Code cleanups

pycroscopy/analysis/optimize.py

@@ -156,7 +156,7 @@ class Optimize(object):
         self.solver_type = solver_type
         self.solver_options = solver_options
         if self.solver_type not in scipy.optimize.__dict__.keys():
-            warn('Solver %s does not exist!. For additional info see scipy.optimize' % (solver_type))
+            warn('Solver %s does not exist!. For additional info see scipy.optimize' % solver_type)
             sys.exit()
         if obj_func['class'] is None:
             self.obj_func = obj_func['obj_func']

pycroscopy/io/hdf_utils.py

@@ -129,8 +129,8 @@ def getAuxData(parent_data, auxDataName=None):
         auxDataName = parent_data.attrs.keys()
     elif type(auxDataName) not in [list, tuple, set]:
         auxDataName = [auxDataName]  # typically a single string
+    data_list = list()
     try:
-        data_list = []
         file_ref = parent_data.file
         for auxName in auxDataName:
             ref = parent_data.attrs[auxName]
@@ -197,14 +197,15 @@ def get_attributes(parent_data, attr_names=None):
         attr_names = [attr_names]
 
     att_dict = {}
-    try:
-        for attr in attr_names:
+
+    for attr in attr_names:
+        try:
             att_dict[attr] = get_attr(parent_data, attr)
-    except KeyError:
-        warn('%s is not an attribute of %s'
-             % (str(attr), parent_data.name))
-    except:
-        raise
+        except KeyError:
+            warn('%s is not an attribute of %s'
+                 % (str(attr), parent_data.name))
+        except:
+            raise
 
     return att_dict
 
@@ -410,6 +411,7 @@ def getH5RegRefIndices(ref, h5_main, return_method='slices'):
             ref_inds = return_func(start, end)
         else:
             warn('No method currently exists for converting this type of reference.')
+            ref_inds = np.empty(0)
     else:
         raise TypeError('Input ref must be an HDF5 Region Reference')
 
@@ -740,11 +742,9 @@ def reshape_to_Ndims(h5_main, h5_pos=None, h5_spec=None, get_labels=False):
 
         ds_labels = np.hstack([pos_labs, spec_labs])
 
-        results = (ds_Nd2, True, ds_labels)
+        return ds_Nd2, True, ds_labels
     else:
-        results = (ds_Nd2, True)
-
-    return results
+        return ds_Nd2, True
 
 
 def reshape_from_Ndims(ds_Nd, h5_pos=None, h5_spec=None):

pycroscopy/io/io_utils.py

@@ -23,14 +23,14 @@ def check_ssh():
     return 'SSH_CLIENT' in os.environ or 'SSH_TTY' in os.environ
 
 
-def uiGetFile(filter='H5 file (*.h5)', caption='Select File'):
+def uiGetFile(file_filter='H5 file (*.h5)', caption='Select File'):
     """
     Presents a File dialog used for selecting the .mat file
     and returns the absolute filepath of the selecte file\n
 
     Parameters
     ----------
-    filter : String or list of strings
+    file_filter : String or list of strings
         file extensions to look for
     caption : (Optional) String
         Title for the file browser window
@@ -51,7 +51,7 @@ def uiGetFile(filter='H5 file (*.h5)', caption='Select File'):
             raise
         else:
             app = QtWidgets.QApplication([])
-            path = QtWidgets.QFileDialog.getOpenFileName(caption=caption, filter=filter)[0]
+            path = QtWidgets.QFileDialog.getOpenFileName(caption=caption, filter=file_filter)[0]
             app.closeAllWindows()
             app.exit()
             del app
@@ -66,7 +66,7 @@ def uiGetFile(filter='H5 file (*.h5)', caption='Select File'):
             raise
         else:
             app = QtGui.QApplication([])
-            path = QtGui.QFileDialog.getOpenFileName(caption=caption, filter=filter)
+            path = QtGui.QFileDialog.getOpenFileName(caption=caption, filter=file_filter)
             app.exit()
             del app
 

