fix some cosmetics

Crunch-io · May 24, 2018 · 1593776 · 1593776
1 parent f3f1869
commit 1593776
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Showing 2 changed files with 13 additions and 43 deletions.
diff --git a/src/cr/cube/crunch_cube.py b/src/cr/cube/crunch_cube.py
@@ -328,12 +328,10 @@ def prune_indices(self, transforms=None):
         if self.ndim >= 3:
             # In case of a 3D cube, return list of tuples
             # (of row and col pruned indices).
-            # return self._prune_3d_indices(self.hs_dims)
             return self._prune_3d_indices(transforms)
 
         # In case of 1 or 2 D cubes, return a list of
         # row indices (or row and col indices)
-        # return self._prune_indices(self.hs_dims)
         return self._prune_indices(transforms)
 
     def _prune_indices(self, transforms):

diff --git a/tests/integration/test_crunch_cube.py b/tests/integration/test_crunch_cube.py
@@ -1,5 +1,4 @@
 from unittest import TestCase
-from mock import patch
 import numpy as np
 
 from cr.cube.crunch_cube import CrunchCube
@@ -502,7 +501,7 @@ def test_as_array_simple_cat_array_include_missing(self):
         np.testing.assert_array_equal(actual, expected)
 
     def test_as_array_cat_x_num_x_datetime(self):
-        '''Test 3D cube, slicing accross first (numerical) variable.'''
+        """Test 3D cube, slicing accross first (numerical) variable."""
         cube = CrunchCube(CAT_X_NUM_X_DATETIME)
         expected = np.array([
             [[1, 1],
@@ -667,7 +666,7 @@ def test_margin_weighted_gender_x_ideology_axis_1(self):
         np.testing.assert_almost_equal(actual, expected)
 
     def test_calculate_standard_error_axis_0(self):
-        '''Calculate standard error across columns.'''
+        """Calculate standard error across columns."""
         cube = CrunchCube(ECON_GENDER_X_IDEOLOGY_WEIGHTED)
         expected = np.array([
             [
@@ -1129,11 +1128,11 @@ def test_pets_x_fruit_proportions_by_row(self):
         np.testing.assert_almost_equal(actual, expected)
 
     def test_cat_x_cat_array_proportions_by_row(self):
-        '''Get the proportions for each slice of the 3D cube.
+        """Get the proportions for each slice of the 3D cube.
 
         The axis is equal to 2, since this is the dimensions across which
         we have to calculate the margin.
-        '''
+        """
         cube = CrunchCube(FRUIT_X_PETS_ARRAY)
         expected = ([
             [[0.52, 0.48],
@@ -1188,7 +1187,7 @@ def test_pets_array_x_pets_cell(self):
         cube = CrunchCube(PETS_ARRAY_X_PETS)
         expected = np.array([
             [0.24992768, 0.        , 0.26901938],  # noqa
-            [0.17298235, 0.44258027, 0.21174429],
+            [0.17298235, 0.44258027, 0.21174429]
         ])
         actual = cube.proportions(axis=None)[0]
         np.testing.assert_almost_equal(actual, expected)
@@ -1200,18 +1199,18 @@ def test_pets_x_pets_array_margin_by_cell(self):
         np.testing.assert_almost_equal(actual, expected)
 
     def test_pets_x_pets_array_percentages(self):
-        '''All directions need to return same percentages.
+        """All directions need to return same percentages.
 
         The only direction that makes sense is across categories, and this is
         handled automatically by the cube.
-        '''
+        """
         cube = CrunchCube(PETS_X_PETS_ARRAY)
         expected = np.array([
             [0.58823529, 0.41176471],  # noqa
             [0.        , 1.        ],  # noqa
             [0.47058824, 0.52941176],  # noqa
         ])
-        # TODO: Consult with jon and Mike. The new expectation is closer to what
+        # TODO Consult with jon and Mike. The new expectation is closer to what
         # whaam displays, but diverges from R.
         # expected = np.array([
         #     [0.55555556, 0.19444444],
@@ -1275,16 +1274,6 @@ def test_cat_x_cat_as_array_prune_cols(self):
             np.testing.assert_array_equal(pruned[i], pruned_expected[i])
 
