Merge 0abcbf4 into 5c74fb1

HISTORY.md

@@ -1,5 +1,7 @@
 # History of Changes
 
+#### 1.6.10 Fix README on pypi
+
 #### 1.6.9 Bugfix
 - When Categorical Array variable is selected in multitable export, and Scale Means is selected, the cube fails, because it tries to access the non-existing slice (the CA is only _interpreted_ as multiple slices in tabbooks). This fix makes sure that the export cube doesn't fail in such case.
 

README.md

@@ -96,6 +96,11 @@ The detailed description can be found
 
 ## Changes
 
+#### 1.7.1 Fix index error
+- Fix peculiar case of CA x CAT (single elem) index error
+- Support with unit tests
+- Create base for fixing exporter issues
+
 #### 1.7.0 Normalization, PEP8 and bugfix
 - Refactored to remove a couple modules
 - Fixed pesky numpy warnings
@@ -106,6 +111,4 @@ The detailed description can be found
 - Use `get_shape(prune=False)` instead
 - Will be removed in future versions
 
-#### 1.6.10 Fix README on pypi
-
 For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).

setup.py

@@ -3,7 +3,7 @@
 
 from setuptools import setup, find_packages
 
-version = '1.7.0'
+version = '1.7.1'
 
 
 def get_long_desc():

src/cr/cube/measures/scale_means.py

@@ -41,15 +41,22 @@ def data(self):
                 continue
 
             # Calculate means
-            num = np.sum(product[self.valid_indices(axis)], axis)
-            den = np.sum(table[self.valid_indices(axis)], axis)
+            num = np.sum(product[self._valid_indices(axis)], axis)
+            den = np.sum(table[self._valid_indices(axis)], axis)
             mean = num / den
             if not isinstance(mean, np.ndarray):
                 mean = np.array([mean])
             means.append(mean)
         return means
 
     def margin(self, axis):
+        '''Return marginal value of the current slice scaled means.
+
+        This value is the the same what you would get from a single variable
+        (constituting a 2D cube/slice), when the "non-missing" filter of the
+        opposite variable would be applied. This behavior is consistent with
+        what is visible in the front-end client.
+        '''
         if self._slice.ndim < 2:
             msg = (
                 'Scale Means marginal cannot be calculated on 1D cubes, as'
@@ -67,16 +74,16 @@ def margin(self, axis):
 
         return np.sum(values * margin) / total
 
-    def valid_indices(self, axis):
+    def _valid_indices(self, axis):
         # --there is an interaction with CrunchCube._fix_shape() which
         # --essentially eliminates length-1 dimensions not in the first
         # --position. We must mirror that reshaping here. The fact this logic
         # --needs to be duplicated indicates we're missing an abstraction
         # --somewhere, like perhaps CrunchCube and/or
         # --CrunchSlice.reshaped_dimensions.
         reshaped_dimensions = [
-            dim for (idx, dim) in enumerate(self._slice.dimensions)
-            if len(dim.elements()) != 1 or idx == 0
+            dimension for dimension in self._slice.dimensions
+            if len(dimension.values) > 1
         ]
 
         return tuple(
@@ -104,14 +111,20 @@ def values(self):
             for dim in self._slice.dimensions
         ]
 
+    def _inflate(self, dim_ind):
+        return (
+            self._slice.ndim > 1 and
+            len(self._slice.get_shape()) > 1 and
+            not dim_ind
+        )
+
     def _inner_prods(self, contents, values):
         products = []
         for i, numeric in enumerate(values):
             if numeric is None:
                 products.append(numeric)
                 continue
-            inflate = self._slice.ndim > 1 and not i
-            numeric = numeric[:, None] if inflate else numeric
+            numeric = numeric[:, None] if self._inflate(i) else numeric
             product = contents * numeric
             products.append(product)
         return products

tests/unit/test_scale_means.py

@@ -1,3 +1,4 @@
+# pylint: disable=missing-docstring, redefined-outer-name, protected-access
 from __future__ import division
 from mock import Mock
 import numpy as np
@@ -22,6 +23,11 @@ def test_scale_means_marginal(scale_means_fixture):
     assert scale_means.margin(axis) == expected
 
 
+def test_inflate(inflate_fixture):
+    dim_ind, scale_means, expected = inflate_fixture
+    assert scale_means._inflate(dim_ind) == expected
+
+
 @pytest.fixture(params=[
     (
         [None, ROWS_DIM_VALUES], margin, 0, 2,
@@ -52,3 +58,45 @@ def scale_means_fixture(request, values_prop_):
 @pytest.fixture
 def values_prop_(request):
     return property_mock(request, ScaleMeans, 'values')
+
+
+@pytest.fixture(params=[
+    (0, 2, (Mock(), Mock()), True),
+    (1, 2, (Mock(), Mock()), False),
+    (0, 2, (Mock(),), False),
+    (0, 1, (Mock(),), False),
+])
+def inflate_fixture(request):
+    dim_ind, ndim, shape, expected = request.param
+    slice_ = Mock()
+    slice_.ndim = ndim
+    slice_.get_shape.return_value = shape
+    scale_means = ScaleMeans(slice_)
+    return dim_ind, scale_means, expected
+
+
+def test_valid_indices(valid_indices_fixture):
+    scale_means, axis, expected = valid_indices_fixture
+    actual = scale_means._valid_indices(axis)
+    np.testing.assert_equal(actual, expected)
+
+
+@pytest.fixture(params=[
+    ([Mock(values=[])], 0, []),
+    ([Mock(values=[1, 2, 3])], 0, [np.array([True, True, True])]),
+    (
+        [Mock(values=[1, 2, np.nan, 4])], 0,
+        [np.array([True, True, False, True])],
+    ),
+    ([Mock(values=[1])], 0, []),
+    (
+        [Mock(values=[1, 2, 3]), Mock(values=[])], 0,
+        [np.array([True, True, True])],
+    ),
+    ([Mock(values=[1, 2, 3]), Mock(values=[])], 1, [slice(None)]),
+])
+def valid_indices_fixture(request):
+    dimensions, axis, expected = request.param
+    slice_ = Mock()
+    slice_.dimensions = dimensions
+    return ScaleMeans(slice_), axis, expected