Merge pull request #1531 from pints-team/numpy-2

Updates for numpy 2 compatibility

pints/_optimisers/_nelder_mead.py

@@ -186,15 +186,15 @@ def ask(self):
                     self._xs[1 + i][i] *= x_grow
 
             # Ask for initial points
-            return np.array(self._xs, copy=True)
+            return np.copy(self._xs)
 
         # Shrink operation
         if self._shrink:
             for i in range(self._n_parameters):
                 self._xs[1 + i] = \
                     self._xs[0] + self._ys * (self._xs[1 + i] - self._xs[0])
 
-            return np.array(self._xs[1:], copy=True)
+            return np.copy(self._xs[1:])
 
         # Start of normal iteration, ask for reflection point
         if self._xr is None:
@@ -240,7 +240,7 @@ def tell(self, fx):
 
         # Initialise
         if self._fs is None:
-            fx = np.array(fx, copy=True)
+            fx = np.copy(fx)
             if np.prod(fx.shape) != self._n_parameters + 1:
                 raise ValueError(
                     'Expecting a vector of length (1 + n_parameters).')
@@ -251,7 +251,7 @@ def tell(self, fx):
 
         # Shrink
         if self._shrink:
-            fx = np.array(fx, copy=False)
+            fx = np.asarray(fx)
             if np.prod(fx.shape) != self._n_parameters:
                 raise ValueError(
                     'Expecting a vector of length n_parameters.')

pints/tests/test_mcmc_relativistic.py

@@ -8,13 +8,19 @@
 #
 import unittest
 import numpy as np
-from scipy.integrate import cumtrapz, quad
+from scipy.integrate import quad
 from scipy.interpolate import interp1d
 import pints
 import pints.toy
 
 from shared import StreamCapture
 
+# For compatibility with scipy<1.12, released 2024-01-20
+try:
+    from scipy.integrate import cumulative_trapezoid
+except ImportError:     # pragma: no cover
+    from scipy.integrate import cumtrapz as cumulative_trapezoid
+
 
 class TestRelativisticMCMC(unittest.TestCase):
     """
@@ -238,7 +244,8 @@ def pdf(u):
         # in this test, the values of m and c are nice enough that this
         # function can be used for the comparison, and we check that the
         # results are the same.
-        cdf = cumtrapz(1 / c * pdf(integration_grid), x=integration_grid)
+        cdf = cumulative_trapezoid(
+            1 / c * pdf(integration_grid), x=integration_grid)
 
         # Interpolate to get approximate inverse
         inv_cdf = interp1d([0.0] + list(cdf), integration_grid)