._get_value(self.accepted_minimum_provider)","def get_f_minimum(self):\n return np.min(self._Y)","def min_value(dtype):\n return _api_internal._min_value(dtype)","def minX(self):\n return min(self.getx())","def take_min(self):\n return self.get_first()","def peek_min(self):\n if self.root:\n return self.root.min().value\n raise ValueError(\"cannot perform peek_min on an empty tree\")","def min(self) -> \"Stream[float]\":\n return self.agg(np.min).astype(\"float\")","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL3_GetMinimum(self)","def find_min(self):\n return min(self.nodes, key=int)","def get_min(cls, data: tuple or list) -> float:\n cls._data_validation(data)\n return min(data)","def getMin(self, field): \n return np.min([self.fitnesses[i][field] for i in range(len(self.fitnesses))])","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL2_GetMinimum(self)","def x_min(self) -> ir.FloatingValue:\n return ops.GeoXMin(self).to_expr()","def getMinX(self):\n return self.minx","def get_fmin(self):\n return self.model.predict(self.model.X)[0].min()","def potential_min(self):\n\n return self._args.min","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS3_GetMinimum(self)","def vmin(self):\n return self._vmin","def extractMinimum(self):\n\n return self.heap[1]","def min(self):\n raise NotImplementedError('must be implemented by subclass')","def min(self):\n raise NotImplementedError('must be implemented by subclass')","def find_min(self):\n\n if self.left:\n return self.left.find_min()\n\n return self.data","def get_tmin(self):\n tmin = min(sorted(self.srcData.keys()))\n return tmin","def userMinimum(self) -> float:\n return self._user_minimum","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def minimum_number(self):\n return self._minimum_number","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS2_GetMinimum(self)","def min_level(self):\n return self.__min","def min(self):\n return self._summarize(DataFrameCpu._cmin)","def min_rec(self):\n return self._min_rec","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUC3_GetMinimum(self)","def min(self):\n return self._min_coords","def _min_in_bounds(self, min):\n if min <= self.valmin:\n if not self.closedmin:\n return self.val[0]\n min = self.valmin\n\n if min > self.val[1]:\n min = self.val[1]\n return self._stepped_value(min)","def min(self):\n return self.heap[1]"],"string":"[\n \"def min(self):\\n if self.kind == 'u':\\n return 0\\n else:\\n try:\\n val = iinfo._min_vals[self.key]\\n except KeyError:\\n val = int(-(1 << (self.bits-1)))\\n iinfo._min_vals[self.key] = val\\n return val\",\n \"def min(self):\\n if self.kind == 'u':\\n return 0\\n else:\\n try:\\n val = iinfo._min_vals[self.key]\\n except KeyError:\\n val = int(-(1 << (self.bits-1)))\\n iinfo._min_vals[self.key] = val\\n return val\",\n \"def _get_minimum(self):\\n return self._minimum\",\n \"def minimum_value(self):\\n return self._fitness[self._minidx]\",\n \"def MinimumValue(self):\\n return self._fitness[self._minIndex]\",\n \"def _get_minimum_value(self):\\n if hasattr(self, '_minimum_value'):\\n return self._minimum_value\\n return None\",\n \"def _minimum(self) -> float:\\n if self._type == \\\"power\\\":\\n return 1.0\\n elif self._type == \\\"setpoint\\\":\\n return self._product.get_data_config_json()[\\\"_value_setpoint_min\\\"]\\n elif self._type == \\\"fan1\\\":\\n fan = 1\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\\n elif self._type == \\\"fan2\\\":\\n fan = 2\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\\n elif self._type == \\\"fan3\\\":\\n fan = 3\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\",\n \"def getMinValue(self):\\n return self.MIN_VALUE\",\n \"def min_value(self) -> Union[int, float]:\\n return self.left_boundary['value']\",\n \"def get_min(self):\\n return self.serie.min()\",\n \"def get_min(self):\\n\\t\\tif self.left:\\n\\t\\t\\treturn self.left.get_min()\\n\\t\\treturn self.value\",\n \"def min(self) -> Union[float, int, str]:\\n return self._data.min()\",\n \"def min(self):\\n return self._min\",\n \"def min(self):\\n return self._min\",\n \"def min(self):\\n return self.__min\",\n \"def min(self):\\r\\n return np.min(self.data_array)\",\n \"def find_min(self):\\n return self.min\",\n \"def find_min(self):\\n return self.min\",\n \"def min(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self):\\n return numpy.ma.min(self.data)\",\n \"def min_value(self) -> float:\\n return DEFAULT_MIN_VALUE\",\n \"def minimum(self) -> Union[int, float]:\\n return self.range[0]\",\n \"def min(self):\\r\\n\\t\\treturn min(self.sample)\",\n \"def min(self):\\n return min(self)\",\n \"def min(self):\\n least = self.data[0]\\n \\n for i in range(len(self.data)):\\n if self.data[i] < least:\\n least = self.data[i]\\n return least\",\n \"def minimum(self):\\n return self.properties.get('minimum')\",\n \"def min(self):\\n return self._reduce_for_stat_function(F.min, only_numeric=False)\",\n \"def native_min_value(self) -> float:\\n return TEMP_MINIMUM\",\n \"def min(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def Min(data):\\n return data.min()\",\n \"def min(self):\\n return self.get_first()\",\n \"def getmin(self):\\n\\n return self.X\",\n \"def min(self) -> float:\\n return stats.min(self)\",\n \"def native_min_value(self) -> float:\\n return -9\",\n \"def get_min(self):\\r\\n df = pd.read_csv(\\\"MonthlyRate.csv\\\")\\r\\n df = df[df.CurrencyCode == self.choice]\\r\\n minimum = df.min(axis=1).values[0]\\r\\n # Round the value to 4 d.p.\\r\\n minimum = round(float(minimum), 4)\\r\\n return minimum\",\n \"def min_flux(self):\\n return np.min(self.flux)\",\n \"def _minimum(self) -> float:\\n return self._config[CONF_MIN]\",\n \"def getMinValue(self):\\n if self.left is None:\\n return self.data\\n return self.left.getMinValue()\",\n \"def get_min_value(self, dim):\\n return self._min_values[dim]\",\n \"def get_min(self):\\n if not self:\\n return None\\n return self.left.get_min() if self.left else self.value #Ternarary Operator\",\n \"def native_min_value(self) -> float:\\n return self._device.min_offset\",\n \"def x_min(self):\\n return self.get_min_value(self.X_INDEX)\",\n \"def get_min(self):\\n min_value= self.df[self.col_name].min()\\n return min_value\",\n \"def z_min(self):\\n return self.get_min_value(self.Z_INDEX)\",\n \"def get_min(self):\\n raise NotImplementedError(\\\"This is an abstract method and needs to be implemented in derived classes.\\\")\",\n \"def min():\\n return KeeperOfMinOrMax(int.__gt__)\",\n \"def minimum(self):\\n return min(self.numbers)\",\n \"def min(self):\\n a = self.array_form\\n min = len(a)\\n for i in xrange(len(a)):\\n if a[i] != i and a[i] < min:\\n min = a[i]\\n return min\",\n \"def min(self):\\n if 0 in type(self).flatten_shape(self.shape):\\n raise ValueError(\\\"zero-size array has no minimum\\\")\\n if self.isscalar():\\n return self.defval\\n # If not all blocks are set, then the tensor has an element of defval\\n # somewhere.\\n m = np.inf if self.is_full() else self.defval\\n for v in self.sects.values():\\n try:\\n m = min(m, np.min(v))\\n except ValueError:\\n # This block was zero-size, and has no elements.\\n pass\\n return m\",\n \"def state_min(self) -> float:\\n raise NotImplementedError\",\n \"def cmin(self):\\n return self[\\\"cmin\\\"]\",\n \"def cmin(self):\\n return self['cmin']\",\n \"def accepted_minimum(self):\\n\\n return self._get_value(self.accepted_minimum_provider)\",\n \"def get_f_minimum(self):\\n return np.min(self._Y)\",\n \"def min_value(dtype):\\n return _api_internal._min_value(dtype)\",\n \"def minX(self):\\n return min(self.getx())\",\n \"def take_min(self):\\n return self.get_first()\",\n \"def peek_min(self):\\n if self.root:\\n return self.root.min().value\\n raise ValueError(\\\"cannot perform peek_min on an empty tree\\\")\",\n \"def min(self) -> \\\"Stream[float]\\\":\\n return self.agg(np.min).astype(\\\"float\\\")\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL3_GetMinimum(self)\",\n \"def find_min(self):\\n return min(self.nodes, key=int)\",\n \"def get_min(cls, data: tuple or list) -> float:\\n cls._data_validation(data)\\n return min(data)\",\n \"def getMin(self, field): \\n return np.min([self.fitnesses[i][field] for i in range(len(self.fitnesses))])\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL2_GetMinimum(self)\",\n \"def x_min(self) -> ir.FloatingValue:\\n return ops.GeoXMin(self).to_expr()\",\n \"def getMinX(self):\\n return self.minx\",\n \"def get_fmin(self):\\n return self.model.predict(self.model.X)[0].min()\",\n \"def potential_min(self):\\n\\n return self._args.min\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS3_GetMinimum(self)\",\n \"def vmin(self):\\n return self._vmin\",\n \"def extractMinimum(self):\\n\\n return self.heap[1]\",\n \"def min(self):\\n raise NotImplementedError('must be implemented by subclass')\",\n \"def min(self):\\n raise NotImplementedError('must be implemented by subclass')\",\n \"def find_min(self):\\n\\n if self.left:\\n return self.left.find_min()\\n\\n return self.data\",\n \"def get_tmin(self):\\n tmin = min(sorted(self.srcData.keys()))\\n return tmin\",\n \"def userMinimum(self) -> float:\\n return self._user_minimum\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def minimum_number(self):\\n return self._minimum_number\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS2_GetMinimum(self)\",\n \"def min_level(self):\\n return self.__min\",\n \"def min(self):\\n return self._summarize(DataFrameCpu._cmin)\",\n \"def min_rec(self):\\n return self._min_rec\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUC3_GetMinimum(self)\",\n \"def min(self):\\n return self._min_coords\",\n \"def _min_in_bounds(self, min):\\n if min <= self.valmin:\\n if not self.closedmin:\\n return self.val[0]\\n min = self.valmin\\n\\n if min > self.val[1]:\\n min = self.val[1]\\n return self._stepped_value(min)\",\n \"def min(self):\\n return self.heap[1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7944226","0.7944226","0.7937631","0.78972983","0.7884411","0.78611416","0.7846386","0.7778144","0.77733576","0.7762124","0.7680303","0.7642864","0.75792414","0.75792414","0.75784636","0.7551094","0.75474113","0.75474113","0.7521111","0.7521111","0.74849975","0.7484885","0.7471194","0.7461117","0.74487036","0.7446084","0.744392","0.74194586","0.7390895","0.73744524","0.73744524","0.73413813","0.7310355","0.7296167","0.7289322","0.7249265","0.7248786","0.72440404","0.7236522","0.7224943","0.72162694","0.72142404","0.7209117","0.71570104","0.7145454","0.714173","0.71323127","0.7109216","0.70772934","0.70617133","0.70600265","0.70433307","0.7028071","0.70215166","0.70111954","0.6988008","0.69834995","0.6979648","0.6968356","0.69448626","0.694202","0.69383395","0.69209516","0.6905302","0.6903685","0.6900909","0.6896162","0.6874685","0.6870821","0.6850932","0.68279237","0.6827528","0.6824322","0.68122953","0.68122953","0.68073714","0.68069035","0.6802727","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67865294","0.67865294","0.67865294","0.67865294","0.67847824","0.67812485","0.6780029","0.6770788","0.6767305","0.6761661","0.67479265","0.6722724","0.67219627"],"string":"[\n \"0.7944226\",\n \"0.7944226\",\n \"0.7937631\",\n \"0.78972983\",\n \"0.7884411\",\n \"0.78611416\",\n \"0.7846386\",\n \"0.7778144\",\n \"0.77733576\",\n \"0.7762124\",\n \"0.7680303\",\n \"0.7642864\",\n \"0.75792414\",\n \"0.75792414\",\n \"0.75784636\",\n \"0.7551094\",\n \"0.75474113\",\n \"0.75474113\",\n \"0.7521111\",\n \"0.7521111\",\n \"0.74849975\",\n \"0.7484885\",\n \"0.7471194\",\n \"0.7461117\",\n \"0.74487036\",\n \"0.7446084\",\n \"0.744392\",\n \"0.74194586\",\n \"0.7390895\",\n \"0.73744524\",\n \"0.73744524\",\n \"0.73413813\",\n \"0.7310355\",\n \"0.7296167\",\n \"0.7289322\",\n \"0.7249265\",\n \"0.7248786\",\n \"0.72440404\",\n \"0.7236522\",\n \"0.7224943\",\n \"0.72162694\",\n \"0.72142404\",\n \"0.7209117\",\n \"0.71570104\",\n \"0.7145454\",\n \"0.714173\",\n \"0.71323127\",\n \"0.7109216\",\n \"0.70772934\",\n \"0.70617133\",\n \"0.70600265\",\n \"0.70433307\",\n \"0.7028071\",\n \"0.70215166\",\n \"0.70111954\",\n \"0.6988008\",\n \"0.69834995\",\n \"0.6979648\",\n \"0.6968356\",\n \"0.69448626\",\n \"0.694202\",\n \"0.69383395\",\n \"0.69209516\",\n \"0.6905302\",\n \"0.6903685\",\n \"0.6900909\",\n \"0.6896162\",\n \"0.6874685\",\n \"0.6870821\",\n \"0.6850932\",\n \"0.68279237\",\n \"0.6827528\",\n \"0.6824322\",\n \"0.68122953\",\n \"0.68122953\",\n \"0.68073714\",\n \"0.68069035\",\n \"0.6802727\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67847824\",\n \"0.67812485\",\n \"0.6780029\",\n \"0.6770788\",\n \"0.6767305\",\n \"0.6761661\",\n \"0.67479265\",\n \"0.6722724\",\n \"0.67219627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7393505"},"document_rank":{"kind":"string","value":"28"}}},{"rowIdx":95690,"cells":{"query":{"kind":"string","value":"Returns the minimum value that can be stored in this band"},"document":{"kind":"string","value":"def MaximumValue(self):\n datatype = self.NumPyDataType\n if issubclass(datatype, numpy.integer):\n return numpy.iinfo(datatype).max\n elif issubclass(datatype, numpy.floating):\n return numpy.inf\n else:\n raise TypeError(\"Cannot handle DataType: {0}\".format(datatype))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val","def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val","def _get_minimum(self):\n return self._minimum","def minimum_value(self):\n return self._fitness[self._minidx]","def MinimumValue(self):\n return self._fitness[self._minIndex]","def _get_minimum_value(self):\n if hasattr(self, '_minimum_value'):\n return self._minimum_value\n return None","def _minimum(self) -> float:\n if self._type == \"power\":\n return 1.0\n elif self._type == \"setpoint\":\n return self._product.get_data_config_json()[\"_value_setpoint_min\"]\n elif self._type == \"fan1\":\n fan = 1\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]\n elif self._type == \"fan2\":\n fan = 2\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]\n elif self._type == \"fan3\":\n fan = 3\n return self._product.get_data_config_json()[\"_value_fan_limits\"][\n ((fan - 1) * 2)\n ]","def getMinValue(self):\n return self.MIN_VALUE","def min_value(self) -> Union[int, float]:\n return self.left_boundary['value']","def get_min(self):\n return self.serie.min()","def get_min(self):\n\t\tif self.left:\n\t\t\treturn self.left.get_min()\n\t\treturn self.value","def min(self) -> Union[float, int, str]:\n return self._data.min()","def min(self):\n return self._min","def min(self):\n return self._min","def min(self):\n return self.__min","def min(self):\r\n return np.min(self.data_array)","def find_min(self):\n return self.min","def find_min(self):\n return self.min","def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")","def min(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"min\")","def min(self):\n return numpy.ma.min(self.data)","def min_value(self) -> float:\n return DEFAULT_MIN_VALUE","def minimum(self) -> Union[int, float]:\n return self.range[0]","def min(self):\r\n\t\treturn min(self.sample)","def min(self):\n return min(self)","def min(self):\n least = self.data[0]\n \n for i in range(len(self.data)):\n if self.data[i] < least:\n least = self.data[i]\n return least","def minimum(self):\n return self.properties.get('minimum')","def min(self):\n return self._reduce_for_stat_function(F.min, only_numeric=False)","def MinimumValue(self):\n datatype = self.NumPyDataType\n if issubclass(datatype, numpy.integer):\n return numpy.iinfo(datatype).min\n elif issubclass(datatype, numpy.floating):\n return -numpy.inf\n else:\n raise TypeError(\"Cannot handle DataType: {0}\".format(datatype))","def native_min_value(self) -> float:\n return TEMP_MINIMUM","def min(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"min\")","def min(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"min\")","def Min(data):\n return data.min()","def min(self):\n return self.get_first()","def getmin(self):\n\n return self.X","def min(self) -> float:\n return stats.min(self)","def native_min_value(self) -> float:\n return -9","def get_min(self):\r\n df = pd.read_csv(\"MonthlyRate.csv\")\r\n df = df[df.CurrencyCode == self.choice]\r\n minimum = df.min(axis=1).values[0]\r\n # Round the value to 4 d.p.\r\n minimum = round(float(minimum), 4)\r\n return minimum","def min_flux(self):\n return np.min(self.flux)","def _minimum(self) -> float:\n return self._config[CONF_MIN]","def getMinValue(self):\n if self.left is None:\n return self.data\n return self.left.getMinValue()","def get_min_value(self, dim):\n return self._min_values[dim]","def get_min(self):\n if not self:\n return None\n return self.left.get_min() if self.left else self.value #Ternarary Operator","def native_min_value(self) -> float:\n return self._device.min_offset","def x_min(self):\n return self.get_min_value(self.X_INDEX)","def get_min(self):\n min_value= self.df[self.col_name].min()\n return min_value","def z_min(self):\n return self.get_min_value(self.Z_INDEX)","def get_min(self):\n raise NotImplementedError(\"This is an abstract method and needs to be implemented in derived classes.\")","def min():\n return KeeperOfMinOrMax(int.__gt__)","def minimum(self):\n return min(self.numbers)","def min(self):\n a = self.array_form\n min = len(a)\n for i in xrange(len(a)):\n if a[i] != i and a[i] < min:\n min = a[i]\n return min","def min(self):\n if 0 in type(self).flatten_shape(self.shape):\n raise ValueError(\"zero-size array has no minimum\")\n if self.isscalar():\n return self.defval\n # If not all blocks are set, then the tensor has an element of defval\n # somewhere.\n m = np.inf if self.is_full() else self.defval\n for v in self.sects.values():\n try:\n m = min(m, np.min(v))\n except ValueError:\n # This block was zero-size, and has no elements.\n pass\n return m","def state_min(self) -> float:\n raise NotImplementedError","def cmin(self):\n return self[\"cmin\"]","def cmin(self):\n return self['cmin']","def accepted_minimum(self):\n\n return self._get_value(self.accepted_minimum_provider)","def get_f_minimum(self):\n return np.min(self._Y)","def min_value(dtype):\n return _api_internal._min_value(dtype)","def minX(self):\n return min(self.getx())","def take_min(self):\n return self.get_first()","def peek_min(self):\n if self.root:\n return self.root.min().value\n raise ValueError(\"cannot perform peek_min on an empty tree\")","def min(self) -> \"Stream[float]\":\n return self.agg(np.min).astype(\"float\")","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL3_GetMinimum(self)","def find_min(self):\n return min(self.nodes, key=int)","def get_min(cls, data: tuple or list) -> float:\n cls._data_validation(data)\n return min(data)","def getMin(self, field): \n return np.min([self.fitnesses[i][field] for i in range(len(self.fitnesses))])","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL2_GetMinimum(self)","def x_min(self) -> ir.FloatingValue:\n return ops.GeoXMin(self).to_expr()","def getMinX(self):\n return self.minx","def get_fmin(self):\n return self.model.predict(self.model.X)[0].min()","def potential_min(self):\n\n return self._args.min","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS3_GetMinimum(self)","def vmin(self):\n return self._vmin","def extractMinimum(self):\n\n return self.heap[1]","def min(self):\n raise NotImplementedError('must be implemented by subclass')","def min(self):\n raise NotImplementedError('must be implemented by subclass')","def find_min(self):\n\n if self.left:\n return self.left.find_min()\n\n return self.data","def get_tmin(self):\n tmin = min(sorted(self.srcData.keys()))\n return tmin","def userMinimum(self) -> float:\n return self._user_minimum","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def minimum(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"minimum\")","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def min(self) -> FrameLike:\n return super().min()","def minimum_number(self):\n return self._minimum_number","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS2_GetMinimum(self)","def min_level(self):\n return self.__min","def min(self):\n return self._summarize(DataFrameCpu._cmin)","def min_rec(self):\n return self._min_rec","def GetMinimum(self):\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUC3_GetMinimum(self)","def min(self):\n return self._min_coords","def _min_in_bounds(self, min):\n if min <= self.valmin:\n if not self.closedmin:\n return self.val[0]\n min = self.valmin\n\n if min > self.val[1]:\n min = self.val[1]\n return self._stepped_value(min)","def min(self):\n return self.heap[1]"],"string":"[\n \"def min(self):\\n if self.kind == 'u':\\n return 0\\n else:\\n try:\\n val = iinfo._min_vals[self.key]\\n except KeyError:\\n val = int(-(1 << (self.bits-1)))\\n iinfo._min_vals[self.key] = val\\n return val\",\n \"def min(self):\\n if self.kind == 'u':\\n return 0\\n else:\\n try:\\n val = iinfo._min_vals[self.key]\\n except KeyError:\\n val = int(-(1 << (self.bits-1)))\\n iinfo._min_vals[self.key] = val\\n return val\",\n \"def _get_minimum(self):\\n return self._minimum\",\n \"def minimum_value(self):\\n return self._fitness[self._minidx]\",\n \"def MinimumValue(self):\\n return self._fitness[self._minIndex]\",\n \"def _get_minimum_value(self):\\n if hasattr(self, '_minimum_value'):\\n return self._minimum_value\\n return None\",\n \"def _minimum(self) -> float:\\n if self._type == \\\"power\\\":\\n return 1.0\\n elif self._type == \\\"setpoint\\\":\\n return self._product.get_data_config_json()[\\\"_value_setpoint_min\\\"]\\n elif self._type == \\\"fan1\\\":\\n fan = 1\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\\n elif self._type == \\\"fan2\\\":\\n fan = 2\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\\n elif self._type == \\\"fan3\\\":\\n fan = 3\\n return self._product.get_data_config_json()[\\\"_value_fan_limits\\\"][\\n ((fan - 1) * 2)\\n ]\",\n \"def getMinValue(self):\\n return self.MIN_VALUE\",\n \"def min_value(self) -> Union[int, float]:\\n return self.left_boundary['value']\",\n \"def get_min(self):\\n return self.serie.min()\",\n \"def get_min(self):\\n\\t\\tif self.left:\\n\\t\\t\\treturn self.left.get_min()\\n\\t\\treturn self.value\",\n \"def min(self) -> Union[float, int, str]:\\n return self._data.min()\",\n \"def min(self):\\n return self._min\",\n \"def min(self):\\n return self._min\",\n \"def min(self):\\n return self.__min\",\n \"def min(self):\\r\\n return np.min(self.data_array)\",\n \"def find_min(self):\\n return self.min\",\n \"def find_min(self):\\n return self.min\",\n \"def min(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self):\\n return numpy.ma.min(self.data)\",\n \"def min_value(self) -> float:\\n return DEFAULT_MIN_VALUE\",\n \"def minimum(self) -> Union[int, float]:\\n return self.range[0]\",\n \"def min(self):\\r\\n\\t\\treturn min(self.sample)\",\n \"def min(self):\\n return min(self)\",\n \"def min(self):\\n least = self.data[0]\\n \\n for i in range(len(self.data)):\\n if self.data[i] < least:\\n least = self.data[i]\\n return least\",\n \"def minimum(self):\\n return self.properties.get('minimum')\",\n \"def min(self):\\n return self._reduce_for_stat_function(F.min, only_numeric=False)\",\n \"def MinimumValue(self):\\n datatype = self.NumPyDataType\\n if issubclass(datatype, numpy.integer):\\n return numpy.iinfo(datatype).min\\n elif issubclass(datatype, numpy.floating):\\n return -numpy.inf\\n else:\\n raise TypeError(\\\"Cannot handle DataType: {0}\\\".format(datatype))\",\n \"def native_min_value(self) -> float:\\n return TEMP_MINIMUM\",\n \"def min(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def min(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"min\\\")\",\n \"def Min(data):\\n return data.min()\",\n \"def min(self):\\n return self.get_first()\",\n \"def getmin(self):\\n\\n return self.X\",\n \"def min(self) -> float:\\n return stats.min(self)\",\n \"def native_min_value(self) -> float:\\n return -9\",\n \"def get_min(self):\\r\\n df = pd.read_csv(\\\"MonthlyRate.csv\\\")\\r\\n df = df[df.CurrencyCode == self.choice]\\r\\n minimum = df.min(axis=1).values[0]\\r\\n # Round the value to 4 d.p.\\r\\n minimum = round(float(minimum), 4)\\r\\n return minimum\",\n \"def min_flux(self):\\n return np.min(self.flux)\",\n \"def _minimum(self) -> float:\\n return self._config[CONF_MIN]\",\n \"def getMinValue(self):\\n if self.left is None:\\n return self.data\\n return self.left.getMinValue()\",\n \"def get_min_value(self, dim):\\n return self._min_values[dim]\",\n \"def get_min(self):\\n if not self:\\n return None\\n return self.left.get_min() if self.left else self.value #Ternarary Operator\",\n \"def native_min_value(self) -> float:\\n return self._device.min_offset\",\n \"def x_min(self):\\n return self.get_min_value(self.X_INDEX)\",\n \"def get_min(self):\\n min_value= self.df[self.col_name].min()\\n return min_value\",\n \"def z_min(self):\\n return self.get_min_value(self.Z_INDEX)\",\n \"def get_min(self):\\n raise NotImplementedError(\\\"This is an abstract method and needs to be implemented in derived classes.\\\")\",\n \"def min():\\n return KeeperOfMinOrMax(int.__gt__)\",\n \"def minimum(self):\\n return min(self.numbers)\",\n \"def min(self):\\n a = self.array_form\\n min = len(a)\\n for i in xrange(len(a)):\\n if a[i] != i and a[i] < min:\\n min = a[i]\\n return min\",\n \"def min(self):\\n if 0 in type(self).flatten_shape(self.shape):\\n raise ValueError(\\\"zero-size array has no minimum\\\")\\n if self.isscalar():\\n return self.defval\\n # If not all blocks are set, then the tensor has an element of defval\\n # somewhere.\\n m = np.inf if self.is_full() else self.defval\\n for v in self.sects.values():\\n try:\\n m = min(m, np.min(v))\\n except ValueError:\\n # This block was zero-size, and has no elements.\\n pass\\n return m\",\n \"def state_min(self) -> float:\\n raise NotImplementedError\",\n \"def cmin(self):\\n return self[\\\"cmin\\\"]\",\n \"def cmin(self):\\n return self['cmin']\",\n \"def accepted_minimum(self):\\n\\n return self._get_value(self.accepted_minimum_provider)\",\n \"def get_f_minimum(self):\\n return np.min(self._Y)\",\n \"def min_value(dtype):\\n return _api_internal._min_value(dtype)\",\n \"def minX(self):\\n return min(self.getx())\",\n \"def take_min(self):\\n return self.get_first()\",\n \"def peek_min(self):\\n if self.root:\\n return self.root.min().value\\n raise ValueError(\\\"cannot perform peek_min on an empty tree\\\")\",\n \"def min(self) -> \\\"Stream[float]\\\":\\n return self.agg(np.min).astype(\\\"float\\\")\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL3_GetMinimum(self)\",\n \"def find_min(self):\\n return min(self.nodes, key=int)\",\n \"def get_min(cls, data: tuple or list) -> float:\\n cls._data_validation(data)\\n return min(data)\",\n \"def getMin(self, field): \\n return np.min([self.fitnesses[i][field] for i in range(len(self.fitnesses))])\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUL2_GetMinimum(self)\",\n \"def x_min(self) -> ir.FloatingValue:\\n return ops.GeoXMin(self).to_expr()\",\n \"def getMinX(self):\\n return self.minx\",\n \"def get_fmin(self):\\n return self.model.predict(self.model.X)[0].min()\",\n \"def potential_min(self):\\n\\n return self._args.min\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS3_GetMinimum(self)\",\n \"def vmin(self):\\n return self._vmin\",\n \"def extractMinimum(self):\\n\\n return self.heap[1]\",\n \"def min(self):\\n raise NotImplementedError('must be implemented by subclass')\",\n \"def min(self):\\n raise NotImplementedError('must be implemented by subclass')\",\n \"def find_min(self):\\n\\n if self.left:\\n return self.left.find_min()\\n\\n return self.data\",\n \"def get_tmin(self):\\n tmin = min(sorted(self.srcData.keys()))\\n return tmin\",\n \"def userMinimum(self) -> float:\\n return self._user_minimum\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def minimum(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"minimum\\\")\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def min(self) -> FrameLike:\\n return super().min()\",\n \"def minimum_number(self):\\n return self._minimum_number\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUS2_GetMinimum(self)\",\n \"def min_level(self):\\n return self.__min\",\n \"def min(self):\\n return self._summarize(DataFrameCpu._cmin)\",\n \"def min_rec(self):\\n return self._min_rec\",\n \"def GetMinimum(self):\\n return _itkStatisticsImageFilterPython.itkStatisticsImageFilterIUC3_GetMinimum(self)\",\n \"def min(self):\\n return self._min_coords\",\n \"def _min_in_bounds(self, min):\\n if min <= self.valmin:\\n if not self.closedmin:\\n return self.val[0]\\n min = self.valmin\\n\\n if min > self.val[1]:\\n min = self.val[1]\\n return self._stepped_value(min)\",\n \"def min(self):\\n return self.heap[1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7944226","0.7944226","0.7937631","0.78972983","0.7884411","0.78611416","0.7846386","0.7778144","0.77733576","0.7762124","0.7680303","0.7642864","0.75792414","0.75792414","0.75784636","0.7551094","0.75474113","0.75474113","0.7521111","0.7521111","0.74849975","0.7484885","0.7471194","0.7461117","0.74487036","0.7446084","0.744392","0.74194586","0.7393505","0.7390895","0.73744524","0.73744524","0.73413813","0.7310355","0.7296167","0.7289322","0.7249265","0.7248786","0.72440404","0.7236522","0.7224943","0.72162694","0.72142404","0.7209117","0.71570104","0.7145454","0.714173","0.71323127","0.7109216","0.70772934","0.70617133","0.70600265","0.70433307","0.7028071","0.70215166","0.70111954","0.6988008","0.69834995","0.6979648","0.6968356","0.69448626","0.694202","0.69383395","0.69209516","0.6905302","0.6903685","0.6900909","0.6896162","0.6874685","0.6870821","0.6850932","0.68279237","0.6827528","0.6824322","0.68122953","0.68122953","0.68073714","0.68069035","0.6802727","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67963773","0.67865294","0.67865294","0.67865294","0.67865294","0.67847824","0.67812485","0.6780029","0.6770788","0.6767305","0.6761661","0.67479265","0.6722724","0.67219627"],"string":"[\n \"0.7944226\",\n \"0.7944226\",\n \"0.7937631\",\n \"0.78972983\",\n \"0.7884411\",\n \"0.78611416\",\n \"0.7846386\",\n \"0.7778144\",\n \"0.77733576\",\n \"0.7762124\",\n \"0.7680303\",\n \"0.7642864\",\n \"0.75792414\",\n \"0.75792414\",\n \"0.75784636\",\n \"0.7551094\",\n \"0.75474113\",\n \"0.75474113\",\n \"0.7521111\",\n \"0.7521111\",\n \"0.74849975\",\n \"0.7484885\",\n \"0.7471194\",\n \"0.7461117\",\n \"0.74487036\",\n \"0.7446084\",\n \"0.744392\",\n \"0.74194586\",\n \"0.7393505\",\n \"0.7390895\",\n \"0.73744524\",\n \"0.73744524\",\n \"0.73413813\",\n \"0.7310355\",\n \"0.7296167\",\n \"0.7289322\",\n \"0.7249265\",\n \"0.7248786\",\n \"0.72440404\",\n \"0.7236522\",\n \"0.7224943\",\n \"0.72162694\",\n \"0.72142404\",\n \"0.7209117\",\n \"0.71570104\",\n \"0.7145454\",\n \"0.714173\",\n \"0.71323127\",\n \"0.7109216\",\n \"0.70772934\",\n \"0.70617133\",\n \"0.70600265\",\n \"0.70433307\",\n \"0.7028071\",\n \"0.70215166\",\n \"0.70111954\",\n \"0.6988008\",\n \"0.69834995\",\n \"0.6979648\",\n \"0.6968356\",\n \"0.69448626\",\n \"0.694202\",\n \"0.69383395\",\n \"0.69209516\",\n \"0.6905302\",\n \"0.6903685\",\n \"0.6900909\",\n \"0.6896162\",\n \"0.6874685\",\n \"0.6870821\",\n \"0.6850932\",\n \"0.68279237\",\n \"0.6827528\",\n \"0.6824322\",\n \"0.68122953\",\n \"0.68122953\",\n \"0.68073714\",\n \"0.68069035\",\n \"0.6802727\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67963773\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67865294\",\n \"0.67847824\",\n \"0.67812485\",\n \"0.6780029\",\n \"0.6770788\",\n \"0.6767305\",\n \"0.6761661\",\n \"0.67479265\",\n \"0.6722724\",\n \"0.67219627\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95691,"cells":{"query":{"kind":"string","value":"Returns the next `value` expressible in this band"},"document":{"kind":"string","value":"def IncrementValue(self, value):\n datatype = self.NumPyDataType\n if issubclass(datatype, numpy.integer):\n if not isinstance(value, (int, numpy.integer)):\n raise TypeError(\n 'value {0!r} must be compatible with {1}'.format(\n value, datatype.__name__\n )\n )\n iinfo = numpy.iinfo(datatype)\n minint, maxint = iinfo.min, iinfo.max\n if not minint <= value <= maxint:\n raise ValueError(\n 'value {0!r} must be between {1} and {2}'.format(\n value, minint, maxint\n )\n )\n if value == maxint:\n return maxint\n return value + 1\n\n elif issubclass(datatype, numpy.floating):\n if not isinstance(value, (int, numpy.integer,\n float, numpy.floating)):\n raise TypeError(\n \"value {0!r} must be compatible with {1}\".format(\n value, datatype.__name__\n )\n )\n if value == numpy.finfo(datatype).max:\n return numpy.inf\n return numpy.nextafter(datatype(value), datatype(numpy.inf))\n\n else:\n raise TypeError(\"Cannot handle DataType: {0}\".format(datatype))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_next(self) -> float:\n return self._current + self._offset","def get_next(self) -> int:\n return self._current * self._step + self._offset","def value(self):\n current_value = self.initial_value * self.schedule(self.step / self.nvalues)\n self.step += 1.\n return current_value","def get_next():\n return \"some_value\"","def get_next(self):\n\t\tassert(len(self.past_values) < 256**3)\n\t\twhile self.advance_state() in self.past_values:\n\t\t\tpass\n\t\tself.past_values.add(self.state)\n\t\treturn self.state","def nextVal(self, n):\n stream = self.stream\n\n stream.append(n) # appends on the right\n\n streamlength = len(stream)\n if streamlength > self.period:\n stream.popleft()\n streamlength -= 1\n if streamlength == 0:\n self.value = 0\n else:\n self.value = sum( stream ) / float( streamlength )\n\n return self.value","def peek(self):\n return self.next_val","def get_next(current):\n return 0.5 * (current + n / current)","def next(self) -> int:\n node = self.stack.pop()\n ans = node.val \n self._sink(node.right)\n return ans","def next_value(self):\n self._lock.acquire()\n try:\n id = self._next_id\n self._next_id += 1\n finally:\n self._lock.release()\n return id","def next(self) -> int:\n value = self.inorder[self.index]\n self.index = self.index + 1\n return value","def next(self):\n return self.cycle.next()","def get_next(self) -> int:\n cur_next = self._bin_iter.get_next()\n\n return self._intvs.get_next(cur_next, self.even)","def get_next(self):\n try:\n return self.the_input[self.index]\n except IndexError:\n return None","def find_next(self, v):\n if v + 1 < self.values[0] or v + 1 > self.values[-1]:\n raise IndexError('element not found')\n\n index = self._bin_search_recursive(v, 0, len(self.values) - 1)\n\n if index < len(self.values) - 1:\n return self.values[index + 1]\n else:\n raise IndexError('element not found')","def next(self):\n if self.signbit.dec_value == 1:\n method = 'prev'\n else:\n method = 'next'\n return self._step(method)","def propose_next(self):\n if self._nvalue > 1 and not self.frozen:\n self._proposednextindex = random_walk_nextindex(\n self._currentindex, self._nvalue, neighborhoods=self._neighborhoods)\n else:\n self._proposednextindex = self._currentindex\n return self.values[self._proposednextindex]","def first_value(self):\n return self._start","def _value(self):\n return self.device.value(*self._id[1:])","def __next__(self):\n if self._current is None:\n raise StopIteration()\n else:\n answer = self._current # hold prev value of _current\n self._advance() # update _current to next value in the\n #progression\n return answer","def value(self):\n return self.head","def next(self) -> int:\n self.idx += 1\n return self.m[self.idx]","def next(self) -> int:\n self.idx += 1\n return self.m[self.idx]","def __next__(self):\n\n # pointer is the current value\n # counter is an item next to pointer\n # take value from pointer position and reduce\n # counter until counter is not 0\n # if counter == 0 move pointer to the next position\n # with value (stride=2)\n if self.counter <= 0:\n # move pointer to the next item\n self.pointer += 2\n try:\n # take counter\n self.counter = self.data[self.pointer + 1]\n except IndexError:\n raise StopIteration\n\n # take value from pointer position and reduce counter\n value = self.data[self.pointer]\n self.counter -= 1\n\n return value","def nextValue( self, connection ):\n\t\tif getattr(self.schema,'defaultValue',None):\n\t\t\tfor row in sqlquery.SQLQuery(\n\t\t\t\t\"\"\"SELECT %(value)s\"\"\"\n\t\t\t)( connection, value = self.schema.defaultValue ):\n\t\t\t\treturn row[0]\n\t\tif getattr(self.schema,'sequenceName',None):\n\t\t\t# this is postgresql specific\n\t\t\tfor row in sqlquery.SQLQuery(\n\t\t\t\t\"\"\"SELECT nextval(%%(sequenceName)s)\"\"\"\n\t\t\t)( connection, sequenceName = self.schema.sequenceName ):\n\t\t\t\treturn row[0]\n\t\traise AttributeError(\n\t\t\t\"\"\"Sorry, don't seem to have a default value or sequence name for %s\"\"\"%(\n\t\t\t\tself.schema.name,\n\t\t\t)\n\t\t)","def first_value(self):\n return self._value","def next(self):\n if self.pointer > len(self.queue) - 1:\n self.pointer = 0\n raise StopIteration\n val = self.queue[self.pointer]\n self.pointer += 1\n return val","def next(self):\n return self.from_date(self.date_b)","def next(self):\n return self.my_next","def get(self, index: int) -> int:\n if index + 1 >self.cnt:\n return -1\n\n tmp = self.dummy\n for i in range(index+1):\n tmp = tmp.next\n return tmp.val","def get_next(self):\n return self.next","def get_next(self):\n return self.next","def next(self) -> int:\n node = self.stack.pop()\n self.push_lefts(node.right)\n return node.val","def value(self):\n if hasattr(self, '_m_value'):\n return self._m_value if hasattr(self, '_m_value') else None\n\n self._m_value = (((((((self.groups[0].value + ((self.groups[1].value << 7) if self.len >= 2 else 0)) + ((self.groups[2].value << 14) if self.len >= 3 else 0)) + ((self.groups[3].value << 21) if self.len >= 4 else 0)) + ((self.groups[4].value << 28) if self.len >= 5 else 0)) + ((self.groups[5].value << 35) if self.len >= 6 else 0)) + ((self.groups[6].value << 42) if self.len >= 7 else 0)) + ((self.groups[7].value << 49) if self.len >= 8 else 0))\n return self._m_value if hasattr(self, '_m_value') else None","def __next__(self):\n if self.returned >= len(self):\n raise StopIteration\n else:\n val = self.buffer[self.current]\n self.current = (self.current + 1) % len(self.buffer)\n self.returned += 1\n return val","def value(self, step):\n raise NotImplementedError","def getNext(self):\n\t\t\treturn self.next","def first_value(self):\n if not self.is_empty():\n return self.data[self.head]\n return None","def next(self):\n self.lock.acquire()\n self.count += self.step;\n result = self.count\n self.lock.release()\n return result","def Value(self) -> _n_0_t_14:","def first_value(self):\n return 0","def getNext(self):","def __next__(self):\n self._iteration_index += 1\n if self._iteration_index < self._length:\n return self._child_values[self._iteration_index]\n raise StopIteration","def next(self, _event):\n self.set_val(self.val + 1)","def next(self):\n temp = self.n\n try:\n self.n = next(self.g)\n except Exception as e:\n self._hasNext = False\n return temp","def value_at(self, index):\n if index==0:\n return self.head.val\n\n temp_node = self.head\n for _ in range(index):\n temp_node = temp_node.next\n return temp_node.val","def getNextValue(self):\n\n if self.si >= len(self.str):\n return -1\n\n # First, count the number of zero chunks until we come to a nonzero chunk.\n zeroCount = 0\n b = ord(self.str[self.si])\n if self.zero_expands:\n bmask = (1 << self.n) - 1\n bv = b & (bmask << (self.bi - self.n))\n while bv == 0:\n zeroCount += 1\n self.bi -= self.n\n if self.bi <= 0:\n self.si += 1\n self.bi = 8\n if self.si >= len(self.str):\n return -1\n\n b = ord(self.str[self.si])\n bv = b & (bmask << (self.bi - self.n))\n\n # Infer from that the number of chunks, and hence the number\n # of bits, that make up the value we will extract.\n numChunks = (zeroCount + 1)\n bitCount = numChunks * self.n\n\n # OK, now we need to extract the next bitCount bits into a word.\n result = 0\n while bitCount >= self.bi:\n mask = (1 << self.bi) - 1\n value = (b & mask)\n result = (result << self.bi) | value\n bitCount -= self.bi\n\n self.si += 1\n self.bi = 8\n if self.si >= len(self.str):\n b = 0\n break\n\n b = ord(self.str[self.si])\n\n if bitCount > 0:\n # A partial word in the middle of the byte.\n bottomCount = self.bi - bitCount\n assert bottomCount > 0\n mask = ((1 << bitCount) - 1)\n value = ((b >> bottomCount) & mask)\n result = (result << bitCount) | value\n self.bi -= bitCount\n\n return result","def getNext(self):\n return self.__next","def get_next(self):\n return self._next_previous_helper('next')","def get_next(self):\n return self.cur_node.next.data","def next(self):\n node = self.stack.pop()\n self.pushLeft(node.right)\n return node.val","def next(self) -> int:\n while (self.stack or self.node):\n if self.node:\n self.stack.append(self.node)\n self.node = self.node.left\n else:\n self.node = self.stack.pop()\n res = self.node.val\n self.node = self.node.right\n return res","def next(self) -> int:\n node = self.stack.pop()\n if node.right:\n self.leftMost(node.right)\n \n return node.val","def __next__(self) -> object:\n if not self.current_node:\n raise StopIteration\n\n current_node_value = self.current_node.value()\n self.current_node = self.current_node.next()\n return current_node_value","def first_value(self):\n return self.samples[0]","def values(self):\n while True:\n try:\n yield self.value\n except GPIODeviceClosed:\n break","def get_next_keystream_value(deck_of_cards):\n get_big_joker_value(deck_of_cards)\n get_small_joker_value(deck_of_cards)\n move_small_joker(deck_of_cards)\n move_big_joker(deck_of_cards)\n triple_cut(deck_of_cards)\n insert_top_to_bottom(deck_of_cards)\n keystream_value = get_card_at_top_index(deck_of_cards)\n \n if keystream_value == get_big_joker_value(deck_of_cards) or \\\n keystream_value == get_small_joker_value(deck_of_cards):\n keystream_value = get_next_keystream_value(deck_of_cards)\n return keystream_value\n\t\n # Condition where if keystream_value is equal to big_joker_value or\n # small_joker_value then this will be repeated. After occuring it is then \n # checked again to see if keystream_value is equal to big_joker_value or\n # small_joker_value. If so, then again repeated until not so.","def get_next_sample(self):","def peg(self):\n return self._val","def read(self):\n return self.comp.getNextValue()","def get_flow_value(self):\n valve = self.valve\n if isinstance(valve, (float, int)):\n flow_val = valve\n elif isinstance(valve, Iterable):\n flow_val = next(valve)\n else:\n raise NotImplementedError(f\"Flow value for valve type {type(valve)} not implemented\")\n return flow_val","def first_value(self):\n return self._waveforms[0].first_value","def get(self, index):\n if index < 0 or index >= self.length:\n return -1\n curr = self.head\n for i in range(1, index + 1):\n curr = curr.next\n return curr.val","def next(self):\n return self._next","def step(self):\n try:\n return next(self.generator)\n except StopIteration:\n return None","def get_current_value(self):\n assert(self.is_started())\n return self.currValue","def next(self) -> int:\n top = self.stack.pop()\n temp = top.right;\n while temp:\n self.stack.append(temp)\n temp = temp.left\n\n return top.val","def next_candidate():\r\n candidate_bidder = -1\r\n candidate_value = -1\r\n for n in range(len(bidders)):\r\n if (is_active[n] == 0 and cur_value(n) is not None\r\n and cur_value(n) > max(candidate_value, cur_bid)):\r\n candidate_value = bidders[n].values[cur_value_idx[n]]\r\n candidate_bidder = n\r\n return candidate_value, candidate_bidder","def get(self, index):\n count = 0\n x = self.begin\n\n while count != index:\n x = x.next\n count += 1\n\n return x.value","def get_next(self):\n try:\n g = next(self.__gnext)\n except StopIteration:\n return None\n\n return g","def __getitem__(self, value) -> Node:\n self.value = value\n self.next_value = None\n if value in map(lambda x: x.value, self.nodes):\n return value\n\n else:\n return False","def next(self) -> int:\n node = self.list.pop()\n t = node.right\n while (t):\n self.list.append(t)\n t = t.left\n\n return node.val","def _get_value(self):\n \n return self._value","def dereference_value(self, value: int) -> int:\n if self.is_register(value):\n return self[value]\n\n return value","def lazy_value(self):\n\n if self.state == Node.State.VALID:\n return self.value\n else:\n return None","def get_next_signal(self):\n row, col = -1, -1\n while row == -1 and col == -1:\n row, col = self.do_polling()\n sleep(10/1000)\n return self.get_symbol(row, col)","def get_next_parameter(self, name, value, settings, id=0):\n return None","def value(t):\r\n return t(0)","def next(self) -> int:\n top = self.stack.pop()\n if top.right:\n t = top.right\n while t:\n self.stack.append(t)\n t = t.left\n return top.val","def next(self) -> object:\n return self._next","def next(self, initial):","def get_next(self, buf):\n if buf is None:\n return None, None, None\n err = _ffi.new(\"JError_t *\")\n p = _cjudy.JudySLNext(self._array[0], buf, err)\n return self._cast(buf, err, p)","def getNext(self):\n return self.__next__","def next(self):\r\n rnd = rand() * self.totals[(-1)]\r\n return bisect.bisect_right(self.totals, rnd)","def __next__(self):\n\t\treturn next()","def _next_interval(self):\n return self.interval_generator()","def next(self) -> int:\n self.index += 1\n return self.nodes_sorted[self.index]","def __next__(self):\n return self.next()","def __next__(self):\n return self.next()","def __next__(self):\n return self.next()","def getValue(self, state):\n return self.values[state]","def get(self, index):\n if index < 0 or index >= self._size:\n return -1\n\n current = self._head\n for _ in range(index):\n current = current.next\n return current.val","def get_next_if_any(self):\n try:\n ret = self.work[deepcopy(self.i)]\n self.i += 1\n # print \"Trickling item\", self.i\n return ret\n except Exception:\n return None","def latestValue(self):\n if len(self.values) > 0:\n return self.values[-1]\n else:\n return 0","def getValue(self) -> int:\n ...","def next(self):\n n, self.iter = self.nums[self.iter], self.iter+1\n return n","def value(self):\n return _osgAnimation.SwigPyIterator_value(self)","def _get_value(self):\n return self.__value","def value(self):\n if hasattr(self, '_m_value'):\n return self._m_value if hasattr(self, '_m_value') else None\n\n self._m_value = (self.b & 127)\n return self._m_value if hasattr(self, '_m_value') else None","def get(self, index):\n if index >= self.len:\n return -1\n p = self.head.next\n while index > 0:\n index -= 1\n p = p.next\n return p.val","def next(self):\n return next(self.gen)"],"string":"[\n \"def get_next(self) -> float:\\n return self._current + self._offset\",\n \"def get_next(self) -> int:\\n return self._current * self._step + self._offset\",\n \"def value(self):\\n current_value = self.initial_value * self.schedule(self.step / self.nvalues)\\n self.step += 1.\\n return current_value\",\n \"def get_next():\\n return \\\"some_value\\\"\",\n \"def get_next(self):\\n\\t\\tassert(len(self.past_values) < 256**3)\\n\\t\\twhile self.advance_state() in self.past_values:\\n\\t\\t\\tpass\\n\\t\\tself.past_values.add(self.state)\\n\\t\\treturn self.state\",\n \"def nextVal(self, n):\\n stream = self.stream\\n\\n stream.append(n) # appends on the right\\n\\n streamlength = len(stream)\\n if streamlength > self.period:\\n stream.popleft()\\n streamlength -= 1\\n if streamlength == 0:\\n self.value = 0\\n else:\\n self.value = sum( stream ) / float( streamlength )\\n\\n return self.value\",\n \"def peek(self):\\n return self.next_val\",\n \"def get_next(current):\\n return 0.5 * (current + n / current)\",\n \"def next(self) -> int:\\n node = self.stack.pop()\\n ans = node.val \\n self._sink(node.right)\\n return ans\",\n \"def next_value(self):\\n self._lock.acquire()\\n try:\\n id = self._next_id\\n self._next_id += 1\\n finally:\\n self._lock.release()\\n return id\",\n \"def next(self) -> int:\\n value = self.inorder[self.index]\\n self.index = self.index + 1\\n return value\",\n \"def next(self):\\n return self.cycle.next()\",\n \"def get_next(self) -> int:\\n cur_next = self._bin_iter.get_next()\\n\\n return self._intvs.get_next(cur_next, self.even)\",\n \"def get_next(self):\\n try:\\n return self.the_input[self.index]\\n except IndexError:\\n return None\",\n \"def find_next(self, v):\\n if v + 1 < self.values[0] or v + 1 > self.values[-1]:\\n raise IndexError('element not found')\\n\\n index = self._bin_search_recursive(v, 0, len(self.values) - 1)\\n\\n if index < len(self.values) - 1:\\n return self.values[index + 1]\\n else:\\n raise IndexError('element not found')\",\n \"def next(self):\\n if self.signbit.dec_value == 1:\\n method = 'prev'\\n else:\\n method = 'next'\\n return self._step(method)\",\n \"def propose_next(self):\\n if self._nvalue > 1 and not self.frozen:\\n self._proposednextindex = random_walk_nextindex(\\n self._currentindex, self._nvalue, neighborhoods=self._neighborhoods)\\n else:\\n self._proposednextindex = self._currentindex\\n return self.values[self._proposednextindex]\",\n \"def first_value(self):\\n return self._start\",\n \"def _value(self):\\n return self.device.value(*self._id[1:])\",\n \"def __next__(self):\\n if self._current is None:\\n raise StopIteration()\\n else:\\n answer = self._current # hold prev value of _current\\n self._advance() # update _current to next value in the\\n #progression\\n return answer\",\n \"def value(self):\\n return self.head\",\n \"def next(self) -> int:\\n self.idx += 1\\n return self.m[self.idx]\",\n \"def next(self) -> int:\\n self.idx += 1\\n return self.m[self.idx]\",\n \"def __next__(self):\\n\\n # pointer is the current value\\n # counter is an item next to pointer\\n # take value from pointer position and reduce\\n # counter until counter is not 0\\n # if counter == 0 move pointer to the next position\\n # with value (stride=2)\\n if self.counter <= 0:\\n # move pointer to the next item\\n self.pointer += 2\\n try:\\n # take counter\\n self.counter = self.data[self.pointer + 1]\\n except IndexError:\\n raise StopIteration\\n\\n # take value from pointer position and reduce counter\\n value = self.data[self.pointer]\\n self.counter -= 1\\n\\n return value\",\n \"def nextValue( self, connection ):\\n\\t\\tif getattr(self.schema,'defaultValue',None):\\n\\t\\t\\tfor row in sqlquery.SQLQuery(\\n\\t\\t\\t\\t\\\"\\\"\\\"SELECT %(value)s\\\"\\\"\\\"\\n\\t\\t\\t)( connection, value = self.schema.defaultValue ):\\n\\t\\t\\t\\treturn row[0]\\n\\t\\tif getattr(self.schema,'sequenceName',None):\\n\\t\\t\\t# this is postgresql specific\\n\\t\\t\\tfor row in sqlquery.SQLQuery(\\n\\t\\t\\t\\t\\\"\\\"\\\"SELECT nextval(%%(sequenceName)s)\\\"\\\"\\\"\\n\\t\\t\\t)( connection, sequenceName = self.schema.sequenceName ):\\n\\t\\t\\t\\treturn row[0]\\n\\t\\traise AttributeError(\\n\\t\\t\\t\\\"\\\"\\\"Sorry, don't seem to have a default value or sequence name for %s\\\"\\\"\\\"%(\\n\\t\\t\\t\\tself.schema.name,\\n\\t\\t\\t)\\n\\t\\t)\",\n \"def first_value(self):\\n return self._value\",\n \"def next(self):\\n if self.pointer > len(self.queue) - 1:\\n self.pointer = 0\\n raise StopIteration\\n val = self.queue[self.pointer]\\n self.pointer += 1\\n return val\",\n \"def next(self):\\n return self.from_date(self.date_b)\",\n \"def next(self):\\n return self.my_next\",\n \"def get(self, index: int) -> int:\\n if index + 1 >self.cnt:\\n return -1\\n\\n tmp = self.dummy\\n for i in range(index+1):\\n tmp = tmp.next\\n return tmp.val\",\n \"def get_next(self):\\n return self.next\",\n \"def get_next(self):\\n return self.next\",\n \"def next(self) -> int:\\n node = self.stack.pop()\\n self.push_lefts(node.right)\\n return node.val\",\n \"def value(self):\\n if hasattr(self, '_m_value'):\\n return self._m_value if hasattr(self, '_m_value') else None\\n\\n self._m_value = (((((((self.groups[0].value + ((self.groups[1].value << 7) if self.len >= 2 else 0)) + ((self.groups[2].value << 14) if self.len >= 3 else 0)) + ((self.groups[3].value << 21) if self.len >= 4 else 0)) + ((self.groups[4].value << 28) if self.len >= 5 else 0)) + ((self.groups[5].value << 35) if self.len >= 6 else 0)) + ((self.groups[6].value << 42) if self.len >= 7 else 0)) + ((self.groups[7].value << 49) if self.len >= 8 else 0))\\n return self._m_value if hasattr(self, '_m_value') else None\",\n \"def __next__(self):\\n if self.returned >= len(self):\\n raise StopIteration\\n else:\\n val = self.buffer[self.current]\\n self.current = (self.current + 1) % len(self.buffer)\\n self.returned += 1\\n return val\",\n \"def value(self, step):\\n raise NotImplementedError\",\n \"def getNext(self):\\n\\t\\t\\treturn self.next\",\n \"def first_value(self):\\n if not self.is_empty():\\n return self.data[self.head]\\n return None\",\n \"def next(self):\\n self.lock.acquire()\\n self.count += self.step;\\n result = self.count\\n self.lock.release()\\n return result\",\n \"def Value(self) -> _n_0_t_14:\",\n \"def first_value(self):\\n return 0\",\n \"def getNext(self):\",\n \"def __next__(self):\\n self._iteration_index += 1\\n if self._iteration_index < self._length:\\n return self._child_values[self._iteration_index]\\n raise StopIteration\",\n \"def next(self, _event):\\n self.set_val(self.val + 1)\",\n \"def next(self):\\n temp = self.n\\n try:\\n self.n = next(self.g)\\n except Exception as e:\\n self._hasNext = False\\n return temp\",\n \"def value_at(self, index):\\n if index==0:\\n return self.head.val\\n\\n temp_node = self.head\\n for _ in range(index):\\n temp_node = temp_node.next\\n return temp_node.val\",\n \"def getNextValue(self):\\n\\n if self.si >= len(self.str):\\n return -1\\n\\n # First, count the number of zero chunks until we come to a nonzero chunk.\\n zeroCount = 0\\n b = ord(self.str[self.si])\\n if self.zero_expands:\\n bmask = (1 << self.n) - 1\\n bv = b & (bmask << (self.bi - self.n))\\n while bv == 0:\\n zeroCount += 1\\n self.bi -= self.n\\n if self.bi <= 0:\\n self.si += 1\\n self.bi = 8\\n if self.si >= len(self.str):\\n return -1\\n\\n b = ord(self.str[self.si])\\n bv = b & (bmask << (self.bi - self.n))\\n\\n # Infer from that the number of chunks, and hence the number\\n # of bits, that make up the value we will extract.\\n numChunks = (zeroCount + 1)\\n bitCount = numChunks * self.n\\n\\n # OK, now we need to extract the next bitCount bits into a word.\\n result = 0\\n while bitCount >= self.bi:\\n mask = (1 << self.bi) - 1\\n value = (b & mask)\\n result = (result << self.bi) | value\\n bitCount -= self.bi\\n\\n self.si += 1\\n self.bi = 8\\n if self.si >= len(self.str):\\n b = 0\\n break\\n\\n b = ord(self.str[self.si])\\n\\n if bitCount > 0:\\n # A partial word in the middle of the byte.\\n bottomCount = self.bi - bitCount\\n assert bottomCount > 0\\n mask = ((1 << bitCount) - 1)\\n value = ((b >> bottomCount) & mask)\\n result = (result << bitCount) | value\\n self.bi -= bitCount\\n\\n return result\",\n \"def getNext(self):\\n return self.__next\",\n \"def get_next(self):\\n return self._next_previous_helper('next')\",\n \"def get_next(self):\\n return self.cur_node.next.data\",\n \"def next(self):\\n node = self.stack.pop()\\n self.pushLeft(node.right)\\n return node.val\",\n \"def next(self) -> int:\\n while (self.stack or self.node):\\n if self.node:\\n self.stack.append(self.node)\\n self.node = self.node.left\\n else:\\n self.node = self.stack.pop()\\n res = self.node.val\\n self.node = self.node.right\\n return res\",\n \"def next(self) -> int:\\n node = self.stack.pop()\\n if node.right:\\n self.leftMost(node.right)\\n \\n return node.val\",\n \"def __next__(self) -> object:\\n if not self.current_node:\\n raise StopIteration\\n\\n current_node_value = self.current_node.value()\\n self.current_node = self.current_node.next()\\n return current_node_value\",\n \"def first_value(self):\\n return self.samples[0]\",\n \"def values(self):\\n while True:\\n try:\\n yield self.value\\n except GPIODeviceClosed:\\n break\",\n \"def get_next_keystream_value(deck_of_cards):\\n get_big_joker_value(deck_of_cards)\\n get_small_joker_value(deck_of_cards)\\n move_small_joker(deck_of_cards)\\n move_big_joker(deck_of_cards)\\n triple_cut(deck_of_cards)\\n insert_top_to_bottom(deck_of_cards)\\n keystream_value = get_card_at_top_index(deck_of_cards)\\n \\n if keystream_value == get_big_joker_value(deck_of_cards) or \\\\\\n keystream_value == get_small_joker_value(deck_of_cards):\\n keystream_value = get_next_keystream_value(deck_of_cards)\\n return keystream_value\\n\\t\\n # Condition where if keystream_value is equal to big_joker_value or\\n # small_joker_value then this will be repeated. After occuring it is then \\n # checked again to see if keystream_value is equal to big_joker_value or\\n # small_joker_value. If so, then again repeated until not so.\",\n \"def get_next_sample(self):\",\n \"def peg(self):\\n return self._val\",\n \"def read(self):\\n return self.comp.getNextValue()\",\n \"def get_flow_value(self):\\n valve = self.valve\\n if isinstance(valve, (float, int)):\\n flow_val = valve\\n elif isinstance(valve, Iterable):\\n flow_val = next(valve)\\n else:\\n raise NotImplementedError(f\\\"Flow value for valve type {type(valve)} not implemented\\\")\\n return flow_val\",\n \"def first_value(self):\\n return self._waveforms[0].first_value\",\n \"def get(self, index):\\n if index < 0 or index >= self.length:\\n return -1\\n curr = self.head\\n for i in range(1, index + 1):\\n curr = curr.next\\n return curr.val\",\n \"def next(self):\\n return self._next\",\n \"def step(self):\\n try:\\n return next(self.generator)\\n except StopIteration:\\n return None\",\n \"def get_current_value(self):\\n assert(self.is_started())\\n return self.currValue\",\n \"def next(self) -> int:\\n top = self.stack.pop()\\n temp = top.right;\\n while temp:\\n self.stack.append(temp)\\n temp = temp.left\\n\\n return top.val\",\n \"def next_candidate():\\r\\n candidate_bidder = -1\\r\\n candidate_value = -1\\r\\n for n in range(len(bidders)):\\r\\n if (is_active[n] == 0 and cur_value(n) is not None\\r\\n and cur_value(n) > max(candidate_value, cur_bid)):\\r\\n candidate_value = bidders[n].values[cur_value_idx[n]]\\r\\n candidate_bidder = n\\r\\n return candidate_value, candidate_bidder\",\n \"def get(self, index):\\n count = 0\\n x = self.begin\\n\\n while count != index:\\n x = x.next\\n count += 1\\n\\n return x.value\",\n \"def get_next(self):\\n try:\\n g = next(self.__gnext)\\n except StopIteration:\\n return None\\n\\n return g\",\n \"def __getitem__(self, value) -> Node:\\n self.value = value\\n self.next_value = None\\n if value in map(lambda x: x.value, self.nodes):\\n return value\\n\\n else:\\n return False\",\n \"def next(self) -> int:\\n node = self.list.pop()\\n t = node.right\\n while (t):\\n self.list.append(t)\\n t = t.left\\n\\n return node.val\",\n \"def _get_value(self):\\n \\n return self._value\",\n \"def dereference_value(self, value: int) -> int:\\n if self.is_register(value):\\n return self[value]\\n\\n return value\",\n \"def lazy_value(self):\\n\\n if self.state == Node.State.VALID:\\n return self.value\\n else:\\n return None\",\n \"def get_next_signal(self):\\n row, col = -1, -1\\n while row == -1 and col == -1:\\n row, col = self.do_polling()\\n sleep(10/1000)\\n return self.get_symbol(row, col)\",\n \"def get_next_parameter(self, name, value, settings, id=0):\\n return None\",\n \"def value(t):\\r\\n return t(0)\",\n \"def next(self) -> int:\\n top = self.stack.pop()\\n if top.right:\\n t = top.right\\n while t:\\n self.stack.append(t)\\n t = t.left\\n return top.val\",\n \"def next(self) -> object:\\n return self._next\",\n \"def next(self, initial):\",\n \"def get_next(self, buf):\\n if buf is None:\\n return None, None, None\\n err = _ffi.new(\\\"JError_t *\\\")\\n p = _cjudy.JudySLNext(self._array[0], buf, err)\\n return self._cast(buf, err, p)\",\n \"def getNext(self):\\n return self.__next__\",\n \"def next(self):\\r\\n rnd = rand() * self.totals[(-1)]\\r\\n return bisect.bisect_right(self.totals, rnd)\",\n \"def __next__(self):\\n\\t\\treturn next()\",\n \"def _next_interval(self):\\n return self.interval_generator()\",\n \"def next(self) -> int:\\n self.index += 1\\n return self.nodes_sorted[self.index]\",\n \"def __next__(self):\\n return self.next()\",\n \"def __next__(self):\\n return self.next()\",\n \"def __next__(self):\\n return self.next()\",\n \"def getValue(self, state):\\n return self.values[state]\",\n \"def get(self, index):\\n if index < 0 or index >= self._size:\\n return -1\\n\\n current = self._head\\n for _ in range(index):\\n current = current.next\\n return current.val\",\n \"def get_next_if_any(self):\\n try:\\n ret = self.work[deepcopy(self.i)]\\n self.i += 1\\n # print \\\"Trickling item\\\", self.i\\n return ret\\n except Exception:\\n return None\",\n \"def latestValue(self):\\n if len(self.values) > 0:\\n return self.values[-1]\\n else:\\n return 0\",\n \"def getValue(self) -> int:\\n ...\",\n \"def next(self):\\n n, self.iter = self.nums[self.iter], self.iter+1\\n return n\",\n \"def value(self):\\n return _osgAnimation.SwigPyIterator_value(self)\",\n \"def _get_value(self):\\n return self.__value\",\n \"def value(self):\\n if hasattr(self, '_m_value'):\\n return self._m_value if hasattr(self, '_m_value') else None\\n\\n self._m_value = (self.b & 127)\\n return self._m_value if hasattr(self, '_m_value') else None\",\n \"def get(self, index):\\n if index >= self.len:\\n return -1\\n p = self.head.next\\n while index > 0:\\n index -= 1\\n p = p.next\\n return p.val\",\n \"def next(self):\\n return next(self.gen)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69069135","0.6903918","0.68510103","0.6794112","0.6782542","0.66370076","0.66035736","0.65092635","0.6481222","0.6430199","0.6414499","0.6410997","0.63769144","0.63592046","0.63583064","0.63552517","0.63462645","0.63152736","0.6294821","0.62673527","0.62468565","0.62436676","0.62436676","0.62214273","0.6199272","0.6186351","0.61850303","0.6164596","0.61604047","0.6148999","0.61227494","0.61227494","0.6120474","0.6105034","0.6093133","0.6081499","0.60768473","0.6039049","0.6034145","0.60310954","0.6006239","0.5993654","0.59921926","0.59907025","0.59854513","0.5982865","0.5982694","0.5981716","0.59811467","0.59754896","0.5967847","0.5945388","0.5940692","0.5926408","0.5921525","0.5920828","0.59208125","0.591883","0.59175825","0.5917147","0.59075195","0.589737","0.58773345","0.5871988","0.5871504","0.58639276","0.5853242","0.58406776","0.58404326","0.58304346","0.58259296","0.58254045","0.581069","0.5802216","0.58016706","0.57952255","0.5793001","0.57927775","0.5792605","0.5785569","0.57830656","0.5766482","0.57641965","0.57547677","0.5742776","0.5741573","0.57335067","0.5731173","0.5731173","0.5731173","0.5724478","0.57240343","0.57191503","0.57187617","0.5717772","0.57150465","0.5712302","0.5705474","0.5705355","0.5693724","0.5683531"],"string":"[\n \"0.69069135\",\n \"0.6903918\",\n \"0.68510103\",\n \"0.6794112\",\n \"0.6782542\",\n \"0.66370076\",\n \"0.66035736\",\n \"0.65092635\",\n \"0.6481222\",\n \"0.6430199\",\n \"0.6414499\",\n \"0.6410997\",\n \"0.63769144\",\n \"0.63592046\",\n \"0.63583064\",\n \"0.63552517\",\n \"0.63462645\",\n \"0.63152736\",\n \"0.6294821\",\n \"0.62673527\",\n \"0.62468565\",\n \"0.62436676\",\n \"0.62436676\",\n \"0.62214273\",\n \"0.6199272\",\n \"0.6186351\",\n \"0.61850303\",\n \"0.6164596\",\n \"0.61604047\",\n \"0.6148999\",\n \"0.61227494\",\n \"0.61227494\",\n \"0.6120474\",\n \"0.6105034\",\n \"0.6093133\",\n \"0.6081499\",\n \"0.60768473\",\n \"0.6039049\",\n \"0.6034145\",\n \"0.60310954\",\n \"0.6006239\",\n \"0.5993654\",\n \"0.59921926\",\n \"0.59907025\",\n \"0.59854513\",\n \"0.5982865\",\n \"0.5982694\",\n \"0.5981716\",\n \"0.59811467\",\n \"0.59754896\",\n \"0.5967847\",\n \"0.5945388\",\n \"0.5940692\",\n \"0.5926408\",\n \"0.5921525\",\n \"0.5920828\",\n \"0.59208125\",\n \"0.591883\",\n \"0.59175825\",\n \"0.5917147\",\n \"0.59075195\",\n \"0.589737\",\n \"0.58773345\",\n \"0.5871988\",\n \"0.5871504\",\n \"0.58639276\",\n \"0.5853242\",\n \"0.58406776\",\n \"0.58404326\",\n \"0.58304346\",\n \"0.58259296\",\n \"0.58254045\",\n \"0.581069\",\n \"0.5802216\",\n \"0.58016706\",\n \"0.57952255\",\n \"0.5793001\",\n \"0.57927775\",\n \"0.5792605\",\n \"0.5785569\",\n \"0.57830656\",\n \"0.5766482\",\n \"0.57641965\",\n \"0.57547677\",\n \"0.5742776\",\n \"0.5741573\",\n \"0.57335067\",\n \"0.5731173\",\n \"0.5731173\",\n \"0.5731173\",\n \"0.5724478\",\n \"0.57240343\",\n \"0.57191503\",\n \"0.57187617\",\n \"0.5717772\",\n \"0.57150465\",\n \"0.5712302\",\n \"0.5705474\",\n \"0.5705355\",\n \"0.5693724\",\n \"0.5683531\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95692,"cells":{"query":{"kind":"string","value":"Opens a GDALreadable file. Raises a GdalError if inputfile is invalid."},"document":{"kind":"string","value":"def __init__(self, inputfile, mode=GA_ReadOnly):\n # Open the input file and read some metadata\n open(inputfile, 'r').close() # HACK: GDAL gives a useless exception\n if not isinstance(inputfile, bytes):\n inputfile = inputfile.encode('utf-8')\n try:\n # Since this is a SWIG object, clone the ``this`` pointer\n self.this = gdal.Open(inputfile, mode).this\n except RuntimeError as e:\n raise GdalError(str(e))\n\n # Shadow for metadata so we can overwrite it without saving\n # it to the original file.\n self._geotransform = None\n self._rastersizes = None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def load(self):\r\n\r\n #Open the dataset read only using GDAL\r\n dataset = gdal.Open(self.inputds, gdal.GA_ReadOnly)\r\n \r\n return dataset\r\n \r\n\r\n #print \"Failed to open %s. Is it a GDAL supported format?\" %(self.inputds)\r","def open_input(self):\n gdal.AllRegister()\n\n self.out_drv = gdal.GetDriverByName(self.tiledriver)\n self.mem_drv = gdal.GetDriverByName('MEM')\n\n if not self.out_drv:\n raise Exception(\"The '%s' driver was not found, is it available in this GDAL build?\",\n self.tiledriver)\n if not self.mem_drv:\n raise Exception(\"The 'MEM' driver was not found, is it available in this GDAL build?\")\n\n # Open the input file\n\n if self.input_file:\n input_dataset = gdal.Open(self.input_file, gdal.GA_ReadOnly)\n else:\n raise Exception(\"No input file was specified\")\n\n if self.options.verbose:\n print(\"Input file:\",\n \"( %sP x %sL - %s bands)\" % (input_dataset.RasterXSize,\n input_dataset.RasterYSize,\n input_dataset.RasterCount))\n\n if not input_dataset:\n # Note: GDAL prints the ERROR message too\n exit_with_error(\"It is not possible to open the input file '%s'.\" % self.input_file)\n\n # Read metadata from the input file\n if input_dataset.RasterCount == 0:\n exit_with_error(\"Input file '%s' has no raster band\" % self.input_file)\n\n if input_dataset.GetRasterBand(1).GetRasterColorTable():\n exit_with_error(\n \"Please convert this file to RGB/RGBA and run gdal2tiles on the result.\",\n \"From paletted file you can create RGBA file (temp.vrt) by:\\n\"\n \"gdal_translate -of vrt -expand rgba %s temp.vrt\\n\"\n \"then run:\\n\"\n \"gdal2tiles temp.vrt\" % self.input_file\n )\n\n in_nodata = setup_no_data_values(input_dataset, self.options)\n\n if self.options.verbose:\n print(\"Preprocessed file:\",\n \"( %sP x %sL - %s bands)\" % (input_dataset.RasterXSize,\n input_dataset.RasterYSize,\n input_dataset.RasterCount))\n\n in_srs, self.in_srs_wkt = setup_input_srs(input_dataset, self.options)\n\n self.out_srs = setup_output_srs(in_srs, self.options)\n\n # If input and output reference systems are different, we reproject the input dataset into\n # the output reference system for easier manipulation\n\n self.warped_input_dataset = None\n\n if self.options.profile in ('mercator', 'geodetic'):\n\n if not in_srs:\n exit_with_error(\n \"Input file has unknown SRS.\",\n \"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\")\n\n if not has_georeference(input_dataset):\n exit_with_error(\n \"There is no georeference - neither affine transformation (worldfile) \"\n \"nor GCPs. You can generate only 'raster' profile tiles.\",\n \"Either gdal2tiles with parameter -p 'raster' or use another GIS \"\n \"software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\"\n )\n\n if ((in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or\n (input_dataset.GetGCPCount() != 0)):\n self.warped_input_dataset = reproject_dataset(\n input_dataset, in_srs, self.out_srs)\n\n if in_nodata:\n self.warped_input_dataset = update_no_data_values(\n self.warped_input_dataset, in_nodata, options=self.options)\n else:\n self.warped_input_dataset = update_alpha_value_for_non_alpha_inputs(\n self.warped_input_dataset, options=self.options)\n\n if self.warped_input_dataset and self.options.verbose:\n print(\"Projected file:\", \"tiles.vrt\", \"( %sP x %sL - %s bands)\" % (\n self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize,\n self.warped_input_dataset.RasterCount))\n\n if not self.warped_input_dataset:\n self.warped_input_dataset = input_dataset\n\n #Salva o arquivo reprojetado\n self.warped_input_dataset.GetDriver().CreateCopy(self.tmp_vrt_filename,\n self.warped_input_dataset)\n\n # Get alpha band (either directly or from NODATA value)\n self.alphaband = self.warped_input_dataset.GetRasterBand(1).GetMaskBand()\n self.dataBandsCount = nb_data_bands(self.warped_input_dataset)\n\n # KML test\n self.isepsg4326 = False\n srs4326 = osr.SpatialReference()\n srs4326.ImportFromEPSG(4326)\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\n self.kml = True\n self.isepsg4326 = True\n if self.options.verbose:\n print(\"KML autotest OK!\")\n\n # Read the georeference\n self.out_gt = self.warped_input_dataset.GetGeoTransform()\n\n # Test the size of the pixel\n\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\n if (self.out_gt[2], self.out_gt[4]) != (0, 0):\n exit_with_error(\"Georeference of the raster contains rotation or skew. \"\n \"Such raster is not supported. Please use gdalwarp first.\")\n\n # Here we expect: pixel is square, no rotation on the raster\n\n # Output Bounds - coordinates in the output SRS\n self.ominx = self.out_gt[0]\n self.omaxx = self.out_gt[0] + self.warped_input_dataset.RasterXSize * self.out_gt[1]\n self.omaxy = self.out_gt[3]\n self.ominy = self.out_gt[3] - self.warped_input_dataset.RasterYSize * self.out_gt[1]\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\n\n if self.options.verbose:\n print(\"Bounds (output srs):\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy)\n\n # Calculating ranges for tiles in different zoom levels\n if self.options.profile == 'mercator':\n\n self.mercator = GlobalMercator()\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.mercator.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, 32))\n for tz in range(0, 32):\n tminx, tminy = self.mercator.MetersToTile(self.ominx, self.ominy, tz)\n tmaxx, tmaxy = self.mercator.MetersToTile(self.omaxx, self.omaxy, tz)\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the minimal zoom level (map covers area equivalent to one tile)\n if self.tminz is None:\n self.tminz = self.mercator.ZoomForPixelSize(\n self.out_gt[1] *\n max(self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize) /\n float(self.tilesize))\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tmaxz is None:\n self.tmaxz = self.mercator.ZoomForPixelSize(self.out_gt[1])\n\n if self.options.verbose:\n print(\"Bounds (latlong):\",\n self.mercator.MetersToLatLon(self.ominx, self.ominy),\n self.mercator.MetersToLatLon(self.omaxx, self.omaxy))\n print('MinZoomLevel:', self.tminz)\n print(\"MaxZoomLevel:\",\n self.tmaxz,\n \"(\",\n self.mercator.Resolution(self.tmaxz),\n \")\")\n\n if self.options.profile == 'geodetic':\n\n self.geodetic = GlobalGeodetic(self.options.tmscompatible)\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.geodetic.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, 32))\n for tz in range(0, 32):\n tminx, tminy = self.geodetic.LonLatToTile(self.ominx, self.ominy, tz)\n tmaxx, tmaxy = self.geodetic.LonLatToTile(self.omaxx, self.omaxy, tz)\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tminz is None:\n self.tminz = self.geodetic.ZoomForPixelSize(\n self.out_gt[1] *\n max(self.warped_input_dataset.RasterXSize,\n self.warped_input_dataset.RasterYSize) /\n float(self.tilesize))\n\n # Get the maximal zoom level\n # (closest possible zoom level up on the resolution of raster)\n if self.tmaxz is None:\n self.tmaxz = self.geodetic.ZoomForPixelSize(self.out_gt[1])\n\n if self.options.verbose:\n print(\"Bounds (latlong):\", self.ominx, self.ominy, self.omaxx, self.omaxy)\n\n if self.options.profile == 'raster':\n\n def log2(x):\n return math.log10(x) / math.log10(2)\n\n self.nativezoom = int(\n max(math.ceil(log2(self.warped_input_dataset.RasterXSize/float(self.tilesize))),\n math.ceil(log2(self.warped_input_dataset.RasterYSize/float(self.tilesize)))))\n\n if self.options.verbose:\n print(\"Native zoom of the raster:\", self.nativezoom)\n\n # Get the minimal zoom level (whole raster in one tile)\n if self.tminz is None:\n self.tminz = 0\n\n # Get the maximal zoom level (native resolution of the raster)\n if self.tmaxz is None:\n self.tmaxz = self.nativezoom\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = list(range(0, self.tmaxz+1))\n self.tsize = list(range(0, self.tmaxz+1))\n for tz in range(0, self.tmaxz+1):\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\n tminx, tminy = 0, 0\n tmaxx = int(math.ceil(self.warped_input_dataset.RasterXSize / tsize)) - 1\n tmaxy = int(math.ceil(self.warped_input_dataset.RasterYSize / tsize)) - 1\n self.tsize[tz] = math.ceil(tsize)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # Function which generates SWNE in LatLong for given tile\n if self.kml and self.in_srs_wkt:\n ct = osr.CoordinateTransformation(in_srs, srs4326)\n\n def rastertileswne(x, y, z):\n pixelsizex = (2**(self.tmaxz-z) * self.out_gt[1]) # X-pixel size in level\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\n east = west + self.tilesize*pixelsizex\n south = self.ominy + y*self.tilesize*pixelsizex\n north = south + self.tilesize*pixelsizex\n if not self.isepsg4326:\n # Transformation to EPSG:4326 (WGS84 datum)\n west, south = ct.TransformPoint(west, south)[:2]\n east, north = ct.TransformPoint(east, north)[:2]\n return south, west, north, east\n\n self.tileswne = rastertileswne\n else:\n self.tileswne = lambda x, y, z: (0, 0, 0, 0) # noqa","def open_image(filename):\n\n dataset = gdal.Open(filename, gdal.GA_ReadOnly)\n if dataset is None:\n raise IOError(\"cannot open %s\" % filename)\n\n return GdalImage(dataset, filename)","def open_input(self):\n gdal.SetConfigOption(\"GDAL_PAM_ENABLED\", \"YES\")\n gdal.AllRegister()\n # self.options.verbose=True\n if self.options.tms_osm:\n self.s_y_type=\"osm\"\n else:\n self.s_y_type=\"tms\"\n if self.options.verbose:\n print \"open_input :\", self.input,\" osm[\",self.options.tms_osm,\",\",self.s_y_type,\"] mbtiles[\",self.options.mbtiles,\"] mbtiles_todisk[\",self.options.mbtiles_todisk,\"] mbtiles_fromdisk[\",self.options.mbtiles_fromdisk,\"]\";\n # Open the input file\n if self.input:\n self.in_ds = gdal.Open(self.input, gdal.GA_ReadOnly)\n else:\n raise Exception(\"No input file was specified\")\n\n if self.options.verbose:\n print \"Input file:\", \"( %sP x %sL - %s bands)\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\n\n if not self.in_ds:\n # Note: GDAL prints the ERROR message too\n self.error(\"It is not possible to open the input file '%s'.\" % self.input )\n\n # Read metadata from the input file\n if self.in_ds.RasterCount == 0:\n self.error( \"Input file '%s' has no raster band\" % self.input )\n\n if self.in_ds.GetRasterBand(1).GetRasterColorTable():\n # TODO: Process directly paletted dataset by generating VRT in memory\n self.error( \"Please convert this file to RGB/RGBA and run gdal2mbtiles on the result.\",\n \"\"\"From paletted file you can create RGBA file (temp.vrt) by:\ngdal_translate -of vrt -expand rgba %s temp.vrt\nthen run:\ngdal2mbtiles temp.vrt\"\"\" % self.input )\n\n # Get NODATA value\n # User supplied values overwrite everything else.\n if self.options.srcnodata is not None:\n nds = map(float, self.options.srcnodata.split(','))\n if len(nds) < self.in_ds.RasterCount:\n self.in_nodata = (nds * self.in_ds.RasterCount)[:self.in_ds.RasterCount]\n else:\n self.in_nodata = nds\n else:\n # If the source dataset has NODATA, use it.\n self.in_nodata = []\n for i in range(1, self.in_ds.RasterCount+1):\n if self.in_ds.GetRasterBand(i).GetNoDataValue() != None:\n self.in_nodata.append( self.in_ds.GetRasterBand(i).GetNoDataValue() )\n\n if self.options.verbose:\n print \"NODATA: %s\" % self.in_nodata\n\n # INIT DEST\n if self.options.init_dest is not None:\n if self.options.tile_format == \"jpeg\":\n if self.in_ds.RasterCount == 4:\n nbands = 3\n else:\n nbands = self.in_ds.RasterCount\n\n nds = map(float, self.options.init_dest.split(','))\n\n if len(nds) == 1:\n init_dest = nds * nbands\n elif len(nds) == nbands:\n init_dest = nds\n else:\n print \"WARNING: you suplied %d '--init-dest' values but the dataset has %d data bands\" % (len(nds), nbands)\n init_dest = None\n else:\n init_dest = None\n print \"WARNING: --init-dest can be used only with 'jpeg' tile format\"\n else:\n if self.options.tile_format == \"jpeg\":\n init_dest = [255,255,255]\n else:\n init_dest = None\n\n #\n # Here we should have RGBA input dataset opened in self.in_ds\n #\n\n if self.options.verbose:\n print \"Preprocessed file:\", \"( %sP x %sL - %s bands)\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\n\n # Spatial Reference System of the input raster\n\n\n self.in_srs = None\n\n if self.options.s_srs:\n self.in_srs = osr.SpatialReference()\n self.in_srs.SetFromUserInput(self.options.s_srs)\n self.in_srs_wkt = self.in_srs.ExportToWkt()\n else:\n self.in_srs_wkt = self.in_ds.GetProjection()\n if not self.in_srs_wkt and self.in_ds.GetGCPCount() != 0:\n self.in_srs_wkt = self.in_ds.GetGCPProjection()\n if self.in_srs_wkt:\n self.in_srs = osr.SpatialReference()\n self.in_srs.ImportFromWkt(self.in_srs_wkt)\n #elif self.options.profile != 'raster':\n # self.error(\"There is no spatial reference system info included in the input file.\",\"You should run gdal2mbtiles with --s_srs EPSG:XXXX or similar.\")\n\n # Spatial Reference System of tiles\n\n self.out_srs = osr.SpatialReference()\n\n if self.options.profile == 'mercator':\n self.out_srs.ImportFromEPSG(900913)\n elif self.options.profile in ('geodetic', 'gearth', 'garmin'):\n self.out_srs.ImportFromEPSG(4326)\n else:\n self.out_srs = self.in_srs\n\n # Are the reference systems the same? Reproject if necessary.\n\n self.out_ds = None\n\n if self.options.profile in ('mercator', 'geodetic', 'gearth', 'garmin'):\n\n if (self.in_ds.GetGeoTransform() == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) and (self.in_ds.GetGCPCount() == 0):\n self.error(\"There is no georeference - neither affine transformation (worldfile) nor GCPs. You can generate only 'raster' profile tiles.\",\n \"Either gdal2mbtiles with parameter -p 'raster' or use another GIS software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\")\n\n if self.in_srs:\n\n if (self.in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or (self.in_ds.GetGCPCount() != 0):\n\n # Generation of VRT dataset in tile projection, default 'nearest neighbour' warping\n self.out_ds = gdal.AutoCreateWarpedVRT( self.in_ds, self.in_srs_wkt, self.out_srs.ExportToWkt() )\n\n # TODO: HIGH PRIORITY: Correction of AutoCreateWarpedVRT according the max zoomlevel for correct direct warping!!!\n\n if self.options.verbose:\n print \"Warping of the raster by AutoCreateWarpedVRT (result saved into 'tiles.vrt')\"\n self.out_ds.GetDriver().CreateCopy(\"tiles.vrt\", self.out_ds)\n\n # Note: self.in_srs and self.in_srs_wkt contain still the non-warped reference system!!!\n\n # Correction of AutoCreateWarpedVRT for NODATA values\n if self.in_nodata != []:\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"<GDALWarpOptions>\"\"\",\"\"\"<GDALWarpOptions>\n <Option name=\"UNIFIED_SRC_NODATA\">YES</Option>\n <Option name=\"INIT_DEST\">NO_DATA</Option>\"\"\")\n # replace BandMapping tag for NODATA bands....\n if init_dest is None:\n dstnodata = self.in_nodata\n else:\n dstnodata = init_dest\n for i in range(len(self.in_nodata)):\n s = s.replace(\"\"\"<BandMapping src=\"%i\" dst=\"%i\"/>\"\"\" % ((i+1),(i+1)),\"\"\"<BandMapping src=\"%i\" dst=\"%i\">\n <SrcNoDataReal>%i</SrcNoDataReal>\n <SrcNoDataImag>0</SrcNoDataImag>\n <DstNoDataReal>%i</DstNoDataReal>\n <DstNoDataImag>0</DstNoDataImag>\n </BandMapping>\"\"\" % ((i+1), (i+1), self.in_nodata[i], dstnodata[i]))\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n # set NODATA_VALUE metadata\n self.out_ds.SetMetadataItem('NODATA_VALUES','%s' % \" \".join(str(int(f)) for f in self.in_nodata))\n\n if self.options.verbose:\n print \"Modified warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n # -----------------------------------\n # Correction of AutoCreateWarpedVRT for Mono (1 band) and RGB (3 bands) files without NODATA:\n # equivalent of gdalwarp -dstalpha\n elif self.in_nodata == [] and self.out_ds.RasterCount in (1,3):\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"<BlockXSize>\"\"\",\"\"\"<VRTRasterBand dataType=\"Byte\" band=\"%i\" subClass=\"VRTWarpedRasterBand\">\n <ColorInterp>Alpha</ColorInterp>\n </VRTRasterBand>\n <BlockXSize>\"\"\" % (self.out_ds.RasterCount + 1))\n s = s.replace(\"\"\"</GDALWarpOptions>\"\"\", \"\"\"<DstAlphaBand>%i</DstAlphaBand>\n </GDALWarpOptions>\"\"\" % (self.out_ds.RasterCount + 1))\n if init_dest is None:\n init_dest_str = \"0\"\n else:\n init_dest_str = \",\".join(str(f) for f in init_dest)\n s = s.replace(\"\"\"</WorkingDataType>\"\"\", \"\"\"</WorkingDataType>\n <Option name=\"INIT_DEST\">%s</Option>\"\"\" % init_dest_str)\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n if self.options.verbose:\n print \"Modified -dstalpha warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n elif init_dest is not None:\n import tempfile\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\n # open as a text file\n s = open(tempfilename).read()\n # Add the warping options\n s = s.replace(\"\"\"</WorkingDataType>\"\"\", \"\"\"</WorkingDataType>\n <Option name=\"INIT_DEST\">%s</Option>\"\"\" % \",\".join(str(f) for f in init_dest))\n # save the corrected VRT\n open(tempfilename,\"w\").write(s)\n # open by GDAL as self.out_ds\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\n # delete the temporary file\n os.unlink(tempfilename)\n\n if self.options.verbose:\n print \"Modified warping result saved into 'tiles1.vrt'\"\n open(\"tiles1.vrt\",\"w\").write(s)\n\n # For raster with 4-bands: 4th unknown band set to alpha\n if (self.out_ds.RasterCount == 4\n and self.out_ds.GetRasterBand(4).GetRasterColorInterpretation() == gdal.GCI_Undefined):\n self.out_ds.GetRasterBand(4).SetRasterColorInterpretation(gdal.GCI_AlphaBand)\n\n s = '''\n '''\n\n else:\n self.error(\"Input file has unknown SRS.\", \"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\" )\n\n if self.out_ds and self.options.verbose:\n print \"Projected file:\", \"tiles.vrt\", \"( %sP x %sL - %s bands)\" % (self.out_ds.RasterXSize, self.out_ds.RasterYSize, self.out_ds.RasterCount)\n\n if not self.out_ds:\n self.out_ds = self.in_ds\n\n #\n # Here we should have a raster (out_ds) in the correct Spatial Reference system\n #\n\n # KML test\n self.isepsg4326 = False\n srs4326 = osr.SpatialReference()\n srs4326.ImportFromEPSG(4326)\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\n self.kml = True\n self.isepsg4326 = True\n if self.options.verbose:\n print \"KML autotest OK!\"\n\n # Read the georeference\n\n self.out_gt = self.out_ds.GetGeoTransform()\n\n #originX, originY = self.out_gt[0], self.out_gt[3]\n #pixelSize = self.out_gt[1] # = self.out_gt[5]\n\n # Test the size of the pixel\n\n # MAPTILER - COMMENTED\n #if self.out_gt[1] != (-1 * self.out_gt[5]) and self.options.profile != 'raster':\n # TODO: Process corectly coordinates with are have swichted Y axis (display in OpenLayers too)\n #self.error(\"Size of the pixel in the output differ for X and Y axes.\")\n\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\n if (self.out_gt[2], self.out_gt[4]) != (0,0):\n self.error(\"Georeference of the raster contains rotation or skew. Such raster is not supported. Please use gdalwarp first.\")\n # TODO: Do the warping in this case automaticaly\n\n #\n # Here we expect: pixel is square, no rotation on the raster\n #\n\n # Output Bounds - coordinates in the output SRS\n self.ominx = self.out_gt[0]\n self.omaxx = self.out_gt[0]+self.out_ds.RasterXSize*self.out_gt[1]\n self.omaxy = self.out_gt[3]\n self.ominy = self.out_gt[3]-self.out_ds.RasterYSize*self.out_gt[1]\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\n # user defined bounds to extract - coordinates in the output SRS\n if self.options.te_bounds != '':\n if self.te_minx >= self.ominx and self.te_minx <= self.omaxx:\n if self.te_maxx >= self.ominx and self.te_maxx <= self.omaxx:\n if self.te_miny >= self.ominy and self.te_miny <= self.omaxy:\n if self.te_maxy >= self.ominy and self.te_maxy <= self.omaxy:\n # replace only if inside the read bounds\n self.ominx = self.te_minx\n self.omaxx = self.te_maxx\n self.ominy = self.te_miny\n self.omaxy = self.te_maxy\n if self.options.verbose:\n print \"User defined Bounds (output srs) have been set:\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\n\n if self.options.verbose:\n print \"Bounds (output srs):\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\n\n if self.options.mbtiles:\n self.options.profile = 'mercator'\n if self.options.profile == 'mercator':\n self.mercator = GlobalMercator(self.options.tms_osm) # from globalmaptiles.py\n\n #\n # Calculating ranges for tiles in different zoom levels\n #\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.mercator.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0,32)\n for tz in range(0, 32):\n tminx, tminy = self.mercator.MetersToTile( self.ominx, self.ominy, tz )\n tmaxx, tmaxy = self.mercator.MetersToTile( self.omaxx, self.omaxy, tz )\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the minimal zoom level (map covers area equivalent to one tile)\n if self.tminz == None:\n self.tminz = self.mercator.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tmaxz == None:\n self.tmaxz = self.mercator.ZoomForPixelSize( self.out_gt[1] )\n\n if self.options.verbose:\n print \"Bounds (latlong):\", self.mercator.MetersToLatLon( self.ominx, self.ominy), self.mercator.MetersToLatLon( self.omaxx, self.omaxy)\n print 'MinZoomLevel:', self.tminz\n print \"MaxZoomLevel:\", self.tmaxz, \"(\", self.mercator.Resolution( self.tmaxz ),\")\"\n\n # this must be call befor ImageOutput is called (self.output may be changed)\n if self.options.mbtiles:\n if not self.mbtiles_db:\n self.mbtiles_setup(1);\n\n # Instantiate image output.\n self.image_output = ImageOutput(self.options.tile_format, self.out_ds, self.tilesize,\n self.options.resampling, init_dest, self.output,\n self.options.verbose,self.options.mbtiles)\n if self.options.profile == 'geodetic':\n\n self.geodetic = GlobalGeodetic() # from globalmaptiles.py\n\n # Function which generates SWNE in LatLong for given tile\n self.tileswne = self.geodetic.TileLatLonBounds\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0,32)\n for tz in range(0, 32):\n tminx, tminy = self.geodetic.LatLonToTile( self.ominx, self.ominy, tz )\n tmaxx, tmaxy = self.geodetic.LatLonToTile( self.omaxx, self.omaxy, tz )\n # crop tiles extending world limits (+-180,+-90)\n tminx, tminy = max(0, tminx), max(0, tminy)\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # TODO: Maps crossing 180E (Alaska?)\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tminz == None:\n self.tminz = self.geodetic.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\n\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\n if self.tmaxz == None:\n self.tmaxz = self.geodetic.ZoomForPixelSize( self.out_gt[1] )\n\n if self.options.verbose:\n print \"Bounds (latlong):\", self.ominx, self.ominy, self.omaxx, self.omaxy\n\n if self.options.profile in ('raster', 'gearth', 'garmin'):\n\n log2 = lambda x: math.log10(x) / math.log10(2) # log2 (base 2 logarithm)\n\n self.nativezoom = int(max( math.ceil(log2(self.out_ds.RasterXSize/float(self.tilesize))),\n math.ceil(log2(self.out_ds.RasterYSize/float(self.tilesize)))))\n\n if self.options.verbose:\n print \"Native zoom of the raster:\", self.nativezoom\n\n # Get the minimal zoom level (whole raster in one tile)\n if self.tminz == None:\n self.tminz = 0\n\n # Get the maximal zoom level (native resolution of the raster)\n if self.tmaxz == None:\n self.tmaxz = self.nativezoom\n\n # Garmin has maximally 100 tiles - lower the tmaxz if necessary\n if self.options.profile == 'garmin':\n tno = math.ceil(self.out_ds.RasterXSize / self.tilesize) * math.ceil(self.out_ds.RasterYSize / self.tilesize)\n for tz in range(self.tmaxz, 1, -1):\n if tno > 100:\n tno /= 4\n self.tmaxz -= 1\n print \"Warning: GARMIN has a limit 100 tiles per device: lowering the max zoom level to:\", self.tmaxz\n else:\n continue\n\n # Force only one zoom level for the 'garmin' tile profile\n if self.options.profile == 'garmin':\n self.tminz = self.tmaxz\n\n # Generate table with min max tile coordinates for all zoomlevels\n self.tminmax = range(0, self.tmaxz+1)\n self.tsize = range(0, self.tmaxz+1)\n for tz in range(0, self.tmaxz+1):\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\n tminx, tminy = 0, 0\n tmaxx = int(math.ceil( self.out_ds.RasterXSize / tsize )) - 1\n tmaxy = int(math.ceil( self.out_ds.RasterYSize / tsize )) - 1\n self.tsize[tz] = math.ceil(tsize)\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\n\n # Function which generates SWNE in LatLong for given tile\n if self.kml and self.in_srs_wkt:\n self.ct = osr.CoordinateTransformation(self.in_srs, srs4326)\n def rastertileswne(x,y,z):\n pixelsizex = (2**(self.nativezoom-z) * self.out_gt[1]) # X-pixel size in level\n pixelsizey = (2**(self.nativezoom-z) * self.out_gt[5]) # Y-pixel size in level (usually -1*pixelsizex)\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\n east = west + self.tilesize*pixelsizex\n south = self.ominy + y*self.tilesize*pixelsizex\n north = south + self.tilesize*pixelsizex\n if not self.isepsg4326:\n # Transformation to EPSG:4326 (WGS84 datum)\n west, south = self.ct.TransformPoint(west, south)[:2]\n east, north = self.ct.TransformPoint(east, north)[:2]\n return south, west, north, east\n\n self.tileswne = rastertileswne\n else:\n self.tileswne = lambda x, y, z: (0,0,0,0)","def open(*args, **kwargs):\n return GDALFileTileSource(*args, **kwargs)","def open_shapefile(file_path):\n datasource = ogr.Open(file_path)\n layer = datasource.GetLayerByIndex(0)\n print(\"Opening {}\".format(file_path))\n print(\"Number of features: {}\".format(layer.GetFeatureCount()))\n return datasource","def get_source(self, file_path, driver=None):\n if driver:\n # Use a driver to open the file\n driver = ogr.GetDriverByName(driver)\n return driver.Open(file_path, 0)\n return gdal.Open(file_path, GA_ReadOnly)","def gdal_read_geotiff_file(sFilename_in):\n \n if os.path.exists(sFilename_in):\n pass\n else:\n print('The file does not exist!')\n return\n\n sDriverName='GTiff'\n pDriver = gdal.GetDriverByName(sDriverName) \n\n if pDriver is None:\n print (\"%s pDriver not available.\\n\" % sDriverName)\n else:\n print (\"%s pDriver IS available.\\n\" % sDriverName) \n\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\n\n if pDataset is None:\n print(\"Couldn't open this file: \" + sFilename_in)\n sys.exit(\"Try again!\")\n else: \n pProjection = pDataset.GetProjection()\n\n pDataset.GetMetadata()\n \n ncolumn = pDataset.RasterXSize\n nrow = pDataset.RasterYSize\n nband = pDataset.RasterCount\n\n pGeotransform = pDataset.GetGeoTransform()\n dOriginX = pGeotransform[0]\n dOriginY = pGeotransform[3]\n dPixelWidth = pGeotransform[1]\n pPixelHeight = pGeotransform[5]\n\n pBand = pDataset.GetRasterBand(1)\n\n # Data type of the values\n gdal.GetDataTypeName(pBand.DataType)\n # Compute statistics if needed\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\n pBand.ComputeStatistics(0)\n\n dMissing_value = pBand.GetNoDataValue()\n \n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\n \n #we will use one of them to keep the consistency\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\n \n\n pDataset = None\n pBand = None \n pBand = None\n\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference","def read_gdal_projection(dataset):\n wkt = dataset.GetProjection()\n srs = osr.SpatialReference()\n srs.ImportFromWkt(wkt)\n # src = None\n return srs","def fromfile(file_h):\n # If given a file name, get a real handle.\n if isinstance(file_h, str):\n with open(file_h, \"rb\") as file_h:\n buf = file_h.read()\n else:\n buf = file_h.read()\n\n # If we get WKB need to wrap in memoryview(), so run through regexes.\n if isinstance(buf, bytes):\n try:\n decoded = buf.decode()\n except UnicodeDecodeError:\n pass\n else:\n if wkt_regex.match(decoded) or hex_regex.match(decoded):\n return GEOSGeometry(decoded)\n else:\n return GEOSGeometry(buf)\n\n return GEOSGeometry(memoryview(buf))","def open(cstr, layername=None, layersql=None, extent=None, sql_dialect=None, open_for_update=False):\n ds = ogr.Open(cstr, update=open_for_update)\n if ds is None:\n raise Exception(\"Failed to open \" + cstr)\n if layersql is not None: # an sql statement will take precedence\n if extent is not None and EXTENT_WKT in layersql:\n wkt = \"'\" + extent_to_wkt(extent) + \"'\"\n layersql = layersql.replace(EXTENT_WKT, wkt)\n # restrict to ASCII encodable chars here -\n # don't know what the datasource\n # is precisely and ogr doesn't like unicode.\n layer = ds.ExecuteSQL(str(layersql), dialect=sql_dialect)\n elif layername is not None: # then a layername\n layer = ds.GetLayerByName(layername)\n else: # fallback - shapefiles etc, use first layer\n layer = ds.GetLayer(0)\n assert(layer is not None)\n\n return ds, layer","def read_geojson(input_file):\n # Please use the python json module (imported above)\n # to solve this one.\n with open(input_file,'r') as f:\n gj = json.load(f)\n return gj","def read_geojson(input_file):\n # Please use the python json module (imported above)\n # to solve this one.\n with open(input_file,'r') as f:\n gj = json.load(f)\n return gj","def open_raster_file(self):\n self.log('Opening raster file as GDALRaster.')\n\n # Open raster file\n self.dataset = GDALRaster(os.path.join(self.tmpdir, self.rastername), write=True)\n\n # Make sure nodata value is set from input\n self.hist_values = []\n self.hist_bins = []\n for i, band in enumerate(self.dataset.bands):\n if self.rasterlayer.nodata is not None:\n band.nodata_value = float(self.rasterlayer.nodata)\n\n # Create band metatdata object\n bandmeta = RasterLayerBandMetadata.objects.create(\n rasterlayer=self.rasterlayer,\n band=i,\n nodata_value=band.nodata_value,\n min=band.min,\n max=band.max\n )\n\n # Prepare numpy hist values and bins\n self.hist_values.append(numpy.array(bandmeta.hist_values))\n self.hist_bins.append(numpy.array(bandmeta.hist_bins))\n\n # Store original metadata for this raster\n meta = self.rasterlayer.metadata\n\n meta.uperleftx = self.dataset.origin.x\n meta.uperlefty = self.dataset.origin.y\n meta.width = self.dataset.width\n meta.height = self.dataset.height\n meta.scalex = self.dataset.scale.x\n meta.scaley = self.dataset.scale.y\n meta.skewx = self.dataset.skew.x\n meta.skewy = self.dataset.skew.y\n meta.numbands = len(self.dataset.bands)\n meta.srs_wkt = self.dataset.srs.wkt\n meta.srid = self.dataset.srs.srid\n\n meta.save()","def readMap(fileName, fileFormat):\n # Open file for binary-reading\n mapFormat = gdal.GetDriverByName(fileFormat)\n mapFormat.Register()\n ds = gdal.Open(fileName)\n if ds is None:\n print 'Could not open ' + fileName + '. Something went wrong!! Shutting down'\n sys.exit(1)\n # Retrieve geoTransform info\n geotrans = ds.GetGeoTransform()\n originX = geotrans[0]\n originY = geotrans[3]\n resX = geotrans[1]\n resY = geotrans[5]\n cols = ds.RasterXSize\n rows = ds.RasterYSize\n x = linspace(originX+resX/2,originX+resX/2+resX*(cols-1),cols)\n y = linspace(originY+resY/2,originY+resY/2+resY*(rows-1),rows)\n # Retrieve raster\n RasterBand = ds.GetRasterBand(1) # there's only 1 band, starting from 1\n data = RasterBand.ReadAsArray(0,0,cols,rows)\n FillVal = RasterBand.GetNoDataValue()\n RasterBand = None\n ds = None\n return x, y, data, FillVal","def read_gds(self,\n infile,\n units='skip',\n rename={},\n layers={},\n datatypes={},\n texttypes={}):\n self._references = []\n if isinstance(infile, basestring):\n infile = open(infile, 'rb')\n close = True\n else:\n close = False\n emitted_warnings = []\n record = self._read_record(infile)\n kwargs = {}\n create_element = None\n factor = 1\n cell = None\n while record is not None:\n # LAYER\n if record[0] == 0x0d:\n kwargs['layer'] = layers.get(record[1][0], record[1][0])\n # DATATYPE\n elif record[0] == 0x0e:\n kwargs['datatype'] = datatypes.get(record[1][0], record[1][0])\n # TEXTTYPE\n elif record[0] == 0x16:\n kwargs['texttype'] = texttypes.get(record[1][0], record[1][0])\n # XY\n elif record[0] == 0x10:\n kwargs['xy'] = factor * record[1]\n # WIDTH\n elif record[0] == 0x0f:\n kwargs['width'] = factor * abs(record[1][0])\n if record[1][0] < 0 and record[0] not in emitted_warnings:\n warnings.warn(\n \"[GDSPY] Paths with absolute width value are not \"\n \"supported. Scaling these paths will also scale \"\n \"their width.\",\n stacklevel=2)\n emitted_warnings.append(record[0])\n # ENDEL\n elif record[0] == 0x11:\n if create_element is not None:\n cell.add(create_element(**kwargs))\n create_element = None\n kwargs = {}\n # BOUNDARY\n elif record[0] == 0x08:\n create_element = self._create_polygon\n # PATH\n elif record[0] == 0x09:\n create_element = self._create_path\n # TEXT\n elif record[0] == 0x0c:\n create_element = self._create_label\n # SNAME\n elif record[0] == 0x12:\n kwargs['ref_cell'] = rename.get(record[1], record[1])\n # COLROW\n elif record[0] == 0x13:\n kwargs['columns'] = record[1][0]\n kwargs['rows'] = record[1][1]\n # STRANS\n elif record[0] == 0x1a:\n kwargs['x_reflection'] = ((int(record[1][0]) & 0x8000) > 0)\n if (int(record[1][0]) &\n 0x0006) and record[0] not in emitted_warnings:\n warnings.warn(\n \"[GDSPY] Absolute magnification or rotation of \"\n \"references is not supported. Transformations will \"\n \"be interpreted as relative.\",\n stacklevel=2)\n emitted_warnings.append(record[0])\n # MAG\n elif record[0] == 0x1b:\n kwargs['magnification'] = record[1][0]\n # ANGLE\n elif record[0] == 0x1c:\n kwargs['rotation'] = record[1][0]\n # SREF\n elif record[0] == 0x0a:\n create_element = self._create_reference\n # AREF\n elif record[0] == 0x0b:\n create_element = self._create_array\n # STRNAME\n elif record[0] == 0x06:\n name = rename.get(record[1], record[1])\n cell = Cell(name, exclude_from_current=True)\n self.cell_dict[name] = cell\n # STRING\n elif record[0] == 0x19:\n kwargs['text'] = record[1]\n # ENDSTR\n elif record[0] == 0x07:\n cell = None\n # UNITS\n elif record[0] == 0x03:\n if units == 'skip':\n factor = record[1][0]\n elif units == 'import':\n self.unit = record[1][1] / record[1][0]\n self.precision = record[1][1]\n factor = record[1][0]\n elif units == 'convert':\n factor = record[1][1] / self.unit\n else:\n raise ValueError(\"[GDSPY] units must be one of 'convert', \"\n \"'import' or 'skip'.\")\n # LIBNAME\n elif record[0] == 0x02:\n self.name = record[1]\n # PRESENTATION\n elif record[0] == 0x17:\n kwargs['anchor'] = GdsLibrary._import_anchors[int(record[1][0])\n & 0x000f]\n # PATHTYPE\n elif record[0] == 0x21:\n if record[1][0] > 2:\n if 0x21 not in emitted_warnings:\n warnings.warn(\n \"[GDSPY] Path ends with custom size are not \"\n \"supported.\",\n RuntimeWarning,\n stacklevel=2)\n emitted_warnings.append(0x21)\n else:\n kwargs['ends'] = record[1][0]\n # ENDLIB\n elif record[0] == 0x04:\n for ref in self._references:\n if ref.ref_cell in self.cell_dict:\n ref.ref_cell = self.cell_dict[ref.ref_cell]\n elif ref.ref_cell in current_library.cell_dict:\n ref.ref_cell = current_library.cell_dict[ref.ref_cell]\n # Not supported\n elif (record[0] not in emitted_warnings and\n record[0] not in GdsLibrary._unused_records):\n warnings.warn(\n \"[GDSPY] Record type {0} ({1:02X}) is not \"\n \"supported.\".format(GdsLibrary._record_name[record[0]],\n record[0]),\n RuntimeWarning,\n stacklevel=2)\n emitted_warnings.append(record[0])\n record = self._read_record(infile)\n if close:\n infile.close()\n return self","def import_file(self, *args, **kwargs):\n filename = self.file\n self.completed_layers = []\n err = GdalErrorHandler()\n gdal.PushErrorHandler(err.handler)\n gdal.UseExceptions()\n configuration_options = kwargs.get('configuration_options', [{'index': 0}])\n\n # Configuration options should be a list at this point since the importer can process multiple layers in a\n # single import\n if isinstance(configuration_options, dict):\n configuration_options = [configuration_options]\n\n data, inspector = self.open_source_datastore(filename, *args, **kwargs)\n\n datastore_layers = inspector.describe_fields()\n\n if len(datastore_layers) == 0:\n logger.debug('No Dataset found')\n\n layers_info = []\n\n # Add index for any layers configured by name\n for layer_configuration in configuration_options:\n if 'layer_name' in layer_configuration:\n lookup = 'layer_name'\n elif 'index' in layer_configuration:\n lookup = 'index'\n else:\n lookup = None\n logger.debug('could not find lookup')\n continue\n\n for datastore_layer in datastore_layers:\n if datastore_layer.get(lookup) == layer_configuration.get(lookup):\n layer_configuration.update(datastore_layer)\n layers_info.append(layer_configuration)\n\n for layer_options in layers_info:\n if layer_options['raster']:\n \"\"\"\n File is a raster, we need to convert into optimized GeoTiff\n and skip any further testing or loading into target_store\n \"\"\"\n # Increment filename to make sure target doesn't exists\n filedir, filebase = os.path.split(filename)\n outfile = '%s.tif' % os.path.splitext(filebase)[0]\n fileout = increment_filename(os.path.join(RASTER_FILES, outfile))\n raster_import(layer_options['path'], fileout)\n self.completed_layers.append([fileout, layer_options])\n else:\n target_file, _ = self.open_target_datastore(self.target_store)\n target_create_options = []\n\n # Prevent numeric field overflow for shapefiles https://trac.osgeo.org/gdal/ticket/5241\n if target_file.GetDriver().GetName() == 'PostgreSQL':\n target_create_options.append('PRECISION=NO')\n\n layer_options['modified_fields'] = {}\n layer = data.GetLayer(layer_options.get('index'))\n layer_name = layer_options.get('name', layer.GetName().lower())\n layer_type = self.get_layer_type(layer, data)\n srs = layer.GetSpatialRef()\n\n if layer_name.lower() == 'ogrgeojson':\n try:\n layer_name = os.path.splitext(os.path.basename(filename))[0].lower()\n except IndexError:\n pass\n\n layer_name = launder(str(layer_name))\n\n # default the layer to 4326 if a spatial reference is not provided\n if not srs:\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n\n # pass the srs authority code to handlers\n if srs.AutoIdentifyEPSG() == 0:\n layer_options['srs'] = '{0}:{1}'.format(srs.GetAuthorityName(None), srs.GetAuthorityCode(None))\n\n n = 0\n while True:\n n += 1\n try:\n target_layer = self.create_target_dataset(target_file, layer_name, srs, layer_type,\n options=target_create_options)\n except RuntimeError as e:\n # logger.exception('exception in creating target dataset')\n # the layer already exists in the target store, increment the name\n if 'Use the layer creation option OVERWRITE=YES to replace it.' in e.message:\n layer_name = increment(layer_name)\n\n # try 100 times to increment then break\n if n >= 100:\n break\n\n continue\n else:\n raise e\n break\n\n # adding fields to new layer\n layer_definition = ogr.Feature(layer.GetLayerDefn())\n source_fid = None\n\n wkb_field = 0\n\n for i in range(layer_definition.GetFieldCount()):\n\n field_def = layer_definition.GetFieldDefnRef(i)\n\n if field_def.GetName() == target_layer.GetFIDColumn() and field_def.GetType() != 0:\n field_def.SetType(0)\n\n if field_def.GetName() != 'wkb_geometry':\n target_layer.CreateField(field_def)\n new_name = target_layer.GetLayerDefn().GetFieldDefn(i - wkb_field).GetName()\n old_name = field_def.GetName()\n\n if new_name != old_name:\n layer_options['modified_fields'][old_name] = new_name\n\n if old_name == target_layer.GetFIDColumn() and not layer.GetFIDColumn():\n source_fid = i\n else:\n wkb_field = 1\n\n if wkb_field is not 0:\n layer.SetIgnoredFields(['wkb_geometry'])\n\n for i in range(0, layer.GetFeatureCount()):\n feature = layer.GetFeature(i)\n\n if feature and feature.geometry():\n\n if not layer.GetFIDColumn():\n feature.SetFID(-1)\n\n if feature.geometry().GetGeometryType() != target_layer.GetGeomType() and \\\n target_layer.GetGeomType() in range(4, 7):\n\n conversion_function = ogr.ForceToMultiPolygon\n\n if target_layer.GetGeomType() == 5:\n conversion_function = ogr.ForceToMultiLineString\n\n elif target_layer.GetGeomType() == 4:\n conversion_function = ogr.ForceToMultiPoint\n\n geom = ogr.CreateGeometryFromWkb(feature.geometry().ExportToWkb())\n feature.SetGeometry(conversion_function(geom))\n\n if source_fid is not None:\n feature.SetFID(feature.GetField(source_fid))\n\n try:\n target_layer.CreateFeature(feature)\n\n except:\n for field in range(0, feature.GetFieldCount()):\n if feature.GetFieldType(field) == ogr.OFTString:\n try:\n feature.GetField(field).decode('utf8')\n except UnicodeDecodeError:\n feature.SetField(field, decode(feature.GetField(field)))\n except AttributeError:\n continue\n try:\n target_layer.CreateFeature(feature)\n except err as e:\n logger.error('Create feature failed: {0}'.format(gdal.GetLastErrorMsg()))\n raise e\n\n self.completed_layers.append([target_layer.GetName(), layer_options])\n\n return self.completed_layers","def load_image(nom):\n print(\"load_image : [\", nom, \"]\")\n fic = gdal.Open(nom)\n print(fic)\n return fic.ReadAsArray(), fic.GetGeoTransform()","def create_gdal_gis_lyr(self, file_path, lyr_name, driver_name='GPKG', add_lyr=False):\n raise EODataDownException(\"Not Implemented\")","def loadGeoTransform(filepath):\n \n from osgeo import gdal\n \n ds = gdal.Open(filepath, 0)\n \n return ds.GetGeoTransform()","def load_raster(input):\n dem = gdal.Open(input)\n\n nodata = []\n layers = []\n for i in range(1, dem.RasterCount+1):\n band = dem.GetRasterBand(i)\n data = band.ReadAsArray(0, 0, dem.RasterXSize, dem.RasterYSize)\n layers.append(data)\n nodata.append(band.GetNoDataValue())\n\n if len(layers) > 1:\n layers = N.dstack(layers) \n\n info = attrdict(\n metadata=dem.GetMetadata_Dict(),\n grid=Grid.from_gdal(dem),\n nodata=nodata)\n\n return (info,layers)","def load_from_geojson(self, filename_or_url):","def from_featureclass(filename, **kwargs):\r\n from .io import from_featureclass\r\n gis = kwargs.pop('gis', arcgis.env.active_gis)\r\n if HASARCPY:\r\n return from_featureclass(filename=filename, **kwargs)\r\n elif isinstance(gis, GIS) and \\\r\n gis._con._auth.lower() != \"anon\":\r\n return from_featureclass(filename=filename, **kwargs)\r\n else:\r\n raise Exception(\"Cannot create the SpatialDataFrame, you must \" +\\\r\n \"have an authenticated GIS.