Merge pull request #404 from Crunch-io/fix-ci-pep625

Fix build name

.github/workflows/release.yml

@@ -8,11 +8,11 @@ jobs:
   pypi:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v5
         with:
-          python-version: 3.8
+          python-version: 3.11
       - name: Autobump version
         run: |
           # from refs/tags/v0.1.2 get 0.1.2
@@ -45,9 +45,9 @@ jobs:
     runs-on: ubuntu-latest
     needs: pypi
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v5
         with:
           python-version: 3.8
       - name: Install dependencies

README.md

@@ -6,15 +6,15 @@ Open Source Python implementation of the API for working with CrunchCubes
 
 This package contains the implementation of the CrunchCube API. It is used to
 extract useful information from CrunchCube responses (we'll refer to them as
-_cubes_ in the subsequent text). _Cubes_ are obtained from the *Crunch.io*
+_cubes_ in the subsequent text). _Cubes_ are obtained from the _Crunch.io_
 platform, as JSON responses to the specific _queries_ created by the user.
 These queries specify which data the user wants to extract from the Crunch.io
 system. The most common usage is to obtain the following:
 
- - Cross correlation between different variable
- - Margins of the cross tab _cube_
- - Proportions of the cross tab _cube_ (e.g. proportions of each single element to the entire sample size)
- - Percentages
+- Cross correlation between different variable
+- Margins of the cross tab _cube_
+- Proportions of the cross tab _cube_ (e.g. proportions of each single element to the entire sample size)
+- Percentages
 
 When the data is obtained from the Crunch.io platform, it needs to be
 interpreted to the form that's convenient for a user. The actual shape of the
@@ -34,7 +34,6 @@ The `cr.cube` package can be installed by using the `pip install`:
 
     pip install cr.cube
 
-
 ### For developers
 
 For development mode, `cr.cube` needs to be installed from the local checkout
@@ -64,7 +63,6 @@ And then tests can be run using `py.test` in the root directory:
 After the `cr.cube` package has been successfully installed, the usage is as
 simple as:
 
-
     >>> from cr.cube.cube import Cube
 
     >>> ### Obtain the crunch cube JSON payload using app.crunch.io, pycrunch, rcrunch or scrunch
@@ -103,50 +101,62 @@ The detailed description can be found
 [here](http://crunch-cube.readthedocs.io/en/latest/cr.cube.html#cr-cube-crunch-cube-module).
 
 ---
-[![Build Status](https://travis-ci.org/Crunch-io/crunch-cube.png?branch=master)](https://travis-ci.org/Crunch-io/crunch-cube)
-[![Coverage Status](https://codecov.io/gh/Crunch-io/crunch-cube/branch/master/graph/badge.svg?token=C6auKOj8tZ)](https://codecov.io/gh/Crunch-io/crunch-cube)
-[![Documentation Status](https://readthedocs.org/projects/crunch-cube/badge/?version=latest)](http://crunch-cube.readthedocs.io/en/latest/?badge=latest)
+
+![Build Status](https://github.com/Crunch-io/crunch-cube/workflows/CI/badge.svg?branch=master)
+![Coverage Status](https://codecov.io/gh/Crunch-io/crunch-cube/branch/master/graph/badge.svg?token=C6auKOj8tZ)
+![Documentation Status](https://readthedocs.org/projects/crunch-cube/badge/?version=latest)
 ---
 
 ## Changes
 
 ### 3.1.0
+
 - Subtotal diff (wave difference) for categorical date dimensions
 
 ### 3.0.45
+
 - Enumerator refactoring
 
 ### 3.0.44
+
 - Bug fix median measure for exporter
 
 ### 3.0.43
+
 - Median measure
 
 ### 3.0.42
+
 - Inflate cubes that are single column filters
 
 ### 3.0.41
+
 - Remove `deepcopy` from dimension module due to a performance issue
 
 ### 3.0.40
+
 - Fix bug with weighted vs unweighted in pairwise effect calculation
 
 ### 3.0.39
+
 - Remove cube response deepcopy due to a performance issue
 
 ### 3.0.38
+
 - Improve calculation of DoF for pairwise comparison
 - Use effective counts as column bases for DoF
 
 ### 3.0.37
+
 - Add squared counts as a cube measure
 - Enable calculating pairwise stats with effective denominator
 
 ### 3.0.36
+
 - Fix bug in pairwise sig values for means.
 
 ### 3.0.35
-- Fix bug where categorical dimension would sometimes be interpreted as `MR_CATS`.
 
+- Fix bug where categorical dimension would sometimes be interpreted as `MR_CATS`.
 
 For a complete list of changes see [history](https://github.com/Crunch-io/crunch-cube/blob/master/HISTORY.md).

docs/source/conf.py

@@ -98,7 +98,7 @@ def ascii_bytes_from(path, *paths):
     "date": datetime.date.today().strftime("%d/%m/%y"),
 }
 html_theme_options = {
-    "display_version": True,
+    "version_selector": True,
     "prev_next_buttons_location": "bottom",
     "style_external_links": False,
     # Toc options

setup.py

@@ -35,7 +35,7 @@ def ascii_bytes_from(path, *paths):
 test_requires = ["pytest", "pytest-cov", "mock", "coverage"]
 
 setup(
-    name="cr.cube",
+    name="cr_cube",
     version=version,
     description="Crunch.io Cube library",
     long_description=readme,

src/cr/cube/cube.py

@@ -275,29 +275,29 @@ def augment_response(self, summary_cube_resp) -> "Cube":
         filter cube) will have a different shape. Basically the filter cube will miss
         the row labes that have 0 as counts.
 
-            | FCUBE | CAT
-        ----+-------+----
-         A  |   1   |  1
-         B  |   1   |  1
-         C  |   1   |  1
-         D  |       |  1
-         E  |       |  1
-         F  |       |  1
-
-        The FUCBE have D E and F missing cause its results doesn't count them. And the
+        |     | FCUBE | CAT  |
+        | --- | ----  | ---- |
+        |  A  |   1   |  1   |
+        |  B  |   1   |  1   |
+        |  C  |   1   |  1   |
+        |  D  |       |  1   |
+        |  E  |       |  1   |
+        |  F  |       |  1   |
+
+        The FCUBE have D E and F missing cause its results doesn't count them. And the
         rendering starts from the top without the correct row label association.
         For a correct result the FCUBE cube_response needs to be augmentes with all the
         elements of the summary cube and position the values in the corresponding
         position of the only existing labels in the response.
 
-            | FCUBE | CAT
-        ----+-------+----
-         A  |   0   |  1
-         B  |   0   |  1
-         C  |   1   |  1
-         D  |   1   |  1
-         E  |   1   |  1
-         F  |   0   |  1
+        |     | FCUBE | CAT  |
+        | --- | ----  | ---- |
+        |  A  |   0   |  1   |
+        |  B  |   0   |  1   |
+        |  C  |   1   |  1   |
+        |  D  |   1   |  1   |
+        |  E  |   1   |  1   |
+        |  F  |   0   |  1   |
         """
         cube_resp = self._cube_response