Merge master into develop (#172)

* Fix Numpy and Scipy deprecation warnings (#163)

* fix numpy deprecation (product to prod)

* fix numpy deprecation (brentq)

* fix numpy deprecation (quad/nquad)

* changes to meet PEP-8

* Fix flake8 and black formatting errors

* Bump version to 1.1.9 (#164)

Co-authored-by: tommyod <tommy.odland>

* Update build.yml with pypi API token (#165)

* Update build.yml with pypi API token

* Remove push condition

* Replaced numpy.asfarray with numpy.asarray using the proper dtype. (#169)

* Replaced numpy.asfarray with numpy.asarray using the proper dtype.

* Newer version of numpy return np.True_ instead of True. Changed this in two doctests.

* Changed to correct dtype from int32 to long.
Changed doctest to use assert

---------

Co-authored-by: skrylow <Sergej.Krylow@sefe.eu>

* Version 1.1.10 with numpy 2.0 support (#171)

---------

Co-authored-by: yanbomb <160951800+yanbomb@users.noreply.github.com>
Co-authored-by: SergejKr <121165023+SergejKr@users.noreply.github.com>
Co-authored-by: skrylow <Sergej.Krylow@sefe.eu>

.github/workflows/build.yml

@@ -79,10 +79,11 @@ jobs:
         with:
           name: python-package-distributions
           path: dist/
-
-      - name: Upload to PyPI
-        env:
-          PYPI_PASSWORD: ${{ secrets.pypi_password }}
-        run: |
-          pip install twine;
-          python -m twine upload dist/* -u tommyod -p "$PYPI_PASSWORD" --skip-existing;
+
+      # https://github.com/pypa/gh-action-pypi-publish
+      - name: Publish Python distribution to PyPI
+        uses: pypa/gh-action-pypi-publish@e53eb8b103ffcb59469888563dc324e3c8ba6f06
+        with:
+          skip-existing: true
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

KDEpy/BaseKDE.py

@@ -190,12 +190,11 @@ def _process_sequence(sequence_array_like):
         Examples
         --------
         >>> res = BaseKDE._process_sequence([1, 2, 3])
-        >>> (res == np.array([[1], [2], [3]])).all()
-        True
+        >>> assert (res == np.array([[1], [2], [3]])).all()
         """
         # Must convert to float to avoid possible interger overflow
         if isinstance(sequence_array_like, Sequence):
-            out = np.asfarray(sequence_array_like).reshape(-1, 1)
+            out = np.asarray(sequence_array_like, dtype=float).reshape(-1, 1)
         elif isinstance(sequence_array_like, np.ndarray):
             if len(sequence_array_like.shape) == 1:
                 out = sequence_array_like.reshape(-1, 1)

KDEpy/NaiveKDE.py

@@ -123,7 +123,7 @@ def evaluate(self, grid_points=None):
         # For every data point, compute the kernel and add to the grid
         bw = self.bw
         if isinstance(bw, numbers.Number):
-            bw = np.asfarray(np.ones(self.data.shape[0]) * bw)
+            bw = np.asarray(np.ones(self.data.shape[0]) * bw, dtype=float)
 
         # TODO: Implementation w.r.t grid points for faster evaluation
         # See the SciPy evaluation for how this can be done

KDEpy/TreeKDE.py

@@ -136,7 +136,7 @@ def evaluate(self, grid_points=None, eps=10e-4):
         obs, dims = self.data.shape
         bw = self.bw
         if isinstance(bw, numbers.Number):
-            bw = np.asfarray(np.ones(obs) * bw)
+            bw = np.asarray(np.ones(obs) * bw, dtype=float)
         else:
             bw = np.asarray_chkfinite(bw, dtype=float)
 

KDEpy/binning.py

@@ -104,16 +104,16 @@ def linbin_cython(data, grid_points, weights=None):
     dx = (max_grid - min_grid) / num_intervals
     transformed_data = (data - min_grid) / dx
 
-    result = np.asfarray(np.zeros(num_intervals + 2))
+    result = np.asarray(np.zeros(num_intervals + 2), dtype=float)
 
     # Two Cython functions are implemented, one for weighted data and one
     # for unweighted data, since creating equal weights is costly w.r.t time
     if weights is None:
         result = _cutils.iterate_data_1D(transformed_data, result)
-        return np.asfarray(result[:-1]) / transformed_data.shape[0]
+        return np.asarray(result[:-1], dtype=float) / transformed_data.shape[0]
     else:
         res = _cutils.iterate_data_1D_weighted(transformed_data, weights, result)
-        return np.asfarray(res[:-1])  # Remove last, outside of grid
+        return np.asarray(res[:-1], dtype=float)  # Remove last, outside of grid
 
 
 def linbin_numpy(data, grid_points, weights=None):
@@ -197,7 +197,7 @@ def linbin_numpy(data, grid_points, weights=None):
     unique_integrals = np.unique(integral)
     unique_integrals = unique_integrals[(unique_integrals >= 0) & (unique_integrals <= len(grid_points))]
 
