docstr: Make a file containing image measurements
  
  image_set_numbers - the image sets whose data gets extracted
  workspace - workspace containing the measurements
  settings_group - the settings group used to choose the image
                   measurements for output or None if everything
                   is to be exported
docstr_tok: ["Make","a","file","containing","image","measurements","image_set_numbers","-","the","image","sets","whose","data","gets","extracted","workspace","-","workspace","containing","the","measurements","settings_group","-","the","settings","group","used","to","choose","the","image","measurements","for","output","or","None","if","everything","is","to","be","exported"]
code: def make_image_file(
      self, image_set_numbers, workspace, settings_group=None
  ):
      """Make a file containing image measurements
  
      image_set_numbers - the image sets whose data gets extracted
      workspace - workspace containing the measurements
      settings_group - the settings group used to choose the image
                       measurements for output or None if everything
                       is to be exported
      """
      m = workspace.measurements
      file_name = self.make_objects_file_name(
          IMAGE, workspace, image_set_numbers[0], settings_group
      )
      image_features = m.get_feature_names(IMAGE)
      image_features.insert(0, IMAGE_NUMBER)
  
      fd = open(file_name, "w", newline="")
      try:
          writer = csv.writer(fd, delimiter=self.delimiter_char)
          for img_number in image_set_numbers:
              aggs = []
              if self.wants_aggregate_means:
                  aggs.append(AGG_MEAN)
              if self.wants_aggregate_medians:
                  aggs.append(AGG_MEDIAN)
              if self.wants_aggregate_std:
                  aggs.append(AGG_STD_DEV)
              agg_measurements = m.compute_aggregate_measurements(
                  img_number, aggs
              )
              if img_number == image_set_numbers[0]:
                  ordered_agg_names = list(agg_measurements.keys())
                  ordered_agg_names.sort()
                  image_features += ordered_agg_names
                  image_features.sort()
                  image_features = self.filter_columns(
                      image_features, IMAGE
                  )
                  if image_features is None:
                      return
                  writer.writerow(image_features)
              row = []
              for feature_name in image_features:
                  if feature_name == IMAGE_NUMBER:
                      row.append(str(img_number))
                  else:
                      if feature_name in agg_measurements:
                          value = agg_measurements[feature_name]
                      else:
                          value = m[IMAGE, feature_name, img_number]
                      if value is None:
                          row.append("")
                      elif isinstance(value, str):
                          row.append(value)
                      elif isinstance(value, bytes):
                          row.append(value.decode())
                      elif (
                          isinstance(value, numpy.ndarray)
                          and value.dtype == numpy.uint8
                      ):
                          row.append(base64.b64encode(value.data))
                      elif numpy.isnan(value):
                          if self.nan_representation == NANS_AS_NULLS:
                              row.append("")
                          else:
                              row.append(str(numpy.NaN))
                      else:
                          row.append(str(value))
              writer.writerow(row)
      finally:
          fd.close()
  
imports: import base64
  import csv
  import logging
  import os
  import numpy
  from cellprofiler_core.constants.image import C_MD5_DIGEST, C_SCALING, C_HEIGHT, C_WIDTH
  from cellprofiler_core.constants.measurement import EXPERIMENT, IMAGE, AGG_MEAN, AGG_MEDIAN, AGG_STD_DEV, C_URL, C_PATH_NAME, C_FILE_NAME, NEIGHBORS, R_FIRST_IMAGE_NUMBER, R_SECOND_IMAGE_NUMBER, R_FIRST_OBJECT_NUMBER, R_SECOND_OBJECT_NUMBER
  from cellprofiler_core.constants.module import IO_FOLDER_CHOICE_HELP_TEXT, IO_WITH_METADATA_HELP_TEXT, USING_METADATA_HELP_REF, USING_METADATA_TAGS_REF
  from cellprofiler_core.constants.pipeline import EXIT_STATUS
  from cellprofiler_core.measurement import Measurements
  from cellprofiler_core.module import Module
  from cellprofiler_core.preferences import ABSOLUTE_FOLDER_NAME
  from cellprofiler_core.preferences import DEFAULT_INPUT_FOLDER_NAME
  from cellprofiler_core.preferences import DEFAULT_INPUT_SUBFOLDER_NAME
  from cellprofiler_core.preferences import DEFAULT_OUTPUT_FOLDER_NAME
  from cellprofiler_core.preferences import DEFAULT_OUTPUT_SUBFOLDER_NAME
  from cellprofiler_core.preferences import get_headless
  from cellprofiler_core.setting import Binary
  from cellprofiler_core.setting import Divider
  from cellprofiler_core.setting import Measurement
  from cellprofiler_core.setting import SettingsGroup
  from cellprofiler_core.setting import ValidationError
  from cellprofiler_core.setting.choice import CustomChoice, Choice
  from cellprofiler_core.setting.do_something import DoSomething, RemoveSettingButton
  from cellprofiler_core.setting.multichoice import MeasurementMultiChoice
  from cellprofiler_core.setting.subscriber import ImageSubscriber, LabelSubscriber
  from cellprofiler_core.setting.text import Directory, Text
  from cellprofiler_core.utilities.core.modules.load_data import is_file_name_feature, is_path_name_feature
  from cellprofiler_core.utilities.measurement import find_metadata_tokens, get_agg_measurement_name
  from cellprofiler.gui.help.content import MEASUREMENT_NAMING_HELP
label_desc: Use image node_id 24 for calling the ExportToSpreadsheet obj's underlying member method code with example usage: obj.make_image_file(image_set_numbers, workspace, settings_group) without return types