\")","def read(self, format=None, epsg=None):\n if not format:\n format = self.default_output\n if self.ext not in formats.VECTOR:\n raise UnsupportedFormatException(\n \"Only the following vector formats are supported: {}\".format(\n ','.join(formats.VECTOR)\n )\n )\n if self.data is None:\n self.data = geopandas.read_file(self.uri)\n if self.filters:\n self.filter_data()\n return self.transform_data(format, epsg)","def load_loss_GDF(filename, lon, lat):\n df = pd.read_csv(filename)\n x, y = np.meshgrid(lon, lat)\n coords = [Point(xval, yval) for xval, yval in zip(x.ravel(), y.ravel())]\n \n df['geometry'] = coords\n df = gpd.GeoDataFrame(df)\n df.crs = {'init': 'epsg:4326'}\n return df","def read(f, missing=np.nan):\n import gdal\n if f.is_zipped():\n f = unzip(f) # Unzip and return a File\n\n geotiff = gdal.Open(f.fullpath())\n if geotiff is None:\n raise RuntimeError(\"Could not open unzipped geotiff file\",f)\n\n band = geotiff.GetRasterBand(1)\n data = band.ReadAsArray(0, 0, geotiff.RasterXSize, geotiff.RasterYSize)\n\n del(geotiff)\n os.remove(f.fullpath())\n\n data[data<0] = missing\n\n return data","def create_dat_from_shapefile(windgrid_file, DAT_header, output_file, wind_field='Vg_mph'):\n t0 = time()\n output_file = output_file + '.dat'\n print('reading windgrid')\n t1 = time()\n gdf = gpd.read_file(windgrid_file)\n create_dat_from_geodataframe(\n gdf, DAT_header, output_file, wind_field=wind_field)","def file_open(path: str, mode: str):\n if _path_on_gcp(path):\n return tf.io.gfile.GFile(path, mode)\n return open(path, mode)","def read_gds(\n self,\n infile,\n units=\"skip\",\n rename={},\n rename_template=\"{name}\",\n layers={},\n datatypes={},\n texttypes={},\n ):\n self._references = []\n close = True\n if hasattr(infile, \"__fspath__\"):\n infile = open(infile.__fspath__(), \"rb\")\n elif isinstance(infile, (basestring, Path)):\n infile = open(infile, \"rb\")\n else:\n close = False\n emitted_warnings = []\n kwargs = {}\n create_element = None\n factor = 1\n cell = None\n properties = {}\n attr = -1\n for record in _record_reader(infile):\n # LAYER\n if record[0] == 0x0D:\n kwargs[\"layer\"] = layers.get(record[1][0], record[1][0])\n # DATATYPE or BOXTYPE\n elif record[0] == 0x0E or record[0] == 0x2E:\n kwargs[\"datatype\"] = datatypes.get(record[1][0], record[1][0])\n # TEXTTYPE\n elif record[0] == 0x16:\n kwargs[\"texttype\"] = texttypes.get(record[1][0], record[1][0])\n # XY\n elif record[0] == 0x10:\n if \"xy\" in kwargs:\n kwargs[\"xy\"] = numpy.concatenate((kwargs[\"xy\"], factor * record[1]))\n else:\n kwargs[\"xy\"] = factor * record[1]\n # WIDTH\n elif record[0] == 0x0F:\n kwargs[\"width\"] = factor * abs(record[1][0])\n if record[1][0] < 0:\n kwargs[\"width_transform\"] = False\n # ENDEL\n elif record[0] == 0x11:\n if create_element is not None:\n el = create_element(**kwargs)\n if len(properties) > 0:\n el.properties = properties\n properties = {}\n cell.add(el)\n create_element = None\n kwargs = {}\n # BOUNDARY\n elif record[0] == 0x08:\n create_element = self._create_polygon\n # PATH\n elif record[0] == 0x09:\n create_element = self._create_path\n # BOX\n elif record[0] == 0x2D:\n create_element = self._create_polygon\n if record[0] not in emitted_warnings:\n warnings.warn(\n \"[GDSPY] GDSII elements of type BOX are imported as polygons.\",\n stacklevel=2,\n )\n emitted_warnings.append(record[0])\n # TEXT\n elif record[0] == 0x0C:\n create_element = self._create_label\n # SNAME\n elif record[0] == 0x12:\n if record[1] in rename:\n name = rename[record[1]]\n else:\n name = rename_template.format(name=record[1])\n kwargs[\"ref_cell\"] = name\n # COLROW\n elif record[0] == 0x13:\n kwargs[\"columns\"] = record[1][0]\n kwargs[\"rows\"] = record[1][1]\n # STRANS\n elif record[0] == 0x1A:\n kwargs[\"x_reflection\"] = (int(record[1][0]) & 0x8000) > 0\n if (int(record[1][0]) & 0x0006) and record[0] not in emitted_warnings:\n warnings.warn(\n \"[GDSPY] Absolute magnification or rotation of \"\n \"references is not supported. Transformations \"\n \"will be interpreted as relative.\",\n stacklevel=2,\n )\n emitted_warnings.append(record[0])\n # MAG\n elif record[0] == 0x1B:\n kwargs[\"magnification\"] = record[1][0]\n # ANGLE\n elif record[0] == 0x1C:\n kwargs[\"rotation\"] = record[1][0]\n # SREF\n elif record[0] == 0x0A:\n create_element = self._create_reference\n # AREF\n elif record[0] == 0x0B:\n create_element = self._create_array\n # STRNAME\n elif record[0] == 0x06:\n if record[1] in rename:\n name = rename[record[1]]\n else:\n name = rename_template.format(name=record[1])\n cell = Cell(name, exclude_from_current=True)\n if name in self.cells:\n raise ValueError(\"[GDSPY] Multiple cells with name: {0} in GDSII file\".format(name))\n self.cells[name] = cell\n # STRING\n elif record[0] == 0x19:\n kwargs[\"text\"] = record[1]\n # ENDSTR\n elif record[0] == 0x07:\n cell = None\n # UNITS\n elif record[0] == 0x03:\n if units == \"skip\":\n factor = record[1][0]\n elif units == \"import\":\n self.unit = record[1][1] / record[1][0]\n self.precision = record[1][1]\n factor = record[1][0]\n elif units == \"convert\":\n factor = record[1][1] / self.unit\n else:\n raise ValueError(\n \"[GDSPY] units must be one of 'convert', 'import' or 'skip'.\"\n )\n # LIBNAME\n elif record[0] == 0x02:\n self.name = record[1]\n # PRESENTATION\n elif record[0] == 0x17:\n kwargs[\"anchor\"] = GdsLibrary._import_anchors[\n int(record[1][0]) & 0x000F\n ]\n # PATHTYPE\n elif record[0] == 0x21:\n kwargs[\"ends\"] = GdsLibrary._pathtype_dict.get(record[1][0], \"extended\")\n # BGNEXTN\n elif record[0] == 0x30:\n kwargs[\"bgnextn\"] = factor * record[1][0]\n # ENDEXTN\n elif record[0] == 0x31:\n kwargs[\"endextn\"] = factor * record[1][0]\n # ENDLIB\n elif record[0] == 0x04:\n for ref in self._references:\n if ref.ref_cell in self.cells:\n ref.ref_cell = self.cells[ref.ref_cell]\n # PROPATTR\n elif record[0] == 0x2B:\n attr = record[1][0]\n # PROPVALUE\n elif record[0] == 0x2C:\n properties[attr] = record[1]\n # Not supported\n elif (\n record[0] not in emitted_warnings\n and record[0] not in GdsLibrary._unused_records\n ):\n warnings.warn(\n \"[GDSPY] Record type {0} ({1:02X}) is not supported.\".format(\n GdsLibrary._record_name[record[0]], record[0]\n ),\n stacklevel=2,\n )\n emitted_warnings.append(record[0])\n if close:\n infile.close()\n return self","def buffer(infile,outfile,erosion,dilatation):\n ds_in=ogr.Open( infile )\n lyr_in=ds_in.GetLayer(0)\n drv=ds_in.GetDriver()\n ds_out = drv.CreateDataSource(outfile)\n layer = ds_out.CreateLayer( lyr_in.GetLayerDefn().GetName(),lyr_in.GetSpatialRef(), ogr.wkbPolygon)\n n_fields = lyr_in.GetLayerDefn().GetFieldCount()\n for i in xrange ( lyr_in.GetLayerDefn().GetFieldCount() ):\n field_in = lyr_in.GetLayerDefn().GetFieldDefn( i )\n fielddef = ogr.FieldDefn( field_in.GetName(), field_in.GetType() ) \n layer.CreateField ( fielddef )\n \n featuredefn = layer.GetLayerDefn()\n \n for feat in lyr_in:\n geom0 = feat.GetGeometryRef()\n feature0 = feat.Clone()\n feature0.SetGeometry(geom0.Buffer(float(erosion)))\n geom = feature0.GetGeometryRef()\n feature = feature0.Clone() \n feature.SetGeometry(geom.Buffer(float(dilatation)))\n layer.CreateFeature(feature)\n del geom0\n del geom\n ds_out.Destroy()\n return 0","def loadArray(filepath):\n \n from osgeo import gdal\n \n ds = gdal.Open(filepath, 0)\n \n return ds.ReadAsArray()","def __init__(self, geom_input, srs=None):\n str_instance = isinstance(geom_input, str)\n\n # If HEX, unpack input to a binary buffer.\n if str_instance and hex_regex.match(geom_input):\n geom_input = memoryview(bytes.fromhex(geom_input))\n str_instance = False\n\n # Constructing the geometry,\n if str_instance:\n wkt_m = wkt_regex.match(geom_input)\n json_m = json_regex.match(geom_input)\n if wkt_m:\n if wkt_m[\"srid\"]:\n # If there's EWKT, set the SRS w/value of the SRID.\n srs = int(wkt_m[\"srid\"])\n if wkt_m[\"type\"].upper() == \"LINEARRING\":\n # OGR_G_CreateFromWkt doesn't work with LINEARRING WKT.\n # See https://trac.osgeo.org/gdal/ticket/1992.\n g = capi.create_geom(OGRGeomType(wkt_m[\"type\"]).num)\n capi.import_wkt(g, byref(c_char_p(wkt_m[\"wkt\"].encode())))\n else:\n g = capi.from_wkt(\n byref(c_char_p(wkt_m[\"wkt\"].encode())), None, byref(c_void_p())\n )\n elif json_m:\n g = self._from_json(geom_input.encode())\n else:\n # Seeing if the input is a valid short-hand string\n # (e.g., 'Point', 'POLYGON').\n OGRGeomType(geom_input)\n g = capi.create_geom(OGRGeomType(geom_input).num)\n elif isinstance(geom_input, memoryview):\n # WKB was passed in\n g = self._from_wkb(geom_input)\n elif isinstance(geom_input, OGRGeomType):\n # OGRGeomType was passed in, an empty geometry will be created.\n g = capi.create_geom(geom_input.num)\n elif isinstance(geom_input, self.ptr_type):\n # OGR pointer (c_void_p) was the input.\n g = geom_input\n else:\n raise GDALException(\n \"Invalid input type for OGR Geometry construction: %s\"\n % type(geom_input)\n )\n\n # Now checking the Geometry pointer before finishing initialization\n # by setting the pointer for the object.\n if not g:\n raise GDALException(\n \"Cannot create OGR Geometry from input: %s\" % geom_input\n )\n self.ptr = g\n\n # Assigning the SpatialReference object to the geometry, if valid.\n if srs:\n self.srs = srs\n\n # Setting the class depending upon the OGR Geometry Type\n self.__class__ = GEO_CLASSES[self.geom_type.num]","def loadProjection(filepath):\n \n from osgeo import gdal\n \n ds = gdal.Open(filepath, 0)\n \n return ds.GetProjection()","def read_locations(db, openfile):\n pass","def load(self, filename):\n LIB.mnt_grid_load.argtypes = [POINTER(c_void_p), c_char_p]\n fm = filename.encode('utf-8')\n ier = LIB.mnt_grid_load(self.obj, fm)\n if ier:\n error_handler(FILE, 'load', ier)","def _open_file(cls, *args, **kwargs):\n try:\n import tensorflow as tf\n open_file = tf.io.gfile.GFile # pragma: no cover\n except Exception: # pragma: no cover\n open_file = open\n return open_file(*args, **kwargs)","def read_geojson(cls, path_or_json_or_string_or_url):\n assert path_or_json_or_string_or_url\n data = None\n if isinstance(path_or_json_or_string_or_url, (dict, list)):\n data = path_or_json_or_string_or_url\n try:\n data = json.loads(path_or_json_or_string_or_url)\n except ValueError:\n pass\n try:\n path = path_or_json_or_string_or_url\n if path.endswith('.gz') or path.endswith('.gzip'):\n import gzip\n contents = gzip.open(path, 'r').read().decode('utf-8')\n else:\n contents = open(path, 'r').read()\n data = json.loads(contents)\n except FileNotFoundError:\n pass\n if not data:\n import urllib.request\n with urllib.request.urlopen(path_or_json_or_string_or_url) as url:\n data = json.loads(url.read().decode())\n assert data, 'MapData accepts a valid geoJSON object, geoJSON string, path to a geoJSON file, or URL'\n return cls(cls._read_geojson_features(data))","def isGeospatial(path):\n try:\n ds = gdal.Open(str(path), gdalconst.GA_ReadOnly)\n except Exception:\n return False\n if ds:\n if ds.GetGCPs() and ds.GetGCPProjection():\n return True\n if ds.GetProjection():\n return True\n if ds.GetGeoTransform(can_return_null=True):\n return True\n if ds.GetDriver().ShortName in {'NITF', 'netCDF'}:\n return True\n return False","def read(self, format=None, epsg=None):\n if self.data is None:\n query, params = self.get_query()\n self.data = df_from_postgis(self.engine, query, params,\n self.geom_column, self.epsg)\n return self.transform_data(outformat=format, epsg=epsg)","def read_gdal_coordinates(dataset, mode=\"center\"):\n coordinates_pixel = _pixel_coordinates(\n dataset.RasterXSize, dataset.RasterYSize, mode\n )\n\n geotransform = dataset.GetGeoTransform()\n coordinates = _pixel_to_map(coordinates_pixel, geotransform)\n\n return coordinates","def gzopen(f):\n return gzip.open(f, 'rb') if f.endswith('.gz') else open(f, 'r')","def from_file(cls, file): \n try:\n import dill as pickle\n except ImportError:\n logger.error(\"Cannot import from file, dill not installed\")\n return None\n model = pickle.load(open(file,'rb'))\n if type(model) == GeologicalModel:\n logger.info('GeologicalModel initialised from file')\n return model\n else:\n logger.error('{} does not contain a geological model'.format(file))\n return None","def open_file(path):\n input_file = os.path.join(path)\n with open(input_file) as f:\n dataset = f.read()\n return dataset","def check_gdf_load(gdf):\n if isinstance(gdf, (str, Path)):\n if not is_url(gdf):\n gdf = to_absolute_path(str(gdf))\n # as of geopandas 0.6.2, using the OGR CSV driver requires some add'nal\n # kwargs to create a valid geodataframe with a geometry column. see\n # https://github.com/geopandas/geopandas/issues/1234\n if str(gdf).lower().endswith(\"csv\"):\n return gpd.read_file(\n gdf, GEOM_POSSIBLE_NAMES=\"geometry\", KEEP_GEOM_COLUMNS=\"NO\"\n )\n try:\n return gpd.read_file(gdf)\n except (DriverError, CPLE_OpenFailedError):\n logging.warning(\n f\"GeoDataFrame couldn't be loaded: either {gdf} isn't a valid\"\n \" path or it isn't a valid vector file. Returning an empty\"\n \" GeoDataFrame.\"\n )\n return gpd.GeoDataFrame()\n elif isinstance(gdf, gpd.GeoDataFrame):\n return gdf\n else:\n raise ValueError(f\"{gdf} is not an accepted GeoDataFrame format.\")","def load(src_path):\n satdat = rasterio.open(src_path)\n return satdat","def geotiff_read(ifile,metaData):\r\n\r\n file = gdal.Open(ifile, GA_ReadOnly)\r\n\r\n projection = file.GetProjection()\r\n src = osr.SpatialReference()\r\n src.ImportFromWkt(projection)\r\n proj = src.ExportToWkt()\r\n\r\n Nx = file.RasterXSize\r\n Ny = file.RasterYSize\r\n\r\n trans = file.GetGeoTransform()\r\n\r\n dx = trans[1]\r\n dy = trans[5]\r\n\r\n if metaData == \"A\":\r\n\r\n xp = np.arange(Nx)\r\n yp = np.arange(Ny)\r\n\r\n (Xp, Yp) = np.meshgrid(xp,yp)\r\n\r\n X = trans[0] + (Xp+0.5)*trans[1] + (Yp+0.5)*trans[2] #FIXME: bottleneck!\r\n Y = trans[3] + (Xp+0.5)*trans[4] + (Yp+0.5)*trans[5]\r\n\r\n if metaData == \"P\":\r\n\r\n xp = np.arange(Nx)\r\n yp = np.arange(Ny)\r\n\r\n (Xp, Yp) = np.meshgrid(xp,yp)\r\n\r\n X = trans[0] + Xp*trans[1] + Yp*trans[2] #FIXME: bottleneck!\r\n Y = trans[3] + Xp*trans[4] + Yp*trans[5]\r\n\r\n band = file.GetRasterBand(1)\r\n\r\n Z = band.ReadAsArray()\r\n\r\n dx = np.abs(dx)\r\n dy = np.abs(dy)\r\n\r\n return X, Y, Z, dx, dy, proj","def open_gzipped(infile, mode='rt'):\n import gzip\n import bz2\n if mode.startswith('r'):\n tmode = 'rt'\n bmode = 'r'\n elif mode.startswith('w'):\n tmode = 'wt'\n bmode = 'w'\n elif mode.startswith('a'):\n tmode = 'at'\n bmode = 'a'\n if hasattr(infile, 'write'):\n return infile\n if isinstance(infile, str):\n if infile.endswith('.gz'):\n return gzip.open(infile, tmode)\n if infile.endswith('.bz2'):\n if hasattr(bz2, 'open'):\n return bz2.open(infile, tmode)\n else:\n return bz2.BZ2File(infile, bmode)\n return open(infile, tmode)","def open(self, file):\n\n # Check if file exists\n if not os.path.exists(file):\n logging.warning(f'{file} does not exist')\n return\n try:\n # Open nx file\n self.file.load(file)\n except:\n logging.exception(f'Unable to open {file}')","def _initialize_geospatial_data(self):\n driver = ogr.GetDriverByName(\"ESRI Shapefile\")\n\n bnd_src = driver.Open(self._spatial_filename, 0)\n bnd_lyr = bnd_src.GetLayer()\n (self.spatial_index,\n self.spatial_feats,\n self.bison_spatial_fields\n ) = self._create_spatial_index(bnd_lyr)","def open_file(file_name):\n pass","def open(self, filename):\n raise NotImplementedError","def openFile(file):\n file = file.lower()\n if file.endswith('.bz2'):\n return bz2.BZ2File(file)\n elif file.endswith('.gz'):\n return gzip.open(file)\n return open(file)","def read_gfs_data(fname, lat, lon, interp=None, step=200.):\n pass","def fileopen(file):\n return _posixfile_().fileopen(file)","def geogrid_from_gdalInMem(ds):\n in_data = ds.GetRasterBand(1).ReadAsArray()\n in_xdim = ds.RasterXSize\n in_ydim = ds.RasterYSize\n in_geotransform = ds.GetGeoTransform()\n in_xvals = np.linspace(\n in_geotransform[0] + 0.5 * in_geotransform[1],\n in_geotransform[0] + (in_xdim - 0.5) * in_geotransform[1],\n in_xdim)\n in_yvals = np.linspace(\n in_geotransform[3] + 0.5 * in_geotransform[5],\n in_geotransform[3] + (in_ydim - 0.5) * in_geotransform[5],\n in_ydim)\n in_wkt = ds.GetProjection()\n\n return GeoGrid(in_data, in_xvals, in_yvals, in_wkt, warn=True)","def load(filename):\n return GesFile(filename)","def geogrid_as_gdalInMem(gg):\n in_xdim = len(gg.x)\n in_ydim = len(gg.y)\n in_datatype = get_gdal_datatype(gg.data_array.dtype)\n in_geotransform = gg.geotransform\n in_wkt = gg.wkt\n in_data = gg.data_array\n\n ds = gdal.GetDriverByName('MEM').Create(\n '', in_xdim, in_ydim, 1, in_datatype)\n ds.SetProjection(in_wkt)\n ds.SetGeoTransform(in_geotransform)\n ds.GetRasterBand(1).WriteArray(in_data)\n\n return ds","def load_from_file(path, bands):\n dataset = gdal.Open(path, gdal.GA_ReadOnly)\n array = dataset.ReadAsArray()\n\n if len(array.shape) == 3:\n # The bands column is in the first position, but we want it last\n array = np.rollaxis(array, 0, 3)\n elif len(array.shape) == 2:\n # This image seems to have one band, so we add an axis for ease\n # of use in the rest of the library\n array = array[:, :, np.newaxis]\n\n image = array.astype('float32')\n\n return SatelliteImage(dataset, image, bands)","def _open(self, file_path=None):\n\t\tif file_path is None:\n\t\t\tfile_path = self.file_path\n\n\t\tif not os.path.exists(file_path):\n\t\t\traise ValueError('Could not find file: {}'.format(file_path))\n\n\t\ttry:\n\t\t\tf = open(file_path, encoding='utf-8', newline='')\n\t\texcept OSError as err:\n\t\t\tself.log.error(str(err))\n\t\t\traise ValueError('Could not open file: {}'.format(file_path))\n\n\t\treturn f","def read(self, as_numpy_array=False, as_single_band=True,\n old_nodata=None, new_nodata=None, epsg=None):\n if self.ext not in formats.RASTER:\n raise UnsupportedFormatException(\n \"Only the following raster formats are supported: {}\".format(\n ','.join(formats.RASTER)\n )\n )\n self.basename = os.path.basename(self.uri)\n if not self.data:\n self.data = gdal.Open(self.uri)\n out_data = self.data\n if epsg and self.get_epsg() != epsg:\n out_data = reproject(self.data, epsg)\n\n if as_numpy_array:\n return raster_to_numpy_array(out_data, as_single_band,\n old_nodata, new_nodata)\n else:\n return out_data","def ReadRaster(cls,infile):\r\n try:\r\n import gdal\r\n import rasterio\r\n except:\r\n raise ImportError(\"Can not import module GDAL or RasterIO\")\r\n\r\n with rasterio.open(infile) as src:\r\n transform = src.meta['transform']\r\n nBands = (src.indexes)\r\n array = src.read_band(1)\r\n return array\r\n #except:\r\n # raise ImportError(\"Can not read band\")\r","def readfile(filename, encoding=None, legacy_mode=False):\n if not is_dxf_file(filename):\n raise IOError(\"File '{}' is not a DXF file.\".format(filename))\n\n info = dxf_file_info(filename)\n with io.open(filename, mode='rt', encoding=info.encoding, errors='ignore') as fp:\n dwg = read(fp, legacy_mode=legacy_mode, dxfversion=info.version)\n\n dwg.filename = filename\n if encoding is not None and is_supported_encoding(encoding):\n dwg.encoding = encoding\n return dwg","def open(self, infile, cache=True):\n return _image.image_open(self, infile, cache)","def open(self, path, mode='r', encoding=None, newline=None):\n return DataSource.open(self, self._fullpath(path), mode,\n encoding=encoding, newline=newline)","def _load_file(self, XYZ_path, bohrs=False):\n\n # Imports\n import numpy as np\n from .const import CIC, PHYS, atom_num, atom_sym\n from .error import XYZError\n from .utils import safe_cast as scast\n\n # Complain if already initialized\n if 'geoms' in dir(self):\n raise XYZError(XYZError.OVERWRITE,\n \"Cannot overwrite contents of existing OpanXYZ\", \"\")\n ## end if\n\n # Open file, read contents, close stream\n # No particular exception handling; that will be the responsibility\n # of the calling routine.\n # May want to add a check for text file format; but, really, a check\n # for no-found-geometry-information will likely cover such a case.\n with open(XYZ_path,'rU') as in_fl:\n self.XYZ_path = XYZ_path #: Path of file\n self.in_str = in_fl.read()\n\n # Check to ensure at least one geom match; else raise some sort of\n # error\n if not OpanXYZ.p_geom.search(self.in_str):\n raise XYZError(XYZError.XYZFILE,\n \"No valid geometry found\",\n \"XYZ file: \" + XYZ_path)\n ## end if\n\n # Store the number of atoms. XYZ files with multiple geometries will\n # be required to have the same number of atoms in all geometries.\n # This is fine for Open Anharmonic, but may be highly undesirable\n # in other applications. Beware.\n # If the .match() call doesn't have a group attribute, catch the\n # resulting AttributeError and throw an XYZError. Can't think of any\n # other situation that would throw an AttributeError here, so probably\n # I don't have to check for a subtype of AttributeError...\n # Can't think of a need for an else or a finally here\n try:\n self.num_atoms = scast(OpanXYZ.p_geom.match(self.in_str)\n .group(\"num\"), np.int_)\n except AttributeError:\n raise XYZError(XYZError.XYZFILE,\n \"No geometry block found at start of file\",\n \"XYZ file: \" + XYZ_path)\n ## end try\n\n # Initialize the description vector and geometry array\n # CANNOT use matrix type for the geometry since it's 3D.\n # Retriever function for geometry should convert the given slice\n # to a vector as an np.matrix, though.\n # Geometry is constructed as a length-3N vector of\n # coordinates, where atom 1's x/y/z coordinates are sequential,\n # then atom 2's x/y/z coordinates are sequential, etc.\n # Multiple geometries in a single XYZ file are stacked in a simple\n # array.\n self.descs = []\n self.geoms = []\n\n # Initialize the atom symbols vector\n self.atom_syms = []\n\n # Define a counter for the number of geometries. Store as a persistent\n # instance variable because it will be useful later.\n self.num_geoms = 0\n\n # Loop over the geometry blocks found in the input file\n for mch in OpanXYZ.p_geom.finditer(self.in_str):\n # Check that the number of atoms is consistent with the spec\n # found in the first geometry block\n if not scast(mch.group(\"num\"), np.int_) == self.num_atoms:\n raise XYZError(XYZError.XYZFILE,\n \"Non-constant number of atoms in multiple geometry\",\n \"XYZ file: \" + XYZ_path)\n ## end if\n\n # Store the description for the current geometry\n self.descs.append(mch.group(\"desc\"))\n\n # Assemble the coordinates vector and assemble/check the element\n # ID vector.\n # Reset the atom counter and the coordinates vector\n atom_count = 0\n coord_vec = np.empty((0,),dtype=np.float_)\n for line_mch in OpanXYZ.p_coords.finditer(mch.group(\"coord\")):\n # Check for whether element list has been fully populated\n if len(self.atom_syms) < self.num_atoms:\n # If not, continue populating it; have to check for\n # whether it's an atomic number or an element symbol\n if line_mch.group(\"el\").isdigit():\n # Atomic number\n # Check for valid number\n at_num = scast(line_mch.group(\"el\"), np.int_)\n if not (CIC.MIN_ATOMIC_NUM <= at_num\n <= CIC.MAX_ATOMIC_NUM):\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is an \\\n unsupported element\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ##end if\n\n # Tag on the new symbol\n self.atom_syms.append(atom_sym[at_num])\n else:\n # Element symbol; store as all caps\n # Check for valid element, first by catching if the\n # specified element string is even valid\n try:\n at_num = atom_num[line_mch.group(\"el\").upper()]\n except KeyError:\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is an \\\n unrecognized element\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end try\n\n # Tag on the new symbol\n self.atom_syms.append(line_mch.group(\"el\").upper())\n ## end if\n else: # List is fully populated\n # If so, confirm that the element specified at the current\n # line matches that of the first geometry of the file\n # Have to check whether it's an atomic number or symbol.\n if line_mch.group(\"el\").isdigit():\n # Atomic number; don't need safe cast; must trap for\n # a bad atomic number\n at_num = scast(line_mch.group(\"el\"), np.int_)\n if not (CIC.MIN_ATOMIC_NUM <= at_num\n <= CIC.MAX_ATOMIC_NUM):\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is an \\\n unsupported element\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end if\n if not atom_sym[at_num] == self.atom_syms[atom_count]:\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is inconsistent \\\n with geometry #0\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end if\n\n else:\n # Element symbol\n # Check for valid element, first by catching if the\n # specified element string is even valid\n try:\n at_num = atom_num[line_mch.group(\"el\").upper()]\n except KeyError:\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is an \\\n unrecognized element\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end try\n # Confirm symbol matches the initial geometry\n if not line_mch.group(\"el\").upper() == \\\n self.atom_syms[atom_count]:\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0}, atom #{1} is inconsistent \\\n with geometry #0\"\n .format(self.num_geoms, atom_count),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end if\n ## end if\n ## end if\n\n # Append the three coordinates of the current atom to the\n # temp coordinates vector, converting to Bohrs if indicated.\n # Working in Bohrs is desired because they are atomic units\n # and thus are part of the internal unit definition of the\n # Hartree.\n for c_str in range(1,4):\n coord_vec = np.concatenate(\n (coord_vec, [\n scast(line_mch.group(\"c{0}\".format(c_str)), np.float_) /\n (1.0 if bohrs else PHYS.ANG_PER_BOHR)\n ]), axis=0)\n ## next c_str\n\n # Increment the atom_count for the atom number\n atom_count += 1\n\n ## next line_mch\n\n # Confirm that number of imported coordinates matches the\n # number expected from self.num_atoms\n if not coord_vec.shape[0] == 3*self.num_atoms:\n raise XYZError(XYZError.XYZFILE,\n \"Geometry #{0} atom count is inconsistent\"\n .format(self.num_geoms),\n \"XYZ file: {0}\".format(XYZ_path))\n ## end if\n\n # Assemble the coordinates vector into the actual coordinates\n # stack for the XYZ\n self.geoms.append(coord_vec)\n\n # Increment the count of the number of geometries. Once the\n # 'mch' iteration is completed, this will accurately reflect\n # the number of geometries read from the file.\n self.num_geoms += 1\n\n ## next mch","def create_dat_from_csv(windgrid_file, DAT_header, output_file, wind_field='gust_mph', latitude_field='lat', longitude_field='lon'):\n df = pd.read_csv(windgrid_file)\n df['geometry'] = [Point(x, y) for x, y in zip(\n df[longitude_field], df[latitude_field])]\n gdf = gpd.GeoDataFrame(df, geometry='geometry')\n create_dat_from_geodataframe(\n gdf, DAT_header, output_file, wind_field=wind_field)","def read_from(self, filename):\n\n lon, lat, field, weight = [], [], [], []\n\n if os.path.exists(filename):\n logger.info(\"Reading data from file {0}\".format(filename))\n with open(filename, 'r') as f:\n line = f.readline()\n ncols = len(line.split())\n while ncols >= 3:\n lon.append(float(line.split()[0]))\n lat.append(float(line.split()[1]))\n field.append(float(line.split()[2]))\n if ncols >= 4:\n weight.append(float(line.split()[3]))\n else:\n weight.append(1.)\n line = f.readline()\n ncols = len(line.split())\n\n self.x = np.array(lon)\n self.y = np.array(lat)\n self.field = np.array(field)\n self.weight = np.array(weight)\n return self\n else:\n logger.error(\"File {0} does not exist\".format(filename))\n raise FileNotFoundError('File does not exist')","def open_gz(filename, mode):\n return gzip.open(filename, mode)","def readArray(input):\n data = gdal.Open(input)\n band = data.GetRasterBand(1)\n \n return band.ReadAsArray()","def cli(source_f, raster_f, output, verbose):\n with fiona.open(source_f, 'r') as source:\n source_driver = source.driver\n source_crs = source.crs\n sink_schema = source.schema.copy()\n\n source_geom = source.schema['geometry']\n if source_geom == 'Point':\n sink_schema['geometry'] = '3D Point'\n elif source_geom == 'LineString':\n sink_schema['geometry'] = '3D LineString'\n elif source_geom == '3D Point' or source_geom == '3D LineString':\n pass\n else:\n click.BadParameter(\"Source geometry type {} not implemented\".format(source_geom))\n\n with rasterio.open(raster_f) as raster:\n if source_crs != raster.crs:\n click.BadParameter(\"Features and raster have different CRS.\")\n if raster.count > 1:\n warnings.warn(\"Found {0} bands in {1}, expected a single band raster\".format(raster.bands, raster_f))\n supported = ['int16', 'int32', 'float32', 'float64']\n if raster.dtypes[0] not in supported:\n warnings.warn(\"Found {0} type in {1}, expected one of {2}\".format(raster.dtypes[0], raster_f, supported))\n with fiona.open(\n output, 'w',\n driver=source_driver,\n crs=source_crs,\n schema=sink_schema) as sink:\n\n for feature in source:\n try:\n feature_z = drapery.drape(raster, feature)\n sink.write({\n 'geometry': mapping(feature_z),\n 'properties': feature['properties'],\n })\n except Exception:\n logging.exception(\"Error processing feature %s:\", feature['id'])\n #print(sink.closed)\n #print(raster.closed)\n #print(source.closed)","def read_file(netcdf_file_name):\n\n dataset_object = netCDF4.Dataset(netcdf_file_name)\n\n saliency_dict = {\n MODEL_FILE_KEY: str(getattr(dataset_object, MODEL_FILE_KEY)),\n IS_LAYER_OUTPUT_KEY: bool(getattr(dataset_object, IS_LAYER_OUTPUT_KEY)),\n LAYER_NAME_KEY: str(getattr(dataset_object, LAYER_NAME_KEY)),\n NEURON_INDICES_KEY: numpy.array(\n getattr(dataset_object, NEURON_INDICES_KEY), dtype=int\n ),\n IDEAL_ACTIVATION_KEY: getattr(dataset_object, IDEAL_ACTIVATION_KEY),\n MULTIPLY_BY_INPUT_KEY:\n bool(getattr(dataset_object, MULTIPLY_BY_INPUT_KEY)),\n VALID_TIMES_KEY: numpy.array(\n dataset_object.variables[VALID_TIMES_KEY][:], dtype=int\n ),\n LATITUDES_KEY: numpy.array(\n dataset_object.variables[LATITUDES_KEY][:], dtype=float\n ),\n LONGITUDES_KEY: numpy.array(\n dataset_object.variables[LONGITUDES_KEY][:], dtype=float\n ),\n SALIENCY_MATRIX_KEY: numpy.array(\n dataset_object.variables[SALIENCY_MATRIX_KEY][:], dtype=float\n )\n }\n\n dataset_object.close()\n return saliency_dict","def get_infile(filename):\r\n if filename.endswith(\".gz\"):\r\n fin = GzipFile(filename, \"rb\")\r\n else:\r\n fin = open(filename, \"U\")\r\n return fin","def get_infile(filename):\r\n if filename.endswith(\".gz\"):\r\n fin = GzipFile(filename, \"rb\")\r\n else:\r\n fin = open(filename, \"U\")\r\n return fin","def open_input_file(input_file):\n if input_file:\n if not os.path.isfile(input_file):\n sys.stderr.write(\n \"ERROR! Input file (%s) is not a normal file.\\n\" % input_file)\n sys.exit(1)\n try:\n return codecs.open(input_file, \"r\", \"utf8\")\n except:\n sys.stderr.write(\n \"ERROR! Could not open input file (%s) for reading:\\n\" % input_file)\n raise\n else:\n return sys.stdin","def open_local_or_gcs(path, mode):\n if path.startswith('gs://'):\n try:\n return gcsio.GcsIO().open(path, mode)\n except Exception as e: # pylint: disable=broad-except\n # Currently we retry exactly once, to work around flaky gcs calls.\n logging.error('Retrying after exception reading gcs file: %s', e)\n time.sleep(10)\n return gcsio.GcsIO().open(path, mode)\n else:\n return open(path, mode)","def import_L1B(cls,infile):\r\n try:\r\n import gdal\r\n import rasterio\r\n except:\r\n raise ImportError(\"Can not import module GDAL or RasterIO\")\r\n\r\n\r\n image=image()\r\n\r\n #except:\r\n # raise ImportError(\"Can not read band\")\r","def open_file(file_path, mode='rb', encoding='iso-8859-1'):\n try:\n return open(file_path, mode=mode, encoding=encoding)\n except IOError:\n raise","def file_open(*args, **kwargs):\n return open(*args, **kwargs)","def open_file(self, path, omode=\"r\"):\n self.root_group = netCDF4.Dataset(path, omode)\n return self.root_group","def open_(filename, *args):\n\n if (filename[-3:] == '.gz'):\n return gzip.open(filename, *args)\n try:\n return open(filename, *args)\n except OSError:\n return gzip.open(filename + \".gz\", *args)","def open(path, mode='r', destpath=os.curdir, encoding=None, newline=None):\n\n ds = DataSource(destpath)\n return ds.open(path, mode, encoding=encoding, newline=newline)","def open_file(path, config):\n\n return fopen(normalize_path(path, config))","def openInputFile(infile, *args):\n if infile is None:\n logging.info(\"Reading input from STDIN\")\n return sys.stdin\n\n if isinstance(infile, str):\n if urlRE.match(infile):\n import urllib2\n return urllib2.urlopen(infile)\n if len(infile)>3 and infile[-3:]=='.gz':\n import gzip\n return gzip.GzipFile(infile,'rb')\n elif len(infile)>4 and infile[-4:]=='.bz2':\n import bz2\n return bz2.BZ2File(infile,'rb')\n else:\n return open(infile,'rt')\n else:\n return infile","def open_input_stream(uri: str) -> BinaryIO:\n parsed = urlparse(uri)\n if parsed.scheme == \"gs\":\n return _gcsfs().open(uri)\n else:\n filesystem, path = fs.FileSystem.from_uri(uri)\n return filesystem.open_input_file(path)","def read(self, format=None, epsg=None):\n if not format:\n format = self.default_output\n if self.data is None and self.features:\n if type(self.features) == str:\n self.features = json.loads(self.features)\n features = self.features\n\n if 'type' in features and features['type'] == 'FeatureCollection':\n self.data = geopandas.GeoDataFrame.from_features(\n self.features['features'])\n else:\n self.data = geopandas.GeoDataFrame.from_features(features)\n if not self.data.crs:\n if hasattr(self, 'crs'):\n self.data.crs = self.crs\n else:\n self.get_epsg()\n\n return self.transform_data(outformat=format, epsg=epsg)","def import_national_boundaries(self, name):\n print \"\\n4.3- importa shape con confini nazionali ISTAT\"\n countrySHP = os.path.join(\"boundaries\", \"italy_2011_WGS84.shp\")\n countrySQL = os.path.join(\"boundaries\", \"italy_%s.sql\" % name)\n if os.path.isfile(countrySQL):\n call(\"rm %s\" % countrySQL, shell=True)\n cmd = \"shp2pgsql -s 4326 -W 'LATIN1' %s italy %s > %s\" % (countrySHP, name, countrySQL)\n print cmd\n call(cmd, shell=True)\n call(\"psql -h localhost -U %s -d %s -f %s\" % (self.user, name, countrySQL), shell=True)\n call(\"rm %s\" % countrySQL, shell=True)\n call(\"echo 'CREATE INDEX ON italy USING GIST (geom);'| psql -U %s -d %s\" % (self.user, name), shell=True)\n call(\"echo 'ANALYZE italy;'| psql -U %s -d %s\" % (self.user, name), shell=True)","def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\n args = [\n \"gdal_calc.py\",\n '-A', infile,\n '--outfile={}'.format(tmpfile_1),\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\n '--type=Byte', '--NoDataValue=0',\n '--co=SPARSE_OK=YES',\n '--co=NBITS=1',\n '--quiet'\n # Could enable compression\n # --co=\"COMPRESS=LZW\"\n ]\n subprocess.run(args)\n\n subprocess.run([\n \"gdal_polygonize.py\",\n tmpfile_1,\n '-q',\n '-f', 'ESRI Shapefile',\n tmpfile_2\n ])\n\n subprocess.run([\n \"ogr2ogr\",\n '-a_srs', 'EPSG:4326',\n outfile,\n tmpfile_2\n ])\n\n subprocess.run([\"rm\", tmpfile_1])\n subprocess.run([\"rm\", tmpfile_2])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'shx')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'dbf')])\n subprocess.run([\"rm\", tmpfile_2.replace('shp', 'prj')])","def load_gldas_elevation_dataset(gldas_elevation_file): \n d1 = xr.open_dataset(gldas_elevation_file).load()\n return d1","def genOpen(filename, mode):\n (name, ext) = os.path.splitext(filename)\n if ext == \".gz\":\n return gzip.open(filename, mode)\n else:\n return open(filename, mode)","def genOpen(filename, mode):\n (name, ext) = os.path.splitext(filename)\n if ext == \".gz\":\n return gzip.open(filename, mode)\n else:\n return open(filename, mode)","def open_(filename, mode=None, compresslevel=9):\n if filename[-3:] == '.gz':\n if mode is None: mode = 'rt'\n return closing(gzip.open(filename, mode, compresslevel))\n else:\n if mode is None: mode = 'r'\n return open(filename, mode)","def from_file(path):\n\n filename = os.path.basename(path)\n\n base, suffix = os.path.splitext(filename);\n\n if suffix == '.bin':\n g = bgy3d.from_file(path)\n elif suffix == '.m':\n g = contf.m2dat(path)\n else:\n print 'Unknown file suffix.'\n exit()\n\n return g","def load_surfer(fname, fmt='ascii'):\n assert fmt in ['ascii', 'binary'], \"Invalid grid format '%s'. Should be \\\n 'ascii' or 'binary'.\" % (fmt)\n if fmt == 'ascii':\n # Surfer ASCII grid structure\n # DSAA Surfer ASCII GRD ID\n # nCols nRows number of columns and rows\n # xMin xMax X min max\n # yMin yMax Y min max\n # zMin zMax Z min max\n # z11 z21 z31 ... List of Z values\n with open(fname) as ftext:\n # DSAA is a Surfer ASCII GRD ID\n id = ftext.readline()\n # Read the number of columns (ny) and rows (nx)\n ny, nx = [int(s) for s in ftext.readline().split()]\n shape = (nx, ny)\n # Read the min/max value of columns/longitue (y direction)\n ymin, ymax = [float(s) for s in ftext.readline().split()]\n # Read the min/max value of rows/latitude (x direction)\n xmin, xmax = [float(s) for s in ftext.readline().split()]\n area = (xmin, xmax, ymin, ymax)\n # Read the min/max value of grid values\n datamin, datamax = [float(s) for s in ftext.readline().split()]\n data = numpy.fromiter((float(i) for line in ftext for i in\n line.split()), dtype='f')\n data = numpy.ma.masked_greater_equal(data, 1.70141e+38)\n assert numpy.allclose(datamin, data.min()) \\\n and numpy.allclose(datamax, data.max()), \\\n \"Min and max values of grid don't match ones read from file.\" \\\n + \"Read: ({}, {}) Actual: ({}, {})\".format(\n datamin, datamax, data.min(), data.max())\n # Create x and y coordinate numpy arrays\n x, y = regular(area, shape)\n if fmt == 'binary':\n raise NotImplementedError(\n \"Binary file support is not implemented yet.\")\n return x, y, data, shape","def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\n \n data = numpy.genfromtxt(datafile, delimiter=',')\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n \n # slice out the last column of null values\n if str(data[Ny-1][Nx-1]) == 'nan':\n data = data[:,:-1]\n \n Ny, Nx = data.shape\n \n #print data.size\n #print data.shape\n #print data\n #print Ny, Nx\n \n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\n \n #print startPos, d_lat, d_long\n \n driver = gdal.GetDriverByName(\"GTiff\")\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\n srs = osr.SpatialReference()\n srs.ImportFromEPSG(4326)\n ds.SetProjection( srs.ExportToWkt() )\n ds.GetRasterBand(1).WriteArray(data)\n ds = None","def open_file(self, path: str, **kwargs) -> OpenFileType:\n full_path = self._full_path(path)\n logger.debug(f\"returning open file for {full_path}\")\n return self.fs.open(full_path, **kwargs)","def smart_open(filename: str, mode: str = \"rt\", ftype: str = \"auto\", errors: str = \"replace\"):\n if ftype == \"gzip\" or ftype == \"gz\" or (ftype == \"auto\" and filename.endswith(\".gz\")):\n return gzip.open(filename, mode=mode, encoding=\"utf-8\", errors=errors)\n else:\n return open(filename, mode=mode, encoding=\"utf-8\", errors=errors)","def _open_file(self,path):\n \n print \"Open File %s\" % path\n \n mapping = self.mappings.GetClientData(self.mappings.GetSelection())\n try:\n delimiter=mapping['_params']['delimiter']\n except:\n delimiter=','\n try:\n skip_last=mapping['_params']['skip_last']\n except:\n skip_last=0\n self.grid_table = SimpleCSVGrid(path,delimiter,skip_last)\n self.grid.SetTable(self.grid_table)\n\tself.opened_path = path","def open_input_files(self):\n self.dictionaryFile = open(self.dictionaryFile, 'r', encoding=self.encoding)\n\n if self.annotationFile :\n self.annotationFile = open(self.annotationFile, 'r', encoding=self.encoding)\n elif self.annotationFile is None:\n try:\n self.annotationFile = open(os.path.join(self.dictionaryPath, self.dictionaryName + '.ann'), 'r', encoding=self.encoding)\n except FileNotFoundError:\n if self.verbose >= 2:\n sys.stdout.write (\"Warning: annotation file is not found.\\n\")\n\n if self.abbreviationsFile :\n self.abbreviationsFile = open(self.abbreviationsFile, 'r', encoding=self.encoding)\n elif self.abbreviationsFile is None:\n try:\n self.abbreviationsFile = open(os.path.join(self.dictionaryPath, self.dictionaryName + '_abrv.dsl'), 'r', encoding=self.encoding)\n except FileNotFoundError:\n if self.verbose >= 2:\n sys.stdout.write (\"Warning: abbreviations file is not found.\\n\")","def get_data(datapath, asfile=False):\n import os\n\n ## The file is a local file - try to get it\n if not os.path.isfile(datapath) :\n print \"The file %s you are trying to access does not exist\" %(datapath)\n raise IOError\n fn = datapath\n if asfile:\n return open(fn)\n else:\n import numpy as np\n return np.loadtxt(fn)","def convert_gpkg_to_geojson(self,shape_fname, destdirectory):\r\n\t\tfeatures = []\r\n\t\tcrs = None\r\n\t\tif not os.path.isfile(shape_fname):\r\n\t\t\tself.logger.error('File not found: %s' % shape_fname)\r\n\t\tself.opstatus.add_info(stage=6, msg = \"Rounding coordinates to six decimal precision\")\r\n\r\n\r\n\t\tout_fname = os.path.join(destdirectory,os.path.basename(shape_fname).replace('.gpkg', '.geojson'))\r\n\t\twith fiona.open(shape_fname, driver='GPKG') as source:\r\n\t\t\twith fiona.open(out_fname, \"w\",driver='GeoJSON',crs = fiona.crs.from_epsg(4326),schema=source.schema) as sink:\r\n\t\t\t\tfor rec in source:\r\n\t\t\t\t\tsink.write(rec)\r\n\r\n\t\tself.logger.info('file written: %s' % out_fname)\r\n\t\tself.opstatus.set_status(stage=6, status=1, statustext =\"File successfully converted to GeoJSON with six decimal precision\")\r\n\t\tself.opstatus.add_success(stage=6, msg = \"GeoJSON file successfully written\")\r\n\t\treturn out_fname"],"string":"[\n \"def load(self):\\r\\n\\r\\n #Open the dataset read only using GDAL\\r\\n dataset = gdal.Open(self.inputds, gdal.GA_ReadOnly)\\r\\n \\r\\n return dataset\\r\\n \\r\\n\\r\\n #print \\\"Failed to open %s. Is it a GDAL supported format?\\\" %(self.inputds)\\r\",\n \"def open_input(self):\\n gdal.AllRegister()\\n\\n self.out_drv = gdal.GetDriverByName(self.tiledriver)\\n self.mem_drv = gdal.GetDriverByName('MEM')\\n\\n if not self.out_drv:\\n raise Exception(\\\"The '%s' driver was not found, is it available in this GDAL build?\\\",\\n self.tiledriver)\\n if not self.mem_drv:\\n raise Exception(\\\"The 'MEM' driver was not found, is it available in this GDAL build?\\\")\\n\\n # Open the input file\\n\\n if self.input_file:\\n input_dataset = gdal.Open(self.input_file, gdal.GA_ReadOnly)\\n else:\\n raise Exception(\\\"No input file was specified\\\")\\n\\n if self.options.verbose:\\n print(\\\"Input file:\\\",\\n \\\"( %sP x %sL - %s bands)\\\" % (input_dataset.RasterXSize,\\n input_dataset.RasterYSize,\\n input_dataset.RasterCount))\\n\\n if not input_dataset:\\n # Note: GDAL prints the ERROR message too\\n exit_with_error(\\\"It is not possible to open the input file '%s'.\\\" % self.input_file)\\n\\n # Read metadata from the input file\\n if input_dataset.RasterCount == 0:\\n exit_with_error(\\\"Input file '%s' has no raster band\\\" % self.input_file)\\n\\n if input_dataset.GetRasterBand(1).GetRasterColorTable():\\n exit_with_error(\\n \\\"Please convert this file to RGB/RGBA and run gdal2tiles on the result.\\\",\\n \\\"From paletted file you can create RGBA file (temp.vrt) by:\\\\n\\\"\\n \\\"gdal_translate -of vrt -expand rgba %s temp.vrt\\\\n\\\"\\n \\\"then run:\\\\n\\\"\\n \\\"gdal2tiles temp.vrt\\\" % self.input_file\\n )\\n\\n in_nodata = setup_no_data_values(input_dataset, self.options)\\n\\n if self.options.verbose:\\n print(\\\"Preprocessed file:\\\",\\n \\\"( %sP x %sL - %s bands)\\\" % (input_dataset.RasterXSize,\\n input_dataset.RasterYSize,\\n input_dataset.RasterCount))\\n\\n in_srs, self.in_srs_wkt = setup_input_srs(input_dataset, self.options)\\n\\n self.out_srs = setup_output_srs(in_srs, self.options)\\n\\n # If input and output reference systems are different, we reproject the input dataset into\\n # the output reference system for easier manipulation\\n\\n self.warped_input_dataset = None\\n\\n if self.options.profile in ('mercator', 'geodetic'):\\n\\n if not in_srs:\\n exit_with_error(\\n \\\"Input file has unknown SRS.\\\",\\n \\\"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\\\")\\n\\n if not has_georeference(input_dataset):\\n exit_with_error(\\n \\\"There is no georeference - neither affine transformation (worldfile) \\\"\\n \\\"nor GCPs. You can generate only 'raster' profile tiles.\\\",\\n \\\"Either gdal2tiles with parameter -p 'raster' or use another GIS \\\"\\n \\\"software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\\\"\\n )\\n\\n if ((in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or\\n (input_dataset.GetGCPCount() != 0)):\\n self.warped_input_dataset = reproject_dataset(\\n input_dataset, in_srs, self.out_srs)\\n\\n if in_nodata:\\n self.warped_input_dataset = update_no_data_values(\\n self.warped_input_dataset, in_nodata, options=self.options)\\n else:\\n self.warped_input_dataset = update_alpha_value_for_non_alpha_inputs(\\n self.warped_input_dataset, options=self.options)\\n\\n if self.warped_input_dataset and self.options.verbose:\\n print(\\\"Projected file:\\\", \\\"tiles.vrt\\\", \\\"( %sP x %sL - %s bands)\\\" % (\\n self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize,\\n self.warped_input_dataset.RasterCount))\\n\\n if not self.warped_input_dataset:\\n self.warped_input_dataset = input_dataset\\n\\n #Salva o arquivo reprojetado\\n self.warped_input_dataset.GetDriver().CreateCopy(self.tmp_vrt_filename,\\n self.warped_input_dataset)\\n\\n # Get alpha band (either directly or from NODATA value)\\n self.alphaband = self.warped_input_dataset.GetRasterBand(1).GetMaskBand()\\n self.dataBandsCount = nb_data_bands(self.warped_input_dataset)\\n\\n # KML test\\n self.isepsg4326 = False\\n srs4326 = osr.SpatialReference()\\n srs4326.ImportFromEPSG(4326)\\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\\n self.kml = True\\n self.isepsg4326 = True\\n if self.options.verbose:\\n print(\\\"KML autotest OK!\\\")\\n\\n # Read the georeference\\n self.out_gt = self.warped_input_dataset.GetGeoTransform()\\n\\n # Test the size of the pixel\\n\\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\\n if (self.out_gt[2], self.out_gt[4]) != (0, 0):\\n exit_with_error(\\\"Georeference of the raster contains rotation or skew. \\\"\\n \\\"Such raster is not supported. Please use gdalwarp first.\\\")\\n\\n # Here we expect: pixel is square, no rotation on the raster\\n\\n # Output Bounds - coordinates in the output SRS\\n self.ominx = self.out_gt[0]\\n self.omaxx = self.out_gt[0] + self.warped_input_dataset.RasterXSize * self.out_gt[1]\\n self.omaxy = self.out_gt[3]\\n self.ominy = self.out_gt[3] - self.warped_input_dataset.RasterYSize * self.out_gt[1]\\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\\n\\n if self.options.verbose:\\n print(\\\"Bounds (output srs):\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy)\\n\\n # Calculating ranges for tiles in different zoom levels\\n if self.options.profile == 'mercator':\\n\\n self.mercator = GlobalMercator()\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.mercator.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, 32))\\n for tz in range(0, 32):\\n tminx, tminy = self.mercator.MetersToTile(self.ominx, self.ominy, tz)\\n tmaxx, tmaxy = self.mercator.MetersToTile(self.omaxx, self.omaxy, tz)\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the minimal zoom level (map covers area equivalent to one tile)\\n if self.tminz is None:\\n self.tminz = self.mercator.ZoomForPixelSize(\\n self.out_gt[1] *\\n max(self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize) /\\n float(self.tilesize))\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.mercator.ZoomForPixelSize(self.out_gt[1])\\n\\n if self.options.verbose:\\n print(\\\"Bounds (latlong):\\\",\\n self.mercator.MetersToLatLon(self.ominx, self.ominy),\\n self.mercator.MetersToLatLon(self.omaxx, self.omaxy))\\n print('MinZoomLevel:', self.tminz)\\n print(\\\"MaxZoomLevel:\\\",\\n self.tmaxz,\\n \\\"(\\\",\\n self.mercator.Resolution(self.tmaxz),\\n \\\")\\\")\\n\\n if self.options.profile == 'geodetic':\\n\\n self.geodetic = GlobalGeodetic(self.options.tmscompatible)\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.geodetic.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, 32))\\n for tz in range(0, 32):\\n tminx, tminy = self.geodetic.LonLatToTile(self.ominx, self.ominy, tz)\\n tmaxx, tmaxy = self.geodetic.LonLatToTile(self.omaxx, self.omaxy, tz)\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tminz is None:\\n self.tminz = self.geodetic.ZoomForPixelSize(\\n self.out_gt[1] *\\n max(self.warped_input_dataset.RasterXSize,\\n self.warped_input_dataset.RasterYSize) /\\n float(self.tilesize))\\n\\n # Get the maximal zoom level\\n # (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.geodetic.ZoomForPixelSize(self.out_gt[1])\\n\\n if self.options.verbose:\\n print(\\\"Bounds (latlong):\\\", self.ominx, self.ominy, self.omaxx, self.omaxy)\\n\\n if self.options.profile == 'raster':\\n\\n def log2(x):\\n return math.log10(x) / math.log10(2)\\n\\n self.nativezoom = int(\\n max(math.ceil(log2(self.warped_input_dataset.RasterXSize/float(self.tilesize))),\\n math.ceil(log2(self.warped_input_dataset.RasterYSize/float(self.tilesize)))))\\n\\n if self.options.verbose:\\n print(\\\"Native zoom of the raster:\\\", self.nativezoom)\\n\\n # Get the minimal zoom level (whole raster in one tile)\\n if self.tminz is None:\\n self.tminz = 0\\n\\n # Get the maximal zoom level (native resolution of the raster)\\n if self.tmaxz is None:\\n self.tmaxz = self.nativezoom\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = list(range(0, self.tmaxz+1))\\n self.tsize = list(range(0, self.tmaxz+1))\\n for tz in range(0, self.tmaxz+1):\\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\\n tminx, tminy = 0, 0\\n tmaxx = int(math.ceil(self.warped_input_dataset.RasterXSize / tsize)) - 1\\n tmaxy = int(math.ceil(self.warped_input_dataset.RasterYSize / tsize)) - 1\\n self.tsize[tz] = math.ceil(tsize)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # Function which generates SWNE in LatLong for given tile\\n if self.kml and self.in_srs_wkt:\\n ct = osr.CoordinateTransformation(in_srs, srs4326)\\n\\n def rastertileswne(x, y, z):\\n pixelsizex = (2**(self.tmaxz-z) * self.out_gt[1]) # X-pixel size in level\\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\\n east = west + self.tilesize*pixelsizex\\n south = self.ominy + y*self.tilesize*pixelsizex\\n north = south + self.tilesize*pixelsizex\\n if not self.isepsg4326:\\n # Transformation to EPSG:4326 (WGS84 datum)\\n west, south = ct.TransformPoint(west, south)[:2]\\n east, north = ct.TransformPoint(east, north)[:2]\\n return south, west, north, east\\n\\n self.tileswne = rastertileswne\\n else:\\n self.tileswne = lambda x, y, z: (0, 0, 0, 0) # noqa\",\n \"def open_image(filename):\\n\\n dataset = gdal.Open(filename, gdal.GA_ReadOnly)\\n if dataset is None:\\n raise IOError(\\\"cannot open %s\\\" % filename)\\n\\n return GdalImage(dataset, filename)\",\n \"def open_input(self):\\n gdal.SetConfigOption(\\\"GDAL_PAM_ENABLED\\\", \\\"YES\\\")\\n gdal.AllRegister()\\n # self.options.verbose=True\\n if self.options.tms_osm:\\n self.s_y_type=\\\"osm\\\"\\n else:\\n self.s_y_type=\\\"tms\\\"\\n if self.options.verbose:\\n print \\\"open_input :\\\", self.input,\\\" osm[\\\",self.options.tms_osm,\\\",\\\",self.s_y_type,\\\"] mbtiles[\\\",self.options.mbtiles,\\\"] mbtiles_todisk[\\\",self.options.mbtiles_todisk,\\\"] mbtiles_fromdisk[\\\",self.options.mbtiles_fromdisk,\\\"]\\\";\\n # Open the input file\\n if self.input:\\n self.in_ds = gdal.Open(self.input, gdal.GA_ReadOnly)\\n else:\\n raise Exception(\\\"No input file was specified\\\")\\n\\n if self.options.verbose:\\n print \\\"Input file:\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\\n\\n if not self.in_ds:\\n # Note: GDAL prints the ERROR message too\\n self.error(\\\"It is not possible to open the input file '%s'.\\\" % self.input )\\n\\n # Read metadata from the input file\\n if self.in_ds.RasterCount == 0:\\n self.error( \\\"Input file '%s' has no raster band\\\" % self.input )\\n\\n if self.in_ds.GetRasterBand(1).GetRasterColorTable():\\n # TODO: Process directly paletted dataset by generating VRT in memory\\n self.error( \\\"Please convert this file to RGB/RGBA and run gdal2mbtiles on the result.\\\",\\n \\\"\\\"\\\"From paletted file you can create RGBA file (temp.vrt) by:\\ngdal_translate -of vrt -expand rgba %s temp.vrt\\nthen run:\\ngdal2mbtiles temp.vrt\\\"\\\"\\\" % self.input )\\n\\n # Get NODATA value\\n # User supplied values overwrite everything else.\\n if self.options.srcnodata is not None:\\n nds = map(float, self.options.srcnodata.split(','))\\n if len(nds) < self.in_ds.RasterCount:\\n self.in_nodata = (nds * self.in_ds.RasterCount)[:self.in_ds.RasterCount]\\n else:\\n self.in_nodata = nds\\n else:\\n # If the source dataset has NODATA, use it.\\n self.in_nodata = []\\n for i in range(1, self.in_ds.RasterCount+1):\\n if self.in_ds.GetRasterBand(i).GetNoDataValue() != None:\\n self.in_nodata.append( self.in_ds.GetRasterBand(i).GetNoDataValue() )\\n\\n if self.options.verbose:\\n print \\\"NODATA: %s\\\" % self.in_nodata\\n\\n # INIT DEST\\n if self.options.init_dest is not None:\\n if self.options.tile_format == \\\"jpeg\\\":\\n if self.in_ds.RasterCount == 4:\\n nbands = 3\\n else:\\n nbands = self.in_ds.RasterCount\\n\\n nds = map(float, self.options.init_dest.split(','))\\n\\n if len(nds) == 1:\\n init_dest = nds * nbands\\n elif len(nds) == nbands:\\n init_dest = nds\\n else:\\n print \\\"WARNING: you suplied %d '--init-dest' values but the dataset has %d data bands\\\" % (len(nds), nbands)\\n init_dest = None\\n else:\\n init_dest = None\\n print \\\"WARNING: --init-dest can be used only with 'jpeg' tile format\\\"\\n else:\\n if self.options.tile_format == \\\"jpeg\\\":\\n init_dest = [255,255,255]\\n else:\\n init_dest = None\\n\\n #\\n # Here we should have RGBA input dataset opened in self.in_ds\\n #\\n\\n if self.options.verbose:\\n print \\\"Preprocessed file:\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.in_ds.RasterXSize, self.in_ds.RasterYSize, self.in_ds.RasterCount)\\n\\n # Spatial Reference System of the input raster\\n\\n\\n self.in_srs = None\\n\\n if self.options.s_srs:\\n self.in_srs = osr.SpatialReference()\\n self.in_srs.SetFromUserInput(self.options.s_srs)\\n self.in_srs_wkt = self.in_srs.ExportToWkt()\\n else:\\n self.in_srs_wkt = self.in_ds.GetProjection()\\n if not self.in_srs_wkt and self.in_ds.GetGCPCount() != 0:\\n self.in_srs_wkt = self.in_ds.GetGCPProjection()\\n if self.in_srs_wkt:\\n self.in_srs = osr.SpatialReference()\\n self.in_srs.ImportFromWkt(self.in_srs_wkt)\\n #elif self.options.profile != 'raster':\\n # self.error(\\\"There is no spatial reference system info included in the input file.\\\",\\\"You should run gdal2mbtiles with --s_srs EPSG:XXXX or similar.\\\")\\n\\n # Spatial Reference System of tiles\\n\\n self.out_srs = osr.SpatialReference()\\n\\n if self.options.profile == 'mercator':\\n self.out_srs.ImportFromEPSG(900913)\\n elif self.options.profile in ('geodetic', 'gearth', 'garmin'):\\n self.out_srs.ImportFromEPSG(4326)\\n else:\\n self.out_srs = self.in_srs\\n\\n # Are the reference systems the same? Reproject if necessary.\\n\\n self.out_ds = None\\n\\n if self.options.profile in ('mercator', 'geodetic', 'gearth', 'garmin'):\\n\\n if (self.in_ds.GetGeoTransform() == (0.0, 1.0, 0.0, 0.0, 0.0, 1.0)) and (self.in_ds.GetGCPCount() == 0):\\n self.error(\\\"There is no georeference - neither affine transformation (worldfile) nor GCPs. You can generate only 'raster' profile tiles.\\\",\\n \\\"Either gdal2mbtiles with parameter -p 'raster' or use another GIS software for georeference e.g. gdal_transform -gcp / -a_ullr / -a_srs\\\")\\n\\n if self.in_srs:\\n\\n if (self.in_srs.ExportToProj4() != self.out_srs.ExportToProj4()) or (self.in_ds.GetGCPCount() != 0):\\n\\n # Generation of VRT dataset in tile projection, default 'nearest neighbour' warping\\n self.out_ds = gdal.AutoCreateWarpedVRT( self.in_ds, self.in_srs_wkt, self.out_srs.ExportToWkt() )\\n\\n # TODO: HIGH PRIORITY: Correction of AutoCreateWarpedVRT according the max zoomlevel for correct direct warping!!!\\n\\n if self.options.verbose:\\n print \\\"Warping of the raster by AutoCreateWarpedVRT (result saved into 'tiles.vrt')\\\"\\n self.out_ds.GetDriver().CreateCopy(\\\"tiles.vrt\\\", self.out_ds)\\n\\n # Note: self.in_srs and self.in_srs_wkt contain still the non-warped reference system!!!\\n\\n # Correction of AutoCreateWarpedVRT for NODATA values\\n if self.in_nodata != []:\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"<GDALWarpOptions>\\\"\\\"\\\",\\\"\\\"\\\"<GDALWarpOptions>\\n <Option name=\\\"UNIFIED_SRC_NODATA\\\">YES</Option>\\n <Option name=\\\"INIT_DEST\\\">NO_DATA</Option>\\\"\\\"\\\")\\n # replace BandMapping tag for NODATA bands....\\n if init_dest is None:\\n dstnodata = self.in_nodata\\n else:\\n dstnodata = init_dest\\n for i in range(len(self.in_nodata)):\\n s = s.replace(\\\"\\\"\\\"<BandMapping src=\\\"%i\\\" dst=\\\"%i\\\"/>\\\"\\\"\\\" % ((i+1),(i+1)),\\\"\\\"\\\"<BandMapping src=\\\"%i\\\" dst=\\\"%i\\\">\\n <SrcNoDataReal>%i</SrcNoDataReal>\\n <SrcNoDataImag>0</SrcNoDataImag>\\n <DstNoDataReal>%i</DstNoDataReal>\\n <DstNoDataImag>0</DstNoDataImag>\\n </BandMapping>\\\"\\\"\\\" % ((i+1), (i+1), self.in_nodata[i], dstnodata[i]))\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n # set NODATA_VALUE metadata\\n self.out_ds.SetMetadataItem('NODATA_VALUES','%s' % \\\" \\\".join(str(int(f)) for f in self.in_nodata))\\n\\n if self.options.verbose:\\n print \\\"Modified warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n # -----------------------------------\\n # Correction of AutoCreateWarpedVRT for Mono (1 band) and RGB (3 bands) files without NODATA:\\n # equivalent of gdalwarp -dstalpha\\n elif self.in_nodata == [] and self.out_ds.RasterCount in (1,3):\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"<BlockXSize>\\\"\\\"\\\",\\\"\\\"\\\"<VRTRasterBand dataType=\\\"Byte\\\" band=\\\"%i\\\" subClass=\\\"VRTWarpedRasterBand\\\">\\n <ColorInterp>Alpha</ColorInterp>\\n </VRTRasterBand>\\n <BlockXSize>\\\"\\\"\\\" % (self.out_ds.RasterCount + 1))\\n s = s.replace(\\\"\\\"\\\"</GDALWarpOptions>\\\"\\\"\\\", \\\"\\\"\\\"<DstAlphaBand>%i</DstAlphaBand>\\n </GDALWarpOptions>\\\"\\\"\\\" % (self.out_ds.RasterCount + 1))\\n if init_dest is None:\\n init_dest_str = \\\"0\\\"\\n else:\\n init_dest_str = \\\",\\\".join(str(f) for f in init_dest)\\n s = s.replace(\\\"\\\"\\\"</WorkingDataType>\\\"\\\"\\\", \\\"\\\"\\\"</WorkingDataType>\\n <Option name=\\\"INIT_DEST\\\">%s</Option>\\\"\\\"\\\" % init_dest_str)\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n if self.options.verbose:\\n print \\\"Modified -dstalpha warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n elif init_dest is not None:\\n import tempfile\\n tempfilename = tempfile.mktemp('-gdal2mbtiles.vrt')\\n self.out_ds.GetDriver().CreateCopy(tempfilename, self.out_ds)\\n # open as a text file\\n s = open(tempfilename).read()\\n # Add the warping options\\n s = s.replace(\\\"\\\"\\\"</WorkingDataType>\\\"\\\"\\\", \\\"\\\"\\\"</WorkingDataType>\\n <Option name=\\\"INIT_DEST\\\">%s</Option>\\\"\\\"\\\" % \\\",\\\".join(str(f) for f in init_dest))\\n # save the corrected VRT\\n open(tempfilename,\\\"w\\\").write(s)\\n # open by GDAL as self.out_ds\\n self.out_ds = gdal.Open(tempfilename) #, gdal.GA_ReadOnly)\\n # delete the temporary file\\n os.unlink(tempfilename)\\n\\n if self.options.verbose:\\n print \\\"Modified warping result saved into 'tiles1.vrt'\\\"\\n open(\\\"tiles1.vrt\\\",\\\"w\\\").write(s)\\n\\n # For raster with 4-bands: 4th unknown band set to alpha\\n if (self.out_ds.RasterCount == 4\\n and self.out_ds.GetRasterBand(4).GetRasterColorInterpretation() == gdal.GCI_Undefined):\\n self.out_ds.GetRasterBand(4).SetRasterColorInterpretation(gdal.GCI_AlphaBand)\\n\\n s = '''\\n '''\\n\\n else:\\n self.error(\\\"Input file has unknown SRS.\\\", \\\"Use --s_srs ESPG:xyz (or similar) to provide source reference system.\\\" )\\n\\n if self.out_ds and self.options.verbose:\\n print \\\"Projected file:\\\", \\\"tiles.vrt\\\", \\\"( %sP x %sL - %s bands)\\\" % (self.out_ds.RasterXSize, self.out_ds.RasterYSize, self.out_ds.RasterCount)\\n\\n if not self.out_ds:\\n self.out_ds = self.in_ds\\n\\n #\\n # Here we should have a raster (out_ds) in the correct Spatial Reference system\\n #\\n\\n # KML test\\n self.isepsg4326 = False\\n srs4326 = osr.SpatialReference()\\n srs4326.ImportFromEPSG(4326)\\n if self.out_srs and srs4326.ExportToProj4() == self.out_srs.ExportToProj4():\\n self.kml = True\\n self.isepsg4326 = True\\n if self.options.verbose:\\n print \\\"KML autotest OK!\\\"\\n\\n # Read the georeference\\n\\n self.out_gt = self.out_ds.GetGeoTransform()\\n\\n #originX, originY = self.out_gt[0], self.out_gt[3]\\n #pixelSize = self.out_gt[1] # = self.out_gt[5]\\n\\n # Test the size of the pixel\\n\\n # MAPTILER - COMMENTED\\n #if self.out_gt[1] != (-1 * self.out_gt[5]) and self.options.profile != 'raster':\\n # TODO: Process corectly coordinates with are have swichted Y axis (display in OpenLayers too)\\n #self.error(\\\"Size of the pixel in the output differ for X and Y axes.\\\")\\n\\n # Report error in case rotation/skew is in geotransform (possible only in 'raster' profile)\\n if (self.out_gt[2], self.out_gt[4]) != (0,0):\\n self.error(\\\"Georeference of the raster contains rotation or skew. Such raster is not supported. Please use gdalwarp first.\\\")\\n # TODO: Do the warping in this case automaticaly\\n\\n #\\n # Here we expect: pixel is square, no rotation on the raster\\n #\\n\\n # Output Bounds - coordinates in the output SRS\\n self.ominx = self.out_gt[0]\\n self.omaxx = self.out_gt[0]+self.out_ds.RasterXSize*self.out_gt[1]\\n self.omaxy = self.out_gt[3]\\n self.ominy = self.out_gt[3]-self.out_ds.RasterYSize*self.out_gt[1]\\n # Note: maybe round(x, 14) to avoid the gdal_translate behaviour, when 0 becomes -1e-15\\n # user defined bounds to extract - coordinates in the output SRS\\n if self.options.te_bounds != '':\\n if self.te_minx >= self.ominx and self.te_minx <= self.omaxx:\\n if self.te_maxx >= self.ominx and self.te_maxx <= self.omaxx:\\n if self.te_miny >= self.ominy and self.te_miny <= self.omaxy:\\n if self.te_maxy >= self.ominy and self.te_maxy <= self.omaxy:\\n # replace only if inside the read bounds\\n self.ominx = self.te_minx\\n self.omaxx = self.te_maxx\\n self.ominy = self.te_miny\\n self.omaxy = self.te_maxy\\n if self.options.verbose:\\n print \\\"User defined Bounds (output srs) have been set:\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.verbose:\\n print \\\"Bounds (output srs):\\\", round(self.ominx, 13), self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.mbtiles:\\n self.options.profile = 'mercator'\\n if self.options.profile == 'mercator':\\n self.mercator = GlobalMercator(self.options.tms_osm) # from globalmaptiles.py\\n\\n #\\n # Calculating ranges for tiles in different zoom levels\\n #\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.mercator.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0,32)\\n for tz in range(0, 32):\\n tminx, tminy = self.mercator.MetersToTile( self.ominx, self.ominy, tz )\\n tmaxx, tmaxy = self.mercator.MetersToTile( self.omaxx, self.omaxy, tz )\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**tz-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the minimal zoom level (map covers area equivalent to one tile)\\n if self.tminz == None:\\n self.tminz = self.mercator.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.mercator.ZoomForPixelSize( self.out_gt[1] )\\n\\n if self.options.verbose:\\n print \\\"Bounds (latlong):\\\", self.mercator.MetersToLatLon( self.ominx, self.ominy), self.mercator.MetersToLatLon( self.omaxx, self.omaxy)\\n print 'MinZoomLevel:', self.tminz\\n print \\\"MaxZoomLevel:\\\", self.tmaxz, \\\"(\\\", self.mercator.Resolution( self.tmaxz ),\\\")\\\"\\n\\n # this must be call befor ImageOutput is called (self.output may be changed)\\n if self.options.mbtiles:\\n if not self.mbtiles_db:\\n self.mbtiles_setup(1);\\n\\n # Instantiate image output.\\n self.image_output = ImageOutput(self.options.tile_format, self.out_ds, self.tilesize,\\n self.options.resampling, init_dest, self.output,\\n self.options.verbose,self.options.mbtiles)\\n if self.options.profile == 'geodetic':\\n\\n self.geodetic = GlobalGeodetic() # from globalmaptiles.py\\n\\n # Function which generates SWNE in LatLong for given tile\\n self.tileswne = self.geodetic.TileLatLonBounds\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0,32)\\n for tz in range(0, 32):\\n tminx, tminy = self.geodetic.LatLonToTile( self.ominx, self.ominy, tz )\\n tmaxx, tmaxy = self.geodetic.LatLonToTile( self.omaxx, self.omaxy, tz )\\n # crop tiles extending world limits (+-180,+-90)\\n tminx, tminy = max(0, tminx), max(0, tminy)\\n tmaxx, tmaxy = min(2**(tz+1)-1, tmaxx), min(2**tz-1, tmaxy)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # TODO: Maps crossing 180E (Alaska?)\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tminz == None:\\n self.tminz = self.geodetic.ZoomForPixelSize( self.out_gt[1] * max( self.out_ds.RasterXSize, self.out_ds.RasterYSize) / float(self.tilesize) )\\n\\n # Get the maximal zoom level (closest possible zoom level up on the resolution of raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.geodetic.ZoomForPixelSize( self.out_gt[1] )\\n\\n if self.options.verbose:\\n print \\\"Bounds (latlong):\\\", self.ominx, self.ominy, self.omaxx, self.omaxy\\n\\n if self.options.profile in ('raster', 'gearth', 'garmin'):\\n\\n log2 = lambda x: math.log10(x) / math.log10(2) # log2 (base 2 logarithm)\\n\\n self.nativezoom = int(max( math.ceil(log2(self.out_ds.RasterXSize/float(self.tilesize))),\\n math.ceil(log2(self.out_ds.RasterYSize/float(self.tilesize)))))\\n\\n if self.options.verbose:\\n print \\\"Native zoom of the raster:\\\", self.nativezoom\\n\\n # Get the minimal zoom level (whole raster in one tile)\\n if self.tminz == None:\\n self.tminz = 0\\n\\n # Get the maximal zoom level (native resolution of the raster)\\n if self.tmaxz == None:\\n self.tmaxz = self.nativezoom\\n\\n # Garmin has maximally 100 tiles - lower the tmaxz if necessary\\n if self.options.profile == 'garmin':\\n tno = math.ceil(self.out_ds.RasterXSize / self.tilesize) * math.ceil(self.out_ds.RasterYSize / self.tilesize)\\n for tz in range(self.tmaxz, 1, -1):\\n if tno > 100:\\n tno /= 4\\n self.tmaxz -= 1\\n print \\\"Warning: GARMIN has a limit 100 tiles per device: lowering the max zoom level to:\\\", self.tmaxz\\n else:\\n continue\\n\\n # Force only one zoom level for the 'garmin' tile profile\\n if self.options.profile == 'garmin':\\n self.tminz = self.tmaxz\\n\\n # Generate table with min max tile coordinates for all zoomlevels\\n self.tminmax = range(0, self.tmaxz+1)\\n self.tsize = range(0, self.tmaxz+1)\\n for tz in range(0, self.tmaxz+1):\\n tsize = 2.0**(self.nativezoom-tz)*self.tilesize\\n tminx, tminy = 0, 0\\n tmaxx = int(math.ceil( self.out_ds.RasterXSize / tsize )) - 1\\n tmaxy = int(math.ceil( self.out_ds.RasterYSize / tsize )) - 1\\n self.tsize[tz] = math.ceil(tsize)\\n self.tminmax[tz] = (tminx, tminy, tmaxx, tmaxy)\\n\\n # Function which generates SWNE in LatLong for given tile\\n if self.kml and self.in_srs_wkt:\\n self.ct = osr.CoordinateTransformation(self.in_srs, srs4326)\\n def rastertileswne(x,y,z):\\n pixelsizex = (2**(self.nativezoom-z) * self.out_gt[1]) # X-pixel size in level\\n pixelsizey = (2**(self.nativezoom-z) * self.out_gt[5]) # Y-pixel size in level (usually -1*pixelsizex)\\n west = self.out_gt[0] + x*self.tilesize*pixelsizex\\n east = west + self.tilesize*pixelsizex\\n south = self.ominy + y*self.tilesize*pixelsizex\\n north = south + self.tilesize*pixelsizex\\n if not self.isepsg4326:\\n # Transformation to EPSG:4326 (WGS84 datum)\\n west, south = self.ct.TransformPoint(west, south)[:2]\\n east, north = self.ct.TransformPoint(east, north)[:2]\\n return south, west, north, east\\n\\n self.tileswne = rastertileswne\\n else:\\n self.tileswne = lambda x, y, z: (0,0,0,0)\",\n \"def open(*args, **kwargs):\\n return GDALFileTileSource(*args, **kwargs)\",\n \"def open_shapefile(file_path):\\n datasource = ogr.Open(file_path)\\n layer = datasource.GetLayerByIndex(0)\\n print(\\\"Opening {}\\\".format(file_path))\\n print(\\\"Number of features: {}\\\".format(layer.GetFeatureCount()))\\n return datasource\",\n \"def get_source(self, file_path, driver=None):\\n if driver:\\n # Use a driver to open the file\\n driver = ogr.GetDriverByName(driver)\\n return driver.Open(file_path, 0)\\n return gdal.Open(file_path, GA_ReadOnly)\",\n \"def gdal_read_geotiff_file(sFilename_in):\\n \\n if os.path.exists(sFilename_in):\\n pass\\n else:\\n print('The file does not exist!')\\n return\\n\\n sDriverName='GTiff'\\n pDriver = gdal.GetDriverByName(sDriverName) \\n\\n if pDriver is None:\\n print (\\\"%s pDriver not available.\\\\n\\\" % sDriverName)\\n else:\\n print (\\\"%s pDriver IS available.\\\\n\\\" % sDriverName) \\n\\n pDataset = gdal.Open(sFilename_in, gdal.GA_ReadOnly)\\n\\n if pDataset is None:\\n print(\\\"Couldn't open this file: \\\" + sFilename_in)\\n sys.exit(\\\"Try again!\\\")\\n else: \\n pProjection = pDataset.GetProjection()\\n\\n pDataset.GetMetadata()\\n \\n ncolumn = pDataset.RasterXSize\\n nrow = pDataset.RasterYSize\\n nband = pDataset.RasterCount\\n\\n pGeotransform = pDataset.GetGeoTransform()\\n dOriginX = pGeotransform[0]\\n dOriginY = pGeotransform[3]\\n dPixelWidth = pGeotransform[1]\\n pPixelHeight = pGeotransform[5]\\n\\n pBand = pDataset.GetRasterBand(1)\\n\\n # Data type of the values\\n gdal.GetDataTypeName(pBand.DataType)\\n # Compute statistics if needed\\n if pBand.GetMinimum() is None or pBand.GetMaximum() is None:\\n pBand.ComputeStatistics(0)\\n\\n dMissing_value = pBand.GetNoDataValue()\\n \\n aData_out = pBand.ReadAsArray(0, 0, ncolumn, nrow)\\n \\n #we will use one of them to keep the consistency\\n pSpatial_reference = osr.SpatialReference(wkt=pProjection)\\n \\n\\n pDataset = None\\n pBand = None \\n pBand = None\\n\\n return aData_out, dPixelWidth, dOriginX, dOriginY, nrow, ncolumn, dMissing_value, pGeotransform, pProjection, pSpatial_reference\",\n \"def read_gdal_projection(dataset):\\n wkt = dataset.GetProjection()\\n srs = osr.SpatialReference()\\n srs.ImportFromWkt(wkt)\\n # src = None\\n return srs\",\n \"def fromfile(file_h):\\n # If given a file name, get a real handle.\\n if isinstance(file_h, str):\\n with open(file_h, \\\"rb\\\") as file_h:\\n buf = file_h.read()\\n else:\\n buf = file_h.read()\\n\\n # If we get WKB need to wrap in memoryview(), so run through regexes.\\n if isinstance(buf, bytes):\\n try:\\n decoded = buf.decode()\\n except UnicodeDecodeError:\\n pass\\n else:\\n if wkt_regex.match(decoded) or hex_regex.match(decoded):\\n return GEOSGeometry(decoded)\\n else:\\n return GEOSGeometry(buf)\\n\\n return GEOSGeometry(memoryview(buf))\",\n \"def open(cstr, layername=None, layersql=None, extent=None, sql_dialect=None, open_for_update=False):\\n ds = ogr.Open(cstr, update=open_for_update)\\n if ds is None:\\n raise Exception(\\\"Failed to open \\\" + cstr)\\n if layersql is not None: # an sql statement will take precedence\\n if extent is not None and EXTENT_WKT in layersql:\\n wkt = \\\"'\\\" + extent_to_wkt(extent) + \\\"'\\\"\\n layersql = layersql.replace(EXTENT_WKT, wkt)\\n # restrict to ASCII encodable chars here -\\n # don't know what the datasource\\n # is precisely and ogr doesn't like unicode.\\n layer = ds.ExecuteSQL(str(layersql), dialect=sql_dialect)\\n elif layername is not None: # then a layername\\n layer = ds.GetLayerByName(layername)\\n else: # fallback - shapefiles etc, use first layer\\n layer = ds.GetLayer(0)\\n assert(layer is not None)\\n\\n return ds, layer\",\n \"def read_geojson(input_file):\\n # Please use the python json module (imported above)\\n # to solve this one.\\n with open(input_file,'r') as f:\\n gj = json.load(f)\\n return gj\",\n \"def read_geojson(input_file):\\n # Please use the python json module (imported above)\\n # to solve this one.\\n with open(input_file,'r') as f:\\n gj = json.load(f)\\n return gj\",\n \"def open_raster_file(self):\\n self.log('Opening raster file as GDALRaster.')\\n\\n # Open raster file\\n self.dataset = GDALRaster(os.path.join(self.tmpdir, self.rastername), write=True)\\n\\n # Make sure nodata value is set from input\\n self.hist_values = []\\n self.hist_bins = []\\n for i, band in enumerate(self.dataset.bands):\\n if self.rasterlayer.nodata is not None:\\n band.nodata_value = float(self.rasterlayer.nodata)\\n\\n # Create band metatdata object\\n bandmeta = RasterLayerBandMetadata.objects.create(\\n rasterlayer=self.rasterlayer,\\n band=i,\\n nodata_value=band.nodata_value,\\n min=band.min,\\n max=band.max\\n )\\n\\n # Prepare numpy hist values and bins\\n self.hist_values.append(numpy.array(bandmeta.hist_values))\\n self.hist_bins.append(numpy.array(bandmeta.hist_bins))\\n\\n # Store original metadata for this raster\\n meta = self.rasterlayer.metadata\\n\\n meta.uperleftx = self.dataset.origin.x\\n meta.uperlefty = self.dataset.origin.y\\n meta.width = self.dataset.width\\n meta.height = self.dataset.height\\n meta.scalex = self.dataset.scale.x\\n meta.scaley = self.dataset.scale.y\\n meta.skewx = self.dataset.skew.x\\n meta.skewy = self.dataset.skew.y\\n meta.numbands = len(self.dataset.bands)\\n meta.srs_wkt = self.dataset.srs.wkt\\n meta.srid = self.dataset.srs.srid\\n\\n meta.save()\",\n \"def readMap(fileName, fileFormat):\\n # Open file for binary-reading\\n mapFormat = gdal.GetDriverByName(fileFormat)\\n mapFormat.Register()\\n ds = gdal.Open(fileName)\\n if ds is None:\\n print 'Could not open ' + fileName + '. Something went wrong!! Shutting down'\\n sys.exit(1)\\n # Retrieve geoTransform info\\n geotrans = ds.GetGeoTransform()\\n originX = geotrans[0]\\n originY = geotrans[3]\\n resX = geotrans[1]\\n resY = geotrans[5]\\n cols = ds.RasterXSize\\n rows = ds.RasterYSize\\n x = linspace(originX+resX/2,originX+resX/2+resX*(cols-1),cols)\\n y = linspace(originY+resY/2,originY+resY/2+resY*(rows-1),rows)\\n # Retrieve raster\\n RasterBand = ds.GetRasterBand(1) # there's only 1 band, starting from 1\\n data = RasterBand.ReadAsArray(0,0,cols,rows)\\n FillVal = RasterBand.GetNoDataValue()\\n RasterBand = None\\n ds = None\\n return x, y, data, FillVal\",\n \"def read_gds(self,\\n infile,\\n units='skip',\\n rename={},\\n layers={},\\n datatypes={},\\n texttypes={}):\\n self._references = []\\n if isinstance(infile, basestring):\\n infile = open(infile, 'rb')\\n close = True\\n else:\\n close = False\\n emitted_warnings = []\\n record = self._read_record(infile)\\n kwargs = {}\\n create_element = None\\n factor = 1\\n cell = None\\n while record is not None:\\n # LAYER\\n if record[0] == 0x0d:\\n kwargs['layer'] = layers.get(record[1][0], record[1][0])\\n # DATATYPE\\n elif record[0] == 0x0e:\\n kwargs['datatype'] = datatypes.get(record[1][0], record[1][0])\\n # TEXTTYPE\\n elif record[0] == 0x16:\\n kwargs['texttype'] = texttypes.get(record[1][0], record[1][0])\\n # XY\\n elif record[0] == 0x10:\\n kwargs['xy'] = factor * record[1]\\n # WIDTH\\n elif record[0] == 0x0f:\\n kwargs['width'] = factor * abs(record[1][0])\\n if record[1][0] < 0 and record[0] not in emitted_warnings:\\n warnings.warn(\\n \\\"[GDSPY] Paths with absolute width value are not \\\"\\n \\\"supported. Scaling these paths will also scale \\\"\\n \\\"their width.\\\",\\n stacklevel=2)\\n emitted_warnings.append(record[0])\\n # ENDEL\\n elif record[0] == 0x11:\\n if create_element is not None:\\n cell.add(create_element(**kwargs))\\n create_element = None\\n kwargs = {}\\n # BOUNDARY\\n elif record[0] == 0x08:\\n create_element = self._create_polygon\\n # PATH\\n elif record[0] == 0x09:\\n create_element = self._create_path\\n # TEXT\\n elif record[0] == 0x0c:\\n create_element = self._create_label\\n # SNAME\\n elif record[0] == 0x12:\\n kwargs['ref_cell'] = rename.get(record[1], record[1])\\n # COLROW\\n elif record[0] == 0x13:\\n kwargs['columns'] = record[1][0]\\n kwargs['rows'] = record[1][1]\\n # STRANS\\n elif record[0] == 0x1a:\\n kwargs['x_reflection'] = ((int(record[1][0]) & 0x8000) > 0)\\n if (int(record[1][0]) &\\n 0x0006) and record[0] not in emitted_warnings:\\n warnings.warn(\\n \\\"[GDSPY] Absolute magnification or rotation of \\\"\\n \\\"references is not supported. Transformations will \\\"\\n \\\"be interpreted as relative.\\\",\\n stacklevel=2)\\n emitted_warnings.append(record[0])\\n # MAG\\n elif record[0] == 0x1b:\\n kwargs['magnification'] = record[1][0]\\n # ANGLE\\n elif record[0] == 0x1c:\\n kwargs['rotation'] = record[1][0]\\n # SREF\\n elif record[0] == 0x0a:\\n create_element = self._create_reference\\n # AREF\\n elif record[0] == 0x0b:\\n create_element = self._create_array\\n # STRNAME\\n elif record[0] == 0x06:\\n name = rename.get(record[1], record[1])\\n cell = Cell(name, exclude_from_current=True)\\n self.cell_dict[name] = cell\\n # STRING\\n elif record[0] == 0x19:\\n kwargs['text'] = record[1]\\n # ENDSTR\\n elif record[0] == 0x07:\\n cell = None\\n # UNITS\\n elif record[0] == 0x03:\\n if units == 'skip':\\n factor = record[1][0]\\n elif units == 'import':\\n self.unit = record[1][1] / record[1][0]\\n self.precision = record[1][1]\\n factor = record[1][0]\\n elif units == 'convert':\\n factor = record[1][1] / self.unit\\n else:\\n raise ValueError(\\\"[GDSPY] units must be one of 'convert', \\\"\\n \\\"'import' or 'skip'.\\\")\\n # LIBNAME\\n elif record[0] == 0x02:\\n self.name = record[1]\\n # PRESENTATION\\n elif record[0] == 0x17:\\n kwargs['anchor'] = GdsLibrary._import_anchors[int(record[1][0])\\n & 0x000f]\\n # PATHTYPE\\n elif record[0] == 0x21:\\n if record[1][0] > 2:\\n if 0x21 not in emitted_warnings:\\n warnings.warn(\\n \\\"[GDSPY] Path ends with custom size are not \\\"\\n \\\"supported.\\\",\\n RuntimeWarning,\\n stacklevel=2)\\n emitted_warnings.append(0x21)\\n else:\\n kwargs['ends'] = record[1][0]\\n # ENDLIB\\n elif record[0] == 0x04:\\n for ref in self._references:\\n if ref.ref_cell in self.cell_dict:\\n ref.ref_cell = self.cell_dict[ref.ref_cell]\\n elif ref.ref_cell in current_library.cell_dict:\\n ref.ref_cell = current_library.cell_dict[ref.ref_cell]\\n # Not supported\\n elif (record[0] not in emitted_warnings and\\n record[0] not in GdsLibrary._unused_records):\\n warnings.warn(\\n \\\"[GDSPY] Record type {0} ({1:02X}) is not \\\"\\n \\\"supported.\\\".format(GdsLibrary._record_name[record[0]],\\n record[0]),\\n RuntimeWarning,\\n stacklevel=2)\\n emitted_warnings.append(record[0])\\n record = self._read_record(infile)\\n if close:\\n infile.close()\\n return self\",\n \"def import_file(self, *args, **kwargs):\\n filename = self.file\\n self.completed_layers = []\\n err = GdalErrorHandler()\\n gdal.PushErrorHandler(err.handler)\\n gdal.UseExceptions()\\n configuration_options = kwargs.get('configuration_options', [{'index': 0}])\\n\\n # Configuration options should be a list at this point since the importer can process multiple layers in a\\n # single import\\n if isinstance(configuration_options, dict):\\n configuration_options = [configuration_options]\\n\\n data, inspector = self.open_source_datastore(filename, *args, **kwargs)\\n\\n datastore_layers = inspector.describe_fields()\\n\\n if len(datastore_layers) == 0:\\n logger.debug('No Dataset found')\\n\\n layers_info = []\\n\\n # Add index for any layers configured by name\\n for layer_configuration in configuration_options:\\n if 'layer_name' in layer_configuration:\\n lookup = 'layer_name'\\n elif 'index' in layer_configuration:\\n lookup = 'index'\\n else:\\n lookup = None\\n logger.debug('could not find lookup')\\n continue\\n\\n for datastore_layer in datastore_layers:\\n if datastore_layer.get(lookup) == layer_configuration.get(lookup):\\n layer_configuration.update(datastore_layer)\\n layers_info.append(layer_configuration)\\n\\n for layer_options in layers_info:\\n if layer_options['raster']:\\n \\\"\\\"\\\"\\n File is a raster, we need to convert into optimized GeoTiff\\n and skip any further testing or loading into target_store\\n \\\"\\\"\\\"\\n # Increment filename to make sure target doesn't exists\\n filedir, filebase = os.path.split(filename)\\n outfile = '%s.tif' % os.path.splitext(filebase)[0]\\n fileout = increment_filename(os.path.join(RASTER_FILES, outfile))\\n raster_import(layer_options['path'], fileout)\\n self.completed_layers.append([fileout, layer_options])\\n else:\\n target_file, _ = self.open_target_datastore(self.target_store)\\n target_create_options = []\\n\\n # Prevent numeric field overflow for shapefiles https://trac.osgeo.org/gdal/ticket/5241\\n if target_file.GetDriver().GetName() == 'PostgreSQL':\\n target_create_options.append('PRECISION=NO')\\n\\n layer_options['modified_fields'] = {}\\n layer = data.GetLayer(layer_options.get('index'))\\n layer_name = layer_options.get('name', layer.GetName().lower())\\n layer_type = self.get_layer_type(layer, data)\\n srs = layer.GetSpatialRef()\\n\\n if layer_name.lower() == 'ogrgeojson':\\n try:\\n layer_name = os.path.splitext(os.path.basename(filename))[0].lower()\\n except IndexError:\\n pass\\n\\n layer_name = launder(str(layer_name))\\n\\n # default the layer to 4326 if a spatial reference is not provided\\n if not srs:\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326)\\n\\n # pass the srs authority code to handlers\\n if srs.AutoIdentifyEPSG() == 0:\\n layer_options['srs'] = '{0}:{1}'.format(srs.GetAuthorityName(None), srs.GetAuthorityCode(None))\\n\\n n = 0\\n while True:\\n n += 1\\n try:\\n target_layer = self.create_target_dataset(target_file, layer_name, srs, layer_type,\\n options=target_create_options)\\n except RuntimeError as e:\\n # logger.exception('exception in creating target dataset')\\n # the layer already exists in the target store, increment the name\\n if 'Use the layer creation option OVERWRITE=YES to replace it.' in e.message:\\n layer_name = increment(layer_name)\\n\\n # try 100 times to increment then break\\n if n >= 100:\\n break\\n\\n continue\\n else:\\n raise e\\n break\\n\\n # adding fields to new layer\\n layer_definition = ogr.Feature(layer.GetLayerDefn())\\n source_fid = None\\n\\n wkb_field = 0\\n\\n for i in range(layer_definition.GetFieldCount()):\\n\\n field_def = layer_definition.GetFieldDefnRef(i)\\n\\n if field_def.GetName() == target_layer.GetFIDColumn() and field_def.GetType() != 0:\\n field_def.SetType(0)\\n\\n if field_def.GetName() != 'wkb_geometry':\\n target_layer.CreateField(field_def)\\n new_name = target_layer.GetLayerDefn().GetFieldDefn(i - wkb_field).GetName()\\n old_name = field_def.GetName()\\n\\n if new_name != old_name:\\n layer_options['modified_fields'][old_name] = new_name\\n\\n if old_name == target_layer.GetFIDColumn() and not layer.GetFIDColumn():\\n source_fid = i\\n else:\\n wkb_field = 1\\n\\n if wkb_field is not 0:\\n layer.SetIgnoredFields(['wkb_geometry'])\\n\\n for i in range(0, layer.GetFeatureCount()):\\n feature = layer.GetFeature(i)\\n\\n if feature and feature.geometry():\\n\\n if not layer.GetFIDColumn():\\n feature.SetFID(-1)\\n\\n if feature.geometry().GetGeometryType() != target_layer.GetGeomType() and \\\\\\n target_layer.GetGeomType() in range(4, 7):\\n\\n conversion_function = ogr.ForceToMultiPolygon\\n\\n if target_layer.GetGeomType() == 5:\\n conversion_function = ogr.ForceToMultiLineString\\n\\n elif target_layer.GetGeomType() == 4:\\n conversion_function = ogr.ForceToMultiPoint\\n\\n geom = ogr.CreateGeometryFromWkb(feature.geometry().ExportToWkb())\\n feature.SetGeometry(conversion_function(geom))\\n\\n if source_fid is not None:\\n feature.SetFID(feature.GetField(source_fid))\\n\\n try:\\n target_layer.CreateFeature(feature)\\n\\n except:\\n for field in range(0, feature.GetFieldCount()):\\n if feature.GetFieldType(field) == ogr.OFTString:\\n try:\\n feature.GetField(field).decode('utf8')\\n except UnicodeDecodeError:\\n feature.SetField(field, decode(feature.GetField(field)))\\n except AttributeError:\\n continue\\n try:\\n target_layer.CreateFeature(feature)\\n except err as e:\\n logger.error('Create feature failed: {0}'.format(gdal.GetLastErrorMsg()))\\n raise e\\n\\n self.completed_layers.append([target_layer.GetName(), layer_options])\\n\\n return self.completed_layers\",\n \"def load_image(nom):\\n print(\\\"load_image : [\\\", nom, \\\"]\\\")\\n fic = gdal.Open(nom)\\n print(fic)\\n return fic.ReadAsArray(), fic.GetGeoTransform()\",\n \"def create_gdal_gis_lyr(self, file_path, lyr_name, driver_name='GPKG', add_lyr=False):\\n raise EODataDownException(\\\"Not Implemented\\\")\",\n \"def loadGeoTransform(filepath):\\n \\n from osgeo import gdal\\n \\n ds = gdal.Open(filepath, 0)\\n \\n return ds.GetGeoTransform()\",\n \"def load_raster(input):\\n dem = gdal.Open(input)\\n\\n nodata = []\\n layers = []\\n for i in range(1, dem.RasterCount+1):\\n band = dem.GetRasterBand(i)\\n data = band.ReadAsArray(0, 0, dem.RasterXSize, dem.RasterYSize)\\n layers.append(data)\\n nodata.append(band.GetNoDataValue())\\n\\n if len(layers) > 1:\\n layers = N.dstack(layers) \\n\\n info = attrdict(\\n metadata=dem.GetMetadata_Dict(),\\n grid=Grid.from_gdal(dem),\\n nodata=nodata)\\n\\n return (info,layers)\",\n \"def load_from_geojson(self, filename_or_url):\",\n \"def from_featureclass(filename, **kwargs):\\r\\n from .io import from_featureclass\\r\\n gis = kwargs.pop('gis', arcgis.env.active_gis)\\r\\n if HASARCPY:\\r\\n return from_featureclass(filename=filename, **kwargs)\\r\\n elif isinstance(gis, GIS) and \\\\\\r\\n gis._con._auth.lower() != \\\"anon\\\":\\r\\n return from_featureclass(filename=filename, **kwargs)\\r\\n else:\\r\\n raise Exception(\\\"Cannot create the SpatialDataFrame, you must \\\" +\\\\\\r\\n \\\"have an authenticated GIS.\\\")\",\n \"def read(self, format=None, epsg=None):\\n if not format:\\n format = self.default_output\\n if self.ext not in formats.VECTOR:\\n raise UnsupportedFormatException(\\n \\\"Only the following vector formats are supported: {}\\\".format(\\n ','.join(formats.VECTOR)\\n )\\n )\\n if self.data is None:\\n self.data = geopandas.read_file(self.uri)\\n if self.filters:\\n self.filter_data()\\n return self.transform_data(format, epsg)\",\n \"def load_loss_GDF(filename, lon, lat):\\n df = pd.read_csv(filename)\\n x, y = np.meshgrid(lon, lat)\\n coords = [Point(xval, yval) for xval, yval in zip(x.ravel(), y.ravel())]\\n \\n df['geometry'] = coords\\n df = gpd.GeoDataFrame(df)\\n df.crs = {'init': 'epsg:4326'}\\n return df\",\n \"def read(f, missing=np.nan):\\n import gdal\\n if f.is_zipped():\\n f = unzip(f) # Unzip and return a File\\n\\n geotiff = gdal.Open(f.fullpath())\\n if geotiff is None:\\n raise RuntimeError(\\\"Could not open unzipped geotiff file\\\",f)\\n\\n band = geotiff.GetRasterBand(1)\\n data = band.ReadAsArray(0, 0, geotiff.RasterXSize, geotiff.RasterYSize)\\n\\n del(geotiff)\\n os.remove(f.fullpath())\\n\\n data[data<0] = missing\\n\\n return data\",\n \"def create_dat_from_shapefile(windgrid_file, DAT_header, output_file, wind_field='Vg_mph'):\\n t0 = time()\\n output_file = output_file + '.dat'\\n print('reading windgrid')\\n t1 = time()\\n gdf = gpd.read_file(windgrid_file)\\n create_dat_from_geodataframe(\\n gdf, DAT_header, output_file, wind_field=wind_field)\",\n \"def file_open(path: str, mode: str):\\n if _path_on_gcp(path):\\n return tf.io.gfile.GFile(path, mode)\\n return open(path, mode)\",\n \"def read_gds(\\n self,\\n infile,\\n units=\\\"skip\\\",\\n rename={},\\n rename_template=\\\"{name}\\\",\\n layers={},\\n datatypes={},\\n texttypes={},\\n ):\\n self._references = []\\n close = True\\n if hasattr(infile, \\\"__fspath__\\\"):\\n infile = open(infile.__fspath__(), \\\"rb\\\")\\n elif isinstance(infile, (basestring, Path)):\\n infile = open(infile, \\\"rb\\\")\\n else:\\n close = False\\n emitted_warnings = []\\n kwargs = {}\\n create_element = None\\n factor = 1\\n cell = None\\n properties = {}\\n attr = -1\\n for record in _record_reader(infile):\\n # LAYER\\n if record[0] == 0x0D:\\n kwargs[\\\"layer\\\"] = layers.get(record[1][0], record[1][0])\\n # DATATYPE or BOXTYPE\\n elif record[0] == 0x0E or record[0] == 0x2E:\\n kwargs[\\\"datatype\\\"] = datatypes.get(record[1][0], record[1][0])\\n # TEXTTYPE\\n elif record[0] == 0x16:\\n kwargs[\\\"texttype\\\"] = texttypes.get(record[1][0], record[1][0])\\n # XY\\n elif record[0] == 0x10:\\n if \\\"xy\\\" in kwargs:\\n kwargs[\\\"xy\\\"] = numpy.concatenate((kwargs[\\\"xy\\\"], factor * record[1]))\\n else:\\n kwargs[\\\"xy\\\"] = factor * record[1]\\n # WIDTH\\n elif record[0] == 0x0F:\\n kwargs[\\\"width\\\"] = factor * abs(record[1][0])\\n if record[1][0] < 0:\\n kwargs[\\\"width_transform\\\"] = False\\n # ENDEL\\n elif record[0] == 0x11:\\n if create_element is not None:\\n el = create_element(**kwargs)\\n if len(properties) > 0:\\n el.properties = properties\\n properties = {}\\n cell.add(el)\\n create_element = None\\n kwargs = {}\\n # BOUNDARY\\n elif record[0] == 0x08:\\n create_element = self._create_polygon\\n # PATH\\n elif record[0] == 0x09:\\n create_element = self._create_path\\n # BOX\\n elif record[0] == 0x2D:\\n create_element = self._create_polygon\\n if record[0] not in emitted_warnings:\\n warnings.warn(\\n \\\"[GDSPY] GDSII elements of type BOX are imported as polygons.\\\",\\n stacklevel=2,\\n )\\n emitted_warnings.append(record[0])\\n # TEXT\\n elif record[0] == 0x0C:\\n create_element = self._create_label\\n # SNAME\\n elif record[0] == 0x12:\\n if record[1] in rename:\\n name = rename[record[1]]\\n else:\\n name = rename_template.format(name=record[1])\\n kwargs[\\\"ref_cell\\\"] = name\\n # COLROW\\n elif record[0] == 0x13:\\n kwargs[\\\"columns\\\"] = record[1][0]\\n kwargs[\\\"rows\\\"] = record[1][1]\\n # STRANS\\n elif record[0] == 0x1A:\\n kwargs[\\\"x_reflection\\\"] = (int(record[1][0]) & 0x8000) > 0\\n if (int(record[1][0]) & 0x0006) and record[0] not in emitted_warnings:\\n warnings.warn(\\n \\\"[GDSPY] Absolute magnification or rotation of \\\"\\n \\\"references is not supported. Transformations \\\"\\n \\\"will be interpreted as relative.\\\",\\n stacklevel=2,\\n )\\n emitted_warnings.append(record[0])\\n # MAG\\n elif record[0] == 0x1B:\\n kwargs[\\\"magnification\\\"] = record[1][0]\\n # ANGLE\\n elif record[0] == 0x1C:\\n kwargs[\\\"rotation\\\"] = record[1][0]\\n # SREF\\n elif record[0] == 0x0A:\\n create_element = self._create_reference\\n # AREF\\n elif record[0] == 0x0B:\\n create_element = self._create_array\\n # STRNAME\\n elif record[0] == 0x06:\\n if record[1] in rename:\\n name = rename[record[1]]\\n else:\\n name = rename_template.format(name=record[1])\\n cell = Cell(name, exclude_from_current=True)\\n if name in self.cells:\\n raise ValueError(\\\"[GDSPY] Multiple cells with name: {0} in GDSII file\\\".format(name))\\n self.cells[name] = cell\\n # STRING\\n elif record[0] == 0x19:\\n kwargs[\\\"text\\\"] = record[1]\\n # ENDSTR\\n elif record[0] == 0x07:\\n cell = None\\n # UNITS\\n elif record[0] == 0x03:\\n if units == \\\"skip\\\":\\n factor = record[1][0]\\n elif units == \\\"import\\\":\\n self.unit = record[1][1] / record[1][0]\\n self.precision = record[1][1]\\n factor = record[1][0]\\n elif units == \\\"convert\\\":\\n factor = record[1][1] / self.unit\\n else:\\n raise ValueError(\\n \\\"[GDSPY] units must be one of 'convert', 'import' or 'skip'.\\\"\\n )\\n # LIBNAME\\n elif record[0] == 0x02:\\n self.name = record[1]\\n # PRESENTATION\\n elif record[0] == 0x17:\\n kwargs[\\\"anchor\\\"] = GdsLibrary._import_anchors[\\n int(record[1][0]) & 0x000F\\n ]\\n # PATHTYPE\\n elif record[0] == 0x21:\\n kwargs[\\\"ends\\\"] = GdsLibrary._pathtype_dict.get(record[1][0], \\\"extended\\\")\\n # BGNEXTN\\n elif record[0] == 0x30:\\n kwargs[\\\"bgnextn\\\"] = factor * record[1][0]\\n # ENDEXTN\\n elif record[0] == 0x31:\\n kwargs[\\\"endextn\\\"] = factor * record[1][0]\\n # ENDLIB\\n elif record[0] == 0x04:\\n for ref in self._references:\\n if ref.ref_cell in self.cells:\\n ref.ref_cell = self.cells[ref.ref_cell]\\n # PROPATTR\\n elif record[0] == 0x2B:\\n attr = record[1][0]\\n # PROPVALUE\\n elif record[0] == 0x2C:\\n properties[attr] = record[1]\\n # Not supported\\n elif (\\n record[0] not in emitted_warnings\\n and record[0] not in GdsLibrary._unused_records\\n ):\\n warnings.warn(\\n \\\"[GDSPY] Record type {0} ({1:02X}) is not supported.\\\".format(\\n GdsLibrary._record_name[record[0]], record[0]\\n ),\\n stacklevel=2,\\n )\\n emitted_warnings.append(record[0])\\n if close:\\n infile.close()\\n return self\",\n \"def buffer(infile,outfile,erosion,dilatation):\\n ds_in=ogr.Open( infile )\\n lyr_in=ds_in.GetLayer(0)\\n drv=ds_in.GetDriver()\\n ds_out = drv.CreateDataSource(outfile)\\n layer = ds_out.CreateLayer( lyr_in.GetLayerDefn().GetName(),lyr_in.GetSpatialRef(), ogr.wkbPolygon)\\n n_fields = lyr_in.GetLayerDefn().GetFieldCount()\\n for i in xrange ( lyr_in.GetLayerDefn().GetFieldCount() ):\\n field_in = lyr_in.GetLayerDefn().GetFieldDefn( i )\\n fielddef = ogr.FieldDefn( field_in.GetName(), field_in.GetType() ) \\n layer.CreateField ( fielddef )\\n \\n featuredefn = layer.GetLayerDefn()\\n \\n for feat in lyr_in:\\n geom0 = feat.GetGeometryRef()\\n feature0 = feat.Clone()\\n feature0.SetGeometry(geom0.Buffer(float(erosion)))\\n geom = feature0.GetGeometryRef()\\n feature = feature0.Clone() \\n feature.SetGeometry(geom.Buffer(float(dilatation)))\\n layer.CreateFeature(feature)\\n del geom0\\n del geom\\n ds_out.Destroy()\\n return 0\",\n \"def loadArray(filepath):\\n \\n from osgeo import gdal\\n \\n ds = gdal.Open(filepath, 0)\\n \\n return ds.ReadAsArray()\",\n \"def __init__(self, geom_input, srs=None):\\n str_instance = isinstance(geom_input, str)\\n\\n # If HEX, unpack input to a binary buffer.\\n if str_instance and hex_regex.match(geom_input):\\n geom_input = memoryview(bytes.fromhex(geom_input))\\n str_instance = False\\n\\n # Constructing the geometry,\\n if str_instance:\\n wkt_m = wkt_regex.match(geom_input)\\n json_m = json_regex.match(geom_input)\\n if wkt_m:\\n if wkt_m[\\\"srid\\\"]:\\n # If there's EWKT, set the SRS w/value of the SRID.\\n srs = int(wkt_m[\\\"srid\\\"])\\n if wkt_m[\\\"type\\\"].upper() == \\\"LINEARRING\\\":\\n # OGR_G_CreateFromWkt doesn't work with LINEARRING WKT.\\n # See https://trac.osgeo.org/gdal/ticket/1992.\\n g = capi.create_geom(OGRGeomType(wkt_m[\\\"type\\\"]).num)\\n capi.import_wkt(g, byref(c_char_p(wkt_m[\\\"wkt\\\"].encode())))\\n else:\\n g = capi.from_wkt(\\n byref(c_char_p(wkt_m[\\\"wkt\\\"].encode())), None, byref(c_void_p())\\n )\\n elif json_m:\\n g = self._from_json(geom_input.encode())\\n else:\\n # Seeing if the input is a valid short-hand string\\n # (e.g., 'Point', 'POLYGON').\\n OGRGeomType(geom_input)\\n g = capi.create_geom(OGRGeomType(geom_input).num)\\n elif isinstance(geom_input, memoryview):\\n # WKB was passed in\\n g = self._from_wkb(geom_input)\\n elif isinstance(geom_input, OGRGeomType):\\n # OGRGeomType was passed in, an empty geometry will be created.\\n g = capi.create_geom(geom_input.num)\\n elif isinstance(geom_input, self.ptr_type):\\n # OGR pointer (c_void_p) was the input.\\n g = geom_input\\n else:\\n raise GDALException(\\n \\\"Invalid input type for OGR Geometry construction: %s\\\"\\n % type(geom_input)\\n )\\n\\n # Now checking the Geometry pointer before finishing initialization\\n # by setting the pointer for the object.\\n if not g:\\n raise GDALException(\\n \\\"Cannot create OGR Geometry from input: %s\\\" % geom_input\\n )\\n self.ptr = g\\n\\n # Assigning the SpatialReference object to the geometry, if valid.\\n if srs:\\n self.srs = srs\\n\\n # Setting the class depending upon the OGR Geometry Type\\n self.__class__ = GEO_CLASSES[self.geom_type.num]\",\n \"def loadProjection(filepath):\\n \\n from osgeo import gdal\\n \\n ds = gdal.Open(filepath, 0)\\n \\n return ds.GetProjection()\",\n \"def read_locations(db, openfile):\\n pass\",\n \"def load(self, filename):\\n LIB.mnt_grid_load.argtypes = [POINTER(c_void_p), c_char_p]\\n fm = filename.encode('utf-8')\\n ier = LIB.mnt_grid_load(self.obj, fm)\\n if ier:\\n error_handler(FILE, 'load', ier)\",\n \"def _open_file(cls, *args, **kwargs):\\n try:\\n import tensorflow as tf\\n open_file = tf.io.gfile.GFile # pragma: no cover\\n except Exception: # pragma: no cover\\n open_file = open\\n return open_file(*args, **kwargs)\",\n \"def read_geojson(cls, path_or_json_or_string_or_url):\\n assert path_or_json_or_string_or_url\\n data = None\\n if isinstance(path_or_json_or_string_or_url, (dict, list)):\\n data = path_or_json_or_string_or_url\\n try:\\n data = json.loads(path_or_json_or_string_or_url)\\n except ValueError:\\n pass\\n try:\\n path = path_or_json_or_string_or_url\\n if path.endswith('.gz') or path.endswith('.gzip'):\\n import gzip\\n contents = gzip.open(path, 'r').read().decode('utf-8')\\n else:\\n contents = open(path, 'r').read()\\n data = json.loads(contents)\\n except FileNotFoundError:\\n pass\\n if not data:\\n import urllib.request\\n with urllib.request.urlopen(path_or_json_or_string_or_url) as url:\\n data = json.loads(url.read().decode())\\n assert data, 'MapData accepts a valid geoJSON object, geoJSON string, path to a geoJSON file, or URL'\\n return cls(cls._read_geojson_features(data))\",\n \"def isGeospatial(path):\\n try:\\n ds = gdal.Open(str(path), gdalconst.GA_ReadOnly)\\n except Exception:\\n return False\\n if ds:\\n if ds.GetGCPs() and ds.GetGCPProjection():\\n return True\\n if ds.GetProjection():\\n return True\\n if ds.GetGeoTransform(can_return_null=True):\\n return True\\n if ds.GetDriver().ShortName in {'NITF', 'netCDF'}:\\n return True\\n return False\",\n \"def read(self, format=None, epsg=None):\\n if self.data is None:\\n query, params = self.get_query()\\n self.data = df_from_postgis(self.engine, query, params,\\n self.geom_column, self.epsg)\\n return self.transform_data(outformat=format, epsg=epsg)\",\n \"def read_gdal_coordinates(dataset, mode=\\\"center\\\"):\\n coordinates_pixel = _pixel_coordinates(\\n dataset.RasterXSize, dataset.RasterYSize, mode\\n )\\n\\n geotransform = dataset.GetGeoTransform()\\n coordinates = _pixel_to_map(coordinates_pixel, geotransform)\\n\\n return coordinates\",\n \"def gzopen(f):\\n return gzip.open(f, 'rb') if f.endswith('.gz') else open(f, 'r')\",\n \"def from_file(cls, file): \\n try:\\n import dill as pickle\\n except ImportError:\\n logger.error(\\\"Cannot import from file, dill not installed\\\")\\n return None\\n model = pickle.load(open(file,'rb'))\\n if type(model) == GeologicalModel:\\n logger.info('GeologicalModel initialised from file')\\n return model\\n else:\\n logger.error('{} does not contain a geological model'.format(file))\\n return None\",\n \"def open_file(path):\\n input_file = os.path.join(path)\\n with open(input_file) as f:\\n dataset = f.read()\\n return dataset\",\n \"def check_gdf_load(gdf):\\n if isinstance(gdf, (str, Path)):\\n if not is_url(gdf):\\n gdf = to_absolute_path(str(gdf))\\n # as of geopandas 0.6.2, using the OGR CSV driver requires some add'nal\\n # kwargs to create a valid geodataframe with a geometry column. see\\n # https://github.com/geopandas/geopandas/issues/1234\\n if str(gdf).lower().endswith(\\\"csv\\\"):\\n return gpd.read_file(\\n gdf, GEOM_POSSIBLE_NAMES=\\\"geometry\\\", KEEP_GEOM_COLUMNS=\\\"NO\\\"\\n )\\n try:\\n return gpd.read_file(gdf)\\n except (DriverError, CPLE_OpenFailedError):\\n logging.warning(\\n f\\\"GeoDataFrame couldn't be loaded: either {gdf} isn't a valid\\\"\\n \\\" path or it isn't a valid vector file. Returning an empty\\\"\\n \\\" GeoDataFrame.\\\"\\n )\\n return gpd.GeoDataFrame()\\n elif isinstance(gdf, gpd.GeoDataFrame):\\n return gdf\\n else:\\n raise ValueError(f\\\"{gdf} is not an accepted GeoDataFrame format.\\\")\",\n \"def load(src_path):\\n satdat = rasterio.open(src_path)\\n return satdat\",\n \"def geotiff_read(ifile,metaData):\\r\\n\\r\\n file = gdal.Open(ifile, GA_ReadOnly)\\r\\n\\r\\n projection = file.GetProjection()\\r\\n src = osr.SpatialReference()\\r\\n src.ImportFromWkt(projection)\\r\\n proj = src.ExportToWkt()\\r\\n\\r\\n Nx = file.RasterXSize\\r\\n Ny = file.RasterYSize\\r\\n\\r\\n trans = file.GetGeoTransform()\\r\\n\\r\\n dx = trans[1]\\r\\n dy = trans[5]\\r\\n\\r\\n if metaData == \\\"A\\\":\\r\\n\\r\\n xp = np.arange(Nx)\\r\\n yp = np.arange(Ny)\\r\\n\\r\\n (Xp, Yp) = np.meshgrid(xp,yp)\\r\\n\\r\\n X = trans[0] + (Xp+0.5)*trans[1] + (Yp+0.5)*trans[2] #FIXME: bottleneck!\\r\\n Y = trans[3] + (Xp+0.5)*trans[4] + (Yp+0.5)*trans[5]\\r\\n\\r\\n if metaData == \\\"P\\\":\\r\\n\\r\\n xp = np.arange(Nx)\\r\\n yp = np.arange(Ny)\\r\\n\\r\\n (Xp, Yp) = np.meshgrid(xp,yp)\\r\\n\\r\\n X = trans[0] + Xp*trans[1] + Yp*trans[2] #FIXME: bottleneck!\\r\\n Y = trans[3] + Xp*trans[4] + Yp*trans[5]\\r\\n\\r\\n band = file.GetRasterBand(1)\\r\\n\\r\\n Z = band.ReadAsArray()\\r\\n\\r\\n dx = np.abs(dx)\\r\\n dy = np.abs(dy)\\r\\n\\r\\n return X, Y, Z, dx, dy, proj\",\n \"def open_gzipped(infile, mode='rt'):\\n import gzip\\n import bz2\\n if mode.startswith('r'):\\n tmode = 'rt'\\n bmode = 'r'\\n elif mode.startswith('w'):\\n tmode = 'wt'\\n bmode = 'w'\\n elif mode.startswith('a'):\\n tmode = 'at'\\n bmode = 'a'\\n if hasattr(infile, 'write'):\\n return infile\\n if isinstance(infile, str):\\n if infile.endswith('.gz'):\\n return gzip.open(infile, tmode)\\n if infile.endswith('.bz2'):\\n if hasattr(bz2, 'open'):\\n return bz2.open(infile, tmode)\\n else:\\n return bz2.BZ2File(infile, bmode)\\n return open(infile, tmode)\",\n \"def open(self, file):\\n\\n # Check if file exists\\n if not os.path.exists(file):\\n logging.warning(f'{file} does not exist')\\n return\\n try:\\n # Open nx file\\n self.file.load(file)\\n except:\\n logging.exception(f'Unable to open {file}')\",\n \"def _initialize_geospatial_data(self):\\n driver = ogr.GetDriverByName(\\\"ESRI Shapefile\\\")\\n\\n bnd_src = driver.Open(self._spatial_filename, 0)\\n bnd_lyr = bnd_src.GetLayer()\\n (self.spatial_index,\\n self.spatial_feats,\\n self.bison_spatial_fields\\n ) = self._create_spatial_index(bnd_lyr)\",\n \"def open_file(file_name):\\n pass\",\n \"def open(self, filename):\\n raise NotImplementedError\",\n \"def openFile(file):\\n file = file.lower()\\n if file.endswith('.bz2'):\\n return bz2.BZ2File(file)\\n elif file.endswith('.gz'):\\n return gzip.open(file)\\n return open(file)\",\n \"def read_gfs_data(fname, lat, lon, interp=None, step=200.):\\n pass\",\n \"def fileopen(file):\\n return _posixfile_().fileopen(file)\",\n \"def geogrid_from_gdalInMem(ds):\\n in_data = ds.GetRasterBand(1).ReadAsArray()\\n in_xdim = ds.RasterXSize\\n in_ydim = ds.RasterYSize\\n in_geotransform = ds.GetGeoTransform()\\n in_xvals = np.linspace(\\n in_geotransform[0] + 0.5 * in_geotransform[1],\\n in_geotransform[0] + (in_xdim - 0.5) * in_geotransform[1],\\n in_xdim)\\n in_yvals = np.linspace(\\n in_geotransform[3] + 0.5 * in_geotransform[5],\\n in_geotransform[3] + (in_ydim - 0.5) * in_geotransform[5],\\n in_ydim)\\n in_wkt = ds.GetProjection()\\n\\n return GeoGrid(in_data, in_xvals, in_yvals, in_wkt, warn=True)\",\n \"def load(filename):\\n return GesFile(filename)\",\n \"def geogrid_as_gdalInMem(gg):\\n in_xdim = len(gg.x)\\n in_ydim = len(gg.y)\\n in_datatype = get_gdal_datatype(gg.data_array.dtype)\\n in_geotransform = gg.geotransform\\n in_wkt = gg.wkt\\n in_data = gg.data_array\\n\\n ds = gdal.GetDriverByName('MEM').Create(\\n '', in_xdim, in_ydim, 1, in_datatype)\\n ds.SetProjection(in_wkt)\\n ds.SetGeoTransform(in_geotransform)\\n ds.GetRasterBand(1).WriteArray(in_data)\\n\\n return ds\",\n \"def load_from_file(path, bands):\\n dataset = gdal.Open(path, gdal.GA_ReadOnly)\\n array = dataset.ReadAsArray()\\n\\n if len(array.shape) == 3:\\n # The bands column is in the first position, but we want it last\\n array = np.rollaxis(array, 0, 3)\\n elif len(array.shape) == 2:\\n # This image seems to have one band, so we add an axis for ease\\n # of use in the rest of the library\\n array = array[:, :, np.newaxis]\\n\\n image = array.astype('float32')\\n\\n return SatelliteImage(dataset, image, bands)\",\n \"def _open(self, file_path=None):\\n\\t\\tif file_path is None:\\n\\t\\t\\tfile_path = self.file_path\\n\\n\\t\\tif not os.path.exists(file_path):\\n\\t\\t\\traise ValueError('Could not find file: {}'.format(file_path))\\n\\n\\t\\ttry:\\n\\t\\t\\tf = open(file_path, encoding='utf-8', newline='')\\n\\t\\texcept OSError as err:\\n\\t\\t\\tself.log.error(str(err))\\n\\t\\t\\traise ValueError('Could not open file: {}'.format(file_path))\\n\\n\\t\\treturn f\",\n \"def read(self, as_numpy_array=False, as_single_band=True,\\n old_nodata=None, new_nodata=None, epsg=None):\\n if self.ext not in formats.RASTER:\\n raise UnsupportedFormatException(\\n \\\"Only the following raster formats are supported: {}\\\".format(\\n ','.join(formats.RASTER)\\n )\\n )\\n self.basename = os.path.basename(self.uri)\\n if not self.data:\\n self.data = gdal.Open(self.uri)\\n out_data = self.data\\n if epsg and self.get_epsg() != epsg:\\n out_data = reproject(self.data, epsg)\\n\\n if as_numpy_array:\\n return raster_to_numpy_array(out_data, as_single_band,\\n old_nodata, new_nodata)\\n else:\\n return out_data\",\n \"def ReadRaster(cls,infile):\\r\\n try:\\r\\n import gdal\\r\\n import rasterio\\r\\n except:\\r\\n raise ImportError(\\\"Can not import module GDAL or RasterIO\\\")\\r\\n\\r\\n with rasterio.open(infile) as src:\\r\\n transform = src.meta['transform']\\r\\n nBands = (src.indexes)\\r\\n array = src.read_band(1)\\r\\n return array\\r\\n #except:\\r\\n # raise ImportError(\\\"Can not read band\\\")\\r\",\n \"def readfile(filename, encoding=None, legacy_mode=False):\\n if not is_dxf_file(filename):\\n raise IOError(\\\"File '{}' is not a DXF file.