-    result = np.asfarray(np.zeros(len(grid_points) + 1))
+    result = np.asarray(np.zeros(len(grid_points) + 1), dtype=float)
     for grid_point in unique_integrals:
         # Use binary search to find indices for the grid point
         # Then sum the data assigned to that grid point
@@ -337,7 +337,7 @@ def linbin_Ndim(data, grid_points, weights=None):
 
     # Compute the number of grid points for each dimension in the grid
     grid_num = (grid_points[:, i] for i in range(dims))
-    grid_num = np.array(list(len(np.unique(g)) for g in grid_num))
+    grid_num = np.array(list(len(np.unique(g)) for g in grid_num), dtype="long")
 
     # Scale the data to the grid
     min_grid = np.min(grid_points, axis=0)
@@ -356,7 +356,7 @@ def linbin_Ndim(data, grid_points, weights=None):
     # Weighted data has two specific routines
     if weights is not None:
         if data_dims >= 3:
-            binary_flgs = cartesian(([0, 1],) * dims)
+            binary_flgs = cartesian(([0, 1],) * dims).astype("long")
             result = _cutils.iterate_data_ND_weighted(data, weights, result, grid_num, obs_tot, binary_flgs)
         else:
             result = _cutils.iterate_data_2D_weighted(data, weights, result, grid_num, obs_tot)
@@ -367,7 +367,7 @@ def linbin_Ndim(data, grid_points, weights=None):
     # specialize routine for this case.
     else:
         if data_dims >= 3:
-            binary_flgs = cartesian(([0, 1],) * dims)
+            binary_flgs = cartesian(([0, 1],) * dims).astype("long")
             result = _cutils.iterate_data_ND(data, result, grid_num, obs_tot, binary_flgs)
         else:
             result = _cutils.iterate_data_2D(data, result, grid_num, obs_tot)

KDEpy/bw_selection.py

@@ -55,8 +55,8 @@ def _fixed_point(t, N, I_sq, a2):
     """
 
     # This is important, as the powers might overflow if not done
-    I_sq = np.asfarray(I_sq, dtype=FLOAT)
-    a2 = np.asfarray(a2, dtype=FLOAT)
+    I_sq = np.asarray(I_sq, dtype=FLOAT)
+    a2 = np.asarray(a2, dtype=FLOAT)
 
     # ell = 7 corresponds to the 5 steps recommended in the paper
     ell = 7
@@ -72,7 +72,8 @@ def _fixed_point(t, N, I_sq, a2):
         # but this is faster so and requires an import less
 
         # Step one: estimate t_s from |f^(s+1)|^2
-        odd_numbers_prod = np.product(np.arange(1, 2 * s + 1, 2, dtype=FLOAT))
+        # odd_numbers_prod = np.product(np.arange(1, 2 * s + 1, 2, dtype=FLOAT))
+        odd_numbers_prod = np.prod(np.arange(1, 2 * s + 1, 2, dtype=FLOAT))
         K0 = odd_numbers_prod / np.sqrt(2 * np.pi)
         const = (1 + (1 / 2) ** (s + 1 / 2)) / 3
         time = np.power((2 * const * K0 / (N * f)), (2.0 / (3.0 + 2.0 * s)))

KDEpy/kernel_funcs.py

@@ -252,8 +252,7 @@ def __init__(self, function, var=1, support=3):
         ...     return np.exp(-x) / normalization
         >>> kernel = Kernel(exp, var=4, support=np.inf)
         >>> # The function is scaled so that the standard deviation (bw) = 1
-        >>> kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0]
-        True
+        >>> assert kernel(0, bw=1, norm=2)[0] > kernel(1, bw=1, norm=2)[0]
         >>> np.allclose(kernel(np.array([0, 1, 2])), kernel([0, 1, 2]))
         True
         >>> np.allclose(kernel(0), kernel([0]))
@@ -292,7 +291,7 @@ def practical_support(self, bw, atol=10e-5):
         else:
 
             def f(x):
-                return self.evaluate(x, bw=bw) - atol
+                return self.evaluate(x, bw=bw)[0] - atol
 
             try:
                 xtol = 1e-3

KDEpy/tests/test_api.py

@@ -51,7 +51,7 @@ def test_api_models_kernels_bandwidths(kde1, kde2, bw, kernel):
         assert err < 0.002
 
