Update numpy compatibility

.devcontainer/devcontainer.json

@@ -0,0 +1,19 @@
+{
+  "name": "paramz",
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.13-bullseye",
+  "customizations": {
+    "vscode": {
+      "extensions": [
+        "ms-python.python",
+        "ms-python.debugpy"
+      ],
+      "settings": {
+        "python.defaultInterpreterPath": "/usr/local/bin/python",
+        "python.testing.pytestEnabled": true,
+        "python.testing.unittestEnabled": false
+      }
+    }
+  },
+  "postCreateCommand": "python -m pip install --upgrade pip && python -m pip install -e '.[test,docs,notebook]' nose",
+  "remoteUser": "vscode"
+}

.github/workflows/actions.yml

@@ -6,6 +6,9 @@ on:
       - development
       - deploy
   pull_request:
+  workflow_dispatch:
+  release:
+    types: [published]
 
 permissions:
   contents: read
@@ -16,21 +19,34 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python: ['3.9', '3.10', '3.11', '3.12']
+        python: ['3.11', '3.12', '3.13', '3.14']
     runs-on: ${{ matrix.os }}
     steps:
       - name: Checkout
         uses: actions/checkout@v4
 
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python }}
 
-      - name: Install lib
+      - name: Install and test
         run: |
           pip install --upgrade pip
-          pip install -e .
+          pip install ".[test]"
+          python -m pytest
+
+      - name: Build and validate distribution
+        if: matrix.os == 'ubuntu-latest' && matrix.python == '3.14'
+        run: |
+          pip install build twine
+          python -m build
+          twine check dist/*
+          python -m venv /tmp/paramz-wheel-test
+          /tmp/paramz-wheel-test/bin/python -m pip install --upgrade pip
+          /tmp/paramz-wheel-test/bin/python -m pip install dist/*.whl
+          cd /tmp
+          /tmp/paramz-wheel-test/bin/python -c "import paramz; print(paramz.__version__)"
 
   deploy:
     runs-on: ubuntu-latest
@@ -41,20 +57,16 @@ jobs:
         uses: actions/checkout@v4
 
       - name: Setup python
-        uses: actions/setup-python@v4
+        uses: actions/setup-python@v6
         with:
-          python-version: '3.9'
+          python-version: '3.11'
 
-      - name: Install twine
+      - name: Build and validate distribution
         run: |
           pip install --upgrade pip
-          pip install twine
-
-      - name: Build wheel
-        run: |
-          pip install setuptools
-          pip install wheel
-          python setup.py sdist bdist_wheel
+          pip install build twine
+          python -m build
+          twine check dist/*
 
       - name: Upload to PyPI using twine
         run: |

.vscode/launch.json

@@ -0,0 +1,17 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Test: Parameterized Tests",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "parameterized_tests.py",
+            "console": "integratedTerminal",
+            "cwd": "${workspaceFolder}/paramz/tests",
+            "justMyCode": false
+        }
+    ]
+}

CHANGELOG.md

@@ -2,6 +2,34 @@ Changelog
 =========
 
 
+0.10.0 (2026-07-29)
+-------------------
+
+New
+~~~
+- Full compatibility with NumPy 2.x and Python 3.11-3.14. [fbordeu]
+- Modernized package setup and build configuration. [fbordeu]
+- Updated GitHub Actions workflows for modern CI/CD. [fbordeu]
+- Added replacement wrapper for numpy's .data access for compatibility. [fbordeu]
+- Refactored numerical operations and removed unnecessary float conversions. [fbordeu]
+- Improved error handling in HDF5 parameter writing. [fbordeu]
+- Updated AssertionsMixin to inherit from unittest.TestCase with improved tests. [fbordeu]
+
+Fix
+~~~
+- Restored compatibility with NumPy 1.26.x while supporting NumPy 2.x. [fbordeu]
+- Updated deprecated assertRegexp calls to modern equivalents. [fbordeu]
+- Deactivated catch warnings causing issues with NumPy. [fbordeu]
+- Added debugging config to resolve NumPy memory address issues in tests. [fbordeu]
+
+Other
+~~~~~
+- Builds on preparatory NumPy compatibility work by [MartinBubel]. [fbordeu]
+- Dropped support for Python < 3.11. [fbordeu]
+- Set minimum dependency versions: numpy>=2, scipy>=1.15. [fbordeu]
+- Bumped version: 0.9.6 → 0.10.0. [fbordeu]
+
+
 (unreleased)
 ------------
 

paramz/__version__.py

@@ -1 +1 @@
-__version__ = "0.9.6"
+__version__ = "0.10.0"

