original_docstring: Uses regression based on image intensities to select tracking parameters.
  
      This function uses regression methods from the scikit-learn module to
      predict the lower quality cutoff values for particle filtering in TrackMate
      based on the intensity distributions of input images. Currently only uses
      the first frame of videos for analysis, and is limited to predicting
      quality values.
  
      In practice, users will run regress_sys twice in different modes to build
      a regression system. First, set have_output to False. Function will return
      list of randomly selected videos to include in the training dataset. The
      user should then manually track particles using the Trackmate GUI, and enter
      these values in during the next round as the input yfit variable.
  
      Parameters
      ----------
      folder : str
          S3 directory containing video files specified in all_videos.
      all_videos: list of str
          Contains prefixes of video filenames of entire video set to be
          tracked.  Training dataset will be some subset of these videos.
      yfit: numpy.ndarray
          Contains manually acquired quality levels using Trackmate for the
          files contained in the training dataset.
      training_size : int
          Number of files in training dataset.
      randselect : bool
          If True, will randomly select training videos from all_videos.
          If False, will use trainingdata as input training dataset.
      trainingdata : list of str
          Optional manually selected prefixes of video filenames to be
          used as training dataset.
      have_output: bool
          If you have already acquired the quality values (yfit) for the
          training dataset, set to True.  If False, it will output the files
          the user will need to acquire quality values for.
      bucket_name : str
          S3 bucket containing videos to be downloaded for regression
          calculations.
  
      Returns
      -------
      regress_object : list of sklearn.svm.classes.
          Contains list of regression objects assembled from the training
          datasets.  Uses the mean, 10th percentile, 90th percentile, and
          standard deviation intensities to predict the quality parameter
          in Trackmate.
      tprefix : list of str
          Contains randomly selected images from all_videos to be included in
          training dataset.
  
      
docstring: Uses regression based on image intensities to select tracking parameters.
  This function uses regression methods from the scikit-learn module to
  predict the lower quality cutoff values for particle filtering in TrackMate
  based on the intensity distributions of input images. Currently only uses
  the first frame of videos for analysis, and is limited to predicting
  quality values.
  
  In practice, users will run regress_sys twice in different modes to build
  a regression system. First, set have_output to False. Function will return
  list of randomly selected videos to include in the training dataset. The
  user should then manually track particles using the Trackmate GUI, and enter
  these values in during the next round as the input yfit variable.
  
  Parameters
  
  folder : str
  S3 directory containing video files specified in all_videos.
  all_videos: list of str
  Contains prefixes of video filenames of entire video set to be
  tracked.  Training dataset will be some subset of these videos.
  yfit: numpy.ndarray
  Contains manually acquired quality levels using Trackmate for the
  files contained in the training dataset.
  training_size : int
  Number of files in training dataset.
  randselect : bool
  If True, will randomly select training videos from all_videos.
  If False, will use trainingdata as input training dataset.
  trainingdata : list of str
  Optional manually selected prefixes of video filenames to be
  used as training dataset.
  have_output: bool
  If you have already acquired the quality values (yfit) for the
  training dataset, set to True.  If False, it will output the files
  the user will need to acquire quality values for.
  bucket_name : str
  S3 bucket containing videos to be downloaded for regression
  calculations.
  