\\\".format(filename))\\n\\n info = dxf_file_info(filename)\\n with io.open(filename, mode='rt', encoding=info.encoding, errors='ignore') as fp:\\n dwg = read(fp, legacy_mode=legacy_mode, dxfversion=info.version)\\n\\n dwg.filename = filename\\n if encoding is not None and is_supported_encoding(encoding):\\n dwg.encoding = encoding\\n return dwg\",\n \"def open(self, infile, cache=True):\\n return _image.image_open(self, infile, cache)\",\n \"def open(self, path, mode='r', encoding=None, newline=None):\\n return DataSource.open(self, self._fullpath(path), mode,\\n encoding=encoding, newline=newline)\",\n \"def _load_file(self, XYZ_path, bohrs=False):\\n\\n # Imports\\n import numpy as np\\n from .const import CIC, PHYS, atom_num, atom_sym\\n from .error import XYZError\\n from .utils import safe_cast as scast\\n\\n # Complain if already initialized\\n if 'geoms' in dir(self):\\n raise XYZError(XYZError.OVERWRITE,\\n \\\"Cannot overwrite contents of existing OpanXYZ\\\", \\\"\\\")\\n ## end if\\n\\n # Open file, read contents, close stream\\n # No particular exception handling; that will be the responsibility\\n # of the calling routine.\\n # May want to add a check for text file format; but, really, a check\\n # for no-found-geometry-information will likely cover such a case.\\n with open(XYZ_path,'rU') as in_fl:\\n self.XYZ_path = XYZ_path #: Path of file\\n self.in_str = in_fl.read()\\n\\n # Check to ensure at least one geom match; else raise some sort of\\n # error\\n if not OpanXYZ.p_geom.search(self.in_str):\\n raise XYZError(XYZError.XYZFILE,\\n \\\"No valid geometry found\\\",\\n \\\"XYZ file: \\\" + XYZ_path)\\n ## end if\\n\\n # Store the number of atoms. XYZ files with multiple geometries will\\n # be required to have the same number of atoms in all geometries.\\n # This is fine for Open Anharmonic, but may be highly undesirable\\n # in other applications. Beware.\\n # If the .match() call doesn't have a group attribute, catch the\\n # resulting AttributeError and throw an XYZError. Can't think of any\\n # other situation that would throw an AttributeError here, so probably\\n # I don't have to check for a subtype of AttributeError...\\n # Can't think of a need for an else or a finally here\\n try:\\n self.num_atoms = scast(OpanXYZ.p_geom.match(self.in_str)\\n .group(\\\"num\\\"), np.int_)\\n except AttributeError:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"No geometry block found at start of file\\\",\\n \\\"XYZ file: \\\" + XYZ_path)\\n ## end try\\n\\n # Initialize the description vector and geometry array\\n # CANNOT use matrix type for the geometry since it's 3D.\\n # Retriever function for geometry should convert the given slice\\n # to a vector as an np.matrix, though.\\n # Geometry is constructed as a length-3N vector of\\n # coordinates, where atom 1's x/y/z coordinates are sequential,\\n # then atom 2's x/y/z coordinates are sequential, etc.\\n # Multiple geometries in a single XYZ file are stacked in a simple\\n # array.\\n self.descs = []\\n self.geoms = []\\n\\n # Initialize the atom symbols vector\\n self.atom_syms = []\\n\\n # Define a counter for the number of geometries. Store as a persistent\\n # instance variable because it will be useful later.\\n self.num_geoms = 0\\n\\n # Loop over the geometry blocks found in the input file\\n for mch in OpanXYZ.p_geom.finditer(self.in_str):\\n # Check that the number of atoms is consistent with the spec\\n # found in the first geometry block\\n if not scast(mch.group(\\\"num\\\"), np.int_) == self.num_atoms:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Non-constant number of atoms in multiple geometry\\\",\\n \\\"XYZ file: \\\" + XYZ_path)\\n ## end if\\n\\n # Store the description for the current geometry\\n self.descs.append(mch.group(\\\"desc\\\"))\\n\\n # Assemble the coordinates vector and assemble/check the element\\n # ID vector.\\n # Reset the atom counter and the coordinates vector\\n atom_count = 0\\n coord_vec = np.empty((0,),dtype=np.float_)\\n for line_mch in OpanXYZ.p_coords.finditer(mch.group(\\\"coord\\\")):\\n # Check for whether element list has been fully populated\\n if len(self.atom_syms) < self.num_atoms:\\n # If not, continue populating it; have to check for\\n # whether it's an atomic number or an element symbol\\n if line_mch.group(\\\"el\\\").isdigit():\\n # Atomic number\\n # Check for valid number\\n at_num = scast(line_mch.group(\\\"el\\\"), np.int_)\\n if not (CIC.MIN_ATOMIC_NUM <= at_num\\n <= CIC.MAX_ATOMIC_NUM):\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is an \\\\\\n unsupported element\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ##end if\\n\\n # Tag on the new symbol\\n self.atom_syms.append(atom_sym[at_num])\\n else:\\n # Element symbol; store as all caps\\n # Check for valid element, first by catching if the\\n # specified element string is even valid\\n try:\\n at_num = atom_num[line_mch.group(\\\"el\\\").upper()]\\n except KeyError:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is an \\\\\\n unrecognized element\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end try\\n\\n # Tag on the new symbol\\n self.atom_syms.append(line_mch.group(\\\"el\\\").upper())\\n ## end if\\n else: # List is fully populated\\n # If so, confirm that the element specified at the current\\n # line matches that of the first geometry of the file\\n # Have to check whether it's an atomic number or symbol.\\n if line_mch.group(\\\"el\\\").isdigit():\\n # Atomic number; don't need safe cast; must trap for\\n # a bad atomic number\\n at_num = scast(line_mch.group(\\\"el\\\"), np.int_)\\n if not (CIC.MIN_ATOMIC_NUM <= at_num\\n <= CIC.MAX_ATOMIC_NUM):\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is an \\\\\\n unsupported element\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end if\\n if not atom_sym[at_num] == self.atom_syms[atom_count]:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is inconsistent \\\\\\n with geometry #0\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end if\\n\\n else:\\n # Element symbol\\n # Check for valid element, first by catching if the\\n # specified element string is even valid\\n try:\\n at_num = atom_num[line_mch.group(\\\"el\\\").upper()]\\n except KeyError:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is an \\\\\\n unrecognized element\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end try\\n # Confirm symbol matches the initial geometry\\n if not line_mch.group(\\\"el\\\").upper() == \\\\\\n self.atom_syms[atom_count]:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0}, atom #{1} is inconsistent \\\\\\n with geometry #0\\\"\\n .format(self.num_geoms, atom_count),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end if\\n ## end if\\n ## end if\\n\\n # Append the three coordinates of the current atom to the\\n # temp coordinates vector, converting to Bohrs if indicated.\\n # Working in Bohrs is desired because they are atomic units\\n # and thus are part of the internal unit definition of the\\n # Hartree.\\n for c_str in range(1,4):\\n coord_vec = np.concatenate(\\n (coord_vec, [\\n scast(line_mch.group(\\\"c{0}\\\".format(c_str)), np.float_) /\\n (1.0 if bohrs else PHYS.ANG_PER_BOHR)\\n ]), axis=0)\\n ## next c_str\\n\\n # Increment the atom_count for the atom number\\n atom_count += 1\\n\\n ## next line_mch\\n\\n # Confirm that number of imported coordinates matches the\\n # number expected from self.num_atoms\\n if not coord_vec.shape[0] == 3*self.num_atoms:\\n raise XYZError(XYZError.XYZFILE,\\n \\\"Geometry #{0} atom count is inconsistent\\\"\\n .format(self.num_geoms),\\n \\\"XYZ file: {0}\\\".format(XYZ_path))\\n ## end if\\n\\n # Assemble the coordinates vector into the actual coordinates\\n # stack for the XYZ\\n self.geoms.append(coord_vec)\\n\\n # Increment the count of the number of geometries. Once the\\n # 'mch' iteration is completed, this will accurately reflect\\n # the number of geometries read from the file.\\n self.num_geoms += 1\\n\\n ## next mch\",\n \"def create_dat_from_csv(windgrid_file, DAT_header, output_file, wind_field='gust_mph', latitude_field='lat', longitude_field='lon'):\\n df = pd.read_csv(windgrid_file)\\n df['geometry'] = [Point(x, y) for x, y in zip(\\n df[longitude_field], df[latitude_field])]\\n gdf = gpd.GeoDataFrame(df, geometry='geometry')\\n create_dat_from_geodataframe(\\n gdf, DAT_header, output_file, wind_field=wind_field)\",\n \"def read_from(self, filename):\\n\\n lon, lat, field, weight = [], [], [], []\\n\\n if os.path.exists(filename):\\n logger.info(\\\"Reading data from file {0}\\\".format(filename))\\n with open(filename, 'r') as f:\\n line = f.readline()\\n ncols = len(line.split())\\n while ncols >= 3:\\n lon.append(float(line.split()[0]))\\n lat.append(float(line.split()[1]))\\n field.append(float(line.split()[2]))\\n if ncols >= 4:\\n weight.append(float(line.split()[3]))\\n else:\\n weight.append(1.)\\n line = f.readline()\\n ncols = len(line.split())\\n\\n self.x = np.array(lon)\\n self.y = np.array(lat)\\n self.field = np.array(field)\\n self.weight = np.array(weight)\\n return self\\n else:\\n logger.error(\\\"File {0} does not exist\\\".format(filename))\\n raise FileNotFoundError('File does not exist')\",\n \"def open_gz(filename, mode):\\n return gzip.open(filename, mode)\",\n \"def readArray(input):\\n data = gdal.Open(input)\\n band = data.GetRasterBand(1)\\n \\n return band.ReadAsArray()\",\n \"def cli(source_f, raster_f, output, verbose):\\n with fiona.open(source_f, 'r') as source:\\n source_driver = source.driver\\n source_crs = source.crs\\n sink_schema = source.schema.copy()\\n\\n source_geom = source.schema['geometry']\\n if source_geom == 'Point':\\n sink_schema['geometry'] = '3D Point'\\n elif source_geom == 'LineString':\\n sink_schema['geometry'] = '3D LineString'\\n elif source_geom == '3D Point' or source_geom == '3D LineString':\\n pass\\n else:\\n click.BadParameter(\\\"Source geometry type {} not implemented\\\".format(source_geom))\\n\\n with rasterio.open(raster_f) as raster:\\n if source_crs != raster.crs:\\n click.BadParameter(\\\"Features and raster have different CRS.\\\")\\n if raster.count > 1:\\n warnings.warn(\\\"Found {0} bands in {1}, expected a single band raster\\\".format(raster.bands, raster_f))\\n supported = ['int16', 'int32', 'float32', 'float64']\\n if raster.dtypes[0] not in supported:\\n warnings.warn(\\\"Found {0} type in {1}, expected one of {2}\\\".format(raster.dtypes[0], raster_f, supported))\\n with fiona.open(\\n output, 'w',\\n driver=source_driver,\\n crs=source_crs,\\n schema=sink_schema) as sink:\\n\\n for feature in source:\\n try:\\n feature_z = drapery.drape(raster, feature)\\n sink.write({\\n 'geometry': mapping(feature_z),\\n 'properties': feature['properties'],\\n })\\n except Exception:\\n logging.exception(\\\"Error processing feature %s:\\\", feature['id'])\\n #print(sink.closed)\\n #print(raster.closed)\\n #print(source.closed)\",\n \"def read_file(netcdf_file_name):\\n\\n dataset_object = netCDF4.Dataset(netcdf_file_name)\\n\\n saliency_dict = {\\n MODEL_FILE_KEY: str(getattr(dataset_object, MODEL_FILE_KEY)),\\n IS_LAYER_OUTPUT_KEY: bool(getattr(dataset_object, IS_LAYER_OUTPUT_KEY)),\\n LAYER_NAME_KEY: str(getattr(dataset_object, LAYER_NAME_KEY)),\\n NEURON_INDICES_KEY: numpy.array(\\n getattr(dataset_object, NEURON_INDICES_KEY), dtype=int\\n ),\\n IDEAL_ACTIVATION_KEY: getattr(dataset_object, IDEAL_ACTIVATION_KEY),\\n MULTIPLY_BY_INPUT_KEY:\\n bool(getattr(dataset_object, MULTIPLY_BY_INPUT_KEY)),\\n VALID_TIMES_KEY: numpy.array(\\n dataset_object.variables[VALID_TIMES_KEY][:], dtype=int\\n ),\\n LATITUDES_KEY: numpy.array(\\n dataset_object.variables[LATITUDES_KEY][:], dtype=float\\n ),\\n LONGITUDES_KEY: numpy.array(\\n dataset_object.variables[LONGITUDES_KEY][:], dtype=float\\n ),\\n SALIENCY_MATRIX_KEY: numpy.array(\\n dataset_object.variables[SALIENCY_MATRIX_KEY][:], dtype=float\\n )\\n }\\n\\n dataset_object.close()\\n return saliency_dict\",\n \"def get_infile(filename):\\r\\n if filename.endswith(\\\".gz\\\"):\\r\\n fin = GzipFile(filename, \\\"rb\\\")\\r\\n else:\\r\\n fin = open(filename, \\\"U\\\")\\r\\n return fin\",\n \"def get_infile(filename):\\r\\n if filename.endswith(\\\".gz\\\"):\\r\\n fin = GzipFile(filename, \\\"rb\\\")\\r\\n else:\\r\\n fin = open(filename, \\\"U\\\")\\r\\n return fin\",\n \"def open_input_file(input_file):\\n if input_file:\\n if not os.path.isfile(input_file):\\n sys.stderr.write(\\n \\\"ERROR! Input file (%s) is not a normal file.\\\\n\\\" % input_file)\\n sys.exit(1)\\n try:\\n return codecs.open(input_file, \\\"r\\\", \\\"utf8\\\")\\n except:\\n sys.stderr.write(\\n \\\"ERROR! Could not open input file (%s) for reading:\\\\n\\\" % input_file)\\n raise\\n else:\\n return sys.stdin\",\n \"def open_local_or_gcs(path, mode):\\n if path.startswith('gs://'):\\n try:\\n return gcsio.GcsIO().open(path, mode)\\n except Exception as e: # pylint: disable=broad-except\\n # Currently we retry exactly once, to work around flaky gcs calls.\\n logging.error('Retrying after exception reading gcs file: %s', e)\\n time.sleep(10)\\n return gcsio.GcsIO().open(path, mode)\\n else:\\n return open(path, mode)\",\n \"def import_L1B(cls,infile):\\r\\n try:\\r\\n import gdal\\r\\n import rasterio\\r\\n except:\\r\\n raise ImportError(\\\"Can not import module GDAL or RasterIO\\\")\\r\\n\\r\\n\\r\\n image=image()\\r\\n\\r\\n #except:\\r\\n # raise ImportError(\\\"Can not read band\\\")\\r\",\n \"def open_file(file_path, mode='rb', encoding='iso-8859-1'):\\n try:\\n return open(file_path, mode=mode, encoding=encoding)\\n except IOError:\\n raise\",\n \"def file_open(*args, **kwargs):\\n return open(*args, **kwargs)\",\n \"def open_file(self, path, omode=\\\"r\\\"):\\n self.root_group = netCDF4.Dataset(path, omode)\\n return self.root_group\",\n \"def open_(filename, *args):\\n\\n if (filename[-3:] == '.gz'):\\n return gzip.open(filename, *args)\\n try:\\n return open(filename, *args)\\n except OSError:\\n return gzip.open(filename + \\\".gz\\\", *args)\",\n \"def open(path, mode='r', destpath=os.curdir, encoding=None, newline=None):\\n\\n ds = DataSource(destpath)\\n return ds.open(path, mode, encoding=encoding, newline=newline)\",\n \"def open_file(path, config):\\n\\n return fopen(normalize_path(path, config))\",\n \"def openInputFile(infile, *args):\\n if infile is None:\\n logging.info(\\\"Reading input from STDIN\\\")\\n return sys.stdin\\n\\n if isinstance(infile, str):\\n if urlRE.match(infile):\\n import urllib2\\n return urllib2.urlopen(infile)\\n if len(infile)>3 and infile[-3:]=='.gz':\\n import gzip\\n return gzip.GzipFile(infile,'rb')\\n elif len(infile)>4 and infile[-4:]=='.bz2':\\n import bz2\\n return bz2.BZ2File(infile,'rb')\\n else:\\n return open(infile,'rt')\\n else:\\n return infile\",\n \"def open_input_stream(uri: str) -> BinaryIO:\\n parsed = urlparse(uri)\\n if parsed.scheme == \\\"gs\\\":\\n return _gcsfs().open(uri)\\n else:\\n filesystem, path = fs.FileSystem.from_uri(uri)\\n return filesystem.open_input_file(path)\",\n \"def read(self, format=None, epsg=None):\\n if not format:\\n format = self.default_output\\n if self.data is None and self.features:\\n if type(self.features) == str:\\n self.features = json.loads(self.features)\\n features = self.features\\n\\n if 'type' in features and features['type'] == 'FeatureCollection':\\n self.data = geopandas.GeoDataFrame.from_features(\\n self.features['features'])\\n else:\\n self.data = geopandas.GeoDataFrame.from_features(features)\\n if not self.data.crs:\\n if hasattr(self, 'crs'):\\n self.data.crs = self.crs\\n else:\\n self.get_epsg()\\n\\n return self.transform_data(outformat=format, epsg=epsg)\",\n \"def import_national_boundaries(self, name):\\n print \\\"\\\\n4.3- importa shape con confini nazionali ISTAT\\\"\\n countrySHP = os.path.join(\\\"boundaries\\\", \\\"italy_2011_WGS84.shp\\\")\\n countrySQL = os.path.join(\\\"boundaries\\\", \\\"italy_%s.sql\\\" % name)\\n if os.path.isfile(countrySQL):\\n call(\\\"rm %s\\\" % countrySQL, shell=True)\\n cmd = \\\"shp2pgsql -s 4326 -W 'LATIN1' %s italy %s > %s\\\" % (countrySHP, name, countrySQL)\\n print cmd\\n call(cmd, shell=True)\\n call(\\\"psql -h localhost -U %s -d %s -f %s\\\" % (self.user, name, countrySQL), shell=True)\\n call(\\\"rm %s\\\" % countrySQL, shell=True)\\n call(\\\"echo 'CREATE INDEX ON italy USING GIST (geom);'| psql -U %s -d %s\\\" % (self.user, name), shell=True)\\n call(\\\"echo 'ANALYZE italy;'| psql -U %s -d %s\\\" % (self.user, name), shell=True)\",\n \"def convert(threshold, infile, tmpfile_1, tmpfile_2, outfile):\\n args = [\\n \\\"gdal_calc.py\\\",\\n '-A', infile,\\n '--outfile={}'.format(tmpfile_1),\\n '--calc=logical_and(A>={}, A<999)'.format(threshold),\\n '--type=Byte', '--NoDataValue=0',\\n '--co=SPARSE_OK=YES',\\n '--co=NBITS=1',\\n '--quiet'\\n # Could enable compression\\n # --co=\\\"COMPRESS=LZW\\\"\\n ]\\n subprocess.run(args)\\n\\n subprocess.run([\\n \\\"gdal_polygonize.py\\\",\\n tmpfile_1,\\n '-q',\\n '-f', 'ESRI Shapefile',\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\n \\\"ogr2ogr\\\",\\n '-a_srs', 'EPSG:4326',\\n outfile,\\n tmpfile_2\\n ])\\n\\n subprocess.run([\\\"rm\\\", tmpfile_1])\\n subprocess.run([\\\"rm\\\", tmpfile_2])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'shx')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'dbf')])\\n subprocess.run([\\\"rm\\\", tmpfile_2.replace('shp', 'prj')])\",\n \"def load_gldas_elevation_dataset(gldas_elevation_file): \\n d1 = xr.open_dataset(gldas_elevation_file).load()\\n return d1\",\n \"def genOpen(filename, mode):\\n (name, ext) = os.path.splitext(filename)\\n if ext == \\\".gz\\\":\\n return gzip.open(filename, mode)\\n else:\\n return open(filename, mode)\",\n \"def genOpen(filename, mode):\\n (name, ext) = os.path.splitext(filename)\\n if ext == \\\".gz\\\":\\n return gzip.open(filename, mode)\\n else:\\n return open(filename, mode)\",\n \"def open_(filename, mode=None, compresslevel=9):\\n if filename[-3:] == '.gz':\\n if mode is None: mode = 'rt'\\n return closing(gzip.open(filename, mode, compresslevel))\\n else:\\n if mode is None: mode = 'r'\\n return open(filename, mode)\",\n \"def from_file(path):\\n\\n filename = os.path.basename(path)\\n\\n base, suffix = os.path.splitext(filename);\\n\\n if suffix == '.bin':\\n g = bgy3d.from_file(path)\\n elif suffix == '.m':\\n g = contf.m2dat(path)\\n else:\\n print 'Unknown file suffix.'\\n exit()\\n\\n return g\",\n \"def load_surfer(fname, fmt='ascii'):\\n assert fmt in ['ascii', 'binary'], \\\"Invalid grid format '%s'. Should be \\\\\\n 'ascii' or 'binary'.\\\" % (fmt)\\n if fmt == 'ascii':\\n # Surfer ASCII grid structure\\n # DSAA Surfer ASCII GRD ID\\n # nCols nRows number of columns and rows\\n # xMin xMax X min max\\n # yMin yMax Y min max\\n # zMin zMax Z min max\\n # z11 z21 z31 ... List of Z values\\n with open(fname) as ftext:\\n # DSAA is a Surfer ASCII GRD ID\\n id = ftext.readline()\\n # Read the number of columns (ny) and rows (nx)\\n ny, nx = [int(s) for s in ftext.readline().split()]\\n shape = (nx, ny)\\n # Read the min/max value of columns/longitue (y direction)\\n ymin, ymax = [float(s) for s in ftext.readline().split()]\\n # Read the min/max value of rows/latitude (x direction)\\n xmin, xmax = [float(s) for s in ftext.readline().split()]\\n area = (xmin, xmax, ymin, ymax)\\n # Read the min/max value of grid values\\n datamin, datamax = [float(s) for s in ftext.readline().split()]\\n data = numpy.fromiter((float(i) for line in ftext for i in\\n line.split()), dtype='f')\\n data = numpy.ma.masked_greater_equal(data, 1.70141e+38)\\n assert numpy.allclose(datamin, data.min()) \\\\\\n and numpy.allclose(datamax, data.max()), \\\\\\n \\\"Min and max values of grid don't match ones read from file.\\\" \\\\\\n + \\\"Read: ({}, {}) Actual: ({}, {})\\\".format(\\n datamin, datamax, data.min(), data.max())\\n # Create x and y coordinate numpy arrays\\n x, y = regular(area, shape)\\n if fmt == 'binary':\\n raise NotImplementedError(\\n \\\"Binary file support is not implemented yet.\\\")\\n return x, y, data, shape\",\n \"def convertCSVToGeoTIFF(geoproperties, datafile, outputdir, outfile):\\n \\n data = numpy.genfromtxt(datafile, delimiter=',')\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n \\n # slice out the last column of null values\\n if str(data[Ny-1][Nx-1]) == 'nan':\\n data = data[:,:-1]\\n \\n Ny, Nx = data.shape\\n \\n #print data.size\\n #print data.shape\\n #print data\\n #print Ny, Nx\\n \\n startPos = [geoproperties['tl']['long'],geoproperties['tl']['lat']]\\n d_lat = (geoproperties['br']['lat'] - geoproperties['tl']['lat']) / (Ny - 1)\\n d_long = (geoproperties['br']['long'] - geoproperties['tl']['long']) / (Nx - 1)\\n \\n #print startPos, d_lat, d_long\\n \\n driver = gdal.GetDriverByName(\\\"GTiff\\\")\\n ds = driver.Create(os.path.join(outputdir,outfile),Nx,Ny,1,gdal.GDT_Float32)\\n #ds = driver.Create('output/output.tif',Nx,Ny,1,gdal.GDT_Byte)\\n #ds.SetGeoTransform( [ -158.584, .008, 0, 21.108, 0, .008 ] )\\n ds.SetGeoTransform( [ startPos[0], d_long, 0, startPos[1], 0, d_lat ] )\\n srs = osr.SpatialReference()\\n srs.ImportFromEPSG(4326)\\n ds.SetProjection( srs.ExportToWkt() )\\n ds.GetRasterBand(1).WriteArray(data)\\n ds = None\",\n \"def open_file(self, path: str, **kwargs) -> OpenFileType:\\n full_path = self._full_path(path)\\n logger.debug(f\\\"returning open file for {full_path}\\\")\\n return self.fs.open(full_path, **kwargs)\",\n \"def smart_open(filename: str, mode: str = \\\"rt\\\", ftype: str = \\\"auto\\\", errors: str = \\\"replace\\\"):\\n if ftype == \\\"gzip\\\" or ftype == \\\"gz\\\" or (ftype == \\\"auto\\\" and filename.endswith(\\\".gz\\\")):\\n return gzip.open(filename, mode=mode, encoding=\\\"utf-8\\\", errors=errors)\\n else:\\n return open(filename, mode=mode, encoding=\\\"utf-8\\\", errors=errors)\",\n \"def _open_file(self,path):\\n \\n print \\\"Open File %s\\\" % path\\n \\n mapping = self.mappings.GetClientData(self.mappings.GetSelection())\\n try:\\n delimiter=mapping['_params']['delimiter']\\n except:\\n delimiter=','\\n try:\\n skip_last=mapping['_params']['skip_last']\\n except:\\n skip_last=0\\n self.grid_table = SimpleCSVGrid(path,delimiter,skip_last)\\n self.grid.SetTable(self.grid_table)\\n\\tself.opened_path = path\",\n \"def open_input_files(self):\\n self.dictionaryFile = open(self.dictionaryFile, 'r', encoding=self.encoding)\\n\\n if self.annotationFile :\\n self.annotationFile = open(self.annotationFile, 'r', encoding=self.encoding)\\n elif self.annotationFile is None:\\n try:\\n self.annotationFile = open(os.path.join(self.dictionaryPath, self.dictionaryName + '.ann'), 'r', encoding=self.encoding)\\n except FileNotFoundError:\\n if self.verbose >= 2:\\n sys.stdout.write (\\\"Warning: annotation file is not found.\\\\n\\\")\\n\\n if self.abbreviationsFile :\\n self.abbreviationsFile = open(self.abbreviationsFile, 'r', encoding=self.encoding)\\n elif self.abbreviationsFile is None:\\n try:\\n self.abbreviationsFile = open(os.path.join(self.dictionaryPath, self.dictionaryName + '_abrv.dsl'), 'r', encoding=self.encoding)\\n except FileNotFoundError:\\n if self.verbose >= 2:\\n sys.stdout.write (\\\"Warning: abbreviations file is not found.\\\\n\\\")\",\n \"def get_data(datapath, asfile=False):\\n import os\\n\\n ## The file is a local file - try to get it\\n if not os.path.isfile(datapath) :\\n print \\\"The file %s you are trying to access does not exist\\\" %(datapath)\\n raise IOError\\n fn = datapath\\n if asfile:\\n return open(fn)\\n else:\\n import numpy as np\\n return np.loadtxt(fn)\",\n \"def convert_gpkg_to_geojson(self,shape_fname, destdirectory):\\r\\n\\t\\tfeatures = []\\r\\n\\t\\tcrs = None\\r\\n\\t\\tif not os.path.isfile(shape_fname):\\r\\n\\t\\t\\tself.logger.error('File not found: %s' % shape_fname)\\r\\n\\t\\tself.opstatus.add_info(stage=6, msg = \\\"Rounding coordinates to six decimal precision\\\")\\r\\n\\r\\n\\r\\n\\t\\tout_fname = os.path.join(destdirectory,os.path.basename(shape_fname).replace('.gpkg', '.geojson'))\\r\\n\\t\\twith fiona.open(shape_fname, driver='GPKG') as source:\\r\\n\\t\\t\\twith fiona.open(out_fname, \\\"w\\\",driver='GeoJSON',crs = fiona.crs.from_epsg(4326),schema=source.schema) as sink:\\r\\n\\t\\t\\t\\tfor rec in source:\\r\\n\\t\\t\\t\\t\\tsink.write(rec)\\r\\n\\r\\n\\t\\tself.logger.info('file written: %s' % out_fname)\\r\\n\\t\\tself.opstatus.set_status(stage=6, status=1, statustext =\\\"File successfully converted to GeoJSON with six decimal precision\\\")\\r\\n\\t\\tself.opstatus.add_success(stage=6, msg = \\\"GeoJSON file successfully written\\\")\\r\\n\\t\\treturn out_fname\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67583877","0.6669646","0.6662748","0.6427024","0.5995178","0.57921493","0.5738032","0.5566471","0.5458556","0.5453362","0.5418001","0.5346144","0.5346144","0.5330134","0.52282006","0.52168894","0.5216014","0.5212678","0.5209175","0.51660335","0.5159364","0.5139224","0.5109491","0.5063188","0.5039358","0.50391394","0.5036992","0.5032136","0.49825037","0.49768433","0.49634218","0.49291316","0.49258372","0.49227995","0.49173376","0.48916772","0.48818195","0.4878031","0.48741856","0.48651612","0.4848519","0.4846156","0.4844945","0.48447692","0.48424593","0.48284596","0.48215058","0.48200557","0.48179197","0.47830737","0.4771","0.47534102","0.47239733","0.47052342","0.47002077","0.46981978","0.46945617","0.46943188","0.46895388","0.46886396","0.46577543","0.46577388","0.4653829","0.46536294","0.46345222","0.46340176","0.46242735","0.46226275","0.46201566","0.46136242","0.4609307","0.46048144","0.46048144","0.46027505","0.4596128","0.45946392","0.4592517","0.45831355","0.45733684","0.45672545","0.45659703","0.4560109","0.45539266","0.45529532","0.45514315","0.45399165","0.45339638","0.4533894","0.45324218","0.45324218","0.45311448","0.45118484","0.45111206","0.45072922","0.4507189","0.45051438","0.4503528","0.44913948","0.44837382","0.44752026"],"string":"[\n \"0.67583877\",\n \"0.6669646\",\n \"0.6662748\",\n \"0.6427024\",\n \"0.5995178\",\n \"0.57921493\",\n \"0.5738032\",\n \"0.5566471\",\n \"0.5458556\",\n \"0.5453362\",\n \"0.5418001\",\n \"0.5346144\",\n \"0.5346144\",\n \"0.5330134\",\n \"0.52282006\",\n \"0.52168894\",\n \"0.5216014\",\n \"0.5212678\",\n \"0.5209175\",\n \"0.51660335\",\n \"0.5159364\",\n \"0.5139224\",\n \"0.5109491\",\n \"0.5063188\",\n \"0.5039358\",\n \"0.50391394\",\n \"0.5036992\",\n \"0.5032136\",\n \"0.49825037\",\n \"0.49768433\",\n \"0.49634218\",\n \"0.49291316\",\n \"0.49258372\",\n \"0.49227995\",\n \"0.49173376\",\n \"0.48916772\",\n \"0.48818195\",\n \"0.4878031\",\n \"0.48741856\",\n \"0.48651612\",\n \"0.4848519\",\n \"0.4846156\",\n \"0.4844945\",\n \"0.48447692\",\n \"0.48424593\",\n \"0.48284596\",\n \"0.48215058\",\n \"0.48200557\",\n \"0.48179197\",\n \"0.47830737\",\n \"0.4771\",\n \"0.47534102\",\n \"0.47239733\",\n \"0.47052342\",\n \"0.47002077\",\n \"0.46981978\",\n \"0.46945617\",\n \"0.46943188\",\n \"0.46895388\",\n \"0.46886396\",\n \"0.46577543\",\n \"0.46577388\",\n \"0.4653829\",\n \"0.46536294\",\n \"0.46345222\",\n \"0.46340176\",\n \"0.46242735\",\n \"0.46226275\",\n \"0.46201566\",\n \"0.46136242\",\n \"0.4609307\",\n \"0.46048144\",\n \"0.46048144\",\n \"0.46027505\",\n \"0.4596128\",\n \"0.45946392\",\n \"0.4592517\",\n \"0.45831355\",\n \"0.45733684\",\n \"0.45672545\",\n \"0.45659703\",\n \"0.4560109\",\n \"0.45539266\",\n \"0.45529532\",\n \"0.45514315\",\n \"0.45399165\",\n \"0.45339638\",\n \"0.4533894\",\n \"0.45324218\",\n \"0.45324218\",\n \"0.45311448\",\n \"0.45118484\",\n \"0.45111206\",\n \"0.45072922\",\n \"0.4507189\",\n \"0.45051438\",\n \"0.4503528\",\n \"0.44913948\",\n \"0.44837382\",\n \"0.44752026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62465745"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":95693,"cells":{"query":{"kind":"string","value":"Returns whether the dataset covers the whole world or not."},"document":{"kind":"string","value":"def IsWholeWorld(self, resolution=None):\n if resolution is None:\n resolution = self.GetNativeResolution()\n\n spatial_ref = self.GetSpatialReference()\n world_extents = spatial_ref.GetWorldExtents()\n extents = self.GetExtents()\n ll_offset = world_extents.lower_left - extents.lower_left\n ur_offset = world_extents.upper_right - extents.upper_right\n\n pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\n return (abs(ll_offset.x) <= pixel_sizes.x and\n abs(ll_offset.y) <= pixel_sizes.y and\n abs(ur_offset.x) <= pixel_sizes.x and\n abs(ur_offset.y) <= pixel_sizes.y)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_full_dimensional(self):\n\n return self.affine_dimension() == self.space_dimension()","def is_dataset(self):\n return self._dataset is not None","def ejscreen_areas_of_concern_data_exists(cls):\n return cls.EJSCREEN_AREAS_OF_CONCERN_SOURCE.is_file()","def is_mainland(self):\n return self._is_mainland","def all(self):\n for v in self.sects.values():\n if not np.all(v):\n return False\n if self.is_full():\n return True\n else:\n return np.all(self.defval)","def is_full(self) -> bool:\n return self._array[0].all()","def contains_origin(self):\n return self.contains(self.ambient_space().zero())","def geospatial(self):\n return bool(\n self.dataset.GetProjection() or\n (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()) or\n self.dataset.GetGeoTransform(can_return_null=True) or\n hasattr(self, '_netcdf'))","def has_data(self):\n return ([0] != self.__contexts) and ([0] != self.__weights)","def is_full(self):\n elements_in_sects = sum(\n map(opr.attrgetter(\"size\"), self.sects.values())\n )\n elements_in_total = fct.reduce(\n opr.mul, type(self).flatten_shape(self.shape), 1\n )\n res = elements_in_sects >= elements_in_total\n return res","def test_contains(self):\n dim = Fidelity(\"epoch\", 1, 10)\n\n assert 0 not in dim\n assert 1 in dim\n assert 5 in dim\n assert 10 in dim\n assert 20 not in dim","def full(self):\n for x in range(0,3):\n for y in range(0,3):\n if self[x,y] is None:\n return False\n return True","def is_subset_of(self, uspec):\n \n if self.is_power_onoff() or uspec.is_power_onoff():\n return False\n \n if (uspec.is_bias() or not uspec.is_calib()) and self['speed'] != uspec['speed']:\n return False\n\n if int(self['x_bin']) % int(uspec['x_bin']) != 0 or int(self['y_bin']) % int(uspec['y_bin']) != 0:\n return False\n\n if self.number_windows() > 0:\n\n if not uspec.contains_window(self['x1_start'], self['y1_start'], self['x1_size'], self['y1_size'], self['x_bin'], self['y_bin']):\n return False\n\n if self.number_windows() > 1:\n\n if not uspec.contains_window(self['x2_start'], self['y2_start'], self['x2_size'], self['y2_size'], self['x_bin'], self['y_bin']):\n return False\n\n return True","def any(self):\n for v in self.sects.values():\n if np.any(v):\n return True\n if self.is_full():\n return False\n else:\n return np.any(self.defval)","def is_land(self, only_land: bool = False) -> bool:\n generator = (\n any(_type.name == \"Land\" for _type in face.types.all())\n for face in self.faces.all()\n )\n if only_land:\n return all(generator)\n return any(generator)","def _is_legal(self, h5_main, variables=None):\n if variables is None:\n variables = ['DC_Offset']\n\n file_data_type = get_attr(h5_main.file, 'data_type')\n meas_grp_name = h5_main.name.split('/')\n h5_meas_grp = h5_main.file[meas_grp_name[1]]\n meas_data_type = get_attr(h5_meas_grp, 'data_type')\n\n if h5_main.dtype != sho32:\n warn('Provided dataset is not a SHO results dataset.')\n return False\n\n # This check is clunky but should account for case differences. If Python2 support is dropped, simplify with\n # single check using casefold.\n if not (meas_data_type.lower != file_data_type.lower or meas_data_type.upper != file_data_type.upper):\n warn('Mismatch between file and Measurement group data types for the chosen dataset.')\n print('File data type is {}. The data type for Measurement group {} is {}'.format(file_data_type,\n h5_meas_grp.name,\n meas_data_type))\n return False\n\n if file_data_type == 'BEPSData':\n if get_attr(h5_meas_grp, 'VS_mode') not in ['DC modulation mode', 'current mode']:\n warn('Provided dataset is not a DC modulation or current mode BEPS dataset')\n return False\n elif get_attr(h5_meas_grp, 'VS_cycle_fraction') != 'full':\n warn('Provided dataset does not have full cycles')\n return False\n\n elif file_data_type == 'cKPFMData':\n if get_attr(h5_meas_grp, 'VS_mode') != 'cKPFM':\n warn('Provided dataset has an unsupported VS_mode.')\n return False\n\n return super(BELoopFitter, self)._is_legal(h5_main, variables)","def is_consistent_dataset(data: dict, parameter_box: np.ndarray = None) -> bool:\n train_set = copy.deepcopy(data)\n y, phi = train_set[\"outputs\"].pop(-1), train_set[\"features\"].pop(-1)\n y, phi = np.array(y)[..., np.newaxis], np.array(phi)[..., np.newaxis]\n if train_set[\"outputs\"] and train_set[\"features\"]:\n theta, _, gramian, beta = confidence_polytope(train_set, parameter_box=parameter_box)\n return is_valid_observation(y, phi, theta, gramian, beta)\n else:\n return True","def is_full(self) -> bool:","def has_dimension(self, dim):\n\n return self.units.dimensions == dim","def isEmptyLandmarkset(self):\n return self.subsetpointcloud is None","def isComplete(self):\n assert len(self._x) > 0\n assert len(self._y) > 0\n assert 2 == len(self._data_array.shape)\n assert self.wkt is not None\n assert self.wkt != ''\n\n return True","def maybe_distal(self):\n return bool(set(self.locations) & set(StandardTerminology.DISTAL_LOCATIONS))","def test_outside_grid(dataset):\n\n tf = Delft3D_Mudflats(dataset, dry_depth=-10000) # make sure nothing is dry\n\n points = ((3.5, 54.0), # off\n (7.5, 53.4), # off\n (6.0, 52.0), # off\n (5.3, 53.3), # on\n (5.2, 53.25), # on\n )\n\n time = datetime(2009, 1, 15, 0)\n\n result = tf.is_dry(points, time)\n\n print \"results\", result\n\n assert list(result) == [True, True, True, False, False]","def get_allsky(self):\n band = self.get_band()\n septon = self.is_septon()\n if band == '10_90' or band == '30_90' or septon:\n allsky = True\n else:\n allsky = False\n return allsky","def check_empty(self, coord):\n x, y, z = coord\n if self.perlin_3d(x, y, z) <= 0:\n return True\n else:\n return False","def test_contains_bounds(self):\n dim = Dimension(\"yolo\", \"uniform\", -3, 4)\n with pytest.raises(NotImplementedError):\n assert -3 in dim","def is_full(self):\n return len(self.walls) == 4","def check_visibility(self):\r\n\r\n for gs in self.ground_stations:\r\n if self.visible ^ (elevation_dot_product(self.r_ecef,self.ground_stations[gs][1],self.earth) > 0.0):\r\n self.visible ^= 1\r\n self.gs_id = self.ground_stations[gs][0]\r\n return True","def is_distal(self):\n return bool(set(self.locations) and set(self.locations) <= set(StandardTerminology.DISTAL_LOCATIONS)) \\\n or bool(self.depth and 16 < self.depth < 82)","def __contains__(self, point):\n for component, dim in zip(point, self.dimensions):\n if component not in dim:\n return False\n return True","def contains(self, x):\n # need more to assure its a real SSP - ie on right torus\n return (len(x) == self._shape[0])","def is_fitted(self):\n return self.__fdata is not None","def is_infrastructure (self):\n return sum([1 for i in self.infras]) != 0","def _in_bounds(self, x, y):\r\n return 0 <= x < 8 and 0 <= y < 8","def has_osm_data(self):\n return settings.MSPRAY_OSM_PRESENCE_FIELD in self.data","def exists_dataset(self, dataset):\n assert dataset, \"Must input a valid dataset name.\"\n return any(self.get_by_dataset(dataset))","def is_full(self):\n return len(self._data) == 1","def is_full(self):\n return False","def _has_coordinates_and_gradient(self) -> bool:\n return self._coords is not None and self._coords.g is not None","def is_artificial(self):\n\t\treturn 0","def is_on_ground(self):\n return bool(self.ground_sprites())","def detect_in_bounds(self):\n creature_x, creature_y = self.creature.current_location\n if creature_x < 0 or creature_x >= self.world_width\\\n or creature_y < 0 or creature_y >= self.world_height:\n print('The creature is out of bounds!')\n return False\n return True","def healthy_test(obj: np.ndarray) -> bool:\n nb_rows, nb_cols = obj.shape\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))","def check_tile_covers_land(self, tilename=None):\n land_tiles = self.list_tiles_covering_land()\n if self.check_tilename(tilename):\n tilename = self.tilename2short(tilename)\n return tilename in land_tiles","def _is_vessel_full(self):\n return np.size(np.where(self.end_of_lanes + (np.ones(self.lanes) * self.minimal_package) <= self.rows)) == 0","def can_attack_world(self, world: World) -> bool:\n forces = self.context.calculate_forces(world)\n return (\n forces.space_forces <= self.max_space_force\n and forces.ground_forces <= self.max_ground_force\n )","def in_bounds(self, x, y):\n return x >= 0 and x < 8 and y >= 0 and y < 8","def exposed(self, position):\r\n x, y, z = position\r\n for dx, dy, dz in FACES:\r\n if (x + dx, y + dy, z + dz) not in self.world:\r\n return True\r\n return False","def _loaded_data(self):\n try:\n dsize = [int(d) for d\n in self.run('fits size', via='get').split()]\n except (ValueError, TypeError, AttributeError) as err:\n log.debug(f' FITS size error: {err}')\n return False\n else:\n if 0 in dsize:\n return False\n else:\n return True","def is_alld(self):\n g = self.get_gene().get_seq()\n for i in range(1, len(g)):\n if g[i] is 'c':\n return False\n return True","def contains(self, coord):\n # print(coord, self.position, self.size)\n return (0 <= coord[0] - self.position[0] < self.size[0] and\n 0 <= coord[1] - self.position[1] < self.size[1])","def wid_in(self, wid):\n \n for row in self.tiles:\n if wid in row:\n return True\n return False","def is_full(self):\n return self.name and self.variables and self.assumptions and self.guarantees","def in_water(latitude: float, longitude: float) -> bool:\n path = os.path.abspath('water_polygons.shp')\n with fiona.open(path) as fiona_collection:\n box_detail = 0.0001\n point = Point(longitude, latitude)\n # here we filter to only scan results near the point in question.\n for record in fiona_collection.filter(bbox=(\n longitude+box_detail, latitude+box_detail,\n longitude-box_detail, latitude-box_detail)):\n if record['geometry']:\n shape = asShape(record['geometry'])\n if shape.contains(point):\n return True\n return False","def exists(dtype, name, rootdir=None):\n return FreezableAPI.to_slug(dtype,name) in FreezableAPI.datasets(rootdir=rootdir)","def has_dominance(self):\n trait = self.traitDao.get_dominance(self.name)\n if trait is None:\n return False\n else:\n return True","def has_data(self, fit_id, species_id, start=None, stop=None):\n if not (fit_id in self.raw_results and species_id in\\\n self.raw_results[fit_id]):\n return False\n if all([isinstance(x, datetime) for x in [start, stop]]):\n ts = self.raw_results[fit_id][\"start\"]\n if not any([start < x < stop for x in ts]):\n return False\n return True","def is_dataset_in_t_range(dataset, t_min, t_max):\n for obj in dataset.object_names:\n obj_data = dataset.data[obj]\n t_min_obj = np.min(obj_data['mjd'])\n t_max_obj = np.max(obj_data['mjd'])\n if (t_min_obj < t_min) or (t_max_obj > t_max):\n return False\n return True","def is_island(self):\n return bool(not self.children.exists() and not self.parents.exists())","def has_2D(self):\n\t\tif self.have_fastas is False:\n\t\t\tself._extract_fastas_from_fast5()\n\t\t\tself.have_fastas = True\n\n\t\tif self.fastas.get('twodirections') is not None:\n\t\t\treturn True\n\t\treturn False","def verify(self):\n D,S,I,C = False,False,False,False\n if self.geoData and os.path.exists(self.geoData):\n D = True\n if self.scales:\n S = True\n if type(self.idVariable) == int:\n I = True\n if self.cacheFile:\n C = True\n if D and S and I and C:\n return True\n return False","def covers(self, right: GeoSpatialValue) -> ir.BooleanValue:\n return ops.GeoCovers(self, right).to_expr()","def HasData(self, search_all_index = False):\n\t\tif not search_all_index:\n\t\t\treturn ExistsFile(self.__datafilename(self.__pindex))\n\t\tfor pindex in range(0, len(GV.provinces)):\n\t\t\tif ExistsFile(self.__datafilename(pindex)):\n\t\t\t\treturn True\n\t\treturn False","def is_full(self) -> bool:\n pass","def has_fullhouse(self):\n if self.has_pair() & self.has_three_of_a_kind():\n self.rank_per_hand['5'] = \"full house\"\n return True\n return False","def is_valid(self, dataset):\n pass","def contains(self, point):\n return 0 <= point.x <= 1 \\\n and 0 <= point.y <= 1 \\\n and 0 <= point.z <= 1","def has_guardian(self):\n return self.tiles.count(3) > 0","def __contains__(self, item):\n return item in self.default_dataset","def in_bounds(self, location: tuple) -> bool:\n return 0 <= min(location) and max(location) <= 7","def can_exist_outside_of_game(self):\n return True","def can_exist_outside_of_game(self):\n return True","def checkBottom(self):\n exposed = True\n for sprite in self.overlapping_sprites:\n if sprite not in self.game.neutrinos:\n a = abs(self.bottom - sprite.top)\n b = abs(self.top - sprite.bottom)\n c = abs(self.left - sprite.right)\n d = abs(self.right - sprite.left)\n if a < b and a < c and a < d:\n exposed = False\n break\n return exposed","def is_allc(self):\n g = self.get_gene().get_seq()\n for i in range(1, len(g)):\n if g[i] is 'd':\n return False\n return True","def in_geo_limits(args: argparse.Namespace, track_data: dict) -> bool:\n return (track_data['boundaries']['north'] <= args.north_lim and\n track_data['boundaries']['south'] >= args.south_lim and\n track_data['boundaries']['east'] <= args.east_lim and\n track_data['boundaries']['west'] >= args.west_lim)","def has_datapoint_with_all_dims(fake_ingest, dims):\n for datapoint in fake_ingest.datapoints:\n if has_all_dims(datapoint, dims):\n return True\n return False","def is_full(self):","def is_loaded(self):\n return self.known_stations != {}","def any(self):\n boolean = True\n if type(self.idxs) == np.ndarray:\n boolean = all(self.idxs.shape)\n elif type(self.idxs) == list:\n sh = np.array(self.idxs).shape\n if len(sh) >= 2:\n boolean = np.all(sh)\n return boolean","def is_worthless(self):\n self.normalize()\n return self.all_details['normalized'] in WORTHLESS_UA_TYPES","def can_contain(self):\n return False","def test_contains_extra_bounds(self):\n dim = Real(\"yolo\", \"norm\", 0, 3, low=-3, high=+3)\n assert dists.uniform.rvs(-3, 3) in dim\n assert -4 not in dim\n assert +4 not in dim\n assert (1, 2) not in dim","def hasAtlasPos(self):\n return self.doesHaveAtlasPos","def contains(self, point):\n if in_range(point[0], self.xrange) and in_range(point[0], self.yrange) and in_range(point[0], self.zrange):\n return True\n return False","def IsBound(self) -> bool:","def __cell_is_in_map(self, x, y) -> bool:\n return x >= 0 and y >= 0 and x < self.occupancy_map.info.width and y < self.occupancy_map.info.height","def is_won(self):\n for tile in self:\n if not tile.is_mine and tile.visibility != 1:\n return False\n return True","def ValidClusterRanges(self):\n for cluster_range in self.cluster_ranges:\n the_range = cluster_range.split(\"-\")\n print(f\"Checking that range {the_range} falls within our data area\")\n try:\n if int(the_range[0]) < self.low_data_cluster or int(the_range[1]) > self.high_data_cluster:\n print(f\"False. {the_range[0]} or {the_range[1]} is outside of our data area\")\n return False\n except TypeError as t_err:\n print(f\"Error. Range does not appear to be an int\")\n return False\n return True","def is_sealed(self):\n return self.walls == Direction.All","def has_geom(self):\n return bool(self.give_geom())","def is_water(self):\n return False","def is_water(self):\n return False","def can_attack_world(self, world: World) -> bool:\n forces = self.context.calculate_forces(world)\n return (\n forces.space_forces <= self.max_space_force\n and (forces.space_forces - forces.missile_forces)\n < self.max_nonmissile_forces\n )","def satifiesWinConditions(self, coordinates):\n if self.treasureCaptured and (self.x, self.y) in coordinates:\n return True\n else:\n return False","def _inside(self, x, y):\n wx, wy, w, h = self._raw_graph_window_dim()\n if wx <= x < wx + w and wy <= y < wy + h:\n return True\n return False","def isValidForSimulation(self):\n for position, myQuad in self.myDesign.quads.iteritems():\n if myQuad.components != {}:\n return 1\n return 0","def is_full(self):\n\n current_board = self.current_board\n remaining_rows = 0\n\n for row in current_board:\n if \"-\" in set(row):\n remaining_rows += 1\n\n if remaining_rows == 0:\n return True\n else:\n return False","def isWin(self):\n\n return self.tiles == self.winCdt","def _is_small_net(self):\n dataset_name = self.params.dataset_name.lower()\n key_words = ['cifar', 'tinyimages', 'svhn']\n return any([k in dataset_name for k in key_words])","def isPlayed(self):\n return bool(self.viewedLeafCount == self.leafCount)"],"string":"[\n \"def is_full_dimensional(self):\\n\\n return self.affine_dimension() == self.space_dimension()\",\n \"def is_dataset(self):\\n return self._dataset is not None\",\n \"def ejscreen_areas_of_concern_data_exists(cls):\\n return cls.EJSCREEN_AREAS_OF_CONCERN_SOURCE.is_file()\",\n \"def is_mainland(self):\\n return self._is_mainland\",\n \"def all(self):\\n for v in self.sects.values():\\n if not np.all(v):\\n return False\\n if self.is_full():\\n return True\\n else:\\n return np.all(self.defval)\",\n \"def is_full(self) -> bool:\\n return self._array[0].all()\",\n \"def contains_origin(self):\\n return self.contains(self.ambient_space().zero())\",\n \"def geospatial(self):\\n return bool(\\n self.dataset.GetProjection() or\\n (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()) or\\n self.dataset.GetGeoTransform(can_return_null=True) or\\n hasattr(self, '_netcdf'))\",\n \"def has_data(self):\\n return ([0] != self.__contexts) and ([0] != self.__weights)\",\n \"def is_full(self):\\n elements_in_sects = sum(\\n map(opr.attrgetter(\\\"size\\\"), self.sects.values())\\n )\\n elements_in_total = fct.reduce(\\n opr.mul, type(self).flatten_shape(self.shape), 1\\n )\\n res = elements_in_sects >= elements_in_total\\n return res\",\n \"def test_contains(self):\\n dim = Fidelity(\\\"epoch\\\", 1, 10)\\n\\n assert 0 not in dim\\n assert 1 in dim\\n assert 5 in dim\\n assert 10 in dim\\n assert 20 not in dim\",\n \"def full(self):\\n for x in range(0,3):\\n for y in range(0,3):\\n if self[x,y] is None:\\n return False\\n return True\",\n \"def is_subset_of(self, uspec):\\n \\n if self.is_power_onoff() or uspec.is_power_onoff():\\n return False\\n \\n if (uspec.is_bias() or not uspec.is_calib()) and self['speed'] != uspec['speed']:\\n return False\\n\\n if int(self['x_bin']) % int(uspec['x_bin']) != 0 or int(self['y_bin']) % int(uspec['y_bin']) != 0:\\n return False\\n\\n if self.number_windows() > 0:\\n\\n if not uspec.contains_window(self['x1_start'], self['y1_start'], self['x1_size'], self['y1_size'], self['x_bin'], self['y_bin']):\\n return False\\n\\n if self.number_windows() > 1:\\n\\n if not uspec.contains_window(self['x2_start'], self['y2_start'], self['x2_size'], self['y2_size'], self['x_bin'], self['y_bin']):\\n return False\\n\\n return True\",\n \"def any(self):\\n for v in self.sects.values():\\n if np.any(v):\\n return True\\n if self.is_full():\\n return False\\n else:\\n return np.any(self.defval)\",\n \"def is_land(self, only_land: bool = False) -> bool:\\n generator = (\\n any(_type.name == \\\"Land\\\" for _type in face.types.all())\\n for face in self.faces.all()\\n )\\n if only_land:\\n return all(generator)\\n return any(generator)\",\n \"def _is_legal(self, h5_main, variables=None):\\n if variables is None:\\n variables = ['DC_Offset']\\n\\n file_data_type = get_attr(h5_main.file, 'data_type')\\n meas_grp_name = h5_main.name.split('/')\\n h5_meas_grp = h5_main.file[meas_grp_name[1]]\\n meas_data_type = get_attr(h5_meas_grp, 'data_type')\\n\\n if h5_main.dtype != sho32:\\n warn('Provided dataset is not a SHO results dataset.')\\n return False\\n\\n # This check is clunky but should account for case differences. If Python2 support is dropped, simplify with\\n # single check using casefold.\\n if not (meas_data_type.lower != file_data_type.lower or meas_data_type.upper != file_data_type.upper):\\n warn('Mismatch between file and Measurement group data types for the chosen dataset.')\\n print('File data type is {}. The data type for Measurement group {} is {}'.format(file_data_type,\\n h5_meas_grp.name,\\n meas_data_type))\\n return False\\n\\n if file_data_type == 'BEPSData':\\n if get_attr(h5_meas_grp, 'VS_mode') not in ['DC modulation mode', 'current mode']:\\n warn('Provided dataset is not a DC modulation or current mode BEPS dataset')\\n return False\\n elif get_attr(h5_meas_grp, 'VS_cycle_fraction') != 'full':\\n warn('Provided dataset does not have full cycles')\\n return False\\n\\n elif file_data_type == 'cKPFMData':\\n if get_attr(h5_meas_grp, 'VS_mode') != 'cKPFM':\\n warn('Provided dataset has an unsupported VS_mode.')\\n return False\\n\\n return super(BELoopFitter, self)._is_legal(h5_main, variables)\",\n \"def is_consistent_dataset(data: dict, parameter_box: np.ndarray = None) -> bool:\\n train_set = copy.deepcopy(data)\\n y, phi = train_set[\\\"outputs\\\"].pop(-1), train_set[\\\"features\\\"].pop(-1)\\n y, phi = np.array(y)[..., np.newaxis], np.array(phi)[..., np.newaxis]\\n if train_set[\\\"outputs\\\"] and train_set[\\\"features\\\"]:\\n theta, _, gramian, beta = confidence_polytope(train_set, parameter_box=parameter_box)\\n return is_valid_observation(y, phi, theta, gramian, beta)\\n else:\\n return True\",\n \"def is_full(self) -> bool:\",\n \"def has_dimension(self, dim):\\n\\n return self.units.dimensions == dim\",\n \"def isEmptyLandmarkset(self):\\n return self.subsetpointcloud is None\",\n \"def isComplete(self):\\n assert len(self._x) > 0\\n assert len(self._y) > 0\\n assert 2 == len(self._data_array.shape)\\n assert self.wkt is not None\\n assert self.wkt != ''\\n\\n return True\",\n \"def maybe_distal(self):\\n return bool(set(self.locations) & set(StandardTerminology.DISTAL_LOCATIONS))\",\n \"def test_outside_grid(dataset):\\n\\n tf = Delft3D_Mudflats(dataset, dry_depth=-10000) # make sure nothing is dry\\n\\n points = ((3.5, 54.0), # off\\n (7.5, 53.4), # off\\n (6.0, 52.0), # off\\n (5.3, 53.3), # on\\n (5.2, 53.25), # on\\n )\\n\\n time = datetime(2009, 1, 15, 0)\\n\\n result = tf.is_dry(points, time)\\n\\n print \\\"results\\\", result\\n\\n assert list(result) == [True, True, True, False, False]\",\n \"def get_allsky(self):\\n band = self.get_band()\\n septon = self.is_septon()\\n if band == '10_90' or band == '30_90' or septon:\\n allsky = True\\n else:\\n allsky = False\\n return allsky\",\n \"def check_empty(self, coord):\\n x, y, z = coord\\n if self.perlin_3d(x, y, z) <= 0:\\n return True\\n else:\\n return False\",\n \"def test_contains_bounds(self):\\n dim = Dimension(\\\"yolo\\\", \\\"uniform\\\", -3, 4)\\n with pytest.raises(NotImplementedError):\\n assert -3 in dim\",\n \"def is_full(self):\\n return len(self.walls) == 4\",\n \"def check_visibility(self):\\r\\n\\r\\n for gs in self.ground_stations:\\r\\n if self.visible ^ (elevation_dot_product(self.r_ecef,self.ground_stations[gs][1],self.earth) > 0.0):\\r\\n self.visible ^= 1\\r\\n self.gs_id = self.ground_stations[gs][0]\\r\\n return True\",\n \"def is_distal(self):\\n return bool(set(self.locations) and set(self.locations) <= set(StandardTerminology.DISTAL_LOCATIONS)) \\\\\\n or bool(self.depth and 16 < self.depth < 82)\",\n \"def __contains__(self, point):\\n for component, dim in zip(point, self.dimensions):\\n if component not in dim:\\n return False\\n return True\",\n \"def contains(self, x):\\n # need more to assure its a real SSP - ie on right torus\\n return (len(x) == self._shape[0])\",\n \"def is_fitted(self):\\n return self.__fdata is not None\",\n \"def is_infrastructure (self):\\n return sum([1 for i in self.infras]) != 0\",\n \"def _in_bounds(self, x, y):\\r\\n return 0 <= x < 8 and 0 <= y < 8\",\n \"def has_osm_data(self):\\n return settings.MSPRAY_OSM_PRESENCE_FIELD in self.data\",\n \"def exists_dataset(self, dataset):\\n assert dataset, \\\"Must input a valid dataset name.\\\"\\n return any(self.get_by_dataset(dataset))\",\n \"def is_full(self):\\n return len(self._data) == 1\",\n \"def is_full(self):\\n return False\",\n \"def _has_coordinates_and_gradient(self) -> bool:\\n return self._coords is not None and self._coords.g is not None\",\n \"def is_artificial(self):\\n\\t\\treturn 0\",\n \"def is_on_ground(self):\\n return bool(self.ground_sprites())\",\n \"def detect_in_bounds(self):\\n creature_x, creature_y = self.creature.current_location\\n if creature_x < 0 or creature_x >= self.world_width\\\\\\n or creature_y < 0 or creature_y >= self.world_height:\\n print('The creature is out of bounds!')\\n return False\\n return True\",\n \"def healthy_test(obj: np.ndarray) -> bool:\\n nb_rows, nb_cols = obj.shape\\n return nb_rows == nb_cols > 1 and np.array_equal(obj, colony(nb_rows))\",\n \"def check_tile_covers_land(self, tilename=None):\\n land_tiles = self.list_tiles_covering_land()\\n if self.check_tilename(tilename):\\n tilename = self.tilename2short(tilename)\\n return tilename in land_tiles\",\n \"def _is_vessel_full(self):\\n return np.size(np.where(self.end_of_lanes + (np.ones(self.lanes) * self.minimal_package) <= self.rows)) == 0\",\n \"def can_attack_world(self, world: World) -> bool:\\n forces = self.context.calculate_forces(world)\\n return (\\n forces.space_forces <= self.max_space_force\\n and forces.ground_forces <= self.max_ground_force\\n )\",\n \"def in_bounds(self, x, y):\\n return x >= 0 and x < 8 and y >= 0 and y < 8\",\n \"def exposed(self, position):\\r\\n x, y, z = position\\r\\n for dx, dy, dz in FACES:\\r\\n if (x + dx, y + dy, z + dz) not in self.world:\\r\\n return True\\r\\n return False\",\n \"def _loaded_data(self):\\n try:\\n dsize = [int(d) for d\\n in self.run('fits size', via='get').split()]\\n except (ValueError, TypeError, AttributeError) as err:\\n log.debug(f' FITS size error: {err}')\\n return False\\n else:\\n if 0 in dsize:\\n return False\\n else:\\n return True\",\n \"def is_alld(self):\\n g = self.get_gene().get_seq()\\n for i in range(1, len(g)):\\n if g[i] is 'c':\\n return False\\n return True\",\n \"def contains(self, coord):\\n # print(coord, self.position, self.size)\\n return (0 <= coord[0] - self.position[0] < self.size[0] and\\n 0 <= coord[1] - self.position[1] < self.size[1])\",\n \"def wid_in(self, wid):\\n \\n for row in self.tiles:\\n if wid in row:\\n return True\\n return False\",\n \"def is_full(self):\\n return self.name and self.variables and self.assumptions and self.guarantees\",\n \"def in_water(latitude: float, longitude: float) -> bool:\\n path = os.path.abspath('water_polygons.shp')\\n with fiona.open(path) as fiona_collection:\\n box_detail = 0.0001\\n point = Point(longitude, latitude)\\n # here we filter to only scan results near the point in question.\\n for record in fiona_collection.filter(bbox=(\\n longitude+box_detail, latitude+box_detail,\\n longitude-box_detail, latitude-box_detail)):\\n if record['geometry']:\\n shape = asShape(record['geometry'])\\n if shape.contains(point):\\n return True\\n return False\",\n \"def exists(dtype, name, rootdir=None):\\n return FreezableAPI.to_slug(dtype,name) in FreezableAPI.datasets(rootdir=rootdir)\",\n \"def has_dominance(self):\\n trait = self.traitDao.get_dominance(self.name)\\n if trait is None:\\n return False\\n else:\\n return True\",\n \"def has_data(self, fit_id, species_id, start=None, stop=None):\\n if not (fit_id in self.raw_results and species_id in\\\\\\n self.raw_results[fit_id]):\\n return False\\n if all([isinstance(x, datetime) for x in [start, stop]]):\\n ts = self.raw_results[fit_id][\\\"start\\\"]\\n if not any([start < x < stop for x in ts]):\\n return False\\n return True\",\n \"def is_dataset_in_t_range(dataset, t_min, t_max):\\n for obj in dataset.object_names:\\n obj_data = dataset.data[obj]\\n t_min_obj = np.min(obj_data['mjd'])\\n t_max_obj = np.max(obj_data['mjd'])\\n if (t_min_obj < t_min) or (t_max_obj > t_max):\\n return False\\n return True\",\n \"def is_island(self):\\n return bool(not self.children.exists() and not self.parents.exists())\",\n \"def has_2D(self):\\n\\t\\tif self.have_fastas is False:\\n\\t\\t\\tself._extract_fastas_from_fast5()\\n\\t\\t\\tself.have_fastas = True\\n\\n\\t\\tif self.fastas.get('twodirections') is not None:\\n\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def verify(self):\\n D,S,I,C = False,False,False,False\\n if self.geoData and os.path.exists(self.geoData):\\n D = True\\n if self.scales:\\n S = True\\n if type(self.idVariable) == int:\\n I = True\\n if self.cacheFile:\\n C = True\\n if D and S and I and C:\\n return True\\n return False\",\n \"def covers(self, right: GeoSpatialValue) -> ir.BooleanValue:\\n return ops.GeoCovers(self, right).to_expr()\",\n \"def HasData(self, search_all_index = False):\\n\\t\\tif not search_all_index:\\n\\t\\t\\treturn ExistsFile(self.__datafilename(self.__pindex))\\n\\t\\tfor pindex in range(0, len(GV.provinces)):\\n\\t\\t\\tif ExistsFile(self.__datafilename(pindex)):\\n\\t\\t\\t\\treturn True\\n\\t\\treturn False\",\n \"def is_full(self) -> bool:\\n pass\",\n \"def has_fullhouse(self):\\n if self.has_pair() & self.has_three_of_a_kind():\\n self.rank_per_hand['5'] = \\\"full house\\\"\\n return True\\n return False\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def contains(self, point):\\n return 0 <= point.x <= 1 \\\\\\n and 0 <= point.y <= 1 \\\\\\n and 0 <= point.z <= 1\",\n \"def has_guardian(self):\\n return self.tiles.count(3) > 0\",\n \"def __contains__(self, item):\\n return item in self.default_dataset\",\n \"def in_bounds(self, location: tuple) -> bool:\\n return 0 <= min(location) and max(location) <= 7\",\n \"def can_exist_outside_of_game(self):\\n return True\",\n \"def can_exist_outside_of_game(self):\\n return True\",\n \"def checkBottom(self):\\n exposed = True\\n for sprite in self.overlapping_sprites:\\n if sprite not in self.game.neutrinos:\\n a = abs(self.bottom - sprite.top)\\n b = abs(self.top - sprite.bottom)\\n c = abs(self.left - sprite.right)\\n d = abs(self.right - sprite.left)\\n if a < b and a < c and a < d:\\n exposed = False\\n break\\n return exposed\",\n \"def is_allc(self):\\n g = self.get_gene().get_seq()\\n for i in range(1, len(g)):\\n if g[i] is 'd':\\n return False\\n return True\",\n \"def in_geo_limits(args: argparse.Namespace, track_data: dict) -> bool:\\n return (track_data['boundaries']['north'] <= args.north_lim and\\n track_data['boundaries']['south'] >= args.south_lim and\\n track_data['boundaries']['east'] <= args.east_lim and\\n track_data['boundaries']['west'] >= args.west_lim)\",\n \"def has_datapoint_with_all_dims(fake_ingest, dims):\\n for datapoint in fake_ingest.datapoints:\\n if has_all_dims(datapoint, dims):\\n return True\\n return False\",\n \"def is_full(self):\",\n \"def is_loaded(self):\\n return self.known_stations != {}\",\n \"def any(self):\\n boolean = True\\n if type(self.idxs) == np.ndarray:\\n boolean = all(self.idxs.shape)\\n elif type(self.idxs) == list:\\n sh = np.array(self.idxs).shape\\n if len(sh) >= 2:\\n boolean = np.all(sh)\\n return boolean\",\n \"def is_worthless(self):\\n self.normalize()\\n return self.all_details['normalized'] in WORTHLESS_UA_TYPES\",\n \"def can_contain(self):\\n return False\",\n \"def test_contains_extra_bounds(self):\\n dim = Real(\\\"yolo\\\", \\\"norm\\\", 0, 3, low=-3, high=+3)\\n assert dists.uniform.rvs(-3, 3) in dim\\n assert -4 not in dim\\n assert +4 not in dim\\n assert (1, 2) not in dim\",\n \"def hasAtlasPos(self):\\n return self.doesHaveAtlasPos\",\n \"def contains(self, point):\\n if in_range(point[0], self.xrange) and in_range(point[0], self.yrange) and in_range(point[0], self.zrange):\\n return True\\n return False\",\n \"def IsBound(self) -> bool:\",\n \"def __cell_is_in_map(self, x, y) -> bool:\\n return x >= 0 and y >= 0 and x < self.occupancy_map.info.width and y < self.occupancy_map.info.height\",\n \"def is_won(self):\\n for tile in self:\\n if not tile.is_mine and tile.visibility != 1:\\n return False\\n return True\",\n \"def ValidClusterRanges(self):\\n for cluster_range in self.cluster_ranges:\\n the_range = cluster_range.split(\\\"-\\\")\\n print(f\\\"Checking that range {the_range} falls within our data area\\\")\\n try:\\n if int(the_range[0]) < self.low_data_cluster or int(the_range[1]) > self.high_data_cluster:\\n print(f\\\"False. {the_range[0]} or {the_range[1]} is outside of our data area\\\")\\n return False\\n except TypeError as t_err:\\n print(f\\\"Error. Range does not appear to be an int\\\")\\n return False\\n return True\",\n \"def is_sealed(self):\\n return self.walls == Direction.All\",\n \"def has_geom(self):\\n return bool(self.give_geom())\",\n \"def is_water(self):\\n return False\",\n \"def is_water(self):\\n return False\",\n \"def can_attack_world(self, world: World) -> bool:\\n forces = self.context.calculate_forces(world)\\n return (\\n forces.space_forces <= self.max_space_force\\n and (forces.space_forces - forces.missile_forces)\\n < self.max_nonmissile_forces\\n )\",\n \"def satifiesWinConditions(self, coordinates):\\n if self.treasureCaptured and (self.x, self.y) in coordinates:\\n return True\\n else:\\n return False\",\n \"def _inside(self, x, y):\\n wx, wy, w, h = self._raw_graph_window_dim()\\n if wx <= x < wx + w and wy <= y < wy + h:\\n return True\\n return False\",\n \"def isValidForSimulation(self):\\n for position, myQuad in self.myDesign.quads.iteritems():\\n if myQuad.components != {}:\\n return 1\\n return 0\",\n \"def is_full(self):\\n\\n current_board = self.current_board\\n remaining_rows = 0\\n\\n for row in current_board:\\n if \\\"-\\\" in set(row):\\n remaining_rows += 1\\n\\n if remaining_rows == 0:\\n return True\\n else:\\n return False\",\n \"def isWin(self):\\n\\n return self.tiles == self.winCdt\",\n \"def _is_small_net(self):\\n dataset_name = self.params.dataset_name.lower()\\n key_words = ['cifar', 'tinyimages', 'svhn']\\n return any([k in dataset_name for k in key_words])\",\n \"def isPlayed(self):\\n return bool(self.viewedLeafCount == self.leafCount)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6345008","0.63167757","0.61752814","0.60833305","0.60241663","0.6015851","0.5980115","0.59584624","0.5937705","0.5922513","0.5917669","0.59084296","0.5877617","0.5835224","0.5827307","0.5806919","0.5801645","0.57821","0.5778381","0.57413006","0.57294166","0.57176024","0.57168955","0.5715005","0.57148254","0.5713452","0.5700437","0.56934506","0.5690477","0.56834704","0.5671516","0.56495833","0.56487656","0.5633129","0.5620331","0.5585798","0.55845815","0.5579061","0.5575254","0.5565395","0.5564361","0.5554601","0.5553543","0.555065","0.5549018","0.55473197","0.5545934","0.5545503","0.55440986","0.55415726","0.5528029","0.5523839","0.5516372","0.5513708","0.55035424","0.5502852","0.5502389","0.54958874","0.5494785","0.5493976","0.54938775","0.54918957","0.54900086","0.5489074","0.5485017","0.548476","0.5484562","0.54776025","0.5477495","0.5473537","0.5471521","0.5471521","0.5466949","0.5460795","0.54567075","0.5456264","0.5456242","0.54510087","0.5448426","0.5447151","0.5446548","0.5445015","0.5434507","0.54247504","0.54246503","0.54240036","0.54225314","0.5421749","0.5415321","0.54148155","0.54107237","0.54107237","0.5406654","0.54047304","0.5396586","0.53917164","0.5391677","0.53868324","0.5386621","0.5386443"],"string":"[\n \"0.6345008\",\n \"0.63167757\",\n \"0.61752814\",\n \"0.60833305\",\n \"0.60241663\",\n \"0.6015851\",\n \"0.5980115\",\n \"0.59584624\",\n \"0.5937705\",\n \"0.5922513\",\n \"0.5917669\",\n \"0.59084296\",\n \"0.5877617\",\n \"0.5835224\",\n \"0.5827307\",\n \"0.5806919\",\n \"0.5801645\",\n \"0.57821\",\n \"0.5778381\",\n \"0.57413006\",\n \"0.57294166\",\n \"0.57176024\",\n \"0.57168955\",\n \"0.5715005\",\n \"0.57148254\",\n \"0.5713452\",\n \"0.5700437\",\n \"0.56934506\",\n \"0.5690477\",\n \"0.56834704\",\n \"0.5671516\",\n \"0.56495833\",\n \"0.56487656\",\n \"0.5633129\",\n \"0.5620331\",\n \"0.5585798\",\n \"0.55845815\",\n \"0.5579061\",\n \"0.5575254\",\n \"0.5565395\",\n \"0.5564361\",\n \"0.5554601\",\n \"0.5553543\",\n \"0.555065\",\n \"0.5549018\",\n \"0.55473197\",\n \"0.5545934\",\n \"0.5545503\",\n \"0.55440986\",\n \"0.55415726\",\n \"0.5528029\",\n \"0.5523839\",\n \"0.5516372\",\n \"0.5513708\",\n \"0.55035424\",\n \"0.5502852\",\n \"0.5502389\",\n \"0.54958874\",\n \"0.5494785\",\n \"0.5493976\",\n \"0.54938775\",\n \"0.54918957\",\n \"0.54900086\",\n \"0.5489074\",\n \"0.5485017\",\n \"0.548476\",\n \"0.5484562\",\n \"0.54776025\",\n \"0.5477495\",\n \"0.5473537\",\n \"0.5471521\",\n \"0.5471521\",\n \"0.5466949\",\n \"0.5460795\",\n \"0.54567075\",\n \"0.5456264\",\n \"0.5456242\",\n \"0.54510087\",\n \"0.5448426\",\n \"0.5447151\",\n \"0.5446548\",\n \"0.5445015\",\n \"0.5434507\",\n \"0.54247504\",\n \"0.54246503\",\n \"0.54240036\",\n \"0.54225314\",\n \"0.5421749\",\n \"0.5415321\",\n \"0.54148155\",\n \"0.54107237\",\n \"0.54107237\",\n \"0.5406654\",\n \"0.54047304\",\n \"0.5396586\",\n \"0.53917164\",\n \"0.5391677\",\n \"0.53868324\",\n \"0.5386621\",\n \"0.5386443\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68909526"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95694,"cells":{"query":{"kind":"string","value":"of the source data; this usually means upsampling the data, but if the pixel dimensions are slightly smaller than a given resolution, and equal within error tolerance, that resolution will get chosen as the native one."},"document":{"kind":"string","value":"def GetNativeResolution(self, transform=None, maximum=None):\n # Get the source projection's units for a 1x1 pixel, assuming square\n # pixels.\n width, height = self.GetPixelDimensions()\n src_pixel_size = min(abs(width), abs(height))\n\n if transform is None:\n dst_pixel_size = src_pixel_size\n dst_ref = self.GetSpatialReference()\n else:\n # Transform these dimensions into the destination projection\n dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]\n dst_pixel_size = abs(dst_pixel_size)\n dst_ref = transform.dst_ref\n\n # We allow some floating point error between src_pixel_size and\n # dst_pixel_size based on the major circumference so that the error is\n # in the destination units\n error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128\n\n # Find the resolution where the pixels are smaller than dst_pixel_size.\n for resolution in count():\n if maximum is not None and resolution >= maximum:\n return resolution\n\n res_pixel_size = max(\n *dst_ref.GetPixelDimensions(resolution=resolution)\n )\n if (res_pixel_size - dst_pixel_size) <= error:\n return resolution\n\n # Halve error each resolution\n error /= 2"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def data_resampler (self, data):\r\n data = librosa.resample(data, orig_sr = original_sampling, target_sr = self.target_sampling)\r\n \r\n return data","def rasterio_resample(dataset: Dataset, target_resolution: float, new_nodata: object = 'auto',\n resampling_method: Optional[rio_warp.Resampling] = rio_warp.Resampling.nearest,\n output_datatype: Optional[np.dtype] = np.float32) -> Dataset:\n\n # Detect if resolution is not square\n if dataset.meta[\"resolution\"][0] != dataset.meta[\"resolution\"][1]:\n raise RuntimeError(\"Tried to resample a dataset that doesn't have a square resolution!\")\n\n # Calculate scale factor\n scaling = int(dataset.meta[\"resolution\"][0]) / float(target_resolution)\n\n # Copy data from source Dataset\n source_crs = deepcopy(dataset.profile[\"crs\"])\n source_transform = deepcopy(dataset.profile[\"transform\"])\n source_nodata = deepcopy(dataset.nodata)\n\n # Calculate profile/transform and transfer elements\n new_transform = Affine(source_transform.a / scaling, source_transform.b, source_transform.c, source_transform.d,\n source_transform.e / scaling, source_transform.f)\n new_height = int(dataset.profile[\"height\"] * scaling)\n new_width = int(dataset.profile[\"width\"] * scaling)\n new_resolution = (target_resolution, target_resolution)\n new_nodata = source_nodata if new_nodata == \"auto\" else new_nodata\n new_crs = source_crs # [TODO] Add ability to change CRS in rasterio resample function\n\n # Update profile to match output\n dataset.nodata = new_nodata\n dataset.profile.update(\n res=(float(target_resolution), float(target_resolution)),\n transform=new_transform,\n height=new_height,\n width=new_width,\n nodata=new_nodata\n )\n dataset.profile.data[\"pixel_dimensions\"] = (float(target_resolution), float(target_resolution))\n dataset.meta[\"resolution\"] = new_resolution\n\n # Change datatype of all bands\n # [TODO] There may be a way to save memory by freeing old type ndarrays\n for band_id, band_value in dataset.bands.items():\n dataset.bands[band_id] = band_value.astype(output_datatype)\n\n # Run resampling on each band\n for band_key, band_data in dataset.bands.items():\n # Create an array in the correct resampled dimensions\n resampled_array = np.ma.asanyarray(np.empty(shape=(new_width, new_height), dtype=output_datatype))\n\n # Resample/warp\n rio_warp.reproject(band_data, resampled_array, src_transform=source_transform, dst_transform=new_transform,\n width=new_width, height=new_height, src_nodata=source_nodata, dst_nodata=new_nodata,\n src_crs=source_crs, dst_crs=new_crs, resample=resampling_method)\n\n # Set value in dictionary\n dataset.bands[band_key] = resampled_array\n\n return dataset","def _sample_single(self):\n ss = super(SuperResolutions, self)._sample_single()\n image = ss['data']\n # Down sample\n max_down_sample = max(self.data_down_sample, self.label_down_sample)\n if self.is_down_sample:\n images = []\n images.append(image)\n for i in range(max_down_sample):\n image = self.downsample(image)\n images.append(image)\n data = images[self.data_down_sample]\n label = images[self.label_down_sample]\n return {'data': data, 'label': label}","def get_raw_image_sizes() -> set:\n sizes = set()\n data = SUNRGBDTrainDataset(True, augment=False)\n for i in range(len(data)):\n sizes.add(data[i][0].shape)\n return sizes","def resample(self):\n pass","def data_type_ratio(self):\n if self.sample_size:\n return float(self.match_count) / self.sample_size\n return None","def minimum_sampling(self):\n # TODO: Allow `Source` to understand when this returns None?\n return 1.","def data_resolution_and_offset(data):\n res = (data[data.size - 1] - data[0]) / (data.size - 1.0)\n off = data[0] - 0.5 * res\n return numpy.asscalar(res), numpy.asscalar(off)","def _compute_output_resolution(input_spatial_resolution, kernel_size, stride,\n total_padding):\n if (input_spatial_resolution is None) or (kernel_size is None) or (\n stride is None) or (total_padding is None):\n return None\n return int(\n math.ceil((\n input_spatial_resolution + total_padding - kernel_size + 1) / stride))","def upsample(self, method):\n from scipy.signal import resample\n from scipy.ndimage.interpolation import zoom\n #print \"mm: 100 x 100 x 131\"\n #print \"Dims:\", self.D.shape\n fact = np.array(self.info.shape).astype(\"float32\") / np.array(self.info.read_shape).astype(\"float32\")+0.00001 # hrmpf!!\n if method == \"zoom\":\n print \"Resampling...\"\n self.D = zoom(self.D, fact).astype(\"float32\")\n elif method == \"resample\":\n print \"Resampling...\"\n a = self.info.resample_ax\n s = self.info.shape[a]\n self.D = resample(self.D, s, axis=a, window=10).astype(\"float32\")\n elif method == None:\n pass\n else:\n raise NotImplementedError(\"Unknown upsampling method: %s\" % method)\n #print \"Dims:\", self.D.shape\n print \"done.\"","def get_resolution(testdata):\n diffs = np.transpose([testdata[1:, 0], np.diff(testdata[:, 0])])\n resolutions = ud.safedivide(diffs[:, 0], diffs[:, 1])\n # popt, pcov = scipy.optimize.curve_fit(fit_line, diffs[:, 0], resolutions, maxfev=1000000)\n # fitLine = fit_line(diffs[:, 0], *popt)\n # fitMax = np.max(fitLine)\n # fitMin = np.min(fitLine)\n # diffs_new = diffs[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\n # resolutions_new = resolutions[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\n # popt2, pcov2 = scipy.optimize.curve_fit(fit_line, diffs_new[:, 0], resolutions_new, maxfev=1000000)\n # plt.figure()\n # plt.plot(diffs[:,0], resolutions)\n # plt.plot(diffs[:, 0], fit_line(diffs[:, 0], *popt2), 'r-')\n # plt.show()\n # Currently use A * m ^1.5 (0.5?)\n # Maybe use a*M^b\n # return popt2\n return np.median(resolutions)","def quickMinMax(self, targetSize=1e6):\n data = self.image\n if targetSize < 2: # keep at least two pixels\n targetSize = 2\n while True:\n h, w = data.shape[:2]\n if h * w <= targetSize: break\n if h > w:\n data = data[::2, ::] # downsample first axis\n else:\n data = data[::, ::2] # downsample second axis\n return self._xp.nanmin(data), self._xp.nanmax(data)","def test_alt_source(self):\n sp2 = SourceSpectrum(\n GaussianFlux1D, amplitude=self.sp.model.amplitude.value,\n mean=self.sp.model.mean.value, stddev=self.sp.model.stddev.value)\n w = [3900, 4000, 4060] * u.AA\n assert_quantity_allclose(sp2(w), self.sp(w))","def test_resample_methods():\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\n try:\n cvs.raster(src, upsample_method='santaclaus', downsample_method='toothfairy')\n except ValueError:\n pass\n else:\n assert False\n\n try:\n cvs.raster(src, upsample_method='honestlawyer')\n except ValueError:\n pass\n else:\n assert False\n\n try:\n cvs.raster(src, downsample_method='tenantfriendlylease')\n except ValueError:\n pass\n else:\n assert False","def get_resolution(self):\n ret_val = False\n width = 0\n height = 0\n try:\n sink = self.player.get_by_name('sink')\n sample = GstBase.BaseSink.get_last_sample(sink)\n caps = Gst.Sample.get_caps(sample)\n struct = Gst.Caps.get_structure(caps, 0)\n h_result, height = Gst.Structure.get_int(struct, \"height\")\n w_result, width = Gst.Structure.get_int(struct, \"width\")\n if h_result and w_result:\n ret_val = True\n except:\n ret_val = False\n\n return ret_val, width, height","def preprocess_data(tensors_lr):\n n = input_radius\n m = upsample_rate\n\n # flatten DT matrices\n s = tensors_lr.shape\n print(\"Low resolution tensors shape:\", s)\n tensors_lr = np.reshape(tensors_lr, (s[0], s[1], s[2], 9))\n\n # the target resolution after upsampling\n target_resolution = (s[0]*m, s[1]*m, s[2]*m, 3, 3)\n print(\"Target resolution after upsampling:\", target_resolution)\n\n # remove duplicate entries to obtain the 6 unique parameters\n tensors_lr = np.delete(tensors_lr, [3, 6, 7], axis=3)\n\n all_indices = utils.create_triples(s[0], s[1], s[2])\n\n return all_indices, tensors_lr, target_resolution","def _resolution(self):\n _, xres, _, _, _, yres = self.geotransform\n return xres, yres","def minimum_sampling(self):\n # Sampling in arcsec / pixel\n sampling = self.seeing.minimum_sampling()\n try:\n # Try using `intrinsic` as an object\n sampling = min(self.intrinsic.minimum_sampling(), sampling)\n except AttributeError:\n pass\n return sampling","def test_read_unscale():\n with rasterio.open(COG_SCALE) as src_dst:\n arr, mask = reader.tile(src_dst, 218, 99, 8, tilesize=128)\n arrS, maskS = reader.tile(src_dst, 218, 99, 8, tilesize=128, unscale=True)\n\n assert arr.dtype == \"int16\"\n assert arrS.dtype == \"float32\"\n assert not numpy.array_equal(arr, arrS)\n numpy.testing.assert_array_equal(mask, maskS)\n\n meta = reader.metadata(src_dst)\n assert isinstance(meta[\"statistics\"][1][\"min\"], int)\n\n meta = reader.metadata(src_dst, unscale=True)\n assert isinstance(meta[\"statistics\"][1][\"min\"], float)\n\n p = reader.point(src_dst, [310000, 4100000], coord_crs=src_dst.crs)\n assert p == [8917]\n\n p = reader.point(\n src_dst, [310000, 4100000], coord_crs=src_dst.crs, unscale=True\n )\n assert round(p[0], 3) == 1000.892","def GetResolution(vDataSet):\r\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\r\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\r\n\r\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz","def test_fit(self, model):\r\n fit = model/np.linalg.norm(model, axis=1, keepdims=True)\r\n if self.white:\r\n sources = self.sources.dot(self.zca_matrix)\r\n sources /= np.linalg.norm(sources, axis=1, keepdims=True)\r\n else:\r\n sources = self.sources\r\n allthedots = sources.dot(fit.T)\r\n # how close is the closest model source to each true source?\r\n bestfits = np.max(np.abs(allthedots), axis=1)\r\n return np.median(bestfits)","def __call__(self, src, label):\r\n # img = mx.nd.image.to_tensor(src)\r\n # img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\r\n src = mx.nd.array(src)\r\n img = mx.nd.image.to_tensor(src)\r\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\r\n return img, mx.nd.array(label, dtype=img.dtype)","def resample(self):\n # propagate networks\n self.z = self.prior_latent_distribution.sample()\n # reconstruct image\n self.y_hat_raw = self.fcomb(self.unet_features, self.z)\n\n return self.y_hat_raw","def test_single_image_dataset_train_interpolation(mocker, simple_img):\n\n # This is in X, Y cooridnates\n fake_random_data = torch.Tensor(\n np.array(\n [\n [0.001, 0], # top left\n [0, 0.999], # bottom left\n [0.999, 0], # top right\n ]\n )\n )\n\n mock_random = mocker.patch(\"dataset.torch.rand\")\n mock_random.return_value = fake_random_data\n\n dataset = SingleImageDataset(simple_img, 3, normalize=False)\n assert len(dataset) == 9\n\n dataloader = DataLoader(dataset, batch_size=None, batch_sampler=None)\n\n n_iter = 0\n for x, y in dataloader:\n n_iter += 1\n x = x.numpy()\n y = y.numpy()\n expected_x = np.array(\n [\n [-0.998, -1], # Top left\n [-1, 0.998], # Bottom left\n [0.998, -1], # Top right\n ]\n )\n assert np.isclose(expected_x, x).all()\n\n expected_y = np.array(\n [\n [1.0, 2.0, 3.0],\n [4.0, 5.0, 6.0],\n [7.0, 8.0, 9.0],\n ]\n )\n assert np.isclose(expected_y, y, atol=0.05).all()\n\n assert n_iter == 9","def __call__(self, src, label, mask):\n # resize shorter side but keep in max_size\n h, _, _ = src.shape\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n # no scaling ground-truth, return image scaling ratio instead\n im_scale = float(img.shape[0]) / h\n\n img = mx.nd.image.to_tensor(img)\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\n return img, mx.nd.array([img.shape[-2], img.shape[-1], im_scale])","def __call__(self, src, label):\n # resize shorter side but keep in max_size\n h, w, _ = src.shape\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n # no scaling ground-truth, return image scaling ratio instead\n bbox = tbbox.resize(label, (w, h), (img.shape[1], img.shape[0]))\n im_scale = h / float(img.shape[0])\n\n img = mx.nd.image.to_tensor(img)\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\n return img, bbox.astype('float32'), mx.nd.array([im_scale])","def upsampleImage( arr, kernelSize ):\n return scipy.ndimage.zoom( arr, kernelSize )","def resample(source, target_size, transform):\n dtype = source.dtype\n dev = source.device\n\n height_, width_ = target_size\n ur_ = torch.arange(width_, dtype=dtype, device=dev) + 0.5\n vr_ = torch.arange(height_, dtype=dtype, device=dev) + 0.5\n\n height, weight = source.shape[2:]\n ur = 2 * ((ur_ + transform[0, 1]) / transform[0, 0]) / weight - 1\n vr = 2 * ((vr_ + transform[1, 1]) / transform[1, 0]) / height - 1\n\n v, u = torch.meshgrid(vr, ur)\n v = v.unsqueeze(2)\n u = u.unsqueeze(2)\n\n grid = torch.cat((u, v), dim=2)\n grid = grid.unsqueeze(0).expand(len(source), -1, -1, -1)\n\n return torch.nn.functional.grid_sample(source, grid)","def calculate_image_scale(source_width, source_height, target_width, target_height):\n if source_width == target_width and source_height == target_height:\n return 1.0\n\n source_ratio = source_width / source_height\n target_ratio = target_width / target_height\n\n if target_ratio < source_ratio:\n scale = target_width / source_width\n else:\n scale = target_height / source_height\n\n return scale","def _get_sample_size(self, data):\n if self._samples_per_update:\n return self._samples_per_update\n\n len_data = len(data)\n if len_data <= self._min_sample_size:\n return int(len_data)\n return max(int(self._sampling_ratio * len_data), self._min_sample_size)","def __init__(self, data, pixscale = 7.77/43):\n self.data = data\n self.pixscale = pixscale","def upsample_nearest(input, size=None, scale_factor=None):\n return interpolate(input, size, scale_factor, 'nearest')","def src_simple(input_data, output_data, ratio, converter_type, channels):\n input_frames, _ = _check_data(input_data)\n output_frames, _ = _check_data(output_data)\n data = ffi.new('SRC_DATA*')\n data.input_frames = input_frames\n data.output_frames = output_frames\n data.src_ratio = ratio\n data.data_in = ffi.cast('float*', ffi.from_buffer(input_data))\n data.data_out = ffi.cast('float*', ffi.from_buffer(output_data))\n error = _lib.src_simple(data, converter_type, channels)\n return error, data.input_frames_used, data.output_frames_gen","def set_resolution(self):\n file_name = os.path.basename(self.in_file)\n if '1KM' in file_name:\n self.resolution = 1000\n else:\n raise ValueError(\n 'Cant read this data, please check its resolution: {}'.format(self.in_file))","def mold_image(images, config):\n return images.astype(np.float32) - config.MEAN_PIXEL","def mold_image(images, config):\n return images.astype(np.float32) - config.MEAN_PIXEL","def __call__(self, src, label):\n\n h, w, _ = src.shape\n # interp = np.random.randint(0, 5)\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n img, flips = timage.random_flip(img, px=0.5)\n img = img.astype(np.float32)\n\n if self.teacher_aug:\n target_image_1 = self.random_color_aug(img)\n else:\n target_image_1 = img\n target_image_2 = self.random_color_aug(img)\n\n # target_image_1 = mx.nd.image.to_tensor(target_image_1)\n target_image_1 = mx.nd.image.to_tensor(target_image_1)\n target_image_1 = mx.nd.image.normalize(target_image_1, mean=self._mean, std=self._std)\n\n target_image_2 = mx.nd.image.to_tensor(target_image_2)\n target_image_2 = mx.nd.image.normalize(target_image_2, mean=self._mean, std=self._std)\n\n return target_image_1, target_image_2","def prep(self):\n print\n print 'Filtering rawdata to data as masked array...'\n# using 0 as flag\n# self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.rawdata[:self.nints,:, self.chans,:] == 0j)\n# using standard flags\n self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.flags[:self.nints,:, self.chans,:] == 0) # mask of True for flagged data (flags=0 in tpipe, which is flags=False in Miriad and flags=True in MS)\n self.dataph = (self.data.mean(axis=3).mean(axis=1)).real #dataph is summed and detected to form TP beam at phase center, multi-pol\n self.min = self.dataph.min()\n self.max = self.dataph.max()\n print 'Shape of data:'\n print self.data.shape\n print 'Dataph min, max:'\n print self.min, self.max\n\n self.freq = self.freq_orig[self.chans]\n\n self.track0 = self.track(0.)\n self.twidth = 0\n for k in self.track0[1]:\n self.twidth = max(self.twidth, len(n.where(n.array(self.track0[1]) == k)[0]))\n\n print 'Track width in time: %d. Iteration could step by %d/2.' % (self.twidth, self.twidth)","def _get_normalized_flow_countrywide(x_sample):\n global win; win /= 3\n global nebr; nebr = 7 # nebr /= 3\n global norm_min; norm_min = norm_min * 1. / 3\n global MIN_FLOW_NORM; MIN_FLOW_NORM = MIN_FLOW_NORM * 1. / 3\n global MIN_MOVE_PIXEL; MIN_MOVE_PIXEL /= (6*6)\n \n prev_frame = norm_trans(x_sample[-2])\n next_frame = norm_trans(x_sample[-1])\n kernel_shape = (79, 79) # (477/6, 477/6)\n flow = cv2.calcOpticalFlowFarneback(prev_frame, next_frame, 0.5,3,win, 3, nebr, nebr/4, cv2.OPTFLOW_FARNEBACK_GAUSSIAN)\n \n # flow_norm = numpy.linalg.norm(flow, axis=2) # for numpy version >= 1.8\n flow_norm = np.sum(flow**2, axis=2)**(1./2) # for numpy version < 1.8\n \n kernel = np.ones(kernel_shape, np.float32)\n\n# num_moved_flows = numpy.sum(flow_norm>norm_min)\n num_moved_flows = cv2.filter2D((flow_norm>norm_min).astype('float32'), -1, kernel, borderType=cv2.BORDER_REPLICATE)\n\n# if num_moved_flows > MIN_MOVE_PIXEL:\n# flow_fliter = numpy.zeros(shape=flow.shape);\n# flow_fliter[:,:,0] = flow[:,:,0] * (flow_norm > norm_min)\n# flow_fliter[:,:,1] = flow[:,:,1] * (flow_norm > norm_min)\n# \n# flow_mean = numpy.sum(flow_fliter, axis=(0,1)) / num_moved_flows\n# else:\n# flow_mean = numpy.array([0,0])\n \n flow_filter = flow * (flow_norm > norm_min)[:, :, np.newaxis]\n flow_mean = np.zeros_like(flow)\n flow_mean[:,:,0] = cv2.filter2D(flow_filter[:,:,0], -1, kernel, borderType=cv2.BORDER_REPLICATE) / (num_moved_flows + 0.00001)\n flow_mean[:,:,1] = cv2.filter2D(flow_filter[:,:,1], -1, kernel, borderType=cv2.BORDER_REPLICATE) / (num_moved_flows + 0.00001)\n flow_mean = flow_mean * (num_moved_flows > MIN_MOVE_PIXEL)[:, :, np.newaxis]\n\n# flow_mean_norm = np.sum(flow_mean**2)**(1./2)\n# if flow_mean_norm > MIN_FLOW_NORM:\n# flow_norm = flow_norm.reshape((flow_norm.shape[0], flow_norm.shape[1], 1)) \n# flow = flow * (flow_norm < MIN_FLOW_NORM) * flow_mean_norm / flow_norm + flow * (flow_norm >= MIN_FLOW_NORM)\n flow_mean_norm = np.sum(flow_mean**2, axis=2)**(1./2)\n flow = flow * ((flow_norm < MIN_FLOW_NORM) * (flow_mean_norm > MIN_FLOW_NORM) * flow_mean_norm / (flow_norm + 0.000001))[:, :, np.newaxis] + \\\n flow * ((flow_norm >= MIN_FLOW_NORM) | (flow_mean_norm <= MIN_FLOW_NORM))[:, :, np.newaxis] \n return flow","def normalize_data_unit_interval(data):\n if data.dtype == 'float32':\n return\n return data.astype('float32') / 255.0","def test_thresholded_image(self):\n orig_size = self._image.size\n self._api.SetImage(self._image)\n image = self._api.GetThresholdedImage()\n self.assertIsNot(image, None)\n self.assertIsInstance(image, Image.Image)\n self.assertEqual(image.size, orig_size)\n self.assertEqual(self._api.GetThresholdedImageScaleFactor(), 1)","def normalize_dataset(self):","def get_url_for_min_resolution(self, min_height, min_width, image):","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 1] * self.scale","def testSmallSrc(self):\n fromWcs = afwGeom.makeSkyWcs(\n crpix=lsst.geom.Point2D(0, 0),\n crval=lsst.geom.SpherePoint(359, 0, lsst.geom.degrees),\n cdMatrix=afwGeom.makeCdMatrix(scale=1.0e-8*lsst.geom.degrees),\n )\n fromExp = afwImage.ExposureF(afwImage.MaskedImageF(1, 1), fromWcs)\n\n toWcs = afwGeom.makeSkyWcs(\n crpix=lsst.geom.Point2D(0, 0),\n crval=lsst.geom.SpherePoint(358, 0, lsst.geom.degrees),\n cdMatrix=afwGeom.makeCdMatrix(scale=1.1e-8*lsst.geom.degrees),\n )\n toExp = afwImage.ExposureF(afwImage.MaskedImageF(10, 10), toWcs)\n\n warpControl = afwMath.WarpingControl(\"lanczos3\")\n # if a bug described in ticket #2441 is present, this will raise an\n # exception:\n numGoodPix = afwMath.warpExposure(toExp, fromExp, warpControl)\n self.assertEqual(numGoodPix, 0)\n self.assertTrue(np.all(np.isnan(toExp.image.array)))\n self.assertTrue(np.all(np.isinf(toExp.variance.array)))\n noDataBitMask = afwImage.Mask.getPlaneBitMask(\"NO_DATA\")\n self.assertTrue(np.all(toExp.mask.array == noDataBitMask))","def upsample(x):\n return F.interpolate(x, scale_factor=2, mode=\"nearest\")","def __reduce__(self):\n return ImageNetDownsample, (self.cutout,)","def __len__(self):\n return self._num_samples_per_file * len(self._files) // self._world_size","def read(self):\n try:\n if self.Data.Sync.IsWritten == 1:\n\n if self._IsPauseOn:\n self.Data.Sync.IsPauseOn = 1\n else:\n self.Data.Sync.IsPauseOn = 0\n\n Width = self.Data.Image.ImageWidth\n Height = self.Data.Image.ImageHeight\n\n # Image = np.fromstring(self.Data.Image.Data, np.uint8, Width * Height * self.TARGET_IMAGE_CHANNELS)\n Image = np.frombuffer(self.Data.Image.Data, np.uint8, Width * Height * self.TARGET_IMAGE_CHANNELS)\n Image = Image.reshape(Height, Width, self.TARGET_IMAGE_CHANNELS)\n\n AspectRatio = Width / Height\n TargetWidth = int(self._TargetResolution[1] * AspectRatio)\n\n if TargetWidth >= self._TargetResolution[0]:\n if Width != TargetWidth or Height != self._TargetResolution[1]:\n Image = cv2.resize(Image, (TargetWidth, self._TargetResolution[1]))\n\n if TargetWidth != self._TargetResolution[0]:\n XStart = int(TargetWidth/2 - self._TargetResolution[0]/2)\n XStop = int(TargetWidth/2 + self._TargetResolution[0]/2)\n Image = Image[:, XStart:XStop]\n\n else:\n TargetHeight = int(self._TargetResolution[0]/AspectRatio)\n\n if Width != self._TargetResolution[0] or Height != TargetHeight:\n Image = cv2.resize(Image, (self._TargetResolution[1], TargetHeight))\n\n if TargetHeight != self._TargetResolution[1]:\n YStart = int(TargetHeight/2 - self._TargetResolution[1]/2)\n YStop = int(TargetHeight/2 + self._TargetResolution[1]/2)\n Image = Image[YStart:YStop, :]\n\n # Shall we convert this to 0 - 1 ?\n self._RawImage = Image\n self._Image = cv2.flip(Image, 0)\n\n # This one does not flip the image, but it rotate and crop !!\n # self._Image = np.array(cv2.flip(Image, 0)/255, dtype=np.float32)\n # self._Image = cv2.flip(Image, 0)\n\n\n # This one is flipped upside/down\n # print(\"Image from memory reshaped as WxH with Mean\", Width, Height, np.mean((self._Image), axis=(0, 1)))\n # self.store_to_file(self._Image)\n\n return True\n except:\n print(\"Unexpected error in Shared Memory Read\", sys.exc_info()[0])\n\n return False","def preprocess(self, data):\n (w,h,f) = self.rawinputformat()\n dt = numpy.dtype(numpy.uint8)\n nb = numpy.frombuffer(data,dt,-1,0)\n actual_stream_width = (w&1)+w # hack, rather get this from the app sink\n if(actual_stream_width != self.reqsize):\n nb = nb.reshape(h,actual_stream_width,3)\n nb = nb[0:h,0:w,0:3] # crop to network input size\n else:\n nb = nb.reshape((actual_stream_width,actual_stream_width,3))\n img = nb.astype('float32')\n #Preprocess image\n #for i in range(3):\n # img[:,:,i] = (img[:,:,i] - self.mean[i]) * self.std[i]\n #img = resize(img/255.0,(w,h),1)\n img = img/255.0\n print(img.shape)\n #print(img[0,0,:])\n return img.astype(numpy.float16)","def minimum_sampling(self):\n return self.r_eff/3.","def prep(self):\n print\n print 'Filtering rawdata to data as masked array...'\n# using 0 as flag\n# self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.rawdata[:self.nints,:, self.chans,:] == 0j)\n# using standard flags\n self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.flags[:self.nints,:, self.chans,:] == 0) # mask of True for flagged data (flags=0 in tpipe, which is flags=False in Miriad and flags=True in MS)\n self.dataph = (self.data.mean(axis=3).mean(axis=1)).real #dataph is summed and detected to form TP beam at phase center, multi-pol\n self.min = self.dataph.min()\n self.max = self.dataph.max()\n print 'Shape of data:'\n print self.data.shape\n print 'Dataph min, max:'\n print self.min, self.max\n\n self.freq = self.freq_orig[self.chans]\n\n # set up ur tracks (lol)\n self.dmtrack0 = {}\n self.twidths = {}\n for dmbin in xrange(len(self.dmarr)):\n self.dmtrack0[dmbin] = self.dmtrack(self.dmarr[dmbin],0) # track crosses high-freq channel in first integration\n self.twidths[dmbin] = 0\n for k in self.dmtrack0[dmbin][1]:\n self.twidths[dmbin] = max(self.twidths[dmbin], len(n.where(n.array(self.dmtrack0[dmbin][1]) == k)[0]))\n\n print 'Track width in time: '\n for dmbin in self.twidths:\n print 'DM=%.1f, twidth=%d. Iteration could step by %d/2.' % (self.dmarr[dmbin], self.twidths[dmbin], self.twidths[dmbin])","def upsample(X, y, seed):\n ros = RandomOverSampler(random_state=seed)\n X_resampled, y_resampled = ros.fit_resample(X, y)\n return X_resampled, y_resampled","def near_surface_samples(self):\n if self._near_surface_samples is None:\n if self.is_from_directory:\n nss_sample_path = f'{self._directory_root}/nss_points.sdf'\n nss = gaps_util.read_pts_file(nss_sample_path)\n # log.info(f'The points have shape {nss.shape}')\n else:\n nss = self._archive['axis_samples']\n self._near_surface_samples = np.reshape(nss,\n [100000, 4]).astype(np.float32)\n return self._near_surface_samples","def preprocess(self, data):\n data_unnorm = data / 2.0 + 0.5\n \n if self.permute == 1:\n permute = [2, 1, 0]\n data_rgb_unnorm = data_unnorm[:, permute]\n elif self.permute == 0:\n data_rgb_unnorm = data_unnorm\n \n data_rgb_unnorm = F.upsample(data_rgb_unnorm, size=self.size, mode='bilinear')\n data_rgb = (data_rgb_unnorm - self.normalize_mean) / self.normalize_std\n return data_rgb","def regrid_lowmemory(sourceimage,targetimage,fillval = NAN,theader = 0):\n # Start timer\n start = time.time()\n # Load in source data and header information\n sdata,sheader = fits.getdata(sourceimage,header = True)\n # Create WCS object for the source image\n sourcewcs = wcs.WCS(sheader)\n # Create array of pixel indices in source image\n x = arange(sdata.shape[1])\n y = arange(sdata.shape[0])\n # Interpolate the image data over pixel indices\n interp = scipy.interpolate.RectBivariateSpline(y,x,sdata)\n # Load in target grid data\n if theader == 0:\n tdata,theader = fits.getdata(targetimage,header=True)\n if theader != 0:\n tdata = fits.getdata(targetimage)\n # Create WCS object for target grid\n targetwcs = wcs.WCS(theader)\n # Create all possible pairs of pixel coordinates in target grid\n coords = cartesian([arange(tdata.shape[1]),arange(tdata.shape[0])])\n # Extract x and y columns of pixel pairs\n xpixs = coords[:,0]\n ypixs= coords[:,1]\n # Convert target grid pixels to ra/dec \n world = targetwcs.wcs_pix2world(coords,0)\n # Convert target grid ra/dec to source pixel coordinates\n dpix = sourcewcs.wcs_world2pix(world,0)\n # Extract x and y columns of converted pixel pairs\n xdpixs = dpix[:,0]\n ydpixs = dpix[:,1]\n # Find where target grid corresponds to actual source image data\n good = where((xdpixs >= min(x)) & (xdpixs <= max(x)) & (ydpixs >= min(y)) & (ydpixs <= max(y)))\n # Pick out only pixels with relevant image data\n xpixs = xpixs[good]\n ypixs = ypixs[good]\n xdpixs = xdpixs[good]\n ydpixs = ydpixs[good]\n # Create grid to fill up with source image regrid\n tofill = copy(tdata)\n tofill[:] = fillval\n # Loop over relevant pixels and fill up source image regrid\n for i in range(len(ypixs)):\n ypix = ypixs[i]\n xpix = xpixs[i]\n xdpix = xdpixs[i]\n ydpix = ydpixs[i]\n tofill[ypix,xpix] = interp(ydpix,xdpix)\n # End timer\n end = time.time()\n # Print time to run\n print 'Regridded in ',(end-start)/60.,' min'\n return tofill,tdata","def check_dataset(*, low_path: str, high_path: str, count: int = 1):\n with open(high_path, \"rb\") as s_file:\n src_data: np.array = np.load(s_file)\n\n with open(low_path, \"rb\") as s_file:\n res_data: np.array = np.load(s_file)\n\n assert src_data.shape == res_data.shape\n n, m = res_data.shape\n core_size = int(np.sqrt(m / LAYERS))\n assert core_size ** 2 * LAYERS == m\n k = core_size * 4\n\n for _ in range(count):\n img = np.zeros(\n (core_size, k, LAYERS), dtype=res_data.dtype\n )\n i = random.randint(0, n)\n res_row = res_data[i]\n src_row = src_data[i]\n\n mask = create_percent_diff(src_row, res_row)\n restored_src = apply_diff(res_row, mask)\n for l_i, layer_mask in enumerate(np.reshape(mask, (LAYERS, core_size, core_size))): # noqa\n print(f\"layer {l_i} mask:\")\n for row in layer_mask:\n print(\",\".join(map(\"{: >3}\".format, row)))\n\n nopy_restore_area(\n img[:, 0:core_size, :], src_row, core_size, LAYERS\n )\n nopy_restore_area(\n img[:, core_size:core_size * 2, :], res_row, core_size, LAYERS\n )\n nopy_restore_area(\n img[:, core_size * 2:core_size * 3, :], mask, core_size, LAYERS\n )\n nopy_restore_area(\n img[:, core_size * 3:k, :], restored_src, core_size, LAYERS\n )\n plt.imshow(Image.fromarray(img))\n plt.show(block=True)","def test_get_image(self):\n\n spine_data_loader = SpineDataLoader(dirpath_data=self.dirpath,\n batch_size=4)\n\n for idx in range(4):\n image = spine_data_loader.get_image(str(idx))\n assert image.shape == (256, 256, 1)\n assert image.min() == 0.0\n assert image.max() == 1.0\n assert image.dtype == 'float64'","def UResolution(self, *args):\n return _Adaptor3d.Adaptor3d_Surface_UResolution(self, *args)","def __init__(self, data_source, batch_size):\n self.batch_size = batch_size\n self.samples_per_row = ceil(len(data_source) / batch_size)\n self.num_samples = self.samples_per_row * batch_size","def adjust_image_data(self):\r\n\r\n print('Adjusting image data: ')\r\n\r\n if self.removeFirstSequence: # used to remove the first trial from the sequence\r\n\r\n frames_per_rep = self.nFrames/self.nrepetitions\r\n\r\n self.imageData = self.imageData[frames_per_rep:, :, :]\r\n\r\n self.nFrames = self.imageData.shape[0]\r\n\r\n self.nrepetitions = int(self.nFrames/(self.period * self.framerate))\r\n\r\n self.times = np.arange(0, self.nFrames/self.framerate, 1.0/self.framerate)\r\n\r\n \r\n\r\n # first squeeze the image to 3d if it is 4d\r\n\r\n maxt = np.max(self.times) # find last image time\r\n\r\n sh = self.imageData.shape\r\n\r\n if len(sh) == 4:\r\n\r\n self.imageData = self.imageData.squeeze()\r\n\r\n sh = self.imageData.shape\r\n\r\n dt = np.mean(np.diff(self.times)) # get the mean dt\r\n\r\n n_Periods = int((maxt+dt)/self.period) # how many full periods in the image set - include the first?\r\n\r\n if self.nrepetitions > 0 and self.nrepetitions < n_Periods:\r\n\r\n n_Periods = self.nrepetitions\r\n\r\n n_PtsPerCycle = int(np.floor(self.period/dt)); # estimate image points in a stimulus cycle\r\n\r\n ndt = self.period/n_PtsPerCycle\r\n\r\n self.imageData = self.imageData[range(0, n_Periods*n_PtsPerCycle),:,:] # reduce to only what we need\r\n\r\n print (' Adjusted image info')\r\n\r\n print (\" # Periods: %d Pts/cycle: %d Cycle dt %8.4fs (%8.3fHz) Cycle: %7.4fs\" %(n_Periods, n_PtsPerCycle, ndt, 1.0/ndt, self.period))\r\n\r\n self.print_image_info()","def downsample_sam(self, factor):","def test_oss_fit_sample():\n\n # Resample the data\n oss = OneSidedSelection(random_state=RND_SEED)\n X_resampled, y_resampled = oss.fit_sample(X, Y)\n\n currdir = os.path.dirname(os.path.abspath(__file__))\n X_gt = np.load(os.path.join(currdir, 'data', 'oss_x.npy'))\n y_gt = np.load(os.path.join(currdir, 'data', 'oss_y.npy'))\n assert_array_equal(X_resampled, X_gt)\n assert_array_equal(y_resampled, y_gt)","def lensedImage(self, source, scale, xl=8., yl=4., gamma=0.):\n image, magMap = lens(self.ltype, self.dist, source, self.x01, self.x02, xl, yl, gamma)\n\n return image, magMap","def change_resolution(self):","def getResolution(self):\n return self.resolution","def resolution(self):\n return next(iter(self.resolutions()), None)","def __call__(self, src, label, mask):\n # resize shorter side but keep in max_size\n h, _, _ = src.shape\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n # no scaling ground-truth, return image scaling ratio instead\n im_scale = float(img.shape[0]) / h\n\n img = vf.to_tensor(img)\n img = vf.normalize(img, mean=self._mean, std=self._std)\n return img, torch.tensor([img.shape[-2], img.shape[-1], im_scale], dtype=torch.float32)","def _default_sampling_xrange(self):\n from scipy.stats import rv_continuous\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\n else self.rvdist.dataset\n scale = np.nanmax(dataset) - np.nanmin(dataset)\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]","def get_data(name):\n\n if name == 'train' or name == 'unlabeled':\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_train_X.npy')), axis=-1), \\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_train_y.npy')), axis=1)\n # custom svhn has only training/validation set\n elif name == 'validation' or name == 'val':\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_val_X.npy')), axis=-1), \\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_val_y.npy')), axis=1)","def get_resolution(ds):\n\n if 'x' in ds.coords and 'y' in ds.coords:\n x = ds.coords['x'].values\n y = ds.coords['y'].values\n resx = abs(x[-1] - x[0]) / (len(x) - 1)\n resy = abs(y[-1] - y[0]) / (len(y) - 1)\n return (resx, resy)\n else:\n transform = get_transform(ds)\n if transform is not None:\n return (abs(transform.a), abs(transform.e))\n elif 'res' in ds.attrs:\n return ds.attrs['res']\n\n return None","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 0] * self.scale","def _compute_raw_image_norm(self):\n return np.sum(self._data, dtype=float)","def __call__(self, src, label):\n # resize shorter side but keep in max_size\n h, w, _ = src.shape\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\n # no scaling ground-truth, return image scaling ratio instead\n bbox = tbbox.resize(label, (w, h), (img.shape[1], img.shape[0]))\n im_scale = h / float(img.shape[0])\n\n img = vf.to_tensor(img)\n img = vf.normalize(img, mean=self._mean, std=self._std)\n return img, bbox.astype('float32'), torch.tensor([im_scale], dtype=torch.float32)","def resample_img(image, target_shape, mode='nearest'):\n print(target_shape)\n resize_factor = np.array(target_shape)/image.shape\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\n mode=mode)\n return resampled","def test_copy_sources(self):\n metric_copy = copy_metric(self.metric, self.DATA_MODEL)\n self.assertEqual(\"Source\", first(metric_copy[\"sources\"].values())[\"name\"])","def _number_of_samples(self):\n return len(self._raw_data.samples)","def resample(self):\n self.variational_strategy.resample()","def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\n assert img_r.get_data().shape == (29, 39, 19)\n assert img_r.get_data()[4, 4, 4] == 1.0","def quality_data(self, s):\n known_symbols = np.mod(range(176),48)>=32\n print('quality_data',np.sum(np.real(s[known_symbols])<0))\n success = np.sum(np.real(s[known_symbols])<0) < 20\n return success,0 ## no doppler estimate for data frames","def oversampling(self):\n return self._oversampling","def get_image(self):\n if self._image is None:\n image_data = np.load(self.image_file)\n if not isinstance(image_data, np.ndarray):\n image_data = image_data['arr_0']\n self.meta_data = ImageWrapper.load_metadata(self.image_file+\".meta\")\n exposure_time = self.meta_data['exposure_time_us'] * 1e-6\n dark_level = float(self.meta_data['black_level'])\n # saturation_mask = image_data.max(axis=2) >= 4094\n image_data = np.clip((image_data.astype(np.float32) - dark_level),\n a_min=0.0, a_max=None) / exposure_time\n if self.original_vignetting is not None:\n image_data = image_data / self.original_vignetting\n if self.crop is not None:\n image_data = image_data[\n self.crop[1,0]:self.crop[1,1],\n self.crop[0,0]:self.crop[0,1]\n ]\n # saturation_mask = saturation_mask[\n # self.crop[1,0]:self.crop[1,1],\n # self.crop[0,0]:self.crop[0,1]\n # ]\n if self.down_sample is not None:\n image_data = cv2.resize(\n image_data,\n dsize=None,\n fx=1./self.down_sample,\n fy=1./self.down_sample,\n interpolation=cv2.INTER_AREA\n )\n # saturation_mask = cv2.resize(\n # saturation_mask,\n # dsize=None,\n # fx=1./self.down_sample,\n # fy=1./self.down_sample,\n # interpolation=cv2.INTER_AREA\n # )\n if self.reup_sample is not None:\n image_data = cv2.resize(\n image_data,\n dsize=None,\n fx=self.reup_sample,\n fy=self.reup_sample,\n interpolation=cv2.INTER_CUBIC\n )\n # saturation_mask = cv2.resize(\n # saturation_mask,\n # dsize=None,\n # fx=self.reup_sample,\n # fy=self.reup_sample,\n # interpolation=cv2.INTER_CUBIC\n # )\n image = torch.tensor(np.transpose(image_data, (2,0,1)), dtype=torch.float32, device=self.device)\n # saturation_mask = torch.tensor(saturation_mask, dtype=torch.float32, device=self.device)\n if not self.lazy:\n self._image = image\n # self._saturation_mask = saturation_mask\n else:\n image = self._image\n # saturation_mask = self._saturation_mask\n\n return image#, saturation_mask","def merge(sources, bounds=None, res=None, nodata=None, precision=7):\n first = sources[0]\n first_res = first.res\n nodataval = first.nodatavals[0]\n dtype = first.dtypes[0]\n\n # Extent from option or extent of all inputs.\n if bounds:\n dst_w, dst_s, dst_e, dst_n = bounds\n else:\n # scan input files.\n xs = []\n ys = []\n for src in sources:\n left, bottom, right, top = src.bounds\n xs.extend([left, right])\n ys.extend([bottom, top])\n dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)\n\n logger.debug(\"Output bounds: %r\", (dst_w, dst_s, dst_e, dst_n))\n output_transform = Affine.translation(dst_w, dst_n)\n logger.debug(\"Output transform, before scaling: %r\", output_transform)\n\n # Resolution/pixel size.\n if not res:\n res = first_res\n elif not np.iterable(res):\n res = (res, res)\n elif len(res) == 1:\n res = (res[0], res[0])\n output_transform *= Affine.scale(res[0], -res[1])\n logger.debug(\"Output transform, after scaling: %r\", output_transform)\n\n # Compute output array shape. We guarantee it will cover the output\n # bounds completely.\n output_width = int(math.ceil((dst_e - dst_w) / res[0]))\n output_height = int(math.ceil((dst_n - dst_s) / res[1]))\n\n # Adjust bounds to fit.