     def test_cat_x_cat_props_by_col_prune_cols(self):
-        cube = CrunchCube(CAT_X_CAT_WITH_EMPTY_COLS)
-        expected = np.array([
-            [1., 0.25, np.nan, 0.25],
-            [0., 0., np.nan, 0.],
-            [0., 0.125, np.nan, 0.5],
-            [0., 0.25, np.nan, 0.],
-            [0., 0.25, np.nan, 0.25],
-            [0., 0.125, np.nan, 0.]
-        ])
-        actual = cube.proportions(axis=0, prune=False)
         expected = np.array([
             [1., 0.25, 0.25],
             [0., 0.125, 0.5],
@@ -1293,6 +1282,7 @@ def test_cat_x_cat_props_by_col_prune_cols(self):
             [0., 0.125, 0.]
         ])
         # Use API method parameter to prune
+        cube = CrunchCube(CAT_X_CAT_WITH_EMPTY_COLS)
         table = cube.proportions(axis=0, prune=True)
         actual = table[:, ~table.mask.all(axis=0)][~table.mask.all(axis=1), :]
         np.testing.assert_array_equal(actual, expected)
@@ -1314,15 +1304,6 @@ def test_cat_x_cat_props_by_col_prune_cols(self):
 
     def test_cat_x_cat_props_by_row_prune_cols(self):
         cube = CrunchCube(CAT_X_CAT_WITH_EMPTY_COLS)
-        expected = np.array([
-            [0.4, 0.4, 0., 0.2],
-            [np.nan, np.nan, np.nan, np.nan],
-            [0., 0.33333333, 0., 0.66666667],
-            [0., 1., 0., 0.],
-            [0., 0.66666667, 0., 0.33333333],
-            [0., 1., 0., 0.],
-        ])
-        actual = cube.proportions(axis=1, prune=False)
         expected = np.array([
             [0.4, 0.4, 0.2],
             [0., 0.33333333, 0.66666667],
@@ -1352,15 +1333,6 @@ def test_cat_x_cat_props_by_row_prune_cols(self):
 
     def test_cat_x_cat_props_by_cell_prune_cols(self):
         cube = CrunchCube(CAT_X_CAT_WITH_EMPTY_COLS)
-        expected = np.array([
-            [0.14285714, 0.14285714, 0., 0.07142857],
-            [0., 0., 0., 0.],
-            [0., 0.07142857, 0., 0.14285714],
-            [0., 0.14285714, 0., 0.],
-            [0., 0.14285714, 0., 0.07142857],
-            [0., 0.07142857, 0., 0.],
-        ])
-        actual = cube.proportions(axis=None, prune=False)
         expected = np.array([
             [0.14285714, 0.14285714, 0.07142857],
             [0., 0.07142857, 0.14285714],
@@ -1492,14 +1464,14 @@ def test_mr_dim_ind_for_cat_cube(self):
         self.assertEqual(actual, expected)
 
     def test_total_unweighted_margin_when_has_means(self):
-        '''Tests that total margin is Unweighted N, when cube has means.'''
+        """Tests that total margin is Unweighted N, when cube has means."""
         cube = CrunchCube(SINGLE_CAT_MEANS)
         expected = 17615
         actual = cube.margin(weighted=False)
         assert actual == expected
 
     def test_row_unweighted_margin_when_has_means(self):
-        '''Tests that total margin is Unweighted N, when cube has means.'''
+        """Tests that total margin is Unweighted N, when cube has means."""
         cube = CrunchCube(SINGLE_CAT_MEANS)
         expected = np.array([
             806, 14, 14, 28, 780, 42, 1114, 28, 24, 746, 2, 12, 6, 2178, 2026,
@@ -1602,7 +1574,7 @@ def test_ca_x_single_cat_row_margins(self):
     def test_ca_x_single_cat_cell_margins(self):
         cube = CrunchCube(CA_X_SINGLE_CAT)
         expected = np.array([25, 28, 23])
-        # Axis equals to (1, 2), because the total is calculated for each slice.
+        # axis equals to (1, 2), because the total is calculated for each slice
         actual = cube.margin(axis=(1, 2))
         np.testing.assert_array_equal(actual, expected)
 
@@ -1757,7 +1729,7 @@ def test_mr_props_with_hs_by_col(self):
         np.testing.assert_almost_equal(actual, expected)
 
     def test_3d_pruning_indices(self):
-        '''Test pruning indices for a simple XYZ cube.'''
+        """Test pruning indices for a simple XYZ cube."""
         cube = CrunchCube(XYZ_SIMPLE_ALLTYPES)
 
         # Zeroth slice of the XYZ array: