Implemented changes needed to update to Numpy 2.0 and other dependencies

.github/workflows/builds.yml

@@ -12,9 +12,9 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10", "3.11", "3.12"]
+        python-version: ["3.10", "3.11", "3.12.6"]
     env:
-      PY_LATEST: "3.12"
+      PY_LATEST: "3.12.6"
 
     steps:
       - name: Checkout
@@ -65,6 +65,7 @@ jobs:
         if: matrix.python-version == env.PY_LATEST
         run: |
           python -m pip install --upgrade .['dev']
+          python -m ipykernel install --user
           make -C docs/ html
           touch docs/build/html/.nojekyll
 

.github/workflows/testpypi.yml

@@ -14,7 +14,7 @@ jobs:
     name: Build Python package
     runs-on: ubuntu-latest
     env:
-      PY_LATEST: "3.12"
+      PY_LATEST: "3.12.6"
 
     steps:
       - uses: actions/checkout@v4
@@ -53,13 +53,12 @@ jobs:
       - name: Build Sphinx docs
         run: |
           python -m pip install --upgrade .['dev']
+          python -m ipykernel install --user
           make -C docs/ html
 
       - name: Upload products
         uses: actions/upload-artifact@v4
-        if:
-          (matrix.python-version == env.PY_LATEST) &&
-          startsWith(github.ref, 'refs/tags')
+        if: startsWith(github.ref, 'refs/tags')
         with:
           name: dist-pkg
           path: |

docs/source/demos/demo_simulbouts.rst

@@ -124,7 +124,7 @@ Collect and display NLS results from the simulations:
 .. jupyter-execute::
    :linenos:
 
-   nls_coefs = pd.DataFrame(np.row_stack(coefs_nls),
+   nls_coefs = pd.DataFrame(np.vstack(coefs_nls),
                             columns=["lambda0", "lambda1", "p"])
    # Centrality and variance
    nls_coefs.describe()
@@ -138,7 +138,7 @@ Collect and display MLE results from the simulations:
 .. jupyter-execute::
    :linenos:
 
-   mle_coefs = pd.DataFrame(np.row_stack(coefs_mle),
+   mle_coefs = pd.DataFrame(np.vstack(coefs_mle),
                             columns=["lambda0", "lambda1", "p"])
    # Centrality and variance
    mle_coefs.describe()

pyproject.toml

@@ -16,13 +16,13 @@ dependencies = ["ahrs",
 		"docutils>=0.3",
 		"matplotlib>=3.0",
 		"netcdf4>=1.5",
-		"numpy>=1.18",
-		"pandas>=1.0",
+		"numpy>=2.0",
+		"pandas>=2.0",
 		"rpy2>=3.5",
 		"scikit-learn>=0.20.0",
-		"scipy>=1.5",
+		"scipy>=1.14",
 		"statsmodels>=0.11",
-		"xarray>=0.16"]
+		"xarray>=2024"]
 classifiers = [
     "Development Status :: 4 - Beta",
     "Programming Language :: Python :: 3",

skdiveMove/__init__.py

@@ -88,5 +88,5 @@
 
 __author__ = "Sebastian Luque <spluque@gmail.com>"
 __license__ = "AGPLv3"
-__version__ = "0.3.4"
+__version__ = "0.4.0"
 __all__ = ["TDR", "calibrate", "dump_config_template"]

skdiveMove/calibspeed.py

@@ -94,7 +94,7 @@ def calibrate_speed(x, tau, contour_level, z=0, bad=[0, 0],
         ax.clabel(cntr, fmt="%1.2f")
         # Plot the binned data, adding some noise for clarity
         xjit_binned = np.random.normal(binned[:, 0],
-                                       xnpy[:, 0].ptp() / (2 * n_eval))
+                                       np.ptp(xnpy[:, 0]) / (2 * n_eval))
         ax.scatter(xjit_binned, binned[:, 1], s=6, alpha=0.3)
         # Plot line
         xnew = np.linspace(mins[0], maxs[0])