🎨 make line 88 characters long, remove some expections from flake8

setup.cfg

@@ -38,5 +38,4 @@ console_scripts =
 [flake8]
 max-line-length = 88
 aggressive = 2
-extend-select = B950
-extend-ignore = E203,E501,E701
+extend-ignore = E203 

src/move/data/perturbations.py

@@ -127,8 +127,8 @@ def perturb_continuous_data_extended(
 ) -> list[DataLoader]:
     """Add perturbations to continuous data. For each feature in the target
     dataset, change the feature's value in all samples (in rows):
-    1,2) substituting this feature in all samples by the feature's minimum/maximum value.
-    3,4) Adding/Substracting one standard deviation to the sample's feature value.
+    1,2) substituting this feature in all samples by the feature's minimum/maximum value
+    3,4) Adding/Substracting one standard deviation to the sample's feature value
 
     Args:
         baseline_dataloader: Baseline dataloader
@@ -144,8 +144,8 @@ def perturb_continuous_data_extended(
 
     Note:
         This function was created so that it could generalize to non-normalized
-        datasets. Scaling is done per dataset, not per feature -> slightly different stds
-        feature to feature.
+        datasets. Scaling is done per dataset, not per feature -> slightly different
+        stds feature to feature.
     """
 
     baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset)

src/move/tasks/encode_data.py

@@ -51,7 +51,8 @@ def encode_data(config: DataConfig):
         filepath = raw_data_path / f"{dataset_name}.tsv"
         names, values = io.read_tsv(filepath, sample_names)
 
-        # Plotting the value distribution for all continuous datasets before preprocessing:
+        # Plotting the value distribution for all continuous datasets
+        # before preprocessing:
         fig = plot_value_distributions(values)
         fig_path = str(
             output_path / "Value_distribution_{}_unprocessed.png".format(dataset_name)

src/move/tasks/identify_associations.py

@@ -437,26 +437,30 @@ def _ks_approach(
         task_config: IdentifyAssociationsKSConfig configuration.
         train_dataloader: training DataLoader.
         baseline_dataloader: unperturbed DataLoader.
-        dataloaders: list of DataLoaders where DataLoader[i] is obtained by perturbing feature i
-                     in the target dataset.
+        dataloaders: list of DataLoaders where DataLoader[i] is obtained by perturbing
+                     feature i in the target dataset.
         models_path: path to the models.
         num_perturbed: number of perturbed features.
         num_samples: total number of samples
-        num_continuous: number of continuous features (all continuous datasets concatenated).
-        con_names: list of lists where eah inner list contains the feature names of a specific continuous dataset
+        num_continuous: number of continuous features
+                        (all continuous datasets concatenated).
+        con_names: list of lists where eah inner list
+                   contains the feature names of a specific continuous dataset
         output_path: path where QC summary metrics will be saved.
 
     Returns:
-        sort_ids: list with flattened IDs of the associations above the significance threshold.
-        ks_distance: Ordered list with signed KS scores. KS scores quantify the direction and
-                     magnitude of the shift in feature B's reconstruction when perturbing feature A.
+        sort_ids: list with flattened IDs of the associations
+                  above the significance threshold.
+        ks_distance: Ordered list with signed KS scores. KS scores quantify the
+                    direction and magnitude of the shift in feature B's reconstruction
+                    when perturbing feature A.
 
 
     !!! Note !!!:
 
     The sign of the KS score can be misleading: negative sign means positive shift.
-    since the cumulative distribution starts growing later and is found below the reference
-    (baseline). Hence:
+    since the cumulative distribution starts growing later and is found below
+    the reference (baseline). Hence:
     a) with plus_std, negative sign means a positive correlation.
     b) with minus_std, negative sign means a negative correlation.
     """
@@ -598,11 +602,13 @@ def _ks_approach(
                         edges,
                         hist_base,
                         hist_pert,
-                        f"Cumulative_perturbed_{i}_measuring_{k}_stats_{stats[j, i, k]}",
+                        f"Cumulative_perturbed_{i}_measuring_{
+                            k}_stats_{stats[j, i, k]}",
                     )
                     fig.savefig(
                         figure_path
-                        / f"Cumulative_refit_{j}_perturbed_{i}_measuring_{k}_stats_{stats[j, i, k]}.png"
+                        / f"Cumulative_refit_{j}_perturbed_{
+                            i}_measuring_{k}_stats_{stats[j, i, k]}.png"
                     )
 
