Merge branch 'sig-test-3d-tables-157314298' into rel-5.2.84

src/cr/cube/crunch_cube.py

@@ -13,6 +13,7 @@
 from scipy.stats.contingency import expected_freq
 
 from .mixins.data_table import DataTable
+from .cube_slice import CubeSlice
 from .measures.index import Index
 from .measures.scale_means import ScaleMeans
 from .utils import lazyproperty
@@ -59,16 +60,9 @@ def __init__(self, response):
             So we need to check its type, and convert it to a dictionary if
             it's JSON, if possible.
         '''
-
-        # If the provided response is dict, create cube immediately
-        if isinstance(response, dict):
-            super(CrunchCube, self).__init__(response.get('value', response))
-            # self._table = DataTable(response.get('value', response))
-            return
-
         try:
-            response = json.loads(response)
-            # self._table = DataTable(response.get('value', response))
+            if not isinstance(response, dict):
+                response = json.loads(response)
             super(CrunchCube, self).__init__(response.get('value', response))
         except TypeError:
             # If an unexpected type is provided raise descriptive exception.
@@ -78,6 +72,8 @@ def __init__(self, response):
                     'A `cube` must be JSON or `dict`.'
                 ).format(type(response)))
 
+        self.slices = self._get_slices()
+
     def _fix_shape(self, array):
         '''Fixes shape of MR variables.
         For MR variables, where 'selections' dims are dropped, the ndarray
@@ -1135,16 +1131,12 @@ def index(self, weighted=True, prune=False):
         '''Get cube index measurement.'''
         return Index(self, weighted, prune).data
 
-    def zscore(self, weighted=True, prune=False, hs_dims=None):
-        '''Get cube zscore measurement.'''
-        counts = self.as_array(weighted=weighted, prune=prune)
-
-        total = self.margin(weighted=weighted, prune=prune)
-        colsum = self.margin(axis=0, weighted=weighted, prune=prune)
-        rowsum = self.margin(axis=1, weighted=weighted, prune=prune)
-
-        if self.has_mr or self.ca_dim_ind is not None:
-            if not self.is_double_mr and self.mr_dim_ind == 0:
+    def _calculate_std_res(self, counts, total, colsum, rowsum, slice_):
+        dim_types = slice_.dim_types
+        has_mr_or_ca = 'categorical_array' in dim_types or 'multiple_response' in dim_types
+        # if self.has_mr or self.ca_dim_ind is not None:
+        if has_mr_or_ca:
+            if not self.is_double_mr and (self.mr_dim_ind == 0 or self.mr_dim_ind == 1 and self.ndim == 3):
                 total = total[:, np.newaxis]
                 rowsum = rowsum[:, np.newaxis]
 
@@ -1161,6 +1153,24 @@ def zscore(self, weighted=True, prune=False, hs_dims=None):
                 np.outer(total - rowsum, total - colsum) / total**3
             )
             res = residuals / np.sqrt(variance)
+        return res
+
+    def zscore(self, weighted=True, prune=False, hs_dims=None):
+        '''Get cube zscore measurement.'''
+
+        res = []
+        for slice_ in self.slices:
+            counts = slice_.as_array(weighted=weighted, prune=prune)
+            total = slice_.margin(weighted=weighted, prune=prune)
+            colsum = slice_.margin(axis=0, weighted=weighted, prune=prune)
+            rowsum = slice_.margin(axis=1, weighted=weighted, prune=prune)
+            std_res = self._calculate_std_res(counts, total, colsum, rowsum, slice_)
+            res.append(std_res)
+
+        if len(res) == 1:
+            res = res[0]
+        else:
+            res = np.array(res)
 
         if hs_dims:
             res = self._intersperse_hs_in_std_res(hs_dims, res)
@@ -1198,3 +1208,9 @@ def pvals(self, weighted=True, prune=False, hs_dims=None):
     def scale_means(self):
         '''Get cube means.'''
         return ScaleMeans(self).data
+
+    def _get_slices(self):
+        if self.ndim < 3:
+            return [CubeSlice(self, 0)]
+
+        return [CubeSlice(self, i) for i, _ in enumerate(self.labels()[0])]

src/cr/cube/cube_slice.py

@@ -149,10 +149,9 @@ def dim_types(self):
         '''Get dimension types of the cube slice.'''
         return self._cube.dim_types[-2:]
 
-    @lazyproperty
-    def pvals(self):
+    def pvals(self, *args, **kwargs):
         '''Get pvals of the cube.'''
-        return self._cube.pvals
+        return self._call_cube_method('pvals', *args, **kwargs)
 
     def inserted_hs_indices(self, *args, **kwargs):
         '''Get inserted H&S indices.'''

tests/integration/fixtures/__init__.py

@@ -129,3 +129,10 @@ def _load(cube_file):
 CA_X_MR_HS = _load('ca-x-mr-hs.json')
 MR_X_CAT_HS = _load('mr-x-cat-hs.json')
 CAT_X_MR_HS = _load('cat-x-mr-hs.json')
+CA_X_MR_SIG_TESTING_SUBTOTALS = _load('ca-x-mr-sig-testing-subtotals.json')
+STARTTIME_X_NORDIC_COUNTRIES_X_FOOD_GROOPS = _load(
+    'starttime-x-nordic-countries-x-food-groops.json',
+)
+FOOD_GROOPS_X_STARTTIME_X_NORDIC_COUNTRIES = _load(
+    'food-groops-x-starttime-x-nordic-countries.json',
+)