  Returns
  
  regress_object : list of sklearn.svm.classes.
  Contains list of regression objects assembled from the training
  datasets.  Uses the mean, 10th percentile, 90th percentile, and
  standard deviation intensities to predict the quality parameter
  in Trackmate.
  tprefix : list of str
  Contains randomly selected images from all_videos to be included in
  training dataset.
docstring_tokens: Uses regression based on image intensities to select tracking parameters . This function uses regression methods from the scikit - learn module to predict the lower quality cutoff values for particle filtering in TrackMate based on the intensity distributions of input images . Currently only uses the first frame of videos for analysis and is limited to predicting quality values . In practice users will run regress_sys twice in different modes to build a regression system . First set have_output to False . Function will return list of randomly selected videos to include in the training dataset . The user should then manually track particles using the Trackmate GUI and enter these values in during the next round as the input yfit variable . Parameters folder : str S3 directory containing video files specified in all_videos . all_videos : list of str Contains prefixes of video filenames of entire video set to be tracked . Training dataset will be some subset of these videos . yfit : numpy . ndarray Contains manually acquired quality levels using Trackmate for the files contained in the training dataset . training_size : int Number of files in training dataset . randselect : bool If True will randomly select training videos from all_videos . If False will use trainingdata as input training dataset . trainingdata : list of str Optional manually selected prefixes of video filenames to be used as training dataset . have_output : bool If you have already acquired the quality values ( yfit ) for the training dataset set to True . If False it will output the files the user will need to acquire quality values for . bucket_name : str S3 bucket containing videos to be downloaded for regression calculations . Returns regress_object : list of sklearn . svm . classes . Contains list of regression objects assembled from the training datasets . Uses the mean 10th percentile 90th percentile and standard deviation intensities to predict the quality parameter in Trackmate . tprefix : list of str Contains randomly selected images from all_videos to be included in training dataset .
code: def regress_sys(folder, all_videos, yfit, training_size, randselect=True,
                  trainingdata=[], frame=0, have_output=True, download=True,
                  bucket_name='ccurtis.data'):
      """Uses regression based on image intensities to select tracking parameters.
  
      This function uses regression methods from the scikit-learn module to
      predict the lower quality cutoff values for particle filtering in TrackMate
      based on the intensity distributions of input images. Currently only uses
      the first frame of videos for analysis, and is limited to predicting
      quality values.
  
      In practice, users will run regress_sys twice in different modes to build
      a regression system. First, set have_output to False. Function will return
      list of randomly selected videos to include in the training dataset. The
      user should then manually track particles using the Trackmate GUI, and enter
      these values in during the next round as the input yfit variable.
  
      Parameters
      ----------
      folder : str
          S3 directory containing video files specified in all_videos.
      all_videos: list of str
          Contains prefixes of video filenames of entire video set to be
          tracked.  Training dataset will be some subset of these videos.
      yfit: numpy.ndarray
          Contains manually acquired quality levels using Trackmate for the
          files contained in the training dataset.
      training_size : int
          Number of files in training dataset.
      randselect : bool
          If True, will randomly select training videos from all_videos.
          If False, will use trainingdata as input training dataset.
      trainingdata : list of str
          Optional manually selected prefixes of video filenames to be
          used as training dataset.
      have_output: bool
          If you have already acquired the quality values (yfit) for the
          training dataset, set to True.  If False, it will output the files
          the user will need to acquire quality values for.
      bucket_name : str
          S3 bucket containing videos to be downloaded for regression
          calculations.
  
      Returns
      -------
      regress_object : list of sklearn.svm.classes.
          Contains list of regression objects assembled from the training
          datasets.  Uses the mean, 10th percentile, 90th percentile, and
          standard deviation intensities to predict the quality parameter
          in Trackmate.
      tprefix : list of str
          Contains randomly selected images from all_videos to be included in
          training dataset.
  
      """
  
      if randselect:
          tprefix = []
          for i in range(0, training_size):
              random.seed(i+1)
              tprefix.append(all_videos[random.randint(0, len(all_videos))])
              if have_output is False:
                  print("Get parameters for: {}".format(tprefix[i]))
      else:
          tprefix = trainingdata
  
      if have_output is True:
          # Define descriptors
          descriptors = np.zeros((training_size, 4))
          counter = 0
          for name in tprefix:
              local_im = name + '.tif'
              remote_im = "{}/{}".format(folder, local_im)
              if download:
                  aws.download_s3(remote_im, local_im, bucket_name=bucket_name)
              test_image = sio.imread(local_im)
              descriptors[counter, 0] = np.mean(test_image[frame, :, :])
              descriptors[counter, 1] = np.std(test_image[frame, :, :])
              descriptors[counter, 2] = np.percentile(test_image[frame, :, :], 10)
              descriptors[counter, 3] = np.percentile(test_image[frame, :, :], 90)
              counter = counter + 1
  