                     # Feature changes:
@@ -640,7 +646,8 @@ def _ks_approach(
     sort_ids = np.argsort(abs(final_stats), axis=None)[::-1]  # 1D: N x C
     ks_distance = np.take(final_stats, sort_ids)  # 1D: N x C
 
-    # Writing Quality control csv file. Mean slope and correlation over refits as qc metrics.
+    # Writing Quality control csv file.
+    # Mean slope and correlation over refits as qc metrics.
     logger.info("Writing QC file")
     qc_df = pd.DataFrame({"Feature names": feature_names})
     qc_df["slope"] = np.nanmean(slope, axis=0)

src/move/training/training_loop.py

@@ -44,22 +44,26 @@ def training_loop(
 
     Args:
         model (VAE): trained VAE model object
-        train_dataloader (DataLoader):  An object feeding data to the VAE with training data
-        valid_dataloader (Optional[DataLoader], optional): An object feeding data to the VAE with validation data.
-                                                           Defaults to None.
+        train_dataloader (DataLoader):  An object feeding data to the VAE
+                                        with training data
+        valid_dataloader (Optional[DataLoader], optional): An object feeding data to the
+                                            VAE with validation data. Defaults to None.
         lr (float, optional): learning rate. Defaults to 1e-4.
         num_epochs (int, optional): number of epochs. Defaults to 100.
-        batch_dilation_steps (list[int], optional): a list with integers corresponding to epochs when batch size is
-                                                    increased. Defaults to [].
-        kld_warmup_steps (list[int], optional):  a list with integers corresponding to epochs when kld is decreased by
-                                                 the selected rate. Defaults to [].
-        early_stopping (bool, optional):  boolean if use early stopping . Defaults to False.
-        patience (int, optional): number of epochs to wait before early stop if no progress on the validation set.
-                                  Defaults to 0.
+        batch_dilation_steps (list[int], optional): a list with integers corresponding
+                                to epochs when batch size is increased. Defaults to [].
+        kld_warmup_steps (list[int], optional):  a list with integers corresponding to
+                    epochs when kld is decreased by the selected rate. Defaults to [].
+        early_stopping (bool, optional): boolean if use early stopping.
+                                         Defaults to False.
+
+        patience (int, optional): number of epochs to wait before early stop
+                                  if no progress on the validation set. Defaults to 0.
 
     Returns:
         (tuple): a tuple containing:
-            *outputs (*list): lists containing information of epoch loss, BCE loss, SSE loss, KLD loss
+            *outputs (*list): lists containing information of epoch loss, BCE loss,
+                              SSE loss, KLD loss
             kld_weight (float): final KLD after dilations during the training
     """
 

src/move/visualization/dataset_distributions.py

@@ -88,8 +88,8 @@ def plot_feature_association_graph(
 ) -> matplotlib.figure.Figure:
     """
     This function plots a graph where each node corresponds to a feature and the edges
-    represent the associations between features. Edge width represents the probability of
-    said association, not the association's effect size.
+    represent the associations between features. Edge width represents the probability
+    of said association, not the association's effect size.
 
     Input:
         association_df: pandas dataframe containing the following columns:
@@ -190,7 +190,8 @@ def plot_feature_mean_median(
     array: FloatArray, axis=0, style: str = DEFAULT_PLOT_STYLE
 ) -> matplotlib.figure.Figure:
     """
-    Plot feature values together with the mean, median, min and max values at each array position.
+    Plot feature values together with the mean, median, min and max values
+    at each array position.
     """
     with style_settings(style):
         fig = plt.figure(figsize=(15, 3))
@@ -216,14 +217,16 @@ def plot_reconstruction_movement(
     style: str = DEFAULT_PLOT_STYLE,
 ) -> matplotlib.figure.Figure:
     """
-    Plot, for each sample, the change in value from the unperturbed reconstruction to the perturbed reconstruction.
-    Blue lines are left/negative shifts, red lines are right/positive shifts.
+    Plot, for each sample, the change in value from the unperturbed reconstruction to
+    the perturbed reconstruction. Blue lines are left/negative shifts,
+    red lines are right/positive shifts.
 
     Args:
-        baseline_recon: baseline reconstruction array with s samples and k features (s,k).
-        perturb_recon:  perturbed "                                                      "
-        k: feature index. The shift (movement) of this feature's reconstruction will be plotted for all
-           samples s.
+        baseline_recon: baseline reconstruction array with s samples
+                        and k features (s,k).
+        perturb_recon:  perturbed
+        k: feature index. The shift (movement) of this feature's reconstruction
+                          will be plotted for all samples s.
     """
     with style_settings(style):
         # Feature changes