linting

Author: yavorska-iryna

visual_behavior/dimensionality_reduction/clustering/plotting.py

@@ -2077,7 +2077,7 @@ def plot_eigengap_values(eigenvalues_cre, cre_lines, n_clusters_cre=None, save_d
         n_clusters_cre = processing.get_n_clusters_cre()
 
     for cre_line in cre_lines:
-        if len(eigenvalues_cre[cre_line])<4: # patchwork her. 
+        if len(eigenvalues_cre[cre_line]) < 4:  # patchwork her.
             eigenvalues = eigenvalues_cre[cre_line][1]
         else:
             eigenvalues = eigenvalues_cre[cre_line]
@@ -2766,7 +2766,7 @@ def plot_cluster_size_for_cluster(cluster_size_df, cluster_id, stats_table, diff
     return ax
 
 
-def plot_cluster_size(cluster_size_df, cre_line=None, shuffle_type=None, stats_table=None, diff_column = 'cluster_size_diff',
+def plot_cluster_size(cluster_size_df, cre_line=None, shuffle_type=None, stats_table=None, diff_column='cluster_size_diff',
                       ax=None, figsize=None, save_dir=None, folder=None):
     if cre_line is not None:
         if isinstance(cre_line, str):
@@ -2788,7 +2788,7 @@ def plot_cluster_size(cluster_size_df, cre_line=None, shuffle_type=None, stats_t
 
     # plot cluster size first
     for i, cluster_id in enumerate(cluster_ids):
-        ax[i] = plot_cluster_size_for_cluster(cluster_size_df, cluster_id, stats_table=stats_table, diff_column = diff_column, ax=ax[i])
+        ax[i] = plot_cluster_size_for_cluster(cluster_size_df, cluster_id, stats_table=stats_table, diff_column=diff_column, ax=ax[i])
 
     fig.subplots_adjust(hspace=1.2, wspace=0.6)
     plt.suptitle(processing.get_shuffle_label(shuffle_type), x=0.52, y=1.15)

visual_behavior/dimensionality_reduction/clustering/processing.py

@@ -239,16 +239,16 @@ def get_glm_results_pivoted_for_clustering(glm_version='24_events_all_L2_optimiz
         cells_table = utilities.limit_to_containers_with_all_experience_levels(cells_table)
         cells_table = utilities.limit_to_cell_specimen_ids_matched_in_all_experience_levels(cells_table)
         print(len(cells_table.cell_specimen_id.unique()),
-                  'cells in cells_table after limiting to strictly matched cells')
+              'cells in cells_table after limiting to strictly matched cells')
 
         # get matched cells and experiments to limit to
         matched_experiments = cells_table.ophys_experiment_id.unique()
         matched_cells = cells_table.cell_specimen_id.unique()
 
         if across_sessions_normalized is False:
             results_pivoted = gat.build_pivoted_results_summary(value_to_use=model_output_type,
-                                                                    results_summary=None,
-                                                                    glm_version=glm_version, cutoff=None)
+                                                                results_summary=None,
+                                                                glm_version=glm_version, cutoff=None)
 
             # get rid of passive sessions
             results_pivoted = results_pivoted[results_pivoted.passive == False]
@@ -259,7 +259,7 @@ def get_glm_results_pivoted_for_clustering(glm_version='24_events_all_L2_optimiz
         results_pivoted = results_pivoted[results_pivoted.ophys_experiment_id.isin(matched_experiments)]
         results_pivoted = results_pivoted[results_pivoted.cell_specimen_id.isin(matched_cells)]
         print(len(results_pivoted.cell_specimen_id.unique()),
-                  'cells in results_pivoted after limiting to strictly matched cells')
+              'cells in results_pivoted after limiting to strictly matched cells')
 
         # if save_dir:
         #     # save filtered results to save_dir
@@ -2453,6 +2453,7 @@ def compute_sse(feature_matrix):
 
     return SSE
 
+
 def get_variability_df(feature_matrix, cluster_df, columns=['cluster_id', 'cre_line', 'clustered'], metric='sse'):
     '''
     INPUT:
@@ -2501,5 +2502,3 @@ def get_variability_df(feature_matrix, cluster_df, columns=['cluster_id', 'cre_l
                                                ignore_index=True)
 
     return variability_df
-
-