Add columns and rows margin percentage

README.md

@@ -88,6 +88,14 @@ Please visit <https://crunch-cube.readthedocs.io/en/latest> for the API referenc
 
 ## Changes
 
+### 3.4.0
+
+- Add rows_margin_percentage and columns_margin_percentage to _Slice
+
+### 3.3.8
+
+- Use native namespace packages for cr instead of deprecated pkg_resources / pkgutil
+
 ### 3.3.7
 
 - Rebuild

setup.py

@@ -4,7 +4,7 @@
 import os
 import re
 
-from setuptools import find_packages, setup
+from setuptools import setup
 
 
 def ascii_bytes_from(path, *paths):
@@ -49,12 +49,9 @@ def ascii_bytes_from(path, *paths):
     author_email="dev@crunch.io",
     license="MIT License",
     install_requires=install_requires,
-    tests_require=test_requires,
     extras_require={"testing": test_requires},
-    packages=find_packages("src", exclude=["tests"]),
     package_dir={"": "src"},
     include_package_data=True,
     entry_points={},
-    namespace_packages=["cr"],
     zip_safe=True,
 )

src/cr/cube/__init__.py

@@ -2,4 +2,4 @@
 
 """Initialization module for crunch-cube package."""
 
-__version__ = "3.3.7"
+__version__ = "3.4.0"

src/cr/cube/cubepart.py

@@ -558,6 +558,18 @@ def columns_margin_proportion(self):
         # --- otherwise columns-margin-proportion is a maginal ---
         return self._assemble_marginal(self._measures.columns_table_proportion)
 
+    @lazyproperty
+    def columns_margin_percentage(self):
+        """1D or 2D np.float64 ndarray of weighted-percentage for each column of slice
+
+        This array is 2D (a distinct margin value for each cell) when the rows
+        dimension is MR, because each MR-subvariable has its own weighted N. This is
+        because not every possible response is necessarily offered to every respondent.
+
+        In all other cases, the array is 1D, containing one value for each column.
+        """
+        return self.columns_margin_proportion * 100
+
     @lazyproperty
     def columns_scale_mean(self):
         """Optional 1D np.float64 ndarray of scale mean for each column.
@@ -1431,6 +1443,18 @@ def rows_margin_proportion(self):
         # --- otherwise rows-margin is a vector ---
         return self._assemble_marginal(self._measures.rows_table_proportion)
 
+    @lazyproperty
+    def rows_margin_percentage(self):
+        """1D or 2D np.float64 ndarray of weighted-percentage for each column of slice
+
+        This array is 2D (a distinct margin value for each cell) when the columns
+        dimension is MR, because each MR-subvariable has its own weighted N. This is
+        because not every possible response is necessarily offered to every respondent.
+
+        In all other cases, the array is 1D, containing one value for each column.
+        """
+        return self.rows_margin_proportion * 100
+
     @lazyproperty
     def rows_missing(self):
         """1D/2D np.float64 ndarray of unweighted counts of missing for each cell/row
@@ -1754,6 +1778,11 @@ def table_code(self):
 
     @lazyproperty
     def table_percentages(self):
+        """2D np.float64 ndarray of table-percentage.
+
+        Table-percentage is the fraction of the table weighted-N contributed by each
+        cell, expressed as a percentage (float between 0.0 and 100.0 inclusive).
+        """
         return self.table_proportions * 100
 
     @lazyproperty

tests/expectations/cat-hs-x-mr-columns-margin-percentages.py

@@ -0,0 +1,7 @@
+[
+    17.935203679537608,
+    33.454988861780095,
+    50.762387573092646,
+    80.27297327014429,
+    79.83476971629214,
+]

tests/expectations/cat-hs-x-mr-row-margin-percentages.py

@@ -0,0 +1,46 @@
+[
+    [
+        12.354283136548759,
+        7.444231360707625,
+        4.93318248692712,
+        4.584390457713685,
+        6.55671553927186,
+    ],
+    [
+        18.60667239281892,
+        19.291282000683193,
+        16.542087034551418,
+        13.813767505048451,
+        18.69747651879045,
+    ],
+    [
+        30.960955529367677,
+        26.735513361390815,
+        21.475269521478538,
+        18.398157962762134,
+        25.25419205806231,
+    ],
+    [
+        30.960955529367677,
+        26.735513361390815,
+        21.475269521478538,
+        18.398157962762138,
+        25.25419205806231,
+    ],
+    [0.0, 0.0, 0.0, 0.0, 0.0],
+    [
+        33.37741042168464,
+        36.41410555192866,
+        37.11714542482715,
+        36.248045110476504,
+        35.13675072713398,
+    ],
+    [
+        35.66163404894768,
+        36.850381086680535,
+        41.40758505369432,
+        45.35379692676135,
+        39.60905721480371,
+    ],
+    [100.0, 100.0, 100.0, 99.99999999999999, 100.0],
+]

