fix: support for numpy 2.0 (#220)

* remove upper bound requirement on numpy 2.0

* always return float from GetEntropy even if scipy returns a np.float64

* separate pyp uploading step into separate job in release workflow

.github/workflows/release.yml

@@ -9,12 +9,10 @@ jobs:
   release:
     name: Release
     runs-on: ubuntu-latest
-    environment: release
-    permissions:
-      # IMPORTANT: this permission is mandatory for trusted publishing
-      id-token: write
+    outputs:
+      release_created: ${{ steps.release.outputs.release_created }}
     steps:
-      - uses: GoogleCloudPlatform/release-please-action@v4.0.2
+      - uses: googleapis/release-please-action@v4
         id: release
         with:
           release-type: python
@@ -47,6 +45,26 @@ jobs:
         run: |
           poetry build --ansi -n
 
-      - name: Publish package on PyPI
+      - uses: actions/upload-artifact@v4
         if: ${{ steps.release.outputs.release_created }}
+        with:
+          name: dist
+          path: dist/
+
+  upload_pypi:
+    needs: release
+    runs-on: ubuntu-latest
+    if: ${{ needs.release.outputs.release_created }}
+    environment: release
+    permissions:
+      # IMPORTANT: this permission is mandatory for trusted publishing
+      id-token: write
+    steps:
+      - name: Get artifacts
+        uses: actions/download-artifact@v4
+        with:
+          merge-multiple: true
+          path: dist
+
+      - name: Publish package on PyPI
         uses: pypa/[email protected]

pyproject.toml

@@ -35,7 +35,7 @@ python = ">=3.7.1,<4.0"
 importlib-metadata = {version = ">=3.0.0", python = "<3.8"}
 cyvcf2 = ">=0.30.4"
 matplotlib = ">=3.1.2"
-numpy = ">=1.17.3,<2.0"
+numpy = ">=1.17.3"
 pandas = ">=1.2.0"
 pysam = ">=0.15.4"
 scikit-learn = ">=0.23.1"

trtools/utils/utils.py

@@ -196,7 +196,7 @@ def GetEntropy(allele_freqs: Dict[Any, float]) -> float:
     """
     if not ValidateAlleleFreqs(allele_freqs):
         return np.nan
-    return scipy.stats.entropy(list(x for x in allele_freqs.values()), base=2)
+    return float(scipy.stats.entropy(list(x for x in allele_freqs.values()), base=2))
 
 
 def GetMean(allele_freqs):