          # Define regression techniques
          xfit = descriptors
          classifiers = [
              svm.SVR(),
              linear_model.SGDRegressor(),
              linear_model.BayesianRidge(),
              linear_model.LassoLars(),
              linear_model.ARDRegression(),
              linear_model.PassiveAggressiveRegressor(),
              linear_model.TheilSenRegressor(),
              linear_model.LinearRegression()]
  
          regress_object = []
          for item in classifiers:
              clf = item
              regress_object.append(clf.fit(xfit, yfit))
  
          return regress_object
  
      else:
          return tprefix
code_tokens: def regress_sys ( folder , all_videos , yfit , training_size , randselect = True , trainingdata = [ ] , frame = 0 , have_output = True , download = True , bucket_name = 'ccurtis.data' ) : if randselect : tprefix = [ ] for i in range ( 0 , training_size ) : random . seed ( i + 1 ) tprefix . append ( all_videos [ random . randint ( 0 , len ( all_videos ) ) ] ) if have_output is False : print ( "Get parameters for: {}" . format ( tprefix [ i ] ) ) else : tprefix = trainingdata if have_output is True : descriptors = np . zeros ( ( training_size , 4 ) ) counter = 0 for name in tprefix : local_im = name + '.tif' remote_im = "{}/{}" . format ( folder , local_im ) if download : aws . download_s3 ( remote_im , local_im , bucket_name = bucket_name ) test_image = sio . imread ( local_im ) descriptors [ counter , 0 ] = np . mean ( test_image [ frame , : , : ] ) descriptors [ counter , 1 ] = np . std ( test_image [ frame , : , : ] ) descriptors [ counter , 2 ] = np . percentile ( test_image [ frame , : , : ] , 10 ) descriptors [ counter , 3 ] = np . percentile ( test_image [ frame , : , : ] , 90 ) counter = counter + 1 xfit = descriptors classifiers = [ svm . SVR ( ) , linear_model . SGDRegressor ( ) , linear_model . BayesianRidge ( ) , linear_model . LassoLars ( ) , linear_model . ARDRegression ( ) , linear_model . PassiveAggressiveRegressor ( ) , linear_model . TheilSenRegressor ( ) , linear_model . LinearRegression ( ) ] regress_object = [ ] for item in classifiers : clf = item regress_object . append ( clf . fit ( xfit , yfit ) ) return regress_object else : return tprefix
short_docstring: Uses regression based on image intensities to select tracking parameters.
short_docstring_tokens: Uses regression based on image intensities to select tracking parameters .
comment: """Uses regression based on image intensities to select tracking parameters.
  
      This function uses regression methods from the scikit-learn module to
      predict the lower quality cutoff values for particle filtering in TrackMate
      based on the intensity distributions of input images. Currently only uses
      the first frame of videos for analysis, and is limited to predicting
      quality values.
  
      In practice, users will run regress_sys twice in different modes to build
      a regression system. First, set have_output to False. Function will return
      list of randomly selected videos to include in the training dataset. The
      user should then manually track particles using the Trackmate GUI, and enter
      these values in during the next round as the input yfit variable.
  
      Parameters
      ----------
      folder : str
          S3 directory containing video files specified in all_videos.
      all_videos: list of str
          Contains prefixes of video filenames of entire video set to be
          tracked.  Training dataset will be some subset of these videos.
      yfit: numpy.ndarray
          Contains manually acquired quality levels using Trackmate for the
          files contained in the training dataset.
      training_size : int
          Number of files in training dataset.
      randselect : bool
          If True, will randomly select training videos from all_videos.
          If False, will use trainingdata as input training dataset.
      trainingdata : list of str
          Optional manually selected prefixes of video filenames to be
          used as training dataset.
      have_output: bool
          If you have already acquired the quality values (yfit) for the
          training dataset, set to True.  If False, it will output the files
          the user will need to acquire quality values for.
      bucket_name : str
          S3 bucket containing videos to be downloaded for regression
          calculations.
  
      Returns
      -------
      regress_object : list of sklearn.svm.classes.
          Contains list of regression objects assembled from the training
          datasets.  Uses the mean, 10th percentile, 90th percentile, and
          standard deviation intensities to predict the quality parameter
          in Trackmate.
      tprefix : list of str
          Contains randomly selected images from all_videos to be included in
          training dataset.
  
      """$SEP$# Define descriptors$SEP$# Define regression techniques