tests/expectations/mr-x-cat-hs-columns-margin-percentages.py

@@ -0,0 +1,52 @@
+[
+    [
+        12.354283158588755,
+        18.60667238084944,
+        30.960955539438196,
+        30.960955539438196,
+        0.0,
+        33.37741042334933,
+        35.66163403721248,
+        100.0,
+    ],
+    [
+        7.444231365436714,
+        19.291281991786978,
+        26.735513357223688,
+        26.735513357223688,
+        0.0,
+        36.41410557725717,
+        36.85038106551913,
+        99.99999999999997,
+    ],
+    [
+        4.93318248486953,
+        16.54208703286872,
+        21.47526951773825,
+        21.475269517738248,
+        0.0,
+        37.11714541914551,
+        41.40758506311624,
+        100.0,
+    ],
+    [
+        4.5843904451220485,
+        13.813767515853032,
+        18.39815796097508,
+        18.39815796097508,
+        0.0,
+        36.248045116670035,
+        45.35379692235489,
+        100.0,
+    ],
+    [
+        6.556715546598428,
+        18.697476512833678,
+        25.254192059432107,
+        25.2541920594321,
+        0.0,
+        35.1367507213182,
+        39.60905721924969,
+        100.0,
+    ],
+]

tests/expectations/mr-x-cat-hs-row-margin-percentages.py

@@ -0,0 +1,7 @@
+[
+    17.935203742867287,
+    33.454988896294154,
+    50.76238756298953,
+    80.27297326388751,
+    79.83476971842138,
+]

tests/expectations/mr-x-mr-columns-margin-percentages.py

@@ -0,0 +1,6 @@
+[
+    [13.835522515396878, 26.54735977789324, 55.81110549484439, 60.67041874287763],
+    [12.36197264278271, 32.26874248151863, 53.05910795058967, 67.57591837745224],
+    [15.913981148498676, 35.664811199716155, 48.05298239913407, 65.97161566200931],
+    [13.835522515396914, 32.26874248151863, 48.05298239913407, 55.24609836123409],
+]

tests/expectations/mr-x-mr-row-margin-percentages.py

@@ -0,0 +1,6 @@
+[
+    [13.835522515396878, 12.36197264278271, 15.913981148498676, 13.835522515396912],
+    [26.54735977789324, 32.26874248151863, 35.664811199716155, 32.26874248151863],
+    [55.81110549484439, 53.05910795058967, 48.05298239913407, 48.05298239913407],
+    [60.670418742877644, 67.57591837745224, 65.97161566200931, 55.24609836123409],
+]

tests/integration/test_cubepart.py

@@ -819,6 +819,23 @@ def test_it_knows_the_rows_margin_proportion(self, fixture, expectation):
 
         assert rows_margin_proportions.tolist() == load_python_expression(expectation)
 
+    @pytest.mark.parametrize(
+        "fixture, expectation",
+        (
+            (CR.CAT_HS_X_MR, "cat-hs-x-mr-row-margin-percentages"),
+            (CR.MR_X_CAT_HS_MT, "mr-x-cat-hs-row-margin-percentages"),
+            (CR.MR_X_MR, "mr-x-mr-row-margin-percentages"),
+        ),
+    )
+    def test_it_knows_the_rows_margin_percentage(self, fixture, expectation):
+        slice_ = _Slice(
+            Cube(fixture), slice_idx=0, transforms={}, population=None, mask_size=0
+        )
+
+        rows_margin_percentages = slice_.rows_margin_percentage
+
+        assert rows_margin_percentages.tolist() == load_python_expression(expectation)
+
     @pytest.mark.parametrize(
         "fixture, expectation",
         (
@@ -855,6 +872,23 @@ def test_it_knows_the_column_margin_proportions(self, fixture, expectation):
 
         assert columns_margin_proportion.tolist() == load_python_expression(expectation)
 
+    @pytest.mark.parametrize(
+        "fixture, expectation",
+        (
+            (CR.CAT_HS_X_MR, "cat-hs-x-mr-columns-margin-percentages"),
+            (CR.MR_X_CAT_HS_MT, "mr-x-cat-hs-columns-margin-percentages"),
+            (CR.MR_X_MR, "mr-x-mr-columns-margin-percentages"),
+        ),
+    )
+    def test_it_knows_the_column_margin_percentages(self, fixture, expectation):
+        slice_ = _Slice(
+            Cube(fixture), slice_idx=0, transforms={}, population=None, mask_size=0
+        )
+
+        columns_margin_percentage = slice_.columns_margin_percentage
+
+        assert columns_margin_percentage.tolist() == load_python_expression(expectation)
+
     def test_it_knows_the_margins_of_mr_x_mr(self):
         slice_ = _Slice(
             Cube(CR.MR_X_MR), slice_idx=0, transforms={}, population=None, mask_size=0

tests/integration/test_headers_and_subtotals.py

@@ -3212,7 +3212,9 @@ def test_it_computes_measures_for_cat_x_cat_with_subdiffs_on_both(self):
         assert slice_.columns_margin[0] == pytest.approx(np.nan, nan_ok=True)
         assert slice_.rows_margin[0] == pytest.approx(np.nan, nan_ok=True)
         assert slice_.columns_margin_proportion[0] == pytest.approx(11 / 266)
+        assert slice_.columns_margin_percentage[0] == pytest.approx(11 / 266 * 100)
         assert slice_.rows_margin_proportion[0] == pytest.approx(-17 / 266)
+        assert slice_.rows_margin_percentage[0] == pytest.approx(-17 / 266 * 100)
         assert slice_.columns_base[0] == pytest.approx(np.nan, nan_ok=True)
         assert slice_.rows_base[0] == pytest.approx(np.nan, nan_ok=True)
         assert slice_.column_weighted_bases[:, 0] == pytest.approx(