paramz/core/index_operations.py

@@ -1,21 +1,21 @@
 #===============================================================================
 # Copyright (c) 2015, Max Zwiessele
 # All rights reserved.
-# 
+#
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions are met:
-# 
+#
 # * Redistributions of source code must retain the above copyright notice, this
 #   list of conditions and the following disclaimer.
-# 
+#
 # * Redistributions in binary form must reproduce the above copyright notice,
 #   this list of conditions and the following disclaimer in the documentation
 #   and/or other materials provided with the distribution.
-# 
+#
 # * Neither the name of paramax nor the names of its
 #   contributors may be used to endorse or promote products derived from
 #   this software without specific prior written permission.
-# 
+#
 # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
@@ -29,10 +29,11 @@
 #===============================================================================
 
 import numpy
-from numpy.lib.function_base import vectorize
 from .lists_and_dicts import IntArrayDict
 from functools import reduce
 
+vectorize = numpy.vectorize
+
 def extract_properties_to_index(index, props):
     prop_index = dict()
     for i, cl in enumerate(props):
@@ -64,11 +65,11 @@ class ParameterIndexOperations(object):
         model : 0 1 2 3 4 5 6 7 8 9
         key1: 4 5
         key2: 7 8
-    
+
         param1: 0 1 2 3 4 5
         key1: 2 3
         key2: 5
-    
+
         param2: 0 1 2 3 4
         key1: 0
         key2: 2 3
@@ -226,7 +227,7 @@ def items(self):
             ind2 = self._filter_index(ind)
             if ind2.size > 0:
                 yield i, ind2
-    
+
     def properties(self):
         return [i[0] for i in self.items()]
 

paramz/core/indexable.py

@@ -158,7 +158,7 @@ def _raveled_index_for(self, param):
         """
         from ..param import ParamConcatenation
         if isinstance(param, ParamConcatenation):
-            return np.hstack((self._raveled_index_for(p) for p in param.params))
+            return np.hstack([self._raveled_index_for(p) for p in param.params])
         return param._raveled_index() + self._offset_for(param)
 
     def _raveled_index_for_transformed(self, param):

paramz/core/lists_and_dicts.py

@@ -33,7 +33,7 @@
 
 def intarray_default_factory():
     import numpy as np
-    return np.int_([])
+    return np.array([], dtype=np.int64)
 
 class IntArrayDict(defaultdict):
     def __init__(self, default_factory=None):

paramz/core/observable_array.py

@@ -58,7 +58,7 @@ def __new__(cls, input_array, *a, **kw):
         if not isinstance(input_array, ObsAr):
             try:
                 # try to cast ints to floats
-                obj = np.atleast_1d(np.require(input_array, dtype=np.float_, requirements=['W', 'C'])).view(cls)
+                obj = np.atleast_1d(np.require(input_array, dtype=np.float64, requirements=['W', 'C'])).view(cls)
             except ValueError:
                 # do we have other dtypes in the array?
                 obj = np.atleast_1d(np.require(input_array, requirements=['W', 'C'])).view(cls)
@@ -72,10 +72,11 @@ def __array_finalize__(self, obj):
         self.observers = getattr(obj, 'observers', None)
         self._update_on = getattr(obj, '_update_on', None)
 
-    def __array_wrap__(self, out_arr, context=None):
+    def __array_wrap__(self, out_arr, context=None, return_scalar=False):
         #np.ndarray.__array_wrap__(self, out_arr, context)
         #return out_arr
-        return out_arr.view(np.ndarray)
+        result = out_arr.view(np.ndarray)
+        return result[()] if return_scalar else result
 
     def _setup_observers(self):
         # do not setup anything, as observable arrays do not have default observers

paramz/core/parameter_core.py

@@ -130,6 +130,7 @@ def _trigger_params_changed(self, trigger_parent=True):
 
         If trigger_parent is True, we will tell the parent, otherwise not.
         """
+        self._sync_params_to_children()
         [p._trigger_params_changed(trigger_parent=False) for p in self.parameters if not p.is_fixed]
         self.notify_observers(None, None if trigger_parent else -np.inf)
 
@@ -280,21 +281,51 @@ def _propagate_param_grad(self, parray, garray):
         #if self.gradient.size != self.size:
         #    self._gradient_array_ = np.empty(self.size, dtype=np.float64)
 
-        pi_old_size = 0
-        for pi in self.parameters:
-            pislice = slice(pi_old_size, pi_old_size + pi.size)
+        from ..param import Param
 