pycroscopy/io/microdata.py

@@ -21,7 +21,7 @@ class MicroData(object):
     """
 
     def __init__(self, name, parent):
-        '''
+        """
         Parameters
         ----------
         name : String
@@ -29,7 +29,7 @@ class MicroData(object):
         parent : String
             HDF5 path to the parent of this object. Typically used when
             appending to an existing HDF5 file
-        '''
+        """
         self.name = name
         self.attrs = dict()
         self.parent = parent
@@ -65,18 +65,18 @@ class MicroDataGroup(MicroData):
         pass
 
     def addChildren(self, children):
-        '''
+        """
         Adds Children to the class to make a tree structure.
-        
+
         Parameters
         ----------
         children : list of MicroData objects
             Children can be a mixture of groups and datasets
-            
+
         Returns
         -------
         None
-        '''
+        """
         for child in children:
             if isinstance(child, MicroData):
                 child.parent = self.parent + self.name

pycroscopy/io/translators/be_odf_relaxation.py

@@ -181,8 +181,11 @@ class BEodfRelaxationTranslator(Translator):
         ds_wfm_typ = MicroDataset('Bin_Wfm_Type', exec_bin_vec)
 
         # Create Spectroscopic Values and Spectroscopic Values Labels datasets
-        spec_vals, spec_vals_labs, spec_vals_units = createSpecVals(UDVS_mat, spec_inds, bin_freqs, exec_bin_vec,
-                                                                    parm_dict, UDVS_labs, UDVS_units)
+        spec_vals, spec_inds, spec_vals_labs, spec_vals_units, spec_vals_names = createSpecVals(UDVS_mat, spec_inds,
+                                                                                                bin_freqs,
+                                                                                                exec_bin_vec,
+                                                                                                parm_dict, UDVS_labs,
+                                                                                                UDVS_units)
 
         spec_vals_slices = dict()
         for row_ind, row_name in enumerate(spec_vals_labs):
@@ -359,7 +362,7 @@ class BEodfRelaxationTranslator(Translator):
         FFT_full = np.fft.fftshift(np.fft.fft(BE_wave))
         bin_FFT = np.conjugate(FFT_full[bin_inds])
 
-        return (bin_inds, bin_w, bin_FFT, BE_wave, dc_amp_vec_full)
+        return bin_inds, bin_w, bin_FFT, BE_wave, dc_amp_vec_full
 
     def _parse_file_path(self, data_filepath):
         """
@@ -393,7 +396,7 @@ class BEodfRelaxationTranslator(Translator):
         path_dict['read_imag'] = imag_path
         path_dict['old_mat_parms'] = data_filepath
 
-        return (basename, path_dict)
+        return basename, path_dict
 
     @staticmethod
     def __getParmsFromOldMat(file_path):
@@ -493,7 +496,7 @@ class BEodfRelaxationTranslator(Translator):
         elif VS_parms[0] == 2:
             # AC mode 
             parm_dict['VS_mode'] = 'AC modulation mode with time reversal'
-            parm_dict['VS_amplitude_[V]'] = 0.5 * (VS_final_loop_amp)
+            parm_dict['VS_amplitude_[V]'] = 0.5 * VS_final_loop_amp
             parm_dict[
                 'VS_offset_[V]'] = 0  # this is not correct. Fix manually when it comes to UDVS generation?
         else:
@@ -620,4 +623,4 @@ class BEodfRelaxationTranslator(Translator):
             UD_VS_table[BE_IF_switch == 1, 5] = UD_VS_table[BE_IF_switch == 1, 1]
             UD_VS_table[BE_OF_switch == 1, 6] = UD_VS_table[BE_IF_switch == 1, 1]
 
