clean up, use pypa/gh-action-pypi-publish in CI

Author: wpbonelli

.github/workflows/ci.yaml

@@ -64,19 +64,14 @@ jobs:
           python -m pip install --upgrade pip
           pip install flake8 pytest coveralls setuptools wheel twine
           pip install -e .
+      - name: Build package
+        run: python setup.py sdist bdist_wheel
       - name: Publish package
-        env:
-          TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
-          TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
-        run: |
-          old_version=$(pip install cactice==random 2>&1 >/dev/null | sed 's|.*, \([^ ]*\)).*|\1|g' | head -n 1 | xargs)
-          new_version=$(pip show cactice | sed 's|.*Version: \([^ ]*\).*|\1|g' | sed -n 2p)
-          echo "$old_version"
-          echo "$new_version"
-          if ! [[ "$new_version" = "$old_version" ]]; then
-            python setup.py sdist bdist_wheel
-            python -m twine upload dist/*
-          fi
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          user: ${{ secrets.PYPI_USERNAME }}
+          password: ${{ secrets.PYPI_PASSWORD }}
+          skip_existing: true
       - name: Slack success notification
         uses: rtCamp/action-slack-notify@v2
         env:

cactice/grids.py

@@ -594,18 +594,6 @@ def configurational_entropy(grid: np.ndarray, n_perms=100000, macrostate='edge_l
 
         # return ln(W)
         return log(len([l for l in lengths if l == length]))
-
-        # compute proportion of total edge lengths equal to the original grid's total
-        # prop = len([l for l in lengths if l == length]) / (len(lengths) + 1)
-        # return prop
-
-        # if the grid is small enough that we enumerated all permutations, no need to rescale
-        all_perms = (grid.shape[0] * grid.shape[1]) ** len(list(set(flatten([grid]))))
-        if all_perms <= n_perms:
-            return prop
-        # otherwise, rescale the proportion from [0, n_permutations] to [0, n!] where n is the number of grid cells
-        else:
-            return prop / n_perms * factorial(grid.shape[0] * grid.shape[1])
     elif macrostate == 'kl_divergence':
         # TODO
         # dists = [undirected_bond_distribution([p]) for p in perms]