[MAIN] add support for numpy > 2.0

.github/workflows/CI.yml

@@ -511,6 +511,7 @@ jobs:
           # create an environment and install everything
           python -m venv ~/test_env
           source ~/test_env/bin/activate
+          sudo apt-get update
           sudo apt install -y libopenmpi-dev openmpi-bin
 
           python -m pip install --upgrade pip
@@ -527,4 +528,5 @@ jobs:
       - name: Run doctests
         run: |
           source ~/test_env/bin/activate
+          printf "def pytest_configure(config):\n    import numpy as np\n    import builtins\n    np.set_printoptions(legacy='1.25')\n    builtins.np = np\n" > conftest.py
           pytest elephant --doctest-modules --ignore=elephant/test/

doc/conf.py

@@ -31,7 +31,6 @@
 extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.autosummary',
-    'sphinx.ext.doctest',
     'sphinx.ext.intersphinx',
     'sphinx.ext.todo',
     'sphinx.ext.viewcode',

elephant/causality/granger.py

@@ -357,7 +357,7 @@ def _optimal_vector_arm(signals, dimension, max_order,
 
     length = np.size(signals[0])
 
-    optimal_ic = np.infty
+    optimal_ic = np.inf
     optimal_order = 1
     optimal_coeffs = np.zeros((dimension, dimension, optimal_order))
     optimal_cov_matrix = np.zeros((dimension, dimension))

elephant/current_source_density_src/icsd.py

@@ -344,7 +344,7 @@ def get_f_matrix(self):
                               self.coord_electrode[i])**2 + (self.diam[j] / 2)**2)-
                     abs(self.coord_electrode[j] + self.coord_electrode[i])))
 
-        f_matrix /= (2 * self.sigma)
+        f_matrix = f_matrix / (2 * self.sigma)
         return f_matrix
 
 

elephant/signal_processing.py

@@ -964,7 +964,7 @@ def rauc(signal, baseline=None, bin_duration=None, t_start=None, t_stop=None):
     sig_binned = sig_binned.reshape(n_bins, samples_per_bin, n_channels)
 
     # rectify and integrate over each bin
-    rauc = np.trapz(np.abs(sig_binned), dx=signal.sampling_period, axis=1)
+    rauc = np.trapezoid(np.abs(sig_binned), dx=signal.sampling_period, axis=1)
 
     if n_bins == 1:
         # return a single value for each channel

elephant/spade.py

@@ -1450,7 +1450,7 @@ def _get_pvalue_spec(max_occs, min_spikes, max_spikes, min_occ, n_surr, winlen,
             counts, occs = np.histogram(
                 max_occs_size_dur,
                 bins=np.arange(min_occ,
-                               np.max(max_occs_size_dur) + 2))
+                               np.max(max_occs_size_dur).astype(np.int16) + 2))
             occs = occs[:-1].astype(np.uint16)
             pvalues = np.cumsum(counts[::-1])[::-1] / n_surr
             for occ_id, occ in enumerate(occs):

elephant/statistics.py

@@ -365,7 +365,7 @@ def __variation_check(v, with_nan):
             warnings.warn("The input size is too small. Please provide"
                           "an input with more than 1 entry. Returning `NaN`"
                           "since the argument `with_nan` is `True`")
-            return np.NaN
+            return np.nan
         raise ValueError("Input size is too small. Please provide "
                          "an input with more than 1 entry. Set 'with_nan' "
                          "to True to replace the error by a warning.")

elephant/test/test_gpfa.py

@@ -188,7 +188,7 @@ def test_get_seq_sqrt(self):
         self.assertEqual(seqs['y'].shape, seqs_not_sqrt['y'].shape)
 
     def test_cut_trials_inf(self):
-        same_data = gpfa_util.cut_trials(self.data2, seg_length=np.Inf)
+        same_data = gpfa_util.cut_trials(self.data2, seg_length=np.inf)
         assert same_data is self.data2
 
     def test_cut_trials_zero_length(self):

elephant/test/test_phase_analysis.py

@@ -13,7 +13,7 @@
 import quantities as pq
 import scipy.io
 from neo import SpikeTrain, AnalogSignal
-from numpy.ma.testutils import assert_allclose
+from numpy.testing import assert_allclose
 
 import elephant.phase_analysis
 from elephant.datasets import download_datasets
@@ -45,7 +45,7 @@ def test_perfect_locking_one_spiketrain_one_signal(self):
             interpolate=True)
 
         assert_allclose(phases[0], - np.pi / 2.)
-        assert_allclose(amps[0], 1, atol=0.1)
+        assert_allclose(amps[0].magnitude, 1, atol=0.1)
         assert_allclose(times[0].magnitude, self.st0.magnitude)
         self.assertEqual(len(phases[0]), len(self.st0))
         self.assertEqual(len(amps[0]), len(self.st0))
@@ -60,7 +60,7 @@ def test_perfect_locking_many_spiketrains_many_signals(self):
             interpolate=True)
 
         assert_allclose(phases[0], -np.pi / 2.)
-        assert_allclose(amps[0], 1, atol=0.1)
+        assert_allclose(amps[0].magnitude, 1, atol=0.1)
         assert_allclose(times[0].magnitude, self.st0.magnitude)
         self.assertEqual(len(phases[0]), len(self.st0))
         self.assertEqual(len(amps[0]), len(self.st0))
@@ -75,7 +75,7 @@ def test_perfect_locking_one_spiketrains_many_signals(self):
             interpolate=True)
 