-        return (UD_VS_table_label, UD_VS_table_unit, UD_VS_table)
+        return UD_VS_table_label, UD_VS_table_unit, UD_VS_table

pycroscopy/io/translators/df_utils/be_utils.py

@@ -299,7 +299,7 @@ def normalizeBEresponse(spectrogram_mat, FFT_BE_wave, harmonic):
     # Generate transfer functions
     F_AO_spectrogram = np.transpose(np.tile(FFT_BE_wave / scaling_factor, [spectrogram_mat.shape[1], 1]))
     # Divide by transfer function
-    spectrogram_mat = spectrogram_mat / (F_AO_spectrogram)
+    spectrogram_mat = spectrogram_mat / F_AO_spectrogram
 
     return spectrogram_mat
 
@@ -729,7 +729,7 @@ def createSpecVals(udvs_mat, spec_inds, bin_freqs, bin_wfm_type, parm_dict,
             Check if more that one unique value
             Append column number to iSpec_var if true
             """
-            if (uvals.size > 1):
+            if uvals.size > 1:
                 iSpec_var = np.append(iSpec_var, int(i))
 
         iSpec_var = np.asarray(iSpec_var, np.int)
@@ -1179,7 +1179,7 @@ BEHistogram Class and Functions
 """
 
 
-class BEHistogram():
+class BEHistogram:
     # TODO: Turn into proper class
     # TODO: Parallelize Histogram generation
     """
@@ -1552,7 +1552,7 @@ class BEHistogram():
                 udvs_bins = np.where(x_hist[1] == udvs_step)[0]
                 if debug:
                     print(np.shape(x_hist))
-                data_mat = h5_main[pix_chunks[ichunk]:pix_chunks[ichunk + 1], (udvs_bins)]
+                data_mat = h5_main[pix_chunks[ichunk]:pix_chunks[ichunk + 1], udvs_bins]
 
                 """
         Get the frequecies that correspond to the current UDVS bins from the total x_hist

pycroscopy/io/translators/df_utils/parse_dm3.py

@@ -357,7 +357,7 @@ def dm_read_string(f, outdata=None):
         put_into_file(f, ">" + str(slen) + "s", outdata)
         return header_size
     else:
-        assert(False)
+        assert False
         slen = get_from_file(f, ">L")
         raws = get_from_file(f, ">" + str(slen) + "s")
         if verbose:

pycroscopy/io/translators/general_dynamic_mode.py

@@ -162,7 +162,7 @@ class GDMTranslator(Translator):
                 else:
                     print('File not found for: row {} col {}'.format(row_ind, col_ind))
                 pos_ind += 1
-                if (100.0 * (pos_ind) / num_pix) % 10 == 0:
+                if (100.0 * pos_ind / num_pix) % 10 == 0:
                     print('completed translating {} %'.format(int(100 * pos_ind / num_pix)))
 
         hdf.close()

pycroscopy/io/translators/sporc.py

@@ -146,7 +146,7 @@ class SporcTranslator(Translator):
                 else:
                     print('File for row {} col {} not found'.format(row_ind, col_ind))
                 pos_ind += 1
-                if (100.0 * (pos_ind) / num_pix) % 10 == 0:
+                if (100.0 * pos_ind / num_pix) % 10 == 0:
                     print('Finished reading {} % of data'.format(int(100 * pos_ind / num_pix)))
 
         hdf.close()

pycroscopy/processing/feature_extraction.py

@@ -157,9 +157,9 @@ class FeatureExtractorParallel(object):
             return keypts, descs
 
         # start pool of workers
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
         pool = mp.Pool(processes)
-        tasks = [(imp) for imp in self.data]
+        tasks = [imp for imp in self.data]
         chunk = int(self.data.shape[0] / processes)
         jobs = pool.imap(detect, tasks, chunksize=chunk)
 

pycroscopy/processing/gmode_utils.py

@@ -744,7 +744,7 @@ def reshape_from_lines_to_pixels(h5_main, pts_per_cycle, scan_step_x_m=1):
     if h5_main.shape[1] % pts_per_cycle != 0:
         warn('Error in reshaping the provided dataset to pixels. Check points per pixel')
         raise ValueError
-        return
+
     num_cols = int(h5_main.shape[1] / pts_per_cycle)
 
