Lint transform.

afqinsight/transform.py

@@ -1,8 +1,9 @@
 """Transform AFQ data."""
 
+from collections import OrderedDict
+
 import numpy as np
 import pandas as pd
-from collections import OrderedDict
 from sklearn_pandas import DataFrameMapper
 
 from .utils import CANONICAL_TRACT_NAMES
@@ -33,7 +34,8 @@ class AFQDataFrameMapper(DataFrameMapper):
         Keyword arguments passed to sklearn_pandas.DataFrameMapper. You will
         probably not need to change these defaults.
 
-    pd_interpolate_params : kwargs, default=dict(method="linear", limit_direction="both", limit_area="inside")
+    pd_interpolate_params : kwargs,
+        default=dict(method="linear", limit_direction="both", limit_area="inside")
         Keyword arguments passed to pandas.DataFrame.interpolate. Missing
         values are interpolated within the tract profile so that no data is
         used from other subjects, tracts, or metrics, minimizing the chance
@@ -280,7 +282,7 @@ def multicol2sets(columns, tract_symmetry=True):
         col_vals = np.array([x + (bilateral_symmetry[x[tract_idx]],) for x in col_vals])
 
     col_vals = np.array([tuple([str(el) for el in tup]) for tup in col_vals])
-    col_sets = np.array(list(map(lambda c: set(c), col_vals)))
+    col_sets = np.array([set(c) for c in col_vals])
 
     return col_sets
 
@@ -343,7 +345,7 @@ def sort_features(features, scores):
         Sorted list of columns and scores
     """
     res = sorted(
-        [(feat, score) for feat, score in zip(features, scores)],
+        zip(features, scores),
         key=lambda s: np.abs(s[1]),
         reverse=True,
     )
@@ -383,12 +385,12 @@ def beta_hat_by_groups(beta_hat, columns, drop_zeros=False):
     label_sets = multicol2sets(columns, tract_symmetry=False)
 
     for tract in columns.levels[columns.names.index("tractID")]:
-        tract_mask = set([tract]) <= label_sets
+        tract_mask = set(tract) <= label_sets
         all_metrics = np.copy(beta_hat[tract_mask])
         if not drop_zeros or any(all_metrics != 0):
             betas[tract] = OrderedDict()
             for metric in columns.levels[columns.names.index("metric")]:
-                metric_mask = set([tract, metric]) <= label_sets
+                metric_mask = set([tract, metric]) <= label_sets  # noqa C405
                 x = np.copy(beta_hat[metric_mask])
                 if not drop_zeros or any(x != 0):
                     betas[tract][metric] = x