original_docstring: Predicts quality values to be used in particle tracking.
  
      Uses the regress object from regress_sys to predict tracking
      parameters for TrackMate analysis.
  
      Parameters
      ----------
      regress_object: list of sklearn.svm.classes.
          Obtained from regress_sys
      to_track: string
          Prefix of video files to be tracked.
      regmethod: {'LinearRegression', 'SVR', 'SGDRegressor', 'BayesianRidge',
          'LassoLars', 'ARDRegression', 'PassiveAggressiveRegressor',
          'TheilSenRegressor'}
          Desired regression method.
  
      Returns
      -------
      fqual: float
          Predicted quality factor used in TrackMate analysis.
  
      
docstring: Predicts quality values to be used in particle tracking.
  Uses the regress object from regress_sys to predict tracking
  parameters for TrackMate analysis.
  
  Parameters
  
  
  
  Returns
  
  float
  Predicted quality factor used in TrackMate analysis.
docstring_tokens: Predicts quality values to be used in particle tracking . Uses the regress object from regress_sys to predict tracking parameters for TrackMate analysis . Parameters Returns float Predicted quality factor used in TrackMate analysis .
code: def regress_tracking_params(regress_object, to_track,
                              regmethod='LinearRegression', frame=0):
      """Predicts quality values to be used in particle tracking.
  
      Uses the regress object from regress_sys to predict tracking
      parameters for TrackMate analysis.
  
      Parameters
      ----------
      regress_object: list of sklearn.svm.classes.
          Obtained from regress_sys
      to_track: string
          Prefix of video files to be tracked.
      regmethod: {'LinearRegression', 'SVR', 'SGDRegressor', 'BayesianRidge',
          'LassoLars', 'ARDRegression', 'PassiveAggressiveRegressor',
          'TheilSenRegressor'}
          Desired regression method.
  
      Returns
      -------
      fqual: float
          Predicted quality factor used in TrackMate analysis.
  
      """
  
      local_im = to_track + '.tif'
      pX = np.zeros((1, 4))
      test_image = sio.imread(local_im)
      pX[0, 0] = np.mean(test_image[frame, :, :])
      pX[0, 1] = np.std(test_image[frame, :, :])
      pX[0, 2] = np.percentile(test_image[frame, :, :], 10)
      pX[0, 3] = np.percentile(test_image[frame:, :, :], 90)
  
      quality = []
      for item in regress_object:
          quality.append(item.predict(pX)[0])
  
      if regmethod == 'SVR':
          fqual = quality[0]
      elif regmethod == 'SGDRegressor':
          fqual = quality[1]
      elif regmethod == 'BayesianRidge':
          fqual = quality[2]
      elif regmethod == 'LassoLars':
          fqual = quality[3]
      elif regmethod == 'ARDRegression':
          fqual = quality[4]
      elif regmethod == 'PassiveAggressiveRegressor':
          fqual = quality[5]
      elif regmethod == 'TheilSenRegressor':
          fqual = quality[6]
      elif regmethod == 'LinearRegression':
          fqual = quality[7]
      else:
          fqual = 3.0
  
      return fqual
code_tokens: def regress_tracking_params ( regress_object , to_track , regmethod = 'LinearRegression' , frame = 0 ) : local_im = to_track + '.tif' pX = np . zeros ( ( 1 , 4 ) ) test_image = sio . imread ( local_im ) pX [ 0 , 0 ] = np . mean ( test_image [ frame , : , : ] ) pX [ 0 , 1 ] = np . std ( test_image [ frame , : , : ] ) pX [ 0 , 2 ] = np . percentile ( test_image [ frame , : , : ] , 10 ) pX [ 0 , 3 ] = np . percentile ( test_image [ frame : , : , : ] , 90 ) quality = [ ] for item in regress_object : quality . append ( item . predict ( pX ) [ 0 ] ) if regmethod == 'SVR' : fqual = quality [ 0 ] elif regmethod == 'SGDRegressor' : fqual = quality [ 1 ] elif regmethod == 'BayesianRidge' : fqual = quality [ 2 ] elif regmethod == 'LassoLars' : fqual = quality [ 3 ] elif regmethod == 'ARDRegression' : fqual = quality [ 4 ] elif regmethod == 'PassiveAggressiveRegressor' : fqual = quality [ 5 ] elif regmethod == 'TheilSenRegressor' : fqual = quality [ 6 ] elif regmethod == 'LinearRegression' : fqual = quality [ 7 ] else : fqual = 3.0 return fqual
short_docstring: Predicts quality values to be used in particle tracking.
short_docstring_tokens: Predicts quality values to be used in particle tracking .
comment: """Predicts quality values to be used in particle tracking.
  
      Uses the regress object from regress_sys to predict tracking
      parameters for TrackMate analysis.
  
      Parameters
      ----------
      regress_object: list of sklearn.svm.classes.
          Obtained from regress_sys
      to_track: string
          Prefix of video files to be tracked.
      regmethod: {'LinearRegression', 'SVR', 'SGDRegressor', 'BayesianRidge',
          'LassoLars', 'ARDRegression', 'PassiveAggressiveRegressor',
          'TheilSenRegressor'}
          Desired regression method.
  
      Returns
      -------
      fqual: float
          Predicted quality factor used in TrackMate analysis.
  
      """