-            self.param_array[pislice] = pi.param_array.flat  # , requirements=['C', 'W']).flat
-            self.gradient_full[pislice] = pi.gradient_full.flat  # , requirements=['C', 'W']).flat
+        if isinstance(self, Param):
+            self._gradient_array_ = garray.reshape(self.shape)
+            np.ndarray.__setitem__(self, Ellipsis, parray.reshape(self.shape))
+            return
 
-            pi.param_array.data = parray[pislice].data
-            pi.gradient_full.data = garray[pislice].data
+        self._param_array_ = parray
+        self._gradient_array_ = garray
 
+        pi_old_size = 0
+        for pi in self.parameters:
+            pislice = slice(pi_old_size, pi_old_size + pi.size)
             pi._propagate_param_grad(parray[pislice], garray[pislice])
             pi_old_size += pi.size
 
         self._model_initialized_ = True
 
+    def _sync_params_to_children(self):
+        """Copy the canonical flat parameter array into ndarray leaf parameters."""
+        from ..param import Param
+
+        offset = 0
+        for parameter in self.parameters:
+            pslice = slice(offset, offset + parameter.size)
+            if isinstance(parameter, Param):
+                values = self.param_array[pslice].reshape(parameter.shape)
+                np.ndarray.__setitem__(parameter, Ellipsis, values)
+            offset += parameter.size
+
+    def _sync_from_child(self, child):
+        """Copy a changed immediate child's values and gradients into this object."""
+        child = getattr(child, '_original_', child)
+        while child not in self.parameters and child.has_parent():
+            child = child._parent_
+        try:
+            index = self.parameters.index(child)
+        except ValueError:
+            return
+
+        start = sum(parameter.size for parameter in self.parameters[:index])
+        pslice = slice(start, start + child.size)
+        self.param_array[pslice] = child.param_array.flat
+        self.gradient_full[pslice] = child.gradient_full.flat
+
     def _connect_parameters(self):
         pass
 
@@ -444,7 +475,7 @@ def num_params(self):
     def _add_parameter_name(self, param):
         try:
             pname = adjust_name_for_printing(param.name)
-    
+
             def warn_and_retry(param, match=None):
                 #===================================================================
                 # print """
@@ -507,6 +538,7 @@ def _parameters_changed_notification(self, me, which=None):
         self._optimizer_copy_transformed = False # tells the optimizer array to update on next request
         self.parameters_changed()
     def _pass_through_notify_observers(self, me, which=None):
+        self._sync_from_child(me)
         self.notify_observers(which=which)
     def _setup_observers(self):
         """
@@ -573,6 +605,6 @@ def gather_params(self, plist):
                     d = f.create_dataset('param_array',self.param_array.shape, dtype=self.param_array.dtype)
                     d[:] = self.param_array
                 f.close()
-            except:
-                raise 'Fails to write the parameters into a HDF5 file!'
+            except Exception as error:
+                raise RuntimeError('Failed to write parameters to an HDF5 file') from error
 

paramz/model.py

@@ -33,7 +33,7 @@
 
 
 import numpy as np
-from numpy.linalg.linalg import LinAlgError
+from numpy.linalg import LinAlgError
 
 from . import optimization
 from .parameterized import Parameterized
@@ -370,9 +370,9 @@ def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3
             for xind in zip(transformed_index):
                 xx = x.copy()
                 xx[xind] += step
-                f1 = float(self._objective(xx))
+                f1 = self._objective(xx)
                 xx[xind] -= 2.*step
-                f2 = float(self._objective(xx))
+                f2 = self._objective(xx)
                 #Avoid divide by zero, if any of the values are above 1e-15, otherwise both values are essentiall
                 #the same
                 if f1 > 1e-15 or f1 < -1e-15 or f2 > 1e-15 or f2 < -1e-15:
@@ -396,11 +396,11 @@ def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3
                 if df_unstable:  # pragma: no cover
                     formatted_name = "\033[94m {0} \033[0m".format(names[xind])
 
-                r = '%.6f' % float(ratio)
-                d = '%.6f' % float(difference)
+                r = '%.6f' % ratio
+                d = '%.6f' % difference
                 g = '%.6f' % gradient[xind]
-                ng = '%.6f' % float(numerical_gradient)
-                df = '%1.e' % float(df_ratio)
+                ng = '%.6f' % numerical_gradient
+                df = '%1.e' % df_ratio
                 grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}|{5:^{c5}}".format(formatted_name, r, d, g, ng, df, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4], c5=cols[5])
                 print(grad_string)