\n dst_e, dst_s = output_transform * (output_width, output_height)\n logger.debug(\"Output width: %d, height: %d\", output_width, output_height)\n logger.debug(\"Adjusted bounds: %r\", (dst_w, dst_s, dst_e, dst_n))\n\n # create destination array\n dest = np.zeros((first.count, output_height, output_width), dtype=dtype)\n\n if nodata is not None:\n nodataval = nodata\n logger.debug(\"Set nodataval: %r\", nodataval)\n\n if nodataval is not None:\n # Only fill if the nodataval is within dtype's range.\n inrange = False\n if np.dtype(dtype).kind in ('i', 'u'):\n info = np.iinfo(dtype)\n inrange = (info.min <= nodataval <= info.max)\n elif np.dtype(dtype).kind == 'f':\n info = np.finfo(dtype)\n inrange = (info.min <= nodataval <= info.max)\n if inrange:\n dest.fill(nodataval)\n else:\n warnings.warn(\n \"Input file's nodata value, %s, is beyond the valid \"\n \"range of its data type, %s. Consider overriding it \"\n \"using the --nodata option for better results.\" % (\n nodataval, dtype))\n else:\n nodataval = 0\n\n for src in sources:\n # Real World (tm) use of boundless reads.\n # This approach uses the maximum amount of memory to solve the problem.\n # Making it more efficient is a TODO.\n\n # 1. Compute spatial intersection of destination and source.\n src_w, src_s, src_e, src_n = src.bounds\n\n int_w = src_w if src_w > dst_w else dst_w\n int_s = src_s if src_s > dst_s else dst_s\n int_e = src_e if src_e < dst_e else dst_e\n int_n = src_n if src_n < dst_n else dst_n\n\n # 2. Compute the source window.\n src_window = get_window(\n int_w, int_s, int_e, int_n, src.affine, precision=precision)\n logger.debug(\"Src %s window: %r\", src.name, src_window)\n\n # 3. Compute the destination window.\n dst_window = get_window(\n int_w, int_s, int_e, int_n, output_transform, precision=precision)\n logger.debug(\"Dst window: %r\", dst_window)\n\n # 4. Initialize temp array.\n tcount = first.count\n trows, tcols = tuple(b - a for a, b in dst_window)\n\n temp_shape = (tcount, trows, tcols)\n logger.debug(\"Temp shape: %r\", temp_shape)\n\n temp = np.zeros(temp_shape, dtype=dtype)\n temp = src.read(out=temp, window=src_window, boundless=False,\n masked=True)\n\n # 5. Copy elements of temp into dest.\n roff, coff = dst_window[0][0], dst_window[1][0]\n\n region = dest[:, roff:roff + trows, coff:coff + tcols]\n np.copyto(\n region, temp,\n where=np.logical_and(region == nodataval, temp.mask == False))\n\n return dest, output_transform","def set_data_shape(self):\n # 如果分辨率是 1000 米\n if self.resolution == 1000:\n satellite_type = ['AQUA', 'TERRA']\n if self.satellite in satellite_type:\n try:\n h4File = SD(self.in_file, SDC.READ)\n in_data_r250 = h4File.select('EV_250_Aggr1km_RefSB').get()\n self.data_shape = in_data_r250.shape[1:]\n except Exception as e:\n print(e)\n else:\n raise ValueError(\n 'Cant read this satellite`s data.: {}'.format(self.satellite))\n else:\n raise ValueError(\n \"Cant handle this resolution: \".format(self.resolution))","def normalize_sample(sample_data):\n BASE = 255\n sample_data = np.array(sample_data, dtype='float32')\n return sample_data/BASE","def minimum_sampling(self):\n return self.fwhm/2.","def getDimensions():","def downsampling(inp_img):\n\n\n img = np.array(inp_img)\n f = max(1, np.rint(np.amin(img)/256))\n\n if f > 1:\n lpf = np.ones((f, f))\n f = (1/(f*f))*lpf\n img = cv2.filter2D(img, -1, kernel=f)\n out = np.hstack((img[:, :, 0], img[:, :, 1], img[:, :, 2]))\n\n return out","def test_full_resize(self):\n number_of_pixels = 300\n destination = base_path +'/test_data/rendering_tests/resized_images/'\n source_folder = base_path + '/test_data/rendering_tests/filter_database/'\n\n\n for the_file in os.listdir(destination):\n file_path = os.path.join(destination, the_file)\n if os.path.isfile(file_path):\n os.unlink(file_path)\n\n\n self.assertEqual(0, len(os.listdir(destination)))\n rb.find_all_files(number_of_pixels,source_folder, destination)\n self.assertEqual(6, len(os.listdir(destination)))\n for the_file in os.listdir(destination):\n file_path = os.path.join(destination,the_file)\n with Image.open(file_path) as f:\n self.assertNotEqual(number_of_pixels+5, f.size[0])\n self.assertNotEqual(number_of_pixels+5, f.size[1])\n # the above checks that the size does not vary as needed\n # probably not necessary\n self.assertEqual(number_of_pixels, f.size[0])\n self.assertEqual(number_of_pixels, f.size[1])","def source_freq(self) -> int:","def prepare(dataset):\n dataset = dataset.reshape(dataset.shape[0], 1, 28, 28)\n dataset = dataset.astype('float32')\n dataset /= 255\n return dataset","def test_calc_res():\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\n xr_res = ds.utils.calc_res(src)\n with rasterio.open(TEST_RASTER_PATH) as src:\n rio_res = src.res\n assert np.allclose(xr_res, rio_res)","def resample(self, data_arr):\n data_arr = np.asarray(data_arr, dtype=np.float32, order='C')\n check_audio(data_arr, is_mono=False)\n\n fn = self._lib['resampleObj_resample']\n fn.argtypes = [POINTER(OpaqueResample),\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'),\n c_int,\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')]\n data_len = data_arr.shape[-1]\n\n if data_arr.ndim == 1:\n ret_arr = np.zeros(data_len * 5, dtype=np.float32)\n new_arr_len = fn(self._obj, data_arr, c_int(data_len), ret_arr)\n else:\n data_arr, o_channel_shape = format_channel(data_arr, 1)\n channel_num = data_arr.shape[0]\n\n ret_arr = np.zeros((channel_num, data_len * 5), dtype=np.float32)\n new_arr_len = 0\n for i in range(channel_num):\n _new_arr_len = fn(self._obj, data_arr[i], c_int(data_len), ret_arr[i])\n new_arr_len = max(new_arr_len, _new_arr_len)\n ret_arr = revoke_channel(ret_arr, o_channel_shape, 1)\n\n return ret_arr[..., :new_arr_len]","def process_image(self, image):\r\n img = cv2.imread(image)\r\n img = img.astype(float)/127 - 1\r\n return np.expand_dims(img, axis=0)","def FindScale(self):\n\n ## 6 and from the cv code the distance is 6 then we are good\n print(\"TODO: Very hard\")","def getUnscaledSamples(self, **kwargs) -> TimeData:\n # initialise chans, startSample and endSample with the whole dataset\n options = self.parseGetDataKeywords(kwargs)\n\n # get the files to read and the samples to take from them, in the correct order\n dataFilesToRead, samplesToRead, scalings = self.getDataFilesForSamples(\n options[\"startSample\"], options[\"endSample\"]\n )\n numSamples = options[\"endSample\"] - options[\"startSample\"] + 1\n # set up the dictionary to hold the data\n data = {}\n for chan in options[\"chans\"]:\n data[chan] = np.zeros(shape=(numSamples), dtype=self.dtype)\n\n # loop through chans and get data\n sampleCounter = 0\n for dFile, sToRead, scalar in zip(dataFilesToRead, samplesToRead, scalings):\n # get samples - this is inclusive\n dSamples = sToRead[1] - sToRead[0] + 1\n # spam files always record 5 channels\n dSamplesRead = dSamples * self.recChannels[dFile]\n # read the data\n byteOff = (\n self.dataByteOffset[dFile]\n + sToRead[0] * self.recChannels[dFile] * self.dataByteSize\n )\n dFilePath = os.path.join(self.dataPath, dFile)\n dataRead = np.memmap(\n dFilePath,\n dtype=self.dtype,\n mode=\"r\",\n offset=byteOff,\n shape=(dSamplesRead),\n )\n # now need to unpack this\n for chan in options[\"chans\"]:\n # check to make sure channel exists\n self.checkChan(chan)\n # get the channel index - the chanIndex should give the right order in the data file\n # as it is the same order as in the header file\n chanIndex = self.chanMap[chan]\n # use the range sampleCounter -> sampleCounter + dSamples, because this actually means sampleCounter + dSamples - 1 as python ranges are not inclusive of the end value\n # scale by the lsb scalar here - note that these can be different for each file in the run\n data[chan][sampleCounter : sampleCounter + dSamples] = (\n dataRead[chanIndex : dSamplesRead : self.recChannels[dFile]]\n * scalar[chan]\n )\n # increment sample counter\n sampleCounter = sampleCounter + dSamples # get ready for the next data read\n\n # return data\n startTime, stopTime = self.sample2time(\n options[\"startSample\"], options[\"endSample\"]\n )\n comments = []\n comments.append(\n \"Unscaled data {} to {} read in from measurement {}, samples {} to {}\".format(\n startTime,\n stopTime,\n self.dataPath,\n options[\"startSample\"],\n options[\"endSample\"],\n )\n )\n comments.append(\"Data read from {} files in total\".format(len(dataFilesToRead)))\n comments.append(\n \"Data scaled to mV for all channels using scalings in header files\"\n )\n comments.append(\"Sampling frequency {}\".format(self.getSampleFreq()))\n return TimeData(\n sampleFreq=self.getSampleFreq(),\n startTime=startTime,\n stopTime=stopTime,\n data=data,\n comments=comments,\n )","def resize(data, data_type, interpolation_method, target_height, target_width):\r\n if target_width == data.shape[1] and target_height == data.shape[0]:\r\n return data\r\n\r\n if data_type == 'stereo':\r\n # disparity map\r\n if interpolation_method == 'bilinear':\r\n result = util_cython.resize(data, target_width, target_height, 0.5)\r\n else:\r\n result = cv2.resize(data, (target_width, target_height), interpolation=cv2.INTER_NEAREST)\r\n else:\r\n # optical flow field\r\n result = np.zeros((target_height, target_width, 2))\r\n if interpolation_method == 'bilinear':\r\n result[:, :, 0] = util_cython.resize(data[:, :, 0], target_width, target_height, 0.5)\r\n result[:, :, 1] = util_cython.resize(data[:, :, 1], target_width, target_height, 0.5)\r\n else:\r\n result = cv2.resize(data, (target_width, target_height), interpolation=cv2.INTER_NEAREST)\r\n return result","def query_frame(self):\n x, y = N.ogrid[0:self._resolution[1], 0:self._resolution[0]]\n x0, y0 = int(self._resolution[1] / 2), int(self._resolution[0] / 2)\n r = N.hypot(x - x0, y - y0)\n w0 = 75.0\n self.frame = N.array(N.exp(-r ** 2 / w0 ** 2) * 60000, dtype=N.uint16)\n self.frame += N.random.uniform(low=0, high=5535, size=self._resolution[::-1])","def resample(self, data):\n # Resample signal\n if self._fs_out != self._fs_in:\n\n # Check that new sample rate is compatable with batch size\n if self._next_frame_idx != 0:\n raise ValueError('New sample rate incompatable with batch size.')\n\n # Input dimensions\n frames_in = data.shape[0] # samples\n len_data = float(frames_in) / self._fs_in # seconds\n\n # Output dimensions\n frames_out = int(np.round(len_data*self._fs_out)) # samples\n delta_out = 1.0 / self._fs_out # seconds\n\n # Predict next frame\n self._next_frame_idx = int(np.round(frames_out*delta_out*self._fs_in)) - \\\n frames_in\n\n # Compute indices to output\n idx_out = np.round(np.arange(frames_out)*delta_out*self._fs_in)\n\n # Sample data using output indices\n data_out = np.zeros((frames_out, data.shape[1]))\n for i in range(frames_out):\n idx = min(frames_in - 1, idx_out[i])\n data_out[i, :] = data[int(idx), :]\n\n else:\n\n # No resampling\n data_out = data\n\n return data_out","def resample(image, flow):\r\n assert flow.shape[1] == 2\r\n b, c, h, w = image.shape\r\n grid = get_grid(b, (h, w))\r\n flow = L.concat([flow[:, 0:1, :, :] / ((w - 1.0) / 2.0),\r\n flow[:, 1:2, :, :] / ((h - 1.0) / 2.0)], 1)\r\n final_grid = L.transpose((grid + flow), (0, 2, 3, 1))\r\n image.stop_gradient = False\r\n try:\r\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border', align_corners=True)\r\n except Exception:\r\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border')\r\n \r\n return output\r\n # return image\r\n # return L.zeros_like(image)\r"],"string":"[\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def data_resampler (self, data):\\r\\n data = librosa.resample(data, orig_sr = original_sampling, target_sr = self.target_sampling)\\r\\n \\r\\n return data\",\n \"def rasterio_resample(dataset: Dataset, target_resolution: float, new_nodata: object = 'auto',\\n resampling_method: Optional[rio_warp.Resampling] = rio_warp.Resampling.nearest,\\n output_datatype: Optional[np.dtype] = np.float32) -> Dataset:\\n\\n # Detect if resolution is not square\\n if dataset.meta[\\\"resolution\\\"][0] != dataset.meta[\\\"resolution\\\"][1]:\\n raise RuntimeError(\\\"Tried to resample a dataset that doesn't have a square resolution!\\\")\\n\\n # Calculate scale factor\\n scaling = int(dataset.meta[\\\"resolution\\\"][0]) / float(target_resolution)\\n\\n # Copy data from source Dataset\\n source_crs = deepcopy(dataset.profile[\\\"crs\\\"])\\n source_transform = deepcopy(dataset.profile[\\\"transform\\\"])\\n source_nodata = deepcopy(dataset.nodata)\\n\\n # Calculate profile/transform and transfer elements\\n new_transform = Affine(source_transform.a / scaling, source_transform.b, source_transform.c, source_transform.d,\\n source_transform.e / scaling, source_transform.f)\\n new_height = int(dataset.profile[\\\"height\\\"] * scaling)\\n new_width = int(dataset.profile[\\\"width\\\"] * scaling)\\n new_resolution = (target_resolution, target_resolution)\\n new_nodata = source_nodata if new_nodata == \\\"auto\\\" else new_nodata\\n new_crs = source_crs # [TODO] Add ability to change CRS in rasterio resample function\\n\\n # Update profile to match output\\n dataset.nodata = new_nodata\\n dataset.profile.update(\\n res=(float(target_resolution), float(target_resolution)),\\n transform=new_transform,\\n height=new_height,\\n width=new_width,\\n nodata=new_nodata\\n )\\n dataset.profile.data[\\\"pixel_dimensions\\\"] = (float(target_resolution), float(target_resolution))\\n dataset.meta[\\\"resolution\\\"] = new_resolution\\n\\n # Change datatype of all bands\\n # [TODO] There may be a way to save memory by freeing old type ndarrays\\n for band_id, band_value in dataset.bands.items():\\n dataset.bands[band_id] = band_value.astype(output_datatype)\\n\\n # Run resampling on each band\\n for band_key, band_data in dataset.bands.items():\\n # Create an array in the correct resampled dimensions\\n resampled_array = np.ma.asanyarray(np.empty(shape=(new_width, new_height), dtype=output_datatype))\\n\\n # Resample/warp\\n rio_warp.reproject(band_data, resampled_array, src_transform=source_transform, dst_transform=new_transform,\\n width=new_width, height=new_height, src_nodata=source_nodata, dst_nodata=new_nodata,\\n src_crs=source_crs, dst_crs=new_crs, resample=resampling_method)\\n\\n # Set value in dictionary\\n dataset.bands[band_key] = resampled_array\\n\\n return dataset\",\n \"def _sample_single(self):\\n ss = super(SuperResolutions, self)._sample_single()\\n image = ss['data']\\n # Down sample\\n max_down_sample = max(self.data_down_sample, self.label_down_sample)\\n if self.is_down_sample:\\n images = []\\n images.append(image)\\n for i in range(max_down_sample):\\n image = self.downsample(image)\\n images.append(image)\\n data = images[self.data_down_sample]\\n label = images[self.label_down_sample]\\n return {'data': data, 'label': label}\",\n \"def get_raw_image_sizes() -> set:\\n sizes = set()\\n data = SUNRGBDTrainDataset(True, augment=False)\\n for i in range(len(data)):\\n sizes.add(data[i][0].shape)\\n return sizes\",\n \"def resample(self):\\n pass\",\n \"def data_type_ratio(self):\\n if self.sample_size:\\n return float(self.match_count) / self.sample_size\\n return None\",\n \"def minimum_sampling(self):\\n # TODO: Allow `Source` to understand when this returns None?\\n return 1.\",\n \"def data_resolution_and_offset(data):\\n res = (data[data.size - 1] - data[0]) / (data.size - 1.0)\\n off = data[0] - 0.5 * res\\n return numpy.asscalar(res), numpy.asscalar(off)\",\n \"def _compute_output_resolution(input_spatial_resolution, kernel_size, stride,\\n total_padding):\\n if (input_spatial_resolution is None) or (kernel_size is None) or (\\n stride is None) or (total_padding is None):\\n return None\\n return int(\\n math.ceil((\\n input_spatial_resolution + total_padding - kernel_size + 1) / stride))\",\n \"def upsample(self, method):\\n from scipy.signal import resample\\n from scipy.ndimage.interpolation import zoom\\n #print \\\"mm: 100 x 100 x 131\\\"\\n #print \\\"Dims:\\\", self.D.shape\\n fact = np.array(self.info.shape).astype(\\\"float32\\\") / np.array(self.info.read_shape).astype(\\\"float32\\\")+0.00001 # hrmpf!!\\n if method == \\\"zoom\\\":\\n print \\\"Resampling...\\\"\\n self.D = zoom(self.D, fact).astype(\\\"float32\\\")\\n elif method == \\\"resample\\\":\\n print \\\"Resampling...\\\"\\n a = self.info.resample_ax\\n s = self.info.shape[a]\\n self.D = resample(self.D, s, axis=a, window=10).astype(\\\"float32\\\")\\n elif method == None:\\n pass\\n else:\\n raise NotImplementedError(\\\"Unknown upsampling method: %s\\\" % method)\\n #print \\\"Dims:\\\", self.D.shape\\n print \\\"done.\\\"\",\n \"def get_resolution(testdata):\\n diffs = np.transpose([testdata[1:, 0], np.diff(testdata[:, 0])])\\n resolutions = ud.safedivide(diffs[:, 0], diffs[:, 1])\\n # popt, pcov = scipy.optimize.curve_fit(fit_line, diffs[:, 0], resolutions, maxfev=1000000)\\n # fitLine = fit_line(diffs[:, 0], *popt)\\n # fitMax = np.max(fitLine)\\n # fitMin = np.min(fitLine)\\n # diffs_new = diffs[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\\n # resolutions_new = resolutions[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\\n # popt2, pcov2 = scipy.optimize.curve_fit(fit_line, diffs_new[:, 0], resolutions_new, maxfev=1000000)\\n # plt.figure()\\n # plt.plot(diffs[:,0], resolutions)\\n # plt.plot(diffs[:, 0], fit_line(diffs[:, 0], *popt2), 'r-')\\n # plt.show()\\n # Currently use A * m ^1.5 (0.5?)\\n # Maybe use a*M^b\\n # return popt2\\n return np.median(resolutions)\",\n \"def quickMinMax(self, targetSize=1e6):\\n data = self.image\\n if targetSize < 2: # keep at least two pixels\\n targetSize = 2\\n while True:\\n h, w = data.shape[:2]\\n if h * w <= targetSize: break\\n if h > w:\\n data = data[::2, ::] # downsample first axis\\n else:\\n data = data[::, ::2] # downsample second axis\\n return self._xp.nanmin(data), self._xp.nanmax(data)\",\n \"def test_alt_source(self):\\n sp2 = SourceSpectrum(\\n GaussianFlux1D, amplitude=self.sp.model.amplitude.value,\\n mean=self.sp.model.mean.value, stddev=self.sp.model.stddev.value)\\n w = [3900, 4000, 4060] * u.AA\\n assert_quantity_allclose(sp2(w), self.sp(w))\",\n \"def test_resample_methods():\\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\\n try:\\n cvs.raster(src, upsample_method='santaclaus', downsample_method='toothfairy')\\n except ValueError:\\n pass\\n else:\\n assert False\\n\\n try:\\n cvs.raster(src, upsample_method='honestlawyer')\\n except ValueError:\\n pass\\n else:\\n assert False\\n\\n try:\\n cvs.raster(src, downsample_method='tenantfriendlylease')\\n except ValueError:\\n pass\\n else:\\n assert False\",\n \"def get_resolution(self):\\n ret_val = False\\n width = 0\\n height = 0\\n try:\\n sink = self.player.get_by_name('sink')\\n sample = GstBase.BaseSink.get_last_sample(sink)\\n caps = Gst.Sample.get_caps(sample)\\n struct = Gst.Caps.get_structure(caps, 0)\\n h_result, height = Gst.Structure.get_int(struct, \\\"height\\\")\\n w_result, width = Gst.Structure.get_int(struct, \\\"width\\\")\\n if h_result and w_result:\\n ret_val = True\\n except:\\n ret_val = False\\n\\n return ret_val, width, height\",\n \"def preprocess_data(tensors_lr):\\n n = input_radius\\n m = upsample_rate\\n\\n # flatten DT matrices\\n s = tensors_lr.shape\\n print(\\\"Low resolution tensors shape:\\\", s)\\n tensors_lr = np.reshape(tensors_lr, (s[0], s[1], s[2], 9))\\n\\n # the target resolution after upsampling\\n target_resolution = (s[0]*m, s[1]*m, s[2]*m, 3, 3)\\n print(\\\"Target resolution after upsampling:\\\", target_resolution)\\n\\n # remove duplicate entries to obtain the 6 unique parameters\\n tensors_lr = np.delete(tensors_lr, [3, 6, 7], axis=3)\\n\\n all_indices = utils.create_triples(s[0], s[1], s[2])\\n\\n return all_indices, tensors_lr, target_resolution\",\n \"def _resolution(self):\\n _, xres, _, _, _, yres = self.geotransform\\n return xres, yres\",\n \"def minimum_sampling(self):\\n # Sampling in arcsec / pixel\\n sampling = self.seeing.minimum_sampling()\\n try:\\n # Try using `intrinsic` as an object\\n sampling = min(self.intrinsic.minimum_sampling(), sampling)\\n except AttributeError:\\n pass\\n return sampling\",\n \"def test_read_unscale():\\n with rasterio.open(COG_SCALE) as src_dst:\\n arr, mask = reader.tile(src_dst, 218, 99, 8, tilesize=128)\\n arrS, maskS = reader.tile(src_dst, 218, 99, 8, tilesize=128, unscale=True)\\n\\n assert arr.dtype == \\\"int16\\\"\\n assert arrS.dtype == \\\"float32\\\"\\n assert not numpy.array_equal(arr, arrS)\\n numpy.testing.assert_array_equal(mask, maskS)\\n\\n meta = reader.metadata(src_dst)\\n assert isinstance(meta[\\\"statistics\\\"][1][\\\"min\\\"], int)\\n\\n meta = reader.metadata(src_dst, unscale=True)\\n assert isinstance(meta[\\\"statistics\\\"][1][\\\"min\\\"], float)\\n\\n p = reader.point(src_dst, [310000, 4100000], coord_crs=src_dst.crs)\\n assert p == [8917]\\n\\n p = reader.point(\\n src_dst, [310000, 4100000], coord_crs=src_dst.crs, unscale=True\\n )\\n assert round(p[0], 3) == 1000.892\",\n \"def GetResolution(vDataSet):\\r\\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\\r\\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\\r\\n\\r\\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz\",\n \"def test_fit(self, model):\\r\\n fit = model/np.linalg.norm(model, axis=1, keepdims=True)\\r\\n if self.white:\\r\\n sources = self.sources.dot(self.zca_matrix)\\r\\n sources /= np.linalg.norm(sources, axis=1, keepdims=True)\\r\\n else:\\r\\n sources = self.sources\\r\\n allthedots = sources.dot(fit.T)\\r\\n # how close is the closest model source to each true source?\\r\\n bestfits = np.max(np.abs(allthedots), axis=1)\\r\\n return np.median(bestfits)\",\n \"def __call__(self, src, label):\\r\\n # img = mx.nd.image.to_tensor(src)\\r\\n # img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\r\\n src = mx.nd.array(src)\\r\\n img = mx.nd.image.to_tensor(src)\\r\\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\r\\n return img, mx.nd.array(label, dtype=img.dtype)\",\n \"def resample(self):\\n # propagate networks\\n self.z = self.prior_latent_distribution.sample()\\n # reconstruct image\\n self.y_hat_raw = self.fcomb(self.unet_features, self.z)\\n\\n return self.y_hat_raw\",\n \"def test_single_image_dataset_train_interpolation(mocker, simple_img):\\n\\n # This is in X, Y cooridnates\\n fake_random_data = torch.Tensor(\\n np.array(\\n [\\n [0.001, 0], # top left\\n [0, 0.999], # bottom left\\n [0.999, 0], # top right\\n ]\\n )\\n )\\n\\n mock_random = mocker.patch(\\\"dataset.torch.rand\\\")\\n mock_random.return_value = fake_random_data\\n\\n dataset = SingleImageDataset(simple_img, 3, normalize=False)\\n assert len(dataset) == 9\\n\\n dataloader = DataLoader(dataset, batch_size=None, batch_sampler=None)\\n\\n n_iter = 0\\n for x, y in dataloader:\\n n_iter += 1\\n x = x.numpy()\\n y = y.numpy()\\n expected_x = np.array(\\n [\\n [-0.998, -1], # Top left\\n [-1, 0.998], # Bottom left\\n [0.998, -1], # Top right\\n ]\\n )\\n assert np.isclose(expected_x, x).all()\\n\\n expected_y = np.array(\\n [\\n [1.0, 2.0, 3.0],\\n [4.0, 5.0, 6.0],\\n [7.0, 8.0, 9.0],\\n ]\\n )\\n assert np.isclose(expected_y, y, atol=0.05).all()\\n\\n assert n_iter == 9\",\n \"def __call__(self, src, label, mask):\\n # resize shorter side but keep in max_size\\n h, _, _ = src.shape\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n # no scaling ground-truth, return image scaling ratio instead\\n im_scale = float(img.shape[0]) / h\\n\\n img = mx.nd.image.to_tensor(img)\\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\n return img, mx.nd.array([img.shape[-2], img.shape[-1], im_scale])\",\n \"def __call__(self, src, label):\\n # resize shorter side but keep in max_size\\n h, w, _ = src.shape\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n # no scaling ground-truth, return image scaling ratio instead\\n bbox = tbbox.resize(label, (w, h), (img.shape[1], img.shape[0]))\\n im_scale = h / float(img.shape[0])\\n\\n img = mx.nd.image.to_tensor(img)\\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\n return img, bbox.astype('float32'), mx.nd.array([im_scale])\",\n \"def upsampleImage( arr, kernelSize ):\\n return scipy.ndimage.zoom( arr, kernelSize )\",\n \"def resample(source, target_size, transform):\\n dtype = source.dtype\\n dev = source.device\\n\\n height_, width_ = target_size\\n ur_ = torch.arange(width_, dtype=dtype, device=dev) + 0.5\\n vr_ = torch.arange(height_, dtype=dtype, device=dev) + 0.5\\n\\n height, weight = source.shape[2:]\\n ur = 2 * ((ur_ + transform[0, 1]) / transform[0, 0]) / weight - 1\\n vr = 2 * ((vr_ + transform[1, 1]) / transform[1, 0]) / height - 1\\n\\n v, u = torch.meshgrid(vr, ur)\\n v = v.unsqueeze(2)\\n u = u.unsqueeze(2)\\n\\n grid = torch.cat((u, v), dim=2)\\n grid = grid.unsqueeze(0).expand(len(source), -1, -1, -1)\\n\\n return torch.nn.functional.grid_sample(source, grid)\",\n \"def calculate_image_scale(source_width, source_height, target_width, target_height):\\n if source_width == target_width and source_height == target_height:\\n return 1.0\\n\\n source_ratio = source_width / source_height\\n target_ratio = target_width / target_height\\n\\n if target_ratio < source_ratio:\\n scale = target_width / source_width\\n else:\\n scale = target_height / source_height\\n\\n return scale\",\n \"def _get_sample_size(self, data):\\n if self._samples_per_update:\\n return self._samples_per_update\\n\\n len_data = len(data)\\n if len_data <= self._min_sample_size:\\n return int(len_data)\\n return max(int(self._sampling_ratio * len_data), self._min_sample_size)\",\n \"def __init__(self, data, pixscale = 7.77/43):\\n self.data = data\\n self.pixscale = pixscale\",\n \"def upsample_nearest(input, size=None, scale_factor=None):\\n return interpolate(input, size, scale_factor, 'nearest')\",\n \"def src_simple(input_data, output_data, ratio, converter_type, channels):\\n input_frames, _ = _check_data(input_data)\\n output_frames, _ = _check_data(output_data)\\n data = ffi.new('SRC_DATA*')\\n data.input_frames = input_frames\\n data.output_frames = output_frames\\n data.src_ratio = ratio\\n data.data_in = ffi.cast('float*', ffi.from_buffer(input_data))\\n data.data_out = ffi.cast('float*', ffi.from_buffer(output_data))\\n error = _lib.src_simple(data, converter_type, channels)\\n return error, data.input_frames_used, data.output_frames_gen\",\n \"def set_resolution(self):\\n file_name = os.path.basename(self.in_file)\\n if '1KM' in file_name:\\n self.resolution = 1000\\n else:\\n raise ValueError(\\n 'Cant read this data, please check its resolution: {}'.format(self.in_file))\",\n \"def mold_image(images, config):\\n return images.astype(np.float32) - config.MEAN_PIXEL\",\n \"def mold_image(images, config):\\n return images.astype(np.float32) - config.MEAN_PIXEL\",\n \"def __call__(self, src, label):\\n\\n h, w, _ = src.shape\\n # interp = np.random.randint(0, 5)\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n img, flips = timage.random_flip(img, px=0.5)\\n img = img.astype(np.float32)\\n\\n if self.teacher_aug:\\n target_image_1 = self.random_color_aug(img)\\n else:\\n target_image_1 = img\\n target_image_2 = self.random_color_aug(img)\\n\\n # target_image_1 = mx.nd.image.to_tensor(target_image_1)\\n target_image_1 = mx.nd.image.to_tensor(target_image_1)\\n target_image_1 = mx.nd.image.normalize(target_image_1, mean=self._mean, std=self._std)\\n\\n target_image_2 = mx.nd.image.to_tensor(target_image_2)\\n target_image_2 = mx.nd.image.normalize(target_image_2, mean=self._mean, std=self._std)\\n\\n return target_image_1, target_image_2\",\n \"def prep(self):\\n print\\n print 'Filtering rawdata to data as masked array...'\\n# using 0 as flag\\n# self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.rawdata[:self.nints,:, self.chans,:] == 0j)\\n# using standard flags\\n self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.flags[:self.nints,:, self.chans,:] == 0) # mask of True for flagged data (flags=0 in tpipe, which is flags=False in Miriad and flags=True in MS)\\n self.dataph = (self.data.mean(axis=3).mean(axis=1)).real #dataph is summed and detected to form TP beam at phase center, multi-pol\\n self.min = self.dataph.min()\\n self.max = self.dataph.max()\\n print 'Shape of data:'\\n print self.data.shape\\n print 'Dataph min, max:'\\n print self.min, self.max\\n\\n self.freq = self.freq_orig[self.chans]\\n\\n self.track0 = self.track(0.)\\n self.twidth = 0\\n for k in self.track0[1]:\\n self.twidth = max(self.twidth, len(n.where(n.array(self.track0[1]) == k)[0]))\\n\\n print 'Track width in time: %d. Iteration could step by %d/2.' % (self.twidth, self.twidth)\",\n \"def _get_normalized_flow_countrywide(x_sample):\\n global win; win /= 3\\n global nebr; nebr = 7 # nebr /= 3\\n global norm_min; norm_min = norm_min * 1. / 3\\n global MIN_FLOW_NORM; MIN_FLOW_NORM = MIN_FLOW_NORM * 1. / 3\\n global MIN_MOVE_PIXEL; MIN_MOVE_PIXEL /= (6*6)\\n \\n prev_frame = norm_trans(x_sample[-2])\\n next_frame = norm_trans(x_sample[-1])\\n kernel_shape = (79, 79) # (477/6, 477/6)\\n flow = cv2.calcOpticalFlowFarneback(prev_frame, next_frame, 0.5,3,win, 3, nebr, nebr/4, cv2.OPTFLOW_FARNEBACK_GAUSSIAN)\\n \\n # flow_norm = numpy.linalg.norm(flow, axis=2) # for numpy version >= 1.8\\n flow_norm = np.sum(flow**2, axis=2)**(1./2) # for numpy version < 1.8\\n \\n kernel = np.ones(kernel_shape, np.float32)\\n\\n# num_moved_flows = numpy.sum(flow_norm>norm_min)\\n num_moved_flows = cv2.filter2D((flow_norm>norm_min).astype('float32'), -1, kernel, borderType=cv2.BORDER_REPLICATE)\\n\\n# if num_moved_flows > MIN_MOVE_PIXEL:\\n# flow_fliter = numpy.zeros(shape=flow.shape);\\n# flow_fliter[:,:,0] = flow[:,:,0] * (flow_norm > norm_min)\\n# flow_fliter[:,:,1] = flow[:,:,1] * (flow_norm > norm_min)\\n# \\n# flow_mean = numpy.sum(flow_fliter, axis=(0,1)) / num_moved_flows\\n# else:\\n# flow_mean = numpy.array([0,0])\\n \\n flow_filter = flow * (flow_norm > norm_min)[:, :, np.newaxis]\\n flow_mean = np.zeros_like(flow)\\n flow_mean[:,:,0] = cv2.filter2D(flow_filter[:,:,0], -1, kernel, borderType=cv2.BORDER_REPLICATE) / (num_moved_flows + 0.00001)\\n flow_mean[:,:,1] = cv2.filter2D(flow_filter[:,:,1], -1, kernel, borderType=cv2.BORDER_REPLICATE) / (num_moved_flows + 0.00001)\\n flow_mean = flow_mean * (num_moved_flows > MIN_MOVE_PIXEL)[:, :, np.newaxis]\\n\\n# flow_mean_norm = np.sum(flow_mean**2)**(1./2)\\n# if flow_mean_norm > MIN_FLOW_NORM:\\n# flow_norm = flow_norm.reshape((flow_norm.shape[0], flow_norm.shape[1], 1)) \\n# flow = flow * (flow_norm < MIN_FLOW_NORM) * flow_mean_norm / flow_norm + flow * (flow_norm >= MIN_FLOW_NORM)\\n flow_mean_norm = np.sum(flow_mean**2, axis=2)**(1./2)\\n flow = flow * ((flow_norm < MIN_FLOW_NORM) * (flow_mean_norm > MIN_FLOW_NORM) * flow_mean_norm / (flow_norm + 0.000001))[:, :, np.newaxis] + \\\\\\n flow * ((flow_norm >= MIN_FLOW_NORM) | (flow_mean_norm <= MIN_FLOW_NORM))[:, :, np.newaxis] \\n return flow\",\n \"def normalize_data_unit_interval(data):\\n if data.dtype == 'float32':\\n return\\n return data.astype('float32') / 255.0\",\n \"def test_thresholded_image(self):\\n orig_size = self._image.size\\n self._api.SetImage(self._image)\\n image = self._api.GetThresholdedImage()\\n self.assertIsNot(image, None)\\n self.assertIsInstance(image, Image.Image)\\n self.assertEqual(image.size, orig_size)\\n self.assertEqual(self._api.GetThresholdedImageScaleFactor(), 1)\",\n \"def normalize_dataset(self):\",\n \"def get_url_for_min_resolution(self, min_height, min_width, image):\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 1] * self.scale\",\n \"def testSmallSrc(self):\\n fromWcs = afwGeom.makeSkyWcs(\\n crpix=lsst.geom.Point2D(0, 0),\\n crval=lsst.geom.SpherePoint(359, 0, lsst.geom.degrees),\\n cdMatrix=afwGeom.makeCdMatrix(scale=1.0e-8*lsst.geom.degrees),\\n )\\n fromExp = afwImage.ExposureF(afwImage.MaskedImageF(1, 1), fromWcs)\\n\\n toWcs = afwGeom.makeSkyWcs(\\n crpix=lsst.geom.Point2D(0, 0),\\n crval=lsst.geom.SpherePoint(358, 0, lsst.geom.degrees),\\n cdMatrix=afwGeom.makeCdMatrix(scale=1.1e-8*lsst.geom.degrees),\\n )\\n toExp = afwImage.ExposureF(afwImage.MaskedImageF(10, 10), toWcs)\\n\\n warpControl = afwMath.WarpingControl(\\\"lanczos3\\\")\\n # if a bug described in ticket #2441 is present, this will raise an\\n # exception:\\n numGoodPix = afwMath.warpExposure(toExp, fromExp, warpControl)\\n self.assertEqual(numGoodPix, 0)\\n self.assertTrue(np.all(np.isnan(toExp.image.array)))\\n self.assertTrue(np.all(np.isinf(toExp.variance.array)))\\n noDataBitMask = afwImage.Mask.getPlaneBitMask(\\\"NO_DATA\\\")\\n self.assertTrue(np.all(toExp.mask.array == noDataBitMask))\",\n \"def upsample(x):\\n return F.interpolate(x, scale_factor=2, mode=\\\"nearest\\\")\",\n \"def __reduce__(self):\\n return ImageNetDownsample, (self.cutout,)\",\n \"def __len__(self):\\n return self._num_samples_per_file * len(self._files) // self._world_size\",\n \"def read(self):\\n try:\\n if self.Data.Sync.IsWritten == 1:\\n\\n if self._IsPauseOn:\\n self.Data.Sync.IsPauseOn = 1\\n else:\\n self.Data.Sync.IsPauseOn = 0\\n\\n Width = self.Data.Image.ImageWidth\\n Height = self.Data.Image.ImageHeight\\n\\n # Image = np.fromstring(self.Data.Image.Data, np.uint8, Width * Height * self.TARGET_IMAGE_CHANNELS)\\n Image = np.frombuffer(self.Data.Image.Data, np.uint8, Width * Height * self.TARGET_IMAGE_CHANNELS)\\n Image = Image.reshape(Height, Width, self.TARGET_IMAGE_CHANNELS)\\n\\n AspectRatio = Width / Height\\n TargetWidth = int(self._TargetResolution[1] * AspectRatio)\\n\\n if TargetWidth >= self._TargetResolution[0]:\\n if Width != TargetWidth or Height != self._TargetResolution[1]:\\n Image = cv2.resize(Image, (TargetWidth, self._TargetResolution[1]))\\n\\n if TargetWidth != self._TargetResolution[0]:\\n XStart = int(TargetWidth/2 - self._TargetResolution[0]/2)\\n XStop = int(TargetWidth/2 + self._TargetResolution[0]/2)\\n Image = Image[:, XStart:XStop]\\n\\n else:\\n TargetHeight = int(self._TargetResolution[0]/AspectRatio)\\n\\n if Width != self._TargetResolution[0] or Height != TargetHeight:\\n Image = cv2.resize(Image, (self._TargetResolution[1], TargetHeight))\\n\\n if TargetHeight != self._TargetResolution[1]:\\n YStart = int(TargetHeight/2 - self._TargetResolution[1]/2)\\n YStop = int(TargetHeight/2 + self._TargetResolution[1]/2)\\n Image = Image[YStart:YStop, :]\\n\\n # Shall we convert this to 0 - 1 ?\\n self._RawImage = Image\\n self._Image = cv2.flip(Image, 0)\\n\\n # This one does not flip the image, but it rotate and crop !!\\n # self._Image = np.array(cv2.flip(Image, 0)/255, dtype=np.float32)\\n # self._Image = cv2.flip(Image, 0)\\n\\n\\n # This one is flipped upside/down\\n # print(\\\"Image from memory reshaped as WxH with Mean\\\", Width, Height, np.mean((self._Image), axis=(0, 1)))\\n # self.store_to_file(self._Image)\\n\\n return True\\n except:\\n print(\\\"Unexpected error in Shared Memory Read\\\", sys.exc_info()[0])\\n\\n return False\",\n \"def preprocess(self, data):\\n (w,h,f) = self.rawinputformat()\\n dt = numpy.dtype(numpy.uint8)\\n nb = numpy.frombuffer(data,dt,-1,0)\\n actual_stream_width = (w&1)+w # hack, rather get this from the app sink\\n if(actual_stream_width != self.reqsize):\\n nb = nb.reshape(h,actual_stream_width,3)\\n nb = nb[0:h,0:w,0:3] # crop to network input size\\n else:\\n nb = nb.reshape((actual_stream_width,actual_stream_width,3))\\n img = nb.astype('float32')\\n #Preprocess image\\n #for i in range(3):\\n # img[:,:,i] = (img[:,:,i] - self.mean[i]) * self.std[i]\\n #img = resize(img/255.0,(w,h),1)\\n img = img/255.0\\n print(img.shape)\\n #print(img[0,0,:])\\n return img.astype(numpy.float16)\",\n \"def minimum_sampling(self):\\n return self.r_eff/3.\",\n \"def prep(self):\\n print\\n print 'Filtering rawdata to data as masked array...'\\n# using 0 as flag\\n# self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.rawdata[:self.nints,:, self.chans,:] == 0j)\\n# using standard flags\\n self.data = n.ma.masked_array(self.rawdata[:self.nints,:, self.chans,:], self.flags[:self.nints,:, self.chans,:] == 0) # mask of True for flagged data (flags=0 in tpipe, which is flags=False in Miriad and flags=True in MS)\\n self.dataph = (self.data.mean(axis=3).mean(axis=1)).real #dataph is summed and detected to form TP beam at phase center, multi-pol\\n self.min = self.dataph.min()\\n self.max = self.dataph.max()\\n print 'Shape of data:'\\n print self.data.shape\\n print 'Dataph min, max:'\\n print self.min, self.max\\n\\n self.freq = self.freq_orig[self.chans]\\n\\n # set up ur tracks (lol)\\n self.dmtrack0 = {}\\n self.twidths = {}\\n for dmbin in xrange(len(self.dmarr)):\\n self.dmtrack0[dmbin] = self.dmtrack(self.dmarr[dmbin],0) # track crosses high-freq channel in first integration\\n self.twidths[dmbin] = 0\\n for k in self.dmtrack0[dmbin][1]:\\n self.twidths[dmbin] = max(self.twidths[dmbin], len(n.where(n.array(self.dmtrack0[dmbin][1]) == k)[0]))\\n\\n print 'Track width in time: '\\n for dmbin in self.twidths:\\n print 'DM=%.1f, twidth=%d. Iteration could step by %d/2.' % (self.dmarr[dmbin], self.twidths[dmbin], self.twidths[dmbin])\",\n \"def upsample(X, y, seed):\\n ros = RandomOverSampler(random_state=seed)\\n X_resampled, y_resampled = ros.fit_resample(X, y)\\n return X_resampled, y_resampled\",\n \"def near_surface_samples(self):\\n if self._near_surface_samples is None:\\n if self.is_from_directory:\\n nss_sample_path = f'{self._directory_root}/nss_points.sdf'\\n nss = gaps_util.read_pts_file(nss_sample_path)\\n # log.info(f'The points have shape {nss.shape}')\\n else:\\n nss = self._archive['axis_samples']\\n self._near_surface_samples = np.reshape(nss,\\n [100000, 4]).astype(np.float32)\\n return self._near_surface_samples\",\n \"def preprocess(self, data):\\n data_unnorm = data / 2.0 + 0.5\\n \\n if self.permute == 1:\\n permute = [2, 1, 0]\\n data_rgb_unnorm = data_unnorm[:, permute]\\n elif self.permute == 0:\\n data_rgb_unnorm = data_unnorm\\n \\n data_rgb_unnorm = F.upsample(data_rgb_unnorm, size=self.size, mode='bilinear')\\n data_rgb = (data_rgb_unnorm - self.normalize_mean) / self.normalize_std\\n return data_rgb\",\n \"def regrid_lowmemory(sourceimage,targetimage,fillval = NAN,theader = 0):\\n # Start timer\\n start = time.time()\\n # Load in source data and header information\\n sdata,sheader = fits.getdata(sourceimage,header = True)\\n # Create WCS object for the source image\\n sourcewcs = wcs.WCS(sheader)\\n # Create array of pixel indices in source image\\n x = arange(sdata.shape[1])\\n y = arange(sdata.shape[0])\\n # Interpolate the image data over pixel indices\\n interp = scipy.interpolate.RectBivariateSpline(y,x,sdata)\\n # Load in target grid data\\n if theader == 0:\\n tdata,theader = fits.getdata(targetimage,header=True)\\n if theader != 0:\\n tdata = fits.getdata(targetimage)\\n # Create WCS object for target grid\\n targetwcs = wcs.WCS(theader)\\n # Create all possible pairs of pixel coordinates in target grid\\n coords = cartesian([arange(tdata.shape[1]),arange(tdata.shape[0])])\\n # Extract x and y columns of pixel pairs\\n xpixs = coords[:,0]\\n ypixs= coords[:,1]\\n # Convert target grid pixels to ra/dec \\n world = targetwcs.wcs_pix2world(coords,0)\\n # Convert target grid ra/dec to source pixel coordinates\\n dpix = sourcewcs.wcs_world2pix(world,0)\\n # Extract x and y columns of converted pixel pairs\\n xdpixs = dpix[:,0]\\n ydpixs = dpix[:,1]\\n # Find where target grid corresponds to actual source image data\\n good = where((xdpixs >= min(x)) & (xdpixs <= max(x)) & (ydpixs >= min(y)) & (ydpixs <= max(y)))\\n # Pick out only pixels with relevant image data\\n xpixs = xpixs[good]\\n ypixs = ypixs[good]\\n xdpixs = xdpixs[good]\\n ydpixs = ydpixs[good]\\n # Create grid to fill up with source image regrid\\n tofill = copy(tdata)\\n tofill[:] = fillval\\n # Loop over relevant pixels and fill up source image regrid\\n for i in range(len(ypixs)):\\n ypix = ypixs[i]\\n xpix = xpixs[i]\\n xdpix = xdpixs[i]\\n ydpix = ydpixs[i]\\n tofill[ypix,xpix] = interp(ydpix,xdpix)\\n # End timer\\n end = time.time()\\n # Print time to run\\n print 'Regridded in ',(end-start)/60.,' min'\\n return tofill,tdata\",\n \"def check_dataset(*, low_path: str, high_path: str, count: int = 1):\\n with open(high_path, \\\"rb\\\") as s_file:\\n src_data: np.array = np.load(s_file)\\n\\n with open(low_path, \\\"rb\\\") as s_file:\\n res_data: np.array = np.load(s_file)\\n\\n assert src_data.shape == res_data.shape\\n n, m = res_data.shape\\n core_size = int(np.sqrt(m / LAYERS))\\n assert core_size ** 2 * LAYERS == m\\n k = core_size * 4\\n\\n for _ in range(count):\\n img = np.zeros(\\n (core_size, k, LAYERS), dtype=res_data.dtype\\n )\\n i = random.randint(0, n)\\n res_row = res_data[i]\\n src_row = src_data[i]\\n\\n mask = create_percent_diff(src_row, res_row)\\n restored_src = apply_diff(res_row, mask)\\n for l_i, layer_mask in enumerate(np.reshape(mask, (LAYERS, core_size, core_size))): # noqa\\n print(f\\\"layer {l_i} mask:\\\")\\n for row in layer_mask:\\n print(\\\",\\\".join(map(\\\"{: >3}\\\".format, row)))\\n\\n nopy_restore_area(\\n img[:, 0:core_size, :], src_row, core_size, LAYERS\\n )\\n nopy_restore_area(\\n img[:, core_size:core_size * 2, :], res_row, core_size, LAYERS\\n )\\n nopy_restore_area(\\n img[:, core_size * 2:core_size * 3, :], mask, core_size, LAYERS\\n )\\n nopy_restore_area(\\n img[:, core_size * 3:k, :], restored_src, core_size, LAYERS\\n )\\n plt.imshow(Image.fromarray(img))\\n plt.show(block=True)\",\n \"def test_get_image(self):\\n\\n spine_data_loader = SpineDataLoader(dirpath_data=self.dirpath,\\n batch_size=4)\\n\\n for idx in range(4):\\n image = spine_data_loader.get_image(str(idx))\\n assert image.shape == (256, 256, 1)\\n assert image.min() == 0.0\\n assert image.max() == 1.0\\n assert image.dtype == 'float64'\",\n \"def UResolution(self, *args):\\n return _Adaptor3d.Adaptor3d_Surface_UResolution(self, *args)\",\n \"def __init__(self, data_source, batch_size):\\n self.batch_size = batch_size\\n self.samples_per_row = ceil(len(data_source) / batch_size)\\n self.num_samples = self.samples_per_row * batch_size\",\n \"def adjust_image_data(self):\\r\\n\\r\\n print('Adjusting image data: ')\\r\\n\\r\\n if self.removeFirstSequence: # used to remove the first trial from the sequence\\r\\n\\r\\n frames_per_rep = self.nFrames/self.nrepetitions\\r\\n\\r\\n self.imageData = self.imageData[frames_per_rep:, :, :]\\r\\n\\r\\n self.nFrames = self.imageData.shape[0]\\r\\n\\r\\n self.nrepetitions = int(self.nFrames/(self.period * self.framerate))\\r\\n\\r\\n self.times = np.arange(0, self.nFrames/self.framerate, 1.0/self.framerate)\\r\\n\\r\\n \\r\\n\\r\\n # first squeeze the image to 3d if it is 4d\\r\\n\\r\\n maxt = np.max(self.times) # find last image time\\r\\n\\r\\n sh = self.imageData.shape\\r\\n\\r\\n if len(sh) == 4:\\r\\n\\r\\n self.imageData = self.imageData.squeeze()\\r\\n\\r\\n sh = self.imageData.shape\\r\\n\\r\\n dt = np.mean(np.diff(self.times)) # get the mean dt\\r\\n\\r\\n n_Periods = int((maxt+dt)/self.period) # how many full periods in the image set - include the first?\\r\\n\\r\\n if self.nrepetitions > 0 and self.nrepetitions < n_Periods:\\r\\n\\r\\n n_Periods = self.nrepetitions\\r\\n\\r\\n n_PtsPerCycle = int(np.floor(self.period/dt)); # estimate image points in a stimulus cycle\\r\\n\\r\\n ndt = self.period/n_PtsPerCycle\\r\\n\\r\\n self.imageData = self.imageData[range(0, n_Periods*n_PtsPerCycle),:,:] # reduce to only what we need\\r\\n\\r\\n print (' Adjusted image info')\\r\\n\\r\\n print (\\\" # Periods: %d Pts/cycle: %d Cycle dt %8.4fs (%8.3fHz) Cycle: %7.4fs\\\" %(n_Periods, n_PtsPerCycle, ndt, 1.0/ndt, self.period))\\r\\n\\r\\n self.print_image_info()\",\n \"def downsample_sam(self, factor):\",\n \"def test_oss_fit_sample():\\n\\n # Resample the data\\n oss = OneSidedSelection(random_state=RND_SEED)\\n X_resampled, y_resampled = oss.fit_sample(X, Y)\\n\\n currdir = os.path.dirname(os.path.abspath(__file__))\\n X_gt = np.load(os.path.join(currdir, 'data', 'oss_x.npy'))\\n y_gt = np.load(os.path.join(currdir, 'data', 'oss_y.npy'))\\n assert_array_equal(X_resampled, X_gt)\\n assert_array_equal(y_resampled, y_gt)\",\n \"def lensedImage(self, source, scale, xl=8., yl=4., gamma=0.):\\n image, magMap = lens(self.ltype, self.dist, source, self.x01, self.x02, xl, yl, gamma)\\n\\n return image, magMap\",\n \"def change_resolution(self):\",\n \"def getResolution(self):\\n return self.resolution\",\n \"def resolution(self):\\n return next(iter(self.resolutions()), None)\",\n \"def __call__(self, src, label, mask):\\n # resize shorter side but keep in max_size\\n h, _, _ = src.shape\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n # no scaling ground-truth, return image scaling ratio instead\\n im_scale = float(img.shape[0]) / h\\n\\n img = vf.to_tensor(img)\\n img = vf.normalize(img, mean=self._mean, std=self._std)\\n return img, torch.tensor([img.shape[-2], img.shape[-1], im_scale], dtype=torch.float32)\",\n \"def _default_sampling_xrange(self):\\n from scipy.stats import rv_continuous\\n dataset = self.rvdist.rvs(1000) if rv_continuous in self.rvdist.__class__.__mro__ \\\\\\n else self.rvdist.dataset\\n scale = np.nanmax(dataset) - np.nanmin(dataset)\\n return [np.nanmin(dataset) - scale*0.05, np.nanmax(dataset) + scale*0.05]\",\n \"def get_data(name):\\n\\n if name == 'train' or name == 'unlabeled':\\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_train_X.npy')), axis=-1), \\\\\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_train_y.npy')), axis=1)\\n # custom svhn has only training/validation set\\n elif name == 'validation' or name == 'val':\\n return np.expand_dims(np.load(os.path.join(DATADIR, 'source_d10_val_X.npy')), axis=-1), \\\\\\n np.argmax(np.load(os.path.join(DATADIR, 'source_d10_val_y.npy')), axis=1)\",\n \"def get_resolution(ds):\\n\\n if 'x' in ds.coords and 'y' in ds.coords:\\n x = ds.coords['x'].values\\n y = ds.coords['y'].values\\n resx = abs(x[-1] - x[0]) / (len(x) - 1)\\n resy = abs(y[-1] - y[0]) / (len(y) - 1)\\n return (resx, resy)\\n else:\\n transform = get_transform(ds)\\n if transform is not None:\\n return (abs(transform.a), abs(transform.e))\\n elif 'res' in ds.attrs:\\n return ds.attrs['res']\\n\\n return None\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 0] * self.scale\",\n \"def _compute_raw_image_norm(self):\\n return np.sum(self._data, dtype=float)\",\n \"def __call__(self, src, label):\\n # resize shorter side but keep in max_size\\n h, w, _ = src.shape\\n img = timage.resize_short_within(src, self._short, self._max_size, interp=1)\\n # no scaling ground-truth, return image scaling ratio instead\\n bbox = tbbox.resize(label, (w, h), (img.shape[1], img.shape[0]))\\n im_scale = h / float(img.shape[0])\\n\\n img = vf.to_tensor(img)\\n img = vf.normalize(img, mean=self._mean, std=self._std)\\n return img, bbox.astype('float32'), torch.tensor([im_scale], dtype=torch.float32)\",\n \"def resample_img(image, target_shape, mode='nearest'):\\n print(target_shape)\\n resize_factor = np.array(target_shape)/image.shape\\n resampled = scipy.ndimage.interpolation.zoom(image, resize_factor,\\n mode=mode)\\n return resampled\",\n \"def test_copy_sources(self):\\n metric_copy = copy_metric(self.metric, self.DATA_MODEL)\\n self.assertEqual(\\\"Source\\\", first(metric_copy[\\\"sources\\\"].values())[\\\"name\\\"])\",\n \"def _number_of_samples(self):\\n return len(self._raw_data.samples)\",\n \"def resample(self):\\n self.variational_strategy.resample()\",\n \"def test_nib_resample_image_3d_to_dest(fake_3dimage_nib, fake_3dimage_nib_big):\\n img_r = resampling.resample_nib(fake_3dimage_nib, image_dest=fake_3dimage_nib_big, interpolation='linear')\\n assert img_r.get_data().shape == (29, 39, 19)\\n assert img_r.get_data()[4, 4, 4] == 1.0\",\n \"def quality_data(self, s):\\n known_symbols = np.mod(range(176),48)>=32\\n print('quality_data',np.sum(np.real(s[known_symbols])<0))\\n success = np.sum(np.real(s[known_symbols])<0) < 20\\n return success,0 ## no doppler estimate for data frames\",\n \"def oversampling(self):\\n return self._oversampling\",\n \"def get_image(self):\\n if self._image is None:\\n image_data = np.load(self.image_file)\\n if not isinstance(image_data, np.ndarray):\\n image_data = image_data['arr_0']\\n self.meta_data = ImageWrapper.load_metadata(self.image_file+\\\".meta\\\")\\n exposure_time = self.meta_data['exposure_time_us'] * 1e-6\\n dark_level = float(self.meta_data['black_level'])\\n # saturation_mask = image_data.max(axis=2) >= 4094\\n image_data = np.clip((image_data.astype(np.float32) - dark_level),\\n a_min=0.0, a_max=None) / exposure_time\\n if self.original_vignetting is not None:\\n image_data = image_data / self.original_vignetting\\n if self.crop is not None:\\n image_data = image_data[\\n self.crop[1,0]:self.crop[1,1],\\n self.crop[0,0]:self.crop[0,1]\\n ]\\n # saturation_mask = saturation_mask[\\n # self.crop[1,0]:self.crop[1,1],\\n # self.crop[0,0]:self.crop[0,1]\\n # ]\\n if self.down_sample is not None:\\n image_data = cv2.resize(\\n image_data,\\n dsize=None,\\n fx=1./self.down_sample,\\n fy=1./self.down_sample,\\n interpolation=cv2.INTER_AREA\\n )\\n # saturation_mask = cv2.resize(\\n # saturation_mask,\\n # dsize=None,\\n # fx=1./self.down_sample,\\n # fy=1./self.down_sample,\\n # interpolation=cv2.INTER_AREA\\n # )\\n if self.reup_sample is not None:\\n image_data = cv2.resize(\\n image_data,\\n dsize=None,\\n fx=self.reup_sample,\\n fy=self.reup_sample,\\n interpolation=cv2.INTER_CUBIC\\n )\\n # saturation_mask = cv2.resize(\\n # saturation_mask,\\n # dsize=None,\\n # fx=self.reup_sample,\\n # fy=self.reup_sample,\\n # interpolation=cv2.INTER_CUBIC\\n # )\\n image = torch.tensor(np.transpose(image_data, (2,0,1)), dtype=torch.float32, device=self.device)\\n # saturation_mask = torch.tensor(saturation_mask, dtype=torch.float32, device=self.device)\\n if not self.lazy:\\n self._image = image\\n # self._saturation_mask = saturation_mask\\n else:\\n image = self._image\\n # saturation_mask = self._saturation_mask\\n\\n return image#, saturation_mask\",\n \"def merge(sources, bounds=None, res=None, nodata=None, precision=7):\\n first = sources[0]\\n first_res = first.res\\n nodataval = first.nodatavals[0]\\n dtype = first.dtypes[0]\\n\\n # Extent from option or extent of all inputs.\\n if bounds:\\n dst_w, dst_s, dst_e, dst_n = bounds\\n else:\\n # scan input files.\\n xs = []\\n ys = []\\n for src in sources:\\n left, bottom, right, top = src.bounds\\n xs.extend([left, right])\\n ys.extend([bottom, top])\\n dst_w, dst_s, dst_e, dst_n = min(xs), min(ys), max(xs), max(ys)\\n\\n logger.debug(\\\"Output bounds: %r\\\", (dst_w, dst_s, dst_e, dst_n))\\n output_transform = Affine.translation(dst_w, dst_n)\\n logger.debug(\\\"Output transform, before scaling: %r\\\", output_transform)\\n\\n # Resolution/pixel size.\\n if not res:\\n res = first_res\\n elif not np.iterable(res):\\n res = (res, res)\\n elif len(res) == 1:\\n res = (res[0], res[0])\\n output_transform *= Affine.scale(res[0], -res[1])\\n logger.debug(\\\"Output transform, after scaling: %r\\\", output_transform)\\n\\n # Compute output array shape. We guarantee it will cover the output\\n # bounds completely.\\n output_width = int(math.ceil((dst_e - dst_w) / res[0]))\\n output_height = int(math.ceil((dst_n - dst_s) / res[1]))\\n\\n # Adjust bounds to fit.\\n dst_e, dst_s = output_transform * (output_width, output_height)\\n logger.debug(\\\"Output width: %d, height: %d\\\", output_width, output_height)\\n logger.debug(\\\"Adjusted bounds: %r\\\", (dst_w, dst_s, dst_e, dst_n))\\n\\n # create destination array\\n dest = np.zeros((first.count, output_height, output_width), dtype=dtype)\\n\\n if nodata is not None:\\n nodataval = nodata\\n logger.debug(\\\"Set nodataval: %r\\\", nodataval)\\n\\n if nodataval is not None:\\n # Only fill if the nodataval is within dtype's range.\\n inrange = False\\n if np.dtype(dtype).kind in ('i', 'u'):\\n info = np.iinfo(dtype)\\n inrange = (info.min <= nodataval <= info.max)\\n elif np.dtype(dtype).kind == 'f':\\n info = np.finfo(dtype)\\n inrange = (info.min <= nodataval <= info.max)\\n if inrange:\\n dest.fill(nodataval)\\n else:\\n warnings.warn(\\n \\\"Input file's nodata value, %s, is beyond the valid \\\"\\n \\\"range of its data type, %s. Consider overriding it \\\"\\n \\\"using the --nodata option for better results.\\\" % (\\n nodataval, dtype))\\n else:\\n nodataval = 0\\n\\n for src in sources:\\n # Real World (tm) use of boundless reads.\\n # This approach uses the maximum amount of memory to solve the problem.\\n # Making it more efficient is a TODO.\\n\\n # 1. Compute spatial intersection of destination and source.\\n src_w, src_s, src_e, src_n = src.bounds\\n\\n int_w = src_w if src_w > dst_w else dst_w\\n int_s = src_s if src_s > dst_s else dst_s\\n int_e = src_e if src_e < dst_e else dst_e\\n int_n = src_n if src_n < dst_n else dst_n\\n\\n # 2. Compute the source window.\\n src_window = get_window(\\n int_w, int_s, int_e, int_n, src.affine, precision=precision)\\n logger.debug(\\\"Src %s window: %r\\\", src.name, src_window)\\n\\n # 3. Compute the destination window.\\n dst_window = get_window(\\n int_w, int_s, int_e, int_n, output_transform, precision=precision)\\n logger.debug(\\\"Dst window: %r\\\", dst_window)\\n\\n # 4. Initialize temp array.\\n tcount = first.count\\n trows, tcols = tuple(b - a for a, b in dst_window)\\n\\n temp_shape = (tcount, trows, tcols)\\n logger.debug(\\\"Temp shape: %r\\\", temp_shape)\\n\\n temp = np.zeros(temp_shape, dtype=dtype)\\n temp = src.read(out=temp, window=src_window, boundless=False,\\n masked=True)\\n\\n # 5. Copy elements of temp into dest.\\n roff, coff = dst_window[0][0], dst_window[1][0]\\n\\n region = dest[:, roff:roff + trows, coff:coff + tcols]\\n np.copyto(\\n region, temp,\\n where=np.logical_and(region == nodataval, temp.mask == False))\\n\\n return dest, output_transform\",\n \"def set_data_shape(self):\\n # 如果分辨率是 1000 米\\n if self.resolution == 1000:\\n satellite_type = ['AQUA', 'TERRA']\\n if self.satellite in satellite_type:\\n try:\\n h4File = SD(self.in_file, SDC.READ)\\n in_data_r250 = h4File.select('EV_250_Aggr1km_RefSB').get()\\n self.data_shape = in_data_r250.shape[1:]\\n except Exception as e:\\n print(e)\\n else:\\n raise ValueError(\\n 'Cant read this satellite`s data.: {}'.format(self.satellite))\\n else:\\n raise ValueError(\\n \\\"Cant handle this resolution: \\\".format(self.resolution))\",\n \"def normalize_sample(sample_data):\\n BASE = 255\\n sample_data = np.array(sample_data, dtype='float32')\\n return sample_data/BASE\",\n \"def minimum_sampling(self):\\n return self.fwhm/2.\",\n \"def getDimensions():\",\n \"def downsampling(inp_img):\\n\\n\\n img = np.array(inp_img)\\n f = max(1, np.rint(np.amin(img)/256))\\n\\n if f > 1:\\n lpf = np.ones((f, f))\\n f = (1/(f*f))*lpf\\n img = cv2.filter2D(img, -1, kernel=f)\\n out = np.hstack((img[:, :, 0], img[:, :, 1], img[:, :, 2]))\\n\\n return out\",\n \"def test_full_resize(self):\\n number_of_pixels = 300\\n destination = base_path +'/test_data/rendering_tests/resized_images/'\\n source_folder = base_path + '/test_data/rendering_tests/filter_database/'\\n\\n\\n for the_file in os.listdir(destination):\\n file_path = os.path.join(destination, the_file)\\n if os.path.isfile(file_path):\\n os.unlink(file_path)\\n\\n\\n self.assertEqual(0, len(os.listdir(destination)))\\n rb.find_all_files(number_of_pixels,source_folder, destination)\\n self.assertEqual(6, len(os.listdir(destination)))\\n for the_file in os.listdir(destination):\\n file_path = os.path.join(destination,the_file)\\n with Image.open(file_path) as f:\\n self.assertNotEqual(number_of_pixels+5, f.size[0])\\n self.assertNotEqual(number_of_pixels+5, f.size[1])\\n # the above checks that the size does not vary as needed\\n # probably not necessary\\n self.assertEqual(number_of_pixels, f.size[0])\\n self.assertEqual(number_of_pixels, f.size[1])\",\n \"def source_freq(self) -> int:\",\n \"def prepare(dataset):\\n dataset = dataset.reshape(dataset.shape[0], 1, 28, 28)\\n dataset = dataset.astype('float32')\\n dataset /= 255\\n return dataset\",\n \"def test_calc_res():\\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\\n xr_res = ds.utils.calc_res(src)\\n with rasterio.open(TEST_RASTER_PATH) as src:\\n rio_res = src.res\\n assert np.allclose(xr_res, rio_res)\",\n \"def resample(self, data_arr):\\n data_arr = np.asarray(data_arr, dtype=np.float32, order='C')\\n check_audio(data_arr, is_mono=False)\\n\\n fn = self._lib['resampleObj_resample']\\n fn.argtypes = [POINTER(OpaqueResample),\\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS'),\\n c_int,\\n np.ctypeslib.ndpointer(dtype=np.float32, ndim=1, flags='C_CONTIGUOUS')]\\n data_len = data_arr.shape[-1]\\n\\n if data_arr.ndim == 1:\\n ret_arr = np.zeros(data_len * 5, dtype=np.float32)\\n new_arr_len = fn(self._obj, data_arr, c_int(data_len), ret_arr)\\n else:\\n data_arr, o_channel_shape = format_channel(data_arr, 1)\\n channel_num = data_arr.shape[0]\\n\\n ret_arr = np.zeros((channel_num, data_len * 5), dtype=np.float32)\\n new_arr_len = 0\\n for i in range(channel_num):\\n _new_arr_len = fn(self._obj, data_arr[i], c_int(data_len), ret_arr[i])\\n new_arr_len = max(new_arr_len, _new_arr_len)\\n ret_arr = revoke_channel(ret_arr, o_channel_shape, 1)\\n\\n return ret_arr[..., :new_arr_len]\",\n \"def process_image(self, image):\\r\\n img = cv2.imread(image)\\r\\n img = img.astype(float)/127 - 1\\r\\n return np.expand_dims(img, axis=0)\",\n \"def FindScale(self):\\n\\n ## 6 and from the cv code the distance is 6 then we are good\\n print(\\\"TODO: Very hard\\\")\",\n \"def getUnscaledSamples(self, **kwargs) -> TimeData:\\n # initialise chans, startSample and endSample with the whole dataset\\n options = self.parseGetDataKeywords(kwargs)\\n\\n # get the files to read and the samples to take from them, in the correct order\\n dataFilesToRead, samplesToRead, scalings = self.getDataFilesForSamples(\\n options[\\\"startSample\\\"], options[\\\"endSample\\\"]\\n )\\n numSamples = options[\\\"endSample\\\"] - options[\\\"startSample\\\"] + 1\\n # set up the dictionary to hold the data\\n data = {}\\n for chan in options[\\\"chans\\\"]:\\n data[chan] = np.zeros(shape=(numSamples), dtype=self.dtype)\\n\\n # loop through chans and get data\\n sampleCounter = 0\\n for dFile, sToRead, scalar in zip(dataFilesToRead, samplesToRead, scalings):\\n # get samples - this is inclusive\\n dSamples = sToRead[1] - sToRead[0] + 1\\n # spam files always record 5 channels\\n dSamplesRead = dSamples * self.recChannels[dFile]\\n # read the data\\n byteOff = (\\n self.dataByteOffset[dFile]\\n + sToRead[0] * self.recChannels[dFile] * self.dataByteSize\\n )\\n dFilePath = os.path.join(self.dataPath, dFile)\\n dataRead = np.memmap(\\n dFilePath,\\n dtype=self.dtype,\\n mode=\\\"r\\\",\\n offset=byteOff,\\n shape=(dSamplesRead),\\n )\\n # now need to unpack this\\n for chan in options[\\\"chans\\\"]:\\n # check to make sure channel exists\\n self.checkChan(chan)\\n # get the channel index - the chanIndex should give the right order in the data file\\n # as it is the same order as in the header file\\n chanIndex = self.chanMap[chan]\\n # use the range sampleCounter -> sampleCounter + dSamples, because this actually means sampleCounter + dSamples - 1 as python ranges are not inclusive of the end value\\n # scale by the lsb scalar here - note that these can be different for each file in the run\\n data[chan][sampleCounter : sampleCounter + dSamples] = (\\n dataRead[chanIndex : dSamplesRead : self.recChannels[dFile]]\\n * scalar[chan]\\n )\\n # increment sample counter\\n sampleCounter = sampleCounter + dSamples # get ready for the next data read\\n\\n # return data\\n startTime, stopTime = self.sample2time(\\n options[\\\"startSample\\\"], options[\\\"endSample\\\"]\\n )\\n comments = []\\n comments.append(\\n \\\"Unscaled data {} to {} read in from measurement {}, samples {} to {}\\\".format(\\n startTime,\\n stopTime,\\n self.dataPath,\\n options[\\\"startSample\\\"],\\n options[\\\"endSample\\\"],\\n )\\n )\\n comments.append(\\\"Data read from {} files in total\\\".format(len(dataFilesToRead)))\\n comments.append(\\n \\\"Data scaled to mV for all channels using scalings in header files\\\"\\n )\\n comments.append(\\\"Sampling frequency {}\\\".format(self.getSampleFreq()))\\n return TimeData(\\n sampleFreq=self.getSampleFreq(),\\n startTime=startTime,\\n stopTime=stopTime,\\n data=data,\\n comments=comments,\\n )\",\n \"def resize(data, data_type, interpolation_method, target_height, target_width):\\r\\n if target_width == data.shape[1] and target_height == data.shape[0]:\\r\\n return data\\r\\n\\r\\n if data_type == 'stereo':\\r\\n # disparity map\\r\\n if interpolation_method == 'bilinear':\\r\\n result = util_cython.resize(data, target_width, target_height, 0.5)\\r\\n else:\\r\\n result = cv2.resize(data, (target_width, target_height), interpolation=cv2.INTER_NEAREST)\\r\\n else:\\r\\n # optical flow field\\r\\n result = np.zeros((target_height, target_width, 2))\\r\\n if interpolation_method == 'bilinear':\\r\\n result[:, :, 0] = util_cython.resize(data[:, :, 0], target_width, target_height, 0.5)\\r\\n result[:, :, 1] = util_cython.resize(data[:, :, 1], target_width, target_height, 0.5)\\r\\n else:\\r\\n result = cv2.resize(data, (target_width, target_height), interpolation=cv2.INTER_NEAREST)\\r\\n return result\",\n \"def query_frame(self):\\n x, y = N.ogrid[0:self._resolution[1], 0:self._resolution[0]]\\n x0, y0 = int(self._resolution[1] / 2), int(self._resolution[0] / 2)\\n r = N.hypot(x - x0, y - y0)\\n w0 = 75.0\\n self.frame = N.array(N.exp(-r ** 2 / w0 ** 2) * 60000, dtype=N.uint16)\\n self.frame += N.random.uniform(low=0, high=5535, size=self._resolution[::-1])\",\n \"def resample(self, data):\\n # Resample signal\\n if self._fs_out != self._fs_in:\\n\\n # Check that new sample rate is compatable with batch size\\n if self._next_frame_idx != 0:\\n raise ValueError('New sample rate incompatable with batch size.')\\n\\n # Input dimensions\\n frames_in = data.shape[0] # samples\\n len_data = float(frames_in) / self._fs_in # seconds\\n\\n # Output dimensions\\n frames_out = int(np.round(len_data*self._fs_out)) # samples\\n delta_out = 1.0 / self._fs_out # seconds\\n\\n # Predict next frame\\n self._next_frame_idx = int(np.round(frames_out*delta_out*self._fs_in)) - \\\\\\n frames_in\\n\\n # Compute indices to output\\n idx_out = np.round(np.arange(frames_out)*delta_out*self._fs_in)\\n\\n # Sample data using output indices\\n data_out = np.zeros((frames_out, data.shape[1]))\\n for i in range(frames_out):\\n idx = min(frames_in - 1, idx_out[i])\\n data_out[i, :] = data[int(idx), :]\\n\\n else:\\n\\n # No resampling\\n data_out = data\\n\\n return data_out\",\n \"def resample(image, flow):\\r\\n assert flow.shape[1] == 2\\r\\n b, c, h, w = image.shape\\r\\n grid = get_grid(b, (h, w))\\r\\n flow = L.concat([flow[:, 0:1, :, :] / ((w - 1.0) / 2.0),\\r\\n flow[:, 1:2, :, :] / ((h - 1.0) / 2.0)], 1)\\r\\n final_grid = L.transpose((grid + flow), (0, 2, 3, 1))\\r\\n image.stop_gradient = False\\r\\n try:\\r\\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border', align_corners=True)\\r\\n except Exception:\\r\\n output = nn.functional.grid_sample(image, final_grid, mode='bilinear', padding_mode='border')\\r\\n \\r\\n return output\\r\\n # return image\\r\\n # return L.zeros_like(image)\\r\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5770578","0.5644546","0.56255263","0.55761415","0.54033136","0.5392939","0.536528","0.5364385","0.5331956","0.5313504","0.5307943","0.5276826","0.52704465","0.52479804","0.5238506","0.5231732","0.52003103","0.517683","0.5145478","0.51325125","0.51163715","0.5104453","0.5092835","0.50851905","0.5081938","0.5079806","0.50769687","0.50678796","0.5065648","0.5063772","0.50579244","0.50506955","0.50435823","0.50368625","0.5019365","0.5019097","0.5019097","0.50190884","0.50138164","0.5010639","0.5004717","0.50035745","0.49978384","0.49829066","0.49809545","0.49789864","0.4972913","0.49693954","0.49625567","0.496158","0.49612066","0.49558938","0.4953811","0.495172","0.49499387","0.4943389","0.49392447","0.49276426","0.49260318","0.49247807","0.49199995","0.49087572","0.4906858","0.49059227","0.4905121","0.48984513","0.48974463","0.48934284","0.48925644","0.48902303","0.488924","0.48885387","0.4886625","0.48864132","0.48849285","0.4874222","0.48653203","0.48571238","0.48525682","0.48515457","0.48468876","0.48450205","0.48409206","0.48406142","0.4839113","0.48377684","0.48320112","0.4829218","0.48279652","0.48271546","0.48214445","0.4820808","0.48203573","0.48198593","0.48190567","0.48169672","0.48118308","0.48097494","0.4806397","0.48053595","0.47959656"],"string":"[\n \"0.5770578\",\n \"0.5644546\",\n \"0.56255263\",\n \"0.55761415\",\n \"0.54033136\",\n \"0.5392939\",\n \"0.536528\",\n \"0.5364385\",\n \"0.5331956\",\n \"0.5313504\",\n \"0.5307943\",\n \"0.5276826\",\n \"0.52704465\",\n \"0.52479804\",\n \"0.5238506\",\n \"0.5231732\",\n \"0.52003103\",\n \"0.517683\",\n \"0.5145478\",\n \"0.51325125\",\n \"0.51163715\",\n \"0.5104453\",\n \"0.5092835\",\n \"0.50851905\",\n \"0.5081938\",\n \"0.5079806\",\n \"0.50769687\",\n \"0.50678796\",\n \"0.5065648\",\n \"0.5063772\",\n \"0.50579244\",\n \"0.50506955\",\n \"0.50435823\",\n \"0.50368625\",\n \"0.5019365\",\n \"0.5019097\",\n \"0.5019097\",\n \"0.50190884\",\n \"0.50138164\",\n \"0.5010639\",\n \"0.5004717\",\n \"0.50035745\",\n \"0.49978384\",\n \"0.49829066\",\n \"0.49809545\",\n \"0.49789864\",\n \"0.4972913\",\n \"0.49693954\",\n \"0.49625567\",\n \"0.496158\",\n \"0.49612066\",\n \"0.49558938\",\n \"0.4953811\",\n \"0.495172\",\n \"0.49499387\",\n \"0.4943389\",\n \"0.49392447\",\n \"0.49276426\",\n \"0.49260318\",\n \"0.49247807\",\n \"0.49199995\",\n \"0.49087572\",\n \"0.4906858\",\n \"0.49059227\",\n \"0.4905121\",\n \"0.48984513\",\n \"0.48974463\",\n \"0.48934284\",\n \"0.48925644\",\n \"0.48902303\",\n \"0.488924\",\n \"0.48885387\",\n \"0.4886625\",\n \"0.48864132\",\n \"0.48849285\",\n \"0.4874222\",\n \"0.48653203\",\n \"0.48571238\",\n \"0.48525682\",\n \"0.48515457\",\n \"0.48468876\",\n \"0.48450205\",\n \"0.48409206\",\n \"0.48406142\",\n \"0.4839113\",\n \"0.48377684\",\n \"0.48320112\",\n \"0.4829218\",\n \"0.48279652\",\n \"0.48271546\",\n \"0.48214445\",\n \"0.4820808\",\n \"0.48203573\",\n \"0.48198593\",\n \"0.48190567\",\n \"0.48169672\",\n \"0.48118308\",\n \"0.48097494\",\n \"0.4806397\",\n \"0.48053595\",\n \"0.47959656\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":95695,"cells":{"query":{"kind":"string","value":"Returns the (width, height) of pixels in this Dataset's units."},"document":{"kind":"string","value":"def GetPixelDimensions(self):\n _, width, _, _, _, height = self.GetGeoTransform()\n return XY(x=width, y=height)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dimensions (self):\n return (self.width, self.height)","def get_size(self) -> Tuple2IntType:\n return self.get_width(), self.get_height()","def size(self):\n return self.width, self.height","def get_size_inches(self):\n width, height = self.figure.get_size_inches()\n bbox = self.get_position()\n width = width * abs(bbox.width)\n height = height * abs(bbox.height)\n return width, height","def pixelSize(self):\n br = self.sceneBoundingRect()\n if self.image is None:\n return 1,1\n return br.width()/self.width(), br.height()/self.height()","def getPixelSize(self):\n return (0.000013, 0.000013)","def size(self) -> typing.Tuple[int, int]:\n return self.width, self.height","def getSize(self):\n return self.__width * self.__height;","def getSize(self):\n return (int(self.getWidth()), int(self.getHeight()))","def get_pixel_size(self):\n raise NotImplementedError","def size(self) -> Tuple[int, int]:\n return self._width, self._height","def size(self) -> Tuple[int, int]:\n return (self.width, self.height)","def size(self):\n return (self.width, self.height)","def getDimensions():","def size(self):\n return (self.width(), self.height())","def size_in(self):\n return self.dimensions","def size(self):\n return (self.width)","def size(self):\n return (self._width, self._height)","def get_image_size(self, **kwargs):\n points = kwargs['points']\n max_val = points.max(0)\n min_val = points.min(0)\n height = np.ceil((max_val[0] - min_val[0]) * self.res_x).astype(int)\n width = np.ceil((max_val[1] - min_val[1]) * self.res_y).astype(int)\n\n return height, width","def px_size(self):\n xp, yp = ct.c_float(), ct.c_float()\n\n self.lib.GetPixelSize(ct.pointer(xp), ct.pointer(yp))\n\n return (xp.value, yp.value)","def getDimensions(self):\n\ttop = self.getTop()\n\tleft = self.getLeft()\n\twidth = self.getWidth()\n\theight = self.getHeight()\n\treturn top, left, width, height","def _size_pixels(self, renderer):\n return renderer.points_to_pixels(self.size)","def numPixels(self):\n\t\treturn self.size","def numPixels(self):\n\t\treturn self.size","def getDimensions(self):\n return _libsbml.Layout_getDimensions(self)","def resolution_pixels_xy(self):\n return np.array([self._photo_size_pixels, self._photo_size_pixels])","def getDims(self):\n size = self.screenSize\n height = int(size.height())\n width = int(size.width())\n return height, width","def pix_size(self):\n return self._pix_size","def resolution(self):\n return {'x': self.width, 'y': self.height}","def get_display_px(self):\n return self.image.size","def _get_image_dimensions(self):\n\t\timageWidth = int(self.labels['IMAGE']['LINE_SAMPLES'])\n\t\timageHeight = int(self.labels['IMAGE']['LINES'])\n\t\treturn imageWidth, imageHeight","def get_dim():\n return (Settings.width, Settings.height)","def size(self):\n return self.width","def size(self):\n return self.width","def size(self):\n return self.width","def size(self):\n return self.width","def size(self):\n return self.width","def size(self):\n return self.width","def size(self):\n return self.width","def dimension(self) -> float:\n return self._dimensions","def shape(self) -> tuple[int, int]:\n return self.height, self.width","def get_data_dimensions(self):\n return image_utils.convert_shape_indexing(self._get_data_dimensions_rc(),\"rc\",self.image_indexing)","def size_out(self):\n return self.dimensions","def size(self) -> (float, float):\n\n return self.screen.get_surface().get_size()","def get_pixel_size(self):\n p0 = core.PointF(0, 0)\n p1 = core.PointF(1, 1)\n tr = self.transform().inverted()[0]\n p01 = tr.map(p0)\n p11 = tr.map(p1)\n return core.PointF(p11 - p01)","def get_size(self):\n result_str = subprocess.check_output([\n ADB_EXECUTOR, '-s', self.device_id, 'shell',\n 'wm', 'size'\n ]).decode(DEFAULT_CHARSET)\n width, height = result_str.replace('\\n', '').replace('\\r', '').split(' ')[-1].split('x')\n return width, height","def size(self):\n\n return self.width","def dimensions(self) -> typing.Tuple[int, int]:\n dimensions = self.data[2]\n dimensions = re.findall(r'(\\d+)\\s+x\\s+(\\d+)\\s+M', dimensions.replace('-', '0'))\n return dimensions[0] if dimensions else (0, 0)","def get_dimension_width(self):\n pass","def getDim(self):\n return \"%dx%d\" % (self.rows, self.cols)","def dimensions():","def get_data_extent(self):\n x, y = self.xy[0], self.xy[1]\n w, h = self.width, self.height\n return x, y, w, h","def getDimensions(self):\n return self._majax, self._minax, self._pa","def dimensions(self) -> int:\n return pulumi.get(self, \"dimensions\")","def get_dimension_height(self):\n pass","def canvas_size(self):\r\n width = height = 0\r\n for image in self.images:\r\n x = image.x + image.absolute_width\r\n y = image.y + image.absolute_height\r\n if width < x:\r\n width = x\r\n if height < y:\r\n height = y\r\n return round_up(width), round_up(height)","def get_detector_size(self):\n sensor=self._get_sensor_info()\n return sensor.nMaxWidth,sensor.nMaxHeight","def dimension_size(self):\n return self._dim","def getShape(self):\n if self.initDone:\n return self.pixelHeight,self.pixelWidth\n\n self._waitForInit()\n\n return self.pixelHeight,self.pixelWidth","def __len__(self):\n return self.width * self.height","def toPixels(self):\n return (self.tile * AxisDistance.tilesize) + self.pixel","def area(self):\r\n return self.width * self.height","def DoGetSize(self):\r\n\r\n return self._rect.width, self._rect.height","def get_state_size(self) -> Tuple[int, int]:\n return self.height, self.width","def get_current_resolution(self):\n return self.display_info[\"width\"], self.display_info[\"height\"]","def get_dimensions(self):\n return self.lon_arr.shape","def area(self):\n\t\treturn self.width * self.height","def width(self) -> int:\n return self._image_data.width","def area(self):\n return self.__width * self.__height","def area(self):\n return self.__width * self.__height","def area(self):\n return self.__width * self.__height","def area(self):\n return self.__width * self.__height","def area(self):\n return self.__width * self.__height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def area(self):\n return self.width * self.height","def get_target_dimensions(self):\n if self.target_height is None:\n self._calculate_target_dimensions()\n return int(self.target_width), int(self.target_height)","def tile_size_2d(self):\n return 32.0, 32.0","def __len__(self) -> int:\n return self.width * self.height","def width(self):\n return _libsbml.Dimensions_width(self)","def area(self):\n return (self.__width * self.__height)","def area(self):\n return (self.__width * self.__height)","def width(self):\n return self._get_mean_and_samples_attribute('width')","def area(self):\n return self.__height * self.__width","def area(self):\n return self.__height * self.__width","def area(self):\n return self.__height * self.__width","def getDimension(data):\r\n # open image for reading in binary mode\r\n\r\n # read the 2 bytes\r\n a = data[163:165]\r\n\r\n # calculate height\r\n height = (a[0] << 8) + a[1]\r\n\r\n # next 2 bytes is width\r\n a = data[165:167]\r\n\r\n # calculate width\r\n width = (a[0] << 8) + a[1]\r\n\r\n return (width, height)","def area(self):\n return self._width * self._height","def get_image_size(self):","def pixelsize(self):\n if hasattr(self, \"_pixelsize\"):\n return self._pixelsize\n\n try:\n return self.header[\"PixSize\"] # [arcsec]\n except KeyError:\n try:\n return abs(self.header[\"CDELT1\"]) * 3600 # [deg] -> [arcsec]\n except KeyError:\n return None","def _get_dimensions(self):\n corners = []\n for module in self.modules:\n for tile in module:\n corners.append(tile.corners())\n corners = np.concatenate(corners)[:, :2] / self._pixel_shape\n\n # Find extremes, add 1 px margin to allow for rounding errors\n min_xy = corners.min(axis=0).astype(int) - 1\n max_xy = corners.max(axis=0).astype(int) + 1\n\n size = max_xy - min_xy\n centre = -min_xy\n # Switch xy -> yx\n return tuple(size[::-1]), centre[::-1]","def get_density(self):\n xpx, ypx = self.get_axes_pixelsize()\n\n xmin, xmax = self.axes.get_xlim()\n ymin, ymax = self.axes.get_ylim()\n width = xmax - xmin\n height = ymax - ymin\n\n return width / xpx, height / ypx","def get_image_shape(self) -> Tuple[int, int]:\n x = self.header.get(\"Rows\")\n y = self.header.get(\"Columns\")\n if x is not None and y is not None:\n return (x // self.size, y // self.size)","def GetDimensions(filename):\n with Image(filename=filename) as img:\n dimensions = (img.width, img.height)\n return(dimensions)","def area(self):\n\n return self.__width * self.__height"],"string":"[\n \"def dimensions (self):\\n return (self.width, self.height)\",\n \"def get_size(self) -> Tuple2IntType:\\n return self.get_width(), self.get_height()\",\n \"def size(self):\\n return self.width, self.height\",\n \"def get_size_inches(self):\\n width, height = self.figure.get_size_inches()\\n bbox = self.get_position()\\n width = width * abs(bbox.width)\\n height = height * abs(bbox.height)\\n return width, height\",\n \"def pixelSize(self):\\n br = self.sceneBoundingRect()\\n if self.image is None:\\n return 1,1\\n return br.width()/self.width(), br.height()/self.height()\",\n \"def getPixelSize(self):\\n return (0.000013, 0.000013)\",\n \"def size(self) -> typing.Tuple[int, int]:\\n return self.width, self.height\",\n \"def getSize(self):\\n return self.__width * self.__height;\",\n \"def getSize(self):\\n return (int(self.getWidth()), int(self.getHeight()))\",\n \"def get_pixel_size(self):\\n raise NotImplementedError\",\n \"def size(self) -> Tuple[int, int]:\\n return self._width, self._height\",\n \"def size(self) -> Tuple[int, int]:\\n return (self.width, self.height)\",\n \"def size(self):\\n return (self.width, self.height)\",\n \"def getDimensions():\",\n \"def size(self):\\n return (self.width(), self.height())\",\n \"def size_in(self):\\n return self.dimensions\",\n \"def size(self):\\n return (self.width)\",\n \"def size(self):\\n return (self._width, self._height)\",\n \"def get_image_size(self, **kwargs):\\n points = kwargs['points']\\n max_val = points.max(0)\\n min_val = points.min(0)\\n height = np.ceil((max_val[0] - min_val[0]) * self.res_x).astype(int)\\n width = np.ceil((max_val[1] - min_val[1]) * self.res_y).astype(int)\\n\\n return height, width\",\n \"def px_size(self):\\n xp, yp = ct.c_float(), ct.c_float()\\n\\n self.lib.GetPixelSize(ct.pointer(xp), ct.pointer(yp))\\n\\n return (xp.value, yp.value)\",\n \"def getDimensions(self):\\n\\ttop = self.getTop()\\n\\tleft = self.getLeft()\\n\\twidth = self.getWidth()\\n\\theight = self.getHeight()\\n\\treturn top, left, width, height\",\n \"def _size_pixels(self, renderer):\\n return renderer.points_to_pixels(self.size)\",\n \"def numPixels(self):\\n\\t\\treturn self.size\",\n \"def numPixels(self):\\n\\t\\treturn self.size\",\n \"def getDimensions(self):\\n return _libsbml.Layout_getDimensions(self)\",\n \"def resolution_pixels_xy(self):\\n return np.array([self._photo_size_pixels, self._photo_size_pixels])\",\n \"def getDims(self):\\n size = self.screenSize\\n height = int(size.height())\\n width = int(size.width())\\n return height, width\",\n \"def pix_size(self):\\n return self._pix_size\",\n \"def resolution(self):\\n return {'x': self.width, 'y': self.height}\",\n \"def get_display_px(self):\\n return self.image.size\",\n \"def _get_image_dimensions(self):\\n\\t\\timageWidth = int(self.labels['IMAGE']['LINE_SAMPLES'])\\n\\t\\timageHeight = int(self.labels['IMAGE']['LINES'])\\n\\t\\treturn imageWidth, imageHeight\",\n \"def get_dim():\\n return (Settings.width, Settings.height)\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def size(self):\\n return self.width\",\n \"def dimension(self) -> float:\\n return self._dimensions\",\n \"def shape(self) -> tuple[int, int]:\\n return self.height, self.width\",\n \"def get_data_dimensions(self):\\n return image_utils.convert_shape_indexing(self._get_data_dimensions_rc(),\\\"rc\\\",self.image_indexing)\",\n \"def size_out(self):\\n return self.dimensions\",\n \"def size(self) -> (float, float):\\n\\n return self.screen.get_surface().get_size()\",\n \"def get_pixel_size(self):\\n p0 = core.PointF(0, 0)\\n p1 = core.PointF(1, 1)\\n tr = self.transform().inverted()[0]\\n p01 = tr.map(p0)\\n p11 = tr.map(p1)\\n return core.PointF(p11 - p01)\",\n \"def get_size(self):\\n result_str = subprocess.check_output([\\n ADB_EXECUTOR, '-s', self.device_id, 'shell',\\n 'wm', 'size'\\n ]).decode(DEFAULT_CHARSET)\\n width, height = result_str.replace('\\\\n', '').replace('\\\\r', '').split(' ')[-1].split('x')\\n return width, height\",\n \"def size(self):\\n\\n return self.width\",\n \"def dimensions(self) -> typing.Tuple[int, int]:\\n dimensions = self.data[2]\\n dimensions = re.findall(r'(\\\\d+)\\\\s+x\\\\s+(\\\\d+)\\\\s+M', dimensions.replace('-', '0'))\\n return dimensions[0] if dimensions else (0, 0)\",\n \"def get_dimension_width(self):\\n pass\",\n \"def getDim(self):\\n return \\\"%dx%d\\\" % (self.rows, self.cols)\",\n \"def dimensions():\",\n \"def get_data_extent(self):\\n x, y = self.xy[0], self.xy[1]\\n w, h = self.width, self.height\\n return x, y, w, h\",\n \"def getDimensions(self):\\n return self._majax, self._minax, self._pa\",\n \"def dimensions(self) -> int:\\n return pulumi.get(self, \\\"dimensions\\\")\",\n \"def get_dimension_height(self):\\n pass\",\n \"def canvas_size(self):\\r\\n width = height = 0\\r\\n for image in self.images:\\r\\n x = image.x + image.absolute_width\\r\\n y = image.y + image.absolute_height\\r\\n if width < x:\\r\\n width = x\\r\\n if height < y:\\r\\n height = y\\r\\n return round_up(width), round_up(height)\",\n \"def get_detector_size(self):\\n sensor=self._get_sensor_info()\\n return sensor.nMaxWidth,sensor.nMaxHeight\",\n \"def dimension_size(self):\\n return self._dim\",\n \"def getShape(self):\\n if self.initDone:\\n return self.pixelHeight,self.pixelWidth\\n\\n self._waitForInit()\\n\\n return self.pixelHeight,self.pixelWidth\",\n \"def __len__(self):\\n return self.width * self.height\",\n \"def toPixels(self):\\n return (self.tile * AxisDistance.tilesize) + self.pixel\",\n \"def area(self):\\r\\n return self.width * self.height\",\n \"def DoGetSize(self):\\r\\n\\r\\n return self._rect.width, self._rect.height\",\n \"def get_state_size(self) -> Tuple[int, int]:\\n return self.height, self.width\",\n \"def get_current_resolution(self):\\n return self.display_info[\\\"width\\\"], self.display_info[\\\"height\\\"]\",\n \"def get_dimensions(self):\\n return self.lon_arr.shape\",\n \"def area(self):\\n\\t\\treturn self.width * self.height\",\n \"def width(self) -> int:\\n return self._image_data.width\",\n \"def area(self):\\n return self.__width * self.__height\",\n \"def area(self):\\n return self.__width * self.__height\",\n \"def area(self):\\n return self.__width * self.__height\",\n \"def area(self):\\n return self.__width * self.__height\",\n \"def area(self):\\n return self.__width * self.__height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def area(self):\\n return self.width * self.height\",\n \"def get_target_dimensions(self):\\n if self.target_height is None:\\n self._calculate_target_dimensions()\\n return int(self.target_width), int(self.target_height)\",\n \"def tile_size_2d(self):\\n return 32.0, 32.0\",\n \"def __len__(self) -> int:\\n return self.width * self.height\",\n \"def width(self):\\n return _libsbml.Dimensions_width(self)\",\n \"def area(self):\\n return (self.__width * self.__height)\",\n \"def area(self):\\n return (self.__width * self.__height)\",\n \"def width(self):\\n return self._get_mean_and_samples_attribute('width')\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def area(self):\\n return self.__height * self.__width\",\n \"def getDimension(data):\\r\\n # open image for reading in binary mode\\r\\n\\r\\n # read the 2 bytes\\r\\n a = data[163:165]\\r\\n\\r\\n # calculate height\\r\\n height = (a[0] << 8) + a[1]\\r\\n\\r\\n # next 2 bytes is width\\r\\n a = data[165:167]\\r\\n\\r\\n # calculate width\\r\\n width = (a[0] << 8) + a[1]\\r\\n\\r\\n return (width, height)\",\n \"def area(self):\\n return self._width * self._height\",\n \"def get_image_size(self):\",\n \"def pixelsize(self):\\n if hasattr(self, \\\"_pixelsize\\\"):\\n return self._pixelsize\\n\\n try:\\n return self.header[\\\"PixSize\\\"] # [arcsec]\\n except KeyError:\\n try:\\n return abs(self.header[\\\"CDELT1\\\"]) * 3600 # [deg] -> [arcsec]\\n except KeyError:\\n return None\",\n \"def _get_dimensions(self):\\n corners = []\\n for module in self.modules:\\n for tile in module:\\n corners.append(tile.corners())\\n corners = np.concatenate(corners)[:, :2] / self._pixel_shape\\n\\n # Find extremes, add 1 px margin to allow for rounding errors\\n min_xy = corners.min(axis=0).astype(int) - 1\\n max_xy = corners.max(axis=0).astype(int) + 1\\n\\n size = max_xy - min_xy\\n centre = -min_xy\\n # Switch xy -> yx\\n return tuple(size[::-1]), centre[::-1]\",\n \"def get_density(self):\\n xpx, ypx = self.get_axes_pixelsize()\\n\\n xmin, xmax = self.axes.get_xlim()\\n ymin, ymax = self.axes.get_ylim()\\n width = xmax - xmin\\n height = ymax - ymin\\n\\n return width / xpx, height / ypx\",\n \"def get_image_shape(self) -> Tuple[int, int]:\\n x = self.header.get(\\\"Rows\\\")\\n y = self.header.get(\\\"Columns\\\")\\n if x is not None and y is not None:\\n return (x // self.size, y // self.size)\",\n \"def GetDimensions(filename):\\n with Image(filename=filename) as img:\\n dimensions = (img.width, img.height)\\n return(dimensions)\",\n \"def area(self):\\n\\n return self.__width * self.__height\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7506064","0.7479907","0.7441489","0.74142814","0.7389819","0.7367811","0.73565596","0.73126996","0.72868365","0.7277576","0.72565204","0.7253058","0.72077274","0.72069263","0.71481836","0.7120468","0.7104735","0.70551723","0.7054781","0.70520395","0.7045539","0.7022868","0.70155936","0.70155936","0.6977367","0.69575024","0.6944623","0.6929413","0.6910823","0.6899658","0.689238","0.6873743","0.68591344","0.68591344","0.68591344","0.68591344","0.68591344","0.68591344","0.68591344","0.6854056","0.6833276","0.6830843","0.6825155","0.68158436","0.68019533","0.6787426","0.6783572","0.6743567","0.67329556","0.6702341","0.66861486","0.66801685","0.6671783","0.66553265","0.66523576","0.66421914","0.6629218","0.66253066","0.6623292","0.66197604","0.6608857","0.66083455","0.65971553","0.659455","0.6589303","0.6587672","0.65810436","0.658047","0.6580235","0.6580235","0.6580235","0.6580235","0.6580235","0.6573088","0.6573088","0.6573088","0.6573088","0.6573088","0.6573088","0.6573088","0.6573088","0.6571182","0.65707433","0.6566482","0.6558894","0.65566564","0.65566564","0.65477437","0.65453875","0.65453875","0.65453875","0.65435326","0.65427756","0.6540359","0.6537701","0.6531522","0.6530893","0.6529505","0.65264636","0.652547"],"string":"[\n \"0.7506064\",\n \"0.7479907\",\n \"0.7441489\",\n \"0.74142814\",\n \"0.7389819\",\n \"0.7367811\",\n \"0.73565596\",\n \"0.73126996\",\n \"0.72868365\",\n \"0.7277576\",\n \"0.72565204\",\n \"0.7253058\",\n \"0.72077274\",\n \"0.72069263\",\n \"0.71481836\",\n \"0.7120468\",\n \"0.7104735\",\n \"0.70551723\",\n \"0.7054781\",\n \"0.70520395\",\n \"0.7045539\",\n \"0.7022868\",\n \"0.70155936\",\n \"0.70155936\",\n \"0.6977367\",\n \"0.69575024\",\n \"0.6944623\",\n \"0.6929413\",\n \"0.6910823\",\n \"0.6899658\",\n \"0.689238\",\n \"0.6873743\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.68591344\",\n \"0.6854056\",\n \"0.6833276\",\n \"0.6830843\",\n \"0.6825155\",\n \"0.68158436\",\n \"0.68019533\",\n \"0.6787426\",\n \"0.6783572\",\n \"0.6743567\",\n \"0.67329556\",\n \"0.6702341\",\n \"0.66861486\",\n \"0.66801685\",\n \"0.6671783\",\n \"0.66553265\",\n \"0.66523576\",\n \"0.66421914\",\n \"0.6629218\",\n \"0.66253066\",\n \"0.6623292\",\n \"0.66197604\",\n \"0.6608857\",\n \"0.66083455\",\n \"0.65971553\",\n \"0.659455\",\n \"0.6589303\",\n \"0.6587672\",\n \"0.65810436\",\n \"0.658047\",\n \"0.6580235\",\n \"0.6580235\",\n \"0.6580235\",\n \"0.6580235\",\n \"0.6580235\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6573088\",\n \"0.6571182\",\n \"0.65707433\",\n \"0.6566482\",\n \"0.6558894\",\n \"0.65566564\",\n \"0.65566564\",\n \"0.65477437\",\n \"0.65453875\",\n \"0.65453875\",\n \"0.65453875\",\n \"0.65435326\",\n \"0.65427756\",\n \"0.6540359\",\n \"0.6537701\",\n \"0.6531522\",\n \"0.6530893\",\n \"0.6529505\",\n \"0.65264636\",\n \"0.652547\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77364904"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95696,"cells":{"query":{"kind":"string","value":"Transforms pixel coordinates into the destination projection. If transform is None, no reprojection occurs and the dataset's SpatialReference is used."},"document":{"kind":"string","value":"def PixelCoordinates(self, x, y, transform=None):\n # Assert that pixel_x and pixel_y are valid\n if not 0 <= x <= self.RasterXSize:\n raise ValueError('x %r is not between 0 and %d' %\n (x, self.RasterXSize))\n if not 0 <= y <= self.RasterYSize:\n raise ValueError('y %r is not between 0 and %d' %\n (y, self.RasterYSize))\n\n geotransform = self.GetGeoTransform()\n coords = XY(\n geotransform[0] + geotransform[1] * x + geotransform[2] * y,\n geotransform[3] + geotransform[4] * x + geotransform[5] * y\n )\n\n if transform is None:\n return coords\n\n # Reproject\n return XY(*transform.TransformPoint(coords.x, coords.y)[0:2])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _reproject(ds, dst_crs=None, dst_transform=None, width=None, height=None,\n res=None, extent=None, **kwargs):\n\n if 'resampling' not in kwargs:\n kwargs['resampling'] = rasterio.warp.Resampling.cubic\n\n src_crs = get_crs(ds)\n src_bounds = get_bounds(ds)\n if extent is not None:\n extent = BoundingBox(*extent)\n\n #\n # Only allow inferring of width or height from aspect ratio\n # if the CRS is not changed.\n #\n if dst_crs is None:\n dst_crs = src_crs\n if width is None and height is not None:\n width = int(ncols(ds) * height / nrows(ds))\n elif height is None and width is not None:\n height = int(nrows(ds) * width / ncols(ds))\n\n # Given: transform, shape\n # Given: transform, extent\n # Given: res, extent\n # Given: shape, res\n # Given: shape, extent\n\n if dst_transform is not None:\n #\n # If the transform is given, we also need the width and height or\n # the extent.\n #\n if width is not None and height is not None:\n pass\n elif extent is not None:\n # Calculate width and height from extent\n width = int(abs(\n (extent.right - extent.left) / dst_transform.a)) + 1\n height = int(abs(\n (extent.top - extent.bottom) / dst_transform.e)) + 1\n else:\n raise ValueError('Not enough information provided.')\n\n elif extent is not None:\n #\n # Transform can be calculated from extent, if either width and height\n # or the resolution are given.\n #\n if res is not None:\n width = int(abs(\n (extent.right - extent.left) / res[0])) + 1\n height = int(abs(\n (extent.top - extent.bottom) / res[1])) + 1\n\n # The following doesn't give the correct result.\n dst_transform = rasterio.transform.from_bounds(\n *extent, width=width-1, height=height-1\n )\n\n else:\n #\n # If neither the transform nor the extent are given, infer the best\n # possible parameters from the width, height, and the resolution.\n #\n dst_transform, width, height = \\\n rasterio.warp.calculate_default_transform(\n src_crs, dst_crs,\n ncols(ds), nrows(ds),\n resolution=res,\n dst_width=width,\n dst_height=height,\n **src_bounds._asdict())\n\n src_transform = get_transform(ds)\n src_dims = get_dims(ds)\n dst_crs = _parse_crs(dst_crs)\n\n #\n # Prepare new x and y coordinate arrays\n #\n dst_x, _ = dst_transform * (np.arange(width), np.zeros(width, dtype=int))\n _, dst_y = dst_transform * (np.zeros(height, dtype=int), np.arange(height))\n dst_coords = {'x': dst_x, 'y': dst_y}\n\n #\n # Handle the case where there are extra dimensions, e.g. 'time'\n # or 'band'\n #\n extra_dims = src_dims.keys() - {'y', 'x'}\n if len(extra_dims) == 0:\n dst_shape = (height, width)\n dst_dims = ('y', 'x')\n elif len(extra_dims) == 1:\n extra_dim = extra_dims.pop()\n dst_shape = (src_dims[extra_dim], height, width)\n dst_dims = (extra_dim, 'y', 'x')\n dst_coords[extra_dim] = ds.coords[extra_dim]\n else:\n raise ValueError('More than three dimensions are not supported.')\n\n def _reproject_da(da, shape):\n #\n # Reproject a single data array\n #\n extra_dims = set(da.dims) - {'y', 'x'}\n dim_order = tuple(extra_dims) + ('y', 'x')\n values = da.transpose(*dim_order).values\n output = np.zeros(shape, dtype=da.dtype)\n output[:] = np.nan\n\n # Fix data shape for one-dimensional data\n if da.ndim == 1:\n #\n # NOTE: The stretch factor is necessary because the input data\n # must extend at least half an original resolution cell in the\n # independent dimension.\n #\n if da.dims == ('x',):\n stretch_x = int((~src_transform * dst_transform).a / 2 + 1)\n values = np.vstack([values] * stretch_x)\n output.shape = (1,) + output.shape\n elif da.dims == ('y',):\n stretch_y = int((~src_transform * dst_transform).e / 2 + 1)\n values = np.vstack([values] * stretch_y).T\n output.shape = output.shape + (1,)\n\n rasterio.warp.reproject(\n values,\n output,\n src_transform=src_transform,\n src_crs=src_crs,\n dst_transform=dst_transform,\n dst_crs=dst_crs,\n dst_nodata=np.nan,\n **kwargs\n )\n\n # Final reshape in case the input was one-dimensional\n return output.reshape(shape)\n\n if isinstance(ds, xr.Dataset):\n result = xr.Dataset(coords=dst_coords)\n\n #\n # Also reproject coordinate arrays that are defined over\n # x and y\n #\n for v in ds.coords:\n #\n # If the projection is the same, also reproject coordinate arrays\n # that are defined over only one variable.\n #\n if dst_crs == src_crs and v not in ds.dims:\n if ds.coords[v].dims == ('x',):\n result.coords[v] = \\\n (('x',), _reproject_da(ds.coords[v], (width,)))\n elif ds.coords[v].dims == ('y',):\n result.coords[v] = \\\n (('y',), _reproject_da(ds.coords[v], (height,)))\n\n if not set(ds.coords[v].dims).issuperset({'x', 'y'}):\n continue\n\n shape = (height, width)\n result.coords[v] = (('y', 'x'),\n _reproject_da(ds.coords[v], shape))\n\n #\n # Reproject the actual data\n #\n for v in ds.data_vars:\n if set(ds[v].dims) == set(dst_dims):\n result[v] = (dst_dims, _reproject_da(ds[v], dst_shape))\n elif set(ds[v].dims) == {'y', 'x'}:\n shape = (height, width)\n result[v] = (dst_dims, _reproject_da(ds[v], shape))\n else:\n result[v] = (ds[v].dims, ds[v])\n\n #\n # Create lat and lon coordinates\n #\n # if 'lat' in ds.coords and 'lon' in ds.coords:\n # lon, lat = rasterio.warp.transform(\n # src_crs, dst_crs, ds.coords['x'], ds.coords['y'])\n # result.coords['lon'] = (('x',), lon)\n # result.coords['lat'] = (('y',), lat)\n\n elif isinstance(ds, xr.DataArray):\n result = xr.DataArray(_reproject_da(ds, dst_shape), dims=dst_dims,\n coords=dst_coords, name=ds.name)\n\n result.attrs = ds.attrs\n\n # Serialize transform to tuple and store in metadata\n result.attrs['transform'] = dst_transform[:6]\n # Store CRS info in metadata\n result.attrs['crs'] = dst_crs.to_string()\n result.attrs['coordinate_system_string'] = dst_crs.wkt\n # Store new data shape in metadata\n result.attrs['lines'] = nrows(result)\n result.attrs['samples'] = ncols(result)\n\n _add_latlon(result)\n\n return result","def reproject(self, lon, lat):\n if self.xform is None:\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\n return self.xform.transform(QgsPointXY(lon, lat))","def apply(self, ds):\n\n return _reproject(ds, dst_crs=self.crs, dst_transform=self.transform,\n width=self.width, height=self.height, res=self.res,\n extent=self.extent, **self.kwargs)","def reproject(\n self,\n dst_crs=None,\n dst_res=None,\n dst_transform=None,\n dst_width=None,\n dst_height=None,\n dst_nodata=None,\n method=\"nearest\",\n align=False,\n ):\n\n def _reproj(da, **kwargs):\n return da.raster._reproject(**kwargs)\n\n # parse and check destination grid and crs\n dst_crs = self._dst_crs(dst_crs)\n dst_transform, dst_width, dst_height = self._dst_transform(\n dst_crs, dst_res, dst_transform, dst_width, dst_height, align\n )\n reproj_kwargs = dict(\n dst_crs=dst_crs,\n dst_transform=dst_transform,\n dst_width=dst_width,\n dst_height=dst_height,\n )\n # gdal resampling method with exception for index based resampling\n method = method.lower()\n if method == \"nearest_index\":\n index = self.nearest_index(**reproj_kwargs)\n return self.reindex2d(index, dst_nodata)\n # update reproject settings\n if dst_nodata is None:\n dst_nodata = self.nodata if self.nodata is not None else np.nan\n reproj_kwargs.update(method=method, dst_nodata=dst_nodata)\n if self._obj.chunks is None:\n da_reproj = _reproj(self._obj, **reproj_kwargs)\n else:\n # create template with dask data\n dst_coords = {\n d: self._obj.coords[d]\n for d in self._obj.dims\n if d not in [self.x_dim, self.y_dim]\n }\n coords = gis_utils.affine_to_coords(\n dst_transform,\n (dst_height, dst_width),\n x_dim=self.x_dim,\n y_dim=self.y_dim,\n )\n dst_coords.update(coords)\n da_temp = full(\n dst_coords,\n nodata=dst_nodata,\n dtype=self._obj.dtype,\n name=self._obj.name,\n attrs=self._obj.attrs,\n crs=dst_crs,\n lazy=True,\n shape=(dst_height, dst_width)\n if self.dim0 is None\n else (self._obj.shape[0], dst_height, dst_width),\n dims=self.dims if self.dim0 is None else (self.dim0, *self.dims),\n )\n # no chunks on spatial dims\n chunksize = max(self._obj.chunks[0])\n chunks = {d: chunksize if d == self.dim0 else -1 for d in self._obj.dims}\n _da = self._obj.chunk(chunks)\n da_temp = da_temp.chunk(chunks)\n da_reproj = _da.map_blocks(_reproj, kwargs=reproj_kwargs, template=da_temp)\n da_reproj.raster.set_crs(dst_crs)\n return da_reproj.raster.reset_spatial_dims_attrs()","def geotransform(self):\n return self.dataset.GetGeoTransform() if self.dataset else None","def reproject(self, spatial_reference, transformation=None, inplace=False):\r\n if HASARCPY:\r\n if isinstance(spatial_reference, arcpy.SpatialReference):\r\n wkt = spatial_reference.exportToString()\r\n wkid = spatial_reference.factoryCode\r\n if wkid:\r\n sr = _types.SpatialReference({'wkid' : wkid})\r\n elif wkt:\r\n sr = _types.SpatialReference({'wkt': wkt})\r\n else:\r\n sr = None\r\n elif isinstance(spatial_reference, int):\r\n sr = _types.SpatialReference({'wkid' : spatial_reference})\r\n elif isinstance(spatial_reference, string_types):\r\n sr = _types.SpatialReference({'wkt' : spatial_reference})\r\n elif isinstance(spatial_reference, _types.SpatialReference):\r\n sr = spatial_reference\r\n else:\r\n raise ValueError(\"spatial_referernce must be of type: int, string, _types.SpatialReference, or arcpy.SpatialReference\")\r\n\r\n if inplace:\r\n df = self\r\n else:\r\n df = self.copy()\r\n sarcpy = sr.as_arcpy\r\n if sarcpy:\r\n geom = df.geometry.project_as(sarcpy, transformation)\r\n geom.sr = sr\r\n df.geometry = geom\r\n if inplace:\r\n return df\r\n else:\r\n raise Exception(\"could not reproject the dataframe.\")\r\n return df","def reproject(dataset, epsg):\n dataclass = dataset.__class__.__name__\n # Run appropriate reprojection method\n if dataclass == 'GeoDataFrame':\n repro = geopandas.GeoDataFrame.copy(dataclass)\n repro[repro.geometry.name] = repro.geometry.to_crs(epsg=epsg)\n repro.crs = fiona.crs.from_epsg(epsg)\n elif dataclass == 'Dataset':\n repro = gdal_reproject(dataset, '', epsg=epsg)\n return repro","def reproject_dataset_example(dataset, dataset_example, method=1):\n # open dataset that must be transformed\n try:\n if (os.path.splitext(dataset)[-1] == '.tif' or os.path.splitext(dataset)[-1] == '.TIF'):\n g = gdal.Open(dataset)\n else:\n g = dataset\n except:\n g = dataset\n epsg_from = Get_epsg(g)\n\n #exceptions\n if epsg_from == 9001:\n epsg_from = 5070\n\n # open dataset that is used for transforming the dataset\n try:\n if (os.path.splitext(dataset_example)[-1] == '.tif' or os.path.splitext(dataset_example)[-1] == '.TIF'):\n gland = gdal.Open(dataset_example)\n epsg_to = Get_epsg(gland)\n else:\n gland = dataset_example\n epsg_to = Get_epsg(gland)\n except:\n gland = dataset_example\n epsg_to = Get_epsg(gland)\n\n # Set the EPSG codes\n osng = osr.SpatialReference()\n osng.ImportFromEPSG(epsg_to)\n wgs84 = osr.SpatialReference()\n wgs84.ImportFromEPSG(epsg_from)\n\n # Get shape and geo transform from example\n geo_land = gland.GetGeoTransform()\n col=gland.RasterXSize\n rows=gland.RasterYSize\n\n # Create new raster\n mem_drv = gdal.GetDriverByName('MEM')\n dest1 = mem_drv.Create('', col, rows, 1, gdal.GDT_Float32)\n dest1.SetGeoTransform(geo_land)\n dest1.SetProjection(osng.ExportToWkt())\n\n # Perform the projection/resampling\n if method == 1:\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_NearestNeighbour)\n if method == 2:\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Bilinear)\n if method == 3:\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Lanczos)\n if method == 4:\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Average)\n \n return(dest1)","def imageTransform(self):\n ims = self.imageShape\n acs = self.activeShape\n dx = self.colVector\n dy = self.rowVector\n\n p0 = self.activeOrigin\n p1 = p0 + acs[2] * dx\n p2 = p0 + acs[1] * dy\n\n # print p0, p1, p2\n # print acs, dx, dy\n\n localPts = list(map(pg.Vector, [[0,0], [ims[2],0], [0,ims[1]], [0,0,1]])) # w and h of data of image in pixels.\n globalPts = list(map(pg.Vector, [p0, p1, p2, [0,0,1]]))\n m = pg.solve3DTransform(localPts, globalPts)\n m[:,2] = m[:,3]\n m[2] = m[3]\n m[2,2] = 1\n tr = Qt.QTransform(*m[:3,:3].transpose().reshape(9))\n return tr","def geo_transform(self):\n pass","def TransformPoint(*args, **kwargs):\n return _gdi_.GraphicsMatrix_TransformPoint(*args, **kwargs)","def _getGeoTransform(self):\n with self._getDatasetLock:\n gt = self.dataset.GetGeoTransform()\n if (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()):\n gt = gdal.GCPsToGeoTransform(self.dataset.GetGCPs())\n return gt","def warp(\r\n self,\r\n dst_crs,\r\n resampling_method=0,\r\n num_threads=4,\r\n resolution=None,\r\n nodata=None,\r\n target_align=None,\r\n ):\r\n if target_align:\r\n transform = target_align.dataset.transform\r\n width = target_align.dataset.width\r\n height = target_align.dataset.height\r\n\r\n else:\r\n if resolution:\r\n transform, width, height = rasterio.warp.calculate_default_transform(\r\n self.dataset.crs,\r\n dst_crs,\r\n self.dataset.width,\r\n self.dataset.height,\r\n *self.dataset.bounds,\r\n resolution=resolution,\r\n )\r\n else:\r\n transform, width, height = rasterio.warp.calculate_default_transform(\r\n self.dataset.crs,\r\n dst_crs,\r\n self.dataset.width,\r\n self.dataset.height,\r\n *self.dataset.bounds,\r\n )\r\n\r\n destination = np.zeros((self.dataset.count, height, width), self.__arr.dtype)\r\n\r\n self.__arr, transform = rasterio.warp.reproject(\r\n source=self.__arr,\r\n destination=destination,\r\n src_transform=self.dataset.transform,\r\n src_crs=self.dataset.crs,\r\n src_nodata=nodata,\r\n dst_transform=transform,\r\n dst_crs=dst_crs,\r\n dst_nodata=nodata,\r\n resampling=resampling_method,\r\n num_threads=num_threads,\r\n )\r\n\r\n self.__update_dataset(dst_crs, transform, nodata=nodata)","def transform(self, x, y):\n # return self.transform_2D(x, y)\n return self.transform_perspective(x, y)","def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\n\n x, y = np.ma.array(x), np.ma.array(y)\n\n # First to local proj\n _crs = check_crs(crs, raise_on_error=True)\n if isinstance(_crs, pyproj.Proj):\n x, y = transform_proj(_crs, self.proj, x, y)\n elif isinstance(_crs, Grid):\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\n\n # Then to local grid\n x = (x - self.x0) / self.dx\n y = (y - self.y0) / self.dy\n\n # See if we need to round\n if nearest:\n f = np.rint if self.pixel_ref == 'center' else np.floor\n x = f(x).astype(int)\n y = f(y).astype(int)\n\n # Mask?\n if maskout:\n if self.pixel_ref == 'center':\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\n (y >= -0.5) & (y < self.ny-0.5))\n else:\n mask = ~((x >= 0) & (x < self.nx) &\n (y >= 0) & (y < self.ny))\n x = np.ma.array(x, mask=mask)\n y = np.ma.array(y, mask=mask)\n\n return x, y","def dataTransform(self):\n # Might eventually need to account for downsampling / clipping here\n # transforms are updated in setOpts call.\n return self._dataTransform","def _project(self):\n ghosts_w = self.input_field.topology.ghosts()\n self.input_field.data[0], self.input_field.data[1], \\\n self.input_field.data[2] = \\\n fftw2py.projection_om_3d(self.input_field.data[0],\n self.input_field.data[1],\n self.input_field.data[2], ghosts_w)","def _apply_transform(self, img: np.ndarray): \n img = self.transform(image=img)[\"image\"]\n return img","def _apply_transform(self, img: np.ndarray): \n img = self.transform(image=img)[\"image\"]\n return img","def projection(self, point):\n return gs.copy(point)","def apply_projection_transform(Xb, batch_size, image_size):\n d = image_size * 0.3 * intensity\n for i in np.random.choice(batch_size, int(batch_size * p), replace = False): \n tl_top = random.uniform(-d, d) # Top left corner, top margin\n tl_left = random.uniform(-d, d) # Top left corner, left margin\n bl_bottom = random.uniform(-d, d) # Bottom left corner, bottom margin\n bl_left = random.uniform(-d, d) # Bottom left corner, left margin\n tr_top = random.uniform(-d, d) # Top right corner, top margin\n tr_right = random.uniform(-d, d) # Top right corner, right margin\n br_bottom = random.uniform(-d, d) # Bottom right corner, bottom margin\n br_right = random.uniform(-d, d) # Bottom right corner, right margin\n\n transform = ProjectiveTransform()\n transform.estimate(np.array((\n (tl_left, tl_top),\n (bl_left, image_size - bl_bottom),\n (image_size - br_right, image_size - br_bottom),\n (image_size - tr_right, tr_top)\n )), np.array((\n (0, 0),\n (0, image_size),\n (image_size, image_size),\n (image_size, 0)\n )))\n Xb[i] = warp(Xb[i], transform, output_shape=(image_size, image_size), order = 1, mode = 'edge')\n\n return Xb","def TransformPoint(transform, x, y, z):\n result = np.matmul(transform, np.array([x, y, z, 1.]))\n return result[0], result[1], result[2]","def transform_point(transform, x_in, y_in):\n # create point geometry from coordinates\n point = ogr.Geometry(ogr.wkbPoint)\n point.AddPoint(x_in, y_in)\n point.Transform(transform)\n\n x_out = point.GetX()\n y_out = point.GetY()\n return x_out, y_out","def reproject_coordinates(x_in, y_in, spatial_reference_source, spatial_reference_target=None): \n if spatial_reference_target is not None:\n pass\n else:\n spatial_reference_target = osr.SpatialReference()\n spatial_reference_target.ImportFromEPSG(4326) \n pass\n \n if int(osgeo.