code: def calculate_power_spectrum(self, image_group, workspace):
      result = []
      for image_name in self.images_to_process(image_group, workspace):
          image = workspace.image_set.get_image(
              image_name, must_be_grayscale=True
          )
  
          if image.dimensions == 3:
              # TODO: calculate "radial power spectrum" for volumes.
              continue
  
          pixel_data = image.pixel_data
  
          if image.has_mask:
              pixel_data = numpy.array(pixel_data)  # make a copy
              masked_pixels = pixel_data[image.mask]
              pixel_count = numpy.product(masked_pixels.shape)
              if pixel_count > 0:
                  pixel_data[~image.mask] = numpy.mean(masked_pixels)
              else:
                  pixel_data[~image.mask] = 0
  
          (
              radii,
              magnitude,
              power,
          ) = centrosome.radial_power_spectrum.rps(pixel_data)
          if sum(magnitude) > 0 and len(numpy.unique(pixel_data)) > 1:
              valid = magnitude > 0
              radii = radii[valid].reshape((-1, 1))
              power = power[valid].reshape((-1, 1))
              if radii.shape[0] > 1:
                  idx = numpy.isfinite(numpy.log(power))
                  powerslope = scipy.linalg.basic.lstsq(
                      numpy.hstack(
                          (
                              numpy.log(radii)[idx][:, numpy.newaxis],
                              numpy.ones(radii.shape)[idx][
                                  :, numpy.newaxis
                              ],
                          )
                      ),
                      numpy.log(power)[idx][:, numpy.newaxis],
                  )[0][0]
              else:
                  powerslope = 0
          else:
              powerslope = 0
  
          workspace.add_measurement(
              "Image",
              "{}_{}_{}".format(
                  C_IMAGE_QUALITY, F_POWER_SPECTRUM_SLOPE, image_name
              ),
              powerslope,
          )
          result += [
              [
                  "{} {}".format(image_name, F_POWER_SPECTRUM_SLOPE),
                  "{:.1f}".format(float(powerslope)),
              ]
          ]
      return result
  
imports: import itertools
  import logging
  import cellprofiler_core.utilities.image
  import centrosome.cpmorphology
  import centrosome.haralick
  import centrosome.radial_power_spectrum
  import centrosome.threshold
  import centrosome.threshold
  import numpy
  import scipy.linalg.basic
  import scipy.ndimage
  from cellprofiler_core.constants.image import C_SCALING
  from cellprofiler_core.constants.measurement import COLTYPE_FLOAT, EXPERIMENT, MCA_AVAILABLE_POST_RUN
  from cellprofiler_core.constants.module._identify import O_WEIGHTED_VARIANCE, O_ENTROPY, O_FOREGROUND, O_BACKGROUND
  from cellprofiler_core.module import Module
  from cellprofiler_core.preferences import get_headless
  from cellprofiler_core.setting import Divider, HiddenCount, SettingsGroup, Binary, ValidationError
  from cellprofiler_core.setting.choice import Choice
  from cellprofiler_core.setting.do_something import DoSomething, RemoveSettingButton
  from cellprofiler_core.setting.subscriber import ImageListSubscriber
  from cellprofiler_core.setting.text import ImageName, Integer, Float
  from cellprofiler.modules.threshold import O_THREE_CLASS, O_TWO_CLASS
label_desc: Use image node_id 33 for calling the MeasureImageQuality obj's underlying member method code with example usage: obj.calculate_power_spectrum(image_group, workspace) and returns: result