         assert_allclose(phases[0], -np.pi / 2.)
-        assert_allclose(amps[0], 1, atol=0.1)
+        assert_allclose(amps[0].magnitude, 1, atol=0.1)
         assert_allclose(times[0].magnitude, self.st0.magnitude)
         self.assertEqual(len(phases[0]), len(self.st0))
         self.assertEqual(len(amps[0]), len(self.st0))
@@ -88,7 +88,7 @@ def test_perfect_locking_many_spiketrains_one_signal(self):
             interpolate=True)
 
         assert_allclose(phases[0], -np.pi / 2.)
-        assert_allclose(amps[0], 1, atol=0.1)
+        assert_allclose(amps[0].magnitude, 1, atol=0.1)
         assert_allclose(times[0].magnitude, self.st0.magnitude)
         self.assertEqual(len(phases[0]), len(self.st0))
         self.assertEqual(len(amps[0]), len(self.st0))

elephant/test/test_spade.py

@@ -404,7 +404,7 @@ def test_spade_msip_3d(self):
         assert_equal(lags_msip, self.lags_msip)
 
     # test under different configuration of parameters than the default one
-    def test_parameters_3d(self):
+    def test_parameters_3d_min_spikes(self):
         # test min_spikes parameter
         output_msip_min_spikes = spade.spade(
             self.msip,
@@ -438,6 +438,7 @@ def test_parameters_3d(self):
             el for el in self.elements_msip if len(el) >= self.min_neu and len(
                 el) >= self.min_spikes])
 
+    def test_parameters_3d_min_occ(self):
         # test min_occ parameter
         output_msip_min_occ = spade.spade(
             self.msip,

elephant/test/test_spectral.py

@@ -16,7 +16,7 @@
 import scipy.fft
 import scipy.signal as spsig
 from neo import AnalogSignal
-from numpy.testing import assert_array_equal
+from numpy.testing import assert_array_equal, assert_array_almost_equal
 from packaging import version
 
 import elephant.spectral
@@ -569,8 +569,8 @@ def test_multitaper_cross_spectrum_behavior(self):
             elephant.spectral.multitaper_cross_spectrum(
                     data.magnitude.T, fs=1 / sampling_period,
                     peak_resolution=peak_res)
-        self.assertTrue((freqs == freqs_np).all()
-                        and (cross_spec == cross_spec_np).all())
+        assert_array_equal(freqs.magnitude, freqs_np)
+        assert_array_almost_equal(cross_spec.magnitude, cross_spec_np)
 
         # one-sided vs two-sided spectrum
         freqs_os, cross_spec_os = \
@@ -929,8 +929,8 @@ def test_multitaper_coherence_input_types(self):
                                                    anasig_signal_j)
 
         np.testing.assert_array_equal(arr_f, anasig_f)
-        np.testing.assert_allclose(arr_coh, anasig_coh, atol=1e-6)
-        np.testing.assert_array_equal(arr_phi, anasig_phi)
+        np.testing.assert_allclose(arr_coh, anasig_coh.magnitude, atol=1e-6)
+        np.testing.assert_array_almost_equal(arr_phi, anasig_phi.magnitude)
 
     def test_multitaper_cohere_peak(self):
         # Generate dummy data

elephant/test/test_spike_train_correlation.py

@@ -264,7 +264,7 @@ def test_empty_spike_train(self):
             result = sc.correlation_coefficient(binned_12, fast=False)
 
         # test for NaNs in the output array
-        target = np.zeros((2, 2)) * np.NaN
+        target = np.zeros((2, 2)) * np.nan
         target[0, 0] = 1.0
         assert_array_almost_equal(result, target)
 

elephant/test/test_total_spiking_probability_edges.py

@@ -87,25 +87,26 @@ def test_total_spiking_probability_edges(self):
                  "ER10/new_sim0_100.mat",
                  "ER15/new_sim0_100.mat",
                  ]
+        repo_base_path = 'unittest/functional_connectivity/' \
+                         'total_spiking_probability_edges/data/'
 
         for datafile in files:
-            repo_base_path = 'unittest/functional_connectivity/' \
-                             'total_spiking_probability_edges/data/'
-            downloaded_dataset_path = download_datasets(repo_base_path +
-                                                        datafile)
+            with self.subTest(datafile=datafile):
+                downloaded_dataset_path = download_datasets(repo_base_path +
+                                                            datafile)
 
-            spiketrains, original_data = load_spike_train_simulated(
-                downloaded_dataset_path)
+                spiketrains, original_data = load_spike_train_simulated(
+                    downloaded_dataset_path)
 
-            connectivity_matrix, delay_matrix = \
-                total_spiking_probability_edges(spiketrains)
+                connectivity_matrix, delay_matrix = \
+                    total_spiking_probability_edges(spiketrains)
 
-            # Remove self-connections
-            np.fill_diagonal(connectivity_matrix, 0)
+                # Remove self-connections
+                np.fill_diagonal(connectivity_matrix, 0)
 
-            _, _, _, auc = roc_curve(connectivity_matrix, original_data)
+                _, _, _, auc = roc_curve(connectivity_matrix, original_data)
 
-            self.assertGreater(auc, 0.95)
+                self.assertGreater(auc, 0.95)
 
 # ====== HELPER FUNCTIONS ======
 
@@ -173,7 +174,7 @@ def roc_curve(estimate, original):
         tpr_list.append(sensitivity(*conf_matrix))
         fpr_list.append(fall_out(*conf_matrix))
 
-    auc = np.trapz(tpr_list, fpr_list)
+    auc = np.trapezoid(tpr_list, fpr_list)
 
     return tpr_list, fpr_list, thresholds, auc
 

requirements/requirements.txt

@@ -1,5 +1,5 @@
 neo>=0.10.0
-numpy>=1.19.5, <2
+numpy>=2.0.0
 quantities>=0.14.1
 scipy>=1.10.0
 six>=1.10.0