flake8 corrections

Author: jmarinllao

src/diffupy/process_input.py

@@ -266,32 +266,6 @@ def _codify_input_data(
                                     threshold
                                     )
 
-    # Standardize the title of the node column labeling column to 'Label', for later processing.
-    if LABEL not in df.columns:
-        for l in list(df.columns):
-            if l in NODE_LABELING:
-                df = df.rename(columns={l: LABEL})
-                break
-
-    # If node type provided in a column, classify in a dictionary the input codification by its node type.
-    if NODE_TYPE in df.columns:
-
-        node_types = list(set(df[NODE_TYPE]))  # Get the node types list set.
-        codified_by_type_dict = {}
-
-        for node_type in node_types:
-            # Filter the nodes by the iterable type.
-            df_by_type = df.loc[df[NODE_TYPE] == node_type]
-
-            # Codify the nodes for the iterable type.
-            codified_by_type_dict[node_type] = _codify_method_check(df_by_type,
-                                                                    method,
-                                                                    binning,
-                                                                    absolute_value,
-                                                                    p_value,
-                                                                    threshold
-                                                                    )
-        return codified_by_type_dict
 
 def _codify_method_check(
         df: pd.DataFrame,
@@ -613,7 +587,6 @@ def mapping_statistics(
 
             total_mapping.update(mapping)
 
-
         if subtotals:
             statistics_dict['total_mapping'] = total_mapping
             statistics_dict['total_input'] = total_input
@@ -782,7 +755,7 @@ def _map_label_dict(
             label_bck = _check_label_to_background_labels(label, background_labels, check_substrings)
             if label_bck is not None:
                 mapped_dict[label_bck] = v
-   
+
         elif isinstance(label, set) or isinstance(label, tuple) or isinstance(label, list):
             for sublabel in set(label):
                 label_bck = _check_label_to_background_labels(sublabel, background_labels, check_substrings)

src/diffupy/utils.py

@@ -24,7 +24,6 @@
 """Matrix/graph handling utils."""
 
 
-
 def get_laplacian(graph: Graph, normalized: bool = False) -> np.ndarray:
     """Return Laplacian matrix."""
     if nx.is_directed(graph):
@@ -145,9 +144,10 @@ def print_dict_dimensions(entities_db, title='Title', message=''):
                 m += f'{k2}  ({v2})\n'
         else:
             m += f'{v1}'
-            
+
     print(f'{m}\n\n')
 
+
 def log_dict(dict_to_print: dict, message: str = ''):
     """Print dictionary as list with a message."""
     for k1, v1 in dict_to_print.items():
@@ -300,12 +300,6 @@ def munge_cell(cell):
     else:
         raise TypeError(f'The cell "{cell}" could not be processed.')
 
-def parse_xls_sheet_to_df(sheet: opxl.workbook,
-                          min_row: Optional[int] = 1,
-                          relevant_cols: Optional[list] = None,
-                          irrelevant_cols: Optional[list] = None) -> pd.DataFrame:
-    """Process/format excel sheets to DataFrame."""
-    parsed_sheet_dict = {}
 
 def parse_xls_sheet_to_df(sheet: opxl.workbook,
                           min_row: Optional[int] = 1,

tests/test_input.py

@@ -271,7 +271,7 @@ def test_format_input_for_diffusion_label_list(self):
             map_labels_input({'Metabolite': {'C': -1}, 'Gene': {'A': 2, 'B': 1}, 'mirnas': {'A': 1, 'T': 1}},
                              self.kernel_test_1.rows_labels),
             self.kernel_test_1,
-        
+        )
 
         # TODO: Implement in Matrix equal, now if the col order is mixed it raises error
         # assert(np.allclose(processed_mapped_nodes_list.mat,