     h5_spec_vals = getAuxData(h5_main, auxDataName=['Spectroscopic_Values'])[0]

pycroscopy/processing/image_processing.py

@@ -584,7 +584,7 @@ class ImageWindow(object):
         for islice, this_slice in enumerate(win_slices):
             selected = islice % np.rint(n_wins / 10) == 0
             if selected:
-                per_done = np.rint(100 * (islice) / (n_wins))
+                per_done = np.rint(100 * islice / n_wins)
                 print('Reconstructing Image...{}% -- step # {}'.format(per_done, islice))
             counts[this_slice] += ones
 
@@ -708,7 +708,7 @@ class ImageWindow(object):
 
         for islice, this_slice in enumerate(win_slices):
             if islice % np.rint(n_wins / 10) == 0:
-                per_done = np.rint(100 * (islice) / (n_wins))
+                per_done = np.rint(100 * islice / n_wins)
                 print('Reconstructing Image...{}% -- step # {}'.format(per_done, islice))
 
             counts[this_slice] += ones

pycroscopy/processing/image_transformation.py

@@ -160,9 +160,9 @@ class FeatureExtractorParallel(object):
             return keypts, descs
 
         # start pool of workers
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
         pool = mp.Pool(processes)
-        tasks = [(imp) for imp in self.data]
+        tasks = [imp for imp in self.data]
         chunk = int(self.data.shape[0] / processes)
         jobs = pool.imap(detect, tasks, chunksize=chunk)
 
@@ -769,7 +769,6 @@ class geoTransformerParallel(object):
     """
 
     def __init__(self):
-        self.__init__
         self.data = []
         self.features = []
 
@@ -844,7 +843,7 @@ class geoTransformerParallel(object):
 
         # start pool of workers
         pool = mp.Pool(processes)
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
 
         tasks = [(desc1, desc2) for desc1, desc2 in zip(desc[:], desc[1:])]
         chunk = int(len(desc) / processes)
@@ -898,7 +897,7 @@ class geoTransformerParallel(object):
 
             # start pool of workers
 
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
         pool = mp.Pool(processes)
         tasks = [(key1[match[:, 0]], key2[match[:, 1]])
                  for match, key1, key2 in zip(matches, keypts[:], keypts[1:])]
@@ -1039,7 +1038,7 @@ class geoTransformerParallel(object):
         for imp, transform, itm in zip(transImages, chainTransforms, range(0, transImages.shape[0])):
             transimp = warping([imp, transform])
             transImages[itm] = transimp
-            print('Image #%i' % (itm))
+            print('Image #%i' % itm)
 
         return transImages, chainTransforms
 
@@ -1061,7 +1060,7 @@ class geoTransformerParallel(object):
         processes = kwargs.get('processors', 1)
 
         pool = mp.Pool(processes)
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
 
         def register(images):
             imp1, imp2 = images[0], images[1]
@@ -1100,7 +1099,6 @@ class geoTransformerSerial(object):
     """
 
     def __init__(self):
-        self.__init__
         self.data = []
         self.features = []
 
@@ -1173,7 +1171,7 @@ class geoTransformerSerial(object):
 
         # start pool of workers
         pool = mp.Pool(processes)
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
 
         tasks = [(desc1, desc2) for desc1, desc2 in zip(desc[:], desc[1:])]
         chunk = int(len(desc) / processes)
@@ -1343,7 +1341,7 @@ class geoTransformerSerial(object):
         for imp, transform, itm in zip(transImages, chainTransforms, range(0, transImages.shape[0])):
             transimp = warping([imp, transform])
             transImages[itm] = transimp
-            print('Image #%i' % (itm))
+            print('Image #%i' % itm)
 
         return transImages, chainTransforms
 
@@ -1365,7 +1363,7 @@ class geoTransformerSerial(object):
         processes = kwargs.get('processors', 1)
 
