More PEP8

pycroscopy/analysis/be_loop_model.py

@@ -624,9 +624,9 @@ class BELoopModel(Model):
 
         # step 5: Move the voltage dimension to the first dim
         order_dc_outside_nd = [self._dc_offset_index] + list(range(self._dc_offset_index)) + \
-                              list(range(self._dc_offset_index + 1, len(fit_nd.shape)))
+            list(range(self._dc_offset_index + 1, len(fit_nd.shape)))
         order_dc_offset_reverse = list(range(1, self._dc_offset_index + 1)) + [0] + \
-                                  list(range(self._dc_offset_index + 1, len(fit_nd.shape)))
+            list(range(self._dc_offset_index + 1, len(fit_nd.shape)))
         fit_nd2 = np.transpose(fit_nd, tuple(order_dc_outside_nd))
         dim_names_dc_out = dim_names_orig[order_dc_outside_nd]
         if verbose:
@@ -946,7 +946,7 @@ class BELoopModel(Model):
             self.data = None
 
         guess = self.h5_guess[self._start_pos:self._end_pos,
-                self._current_met_spec_slice].reshape([-1, 1])
+                              self._current_met_spec_slice].reshape([-1, 1])
         self.guess = compound_to_scalar(guess)[:, :-1]
 
     def _create_guess_datasets(self):

pycroscopy/io/be_hdf_utils.py

@@ -208,7 +208,7 @@ def getIndicesforPlotGroup(h5_udvs_inds, ds_udvs, plt_grp_name):
         step_bin_indices[indx, :] = np.where(spec_ind_udvs_step_col == step)[0]
 
     oneD_indices = step_bin_indices.reshape((step_bin_indices.shape[0] * step_bin_indices.shape[1]))
-    return (step_bin_indices, oneD_indices, udvs_plt_grp_col)
+    return step_bin_indices, oneD_indices, udvs_plt_grp_col
 
 
 def reshapeToOneStep(raw_mat, num_steps):

pycroscopy/io/translators/be_odf_relaxation.py

@@ -311,7 +311,7 @@ class BEodfRelaxationTranslator(Translator):
         for pix_ind in range(num_pix):
             print('Reading pixel #{}, file position {}'.format(pix_ind, hex(pix_ind * bytes_per_pix)))
             pix_vec = np.fromstring(f_real.read(int(bytes_per_pix)), dtype='f') + \
-                      1j * np.fromstring(f_imag.read(int(bytes_per_pix)), dtype='f')
+                1j * np.fromstring(f_imag.read(int(bytes_per_pix)), dtype='f')
 
             # Make chronologically correct
             pix_mat = np.reshape(pix_vec, (parm_dict['BE_bins_per_read'],

pycroscopy/io/translators/beps_ndf.py

@@ -194,11 +194,11 @@ class BEPSndfTranslator(Translator):
             for prsr in parsers:
                 wave_type = prsr.get_wave_type()
                 if self.parm_dict['VS_mode'] == 'AC modulation mode with time reversal' and \
-                    self.BE_bin_inds is not None:
-                        if np.sign(wave_type) == -1:
-                            bin_fft = self.BE_wave[self.BE_bin_inds]
-                        elif np.sign(wave_type) == 1:
-                            bin_fft = self.BE_wave_rev[self.BE_bin_inds]
+                        self.BE_bin_inds is not None:
+                    if np.sign(wave_type) == -1:
+                        bin_fft = self.BE_wave[self.BE_bin_inds]
+                    elif np.sign(wave_type) == 1:
+                        bin_fft = self.BE_wave_rev[self.BE_bin_inds]
                 else:
                     bin_fft = None
 
@@ -258,7 +258,7 @@ class BEPSndfTranslator(Translator):
 
         ds_pos_ind = MicroDataset('Position_Indices',
                                   self.pos_mat[self.ds_pixel_start_indx:self.ds_pixel_start_indx +
-                                                                        self.ds_pixel_index, :],
+                                               self.ds_pixel_index, :],
                                   dtype=np.uint)
 
         ds_pos_ind.attrs['labels'] = pos_slice_dict
@@ -283,7 +283,7 @@ class BEPSndfTranslator(Translator):
             self.pos_vals_list[:, 2] *= 1E+6  # convert to microns
 
         pos_val_mat = np.float32(self.pos_mat[self.ds_pixel_start_indx:self.ds_pixel_start_indx +
-                                                                       self.ds_pixel_index, :])
+                                              self.ds_pixel_index, :])
 
         for col_ind, targ_dim_name in enumerate(['X', 'Y', 'Z']):
             if targ_dim_name in self.pos_labels:

pycroscopy/processing/atom_finding.py

@@ -369,9 +369,11 @@ def apply_find(file_path_h5, file_name_h5, file_path_png, file_name_png, filter_
     for k1 in range(-filter_width, filter_width + 1):
         for k2 in range(-filter_width, filter_width + 1):
             mat_large[filter_width - k1:-(filter_width + k1) - 1,
-            filter_width - k2:-(filter_width + k2) - 1] = np.minimum(mat_large[filter_width - k1:-filter_width - k1 - 1,
-                                                                     filter_width - k2:-filter_width - k2 - 1],
-                                                                     h5_image)
+                      filter_width - k2:-(filter_width + k2) - 1] = np.minimum(mat_large[filter_width - k1:
+                                                                                         -filter_width - k1 - 1,
+                                                                                         filter_width - k2:
+                                                                                         -filter_width - k2 - 1],
+                                                                                h5_image)
 
     deconv_mat_temp = mat_large[filter_width:len(mat_larg[1, :]) - filter_width,
                       filter_width:len(mat_larg[:, 1]) - filter_width]