__version__[0]) >= 3:\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\n \n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\n\n \n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\n \n x_new,y_new, z = pTransform.TransformPoint( x_in,y_in)\n \n return x_new,y_new","def apply_transform(transform):\n vg.shape.check(locals(), \"transform\", (4, 4))\n\n def apply(points, discard_z_coord=False, treat_input_as_vector=False):\n points, is_columnized, maybe_decolumnize = columnize(\n points, (-1, 3), name=\"points\"\n )\n\n homogenous_coordinate_value = 0 if treat_input_as_vector else 1\n padded_points = np.pad(\n points,\n ((0, 0), (0, 1)),\n mode=\"constant\",\n constant_values=homogenous_coordinate_value,\n )\n transformed_padded_points = np.dot(transform, padded_points.T).T\n transformed_points = np.delete(transformed_padded_points, 3, axis=1)\n\n result = maybe_decolumnize(transformed_points)\n if discard_z_coord:\n return result[:, 0:2] if is_columnized else result[0:2]\n else:\n return result\n\n return apply","def geotransform(self):\n return self._geotransform","def Reproject(x, y, in_grid = 4326, out_grid = 32737):\n \n inProj = Proj(init='epsg:'+str(in_grid))\n outProj = Proj(init='epsg:'+str(out_grid))\n \n \n x2,y2 = transform(inProj,outProj,x,y)\n \n return x2, y2","def transform_to(self, dst_crs):\n self.nodes.transform_to(dst_crs)","def transform_proj(p1, p2, x, y, nocopy=False):\n\n try:\n # This always makes a copy, even if projections are equivalent\n return _transform_internal(p1, p2, x, y, always_xy=True)\n except TypeError:\n if proj_is_same(p1, p2):\n if nocopy:\n return x, y\n else:\n return copy.deepcopy(x), copy.deepcopy(y)\n\n return _transform_internal(p1, p2, x, y)","def _transform(self, dataset):\n raise NotImplementedError()","def get_transform(ds):\n\n if 'transform' in ds.attrs:\n ds_trans = ds.attrs['transform']\n if isinstance(ds_trans, Affine):\n return ds_trans\n else:\n return Affine(*ds_trans)\n\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\n transf_str = ds.data_vars['crs'].attrs['i2m']\n a = list(map(float, transf_str.split(',')))\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\n\n else:\n resx, resy = get_resolution(ds)\n xoff = ds['x'].values.min()\n yoff = ds['y'].values.max()\n return Affine(resx, 0, xoff, 0, resy, yoff)","def _apply_transform(self, x, transform_parameters):\n # x is a single image, so it doesn't have image number at index 0\n img_row_axis = self.row_axis - 1\n img_col_axis = self.col_axis - 1\n img_channel_axis = self.channel_axis - 1\n\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\n transform_parameters.get('tx', 0),\n transform_parameters.get('ty', 0),\n transform_parameters.get('shear', 0),\n transform_parameters.get('zx', 1),\n transform_parameters.get('zy', 1),\n row_axis=img_row_axis,\n col_axis=img_col_axis,\n channel_axis=img_channel_axis,\n fill_mode=self.fill_mode,\n cval=self.cval)\n\n if transform_parameters.get('channel_shift_intensity') is not None:\n x = apply_channel_shift(x,\n transform_parameters['channel_shift_intensity'],\n img_channel_axis)\n\n if transform_parameters.get('flip_horizontal', False):\n x = self._flip_axis(x, img_col_axis)\n\n if transform_parameters.get('flip_vertical', False):\n x = self._flip_axis(x, img_row_axis)\n\n if transform_parameters.get('brightness') is not None:\n x = apply_brightness_shift(x, transform_parameters['brightness'])\n\n return x","def projection(self):\n return self.dataset.GetProjection() if self.dataset else None","def reproject(point):\n wgs84 = pyproj.Proj('+init=epsg:4326')\n native = pyproj.Proj(DEM_PROJECTION)\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\n return geom.Point(x, y)","def transform(dataset):\n\n from tomviz import utils\n import scipy.ndimage\n import numpy as np\n import warnings\n\n array = dataset.active_scalars\n\n zoom = (0.5, 0.5, 1)\n result_shape = utils.zoom_shape(array, zoom)\n result = np.empty(result_shape, array.dtype, order='F')\n # Downsample the dataset x2 using order 1 spline (linear)\n warnings.filterwarnings('ignore', '.*output shape of zoom.*')\n scipy.ndimage.interpolation.zoom(array, zoom,\n output=result,\n order=1,\n mode='constant',\n cval=0.0, prefilter=False)\n\n # Set the result as the new scalars.\n dataset.active_scalars = result","def projective_transform(self, x):\n\n x = np.asarray(x)\n # Assume no intensity column\n x0, y0, z0 = x\n\n # Camera coors to pixel coors\n u = ((x0 / z0) * self.f) + (self.sensor_size[0] // 2)\n v = ((y0 / z0) * self.f) + (self.sensor_size[1] // 2)\n\n u_min = np.min(u)\n v_min = np.min(v)\n\n n = len(u)\n u_list = []\n v_list = []\n if self.error_on_oob:\n for i in range(n):\n if (u[i] >= u_min and u[i] <= self.sensor_size[0] and v[i] >= v_min and v[i] <= self.sensor_size[1]):\n u_list.append(u[i])\n v_list.append(v[i])\n else:\n raise OutOfSensorBoundsError(\"Projected coordinate was outside the sensor\")\n else:\n for i in range(n):\n u_list.append(u[i])\n v_list.append(v[i])\n\n u = np.asarray(u_list)\n v = np.asarray(v_list)\n\n return np.vstack((u, v))","def apply_transform(img,\n transform_matrix):\n rows,cols = img.shape[:2]\n dst = cv2.warpAffine(img,transform_matrix,(cols,rows))\n\n\n return dst","def composed_with_transform(self, w2w_transform):\n if not w2w_transform.input_space == self.world_space:\n raise CompositionError(\n \"The transform given does not apply to \"\n \"the image's world space:\\n%s\\n\\n%s\" % \n (w2w_transform, self)\n )\n new_img = self._apply_transform(w2w_transform)\n new_img.world_space = w2w_transform.output_space\n return new_img","def do_transform_point(transform: Transform, point: Point) -> Point:\n\n transform_mat = alloy.math.transformation_matrix_from_array(alloy.ros.transform_to_numpy(transform))\n point_np = alloy.ros.point_to_numpy(point)\n point_np = np.append(point_np, 1).reshape((4,1))\n trans_point = np.matmul(transform_mat, point_np)\n return alloy.ros.numpy_to_point(trans_point[0:3,0])","def transform_to(self, frame):\n if self.component_type == \"healpix\":\n raise ValueError(\n \"Direct coordinate transformation between frames is not valid \"\n \"for `healpix` type catalogs. Please use the `healpix_interp_transform` \"\n \"to transform to a new frame and interpolate to the new pixel centers. \"\n \"Alternatively, you can call `healpix_to_point` to convert the healpix map \"\n \"to a point source catalog before calling this function.\"\n )\n\n new_skycoord = self.skycoord.transform_to(frame)\n self.skycoord = new_skycoord\n\n return","def reproject_GeoGrid(geogrid_in, srs_string,\n out_xdim=None, out_ydim=None, out_geotransform=None,\n out_nodata_value=None, interp_method=None):\n src = geogrid_as_gdalInMem(geogrid_in)\n\n out_srs = osr.SpatialReference()\n assign_projection_to_srs(out_srs, srs_string)\n out_wkt = out_srs.ExportToWkt()\n\n dst_gdal_datatype = get_gdal_datatype(geogrid_in.data_array.dtype)\n\n try:\n dst = gdal.GetDriverByName('MEM').Create(\n '',\n out_xdim,\n out_ydim,\n 1,\n dst_gdal_datatype,\n )\n dst.SetGeoTransform(out_geotransform)\n dst.SetProjection(out_wkt)\n except ValueError:\n raise ValueError('Error creating dst in reproject_GeoGrid()')\n except AttributeError:\n raise ValueError('AttributeError in dst creation')\n\n\n gdal_interp_method = getGdalInterpMethod(interp_method)\n res = gdal.ReprojectImage(src,\n dst,\n src.GetProjection(),\n dst.GetProjection(),\n gdal_interp_method,\n )\n\n\n return geogrid_from_gdalInMem(dst)","def apply_transform_to_image(self,img, transform, center=None):\n \n if center is None:\n center = (np.array(img.shape)[::-1]-1)/2.0\n \n displacement = np.dot(transform, center)\n shift = center - displacement\n \n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\"constant\", order=3, cval=0.0)\n return img_tf","def apply_transform_matrix(self, img: np.ndarray, transform_matrix):\n h, w = img.shape[0], img.shape[1]\n transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)\n img = np.rollaxis(img, 2, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n\n channel_images = [scipy.ndimage.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=1,\n mode=self.fill_mode,\n cval=self.cval) for x_channel in img]\n img = np.stack(channel_images, axis=0)\n img = np.rollaxis(img, 0, 2 + 1)\n # img = apply_affine_transform(img, transform_matrix, channel_axis=2, fill_mode=self.fill_mode, cval=self.cval) # apply_transform\n return img","def replace_current_transform_group(self, transform):\n datacopy = self._shallow_clone_dataset()\n fdata = datacopy._flat_data\n datacopy._flat_data = fdata.replace_current_transform_group(transform)\n return datacopy","def projection(self, point):\n projected_point = self._iterate_over_factors(\"projection\", {\"point\": point})\n return projected_point","def transform(self, coord_trans, clone=False):\n if clone:\n klone = self.clone()\n klone.transform(coord_trans)\n return klone\n\n # Depending on the input type, use the appropriate OGR routine\n # to perform the transformation.\n if isinstance(coord_trans, CoordTransform):\n capi.geom_transform(self.ptr, coord_trans.ptr)\n elif isinstance(coord_trans, SpatialReference):\n capi.geom_transform_to(self.ptr, coord_trans.ptr)\n elif isinstance(coord_trans, (int, str)):\n sr = SpatialReference(coord_trans)\n capi.geom_transform_to(self.ptr, sr.ptr)\n else:\n raise TypeError(\n \"Transform only accepts CoordTransform, \"\n \"SpatialReference, string, and integer objects.\"\n )","def reproject_raster_dataset(src_ds, **kwargs):\n\n # checking kwargs\n spacing = kwargs.pop(\"spacing\", None)\n size = kwargs.pop(\"size\", None)\n resample = kwargs.pop(\"resample\", gdal.GRA_Bilinear)\n src_srs = kwargs.pop(\"projection_source\", None)\n dst_srs = kwargs.pop(\"projection_target\", None)\n align = kwargs.pop(\"align\", False)\n\n if spacing is None and size is None:\n raise NameError(\"Whether keyword 'spacing' or 'size' must be given\")\n\n if spacing is not None and size is not None:\n warnings.warn(\n \"both ``spacing`` and ``size`` kwargs given, ``size`` will be ignored.\",\n UserWarning,\n )\n\n # Get the GeoTransform vector\n src_geo = src_ds.GetGeoTransform()\n x_size = src_ds.RasterXSize\n y_size = src_ds.RasterYSize\n\n # get extent\n ulx = src_geo[0]\n uly = src_geo[3]\n lrx = src_geo[0] + src_geo[1] * x_size\n lry = src_geo[3] + src_geo[5] * y_size\n\n extent = np.array([[[ulx, uly], [lrx, uly]], [[ulx, lry], [lrx, lry]]])\n\n if dst_srs:\n # try to load projection from source dataset if None is given\n if src_srs is None:\n src_proj = src_ds.GetProjection()\n if not src_proj:\n raise ValueError(\n \"src_ds is missing projection information, please use ``projection_source`` kwarg and provide a fitting OSR SRS object.\"\n )\n src_srs = osr.SpatialReference()\n src_srs.ImportFromWkt(src_ds.GetProjection())\n\n # Transformation\n extent = georef.reproject(\n extent, projection_source=src_srs, projection_target=dst_srs\n )\n\n # wkt needed\n src_srs = src_srs.ExportToWkt()\n dst_srs = dst_srs.ExportToWkt()\n\n (ulx, uly, urx, ury, llx, lly, lrx, lry) = tuple(list(extent.flatten().tolist()))\n\n # align grid to destination raster or UL-corner point\n if align:\n try:\n ulx, uly = align\n except TypeError:\n pass\n\n ulx = int(max(np.floor(ulx), np.floor(llx)))\n uly = int(min(np.ceil(uly), np.ceil(ury)))\n lrx = int(min(np.ceil(lrx), np.ceil(urx)))\n lry = int(max(np.floor(lry), np.floor(lly)))\n\n # calculate cols/rows or xspacing/yspacing\n if spacing:\n try:\n x_ps, y_ps = spacing\n except TypeError:\n x_ps = spacing\n y_ps = spacing\n\n cols = int(abs(lrx - ulx) / x_ps)\n rows = int(abs(uly - lry) / y_ps)\n elif size:\n cols, rows = size\n x_ps = x_size * src_geo[1] / cols\n y_ps = y_size * abs(src_geo[5]) / rows\n\n # create destination in-memory raster\n mem_drv = gdal.GetDriverByName(\"MEM\")\n\n # and set RasterSize according ro cols/rows\n dst_ds = mem_drv.Create(\"\", cols, rows, 1, gdal.GDT_Float32)\n\n # Create the destination GeoTransform with changed x/y spacing\n dst_geo = (ulx, x_ps, src_geo[2], uly, src_geo[4], -y_ps)\n\n # apply GeoTransform to destination dataset\n dst_ds.SetGeoTransform(dst_geo)\n\n # apply Projection to destination dataset\n if dst_srs is not None:\n dst_ds.SetProjection(dst_srs)\n\n # nodata handling, need to initialize dst_ds with nodata\n src_band = src_ds.GetRasterBand(1)\n nodata = src_band.GetNoDataValue()\n dst_band = dst_ds.GetRasterBand(1)\n if nodata is not None:\n dst_band.SetNoDataValue(nodata)\n dst_band.WriteArray(np.ones((rows, cols)) * nodata)\n dst_band.FlushCache()\n\n # resample and reproject dataset\n gdal.ReprojectImage(src_ds, dst_ds, src_srs, dst_srs, resample)\n\n return dst_ds","def _get_transform(self, transform):\n try:\n from cartopy.crs import CRS\n except ModuleNotFoundError:\n CRS = None\n cartopy = getattr(self, 'name', None) == 'proplot_cartopy'\n if (\n isinstance(transform, mtransforms.Transform)\n or CRS and isinstance(transform, CRS)\n ):\n return transform\n elif transform == 'figure':\n return self.figure.transFigure\n elif transform == 'axes':\n return self.transAxes\n elif transform == 'data':\n return PlateCarree() if cartopy else self.transData\n elif cartopy and transform == 'map':\n return self.transData\n else:\n raise ValueError(f'Unknown transform {transform!r}.')","def __update_dataset(self, crs, transform, nodata=None):\r\n\r\n meta = {\r\n \"driver\": \"GTiff\",\r\n \"dtype\": self.__arr.dtype,\r\n \"nodata\": nodata,\r\n \"height\": self.__arr.shape[-2],\r\n \"width\": self.__arr.shape[-1],\r\n \"count\": self.__arr.shape[0],\r\n \"crs\": crs,\r\n \"transform\": transform,\r\n }\r\n\r\n memfile = MemoryFile()\r\n with memfile.open(**meta) as ds:\r\n ds.write(self.__arr)\r\n self.dataset = memfile.open()\r\n memfile.close()","def reproject_dataset(filename):\n\n # We want to project the GeoTIFF coordinate reference system (crs)\n # to WGS84 (e.g. into the familiar Lat/Lon pairs). WGS84 is analogous\n # to EPSG:4326\n dst_crs = 'EPSG:4326'\n\n with rasterio.open(filename) as src:\n transform, width, height = rasterio.warp.calculate_default_transform(\n src.crs, dst_crs, src.width, src.height, *src.bounds)\n kwargs = src.meta.copy()\n kwargs.update({\n 'crs': dst_crs,\n 'transform': transform,\n 'width': width,\n 'height': height,\n 'compress': 'lzw'\n })\n\n out_path = filename.split('.tif')[0] + '.wgs84.tif'\n with rasterio.open(out_path, 'w', **kwargs) as dst:\n for i in range(1, src.count + 1):\n rasterio.warp.reproject(\n source=rasterio.band(src, i),\n destination=rasterio.band(dst, i),\n src_transform=src.transform,\n src_crs=src.crs,\n dst_transform=transform,\n dst_crs=dst_crs,\n resampling=rasterio.warp.Resampling.nearest)\n\n return out_path","def project_points(X, K, R, T, distortion_flag=False, distortion_params=None):\n # Project points from 3d world coordinates to 2d image coordinates\n\n #get projection matrix\n pmatrix = projection_matrix(R, T, K)\n\n #add 4th component to points\n ones = np.ones([1,len(X[0])])\n xones=np.row_stack((X,ones))\n\n #calculate pixel coordinates\n X_camera = pmatrix.dot(xones)\n\n return X_camera","def reproject(src_path, out_path, target_crs='EPSG:4326'):\n\n # load satdata\n satdata = load(src_path)\n\n # calculate a transform and new dimensions using our dataset's current CRS and dimensions\n transform, width, height = calculate_default_transform(satdata.crs,\n target_crs,\n satdata.width,\n satdata.height,\n *satdata.bounds)\n\n # Using a copy of the metadata from the clipped raster dataset and the transform we defined above,\n # we can write a new geoTIFF containing the reprojected and clipped raster data:\n metadata = satdata.meta.copy()\n\n # Change the CRS, transform, and dimensions in metadata to match our desired output dataset\n metadata.update({'crs':target_crs,\n 'transform':transform,\n 'width':width,\n 'height':height})\n\n # apply the transform & metadata to perform the reprojection\n with rasterio.open(out_path, 'w', **metadata) as reprojected:\n for band in range(1, satdata.count + 1):\n rasterio_reproject(\n source=rasterio.band(satdata, band),\n destination=rasterio.band(reprojected, band),\n src_transform=satdata.transform,\n src_crs=satdata.crs,\n dst_transform=transform,\n dst_crs=target_crs\n )","def CopyTransform(transform):\n return copy.copy(transform)","def apply_transform_on_3d_image(image: torch.Tensor, transforms: List[Callable]) -> torch.Tensor:\n for z in range(image.shape[0]):\n pil = TF.to_pil_image(image[z])\n for transform_fn in transforms:\n pil = transform_fn(pil)\n image[z] = TF.to_tensor(pil).squeeze()\n return image","def reproject_coordinates(coordinates, inproj, outproj, flat=False):\n if flat:\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]","def testCalculateTransform(self):\n # Create some points in the first frame.\n z = 4.0\n self.evaluator.camera_height = z\n first_points = numpy.array(\n [[0, 0, z], [2, 0, z], [2, 5, z], [0, 5, z]], dtype=numpy.float32)\n # Create a transformation that will move the camera\n R = numpy.array([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])\n t = numpy.array([[3.0], [-5.0], [0.0]])\n expected_result = numpy.eye(4)\n expected_result[0:3, 0:3] = R\n expected_result[0:3, 3:] = t\n # Determine where the second points would be given that.\n second_points = (numpy.matmul(\n R, first_points.transpose()) + t).transpose()\n # Create a simple intrinsic matrix to project onto a fictional camera\n intrinsic = numpy.array(\n [[1.0, 0.0, 20.0], [0.0, 1.0, 20.0], [0.0, 0.0, 1.0]])\n # Use no distortion or transformations\n rvec = numpy.zeros((3, 1))\n tvec = rvec\n distortion = numpy.zeros((5, 1))\n # Project the points into the camera\n (camera_first_points, _) = cv2.projectPoints(\n first_points, rvec, tvec, intrinsic, distortion)\n camera_first_points = camera_first_points.squeeze()\n (camera_second_points, _) = cv2.projectPoints(\n second_points, rvec, tvec, intrinsic, distortion)\n camera_second_points = camera_second_points.squeeze()\n # Using these projected points, can the object recover the correct initial transform\n result = self.evaluator._calculateTransform(\n camera_first_points, camera_second_points, intrinsic)\n # The matrix comparisions aren't reliable near zero, so check elements manually.\n for i in range(expected_result.shape[0]):\n for j in range(expected_result.shape[1]):\n result_element = result[i, j]\n expected_element = expected_result[i, j]\n self.assertAlmostEqual(result_element, expected_element, 6,\n 'Matrix element ({0:d}, {1:d}) is incorrect.'.format(i, j))","def transform(self, X, copy=...):\n ...","def transform(self, X, copy=...):\n ...","def transform(self, X, copy=...):\n ...","def project_point(self, point: Point3D) -> Point3D:\n x, y, z = point\n cam_x, cam_y, cam_z = self._pos\n x -= cam_x\n y -= cam_y\n z -= cam_z\n dx = self._cy*(self._sz*y + self._cz*x) - self._sy*z\n dy = self._sx*(self._sy*(self._sz*y + self._cz*x) + self._cy*z) + self._cx*(self._cz*y - self._sz*x)\n dz = self._cx*(self._sy*(self._sz*y + self._cz*x) + self._cy*z) - self._sx*(self._cz*y - self._sz*x)\n return self._scale * dx/dz, self._scale * dy/dz, dz","def transform(self, R, t, scale = 1):\n\n # Build 4-by-4 projection matrix from args ----------------------------\n # This is what we are doing internally:\n # Proj = np.r_[ scale * np.c_[R, t], [[0, 0, 0, 1]] ]\n # InvProj = np.r_[ scale * np.c_[R.T, -np.dot(R.T, t)], [[0,0,0,scale]] ]\n Proj = tf_format.tf_format('4x4', R, t)\n Proj[:-1,:] *= scale\n InvProj = tf_format.tf_format('i4x4', R, t) * scale\n \n \n # Apply transformation to pts3D ---------------------------------------\n if self.pts3D is not None and self.pts3D.shape[1] > 0:\n # Use homogeneous coords\n pts3D = np.r_[self.pts3D, np.ones((1, self.pts3D.shape[1]))]\n pts3D = np.dot(Proj, pts3D)\n self.pts3D = pts3D[:3, :]\n\n # Apply transformation to cameras -------------------------------------\n # Camera poses are stored using camera-to-world transformations, we \n # need to invert the projection matrix for this to work --> \n # we use InvProj\n\n cposes = self.cam_poses\n for i in range(cposes.shape[1]):\n\n # Extract camera projection matrix\n p_cam = tf_format.tf_format('4x4', cposes[:, i])\n\n # Transform camera projection matrix\n new_p_cam = np.dot(p_cam, InvProj)\n \n # Make sure it's a true rotation!\n [u, s, vT] = np.linalg.svd(new_p_cam[:3,:3])\n cposes[:3, i] = tf_format.rodrigues( np.dot(u,vT) ).ravel()\n cposes[3:, i] = new_p_cam[:3, 3]\n\n self.cam_poses = cposes","def transform(self, X, y=None):\n X = X.copy()\n if isinstance(self.transformation, BaseEstimator):\n X[self.columns_to_transform_] = self.transformation.transform(\n X[self.columns_to_transform_]\n )\n else:\n X[self.columns_to_transform_] = X[self.columns_to_transform_].applymap(\n self.transformation\n )\n\n return X","def four_point_transform(image, pts):\n\n\tmax_x, max_y = np.max(pts[:, 0]).astype(np.int32), np.max(pts[:, 1]).astype(np.int32)\n\n\tdst = np.array([\n\t\t[0, 0],\n\t\t[image.shape[1] - 1, 0],\n\t\t[image.shape[1] - 1, image.shape[0] - 1],\n\t\t[0, image.shape[0] - 1]], dtype=\"float32\")\n\n\twarped = cv2.warpPerspective(image, cv2.getPerspectiveTransform(dst, pts), (max_x, max_y))\n\n\treturn warped","def _build_geotransform(self, i, j):\n assert isinstance(i, int), (\"i is not an integer\")\n assert isinstance(j, int), (\"j is not an integer\")\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\n self.image_metadata.geotransform)\n # integer conversion to reduce floating point error\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \n y_ignore, y_res) \n return geotransform","def project(self, point_cloud, round_px=True):\n if not isinstance(point_cloud, PointCloud) and not (isinstance(point_cloud, Point) and point_cloud.dim == 3):\n raise ValueError('Must provide PointCloud or 3D Point object for projection')\n if point_cloud.frame != self._frame:\n raise ValueError('Cannot project points in frame %s into camera with frame %s' %(point_cloud.frame, self._frame))\n\n points_proj = self.S.dot(point_cloud.data) + self.t\n if len(points_proj.shape) == 1:\n points_proj = points_proj[:, np.newaxis]\n point_depths = np.tile(points_proj[2,:], [3, 1])\n points_proj = np.divide(points_proj, point_depths)\n if round_px:\n points_proj = np.round(points_proj)\n\n if isinstance(point_cloud, Point):\n return Point(data=points_proj[:2,:].astype(np.int16), frame=self._frame)\n return ImageCoords(data=points_proj[:2,:].astype(np.int16), frame=self._frame)","def project(self, point):\n return np.round(project(self.camera.P, point)).astype(int)","def map(self, obj):\n if isinstance(obj, np.ndarray) and obj.ndim >= 2 and obj.shape[0] in (2,3):\n return fn.transformCoordinates(self, obj)\n else:\n return QtGui.QMatrix4x4.map(self, obj)","def apply_projection(projection, dataset):\n out = DatasetType(name=dataset.name, attributes=dataset.attributes)\n\n for var in projection:\n target, template = out, dataset\n while var:\n name, slice_ = var.pop(0)\n candidate = template[name]\n \n # apply slice\n if slice_:\n if isinstance(candidate, BaseType):\n candidate.data = candidate[slice_]\n elif isinstance(candidate, SequenceType):\n candidate = candidate[slice_[0]]\n elif isinstance(candidate, GridType):\n candidate = candidate[slice_]\n\n # handle structures\n if isinstance(candidate, StructureType):\n # add variable to target\n if name not in target.keys():\n if var:\n # if there are more children to add we need to clear the\n # candidate so it has only explicitly added children; \n # also, Grids are degenerated into Structures\n if isinstance(candidate, GridType):\n candidate = StructureType(candidate.name, candidate.attributes)\n candidate._keys = []\n target[name] = candidate\n target, template = target[name], template[name]\n else:\n target[name] = candidate\n\n # fix sequence data, including only variables that are in the sequence\n for seq in walk(out, SequenceType):\n seq.data = get_var(dataset, seq.id)[tuple(seq.keys())].data\n\n return out","def project(points, camera_params, theta):\n \"\"\"\n Function takes input of 3d_points, transformations and Convert 3-D points to 2-D by projecting onto images. \n Input:\n points: 3D points in world frame\n camera_params: parameters of camera corrosponding to the point\n theta: Needed For PAL camera to specify the sub camera index for the points\n Output:\n points_proj: 2D reprojected points for 3D points \n\n \"\"\"\n # Convert the 3D points to Camera Frame by rotaion followes by translation\n points_proj1 = rotate(points[:,0:3], camera_params[:, :3])\n points_proj1 += camera_params[:, 3:6]\n # FOR PAL: Converting into the Sub-camera Frame by respective rotation\n thetas = theta * np.pi / 3 \n points_proj = np.copy(points_proj1)\n points_proj[:,0] = points_proj1[:,0]*np.cos(thetas) - points_proj1[:,2]*np.sin(thetas)\n points_proj[:,2] = points_proj1[:,0]*np.sin(thetas) + points_proj1[:,2]*np.cos(thetas)\n # Avoiding Zero error\n for i in range(len(points_proj)):\n if(points_proj[i,2]==0):\n points_proj[i,0] = 0\n points_proj[i,1] = 0\n points_proj[i,2] = 1\n # 2D projection\n points_proj = points_proj[:, :2] / points_proj[:, 2, np.newaxis]\n f = camera_params[:, 6]\n k1 = camera_params[:, 7]\n k2 = camera_params[:, 8]\n n = np.sum(points_proj**2, axis=1)\n r = 1 + k1 * n + k2 * n**2\n points_proj *= (r * f)[:, np.newaxis]\n return points_proj","def update_transform(self, transform: Optional[torchvision.transforms.Compose],\n train: bool = True) -> 'BaseDataset':\n if train:\n self.train_transform = transform\n else:\n self.val_transform = transform\n return self","def project_drawn(cb, msg):\n stream = cb.streams[0]\n old_data = stream.data\n stream.update(data=msg['data'])\n element = stream.element\n stream.update(data=old_data)\n proj = cb.plot.projection\n if not isinstance(element, _Element) or element.crs == proj:\n return None\n crs = element.crs\n element.crs = proj\n return project(element, projection=crs)","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=1,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def coordinate_transform_proj4(proj1, proj2, coords):\n\n srs1 = osr.SpatialReference()\n srs2 = osr.SpatialReference()\n srs1.ImportFromProj4(proj1)\n srs2.ImportFromProj4(proj2)\n\n return coordinate_transform(srs1, srs2, coords)","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=0,\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x","def deproject_pixel(self, depth, pixel):\n if not isinstance(pixel, Point) and not pixel.dim == 2:\n raise ValueError('Must provide 2D Point object for pixel projection')\n if pixel.frame != self._frame:\n raise ValueError('Cannot deproject pixel in frame %s from camera with frame %s' %(pixel.frame, self._frame))\n\n point = np.r_[pixel.data, depth]\n point_3d = np.linalg.inv(self.S).dot(point - self.t)\n return Point(data=point_3d, frame=self._frame)","def transform_coord(proj1, proj2, x, y):\r\n\r\n # Set full EPSG projection strings\r\n proj1 = pyproj.Proj(\"+init=EPSG:\"+proj1)\r\n proj2 = pyproj.Proj(\"+init=EPSG:\"+proj2)\r\n\r\n # Convert coordinates\r\n return pyproj.transform(proj1, proj2, x, y)","def transform_coord(proj1, proj2, x, y):\r\n\r\n # Set full EPSG projection strings\r\n proj1 = pyproj.Proj(\"+init=EPSG:\"+proj1)\r\n proj2 = pyproj.Proj(\"+init=EPSG:\"+proj2)\r\n\r\n # Convert coordinates\r\n return pyproj.transform(proj1, proj2, x, y)","def computeProjection(skyPictureInstance_id):\n \n skyPictureInstance = SkyPicture.objects.get(id = skyPictureInstance_id)\n \n # Check if it has not already been computed for this instance\n if not skyPictureInstance.undistorted or skyPictureInstance.undistorted == 'undistorted/TODO.png':\n img = np.asarray(Image.open(os.path.join(settings.BASE_DIR, skyPictureInstance.image.url[1:])))\n \n if img.shape[0] > img.shape[1]:\n img = np.rot90(img)\n \n img = np.flip(img,0)\n \n img = img[:, (np.shape(img)[1]/2 - np.shape(img)[0]/2):(np.shape(img)[1]/2 + np.shape(img)[0]/2), :]\n img = transparency(img)\n img_pil_response = Image.fromarray(img, \"RGBA\")\n \n # fetch image into memory\n temp_handle = StringIO()\n img_pil_response.save(temp_handle, 'PNG', option='optimize')\n temp_handle.seek(0)\n \n filename = skyPictureInstance.date.strftime(\"%Y-%m-%d-\") + skyPictureInstance.time.strftime(\"%H-%M-%S.png\")\n suf = SimpleUploadedFile(filename, temp_handle.read(), content_type='image/png')\n skyPictureInstance.undistorted.save(filename, suf, False)\n skyPictureInstance.save()","def project_points(points, cam_matrix, trans, rot):\n\n # STEP 1: Transform pointcloud into new reference frame.\n points = np.dot(rot, points) + trans[:, None]\n\n # STEP 2: Project new pointcloud onto image frame using K matrix.\n # gives a 3 x N array of image plane coordinates in homogenous coordinates.\n homo_pixel_coords = np.dot(cam_matrix, points)\n\n # STEP 3: Convert homogenous coordinates to regular 2D coordinates.\n # To do this, you need to divide the first two coordinates of homo_pixel_coords\n # by the third coordinate.\n pixel_coords = homo_pixel_coords[:2] / homo_pixel_coords[2]\n\n # STEP 4: Convert to integers. Take the floor of pixel_coords then cast it\n # to an integer type, like numpy.int32\n pixel_coords = np.int32(np.floor(pixel_coords))\n\n return pixel_coords","def xprojection(self):\n return self.image.sum(axis=0)","def transform_coord(proj1, proj2, x, y):\r\n # Set full EPSG projection strings\r\n proj1 = pyproj.Proj(\"+init=EPSG:\"+proj1)\r\n proj2 = pyproj.Proj(\"+init=EPSG:\"+proj2)\r\n\r\n # Convert coordinates\r\n return pyproj.transform(proj1, proj2, x, y)","def displace_to_pose(img, depth, pose, intrinsics, intrinsics_inv, rotation_mode='euler', padding_mode='zeros', return_coordinates=False):\n check_sizes(img, 'img', 'B3HW')\n\n src_pixel_coords = get_displacement_pixel_transformation(img, depth, pose, intrinsics, intrinsics_inv, rotation_mode=rotation_mode, padding_mode=padding_mode)\n projected_img = torch.nn.functional.grid_sample(img, src_pixel_coords, padding_mode=padding_mode)\n if return_coordinates:\n return projected_img, src_pixel_coords\n else:\n return projected_img","def transform(self, data):\n return self.fit_transform(data, fitting=False)","def _transform(\n self, x: \"torch.Tensor\", y: Optional[\"torch.Tensor\"], **kwargs\n ) -> Tuple[\"torch.Tensor\", Optional[\"torch.Tensor\"]]:\n import torch\n import torchvision.transforms.functional as F\n\n img_size = x.shape[:2]\n\n angle = float(\n torch.empty(1)\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\n .item()\n )\n\n max_dx = float(self.translate[0] * img_size[1])\n max_dy = float(self.translate[1] * img_size[0])\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\n translations = (tx, ty)\n\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\n\n # x needs to have channel first\n x = x.permute(2, 0, 1)\n x = F.affine(\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\n )\n x = x.permute(1, 2, 0)\n\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y","def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\n # checks here\n super().transform(dataset)\n # convert to accepted dtype and get attributes\n dataset = dataset.to_pandas()\n df = dataset.data\n\n # transform\n roles = NumericRole()\n outputs = []\n\n for n, conlumn_name in enumerate(df.columns):\n if self.cache_dir is not None:\n full_hash = get_textarr_hash(df[conlumn_name]) + get_textarr_hash(self.dicts[conlumn_name][\"feats\"])\n fname = os.path.join(self.cache_dir, full_hash + \".pkl\")\n\n if os.path.exists(fname):\n logger.info3(f\"Load saved dataset for {conlumn_name}\")\n\n with open(fname, \"rb\") as f:\n new_arr = pickle.load(f)\n\n else:\n new_arr = self.dicts[conlumn_name][\"transformer\"].transform(df[conlumn_name])\n with open(fname, \"wb\") as f:\n pickle.dump(new_arr, f)\n else:\n new_arr = self.dicts[conlumn_name][\"transformer\"].transform(df[conlumn_name])\n\n output = dataset.empty().to_numpy()\n output.set_data(new_arr, self.dicts[conlumn_name][\"feats\"], roles)\n outputs.append(output)\n logger.info3(f\"Feature {conlumn_name} transformed\")\n # create resulted\n return dataset.empty().to_numpy().concat(outputs)","def get_transform(self, map_from='visual', map_to='render'):\n return self.transforms.get_transform(map_from, map_to)","def get_transform(self, from_frame, to_frame):\n if not self._pipeline:\n return None\n try:\n from_ind = self._get_frame_index(from_frame)\n except ValueError:\n raise CoordinateFrameError(\"Frame {0} is not in the available \"\n \"frames\".format(from_frame))\n try:\n to_ind = self._get_frame_index(to_frame)\n except ValueError:\n raise CoordinateFrameError(\"Frame {0} is not in the available frames\".format(to_frame))\n if to_ind < from_ind:\n #transforms = np.array(self._pipeline[to_ind: from_ind], dtype=\"object\")[:, 1].tolist()\n transforms = [step.transform for step in self._pipeline[to_ind: from_ind]]\n transforms = [tr.inverse for tr in transforms[::-1]]\n elif to_ind == from_ind:\n return None\n else:\n #transforms = np.array(self._pipeline[from_ind: to_ind], dtype=\"object\")[:, 1].copy()\n transforms = [step.transform for step in self._pipeline[from_ind: to_ind]]\n return functools.reduce(lambda x, y: x | y, transforms)","def _transform(self, X, y=None):\n return clone(self.transformer).fit_transform(X=X, y=y)","def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):\n x = np.rollaxis(x, channel_index, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n channel_images = [ndi.interpolation.affine_transform(x_channel,\n final_affine_matrix,\n final_offset, order=0, mode=fill_mode, cval=cval)\n for x_channel in x]\n x = np.stack(channel_images, axis=0)\n x = np.rollaxis(x, 0, channel_index+1)\n return x","def get_dataset_transform(dataset_name):\n if dataset_name == \"MNIST\":\n train_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])\n test_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])\n elif dataset_name == \"CIFAR10\":\n train_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ])\n test_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ])\n elif dataset_name == \"CIFAR100\":\n train_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ])\n test_transform = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\n ])\n elif dataset_name == \"EMNIST\":\n train_transform = transforms.Compose([\n Transpose(),\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])\n test_transform = transforms.Compose([\n Transpose(),\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])\n else:\n print(\"current dataset name:{} is not right\".format(dataset_name))\n sys.exit(1)\n return train_transform, test_transform","def transformAffine(self, path=None, src=None, dst=None):\n if path is not None:\n landmarks = pd.read_csv(path, skiprows=1,engine=\"c\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\n dst = landmarks[:,3:5]\n src = landmarks[:,1:3]\n affine = transform.estimate_transform(\"affine\",src,dst)\n data = self.stormData[0][:,0:2]\n data = affine(data)\n self.stormData[0][:,0:2] = data","def _pixel_to_map(coordinates, geotransform):\n coordinates_map = np.empty(coordinates.shape)\n coordinates_map[..., 0] = (\n geotransform[0]\n + geotransform[1] * coordinates[..., 0]\n + geotransform[2] * coordinates[..., 1]\n )\n coordinates_map[..., 1] = (\n geotransform[3]\n + geotransform[4] * coordinates[..., 0]\n + geotransform[5] * coordinates[..., 1]\n )\n return coordinates_map","def test_coords_transformation():\n\n # H+R+S+T, not reverse, depth\n img_meta = {\n 'pcd_scale_factor':\n 1.2311e+00,\n 'pcd_rotation': [[8.660254e-01, 0.5, 0], [-0.5, 8.660254e-01, 0],\n [0, 0, 1.0e+00]],\n 'pcd_trans': [1.111e-02, -8.88e-03, 0.0],\n 'pcd_horizontal_flip':\n True,\n 'transformation_3d_flow': ['HF', 'R', 'S', 'T']\n }\n\n pcd = torch.tensor([[-5.2422e+00, -2.9757e-01, 4.0021e+01],\n [-9.1435e-01, 2.6675e+01, -5.5950e+00],\n [2.0089e-01, 5.8098e+00, -3.5409e+01],\n [-1.9461e-01, 3.1309e+01, -1.0901e+00]])\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'DEPTH', img_meta, reverse=False)\n\n expected_tensor = torch.tensor(\n [[5.78332345e+00, 2.900697e+00, 4.92698531e+01],\n [-1.5433839e+01, 2.8993850e+01, -6.8880045e+00],\n [-3.77929405e+00, 6.061661e+00, -4.35920199e+01],\n [-1.9053658e+01, 3.3491436e+01, -1.34202211e+00]])\n\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\n\n # H+R+S+T, reverse, depth\n img_meta = {\n 'pcd_scale_factor':\n 7.07106781e-01,\n 'pcd_rotation': [[7.07106781e-01, 7.07106781e-01, 0.0],\n [-7.07106781e-01, 7.07106781e-01, 0.0],\n [0.0, 0.0, 1.0e+00]],\n 'pcd_trans': [0.0, 0.0, 0.0],\n 'pcd_horizontal_flip':\n False,\n 'transformation_3d_flow': ['HF', 'R', 'S', 'T']\n }\n\n pcd = torch.tensor([[-5.2422e+00, -2.9757e-01, 4.0021e+01],\n [-9.1435e+01, 2.6675e+01, -5.5950e+00],\n [6.061661e+00, -0.0, -1.0e+02]])\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'DEPTH', img_meta, reverse=True)\n\n expected_tensor = torch.tensor(\n [[-5.53977e+00, 4.94463e+00, 5.65982409e+01],\n [-6.476e+01, 1.1811e+02, -7.91252488e+00],\n [6.061661e+00, -6.061661e+00, -1.41421356e+02]])\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\n\n # H+R+S+T, not reverse, camera\n img_meta = {\n 'pcd_scale_factor':\n 1.0 / 7.07106781e-01,\n 'pcd_rotation': [[7.07106781e-01, 0.0, 7.07106781e-01],\n [0.0, 1.0e+00, 0.0],\n [-7.07106781e-01, 0.0, 7.07106781e-01]],\n 'pcd_trans': [1.0e+00, -1.0e+00, 0.0],\n 'pcd_horizontal_flip':\n True,\n 'transformation_3d_flow': ['HF', 'S', 'R', 'T']\n }\n\n pcd = torch.tensor([[-5.2422e+00, 4.0021e+01, -2.9757e-01],\n [-9.1435e+01, -5.5950e+00, 2.6675e+01],\n [6.061661e+00, -1.0e+02, -0.0]])\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'CAMERA', img_meta, reverse=False)\n\n expected_tensor = torch.tensor(\n [[6.53977e+00, 5.55982409e+01, 4.94463e+00],\n [6.576e+01, -8.91252488e+00, 1.1811e+02],\n [-5.061661e+00, -1.42421356e+02, -6.061661e+00]])\n\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\n\n # V, reverse, camera\n img_meta = {'pcd_vertical_flip': True, 'transformation_3d_flow': ['VF']}\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'CAMERA', img_meta, reverse=True)\n\n expected_tensor = torch.tensor([[-5.2422e+00, 4.0021e+01, 2.9757e-01],\n [-9.1435e+01, -5.5950e+00, -2.6675e+01],\n [6.061661e+00, -1.0e+02, 0.0]])\n\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\n\n # V+H, not reverse, depth\n img_meta = {\n 'pcd_vertical_flip': True,\n 'pcd_horizontal_flip': True,\n 'transformation_3d_flow': ['VF', 'HF']\n }\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'DEPTH', img_meta, reverse=False)\n\n expected_tensor = torch.tensor([[5.2422e+00, -4.0021e+01, -2.9757e-01],\n [9.1435e+01, 5.5950e+00, 2.6675e+01],\n [-6.061661e+00, 1.0e+02, 0.0]])\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\n\n # V+H, reverse, lidar\n img_meta = {\n 'pcd_vertical_flip': True,\n 'pcd_horizontal_flip': True,\n 'transformation_3d_flow': ['VF', 'HF']\n }\n\n pcd_transformed = apply_3d_transformation(\n pcd, 'LIDAR', img_meta, reverse=True)\n\n expected_tensor = torch.tensor([[5.2422e+00, -4.0021e+01, -2.9757e-01],\n [9.1435e+01, 5.5950e+00, 2.6675e+01],\n [-6.061661e+00, 1.0e+02, 0.0]])\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)","def transform(self, X):\n\n t0 = time.perf_counter()\n check_is_fitted(self)\n self.check_external_components_modified()#[WARN] in d3m, primitives can \"restore\" private class variables...\n X = self._validate_data(X, accept_sparse=[\"csr\", \"csc\"], reset=False)\n t1 = time.perf_counter()\n\n if X.shape[1] != self.components_af_.shape[1]:\n raise ValueError(\n \"Impossible to perform projection:\"\n \"X at fit stage had a different number of features. \"\n \"(%s != %s)\" % (X.shape[1], self.components_af_.shape[1])\n )\n\n #X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)\n #import pdb; pdb.set_trace()\n X_af = af.interop.from_ndarray(X).as_type(self.components_af_.dtype())\n X_new = af.matmulNT(X_af, self.components_af_)\n X_new = X_new.to_ndarray()\n t2 = time.perf_counter()\n return X_new","def setCrsIsProjection(self):\n self.isgeographic = False","def set_transform(self, tf):\n self.camera.SetTransform(tf)","def transform(self, srid: ir.IntegerValue) -> GeoSpatialValue:\n return ops.GeoTransform(self, srid).to_expr()","def _select_transform(self):\n for transform in self.transforms:\n if transform.applies is None or transform.applies(self.ti_dict) is True:\n self.transform = transform\n break\n else:\n raise RuntimeError('No transform found for TI data')","def set_transform(self, transform):\n self._transform = transform","def apply_transform(x,\n transform_matrix,\n channel_axis=0,\n fill_mode='nearest',\n cval=0.,\n interp_order=0):\n x = np.rollaxis(x, channel_axis, 0)\n final_affine_matrix = transform_matrix[:3, :3]\n final_offset = transform_matrix[:3, -1]\n channel_volumes = [ndi.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=interp_order, # NOTE: The order of the spline interpolation\n mode=fill_mode,\n cval=cval) for x_channel in x]\n x = np.stack(channel_volumes, axis=0)\n x = np.rollaxis(x, 0, channel_axis + 1)\n return x"],"string":"[\n \"def _reproject(ds, dst_crs=None, dst_transform=None, width=None, height=None,\\n res=None, extent=None, **kwargs):\\n\\n if 'resampling' not in kwargs:\\n kwargs['resampling'] = rasterio.warp.Resampling.cubic\\n\\n src_crs = get_crs(ds)\\n src_bounds = get_bounds(ds)\\n if extent is not None:\\n extent = BoundingBox(*extent)\\n\\n #\\n # Only allow inferring of width or height from aspect ratio\\n # if the CRS is not changed.\\n #\\n if dst_crs is None:\\n dst_crs = src_crs\\n if width is None and height is not None:\\n width = int(ncols(ds) * height / nrows(ds))\\n elif height is None and width is not None:\\n height = int(nrows(ds) * width / ncols(ds))\\n\\n # Given: transform, shape\\n # Given: transform, extent\\n # Given: res, extent\\n # Given: shape, res\\n # Given: shape, extent\\n\\n if dst_transform is not None:\\n #\\n # If the transform is given, we also need the width and height or\\n # the extent.\\n #\\n if width is not None and height is not None:\\n pass\\n elif extent is not None:\\n # Calculate width and height from extent\\n width = int(abs(\\n (extent.right - extent.left) / dst_transform.a)) + 1\\n height = int(abs(\\n (extent.top - extent.bottom) / dst_transform.e)) + 1\\n else:\\n raise ValueError('Not enough information provided.')\\n\\n elif extent is not None:\\n #\\n # Transform can be calculated from extent, if either width and height\\n # or the resolution are given.\\n #\\n if res is not None:\\n width = int(abs(\\n (extent.right - extent.left) / res[0])) + 1\\n height = int(abs(\\n (extent.top - extent.bottom) / res[1])) + 1\\n\\n # The following doesn't give the correct result.\\n dst_transform = rasterio.transform.from_bounds(\\n *extent, width=width-1, height=height-1\\n )\\n\\n else:\\n #\\n # If neither the transform nor the extent are given, infer the best\\n # possible parameters from the width, height, and the resolution.\\n #\\n dst_transform, width, height = \\\\\\n rasterio.warp.calculate_default_transform(\\n src_crs, dst_crs,\\n ncols(ds), nrows(ds),\\n resolution=res,\\n dst_width=width,\\n dst_height=height,\\n **src_bounds._asdict())\\n\\n src_transform = get_transform(ds)\\n src_dims = get_dims(ds)\\n dst_crs = _parse_crs(dst_crs)\\n\\n #\\n # Prepare new x and y coordinate arrays\\n #\\n dst_x, _ = dst_transform * (np.arange(width), np.zeros(width, dtype=int))\\n _, dst_y = dst_transform * (np.zeros(height, dtype=int), np.arange(height))\\n dst_coords = {'x': dst_x, 'y': dst_y}\\n\\n #\\n # Handle the case where there are extra dimensions, e.g. 'time'\\n # or 'band'\\n #\\n extra_dims = src_dims.keys() - {'y', 'x'}\\n if len(extra_dims) == 0:\\n dst_shape = (height, width)\\n dst_dims = ('y', 'x')\\n elif len(extra_dims) == 1:\\n extra_dim = extra_dims.pop()\\n dst_shape = (src_dims[extra_dim], height, width)\\n dst_dims = (extra_dim, 'y', 'x')\\n dst_coords[extra_dim] = ds.coords[extra_dim]\\n else:\\n raise ValueError('More than three dimensions are not supported.')\\n\\n def _reproject_da(da, shape):\\n #\\n # Reproject a single data array\\n #\\n extra_dims = set(da.dims) - {'y', 'x'}\\n dim_order = tuple(extra_dims) + ('y', 'x')\\n values = da.transpose(*dim_order).values\\n output = np.zeros(shape, dtype=da.dtype)\\n output[:] = np.nan\\n\\n # Fix data shape for one-dimensional data\\n if da.ndim == 1:\\n #\\n # NOTE: The stretch factor is necessary because the input data\\n # must extend at least half an original resolution cell in the\\n # independent dimension.\\n #\\n if da.dims == ('x',):\\n stretch_x = int((~src_transform * dst_transform).a / 2 + 1)\\n values = np.vstack([values] * stretch_x)\\n output.shape = (1,) + output.shape\\n elif da.dims == ('y',):\\n stretch_y = int((~src_transform * dst_transform).e / 2 + 1)\\n values = np.vstack([values] * stretch_y).T\\n output.shape = output.shape + (1,)\\n\\n rasterio.warp.reproject(\\n values,\\n output,\\n src_transform=src_transform,\\n src_crs=src_crs,\\n dst_transform=dst_transform,\\n dst_crs=dst_crs,\\n dst_nodata=np.nan,\\n **kwargs\\n )\\n\\n # Final reshape in case the input was one-dimensional\\n return output.reshape(shape)\\n\\n if isinstance(ds, xr.Dataset):\\n result = xr.Dataset(coords=dst_coords)\\n\\n #\\n # Also reproject coordinate arrays that are defined over\\n # x and y\\n #\\n for v in ds.coords:\\n #\\n # If the projection is the same, also reproject coordinate arrays\\n # that are defined over only one variable.\\n #\\n if dst_crs == src_crs and v not in ds.dims:\\n if ds.coords[v].dims == ('x',):\\n result.coords[v] = \\\\\\n (('x',), _reproject_da(ds.coords[v], (width,)))\\n elif ds.coords[v].dims == ('y',):\\n result.coords[v] = \\\\\\n (('y',), _reproject_da(ds.coords[v], (height,)))\\n\\n if not set(ds.coords[v].dims).issuperset({'x', 'y'}):\\n continue\\n\\n shape = (height, width)\\n result.coords[v] = (('y', 'x'),\\n _reproject_da(ds.coords[v], shape))\\n\\n #\\n # Reproject the actual data\\n #\\n for v in ds.data_vars:\\n if set(ds[v].dims) == set(dst_dims):\\n result[v] = (dst_dims, _reproject_da(ds[v], dst_shape))\\n elif set(ds[v].dims) == {'y', 'x'}:\\n shape = (height, width)\\n result[v] = (dst_dims, _reproject_da(ds[v], shape))\\n else:\\n result[v] = (ds[v].dims, ds[v])\\n\\n #\\n # Create lat and lon coordinates\\n #\\n # if 'lat' in ds.coords and 'lon' in ds.coords:\\n # lon, lat = rasterio.warp.transform(\\n # src_crs, dst_crs, ds.coords['x'], ds.coords['y'])\\n # result.coords['lon'] = (('x',), lon)\\n # result.coords['lat'] = (('y',), lat)\\n\\n elif isinstance(ds, xr.DataArray):\\n result = xr.DataArray(_reproject_da(ds, dst_shape), dims=dst_dims,\\n coords=dst_coords, name=ds.name)\\n\\n result.attrs = ds.attrs\\n\\n # Serialize transform to tuple and store in metadata\\n result.attrs['transform'] = dst_transform[:6]\\n # Store CRS info in metadata\\n result.attrs['crs'] = dst_crs.to_string()\\n result.attrs['coordinate_system_string'] = dst_crs.wkt\\n # Store new data shape in metadata\\n result.attrs['lines'] = nrows(result)\\n result.attrs['samples'] = ncols(result)\\n\\n _add_latlon(result)\\n\\n return result\",\n \"def reproject(self, lon, lat):\\n if self.xform is None:\\n # if the CRS hasn't been determined yet, we set it from the first image's lat/lon (take the UTM crs)\\n utm_i = str(int(math.floor((self.images[0].lon + 180) / 6 ) % 60) + 1).zfill(2)\\n epsg_code = int('326' + utm_i) if (self.images[0].lat >= 0) else int('327' + utm_i)\\n self.crs_dest = QgsCoordinateReferenceSystem(epsg_code)\\n self.xform = QgsCoordinateTransform(self.crs_src, self.crs_dest, QgsProject.instance())\\n return self.xform.transform(QgsPointXY(lon, lat))\",\n \"def apply(self, ds):\\n\\n return _reproject(ds, dst_crs=self.crs, dst_transform=self.transform,\\n width=self.width, height=self.height, res=self.res,\\n extent=self.extent, **self.kwargs)\",\n \"def reproject(\\n self,\\n dst_crs=None,\\n dst_res=None,\\n dst_transform=None,\\n dst_width=None,\\n dst_height=None,\\n dst_nodata=None,\\n method=\\\"nearest\\\",\\n align=False,\\n ):\\n\\n def _reproj(da, **kwargs):\\n return da.raster._reproject(**kwargs)\\n\\n # parse and check destination grid and crs\\n dst_crs = self._dst_crs(dst_crs)\\n dst_transform, dst_width, dst_height = self._dst_transform(\\n dst_crs, dst_res, dst_transform, dst_width, dst_height, align\\n )\\n reproj_kwargs = dict(\\n dst_crs=dst_crs,\\n dst_transform=dst_transform,\\n dst_width=dst_width,\\n dst_height=dst_height,\\n )\\n # gdal resampling method with exception for index based resampling\\n method = method.lower()\\n if method == \\\"nearest_index\\\":\\n index = self.nearest_index(**reproj_kwargs)\\n return self.reindex2d(index, dst_nodata)\\n # update reproject settings\\n if dst_nodata is None:\\n dst_nodata = self.nodata if self.nodata is not None else np.nan\\n reproj_kwargs.update(method=method, dst_nodata=dst_nodata)\\n if self._obj.chunks is None:\\n da_reproj = _reproj(self._obj, **reproj_kwargs)\\n else:\\n # create template with dask data\\n dst_coords = {\\n d: self._obj.coords[d]\\n for d in self._obj.dims\\n if d not in [self.x_dim, self.y_dim]\\n }\\n coords = gis_utils.affine_to_coords(\\n dst_transform,\\n (dst_height, dst_width),\\n x_dim=self.x_dim,\\n y_dim=self.y_dim,\\n )\\n dst_coords.update(coords)\\n da_temp = full(\\n dst_coords,\\n nodata=dst_nodata,\\n dtype=self._obj.dtype,\\n name=self._obj.name,\\n attrs=self._obj.attrs,\\n crs=dst_crs,\\n lazy=True,\\n shape=(dst_height, dst_width)\\n if self.dim0 is None\\n else (self._obj.shape[0], dst_height, dst_width),\\n dims=self.dims if self.dim0 is None else (self.dim0, *self.dims),\\n )\\n # no chunks on spatial dims\\n chunksize = max(self._obj.chunks[0])\\n chunks = {d: chunksize if d == self.dim0 else -1 for d in self._obj.dims}\\n _da = self._obj.chunk(chunks)\\n da_temp = da_temp.chunk(chunks)\\n da_reproj = _da.map_blocks(_reproj, kwargs=reproj_kwargs, template=da_temp)\\n da_reproj.raster.set_crs(dst_crs)\\n return da_reproj.raster.reset_spatial_dims_attrs()\",\n \"def geotransform(self):\\n return self.dataset.GetGeoTransform() if self.dataset else None\",\n \"def reproject(self, spatial_reference, transformation=None, inplace=False):\\r\\n if HASARCPY:\\r\\n if isinstance(spatial_reference, arcpy.SpatialReference):\\r\\n wkt = spatial_reference.exportToString()\\r\\n wkid = spatial_reference.factoryCode\\r\\n if wkid:\\r\\n sr = _types.SpatialReference({'wkid' : wkid})\\r\\n elif wkt:\\r\\n sr = _types.SpatialReference({'wkt': wkt})\\r\\n else:\\r\\n sr = None\\r\\n elif isinstance(spatial_reference, int):\\r\\n sr = _types.SpatialReference({'wkid' : spatial_reference})\\r\\n elif isinstance(spatial_reference, string_types):\\r\\n sr = _types.SpatialReference({'wkt' : spatial_reference})\\r\\n elif isinstance(spatial_reference, _types.SpatialReference):\\r\\n sr = spatial_reference\\r\\n else:\\r\\n raise ValueError(\\\"spatial_referernce must be of type: int, string, _types.SpatialReference, or arcpy.SpatialReference\\\")\\r\\n\\r\\n if inplace:\\r\\n df = self\\r\\n else:\\r\\n df = self.copy()\\r\\n sarcpy = sr.as_arcpy\\r\\n if sarcpy:\\r\\n geom = df.geometry.project_as(sarcpy, transformation)\\r\\n geom.sr = sr\\r\\n df.geometry = geom\\r\\n if inplace:\\r\\n return df\\r\\n else:\\r\\n raise Exception(\\\"could not reproject the dataframe.\\\")\\r\\n return df\",\n \"def reproject(dataset, epsg):\\n dataclass = dataset.__class__.__name__\\n # Run appropriate reprojection method\\n if dataclass == 'GeoDataFrame':\\n repro = geopandas.GeoDataFrame.copy(dataclass)\\n repro[repro.geometry.name] = repro.geometry.to_crs(epsg=epsg)\\n repro.crs = fiona.crs.from_epsg(epsg)\\n elif dataclass == 'Dataset':\\n repro = gdal_reproject(dataset, '', epsg=epsg)\\n return repro\",\n \"def reproject_dataset_example(dataset, dataset_example, method=1):\\n # open dataset that must be transformed\\n try:\\n if (os.path.splitext(dataset)[-1] == '.tif' or os.path.splitext(dataset)[-1] == '.TIF'):\\n g = gdal.Open(dataset)\\n else:\\n g = dataset\\n except:\\n g = dataset\\n epsg_from = Get_epsg(g)\\n\\n #exceptions\\n if epsg_from == 9001:\\n epsg_from = 5070\\n\\n # open dataset that is used for transforming the dataset\\n try:\\n if (os.path.splitext(dataset_example)[-1] == '.tif' or os.path.splitext(dataset_example)[-1] == '.TIF'):\\n gland = gdal.Open(dataset_example)\\n epsg_to = Get_epsg(gland)\\n else:\\n gland = dataset_example\\n epsg_to = Get_epsg(gland)\\n except:\\n gland = dataset_example\\n epsg_to = Get_epsg(gland)\\n\\n # Set the EPSG codes\\n osng = osr.SpatialReference()\\n osng.ImportFromEPSG(epsg_to)\\n wgs84 = osr.SpatialReference()\\n wgs84.ImportFromEPSG(epsg_from)\\n\\n # Get shape and geo transform from example\\n geo_land = gland.GetGeoTransform()\\n col=gland.RasterXSize\\n rows=gland.RasterYSize\\n\\n # Create new raster\\n mem_drv = gdal.GetDriverByName('MEM')\\n dest1 = mem_drv.Create('', col, rows, 1, gdal.GDT_Float32)\\n dest1.SetGeoTransform(geo_land)\\n dest1.SetProjection(osng.ExportToWkt())\\n\\n # Perform the projection/resampling\\n if method == 1:\\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_NearestNeighbour)\\n if method == 2:\\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Bilinear)\\n if method == 3:\\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Lanczos)\\n if method == 4:\\n gdal.ReprojectImage(g, dest1, wgs84.ExportToWkt(), osng.ExportToWkt(), gdal.GRA_Average)\\n \\n return(dest1)\",\n \"def imageTransform(self):\\n ims = self.imageShape\\n acs = self.activeShape\\n dx = self.colVector\\n dy = self.rowVector\\n\\n p0 = self.activeOrigin\\n p1 = p0 + acs[2] * dx\\n p2 = p0 + acs[1] * dy\\n\\n # print p0, p1, p2\\n # print acs, dx, dy\\n\\n localPts = list(map(pg.Vector, [[0,0], [ims[2],0], [0,ims[1]], [0,0,1]])) # w and h of data of image in pixels.\\n globalPts = list(map(pg.Vector, [p0, p1, p2, [0,0,1]]))\\n m = pg.solve3DTransform(localPts, globalPts)\\n m[:,2] = m[:,3]\\n m[2] = m[3]\\n m[2,2] = 1\\n tr = Qt.QTransform(*m[:3,:3].transpose().reshape(9))\\n return tr\",\n \"def geo_transform(self):\\n pass\",\n \"def TransformPoint(*args, **kwargs):\\n return _gdi_.GraphicsMatrix_TransformPoint(*args, **kwargs)\",\n \"def _getGeoTransform(self):\\n with self._getDatasetLock:\\n gt = self.dataset.GetGeoTransform()\\n if (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()):\\n gt = gdal.GCPsToGeoTransform(self.dataset.GetGCPs())\\n return gt\",\n \"def warp(\\r\\n self,\\r\\n dst_crs,\\r\\n resampling_method=0,\\r\\n num_threads=4,\\r\\n resolution=None,\\r\\n nodata=None,\\r\\n target_align=None,\\r\\n ):\\r\\n if target_align:\\r\\n transform = target_align.dataset.transform\\r\\n width = target_align.dataset.width\\r\\n height = target_align.dataset.height\\r\\n\\r\\n else:\\r\\n if resolution:\\r\\n transform, width, height = rasterio.warp.calculate_default_transform(\\r\\n self.dataset.crs,\\r\\n dst_crs,\\r\\n self.dataset.width,\\r\\n self.dataset.height,\\r\\n *self.dataset.bounds,\\r\\n resolution=resolution,\\r\\n )\\r\\n else:\\r\\n transform, width, height = rasterio.warp.calculate_default_transform(\\r\\n self.dataset.crs,\\r\\n dst_crs,\\r\\n self.dataset.width,\\r\\n self.dataset.height,\\r\\n *self.dataset.bounds,\\r\\n )\\r\\n\\r\\n destination = np.zeros((self.dataset.count, height, width), self.__arr.dtype)\\r\\n\\r\\n self.__arr, transform = rasterio.warp.reproject(\\r\\n source=self.__arr,\\r\\n destination=destination,\\r\\n src_transform=self.dataset.transform,\\r\\n src_crs=self.dataset.crs,\\r\\n src_nodata=nodata,\\r\\n dst_transform=transform,\\r\\n dst_crs=dst_crs,\\r\\n dst_nodata=nodata,\\r\\n resampling=resampling_method,\\r\\n num_threads=num_threads,\\r\\n )\\r\\n\\r\\n self.__update_dataset(dst_crs, transform, nodata=nodata)\",\n \"def transform(self, x, y):\\n # return self.transform_2D(x, y)\\n return self.transform_perspective(x, y)\",\n \"def transform(self, x, y, z=None, crs=wgs84, nearest=False, maskout=False):\\n\\n x, y = np.ma.array(x), np.ma.array(y)\\n\\n # First to local proj\\n _crs = check_crs(crs, raise_on_error=True)\\n if isinstance(_crs, pyproj.Proj):\\n x, y = transform_proj(_crs, self.proj, x, y)\\n elif isinstance(_crs, Grid):\\n x, y = _crs.ij_to_crs(x, y, crs=self.proj)\\n\\n # Then to local grid\\n x = (x - self.x0) / self.dx\\n y = (y - self.y0) / self.dy\\n\\n # See if we need to round\\n if nearest:\\n f = np.rint if self.pixel_ref == 'center' else np.floor\\n x = f(x).astype(int)\\n y = f(y).astype(int)\\n\\n # Mask?\\n if maskout:\\n if self.pixel_ref == 'center':\\n mask = ~((x >= -0.5) & (x < self.nx-0.5) &\\n (y >= -0.5) & (y < self.ny-0.5))\\n else:\\n mask = ~((x >= 0) & (x < self.nx) &\\n (y >= 0) & (y < self.ny))\\n x = np.ma.array(x, mask=mask)\\n y = np.ma.array(y, mask=mask)\\n\\n return x, y\",\n \"def dataTransform(self):\\n # Might eventually need to account for downsampling / clipping here\\n # transforms are updated in setOpts call.\\n return self._dataTransform\",\n \"def _project(self):\\n ghosts_w = self.input_field.topology.ghosts()\\n self.input_field.data[0], self.input_field.data[1], \\\\\\n self.input_field.data[2] = \\\\\\n fftw2py.projection_om_3d(self.input_field.data[0],\\n self.input_field.data[1],\\n self.input_field.data[2], ghosts_w)\",\n \"def _apply_transform(self, img: np.ndarray): \\n img = self.transform(image=img)[\\\"image\\\"]\\n return img\",\n \"def _apply_transform(self, img: np.ndarray): \\n img = self.transform(image=img)[\\\"image\\\"]\\n return img\",\n \"def projection(self, point):\\n return gs.copy(point)\",\n \"def apply_projection_transform(Xb, batch_size, image_size):\\n d = image_size * 0.3 * intensity\\n for i in np.random.choice(batch_size, int(batch_size * p), replace = False): \\n tl_top = random.uniform(-d, d) # Top left corner, top margin\\n tl_left = random.uniform(-d, d) # Top left corner, left margin\\n bl_bottom = random.uniform(-d, d) # Bottom left corner, bottom margin\\n bl_left = random.uniform(-d, d) # Bottom left corner, left margin\\n tr_top = random.uniform(-d, d) # Top right corner, top margin\\n tr_right = random.uniform(-d, d) # Top right corner, right margin\\n br_bottom = random.uniform(-d, d) # Bottom right corner, bottom margin\\n br_right = random.uniform(-d, d) # Bottom right corner, right margin\\n\\n transform = ProjectiveTransform()\\n transform.estimate(np.array((\\n (tl_left, tl_top),\\n (bl_left, image_size - bl_bottom),\\n (image_size - br_right, image_size - br_bottom),\\n (image_size - tr_right, tr_top)\\n )), np.array((\\n (0, 0),\\n (0, image_size),\\n (image_size, image_size),\\n (image_size, 0)\\n )))\\n Xb[i] = warp(Xb[i], transform, output_shape=(image_size, image_size), order = 1, mode = 'edge')\\n\\n return Xb\",\n \"def TransformPoint(transform, x, y, z):\\n result = np.matmul(transform, np.array([x, y, z, 1.]))\\n return result[0], result[1], result[2]\",\n \"def transform_point(transform, x_in, y_in):\\n # create point geometry from coordinates\\n point = ogr.Geometry(ogr.wkbPoint)\\n point.AddPoint(x_in, y_in)\\n point.Transform(transform)\\n\\n x_out = point.GetX()\\n y_out = point.GetY()\\n return x_out, y_out\",\n \"def reproject_coordinates(x_in, y_in, spatial_reference_source, spatial_reference_target=None): \\n if spatial_reference_target is not None:\\n pass\\n else:\\n spatial_reference_target = osr.SpatialReference()\\n spatial_reference_target.ImportFromEPSG(4326) \\n pass\\n \\n if int(osgeo.__version__[0]) >= 3:\\n # GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546\\n \\n spatial_reference_source.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n spatial_reference_target.SetAxisMappingStrategy(osgeo.osr.OAMS_TRADITIONAL_GIS_ORDER)\\n\\n \\n pTransform = osr.CoordinateTransformation( spatial_reference_source, spatial_reference_target)\\n \\n x_new,y_new, z = pTransform.TransformPoint( x_in,y_in)\\n \\n return x_new,y_new\",\n \"def apply_transform(transform):\\n vg.shape.check(locals(), \\\"transform\\\", (4, 4))\\n\\n def apply(points, discard_z_coord=False, treat_input_as_vector=False):\\n points, is_columnized, maybe_decolumnize = columnize(\\n points, (-1, 3), name=\\\"points\\\"\\n )\\n\\n homogenous_coordinate_value = 0 if treat_input_as_vector else 1\\n padded_points = np.pad(\\n points,\\n ((0, 0), (0, 1)),\\n mode=\\\"constant\\\",\\n constant_values=homogenous_coordinate_value,\\n )\\n transformed_padded_points = np.dot(transform, padded_points.T).T\\n transformed_points = np.delete(transformed_padded_points, 3, axis=1)\\n\\n result = maybe_decolumnize(transformed_points)\\n if discard_z_coord:\\n return result[:, 0:2] if is_columnized else result[0:2]\\n else:\\n return result\\n\\n return apply\",\n \"def geotransform(self):\\n return self._geotransform\",\n \"def Reproject(x, y, in_grid = 4326, out_grid = 32737):\\n \\n inProj = Proj(init='epsg:'+str(in_grid))\\n outProj = Proj(init='epsg:'+str(out_grid))\\n \\n \\n x2,y2 = transform(inProj,outProj,x,y)\\n \\n return x2, y2\",\n \"def transform_to(self, dst_crs):\\n self.nodes.transform_to(dst_crs)\",\n \"def transform_proj(p1, p2, x, y, nocopy=False):\\n\\n try:\\n # This always makes a copy, even if projections are equivalent\\n return _transform_internal(p1, p2, x, y, always_xy=True)\\n except TypeError:\\n if proj_is_same(p1, p2):\\n if nocopy:\\n return x, y\\n else:\\n return copy.deepcopy(x), copy.deepcopy(y)\\n\\n return _transform_internal(p1, p2, x, y)\",\n \"def _transform(self, dataset):\\n raise NotImplementedError()\",\n \"def get_transform(ds):\\n\\n if 'transform' in ds.attrs:\\n ds_trans = ds.attrs['transform']\\n if isinstance(ds_trans, Affine):\\n return ds_trans\\n else:\\n return Affine(*ds_trans)\\n\\n elif 'crs' in ds.data_vars and 'i2m' in ds.data_vars['crs'].attrs:\\n transf_str = ds.data_vars['crs'].attrs['i2m']\\n a = list(map(float, transf_str.split(',')))\\n return Affine(a[0], a[2], a[4], a[1], a[3], a[5])\\n\\n else:\\n resx, resy = get_resolution(ds)\\n xoff = ds['x'].values.min()\\n yoff = ds['y'].values.max()\\n return Affine(resx, 0, xoff, 0, resy, yoff)\",\n \"def _apply_transform(self, x, transform_parameters):\\n # x is a single image, so it doesn't have image number at index 0\\n img_row_axis = self.row_axis - 1\\n img_col_axis = self.col_axis - 1\\n img_channel_axis = self.channel_axis - 1\\n\\n x = apply_affine_transform(x, transform_parameters.get('theta', 0),\\n transform_parameters.get('tx', 0),\\n transform_parameters.get('ty', 0),\\n transform_parameters.get('shear', 0),\\n transform_parameters.get('zx', 1),\\n transform_parameters.get('zy', 1),\\n row_axis=img_row_axis,\\n col_axis=img_col_axis,\\n channel_axis=img_channel_axis,\\n fill_mode=self.fill_mode,\\n cval=self.cval)\\n\\n if transform_parameters.get('channel_shift_intensity') is not None:\\n x = apply_channel_shift(x,\\n transform_parameters['channel_shift_intensity'],\\n img_channel_axis)\\n\\n if transform_parameters.get('flip_horizontal', False):\\n x = self._flip_axis(x, img_col_axis)\\n\\n if transform_parameters.get('flip_vertical', False):\\n x = self._flip_axis(x, img_row_axis)\\n\\n if transform_parameters.get('brightness') is not None:\\n x = apply_brightness_shift(x, transform_parameters['brightness'])\\n\\n return x\",\n \"def projection(self):\\n return self.dataset.GetProjection() if self.dataset else None\",\n \"def reproject(point):\\n wgs84 = pyproj.Proj('+init=epsg:4326')\\n native = pyproj.Proj(DEM_PROJECTION)\\n x, y = pyproj.transform(wgs84, native, point.x, point.y)\\n return geom.Point(x, y)\",\n \"def transform(dataset):\\n\\n from tomviz import utils\\n import scipy.ndimage\\n import numpy as np\\n import warnings\\n\\n array = dataset.active_scalars\\n\\n zoom = (0.5, 0.5, 1)\\n result_shape = utils.zoom_shape(array, zoom)\\n result = np.empty(result_shape, array.dtype, order='F')\\n # Downsample the dataset x2 using order 1 spline (linear)\\n warnings.filterwarnings('ignore', '.*output shape of zoom.*')\\n scipy.ndimage.interpolation.zoom(array, zoom,\\n output=result,\\n order=1,\\n mode='constant',\\n cval=0.0, prefilter=False)\\n\\n # Set the result as the new scalars.\\n dataset.active_scalars = result\",\n \"def projective_transform(self, x):\\n\\n x = np.asarray(x)\\n # Assume no intensity column\\n x0, y0, z0 = x\\n\\n # Camera coors to pixel coors\\n u = ((x0 / z0) * self.f) + (self.sensor_size[0] // 2)\\n v = ((y0 / z0) * self.f) + (self.sensor_size[1] // 2)\\n\\n u_min = np.min(u)\\n v_min = np.min(v)\\n\\n n = len(u)\\n u_list = []\\n v_list = []\\n if self.error_on_oob:\\n for i in range(n):\\n if (u[i] >= u_min and u[i] <= self.sensor_size[0] and v[i] >= v_min and v[i] <= self.sensor_size[1]):\\n u_list.append(u[i])\\n v_list.append(v[i])\\n else:\\n raise OutOfSensorBoundsError(\\\"Projected coordinate was outside the sensor\\\")\\n else:\\n for i in range(n):\\n u_list.append(u[i])\\n v_list.append(v[i])\\n\\n u = np.asarray(u_list)\\n v = np.asarray(v_list)\\n\\n return np.vstack((u, v))\",\n \"def apply_transform(img,\\n transform_matrix):\\n rows,cols = img.shape[:2]\\n dst = cv2.warpAffine(img,transform_matrix,(cols,rows))\\n\\n\\n return dst\",\n \"def composed_with_transform(self, w2w_transform):\\n if not w2w_transform.input_space == self.world_space:\\n raise CompositionError(\\n \\\"The transform given does not apply to \\\"\\n \\\"the image's world space:\\\\n%s\\\\n\\\\n%s\\\" % \\n (w2w_transform, self)\\n )\\n new_img = self._apply_transform(w2w_transform)\\n new_img.world_space = w2w_transform.output_space\\n return new_img\",\n \"def do_transform_point(transform: Transform, point: Point) -> Point:\\n\\n transform_mat = alloy.math.transformation_matrix_from_array(alloy.ros.transform_to_numpy(transform))\\n point_np = alloy.ros.point_to_numpy(point)\\n point_np = np.append(point_np, 1).reshape((4,1))\\n trans_point = np.matmul(transform_mat, point_np)\\n return alloy.ros.numpy_to_point(trans_point[0:3,0])\",\n \"def transform_to(self, frame):\\n if self.component_type == \\\"healpix\\\":\\n raise ValueError(\\n \\\"Direct coordinate transformation between frames is not valid \\\"\\n \\\"for `healpix` type catalogs. Please use the `healpix_interp_transform` \\\"\\n \\\"to transform to a new frame and interpolate to the new pixel centers. \\\"\\n \\\"Alternatively, you can call `healpix_to_point` to convert the healpix map \\\"\\n \\\"to a point source catalog before calling this function.\\\"\\n )\\n\\n new_skycoord = self.skycoord.transform_to(frame)\\n self.skycoord = new_skycoord\\n\\n return\",\n \"def reproject_GeoGrid(geogrid_in, srs_string,\\n out_xdim=None, out_ydim=None, out_geotransform=None,\\n out_nodata_value=None, interp_method=None):\\n src = geogrid_as_gdalInMem(geogrid_in)\\n\\n out_srs = osr.SpatialReference()\\n assign_projection_to_srs(out_srs, srs_string)\\n out_wkt = out_srs.ExportToWkt()\\n\\n dst_gdal_datatype = get_gdal_datatype(geogrid_in.data_array.dtype)\\n\\n try:\\n dst = gdal.GetDriverByName('MEM').Create(\\n '',\\n out_xdim,\\n out_ydim,\\n 1,\\n dst_gdal_datatype,\\n )\\n dst.SetGeoTransform(out_geotransform)\\n dst.SetProjection(out_wkt)\\n except ValueError:\\n raise ValueError('Error creating dst in reproject_GeoGrid()')\\n except AttributeError:\\n raise ValueError('AttributeError in dst creation')\\n\\n\\n gdal_interp_method = getGdalInterpMethod(interp_method)\\n res = gdal.ReprojectImage(src,\\n dst,\\n src.GetProjection(),\\n dst.GetProjection(),\\n gdal_interp_method,\\n )\\n\\n\\n return geogrid_from_gdalInMem(dst)\",\n \"def apply_transform_to_image(self,img, transform, center=None):\\n \\n if center is None:\\n center = (np.array(img.shape)[::-1]-1)/2.0\\n \\n displacement = np.dot(transform, center)\\n shift = center - displacement\\n \\n img_tf = ndimage.interpolation.affine_transform(img, transform, offset=shift, mode=\\\"constant\\\", order=3, cval=0.0)\\n return img_tf\",\n \"def apply_transform_matrix(self, img: np.ndarray, transform_matrix):\\n h, w = img.shape[0], img.shape[1]\\n transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)\\n img = np.rollaxis(img, 2, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n\\n channel_images = [scipy.ndimage.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=1,\\n mode=self.fill_mode,\\n cval=self.cval) for x_channel in img]\\n img = np.stack(channel_images, axis=0)\\n img = np.rollaxis(img, 0, 2 + 1)\\n # img = apply_affine_transform(img, transform_matrix, channel_axis=2, fill_mode=self.fill_mode, cval=self.cval) # apply_transform\\n return img\",\n \"def replace_current_transform_group(self, transform):\\n datacopy = self._shallow_clone_dataset()\\n fdata = datacopy._flat_data\\n datacopy._flat_data = fdata.replace_current_transform_group(transform)\\n return datacopy\",\n \"def projection(self, point):\\n projected_point = self._iterate_over_factors(\\\"projection\\\", {\\\"point\\\": point})\\n return projected_point\",\n \"def transform(self, coord_trans, clone=False):\\n if clone:\\n klone = self.clone()\\n klone.transform(coord_trans)\\n return klone\\n\\n # Depending on the input type, use the appropriate OGR routine\\n # to perform the transformation.\\n if isinstance(coord_trans, CoordTransform):\\n capi.geom_transform(self.ptr, coord_trans.ptr)\\n elif isinstance(coord_trans, SpatialReference):\\n capi.geom_transform_to(self.ptr, coord_trans.ptr)\\n elif isinstance(coord_trans, (int, str)):\\n sr = SpatialReference(coord_trans)\\n capi.geom_transform_to(self.ptr, sr.ptr)\\n else:\\n raise TypeError(\\n \\\"Transform only accepts CoordTransform, \\\"\\n \\\"SpatialReference, string, and integer objects.\\\"\\n )\",\n \"def reproject_raster_dataset(src_ds, **kwargs):\\n\\n # checking kwargs\\n spacing = kwargs.pop(\\\"spacing\\\", None)\\n size = kwargs.pop(\\\"size\\\", None)\\n resample = kwargs.pop(\\\"resample\\\", gdal.GRA_Bilinear)\\n src_srs = kwargs.pop(\\\"projection_source\\\", None)\\n dst_srs = kwargs.pop(\\\"projection_target\\\", None)\\n align = kwargs.pop(\\\"align\\\", False)\\n\\n if spacing is None and size is None:\\n raise NameError(\\\"Whether keyword 'spacing' or 'size' must be given\\\")\\n\\n if spacing is not None and size is not None:\\n warnings.warn(\\n \\\"both ``spacing`` and ``size`` kwargs given, ``size`` will be ignored.\\\",\\n UserWarning,\\n )\\n\\n # Get the GeoTransform vector\\n src_geo = src_ds.GetGeoTransform()\\n x_size = src_ds.RasterXSize\\n y_size = src_ds.RasterYSize\\n\\n # get extent\\n ulx = src_geo[0]\\n uly = src_geo[3]\\n lrx = src_geo[0] + src_geo[1] * x_size\\n lry = src_geo[3] + src_geo[5] * y_size\\n\\n extent = np.array([[[ulx, uly], [lrx, uly]], [[ulx, lry], [lrx, lry]]])\\n\\n if dst_srs:\\n # try to load projection from source dataset if None is given\\n if src_srs is None:\\n src_proj = src_ds.GetProjection()\\n if not src_proj:\\n raise ValueError(\\n \\\"src_ds is missing projection information, please use ``projection_source`` kwarg and provide a fitting OSR SRS object.\\\"\\n )\\n src_srs = osr.SpatialReference()\\n src_srs.ImportFromWkt(src_ds.GetProjection())\\n\\n # Transformation\\n extent = georef.reproject(\\n extent, projection_source=src_srs, projection_target=dst_srs\\n )\\n\\n # wkt needed\\n src_srs = src_srs.ExportToWkt()\\n dst_srs = dst_srs.ExportToWkt()\\n\\n (ulx, uly, urx, ury, llx, lly, lrx, lry) = tuple(list(extent.flatten().tolist()))\\n\\n # align grid to destination raster or UL-corner point\\n if align:\\n try:\\n ulx, uly = align\\n except TypeError:\\n pass\\n\\n ulx = int(max(np.floor(ulx), np.floor(llx)))\\n uly = int(min(np.ceil(uly), np.ceil(ury)))\\n lrx = int(min(np.ceil(lrx), np.ceil(urx)))\\n lry = int(max(np.floor(lry), np.floor(lly)))\\n\\n # calculate cols/rows or xspacing/yspacing\\n if spacing:\\n try:\\n x_ps, y_ps = spacing\\n except TypeError:\\n x_ps = spacing\\n y_ps = spacing\\n\\n cols = int(abs(lrx - ulx) / x_ps)\\n rows = int(abs(uly - lry) / y_ps)\\n elif size:\\n cols, rows = size\\n x_ps = x_size * src_geo[1] / cols\\n y_ps = y_size * abs(src_geo[5]) / rows\\n\\n # create destination in-memory raster\\n mem_drv = gdal.GetDriverByName(\\\"MEM\\\")\\n\\n # and set RasterSize according ro cols/rows\\n dst_ds = mem_drv.Create(\\\"\\\", cols, rows, 1, gdal.GDT_Float32)\\n\\n # Create the destination GeoTransform with changed x/y spacing\\n dst_geo = (ulx, x_ps, src_geo[2], uly, src_geo[4], -y_ps)\\n\\n # apply GeoTransform to destination dataset\\n dst_ds.SetGeoTransform(dst_geo)\\n\\n # apply Projection to destination dataset\\n if dst_srs is not None:\\n dst_ds.SetProjection(dst_srs)\\n\\n # nodata handling, need to initialize dst_ds with nodata\\n src_band = src_ds.GetRasterBand(1)\\n nodata = src_band.GetNoDataValue()\\n dst_band = dst_ds.GetRasterBand(1)\\n if nodata is not None:\\n dst_band.SetNoDataValue(nodata)\\n dst_band.WriteArray(np.ones((rows, cols)) * nodata)\\n dst_band.FlushCache()\\n\\n # resample and reproject dataset\\n gdal.ReprojectImage(src_ds, dst_ds, src_srs, dst_srs, resample)\\n\\n return dst_ds\",\n \"def _get_transform(self, transform):\\n try:\\n from cartopy.crs import CRS\\n except ModuleNotFoundError:\\n CRS = None\\n cartopy = getattr(self, 'name', None) == 'proplot_cartopy'\\n if (\\n isinstance(transform, mtransforms.Transform)\\n or CRS and isinstance(transform, CRS)\\n ):\\n return transform\\n elif transform == 'figure':\\n return self.figure.transFigure\\n elif transform == 'axes':\\n return self.transAxes\\n elif transform == 'data':\\n return PlateCarree() if cartopy else self.transData\\n elif cartopy and transform == 'map':\\n return self.transData\\n else:\\n raise ValueError(f'Unknown transform {transform!r}.')\",\n \"def __update_dataset(self, crs, transform, nodata=None):\\r\\n\\r\\n meta = {\\r\\n \\\"driver\\\": \\\"GTiff\\\",\\r\\n \\\"dtype\\\": self.__arr.dtype,\\r\\n \\\"nodata\\\": nodata,\\r\\n \\\"height\\\": self.__arr.shape[-2],\\r\\n \\\"width\\\": self.__arr.shape[-1],\\r\\n \\\"count\\\": self.__arr.shape[0],\\r\\n \\\"crs\\\": crs,\\r\\n \\\"transform\\\": transform,\\r\\n }\\r\\n\\r\\n memfile = MemoryFile()\\r\\n with memfile.open(**meta) as ds:\\r\\n ds.write(self.__arr)\\r\\n self.dataset = memfile.open()\\r\\n memfile.close()\",\n \"def reproject_dataset(filename):\\n\\n # We want to project the GeoTIFF coordinate reference system (crs)\\n # to WGS84 (e.g. into the familiar Lat/Lon pairs). WGS84 is analogous\\n # to EPSG:4326\\n dst_crs = 'EPSG:4326'\\n\\n with rasterio.open(filename) as src:\\n transform, width, height = rasterio.warp.calculate_default_transform(\\n src.crs, dst_crs, src.width, src.height, *src.bounds)\\n kwargs = src.meta.copy()\\n kwargs.update({\\n 'crs': dst_crs,\\n 'transform': transform,\\n 'width': width,\\n 'height': height,\\n 'compress': 'lzw'\\n })\\n\\n out_path = filename.split('.tif')[0] + '.wgs84.tif'\\n with rasterio.open(out_path, 'w', **kwargs) as dst:\\n for i in range(1, src.count + 1):\\n rasterio.warp.reproject(\\n source=rasterio.band(src, i),\\n destination=rasterio.band(dst, i),\\n src_transform=src.transform,\\n src_crs=src.crs,\\n dst_transform=transform,\\n dst_crs=dst_crs,\\n resampling=rasterio.warp.Resampling.nearest)\\n\\n return out_path\",\n \"def project_points(X, K, R, T, distortion_flag=False, distortion_params=None):\\n # Project points from 3d world coordinates to 2d image coordinates\\n\\n #get projection matrix\\n pmatrix = projection_matrix(R, T, K)\\n\\n #add 4th component to points\\n ones = np.ones([1,len(X[0])])\\n xones=np.row_stack((X,ones))\\n\\n #calculate pixel coordinates\\n X_camera = pmatrix.dot(xones)\\n\\n return X_camera\",\n \"def reproject(src_path, out_path, target_crs='EPSG:4326'):\\n\\n # load satdata\\n satdata = load(src_path)\\n\\n # calculate a transform and new dimensions using our dataset's current CRS and dimensions\\n transform, width, height = calculate_default_transform(satdata.crs,\\n target_crs,\\n satdata.width,\\n satdata.height,\\n *satdata.bounds)\\n\\n # Using a copy of the metadata from the clipped raster dataset and the transform we defined above,\\n # we can write a new geoTIFF containing the reprojected and clipped raster data:\\n metadata = satdata.meta.copy()\\n\\n # Change the CRS, transform, and dimensions in metadata to match our desired output dataset\\n metadata.update({'crs':target_crs,\\n 'transform':transform,\\n 'width':width,\\n 'height':height})\\n\\n # apply the transform & metadata to perform the reprojection\\n with rasterio.open(out_path, 'w', **metadata) as reprojected:\\n for band in range(1, satdata.count + 1):\\n rasterio_reproject(\\n source=rasterio.band(satdata, band),\\n destination=rasterio.band(reprojected, band),\\n src_transform=satdata.transform,\\n src_crs=satdata.crs,\\n dst_transform=transform,\\n dst_crs=target_crs\\n )\",\n \"def CopyTransform(transform):\\n return copy.copy(transform)\",\n \"def apply_transform_on_3d_image(image: torch.Tensor, transforms: List[Callable]) -> torch.Tensor:\\n for z in range(image.shape[0]):\\n pil = TF.to_pil_image(image[z])\\n for transform_fn in transforms:\\n pil = transform_fn(pil)\\n image[z] = TF.to_tensor(pil).squeeze()\\n return image\",\n \"def reproject_coordinates(coordinates, inproj, outproj, flat=False):\\n if flat:\\n return np.array([transform(inproj, outproj, coord[0], coord[1]) for coord in coordinates]).flatten()\\n return [list(transform(inproj, outproj, coord[0], coord[1])) for coord in coordinates]\",\n \"def testCalculateTransform(self):\\n # Create some points in the first frame.\\n z = 4.0\\n self.evaluator.camera_height = z\\n first_points = numpy.array(\\n [[0, 0, z], [2, 0, z], [2, 5, z], [0, 5, z]], dtype=numpy.float32)\\n # Create a transformation that will move the camera\\n R = numpy.array([[0.0, -1.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 1.0]])\\n t = numpy.array([[3.0], [-5.0], [0.0]])\\n expected_result = numpy.eye(4)\\n expected_result[0:3, 0:3] = R\\n expected_result[0:3, 3:] = t\\n # Determine where the second points would be given that.\\n second_points = (numpy.matmul(\\n R, first_points.transpose()) + t).transpose()\\n # Create a simple intrinsic matrix to project onto a fictional camera\\n intrinsic = numpy.array(\\n [[1.0, 0.0, 20.0], [0.0, 1.0, 20.0], [0.0, 0.0, 1.0]])\\n # Use no distortion or transformations\\n rvec = numpy.zeros((3, 1))\\n tvec = rvec\\n distortion = numpy.zeros((5, 1))\\n # Project the points into the camera\\n (camera_first_points, _) = cv2.projectPoints(\\n first_points, rvec, tvec, intrinsic, distortion)\\n camera_first_points = camera_first_points.squeeze()\\n (camera_second_points, _) = cv2.projectPoints(\\n second_points, rvec, tvec, intrinsic, distortion)\\n camera_second_points = camera_second_points.squeeze()\\n # Using these projected points, can the object recover the correct initial transform\\n result = self.evaluator._calculateTransform(\\n camera_first_points, camera_second_points, intrinsic)\\n # The matrix comparisions aren't reliable near zero, so check elements manually.\\n for i in range(expected_result.shape[0]):\\n for j in range(expected_result.shape[1]):\\n result_element = result[i, j]\\n expected_element = expected_result[i, j]\\n self.assertAlmostEqual(result_element, expected_element, 6,\\n 'Matrix element ({0:d}, {1:d}) is incorrect.'.format(i, j))\",\n \"def transform(self, X, copy=...):\\n ...\",\n \"def transform(self, X, copy=...):\\n ...\",\n \"def transform(self, X, copy=...):\\n ...\",\n \"def project_point(self, point: Point3D) -> Point3D:\\n x, y, z = point\\n cam_x, cam_y, cam_z = self._pos\\n x -= cam_x\\n y -= cam_y\\n z -= cam_z\\n dx = self._cy*(self._sz*y + self._cz*x) - self._sy*z\\n dy = self._sx*(self._sy*(self._sz*y + self._cz*x) + self._cy*z) + self._cx*(self._cz*y - self._sz*x)\\n dz = self._cx*(self._sy*(self._sz*y + self._cz*x) + self._cy*z) - self._sx*(self._cz*y - self._sz*x)\\n return self._scale * dx/dz, self._scale * dy/dz, dz\",\n \"def transform(self, R, t, scale = 1):\\n\\n # Build 4-by-4 projection matrix from args ----------------------------\\n # This is what we are doing internally:\\n # Proj = np.r_[ scale * np.c_[R, t], [[0, 0, 0, 1]] ]\\n # InvProj = np.r_[ scale * np.c_[R.T, -np.dot(R.T, t)], [[0,0,0,scale]] ]\\n Proj = tf_format.tf_format('4x4', R, t)\\n Proj[:-1,:] *= scale\\n InvProj = tf_format.tf_format('i4x4', R, t) * scale\\n \\n \\n # Apply transformation to pts3D ---------------------------------------\\n if self.pts3D is not None and self.pts3D.shape[1] > 0:\\n # Use homogeneous coords\\n pts3D = np.r_[self.pts3D, np.ones((1, self.pts3D.shape[1]))]\\n pts3D = np.dot(Proj, pts3D)\\n self.pts3D = pts3D[:3, :]\\n\\n # Apply transformation to cameras -------------------------------------\\n # Camera poses are stored using camera-to-world transformations, we \\n # need to invert the projection matrix for this to work --> \\n # we use InvProj\\n\\n cposes = self.cam_poses\\n for i in range(cposes.shape[1]):\\n\\n # Extract camera projection matrix\\n p_cam = tf_format.tf_format('4x4', cposes[:, i])\\n\\n # Transform camera projection matrix\\n new_p_cam = np.dot(p_cam, InvProj)\\n \\n # Make sure it's a true rotation!\\n [u, s, vT] = np.linalg.svd(new_p_cam[:3,:3])\\n cposes[:3, i] = tf_format.rodrigues( np.dot(u,vT) ).ravel()\\n cposes[3:, i] = new_p_cam[:3, 3]\\n\\n self.cam_poses = cposes\",\n \"def transform(self, X, y=None):\\n X = X.copy()\\n if isinstance(self.transformation, BaseEstimator):\\n X[self.columns_to_transform_] = self.transformation.transform(\\n X[self.columns_to_transform_]\\n )\\n else:\\n X[self.columns_to_transform_] = X[self.columns_to_transform_].applymap(\\n self.transformation\\n )\\n\\n return X\",\n \"def four_point_transform(image, pts):\\n\\n\\tmax_x, max_y = np.max(pts[:, 0]).astype(np.int32), np.max(pts[:, 1]).astype(np.int32)\\n\\n\\tdst = np.array([\\n\\t\\t[0, 0],\\n\\t\\t[image.shape[1] - 1, 0],\\n\\t\\t[image.shape[1] - 1, image.shape[0] - 1],\\n\\t\\t[0, image.shape[0] - 1]], dtype=\\\"float32\\\")\\n\\n\\twarped = cv2.warpPerspective(image, cv2.getPerspectiveTransform(dst, pts), (max_x, max_y))\\n\\n\\treturn warped\",\n \"def _build_geotransform(self, i, j):\\n assert isinstance(i, int), (\\\"i is not an integer\\\")\\n assert isinstance(j, int), (\\\"j is not an integer\\\")\\n x_origin, x_res, x_ignore, y_origin, y_ignore, y_res = (\\n self.image_metadata.geotransform)\\n # integer conversion to reduce floating point error\\n new_x_origin = self._calculate_origin(x_origin, x_res, self.offset, j)\\n new_y_origin = self._calculate_origin(y_origin, y_res, self.offset, i)\\n geotransform = (new_x_origin, x_res, x_ignore, new_y_origin, \\n y_ignore, y_res) \\n return geotransform\",\n \"def project(self, point_cloud, round_px=True):\\n if not isinstance(point_cloud, PointCloud) and not (isinstance(point_cloud, Point) and point_cloud.dim == 3):\\n raise ValueError('Must provide PointCloud or 3D Point object for projection')\\n if point_cloud.frame != self._frame:\\n raise ValueError('Cannot project points in frame %s into camera with frame %s' %(point_cloud.frame, self._frame))\\n\\n points_proj = self.S.dot(point_cloud.data) + self.t\\n if len(points_proj.shape) == 1:\\n points_proj = points_proj[:, np.newaxis]\\n point_depths = np.tile(points_proj[2,:], [3, 1])\\n points_proj = np.divide(points_proj, point_depths)\\n if round_px:\\n points_proj = np.round(points_proj)\\n\\n if isinstance(point_cloud, Point):\\n return Point(data=points_proj[:2,:].astype(np.int16), frame=self._frame)\\n return ImageCoords(data=points_proj[:2,:].astype(np.int16), frame=self._frame)\",\n \"def project(self, point):\\n return np.round(project(self.camera.P, point)).astype(int)\",\n \"def map(self, obj):\\n if isinstance(obj, np.ndarray) and obj.ndim >= 2 and obj.shape[0] in (2,3):\\n return fn.transformCoordinates(self, obj)\\n else:\\n return QtGui.QMatrix4x4.map(self, obj)\",\n \"def apply_projection(projection, dataset):\\n out = DatasetType(name=dataset.name, attributes=dataset.attributes)\\n\\n for var in projection:\\n target, template = out, dataset\\n while var:\\n name, slice_ = var.pop(0)\\n candidate = template[name]\\n \\n # apply slice\\n if slice_:\\n if isinstance(candidate, BaseType):\\n candidate.data = candidate[slice_]\\n elif isinstance(candidate, SequenceType):\\n candidate = candidate[slice_[0]]\\n elif isinstance(candidate, GridType):\\n candidate = candidate[slice_]\\n\\n # handle structures\\n if isinstance(candidate, StructureType):\\n # add variable to target\\n if name not in target.keys():\\n if var:\\n # if there are more children to add we need to clear the\\n # candidate so it has only explicitly added children; \\n # also, Grids are degenerated into Structures\\n if isinstance(candidate, GridType):\\n candidate = StructureType(candidate.name, candidate.attributes)\\n candidate._keys = []\\n target[name] = candidate\\n target, template = target[name], template[name]\\n else:\\n target[name] = candidate\\n\\n # fix sequence data, including only variables that are in the sequence\\n for seq in walk(out, SequenceType):\\n seq.data = get_var(dataset, seq.id)[tuple(seq.keys())].data\\n\\n return out\",\n \"def project(points, camera_params, theta):\\n \\\"\\\"\\\"\\n Function takes input of 3d_points, transformations and Convert 3-D points to 2-D by projecting onto images. \\n Input:\\n points: 3D points in world frame\\n camera_params: parameters of camera corrosponding to the point\\n theta: Needed For PAL camera to specify the sub camera index for the points\\n Output:\\n points_proj: 2D reprojected points for 3D points \\n\\n \\\"\\\"\\\"\\n # Convert the 3D points to Camera Frame by rotaion followes by translation\\n points_proj1 = rotate(points[:,0:3], camera_params[:, :3])\\n points_proj1 += camera_params[:, 3:6]\\n # FOR PAL: Converting into the Sub-camera Frame by respective rotation\\n thetas = theta * np.pi / 3 \\n points_proj = np.copy(points_proj1)\\n points_proj[:,0] = points_proj1[:,0]*np.cos(thetas) - points_proj1[:,2]*np.sin(thetas)\\n points_proj[:,2] = points_proj1[:,0]*np.sin(thetas) + points_proj1[:,2]*np.cos(thetas)\\n # Avoiding Zero error\\n for i in range(len(points_proj)):\\n if(points_proj[i,2]==0):\\n points_proj[i,0] = 0\\n points_proj[i,1] = 0\\n points_proj[i,2] = 1\\n # 2D projection\\n points_proj = points_proj[:, :2] / points_proj[:, 2, np.newaxis]\\n f = camera_params[:, 6]\\n k1 = camera_params[:, 7]\\n k2 = camera_params[:, 8]\\n n = np.sum(points_proj**2, axis=1)\\n r = 1 + k1 * n + k2 * n**2\\n points_proj *= (r * f)[:, np.newaxis]\\n return points_proj\",\n \"def update_transform(self, transform: Optional[torchvision.transforms.Compose],\\n train: bool = True) -> 'BaseDataset':\\n if train:\\n self.train_transform = transform\\n else:\\n self.val_transform = transform\\n return self\",\n \"def project_drawn(cb, msg):\\n stream = cb.streams[0]\\n old_data = stream.data\\n stream.update(data=msg['data'])\\n element = stream.element\\n stream.update(data=old_data)\\n proj = cb.plot.projection\\n if not isinstance(element, _Element) or element.crs == proj:\\n return None\\n crs = element.crs\\n element.crs = proj\\n return project(element, projection=crs)\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=1,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def coordinate_transform_proj4(proj1, proj2, coords):\\n\\n srs1 = osr.SpatialReference()\\n srs2 = osr.SpatialReference()\\n srs1.ImportFromProj4(proj1)\\n srs2.ImportFromProj4(proj2)\\n\\n return coordinate_transform(srs1, srs2, coords)\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=0,\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\",\n \"def deproject_pixel(self, depth, pixel):\\n if not isinstance(pixel, Point) and not pixel.dim == 2:\\n raise ValueError('Must provide 2D Point object for pixel projection')\\n if pixel.frame != self._frame:\\n raise ValueError('Cannot deproject pixel in frame %s from camera with frame %s' %(pixel.frame, self._frame))\\n\\n point = np.r_[pixel.data, depth]\\n point_3d = np.linalg.inv(self.S).dot(point - self.t)\\n return Point(data=point_3d, frame=self._frame)\",\n \"def transform_coord(proj1, proj2, x, y):\\r\\n\\r\\n # Set full EPSG projection strings\\r\\n proj1 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj1)\\r\\n proj2 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj2)\\r\\n\\r\\n # Convert coordinates\\r\\n return pyproj.transform(proj1, proj2, x, y)\",\n \"def transform_coord(proj1, proj2, x, y):\\r\\n\\r\\n # Set full EPSG projection strings\\r\\n proj1 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj1)\\r\\n proj2 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj2)\\r\\n\\r\\n # Convert coordinates\\r\\n return pyproj.transform(proj1, proj2, x, y)\",\n \"def computeProjection(skyPictureInstance_id):\\n \\n skyPictureInstance = SkyPicture.objects.get(id = skyPictureInstance_id)\\n \\n # Check if it has not already been computed for this instance\\n if not skyPictureInstance.undistorted or skyPictureInstance.undistorted == 'undistorted/TODO.png':\\n img = np.asarray(Image.open(os.path.join(settings.BASE_DIR, skyPictureInstance.image.url[1:])))\\n \\n if img.shape[0] > img.shape[1]:\\n img = np.rot90(img)\\n \\n img = np.flip(img,0)\\n \\n img = img[:, (np.shape(img)[1]/2 - np.shape(img)[0]/2):(np.shape(img)[1]/2 + np.shape(img)[0]/2), :]\\n img = transparency(img)\\n img_pil_response = Image.fromarray(img, \\\"RGBA\\\")\\n \\n # fetch image into memory\\n temp_handle = StringIO()\\n img_pil_response.save(temp_handle, 'PNG', option='optimize')\\n temp_handle.seek(0)\\n \\n filename = skyPictureInstance.date.strftime(\\\"%Y-%m-%d-\\\") + skyPictureInstance.time.strftime(\\\"%H-%M-%S.png\\\")\\n suf = SimpleUploadedFile(filename, temp_handle.read(), content_type='image/png')\\n skyPictureInstance.undistorted.save(filename, suf, False)\\n skyPictureInstance.save()\",\n \"def project_points(points, cam_matrix, trans, rot):\\n\\n # STEP 1: Transform pointcloud into new reference frame.\\n points = np.dot(rot, points) + trans[:, None]\\n\\n # STEP 2: Project new pointcloud onto image frame using K matrix.\\n # gives a 3 x N array of image plane coordinates in homogenous coordinates.\\n homo_pixel_coords = np.dot(cam_matrix, points)\\n\\n # STEP 3: Convert homogenous coordinates to regular 2D coordinates.\\n # To do this, you need to divide the first two coordinates of homo_pixel_coords\\n # by the third coordinate.\\n pixel_coords = homo_pixel_coords[:2] / homo_pixel_coords[2]\\n\\n # STEP 4: Convert to integers. Take the floor of pixel_coords then cast it\\n # to an integer type, like numpy.int32\\n pixel_coords = np.int32(np.floor(pixel_coords))\\n\\n return pixel_coords\",\n \"def xprojection(self):\\n return self.image.sum(axis=0)\",\n \"def transform_coord(proj1, proj2, x, y):\\r\\n # Set full EPSG projection strings\\r\\n proj1 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj1)\\r\\n proj2 = pyproj.Proj(\\\"+init=EPSG:\\\"+proj2)\\r\\n\\r\\n # Convert coordinates\\r\\n return pyproj.transform(proj1, proj2, x, y)\",\n \"def displace_to_pose(img, depth, pose, intrinsics, intrinsics_inv, rotation_mode='euler', padding_mode='zeros', return_coordinates=False):\\n check_sizes(img, 'img', 'B3HW')\\n\\n src_pixel_coords = get_displacement_pixel_transformation(img, depth, pose, intrinsics, intrinsics_inv, rotation_mode=rotation_mode, padding_mode=padding_mode)\\n projected_img = torch.nn.functional.grid_sample(img, src_pixel_coords, padding_mode=padding_mode)\\n if return_coordinates:\\n return projected_img, src_pixel_coords\\n else:\\n return projected_img\",\n \"def transform(self, data):\\n return self.fit_transform(data, fitting=False)\",\n \"def _transform(\\n self, x: \\\"torch.Tensor\\\", y: Optional[\\\"torch.Tensor\\\"], **kwargs\\n ) -> Tuple[\\\"torch.Tensor\\\", Optional[\\\"torch.Tensor\\\"]]:\\n import torch\\n import torchvision.transforms.functional as F\\n\\n img_size = x.shape[:2]\\n\\n angle = float(\\n torch.empty(1)\\n .uniform_(float(self.degree_range[0]), float(self.degree_range[1]))\\n .item()\\n )\\n\\n max_dx = float(self.translate[0] * img_size[1])\\n max_dy = float(self.translate[1] * img_size[0])\\n tx = int(round(torch.empty(1).uniform_(-max_dx, max_dx).item()))\\n ty = int(round(torch.empty(1).uniform_(-max_dy, max_dy).item()))\\n translations = (tx, ty)\\n\\n scale = float(torch.empty(1).uniform_(self.scale[0], self.scale[1]).item())\\n\\n # x needs to have channel first\\n x = x.permute(2, 0, 1)\\n x = F.affine(\\n img=x, angle=angle, translate=translations, scale=scale, shear=(0.0, 0.0)\\n )\\n x = x.permute(1, 2, 0)\\n\\n return torch.clamp(x, min=self.clip_values[0], max=self.clip_values[1]), y\",\n \"def transform(self, dataset: NumpyOrPandas) -> NumpyDataset:\\n # checks here\\n super().transform(dataset)\\n # convert to accepted dtype and get attributes\\n dataset = dataset.to_pandas()\\n df = dataset.data\\n\\n # transform\\n roles = NumericRole()\\n outputs = []\\n\\n for n, conlumn_name in enumerate(df.columns):\\n if self.cache_dir is not None:\\n full_hash = get_textarr_hash(df[conlumn_name]) + get_textarr_hash(self.dicts[conlumn_name][\\\"feats\\\"])\\n fname = os.path.join(self.cache_dir, full_hash + \\\".pkl\\\")\\n\\n if os.path.exists(fname):\\n logger.info3(f\\\"Load saved dataset for {conlumn_name}\\\")\\n\\n with open(fname, \\\"rb\\\") as f:\\n new_arr = pickle.load(f)\\n\\n else:\\n new_arr = self.dicts[conlumn_name][\\\"transformer\\\"].transform(df[conlumn_name])\\n with open(fname, \\\"wb\\\") as f:\\n pickle.dump(new_arr, f)\\n else:\\n new_arr = self.dicts[conlumn_name][\\\"transformer\\\"].transform(df[conlumn_name])\\n\\n output = dataset.empty().to_numpy()\\n output.set_data(new_arr, self.dicts[conlumn_name][\\\"feats\\\"], roles)\\n outputs.append(output)\\n logger.info3(f\\\"Feature {conlumn_name} transformed\\\")\\n # create resulted\\n return dataset.empty().to_numpy().concat(outputs)\",\n \"def get_transform(self, map_from='visual', map_to='render'):\\n return self.transforms.get_transform(map_from, map_to)\",\n \"def get_transform(self, from_frame, to_frame):\\n if not self._pipeline:\\n return None\\n try:\\n from_ind = self._get_frame_index(from_frame)\\n except ValueError:\\n raise CoordinateFrameError(\\\"Frame {0} is not in the available \\\"\\n \\\"frames\\\".format(from_frame))\\n try:\\n to_ind = self._get_frame_index(to_frame)\\n except ValueError:\\n raise CoordinateFrameError(\\\"Frame {0} is not in the available frames\\\".format(to_frame))\\n if to_ind < from_ind:\\n #transforms = np.array(self._pipeline[to_ind: from_ind], dtype=\\\"object\\\")[:, 1].tolist()\\n transforms = [step.transform for step in self._pipeline[to_ind: from_ind]]\\n transforms = [tr.inverse for tr in transforms[::-1]]\\n elif to_ind == from_ind:\\n return None\\n else:\\n #transforms = np.array(self._pipeline[from_ind: to_ind], dtype=\\\"object\\\")[:, 1].copy()\\n transforms = [step.transform for step in self._pipeline[from_ind: to_ind]]\\n return functools.reduce(lambda x, y: x | y, transforms)\",\n \"def _transform(self, X, y=None):\\n return clone(self.transformer).fit_transform(X=X, y=y)\",\n \"def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nearest', cval=0.):\\n x = np.rollaxis(x, channel_index, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n channel_images = [ndi.interpolation.affine_transform(x_channel,\\n final_affine_matrix,\\n final_offset, order=0, mode=fill_mode, cval=cval)\\n for x_channel in x]\\n x = np.stack(channel_images, axis=0)\\n x = np.rollaxis(x, 0, channel_index+1)\\n return x\",\n \"def get_dataset_transform(dataset_name):\\n if dataset_name == \\\"MNIST\\\":\\n train_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ])\\n test_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ])\\n elif dataset_name == \\\"CIFAR10\\\":\\n train_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\\n ])\\n test_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\\n ])\\n elif dataset_name == \\\"CIFAR100\\\":\\n train_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\\n ])\\n test_transform = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.4914, 0.4822, 0.4465), (0.247, 0.243, 0.261))\\n ])\\n elif dataset_name == \\\"EMNIST\\\":\\n train_transform = transforms.Compose([\\n Transpose(),\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ])\\n test_transform = transforms.Compose([\\n Transpose(),\\n transforms.ToTensor(),\\n transforms.Normalize((0.1307,), (0.3081,))\\n ])\\n else:\\n print(\\\"current dataset name:{} is not right\\\".format(dataset_name))\\n sys.exit(1)\\n return train_transform, test_transform\",\n \"def transformAffine(self, path=None, src=None, dst=None):\\n if path is not None:\\n landmarks = pd.read_csv(path, skiprows=1,engine=\\\"c\\\", na_filter=False, header=None, delim_whitespace=True, dtype=np.float32).as_matrix()\\n dst = landmarks[:,3:5]\\n src = landmarks[:,1:3]\\n affine = transform.estimate_transform(\\\"affine\\\",src,dst)\\n data = self.stormData[0][:,0:2]\\n data = affine(data)\\n self.stormData[0][:,0:2] = data\",\n \"def _pixel_to_map(coordinates, geotransform):\\n coordinates_map = np.empty(coordinates.shape)\\n coordinates_map[..., 0] = (\\n geotransform[0]\\n + geotransform[1] * coordinates[..., 0]\\n + geotransform[2] * coordinates[..., 1]\\n )\\n coordinates_map[..., 1] = (\\n geotransform[3]\\n + geotransform[4] * coordinates[..., 0]\\n + geotransform[5] * coordinates[..., 1]\\n )\\n return coordinates_map\",\n \"def test_coords_transformation():\\n\\n # H+R+S+T, not reverse, depth\\n img_meta = {\\n 'pcd_scale_factor':\\n 1.2311e+00,\\n 'pcd_rotation': [[8.660254e-01, 0.5, 0], [-0.5, 8.660254e-01, 0],\\n [0, 0, 1.0e+00]],\\n 'pcd_trans': [1.111e-02, -8.88e-03, 0.0],\\n 'pcd_horizontal_flip':\\n True,\\n 'transformation_3d_flow': ['HF', 'R', 'S', 'T']\\n }\\n\\n pcd = torch.tensor([[-5.2422e+00, -2.9757e-01, 4.0021e+01],\\n [-9.1435e-01, 2.6675e+01, -5.5950e+00],\\n [2.0089e-01, 5.8098e+00, -3.5409e+01],\\n [-1.9461e-01, 3.1309e+01, -1.0901e+00]])\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'DEPTH', img_meta, reverse=False)\\n\\n expected_tensor = torch.tensor(\\n [[5.78332345e+00, 2.900697e+00, 4.92698531e+01],\\n [-1.5433839e+01, 2.8993850e+01, -6.8880045e+00],\\n [-3.77929405e+00, 6.061661e+00, -4.35920199e+01],\\n [-1.9053658e+01, 3.3491436e+01, -1.34202211e+00]])\\n\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\\n\\n # H+R+S+T, reverse, depth\\n img_meta = {\\n 'pcd_scale_factor':\\n 7.07106781e-01,\\n 'pcd_rotation': [[7.07106781e-01, 7.07106781e-01, 0.0],\\n [-7.07106781e-01, 7.07106781e-01, 0.0],\\n [0.0, 0.0, 1.0e+00]],\\n 'pcd_trans': [0.0, 0.0, 0.0],\\n 'pcd_horizontal_flip':\\n False,\\n 'transformation_3d_flow': ['HF', 'R', 'S', 'T']\\n }\\n\\n pcd = torch.tensor([[-5.2422e+00, -2.9757e-01, 4.0021e+01],\\n [-9.1435e+01, 2.6675e+01, -5.5950e+00],\\n [6.061661e+00, -0.0, -1.0e+02]])\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'DEPTH', img_meta, reverse=True)\\n\\n expected_tensor = torch.tensor(\\n [[-5.53977e+00, 4.94463e+00, 5.65982409e+01],\\n [-6.476e+01, 1.1811e+02, -7.91252488e+00],\\n [6.061661e+00, -6.061661e+00, -1.41421356e+02]])\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\\n\\n # H+R+S+T, not reverse, camera\\n img_meta = {\\n 'pcd_scale_factor':\\n 1.0 / 7.07106781e-01,\\n 'pcd_rotation': [[7.07106781e-01, 0.0, 7.07106781e-01],\\n [0.0, 1.0e+00, 0.0],\\n [-7.07106781e-01, 0.0, 7.07106781e-01]],\\n 'pcd_trans': [1.0e+00, -1.0e+00, 0.0],\\n 'pcd_horizontal_flip':\\n True,\\n 'transformation_3d_flow': ['HF', 'S', 'R', 'T']\\n }\\n\\n pcd = torch.tensor([[-5.2422e+00, 4.0021e+01, -2.9757e-01],\\n [-9.1435e+01, -5.5950e+00, 2.6675e+01],\\n [6.061661e+00, -1.0e+02, -0.0]])\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'CAMERA', img_meta, reverse=False)\\n\\n expected_tensor = torch.tensor(\\n [[6.53977e+00, 5.55982409e+01, 4.94463e+00],\\n [6.576e+01, -8.91252488e+00, 1.1811e+02],\\n [-5.061661e+00, -1.42421356e+02, -6.061661e+00]])\\n\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\\n\\n # V, reverse, camera\\n img_meta = {'pcd_vertical_flip': True, 'transformation_3d_flow': ['VF']}\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'CAMERA', img_meta, reverse=True)\\n\\n expected_tensor = torch.tensor([[-5.2422e+00, 4.0021e+01, 2.9757e-01],\\n [-9.1435e+01, -5.5950e+00, -2.6675e+01],\\n [6.061661e+00, -1.0e+02, 0.0]])\\n\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\\n\\n # V+H, not reverse, depth\\n img_meta = {\\n 'pcd_vertical_flip': True,\\n 'pcd_horizontal_flip': True,\\n 'transformation_3d_flow': ['VF', 'HF']\\n }\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'DEPTH', img_meta, reverse=False)\\n\\n expected_tensor = torch.tensor([[5.2422e+00, -4.0021e+01, -2.9757e-01],\\n [9.1435e+01, 5.5950e+00, 2.6675e+01],\\n [-6.061661e+00, 1.0e+02, 0.0]])\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\\n\\n # V+H, reverse, lidar\\n img_meta = {\\n 'pcd_vertical_flip': True,\\n 'pcd_horizontal_flip': True,\\n 'transformation_3d_flow': ['VF', 'HF']\\n }\\n\\n pcd_transformed = apply_3d_transformation(\\n pcd, 'LIDAR', img_meta, reverse=True)\\n\\n expected_tensor = torch.tensor([[5.2422e+00, -4.0021e+01, -2.9757e-01],\\n [9.1435e+01, 5.5950e+00, 2.6675e+01],\\n [-6.061661e+00, 1.0e+02, 0.0]])\\n assert torch.allclose(expected_tensor, pcd_transformed, 1e-4)\",\n \"def transform(self, X):\\n\\n t0 = time.perf_counter()\\n check_is_fitted(self)\\n self.check_external_components_modified()#[WARN] in d3m, primitives can \\\"restore\\\" private class variables...\\n X = self._validate_data(X, accept_sparse=[\\\"csr\\\", \\\"csc\\\"], reset=False)\\n t1 = time.perf_counter()\\n\\n if X.shape[1] != self.components_af_.shape[1]:\\n raise ValueError(\\n \\\"Impossible to perform projection:\\\"\\n \\\"X at fit stage had a different number of features. \\\"\\n \\\"(%s != %s)\\\" % (X.shape[1], self.components_af_.shape[1])\\n )\\n\\n #X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)\\n #import pdb; pdb.set_trace()\\n X_af = af.interop.from_ndarray(X).as_type(self.components_af_.dtype())\\n X_new = af.matmulNT(X_af, self.components_af_)\\n X_new = X_new.to_ndarray()\\n t2 = time.perf_counter()\\n return X_new\",\n \"def setCrsIsProjection(self):\\n self.isgeographic = False\",\n \"def set_transform(self, tf):\\n self.camera.SetTransform(tf)\",\n \"def transform(self, srid: ir.IntegerValue) -> GeoSpatialValue:\\n return ops.GeoTransform(self, srid).to_expr()\",\n \"def _select_transform(self):\\n for transform in self.transforms:\\n if transform.applies is None or transform.applies(self.ti_dict) is True:\\n self.transform = transform\\n break\\n else:\\n raise RuntimeError('No transform found for TI data')\",\n \"def set_transform(self, transform):\\n self._transform = transform\",\n \"def apply_transform(x,\\n transform_matrix,\\n channel_axis=0,\\n fill_mode='nearest',\\n cval=0.,\\n interp_order=0):\\n x = np.rollaxis(x, channel_axis, 0)\\n final_affine_matrix = transform_matrix[:3, :3]\\n final_offset = transform_matrix[:3, -1]\\n channel_volumes = [ndi.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=interp_order, # NOTE: The order of the spline interpolation\\n mode=fill_mode,\\n cval=cval) for x_channel in x]\\n x = np.stack(channel_volumes, axis=0)\\n x = np.rollaxis(x, 0, channel_axis + 1)\\n return x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6720557","0.6548644","0.6337521","0.6230713","0.62149775","0.58759457","0.58334744","0.5782975","0.5738817","0.56981885","0.5647636","0.5635617","0.5624577","0.5602451","0.55902624","0.5584441","0.5567373","0.55569345","0.55569345","0.5550751","0.552929","0.54919827","0.5490159","0.54779094","0.5475191","0.5470257","0.54520786","0.54448014","0.54384285","0.5435653","0.5430392","0.5413855","0.5407643","0.53956044","0.53721946","0.5369844","0.53660345","0.53596693","0.5338315","0.53264916","0.5308238","0.53082347","0.5279875","0.5273014","0.52558863","0.52554613","0.52545464","0.5242427","0.52370423","0.52360666","0.52293295","0.5228455","0.5212772","0.5207434","0.52031183","0.5182809","0.51702833","0.51702833","0.51702833","0.5169828","0.51623565","0.5145855","0.5145717","0.5145486","0.513241","0.51280165","0.51212513","0.5119788","0.5095306","0.5093604","0.5087195","0.5083273","0.5082387","0.50799567","0.5079166","0.5069819","0.5069819","0.5069103","0.5064534","0.50637126","0.5062107","0.5060045","0.5055635","0.5045152","0.5039407","0.50366175","0.5021306","0.50170046","0.50162876","0.50110936","0.50040156","0.49997258","0.4996688","0.49913839","0.49875358","0.49870628","0.49853915","0.49759674","0.4974922","0.49618715"],"string":"[\n \"0.6720557\",\n \"0.6548644\",\n \"0.6337521\",\n \"0.6230713\",\n \"0.62149775\",\n \"0.58759457\",\n \"0.58334744\",\n \"0.5782975\",\n \"0.5738817\",\n \"0.56981885\",\n \"0.5647636\",\n \"0.5635617\",\n \"0.5624577\",\n \"0.5602451\",\n \"0.55902624\",\n \"0.5584441\",\n \"0.5567373\",\n \"0.55569345\",\n \"0.55569345\",\n \"0.5550751\",\n \"0.552929\",\n \"0.54919827\",\n \"0.5490159\",\n \"0.54779094\",\n \"0.5475191\",\n \"0.5470257\",\n \"0.54520786\",\n \"0.54448014\",\n \"0.54384285\",\n \"0.5435653\",\n \"0.5430392\",\n \"0.5413855\",\n \"0.5407643\",\n \"0.53956044\",\n \"0.53721946\",\n \"0.5369844\",\n \"0.53660345\",\n \"0.53596693\",\n \"0.5338315\",\n \"0.53264916\",\n \"0.5308238\",\n \"0.53082347\",\n \"0.5279875\",\n \"0.5273014\",\n \"0.52558863\",\n \"0.52554613\",\n \"0.52545464\",\n \"0.5242427\",\n \"0.52370423\",\n \"0.52360666\",\n \"0.52293295\",\n \"0.5228455\",\n \"0.5212772\",\n \"0.5207434\",\n \"0.52031183\",\n \"0.5182809\",\n \"0.51702833\",\n \"0.51702833\",\n \"0.51702833\",\n \"0.5169828\",\n \"0.51623565\",\n \"0.5145855\",\n \"0.5145717\",\n \"0.5145486\",\n \"0.513241\",\n \"0.51280165\",\n \"0.51212513\",\n \"0.5119788\",\n \"0.5095306\",\n \"0.5093604\",\n \"0.5087195\",\n \"0.5083273\",\n \"0.5082387\",\n \"0.50799567\",\n \"0.5079166\",\n \"0.5069819\",\n \"0.5069819\",\n \"0.5069103\",\n \"0.5064534\",\n \"0.50637126\",\n \"0.5062107\",\n \"0.5060045\",\n \"0.5055635\",\n \"0.5045152\",\n \"0.5039407\",\n \"0.50366175\",\n \"0.5021306\",\n \"0.50170046\",\n \"0.50162876\",\n \"0.50110936\",\n \"0.50040156\",\n \"0.49997258\",\n \"0.4996688\",\n \"0.49913839\",\n \"0.49875358\",\n \"0.49870628\",\n \"0.49853915\",\n \"0.49759674\",\n \"0.4974922\",\n \"0.49618715\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5615591"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":95697,"cells":{"query":{"kind":"string","value":"Returns (lowerleft, upperright) extents in transform's destination projection. If transform is None, no reprojection occurs and the dataset's SpatialReference is used."