         pool = mp.Pool(processes)
-        print('launching %i kernels...' % (processes))
+        print('launching %i kernels...' % processes)
 
         def register(images):
             imp1, imp2 = images[0], images[1]

pycroscopy/visualizers/BEPSvisSHO/ioFuncs.py

-'''
+"""
 Created on Apr 20, 2016
 
 @author: Chris Smith -- csmith55@utk.edu
-'''
+"""
 
 import sys
 import numpy as np
@@ -15,11 +15,11 @@ from pycroscopy.io.hdf_utils import reshape_to_Ndims
 
 
 def loadDataFunc(filePath, **kwargs):
-    '''
+    """
     Function to load the N-D data from a .mat file
     Output:  N-D numpy data array, Nx2 x-vector array
             array indices are (Step, #rows, #cols, cycle#)
-    '''
+    """
     data = loadmat(filePath)
     data_mat = data['loop_mat']
     data_mat = data_mat[:, :, :, :]
@@ -32,18 +32,18 @@ def loadDataFunc(filePath, **kwargs):
 
 
 def readData(h5_path, dset_name='SHO_Fit_Guess'):
-    '''
+    """
     Reads the hdf5 data file and calls appropriate reader based on data type
     Input:
         h5_path -- the absolute file path to the hdf5 file to be read in.
         dset_name -- the name of the main dataset
-        
+
     Outputs:
         data_mat -- the transformed data read to be plotted
         xvec -- numpy array containing the possible plot data of the slice viewer
         xvec_labs -- numpy array of labels and units for the xvec array
-        
-    '''
+
+    """
 
     hdf = ioHDF5(h5_path)
 
@@ -64,18 +64,18 @@ def readData(h5_path, dset_name='SHO_Fit_Guess'):
 
 
 def readDCData(h5_group):
-    '''
+    """
     Reads the data for DC modulation experiments
-    
+
     Inputs:
-        h5_group -- hdf5 group holding the SHO_Fit Data for the chosen 
+        h5_group -- hdf5 group holding the SHO_Fit Data for the chosen
                     Measurement group
-    
+
     Outputs:
         data_guess -- the transformed data to be plotted
         xvec -- numpy array containing the possible plot data of the slice viewer
         xvec_labs -- numpy array of labels and units for the xvec array
-    '''
+    """
     h5_chan = h5_group['Channel_000']
     h5_main = h5_chan['Raw_Data']
     h5_file = h5_main.file
@@ -212,18 +212,18 @@ def readDCData(h5_group):
 
 
 def readACData(h5_group):
-    '''
+    """
     Reads the data for AC modulation experiments
-    
+
     Inputs:
-        h5_group -- hdf5 group holding the SHO_Fit Data for the chosen 
+        h5_group -- hdf5 group holding the SHO_Fit Data for the chosen
                     Measurement group
-    
+
     Outputs:
         data_guess -- the transformed data to be plotted
         xvec -- numpy array containing the possible plot data of the slice viewer
         xvec_labs -- numpy array of labels and units for the xvec array
-    '''
+    """
     h5_chan = h5_group['Channel_000']
     h5_main = h5_chan['Raw_Data']
     h5_specv = h5_chan['Spectroscopic_Values']
@@ -367,9 +367,9 @@ def getSpectralData(point, data_mat):
 
 
 def __getPos(h5_pos):
-    '''
+    """
     Return the number of rows and columns in the dataset
-    '''
+    """
     num_rows = len(np.unique(h5_pos[:, 0]))
     try:
         num_cols = len(np.unique(h5_pos[:, 1]))
@@ -380,9 +380,9 @@ def __getPos(h5_pos):
 
 
 def __findDataset(h5_file, ds_name):
-    '''
+    """
     Uses visit() to find all datasets with the desired name
-    '''
+    """
     print('Finding all instances of', ds_name)
     ds = []
 

