Merge pull request #3640 from alejoe91/support_numpy_2

Support numpy 2.0

.github/workflows/full-test-with-codecov.yml

@@ -23,7 +23,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: actions/setup-python@v5
         with:
-          python-version: '3.10'
+          python-version: '3.12'
       - name: Get ephy_testing_data current head hash
         # the key depends on the last comit repo https://gin.g-node.org/NeuralEnsemble/ephy_testing_data.git
         id: vars

pyproject.toml

@@ -7,7 +7,7 @@ authors = [
 ]
 description = "Python toolkit for analysis, visualization, and comparison of spike sorting output"
 readme = "README.md"
-requires-python = ">=3.9,<3.13"  # Only numpy 2.1 supported on python 3.13 for windows. We need to wait for fix on neo
+requires-python = ">=3.9,<3.13"
 classifiers = [
     "Programming Language :: Python :: 3 :: Only",
     "License :: OSI Approved :: MIT License",
@@ -20,11 +20,11 @@ classifiers = [
 
 
 dependencies = [
-    "numpy>=1.20, <2.0",  # 1.20 np.ptp, 1.26 might be necessary for avoiding pickling errors when numpy >2.0
+    "numpy>=1.20",
     "threadpoolctl>=3.0.0",
     "tqdm",
     "zarr>=2.18,<3",
-    "neo>=0.13.0",
+    "neo>=0.14.0",
     "probeinterface>=0.2.23",
     "packaging",
 ]

src/spikeinterface/core/waveform_tools.py

@@ -170,7 +170,7 @@ def allocate_waveforms_buffers(
         Dictionary to "construct" array in workers process (memmap file or sharemem)
     """
 
-    nsamples = nbefore + nafter
+    n_samples = nbefore + nafter
 
     dtype = np.dtype(dtype)
     if mode == "shared_memory":
@@ -187,11 +187,11 @@ def allocate_waveforms_buffers(
             num_chans = recording.get_num_channels()
         else:
             num_chans = np.sum(sparsity_mask[unit_ind, :])
-        shape = (n_spikes, nsamples, num_chans)
+        shape = (int(n_spikes), int(n_samples), int(num_chans))
 
         if mode == "memmap":
             filename = str(folder / f"waveforms_{unit_id}.npy")
-            arr = np.lib.format.open_memmap(filename, mode="w+", dtype=dtype, shape=shape)
+            arr = np.lib.format.open_memmap(filename, mode="w+", dtype=dtype.str, shape=shape)
             waveforms_by_units[unit_id] = arr
             arrays_info[unit_id] = filename
         elif mode == "shared_memory":
@@ -476,7 +476,7 @@ def extract_waveforms_to_single_buffer(
         Optionally return in case of shared_memory if copy=False.
         Dictionary to "construct" array in workers process (memmap file or sharemem info)
     """
-    nsamples = nbefore + nafter
+    n_samples = nbefore + nafter
 
     dtype = np.dtype(dtype)
     if mode == "shared_memory":
@@ -489,7 +489,7 @@ def extract_waveforms_to_single_buffer(
         num_chans = recording.get_num_channels()
     else:
         num_chans = int(max(np.sum(sparsity_mask, axis=1)))  # This is a numpy scalar, so we cast to int
-    shape = (num_spikes, nsamples, num_chans)
+    shape = (int(num_spikes), int(n_samples), int(num_chans))
 
     if mode == "memmap":
         all_waveforms = np.lib.format.open_memmap(file_path, mode="w+", dtype=dtype, shape=shape)