},"document":{"kind":"string","value":"def GetExtents(self, transform=None):\n # Prepare GDAL functions to compute extents\n x_size, y_size = self.RasterXSize, self.RasterYSize\n\n # Compute four corners in destination projection\n upper_left = self.PixelCoordinates(0, 0,\n transform=transform)\n upper_right = self.PixelCoordinates(x_size, 0,\n transform=transform)\n lower_left = self.PixelCoordinates(0, y_size,\n transform=transform)\n lower_right = self.PixelCoordinates(x_size, y_size,\n transform=transform)\n x_values, y_values = list(zip(upper_left, upper_right,\n lower_left, lower_right))\n\n # Return lower-left and upper-right extents\n return Extents(lower_left=XY(min(x_values), min(y_values)),\n upper_right=XY(max(x_values), max(y_values)))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extent(self):\n left = self.transform[0]\n right = left + self.transform[1] * self.shape[1]\n top = self.transform[3]\n bottom = top + self.transform[5] * self.shape[0]\n return left, right, bottom, top","def extent(self):\n if self.x is not None:\n if self.y is not None:\n if self.z is not None:\n return (self.x.min(), self.x.max(),\n self.y.min(), self.y.max(),\n self.z.min(), self.z.max())\n return (self.x.min(), self.x.max(),\n self.y.min(), self.y.max())\n return (self.x.min(), self.x.max())\n\n elif self.r is not None and self.t is not None:\n if self.z is not None:\n return (self.z.min(), self.z.max(),\n self.r.min(), self.r.max(),\n self.t.min(), self.t.max())\n return (self.r.min(), self.r.max(),\n self.t.min(), self.t.max())\n\n return ()","def get_extent(self):\n geot = self.geotransform()\n return (geot[0], geot[3] + self.YSize() * geot[5],\n geot[0] + self.XSize() * geot[1], geot[3])","def get_extent_from_dataset(ds):\n\n transform = ds.GetGeoTransform()\n rows = ds.RasterYSize\n cols = ds.RasterXSize\n\n west = transform[0]\n ewres = transform[1]\n north = transform[3]\n nsres = transform[5]\n\n south = north + (rows * nsres)\n east = west + (cols * ewres)\n\n # print(\"crs\", ds.GetProjection())\n # print(\"cols\", cols)\n # print(\"north\", north)\n # print(\"south\", south)\n # print(\"west\", west)\n # print(\"east\", east)\n # print(\"ewres\", ewres)\n # print(\"nsres\", nsres)\n\n extent = SpatialExtent(top=north, bottom=south, left=west,\n right=east, width=abs(ewres), height=abs(nsres))\n\n return extent","def extent(self):\n rx0 = gxapi.float_ref()\n ry0 = gxapi.float_ref()\n rz0 = gxapi.float_ref()\n rx1 = gxapi.float_ref()\n ry1 = gxapi.float_ref()\n rz1 = gxapi.float_ref()\n self.gxvox.get_area(rx0, ry0, rz0, rx1, ry1, rz1)\n if self.is_depth:\n return gxgm.Point2(((rx0.value, ry0.value, -rz1.value), (rx1.value, ry1.value, -rz0.value)))\n return gxgm.Point2(((rx0.value, ry0.value, rz0.value), (rx1.value, ry1.value, rz1.value)),\n self.coordinate_system)","def geotransform(self):\n return self.dataset.GetGeoTransform() if self.dataset else None","def GetTmsExtents(self, resolution=None, transform=None):\n if resolution is None:\n resolution = self.GetNativeResolution(transform=transform)\n\n # Get the tile dimensions in map units\n if transform is None:\n spatial_ref = self.GetSpatialReference()\n else:\n spatial_ref = transform.dst_ref\n\n tile_width, tile_height = spatial_ref.GetTileDimensions(\n resolution=resolution\n )\n\n # Validate that the native resolution extents are tile-aligned.\n extents = self.GetTiledExtents(transform=transform)\n pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\n if not extents.almost_equal(self.GetExtents(transform=transform),\n delta=min(*pixel_sizes)):\n raise UnalignedInputError('Dataset is not aligned to TMS grid')\n\n # Correct for origin, because you can't do modular arithmetic on\n # half-tiles.\n left, bottom = spatial_ref.OffsetPoint(*extents.lower_left)\n right, top = spatial_ref.OffsetPoint(*extents.upper_right)\n\n # Divide by number of tiles\n return Extents(lower_left=XY(int(round(left / tile_width)),\n int(round(bottom / tile_height))),\n upper_right=XY(int(round(right / tile_width)),\n int(round(top / tile_height))))","def get_extents(self, view, ranges):\n ndims = len(view.dimensions())\n num = 6 if self.projection == '3d' else 4\n if self.apply_ranges:\n if ranges:\n dims = view.dimensions()\n x0, x1 = ranges[dims[0].name]\n if ndims > 1:\n y0, y1 = ranges[dims[1].name]\n else:\n y0, y1 = (np.NaN, np.NaN)\n if self.projection == '3d':\n if len(dims) > 2:\n z0, z1 = ranges[dims[2].name]\n else:\n z0, z1 = np.NaN, np.NaN\n else:\n x0, x1 = view.range(0)\n y0, y1 = view.range(1) if ndims > 1 else (np.NaN, np.NaN)\n if self.projection == '3d':\n z0, z1 = view.range(2)\n if self.projection == '3d':\n range_extents = (x0, y0, z0, x1, y1, z1)\n else:\n range_extents = (x0, y0, x1, y1)\n else:\n range_extents = (np.NaN,) * num\n\n if self.apply_extents:\n norm_opts = self.lookup_options(view, 'norm').options\n if norm_opts.get('framewise', False) or self.dynamic:\n extents = view.extents\n else:\n extent_list = self.hmap.traverse(lambda x: x.extents, [Element])\n extents = util.max_extents(extent_list, self.projection == '3d')\n else:\n extents = (np.NaN,) * num\n return tuple(l1 if l2 is None or not np.isfinite(l2) else\n l2 for l1, l2 in zip(range_extents, extents))","def get_extent(self):\n pass","def get_extent(ds):\n\n #\n # Check if latitude and longitude are stored as coordinates.\n #\n if 'lon' in ds.coords and 'lat' in ds.coords:\n return BoundingBox(\n left=ds.lon.values.min(),\n bottom=ds.lat.values.min(),\n right=ds.lon.values.max(),\n top=ds.lat.values.max()\n )\n\n #\n # Otherwise, get extent from projection information\n # by projecting the corner coordinates onto EPSG:4326\n # to obtain the latitude and longitude at the four corners.\n #\n src_crs = get_crs(ds)\n if src_crs is None:\n raise CRSError('Could not determine the CRS.')\n\n dst_crs = CRS(init='epsg:4326')\n proj_bounds = get_bounds(ds)\n bounds = rasterio.warp.transform_bounds(\n src_crs, dst_crs, **proj_bounds._asdict()\n )\n return BoundingBox(*bounds)","def extent(self):\n\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\n\n return [x[0], x[1], y[0], y[1]]","def extent_in_crs(self, crs=wgs84):\n\n # this is not so trivial\n # for optimisation we will transform the boundaries only\n poly = self.extent_as_polygon(crs=crs)\n _i, _j = poly.exterior.xy\n return [np.min(_i), np.max(_i), np.min(_j), np.max(_j)]","def get_data_extent(self):\n xs, ys = self.xs, self.ys\n xmin, xmax = min(xs), max(xs)\n ymin, ymax = min(xy), max(ys)\n w = maxx - minx\n h = maxy - miny\n return xmin, ymax, w, h","def extents(self):\n x0, y0, width, height = self._rect_bbox\n xmin, xmax = sorted([x0, x0 + width])\n ymin, ymax = sorted([y0, y0 + height])\n return xmin, xmax, ymin, ymax","def _getGeoTransform(self):\n with self._getDatasetLock:\n gt = self.dataset.GetGeoTransform()\n if (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()):\n gt = gdal.GCPsToGeoTransform(self.dataset.GetGCPs())\n return gt","def extent(self) -> Tuple[float]:\n ext = (\n min(self.putin.geometry.x, self.takeout.geometry.x),\n min(self.putin.geometry.y, self.takeout.geometry.y),\n max(self.putin.geometry.x, self.takeout.geometry.x),\n max(self.putin.geometry.y, self.takeout.geometry.y),\n )\n return ext","def extent(self):\n ulx, uly, lrx, lry = self.ul_lr\n return ulx, lry, lrx, uly","def extents(self):\n\n return self._local","def extent_from_metadata(meta):\n\n meta = find_meta_branch(meta, 'geosoft')\n if meta:\n cs = coordinate_system_from_metadata(meta)\n try:\n ex = meta['geosoft']['dataset']['georeference']['dataextents']['extent3d']\n minp = gxgm.Point((float(ex['@minx']), float(ex['@miny']), float(ex['@minz'])))\n maxp = gxgm.Point((float(ex['@maxx']), float(ex['@maxy']), float(ex['@maxz'])))\n return gxgm.Point2((minp, maxp), cs)\n\n except KeyError:\n pass\n\n return None","def get_geographic_extent(self, project):\n if project.geographic_extent is not None:\n return json.loads(project.geographic_extent.json)\n else:\n return None","def get_raster_extent(dataset, geo=False, window=True):\n\n x_size = dataset.RasterXSize\n y_size = dataset.RasterYSize\n geotrans = dataset.GetGeoTransform()\n xmin = geotrans[0]\n ymax = geotrans[3]\n xmax = geotrans[0] + geotrans[1] * x_size\n ymin = geotrans[3] + geotrans[5] * y_size\n\n extent = np.array([[xmin, ymax], [xmin, ymin], [xmax, ymin], [xmax, ymax]])\n\n if geo:\n projection = read_gdal_projection(dataset)\n extent = georef.reproject(extent, projection_source=projection)\n\n if window:\n x = extent[:, 0]\n y = extent[:, 1]\n extent = np.array([x.min(), x.max(), y.min(), y.max()])\n\n return extent","def getExtent(self):\n extent = self.parent.biomeGeometry.extent\n return extent","def extent(self):\n return self._extent","def get_data_extent(self):\n \n x, y = self.xy[0], self.xy[1]\n w, h = self.radius, self.radius\n return x-w, y+w, w, h","def extents(self):\n if self.direction == 'horizontal':\n vmin = self._selection_artist.get_x()\n vmax = vmin + self._selection_artist.get_width()\n else:\n vmin = self._selection_artist.get_y()\n vmax = vmin + self._selection_artist.get_height()\n return vmin, vmax","def get_data_extent(self):\n x, y = self.xy[0], self.xy[1]\n w, h = self.width, self.height\n return x, y, w, h","def transform_bounds(\n self, dst_crs: CRS | int | str | dict, densify_pts: int = 21\n ) -> tuple[float, float, float, float]:\n if self.crs != dst_crs:\n bounds = rasterio.warp.transform_bounds(\n self.crs, dst_crs, *self.bounds, densify_pts=densify_pts\n )\n else:\n bounds = self.bounds\n return bounds","def extent(self, start=None, finish=None):\n start, finish = self.bounds(start, finish)\n try:\n return finish - start + 1\n except TypeError:\n return None","def bounds(self) -> tuple[float, float, float, float]:\n transform = self.transform\n a, b, c, d, e, f, _, _, _ = transform\n if b == d == 0:\n xs = (c, c + a * self.width)\n ys = (f, f + e * self.height)\n else: # rotated\n c0x, c0y = c, f\n c1x, c1y = transform * (0, self.height)\n c2x, c2y = transform * (self.width, self.height)\n c3x, c3y = transform * (self.width, 0)\n xs = (c0x, c1x, c2x, c3x)\n ys = (c0y, c1y, c2y, c3y)\n return min(xs), min(ys), max(xs), max(ys)","def _reproject(ds, dst_crs=None, dst_transform=None, width=None, height=None,\n res=None, extent=None, **kwargs):\n\n if 'resampling' not in kwargs:\n kwargs['resampling'] = rasterio.warp.Resampling.cubic\n\n src_crs = get_crs(ds)\n src_bounds = get_bounds(ds)\n if extent is not None:\n extent = BoundingBox(*extent)\n\n #\n # Only allow inferring of width or height from aspect ratio\n # if the CRS is not changed.\n #\n if dst_crs is None:\n dst_crs = src_crs\n if width is None and height is not None:\n width = int(ncols(ds) * height / nrows(ds))\n elif height is None and width is not None:\n height = int(nrows(ds) * width / ncols(ds))\n\n # Given: transform, shape\n # Given: transform, extent\n # Given: res, extent\n # Given: shape, res\n # Given: shape, extent\n\n if dst_transform is not None:\n #\n # If the transform is given, we also need the width and height or\n # the extent.\n #\n if width is not None and height is not None:\n pass\n elif extent is not None:\n # Calculate width and height from extent\n width = int(abs(\n (extent.right - extent.left) / dst_transform.a)) + 1\n height = int(abs(\n (extent.top - extent.bottom) / dst_transform.e)) + 1\n else:\n raise ValueError('Not enough information provided.')\n\n elif extent is not None:\n #\n # Transform can be calculated from extent, if either width and height\n # or the resolution are given.\n #\n if res is not None:\n width = int(abs(\n (extent.right - extent.left) / res[0])) + 1\n height = int(abs(\n (extent.top - extent.bottom) / res[1])) + 1\n\n # The following doesn't give the correct result.\n dst_transform = rasterio.transform.from_bounds(\n *extent, width=width-1, height=height-1\n )\n\n else:\n #\n # If neither the transform nor the extent are given, infer the best\n # possible parameters from the width, height, and the resolution.\n #\n dst_transform, width, height = \\\n rasterio.warp.calculate_default_transform(\n src_crs, dst_crs,\n ncols(ds), nrows(ds),\n resolution=res,\n dst_width=width,\n dst_height=height,\n **src_bounds._asdict())\n\n src_transform = get_transform(ds)\n src_dims = get_dims(ds)\n dst_crs = _parse_crs(dst_crs)\n\n #\n # Prepare new x and y coordinate arrays\n #\n dst_x, _ = dst_transform * (np.arange(width), np.zeros(width, dtype=int))\n _, dst_y = dst_transform * (np.zeros(height, dtype=int), np.arange(height))\n dst_coords = {'x': dst_x, 'y': dst_y}\n\n #\n # Handle the case where there are extra dimensions, e.g. 'time'\n # or 'band'\n #\n extra_dims = src_dims.keys() - {'y', 'x'}\n if len(extra_dims) == 0:\n dst_shape = (height, width)\n dst_dims = ('y', 'x')\n elif len(extra_dims) == 1:\n extra_dim = extra_dims.pop()\n dst_shape = (src_dims[extra_dim], height, width)\n dst_dims = (extra_dim, 'y', 'x')\n dst_coords[extra_dim] = ds.coords[extra_dim]\n else:\n raise ValueError('More than three dimensions are not supported.')\n\n def _reproject_da(da, shape):\n #\n # Reproject a single data array\n #\n extra_dims = set(da.dims) - {'y', 'x'}\n dim_order = tuple(extra_dims) + ('y', 'x')\n values = da.transpose(*dim_order).values\n output = np.zeros(shape, dtype=da.dtype)\n output[:] = np.nan\n\n # Fix data shape for one-dimensional data\n if da.ndim == 1:\n #\n # NOTE: The stretch factor is necessary because the input data\n # must extend at least half an original resolution cell in the\n # independent dimension.\n #\n if da.dims == ('x',):\n stretch_x = int((~src_transform * dst_transform).a / 2 + 1)\n values = np.vstack([values] * stretch_x)\n output.shape = (1,) + output.shape\n elif da.dims == ('y',):\n stretch_y = int((~src_transform * dst_transform).e / 2 + 1)\n values = np.vstack([values] * stretch_y).T\n output.shape = output.shape + (1,)\n\n rasterio.warp.reproject(\n values,\n output,\n src_transform=src_transform,\n src_crs=src_crs,\n dst_transform=dst_transform,\n dst_crs=dst_crs,\n dst_nodata=np.nan,\n **kwargs\n )\n\n # Final reshape in case the input was one-dimensional\n return output.reshape(shape)\n\n if isinstance(ds, xr.Dataset):\n result = xr.Dataset(coords=dst_coords)\n\n #\n # Also reproject coordinate arrays that are defined over\n # x and y\n #\n for v in ds.coords:\n #\n # If the projection is the same, also reproject coordinate arrays\n # that are defined over only one variable.\n #\n if dst_crs == src_crs and v not in ds.dims:\n if ds.coords[v].dims == ('x',):\n result.coords[v] = \\\n (('x',), _reproject_da(ds.coords[v], (width,)))\n elif ds.coords[v].dims == ('y',):\n result.coords[v] = \\\n (('y',), _reproject_da(ds.coords[v], (height,)))\n\n if not set(ds.coords[v].dims).issuperset({'x', 'y'}):\n continue\n\n shape = (height, width)\n result.coords[v] = (('y', 'x'),\n _reproject_da(ds.coords[v], shape))\n\n #\n # Reproject the actual data\n #\n for v in ds.data_vars:\n if set(ds[v].dims) == set(dst_dims):\n result[v] = (dst_dims, _reproject_da(ds[v], dst_shape))\n elif set(ds[v].dims) == {'y', 'x'}:\n shape = (height, width)\n result[v] = (dst_dims, _reproject_da(ds[v], shape))\n else:\n result[v] = (ds[v].dims, ds[v])\n\n #\n # Create lat and lon coordinates\n #\n # if 'lat' in ds.coords and 'lon' in ds.coords:\n # lon, lat = rasterio.warp.transform(\n # src_crs, dst_crs, ds.coords['x'], ds.coords['y'])\n # result.coords['lon'] = (('x',), lon)\n # result.coords['lat'] = (('y',), lat)\n\n elif isinstance(ds, xr.DataArray):\n result = xr.DataArray(_reproject_da(ds, dst_shape), dims=dst_dims,\n coords=dst_coords, name=ds.name)\n\n result.attrs = ds.attrs\n\n # Serialize transform to tuple and store in metadata\n result.attrs['transform'] = dst_transform[:6]\n # Store CRS info in metadata\n result.attrs['crs'] = dst_crs.to_string()\n result.attrs['coordinate_system_string'] = dst_crs.wkt\n # Store new data shape in metadata\n result.attrs['lines'] = nrows(result)\n result.attrs['samples'] = ncols(result)\n\n _add_latlon(result)\n\n return result","def GetExtent(vDataSet):\r\n return [vDataSet.GetExtendMinX(),vDataSet.GetExtendMaxX(),\r\n vDataSet.GetExtendMinY(),vDataSet.GetExtendMaxY(),\r\n vDataSet.GetExtendMinZ(),vDataSet.GetExtendMaxZ()]","def GetExtent(ds):\n xmin, xpixel, _, ymax, _, ypixel = ds.GetGeoTransform()\n width, height = ds.RasterXSize, ds.RasterYSize\n xmax = xmin + width * xpixel\n ymin = ymax + height * ypixel\n\n return (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)","def extent(self):\n return self.index.max() - self.index.min(), self.columns.max() - self.columns.min()","def extent_to_gdal_transform(extent):\n\n # extent = SpatialExtent()\n\n pixel_height = math.copysign(extent.height, extent.bottom - extent.top)\n pixel_width = math.copysign(extent.width, extent.right - extent.left)\n\n transform = [extent.top, pixel_width, 0, extent.left, 0, pixel_height]\n return transform","def extents(nodes):\n from numpy import min, max\n return ( min(nodes[:,0]), max(nodes[:,0]),\n min(nodes[:,1]), max(nodes[:,1]),\n min(nodes[:,2]), max(nodes[:,2]) )","def extents(self):\n self._updateExtents()\n return self.mExtents","def get_data_extent(self):\n raise NotImplementedError, \"Derived must override\"","def GetResolution(vDataSet):\r\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\r\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\r\n\r\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz","def geoextent(self):\r\n return self.series_extent","def return_extents(self):\n\n return [qm.tree.mins, qm.tree.maxs]","def extent(self):\n return np.array(self._extent)","def extent(self):\n return self._ax.extent","def dataPointExtent(dataset):\n ext = tuple(np.flip(dataset.GetDimensions()))\n return ext","def geotransform(self):\n return self._geotransform","def _resolution(self):\n _, xres, _, _, _, yres = self.geotransform\n return xres, yres","def GetNativeResolution(self, transform=None, maximum=None):\n # Get the source projection's units for a 1x1 pixel, assuming square\n # pixels.\n width, height = self.GetPixelDimensions()\n src_pixel_size = min(abs(width), abs(height))\n\n if transform is None:\n dst_pixel_size = src_pixel_size\n dst_ref = self.GetSpatialReference()\n else:\n # Transform these dimensions into the destination projection\n dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]\n dst_pixel_size = abs(dst_pixel_size)\n dst_ref = transform.dst_ref\n\n # We allow some floating point error between src_pixel_size and\n # dst_pixel_size based on the major circumference so that the error is\n # in the destination units\n error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128\n\n # Find the resolution where the pixels are smaller than dst_pixel_size.\n for resolution in count():\n if maximum is not None and resolution >= maximum:\n return resolution\n\n res_pixel_size = max(\n *dst_ref.GetPixelDimensions(resolution=resolution)\n )\n if (res_pixel_size - dst_pixel_size) <= error:\n return resolution\n\n # Halve error each resolution\n error /= 2","def get_common_extent(datasets):\n common_bounds = get_common_bounds(datasets)\n common_crs = get_crs(datasets[0])\n dst_crs = CRS(init='epsg:4326')\n extent = rasterio.warp.transform_bounds(\n common_crs, dst_crs, **common_bounds._asdict()\n )\n return BoundingBox(*extent)","def getCurrentExtent(self):\n if not self.currentBox:\n extent = None\n else:\n extent = boxToExtent(self.currentBox)\n return extent","def extent(self):\r\n if not hasattr(self, '_extent'):\r\n self._extent = conf.lib.clang_getCursorExtent(self)\r\n\r\n return self._extent","def Extrema(self):\n ymin = np.min(self._corners[:, 1])\n xmin = np.min(self._corners[:, 0])\n ymax = np.max(self._corners[:, 1])\n xmax = np.max(self._corners[:, 0])\n return ymin, xmin, ymax, xmax","def bounding_box(self):\n frames = self.available_frames\n transform_0 = self.get_transform(frames[0], frames[1])\n try:\n bb = transform_0.bounding_box\n except NotImplementedError:\n return None\n if transform_0.n_inputs == 1:\n return bb\n try:\n axes_order = self.input_frame.axes_order\n except AttributeError:\n axes_order = np.arange(transform_0.n_inputs)\n # Model.bounding_box is in python order, need to reverse it first.\n return tuple(bb[::-1][i] for i in axes_order)","def major_extent(self) -> complex:\n return max((self.max() - self.null, self.null - self.min()))","def get_bounds(self):\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","def check_extent(self):\n if self.lower_left.x > self.upper_right.x:\n dlx = self.lower_left.x\n self.lower_left.x = self.upper_right.x\n self.upper_right.y = dlx\n\n if self.lower_left.y > self.upper_right.y:\n dly = self.lower_left.y\n self.lower_left.y = self.upper_right.y\n self.upper_right.y = dly","def get_extent(georef, shape):\n extent = (\n georef[0],\n georef[3] +\n shape[1] *\n georef[5],\n georef[0] +\n shape[0] *\n georef[1],\n georef[3]) # x1,y1,x2,y2\n return extent","def get_extent(fpath):\n extents = []\n with h5py.File(fpath, mode='r') as f:\n for key, value in f['label/label-0'].attrs.items():\n if key.lower().endswith('extent') and isinstance(value, np.ndarray):\n extents.append(value)\n \n extents = np.stack(extents, axis=0)\n maxs = np.max(extents, axis=0)\n mins = np.min(extents, axis=0)\n axis_slices = []\n for min_, max_ in zip(mins[::2], maxs[1::2]):\n axis_slices.append(slice(min_, max_, 1))\n return tuple(axis_slices)","def extent(self) -> typing.Tuple[str, ...]:\n return self._extent.members()","def bbox(self):\n return np.array(self.path.get_extents()).ravel(order='F')","def fetchbounds(self):\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\n self.in_end, self.end, self.out_end] \\\n if x is not None]\n return min(pnts), max(pnts)","def pivot_grid_bound(param=None, extent=10.0):\n\n # No pivot set.\n if not hasattr(cdp, param):\n raise RelaxError(\"The pivot point has not been set, cannot determine the grid search bounds.\")\n\n # The value.\n val = getattr(cdp, param)\n\n # Return the bound.\n return val + extent","def getBounds(self, srs=None):\n if srs not in self._bounds:\n gt = self._getGeoTransform()\n nativeSrs = self.getProj4String()\n if not nativeSrs:\n self._bounds[srs] = None\n return\n bounds = {\n 'll': {\n 'x': gt[0] + self.sourceSizeY * gt[2],\n 'y': gt[3] + self.sourceSizeY * gt[5],\n },\n 'ul': {\n 'x': gt[0],\n 'y': gt[3],\n },\n 'lr': {\n 'x': gt[0] + self.sourceSizeX * gt[1] + self.sourceSizeY * gt[2],\n 'y': gt[3] + self.sourceSizeX * gt[4] + self.sourceSizeY * gt[5],\n },\n 'ur': {\n 'x': gt[0] + self.sourceSizeX * gt[1],\n 'y': gt[3] + self.sourceSizeX * gt[4],\n },\n 'srs': nativeSrs,\n }\n # Make sure geographic coordinates do not exceed their limits\n if self._proj4Proj(nativeSrs).crs.is_geographic and srs:\n try:\n self._proj4Proj(srs)(0, 90, errcheck=True)\n yBound = 90.0\n except RuntimeError:\n yBound = 89.999999\n keys = ('ll', 'ul', 'lr', 'ur')\n for key in keys:\n bounds[key]['y'] = max(min(bounds[key]['y'], yBound), -yBound)\n while any(bounds[key]['x'] > 180 for key in keys):\n for key in keys:\n bounds[key]['x'] -= 360\n while any(bounds[key]['x'] < -180 for key in keys):\n for key in keys:\n bounds[key]['x'] += 360\n if any(bounds[key]['x'] >= 180 for key in keys):\n bounds['ul']['x'] = bounds['ll']['x'] = -180\n bounds['ur']['x'] = bounds['lr']['x'] = 180\n if srs and srs != nativeSrs:\n inProj = self._proj4Proj(nativeSrs)\n outProj = self._proj4Proj(srs)\n keys = ('ll', 'ul', 'lr', 'ur')\n pts = pyproj.Transformer.from_proj(inProj, outProj, always_xy=True).itransform([\n (bounds[key]['x'], bounds[key]['y']) for key in keys])\n for idx, pt in enumerate(pts):\n key = keys[idx]\n bounds[key]['x'] = pt[0]\n bounds[key]['y'] = pt[1]\n bounds['srs'] = srs.decode() if isinstance(srs, bytes) else srs\n bounds['xmin'] = min(bounds['ll']['x'], bounds['ul']['x'],\n bounds['lr']['x'], bounds['ur']['x'])\n bounds['xmax'] = max(bounds['ll']['x'], bounds['ul']['x'],\n bounds['lr']['x'], bounds['ur']['x'])\n bounds['ymin'] = min(bounds['ll']['y'], bounds['ul']['y'],\n bounds['lr']['y'], bounds['ur']['y'])\n bounds['ymax'] = max(bounds['ll']['y'], bounds['ul']['y'],\n bounds['lr']['y'], bounds['ur']['y'])\n self._bounds[srs] = bounds\n return self._bounds[srs]","def get_resolution(ds):\n\n if 'x' in ds.coords and 'y' in ds.coords:\n x = ds.coords['x'].values\n y = ds.coords['y'].values\n resx = abs(x[-1] - x[0]) / (len(x) - 1)\n resy = abs(y[-1] - y[0]) / (len(y) - 1)\n return (resx, resy)\n else:\n transform = get_transform(ds)\n if transform is not None:\n return (abs(transform.a), abs(transform.e))\n elif 'res' in ds.attrs:\n return ds.attrs['res']\n\n return None","def map_extent(input_raster):\n\n gdal.UseExceptions()\n raster = gdal.Open(input_raster)\n raster_geotransform = raster.GetGeoTransform()\n raster_extent = (raster_geotransform[0],\n raster_geotransform[0]\n + raster.RasterXSize * raster_geotransform[1],\n raster_geotransform[3]\n + raster.RasterYSize * raster_geotransform[5],\n raster_geotransform[3])\n\n return raster_extent","def get_bounds(self):\n return self._geometry.bounds","def transformed_bounds(self):\n b = []\n\n for dim in self.dimensions:\n if dim.transformed_size == 1:\n b.append(dim.transformed_bounds)\n else:\n b.extend(dim.transformed_bounds)\n\n return b","def _get_inv_gt(self):\r\n # Geotransform the original map\r\n self.in_gt = self.in_ds.GetGeoTransform()\r\n # Create an inverse geotransform for the raster.\r\n # This converts real-world coordinates to pixel offsets.\r\n self.inv_gt = gdal.InvGeoTransform(self.in_gt)\r\n if gdal.VersionInfo()[0] == '1':\r\n if self.inv_gt[0] == 1:\r\n self.inv_gt = self.inv_gt[1]\r\n else:\r\n raise RuntimeError('Inverse geotransform failed')\r\n elif self.inv_gt is None:\r\n raise RuntimeError('Inverse geotransform failed')","def extent(obj):\n return obj.get('startOffset', -1), obj.get('endOffset', -1)","def extent(obj):\n return obj.get('startOffset', -1), obj.get('endOffset', -1)","def get_bounds(self):\n\n northing=self.f.variables['y']\n easting=self.f.variables['x']\n\n lat1,lon1 = utm.to_latlon(np.min(easting),np.min(northing),11,northern=True)\n lat2,lon2 = utm.to_latlon(np.max(easting),np.max(northing),11,northern=True)\n\n return (lon1,lon2,lat1,lat2)","def get_absolute_poses(self):\n if self.backend is not None:\n return self.backend.absolute_poses()\n return compute_absolute_poses(self.odometry.get_relative_poses())","def bounds(self):\n return self.xmin, self.xmax, self.ymin, self.ymax","def dataCellExtent(dataset):\n # Subtracting 1 from GetDimensions accounts for point-counts\n # vs cell-counts, while np.flip(...) switches the axis order:\n ext = tuple(np.flip(dataset.GetDimensions()) - 1)\n return ext","def get_bounds(self, crs=\"default\"):\n\n if crs == \"default\":\n crs = podpac.settings[\"DEFAULT_CRS\"]\n\n bounds = {}\n for coords in self.find_coordinates():\n ct = coords.transform(crs)\n for dim, (lo, hi) in ct.bounds.items():\n if dim not in bounds:\n bounds[dim] = (lo, hi)\n else:\n bounds[dim] = (min(lo, bounds[dim][0]), max(hi, bounds[dim][1]))\n\n return bounds, crs","def get_extents(self, p, x, y, z, w, h):\n x.value, y.value, z.value, w.value, h.value = self._get_extents(p, x.value, y.value, z.value, w.value, h.value)","def projection(self):\n return self.dataset.GetProjection() if self.dataset else None","def _get_dimensions(self):\n corners = []\n for module in self.modules:\n for tile in module:\n corners.append(tile.corners())\n corners = np.concatenate(corners)[:, :2] / self._pixel_shape\n\n # Find extremes, add 1 px margin to allow for rounding errors\n min_xy = corners.min(axis=0).astype(int) - 1\n max_xy = corners.max(axis=0).astype(int) + 1\n\n size = max_xy - min_xy\n centre = -min_xy\n # Switch xy -> yx\n return tuple(size[::-1]), centre[::-1]","def projection(self):\n return self._map_projection","def projection(self):\n return self._map_projection","def getExtentUnits(self):\n return _libsbml.Model_getExtentUnits(self)","def get_height():\n return resize.transforms[1].size","def _set_lim_and_transforms(self):\n # There are three important coordinate spaces going on here:\n #\n # 1. Data space: The space of the data itself\n #\n # 2. Axes space: The unit rectangle (0, 0) to (1, 1)\n # covering the entire plot area.\n #\n # 3. Display space: The coordinates of the resulting image,\n # often in pixels or dpi/inch.\n\n # This function makes heavy use of the Transform classes in\n # ``lib/matplotlib/transforms.py.`` For more information, see\n # the inline documentation there.\n\n # The goal of the first two transformations is to get from the\n # data space (in this case longitude and latitude) to axes\n # space. It is separated into a non-affine and affine part so\n # that the non-affine part does not have to be recomputed when\n # a simple affine change to the figure has been made (such as\n # resizing the window or changing the dpi).\n\n # 1) The core transformation from data space into\n # rectilinear space defined in the HammerTransform class.\n self.transProjection = IdentityTransform()\n # 2) The above has an output range that is not in the unit\n # rectangle, so scale and translate it so it fits correctly\n # within the axes. The peculiar calculations of xscale and\n # yscale are specific to a Aitoff-Hammer projection, so don't\n # worry about them too much.\n self.transAffine = Affine2D.from_values(\n 1., 0, 0.5, np.sqrt(3)/2., 0, 0)\n self.transAffinedep = Affine2D.from_values(\n 1., 0, -0.5, np.sqrt(3)/2., 0, 0)\n #self.transAffine = IdentityTransform()\n \n # 3) This is the transformation from axes space to display\n # space.\n self.transAxes = BboxTransformTo(self.bbox)\n\n # Now put these 3 transforms together -- from data all the way\n # to display coordinates. Using the '+' operator, these\n # transforms will be applied \"in order\". The transforms are\n # automatically simplified, if possible, by the underlying\n # transformation framework.\n self.transData = \\\n self.transProjection + \\\n self.transAffine + \\\n self.transAxes\n\n # The main data transformation is set up. Now deal with\n # gridlines and tick labels.\n\n # Longitude gridlines and ticklabels. The input to these\n # transforms are in display space in x and axes space in y.\n # Therefore, the input values will be in range (-xmin, 0),\n # (xmax, 1). The goal of these transforms is to go from that\n # space to display space. The tick labels will be offset 4\n # pixels from the equator.\n\n self._xaxis_pretransform = IdentityTransform()\n self._xaxis_transform = \\\n self._xaxis_pretransform + \\\n self.transData\n self._xaxis_text1_transform = \\\n Affine2D().scale(1.0, 0.0) + \\\n self.transData + \\\n Affine2D().translate(0.0, -20.0)\n self._xaxis_text2_transform = \\\n Affine2D().scale(1.0, 0.0) + \\\n self.transData + \\\n Affine2D().translate(0.0, -4.0)\n\n # Now set up the transforms for the latitude ticks. The input to\n # these transforms are in axes space in x and display space in\n # y. Therefore, the input values will be in range (0, -ymin),\n # (1, ymax). The goal of these transforms is to go from that\n # space to display space. The tick labels will be offset 4\n # pixels from the edge of the axes ellipse.\n\n self._yaxis_transform = self.transData\n yaxis_text_base = \\\n self.transProjection + \\\n (self.transAffine + \\\n self.transAxes)\n self._yaxis_text1_transform = \\\n yaxis_text_base + \\\n Affine2D().translate(-8.0, 0.0)\n self._yaxis_text2_transform = \\\n yaxis_text_base + \\\n Affine2D().translate(8.0, 0.0)","def get_bounds(ds):\n\n trans = get_transform(ds)\n if trans is not None:\n if isinstance(ds, xr.Dataset):\n dims = ds.dims\n elif isinstance(ds, xr.DataArray):\n dims = dict(zip(ds.dims, ds.shape))\n nrows = dims['y']\n ncols = dims['x']\n corners = (np.array([0, 0, ncols-1, ncols-1]),\n np.array([0, nrows-1, 0, nrows-1]))\n corner_x, corner_y = trans * corners\n return BoundingBox(\n left=corner_x.min(),\n bottom=corner_y.min(),\n right=corner_x.max(),\n top=corner_y.max()\n )\n else:\n return BoundingBox(\n left=ds['x'].min(),\n bottom=ds['y'].min(),\n right=ds['x'].max(),\n top=ds['y'].max()\n )","def get_geo_bounds(self, extent=None, data_sel=None):\n if self.science_product:\n res = self.__nc_geo_bounds(extent, data_sel)\n else:\n res = self.__h5_geo_bounds(extent, data_sel)\n data_sel, lon_bounds, lat_bounds = res\n\n res = {}\n _sz = lon_bounds.shape\n res['longitude'] = np.empty((_sz[0]+1, _sz[1]+1), dtype=np.float)\n res['longitude'][:-1, :-1] = lon_bounds[:, :, 0]\n res['longitude'][-1, :-1] = lon_bounds[-1, :, 1]\n res['longitude'][:-1, -1] = lon_bounds[:, -1, 1]\n res['longitude'][-1, -1] = lon_bounds[-1, -1, 2]\n\n res['latitude'] = np.empty((_sz[0]+1, _sz[1]+1), dtype=np.float)\n res['latitude'][:-1, :-1] = lat_bounds[:, :, 0]\n res['latitude'][-1, :-1] = lat_bounds[-1, :, 1]\n res['latitude'][:-1, -1] = lat_bounds[:, -1, 1]\n res['latitude'][-1, -1] = lat_bounds[-1, -1, 2]\n\n if extent is None:\n return res\n\n return data_sel, res","def bottomLeftCorner(self):\n self._updateExtents()\n return (self._mMinX,self._mMaxY)","def minor_extent(self) -> complex:\n return min((self.max() - self.null, self.null - self.min()))","def get_transform(self, map_from='visual', map_to='render'):\n return self.transforms.get_transform(map_from, map_to)","def coord_proj(self):\r\n return self._coord_proj","def size(self):\n return self.__xmax, self.__ymax","def width_height(bounds):\n try:\n geom = reproject_geometry(\n box(*bounds), src_crs=tile.crs, dst_crs=dst_pyramid.crs\n )\n if geom.is_empty: # Shapely>=2.0\n raise ValueError(\"geometry empty after reprojection\")\n l, b, r, t = geom.bounds\n except ValueError: # pragma: no cover\n raise TopologicalError(\"bounds cannot be translated into target CRS\")\n return r - l, t - b","def domain_bounds(self):\n return self._xmin, self._xmax, self._ymin, self._ymax, self._zmin, self._zmax","def bounds(self):\n \n return self.osmdb.bounds()","def get_bounding_box(self):\n if len(self.polygons) == 0:\n return None\n return numpy.array(((min(pts[:, 0].min() for pts in self.polygons),\n min(pts[:, 1].min() for pts in self.polygons)),\n (max(pts[:, 0].max() for pts in self.polygons),\n max(pts[:, 1].max() for pts in self.polygons))))","def get_bbox(self, obj):\n renderer = self.figure.canvas.get_renderer()\n transformer = self.figure.dpi_scale_trans.inverted()\n return obj.get_window_extent(renderer=renderer).transformed(transformer)","def geo_transform(self):\n pass","def get_object_bounds(self):\n if len(self._object_bounds) == 0:\n # Nothing plotted yet\n return -.01, .01, -.01, .01\n xmins, xmaxs, ymins, ymaxs = np.array(self._object_bounds).T\n xmax = max(xmaxs.max(), xmins.max())\n xmin = min(xmins.min(), xmaxs.min())\n ymax = max(ymaxs.max(), ymins.max())\n ymin = min(ymins.min(), ymaxs.min())\n return xmin, xmax, ymin, ymax","def get_projection(self):\n return self.projection","def get_bbox(self):\n dimsizes = self.get_full_dimensions('lon').values()\n slices = [slice(None, None, dimsizes[0] - 1),\n slice(None, None, dimsizes[1] - 1)]\n lon = self.read_values('lon', slices=slices)\n lat = self.read_values('lat', slices=slices)\n return (lon.min(), lat.min(), lon.max(), lat.max())","def getCurrentTransformSelection():\n node = cmds.ls(sl=True)\n if node:\n node = node[0]\n if cmds.nodeType(node) == 'transform':\n xform = node\n return xform\n else:\n relatives = cmds.listRelatives(node, shapes=True, f=1)\n if relatives:\n for i in relatives:\n if cmds.nodeType(i) == \"transform\":\n xform = i\n return xform\n return None","def GetTransform(*args, **kwargs):\n return _gdi_.GraphicsContext_GetTransform(*args, **kwargs)","def extent(self, **kwargs) -> gpd.GeoDataFrame:\n return gpd.GeoDataFrame(\n geometry=[box(*self.footprint().total_bounds)],\n crs=self.crs(),\n )"],"string":"[\n \"def extent(self):\\n left = self.transform[0]\\n right = left + self.transform[1] * self.shape[1]\\n top = self.transform[3]\\n bottom = top + self.transform[5] * self.shape[0]\\n return left, right, bottom, top\",\n \"def extent(self):\\n if self.x is not None:\\n if self.y is not None:\\n if self.z is not None:\\n return (self.x.min(), self.x.max(),\\n self.y.min(), self.y.max(),\\n self.z.min(), self.z.max())\\n return (self.x.min(), self.x.max(),\\n self.y.min(), self.y.max())\\n return (self.x.min(), self.x.max())\\n\\n elif self.r is not None and self.t is not None:\\n if self.z is not None:\\n return (self.z.min(), self.z.max(),\\n self.r.min(), self.r.max(),\\n self.t.min(), self.t.max())\\n return (self.r.min(), self.r.max(),\\n self.t.min(), self.t.max())\\n\\n return ()\",\n \"def get_extent(self):\\n geot = self.geotransform()\\n return (geot[0], geot[3] + self.YSize() * geot[5],\\n geot[0] + self.XSize() * geot[1], geot[3])\",\n \"def get_extent_from_dataset(ds):\\n\\n transform = ds.GetGeoTransform()\\n rows = ds.RasterYSize\\n cols = ds.RasterXSize\\n\\n west = transform[0]\\n ewres = transform[1]\\n north = transform[3]\\n nsres = transform[5]\\n\\n south = north + (rows * nsres)\\n east = west + (cols * ewres)\\n\\n # print(\\\"crs\\\", ds.GetProjection())\\n # print(\\\"cols\\\", cols)\\n # print(\\\"north\\\", north)\\n # print(\\\"south\\\", south)\\n # print(\\\"west\\\", west)\\n # print(\\\"east\\\", east)\\n # print(\\\"ewres\\\", ewres)\\n # print(\\\"nsres\\\", nsres)\\n\\n extent = SpatialExtent(top=north, bottom=south, left=west,\\n right=east, width=abs(ewres), height=abs(nsres))\\n\\n return extent\",\n \"def extent(self):\\n rx0 = gxapi.float_ref()\\n ry0 = gxapi.float_ref()\\n rz0 = gxapi.float_ref()\\n rx1 = gxapi.float_ref()\\n ry1 = gxapi.float_ref()\\n rz1 = gxapi.float_ref()\\n self.gxvox.get_area(rx0, ry0, rz0, rx1, ry1, rz1)\\n if self.is_depth:\\n return gxgm.Point2(((rx0.value, ry0.value, -rz1.value), (rx1.value, ry1.value, -rz0.value)))\\n return gxgm.Point2(((rx0.value, ry0.value, rz0.value), (rx1.value, ry1.value, rz1.value)),\\n self.coordinate_system)\",\n \"def geotransform(self):\\n return self.dataset.GetGeoTransform() if self.dataset else None\",\n \"def GetTmsExtents(self, resolution=None, transform=None):\\n if resolution is None:\\n resolution = self.GetNativeResolution(transform=transform)\\n\\n # Get the tile dimensions in map units\\n if transform is None:\\n spatial_ref = self.GetSpatialReference()\\n else:\\n spatial_ref = transform.dst_ref\\n\\n tile_width, tile_height = spatial_ref.GetTileDimensions(\\n resolution=resolution\\n )\\n\\n # Validate that the native resolution extents are tile-aligned.\\n extents = self.GetTiledExtents(transform=transform)\\n pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\\n if not extents.almost_equal(self.GetExtents(transform=transform),\\n delta=min(*pixel_sizes)):\\n raise UnalignedInputError('Dataset is not aligned to TMS grid')\\n\\n # Correct for origin, because you can't do modular arithmetic on\\n # half-tiles.\\n left, bottom = spatial_ref.OffsetPoint(*extents.lower_left)\\n right, top = spatial_ref.OffsetPoint(*extents.upper_right)\\n\\n # Divide by number of tiles\\n return Extents(lower_left=XY(int(round(left / tile_width)),\\n int(round(bottom / tile_height))),\\n upper_right=XY(int(round(right / tile_width)),\\n int(round(top / tile_height))))\",\n \"def get_extents(self, view, ranges):\\n ndims = len(view.dimensions())\\n num = 6 if self.projection == '3d' else 4\\n if self.apply_ranges:\\n if ranges:\\n dims = view.dimensions()\\n x0, x1 = ranges[dims[0].name]\\n if ndims > 1:\\n y0, y1 = ranges[dims[1].name]\\n else:\\n y0, y1 = (np.NaN, np.NaN)\\n if self.projection == '3d':\\n if len(dims) > 2:\\n z0, z1 = ranges[dims[2].name]\\n else:\\n z0, z1 = np.NaN, np.NaN\\n else:\\n x0, x1 = view.range(0)\\n y0, y1 = view.range(1) if ndims > 1 else (np.NaN, np.NaN)\\n if self.projection == '3d':\\n z0, z1 = view.range(2)\\n if self.projection == '3d':\\n range_extents = (x0, y0, z0, x1, y1, z1)\\n else:\\n range_extents = (x0, y0, x1, y1)\\n else:\\n range_extents = (np.NaN,) * num\\n\\n if self.apply_extents:\\n norm_opts = self.lookup_options(view, 'norm').options\\n if norm_opts.get('framewise', False) or self.dynamic:\\n extents = view.extents\\n else:\\n extent_list = self.hmap.traverse(lambda x: x.extents, [Element])\\n extents = util.max_extents(extent_list, self.projection == '3d')\\n else:\\n extents = (np.NaN,) * num\\n return tuple(l1 if l2 is None or not np.isfinite(l2) else\\n l2 for l1, l2 in zip(range_extents, extents))\",\n \"def get_extent(self):\\n pass\",\n \"def get_extent(ds):\\n\\n #\\n # Check if latitude and longitude are stored as coordinates.\\n #\\n if 'lon' in ds.coords and 'lat' in ds.coords:\\n return BoundingBox(\\n left=ds.lon.values.min(),\\n bottom=ds.lat.values.min(),\\n right=ds.lon.values.max(),\\n top=ds.lat.values.max()\\n )\\n\\n #\\n # Otherwise, get extent from projection information\\n # by projecting the corner coordinates onto EPSG:4326\\n # to obtain the latitude and longitude at the four corners.\\n #\\n src_crs = get_crs(ds)\\n if src_crs is None:\\n raise CRSError('Could not determine the CRS.')\\n\\n dst_crs = CRS(init='epsg:4326')\\n proj_bounds = get_bounds(ds)\\n bounds = rasterio.warp.transform_bounds(\\n src_crs, dst_crs, **proj_bounds._asdict()\\n )\\n return BoundingBox(*bounds)\",\n \"def extent(self):\\n\\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\\n\\n return [x[0], x[1], y[0], y[1]]\",\n \"def extent_in_crs(self, crs=wgs84):\\n\\n # this is not so trivial\\n # for optimisation we will transform the boundaries only\\n poly = self.extent_as_polygon(crs=crs)\\n _i, _j = poly.exterior.xy\\n return [np.min(_i), np.max(_i), np.min(_j), np.max(_j)]\",\n \"def get_data_extent(self):\\n xs, ys = self.xs, self.ys\\n xmin, xmax = min(xs), max(xs)\\n ymin, ymax = min(xy), max(ys)\\n w = maxx - minx\\n h = maxy - miny\\n return xmin, ymax, w, h\",\n \"def extents(self):\\n x0, y0, width, height = self._rect_bbox\\n xmin, xmax = sorted([x0, x0 + width])\\n ymin, ymax = sorted([y0, y0 + height])\\n return xmin, xmax, ymin, ymax\",\n \"def _getGeoTransform(self):\\n with self._getDatasetLock:\\n gt = self.dataset.GetGeoTransform()\\n if (self.dataset.GetGCPProjection() and self.dataset.GetGCPs()):\\n gt = gdal.GCPsToGeoTransform(self.dataset.GetGCPs())\\n return gt\",\n \"def extent(self) -> Tuple[float]:\\n ext = (\\n min(self.putin.geometry.x, self.takeout.geometry.x),\\n min(self.putin.geometry.y, self.takeout.geometry.y),\\n max(self.putin.geometry.x, self.takeout.geometry.x),\\n max(self.putin.geometry.y, self.takeout.geometry.y),\\n )\\n return ext\",\n \"def extent(self):\\n ulx, uly, lrx, lry = self.ul_lr\\n return ulx, lry, lrx, uly\",\n \"def extents(self):\\n\\n return self._local\",\n \"def extent_from_metadata(meta):\\n\\n meta = find_meta_branch(meta, 'geosoft')\\n if meta:\\n cs = coordinate_system_from_metadata(meta)\\n try:\\n ex = meta['geosoft']['dataset']['georeference']['dataextents']['extent3d']\\n minp = gxgm.Point((float(ex['@minx']), float(ex['@miny']), float(ex['@minz'])))\\n maxp = gxgm.Point((float(ex['@maxx']), float(ex['@maxy']), float(ex['@maxz'])))\\n return gxgm.Point2((minp, maxp), cs)\\n\\n except KeyError:\\n pass\\n\\n return None\",\n \"def get_geographic_extent(self, project):\\n if project.geographic_extent is not None:\\n return json.loads(project.geographic_extent.json)\\n else:\\n return None\",\n \"def get_raster_extent(dataset, geo=False, window=True):\\n\\n x_size = dataset.RasterXSize\\n y_size = dataset.RasterYSize\\n geotrans = dataset.GetGeoTransform()\\n xmin = geotrans[0]\\n ymax = geotrans[3]\\n xmax = geotrans[0] + geotrans[1] * x_size\\n ymin = geotrans[3] + geotrans[5] * y_size\\n\\n extent = np.array([[xmin, ymax], [xmin, ymin], [xmax, ymin], [xmax, ymax]])\\n\\n if geo:\\n projection = read_gdal_projection(dataset)\\n extent = georef.reproject(extent, projection_source=projection)\\n\\n if window:\\n x = extent[:, 0]\\n y = extent[:, 1]\\n extent = np.array([x.min(), x.max(), y.min(), y.max()])\\n\\n return extent\",\n \"def getExtent(self):\\n extent = self.parent.biomeGeometry.extent\\n return extent\",\n \"def extent(self):\\n return self._extent\",\n \"def get_data_extent(self):\\n \\n x, y = self.xy[0], self.xy[1]\\n w, h = self.radius, self.radius\\n return x-w, y+w, w, h\",\n \"def extents(self):\\n if self.direction == 'horizontal':\\n vmin = self._selection_artist.get_x()\\n vmax = vmin + self._selection_artist.get_width()\\n else:\\n vmin = self._selection_artist.get_y()\\n vmax = vmin + self._selection_artist.get_height()\\n return vmin, vmax\",\n \"def get_data_extent(self):\\n x, y = self.xy[0], self.xy[1]\\n w, h = self.width, self.height\\n return x, y, w, h\",\n \"def transform_bounds(\\n self, dst_crs: CRS | int | str | dict, densify_pts: int = 21\\n ) -> tuple[float, float, float, float]:\\n if self.crs != dst_crs:\\n bounds = rasterio.warp.transform_bounds(\\n self.crs, dst_crs, *self.bounds, densify_pts=densify_pts\\n )\\n else:\\n bounds = self.bounds\\n return bounds\",\n \"def extent(self, start=None, finish=None):\\n start, finish = self.bounds(start, finish)\\n try:\\n return finish - start + 1\\n except TypeError:\\n return None\",\n \"def bounds(self) -> tuple[float, float, float, float]:\\n transform = self.transform\\n a, b, c, d, e, f, _, _, _ = transform\\n if b == d == 0:\\n xs = (c, c + a * self.width)\\n ys = (f, f + e * self.height)\\n else: # rotated\\n c0x, c0y = c, f\\n c1x, c1y = transform * (0, self.height)\\n c2x, c2y = transform * (self.width, self.height)\\n c3x, c3y = transform * (self.width, 0)\\n xs = (c0x, c1x, c2x, c3x)\\n ys = (c0y, c1y, c2y, c3y)\\n return min(xs), min(ys), max(xs), max(ys)\",\n \"def _reproject(ds, dst_crs=None, dst_transform=None, width=None, height=None,\\n res=None, extent=None, **kwargs):\\n\\n if 'resampling' not in kwargs:\\n kwargs['resampling'] = rasterio.warp.Resampling.cubic\\n\\n src_crs = get_crs(ds)\\n src_bounds = get_bounds(ds)\\n if extent is not None:\\n extent = BoundingBox(*extent)\\n\\n #\\n # Only allow inferring of width or height from aspect ratio\\n # if the CRS is not changed.\\n #\\n if dst_crs is None:\\n dst_crs = src_crs\\n if width is None and height is not None:\\n width = int(ncols(ds) * height / nrows(ds))\\n elif height is None and width is not None:\\n height = int(nrows(ds) * width / ncols(ds))\\n\\n # Given: transform, shape\\n # Given: transform, extent\\n # Given: res, extent\\n # Given: shape, res\\n # Given: shape, extent\\n\\n if dst_transform is not None:\\n #\\n # If the transform is given, we also need the width and height or\\n # the extent.\\n #\\n if width is not None and height is not None:\\n pass\\n elif extent is not None:\\n # Calculate width and height from extent\\n width = int(abs(\\n (extent.right - extent.left) / dst_transform.a)) + 1\\n height = int(abs(\\n (extent.top - extent.bottom) / dst_transform.e)) + 1\\n else:\\n raise ValueError('Not enough information provided.')\\n\\n elif extent is not None:\\n #\\n # Transform can be calculated from extent, if either width and height\\n # or the resolution are given.\\n #\\n if res is not None:\\n width = int(abs(\\n (extent.right - extent.left) / res[0])) + 1\\n height = int(abs(\\n (extent.top - extent.bottom) / res[1])) + 1\\n\\n # The following doesn't give the correct result.\\n dst_transform = rasterio.transform.from_bounds(\\n *extent, width=width-1, height=height-1\\n )\\n\\n else:\\n #\\n # If neither the transform nor the extent are given, infer the best\\n # possible parameters from the width, height, and the resolution.\\n #\\n dst_transform, width, height = \\\\\\n rasterio.warp.calculate_default_transform(\\n src_crs, dst_crs,\\n ncols(ds), nrows(ds),\\n resolution=res,\\n dst_width=width,\\n dst_height=height,\\n **src_bounds._asdict())\\n\\n src_transform = get_transform(ds)\\n src_dims = get_dims(ds)\\n dst_crs = _parse_crs(dst_crs)\\n\\n #\\n # Prepare new x and y coordinate arrays\\n #\\n dst_x, _ = dst_transform * (np.arange(width), np.zeros(width, dtype=int))\\n _, dst_y = dst_transform * (np.zeros(height, dtype=int), np.arange(height))\\n dst_coords = {'x': dst_x, 'y': dst_y}\\n\\n #\\n # Handle the case where there are extra dimensions, e.g. 'time'\\n # or 'band'\\n #\\n extra_dims = src_dims.keys() - {'y', 'x'}\\n if len(extra_dims) == 0:\\n dst_shape = (height, width)\\n dst_dims = ('y', 'x')\\n elif len(extra_dims) == 1:\\n extra_dim = extra_dims.pop()\\n dst_shape = (src_dims[extra_dim], height, width)\\n dst_dims = (extra_dim, 'y', 'x')\\n dst_coords[extra_dim] = ds.coords[extra_dim]\\n else:\\n raise ValueError('More than three dimensions are not supported.')\\n\\n def _reproject_da(da, shape):\\n #\\n # Reproject a single data array\\n #\\n extra_dims = set(da.dims) - {'y', 'x'}\\n dim_order = tuple(extra_dims) + ('y', 'x')\\n values = da.transpose(*dim_order).values\\n output = np.zeros(shape, dtype=da.dtype)\\n output[:] = np.nan\\n\\n # Fix data shape for one-dimensional data\\n if da.ndim == 1:\\n #\\n # NOTE: The stretch factor is necessary because the input data\\n # must extend at least half an original resolution cell in the\\n # independent dimension.\\n #\\n if da.dims == ('x',):\\n stretch_x = int((~src_transform * dst_transform).a / 2 + 1)\\n values = np.vstack([values] * stretch_x)\\n output.shape = (1,) + output.shape\\n elif da.dims == ('y',):\\n stretch_y = int((~src_transform * dst_transform).e / 2 + 1)\\n values = np.vstack([values] * stretch_y).T\\n output.shape = output.shape + (1,)\\n\\n rasterio.warp.reproject(\\n values,\\n output,\\n src_transform=src_transform,\\n src_crs=src_crs,\\n dst_transform=dst_transform,\\n dst_crs=dst_crs,\\n dst_nodata=np.nan,\\n **kwargs\\n )\\n\\n # Final reshape in case the input was one-dimensional\\n return output.reshape(shape)\\n\\n if isinstance(ds, xr.Dataset):\\n result = xr.Dataset(coords=dst_coords)\\n\\n #\\n # Also reproject coordinate arrays that are defined over\\n # x and y\\n #\\n for v in ds.coords:\\n #\\n # If the projection is the same, also reproject coordinate arrays\\n # that are defined over only one variable.\\n #\\n if dst_crs == src_crs and v not in ds.dims:\\n if ds.coords[v].dims == ('x',):\\n result.coords[v] = \\\\\\n (('x',), _reproject_da(ds.coords[v], (width,)))\\n elif ds.coords[v].dims == ('y',):\\n result.coords[v] = \\\\\\n (('y',), _reproject_da(ds.coords[v], (height,)))\\n\\n if not set(ds.coords[v].dims).issuperset({'x', 'y'}):\\n continue\\n\\n shape = (height, width)\\n result.coords[v] = (('y', 'x'),\\n _reproject_da(ds.coords[v], shape))\\n\\n #\\n # Reproject the actual data\\n #\\n for v in ds.data_vars:\\n if set(ds[v].dims) == set(dst_dims):\\n result[v] = (dst_dims, _reproject_da(ds[v], dst_shape))\\n elif set(ds[v].dims) == {'y', 'x'}:\\n shape = (height, width)\\n result[v] = (dst_dims, _reproject_da(ds[v], shape))\\n else:\\n result[v] = (ds[v].dims, ds[v])\\n\\n #\\n # Create lat and lon coordinates\\n #\\n # if 'lat' in ds.coords and 'lon' in ds.coords:\\n # lon, lat = rasterio.warp.transform(\\n # src_crs, dst_crs, ds.coords['x'], ds.coords['y'])\\n # result.coords['lon'] = (('x',), lon)\\n # result.coords['lat'] = (('y',), lat)\\n\\n elif isinstance(ds, xr.DataArray):\\n result = xr.DataArray(_reproject_da(ds, dst_shape), dims=dst_dims,\\n coords=dst_coords, name=ds.name)\\n\\n result.attrs = ds.attrs\\n\\n # Serialize transform to tuple and store in metadata\\n result.attrs['transform'] = dst_transform[:6]\\n # Store CRS info in metadata\\n result.attrs['crs'] = dst_crs.to_string()\\n result.attrs['coordinate_system_string'] = dst_crs.wkt\\n # Store new data shape in metadata\\n result.attrs['lines'] = nrows(result)\\n result.attrs['samples'] = ncols(result)\\n\\n _add_latlon(result)\\n\\n return result\",\n \"def GetExtent(vDataSet):\\r\\n return [vDataSet.GetExtendMinX(),vDataSet.GetExtendMaxX(),\\r\\n vDataSet.GetExtendMinY(),vDataSet.GetExtendMaxY(),\\r\\n vDataSet.GetExtendMinZ(),vDataSet.GetExtendMaxZ()]\",\n \"def GetExtent(ds):\\n xmin, xpixel, _, ymax, _, ypixel = ds.GetGeoTransform()\\n width, height = ds.RasterXSize, ds.RasterYSize\\n xmax = xmin + width * xpixel\\n ymin = ymax + height * ypixel\\n\\n return (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)\",\n \"def extent(self):\\n return self.index.max() - self.index.min(), self.columns.max() - self.columns.min()\",\n \"def extent_to_gdal_transform(extent):\\n\\n # extent = SpatialExtent()\\n\\n pixel_height = math.copysign(extent.height, extent.bottom - extent.top)\\n pixel_width = math.copysign(extent.width, extent.right - extent.left)\\n\\n transform = [extent.top, pixel_width, 0, extent.left, 0, pixel_height]\\n return transform\",\n \"def extents(nodes):\\n from numpy import min, max\\n return ( min(nodes[:,0]), max(nodes[:,0]),\\n min(nodes[:,1]), max(nodes[:,1]),\\n min(nodes[:,2]), max(nodes[:,2]) )\",\n \"def extents(self):\\n self._updateExtents()\\n return self.mExtents\",\n \"def get_data_extent(self):\\n raise NotImplementedError, \\\"Derived must override\\\"\",\n \"def GetResolution(vDataSet):\\r\\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\\r\\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\\r\\n\\r\\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz\",\n \"def geoextent(self):\\r\\n return self.series_extent\",\n \"def return_extents(self):\\n\\n return [qm.tree.mins, qm.tree.maxs]\",\n \"def extent(self):\\n return np.array(self._extent)\",\n \"def extent(self):\\n return self._ax.extent\",\n \"def dataPointExtent(dataset):\\n ext = tuple(np.flip(dataset.GetDimensions()))\\n return ext\",\n \"def geotransform(self):\\n return self._geotransform\",\n \"def _resolution(self):\\n _, xres, _, _, _, yres = self.geotransform\\n return xres, yres\",\n \"def GetNativeResolution(self, transform=None, maximum=None):\\n # Get the source projection's units for a 1x1 pixel, assuming square\\n # pixels.\\n width, height = self.GetPixelDimensions()\\n src_pixel_size = min(abs(width), abs(height))\\n\\n if transform is None:\\n dst_pixel_size = src_pixel_size\\n dst_ref = self.GetSpatialReference()\\n else:\\n # Transform these dimensions into the destination projection\\n dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]\\n dst_pixel_size = abs(dst_pixel_size)\\n dst_ref = transform.dst_ref\\n\\n # We allow some floating point error between src_pixel_size and\\n # dst_pixel_size based on the major circumference so that the error is\\n # in the destination units\\n error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128\\n\\n # Find the resolution where the pixels are smaller than dst_pixel_size.\\n for resolution in count():\\n if maximum is not None and resolution >= maximum:\\n return resolution\\n\\n res_pixel_size = max(\\n *dst_ref.GetPixelDimensions(resolution=resolution)\\n )\\n if (res_pixel_size - dst_pixel_size) <= error:\\n return resolution\\n\\n # Halve error each resolution\\n error /= 2\",\n \"def get_common_extent(datasets):\\n common_bounds = get_common_bounds(datasets)\\n common_crs = get_crs(datasets[0])\\n dst_crs = CRS(init='epsg:4326')\\n extent = rasterio.warp.transform_bounds(\\n common_crs, dst_crs, **common_bounds._asdict()\\n )\\n return BoundingBox(*extent)\",\n \"def getCurrentExtent(self):\\n if not self.currentBox:\\n extent = None\\n else:\\n extent = boxToExtent(self.currentBox)\\n return extent\",\n \"def extent(self):\\r\\n if not hasattr(self, '_extent'):\\r\\n self._extent = conf.lib.clang_getCursorExtent(self)\\r\\n\\r\\n return self._extent\",\n \"def Extrema(self):\\n ymin = np.min(self._corners[:, 1])\\n xmin = np.min(self._corners[:, 0])\\n ymax = np.max(self._corners[:, 1])\\n xmax = np.max(self._corners[:, 0])\\n return ymin, xmin, ymax, xmax\",\n \"def bounding_box(self):\\n frames = self.available_frames\\n transform_0 = self.get_transform(frames[0], frames[1])\\n try:\\n bb = transform_0.bounding_box\\n except NotImplementedError:\\n return None\\n if transform_0.n_inputs == 1:\\n return bb\\n try:\\n axes_order = self.input_frame.axes_order\\n except AttributeError:\\n axes_order = np.arange(transform_0.n_inputs)\\n # Model.bounding_box is in python order, need to reverse it first.\\n return tuple(bb[::-1][i] for i in axes_order)\",\n \"def major_extent(self) -> complex:\\n return max((self.max() - self.null, self.null - self.min()))\",\n \"def get_bounds(self):\\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\n \"def check_extent(self):\\n if self.lower_left.x > self.upper_right.x:\\n dlx = self.lower_left.x\\n self.lower_left.x = self.upper_right.x\\n self.upper_right.y = dlx\\n\\n if self.lower_left.y > self.upper_right.y:\\n dly = self.lower_left.y\\n self.lower_left.y = self.upper_right.y\\n self.upper_right.y = dly\",\n \"def get_extent(georef, shape):\\n extent = (\\n georef[0],\\n georef[3] +\\n shape[1] *\\n georef[5],\\n georef[0] +\\n shape[0] *\\n georef[1],\\n georef[3]) # x1,y1,x2,y2\\n return extent\",\n \"def get_extent(fpath):\\n extents = []\\n with h5py.File(fpath, mode='r') as f:\\n for key, value in f['label/label-0'].attrs.items():\\n if key.lower().endswith('extent') and isinstance(value, np.ndarray):\\n extents.append(value)\\n \\n extents = np.stack(extents, axis=0)\\n maxs = np.max(extents, axis=0)\\n mins = np.min(extents, axis=0)\\n axis_slices = []\\n for min_, max_ in zip(mins[::2], maxs[1::2]):\\n axis_slices.append(slice(min_, max_, 1))\\n return tuple(axis_slices)\",\n \"def extent(self) -> typing.Tuple[str, ...]:\\n return self._extent.members()\",\n \"def bbox(self):\\n return np.array(self.path.get_extents()).ravel(order='F')\",\n \"def fetchbounds(self):\\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\\\\n self.in_end, self.end, self.out_end] \\\\\\n if x is not None]\\n return min(pnts), max(pnts)\",\n \"def pivot_grid_bound(param=None, extent=10.0):\\n\\n # No pivot set.\\n if not hasattr(cdp, param):\\n raise RelaxError(\\\"The pivot point has not been set, cannot determine the grid search bounds.\\\")\\n\\n # The value.\\n val = getattr(cdp, param)\\n\\n # Return the bound.\\n return val + extent\",\n \"def getBounds(self, srs=None):\\n if srs not in self._bounds:\\n gt = self._getGeoTransform()\\n nativeSrs = self.getProj4String()\\n if not nativeSrs:\\n self._bounds[srs] = None\\n return\\n bounds = {\\n 'll': {\\n 'x': gt[0] + self.sourceSizeY * gt[2],\\n 'y': gt[3] + self.sourceSizeY * gt[5],\\n },\\n 'ul': {\\n 'x': gt[0],\\n 'y': gt[3],\\n },\\n 'lr': {\\n 'x': gt[0] + self.sourceSizeX * gt[1] + self.sourceSizeY * gt[2],\\n 'y': gt[3] + self.sourceSizeX * gt[4] + self.sourceSizeY * gt[5],\\n },\\n 'ur': {\\n 'x': gt[0] + self.sourceSizeX * gt[1],\\n 'y': gt[3] + self.sourceSizeX * gt[4],\\n },\\n 'srs': nativeSrs,\\n }\\n # Make sure geographic coordinates do not exceed their limits\\n if self._proj4Proj(nativeSrs).crs.is_geographic and srs:\\n try:\\n self._proj4Proj(srs)(0, 90, errcheck=True)\\n yBound = 90.0\\n except RuntimeError:\\n yBound = 89.999999\\n keys = ('ll', 'ul', 'lr', 'ur')\\n for key in keys:\\n bounds[key]['y'] = max(min(bounds[key]['y'], yBound), -yBound)\\n while any(bounds[key]['x'] > 180 for key in keys):\\n for key in keys:\\n bounds[key]['x'] -= 360\\n while any(bounds[key]['x'] < -180 for key in keys):\\n for key in keys:\\n bounds[key]['x'] += 360\\n if any(bounds[key]['x'] >= 180 for key in keys):\\n bounds['ul']['x'] = bounds['ll']['x'] = -180\\n bounds['ur']['x'] = bounds['lr']['x'] = 180\\n if srs and srs != nativeSrs:\\n inProj = self._proj4Proj(nativeSrs)\\n outProj = self._proj4Proj(srs)\\n keys = ('ll', 'ul', 'lr', 'ur')\\n pts = pyproj.Transformer.from_proj(inProj, outProj, always_xy=True).itransform([\\n (bounds[key]['x'], bounds[key]['y']) for key in keys])\\n for idx, pt in enumerate(pts):\\n key = keys[idx]\\n bounds[key]['x'] = pt[0]\\n bounds[key]['y'] = pt[1]\\n bounds['srs'] = srs.decode() if isinstance(srs, bytes) else srs\\n bounds['xmin'] = min(bounds['ll']['x'], bounds['ul']['x'],\\n bounds['lr']['x'], bounds['ur']['x'])\\n bounds['xmax'] = max(bounds['ll']['x'], bounds['ul']['x'],\\n bounds['lr']['x'], bounds['ur']['x'])\\n bounds['ymin'] = min(bounds['ll']['y'], bounds['ul']['y'],\\n bounds['lr']['y'], bounds['ur']['y'])\\n bounds['ymax'] = max(bounds['ll']['y'], bounds['ul']['y'],\\n bounds['lr']['y'], bounds['ur']['y'])\\n self._bounds[srs] = bounds\\n return self._bounds[srs]\",\n \"def get_resolution(ds):\\n\\n if 'x' in ds.coords and 'y' in ds.coords:\\n x = ds.coords['x'].values\\n y = ds.coords['y'].values\\n resx = abs(x[-1] - x[0]) / (len(x) - 1)\\n resy = abs(y[-1] - y[0]) / (len(y) - 1)\\n return (resx, resy)\\n else:\\n transform = get_transform(ds)\\n if transform is not None:\\n return (abs(transform.a), abs(transform.e))\\n elif 'res' in ds.attrs:\\n return ds.attrs['res']\\n\\n return None\",\n \"def map_extent(input_raster):\\n\\n gdal.UseExceptions()\\n raster = gdal.Open(input_raster)\\n raster_geotransform = raster.GetGeoTransform()\\n raster_extent = (raster_geotransform[0],\\n raster_geotransform[0]\\n + raster.RasterXSize * raster_geotransform[1],\\n raster_geotransform[3]\\n + raster.RasterYSize * raster_geotransform[5],\\n raster_geotransform[3])\\n\\n return raster_extent\",\n \"def get_bounds(self):\\n return self._geometry.bounds\",\n \"def transformed_bounds(self):\\n b = []\\n\\n for dim in self.dimensions:\\n if dim.transformed_size == 1:\\n b.append(dim.transformed_bounds)\\n else:\\n b.extend(dim.transformed_bounds)\\n\\n return b\",\n \"def _get_inv_gt(self):\\r\\n # Geotransform the original map\\r\\n self.in_gt = self.in_ds.GetGeoTransform()\\r\\n # Create an inverse geotransform for the raster.\\r\\n # This converts real-world coordinates to pixel offsets.\\r\\n self.inv_gt = gdal.InvGeoTransform(self.in_gt)\\r\\n if gdal.VersionInfo()[0] == '1':\\r\\n if self.inv_gt[0] == 1:\\r\\n self.inv_gt = self.inv_gt[1]\\r\\n else:\\r\\n raise RuntimeError('Inverse geotransform failed')\\r\\n elif self.inv_gt is None:\\r\\n raise RuntimeError('Inverse geotransform failed')\",\n \"def extent(obj):\\n return obj.get('startOffset', -1), obj.get('endOffset', -1)\",\n \"def extent(obj):\\n return obj.get('startOffset', -1), obj.get('endOffset', -1)\",\n \"def get_bounds(self):\\n\\n northing=self.f.variables['y']\\n easting=self.f.variables['x']\\n\\n lat1,lon1 = utm.to_latlon(np.min(easting),np.min(northing),11,northern=True)\\n lat2,lon2 = utm.to_latlon(np.max(easting),np.max(northing),11,northern=True)\\n\\n return (lon1,lon2,lat1,lat2)\",\n \"def get_absolute_poses(self):\\n if self.backend is not None:\\n return self.backend.absolute_poses()\\n return compute_absolute_poses(self.odometry.get_relative_poses())\",\n \"def bounds(self):\\n return self.xmin, self.xmax, self.ymin, self.ymax\",\n \"def dataCellExtent(dataset):\\n # Subtracting 1 from GetDimensions accounts for point-counts\\n # vs cell-counts, while np.flip(...) switches the axis order:\\n ext = tuple(np.flip(dataset.GetDimensions()) - 1)\\n return ext\",\n \"def get_bounds(self, crs=\\\"default\\\"):\\n\\n if crs == \\\"default\\\":\\n crs = podpac.settings[\\\"DEFAULT_CRS\\\"]\\n\\n bounds = {}\\n for coords in self.find_coordinates():\\n ct = coords.transform(crs)\\n for dim, (lo, hi) in ct.bounds.items():\\n if dim not in bounds:\\n bounds[dim] = (lo, hi)\\n else:\\n bounds[dim] = (min(lo, bounds[dim][0]), max(hi, bounds[dim][1]))\\n\\n return bounds, crs\",\n \"def get_extents(self, p, x, y, z, w, h):\\n x.value, y.value, z.value, w.value, h.value = self._get_extents(p, x.value, y.value, z.value, w.value, h.value)\",\n \"def projection(self):\\n return self.dataset.GetProjection() if self.dataset else None\",\n \"def _get_dimensions(self):\\n corners = []\\n for module in self.modules:\\n for tile in module:\\n corners.append(tile.corners())\\n corners = np.concatenate(corners)[:, :2] / self._pixel_shape\\n\\n # Find extremes, add 1 px margin to allow for rounding errors\\n min_xy = corners.min(axis=0).astype(int) - 1\\n max_xy = corners.max(axis=0).astype(int) + 1\\n\\n size = max_xy - min_xy\\n centre = -min_xy\\n # Switch xy -> yx\\n return tuple(size[::-1]), centre[::-1]\",\n \"def projection(self):\\n return self._map_projection\",\n \"def projection(self):\\n return self._map_projection\",\n \"def getExtentUnits(self):\\n return _libsbml.Model_getExtentUnits(self)\",\n \"def get_height():\\n return resize.transforms[1].size\",\n \"def _set_lim_and_transforms(self):\\n # There are three important coordinate spaces going on here:\\n #\\n # 1. Data space: The space of the data itself\\n #\\n # 2. Axes space: The unit rectangle (0, 0) to (1, 1)\\n # covering the entire plot area.\\n #\\n # 3. Display space: The coordinates of the resulting image,\\n # often in pixels or dpi/inch.\\n\\n # This function makes heavy use of the Transform classes in\\n # ``lib/matplotlib/transforms.py.`` For more information, see\\n # the inline documentation there.\\n\\n # The goal of the first two transformations is to get from the\\n # data space (in this case longitude and latitude) to axes\\n # space. It is separated into a non-affine and affine part so\\n # that the non-affine part does not have to be recomputed when\\n # a simple affine change to the figure has been made (such as\\n # resizing the window or changing the dpi).\\n\\n # 1) The core transformation from data space into\\n # rectilinear space defined in the HammerTransform class.\\n self.transProjection = IdentityTransform()\\n # 2) The above has an output range that is not in the unit\\n # rectangle, so scale and translate it so it fits correctly\\n # within the axes. The peculiar calculations of xscale and\\n # yscale are specific to a Aitoff-Hammer projection, so don't\\n # worry about them too much.\\n self.transAffine = Affine2D.from_values(\\n 1., 0, 0.5, np.sqrt(3)/2., 0, 0)\\n self.transAffinedep = Affine2D.from_values(\\n 1., 0, -0.5, np.sqrt(3)/2., 0, 0)\\n #self.transAffine = IdentityTransform()\\n \\n # 3) This is the transformation from axes space to display\\n # space.\\n self.transAxes = BboxTransformTo(self.bbox)\\n\\n # Now put these 3 transforms together -- from data all the way\\n # to display coordinates. Using the '+' operator, these\\n # transforms will be applied \\\"in order\\\". The transforms are\\n # automatically simplified, if possible, by the underlying\\n # transformation framework.\\n self.transData = \\\\\\n self.transProjection + \\\\\\n self.transAffine + \\\\\\n self.transAxes\\n\\n # The main data transformation is set up. Now deal with\\n # gridlines and tick labels.\\n\\n # Longitude gridlines and ticklabels. The input to these\\n # transforms are in display space in x and axes space in y.\\n # Therefore, the input values will be in range (-xmin, 0),\\n # (xmax, 1). The goal of these transforms is to go from that\\n # space to display space. The tick labels will be offset 4\\n # pixels from the equator.\\n\\n self._xaxis_pretransform = IdentityTransform()\\n self._xaxis_transform = \\\\\\n self._xaxis_pretransform + \\\\\\n self.transData\\n self._xaxis_text1_transform = \\\\\\n Affine2D().scale(1.0, 0.0) + \\\\\\n self.transData + \\\\\\n Affine2D().translate(0.0, -20.0)\\n self._xaxis_text2_transform = \\\\\\n Affine2D().scale(1.0, 0.0) + \\\\\\n self.transData + \\\\\\n Affine2D().translate(0.0, -4.0)\\n\\n # Now set up the transforms for the latitude ticks. The input to\\n # these transforms are in axes space in x and display space in\\n # y. Therefore, the input values will be in range (0, -ymin),\\n # (1, ymax). The goal of these transforms is to go from that\\n # space to display space. The tick labels will be offset 4\\n # pixels from the edge of the axes ellipse.\\n\\n self._yaxis_transform = self.transData\\n yaxis_text_base = \\\\\\n self.transProjection + \\\\\\n (self.transAffine + \\\\\\n self.transAxes)\\n self._yaxis_text1_transform = \\\\\\n yaxis_text_base + \\\\\\n Affine2D().translate(-8.0, 0.0)\\n self._yaxis_text2_transform = \\\\\\n yaxis_text_base + \\\\\\n Affine2D().translate(8.0, 0.0)\",\n \"def get_bounds(ds):\\n\\n trans = get_transform(ds)\\n if trans is not None:\\n if isinstance(ds, xr.Dataset):\\n dims = ds.dims\\n elif isinstance(ds, xr.DataArray):\\n dims = dict(zip(ds.dims, ds.shape))\\n nrows = dims['y']\\n ncols = dims['x']\\n corners = (np.array([0, 0, ncols-1, ncols-1]),\\n np.array([0, nrows-1, 0, nrows-1]))\\n corner_x, corner_y = trans * corners\\n return BoundingBox(\\n left=corner_x.min(),\\n bottom=corner_y.min(),\\n right=corner_x.max(),\\n top=corner_y.max()\\n )\\n else:\\n return BoundingBox(\\n left=ds['x'].min(),\\n bottom=ds['y'].min(),\\n right=ds['x'].max(),\\n top=ds['y'].max()\\n )\",\n \"def get_geo_bounds(self, extent=None, data_sel=None):\\n if self.science_product:\\n res = self.__nc_geo_bounds(extent, data_sel)\\n else:\\n res = self.__h5_geo_bounds(extent, data_sel)\\n data_sel, lon_bounds, lat_bounds = res\\n\\n res = {}\\n _sz = lon_bounds.shape\\n res['longitude'] = np.empty((_sz[0]+1, _sz[1]+1), dtype=np.float)\\n res['longitude'][:-1, :-1] = lon_bounds[:, :, 0]\\n res['longitude'][-1, :-1] = lon_bounds[-1, :, 1]\\n res['longitude'][:-1, -1] = lon_bounds[:, -1, 1]\\n res['longitude'][-1, -1] = lon_bounds[-1, -1, 2]\\n\\n res['latitude'] = np.empty((_sz[0]+1, _sz[1]+1), dtype=np.float)\\n res['latitude'][:-1, :-1] = lat_bounds[:, :, 0]\\n res['latitude'][-1, :-1] = lat_bounds[-1, :, 1]\\n res['latitude'][:-1, -1] = lat_bounds[:, -1, 1]\\n res['latitude'][-1, -1] = lat_bounds[-1, -1, 2]\\n\\n if extent is None:\\n return res\\n\\n return data_sel, res\",\n \"def bottomLeftCorner(self):\\n self._updateExtents()\\n return (self._mMinX,self._mMaxY)\",\n \"def minor_extent(self) -> complex:\\n return min((self.max() - self.null, self.null - self.min()))\",\n \"def get_transform(self, map_from='visual', map_to='render'):\\n return self.transforms.get_transform(map_from, map_to)\",\n \"def coord_proj(self):\\r\\n return self._coord_proj\",\n \"def size(self):\\n return self.__xmax, self.__ymax\",\n \"def width_height(bounds):\\n try:\\n geom = reproject_geometry(\\n box(*bounds), src_crs=tile.crs, dst_crs=dst_pyramid.crs\\n )\\n if geom.is_empty: # Shapely>=2.0\\n raise ValueError(\\\"geometry empty after reprojection\\\")\\n l, b, r, t = geom.bounds\\n except ValueError: # pragma: no cover\\n raise TopologicalError(\\\"bounds cannot be translated into target CRS\\\")\\n return r - l, t - b\",\n \"def domain_bounds(self):\\n return self._xmin, self._xmax, self._ymin, self._ymax, self._zmin, self._zmax\",\n \"def bounds(self):\\n \\n return self.osmdb.bounds()\",\n \"def get_bounding_box(self):\\n if len(self.polygons) == 0:\\n return None\\n return numpy.array(((min(pts[:, 0].min() for pts in self.polygons),\\n min(pts[:, 1].min() for pts in self.polygons)),\\n (max(pts[:, 0].max() for pts in self.polygons),\\n max(pts[:, 1].max() for pts in self.polygons))))\",\n \"def get_bbox(self, obj):\\n renderer = self.figure.canvas.get_renderer()\\n transformer = self.figure.dpi_scale_trans.inverted()\\n return obj.get_window_extent(renderer=renderer).transformed(transformer)\",\n \"def geo_transform(self):\\n pass\",\n \"def get_object_bounds(self):\\n if len(self._object_bounds) == 0:\\n # Nothing plotted yet\\n return -.01, .01, -.01, .01\\n xmins, xmaxs, ymins, ymaxs = np.array(self._object_bounds).T\\n xmax = max(xmaxs.max(), xmins.max())\\n xmin = min(xmins.min(), xmaxs.min())\\n ymax = max(ymaxs.max(), ymins.max())\\n ymin = min(ymins.min(), ymaxs.min())\\n return xmin, xmax, ymin, ymax\",\n \"def get_projection(self):\\n return self.projection\",\n \"def get_bbox(self):\\n dimsizes = self.get_full_dimensions('lon').values()\\n slices = [slice(None, None, dimsizes[0] - 1),\\n slice(None, None, dimsizes[1] - 1)]\\n lon = self.read_values('lon', slices=slices)\\n lat = self.read_values('lat', slices=slices)\\n return (lon.min(), lat.min(), lon.max(), lat.max())\",\n \"def getCurrentTransformSelection():\\n node = cmds.ls(sl=True)\\n if node:\\n node = node[0]\\n if cmds.nodeType(node) == 'transform':\\n xform = node\\n return xform\\n else:\\n relatives = cmds.listRelatives(node, shapes=True, f=1)\\n if relatives:\\n for i in relatives:\\n if cmds.nodeType(i) == \\\"transform\\\":\\n xform = i\\n return xform\\n return None\",\n \"def GetTransform(*args, **kwargs):\\n return _gdi_.GraphicsContext_GetTransform(*args, **kwargs)\",\n \"def extent(self, **kwargs) -> gpd.GeoDataFrame:\\n return gpd.GeoDataFrame(\\n geometry=[box(*self.footprint().total_bounds)],\\n crs=self.crs(),\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6707964","0.62306535","0.61376554","0.58643377","0.58163196","0.5813695","0.5760055","0.5748703","0.57084984","0.5700975","0.56836563","0.5661239","0.5635688","0.5632432","0.56189066","0.5601325","0.55867517","0.5552704","0.5544527","0.5532464","0.5512576","0.55049706","0.5500104","0.54963976","0.543696","0.5405108","0.5398917","0.5361915","0.5324615","0.5324042","0.5311395","0.53015083","0.53008103","0.525847","0.5256349","0.5168529","0.51329","0.5121131","0.51191145","0.50949913","0.5094101","0.5085745","0.50824857","0.50582695","0.50466925","0.5046534","0.5015328","0.49839884","0.4968262","0.49595347","0.4958106","0.49522194","0.49258733","0.4920959","0.4880789","0.48775324","0.48737133","0.48336482","0.48261192","0.47992152","0.47977766","0.47870633","0.47668493","0.47567892","0.47476017","0.4736752","0.47305942","0.47305942","0.47125965","0.4637063","0.46341562","0.46230805","0.4607761","0.46064207","0.4593204","0.4582184","0.45808795","0.45808795","0.4580255","0.45729274","0.45721936","0.45712063","0.45556805","0.45551962","0.4549101","0.45418063","0.453897","0.45109153","0.4503543","0.45027548","0.44981578","0.44780275","0.44776282","0.4464994","0.44619098","0.44449311","0.44213164","0.4418409","0.44175324","0.4414818"],"string":"[\n \"0.6707964\",\n \"0.62306535\",\n \"0.61376554\",\n \"0.58643377\",\n \"0.58163196\",\n \"0.5813695\",\n \"0.5760055\",\n \"0.5748703\",\n \"0.57084984\",\n \"0.5700975\",\n \"0.56836563\",\n \"0.5661239\",\n \"0.5635688\",\n \"0.5632432\",\n \"0.56189066\",\n \"0.5601325\",\n \"0.55867517\",\n \"0.5552704\",\n \"0.5544527\",\n \"0.5532464\",\n \"0.5512576\",\n \"0.55049706\",\n \"0.5500104\",\n \"0.54963976\",\n \"0.543696\",\n \"0.5405108\",\n \"0.5398917\",\n \"0.5361915\",\n \"0.5324615\",\n \"0.5324042\",\n \"0.5311395\",\n \"0.53015083\",\n \"0.53008103\",\n \"0.525847\",\n \"0.5256349\",\n \"0.5168529\",\n \"0.51329\",\n \"0.5121131\",\n \"0.51191145\",\n \"0.50949913\",\n \"0.5094101\",\n \"0.5085745\",\n \"0.50824857\",\n \"0.50582695\",\n \"0.50466925\",\n \"0.5046534\",\n \"0.5015328\",\n \"0.49839884\",\n \"0.4968262\",\n \"0.49595347\",\n \"0.4958106\",\n \"0.49522194\",\n \"0.49258733\",\n \"0.4920959\",\n \"0.4880789\",\n \"0.48775324\",\n \"0.48737133\",\n \"0.48336482\",\n \"0.48261192\",\n \"0.47992152\",\n \"0.47977766\",\n \"0.47870633\",\n \"0.47668493\",\n \"0.47567892\",\n \"0.47476017\",\n \"0.4736752\",\n \"0.47305942\",\n \"0.47305942\",\n \"0.47125965\",\n \"0.4637063\",\n \"0.46341562\",\n \"0.46230805\",\n \"0.4607761\",\n \"0.46064207\",\n \"0.4593204\",\n \"0.4582184\",\n \"0.45808795\",\n \"0.45808795\",\n \"0.4580255\",\n \"0.45729274\",\n \"0.45721936\",\n \"0.45712063\",\n \"0.45556805\",\n \"0.45551962\",\n \"0.4549101\",\n \"0.45418063\",\n \"0.453897\",\n \"0.45109153\",\n \"0.4503543\",\n \"0.45027548\",\n \"0.44981578\",\n \"0.44780275\",\n \"0.44776282\",\n \"0.4464994\",\n \"0.44619098\",\n \"0.44449311\",\n \"0.44213164\",\n \"0.4418409\",\n \"0.44175324\",\n \"0.4414818\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75209373"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95698,"cells":{"query":{"kind":"string","value":"Get the scaling ratios required to upsample an image to `resolution`. If resolution is None, then assume it will be upsampled to the native destination resolution. See Dataset.GetNativeResolution() If places is not None, rounds the ratios to the number of decimal places specified."},"document":{"kind":"string","value":"def GetScalingRatios(self, resolution=None, places=None):\n if resolution is None:\n resolution = self.GetNativeResolution(transform=None)\n\n # Get the pixel dimensions in map units. There is no custom transform,\n # because it makes no sense to compute a pixel ratio for a\n # reprojection.\n spatial_ref = self.GetSpatialReference()\n dst_pixel_width, dst_pixel_height = spatial_ref.GetPixelDimensions(\n resolution=resolution\n )\n src_pixel_width, src_pixel_height = self.GetPixelDimensions()\n\n xscale = abs(src_pixel_width / dst_pixel_width)\n yscale = abs(src_pixel_height / dst_pixel_height)\n\n if places is not None:\n xscale = round(xscale, places)\n yscale = round(yscale, places)\n\n return XY(x=xscale, y=yscale)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def GetWorldScalingRatios(self, resolution=None, places=None):\n if resolution is None:\n resolution = self.GetNativeResolution()\n\n spatial_ref = self.GetSpatialReference()\n world = spatial_ref.GetWorldExtents().dimensions\n src_pixel_sizes = XY(x=world.x / self.RasterXSize,\n y=world.y / self.RasterYSize)\n dst_pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\n\n xscale = abs(src_pixel_sizes.x / dst_pixel_sizes.x)\n\n # Make sure that yscale fits within the whole world\n yscale = min(xscale, abs(src_pixel_sizes.y / dst_pixel_sizes.y))\n\n if places is not None:\n xscale = round(xscale, places)\n yscale = round(yscale, places)\n\n return XY(x=xscale, y=yscale)","def _compute_output_resolution(input_spatial_resolution, kernel_size, stride,\n total_padding):\n if (input_spatial_resolution is None) or (kernel_size is None) or (\n stride is None) or (total_padding is None):\n return None\n return int(\n math.ceil((\n input_spatial_resolution + total_padding - kernel_size + 1) / stride))","def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return new_res_img","def raw_resolution(resolution, splitter=False):\n width, height = resolution\n if splitter:\n fwidth = (width + 15) & ~15\n else:\n fwidth = (width + 31) & ~31\n fheight = (height + 15) & ~15\n return fwidth, fheight","def compute_resolution(zoom, size_px):\n # Calibration data:\n dist_in_um = 10\n dist_in_px = np.array([21.13, 19.62, 8.93])\n zooms = np.array([1.5, 3, 4.5])\n image_max_sizes = np.array([330, 610, 410])\n \n return np.mean((dist_in_um/dist_in_px) * (zoom/zooms) * (image_max_sizes/size_px))","def UResolution(self, *args):\n return _Adaptor3d.Adaptor3d_Surface_UResolution(self, *args)","def resolution_range(self) -> Optional[float]:\n return self._get_property(RESOLUTION_RANGE_PROP, float)","def _get_scaling(root):\n dpi = root.winfo_fpixels(\"1i\")\n scaling = dpi / 72.0\n logger.debug(\"dpi: %s, scaling: %s'\", dpi, scaling)\n return scaling","def downscale_resolution(self, downscale_resolution):\n\n self._downscale_resolution = downscale_resolution","def getResolution(self):\n return self.resolution","def effective_resolution(self) -> Tuple[int, int]:\n import numpy as np\n\n assert self.info.resolution, 'No base resolution specified'\n rot = (self.info.rotate or 0) * math.pi / 180\n sin = math.sin(rot)\n cos = math.cos(rot)\n scale = np.array([[self.info.scale_x or 1.0, self.info.scale_y or 1.0]])\n resolution = np.array([[self.info.resolution[0], self.info.resolution[1]]])\n rot_matrix = np.array([[sin, cos], [cos, sin]])\n resolution = (scale * abs(np.cross(rot_matrix, resolution)))[0]\n return int(round(resolution[0])), int(round(resolution[1]))","def GetResolution(vDataSet):\r\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\r\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\r\n\r\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz","def getResolution(self):\n return self._lowLevelGetDeviceResolution()","def extend_to_grid(self, resolution):\n return Bounds(\n min_value = math.floor(self.min/resolution)*resolution,\n max_value = math.ceil(self.max/resolution)*resolution\n )","def round_filters(filters, global_params):\n multiplier = global_params.width_coefficient\n if not multiplier:\n return filters\n divisor = global_params.depth_divisor\n min_depth = global_params.min_depth\n filters *= multiplier\n min_depth = min_depth or divisor\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\n new_filters += divisor\n return int(new_filters)","def round_filters(filters, global_params):\n multiplier = global_params.width_coefficient\n if not multiplier:\n return filters\n divisor = global_params.depth_divisor\n min_depth = global_params.min_depth\n filters *= multiplier\n min_depth = min_depth or divisor\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\n new_filters += divisor\n return int(new_filters)","def round_filters(filters, global_params):\n multiplier = global_params.width_coefficient\n if not multiplier:\n return filters\n divisor = global_params.depth_divisor\n min_depth = global_params.min_depth\n filters *= multiplier\n min_depth = min_depth or divisor\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\n new_filters += divisor\n return int(new_filters)","def zoom(self):\n res = np.max(self.metadata[\"resolution\"])\n\n if self.atlas_name == \"allen_human_500um\":\n logger.debug(\n \"ATLAS: setting zoom manually for human atlas, atlas needs fixing\"\n )\n return 350\n else:\n return 40 / res","def downScaleResolution(kv, factor=10):\n sub_img_name = kv[0]\n sub_image = kv[1]\n img_dimension = len(sub_image)\n big_image = sub_image\n Nbig = img_dimension\n Nsmall = Nbig//factor\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\n return (sub_img_name,small_image)","def _round_filters(self, filters):\n filters *= self.width_coefficient\n new_filters = max(\n self.depth_divisor,\n int(filters + self.depth_divisor / 2)\n // self.depth_divisor\n * self.depth_divisor,\n )\n # Make sure that round down does not go down by more than 10%.\n if new_filters < 0.9 * filters:\n new_filters += self.depth_divisor\n return int(new_filters)","def _pixel_scale(self, width=None, height=None, scale=None):\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\n raise ValueError(\"Specify only one of width, height, or scale.\")\n if width is not None:\n scale = width / self._width\n elif height is not None:\n scale = height / self._height\n elif scale is None:\n scale = 1.0\n return scale","def resolution(self) -> int:\n return self.options.resolution","def pixel_size_ratio(self):\n return 2**(self.levels[-1] - self.levels[0])","def reduce_resolution(input_dir, output_dir, factor=2):\n # Get the images.\n downsample_ratio = 1.0 / args.factor\n tiles = glob.glob(os.path.join(args.input_dir, 'S_*/*.tif*'))\n tiles.sort()\n print('Number of Tiles: ', len(tiles))\n print('Downsample Ratio: ', downsample_ratio)\n\n # Downsample the images by the requested factor with OpenCV's resize().\n for t in tqdm(tiles):\n dirname = os.path.basename(os.path.dirname(t))\n filename = os.path.basename(t)\n os.makedirs(os.path.join(args.output_dir, dirname), exist_ok=True)\n f_out = os.path.join(args.output_dir, dirname, filename)\n im = cv2.imread(t, 0)\n im_down = cv2.resize(im, (0, 0), fx=downsample_ratio, fy=downsample_ratio)\n cv2.imwrite(f_out, im_down)","def get_resolution(testdata):\n diffs = np.transpose([testdata[1:, 0], np.diff(testdata[:, 0])])\n resolutions = ud.safedivide(diffs[:, 0], diffs[:, 1])\n # popt, pcov = scipy.optimize.curve_fit(fit_line, diffs[:, 0], resolutions, maxfev=1000000)\n # fitLine = fit_line(diffs[:, 0], *popt)\n # fitMax = np.max(fitLine)\n # fitMin = np.min(fitLine)\n # diffs_new = diffs[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\n # resolutions_new = resolutions[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\n # popt2, pcov2 = scipy.optimize.curve_fit(fit_line, diffs_new[:, 0], resolutions_new, maxfev=1000000)\n # plt.figure()\n # plt.plot(diffs[:,0], resolutions)\n # plt.plot(diffs[:, 0], fit_line(diffs[:, 0], *popt2), 'r-')\n # plt.show()\n # Currently use A * m ^1.5 (0.5?)\n # Maybe use a*M^b\n # return popt2\n return np.median(resolutions)","def scaling(self):\n return self.__scaling","def _round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):\n if not multiplier:\n return channels\n\n channels *= multiplier\n channel_min = channel_min or divisor\n new_channels = max(\n int(channels + divisor / 2) // divisor * divisor,\n channel_min)\n # Make sure that round down does not go down by more than 10%.\n if new_channels < 0.9 * channels:\n new_channels += divisor\n return new_channels","def get_res_size_from_size(sz, factor):\n if (factor is None) :\n print('WARNING: Could not compute low_res_size as factor was ' + str( factor ))\n return sz\n else:\n lowResSize = np.array(sz)\n if not isinstance(factor, list):\n lowResSize[2::] = (np.ceil((np.array(sz[2:]) * factor))).astype('int16')\n else:\n lowResSize[2::] = (np.ceil((np.array(sz[2:]) * np.array(factor)))).astype('int16')\n\n if lowResSize[-1]%2!=0:\n lowResSize[-1]-=1\n print('\\n\\nWARNING: forcing last dimension to be even: fix properly in the Fourier transform later!\\n\\n')\n\n return lowResSize","def upsample_nearest(input, size=None, scale_factor=None):\n return interpolate(input, size, scale_factor, 'nearest')","def setResolution(self, resolution):\n assert(resolution > 1 and resolution <= 8192)\n self.resolution = resolution","def scale(self):\n return self.scale_factor / CONSTANTS.AU","def scaling(self):\n return self._scaling","def scaling(self):\n return self._scaling","def resolution(self) -> int:\n return self._resolution","def upsample(self, method):\n from scipy.signal import resample\n from scipy.ndimage.interpolation import zoom\n #print \"mm: 100 x 100 x 131\"\n #print \"Dims:\", self.D.shape\n fact = np.array(self.info.shape).astype(\"float32\") / np.array(self.info.read_shape).astype(\"float32\")+0.00001 # hrmpf!!\n if method == \"zoom\":\n print \"Resampling...\"\n self.D = zoom(self.D, fact).astype(\"float32\")\n elif method == \"resample\":\n print \"Resampling...\"\n a = self.info.resample_ax\n s = self.info.shape[a]\n self.D = resample(self.D, s, axis=a, window=10).astype(\"float32\")\n elif method == None:\n pass\n else:\n raise NotImplementedError(\"Unknown upsampling method: %s\" % method)\n #print \"Dims:\", self.D.shape\n print \"done.\"","def set_scale_factors_to_output_size(self):\n # Compute the scale_factor using rounded scaled image size.\n height = tf.shape(self._image)[0]\n width = tf.shape(self._image)[1]\n max_image_size = tf.to_float(tf.maximum(height, width))\n image_scale = tf.to_float(self._output_size) / max_image_size\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\n self._image_scale = image_scale\n self._scaled_height = scaled_height\n self._scaled_width = scaled_width","def _set_resolution( self ):\r\n offset = 0\r\n # if current and skinned resolutions differ and skinned resolution is not\r\n # 1080i or 720p (they have no 4:3), calculate widescreen offset\r\n if ( ( not ( self.currentResolution == self.resolution ) ) and self.resolution > 1 ):\r\n # check if current resolution is 16x9\r\n if ( self.currentResolution == 0 or self.currentResolution % 2 ): iCur16x9 = 1\r\n else: iCur16x9 = 0\r\n # check if skinned resolution is 16x9\r\n if ( self.resolution % 2 ): i16x9 = 1\r\n else: i16x9 = 0\r\n # calculate widescreen offset\r\n offset = iCur16x9 - i16x9\r\n self.win.setCoordinateResolution( self.resolution + offset )","def find_supported_scaling_factor(\n current_width: int, current_height: int, target_width: int, target_height: int\n) -> tuple[int, int] | None:\n for idx, supported_sf in enumerate(SUPPORTED_SCALING_FACTORS):\n ratio = supported_sf[0] / supported_sf[1]\n width_after_scale = current_width * ratio\n height_after_scale = current_height * ratio\n if width_after_scale == target_width and height_after_scale == target_height:\n return supported_sf\n if width_after_scale < target_width or height_after_scale < target_height:\n return None if idx == 0 else SUPPORTED_SCALING_FACTORS[idx - 1]\n\n # Giant image, the most we can reduce by is 1/8\n return SUPPORTED_SCALING_FACTORS[-1]","def calculateRatio(levelDims):\n highestReso = np.asarray(levelDims[0])\n lowestReso = np.asarray(levelDims[-1])\n Xratio, Yratio = highestReso/lowestReso\n return (Xratio, Yratio)","def set_rscale(self, top, bottom=0, round_up=False):\n if self.shape == 'circle':\n r = top\n elif self.shape == 'polygon':\n angle_of_slice = 2 * np.pi / self.size\n r = top / np.cos(angle_of_slice / 2.)\n if round_up:\n r = np.ceil(r)\n else:\n # this should never happen since this is checked for in class\n # creation\n raise ValueError('unknown value for `frame`: %s' % self.shape)\n self.set_ylim(bottom, r)","def _get_rupture_dimensions(src, mag, nodal_plane):\n area = src.magnitude_scaling_relationship.get_median_area(\n mag, nodal_plane.rake)\n rup_length = math.sqrt(area * src.rupture_aspect_ratio)\n rup_width = area / rup_length\n seismogenic_layer_width = (src.lower_seismogenic_depth\n - src.upper_seismogenic_depth)\n max_width = (seismogenic_layer_width\n / math.sin(math.radians(nodal_plane.dip)))\n if rup_width > max_width:\n rup_width = max_width\n rup_length = area / rup_width\n return rup_length, rup_width","def determine_real_to_pixel_ratio(\n image_shape: Tuple[int, int],\n min_x: float,\n min_y: float,\n max_x: float,\n max_y: float,\n):\n image_x = image_shape[1]\n image_y = image_shape[0]\n diff_x = max_x - min_x\n diff_y = max_y - min_y\n\n resolution_x = image_x / diff_x\n resolution_y = image_y / diff_y\n\n # Should be similar as cells are rectangles\n if not np.isclose(resolution_x, resolution_y):\n logging.info(\n f\"Resolution in x and y axes differ: x: {resolution_x} y: {resolution_y}\"\n )\n\n resolution = np.mean([resolution_x, resolution_y])\n\n return resolution","def _infer_scale(\n print_h: Measurement, print_w: Measurement, viewbox_h: float, viewbox_w: float\n) -> float:\n if any(x < 0 for x in (print_h.value, print_w.value, viewbox_h, viewbox_w)):\n msg = \"Negative values are not allowed\"\n raise ValueError(msg)\n\n candidate_scales: set[float] = set()\n if print_w.value and viewbox_w:\n candidate_scales.add(print_w.value / viewbox_w)\n if print_h.value and viewbox_h:\n candidate_scales.add(print_h.value / viewbox_h)\n if candidate_scales:\n # size of picture is determined by print area\n return min(candidate_scales)\n if any([print_w.value, print_h.value]):\n msg = \"All potential scales would be infinite.\"\n raise ValueError(msg)\n # a print unit was given, but not a print size. Size of picture is determined\n # by interpreting viewbox dimensions as print_width or print_height units\n return print_w.native_unit.value[1]","def setResolution(self, resolution):\n self._lowLevelSetDeviceResolution(self.ADC_RESOLUTIONS[resolution])","def calculate_image_scale(source_width, source_height, target_width, target_height):\n if source_width == target_width and source_height == target_height:\n return 1.0\n\n source_ratio = source_width / source_height\n target_ratio = target_width / target_height\n\n if target_ratio < source_ratio:\n scale = target_width / source_width\n else:\n scale = target_height / source_height\n\n return scale","def get_current_resolution(self):\n return self.display_info[\"width\"], self.display_info[\"height\"]","def set_resolution(self):\n file_name = os.path.basename(self.in_file)\n if '1KM' in file_name:\n self.resolution = 1000\n else:\n raise ValueError(\n 'Cant read this data, please check its resolution: {}'.format(self.in_file))","def GetNativeResolution(self, transform=None, maximum=None):\n # Get the source projection's units for a 1x1 pixel, assuming square\n # pixels.\n width, height = self.GetPixelDimensions()\n src_pixel_size = min(abs(width), abs(height))\n\n if transform is None:\n dst_pixel_size = src_pixel_size\n dst_ref = self.GetSpatialReference()\n else:\n # Transform these dimensions into the destination projection\n dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]\n dst_pixel_size = abs(dst_pixel_size)\n dst_ref = transform.dst_ref\n\n # We allow some floating point error between src_pixel_size and\n # dst_pixel_size based on the major circumference so that the error is\n # in the destination units\n error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128\n\n # Find the resolution where the pixels are smaller than dst_pixel_size.\n for resolution in count():\n if maximum is not None and resolution >= maximum:\n return resolution\n\n res_pixel_size = max(\n *dst_ref.GetPixelDimensions(resolution=resolution)\n )\n if (res_pixel_size - dst_pixel_size) <= error:\n return resolution\n\n # Halve error each resolution\n error /= 2","def get_resolution(self, curvename):\n\n if curvename == 'flank':\n return self.points_flank\n elif curvename == 'fillet':\n return self.points_fillet\n elif curvename == 'tip':\n return self.points_tip\n elif curvename == 'root':\n return self.points_root\n elif curvename == 'shaft':\n return self.points_shaft\n elif curvename == 'width':\n return self.points_width","def upsample(\n input,\n size=None,\n scale_factor=None,\n mode='nearest',\n align_corners=False,\n):\n return interpolate(input, size, scale_factor, mode, align_corners)","def span_rbw_ratio(self):\r\n res = self._visa.query(f\"SENSE{self._screen()}:BANDWIDTH:RESOLUTION:RATIO?\")\r\n return 1 / float(res)","def ratio(self):\n return float(self.max_width) / self.max_height","def stdsize(image,r=30):\n image = square(image)\n s,_ = image.shape\n return interpolation.zoom(image,(r+0.5)/float(s))","def _scale_filters(filters, multiplier, base=8):\n round_half_up = int(int(filters) * multiplier / base + 0.5)\n result = int(round_half_up * base)\n return max(result, base)","def handle_proportionality_factors(self, scaling_candidates):\n\n if not len(scaling_candidates):\n return\n\n scalingsForHierarchicalIndices = [\n self.optimization_parameter_name_to_index[x] for x in\n scaling_candidates]\n order = np.argsort(scalingsForHierarchicalIndices)\n scalingsForHierarchicalIndices = \\\n [scalingsForHierarchicalIndices[i] for i in order]\n scaling_candidates = [scaling_candidates[i] for i in order]\n\n\n self.f.require_dataset(\"/scalingParameterIndices\",\n shape=(len(scalingsForHierarchicalIndices),),\n dtype='<i4',\n data=scalingsForHierarchicalIndices)\n print(Fore.CYAN, \"Number of proportionality factors for \"\n \"hierarchical optimization: %d\"\n % len(scalingsForHierarchicalIndices))\n\n # find usages for the selected parameters\n use = self.get_analytical_parameter_table(scaling_candidates,\n 'observable')\n\n self.f.require_dataset(\"/scalingParametersMapToObservables\",\n shape=(len(use), 3),\n dtype='<i4', data=use)","def scale(self, factors):\n if isinstance(factors, numbers.Number):\n factors = np.ones(self.dim) * factors;\n self.raw_wires.scale(factors);","def guess_scaling(name, spectrum):\n spectra = '%s/disp/%s.1d.fits' % (name, zerocount(spectrum))\n skyname = '%s/sky.1d.fits' % name\n spectrafits = pyfits.open(spectra)\n skyfits = pyfits.open(skyname)\n scalings = []\n for line in LINES:\n spec_peak, spec_cont = get_peak_cont(spectrafits, line, 5)\n sky_peak, sky_cont = get_peak_cont(skyfits, line, 5)\n scale = ((spec_peak - spec_cont) / (sky_peak - sky_cont))\n scalings.append(scale)\n return avg(*scalings)","def process(self, resolutions):\r\n # This is optional behavior\r\n return resolutions","def round_filters(filters, divisor=depth_divisor):\n filters *= width_coefficient\n new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)\n # Make sure that round down does not go down by more than 10%.\n if new_filters < 0.9 * filters:\n new_filters += divisor\n return int(new_filters)","def __call__(self, results):\n if 'scale' not in results:\n if 'scale_factor' in results:\n img_shape = results['img'].shape[:2]\n scale_factor = results['scale_factor']\n assert isinstance(scale_factor, float)\n results['scale'] = tuple([int(x * scale_factor) for x in img_shape][::-1])\n else:\n self._random_scale(results)\n else:\n if not self.override:\n assert 'scale_factor' not in results, 'scale and scale_factor cannot be both set.'\n else:\n results.pop('scale')\n if 'scale_factor' in results:\n results.pop('scale_factor')\n self._random_scale(results)\n\n self._resize_img(results)\n self._resize_bboxes(results)\n self._resize_cbboxes(results)\n self._resize_masks(results)\n self._resize_seg(results)\n\n return results","def scanResolution(self):\n return self._getAttribute(Attribute.scanResolution)","def change_resolution(self):","def castSize(self, scale):\n return self.camera.sensorSize * scale","def getResolution(s) -> int:\n unit = getDurationUnit(s)\n #number of ticks is 1 / unit (if that is an integer)\n ticksPerQuarter = unit.denominator / unit.numerator\n if ticksPerQuarter.is_integer():\n return int(unit.denominator / unit.numerator)\n else:\n print(s.filePath, ' non integer number of ticks per Quarter')\n return 0","def __call__(self, results):\n\n if 'scale' not in results:\n if 'scale_factor' in results:\n img_shape = results['img'].shape[:2]\n scale_factor = results['scale_factor']\n assert isinstance(scale_factor, float)\n results['scale'] = tuple(\n [int(x * scale_factor) for x in img_shape][::-1])\n else:\n self._random_scale(results)\n else:\n if not self.override:\n assert 'scale_factor' not in results, (\n 'scale and scale_factor cannot be both set.')\n else:\n results.pop('scale')\n if 'scale_factor' in results:\n results.pop('scale_factor')\n self._random_scale(results)\n\n self._resize_img(results)\n self._resize_bboxes(results)\n self._resize_masks(results)\n self._resize_seg(results)\n return results","def __call__(self, results):\n\n if 'scale' not in results:\n if 'scale_factor' in results:\n img_shape = results['img'].shape[:2]\n scale_factor = results['scale_factor']\n assert isinstance(scale_factor, float)\n results['scale'] = tuple(\n [int(x * scale_factor) for x in img_shape][::-1])\n else:\n self._random_scale(results)\n else:\n if not self.override:\n assert 'scale_factor' not in results, (\n 'scale and scale_factor cannot be both set.')\n else:\n results.pop('scale')\n if 'scale_factor' in results:\n results.pop('scale_factor')\n self._random_scale(results)\n\n self._resize_img(results)\n self._resize_bboxes(results)\n self._resize_masks(results)\n self._resize_seg(results)\n return results","def Resolution(self):\n\t\treturn self._get_attribute('resolution')","def upsampling(\n data,\n scale_h,\n scale_w,\n layout=\"NCHW\",\n method=\"nearest_neighbor\",\n align_corners=False,\n output_shape=None,\n):\n base_layout = layout[0:4]\n if base_layout == \"NCHW\":\n if not output_shape: # static case\n scaled_h = data.shape[2] * scale_h\n scaled_w = data.shape[3] * scale_w\n reshape_size = (\n simplify(topi.cast(te.round(scaled_h), data.shape[2].dtype)),\n simplify(topi.cast(te.round(scaled_w), data.shape[3].dtype)),\n )\n else: # dynamic case -- we don't need to scale; already done in shape func\n reshape_size = (\n simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),\n simplify(topi.cast(te.round(output_shape[3]), output_shape[3].dtype)),\n )\n elif layout == \"NHWC\":\n if not output_shape: # static case\n scaled_h = data.shape[1] * scale_h\n scaled_w = data.shape[2] * scale_w\n reshape_size = (\n simplify(topi.cast(te.round(scaled_h), data.shape[1].dtype)),\n simplify(topi.cast(te.round(scaled_w), data.shape[2].dtype)),\n )\n else: # dynamic case\n reshape_size = (\n simplify(topi.cast(te.round(output_shape[1]), output_shape[1].dtype)),\n simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),\n )\n\n else:\n raise ValueError(f\"not support this layout {layout} yet\")\n coord_trans = \"align_corners\" if align_corners else \"asymmetric\"\n if method[0:2] == \"bi\":\n method = method[2:]\n return topi.image.resize2d(\n data,\n [0.0] * 4,\n reshape_size,\n layout=layout,\n method=method,\n coordinate_transformation_mode=coord_trans,\n output_shape=output_shape,\n )","def get_scale_factor(rec, stack):\n \n rec_pixel_size = get_pixel_size_rec(rec)\n stack_pixel_size = get_pixel_size_stack(stack)\n \n return rec_pixel_size / stack_pixel_size","def determine_thresholds(confidence, resolution=100):\n if isinstance(confidence, list):\n confidence = np.array(confidence)\n confidence = confidence.flatten()\n confidence = confidence[~np.isnan(confidence)]\n confidence.sort()\n\n assert len(confidence) > resolution and resolution > 2\n\n thresholds = np.ones((resolution))\n thresholds[0] = - np.inf\n thresholds[-1] = np.inf\n delta = np.floor(len(confidence) / (resolution - 2))\n idxs = np.linspace(delta, len(confidence)-delta, resolution-2, dtype=np.int32)\n thresholds[1:-1] = confidence[idxs]\n return thresholds","def rscale(mag=10.0):\n if mag > 11.5:\n return 0.5\n elif mag > 11.0:\n return 1.0\n elif mag > 10.5:\n return 1.5\n elif mag > 10.0:\n return 1.5\n elif mag > 9.5:\n return 2.0\n elif mag > 9.0:\n return 2.5\n elif mag > 8.5:\n return 3.0\n else:\n return 3.5","def _round_sampling_rate(sampling_rate):\n # check if sampling rate is below 5 Hz in that case always round to one\n if sampling_rate < 5:\n\n # set sampling rate to 1\n rounded_sampling_rate = 1\n\n else:\n\n # round to the nearest 10 digit\n rounded_sampling_rate = round(sampling_rate/10) * 10\n\n return rounded_sampling_rate","def calculate_rf_size(rf_size, downsample):\n h = 61 # 24\" monitor\n d = 10 # 10cm from the right eye\n r = 1080 / downsample # Vertical resolution\n d_px = np.degrees(math.atan2(h / 2, d)) / (r / 2)\n return rf_size * d_px","def scaling_factor(self):\n bin_scale = self.spabins * self.spebins\n return bin_scale * self.int_time","def zoom_to_size(self, *p):\n\t\tif self.image is None or self.allocation is None:\n\t\t\treturn\n\t\tif __debug__: print self.allocation.width, self.image.get_width()\n\t\tif __debug__: print self.allocation.width, self.image.get_width(), self.allocation.width/self.image.get_width()\n\t\tz = min(\n\t\t\tself.allocation.width/self.image.get_width(),\n\t\t\tself.allocation.height/self.image.get_height()\n\t\t\t)\n\t\tif __debug__: print \"zoom_to_size\", \"z=\", z\n\t\tself.zoom = z","def upround(x, base):\n return base * math.ceil(float(x) / base)","def get_resolution(self):\n return self.__resolution","def _round_filters(self, filters, width_coefficient, depth_divisor, min_depth):\n\n if not width_coefficient:\n return filters\n\n filters *= width_coefficient\n min_depth = min_depth or depth_divisor\n new_filters = max(\n min_depth,\n int(filters + depth_divisor / 2) // depth_divisor*depth_divisor\n )\n # Make sure that round down does not go down by more than 10%.\n if new_filters < 0.9 * filters:\n new_filters += depth_divisor\n\n return int(new_filters)","def get_apply_scale(self, applyScaleFactor, scale_quality = 1.0):\n v = self.scale * self.scale_quality * scale_quality\n if applyScaleFactor:\n v *= self.scale_factor\n return v","def resize(orig, factor, method=\"nearest\"):\r\n method_dict = {'nearest': 0, 'bilinear': 1, 'cubic': 2}\r\n if method.lower() not in method_dict:\r\n raise ValueError(\"Invalid interpolation method. Options are: \" + \", \".join(method_dict.keys()))\r\n try:\r\n return zoom(orig, factor, order=method_dict[method.lower()])\r\n except RuntimeError:\r\n # raised by zoom when factor length does not match orig.shape length\r\n raise ValueError(\"Factor sequence length does not match input length\")","def Rescale(self):\r\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\r\n bitmap = self.scaled = self.bitmap\r\n if not bitmap: return\r\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\r\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\r\n image = bitmap.ConvertToImage()\r\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\r\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\r\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\r\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\r","def resolution_init():\n\n defaultResolution = pm.PyNode(\"defaultResolution\")\n task = ftrack.Task(os.environ[\"FTRACK_TASKID\"])\n\n # Adding/Checking ftrack resolution attribute\n resolution_set = False\n if hasattr(defaultResolution, \"ftrackResolutionSet\"):\n attr = pm.Attribute(\"defaultResolution.ftrackResolutionSet\")\n resolution_set = attr.get()\n else:\n pm.addAttr(\n defaultResolution,\n longName=\"ftrackResolutionSet\",\n defaultValue=True,\n attributeType=\"bool\"\n )\n\n if not resolution_set:\n width = task.getParent().get(\"width\")\n defaultResolution.width.set(width)\n pm.warning(\"Changed resolution width to: {0}\".format(width))\n height = task.getParent().get(\"height\")\n defaultResolution.height.set(height)\n pm.warning(\"Changed resolution height to: {0}\".format(height))\n\n # Vray specific resolution\n if pm.objExists(\"vraySettings\"):\n vray_settings = pm.PyNode(\"vraySettings\")\n vray_settings.width.set(width)\n pm.warning(\"Changed vray resolution width to: {0}\".format(width))\n vray_settings.height.set(height)\n pm.warning(\"Changed vray resolution height to: {0}\".format(height))","def resolution(self):\n return {'x': self.width, 'y': self.height}","def largestResolution(resolutions):\n return resolutions[0]","def _upsample_conv(self, x, conv):\n return conv(\n F.interpolate(x,\n scale_factor=2,\n mode='bilinear',\n align_corners=False))","def _apply_resolution(self, Qin, Rin, interpolation):\n Q, dQ = _interpolate_Q(self.Q, self.dQ, interpolation)\n if np.iscomplex(Rin).any():\n R_real = convolve(Qin, Rin.real, Q, dQ, resolution=self.resolution)\n R_imag = convolve(Qin, Rin.imag, Q, dQ, resolution=self.resolution)\n R = R_real + 1j*R_imag\n else:\n R = convolve(Qin, Rin, Q, dQ, resolution=self.resolution)\n return Q, R","def get_scaling_ratio(img):\n\n healthy_img_area = 4872 * 6496\n input_img_area = img.shape[0] * img.shape[1]\n ratio = input_img_area / healthy_img_area\n return ratio","def _set_pixel_size(self) -> None:\n # Not Pansharpened images\n if self.band_combi == Sv1BandCombination.PMS:\n # TODO: manage default resolution for PAN band ?\n self.pixel_size = self._ms_res\n # Pansharpened images\n else:\n self.pixel_size = self._pan_res","def find_suggested_tonemap_scale(session):\n avg_film_luminance = session.GetFilm().GetFilmY()\n return (1.25 / avg_film_luminance * (118 / 255))\n\n # TODO\n # measure this all the time, show a message to the user if\n # abs(old - new) > threshold\n # so the user can set the new value with one click\n\n # imagepipeline = scene.camera.data.luxcore.imagepipeline\n # imagepipeline.tonemapper.linear_scale = suggested_linear_scale\n # imagepipeline.tonemapper.use_autolinear = False","def __init__(self, resolution, normalize=True, eps=1e-6):\n super().