pycroscopy/visualizers/BEPSvisSHO/plotFunctions.py

@@ -10,14 +10,14 @@ from pyqtgraph import QtGui
 
 
 class BEPSwindow(QtGui.QMainWindow):
-    '''
+    """
     Window object that will handle all the plotting
-    '''
+    """
 
     def __init__(self, **kwargs):
-        '''
+        """
         Create the initial window
-        '''
+        """
         super(BEPSwindow, self).__init__()
         winTitle = kwargs.get('winTitle', 'BEPS Visualization')
         self.setWindowTitle(winTitle)
@@ -98,10 +98,10 @@ class BEPSwindow(QtGui.QMainWindow):
     # %%
 
     def setup(self, h5_path=None):
-        '''
-        Call the readData functions from ioFuncs to setup the 
+        """
+        Call the readData functions from ioFuncs to setup the
         arrays for later uses and get the proper parameters
-        '''
+        """
         if not h5_path:
             h5_path = pg.FileDialog.getOpenFileName(caption='Select H5 file',
                                                     filter="H5 file (*.h5)")
@@ -373,26 +373,26 @@ class BEPSwindow(QtGui.QMainWindow):
         return imv1, imv2, imv3
 
     def __setupTwoD(self, xlabel, ylabel):
-        '''
+        """
         Creates the needed widgets for plotting 1D data
-        
+
         Inputs:
             xlabel -- list of labels and units to use for x-axis of plots
             ylabel -- list of labels and units to use for y-axis of plots
-        
+
         Outputs:
             imv1 -- ImageView Widget in which the map will be plotted
             imv2 -- PlotWidget in which the loops will be plotted
             imv3 -- PlotWidget in which the timeline will be plotted
-        
+
         Shared:
-            plt1 -- PlotItem associated with the map to show position axes 
+            plt1 -- PlotItem associated with the map to show position axes
                     and the title
-            roi1 -- The crosshairs roi object that determines the position for 
+            roi1 -- The crosshairs roi object that determines the position for
                     which the loops are plotted in imv2
             roiplt1 -- The box roi object associated with imv1
-            posline -- the 
-        '''
+            posline -- the
+        """
         plt1 = pg.PlotItem()
         imv1 = pg.ImageView(view=plt1)
         imv2 = pg.PlotWidget()
@@ -435,9 +435,9 @@ class BEPSwindow(QtGui.QMainWindow):
         return imv1, imv2, imv3
 
     def __setInitialData(self, data_guess, data_results, xvec, data_main, freq_vec):
-        '''
+        """
         Tell the window what data it should be plotting from
-        '''
+        """
         points = np.arange(len(xvec[0, 0, 0, :]) + 1)
         self.num_bins = len(freq_vec)
 
@@ -465,43 +465,43 @@ class BEPSwindow(QtGui.QMainWindow):
         return
 
     def __setDataOneD(self, data_guess, data_results, xvec, data_main, freq_vec, points):
-        '''
+        """
         Sets the initial data for the case of one spacial dimension
         Inputs:
-            data_guess -- 6D numpy array holding BEPS data.  
+            data_guess -- 6D numpy array holding BEPS data.
                         Indices are:
                         [Field, SHO, Cycle #, UDVS Step, X-Pos, Y-Pos]
 
                     Field:  "In-field" or "Out-of-field" for DC
                             "Forward" or "Reverse" for AC
                     SHO:    SHO parameter to be plotted
-                            Amplitude, Resonance Frequency, Q-factor, Phase, 
+                            Amplitude, Resonance Frequency, Q-factor, Phase,
                             or R^2 value
                     Cycle:  Which cycle should be plotted
                     UDVS:   The UDVS step of the current plot
                     X-Pos:  X position from dataset
                     Y-Pos:  Y position from dataset
 
-                        These indices should be present for all datasets 
+                        These indices should be present for all datasets
                         even if they only have a length of 1
-             
-             xvec -- 4D numpy array containing the values which will be plotted 
+
+             xvec -- 4D numpy array containing the values which will be plotted
                      against