Numpy 2 upgrade

.github/workflows/publish-pypi.yml

@@ -7,28 +7,22 @@ on:
 jobs:
   publish:
     runs-on: ubuntu-latest
+    permissions:
+      id-token: write
     steps:
       - uses: actions/checkout@v4
       - name: Install poetry
         run: pipx install poetry
       - name: Python setup
         uses: actions/setup-python@v5
         with:
-          python-version: 3.9
-          cache: 'poetry'
-          cache-dependency-path: "pyproject.toml"
-      - name: Install package
-        run: |
-          python -m pip install --upgrade pip
-          pip install wheel
-          pip install .
+          python-version: 3.11
       - name: Build package
         run: |
-          poetry build -o dist
+          poetry build
       - name: Publish to PyPI
         uses: pypa/gh-action-pypi-publish@release/v1
         with:
-          user: __token__
           password: ${{ secrets.PYPI_API_TOKEN }}
           packages_dir: ./dist
           verbose: true

elapid/models.py

@@ -4,6 +4,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+from numpy.exceptions import AxisError
 from scipy import stats as scistats
 from sklearn.base import BaseEstimator
 from sklearn.exceptions import NotFittedError
@@ -359,7 +360,7 @@ def fit(
                 a .fit_transform() method. Some examples include a PCA() object or a
                 RobustScaler().
         """
-        
+
         # clear state variables
         self.alpha_ = 0.0
         self.entropy_ = 0.0
@@ -563,7 +564,7 @@ def initialize_sklearn_model(self, C: float, fit_intercept: bool = True) -> None
             solver="liblinear",
             tol=self.convergence_tolerance,
             max_iter=self.n_lambdas,
-            random_state=self.random_state
+            random_state=self.random_state,
         )
 
 
@@ -840,14 +841,14 @@ def format_occurrence_data(y: ArrayLike) -> ArrayLike:
         formatted uint8 ndarray of shape (n_samples,)
 
     Raises:
-        np.AxisError: an array with 2 or more columns is passed
+        np.exceptions.AxisError: an array with 2 or more columns is passed
     """
     if not isinstance(y, np.ndarray):
         y = np.array(y)
 
     if y.ndim > 1:
         if y.shape[1] > 1 or y.ndim > 2:
-            raise np.AxisError(f"Multi-column y data passed of shape {y.shape}. Must be 1d or 1 column.")
+            raise AxisError(f"Multi-column y data passed of shape {y.shape}. Must be 1d or 1 column.")
         y = y.flatten()
 
     return y.astype("uint8")

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "elapid"
-version = "1.0.2"
+version = "1.0.3"
 description = "Species distribution modeling tools"
 authors = ["Christopher Anderson <cbanderson08@gmail.com>"]
 license = "MIT"
@@ -12,14 +12,15 @@ include = [
 
 [tool.poetry.dependencies]
 python = ">=3.9"
-numpy = ">=1.18,<2.0"
-pandas = ">=1.0.3"
+numpy = ">=2.0"
+pandas = ">=1.0.3,<3.0"
 pyproj = ">3.0"
 geopandas = ">=1.0"
 rasterio = ">=1.2.1"
 tqdm = ">=4.60"
 rtree = ">=0.9"
 scikit-learn = ">=1.2,<1.6"
+scipy = ">=1.13"
 matplotlib = ">=3.7"
 
 [tool.poetry.group.dev.dependencies]

tests/test_models.py

@@ -1,5 +1,6 @@
 import numpy as np
 import pytest
+from numpy.exceptions import AxisError
 from sklearn import metrics
 from sklearn.decomposition import PCA
 from sklearn.linear_model import LogisticRegression
@@ -113,6 +114,7 @@ def test_sklearn_MaxentModel():
     assert ypred.max() <= 1.0
     assert ypred.min() >= 0.0
 
+
 def test_MaxentModel_random_state_behavior():
     model1 = models.MaxentModel(random_state=1)
     model2 = models.MaxentModel(random_state=1)
@@ -124,11 +126,15 @@ def test_MaxentModel_random_state_behavior():
 
     model3 = models.MaxentModel(random_state=2)
     model3.fit(x, y)
-    ypred3 = model3.predict(x)  
-    
+    ypred3 = model3.predict(x)
+
     if np.allclose(ypred1, ypred3):
         import warnings
-        warnings.warn("MaxentModel predictions are identical for different random_state values; model may be deterministic for this configuration.")
+
+        warnings.warn(
+            "MaxentModel predictions are identical for different random_state values; model may be deterministic for this configuration."
+        )
+
 
 def test_format_occurrence_data():
     # add a trailing dimension
@@ -137,7 +143,7 @@ def test_format_occurrence_data():
     model.fit(x, yt)
 
     # fail on >2 dims
-    with pytest.raises(np.AxisError):
+    with pytest.raises(AxisError):
         ytt = np.concatenate((yt, yt), axis=1)
         model.fit(x, ytt)