__init__()\n self.r = int(resolution)\n self.normalize = normalize\n self.eps = eps","def Resolution(self, *args):\n return _Adaptor3d.Adaptor3d_Curve_Resolution(self, *args)","def _get_upsample_layer(self, in_channels=None, out_channels=None):\n if self.expand_strategy == \"upsample\":\n return nn.Upsample(scale_factor=2, mode=\"nearest\")\n elif self.expand_strategy == \"transpose_convolution\":\n return nn.ConvTranspose2d(\n in_channels, out_channels, 3, stride=2, padding=1, output_padding=1\n )\n else:\n raise ValueError(\"Unkown expand strategy\")","def _calc_fig_size(self, image_height, dpi, y_range, slice_window, fs):\n duration = slice_window / fs\n width_mm = duration * self.MM_IN_SEC\n height_mm = abs(y_range.max - y_range.min) * self.MM_IN_MV\n\n height_inch = image_height / dpi\n width_inch = height_inch * width_mm / height_mm\n\n return (width_inch, height_inch)","def standardizeRatios( self, ratios ):\n\t\tratios_standardized = ratios.copy()\n\t\tzscore = lambda x: ( x - x.mean() ) / x.std()\n\t\tfor row in ratios.iterrows():\n\t\t\tratios_standardized.loc[ row[0] ] = zscore( row[1] )\n\t\treturn ratios_standardized","def _scale_param(self, resid_us):\n return((resid_us**2).sum().sum() / self.dof)","def resolution(self):\n return self._resolution","def resolution(self, resolution):\n\n self._resolution = resolution","def get_scaling(self):\n if self.constrain_navigation:\n self.activate_navigation_constrain()\n return self.sx, self.sy","def resolution(self):\n return next(iter(self.resolutions()), None)","def testCalculateVNCScreenRatio(self, mock_tk):\n # Get scale-down ratio if screen height is smaller than AVD height.\n mock_tk.return_value = FakeTkinter(height=800, width=1200)\n avd_h = 1920\n avd_w = 1080\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.4)\n\n # Get scale-down ratio if screen width is smaller than AVD width.\n mock_tk.return_value = FakeTkinter(height=800, width=1200)\n avd_h = 900\n avd_w = 1920\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.6)\n\n # Scale ratio = 1 if screen is larger than AVD.\n mock_tk.return_value = FakeTkinter(height=1080, width=1920)\n avd_h = 800\n avd_w = 1280\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 1)\n\n # Get the scale if ratio of width is smaller than the\n # ratio of height.\n mock_tk.return_value = FakeTkinter(height=1200, width=800)\n avd_h = 1920\n avd_w = 1080\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.6)"],"string":"[\n \"def GetWorldScalingRatios(self, resolution=None, places=None):\\n if resolution is None:\\n resolution = self.GetNativeResolution()\\n\\n spatial_ref = self.GetSpatialReference()\\n world = spatial_ref.GetWorldExtents().dimensions\\n src_pixel_sizes = XY(x=world.x / self.RasterXSize,\\n y=world.y / self.RasterYSize)\\n dst_pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\\n\\n xscale = abs(src_pixel_sizes.x / dst_pixel_sizes.x)\\n\\n # Make sure that yscale fits within the whole world\\n yscale = min(xscale, abs(src_pixel_sizes.y / dst_pixel_sizes.y))\\n\\n if places is not None:\\n xscale = round(xscale, places)\\n yscale = round(yscale, places)\\n\\n return XY(x=xscale, y=yscale)\",\n \"def _compute_output_resolution(input_spatial_resolution, kernel_size, stride,\\n total_padding):\\n if (input_spatial_resolution is None) or (kernel_size is None) or (\\n stride is None) or (total_padding is None):\\n return None\\n return int(\\n math.ceil((\\n input_spatial_resolution + total_padding - kernel_size + 1) / stride))\",\n \"def change_resolution(img):\\n scale_factor = np.random.choice(list(range(0, 6, 2)))\\n if scale_factor == 0:\\n return img\\n downsample = nn.AvgPool2d(scale_factor)\\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\\n return new_res_img\",\n \"def raw_resolution(resolution, splitter=False):\\n width, height = resolution\\n if splitter:\\n fwidth = (width + 15) & ~15\\n else:\\n fwidth = (width + 31) & ~31\\n fheight = (height + 15) & ~15\\n return fwidth, fheight\",\n \"def compute_resolution(zoom, size_px):\\n # Calibration data:\\n dist_in_um = 10\\n dist_in_px = np.array([21.13, 19.62, 8.93])\\n zooms = np.array([1.5, 3, 4.5])\\n image_max_sizes = np.array([330, 610, 410])\\n \\n return np.mean((dist_in_um/dist_in_px) * (zoom/zooms) * (image_max_sizes/size_px))\",\n \"def UResolution(self, *args):\\n return _Adaptor3d.Adaptor3d_Surface_UResolution(self, *args)\",\n \"def resolution_range(self) -> Optional[float]:\\n return self._get_property(RESOLUTION_RANGE_PROP, float)\",\n \"def _get_scaling(root):\\n dpi = root.winfo_fpixels(\\\"1i\\\")\\n scaling = dpi / 72.0\\n logger.debug(\\\"dpi: %s, scaling: %s'\\\", dpi, scaling)\\n return scaling\",\n \"def downscale_resolution(self, downscale_resolution):\\n\\n self._downscale_resolution = downscale_resolution\",\n \"def getResolution(self):\\n return self.resolution\",\n \"def effective_resolution(self) -> Tuple[int, int]:\\n import numpy as np\\n\\n assert self.info.resolution, 'No base resolution specified'\\n rot = (self.info.rotate or 0) * math.pi / 180\\n sin = math.sin(rot)\\n cos = math.cos(rot)\\n scale = np.array([[self.info.scale_x or 1.0, self.info.scale_y or 1.0]])\\n resolution = np.array([[self.info.resolution[0], self.info.resolution[1]]])\\n rot_matrix = np.array([[sin, cos], [cos, sin]])\\n resolution = (scale * abs(np.cross(rot_matrix, resolution)))[0]\\n return int(round(resolution[0])), int(round(resolution[1]))\",\n \"def GetResolution(vDataSet):\\r\\n xmin,xmax,ymin,ymax,zmin,zmax = GetExtent(vDataSet)\\r\\n nx,ny,nz = vDataSet.GetSizeX(),vDataSet.GetSizeY(),vDataSet.GetSizeZ()\\r\\n\\r\\n return (xmax-xmin)/nx, (ymax-ymin)/ny, (zmax-zmin)/nz\",\n \"def getResolution(self):\\n return self._lowLevelGetDeviceResolution()\",\n \"def extend_to_grid(self, resolution):\\n return Bounds(\\n min_value = math.floor(self.min/resolution)*resolution,\\n max_value = math.ceil(self.max/resolution)*resolution\\n )\",\n \"def round_filters(filters, global_params):\\n multiplier = global_params.width_coefficient\\n if not multiplier:\\n return filters\\n divisor = global_params.depth_divisor\\n min_depth = global_params.min_depth\\n filters *= multiplier\\n min_depth = min_depth or divisor\\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\\n new_filters += divisor\\n return int(new_filters)\",\n \"def round_filters(filters, global_params):\\n multiplier = global_params.width_coefficient\\n if not multiplier:\\n return filters\\n divisor = global_params.depth_divisor\\n min_depth = global_params.min_depth\\n filters *= multiplier\\n min_depth = min_depth or divisor\\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\\n new_filters += divisor\\n return int(new_filters)\",\n \"def round_filters(filters, global_params):\\n multiplier = global_params.width_coefficient\\n if not multiplier:\\n return filters\\n divisor = global_params.depth_divisor\\n min_depth = global_params.min_depth\\n filters *= multiplier\\n min_depth = min_depth or divisor\\n new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor)\\n if new_filters < 0.9 * filters: # prevent rounding by more than 10%\\n new_filters += divisor\\n return int(new_filters)\",\n \"def zoom(self):\\n res = np.max(self.metadata[\\\"resolution\\\"])\\n\\n if self.atlas_name == \\\"allen_human_500um\\\":\\n logger.debug(\\n \\\"ATLAS: setting zoom manually for human atlas, atlas needs fixing\\\"\\n )\\n return 350\\n else:\\n return 40 / res\",\n \"def downScaleResolution(kv, factor=10):\\n sub_img_name = kv[0]\\n sub_image = kv[1]\\n img_dimension = len(sub_image)\\n big_image = sub_image\\n Nbig = img_dimension\\n Nsmall = Nbig//factor\\n small_image = big_image.reshape([Nsmall, Nbig // Nsmall, Nsmall, Nbig // Nsmall]).mean(3).mean(1)\\n return (sub_img_name,small_image)\",\n \"def _round_filters(self, filters):\\n filters *= self.width_coefficient\\n new_filters = max(\\n self.depth_divisor,\\n int(filters + self.depth_divisor / 2)\\n // self.depth_divisor\\n * self.depth_divisor,\\n )\\n # Make sure that round down does not go down by more than 10%.\\n if new_filters < 0.9 * filters:\\n new_filters += self.depth_divisor\\n return int(new_filters)\",\n \"def _pixel_scale(self, width=None, height=None, scale=None):\\n if numpy.count_nonzero([width is not None, height is not None, scale is not None]) > 1:\\n raise ValueError(\\\"Specify only one of width, height, or scale.\\\")\\n if width is not None:\\n scale = width / self._width\\n elif height is not None:\\n scale = height / self._height\\n elif scale is None:\\n scale = 1.0\\n return scale\",\n \"def resolution(self) -> int:\\n return self.options.resolution\",\n \"def pixel_size_ratio(self):\\n return 2**(self.levels[-1] - self.levels[0])\",\n \"def reduce_resolution(input_dir, output_dir, factor=2):\\n # Get the images.\\n downsample_ratio = 1.0 / args.factor\\n tiles = glob.glob(os.path.join(args.input_dir, 'S_*/*.tif*'))\\n tiles.sort()\\n print('Number of Tiles: ', len(tiles))\\n print('Downsample Ratio: ', downsample_ratio)\\n\\n # Downsample the images by the requested factor with OpenCV's resize().\\n for t in tqdm(tiles):\\n dirname = os.path.basename(os.path.dirname(t))\\n filename = os.path.basename(t)\\n os.makedirs(os.path.join(args.output_dir, dirname), exist_ok=True)\\n f_out = os.path.join(args.output_dir, dirname, filename)\\n im = cv2.imread(t, 0)\\n im_down = cv2.resize(im, (0, 0), fx=downsample_ratio, fy=downsample_ratio)\\n cv2.imwrite(f_out, im_down)\",\n \"def get_resolution(testdata):\\n diffs = np.transpose([testdata[1:, 0], np.diff(testdata[:, 0])])\\n resolutions = ud.safedivide(diffs[:, 0], diffs[:, 1])\\n # popt, pcov = scipy.optimize.curve_fit(fit_line, diffs[:, 0], resolutions, maxfev=1000000)\\n # fitLine = fit_line(diffs[:, 0], *popt)\\n # fitMax = np.max(fitLine)\\n # fitMin = np.min(fitLine)\\n # diffs_new = diffs[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\\n # resolutions_new = resolutions[(1.2 * fitMin < resolutions) & (resolutions < 1.2 * fitMax)]\\n # popt2, pcov2 = scipy.optimize.curve_fit(fit_line, diffs_new[:, 0], resolutions_new, maxfev=1000000)\\n # plt.figure()\\n # plt.plot(diffs[:,0], resolutions)\\n # plt.plot(diffs[:, 0], fit_line(diffs[:, 0], *popt2), 'r-')\\n # plt.show()\\n # Currently use A * m ^1.5 (0.5?)\\n # Maybe use a*M^b\\n # return popt2\\n return np.median(resolutions)\",\n \"def scaling(self):\\n return self.__scaling\",\n \"def _round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):\\n if not multiplier:\\n return channels\\n\\n channels *= multiplier\\n channel_min = channel_min or divisor\\n new_channels = max(\\n int(channels + divisor / 2) // divisor * divisor,\\n channel_min)\\n # Make sure that round down does not go down by more than 10%.\\n if new_channels < 0.9 * channels:\\n new_channels += divisor\\n return new_channels\",\n \"def get_res_size_from_size(sz, factor):\\n if (factor is None) :\\n print('WARNING: Could not compute low_res_size as factor was ' + str( factor ))\\n return sz\\n else:\\n lowResSize = np.array(sz)\\n if not isinstance(factor, list):\\n lowResSize[2::] = (np.ceil((np.array(sz[2:]) * factor))).astype('int16')\\n else:\\n lowResSize[2::] = (np.ceil((np.array(sz[2:]) * np.array(factor)))).astype('int16')\\n\\n if lowResSize[-1]%2!=0:\\n lowResSize[-1]-=1\\n print('\\\\n\\\\nWARNING: forcing last dimension to be even: fix properly in the Fourier transform later!\\\\n\\\\n')\\n\\n return lowResSize\",\n \"def upsample_nearest(input, size=None, scale_factor=None):\\n return interpolate(input, size, scale_factor, 'nearest')\",\n \"def setResolution(self, resolution):\\n assert(resolution > 1 and resolution <= 8192)\\n self.resolution = resolution\",\n \"def scale(self):\\n return self.scale_factor / CONSTANTS.AU\",\n \"def scaling(self):\\n return self._scaling\",\n \"def scaling(self):\\n return self._scaling\",\n \"def resolution(self) -> int:\\n return self._resolution\",\n \"def upsample(self, method):\\n from scipy.signal import resample\\n from scipy.ndimage.interpolation import zoom\\n #print \\\"mm: 100 x 100 x 131\\\"\\n #print \\\"Dims:\\\", self.D.shape\\n fact = np.array(self.info.shape).astype(\\\"float32\\\") / np.array(self.info.read_shape).astype(\\\"float32\\\")+0.00001 # hrmpf!!\\n if method == \\\"zoom\\\":\\n print \\\"Resampling...\\\"\\n self.D = zoom(self.D, fact).astype(\\\"float32\\\")\\n elif method == \\\"resample\\\":\\n print \\\"Resampling...\\\"\\n a = self.info.resample_ax\\n s = self.info.shape[a]\\n self.D = resample(self.D, s, axis=a, window=10).astype(\\\"float32\\\")\\n elif method == None:\\n pass\\n else:\\n raise NotImplementedError(\\\"Unknown upsampling method: %s\\\" % method)\\n #print \\\"Dims:\\\", self.D.shape\\n print \\\"done.\\\"\",\n \"def set_scale_factors_to_output_size(self):\\n # Compute the scale_factor using rounded scaled image size.\\n height = tf.shape(self._image)[0]\\n width = tf.shape(self._image)[1]\\n max_image_size = tf.to_float(tf.maximum(height, width))\\n image_scale = tf.to_float(self._output_size) / max_image_size\\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\\n self._image_scale = image_scale\\n self._scaled_height = scaled_height\\n self._scaled_width = scaled_width\",\n \"def _set_resolution( self ):\\r\\n offset = 0\\r\\n # if current and skinned resolutions differ and skinned resolution is not\\r\\n # 1080i or 720p (they have no 4:3), calculate widescreen offset\\r\\n if ( ( not ( self.currentResolution == self.resolution ) ) and self.resolution > 1 ):\\r\\n # check if current resolution is 16x9\\r\\n if ( self.currentResolution == 0 or self.currentResolution % 2 ): iCur16x9 = 1\\r\\n else: iCur16x9 = 0\\r\\n # check if skinned resolution is 16x9\\r\\n if ( self.resolution % 2 ): i16x9 = 1\\r\\n else: i16x9 = 0\\r\\n # calculate widescreen offset\\r\\n offset = iCur16x9 - i16x9\\r\\n self.win.setCoordinateResolution( self.resolution + offset )\",\n \"def find_supported_scaling_factor(\\n current_width: int, current_height: int, target_width: int, target_height: int\\n) -> tuple[int, int] | None:\\n for idx, supported_sf in enumerate(SUPPORTED_SCALING_FACTORS):\\n ratio = supported_sf[0] / supported_sf[1]\\n width_after_scale = current_width * ratio\\n height_after_scale = current_height * ratio\\n if width_after_scale == target_width and height_after_scale == target_height:\\n return supported_sf\\n if width_after_scale < target_width or height_after_scale < target_height:\\n return None if idx == 0 else SUPPORTED_SCALING_FACTORS[idx - 1]\\n\\n # Giant image, the most we can reduce by is 1/8\\n return SUPPORTED_SCALING_FACTORS[-1]\",\n \"def calculateRatio(levelDims):\\n highestReso = np.asarray(levelDims[0])\\n lowestReso = np.asarray(levelDims[-1])\\n Xratio, Yratio = highestReso/lowestReso\\n return (Xratio, Yratio)\",\n \"def set_rscale(self, top, bottom=0, round_up=False):\\n if self.shape == 'circle':\\n r = top\\n elif self.shape == 'polygon':\\n angle_of_slice = 2 * np.pi / self.size\\n r = top / np.cos(angle_of_slice / 2.)\\n if round_up:\\n r = np.ceil(r)\\n else:\\n # this should never happen since this is checked for in class\\n # creation\\n raise ValueError('unknown value for `frame`: %s' % self.shape)\\n self.set_ylim(bottom, r)\",\n \"def _get_rupture_dimensions(src, mag, nodal_plane):\\n area = src.magnitude_scaling_relationship.get_median_area(\\n mag, nodal_plane.rake)\\n rup_length = math.sqrt(area * src.rupture_aspect_ratio)\\n rup_width = area / rup_length\\n seismogenic_layer_width = (src.lower_seismogenic_depth\\n - src.upper_seismogenic_depth)\\n max_width = (seismogenic_layer_width\\n / math.sin(math.radians(nodal_plane.dip)))\\n if rup_width > max_width:\\n rup_width = max_width\\n rup_length = area / rup_width\\n return rup_length, rup_width\",\n \"def determine_real_to_pixel_ratio(\\n image_shape: Tuple[int, int],\\n min_x: float,\\n min_y: float,\\n max_x: float,\\n max_y: float,\\n):\\n image_x = image_shape[1]\\n image_y = image_shape[0]\\n diff_x = max_x - min_x\\n diff_y = max_y - min_y\\n\\n resolution_x = image_x / diff_x\\n resolution_y = image_y / diff_y\\n\\n # Should be similar as cells are rectangles\\n if not np.isclose(resolution_x, resolution_y):\\n logging.info(\\n f\\\"Resolution in x and y axes differ: x: {resolution_x} y: {resolution_y}\\\"\\n )\\n\\n resolution = np.mean([resolution_x, resolution_y])\\n\\n return resolution\",\n \"def _infer_scale(\\n print_h: Measurement, print_w: Measurement, viewbox_h: float, viewbox_w: float\\n) -> float:\\n if any(x < 0 for x in (print_h.value, print_w.value, viewbox_h, viewbox_w)):\\n msg = \\\"Negative values are not allowed\\\"\\n raise ValueError(msg)\\n\\n candidate_scales: set[float] = set()\\n if print_w.value and viewbox_w:\\n candidate_scales.add(print_w.value / viewbox_w)\\n if print_h.value and viewbox_h:\\n candidate_scales.add(print_h.value / viewbox_h)\\n if candidate_scales:\\n # size of picture is determined by print area\\n return min(candidate_scales)\\n if any([print_w.value, print_h.value]):\\n msg = \\\"All potential scales would be infinite.\\\"\\n raise ValueError(msg)\\n # a print unit was given, but not a print size. Size of picture is determined\\n # by interpreting viewbox dimensions as print_width or print_height units\\n return print_w.native_unit.value[1]\",\n \"def setResolution(self, resolution):\\n self._lowLevelSetDeviceResolution(self.ADC_RESOLUTIONS[resolution])\",\n \"def calculate_image_scale(source_width, source_height, target_width, target_height):\\n if source_width == target_width and source_height == target_height:\\n return 1.0\\n\\n source_ratio = source_width / source_height\\n target_ratio = target_width / target_height\\n\\n if target_ratio < source_ratio:\\n scale = target_width / source_width\\n else:\\n scale = target_height / source_height\\n\\n return scale\",\n \"def get_current_resolution(self):\\n return self.display_info[\\\"width\\\"], self.display_info[\\\"height\\\"]\",\n \"def set_resolution(self):\\n file_name = os.path.basename(self.in_file)\\n if '1KM' in file_name:\\n self.resolution = 1000\\n else:\\n raise ValueError(\\n 'Cant read this data, please check its resolution: {}'.format(self.in_file))\",\n \"def GetNativeResolution(self, transform=None, maximum=None):\\n # Get the source projection's units for a 1x1 pixel, assuming square\\n # pixels.\\n width, height = self.GetPixelDimensions()\\n src_pixel_size = min(abs(width), abs(height))\\n\\n if transform is None:\\n dst_pixel_size = src_pixel_size\\n dst_ref = self.GetSpatialReference()\\n else:\\n # Transform these dimensions into the destination projection\\n dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]\\n dst_pixel_size = abs(dst_pixel_size)\\n dst_ref = transform.dst_ref\\n\\n # We allow some floating point error between src_pixel_size and\\n # dst_pixel_size based on the major circumference so that the error is\\n # in the destination units\\n error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128\\n\\n # Find the resolution where the pixels are smaller than dst_pixel_size.\\n for resolution in count():\\n if maximum is not None and resolution >= maximum:\\n return resolution\\n\\n res_pixel_size = max(\\n *dst_ref.GetPixelDimensions(resolution=resolution)\\n )\\n if (res_pixel_size - dst_pixel_size) <= error:\\n return resolution\\n\\n # Halve error each resolution\\n error /= 2\",\n \"def get_resolution(self, curvename):\\n\\n if curvename == 'flank':\\n return self.points_flank\\n elif curvename == 'fillet':\\n return self.points_fillet\\n elif curvename == 'tip':\\n return self.points_tip\\n elif curvename == 'root':\\n return self.points_root\\n elif curvename == 'shaft':\\n return self.points_shaft\\n elif curvename == 'width':\\n return self.points_width\",\n \"def upsample(\\n input,\\n size=None,\\n scale_factor=None,\\n mode='nearest',\\n align_corners=False,\\n):\\n return interpolate(input, size, scale_factor, mode, align_corners)\",\n \"def span_rbw_ratio(self):\\r\\n res = self._visa.query(f\\\"SENSE{self._screen()}:BANDWIDTH:RESOLUTION:RATIO?\\\")\\r\\n return 1 / float(res)\",\n \"def ratio(self):\\n return float(self.max_width) / self.max_height\",\n \"def stdsize(image,r=30):\\n image = square(image)\\n s,_ = image.shape\\n return interpolation.zoom(image,(r+0.5)/float(s))\",\n \"def _scale_filters(filters, multiplier, base=8):\\n round_half_up = int(int(filters) * multiplier / base + 0.5)\\n result = int(round_half_up * base)\\n return max(result, base)\",\n \"def handle_proportionality_factors(self, scaling_candidates):\\n\\n if not len(scaling_candidates):\\n return\\n\\n scalingsForHierarchicalIndices = [\\n self.optimization_parameter_name_to_index[x] for x in\\n scaling_candidates]\\n order = np.argsort(scalingsForHierarchicalIndices)\\n scalingsForHierarchicalIndices = \\\\\\n [scalingsForHierarchicalIndices[i] for i in order]\\n scaling_candidates = [scaling_candidates[i] for i in order]\\n\\n\\n self.f.require_dataset(\\\"/scalingParameterIndices\\\",\\n shape=(len(scalingsForHierarchicalIndices),),\\n dtype='<i4',\\n data=scalingsForHierarchicalIndices)\\n print(Fore.CYAN, \\\"Number of proportionality factors for \\\"\\n \\\"hierarchical optimization: %d\\\"\\n % len(scalingsForHierarchicalIndices))\\n\\n # find usages for the selected parameters\\n use = self.get_analytical_parameter_table(scaling_candidates,\\n 'observable')\\n\\n self.f.require_dataset(\\\"/scalingParametersMapToObservables\\\",\\n shape=(len(use), 3),\\n dtype='<i4', data=use)\",\n \"def scale(self, factors):\\n if isinstance(factors, numbers.Number):\\n factors = np.ones(self.dim) * factors;\\n self.raw_wires.scale(factors);\",\n \"def guess_scaling(name, spectrum):\\n spectra = '%s/disp/%s.1d.fits' % (name, zerocount(spectrum))\\n skyname = '%s/sky.1d.fits' % name\\n spectrafits = pyfits.open(spectra)\\n skyfits = pyfits.open(skyname)\\n scalings = []\\n for line in LINES:\\n spec_peak, spec_cont = get_peak_cont(spectrafits, line, 5)\\n sky_peak, sky_cont = get_peak_cont(skyfits, line, 5)\\n scale = ((spec_peak - spec_cont) / (sky_peak - sky_cont))\\n scalings.append(scale)\\n return avg(*scalings)\",\n \"def process(self, resolutions):\\r\\n # This is optional behavior\\r\\n return resolutions\",\n \"def round_filters(filters, divisor=depth_divisor):\\n filters *= width_coefficient\\n new_filters = max(divisor, int(filters + divisor / 2) // divisor * divisor)\\n # Make sure that round down does not go down by more than 10%.\\n if new_filters < 0.9 * filters:\\n new_filters += divisor\\n return int(new_filters)\",\n \"def __call__(self, results):\\n if 'scale' not in results:\\n if 'scale_factor' in results:\\n img_shape = results['img'].shape[:2]\\n scale_factor = results['scale_factor']\\n assert isinstance(scale_factor, float)\\n results['scale'] = tuple([int(x * scale_factor) for x in img_shape][::-1])\\n else:\\n self._random_scale(results)\\n else:\\n if not self.override:\\n assert 'scale_factor' not in results, 'scale and scale_factor cannot be both set.'\\n else:\\n results.pop('scale')\\n if 'scale_factor' in results:\\n results.pop('scale_factor')\\n self._random_scale(results)\\n\\n self._resize_img(results)\\n self._resize_bboxes(results)\\n self._resize_cbboxes(results)\\n self._resize_masks(results)\\n self._resize_seg(results)\\n\\n return results\",\n \"def scanResolution(self):\\n return self._getAttribute(Attribute.scanResolution)\",\n \"def change_resolution(self):\",\n \"def castSize(self, scale):\\n return self.camera.sensorSize * scale\",\n \"def getResolution(s) -> int:\\n unit = getDurationUnit(s)\\n #number of ticks is 1 / unit (if that is an integer)\\n ticksPerQuarter = unit.denominator / unit.numerator\\n if ticksPerQuarter.is_integer():\\n return int(unit.denominator / unit.numerator)\\n else:\\n print(s.filePath, ' non integer number of ticks per Quarter')\\n return 0\",\n \"def __call__(self, results):\\n\\n if 'scale' not in results:\\n if 'scale_factor' in results:\\n img_shape = results['img'].shape[:2]\\n scale_factor = results['scale_factor']\\n assert isinstance(scale_factor, float)\\n results['scale'] = tuple(\\n [int(x * scale_factor) for x in img_shape][::-1])\\n else:\\n self._random_scale(results)\\n else:\\n if not self.override:\\n assert 'scale_factor' not in results, (\\n 'scale and scale_factor cannot be both set.')\\n else:\\n results.pop('scale')\\n if 'scale_factor' in results:\\n results.pop('scale_factor')\\n self._random_scale(results)\\n\\n self._resize_img(results)\\n self._resize_bboxes(results)\\n self._resize_masks(results)\\n self._resize_seg(results)\\n return results\",\n \"def __call__(self, results):\\n\\n if 'scale' not in results:\\n if 'scale_factor' in results:\\n img_shape = results['img'].shape[:2]\\n scale_factor = results['scale_factor']\\n assert isinstance(scale_factor, float)\\n results['scale'] = tuple(\\n [int(x * scale_factor) for x in img_shape][::-1])\\n else:\\n self._random_scale(results)\\n else:\\n if not self.override:\\n assert 'scale_factor' not in results, (\\n 'scale and scale_factor cannot be both set.')\\n else:\\n results.pop('scale')\\n if 'scale_factor' in results:\\n results.pop('scale_factor')\\n self._random_scale(results)\\n\\n self._resize_img(results)\\n self._resize_bboxes(results)\\n self._resize_masks(results)\\n self._resize_seg(results)\\n return results\",\n \"def Resolution(self):\\n\\t\\treturn self._get_attribute('resolution')\",\n \"def upsampling(\\n data,\\n scale_h,\\n scale_w,\\n layout=\\\"NCHW\\\",\\n method=\\\"nearest_neighbor\\\",\\n align_corners=False,\\n output_shape=None,\\n):\\n base_layout = layout[0:4]\\n if base_layout == \\\"NCHW\\\":\\n if not output_shape: # static case\\n scaled_h = data.shape[2] * scale_h\\n scaled_w = data.shape[3] * scale_w\\n reshape_size = (\\n simplify(topi.cast(te.round(scaled_h), data.shape[2].dtype)),\\n simplify(topi.cast(te.round(scaled_w), data.shape[3].dtype)),\\n )\\n else: # dynamic case -- we don't need to scale; already done in shape func\\n reshape_size = (\\n simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),\\n simplify(topi.cast(te.round(output_shape[3]), output_shape[3].dtype)),\\n )\\n elif layout == \\\"NHWC\\\":\\n if not output_shape: # static case\\n scaled_h = data.shape[1] * scale_h\\n scaled_w = data.shape[2] * scale_w\\n reshape_size = (\\n simplify(topi.cast(te.round(scaled_h), data.shape[1].dtype)),\\n simplify(topi.cast(te.round(scaled_w), data.shape[2].dtype)),\\n )\\n else: # dynamic case\\n reshape_size = (\\n simplify(topi.cast(te.round(output_shape[1]), output_shape[1].dtype)),\\n simplify(topi.cast(te.round(output_shape[2]), output_shape[2].dtype)),\\n )\\n\\n else:\\n raise ValueError(f\\\"not support this layout {layout} yet\\\")\\n coord_trans = \\\"align_corners\\\" if align_corners else \\\"asymmetric\\\"\\n if method[0:2] == \\\"bi\\\":\\n method = method[2:]\\n return topi.image.resize2d(\\n data,\\n [0.0] * 4,\\n reshape_size,\\n layout=layout,\\n method=method,\\n coordinate_transformation_mode=coord_trans,\\n output_shape=output_shape,\\n )\",\n \"def get_scale_factor(rec, stack):\\n \\n rec_pixel_size = get_pixel_size_rec(rec)\\n stack_pixel_size = get_pixel_size_stack(stack)\\n \\n return rec_pixel_size / stack_pixel_size\",\n \"def determine_thresholds(confidence, resolution=100):\\n if isinstance(confidence, list):\\n confidence = np.array(confidence)\\n confidence = confidence.flatten()\\n confidence = confidence[~np.isnan(confidence)]\\n confidence.sort()\\n\\n assert len(confidence) > resolution and resolution > 2\\n\\n thresholds = np.ones((resolution))\\n thresholds[0] = - np.inf\\n thresholds[-1] = np.inf\\n delta = np.floor(len(confidence) / (resolution - 2))\\n idxs = np.linspace(delta, len(confidence)-delta, resolution-2, dtype=np.int32)\\n thresholds[1:-1] = confidence[idxs]\\n return thresholds\",\n \"def rscale(mag=10.0):\\n if mag > 11.5:\\n return 0.5\\n elif mag > 11.0:\\n return 1.0\\n elif mag > 10.5:\\n return 1.5\\n elif mag > 10.0:\\n return 1.5\\n elif mag > 9.5:\\n return 2.0\\n elif mag > 9.0:\\n return 2.5\\n elif mag > 8.5:\\n return 3.0\\n else:\\n return 3.5\",\n \"def _round_sampling_rate(sampling_rate):\\n # check if sampling rate is below 5 Hz in that case always round to one\\n if sampling_rate < 5:\\n\\n # set sampling rate to 1\\n rounded_sampling_rate = 1\\n\\n else:\\n\\n # round to the nearest 10 digit\\n rounded_sampling_rate = round(sampling_rate/10) * 10\\n\\n return rounded_sampling_rate\",\n \"def calculate_rf_size(rf_size, downsample):\\n h = 61 # 24\\\" monitor\\n d = 10 # 10cm from the right eye\\n r = 1080 / downsample # Vertical resolution\\n d_px = np.degrees(math.atan2(h / 2, d)) / (r / 2)\\n return rf_size * d_px\",\n \"def scaling_factor(self):\\n bin_scale = self.spabins * self.spebins\\n return bin_scale * self.int_time\",\n \"def zoom_to_size(self, *p):\\n\\t\\tif self.image is None or self.allocation is None:\\n\\t\\t\\treturn\\n\\t\\tif __debug__: print self.allocation.width, self.image.get_width()\\n\\t\\tif __debug__: print self.allocation.width, self.image.get_width(), self.allocation.width/self.image.get_width()\\n\\t\\tz = min(\\n\\t\\t\\tself.allocation.width/self.image.get_width(),\\n\\t\\t\\tself.allocation.height/self.image.get_height()\\n\\t\\t\\t)\\n\\t\\tif __debug__: print \\\"zoom_to_size\\\", \\\"z=\\\", z\\n\\t\\tself.zoom = z\",\n \"def upround(x, base):\\n return base * math.ceil(float(x) / base)\",\n \"def get_resolution(self):\\n return self.__resolution\",\n \"def _round_filters(self, filters, width_coefficient, depth_divisor, min_depth):\\n\\n if not width_coefficient:\\n return filters\\n\\n filters *= width_coefficient\\n min_depth = min_depth or depth_divisor\\n new_filters = max(\\n min_depth,\\n int(filters + depth_divisor / 2) // depth_divisor*depth_divisor\\n )\\n # Make sure that round down does not go down by more than 10%.\\n if new_filters < 0.9 * filters:\\n new_filters += depth_divisor\\n\\n return int(new_filters)\",\n \"def get_apply_scale(self, applyScaleFactor, scale_quality = 1.0):\\n v = self.scale * self.scale_quality * scale_quality\\n if applyScaleFactor:\\n v *= self.scale_factor\\n return v\",\n \"def resize(orig, factor, method=\\\"nearest\\\"):\\r\\n method_dict = {'nearest': 0, 'bilinear': 1, 'cubic': 2}\\r\\n if method.lower() not in method_dict:\\r\\n raise ValueError(\\\"Invalid interpolation method. Options are: \\\" + \\\", \\\".join(method_dict.keys()))\\r\\n try:\\r\\n return zoom(orig, factor, order=method_dict[method.lower()])\\r\\n except RuntimeError:\\r\\n # raised by zoom when factor length does not match orig.shape length\\r\\n raise ValueError(\\\"Factor sequence length does not match input length\\\")\",\n \"def Rescale(self):\\r\\n picWidth,picHeight = self.oldSize = self.GetSizeTuple()\\r\\n bitmap = self.scaled = self.bitmap\\r\\n if not bitmap: return\\r\\n imgWidth,imgHeight = bitmap.GetWidth(),bitmap.GetHeight()\\r\\n if self.scaling == 2 or (self.scaling == 1 and (imgWidth > picWidth or imgHeight > picHeight)):\\r\\n image = bitmap.ConvertToImage()\\r\\n factor = min(1.0*picWidth/imgWidth,1.0*picHeight/imgHeight)\\r\\n newWidth,newHeight = int(factor*imgWidth),int(factor*imgHeight)\\r\\n self.scaled = image.Scale(newWidth,newHeight).ConvertToBitmap()\\r\\n #self.scaled = image.Scale(newWidth,newHeight,wx.IMAGE_QUALITY_HIGH ).ConvertToBitmap()\\r\",\n \"def resolution_init():\\n\\n defaultResolution = pm.PyNode(\\\"defaultResolution\\\")\\n task = ftrack.Task(os.environ[\\\"FTRACK_TASKID\\\"])\\n\\n # Adding/Checking ftrack resolution attribute\\n resolution_set = False\\n if hasattr(defaultResolution, \\\"ftrackResolutionSet\\\"):\\n attr = pm.Attribute(\\\"defaultResolution.ftrackResolutionSet\\\")\\n resolution_set = attr.get()\\n else:\\n pm.addAttr(\\n defaultResolution,\\n longName=\\\"ftrackResolutionSet\\\",\\n defaultValue=True,\\n attributeType=\\\"bool\\\"\\n )\\n\\n if not resolution_set:\\n width = task.getParent().get(\\\"width\\\")\\n defaultResolution.width.set(width)\\n pm.warning(\\\"Changed resolution width to: {0}\\\".format(width))\\n height = task.getParent().get(\\\"height\\\")\\n defaultResolution.height.set(height)\\n pm.warning(\\\"Changed resolution height to: {0}\\\".format(height))\\n\\n # Vray specific resolution\\n if pm.objExists(\\\"vraySettings\\\"):\\n vray_settings = pm.PyNode(\\\"vraySettings\\\")\\n vray_settings.width.set(width)\\n pm.warning(\\\"Changed vray resolution width to: {0}\\\".format(width))\\n vray_settings.height.set(height)\\n pm.warning(\\\"Changed vray resolution height to: {0}\\\".format(height))\",\n \"def resolution(self):\\n return {'x': self.width, 'y': self.height}\",\n \"def largestResolution(resolutions):\\n return resolutions[0]\",\n \"def _upsample_conv(self, x, conv):\\n return conv(\\n F.interpolate(x,\\n scale_factor=2,\\n mode='bilinear',\\n align_corners=False))\",\n \"def _apply_resolution(self, Qin, Rin, interpolation):\\n Q, dQ = _interpolate_Q(self.Q, self.dQ, interpolation)\\n if np.iscomplex(Rin).any():\\n R_real = convolve(Qin, Rin.real, Q, dQ, resolution=self.resolution)\\n R_imag = convolve(Qin, Rin.imag, Q, dQ, resolution=self.resolution)\\n R = R_real + 1j*R_imag\\n else:\\n R = convolve(Qin, Rin, Q, dQ, resolution=self.resolution)\\n return Q, R\",\n \"def get_scaling_ratio(img):\\n\\n healthy_img_area = 4872 * 6496\\n input_img_area = img.shape[0] * img.shape[1]\\n ratio = input_img_area / healthy_img_area\\n return ratio\",\n \"def _set_pixel_size(self) -> None:\\n # Not Pansharpened images\\n if self.band_combi == Sv1BandCombination.PMS:\\n # TODO: manage default resolution for PAN band ?\\n self.pixel_size = self._ms_res\\n # Pansharpened images\\n else:\\n self.pixel_size = self._pan_res\",\n \"def find_suggested_tonemap_scale(session):\\n avg_film_luminance = session.GetFilm().GetFilmY()\\n return (1.25 / avg_film_luminance * (118 / 255))\\n\\n # TODO\\n # measure this all the time, show a message to the user if\\n # abs(old - new) > threshold\\n # so the user can set the new value with one click\\n\\n # imagepipeline = scene.camera.data.luxcore.imagepipeline\\n # imagepipeline.tonemapper.linear_scale = suggested_linear_scale\\n # imagepipeline.tonemapper.use_autolinear = False\",\n \"def __init__(self, resolution, normalize=True, eps=1e-6):\\n super().__init__()\\n self.r = int(resolution)\\n self.normalize = normalize\\n self.eps = eps\",\n \"def Resolution(self, *args):\\n return _Adaptor3d.Adaptor3d_Curve_Resolution(self, *args)\",\n \"def _get_upsample_layer(self, in_channels=None, out_channels=None):\\n if self.expand_strategy == \\\"upsample\\\":\\n return nn.Upsample(scale_factor=2, mode=\\\"nearest\\\")\\n elif self.expand_strategy == \\\"transpose_convolution\\\":\\n return nn.ConvTranspose2d(\\n in_channels, out_channels, 3, stride=2, padding=1, output_padding=1\\n )\\n else:\\n raise ValueError(\\\"Unkown expand strategy\\\")\",\n \"def _calc_fig_size(self, image_height, dpi, y_range, slice_window, fs):\\n duration = slice_window / fs\\n width_mm = duration * self.MM_IN_SEC\\n height_mm = abs(y_range.max - y_range.min) * self.MM_IN_MV\\n\\n height_inch = image_height / dpi\\n width_inch = height_inch * width_mm / height_mm\\n\\n return (width_inch, height_inch)\",\n \"def standardizeRatios( self, ratios ):\\n\\t\\tratios_standardized = ratios.copy()\\n\\t\\tzscore = lambda x: ( x - x.mean() ) / x.std()\\n\\t\\tfor row in ratios.iterrows():\\n\\t\\t\\tratios_standardized.loc[ row[0] ] = zscore( row[1] )\\n\\t\\treturn ratios_standardized\",\n \"def _scale_param(self, resid_us):\\n return((resid_us**2).sum().sum() / self.dof)\",\n \"def resolution(self):\\n return self._resolution\",\n \"def resolution(self, resolution):\\n\\n self._resolution = resolution\",\n \"def get_scaling(self):\\n if self.constrain_navigation:\\n self.activate_navigation_constrain()\\n return self.sx, self.sy\",\n \"def resolution(self):\\n return next(iter(self.resolutions()), None)\",\n \"def testCalculateVNCScreenRatio(self, mock_tk):\\n # Get scale-down ratio if screen height is smaller than AVD height.\\n mock_tk.return_value = FakeTkinter(height=800, width=1200)\\n avd_h = 1920\\n avd_w = 1080\\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.4)\\n\\n # Get scale-down ratio if screen width is smaller than AVD width.\\n mock_tk.return_value = FakeTkinter(height=800, width=1200)\\n avd_h = 900\\n avd_w = 1920\\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.6)\\n\\n # Scale ratio = 1 if screen is larger than AVD.\\n mock_tk.return_value = FakeTkinter(height=1080, width=1920)\\n avd_h = 800\\n avd_w = 1280\\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 1)\\n\\n # Get the scale if ratio of width is smaller than the\\n # ratio of height.\\n mock_tk.return_value = FakeTkinter(height=1200, width=800)\\n avd_h = 1920\\n avd_w = 1080\\n self.assertEqual(utils.CalculateVNCScreenRatio(avd_w, avd_h), 0.6)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66777146","0.5513569","0.5297719","0.5258411","0.51001024","0.50386363","0.50341105","0.50095075","0.49838406","0.49593174","0.48953298","0.48382708","0.47824258","0.4761503","0.47418657","0.47418657","0.47418657","0.47353554","0.46934766","0.46716377","0.46686354","0.46632555","0.464116","0.46300888","0.46214852","0.4617522","0.46121708","0.46090895","0.46079633","0.459634","0.4591744","0.4590737","0.4590737","0.4582168","0.45549777","0.45530677","0.45529407","0.45406085","0.45286712","0.45277342","0.45252836","0.45016167","0.45015433","0.4496983","0.44873065","0.4483391","0.44797924","0.44791883","0.4477756","0.4472525","0.44724697","0.44563133","0.4442905","0.44342223","0.44298378","0.4428681","0.44277474","0.44252795","0.4424534","0.4420016","0.44192308","0.44094476","0.44036007","0.44033262","0.44022742","0.44022742","0.43882185","0.43877232","0.43824604","0.4374804","0.43692845","0.4360759","0.43569183","0.43535465","0.43473327","0.43412396","0.43398592","0.43371242","0.43241242","0.43237358","0.4322482","0.43220592","0.4320956","0.43131113","0.43064624","0.43046552","0.43039057","0.4301367","0.42983094","0.42953598","0.42902893","0.42818382","0.4281588","0.42791483","0.42697376","0.42689234","0.4251579","0.4250367","0.42499098","0.42486098"],"string":"[\n \"0.66777146\",\n \"0.5513569\",\n \"0.5297719\",\n \"0.5258411\",\n \"0.51001024\",\n \"0.50386363\",\n \"0.50341105\",\n \"0.50095075\",\n \"0.49838406\",\n \"0.49593174\",\n \"0.48953298\",\n \"0.48382708\",\n \"0.47824258\",\n \"0.4761503\",\n \"0.47418657\",\n \"0.47418657\",\n \"0.47418657\",\n \"0.47353554\",\n \"0.46934766\",\n \"0.46716377\",\n \"0.46686354\",\n \"0.46632555\",\n \"0.464116\",\n \"0.46300888\",\n \"0.46214852\",\n \"0.4617522\",\n \"0.46121708\",\n \"0.46090895\",\n \"0.46079633\",\n \"0.459634\",\n \"0.4591744\",\n \"0.4590737\",\n \"0.4590737\",\n \"0.4582168\",\n \"0.45549777\",\n \"0.45530677\",\n \"0.45529407\",\n \"0.45406085\",\n \"0.45286712\",\n \"0.45277342\",\n \"0.45252836\",\n \"0.45016167\",\n \"0.45015433\",\n \"0.4496983\",\n \"0.44873065\",\n \"0.4483391\",\n \"0.44797924\",\n \"0.44791883\",\n \"0.4477756\",\n \"0.4472525\",\n \"0.44724697\",\n \"0.44563133\",\n \"0.4442905\",\n \"0.44342223\",\n \"0.44298378\",\n \"0.4428681\",\n \"0.44277474\",\n \"0.44252795\",\n \"0.4424534\",\n \"0.4420016\",\n \"0.44192308\",\n \"0.44094476\",\n \"0.44036007\",\n \"0.44033262\",\n \"0.44022742\",\n \"0.44022742\",\n \"0.43882185\",\n \"0.43877232\",\n \"0.43824604\",\n \"0.4374804\",\n \"0.43692845\",\n \"0.4360759\",\n \"0.43569183\",\n \"0.43535465\",\n \"0.43473327\",\n \"0.43412396\",\n \"0.43398592\",\n \"0.43371242\",\n \"0.43241242\",\n \"0.43237358\",\n \"0.4322482\",\n \"0.43220592\",\n \"0.4320956\",\n \"0.43131113\",\n \"0.43064624\",\n \"0.43046552\",\n \"0.43039057\",\n \"0.4301367\",\n \"0.42983094\",\n \"0.42953598\",\n \"0.42902893\",\n \"0.42818382\",\n \"0.4281588\",\n \"0.42791483\",\n \"0.42697376\",\n \"0.42689234\",\n \"0.4251579\",\n \"0.4250367\",\n \"0.42499098\",\n \"0.42486098\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7302088"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":95699,"cells":{"query":{"kind":"string","value":"Returns (lowerleft, upperright) TMS tile coordinates. The upperright coordinates are excluded from the range, while the lowerleft are included."},"document":{"kind":"string","value":"def GetTmsExtents(self, resolution=None, transform=None):\n if resolution is None:\n resolution = self.GetNativeResolution(transform=transform)\n\n # Get the tile dimensions in map units\n if transform is None:\n spatial_ref = self.GetSpatialReference()\n else:\n spatial_ref = transform.dst_ref\n\n tile_width, tile_height = spatial_ref.GetTileDimensions(\n resolution=resolution\n )\n\n # Validate that the native resolution extents are tile-aligned.\n extents = self.GetTiledExtents(transform=transform)\n pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)\n if not extents.almost_equal(self.GetExtents(transform=transform),\n delta=min(*pixel_sizes)):\n raise UnalignedInputError('Dataset is not aligned to TMS grid')\n\n # Correct for origin, because you can't do modular arithmetic on\n # half-tiles.\n left, bottom = spatial_ref.OffsetPoint(*extents.lower_left)\n right, top = spatial_ref.OffsetPoint(*extents.upper_right)\n\n # Divide by number of tiles\n return Extents(lower_left=XY(int(round(left / tile_width)),\n int(round(bottom / tile_height))),\n upper_right=XY(int(round(right / tile_width)),\n int(round(top / tile_height))))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getupperleft(self):\n return (self.rect.x, self.rect.y)","def topLeftCorner(self):\n self._updateExtents()\n return (self._mMinX,self._mMinY)","def topRightCorner(self):\n self._updateExtents()\n return (self._mMaxX,self._mMinY)","def get_tile_location(self):\n if self.rect.x == 0:\n tile_x = 0\n elif self.rect.x % 32 == 0:\n tile_x = (self.rect.x / 32)\n else:\n tile_x = 0\n\n if self.rect.y == 0:\n tile_y = 0\n elif self.rect.y % 32 == 0:\n tile_y = (self.rect.y / 32)\n else:\n tile_y = 0\n\n return [tile_x, tile_y]","def tile_coordinates(text):\n UL = (text[1]), (text[2]) # Upper Left\n UR = (text[3]), (text[2]) # Upper Right\n LR = (text[3]), (text[4]) # Lower Right\n LL = (text[1]), (text[4]) # Lower Left\n coordinates = (UL, UR, LR, LL)\n return text[0], [tuple(float(x) for x in xs) for xs in coordinates]","def topleft(self):\n return (self.left, self.top)","def upper_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\n return self.left, self.top","def __getMaxUpperLeftCoordinate(self, entityNodeList):\r\n minX = sys.maxint\r\n minY = sys.maxint\r\n for node in entityNodeList:\r\n if(node.graphObject_.y < minY):\r\n minY = node.graphObject_.y\r\n if(node.graphObject_.x < minX):\r\n minX = node.graphObject_.x \r\n return (minX, minY)","def get_current_edges(self) -> Tuple[int, int, int, int]:\n top = int(self.tile_rows[0], 2)\n bottom = int(self.tile_rows[-1], 2)\n left = int(''.join([r[0] for r in self.tile_rows]), 2)\n right = int(''.join([r[-1] for r in self.tile_rows]), 2)\n\n return (top, bottom, left, right)","def get_bounds(self):\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","def lower_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\n return self.right, self.bottom","def internal_bounds(self) -> tuple[float, float, float, float]:\n xres, yres = self.res\n w, s, e, n = self.bounds\n y0, y1 = (n, s) if yres < 0 else (s, n)\n x0, x1 = (e, w) if xres < 0 else (w, e)\n return x0, y0, x1, y1","def sw_corner(self):\n return (self.min_lat, self.min_lon)","def lower_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\n return self.left, self.bottom","def get_bounds(self):\n occupied_locations = self.board.keys()\n min_x = min(p[0] for p in occupied_locations)\n max_x = max(p[0] for p in occupied_locations)\n min_y = min(p[1] for p in occupied_locations)\n max_y = max(p[1] for p in occupied_locations)\n return ((min_x, max_x), (min_y, max_y))","def upleft(self):\n return Coord([self.x - 1, self.y - 1])","def get_tile(left, up, right, down):\n tile = 0\n if left:\n tile += 1\n if up:\n tile += 2\n if right:\n tile += 4\n if down:\n tile += 8\n return tile","def extent(self):\n if self.x is not None:\n if self.y is not None:\n if self.z is not None:\n return (self.x.min(), self.x.max(),\n self.y.min(), self.y.max(),\n self.z.min(), self.z.max())\n return (self.x.min(), self.x.max(),\n self.y.min(), self.y.max())\n return (self.x.min(), self.x.max())\n\n elif self.r is not None and self.t is not None:\n if self.z is not None:\n return (self.z.min(), self.z.max(),\n self.r.min(), self.r.max(),\n self.t.min(), self.t.max())\n return (self.r.min(), self.r.max(),\n self.t.min(), self.t.max())\n\n return ()","def calculate_min_max_tiles(self):","def getlefttop(self,xnum,ynum):\n left = self.xmargin + xnum*CELLSIZE\n top = self.ymargin + ynum*CELLSIZE\n return (left,top)","def convertLatLongToPixels(mapImg, leftLongitude, rightLongitude, topLatitude, bottomLatitude, latLong):\n latitude = min(max(latLong[0], bottomLatitude), topLatitude)\n longitude = min(max(latLong[1], leftLongitude), rightLongitude)\n\n diffLat = topLatitude - bottomLatitude\n diffLong = rightLongitude - leftLongitude\n\n pixelX = (longitude - leftLongitude)/diffLong*mapImg.size[0]\n pixelX = max(min(pixelX, mapImg.size[0] - 1), 0)\n pixelY = mapImg.size[1] - (latitude - bottomLatitude)/diffLat*mapImg.size[1]\n pixelY = max(min(pixelY, mapImg.size[1] - 1), 0)\n return (pixelX, pixelY)","def get_bounds(self):\r\n left, bottom, front = 10000, 10000, 10000\r\n right, top, back = -10000, -10000, -10000\r\n for b in self.buf:\r\n for v in b.vertices:\r\n if v[0] < left:\r\n left = v[0]\r\n if v[0] > right:\r\n right = v[0]\r\n if v[1] < bottom:\r\n bottom = v[1]\r\n if v[1] > top:\r\n top = v[1]\r\n if v[2] < front:\r\n front = v[2]\r\n if v[2] > back:\r\n back = v[2]\r\n\r\n return (left, bottom, front, right, top, back)","def bottomLeftCorner(self):\n self._updateExtents()\n return (self._mMinX,self._mMaxY)","def get_bounds(self):\n\n northing=self.f.variables['y']\n easting=self.f.variables['x']\n\n lat1,lon1 = utm.to_latlon(np.min(easting),np.min(northing),11,northern=True)\n lat2,lon2 = utm.to_latlon(np.max(easting),np.max(northing),11,northern=True)\n\n return (lon1,lon2,lat1,lat2)","def create_coords(self, xleft, ytop):\n xbox_left = np.int(xleft*self.scale)\n ytop_draw = np.int(ytop*self.scale)\n win_draw = np.int(self.window*self.scale)\n ystart = self.ys[0]\n\n startx, starty = xbox_left, np.int(ytop_draw+ystart)\n endx, endy = np.int(xbox_left+win_draw), np.int(ytop_draw+win_draw+ystart)\n bbox_coord = ((startx, starty), (endx, endy))\n #bbox_coords.append(((startx, starty), (endx, endy)))\n\n startx, starty = ytop_draw+ystart, ytop_draw+win_draw+ystart\n endx, endy = xbox_left, xbox_left+win_draw\n heatmap_coord = ((startx, starty), (endx, endy))\n #heatmap_coords.append(((startx, starty), (endx, endy)))\n return bbox_coord, heatmap_coord","def _get_tiles_and_coords(\n self, tensor: torch.Tensor\n ) -> Tuple[torch.Tensor, List[List[int]], List[List[List[int]]]]:\n assert tensor.dim() == 4 and tensor.shape[0] == 1\n\n y_coords, y_overlaps = self._calc_tile_coords(\n tensor.shape[2], self._tile_size[0], self._tile_overlap[0]\n )\n x_coords, x_overlaps = self._calc_tile_coords(\n tensor.shape[3], self._tile_size[1], self._tile_overlap[1]\n )\n tile_coords = torch.jit.annotate(List[Tuple[int, int, int, int]], [])\n [\n [\n tile_coords.append(\n (y, y + self._tile_size[0], x, x + self._tile_size[1])\n )\n for x in x_coords\n ]\n for y in y_coords\n ]\n tiles = torch.cat([tensor[..., c[0] : c[1], c[2] : c[3]] for c in tile_coords])\n return tiles, [y_coords, x_coords], [y_overlaps, x_overlaps]","def midtop(self):\n return (self.centerx, self.top)","def world_to_grid(mapdata, wp):\n WX = wp.x\n WY = wp.y\n resol = mapdata.info.resolution\n # -0.5 but coordinates to center\n gx = math.floor((WX - mapdata.info.origin.position.x) / resol - 0.5)\n gy = math.floor((WY - mapdata.info.origin.position.y) / resol - 0.5)\n return gx, gy","def get_grid_position(self, as_int=True):\n if as_int:\n return (\n int(self.x // Constant.TILE_SIZE),\n int(self.y // Constant.TILE_SIZE),\n )\n else:\n return (\n self.x / Constant.TILE_SIZE,\n self.y / Constant.TILE_SIZE,\n )","def get_grid_position(self):\n tile_size_x = constants.WINDOW_WIDTH / constants.GRID_TILE_LENGTH\n tile_size_y = constants.WINDOW_HEIGHT / constants.GRID_TILE_LENGTH\n grid_x = tile_size_x / self.host.x\n grid_y = tile_size_y / self.host.y\n return grid_x, grid_y","def get_coords(self):\n xTK = int(jeu.coords(self.rectangle)[0]) # Coordonnées TKinter x1 et y1 du rectangle correspondant à la voiture\n yTK = int(jeu.coords(self.rectangle)[1])\n # On divise par la largeur d'une case et on renvoie les valeurs obtenues sous la forme d'un tuple\n X = xTK//100\n Y = yTK//100\n resultat = [X, Y]\n return resultat","def get_range(self) -> tuple[int, int]:\n return self.range_from, self.range_to","def get_soft_bounds(self):\n if self.bounds is None:\n hl,hu=(None,None)\n else:\n hl,hu=self.bounds\n\n if self._softbounds is None:\n sl,su=(None,None)\n else:\n sl,su=self._softbounds\n\n \n if sl is None: l = hl\n else: l = sl\n\n if su is None: u = hu\n else: u = su\n\n return (l,u)","def get_bounds(self):\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)","def relative_position(self) -> Tuple[int, int]:\n return self.position[0] - self.region.rect.x, self.position[1] - self.region.rect.y","def relative_position(self) -> Tuple[int, int]:\n return self.position[0] - self.region.rect.x, self.position[1] - self.region.rect.y","def top_left_tile_value(self):\n\t\treturn 1","def bottom_left_tile_value(self):\n\t\treturn self.expected_cols * (self.expected_rows - 1) + 1","def getRange(self) -> Tuple[int, int]:\n return self.validator().bottom(), self.validator().top()","def get_position_coords(cls):\n row = math.floor(cls.position / cls.size)\n col = cls.position - row * cls.size\n return row, col","def getPosition(self):\n\tleft = self.getLeft()\n\ttop = self.getTop()\n\treturn (left,top)","def pupil_left_coords(self):\n if self.pupils_located:\n x = self.eye_left.origin[0] + self.eye_left.pupil.x + self.x_add\n y = self.eye_left.origin[1] + self.eye_left.pupil.y + self.y_add\n return (x, y)","def tile_bbox(self, (z, x, y_tms)):\n topleft = (x * self.tileSize, (y_tms + 1) * self.tileSize)\n bottomright = ((x + 1) * self.tileSize, y_tms * self.tileSize)\n wn = self.unproject_pixels(topleft, z)\n es = self.unproject_pixels(bottomright, z)\n return wn + es","def extent(self):\n left = self.transform[0]\n right = left + self.transform[1] * self.shape[1]\n top = self.transform[3]\n bottom = top + self.transform[5] * self.shape[0]\n return left, right, bottom, top","def positive_left_right_requirements(\n self,\n ) -> Tuple[Tuple[GriddedPerm, ...], Tuple[GriddedPerm, ...]]:\n left, right = [], []\n for (x, y) in self._tiling.active_cells:\n if self._fuse_row and y == self._row_idx:\n left.append(GriddedPerm.single_cell((0,), (x, y)))\n right.append(GriddedPerm.single_cell((0,), (x, y + 1)))\n if not self._fuse_row and x == self._col_idx:\n left.append(GriddedPerm.single_cell((0,), (x, y)))\n right.append(GriddedPerm.single_cell((0,), (x + 1, y)))\n return tuple(sorted(left)), tuple(sorted(right))","def get_tile_index_range(dataset_filename):\n dataset = gdal.Open(dataset_filename)\n assert dataset, 'Unable to open dataset %s' % dataset_filename\n spatial_reference = osr.SpatialReference()\n spatial_reference.ImportFromWkt(dataset.GetProjection())\n geotransform = dataset.GetGeoTransform()\n logger.debug('geotransform = %s', geotransform)\n# latlong_spatial_reference = spatial_reference.CloneGeogCS()\n tile_spatial_reference = osr.SpatialReference()\n s = re.match('EPSG:(\\d+)', tile_type_info['crs'])\n if s:\n epsg_code = int(s.group(1))\n logger.debug('epsg_code = %d', epsg_code)\n assert tile_spatial_reference.ImportFromEPSG(epsg_code) == 0, 'Invalid EPSG code for tile projection'\n else:\n assert tile_spatial_reference.ImportFromWkt(tile_type_info['crs']), 'Invalid WKT for tile projection'\n \n logger.debug('Tile WKT = %s', tile_spatial_reference.ExportToWkt())\n \n coord_transform_to_tile = osr.CoordinateTransformation(spatial_reference, tile_spatial_reference)\n # Upper Left\n xmin, ymax, _z = coord_transform_to_tile.TransformPoint(geotransform[0], geotransform[3], 0)\n # Lower Right\n xmax, ymin, _z = coord_transform_to_tile.TransformPoint(geotransform[0] + geotransform[1] * dataset.RasterXSize, \n geotransform[3] + geotransform[5] * dataset.RasterYSize, \n 0)\n \n logger.debug('Coordinates: xmin = %f, ymin = %f, xmax = %f, ymax = %f', xmin, ymin, xmax, ymax)\n\n return (int(floor((xmin - tile_type_info['x_origin']) / tile_type_info['x_size'])), \n int(floor((ymin - tile_type_info['y_origin']) / tile_type_info['y_size'])), \n int(ceil((xmax - tile_type_info['x_origin']) / tile_type_info['x_size'])), \n int(ceil((ymax - tile_type_info['y_origin']) / tile_type_info['y_size'])))","def get_tile_coordinates_for_registration(self, grid_number, tile_number):\n dx, dy = self.get_tile_coordinates_d(grid_number, tile_number)\n width_d = self.get_tile_width_d(grid_number)\n height_d = self.get_tile_height_d(grid_number)\n return int((dx - width_d/2) * 1000), int((dy - height_d/2) * 1000)","def LongitudinalBounds(lng, num_tiles):\n # Normalize to between -180 and 180 degrees longitude.\n while lng < -180.0:\n lng += 360.0\n while lng >= 180.0:\n lng -= 360.0\n\n degrees_per_tile = 360.0 / num_tiles\n x = int((lng + 180.0) / degrees_per_tile)\n west = x * degrees_per_tile - 180.0\n return (west, west + degrees_per_tile)","def bbox(self):\n lower = (self.x.min(), self.y.min())\n upper = (self.x.max(), self.y.max())\n return (lower, upper)","def coordinates(self) -> Tuple[float, float, float, float, float]:\n return (self.x, self.y, self.x + self.width, self.y + self.height)","def getMachineCoordinates(self):\n return (self.x, self.y, self.z)","def ne_corner(self):\n return (self.max_lat, self.max_lon)","def image_coordinates(self, temp):\n iy = np.array((temp.y[:,None]-self.extent[2])/self.spacing[1],dtype=np.int64)\n ix = np.array((temp.x[None,:]-self.extent[0])/self.spacing[0],dtype=np.int64)\n return (iy,ix)","def bounds(self):\n return self.min_col, self.min_row, self.max_col, self.max_row","def top_left(self):\n return Point(self.left, self.top)","def getStartTlXY(self, lat, lng,zoom):\n tile_size = 256\n # Use a left shift to get the power of 2\n # i.e. a zoom level of 2 will have 2^2 = 4 tiles\n numTiles = 1 << zoom\n # Find the x_point given the longitude\n point_x = (tile_size/ 2 + lng * tile_size / 360.0) * numTiles // tile_size\n # Convert the latitude to radians and take the sine\n sin_y = math.sin(lat * (math.pi / 180.0))\n # Calulate the y coorindate\n point_y = ((tile_size / 2) + 0.5 * math.log((1+sin_y)/(1-sin_y)) * -(tile_size / (2 * math.pi))) * numTiles // tile_size\n return int(point_x), int(point_y)","def get_cell_coords(self, pt):\n\n\t return int(pt[0] // self.a), int(pt[1] // self.a)","def get_boundingbox(self):\n tile_iterator = iter(self)\n (coordinate,tile) = next(tile_iterator)\n assert(tile is not None)\n min_x = coordinate[0]\n max_x = min_x + 1\n min_y = coordinate[1]\n max_y = min_y + 1\n\n for (coordinate,tile) in tile_iterator:\n\n if coordinate[0] < min_x:\n min_x = coordinate[0]\n if coordinate[0]+1> max_x:\n max_x = coordinate[0] +1\n if coordinate[1] < min_y:\n min_y = coordinate[1]\n if coordinate[1]+1> max_y:\n max_y = coordinate[1] +1\n\n return ((min_x, min_y), (max_x, max_y))","def bounds(self) -> tuple[float, float, float, float]:\n transform = self.transform\n a, b, c, d, e, f, _, _, _ = transform\n if b == d == 0:\n xs = (c, c + a * self.width)\n ys = (f, f + e * self.height)\n else: # rotated\n c0x, c0y = c, f\n c1x, c1y = transform * (0, self.height)\n c2x, c2y = transform * (self.width, self.height)\n c3x, c3y = transform * (self.width, 0)\n xs = (c0x, c1x, c2x, c3x)\n ys = (c0y, c1y, c2y, c3y)\n return min(xs), min(ys), max(xs), max(ys)","def get_bounds_halo(self):\n bottom_right = np.asarray([self.coords_halo[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords_halo[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","def get_cell_coords(pt):\n\n return int(pt[0] // a), int(pt[1] // a)","def _get_top_left_coordinates(height, width, patch_size):\n\n n_h = math.ceil(height / patch_size)\n n_w = math.ceil(width / patch_size)\n tops = np.linspace(0, height - patch_size, n_h, dtype=int)\n lefts = np.linspace(0, width - patch_size, n_w, dtype=int)\n\n return product(tops, lefts)","def upright(self):\n return Coord([self.x + 1, self.y - 1])","def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\n\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\n return (-1,-1)\n\n lat_step = abs(max_lat-min_lat)/rows\n lon_step = abs(max_lon-min_lon)/cols\n\n lat_spot = int((max_lat-lat)/lat_step)\n lon_spot = int((lon-min_lon)/lon_step)\n #print \"lat: %f lon: %f lat_spot: %f lon_spot: %f\" % (lat,lon,lat_spot,lon_spot)\n return (lat_spot,lon_spot)","def roi_x_offset():\n def r(x):\n return x & 0xFFF\n\n def w(x):\n return min(x, 0xFFF)\n return r, w","def find_yolo_coordinates(y_top, y_bottom, x_left, x_right, width, height):\n w = (width - x_left - x_right) / width # width of bounding box\n h = (height - y_top - y_bottom) / height # height of bounding box\n x = (1 - w / 2) - x_right / width # x center of box (distance right from UL)\n y = (1 - h / 2) - y_bottom / height # y center of box (distance down from UL)\n\n return x,y,w,h","def get_grid_locations(self, top_left, other_pos):\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\n\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\n cell_x[cell_x == self.grid_size] -= 1\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\n cell_y[cell_y == self.grid_size] -= 1\n grid_pos = cell_x + cell_y * self.grid_size\n\n return grid_pos","def extend (self) :\n return (self.x_min, self.x_max, self.y_min, self.y_max)","def gettpoints(self,left,top,tnum):\n if tnum == 0:\n x1 = left + CELLSIZE/2\n y1 = top\n x2 = x1 + TSIZE\n y2 = y1 + TSIZE\n x3 = x2 - 2*TSIZE\n y3 = y2\n if tnum == 1:\n x1 = left + CELLSIZE\n y1 = top + CELLSIZE/2\n x2 = x1 - TSIZE\n y2 = y1 + TSIZE\n x3 = x2\n y3 = y2 - 2*TSIZE\n if tnum == 2:\n x1 = left + CELLSIZE/2\n y1 = top + CELLSIZE\n x2 = x1 - TSIZE\n y2 = y1 - TSIZE\n x3 = x2 + TSIZE*2\n y3 = y2\n if tnum == 3:\n x1 = left\n y1 = top + CELLSIZE/2\n x2 = x1 + TSIZE\n y2 = y1 + TSIZE\n x3 = x2\n y3 = y2 - TSIZE*2\n\n return ((x1,y1),(x2,y2),(x3,y3))","def bottomleft(self):\n return (self.left, self.bottom)","def pupil_right_coords(self):\n if self.pupils_located:\n x = self.eye_right.origin[0] + self.eye_right.pupil.x + self.x_add\n y = self.eye_right.origin[1] + self.eye_right.pupil.y + self.y_add\n return (x, y)","def get_img_coord_tuple(img):\n\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\n lat = -lat\n\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\n longitude = -longitude\n\n return lat, longitude","def res(self) -> tuple[float, float]:\n xs, ys = self.xcoords.data, self.ycoords.data\n dx, dy = 0, 0\n if xs.ndim == 1:\n dx = xs[1] - xs[0]\n dy = ys[1] - ys[0]\n elif xs.ndim == 2:\n ddx0 = xs[1, 0] - xs[0, 0]\n ddy0 = ys[1, 0] - ys[0, 0]\n ddx1 = xs[0, 1] - xs[0, 0]\n ddy1 = ys[0, 1] - ys[0, 0]\n dx = math.hypot(ddx1, ddy1) # always positive!\n dy = math.hypot(ddx0, ddy0)\n rot = self.rotation\n acos = math.cos(math.radians(rot))\n # find grid top-down orientation\n if (\n (acos < 0 and ddy0 > 0)\n or (acos > 0 and ddy0 < 0)\n or (\n ddy0 == 0\n and (np.isclose(rot, 270) and ddx0 < 0)\n or (np.isclose(rot, 90) and ddx0 > 0)\n )\n ):\n dy = -1 * dy\n return dx, dy","def chr_coords(s):\n return max_y - (max_y - min_y)*s","def get_tile_coordinates_d(self, grid_number, tile_number):\n origin_dx, origin_dy = self.cs.get_grid_origin_d(grid_number)\n return (origin_dx + self.grid_map_d[grid_number][tile_number][0],\n origin_dy + self.grid_map_d[grid_number][tile_number][1])","def fetchbounds(self):\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\n self.in_end, self.end, self.out_end] \\\n if x is not None]\n return min(pnts), max(pnts)","def _get_range(self):\n return tuple((0, m, 1) for m in self.level_shapes[0])","def top_right_tile_value(self):\n\t\treturn self.expected_cols","def get_bottom_right(left, right):\n x = right.x - (right.x - left.x) / 8\n y = right.y - (right.y - left.y) / 8\n return (x, y)","def getTilePos(self, pos = None):\n\n if not pos:\n pos = self.actor.getPos()\n \n for i in range(len(pos)):\n pos[i] = int(math.floor( (pos[i] + self.dimensions[i]) / 2.0))\n #pos[i] = int(math.floor( pos[i] / 2.0))\n\n return pos","def normalized_position(self) -> Tuple[float, float]:\n x, y = self.relative_position\n return x / self.region.rect.width, y / self.region.rect.height","def normalized_position(self) -> Tuple[float, float]:\n x, y = self.relative_position\n return x / self.region.rect.width, y / self.region.rect.height","def test_mines_left_coordinates(self):\n pg.font.init()\n mines_coords = utils.mines_left_coords(1)\n self.assertIsInstance(mines_coords, tuple)","def get_pos(x, y):\r\n return normalize(x) // 2, normalize(y) // 4","def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\n lower_index = np.argmax(self.x >= lower)\n upper_index = np.argmax(self.x >= upper)\n return int(lower_index), int(upper_index)","def _get_xy_lims(self):\n \n x = self.datapos[0] - 1\n y = self.datapos[1] - 1\n\n return x, y","def get_uv_positions(data, image_size, target_grid, up_vector, right_vector, tile_xy, verts, vtx_center):\n\n return get_uv_pos_size(data, image_size, target_grid, tile_xy,\n target_grid.grid[0], target_grid.grid[1],\n up_vector, right_vector,\n verts, vtx_center)","def coordinates(self) -> Tuple[int, int]:\n return self.x, self.y","def region_points(x, y, width, xmin, xmax):\n right = (x, y + width / 2)\n top = (xmax, y)\n left = (x, y - width / 2)\n bottom = (xmin, y)\n return (right, top, left, bottom)","def grid_coords(self):\n return [(x, y) for y in range(self.height) for x in range(self.width)]","def extent(self):\n\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\n\n return [x[0], x[1], y[0], y[1]]","def left(self):\n return self.points['topLeft'].x","def left(self):\n return self.points['topLeft'].x","def bounds(self):\n return (\n self.x, self.y,\n self.x, self.y\n )","def extents(self):\n x0, y0, width, height = self._rect_bbox\n xmin, xmax = sorted([x0, x0 + width])\n ymin, ymax = sorted([y0, y0 + height])\n return xmin, xmax, ymin, ymax","def get_min_max(self):\n\n mr = np.sqrt(2 * np.log(1/self.mth)) * self.ms\n mr[:] = np.max(mr)\n\n mxmin = self.mx - mr\n mxmax = self.mx + mr\n mymin = self.my - mr\n mymax = self.my + mr\n mzmin = self.mz - mr\n mzmax = self.mz + mr\n\n mb_xmin_idx = np.argmin(mxmin[self.ma > 0])\n mb_xmax_idx = np.argmax(mxmax[self.ma > 0])\n mb_ymin_idx = np.argmin(mymin[self.ma > 0])\n mb_ymax_idx = np.argmax(mymax[self.ma > 0])\n mb_zmin_idx = np.argmin(mzmin[self.ma > 0])\n mb_zmax_idx = np.argmax(mzmax[self.ma > 0])\n\n xmin0 = self.mx[mb_xmin_idx] - mr[mb_xmin_idx]\n xmax0 = self.mx[mb_xmax_idx] + mr[mb_xmax_idx]\n ymin0 = self.my[mb_ymin_idx] - mr[mb_ymin_idx]\n ymax0 = self.my[mb_ymax_idx] + mr[mb_ymax_idx]\n zmin0 = self.mz[mb_zmin_idx] - mr[mb_zmin_idx]\n zmax0 = self.mz[mb_zmax_idx] + mr[mb_zmax_idx]\n\n xmin = xmin0 - (xmax0 - xmin0) * 0.25\n xmax = xmax0 + (xmax0 - xmin0) * 0.25\n ymin = ymin0 - (ymax0 - ymin0) * 0.25\n ymax = ymax0 + (ymax0 - ymin0) * 0.25\n zmin = zmin0 - (zmax0 - zmin0) * 0.25\n zmax = zmax0 + (zmax0 - zmin0) * 0.25\n\n return xmin, xmax, ymin, ymax, zmin, zmax","def upper_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\n return self.right, self.top","def restored_position(self) -> Tuple[int, int]:\n x_n, y_n = self.normalized_position\n w, h = self.region.original_size\n return int(x_n * w), int(y_n * h)","def restored_position(self) -> Tuple[int, int]:\n x_n, y_n = self.normalized_position\n w, h = self.region.original_size\n return int(x_n * w), int(y_n * h)","def get_pos(self):\n return (self.x, self.y)","def node_to_coords(self,node_num):\n row = (node_num - 1) / self.cols\n col = (node_num - 1) % self.cols\n return (row,col)"],"string":"[\n \"def getupperleft(self):\\n return (self.rect.x, self.rect.y)\",\n \"def topLeftCorner(self):\\n self._updateExtents()\\n return (self._mMinX,self._mMinY)\",\n \"def topRightCorner(self):\\n self._updateExtents()\\n return (self._mMaxX,self._mMinY)\",\n \"def get_tile_location(self):\\n if self.rect.x == 0:\\n tile_x = 0\\n elif self.rect.x % 32 == 0:\\n tile_x = (self.rect.x / 32)\\n else:\\n tile_x = 0\\n\\n if self.rect.y == 0:\\n tile_y = 0\\n elif self.rect.y % 32 == 0:\\n tile_y = (self.rect.y / 32)\\n else:\\n tile_y = 0\\n\\n return [tile_x, tile_y]\",\n \"def tile_coordinates(text):\\n UL = (text[1]), (text[2]) # Upper Left\\n UR = (text[3]), (text[2]) # Upper Right\\n LR = (text[3]), (text[4]) # Lower Right\\n LL = (text[1]), (text[4]) # Lower Left\\n coordinates = (UL, UR, LR, LL)\\n return text[0], [tuple(float(x) for x in xs) for xs in coordinates]\",\n \"def topleft(self):\\n return (self.left, self.top)\",\n \"def upper_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\\n return self.left, self.top\",\n \"def __getMaxUpperLeftCoordinate(self, entityNodeList):\\r\\n minX = sys.maxint\\r\\n minY = sys.maxint\\r\\n for node in entityNodeList:\\r\\n if(node.graphObject_.y < minY):\\r\\n minY = node.graphObject_.y\\r\\n if(node.graphObject_.x < minX):\\r\\n minX = node.graphObject_.x \\r\\n return (minX, minY)\",\n \"def get_current_edges(self) -> Tuple[int, int, int, int]:\\n top = int(self.tile_rows[0], 2)\\n bottom = int(self.tile_rows[-1], 2)\\n left = int(''.join([r[0] for r in self.tile_rows]), 2)\\n right = int(''.join([r[-1] for r in self.tile_rows]), 2)\\n\\n return (top, bottom, left, right)\",\n \"def get_bounds(self):\\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\n \"def lower_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\\n return self.right, self.bottom\",\n \"def internal_bounds(self) -> tuple[float, float, float, float]:\\n xres, yres = self.res\\n w, s, e, n = self.bounds\\n y0, y1 = (n, s) if yres < 0 else (s, n)\\n x0, x1 = (e, w) if xres < 0 else (w, e)\\n return x0, y0, x1, y1\",\n \"def sw_corner(self):\\n return (self.min_lat, self.min_lon)\",\n \"def lower_left(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\\n return self.left, self.bottom\",\n \"def get_bounds(self):\\n occupied_locations = self.board.keys()\\n min_x = min(p[0] for p in occupied_locations)\\n max_x = max(p[0] for p in occupied_locations)\\n min_y = min(p[1] for p in occupied_locations)\\n max_y = max(p[1] for p in occupied_locations)\\n return ((min_x, max_x), (min_y, max_y))\",\n \"def upleft(self):\\n return Coord([self.x - 1, self.y - 1])\",\n \"def get_tile(left, up, right, down):\\n tile = 0\\n if left:\\n tile += 1\\n if up:\\n tile += 2\\n if right:\\n tile += 4\\n if down:\\n tile += 8\\n return tile\",\n \"def extent(self):\\n if self.x is not None:\\n if self.y is not None:\\n if self.z is not None:\\n return (self.x.min(), self.x.max(),\\n self.y.min(), self.y.max(),\\n self.z.min(), self.z.max())\\n return (self.x.min(), self.x.max(),\\n self.y.min(), self.y.max())\\n return (self.x.min(), self.x.max())\\n\\n elif self.r is not None and self.t is not None:\\n if self.z is not None:\\n return (self.z.min(), self.z.max(),\\n self.r.min(), self.r.max(),\\n self.t.min(), self.t.max())\\n return (self.r.min(), self.r.max(),\\n self.t.min(), self.t.max())\\n\\n return ()\",\n \"def calculate_min_max_tiles(self):\",\n \"def getlefttop(self,xnum,ynum):\\n left = self.xmargin + xnum*CELLSIZE\\n top = self.ymargin + ynum*CELLSIZE\\n return (left,top)\",\n \"def convertLatLongToPixels(mapImg, leftLongitude, rightLongitude, topLatitude, bottomLatitude, latLong):\\n latitude = min(max(latLong[0], bottomLatitude), topLatitude)\\n longitude = min(max(latLong[1], leftLongitude), rightLongitude)\\n\\n diffLat = topLatitude - bottomLatitude\\n diffLong = rightLongitude - leftLongitude\\n\\n pixelX = (longitude - leftLongitude)/diffLong*mapImg.size[0]\\n pixelX = max(min(pixelX, mapImg.size[0] - 1), 0)\\n pixelY = mapImg.size[1] - (latitude - bottomLatitude)/diffLat*mapImg.size[1]\\n pixelY = max(min(pixelY, mapImg.size[1] - 1), 0)\\n return (pixelX, pixelY)\",\n \"def get_bounds(self):\\r\\n left, bottom, front = 10000, 10000, 10000\\r\\n right, top, back = -10000, -10000, -10000\\r\\n for b in self.buf:\\r\\n for v in b.vertices:\\r\\n if v[0] < left:\\r\\n left = v[0]\\r\\n if v[0] > right:\\r\\n right = v[0]\\r\\n if v[1] < bottom:\\r\\n bottom = v[1]\\r\\n if v[1] > top:\\r\\n top = v[1]\\r\\n if v[2] < front:\\r\\n front = v[2]\\r\\n if v[2] > back:\\r\\n back = v[2]\\r\\n\\r\\n return (left, bottom, front, right, top, back)\",\n \"def bottomLeftCorner(self):\\n self._updateExtents()\\n return (self._mMinX,self._mMaxY)\",\n \"def get_bounds(self):\\n\\n northing=self.f.variables['y']\\n easting=self.f.variables['x']\\n\\n lat1,lon1 = utm.to_latlon(np.min(easting),np.min(northing),11,northern=True)\\n lat2,lon2 = utm.to_latlon(np.max(easting),np.max(northing),11,northern=True)\\n\\n return (lon1,lon2,lat1,lat2)\",\n \"def create_coords(self, xleft, ytop):\\n xbox_left = np.int(xleft*self.scale)\\n ytop_draw = np.int(ytop*self.scale)\\n win_draw = np.int(self.window*self.scale)\\n ystart = self.ys[0]\\n\\n startx, starty = xbox_left, np.int(ytop_draw+ystart)\\n endx, endy = np.int(xbox_left+win_draw), np.int(ytop_draw+win_draw+ystart)\\n bbox_coord = ((startx, starty), (endx, endy))\\n #bbox_coords.append(((startx, starty), (endx, endy)))\\n\\n startx, starty = ytop_draw+ystart, ytop_draw+win_draw+ystart\\n endx, endy = xbox_left, xbox_left+win_draw\\n heatmap_coord = ((startx, starty), (endx, endy))\\n #heatmap_coords.append(((startx, starty), (endx, endy)))\\n return bbox_coord, heatmap_coord\",\n \"def _get_tiles_and_coords(\\n self, tensor: torch.Tensor\\n ) -> Tuple[torch.Tensor, List[List[int]], List[List[List[int]]]]:\\n assert tensor.dim() == 4 and tensor.shape[0] == 1\\n\\n y_coords, y_overlaps = self._calc_tile_coords(\\n tensor.shape[2], self._tile_size[0], self._tile_overlap[0]\\n )\\n x_coords, x_overlaps = self._calc_tile_coords(\\n tensor.shape[3], self._tile_size[1], self._tile_overlap[1]\\n )\\n tile_coords = torch.jit.annotate(List[Tuple[int, int, int, int]], [])\\n [\\n [\\n tile_coords.append(\\n (y, y + self._tile_size[0], x, x + self._tile_size[1])\\n )\\n for x in x_coords\\n ]\\n for y in y_coords\\n ]\\n tiles = torch.cat([tensor[..., c[0] : c[1], c[2] : c[3]] for c in tile_coords])\\n return tiles, [y_coords, x_coords], [y_overlaps, x_overlaps]\",\n \"def midtop(self):\\n return (self.centerx, self.top)\",\n \"def world_to_grid(mapdata, wp):\\n WX = wp.x\\n WY = wp.y\\n resol = mapdata.info.resolution\\n # -0.5 but coordinates to center\\n gx = math.floor((WX - mapdata.info.origin.position.x) / resol - 0.5)\\n gy = math.floor((WY - mapdata.info.origin.position.y) / resol - 0.5)\\n return gx, gy\",\n \"def get_grid_position(self, as_int=True):\\n if as_int:\\n return (\\n int(self.x // Constant.TILE_SIZE),\\n int(self.y // Constant.TILE_SIZE),\\n )\\n else:\\n return (\\n self.x / Constant.TILE_SIZE,\\n self.y / Constant.TILE_SIZE,\\n )\",\n \"def get_grid_position(self):\\n tile_size_x = constants.WINDOW_WIDTH / constants.GRID_TILE_LENGTH\\n tile_size_y = constants.WINDOW_HEIGHT / constants.GRID_TILE_LENGTH\\n grid_x = tile_size_x / self.host.x\\n grid_y = tile_size_y / self.host.y\\n return grid_x, grid_y\",\n \"def get_coords(self):\\n xTK = int(jeu.coords(self.rectangle)[0]) # Coordonnées TKinter x1 et y1 du rectangle correspondant à la voiture\\n yTK = int(jeu.coords(self.rectangle)[1])\\n # On divise par la largeur d'une case et on renvoie les valeurs obtenues sous la forme d'un tuple\\n X = xTK//100\\n Y = yTK//100\\n resultat = [X, Y]\\n return resultat\",\n \"def get_range(self) -> tuple[int, int]:\\n return self.range_from, self.range_to\",\n \"def get_soft_bounds(self):\\n if self.bounds is None:\\n hl,hu=(None,None)\\n else:\\n hl,hu=self.bounds\\n\\n if self._softbounds is None:\\n sl,su=(None,None)\\n else:\\n sl,su=self._softbounds\\n\\n \\n if sl is None: l = hl\\n else: l = sl\\n\\n if su is None: u = hu\\n else: u = su\\n\\n return (l,u)\",\n \"def get_bounds(self):\\n return ([self.t_min] * self.dim,[self.t_max] * self.dim)\",\n \"def relative_position(self) -> Tuple[int, int]:\\n return self.position[0] - self.region.rect.x, self.position[1] - self.region.rect.y\",\n \"def relative_position(self) -> Tuple[int, int]:\\n return self.position[0] - self.region.rect.x, self.position[1] - self.region.rect.y\",\n \"def top_left_tile_value(self):\\n\\t\\treturn 1\",\n \"def bottom_left_tile_value(self):\\n\\t\\treturn self.expected_cols * (self.expected_rows - 1) + 1\",\n \"def getRange(self) -> Tuple[int, int]:\\n return self.validator().bottom(), self.validator().top()\",\n \"def get_position_coords(cls):\\n row = math.floor(cls.position / cls.size)\\n col = cls.position - row * cls.size\\n return row, col\",\n \"def getPosition(self):\\n\\tleft = self.getLeft()\\n\\ttop = self.getTop()\\n\\treturn (left,top)\",\n \"def pupil_left_coords(self):\\n if self.pupils_located:\\n x = self.eye_left.origin[0] + self.eye_left.pupil.x + self.x_add\\n y = self.eye_left.origin[1] + self.eye_left.pupil.y + self.y_add\\n return (x, y)\",\n \"def tile_bbox(self, (z, x, y_tms)):\\n topleft = (x * self.tileSize, (y_tms + 1) * self.tileSize)\\n bottomright = ((x + 1) * self.tileSize, y_tms * self.tileSize)\\n wn = self.unproject_pixels(topleft, z)\\n es = self.unproject_pixels(bottomright, z)\\n return wn + es\",\n \"def extent(self):\\n left = self.transform[0]\\n right = left + self.transform[1] * self.shape[1]\\n top = self.transform[3]\\n bottom = top + self.transform[5] * self.shape[0]\\n return left, right, bottom, top\",\n \"def positive_left_right_requirements(\\n self,\\n ) -> Tuple[Tuple[GriddedPerm, ...], Tuple[GriddedPerm, ...]]:\\n left, right = [], []\\n for (x, y) in self._tiling.active_cells:\\n if self._fuse_row and y == self._row_idx:\\n left.append(GriddedPerm.single_cell((0,), (x, y)))\\n right.append(GriddedPerm.single_cell((0,), (x, y + 1)))\\n if not self._fuse_row and x == self._col_idx:\\n left.append(GriddedPerm.single_cell((0,), (x, y)))\\n right.append(GriddedPerm.single_cell((0,), (x + 1, y)))\\n return tuple(sorted(left)), tuple(sorted(right))\",\n \"def get_tile_index_range(dataset_filename):\\n dataset = gdal.Open(dataset_filename)\\n assert dataset, 'Unable to open dataset %s' % dataset_filename\\n spatial_reference = osr.SpatialReference()\\n spatial_reference.ImportFromWkt(dataset.GetProjection())\\n geotransform = dataset.GetGeoTransform()\\n logger.debug('geotransform = %s', geotransform)\\n# latlong_spatial_reference = spatial_reference.CloneGeogCS()\\n tile_spatial_reference = osr.SpatialReference()\\n s = re.match('EPSG:(\\\\d+)', tile_type_info['crs'])\\n if s:\\n epsg_code = int(s.group(1))\\n logger.debug('epsg_code = %d', epsg_code)\\n assert tile_spatial_reference.ImportFromEPSG(epsg_code) == 0, 'Invalid EPSG code for tile projection'\\n else:\\n assert tile_spatial_reference.ImportFromWkt(tile_type_info['crs']), 'Invalid WKT for tile projection'\\n \\n logger.debug('Tile WKT = %s', tile_spatial_reference.ExportToWkt())\\n \\n coord_transform_to_tile = osr.CoordinateTransformation(spatial_reference, tile_spatial_reference)\\n # Upper Left\\n xmin, ymax, _z = coord_transform_to_tile.TransformPoint(geotransform[0], geotransform[3], 0)\\n # Lower Right\\n xmax, ymin, _z = coord_transform_to_tile.TransformPoint(geotransform[0] + geotransform[1] * dataset.RasterXSize, \\n geotransform[3] + geotransform[5] * dataset.RasterYSize, \\n 0)\\n \\n logger.debug('Coordinates: xmin = %f, ymin = %f, xmax = %f, ymax = %f', xmin, ymin, xmax, ymax)\\n\\n return (int(floor((xmin - tile_type_info['x_origin']) / tile_type_info['x_size'])), \\n int(floor((ymin - tile_type_info['y_origin']) / tile_type_info['y_size'])), \\n int(ceil((xmax - tile_type_info['x_origin']) / tile_type_info['x_size'])), \\n int(ceil((ymax - tile_type_info['y_origin']) / tile_type_info['y_size'])))\",\n \"def get_tile_coordinates_for_registration(self, grid_number, tile_number):\\n dx, dy = self.get_tile_coordinates_d(grid_number, tile_number)\\n width_d = self.get_tile_width_d(grid_number)\\n height_d = self.get_tile_height_d(grid_number)\\n return int((dx - width_d/2) * 1000), int((dy - height_d/2) * 1000)\",\n \"def LongitudinalBounds(lng, num_tiles):\\n # Normalize to between -180 and 180 degrees longitude.\\n while lng < -180.0:\\n lng += 360.0\\n while lng >= 180.0:\\n lng -= 360.0\\n\\n degrees_per_tile = 360.0 / num_tiles\\n x = int((lng + 180.0) / degrees_per_tile)\\n west = x * degrees_per_tile - 180.0\\n return (west, west + degrees_per_tile)\",\n \"def bbox(self):\\n lower = (self.x.min(), self.y.min())\\n upper = (self.x.max(), self.y.max())\\n return (lower, upper)\",\n \"def coordinates(self) -> Tuple[float, float, float, float, float]:\\n return (self.x, self.y, self.x + self.width, self.y + self.height)\",\n \"def getMachineCoordinates(self):\\n return (self.x, self.y, self.z)\",\n \"def ne_corner(self):\\n return (self.max_lat, self.max_lon)\",\n \"def image_coordinates(self, temp):\\n iy = np.array((temp.y[:,None]-self.extent[2])/self.spacing[1],dtype=np.int64)\\n ix = np.array((temp.x[None,:]-self.extent[0])/self.spacing[0],dtype=np.int64)\\n return (iy,ix)\",\n \"def bounds(self):\\n return self.min_col, self.min_row, self.max_col, self.max_row\",\n \"def top_left(self):\\n return Point(self.left, self.top)\",\n \"def getStartTlXY(self, lat, lng,zoom):\\n tile_size = 256\\n # Use a left shift to get the power of 2\\n # i.e. a zoom level of 2 will have 2^2 = 4 tiles\\n numTiles = 1 << zoom\\n # Find the x_point given the longitude\\n point_x = (tile_size/ 2 + lng * tile_size / 360.0) * numTiles // tile_size\\n # Convert the latitude to radians and take the sine\\n sin_y = math.sin(lat * (math.pi / 180.0))\\n # Calulate the y coorindate\\n point_y = ((tile_size / 2) + 0.5 * math.log((1+sin_y)/(1-sin_y)) * -(tile_size / (2 * math.pi))) * numTiles // tile_size\\n return int(point_x), int(point_y)\",\n \"def get_cell_coords(self, pt):\\n\\n\\t return int(pt[0] // self.a), int(pt[1] // self.a)\",\n \"def get_boundingbox(self):\\n tile_iterator = iter(self)\\n (coordinate,tile) = next(tile_iterator)\\n assert(tile is not None)\\n min_x = coordinate[0]\\n max_x = min_x + 1\\n min_y = coordinate[1]\\n max_y = min_y + 1\\n\\n for (coordinate,tile) in tile_iterator:\\n\\n if coordinate[0] < min_x:\\n min_x = coordinate[0]\\n if coordinate[0]+1> max_x:\\n max_x = coordinate[0] +1\\n if coordinate[1] < min_y:\\n min_y = coordinate[1]\\n if coordinate[1]+1> max_y:\\n max_y = coordinate[1] +1\\n\\n return ((min_x, min_y), (max_x, max_y))\",\n \"def bounds(self) -> tuple[float, float, float, float]:\\n transform = self.transform\\n a, b, c, d, e, f, _, _, _ = transform\\n if b == d == 0:\\n xs = (c, c + a * self.width)\\n ys = (f, f + e * self.height)\\n else: # rotated\\n c0x, c0y = c, f\\n c1x, c1y = transform * (0, self.height)\\n c2x, c2y = transform * (self.width, self.height)\\n c3x, c3y = transform * (self.width, 0)\\n xs = (c0x, c1x, c2x, c3x)\\n ys = (c0y, c1y, c2y, c3y)\\n return min(xs), min(ys), max(xs), max(ys)\",\n \"def get_bounds_halo(self):\\n bottom_right = np.asarray([self.coords_halo[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords_halo[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\n \"def get_cell_coords(pt):\\n\\n return int(pt[0] // a), int(pt[1] // a)\",\n \"def _get_top_left_coordinates(height, width, patch_size):\\n\\n n_h = math.ceil(height / patch_size)\\n n_w = math.ceil(width / patch_size)\\n tops = np.linspace(0, height - patch_size, n_h, dtype=int)\\n lefts = np.linspace(0, width - patch_size, n_w, dtype=int)\\n\\n return product(tops, lefts)\",\n \"def upright(self):\\n return Coord([self.x + 1, self.y - 1])\",\n \"def gen_gps_to_coords(lat,lon,rows,cols,min_lat,max_lat,min_lon,max_lon):\\n\\n if (lat <= min_lat or lat >= max_lat or lon <= min_lon or lon >= max_lon):\\n return (-1,-1)\\n\\n lat_step = abs(max_lat-min_lat)/rows\\n lon_step = abs(max_lon-min_lon)/cols\\n\\n lat_spot = int((max_lat-lat)/lat_step)\\n lon_spot = int((lon-min_lon)/lon_step)\\n #print \\\"lat: %f lon: %f lat_spot: %f lon_spot: %f\\\" % (lat,lon,lat_spot,lon_spot)\\n return (lat_spot,lon_spot)\",\n \"def roi_x_offset():\\n def r(x):\\n return x & 0xFFF\\n\\n def w(x):\\n return min(x, 0xFFF)\\n return r, w\",\n \"def find_yolo_coordinates(y_top, y_bottom, x_left, x_right, width, height):\\n w = (width - x_left - x_right) / width # width of bounding box\\n h = (height - y_top - y_bottom) / height # height of bounding box\\n x = (1 - w / 2) - x_right / width # x center of box (distance right from UL)\\n y = (1 - h / 2) - y_bottom / height # y center of box (distance down from UL)\\n\\n return x,y,w,h\",\n \"def get_grid_locations(self, top_left, other_pos):\\n cell_x = torch.floor(((other_pos[:, 0] - top_left[:, 0]) / self.neighborhood_size) *self.grid_size)\\n\\n # Added this part to implementation, otherwise the pooling is going to run into an indexing error\\n cell_x[cell_x == self.grid_size] -= 1\\n cell_y = torch.floor(((top_left[:, 1] - other_pos[:, 1]) / self.neighborhood_size) *self.grid_size)\\n cell_y[cell_y == self.grid_size] -= 1\\n grid_pos = cell_x + cell_y * self.grid_size\\n\\n return grid_pos\",\n \"def extend (self) :\\n return (self.x_min, self.x_max, self.y_min, self.y_max)\",\n \"def gettpoints(self,left,top,tnum):\\n if tnum == 0:\\n x1 = left + CELLSIZE/2\\n y1 = top\\n x2 = x1 + TSIZE\\n y2 = y1 + TSIZE\\n x3 = x2 - 2*TSIZE\\n y3 = y2\\n if tnum == 1:\\n x1 = left + CELLSIZE\\n y1 = top + CELLSIZE/2\\n x2 = x1 - TSIZE\\n y2 = y1 + TSIZE\\n x3 = x2\\n y3 = y2 - 2*TSIZE\\n if tnum == 2:\\n x1 = left + CELLSIZE/2\\n y1 = top + CELLSIZE\\n x2 = x1 - TSIZE\\n y2 = y1 - TSIZE\\n x3 = x2 + TSIZE*2\\n y3 = y2\\n if tnum == 3:\\n x1 = left\\n y1 = top + CELLSIZE/2\\n x2 = x1 + TSIZE\\n y2 = y1 + TSIZE\\n x3 = x2\\n y3 = y2 - TSIZE*2\\n\\n return ((x1,y1),(x2,y2),(x3,y3))\",\n \"def bottomleft(self):\\n return (self.left, self.bottom)\",\n \"def pupil_right_coords(self):\\n if self.pupils_located:\\n x = self.eye_right.origin[0] + self.eye_right.pupil.x + self.x_add\\n y = self.eye_right.origin[1] + self.eye_right.pupil.y + self.y_add\\n return (x, y)\",\n \"def get_img_coord_tuple(img):\\n\\n lat = convert_to_degress(get_gps_details(img)['GPSLatitude'])\\n if get_gps_details(img)['GPSLatitudeRef'] == 'S':\\n lat = -lat\\n\\n longitude = convert_to_degress(get_gps_details(img)['GPSLongitude'])\\n if get_gps_details(img)['GPSLongitudeRef'] == 'W':\\n longitude = -longitude\\n\\n return lat, longitude\",\n \"def res(self) -> tuple[float, float]:\\n xs, ys = self.xcoords.data, self.ycoords.data\\n dx, dy = 0, 0\\n if xs.ndim == 1:\\n dx = xs[1] - xs[0]\\n dy = ys[1] - ys[0]\\n elif xs.ndim == 2:\\n ddx0 = xs[1, 0] - xs[0, 0]\\n ddy0 = ys[1, 0] - ys[0, 0]\\n ddx1 = xs[0, 1] - xs[0, 0]\\n ddy1 = ys[0, 1] - ys[0, 0]\\n dx = math.hypot(ddx1, ddy1) # always positive!\\n dy = math.hypot(ddx0, ddy0)\\n rot = self.rotation\\n acos = math.cos(math.radians(rot))\\n # find grid top-down orientation\\n if (\\n (acos < 0 and ddy0 > 0)\\n or (acos > 0 and ddy0 < 0)\\n or (\\n ddy0 == 0\\n and (np.isclose(rot, 270) and ddx0 < 0)\\n or (np.isclose(rot, 90) and ddx0 > 0)\\n )\\n ):\\n dy = -1 * dy\\n return dx, dy\",\n \"def chr_coords(s):\\n return max_y - (max_y - min_y)*s\",\n \"def get_tile_coordinates_d(self, grid_number, tile_number):\\n origin_dx, origin_dy = self.cs.get_grid_origin_d(grid_number)\\n return (origin_dx + self.grid_map_d[grid_number][tile_number][0],\\n origin_dy + self.grid_map_d[grid_number][tile_number][1])\",\n \"def fetchbounds(self):\\n pnts = [x for x in [self.out_start, self.start, self.in_start, \\\\\\n self.in_end, self.end, self.out_end] \\\\\\n if x is not None]\\n return min(pnts), max(pnts)\",\n \"def _get_range(self):\\n return tuple((0, m, 1) for m in self.level_shapes[0])\",\n \"def top_right_tile_value(self):\\n\\t\\treturn self.expected_cols\",\n \"def get_bottom_right(left, right):\\n x = right.x - (right.x - left.x) / 8\\n y = right.y - (right.y - left.y) / 8\\n return (x, y)\",\n \"def getTilePos(self, pos = None):\\n\\n if not pos:\\n pos = self.actor.getPos()\\n \\n for i in range(len(pos)):\\n pos[i] = int(math.floor( (pos[i] + self.dimensions[i]) / 2.0))\\n #pos[i] = int(math.floor( pos[i] / 2.0))\\n\\n return pos\",\n \"def normalized_position(self) -> Tuple[float, float]:\\n x, y = self.relative_position\\n return x / self.region.rect.width, y / self.region.rect.height\",\n \"def normalized_position(self) -> Tuple[float, float]:\\n x, y = self.relative_position\\n return x / self.region.rect.width, y / self.region.rect.height\",\n \"def test_mines_left_coordinates(self):\\n pg.font.init()\\n mines_coords = utils.mines_left_coords(1)\\n self.assertIsInstance(mines_coords, tuple)\",\n \"def get_pos(x, y):\\r\\n return normalize(x) // 2, normalize(y) // 4\",\n \"def get_xrange_indices(self, lower, upper) -> Tuple[int, int]:\\n lower_index = np.argmax(self.x >= lower)\\n upper_index = np.argmax(self.x >= upper)\\n return int(lower_index), int(upper_index)\",\n \"def _get_xy_lims(self):\\n \\n x = self.datapos[0] - 1\\n y = self.datapos[1] - 1\\n\\n return x, y\",\n \"def get_uv_positions(data, image_size, target_grid, up_vector, right_vector, tile_xy, verts, vtx_center):\\n\\n return get_uv_pos_size(data, image_size, target_grid, tile_xy,\\n target_grid.grid[0], target_grid.grid[1],\\n up_vector, right_vector,\\n verts, vtx_center)\",\n \"def coordinates(self) -> Tuple[int, int]:\\n return self.x, self.y\",\n \"def region_points(x, y, width, xmin, xmax):\\n right = (x, y + width / 2)\\n top = (xmax, y)\\n left = (x, y - width / 2)\\n bottom = (xmin, y)\\n return (right, top, left, bottom)\",\n \"def grid_coords(self):\\n return [(x, y) for y in range(self.height) for x in range(self.width)]\",\n \"def extent(self):\\n\\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\\n\\n return [x[0], x[1], y[0], y[1]]\",\n \"def left(self):\\n return self.points['topLeft'].x\",\n \"def left(self):\\n return self.points['topLeft'].x\",\n \"def bounds(self):\\n return (\\n self.x, self.y,\\n self.x, self.y\\n )\",\n \"def extents(self):\\n x0, y0, width, height = self._rect_bbox\\n xmin, xmax = sorted([x0, x0 + width])\\n ymin, ymax = sorted([y0, y0 + height])\\n return xmin, xmax, ymin, ymax\",\n \"def get_min_max(self):\\n\\n mr = np.sqrt(2 * np.log(1/self.mth)) * self.ms\\n mr[:] = np.max(mr)\\n\\n mxmin = self.mx - mr\\n mxmax = self.mx + mr\\n mymin = self.my - mr\\n mymax = self.my + mr\\n mzmin = self.mz - mr\\n mzmax = self.mz + mr\\n\\n mb_xmin_idx = np.argmin(mxmin[self.ma > 0])\\n mb_xmax_idx = np.argmax(mxmax[self.ma > 0])\\n mb_ymin_idx = np.argmin(mymin[self.ma > 0])\\n mb_ymax_idx = np.argmax(mymax[self.ma > 0])\\n mb_zmin_idx = np.argmin(mzmin[self.ma > 0])\\n mb_zmax_idx = np.argmax(mzmax[self.ma > 0])\\n\\n xmin0 = self.mx[mb_xmin_idx] - mr[mb_xmin_idx]\\n xmax0 = self.mx[mb_xmax_idx] + mr[mb_xmax_idx]\\n ymin0 = self.my[mb_ymin_idx] - mr[mb_ymin_idx]\\n ymax0 = self.my[mb_ymax_idx] + mr[mb_ymax_idx]\\n zmin0 = self.mz[mb_zmin_idx] - mr[mb_zmin_idx]\\n zmax0 = self.mz[mb_zmax_idx] + mr[mb_zmax_idx]\\n\\n xmin = xmin0 - (xmax0 - xmin0) * 0.25\\n xmax = xmax0 + (xmax0 - xmin0) * 0.25\\n ymin = ymin0 - (ymax0 - ymin0) * 0.25\\n ymax = ymax0 + (ymax0 - ymin0) * 0.25\\n zmin = zmin0 - (zmax0 - zmin0) * 0.25\\n zmax = zmax0 + (zmax0 - zmin0) * 0.25\\n\\n return xmin, xmax, ymin, ymax, zmin, zmax\",\n \"def upper_right(self) -> Tuple[decimal.Decimal, decimal.Decimal]:\\n return self.right, self.top\",\n \"def restored_position(self) -> Tuple[int, int]:\\n x_n, y_n = self.normalized_position\\n w, h = self.region.original_size\\n return int(x_n * w), int(y_n * h)\",\n \"def restored_position(self) -> Tuple[int, int]:\\n x_n, y_n = self.normalized_position\\n w, h = self.region.original_size\\n return int(x_n * w), int(y_n * h)\",\n \"def get_pos(self):\\n return (self.x, self.y)\",\n \"def node_to_coords(self,node_num):\\n row = (node_num - 1) / self.cols\\n col = (node_num - 1) % self.cols\\n return (row,col)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67502934","0.65008754","0.6489868","0.6368388","0.6322317","0.63027024","0.62114066","0.6206572","0.6173504","0.61361134","0.6121037","0.6087851","0.60849476","0.6061991","0.6028953","0.59848636","0.59517145","0.59340775","0.5899759","0.589703","0.58896315","0.5879526","0.58723646","0.5855794","0.5810314","0.5799515","0.5790533","0.5788975","0.5769757","0.5758871","0.57432973","0.5741471","0.57384056","0.5735267","0.57330817","0.57330817","0.5730476","0.5726652","0.57261693","0.572043","0.57091457","0.5707628","0.57052004","0.56875336","0.5655426","0.5645721","0.5639985","0.56333053","0.5602915","0.5599982","0.55999297","0.55738425","0.5569438","0.55689603","0.5562546","0.55595","0.5557271","0.5557217","0.5553043","0.55524445","0.55473334","0.5540967","0.5538647","0.55335146","0.55302155","0.5520543","0.5520056","0.5512424","0.55102175","0.5508274","0.55039257","0.54996103","0.5498404","0.5490007","0.54894847","0.5489164","0.54871297","0.54815936","0.5479889","0.5478756","0.5473168","0.5473168","0.5471","0.546975","0.54691815","0.5463697","0.54557997","0.5455175","0.5452328","0.5452043","0.5448572","0.544536","0.544536","0.5441362","0.5441037","0.54405576","0.54328036","0.5428839","0.5428839","0.542655","0.5425845"],"string":"[\n \"0.67502934\",\n \"0.65008754\",\n \"0.6489868\",\n \"0.6368388\",\n \"0.6322317\",\n \"0.63027024\",\n \"0.62114066\",\n \"0.6206572\",\n \"0.6173504\",\n \"0.61361134\",\n \"0.6121037\",\n \"0.6087851\",\n \"0.60849476\",\n \"0.6061991\",\n \"0.6028953\",\n \"0.59848636\",\n \"0.59517145\",\n \"0.59340775\",\n \"0.5899759\",\n \"0.589703\",\n \"0.58896315\",\n \"0.5879526\",\n \"0.58723646\",\n \"0.5855794\",\n \"0.5810314\",\n \"0.5799515\",\n \"0.5790533\",\n \"0.5788975\",\n \"0.5769757\",\n \"0.5758871\",\n \"0.57432973\",\n \"0.5741471\",\n \"0.57384056\",\n \"0.5735267\",\n \"0.57330817\",\n \"0.57330817\",\n \"0.5730476\",\n \"0.5726652\",\n \"0.57261693\",\n \"0.572043\",\n \"0.57091457\",\n \"0.5707628\",\n \"0.57052004\",\n \"0.56875336\",\n \"0.5655426\",\n \"0.5645721\",\n \"0.5639985\",\n \"0.56333053\",\n \"0.5602915\",\n \"0.5599982\",\n \"0.55999297\",\n \"0.55738425\",\n \"0.5569438\",\n \"0.55689603\",\n \"0.5562546\",\n \"0.55595\",\n \"0.5557271\",\n \"0.5557217\",\n \"0.5553043\",\n \"0.55524445\",\n \"0.55473334\",\n \"0.5540967\",\n \"0.5538647\",\n \"0.55335146\",\n \"0.55302155\",\n \"0.5520543\",\n \"0.5520056\",\n \"0.5512424\",\n \"0.55102175\",\n \"0.5508274\",\n \"0.55039257\",\n \"0.54996103\",\n \"0.5498404\",\n \"0.5490007\",\n \"0.54894847\",\n \"0.5489164\",\n \"0.54871297\",\n \"0.54815936\",\n \"0.5479889\",\n \"0.5478756\",\n \"0.5473168\",\n \"0.5473168\",\n \"0.5471\",\n \"0.546975\",\n \"0.54691815\",\n \"0.5463697\",\n \"0.54557997\",\n \"0.5455175\",\n \"0.5452328\",\n \"0.5452043\",\n \"0.5448572\",\n \"0.544536\",\n \"0.544536\",\n \"0.5441362\",\n \"0.5441037\",\n \"0.54405576\",\n \"0.54328036\",\n \"0.5428839\",\n \"0.5428839\",\n \"0.542655\",\n \"0.5425845\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":956,"numItemsPerPage":100,"numTotalItems":95772,"offset":95600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3MzUxNDk3OSwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzM1MTg1NzksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.fvMQDErjEAPQFmUqC4AkxO8n1IMQVp-vpD9ZrUzCQi_PUc0HoiWNJf46ZqtYbQfb1KgbtsjkLYsfbzsrgdrQDg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102 values |
|---|---|---|---|---|---|---|
Gets the average GPS coordinates of the rough address interval | def selective_geocode(rough_address: str) -> dict:
lat_results = []; lon_results = []
found = re.findall(r"\d+~\d+", rough_address)
if not found:
raise geo.AddressError(geo.__name__, rough_address)
bound = [int(i) for i in found[0].split('~')]
if bound[0] > bound[1]:
raise geo.AddressError(geo.__name__, rough_address)
interval = int((bound[1] - bound[0] + 1) / settings.GEO_SAMPLES)
samples = [i for i in range(bound[0], bound[1] + 1, interval)]
for sample in samples:
query_address = rough_address.replace(found[0], str(sample))
gps_coordinates = geo.geocode(query_address, culture='zh-TW')["GPS"]
if gps_coordinates["lat"] and gps_coordinates["lon"]:
lat_results.append(gps_coordinates["lat"])
lon_results.append(gps_coordinates["lon"])
return {"lat": lat_results, "lon": lon_results} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gavg(idata):\n\t\n\twgt1=np.cos(np.deg2rad(idata.lat))*(idata*0+1)\n\tga=(wgt1*idata).sum(dim=['lat','lon'])/wgt1.sum(dim=['lat','lon'])\n\n\treturn ga",
"def _get_lat_avg(self, report):\n match = re.search(\"\\s*lat\\s*\\((\\w+)\\).*avg\\=\\s*(\\d+\\.{0,1}\\d*)\",\n report)\n if... | [
"0.66684115",
"0.66580987",
"0.6334983",
"0.62975043",
"0.61769515",
"0.61367",
"0.60557425",
"0.6047087",
"0.6028656",
"0.59404755",
"0.57874817",
"0.57128465",
"0.57006764",
"0.5697978",
"0.5691043",
"0.5690729",
"0.5679087",
"0.56297153",
"0.5596291",
"0.55928093",
"0.5568... | 0.5241144 | 57 |
Geocode address of the same county in quarter fashion | def partition_geocode(con: sqlite3.Connection, cur: sqlite3.Cursor, quarter: str, county_cht: str):
cur.execute('''SELECT 土地區段位置或建物區門牌 FROM "{0}/TRX"
WHERE 縣市 = ?
GROUP BY 土地區段位置或建物區門牌;'''.format(quarter), (county_cht,))
for address, in cur.fetchall():
cur.execute('''SELECT GEO.編號
FROM "{0}/TRX" AS TRX, "{0}/GEO" AS GEO
WHERE TRX.編號 = GEO.編號
AND TRX.土地區段位置或建物區門牌 = ?
AND GEO.LAT_Avg ISNULL;'''.format(quarter), (address,))
identities = cur.fetchall()
if not identities:
continue
print("[%d] "%(len(identities)) + address)
try:
results = selective_geocode(address)
except geo.AddressError:
continue
if len(results["lat"]) != 5 or len(results["lon"]) != 5:
continue
results["lat"].append(sum(results["lat"]) / len(results["lat"]))
results["lon"].append(sum(results["lon"]) / len(results["lon"]))
combined = [num for zipped in zip(results["lat"], results["lon"]) for num in zipped]
values = [(tuple(combined) + identity) for identity in identities]
cur.executemany('''UPDATE "{0}/GEO" SET
LAT_1 = ?, LON_1 = ?,
LAT_2 = ?, LON_2 = ?,
LAT_3 = ?, LON_3 = ?,
LAT_4 = ?, LON_4 = ?,
LAT_5 = ?, LON_5 = ?,
LAT_Avg = ?, LON_Avg = ?
WHERE 編號 = ?;'''.format(quarter), values)
con.commit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geo_coder(house_number, boro_code, street_name, zip_code): \r\n wa1 = '1B{}{}{}{}{}C{}{}'.format(rightpad(house_number, 16), rightpad('', 38), boro_code, rightpad('', 10), rightpad(street_name, 32), rightpad('', 113), rightpad(zip_code, 5))\r\n wa1 = rightpad(wa1, 1200)\r\n wa2 = rightpad('', 4300... | [
"0.6251422",
"0.6227843",
"0.60835624",
"0.59131515",
"0.585577",
"0.5806368",
"0.57388103",
"0.5738554",
"0.5722705",
"0.5614146",
"0.5594229",
"0.55850214",
"0.5574379",
"0.5574237",
"0.5490394",
"0.54893243",
"0.5472008",
"0.5418318",
"0.5418175",
"0.53998554",
"0.5397736"... | 0.6954527 | 0 |
fit offset gaussian, with offset, amplitude, and sigma constrained to be positive | def fitgauss(bin_edges, counts, guess):
"""sigma is constrained to be >= one bin width"""
"""unless guess[3] < 0, then offset set to zero"""
bincen = np.zeros(np.size(bin_edges)-1)
bincen[:] = bin_edges[0:np.size(bin_edges)-1]
binstep = bin_edges[2]-bin_edges[1]
bincen += binstep/2
#print(bincen)
#print(counts)
#plt.plot(bincen, counts, label = 'data')
#plt.show()
if(guess[3]>0): #allow y_offset
coeff, var_matrix = curve_fit(gauss, bincen, counts, guess,
bounds=([0,-np.inf, binstep, 0],[np.inf,np.inf, np.inf, np.inf]))
[A, mu, sigma, yoff] = coeff
else: #force y_offset to be zero - pure gaussian
coeff, var_matrix = curve_fit(puregauss, bincen, counts, guess[0:3],
bounds=([0,-np.inf, binstep],[np.inf,np.inf, np.inf]))
[A, mu, sigma] = coeff
yoff = 0.0
c_exp = gauss(bincen, A, mu, sigma, yoff)
resid2 = sum((c_exp - counts)**2)
#print coeff
# print var_matrix
#display check
#hist_fit=gauss(bincen, *coeff)
#plt.plot(bincen, counts, label='data')
#plt.plot(bincen, hist_fit, label='fit')
#plt.show()
return [A, mu, sigma, yoff, resid2] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gaussian(x, *parameters):\n position, sigma, amplitude, background = parameters\n return amplitude * np.exp(-(x - position)**2 / (2.0 * sigma**2)) + background",
"def gaussian(mu, wid, x):\n return np.exp(-((x - mu) / (0.6005612 * wid))**2)",
"def Gaussian(x, mu=0, sigma=26.4, A=1, y0=0):\r\n #... | [
"0.68242663",
"0.6790471",
"0.67830956",
"0.67823905",
"0.67808473",
"0.67197216",
"0.67171425",
"0.661976",
"0.6550336",
"0.6550336",
"0.6507729",
"0.64968884",
"0.64913034",
"0.64834434",
"0.64469576",
"0.6435371",
"0.6381322",
"0.6359356",
"0.63252896",
"0.6323852",
"0.630... | 0.5754527 | 87 |
method catches all requests that are not api requests ensures routing possibilities within VUE | def catch_all(path=''):
return render_template('index.html') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def serve(self, request, *args, **kwargs):\n raise Http404",
"def serve(self, request, *args, **kwargs):\n raise Http404",
"def serve(self, request, *args, **kwargs):\n raise Http404",
"def serve(self, request, *args, **kwargs):\n raise Http404",
"def serve(self, request, *args,... | [
"0.55937976",
"0.55937976",
"0.55937976",
"0.55937976",
"0.55937976",
"0.5476229",
"0.5457845",
"0.5313464",
"0.5251401",
"0.5202117",
"0.5182233",
"0.5172451",
"0.51495206",
"0.51472974",
"0.51144993",
"0.5110918",
"0.5094427",
"0.50860524",
"0.50750285",
"0.5068474",
"0.505... | 0.0 | -1 |
This creates the generator object yielding squares of the number | def generate_square_number(square_limit):
for i in range(0,square_limit):
yield i**2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def square_numbers_2(nums):\n for i in nums:\n yield(i*i)",
"def perfect_sq_seq_gen(num):\n for i in range(num):\n yield i ** 2",
"def gensquares(n):\n for number in my_range.my_range(n): # note that we are NOT calling range(N), we implemented our own my_range() generator\n yiel... | [
"0.76812726",
"0.7570587",
"0.7127406",
"0.7013921",
"0.6935942",
"0.6566116",
"0.65498954",
"0.65378404",
"0.65034443",
"0.6499686",
"0.64733166",
"0.6452493",
"0.6364364",
"0.6352053",
"0.6324538",
"0.63203084",
"0.62623084",
"0.6218521",
"0.6207027",
"0.6205454",
"0.619938... | 0.7852594 | 0 |
r"""One could also use the Laplacian of Gaussian formula to design the filter. | def laplacian_1d(window_size) -> torch.Tensor:
filter_1d = torch.ones(window_size)
filter_1d[window_size // 2] = 1 - window_size
laplacian_1d: torch.Tensor = filter_1d
return laplacian_1d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def filter(self, op=GaussianFilter):\n\n if self._verbose > 0:\n print(\"Filtering...\")\n\n # Import from utils specified params.\n params = get_filtering_params()\n\n negative = self.image_raw - op(sigma=params['sigma_bgd']).convolve(self.image_raw)\n\n self.image_fi... | [
"0.66963106",
"0.65597415",
"0.6478427",
"0.636755",
"0.6351325",
"0.6253081",
"0.62316626",
"0.62082386",
"0.617469",
"0.61242366",
"0.6101194",
"0.6060973",
"0.60049284",
"0.600304",
"0.60001606",
"0.59720194",
"0.59712464",
"0.59702766",
"0.5951012",
"0.59331256",
"0.59309... | 0.0 | -1 |
r"""Function that returns the coefficients of a 1D Laplacian filter | def get_laplacian_kernel(kernel_size: int) -> torch.Tensor:
if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \
kernel_size <= 0:
raise TypeError("ksize must be an odd positive integer. Got {}"
.format(kernel_size))
window_1d: torch.Tensor = laplacian_1d(kernel_size)
return window_1d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _laplacian_to_image(lpyr, filter_vec, coeff):\n im = lpyr[-1]\n filter_vec = filter_vec.reshape(filter_vec.size, 1)\n for i in reversed(range(len(lpyr) - 1)):\n im = _expand(im, filter_vec) + coeff[i] * lpyr[i]\n\n return im",
"def slip_to_coefficients(x, y, a):\n partials = np.zeros((x... | [
"0.63450897",
"0.59707314",
"0.56549406",
"0.5654393",
"0.56457824",
"0.5604039",
"0.55629796",
"0.554021",
"0.55173355",
"0.55153257",
"0.54377836",
"0.54277223",
"0.5426681",
"0.53971505",
"0.538953",
"0.5377202",
"0.5352815",
"0.53502446",
"0.534966",
"0.53384006",
"0.5319... | 0.0 | -1 |
r"""Function that returns Gaussian filter matrix coefficients. | def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor:
if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \
kernel_size <= 0:
raise TypeError("ksize must be an odd positive integer. Got {}"
.format(kernel_size))
kernel = torch.ones((kernel_size, kernel_size))
mid = kernel_size // 2
kernel[mid, mid] = 1 - kernel_size ** 2
kernel_2d: torch.Tensor = kernel
return kernel_2d | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _gaussian(self, c, sigma):\n d = 2*sigma*sigma\n ax = exp(-power(self._xx-self._xx.T[c], 2)/d)\n ay = exp(-power(self._yy-self._yy.T[c], 2)/d)\n return (ax * ay).T # the external product gives a matrix",
"def apply_gaussian(X, sigma):\n return np.array([ndimage.gaussian_filter... | [
"0.64841884",
"0.6266498",
"0.6249518",
"0.59057564",
"0.5897109",
"0.5803587",
"0.57633173",
"0.5752097",
"0.56212413",
"0.56012994",
"0.5532037",
"0.5527262",
"0.55129933",
"0.55118227",
"0.5507921",
"0.54987395",
"0.5487615",
"0.54819536",
"0.54755914",
"0.5475295",
"0.547... | 0.0 | -1 |
Returns a 2D Laplacian kernel array. | def get_laplacian_kernel(kernel_size) -> torch.Tensor:
kernel: torch.Tensor = get_laplacian_kernel2d(kernel_size)
return kernel | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor:\n if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or \\\n kernel_size <= 0:\n raise TypeError(\"ksize must be an odd positive integer. Got {}\"\n .format(kernel_size))\n\n kernel = torch.ones((k... | [
"0.68943256",
"0.5928996",
"0.5873876",
"0.5794534",
"0.5719824",
"0.568754",
"0.5661878",
"0.56419665",
"0.5635752",
"0.5564691",
"0.5545013",
"0.553446",
"0.54953605",
"0.5416252",
"0.5343913",
"0.53213376",
"0.5320362",
"0.5318403",
"0.5306929",
"0.5298269",
"0.52873653",
... | 0.66206235 | 1 |
r"""Function that returns a tensor using a Laplacian filter. The operator smooths the given tensor with a laplacian kernel by convolving it to each channel. It suports batched operation. | def laplacian(src: torch.Tensor, kernel_size: int) -> torch.Tensor:
return Laplacian(kernel_size)(src) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _laplacian_to_image(lpyr, filter_vec, coeff):\n im = lpyr[-1]\n filter_vec = filter_vec.reshape(filter_vec.size, 1)\n for i in reversed(range(len(lpyr) - 1)):\n im = _expand(im, filter_vec) + coeff[i] * lpyr[i]\n\n return im",
"def lapsharp(image, maskret = False):\n #padded_image =... | [
"0.6398194",
"0.6261045",
"0.6243763",
"0.6211535",
"0.6126479",
"0.6089628",
"0.60353637",
"0.59343386",
"0.5885602",
"0.58751994",
"0.5864428",
"0.58093095",
"0.58002144",
"0.5674847",
"0.56685555",
"0.5641529",
"0.55820704",
"0.5570494",
"0.55570203",
"0.5538427",
"0.55247... | 0.7140893 | 0 |
Slow method that exhaustively find pairs that gives the maximum product | def max_pairwise_product(numbers):
n = len(numbers)
max_product = 0
for first in range(n):
for second in range(first + 1, n):
max_product = max(max_product,
numbers[first] * numbers[second])
return max_product | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max_pairwise_product_brute_force(array):\n\n if len(array) <= 1:\n return 0\n\n max_product = 0\n\n for i in range(len(array)):\n for j in range(len(array)):\n if i != j:\n if array[i] * array[j] > max_product:\n max_product = array[i] * array... | [
"0.76415133",
"0.7435716",
"0.7419571",
"0.73527825",
"0.6995507",
"0.6947662",
"0.6701412",
"0.66534376",
"0.66308546",
"0.6610363",
"0.65937907",
"0.65213996",
"0.65124816",
"0.6395902",
"0.63665193",
"0.63143504",
"0.62744373",
"0.62582433",
"0.62466544",
"0.6241433",
"0.6... | 0.7392273 | 3 |
Sort the list first, then take the last two number (biggest) and multiply them | def max_pairwise_product_sort(numbers):
sorted_list = sorted(numbers)
ans = sorted_list[-1]*sorted_list[-2]
return ans | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def greatest_difference(num_list):",
"def highest_product_2(arr):\n\n # make a list to store the highest three ints, initializing to first three\n maxes = [arr[0], arr[1], arr[2]]\n\n # find the lowest of the highest three ints\n lowest_max = min(maxes)\n\n # go through the rest of the list to che... | [
"0.6987764",
"0.685001",
"0.66746044",
"0.66537255",
"0.6615918",
"0.66083395",
"0.6553763",
"0.64379936",
"0.64302796",
"0.6412502",
"0.6391118",
"0.63837785",
"0.6367723",
"0.6360329",
"0.633378",
"0.6331884",
"0.63131255",
"0.6293756",
"0.628999",
"0.6283172",
"0.6250778",... | 0.7739796 | 0 |
Find the largest 2 numbers by scanning through the list | def max_pairwise_product_fast(numbers):
num_list = numbers.copy()
max_num_1 = max(num_list)
num_list.remove(max_num_1)
max_num_2 = max(num_list)
ans = max_num_1*max_num_2
return ans | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max_in_list(list):\n x=list[0] #set x be the first number in the list\n for i in range(0,len(list)):#go over the number in the list\n if x<=list[i]: #if the second one is bigger than the first\n x=list[i] #assign x to the bigger one\n else:\n continue#repeat until find... | [
"0.7865657",
"0.77563345",
"0.7723488",
"0.77118576",
"0.7655584",
"0.76221126",
"0.7604391",
"0.7559842",
"0.7552165",
"0.7419501",
"0.7415454",
"0.73194754",
"0.72359556",
"0.7183456",
"0.7156729",
"0.71380377",
"0.7110791",
"0.7098136",
"0.7089967",
"0.7050428",
"0.7011190... | 0.0 | -1 |
Get the result of a MATLAB statement. Parameter | def result(self, timeout=None):
self.__validate_engine()
if self._retrieved:
return self._result
"""
Following code is used to poll the Ctrl+C every second from keyboard in
order to cancel a MATLAB function.