 
-type_functions = [tuple, np.array, np.asfarray, lambda x: np.asfarray(x).reshape(-1, 1)]
+type_functions = [tuple, np.array, np.asarray, lambda x: np.asarray(x, dtype=float).reshape(-1, 1)]
 
 
 @pytest.mark.parametrize(

KDEpy/tests/test_kernel_funcs.py

@@ -56,7 +56,11 @@ def test_integral_unity(self, fname, function):
             a, b = -function.support, function.support
         else:
             a, b = -5 * function.var, 5 * function.var
-        integral, abserr = quad(function, a=a, b=b)
+
+        def function_float(x):
+            return function(x)[0]
+
+        integral, _ = quad(function_float, a=a, b=b)
         assert np.isclose(integral, 1)
 
     @pytest.mark.parametrize(
@@ -74,10 +78,11 @@ def test_integral_unity_2D_many_p_norms(self, p, kernel_name):
         a, b = -function.support, function.support
 
         # Perform integration 2D
-        def int2D(x1, x2):
-            return function([[x1, x2]], norm=p)
+        def function_float(x1, x2):
+            return function([[x1, x2]], norm=p)[0]
 
-        ans, err = scipy.integrate.nquad(int2D, [[a, b], [a, b]], opts={"epsabs": 10e-2, "epsrel": 10e-2})
+        opts = {"epsabs": 10e-2, "epsrel": 10e-2}
+        ans, _ = scipy.integrate.nquad(function_float, [[a, b], [a, b]], opts=opts)
 
         assert np.allclose(ans, 1, rtol=10e-4, atol=10e-4)
 
@@ -93,10 +98,11 @@ def test_integral_unity_3D_many_p_norms(self, p):
         a, b = -function.support, function.support
 
         # Perform integration 2D
-        def int2D(x1, x2, x3):
-            return function([[x1, x2, x3]], norm=p)
+        def function_float(x1, x2, x3):
+            return function([[x1, x2, x3]], norm=p)[0]
 
-        ans, err = scipy.integrate.nquad(int2D, [[a, b], [a, b], [a, b]], opts={"epsabs": 10e-1, "epsrel": 10e-1})
+        opts = {"epsabs": 10e-1, "epsrel": 10e-1}
+        ans, _ = scipy.integrate.nquad(function_float, [[a, b], [a, b], [a, b]], opts=opts)
 
         assert np.allclose(ans, 1, rtol=10e-2, atol=10e-2)
 
@@ -121,10 +127,11 @@ def test_integral_unity_2D_p_norm(self, fname, function, p):
             a, b = -6, 6
 
         # Perform integration 2D
-        def int2D(x1, x2):
-            return function([[x1, x2]], norm=p)
+        def function_float(x1, x2):
+            return function([[x1, x2]], norm=p)[0]
 
-        ans, err = scipy.integrate.nquad(int2D, [[a, b], [a, b]], opts={"epsabs": 10e-1, "epsrel": 10e-1})
+        opts = {"epsabs": 10e-1, "epsrel": 10e-1}
+        ans, _ = scipy.integrate.nquad(function_float, [[a, b], [a, b]], opts=opts)
 
         assert np.allclose(ans, 1, rtol=10e-3, atol=10e-3)
 
@@ -150,10 +157,11 @@ def test_integral_unity_3D_p_norm(self, fname, function, p):
             a, b = -4, 4
 
         # Perform integration 2D
-        def int2D(x1, x2, x3):
-            return function([[x1, x2, x3]], norm=p)
+        def function_float(x1, x2, x3):
+            return function([[x1, x2, x3]], norm=p)[0]
 
-        ans, err = scipy.integrate.nquad(int2D, [[a, b], [a, b], [a, b]], opts={"epsabs": 10e-1, "epsrel": 10e-1})
+        opts = {"epsabs": 10e-1, "epsrel": 10e-1}
+        ans, _ = scipy.integrate.nquad(function_float, [[a, b], [a, b], [a, b]], opts=opts)
 
         assert np.allclose(ans, 1, rtol=10e-2, atol=10e-2)
 

pyproject.toml

@@ -1,8 +1,8 @@
 [project]
 name = "KDEpy"
-version = "1.1.8"
+version = "1.1.10"
 dependencies = [
-    "numpy>=1.14.2,<2.0",
+    "numpy>=1.14.2",
     "scipy>=1.0.1,<2.0",
 ]
 description = "Kernel Density Estimation in Python."