"""
try:
result_ready = self.wait(timeout, pythonengine.waitForFEval)
if not result_ready:
raise TimeoutError(pythonengine.getMessage('MatlabFunctionTimeout'))
self._result = pythonengine.getFEvalResult(self._future,self._nargout, None, out=self._out, err=self._err)
self._retrieved = True
return self._result
except KeyboardInterrupt:
self.cancel()
if self.cancelled():
print(pythonengine.getMessage('MatlabFunctionCancelled'))
except:
raise | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_sql_scalar(self, sql):\n msg_type, msg = self.protocol.build_execute_statement(\"sql\", sql)\n self.protocol.send_msg(msg_type, msg)\n result = RowResult(self)\n result.fetch_all()\n if result.count == 0:\n raise InterfaceError(\"No data found\")\n r... | [
"0.5909357",
"0.5690119",
"0.5548101",
"0.5496141",
"0.54489595",
"0.54063964",
"0.5397221",
"0.5366224",
"0.5346653",
"0.52857095",
"0.5272317",
"0.52682745",
"0.5259357",
"0.52247185",
"0.52104545",
"0.51776475",
"0.5130771",
"0.51298857",
"0.51254255",
"0.51205885",
"0.511... | 0.4819287 | 61 |
Cancel the execution of an evaluation of a MATLAB statement. Returns bool True if the corresponding MATLAB statement can be cancelled; False otherwise. Raises RejectedExecutionError an error occurs if the engine is terminated. | def cancel(self):
self.__validate_engine()
return pythonengine.cancelFEval(self._future) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancelled(self):\n self.__validate_engine()\n return pythonengine.isCancelledFEval(self._future)",
"def cancel(self):\n if not self.is_cancelled:\n self.will_change_value_for('is_cancelled')\n self.cancelled = True\n # remove our dependencies so that we're ready, properly beha... | [
"0.63782734",
"0.6360038",
"0.570138",
"0.567854",
"0.5647722",
"0.5620772",
"0.5620772",
"0.5620772",
"0.5620772",
"0.5569046",
"0.5432427",
"0.53818804",
"0.53624535",
"0.5357489",
"0.5344577",
"0.53426117",
"0.53072584",
"0.5285678",
"0.52575773",
"0.52286595",
"0.5207874"... | 0.67627895 | 0 |
Obtain the cancellation status of the asynchronous execution of a MATLAB command. Returns bool True if the execution is cancelled; False otherwise. Raises RejectedExecutionError an error occurs if the engine is terminated. | def cancelled(self):
self.__validate_engine()
return pythonengine.isCancelledFEval(self._future) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cancel(self):\n self.__validate_engine()\n return pythonengine.cancelFEval(self._future)",
"def cancelled(self):\n return self._status == Future.STATUS_CANCELED",
"async def cancelled(self):\n await self._refresh_and_update()\n return (\n self._operation.HasFie... | [
"0.57483846",
"0.5747091",
"0.5745746",
"0.5590403",
"0.55467594",
"0.55077934",
"0.55016315",
"0.5491056",
"0.5442433",
"0.5399497",
"0.5271981",
"0.5269121",
"0.5266887",
"0.5225791",
"0.5185286",
"0.5151515",
"0.51362634",
"0.5117819",
"0.51172787",
"0.50527465",
"0.502697... | 0.6059455 | 0 |
Obtain the completion status of the asynchronous invocation of a MATLAB command. Returns bool True if the execution is finished; False otherwise. It returns True even if there is an error generated from the MATLAB statement or it is cancelled. Raises RejectedExecutionError an error occurs if the engine is terminated. | def done(self):
self.__validate_engine()
return pythonengine.isDoneFEval(self._future) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def _execute(self):\n f = os.popen(self._com)\n if self._exec_time > 0:\n await asyncio.sleep_ms(self._exec_time)\n try:\n r = f.read()\n print(r)\n if self._expected_return is not None:\n if r == self._expected_return:\n ... | [
"0.6209489",
"0.5881146",
"0.57704556",
"0.5739999",
"0.57303506",
"0.56933546",
"0.56448203",
"0.560831",
"0.5604192",
"0.56039965",
"0.55831635",
"0.5569415",
"0.549603",
"0.549603",
"0.549603",
"0.549603",
"0.549603",
"0.549603",
"0.5446532",
"0.5408486",
"0.53901416",
"... | 0.53526616 | 22 |
Test POST a start call registry and expect a response from API containing a job_id and the data posted. Test uses start_call_fx fixture | def test_POST_a_call_and_expect_job_id_and_data_posted(client, start_call_fx):
url = reverse_lazy('calls:registry-list')
response = client.post(url, start_call_fx, content_type='application/json')
response_data = response.json()
assert response.status_code == status.HTTP_201_CREATED
assert 'job_id' in response_data
for item in start_call_fx.items():
assert item in response_data['data'].items() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_call_and_recover_it_using_a_GET_request(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n post_request = client.post(url,\n start_call_fx,\n content_type='application/json')\n\n assert post_request.st... | [
"0.80795336",
"0.7127848",
"0.70870763",
"0.70252454",
"0.690225",
"0.682077",
"0.6728949",
"0.64632064",
"0.61601955",
"0.61388147",
"0.6122343",
"0.61069036",
"0.598875",
"0.5923191",
"0.5838878",
"0.5811853",
"0.581069",
"0.5795599",
"0.5779675",
"0.5745532",
"0.5728147",
... | 0.8062351 | 1 |
Test the job_id URL of a start call registry POST. Test uses start_call_fx fixture | def test_expect_200Ok_response_GETting_a_job_id_URL(client, start_call_fx):
url = reverse_lazy('calls:registry-list')
response = client.post(url, start_call_fx, content_type='application/json')
response_data = response.json()
task_url = response_data.get('job_id', None)
task_response = client.get(task_url)
assert task_response.status_code == status.HTTP_200_OK | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_call_and_recover_it_using_a_GET_request(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n post_request = client.post(url,\n start_call_fx,\n content_type='application/json')\n\n assert post_request.st... | [
"0.7932951",
"0.7636372",
"0.7201175",
"0.70154786",
"0.69145167",
"0.66262203",
"0.626443",
"0.62300295",
"0.61841965",
"0.60890883",
"0.5921425",
"0.58591396",
"0.58441097",
"0.57225126",
"0.57005316",
"0.56909",
"0.5625876",
"0.55742955",
"0.5544029",
"0.55416805",
"0.5526... | 0.784503 | 1 |
Test if there is a 'status' property in a response about registry process, and if it contains a 'DONE' status about this task. Test uses start_call_fx fixture | def test_expect_status_property_about_registry_process(client, start_call_fx):
url = reverse_lazy('calls:registry-list')
response = client.post(url, start_call_fx, content_type='application/json')
job_id = response.data.get('job_id')
job = client.get(job_id)
assert job.data.get('status') == 'DONE' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_expect_data_posted_return_encapsulated_on_message_property_on_response(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n response = client.post(url, start_call_fx, content_type='application/json')\n\n job_id = response.data.get('job_id')\n\n job = client.get(job_id)\n\n... | [
"0.6107858",
"0.6048469",
"0.6048469",
"0.60329425",
"0.5998805",
"0.59744084",
"0.59617215",
"0.59564376",
"0.5914125",
"0.5885923",
"0.5870812",
"0.58333784",
"0.58272076",
"0.5810825",
"0.5786063",
"0.57617617",
"0.57480896",
"0.5696804",
"0.5684844",
"0.5677295",
"0.56715... | 0.81807953 | 0 |
Test if there is a 'result' property containing the result of registry process Test uses start_call_fx fixture | def test_expect_data_posted_return_encapsulated_on_message_property_on_response(client, start_call_fx):
url = reverse_lazy('calls:registry-list')
response = client.post(url, start_call_fx, content_type='application/json')
job_id = response.data.get('job_id')
job = client.get(job_id)
result = job.json()
assert result.get('result')
registry_url = json.loads(result.get('result'))
assert client.get(registry_url.get('url')).status_code == status.HTTP_200_OK | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_expect_status_property_about_registry_process(client, start_call_fx):\n\n url = reverse_lazy('calls:registry-list')\n\n response = client.post(url, start_call_fx, content_type='application/json')\n\n job_id = response.data.get('job_id')\n\n job = client.get(job_id)\n\n assert job.data.get('... | [
"0.7666267",
"0.6480155",
"0.63547766",
"0.63009536",
"0.6242489",
"0.623529",
"0.61790955",
"0.6141608",
"0.61038685",
"0.6090213",
"0.6060126",
"0.6026361",
"0.5984391",
"0.59258854",
"0.5911147",
"0.5894738",
"0.5893941",
"0.5885163",
"0.58836126",
"0.5859428",
"0.5853621"... | 0.6334782 | 3 |
Test POST a start call registry to registry API and expect recover it using a GET request. Test uses start_call_fx fixture | def test_post_a_start_call_and_recover_it_using_a_GET_request(client, start_call_fx):
url = reverse_lazy('calls:registry-list')
post_request = client.post(url,
start_call_fx,
content_type='application/json')
assert post_request.status_code == status.HTTP_201_CREATED
job_url = post_request.data.get('job_id')
job_request = client.get(job_url)
result = json.loads(job_request.data.get('result'))
get_request = client.get(result.get('url'))
response = get_request.json()
assert get_request.status_code == status.HTTP_200_OK
for key, value in start_call_fx.items():
assert value == response.get(key) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_and_stop_registry_and_get_a_call(client, start_call_fx,\n stop_call_fx):\n\n post_url = reverse_lazy('calls:registry-list')\n\n post_data = [start_call_fx, stop_call_fx]\n\n for data in post_data:\n response = client.post(p... | [
"0.77212137",
"0.7614569",
"0.7375702",
"0.69436055",
"0.68681324",
"0.6717918",
"0.66508937",
"0.63880897",
"0.62512255",
"0.61260146",
"0.58564955",
"0.5841628",
"0.5779191",
"0.57728964",
"0.57675964",
"0.56549394",
"0.55403256",
"0.54902625",
"0.54620844",
"0.5461441",
"0... | 0.85472727 | 0 |
Test a GET request on the Call API Endpoint and expect it return 200 Ok | def test_GET_call_api_and_return_200Ok(client):
url = '/api/v1/calls/'
response = client.get(url)
assert response.status_code == status.HTTP_200_OK | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"def test_get(self):\n self.assertEqual(200, self.resp.status_code)",
"def test_1():\n\tassert api_call().status_code == 200",
"def test_get(self):\n self.assertEqual(200, self.response.status_code)",
"def test_get(se... | [
"0.7947968",
"0.7947968",
"0.7879138",
"0.77913356",
"0.77913356",
"0.77886796",
"0.76140255",
"0.75884384",
"0.7562267",
"0.75419384",
"0.75371504",
"0.75371504",
"0.7369333",
"0.7351754",
"0.7339209",
"0.73001033",
"0.72693115",
"0.7259427",
"0.72563964",
"0.721277",
"0.719... | 0.8886346 | 0 |
Test if there is a Call API endpoint and if is defined as "calls" namespace and "calllist" as his name | def test_namespace_of_call_api_endpoint():
url = '/api/v1/calls/'
resolved = resolve(url)
assert resolved.namespace == 'calls'\
and resolved.url_name == 'call-list' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_call_api_return_only_consolidated_calls(client, start_call_fx, stop_call_fx):\n\n post_url = reverse_lazy('calls:registry-list')\n\n start_call_fx_2 = copy(start_call_fx)\n start_call_fx_2['call_id'] = 2\n\n post_data = [start_call_fx, start_call_fx_2, stop_call_fx]\n\n for data in post_dat... | [
"0.62145686",
"0.6031098",
"0.597374",
"0.59650505",
"0.5733628",
"0.55599207",
"0.54912245",
"0.54800916",
"0.54037213",
"0.5337027",
"0.531804",
"0.523795",
"0.5225318",
"0.5171135",
"0.516988",
"0.51654184",
"0.5162458",
"0.51472855",
"0.51333857",
"0.5127361",
"0.5116722"... | 0.77283394 | 0 |
Test for documentation on Call API Endpoint view | def test_documentation_for_call_view():
url = reverse_lazy('calls:call-list')
view = resolve(url).func
assert view.__doc__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_view_detail_as_method(self):\n url = reverse(\n \"django-admindocs-views-detail\",\n args=[\"django.contrib.admin.sites.AdminSite.index\"],\n )\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)",
"def test_view():\n\treturn ... | [
"0.6804205",
"0.65882033",
"0.6567295",
"0.6547328",
"0.6524265",
"0.64814574",
"0.6474007",
"0.6394351",
"0.6393633",
"0.6392588",
"0.6389973",
"0.62900865",
"0.62861174",
"0.62557405",
"0.62550545",
"0.6218095",
"0.61950505",
"0.61669517",
"0.61537933",
"0.6125932",
"0.6122... | 0.7911585 | 0 |
Test POSTing a start and a stop registry and expect get it at Call API Endpoint Test uses start_call_fx fixture Test uses stop_call_fx fixture | def test_post_a_start_and_stop_registry_and_get_a_call(client, start_call_fx,
stop_call_fx):
post_url = reverse_lazy('calls:registry-list')
post_data = [start_call_fx, stop_call_fx]
for data in post_data:
response = client.post(post_url, data, content_type='application/json')
assert response.status_code == status.HTTP_201_CREATED
get_url = reverse_lazy('calls:call-list')
response = client.get(get_url)
assert len(response.data) == 1
assert response.data[0].get('start_timestamp')
assert response.data[0].get('stop_timestamp') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_and_stop_registry_and_get_a_call_using_url(client,\n start_call_fx,\n stop_call_fx):\n\n post_url = reverse_lazy('calls:registry-list')\n\n post_data = [start_... | [
"0.8207508",
"0.7297929",
"0.7013963",
"0.6848967",
"0.6562138",
"0.64560413",
"0.6252057",
"0.6184207",
"0.61758155",
"0.6018559",
"0.59301925",
"0.5880597",
"0.5861513",
"0.58101547",
"0.57913876",
"0.57734543",
"0.5742188",
"0.57399577",
"0.57374406",
"0.5709208",
"0.56703... | 0.85035634 | 0 |
Test POSTing a start and a stop registry and expect get it at Call API Endpoint using a call_id Test uses start_call_fx fixture Test uses stop_call_fx fixture | def test_post_a_start_and_stop_registry_and_get_a_call_using_url(client,
start_call_fx,
stop_call_fx):
post_url = reverse_lazy('calls:registry-list')
post_data = [start_call_fx, stop_call_fx]
for data in post_data:
response = client.post(post_url, data, content_type='application/json')
assert response.status_code == status.HTTP_201_CREATED
get_url = reverse_lazy('calls:call-detail', kwargs={'call_id': 1})
response = client.get(get_url)
assert response.data.get('start_timestamp')
assert response.data.get('stop_timestamp') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_and_stop_registry_and_get_a_call(client, start_call_fx,\n stop_call_fx):\n\n post_url = reverse_lazy('calls:registry-list')\n\n post_data = [start_call_fx, stop_call_fx]\n\n for data in post_data:\n response = client.post(p... | [
"0.8484062",
"0.75393564",
"0.7322953",
"0.722341",
"0.6426046",
"0.62504566",
"0.6230039",
"0.6164438",
"0.59421086",
"0.59283173",
"0.5919254",
"0.58633626",
"0.58341634",
"0.58131343",
"0.5774062",
"0.57502663",
"0.56712496",
"0.5646838",
"0.56402326",
"0.5634627",
"0.5630... | 0.8285663 | 1 |
Test POSTing two start registries and only one stop registry and expect to GET only on record on Call API Endpoint. Test uses start_call_fx fixture Test uses stop_call_fx fixture | def test_call_api_return_only_consolidated_calls(client, start_call_fx, stop_call_fx):
post_url = reverse_lazy('calls:registry-list')
start_call_fx_2 = copy(start_call_fx)
start_call_fx_2['call_id'] = 2
post_data = [start_call_fx, start_call_fx_2, stop_call_fx]
for data in post_data:
response = client.post(post_url, data, content_type='application/json')
assert response.status_code == status.HTTP_201_CREATED
get_url = reverse_lazy('calls:call-list')
response = client.get(get_url)
assert len(response.data) == 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_post_a_start_and_stop_registry_and_get_a_call(client, start_call_fx,\n stop_call_fx):\n\n post_url = reverse_lazy('calls:registry-list')\n\n post_data = [start_call_fx, stop_call_fx]\n\n for data in post_data:\n response = client.post(p... | [
"0.8359559",
"0.81559026",
"0.72383404",
"0.68315816",
"0.6380493",
"0.6169041",
"0.6073902",
"0.60261333",
"0.5987054",
"0.59616095",
"0.5898319",
"0.5815202",
"0.5773632",
"0.5772734",
"0.5764194",
"0.57637644",
"0.57477325",
"0.57452106",
"0.5725453",
"0.569496",
"0.566565... | 0.76799375 | 2 |
Downhill algorithm as described in Kiusalaas Numerical Methods in Engineering with Python 3 | def downhill(F, xStart, args=None, side=0.1, ftol=1.0e-6, xtol=1.0e-6, maxiter=1000, maxfunc=1000, maxiternochange=10):
# TODO: check the types of the input ???
# print "Entering downhill"
n = len(xStart)
x = np.zeros((n+1, n), dtype=float) #point null matrix, n+1 rows, n columns
f = np.zeros(n+1, dtype=float) # null vector, n+1 columns
p_count = 0 # counter for detecting a plateau
f_count = 0 # counter for the number of function call
f_best_count = 0 # counter for the number of iterations in which the best solution does not change
f_best_prev = 0.0 # holds the best value from the previous iteration
epsilon = 0.001 # tolerance for considering two values as equal
# max_iter_no_change = 10 # maximum number of accepted iterations with no change in the optimal solution
precision = 2
round_map = partial(round, ndigits=precision) # partial function for rounding purposes
# initial simplex
x[0] = xStart
for i in xrange(1, n+1):
x[i] = xStart
x[i,i-1] = xStart[i-1] + side
# print "Evaluate the starting points"
# compute the value of F at the vertices of the simplex
for i in xrange(n+1):
f[i] = F(x[i], args)
# p_count += 1
# main loop
# print "Start iterating"
for k in xrange(maxiter):
# check the number of function calls
if f_count > maxfunc:
print "Stopping criteria: maximum number of function calls"
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'MAXFUNCALL'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'MAXFUNCALL'}
# find the best and worst vertex (consider a minimization problem)
iLo = np.argmin(f) # best vertex
iHi = np.argmax(f) # worst vertex
# print k," ", f[iLo]
#
# if f[iLo] < -0.310000:
# print f[iLo]
# print x[iLo]
# print x
# sys.exit(1)
# print "k: ", k, " f_best_prev: ", f_best_prev, " f[iLo]: ", f[iLo], " f_best_count: ", f_best_count
# print "Beginning of iteration: %4d | Best x: %4f %4f %4f | Best value: %f" % (k, x[iLo][0], x[iLo][1], x[iLo][2], f[iLo])
# print "x: ", x, " f: ", f
# print "========================================================================================="
# check if the solution has changed from the previous iterations
if f[iLo] < f_best_prev:
f_best_prev = f[iLo]
f_best_count = 0
else:
f_best_count += 1
if f_best_count > maxiternochange:
print "Stopping criteria: maximum number of iterations with no improvement in the best solution"
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'NOIMPROVEMENT'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'NOIMPROVEMENT'}
if abs(f[iLo] - f[iHi]) < ftol: # If difference between highest and lowest is smaller than ftol, return
print "Stopping criteria: difference between highest and lowest points is smaller than tolerance"
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'MAXTOLERANCE'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'MAXTOLERANCE'}
# compute the move vector d
d = (-(n+1) * x[iHi] + np.sum(x, axis=0)) / n
# print "d: ", d
# check for convergence
if sqrt(np.dot(d, d)/n) < xtol: # length of the vector d
print "Stopping criteria: length of step d smaller than tolerance"
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'SMALLSTEP'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'SMALLSTEP'}
# try reflection
xNew = np.array(map(round_map, x[iHi] + 2 * d))
fNew = F(xNew, args)
f_count += 1
# print "Reflected point: ", xNew, " value: ", fNew
# check for no improvement over the worst point
# and for plateau condition
if f[iHi] - epsilon <= fNew <= f[iHi] + epsilon:
p_count += 1
# print "No improvement here"
if p_count == n+2: # we reflected all vertices with no improvement
print "Stopping criteria: Probably we landed on a plateau... exiting" # TODO: restart instead of exiting
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'PLATEAU'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'PLATEAU'}
else:
p_count = 0
if fNew <= f[iLo]: # if the new value is better than the best so far,
x[iHi] = xNew # substitute the worst vertex with the new one
f[iHi] = fNew
# try to expand the reflection
xNew = np.array(map(round_map, x[iHi] + d))
fNew = F(xNew, args)
f_count += 1
# print "Expanded point: ", xNew, " value: ", fNew
if fNew <= f[iHi]: # in the original source version it is f[iLo] (?)
x[iHi] = xNew
f[iHi] = fNew
else:
# try reflection again
if fNew <= f[iHi]:
x[iHi] = xNew
f[iHi] = fNew
else:
# try contraction
xNew = np.array(map(round_map, x[iHi] + 0.5 * d))
fNew = F(xNew, args)
f_count += 1
# print "Contracted point: ", xNew, " value: ", fNew
if fNew <= f[iHi]: # accept contraction
x[iHi] = xNew
f[iHi] = fNew
else:
# shrink
for i in xrange(len(x)):
if i != iLo:
x[i] = np.array(map(round_map, x[i] - x[iLo] * 0.5))
f[i] = F(x[i], args)
f_count += 1
# print "End of iteration: %4d | Best x: %4f %4f %4f | Best value: %f" % (k, x[iLo][0], x[iLo][1], x[iLo][2], f[iLo])
# print "x: ", x, " f: ", f
# print "*"*50
# print ""
print "Stopping criteria: maximum number of iterations"
print "Best solution so far: ", x[iLo], " value: ", f[iLo], " at iteration:", k
# return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': (args['Q1'], args['Q2'], args['Q3']), 'stopping': 'MAXITERATION'}
return {'point' : x[iLo], 'value': f[iLo], 'iteration': k, 'funcalls': f_count, 'allocation': [args['Q{}'.format(h)] for h in xrange(1, args['retailers']+1)], 'stopping': 'MAXITERATION'} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solution(A):\n \"\"\"method 2 n**2\n east=[] #0\n west=[] #1\n for i in range(len(A)):\n if A[i] == 0:\n east.append(i)\n else:\n west.append(i)\n\n result = 0\n for e in east:\n count = 0\n for j in range(len(west)):\n if e > west[... | [
"0.6305694",
"0.58596754",
"0.5824617",
"0.57462597",
"0.5715227",
"0.5684032",
"0.56557304",
"0.5591559",
"0.5554532",
"0.55386126",
"0.5528498",
"0.55246407",
"0.5522697",
"0.5501514",
"0.5486631",
"0.5482586",
"0.54730695",
"0.546426",
"0.5456875",
"0.5453615",
"0.5421071"... | 0.5833218 | 2 |
Plot balance between classes | def plot_balance_class(classes):
unique, counts = np.unique(classes, return_counts=True)
plt.bar(unique, counts)
plt.title('Class Frequency')
plt.xlabel('Class')
plt.ylabel('Frequency')
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_class_imbalance(df, title='Class Imbalance', PATH=None):\n ax = sns.barplot(x=[\"Normal\", \"Clickbait\"], y=df.groupby(['target']).target.count())\n ax.set_title(title, size=20)\n plt.xticks([0,1],[\"Normal\", \"Clickbait\"], size = 20)\n ax.set_ylabel(\"Document Count\", size=17)\n ax.set... | [
"0.6470689",
"0.62615764",
"0.618939",
"0.5848157",
"0.58320725",
"0.5751623",
"0.5716817",
"0.5682445",
"0.56821924",
"0.56675744",
"0.5648744",
"0.5599107",
"0.5581174",
"0.5550323",
"0.5517062",
"0.5514832",
"0.54800415",
"0.54709786",
"0.5425537",
"0.54238087",
"0.5382205... | 0.7392071 | 0 |
Load data and convert class type to str | def load_data(dataset_path: str):
data = arff.loadarff(dataset_path)
data_frame = pd.DataFrame(data[0])
return data_frame | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _convert_to_str(self, data):\n raise NotImplementedError()",
"def loaddata(cls, data, auto_cls=True):\n keys = data.dtype.fields if hasattr(data, 'dtype') else data\n if auto_cls and 'class' in keys:\n cls_components = str(data['class']).split('.')\n mod_name = '.'.... | [
"0.64354247",
"0.6280863",
"0.6222278",
"0.6192303",
"0.61756396",
"0.61756396",
"0.61756396",
"0.61756396",
"0.61756396",
"0.616021",
"0.616021",
"0.61370295",
"0.61201906",
"0.6067332",
"0.6043132",
"0.60146064",
"0.60138565",
"0.60008734",
"0.5951044",
"0.58738834",
"0.580... | 0.0 | -1 |
Find best parameters for decision tree | def find_best_classifier(x_train, x_test, y_train, y_test):
max_depth, _ = find_best_parameters(
'max_depth', list(range(1, 30)),
x_train, x_test, y_train, y_test)
print("Best max_depth t: ", max_depth)
min_samples_split, _ = find_best_parameters(
'min_samples_split', list(range(2, 400)),
x_train, x_test, y_train, y_test)
min_samples_split = int(min_samples_split)
print("Best min samples split: ", min_samples_split)
min_samples_leaf, _ = find_best_parameters(
'min_samples_leaf', list(range(2, 200)),
x_train, x_test, y_train, y_test)
min_samples_leaf = int(min_samples_leaf)
print("Best sample leaf: ", min_samples_leaf)
max_leaf_nodes, _ = find_best_parameters(
'max_leaf_nodes', list(range(2, 150)),
x_train, x_test, y_train, y_test)
max_leaf_nodes = int(max_leaf_nodes)
print("Best max leaf nodes split: ", max_leaf_nodes)
min_impurity_decrease, _ = find_best_parameters(
'min_impurity_decrease', np.arange(0.0005, 0.1, 0.0005),
x_train, x_test, y_train, y_test)
print("Best min impurity decrease: ", min_impurity_decrease)
clf = DecisionTreeClassifier(
min_impurity_decrease=min_impurity_decrease,
max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes,
min_samples_split=min_samples_split,
random_state=0)
clf = clf.fit(x_train, y_train)
return clf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_model():\r\n from sklearn import tree\r\n import graphviz\r\n\r\n ValidationSetAndLabels = AllSets[1]\r\n ValLabels = ValidationSetAndLabels[:, [-1]] # extract labels (last column)\r\n ValSet = np.delete(ValidationSetAndLabels, -1, axis=1) # delete labels\r\n\r\n TrainingSetAndLabels =... | [
"0.73654014",
"0.6803678",
"0.6700777",
"0.669526",
"0.6686797",
"0.6683199",
"0.6660834",
"0.6638287",
"0.65660405",
"0.65332466",
"0.65164715",
"0.64410394",
"0.64108276",
"0.64031994",
"0.6387443",
"0.6330438",
"0.62947744",
"0.6267406",
"0.6226899",
"0.62210745",
"0.61950... | 0.74380594 | 0 |
Converts a binary adjacency matrix to a list of directed edges | def to_adj_list(adj_matrix):
adj_list = []
assert (adj_matrix.shape[0] == adj_matrix.shape[1])
for idx in range(adj_matrix.shape[0]):
for jdx in range(idx, adj_matrix.shape[0]):
if adj_matrix[idx, jdx] == 1:
adj_list.append([idx, jdx])
return adj_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edges(adj_mat, vertices):\n return [(i,j) for i,j in\n vertices if (i < j and adj_mat[i][j] == 1)]",
"def matrix_to_edges(matrix: numpy.ndarray, include_reverse_edges: bool=True):\n sparse = scipy.sparse.coo_matrix(matrix)\n edges = zip(sparse.row, sparse.col)\n\n if not include_revers... | [
"0.7220524",
"0.7194223",
"0.710978",
"0.68476194",
"0.6835649",
"0.67063034",
"0.67027074",
"0.6680238",
"0.6674114",
"0.6642599",
"0.66176134",
"0.66089475",
"0.6598832",
"0.6533455",
"0.65062785",
"0.64912444",
"0.64912444",
"0.64545166",
"0.6395245",
"0.6378416",
"0.63470... | 0.69720167 | 3 |
Returns a list of successors by vertex, sensitive to the read strand | def get_successor_list(adj_matrix, vertex_map, read_strand):
succ_list = []
assert (adj_matrix.shape[0] == adj_matrix.shape[1])
for idx in range(adj_matrix.shape[0]):
succ_list.append([])
for jdx in range(adj_matrix.shape[0]):
if adj_matrix[idx, jdx] == 1 and \
((read_strand == "+" and vertex_map[0][idx] <= vertex_map[0][jdx]) or \
(read_strand == "-" and vertex_map[1][idx] >= vertex_map[1][jdx])):
succ_list[idx].append(jdx)
return succ_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_successors(vertex, graph):\n successors = list()\n successors.extend(graph.successors(vertex))\n return successors",
"def get_predecessors_successors(vertex, graph):\n overlaps = list()\n pre = get_unique_predecessors(vertex, graph)\n suc = get_unique_successors(vertex, graph)\n over... | [
"0.7708114",
"0.6563205",
"0.6376617",
"0.6307063",
"0.62348616",
"0.61934036",
"0.60396194",
"0.594999",
"0.59440297",
"0.58112705",
"0.57270694",
"0.5720067",
"0.57045156",
"0.5680978",
"0.5673067",
"0.5660702",
"0.56433517",
"0.5618526",
"0.5613443",
"0.5608551",
"0.560492... | 0.6470455 | 2 |
Find overlapping CDS within an exon given a list of CDS starts | def find_overlapping_cds_simple(v_start, v_stop, cds_begins, strand):
# cds_start = cds_begin[0]
if strand == '+':
return list(filter(lambda x: x[0] >= v_start and x[0] < v_stop, cds_begins))
else:
return list(filter(lambda x: x[0] > v_start and x[0] <= v_stop, cds_begins)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def overlap(list1,list2):\n \n coord=[]\n for pos1 in list1:\n #print 'pos in list1 is', pos1\n coord.append(('S',int(pos1.split('-')[0]), 'l1'))\n #print 'S is ', pos1.split('-')[0]\n coord.append(('E',int(pos1.split('-')[1]),'l1'))\n #print 'E is ', pos1.split('-')[1]\... | [
"0.6343779",
"0.5909693",
"0.5851059",
"0.57616645",
"0.5732729",
"0.568256",
"0.5675713",
"0.56729394",
"0.5659118",
"0.56387144",
"0.5623953",
"0.558914",
"0.5588006",
"0.5570414",
"0.55475664",
"0.5545426",
"0.5528489",
"0.5526816",
"0.5525146",
"0.5520476",
"0.5492296",
... | 0.7374044 | 0 |
Encodes chromosome to same cn | def encode_chromosome(in_num):
convert_dict = {23: "X", 24: "Y", 25: "MT"}
return convert_dict[in_num] if in_num in convert_dict else str(in_num) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __encode(self):\n\n for i, char in enumerate(self.__chars):\n self.__char2idx[char] = i\n self.__idx2char[i] = char",
"def encode(self, C, num_rows):\n x = np.zeros((num_rows, len(self.chars)))\n print(C)\n for i, c in enumerate(C):\n x[i, self.cha... | [
"0.65794533",
"0.62438995",
"0.6232326",
"0.62068266",
"0.6048946",
"0.5984779",
"0.5922863",
"0.5862185",
"0.5761406",
"0.57606995",
"0.5690487",
"0.56302047",
"0.5617058",
"0.5587301",
"0.5558331",
"0.5556066",
"0.5555711",
"0.55398685",
"0.54918736",
"0.54819953",
"0.54703... | 0.7192931 | 0 |
Get the mutated dna subsequence according to mutation specified by the variant_comb. | def get_sub_mut_dna(background_seq, coord, variant_comb, somatic_mutation_sub_dict, strand, gene_start):
def _get_variant_pos_offset(variant_pos, coord_pair_list, strand):
offset = 0
takes_effect = False
for p1,p2 in coord_pair_list:
if variant_pos >= p1 and variant_pos < p2:
if strand == '+':
offset += variant_pos - p1
else:
offset += p2 - variant_pos - 1
takes_effect = True
break
else:
offset = p2 - p1
return offset if takes_effect else np.nan
real_coord = list(filter(lambda x: x is not np.nan and x != None, coord))
assert len(real_coord) % 2 == 0
coord_pair_list = list(zip(real_coord[::2], real_coord[1::2]))
if strand == '+':
sub_dna = ''.join([background_seq[pair[0] - gene_start:pair[1] - gene_start] for pair in coord_pair_list])
else:
sub_dna = ''.join([background_seq[pair[0] - gene_start:pair[1] - gene_start][::-1] for pair in coord_pair_list])
if variant_comb is np.nan : # no mutation exist
return sub_dna
relative_variant_pos = [_get_variant_pos_offset(variant_ipos, coord_pair_list, strand) for variant_ipos in variant_comb]
for i,variant_ipos in enumerate(variant_comb):
mut_base = somatic_mutation_sub_dict[variant_ipos]['mut_base']
ref_base = somatic_mutation_sub_dict[variant_ipos]['ref_base']
pos = relative_variant_pos[i]
if pos is not np.nan:
sub_dna = sub_dna[:pos] + mut_base + sub_dna[pos+1:]
return sub_dna | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_DNA_seq(mutant_position, all_lines):\n\n dna_lines = []\n for i in range(mutant_position + 2, len(all_lines)):\n line = all_lines[ i ]\n if not line:\n break\n\n line = line.replace(\"Variant sequence: \", \"\")\n dna_lines.append(line)\n return dna_lines",
... | [
"0.5627289",
"0.5542037",
"0.5341807",
"0.5279429",
"0.49878958",
"0.4956177",
"0.49556637",
"0.49054444",
"0.4834147",
"0.48148525",
"0.47234476",
"0.4720456",
"0.4699101",
"0.46727768",
"0.4651557",
"0.46446463",
"0.46332493",
"0.46132207",
"0.46070266",
"0.45562443",
"0.45... | 0.695729 | 0 |
Get the expression adjustment parameter for certain samples. | def get_size_factor(samples, lib_file_path):
libs = np.loadtxt(lib_file_path, dtype='str', skiprows=1, delimiter='\t')
a, b = np.where(samples[:, np.newaxis] == libs[:, 0])
assert np.all(libs[b, 0] == samples)
libs = libs[b, :]
med = np.median(libs[:, 1].astype('float'))
sf = med / libs[:, 1].astype('float')
return sf | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def adjustment(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"adjustment\")",
"def adjustment(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"adjustment\")",
"def adjustment(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"adjustment\")... | [
"0.5970573",
"0.5970573",
"0.5970573",
"0.5970573",
"0.5970573",
"0.5970573",
"0.5970573",
"0.5970573",
"0.55451614",
"0.5458691",
"0.5382217",
"0.5338391",
"0.52984136",
"0.52497977",
"0.5241062",
"0.5241062",
"0.52158433",
"0.5213984",
"0.5213984",
"0.5159911",
"0.5122732",... | 0.0 | -1 |
Get all combinations of items in the given array Specifically used for generating variant combination | def get_all_comb(array, r=None):
if r is None:
r = len(array)
return [_ for i in range(1, r + 1) for _ in itertools.combinations(array, i)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def combinations(arrays):\n return np.array(np.meshgrid(*arrays)).T.reshape(-1, len(arrays))",
"def combos(array,n=2): \n # base case\n if n==0:\n yield frozenset()\n return\n\n # core recursion\n for c in set(combos(array,n-1)):\n for i in array:\n #added this t... | [
"0.73074627",
"0.7185561",
"0.6950121",
"0.67702305",
"0.67702305",
"0.6750947",
"0.6720031",
"0.6644649",
"0.6399782",
"0.63772047",
"0.63587743",
"0.6318727",
"0.62932175",
"0.62885773",
"0.62456626",
"0.6200499",
"0.6191303",
"0.61892384",
"0.61841613",
"0.6181527",
"0.616... | 0.79023576 | 0 |
Indicate if a peptide is translated by an isolated cds Currently, it is a simple version. If the exon where the cds is has no connected exon, it is isolated. (the method is not true in some cases) | def is_isolated_cds(gene, gene_info, idx):
if len(gene_info.vertex_succ_list[idx]) > 0:
return False
return np.sum(gene.splicegraph.edges[:, idx]) == 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_isolated(self, d):\n if not self.center.is_isolated(d):\n return False\n\n for i in self.leads:\n if not i.is_isolated(d+1):\n return False\n\n return True",
"def is_atomic(self):\n found = True\n if self.ant is not None:\n ... | [
"0.59894097",
"0.5898949",
"0.5672679",
"0.5648508",
"0.5560285",
"0.5529773",
"0.5459054",
"0.54222125",
"0.541582",
"0.5374678",
"0.53390956",
"0.53365326",
"0.53323716",
"0.5328659",
"0.52947295",
"0.5285229",
"0.5278996",
"0.5277022",
"0.5254552",
"0.5249883",
"0.5224548"... | 0.6469092 | 0 |
Split the exon into segments and get the corresponding counts. | def get_exon_expr(gene, vstart, vstop, countinfo, Idx, seg_counts):
out_shape = (seg_counts.shape[1] + 1) if len(seg_counts.shape) > 1 else 2
# Todo: deal with absense of count file
if vstart is np.nan or vstop is np.nan: # isolated exon case
return np.zeros((0, out_shape), dtype='float')
if countinfo is None or Idx.sample is None:
return np.zeros((0, out_shape), dtype='float') #[np.nan]
segments = gene.segmentgraph.segments
sv1_id = bisect.bisect(segments[0], vstart) - 1
sv2_id = bisect.bisect(segments[0], vstop) - 1
if sv1_id == sv2_id:
if len(seg_counts.shape) > 1:
expr_list = np.c_[np.array([vstop - vstart]), [seg_counts[sv1_id, :]]]
else:
expr_list = np.array([(vstop - vstart, seg_counts[sv1_id])])
else:
if len(seg_counts.shape) > 1:
expr_list = np.c_[segments[1, sv1_id:sv2_id + 1] - segments[0, sv1_id:sv2_id + 1], seg_counts[sv1_id:sv2_id + 1, :]]
else:
expr_list = np.c_[segments[1, sv1_id:sv2_id + 1] - segments[0, sv1_id:sv2_id + 1], seg_counts[sv1_id:sv2_id + 1, np.newaxis]]
expr_list[0, 0] -= (vstart - segments[0, sv1_id])
expr_list[-1, 0] -= (segments[1, sv2_id] - vstop)
if gene.strand == '-': # need to reverse epression list to match the order of translation
expr_list = expr_list[::-1]
return expr_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getSegmentCount(self) -> int:\n ...",
"def calculate_number_of_segments(self):\n return sum(len(eg.transcript_file.segments) for eg in self.exemplars)",
"def getSegments(self) -> List[int]:\n ...",
"def Test_NumSegments(Daten):\n N_Leitungen = len(Daten.PipeSegments)\n\n return... | [
"0.65174055",
"0.6436669",
"0.6075838",
"0.5796184",
"0.57153535",
"0.57003266",
"0.56921184",
"0.5661428",
"0.5604101",
"0.5561135",
"0.55508536",
"0.553926",
"0.5515936",
"0.5487367",
"0.54719216",
"0.54696095",
"0.5457705",
"0.54412377",
"0.537007",
"0.5364843",
"0.5354218... | 0.0 | -1 |
Get the segment expression for one exonpair. Apply 'get_exon_expr' for each exon and concatenate them. | def get_segment_expr(gene, coord, countinfo, Idx, seg_counts, cross_graph_expr):
if coord.start_v3 is None:
expr_list = np.vstack([get_exon_expr(gene, coord.start_v1, coord.stop_v1, countinfo, Idx, seg_counts ),
get_exon_expr(gene, coord.start_v2, coord.stop_v2, countinfo, Idx, seg_counts )])
else:
expr_list = np.vstack([get_exon_expr(gene, coord.start_v1, coord.stop_v1, countinfo, Idx, seg_counts ),
get_exon_expr(gene, coord.start_v2, coord.stop_v2, countinfo, Idx, seg_counts ),
get_exon_expr(gene, coord.start_v3, coord.stop_v3, countinfo, Idx, seg_counts )])
seg_len = np.sum(expr_list[:, 0])
n_samples = expr_list[:, 1:].shape[1]
len_factor = np.tile(expr_list[:, 0], n_samples).reshape(n_samples, expr_list.shape[0]).transpose()
mean_expr = (np.sum(expr_list[:, 1:]*len_factor, 0) / seg_len).astype(int) if seg_len > 0 else np.zeros(n_samples).astype(int)
if not cross_graph_expr:
mean_expr = mean_expr[0]
return mean_expr,expr_list | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_exon_expr(gene, vstart, vstop, countinfo, Idx, seg_counts):\n out_shape = (seg_counts.shape[1] + 1) if len(seg_counts.shape) > 1 else 2\n # Todo: deal with absense of count file\n if vstart is np.nan or vstop is np.nan: # isolated exon case\n return np.zeros((0, out_shape), dtype='float')\... | [
"0.5563815",
"0.5293783",
"0.5213351",
"0.49248624",
"0.49004447",
"0.48478526",
"0.4781561",
"0.47813958",
"0.4710072",
"0.4675474",
"0.4543498",
"0.45414433",
"0.45138633",
"0.44763458",
"0.44727144",
"0.44690332",
"0.4463009",
"0.44608518",
"0.44598845",
"0.44542956",
"0.4... | 0.56654406 | 0 |
get total reads count for the given sample and the given gene actually total_expr = reads_lengthtotal_reads_counts | def get_total_gene_expr(gene, countinfo, Idx, seg_expr, cross_graph_expr):
if len(seg_expr.shape) == 1:
n_samples = 1
else:
n_samples = seg_expr.shape[1]
if countinfo is None or Idx.sample is None:
return [np.nan] * n_samples
seg_len = gene.segmentgraph.segments[1] - gene.segmentgraph.segments[0]
if cross_graph_expr:
total_expr = np.sum(seg_len * seg_expr.T, axis=1)
total_expr = total_expr.tolist()
else:
total_expr = [np.sum(seg_len*seg_expr)]
return total_expr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_count_total_reads(self):\n \n bam = pybedtools.BedTool(clipper.test_file(\"allup_test.bam\"))\n gene_dfn = pybedtools.BedTool(clipper.test_file(\"hg19_genes.bed\"))\n \n result = count_total_reads(bam, gene_dfn)\n \n self.assertEqual(result, 2086)",
"def ... | [
"0.71968734",
"0.64680296",
"0.624144",
"0.60355866",
"0.6020118",
"0.58288175",
"0.57842183",
"0.5721398",
"0.5674062",
"0.56387067",
"0.5625821",
"0.5600439",
"0.5572864",
"0.55386955",
"0.55117846",
"0.550808",
"0.5496517",
"0.54497534",
"0.5437835",
"0.5402488",
"0.539430... | 0.5925086 | 5 |
Create a aggregated Index with namedtuple idx Combine the gene_idx, sample_idx | def get_idx(countinfo, sample, gene_idx):
if not countinfo is None:
if not sample in countinfo.sample_idx_dict:
sample_idx = None
logging.warning("utils.py: The sample {} is not in the count file. Program proceeds without outputting expression data.".format(sample))
else:
sample_idx = countinfo.sample_idx_dict[sample]
else:
sample_idx = None
return Idx(gene_idx, sample_idx) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_gene_indexes(df):\n\tgeneDict = OrderedDict()\n\n\tgeneCount = 0\n\tpreviousGeneIndex = 0\n\n\tcurrent_id=\"\"\n\tcurrent_gene=\"\"\n\n\tfor i in range(len(df)):\n\n\t\tif df.loc[i,'feature'] == 'gene':\n\t\t\ttrdict = parse_entry(df.loc[i,'transcript_id'])\n\n\t\t\tcurGeneID = trdict['gene_id'][0]\n\t\t... | [
"0.6321686",
"0.6155408",
"0.60595196",
"0.6051188",
"0.59505635",
"0.5836034",
"0.5680135",
"0.56531954",
"0.5625771",
"0.55557144",
"0.5533098",
"0.5517968",
"0.5480308",
"0.54524577",
"0.54428107",
"0.5442492",
"0.5433063",
"0.543244",
"0.54272753",
"0.54136074",
"0.540922... | 0.64187765 | 0 |
create library_size text file. Calculate the 75% expression and sum of expression for each sample and write into output_fp. | def create_libsize(expr_distr_fp, output_fp, sample, debug=False):
sample_expr_distr = pa.parquet.read_table(expr_distr_fp['path']).to_pandas()
libsize_count= pd.DataFrame({'sample': sample_expr_distr.columns[1:],
'libsize_75percent': np.percentile(sample_expr_distr.iloc[:, 1:], 75, axis=0),
'libsize_total_count': np.sum(sample_expr_distr.iloc[:, 1:], axis=0)}, index = None)
df_libsize = pd.DataFrame(libsize_count)
if os.path.isfile(output_fp):
previous_libsize = pd.read_csv(output_fp, sep = '\t')
df_libsize = pd.concat([previous_libsize, df_libsize], axis=0).drop_duplicates(subset=['sample'], keep='last')
df_libsize.to_csv(output_fp, sep='\t', index=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_libfile():\n # wfc3_obsmodes_uvis\n wfc3_uvis = [\n \"f218w\",\n \"f225w\",\n \"f275w\",\n \"f336w\",\n \"f390m\",\n \"f390w\",\n \"f410m\",\n \"f438w\",\n \"f467m\",\n \"f475w\",\n \"f547m\",\n \"f555w\",\n \... | [
"0.54608756",
"0.5400242",
"0.5211215",
"0.5197263",
"0.5194946",
"0.51773113",
"0.51361793",
"0.51357317",
"0.5126693",
"0.5125787",
"0.5115333",
"0.51034206",
"0.5085765",
"0.50600755",
"0.5036529",
"0.5036529",
"0.5028773",
"0.50280166",
"0.5021801",
"0.50140125",
"0.50085... | 0.6827684 | 0 |
Get the concatenated peptide from possible match peptide. | def get_concat_peptide(front_coord_pair, back_coord_pair, front_peptide, back_peptide, strand, k=None):
def get_longest_match_position(front_str,back_str,L=None):
if L is None:
L = min(len(front_str),len(back_str))
for i in reversed(list(range(1,L+1))):
if front_str[-i:] == back_str[:i]:
return i
return None
if strand == '+':
front_coord = front_coord_pair.stop_v2
back_coord = back_coord_pair.start_v1
else:
front_coord = front_coord_pair.start_v2
back_coord = back_coord_pair.stop_v1
if abs(front_coord-back_coord) % 3 == 0:
if front_coord == back_coord: # no intersection and we concatenate them directly
new_peptide = front_peptide + back_peptide
else:
pep_common_num = get_longest_match_position(front_peptide,back_peptide,L=k)
if pep_common_num is None:
new_peptide = ''
else:
new_peptide = front_peptide + back_peptide[pep_common_num:]
return new_peptide
else:
return '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def concatenate(self):\n if not self.match_count:\n # No concatenating if there are no matches\n return []\n if self.match_count <= 1:\n # Can't combine a single match\n return self.match_list\n # Setup for iterating through\n cont = True\n ... | [
"0.58377457",
"0.57520026",
"0.55693895",
"0.51852643",
"0.5118976",
"0.5036226",
"0.4998545",
"0.49705812",
"0.49523178",
"0.49279532",
"0.4873407",
"0.4863047",
"0.48540068",
"0.48335233",
"0.4816873",
"0.48077628",
"0.48022303",
"0.48017293",
"0.4800456",
"0.4780788",
"0.4... | 0.6651385 | 0 |
Print memory diagnostics including the active resident set size | def print_memory_diags(disable_print=False):
process = psutil.Process(os.getpid())
memory = process.memory_info().rss/1000000000.0
if not disable_print:
logging.info('\tMemory usage: {:.3f} GB'.format(memory))
return memory | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _mem_report(tensors: Iterable, mem_type: str) -> None:\n print(f\"Storage on {mem_type}\")\n print(\"-\" * LEN)\n total_numel = 0\n total_mem = 0\n visited_data: List[Any] = []\n for tensor in tensors:\n if tensor.is_sparse:\n continue\n ... | [
"0.70509243",
"0.7048715",
"0.684797",
"0.6824635",
"0.6806491",
"0.67121667",
"0.6675784",
"0.66524774",
"0.6635588",
"0.66292006",
"0.65855217",
"0.6583841",
"0.6535865",
"0.65259236",
"0.65087706",
"0.6496641",
"0.64578336",
"0.6444816",
"0.64439434",
"0.63657725",
"0.6365... | 0.74818563 | 0 |
Return some simple info for the tables in VotoStudio's workshop. | def get_table_values(self):
table_descriptors = getattr(self, 'table_descriptors', None)
if not table_descriptors:
raise AttributeError(f"Please add the 'table_descriptors' field to the model '{self._meta.label}'")
return {
'id': self.id,
'descriptors': [{
'name': d,
'value': self._get_descriptor_value(d),
} for d in table_descriptors],
'app_label': self._meta.app_label,
'model_name': self._meta.model_name,
'model_label': self._meta.label,
**self._get_user_info(),
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_table():\n\n title_list = ('ID', 'Platform', 'Producer', 'Year', 'Elements')\n \n return table, title_list",
"def PrintInfo(self):\n infoname = list(zip(*self.basicinfo.cursor.execute(\n \"SELECT Name FROM Infoname\").fetchall()))[0]\n for obj in infoname:\n ... | [
"0.6594911",
"0.6500587",
"0.64386666",
"0.6242125",
"0.61695975",
"0.6073596",
"0.6034584",
"0.6016744",
"0.5995429",
"0.5982109",
"0.59196234",
"0.59027016",
"0.5872902",
"0.5864931",
"0.5819785",
"0.57872105",
"0.57864594",
"0.5785509",
"0.57692075",
"0.57690203",
"0.57690... | 0.0 | -1 |
Return more detailed info for the detail view in VotoStudio's workshop. | def get_detail_info(self):
detail_descriptors = getattr(self, 'detail_descriptors', None)
if not detail_descriptors:
raise AttributeError(f"Please add the 'detail_descriptors' field to the model '{self._meta.label}'")
detail_values = [{
'name': d,
'value': getattr(self, d),
'type': 'basic',
} for d in detail_descriptors['basic']]
related_descriptors = detail_descriptors['related']
for related_descriptor in related_descriptors:
field_name = related_descriptor['field']
field = self._meta.get_field(field_name)
field_value = getattr(self, field_name)
# If this field on the instance
# has a value then check its type.
if field:
field_type = field.get_internal_type()
value = None
if field_type == 'ManyToManyField':
value = [[{
'name': attr,
'value': getattr(o, attr)
} for attr in ('id', *related_descriptor['attrs'])] for o in field_value.all()]
if field_type == 'ForeignKey' or field_type == 'OneToOneField':
value = [{
'name': attr,
'value': getattr(field_value, attr, None),
} for attr in related_descriptor['attrs']]
detail_values.append({
'name': field_name,
'model_label': field.related_model._meta.label,
'value': value,
'type': 'related',
})
return detail_values | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_details(self):",
"def print_details(self):\n self.view.print_details()",
"def detail(self):\n info = self.info()\n return info",
"def get_details(self):\n return self.details",
"def get_details(self):\n return self.details",
"def get_details(self):\n retu... | [
"0.6816689",
"0.67486215",
"0.6713748",
"0.65807945",
"0.65807945",
"0.65807945",
"0.65140444",
"0.6467318",
"0.6412233",
"0.6406499",
"0.63699645",
"0.62743896",
"0.62667674",
"0.6252862",
"0.6249236",
"0.6226553",
"0.619854",
"0.61578506",
"0.61264884",
"0.61043566",
"0.606... | 0.0 | -1 |
model_predict will return the preprocessed image | def model_predict(img_path, model_path):
learn = load_model(model_path)
img = open_image(img_path)
# get the outputs from the model
pred_class,pred_idx,outputs = learn.predict(img)
# return the classification the model returns
return pred_class | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict(model, img):\n\tx = image.img_to_array(img)\n\tx = np.expand_dims(x, axis=0)\n\tx = preprocess_input(x)\n\tpreds = model.predict(x)\n\treturn preds[0]",
"def predict(model, img, target_size=(229, 229)): #fixed size for InceptionV3 architecture\r\n if img.size != target_size:\r\n img = img.resize(... | [
"0.8407046",
"0.82272613",
"0.8150348",
"0.8089915",
"0.804863",
"0.804863",
"0.78832704",
"0.7876628",
"0.7739453",
"0.7739021",
"0.77017534",
"0.769704",
"0.76113904",
"0.7572362",
"0.75003004",
"0.7484886",
"0.74482",
"0.7415542",
"0.7312185",
"0.72863543",
"0.728227",
"... | 0.7862426 | 8 |
Randomly permute the training roidb. | def _shuffle_roidb_idx(self):
self.perm = np.random.permutation(np.arange(self.num_images))
self.cur = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _random_permutation(self):\n self.train_permutation = numpy.random.permutation(range(len(self.train_list)))",
"def shuffle(self, random_state=None): \n if random_state is None:\n random_state = self.random_state\n perm_ids = random_state.permutation(self.n_examples)\n ... | [
"0.7341957",
"0.6980299",
"0.6517204",
"0.6517204",
"0.64934677",
"0.64483047",
"0.6303886",
"0.6292093",
"0.6292093",
"0.626896",
"0.61837375",
"0.61757475",
"0.6172573",
"0.6138042",
"0.61206377",
"0.6080488",
"0.6078871",
"0.6064974",
"0.59135115",
"0.59126294",
"0.5903226... | 0.65922296 | 2 |
Return the roidb indices for the next minibatch. | def _get_next_minibatch_idx(self):
if self.cur + self.batch_size >= self.num_images:
self._shuffle_roidb_idx()
db_idx = self.perm[self.cur:self.cur + self.batch_size]
self.cur += self.batch_size
return db_idx | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_next_minibatch_inds(self):\n ## 实际上只需要向后遍历一个即可\n\n if self._cur + cfg.TRAIN.IMS_PER_BATCH >= len(self._roidb):\n self._shuffle_roidb_inds()\n self._cur = 0\n\n db_inds = self._perm[self._cur : self._cur+ cfg.TRAIN.IMS_PER_BATCH]\n self._cur += cfg.TRAIN.IM... | [
"0.76239616",
"0.745489",
"0.7413968",
"0.70359194",
"0.6630719",
"0.65478575",
"0.6362661",
"0.6330628",
"0.6231964",
"0.6105139",
"0.60316145",
"0.5990063",
"0.59887934",
"0.5968671",
"0.5885011",
"0.58408743",
"0.582455",
"0.5802829",
"0.57580876",
"0.5756126",
"0.5748933"... | 0.72795445 | 3 |
We use this function to flip the bunch in s and convert the information from time (t) to the longitudinal length along the bunch(s). | def flip_slice(t, bins=2944):
s = constants.c*np.ravel((np.max(t)-t)) # Flip s
s_sort_index = np.argsort(s) # Ascending order.
plt.clf()
hist_min = np.min(s)
# hist_max = np.min(s)+(np.max(s)-np.min(s))*0.96
hist_max = np.min(s)+(np.max(s)-np.min(s))*0.99
hist, zplot, patches = plt.hist(s, bins,range = (hist_min, hist_max))
id_slices = []
for n in range(0, bins):
num_begin = int(np.sum(hist[0:n]))
num_end = int(np.sum(hist[0:n+1]))
id_slices.append(s_sort_index[num_begin:num_end])
return id_slices, zplot, hist | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MakeLongTime(t, N):\n dt=(t.max()-t.min())/(max(shape(t))-1)\n tout=arange(0,N,dtype='f')\n tout=tout*dt\n return tout",
"def time_unwrap(val_timestamps):\n a=val_timestamps.shape[0]\n val_time =val_timestamps.astype('int64')\n for i in range(a-1):\n if val_time[i+1]-val_time[i]<-... | [
"0.5729294",
"0.535365",
"0.5282586",
"0.52594215",
"0.5227779",
"0.5178457",
"0.51595366",
"0.507217",
"0.5067364",
"0.50368345",
"0.5025668",
"0.49911958",
"0.49690363",
"0.49690363",
"0.49686134",
"0.4950423",
"0.490032",
"0.4897815",
"0.48929238",
"0.4863648",
"0.4863037"... | 0.49709463 | 12 |
We use this function to analyze the beam property along s. | def beam_property_along_s(ps, id_slices):
prop_s = np.zeros((14, len(id_slices)))
for n in range(len(id_slices)):
ps_s = np.take(ps, id_slices[n], axis=1)
prop_s[0, n] = np.average(ps_s[0,:])
prop_s[1, n] = np.average(ps_s[1,:])
prop_s[2, n] = np.average(ps_s[2,:])
prop_s[3, n] = np.average(ps_s[3,:])
prop_s[4, n] = np.std(ps_s[0,:])
prop_s[5, n] = np.std(ps_s[2,:])
prop_s[6:, n] = emittance_and_twiss(ps_s)
return prop_s | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prt_beam_prms(self):\n print '\\nIon Charge States = ', self.M.conf()['IonChargeStates']\n print 'IonEs [MeV] = ', self.M.conf()['IonEs']/1e6\n print 'IonEk [MeV] = ', self.M.conf()['IonEk']/1e6\n print '\\nBaryCenter 0:\\n', self.M.conf()['BaryCenter0']\n print '... | [
"0.55478334",
"0.55464554",
"0.5515675",
"0.54473466",
"0.5417652",
"0.5381833",
"0.533846",
"0.5251011",
"0.5237646",
"0.5210435",
"0.5204704",
"0.51945657",
"0.5185809",
"0.5178469",
"0.5154056",
"0.5144773",
"0.51260614",
"0.51221514",
"0.51218563",
"0.5119552",
"0.5115868... | 0.63167745 | 0 |
I want to use this function to calculate the 4D phase space at the end of some components in the undulator beamline. | def analyze_phase_space_at_end(ps_beg, beamline, beamline_id, id_slices, N_bin):
phase_spc = ps_beg
## Count how many times the class beamline_id occurs in the beamline_id
count_beamline = 0
for element in beamline:
if isinstance(element, beamline_id):
count_beamline += 1
ps_end = np.zeros((4, count_beamline, N_bin))
count_id = 0
for element in beamline:
phase_spc = np.dot( element.M1, phase_spc ) +element.M2
if isinstance(element, beamline_id):
ps_along_s = beam_property_along_s(phase_spc, id_slices)
ps_end[0, count_id, :] = ps_along_s[0, :]
ps_end[1, count_id, :] = ps_along_s[1, :]
ps_end[2, count_id, :] = ps_along_s[2, :]
ps_end[3, count_id, :] = ps_along_s[3, :]
count_id += 1
return ps_end | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_phasedbeam(self):\n\n self.phasedbeam = n.zeros((len(self.dmarr),len(self.reltime)), dtype='float64')\n\n for i in xrange(len(self.dmarr)):\n self.phasedbeam[i] = self.dedisperse(dmbin=i).mean(axis=3).mean(axis=2).mean(axis=1).real # dedisperse and mean\n ... | [
"0.6671196",
"0.64827406",
"0.6373965",
"0.6372035",
"0.6084464",
"0.60772616",
"0.6052406",
"0.59829885",
"0.5869289",
"0.5851694",
"0.580842",
"0.58041",
"0.5711219",
"0.5682935",
"0.5637525",
"0.5630165",
"0.5624642",
"0.56207854",
"0.5617892",
"0.5615495",
"0.5611847",
... | 0.6127306 | 4 |
We want to use this function to analyze which part is on the axis in each undulator section. | def analyze_on_axis(phase_space, id_begin, id_end, ds_slice, zplot):
ps = phase_space[:, (id_begin-1):id_end, :]
# print(np.shape(ps))
# ps = ps[numpy.logical_not(numpy.isnan(ps))]
x = ps[0, :, :]
px = ps[1, :, :]
y = ps[2, :, :]
py = ps[3, :, :]
id_on_axis = np.zeros((4, int(id_end-id_begin+1)))
for n in range(int(id_end-id_begin+1)):
x_this = x[n, :]
px_this = px[n, :]
y_this = y[n, :]
py_this = py[n, :]
# Remove all NAN elements in the phase space array
x_this = x_this[np.logical_not(np.isnan(x_this))]
px_this = px_this[np.logical_not(np.isnan(px_this))]
y_this = y_this[np.logical_not(np.isnan(y_this))]
py_this = py_this[np.logical_not(np.isnan(py_this))]
## Plot X
plt.subplot(2, 2, 1)
plt.plot(zplot[0:len(x_this)]*1e+6, x_this*1e+6)
plt.ylabel('Position in X/ $\mu$m', fontsize=10)
## Plot Y
plt.subplot(2, 2, 2)
plt.plot(zplot[0:len(y_this)]*1e+6, y_this*1e+6)
plt.ylabel('Position in Y/ $\mu$m', fontsize=10)
## Plot px
plt.subplot(2, 2, 3)
plt.plot(zplot[0:len(px_this)]*1e+6, px_this)
plt.ylabel('Angle in X', fontsize=10)
## Plot py
plt.subplot(2, 2, 4)
plt.plot(zplot[0:len(py_this)]*1e+6, py_this)
plt.ylabel('Angle in Y', fontsize=10)
# plt.xlabel('Longitudianl Direction of the Bunch $s$/ $\mu$m')
# plt.title('First Undulator Section')
# plt.title('Second Undulator Section')
# plt.title('Third Undulator Section')
id_on_axis[0, n] = np.argmin(np.abs(x_this))
id_on_axis[1, n] = np.argmin(np.abs(px_this))
id_on_axis[2, n] = np.argmin(np.abs(y_this))
id_on_axis[3, n] = np.argmin(np.abs(py_this))
fig = plt.gcf()
fig.set_size_inches(13.5, 9)
ax = plt.gca()
ax.yaxis.get_major_formatter().set_powerlimits((0,1))
fig.savefig('phase_space_U3_new.png', dpi=100)
plt.show()
s_on_axis = np.average(id_on_axis[2:4,:])*ds_slice
return id_on_axis, s_on_axis | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def othersn(ax):",
"def _sections(self, axis=None, direction=None, cycle=None,\n concat_direction=False, concat_cycle=False, info=False,\n **kwargs):\n if axis is None:\n axis = ['x', 'y']\n if direction is None:\n direction = ['left', 'right'... | [
"0.5563447",
"0.55561185",
"0.55355847",
"0.5400956",
"0.5346773",
"0.5164223",
"0.51494545",
"0.5128714",
"0.5126724",
"0.50905246",
"0.5079524",
"0.5058356",
"0.5053756",
"0.5040289",
"0.50352335",
"0.50240916",
"0.5023575",
"0.50167745",
"0.50061435",
"0.49880677",
"0.4979... | 0.6365625 | 0 |
We want to use this function to optimize parameters for orbit correctors. Indeed, we always need two orbit correctors to do this job. | def analyze_orbit_corrector(OC1, OC2, beamline, phase_beg):
M = np.identity(4)
OC_parameters = np.zeros(4)
for element in beamline:
M = np.dot(element.M1, M)
# Since the X and Y are decoupled, we can treat them separately.
M_x = M[0:2, 0:2]
M_y = M[2:4, 2:4]
L1 = [[OC1.length/2], [1]]
L2 = [[OC2.length/2], [1]]
M_OC1 = np.array(OC1.M1)[0:2, 0:2]
M_OC2 = np.array(OC2.M1)[0:2, 0:2]
# The following part solve the cx_1 and cx_2
M1_x = np.linalg.multi_dot([M_OC2, M_x, L1])
M2_x = np.linalg.multi_dot([M_OC2, M_x, M_OC1])
M_OC_x = np.hstack((M1_x, L2))
OC_parameters[0:2] = -np.linalg.multi_dot([np.linalg.inv(M_OC_x), M2_x, phase_beg[0:2]])
# The end of the X-part
# The following part solve the cy_1 and cy_2
M1_y = np.linalg.multi_dot([M_OC2, M_y, L1])
M2_y = np.linalg.multi_dot([M_OC2, M_y, M_OC1])
M_OC_y = np.hstack((M1_y, L2))
OC_parameters[2:4] = -np.linalg.multi_dot([np.linalg.inv(M_OC_y), M2_y, phase_beg[2:4]])
# The end of the Y-part
return OC_parameters | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve_prep(self):\n\n par = self.par\n sol = self.sol\n\n # a. retirement\n sol.m_ret = np.zeros((par.T,par.Nm_ret))\n sol.c_ret = np.zeros((par.T,par.Nm_ret))\n sol.a_ret = np.zeros((par.T,par.Nm_ret))\n sol.inv_v_ret = np.zeros((par.T,par.Nm_ret))\n sol... | [
"0.6054454",
"0.6029055",
"0.6029055",
"0.6029055",
"0.59086585",
"0.5886708",
"0.585231",
"0.58431363",
"0.57428247",
"0.57226473",
"0.56672513",
"0.5646705",
"0.5617193",
"0.5610204",
"0.56079906",
"0.5607317",
"0.55761766",
"0.5571415",
"0.5559164",
"0.55436873",
"0.552162... | 0.56180215 | 12 |
I want to use this function to directly set up all orbit correctors. | def set_up_orbit_correctors(ps_beg, delay, id_slice1, ds_slice, zplot, id_slices, U_core, lambdaref):
SXSS = Chicane(3.2716, 0.362, 0.830399, delay[0])
HXSS = Chicane(3.2, 0.3636, 0.5828, delay[1])
OC2 = [CORR08, D1_SXSS, SXSS, D2_SXSS, QUAD09, CORR09]
OC3 = [CORR15, D1_HXSS, HXSS, D2_HXSS, QUAD16, CORR16]
ps_end1 = beam_transportation(ps_beg, U_core[0])
# ps_end1 is a 4-by-N array. N is the number of macro-particles. It is the full
# 4D phase space distribution at the end of the first undulator section.
# The id of the slice on the axis in the second undulator section
on_axis_id_U2 = int(id_slice1+delay[0]/ds_slice+ (8*110)*lambdaref/ds_slice) # The last part is slippage
print(on_axis_id_U2)
ps_end_slice1 = beam_property_along_s(ps_end1, id_slices)[0:4, :]
ps_on_axis_2 = np.ravel(ps_end_slice1[:, on_axis_id_U2])
# print(ps_on_axis_2)
OC2_optimized = analyze_orbit_corrector(OC2[0], OC2[-1], OC2[1:-1], ps_on_axis_2)
print(OC2_optimized)
CORR08_new = Orbit_Corrector(OC2[0].length, OC2_optimized[0], OC2_optimized[2])
CORR09_new = Orbit_Corrector(OC2[-1].length, OC2_optimized[1], OC2_optimized[3])
# The whole U2 with optimized orbit correctors
U2_new = [CORR08_new] + OC2[1:-1] + [CORR09_new] + U_core[1]
ps_end2 = beam_transportation(ps_end1, U2_new)
# ps_end2 is a 4-by-N array. N is the number of macro-particles. It is the full
# 4D phase space distribution at the end of the second undulator section.
# The id of the slice on the axis in the third undulator section
on_axis_id_U3 = int(id_slice1+(delay[0]+delay[1])/ds_slice +(14*110*lambdaref)/ds_slice) # The last term is the slipage
print(on_axis_id_U3)
ps_end_slice2 = beam_property_along_s(ps_end2, id_slices)[0:4, :]
ps_on_axis_3 = np.ravel(ps_end_slice2[ :, on_axis_id_U3])
# print(ps_on_axis_3)
OC3_optimized = analyze_orbit_corrector(OC3[0], OC3[-1], OC3[1:-1], ps_on_axis_3)
print(OC3_optimized)
CORR15_new = Orbit_Corrector(OC3[0].length, OC3_optimized[0], OC3_optimized[2])
CORR16_new = Orbit_Corrector(OC3[-1].length, OC3_optimized[1], OC3_optimized[3])
U3_new = [CORR15_new] + OC3[1:-1] + [CORR16_new] + U_core[2]
Undulator_Beamline = U_core[0]+U2_new+U3_new
return Undulator_Beamline | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n self.precip_cube = setup_precipitation_cube()\n self.oe_cube = setup_orographic_enhancement_cube()\n self.vel_x = set_up_xy_velocity_cube(\"advection_velocity_x\")\n self.vel_y = set_up_xy_velocity_cube(\"advection_velocity_y\")\n for cube in [self.precip_cube,... | [
"0.59741217",
"0.59616315",
"0.59439087",
"0.59116036",
"0.58822644",
"0.58440155",
"0.5834586",
"0.57733816",
"0.5746145",
"0.5736931",
"0.57054114",
"0.56986",
"0.56339926",
"0.5609833",
"0.5579601",
"0.5572102",
"0.5556903",
"0.55461323",
"0.5529173",
"0.5519832",
"0.55151... | 0.64385605 | 0 |
Loads the preprocessed celeb_a dataset scaled down to 32x32 | def get_celeb_a32():
path = './data/celeb_a32'
if not os.path.exists(path):
print(path, " does not exits")
return None
images = utils.get_tensor_images_from_path(path)
data = tf.data.Dataset.from_tensor_slices(images)
data = data.map(lambda x: tf.cast(x, tf.float32))
data = data.batch(int(tf.data.experimental.cardinality(data)))
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_celebA():\n channel_mean = [0.50003925, 0.42008925, 0.37377534]\n channel_std = [0.30878809, 0.28794379, 0.28661432]\n\n dataset = CelebA(transform=transforms.Compose([\n transforms.CenterCrop(178),\n transforms.Resize(64),\n transforms.ToTensor(),\n transforms.Normali... | [
"0.7416813",
"0.6421557",
"0.6321571",
"0.60597914",
"0.5983696",
"0.5967315",
"0.5948484",
"0.59380203",
"0.5907359",
"0.5897862",
"0.58657163",
"0.58646065",
"0.5861289",
"0.58553",
"0.58469105",
"0.577485",
"0.5769538",
"0.5751676",
"0.574995",
"0.57490104",
"0.5744094",
... | 0.72671366 | 1 |
listen for message event | async def on_message(self, msg: Message):
from_contact = msg.talker()
text = msg.text()
room = msg.room()
if text == '#ding':
conversation: Union[
Room, Contact] = from_contact if room is None else room
await conversation.ready()
await conversation.say('dong')
file_box = FileBox.from_url(
'https://ss3.bdstatic.com/70cFv8Sh_Q1YnxGkpoWK1HF6hhy/it/'
'u=1116676390,2305043183&fm=26&gp=0.jpg',
name='ding-dong.jpg')
await conversation.say(file_box) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _handle_message(self, msg):\n self.event('message', msg)",
"def on_message(data):\n pass",
"def event_in_cb(self, msg):\n self.event = msg.data",
"def on_message(self, message):\n print \"Client %s received a message : %s\" % (self.id, message)",
"def onMessage(self, message... | [
"0.8410868",
"0.8254583",
"0.7882204",
"0.78618526",
"0.7829602",
"0.7802724",
"0.773378",
"0.7572389",
"0.7512617",
"0.74960405",
"0.74841064",
"0.74824226",
"0.7457746",
"0.7441775",
"0.74085176",
"0.73933196",
"0.7379106",
"0.7322716",
"0.7322716",
"0.730575",
"0.729197",
... | 0.0 | -1 |
all initialization jobs should be done here. | async def on_ready(self, payload: EventReadyPayload):
log.info('ready event <%s>', payload)
# 1. get all of friends
friends: List[Contact] = await self.Contact.find_all()
for friend in friends:
log.info('load friend<%s>', friend)
# 2. get all of rooms
rooms: List[Room] = await self.Room.find_all()
for room in rooms:
log.info('load room<%s>', room) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _initJobs(self):\n pass",
"def do_init(self):\n\n pass",
"def _initialise_run(self) -> None:",
"def initialize():\n manager.initialize()\n logs.exit_great_success()",
"def _manually_initialize(self) -> None:\n # XXX: maybe refactor, this is actually part of the public interfa... | [
"0.7979565",
"0.7962231",
"0.7734526",
"0.76368004",
"0.7622692",
"0.7585923",
"0.7379355",
"0.7354919",
"0.73505986",
"0.7327622",
"0.7325596",
"0.7325596",
"0.7325596",
"0.73226774",
"0.7315183",
"0.7215203",
"0.7204069",
"0.7199315",
"0.7195699",
"0.7195699",
"0.7195699",
... | 0.0 | -1 |
Run all dispatch tests | def dispatch():
suite = ServiceTestSuite()
suite.addTest(unittest.makeSuite(Test, 'test_dispatch'))
return suite | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def runTests(self):\n \n pass",
"def doAllTests(self):\n # Initial offset\n self.getAlertsFile()\n self.offset = self.getOffset(self.config.get('PATHS', 'tempfile'))\n\n # Do all tests\n # As the socket is not persistent, client side attacks have to be done before all... | [
"0.7445618",
"0.72676694",
"0.72357136",
"0.69886786",
"0.69496655",
"0.69481313",
"0.6913679",
"0.6835688",
"0.6833541",
"0.68203133",
"0.68139446",
"0.6779515",
"0.67011243",
"0.6626831",
"0.66243935",
"0.6614983",
"0.65842265",
"0.6561729",
"0.6560582",
"0.6522628",
"0.648... | 0.7137783 | 3 |
Run all local tests | def local():
suite = ServiceTestSuite()
suite.addTest(unittest.makeSuite(Test, 'test_local'))
return suite | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_local_tests(self, *args, **kwargs):\n pass",
"def runAll():\n\n loader = unittest.TestLoader()\n test_dir = pkg_resources.resource_filename('frvcpy.test','.')\n suite = loader.discover(test_dir)\n\n runner = unittest.TextTestRunner(verbosity=2)\n runner.run(suite)",
"def main():\... | [
"0.8297117",
"0.7959936",
"0.7950929",
"0.7797158",
"0.768714",
"0.766804",
"0.76199144",
"0.75607073",
"0.7456279",
"0.744708",
"0.7380929",
"0.7380103",
"0.72950643",
"0.7281221",
"0.727362",
"0.7181298",
"0.71580607",
"0.7139827",
"0.7125284",
"0.70671266",
"0.7064775",
... | 0.0 | -1 |
Run all network tests | def net():
suite = ServiceTestSuite()
suite.addTest(unittest.makeSuite(Test, 'test_net'))
return suite | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_all():\n test_get_to()\n test_error_type()\n test_exchange()\n print(\"All tests passed.\")",
"def run_all_tests():\n remove_dbs()\n run_training_tests()\n run_custom_training_tests()\n run_training_save_tests()\n run_validation_tests()\n run_feature_extraction_tests()",
... | [
"0.7459378",
"0.74455136",
"0.7278033",
"0.71453613",
"0.713573",
"0.7003208",
"0.69377714",
"0.6935737",
"0.6916369",
"0.68943274",
"0.6879508",
"0.6857769",
"0.67931044",
"0.6778059",
"0.6765904",
"0.675124",
"0.67291695",
"0.66894144",
"0.66677123",
"0.6644143",
"0.6638301... | 0.627527 | 56 |
This function will get a vector an construct a polynomial(function) f(x) with each degree having a coefficient from the corrosponding value from the vector. | def vecToFunc(vector):
def f(x):
f = 0
for i in range(len(vector)):
f += vector[i]*x**i
return f
return f | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate_polynomial(f,x):\n degree = len(f)-1\n ans = 0\n for i in f:\n ans += i*x**degree\n degree -= 1\n return(ans)",
"def build_poly(x, degree):\n phi = np.ones(len(x))\n phi = np.vstack((phi, [x**(j+1) for j in range(degree)]))\n \n return phi.T",
"def polynomial_... | [
"0.71002614",
"0.6959504",
"0.6857088",
"0.6731912",
"0.671709",
"0.66995835",
"0.6682442",
"0.6642471",
"0.66166174",
"0.6615598",
"0.6501584",
"0.6486129",
"0.6437595",
"0.64153004",
"0.64109534",
"0.6395708",
"0.63793916",
"0.636425",
"0.63570154",
"0.6323563",
"0.63210785... | 0.67569125 | 3 |
Parse command line arguments | def get_options(cmd_args=None):
cmd_parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
cmd_parser.add_argument(
'-i',
'--input_file',
help="""a log file to be cleaned up""",
type=str,
default='')
cmd_parser.add_argument(
'-s',
'--salt',
help="""the salt for anonymizing IPs [optional, defaults to hardcoded one]""",
type=str,
default=salt)
args = cmd_parser.parse_args(cmd_args)
options = {}
options['input_file'] = args.input_file
options['salt'] = args.salt
return options | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_arguments(args):",
"def parse_args():\n parser = argparse.ArgumentParser(\n description=\"Reads datapacket pcds, interpolates quaternions and generates scans from dataset in config file\")\n parser.add_argument(\"--visualization\", \"-v\", action=\"store_true\", help=\"if generated clouds ... | [
"0.84956664",
"0.77526116",
"0.7492404",
"0.74425805",
"0.74166906",
"0.7415034",
"0.7406717",
"0.7405937",
"0.7394592",
"0.739314",
"0.7353777",
"0.73531276",
"0.73295814",
"0.7326098",
"0.73121136",
"0.7297962",
"0.72947985",
"0.7294619",
"0.7293887",
"0.7288054",
"0.727713... | 0.0 | -1 |
Returns the file for a given URL. If the URL appear to be a static file, we will attempt to load it internally. Otherwise, it will resolve normally as an external URL. Relative URLs are only supported The default behaviour will fetch any http(s) files normally, and will also attempt to resolve staticfiles internally (this should mostly affect development scenarios, but also works if static files are served under a relative URL). | def django_url_fetcher(url: str):
# If the URL looks like a staticfile, try to load it as such.
# Reading it from the storage avoids a network call in many cases (unless the
# storage is remote, in which case this improves nothing:
try:
if url.startswith(staticfiles_storage.base_url):
filename = url.replace(staticfiles_storage.base_url, "", 1)
data = None
path = finders.find(filename)
if path:
# Read static files from source (e.g.: the file that's bundled with the
# Django app that provides it.
# This also picks up uncollected staticfiles (useful when developing /
# in DEBUG mode).
with open(path, "rb") as f:
data = f.read()
else:
# File was not found by a finder. This commonly happens when running in
# DEBUG=True with a storage that uses Manifests or alike, since the
# filename won't match with the source file.
# In these cases, use the _storage_ to find the file instead:
with staticfiles_storage.open(filename) as f:
data = f.read()
return {
"mime_type": mimetypes.guess_type(url)[0],
"string": data,
}
except (ValueError, FileNotFoundError):
# Looks like this wasn't a staticfile (or maybe it was a missing one?)
# Let it resolve as a normal URL.
pass
try:
# If the URL is a relative URL, use Django's resolver to figure out how Django
# would serve this.
#
# This should cover all those funky scenarios like:
# - Custom views that serve dynamically generated files.
# - Media files (if serving them via Django, which is not recommended).
if url.startswith("/"):
view, args, kwargs = resolve(url)
kwargs["request"] = HttpRequest
kwargs["request"].method = "GET"
response = view(*args, **kwargs)
return {
"mime_type": mimetypes.guess_type(url)[0],
"string": response.content,
}
except Resolver404 as e:
raise InvalidRelativeUrl(f"No view matched `{url}`.") from e
return default_url_fetcher(url) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file(cls, url, working_dir):\n if url.lower().startswith(\"s3://\"):\n return cls._s3_get_file(url)\n elif url.lower().startswith(\"http\"):\n return cls._http_get_file(url)\n else:\n return cls._fs_get_file(url, working_dir)",
"def _fs_get_file(url, ... | [
"0.7276085",
"0.7078293",
"0.66783506",
"0.6656778",
"0.643477",
"0.6392354",
"0.6384419",
"0.6384419",
"0.6384419",
"0.63783556",
"0.63404953",
"0.63349915",
"0.6328483",
"0.62485206",
"0.6248228",
"0.6211496",
"0.62018925",
"0.6179627",
"0.61627275",
"0.6156638",
"0.6155268... | 0.68511707 | 2 |
Writes the PDF data into ``file_``. Note that ``file_`` can actually be a Django Response object as well, since these are filelike objects. This function may be used as a helper that can be used to save a PDF file to a file (or anything else outside of a request/response cycle). | def render_pdf(
template: Union[List[str], str],
file_: IO,
url_fetcher=django_url_fetcher,
context: Optional[dict] = None,
):
context = context or {}
if isinstance(template, str):
template = [template]
html = select_template(template).render(context)
HTML(
string=html,
base_url="not-used://",
url_fetcher=url_fetcher,
).write_pdf(
target=file_,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_pdf(self, response):\n\n # get metadata\n file_type = \"__comprovante_de_acesso__\"\n\n # options to save pdf\n file_id = str(uuid.uuid4())\n filename = \"{file_id}.pdf\".format(file_id=file_id)\n file_path = os.path.join(path, \"downloads\", self.scrape_id, filen... | [
"0.67716074",
"0.6708492",
"0.6434953",
"0.6190672",
"0.61892325",
"0.6181426",
"0.6171567",
"0.6031542",
"0.5953594",
"0.59264964",
"0.587159",
"0.5858788",
"0.57871544",
"0.5752377",
"0.57122386",
"0.56981695",
"0.56687397",
"0.5661542",
"0.56381875",
"0.56225604",
"0.56082... | 0.55854136 | 21 |
Set the `order_by_field` on the filterset and ensure that the field name is respected. | def test_ordering_with_overridden_field_name(self):
class F(FilterSet):
class Meta:
model = User
fields = ['username', 'status']
order_by = ['status']
order_by_field = 'order'
f = F({'order': 'status'}, queryset=self.qs)
self.assertQuerysetEqual(
f.qs, ['carl', 'alex', 'jacob', 'aaron'], lambda o: o.username) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ordering_with_overridden_field_name(self):\n class F(FilterSet):\n class Meta:\n model = User\n fields = ['username', 'status']\n order_by = ['status']\n order_by_field = 'order'\n\n f = F().form\n self.assertNotIn... | [
"0.7114981",
"0.6805322",
"0.65821075",
"0.6384245",
"0.6316189",
"0.6254384",
"0.6160399",
"0.60926193",
"0.59577346",
"0.59503293",
"0.59400135",
"0.5909272",
"0.5759706",
"0.57583076",
"0.57493776",
"0.5726326",
"0.5716653",
"0.571355",
"0.5712792",
"0.5647503",
"0.5642596... | 0.7341014 | 0 |
Test ordering descending works when order_by=True. | def test_ordering_descending_unset(self):
class F(FilterSet):
class Meta:
model = User
fields = ['username', 'status']
order_by = True
f = F({'o': '-username'}, queryset=self.qs)
self.assertQuerysetEqual(
f.qs, ['jacob', 'carl', 'alex', 'aaron'], lambda o: o.username) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_orderby(self):\n\n # TODO: make a unit test out of these various combinations\n #m = mapper(User, users, order_by=desc(users.c.user_name))\n mapper(User, users, order_by=None)\n #mapper(User, users)\n\n #l = create_session().query(User).select(order_by=[desc(users.c.user... | [
"0.68660283",
"0.6728724",
"0.6677009",
"0.6650034",
"0.65972036",
"0.6469477",
"0.64451",
"0.6346018",
"0.62945986",
"0.62910104",
"0.6286649",
"0.6240998",
"0.6212203",
"0.619569",
"0.618064",
"0.61099553",
"0.6034809",
"0.5979432",
"0.59784126",
"0.59772485",
"0.59331065",... | 0.6622364 | 4 |
Set the `order_by_field` on the filterset and ensure that the field name is respected. | def test_ordering_with_overridden_field_name(self):
class F(FilterSet):
class Meta:
model = User
fields = ['username', 'status']
order_by = ['status']
order_by_field = 'order'
f = F().form
self.assertNotIn('o', f.fields)
self.assertIn('order', f.fields)
self.assertEqual(f.fields['order'].choices, [('status', 'Status')]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ordering_with_overridden_field_name(self):\n class F(FilterSet):\n class Meta:\n model = User\n fields = ['username', 'status']\n order_by = ['status']\n order_by_field = 'order'\n\n f = F({'order': 'status'}, queryset=se... | [
"0.7341898",
"0.6805477",
"0.6581681",
"0.6385461",
"0.6315338",
"0.6252756",
"0.6159823",
"0.6090766",
"0.5956584",
"0.5951964",
"0.59386057",
"0.59089255",
"0.5759275",
"0.57582235",
"0.5749629",
"0.57229424",
"0.5717913",
"0.5713295",
"0.5711421",
"0.5647316",
"0.56422055"... | 0.7116057 | 1 |
Set the `order_by_field` on the filterset and ensure that the field name is respected. | def test_ordering_with_overridden_field_name_and_descending(self):
class F(FilterSet):
class Meta:
model = User
fields = ['username', 'status']
order_by = ['status', '-status']
order_by_field = 'order'
f = F().form
self.assertNotIn('o', f.fields)
self.assertIn('order', f.fields)
self.assertEqual(f.fields['order'].choices, [('status', 'Status'), ('-status', 'Status (descending)')]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ordering_with_overridden_field_name(self):\n class F(FilterSet):\n class Meta:\n model = User\n fields = ['username', 'status']\n order_by = ['status']\n order_by_field = 'order'\n\n f = F({'order': 'status'}, queryset=se... | [
"0.7341014",
"0.7114981",
"0.65821075",
"0.6384245",
"0.6316189",
"0.6254384",
"0.6160399",
"0.60926193",
"0.59577346",
"0.59503293",
"0.59400135",
"0.5909272",
"0.5759706",
"0.57583076",
"0.57493776",
"0.5726326",
"0.5716653",
"0.571355",
"0.5712792",
"0.5647503",
"0.5642596... | 0.6805322 | 2 |
Given object without dependency, use it's importerManager to get available data | def test_nondependent_object_get(self):
manager = ImporterManager(importer=UserImporter())
for row,name in enumerate(self.usernames):
manager.update_kvs(field_name='username',value=name,row=row)
manager.get_available_rows()
for i in range(self.n_objs):
objs: List[RecordData] = manager.get_objs_and_meta(i) #: Returns a list of objects only if manytomany
self.assertEqual(objs[0].available, True)
self.assertIsNotNone(objs[0].object)
self.assertIsInstance(objs[0].object, User)
self.assertIsNotNone(objs[0].query)
del manager | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def importer():\n pass",
"def get_objects_data(self):\n pass",
"def import_(self, data):\n return self.__import(data)",
"def import_data(self):\n self.models = []\n for o in self.loader.load():\n klass = self.type_for(o)\n if hasattr(klass, \"from_api\"):\... | [
"0.64458275",
"0.62757504",
"0.61903095",
"0.6175268",
"0.59833163",
"0.5957138",
"0.5917701",
"0.58304566",
"0.58275753",
"0.58036554",
"0.57879543",
"0.5749467",
"0.5749467",
"0.5749467",
"0.5749467",
"0.57487833",
"0.574654",
"0.5720211",
"0.5711407",
"0.5699259",
"0.56894... | 0.5439921 | 37 |
Given object without dependency, use it's importerManager to get available data, and assert that after deleting a given user, we can still query the metadata returned by the ImporterManger | def test_partial_nondependent_object_get(self):
MISSING_INDEX = 2
User.objects.filter(username=self.usernames[MISSING_INDEX]).delete()
manager = ImporterManager(importer=UserImporter())
for row,name in enumerate(self.usernames):
manager.update_kvs(field_name='username',value=name,row=row)
manager.get_available_rows()
for i in range(self.n_objs):
objs: List[RecordData] = manager.get_objs_and_meta(i) #: Returns a list of objects only if manytomany
if i==MISSING_INDEX:
self.assertEqual(objs[0].available, False)
self.assertIsNone(objs[0].object)
self.assertIsNotNone(objs[0].query)
continue
self.assertEqual(objs[0].available, True)
self.assertIsNotNone(objs[0].object)
self.assertIsInstance(objs[0].object, User)
self.assertIsNotNone(objs[0].query)
del manager | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_nondependent_object_get(self):\n manager = ImporterManager(importer=UserImporter())\n for row,name in enumerate(self.usernames):\n manager.update_kvs(field_name='username',value=name,row=row)\n\n manager.get_available_rows()\n for i in range(self.n_objs):\n ... | [
"0.6992192",
"0.6501583",
"0.63623047",
"0.60247934",
"0.5967793",
"0.594195",
"0.57997954",
"0.57775795",
"0.56826866",
"0.56701046",
"0.56604755",
"0.5646488",
"0.5642088",
"0.5631375",
"0.5626657",
"0.55903274",
"0.55903274",
"0.5548128",
"0.5526234",
"0.5525164",
"0.55185... | 0.6286618 | 3 |
Ensures any object with an analagous dependency relationship to UserProfile > User && UserProfile > Company can filter based on it's related import kvs | def test_dependent_object_import(self):
# Initialize Importers
up_manager = ImporterManager(importer=UserProfileImporter())
company_manger = ImporterManager(importer=CompanyImporter())
user_manager = ImporterManager(importer=UserImporter())
# Populate leaf models of dependency tree with kv data
for row,name in enumerate(self.usernames):
user_manager.update_kvs(field_name='username', value=name, row=row)
company_manger.update_kvs(field_name='natural_id', value=self.company.natural_id, row=row)
#: Retrieve data associated with kv data
user_manager.get_available_rows()
company_manger.get_available_rows()
#: Populate data up the dependency tree with retrieved rows
for row in range(self.n_objs):
up_manager.update_kvs('company', company_manger.get_object_or_list(row), row=row)
up_manager.update_kvs('user', user_manager.get_object_or_list(row), row=row)
#: Retrieve data associated with models depended upon
up_manager.get_available_rows()
#: Test corresponding UserProfile has been returned
for row in range(self.n_objs):
objs = up_manager.get_objs_and_meta(row) #: Returns a list of objects only if manytomany, o/w just 1
self.assertEqual(objs[0].available, True)
self.assertIsNotNone(objs[0].object)
self.assertIsInstance(objs[0].object, UserProfile)
self.assertIsNotNone(objs[0].query)
self.assertEqual(objs[0].object.user.username, self.usernames[row]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_twice_dependent_object_import(self):\n pass",
"def test_partial_twice_dependent_object_import(self):\n pass",
"def test_m2m_dependent_object_import_precision(self): #: TODO: Come up with a better name\n other_company = Company.objects.create(name='Other Co', natural_id='oc')\n ... | [
"0.55593485",
"0.5515848",
"0.548871",
"0.53242356",
"0.5323796",
"0.5270651",
"0.52381533",
"0.5179091",
"0.51565045",
"0.5153947",
"0.50942135",
"0.50636023",
"0.4970877",
"0.4866266",
"0.48630893",
"0.48102677",
"0.47713712",
"0.47691986",
"0.47338426",
"0.4730474",
"0.470... | 0.6230097 | 0 |
Tag > UserProfile > User | def test_twice_dependent_object_import(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tags(request):\n return Tag.objects.filter(user=request.user)",
"def test_tags_limited_to_user_tags(self):\n\n user2 = create_user(\n fname='Test2',\n lname='User2',\n email='test2@gmail.com',\n password='testpass2'\n )\n\n Tag.objects.creat... | [
"0.6036632",
"0.59151506",
"0.5854669",
"0.5790308",
"0.5763715",
"0.574283",
"0.5727083",
"0.57143545",
"0.57116055",
"0.56976116",
"0.5677059",
"0.5661893",
"0.56557196",
"0.56339324",
"0.5570487",
"0.5566566",
"0.5553686",
"0.5552441",
"0.55499566",
"0.5542527",
"0.5512405... | 0.0 | -1 |
Tag > UserProfile > User | def test_partial_twice_dependent_object_import(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tags(request):\n return Tag.objects.filter(user=request.user)",
"def test_tags_limited_to_user_tags(self):\n\n user2 = create_user(\n fname='Test2',\n lname='User2',\n email='test2@gmail.com',\n password='testpass2'\n )\n\n Tag.objects.creat... | [
"0.6036632",
"0.59151506",
"0.5854669",
"0.5790308",
"0.5763715",
"0.574283",
"0.5727083",
"0.57143545",
"0.57116055",
"0.56976116",
"0.5677059",
"0.5661893",
"0.56557196",
"0.56339324",
"0.5570487",
"0.5566566",
"0.5553686",
"0.5552441",
"0.55499566",
"0.5542527",
"0.5512405... | 0.0 | -1 |
Image (m)(m)> Tag > UserProfile > User e.g. see factory.create_tags_images for the creation of the below items > We'll assert that populating related data on the m2m field > constructs the expect > retrieves the correct results > doesn't retrieve the incrorrect results Image.pk Image.name Tag.name i grass green i grass blue k sun yellow l grass m sun | def test_m2m_dependent_object_import(self):
user_profile: UserProfile = self.user_profiles[0] # See self.setUp()
# ************ First Handle generating the Tags/Images Synthetically Through the Importer ************
# Initialize Importers
image_manager = ImporterManager(importer=ImageImporter())
tag_manager = ImporterManager(importer=TagImporter())
up_manager = ImporterManager(importer=UserProfileImporter())
company_manger = ImporterManager(importer=CompanyImporter())
user_manager = ImporterManager(importer=UserImporter())
# Populate leaf models of dependency tree with kv data
for row,image in enumerate(self.images):
user_manager.update_kvs(field_name='username', value=user_profile.user.username, row=row)
company_manger.update_kvs(field_name='natural_id', value=self.company.natural_id, row=row)
#: Retrieve data associated with kv data
user_manager.get_available_rows()
company_manger.get_available_rows()
#: Populate data up the dependency tree with retrieved rows
for row,image in enumerate(self.images):
up_manager.update_kvs('company', company_manger.get_object_or_list(row), row=row)
up_manager.update_kvs('user', user_manager.get_object_or_list(row), row=row)
#: Retrieve data associated with models depended upon
up_manager.get_available_rows()
tag_manager.update_kvs('slug', 'blue', row=0, col=0)
tag_manager.update_kvs('slug', 'green', row=0, col=1)
tag_manager.update_kvs('company', company_manger.get_object_or_list(0), row=0, col=0)
tag_manager.update_kvs('created_by', up_manager.get_object_or_list(0), row=0, col=0)
tag_manager.update_kvs('slug', 'yellow', row=1, col=0)
tag_manager.update_kvs('company', company_manger.get_object_or_list(1), row=1, col=0)
tag_manager.update_kvs('created_by', up_manager.get_object_or_list(1), row=1, col=0)
#: Retrieve associate intermediate data
tag_manager.get_available_rows()
for row,image in enumerate(self.images):
image_manager.update_kvs('path', image.path, row=row)
image_manager.update_kvs('name', image.name, row=row)
image_manager.update_kvs('tag', tag_manager.get_object_or_list(row), row=row)
image_manager.update_kvs('company', company_manger.get_object_or_list(row), row=row)
image_manager.get_available_rows()
self.assertNotEqual(image_manager.get_object_or_list(0), [])
self.assertIsInstance(image_manager.get_object_or_list(0), Image)
self.assertNotEqual(image_manager.get_object_or_list(1), [])
self.assertIsInstance(image_manager.get_object_or_list(1), Image) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_image(self):\n\n image = Image.query.filter(Image.tag1 == \"Denali\").first()\n self.assertEqual(image.tag1, \"Denali\")",
"def test_users_photos_view_set_get_own_photos(self):\n # Create user and data\n user = account_models.User.objects.create_user(email='mrtest@mypapaya.io... | [
"0.6760434",
"0.6466026",
"0.6373129",
"0.620715",
"0.61913586",
"0.61831266",
"0.6162001",
"0.61448395",
"0.61397374",
"0.61137104",
"0.6010534",
"0.60070205",
"0.59996825",
"0.5987241",
"0.59855795",
"0.59617376",
"0.5952365",
"0.5951813",
"0.594541",
"0.5944202",
"0.594388... | 0.57373446 | 35 |
Image (m)(m)> Tag > UserProfile > User e.g. see factory.create_tags_images for the creation of the below items > We'll assert that populating related data on the m2m field > constructs the expect > retrieves the correct results > doesn't retrieve the incrorrect results Image.pk Image.name Tag.name company user i grass green x y i grass blue x y k sun yellow x y l grass green q l m sun green q l | def test_m2m_dependent_object_import_precision(self): #: TODO: Come up with a better name
other_company = Company.objects.create(name='Other Co', natural_id='oc')
_,other_user_profile = create_base_models(username='other', company=other_company)
#: Create same named tags <-- assert later that they do not get filtered out as they are from a different
#: company
blue = Tag.objects.create(
company=other_company,
created_by=other_user_profile,
name='blue',
slug='blue',
rank=0
)
green = Tag.objects.create(
company=other_company,
created_by=other_user_profile,
name='green',
slug='green',
rank=2
)
user_profile: UserProfile = self.user_profiles[0] # See self.setUp()
# ************ First Handle generating the Tags/Images Synthetically Through the Importer ************
# Initialize Importers
image_manager = ImporterManager(importer=ImageImporter())
tag_manager = ImporterManager(importer=TagImporter())
up_manager = ImporterManager(importer=UserProfileImporter())
company_manger = ImporterManager(importer=CompanyImporter())
user_manager = ImporterManager(importer=UserImporter())
# Populate leaf models of dependency tree with kv data
for row,image in enumerate(self.images):
user_manager.update_kvs(field_name='username', value=user_profile.user.username, row=row)
company_manger.update_kvs(field_name='natural_id', value=self.company.natural_id, row=row)
#: Retrieve data associated with kv data
user_manager.get_available_rows()
company_manger.get_available_rows()
#: Populate data up the dependency tree with retrieved rows
for row,image in enumerate(self.images):
up_manager.update_kvs('company', company_manger.get_object_or_list(row), row=row)
up_manager.update_kvs('user', user_manager.get_object_or_list(row), row=row)
#: Retrieve data associated with models depended upon
up_manager.get_available_rows()
tag_manager.update_kvs('slug', 'blue', row=0, col=0)
tag_manager.update_kvs('slug', 'green', row=0, col=1)
#: Anyway to avoid pushing these redundant kvs accross a row (??)
tag_manager.update_kvs('company', company_manger.get_object_or_list(0), row=0, col=0)
# tag_manager.update_kvs('company', company_manger.get_object_or_list(0), row=0, col=1)
tag_manager.update_kvs('created_by', up_manager.get_object_or_list(0), row=0, col=0)
# tag_manager.update_kvs('created_by', up_manager.get_object_or_list(0), row=0, col=1)
tag_manager.update_kvs('slug', 'yellow', row=1, col=0)
tag_manager.update_kvs('company', company_manger.get_object_or_list(1), row=1, col=0)
tag_manager.update_kvs('created_by', up_manager.get_object_or_list(1), row=1, col=0)
#: Retrieve associate intermediate data
tag_manager.get_available_rows()
self.assertEqual(len(tag_manager.get_object_or_list(0)), 2)
for tag in tag_manager.get_object_or_list(0):
self.assertEqual(tag.company_id, self.company.id)
self.assertNotEqual(tag.company_id, other_company.id)
self.assertIsInstance(tag_manager.get_object_or_list(1), Tag) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_image(self):\n\n image = Image.query.filter(Image.tag1 == \"Denali\").first()\n self.assertEqual(image.tag1, \"Denali\")",
"def test_users_photos_view_set_get_own_photos(self):\n # Create user and data\n user = account_models.User.objects.create_user(email='mrtest@mypapaya.io... | [
"0.66079843",
"0.6477032",
"0.6326245",
"0.6268388",
"0.6267517",
"0.6212116",
"0.6183669",
"0.61595887",
"0.6151405",
"0.61384684",
"0.60875714",
"0.60794526",
"0.6066342",
"0.60580915",
"0.6017938",
"0.60149026",
"0.600587",
"0.6003988",
"0.5976658",
"0.59595704",
"0.595938... | 0.56813097 | 43 |
Method to be implemented | def update_with_fit_args(self, **kwargs):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self):\n raise NotImplementedError",
"def support(self):",
"def __call__(self):\n raise NotImplementedError()",
"def __call__(self):\r\n raise NotImplementedError('override me')",
"def __call__(self):\n\t\treturn",
"def __call__(self) -> None:",
"def __call__( self ):\... | [
"0.78358424",
"0.7723889",
"0.7493241",
"0.7416625",
"0.7341111",
"0.7135667",
"0.7118768",
"0.71088976",
"0.71088976",
"0.70884323",
"0.69750255",
"0.68996793",
"0.6808303",
"0.6796849",
"0.6753698",
"0.67092526",
"0.66899514",
"0.6654799",
"0.6654799",
"0.6654799",
"0.66419... | 0.0 | -1 |
Explore a dataset dataset pandas dataset prj_info dictionnary containing projet information (response...) | def explore_data(data,prj_info,TMP=1234):
print(" Data file rows and columns are : ", data.shape)
#Open pdf
pp = PdfPages(prj_info['OUTPUT_PATH'] + "exploration_" + str(TMP) + ".pdf")
#Plot average
plot_average_reponse(data,prj_info,pp,TMP)
#Close pdf
pp.close()
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_project_details(\n df,\n copy_columns=info_columns,\n column_rename_dict=info_column_rename_dict,\n use_record=0,\n record_index=\"PID_Index\",\n):\n df_details = df.copy().loc[df[record_index] == use_record][copy_columns]\n\n if column_rename_dict:\n df_details = df_details... | [
"0.58241713",
"0.57872564",
"0.5712627",
"0.5473365",
"0.5464034",
"0.5438289",
"0.54039174",
"0.53523386",
"0.5309826",
"0.5266796",
"0.5227246",
"0.5216134",
"0.51905394",
"0.5185898",
"0.51729375",
"0.5161594",
"0.5143175",
"0.5138437",
"0.5124035",
"0.51153386",
"0.510663... | 0.5347947 | 8 |
Plot average response for all variables in dataset and save plot in pdf dataset pandas dataset prj_info dictionnary containing projet information (response...) | def plot_average_reponse(data,prj_info,pp=PdfPages("exploration.pdf"),TMP=1234,bins=20):
#Copy data
data = data.copy()
#Slice data
data = data.sample(n = min(10000,data.shape[0]),random_state=1234)
#Colnames
var_to_plot = list(set(data.columns.values)-set(prj_info['PRJ_COLUMN'].values()))
#Loop figure
pbar = ProgressBar()
for var in pbar(var_to_plot):
#Bins
if data[var].dtype.name != "category" and len(data[var].unique())>bins:
data["var_new"] = pd.qcut(data[var], bins, duplicates='drop')
else:
data["var_new"] = data[var].astype(str)
data_plot = data.groupby("var_new").agg({prj_info['PRJ_COLUMN']["RESPONSE"]: 'mean', "var_new": 'count'})
#Table
data_plot = data.groupby("var_new").agg({prj_info['PRJ_COLUMN']["RESPONSE"]: 'mean', "var_new": 'count'})
#Build plot
f, ax = plt.subplots()
ax2 =ax.twinx()
sns.barplot(x=data_plot.index.tolist(), y="var_new",data=data_plot,ax=ax, color="dodgerblue")
sns.pointplot(x=data_plot.index.tolist(), y=prj_info['PRJ_COLUMN']["RESPONSE"], data=data_plot,ax=ax2, color="chartreuse")
ax.set_xlabel(var)
ax.set_ylabel(var)
ax2.set_ylabel(prj_info['PRJ_COLUMN']["RESPONSE"])
plt.title("Average reponse by " + var)
plt.setp(ax.xaxis.get_majorticklabels(), rotation=60)
pp.savefig(f)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def avg_response_report(df, var_list, y_obs, y_est, file):\n page = PdfPages(file)\n for var in var_list:\n avg_response(df, var, y_obs, y_est, show=False)\n page.savefig()\n page.close()",
"def explore_data(data,prj_info,TMP=1234):\r\n print(\" Data file rows and columns are : \", d... | [
"0.7190156",
"0.6753629",
"0.60996735",
"0.6064925",
"0.6016448",
"0.6008773",
"0.60022396",
"0.60006684",
"0.5999985",
"0.59736115",
"0.5944627",
"0.59434086",
"0.5941799",
"0.5931249",
"0.5897575",
"0.5877158",
"0.5876371",
"0.5840248",
"0.58335423",
"0.58302295",
"0.582852... | 0.7230312 | 0 |
Create weights as defined in Section 5.1 of our paper. | def create_weights(N, weights_type):
if weights_type == "uniform":
weights = np.array([1 / N,] * N)
elif weights_type == "decreasing":
normaliser = sum([1 / i for i in range(1, N + 1)])
weights = np.array([1 / (i * normaliser) for i in range(1, N + 1)])
elif weights_type == "increasing":
normaliser = sum([1 / i for i in range(1, N + 1)])
weights = np.array([1 / ((N + 1 - i) * normaliser) for i in range(1, N + 1)])
elif weights_type == "centred":
if N % 2 == 1:
normaliser = sum([1 / (abs((N + 1) / 2 - i) + 1) for i in range(1, N + 1)])
weights = np.array(
[1 / ((abs((N + 1) / 2 - i) + 1) * normaliser) for i in range(1, N + 1)]
)
else:
normaliser = sum(
[1 / (abs((N + 1) / 2 - i) + 0.5) for i in range(1, N + 1)]
)
weights = np.array(
[
1 / ((abs((N + 1) / 2 - i) + 0.5) * normaliser)
for i in range(1, N + 1)
]
)
else:
raise ValueError(
'The value of weights_type should be "uniform" or'
'"decreasing" or "increasing" or "centred".'
)
return weights | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_weights(self):\n gate_size = self._hidden_size * self._num_gates\n # Compute the shape of weight and bias.\n matrix_shapes, bias_shapes = [], []\n for layer in range(self._num_layers):\n for direction in range(self._num_directions):\n layer_input_si... | [
"0.7466049",
"0.7392558",
"0.7357619",
"0.72760403",
"0.71676654",
"0.7089966",
"0.7077687",
"0.70380753",
"0.7031394",
"0.7028164",
"0.7026333",
"0.70108604",
"0.69093025",
"0.69080865",
"0.68695116",
"0.68506765",
"0.6768174",
"0.6768174",
"0.6765752",
"0.67634475",
"0.6748... | 0.6555952 | 38 |
Runs evaluate on the provided data and generates a detailed error report | def make_report(self, report_name, id_test, x_test, y_test, country_test, frame_test):
if not os.path.exists('Reports/' + report_name):
os.mkdir('Reports/' + report_name)
results = self.predict(x_test)
# Generate detailied evaluation report
header = 'Country,Child,Frame'
for output_layer in self.get_config()['output_layers']:
header += ',{}_Actual'.format(output_layer[0])
for output_layer in self.get_config()['output_layers']:
header += ',{}_Prediction'.format(output_layer[0])
header += '\n'
with open('Reports/{}/evaluation_report.txt'.format(report_name), 'a') as f:
if os.stat('Reports/{}/evaluation_report.txt'.format(report_name)).st_size == 0:
f.write(header)
for row in range(len(results)):
entry = ','.join([str(i) for i in country_test[row]]) + ','
entry += ','.join([str(i) for i in id_test[row]]) + ','
entry += ','.join([str(i) for i in frame_test[row]]) + ','
entry += ','.join([str(i) for i in y_test[row]]) + ','
entry += ','.join([str(i) for i in results[row]]) + '\n'
f.write(entry)
# Generate report of summary statistics
cultures = np.unique(country_test)
for c in cultures:
culture_rows = np.where(country_test == c)[0] # get row numbers for culture c
culture_ids = id_test[culture_rows] # get ID rows for culture c
unique_ids = np.unique(culture_ids) # get unique IDs for culture c
for u in unique_ids:
all_id_rows = np.where(id_test == u)[0]
id_rows = np.intersect1d(all_id_rows, culture_rows) # get ID rows for child u
id_icc = icc(results[id_rows], y_test[id_rows])[0] # compute ICC for child u
id_pcc = pcc(results[id_rows], y_test[id_rows])[0][0] # compute PCC for child u
id_ccc = ccc(results[id_rows], y_test[id_rows]) # compute CCC for child u
id_mae = mae(results[id_rows], y_test[id_rows]) # compute MAE for child u
icc_entry = '{},{},{}\n'.format(c, u, id_icc)
pcc_entry = '{},{},{}\n'.format(c, u, id_pcc)
ccc_entry = '{},{},{}\n'.format(c, u, id_ccc)
mae_entry = '{},{},{}\n'.format(c, u, id_mae)
with open('Reports/{}/icc_report.txt'.format(report_name), 'a') as f:
f.write(icc_entry)
with open('Reports/{}/pcc_report.txt'.format(report_name), 'a') as f:
f.write(pcc_entry)
with open('Reports/{}/ccc_report.txt'.format(report_name), 'a') as f:
f.write(ccc_entry)
with open('Reports/{}/mae_report.txt'.format(report_name), 'a') as f:
f.write(mae_entry)
return results | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(self, test_data):\n result = self.model.run(test_data)\n self._save_result(result)",
"def evaluate(self, dataset):\n\t\tpass",
"def evaluate(\n self,\n test_data=None,\n print_report=True,\n save_path=\"ktrain_classification_report.csv\",\n class_na... | [
"0.6641769",
"0.66378486",
"0.62046313",
"0.6194362",
"0.6182727",
"0.61543846",
"0.6143863",
"0.61434543",
"0.6132906",
"0.61051583",
"0.6086003",
"0.60806614",
"0.60806614",
"0.6062375",
"0.60618806",
"0.6046971",
"0.604346",
"0.603338",
"0.60103774",
"0.60077417",
"0.60042... | 0.0 | -1 |
Applies VRTfunctions to a GDALreadable inputfile, rendering outputfile. Functions must be an iterable of singleparameter functions that take a filename as input. | def pipeline(inputfile, outputfile, functions, **kwargs):
if not functions:
raise ValueError('Must have at least one function')
tmpfiles = []
try:
previous = inputfile
for name, f in functions:
logging.debug(name)
vrt = f(previous)
current = vrt.get_tempfile(suffix='.vrt', prefix='gdal')
tmpfiles.append(current)
previous = current.name
logging.info('Rendering reprojected image')
return vrt.render(outputfile=outputfile, **kwargs)
finally:
for f in tmpfiles:
f.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(fn_input, fn_output):\n # read file\n inter = Interpolator()\n inter.read_file(fn_input)\n inter.write_interpolated(fn_output)",
"def merge(files: List[str], output_file: str, resample: str = \"average\") -> None:\n\n build_vrt(constants.TEMP_VRT_FILE, files, resample)\n\n gdal.SetConf... | [
"0.5867644",
"0.5854852",
"0.55360836",
"0.52705306",
"0.52492493",
"0.52203393",
"0.50498486",
"0.50492585",
"0.49810517",
"0.49677193",
"0.4958387",
"0.49372962",
"0.4903304",
"0.4866099",
"0.4855312",
"0.4854982",
"0.4847482",
"0.48274714",
"0.48194093",
"0.48180053",
"0.4... | 0.7520964 | 0 |
Takes an inputfile (probably a VRT) and generates a singleband VRT. | def extract_color_band(inputfile, band):
dataset = Dataset(inputfile)
if not 1 <= band <= dataset.RasterCount:
raise ValueError(
"band must be between 1 and {0}".format(dataset.RasterCount)
)
command = [
GDALTRANSLATE,
'-q', # Quiet
'-of', 'VRT', # Output to VRT
'-b', band, # Single band
inputfile,
'/vsistdout'
]
try:
return VRT(check_output_gdal([str(e) for e in command]))
except CalledGdalError as e:
if e.error == ("ERROR 6: Read or update mode not supported on /vsistdout"):
# HACK: WTF?!?
return VRT(e.output)
raise | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vrt(vrt: str, files: List[str], resample_name: str) -> None:\n\n options = gdal.BuildVRTOptions(srcNodata=0)\n gdal.BuildVRT(destName=vrt, srcDSOrSrcDSTab=files, options=options)\n add_pixel_fn(vrt, resample_name)",
"def run_vorpaline(self, input_file, *options):\n\n self._input_file = ... | [
"0.5761271",
"0.539191",
"0.53234494",
"0.5275649",
"0.52752906",
"0.527388",
"0.5264173",
"0.5234479",
"0.52175415",
"0.52002573",
"0.51625293",
"0.50720745",
"0.50369114",
"0.50217336",
"0.4970289",
"0.4957576",
"0.4887836",
"0.48874044",
"0.48784587",
"0.48682615",
"0.4843... | 0.546153 | 1 |
Takes an GDALreadable inputfile and generates the VRT to warp it. | def warp(inputfile, spatial_ref=None, cmd=GDALWARP, resampling=None,
maximum_resolution=None):
dataset = Dataset(inputfile)
warp_cmd = [
cmd,
'-q', # Quiet - FIXME: Use logging
'-of', 'VRT', # Output to VRT
]
# Warping to Mercator.
if spatial_ref is None:
spatial_ref = SpatialReference.FromEPSG(EPSG_WEB_MERCATOR)
warp_cmd.extend(['-t_srs', spatial_ref.GetEPSGString()])
# Resampling method
if resampling is not None:
if not isinstance(resampling, basestring):
try:
resampling = RESAMPLING_METHODS[resampling]
except KeyError:
raise UnknownResamplingMethodError(resampling)
elif resampling not in list(RESAMPLING_METHODS.values()):
raise UnknownResamplingMethodError(resampling)
warp_cmd.extend(['-r', resampling])
# Propagate No Data Value
nodata_values = [dataset.GetRasterBand(i).GetNoDataValue()
for i in range(1, dataset.RasterCount + 1)]
if any(nodata_values):
nodata_values = [str(v).lower() for v in nodata_values]
warp_cmd.extend(['-dstnodata', ' '.join(nodata_values)])
# Call gdalwarp
warp_cmd.extend([inputfile, '/vsistdout'])
try:
return VRT(check_output_gdal([str(e) for e in warp_cmd]))
except CalledGdalError as e:
if e.error == ("ERROR 6: Read or update mode not supported on /vsistdout"):
return VRT(e.output)
raise | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_vrt(self):\n for index, i in enumerate(self.months):\n month = str(index + 1)\n if len(month) < 2:\n month = '0' + month\n txt_file = i.joinpath('subnational/tiffs.txt')\n outfile = i.joinpath(f'{self.country}_{month}_normalised.vrt')\n ... | [
"0.6332105",
"0.6148278",
"0.60546756",
"0.6035137",
"0.59595346",
"0.58744794",
"0.58276",
"0.58268255",
"0.57943213",
"0.57037807",
"0.56656307",
"0.5467358",
"0.541127",
"0.5396852",
"0.53866345",
"0.53866345",
"0.53640836",
"0.5348823",
"0.52539855",
"0.5224854",
"0.51994... | 0.6055037 | 2 |
Returns gdal.Band.GetNoDataValue() as a NumPy type | def GetNoDataValue(self):
result = super(Band, self).GetNoDataValue()
if result is not None:
return self.NumPyDataType(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_band_nodata(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n return self.dataset.GetRasterBand(band_idx).GetNoDataValue()",
"def _nodata_value(self):\n try:\n nodata = float(self._i... | [
"0.7544999",
"0.7434405",
"0.7288697",
"0.6567842",
"0.6520311",
"0.6363698",
"0.6319562",
"0.62501335",
"0.6163293",
"0.61502856",
"0.61502856",
"0.61240387",
"0.6110622",
"0.60905856",
"0.590359",
"0.5895869",
"0.58876556",
"0.58424926",
"0.58220965",
"0.5796688",
"0.577856... | 0.86186093 | 0 |
Returns the NumPy type associated with gdal.Band.DataType | def NumPyDataType(self):
datatype = self.DataType
if datatype == gdalconst.GDT_Byte:
pixeltype = self.GetMetadataItem('PIXELTYPE', 'IMAGE_STRUCTURE')
if pixeltype == 'SIGNEDBYTE':
return numpy.int8
return numpy.uint8
elif datatype == gdalconst.GDT_UInt16:
return numpy.uint16
elif datatype == gdalconst.GDT_UInt32:
return numpy.uint32
elif datatype == gdalconst.GDT_Int16:
return numpy.int16
elif datatype == gdalconst.GDT_Int32:
return numpy.int32
elif datatype == gdalconst.GDT_Float32:
return numpy.float32
elif datatype == gdalconst.GDT_Float64:
return numpy.float64
else:
raise ValueError(
"Cannot handle DataType: {0}".format(
gdal.GetDataTypeName(datatype)
)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_band_dtype(self, band_idx=1):\n if band_idx < 1 or band_idx > self.dataset.RasterCount:\n raise IndexError(\"band index is out of range\")\n return self.dataset.GetRasterBand(band_idx).DataType",
"def type(self):\n # type: () -> type\n return _python_type_map[str(se... | [
"0.7380224",
"0.7158212",
"0.6904815",
"0.68386203",
"0.67266405",
"0.6708839",
"0.66951716",
"0.663477",
"0.663243",
"0.6614309",
"0.6602418",
"0.6594213",
"0.65762633",
"0.65756744",
"0.65720206",
"0.657198",
"0.6555877",
"0.6555877",
"0.6546687",
"0.65154874",
"0.6515137",... | 0.76264346 | 0 |
Returns the minimum value that can be stored in this band | def MinimumValue(self):
datatype = self.NumPyDataType
if issubclass(datatype, numpy.integer):
return numpy.iinfo(datatype).min
elif issubclass(datatype, numpy.floating):
return -numpy.inf
else:
raise TypeError("Cannot handle DataType: {0}".format(datatype)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val",
"def min(... | [
"0.7944226",
"0.7944226",
"0.7937631",
"0.78972983",
"0.7884411",
"0.78611416",
"0.7846386",
"0.7778144",
"0.77733576",
"0.7762124",
"0.7680303",
"0.7642864",
"0.75792414",
"0.75792414",
"0.75784636",
"0.7551094",
"0.75474113",
"0.75474113",
"0.7521111",
"0.7521111",
"0.74849... | 0.7393505 | 28 |
Returns the minimum value that can be stored in this band | def MaximumValue(self):
datatype = self.NumPyDataType
if issubclass(datatype, numpy.integer):
return numpy.iinfo(datatype).max
elif issubclass(datatype, numpy.floating):
return numpy.inf
else:
raise TypeError("Cannot handle DataType: {0}".format(datatype)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def min(self):\n if self.kind == 'u':\n return 0\n else:\n try:\n val = iinfo._min_vals[self.key]\n except KeyError:\n val = int(-(1 << (self.bits-1)))\n iinfo._min_vals[self.key] = val\n return val",
"def min(... | [
"0.7944226",
"0.7944226",
"0.7937631",
"0.78972983",
"0.7884411",
"0.78611416",
"0.7846386",
"0.7778144",
"0.77733576",
"0.7762124",
"0.7680303",
"0.7642864",
"0.75792414",
"0.75792414",
"0.75784636",
"0.7551094",
"0.75474113",
"0.75474113",
"0.7521111",
"0.7521111",
"0.74849... | 0.0 | -1 |
Returns the next `value` expressible in this band | def IncrementValue(self, value):
datatype = self.NumPyDataType
if issubclass(datatype, numpy.integer):
if not isinstance(value, (int, numpy.integer)):
raise TypeError(
'value {0!r} must be compatible with {1}'.format(
value, datatype.__name__
)
)
iinfo = numpy.iinfo(datatype)
minint, maxint = iinfo.min, iinfo.max
if not minint <= value <= maxint:
raise ValueError(
'value {0!r} must be between {1} and {2}'.format(
value, minint, maxint
)
)
if value == maxint:
return maxint
return value + 1
elif issubclass(datatype, numpy.floating):
if not isinstance(value, (int, numpy.integer,
float, numpy.floating)):
raise TypeError(
"value {0!r} must be compatible with {1}".format(
value, datatype.__name__
)
)
if value == numpy.finfo(datatype).max:
return numpy.inf
return numpy.nextafter(datatype(value), datatype(numpy.inf))
else:
raise TypeError("Cannot handle DataType: {0}".format(datatype)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_next(self) -> float:\n return self._current + self._offset",
"def get_next(self) -> int:\n return self._current * self._step + self._offset",
"def value(self):\n current_value = self.initial_value * self.schedule(self.step / self.nvalues)\n self.step += 1.\n return cu... | [
"0.69069135",
"0.6903918",
"0.68510103",
"0.6794112",
"0.6782542",
"0.66370076",
"0.66035736",
"0.65092635",
"0.6481222",
"0.6430199",
"0.6414499",
"0.6410997",
"0.63769144",
"0.63592046",
"0.63583064",
"0.63552517",
"0.63462645",
"0.63152736",
"0.6294821",
"0.62673527",
"0.6... | 0.0 | -1 |
Opens a GDALreadable file. Raises a GdalError if inputfile is invalid. | def __init__(self, inputfile, mode=GA_ReadOnly):
# Open the input file and read some metadata
open(inputfile, 'r').close() # HACK: GDAL gives a useless exception
if not isinstance(inputfile, bytes):
inputfile = inputfile.encode('utf-8')
try:
# Since this is a SWIG object, clone the ``this`` pointer
self.this = gdal.Open(inputfile, mode).this
except RuntimeError as e:
raise GdalError(str(e))
# Shadow for metadata so we can overwrite it without saving
# it to the original file.
self._geotransform = None
self._rastersizes = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(self):\r\n\r\n #Open the dataset read only using GDAL\r\n dataset = gdal.Open(self.inputds, gdal.GA_ReadOnly)\r\n \r\n return dataset\r\n \r\n\r\n #print \"Failed to open %s. Is it a GDAL supported format?\" %(self.inputds)\r",
"def open_input(self):\n gd... | [
"0.67583877",
"0.6669646",
"0.6662748",
"0.6427024",
"0.5995178",
"0.57921493",
"0.5738032",
"0.5566471",
"0.5458556",
"0.5453362",
"0.5418001",
"0.5346144",
"0.5346144",
"0.5330134",
"0.52282006",
"0.52168894",
"0.5216014",
"0.5212678",
"0.5209175",
"0.51660335",
"0.5159364"... | 0.62465745 | 4 |
Returns whether the dataset covers the whole world or not. | def IsWholeWorld(self, resolution=None):
if resolution is None:
resolution = self.GetNativeResolution()
spatial_ref = self.GetSpatialReference()
world_extents = spatial_ref.GetWorldExtents()
extents = self.GetExtents()
ll_offset = world_extents.lower_left - extents.lower_left
ur_offset = world_extents.upper_right - extents.upper_right
pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)
return (abs(ll_offset.x) <= pixel_sizes.x and
abs(ll_offset.y) <= pixel_sizes.y and
abs(ur_offset.x) <= pixel_sizes.x and
abs(ur_offset.y) <= pixel_sizes.y) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_full_dimensional(self):\n\n return self.affine_dimension() == self.space_dimension()",
"def is_dataset(self):\n return self._dataset is not None",
"def ejscreen_areas_of_concern_data_exists(cls):\n return cls.EJSCREEN_AREAS_OF_CONCERN_SOURCE.is_file()",
"def is_mainland(self):\n ... | [
"0.6345008",
"0.63167757",
"0.61752814",
"0.60833305",
"0.60241663",
"0.6015851",
"0.5980115",
"0.59584624",
"0.5937705",
"0.5922513",
"0.5917669",
"0.59084296",
"0.5877617",
"0.5835224",
"0.5827307",
"0.5806919",
"0.5801645",
"0.57821",
"0.5778381",
"0.57413006",
"0.57294166... | 0.68909526 | 0 |
of the source data; this usually means upsampling the data, but if the pixel dimensions are slightly smaller than a given resolution, and equal within error tolerance, that resolution will get chosen as the native one. | def GetNativeResolution(self, transform=None, maximum=None):
# Get the source projection's units for a 1x1 pixel, assuming square
# pixels.
width, height = self.GetPixelDimensions()
src_pixel_size = min(abs(width), abs(height))
if transform is None:
dst_pixel_size = src_pixel_size
dst_ref = self.GetSpatialReference()
else:
# Transform these dimensions into the destination projection
dst_pixel_size = transform.TransformPoint(src_pixel_size, 0)[0]
dst_pixel_size = abs(dst_pixel_size)
dst_ref = transform.dst_ref
# We allow some floating point error between src_pixel_size and
# dst_pixel_size based on the major circumference so that the error is
# in the destination units
error = max(*dst_ref.GetPixelDimensions(resolution=0)) / 128
# Find the resolution where the pixels are smaller than dst_pixel_size.
for resolution in count():
if maximum is not None and resolution >= maximum:
return resolution
res_pixel_size = max(
*dst_ref.GetPixelDimensions(resolution=resolution)
)
if (res_pixel_size - dst_pixel_size) <= error:
return resolution
# Halve error each resolution
error /= 2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_resolution(img):\n scale_factor = np.random.choice(list(range(0, 6, 2)))\n if scale_factor == 0:\n return img\n downsample = nn.AvgPool2d(scale_factor)\n upsample = nn.UpsamplingNearest2d(scale_factor=scale_factor)\n new_res_img = upsample(downsample(img.unsqueeze(dim=1))).squeeze()\n return ne... | [
"0.5770578",
"0.5644546",
"0.56255263",
"0.55761415",
"0.54033136",
"0.5392939",
"0.536528",
"0.5364385",
"0.5331956",
"0.5313504",
"0.5307943",
"0.5276826",
"0.52704465",
"0.52479804",
"0.5238506",
"0.5231732",
"0.52003103",
"0.517683",
"0.5145478",
"0.51325125",
"0.51163715... | 0.0 | -1 |
Returns the (width, height) of pixels in this Dataset's units. | def GetPixelDimensions(self):
_, width, _, _, _, height = self.GetGeoTransform()
return XY(x=width, y=height) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dimensions (self):\n return (self.width, self.height)",
"def get_size(self) -> Tuple2IntType:\n return self.get_width(), self.get_height()",
"def size(self):\n return self.width, self.height",
"def get_size_inches(self):\n width, height = self.figure.get_size_inches()\n ... | [
"0.7506064",
"0.7479907",
"0.7441489",
"0.74142814",
"0.7389819",
"0.7367811",
"0.73565596",
"0.73126996",
"0.72868365",
"0.7277576",
"0.72565204",
"0.7253058",
"0.72077274",
"0.72069263",
"0.71481836",
"0.7120468",
"0.7104735",
"0.70551723",
"0.7054781",
"0.70520395",
"0.704... | 0.77364904 | 0 |
Transforms pixel coordinates into the destination projection. If transform is None, no reprojection occurs and the dataset's SpatialReference is used. | def PixelCoordinates(self, x, y, transform=None):
# Assert that pixel_x and pixel_y are valid
if not 0 <= x <= self.RasterXSize:
raise ValueError('x %r is not between 0 and %d' %
(x, self.RasterXSize))
if not 0 <= y <= self.RasterYSize:
raise ValueError('y %r is not between 0 and %d' %
(y, self.RasterYSize))
geotransform = self.GetGeoTransform()
coords = XY(
geotransform[0] + geotransform[1] * x + geotransform[2] * y,
geotransform[3] + geotransform[4] * x + geotransform[5] * y
)
if transform is None:
return coords
# Reproject
return XY(*transform.TransformPoint(coords.x, coords.y)[0:2]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _reproject(ds, dst_crs=None, dst_transform=None, width=None, height=None,\n res=None, extent=None, **kwargs):\n\n if 'resampling' not in kwargs:\n kwargs['resampling'] = rasterio.warp.Resampling.cubic\n\n src_crs = get_crs(ds)\n src_bounds = get_bounds(ds)\n if extent is not No... | [
"0.6720557",
"0.6548644",
"0.6337521",
"0.6230713",
"0.62149775",
"0.58759457",
"0.58334744",
"0.5782975",
"0.5738817",
"0.56981885",
"0.5647636",
"0.5635617",
"0.5624577",
"0.5602451",
"0.55902624",
"0.5584441",
"0.5567373",
"0.55569345",
"0.55569345",
"0.5550751",
"0.552929... | 0.5615591 | 13 |
Returns (lowerleft, upperright) extents in transform's destination projection. If transform is None, no reprojection occurs and the dataset's SpatialReference is used. | def GetExtents(self, transform=None):
# Prepare GDAL functions to compute extents
x_size, y_size = self.RasterXSize, self.RasterYSize
# Compute four corners in destination projection
upper_left = self.PixelCoordinates(0, 0,
transform=transform)
upper_right = self.PixelCoordinates(x_size, 0,
transform=transform)
lower_left = self.PixelCoordinates(0, y_size,
transform=transform)
lower_right = self.PixelCoordinates(x_size, y_size,
transform=transform)
x_values, y_values = list(zip(upper_left, upper_right,
lower_left, lower_right))
# Return lower-left and upper-right extents
return Extents(lower_left=XY(min(x_values), min(y_values)),
upper_right=XY(max(x_values), max(y_values))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extent(self):\n left = self.transform[0]\n right = left + self.transform[1] * self.shape[1]\n top = self.transform[3]\n bottom = top + self.transform[5] * self.shape[0]\n return left, right, bottom, top",
"def extent(self):\n if self.x is not None:\n if se... | [
"0.6707964",
"0.62306535",
"0.61376554",
"0.58643377",
"0.58163196",
"0.5813695",
"0.5760055",
"0.5748703",
"0.57084984",
"0.5700975",
"0.56836563",
"0.5661239",
"0.5635688",
"0.5632432",
"0.56189066",
"0.5601325",
"0.55867517",
"0.5552704",
"0.5544527",
"0.5532464",
"0.55125... | 0.75209373 | 0 |
Get the scaling ratios required to upsample an image to `resolution`. If resolution is None, then assume it will be upsampled to the native destination resolution. See Dataset.GetNativeResolution() If places is not None, rounds the ratios to the number of decimal places specified. | def GetScalingRatios(self, resolution=None, places=None):
if resolution is None:
resolution = self.GetNativeResolution(transform=None)
# Get the pixel dimensions in map units. There is no custom transform,
# because it makes no sense to compute a pixel ratio for a
# reprojection.
spatial_ref = self.GetSpatialReference()
dst_pixel_width, dst_pixel_height = spatial_ref.GetPixelDimensions(
resolution=resolution
)
src_pixel_width, src_pixel_height = self.GetPixelDimensions()
xscale = abs(src_pixel_width / dst_pixel_width)
yscale = abs(src_pixel_height / dst_pixel_height)
if places is not None:
xscale = round(xscale, places)
yscale = round(yscale, places)
return XY(x=xscale, y=yscale) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetWorldScalingRatios(self, resolution=None, places=None):\n if resolution is None:\n resolution = self.GetNativeResolution()\n\n spatial_ref = self.GetSpatialReference()\n world = spatial_ref.GetWorldExtents().dimensions\n src_pixel_sizes = XY(x=world.x / self.RasterXSiz... | [
"0.66777146",
"0.5513569",
"0.5297719",
"0.5258411",
"0.51001024",
"0.50386363",
"0.50341105",
"0.50095075",
"0.49838406",
"0.49593174",
"0.48953298",
"0.48382708",
"0.47824258",
"0.4761503",
"0.47418657",
"0.47418657",
"0.47418657",
"0.47353554",
"0.46934766",
"0.46716377",
... | 0.7302088 | 0 |
Returns (lowerleft, upperright) TMS tile coordinates. The upperright coordinates are excluded from the range, while the lowerleft are included. | def GetTmsExtents(self, resolution=None, transform=None):
if resolution is None:
resolution = self.GetNativeResolution(transform=transform)
# Get the tile dimensions in map units
if transform is None:
spatial_ref = self.GetSpatialReference()
else:
spatial_ref = transform.dst_ref
tile_width, tile_height = spatial_ref.GetTileDimensions(
resolution=resolution
)
# Validate that the native resolution extents are tile-aligned.
extents = self.GetTiledExtents(transform=transform)
pixel_sizes = spatial_ref.GetPixelDimensions(resolution=resolution)
if not extents.almost_equal(self.GetExtents(transform=transform),
delta=min(*pixel_sizes)):
raise UnalignedInputError('Dataset is not aligned to TMS grid')
# Correct for origin, because you can't do modular arithmetic on
# half-tiles.
left, bottom = spatial_ref.OffsetPoint(*extents.lower_left)
right, top = spatial_ref.OffsetPoint(*extents.upper_right)
# Divide by number of tiles
return Extents(lower_left=XY(int(round(left / tile_width)),
int(round(bottom / tile_height))),
upper_right=XY(int(round(right / tile_width)),
int(round(top / tile_height)))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getupperleft(self):\n return (self.rect.x, self.rect.y)",
"def topLeftCorner(self):\n self._updateExtents()\n return (self._mMinX,self._mMinY)",
"def topRightCorner(self):\n self._updateExtents()\n return (self._mMaxX,self._mMinY)",
"def get_tile_location(self):\n ... | [
"0.67502934",
"0.65008754",
"0.6489868",
"0.6368388",
"0.6322317",
"0.63027024",
"0.62114066",
"0.6206572",
"0.6173504",
"0.61361134",
"0.6121037",
"0.6087851",
"0.60849476",
"0.6061991",
"0.6028953",
"0.59848636",
"0.59517145",
"0.59340775",
"0.5899759",
"0.589703",
"0.58896... | 0.0 | -1 |
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