Merge pull request #2776 from alejoe91/prepare-0.100.6

Prepare release 0.100.6

doc/releases/0.100.6.rst

@@ -0,0 +1,13 @@
+.. _release0.100.6:
+
+SpikeInterface 0.100.6 release notes
+------------------------------------
+
+30th April 2024
+
+Minor release with bug fixes
+
+* Avoid np.prod in make_shared_array (#2621)
+* Improve caching of MS5 sorter (#2690)
+* Allow for remove_excess_spikes to remove negative spike times (#2716)
+* Update ks4 wrapper for newer version>=4.0.3 (#2701, #2774)

doc/whatisnew.rst

@@ -8,6 +8,7 @@ Release notes
 .. toctree::
   :maxdepth: 1
 
+  releases/0.100.6.rst
   releases/0.100.5.rst
   releases/0.100.4.rst
   releases/0.100.3.rst
@@ -39,11 +40,18 @@ Release notes
   releases/0.9.1.rst
 
 
+Version 0.100.6
+===============
+
+* Minor release with bug fixes
+
+
 Version 0.100.5
 ===============
 
 * Minor release with bug fixes
 
+
 Version 0.100.4
 ===============
 

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "spikeinterface"
-version = "0.100.5"
+version = "0.100.6"
 authors = [
   { name="Alessio Buccino", email="[email protected]" },
   { name="Samuel Garcia", email="[email protected]" },

src/spikeinterface/core/core_tools.py

@@ -7,6 +7,7 @@
 import json
 from copy import deepcopy
 
+from math import prod
 
 import numpy as np
 from tqdm import tqdm
@@ -163,7 +164,8 @@ def make_shared_array(shape, dtype):
     from multiprocessing.shared_memory import SharedMemory
 
     dtype = np.dtype(dtype)
-    nbytes = int(np.prod(shape) * dtype.itemsize)
+    shape = tuple(int(x) for x in shape)  # We need to be sure that shape comes in int instead of numpy scalars
+    nbytes = prod(shape) * dtype.itemsize
     shm = SharedMemory(name=None, create=True, size=nbytes)
     arr = np.ndarray(shape=shape, dtype=dtype, buffer=shm.buf)
     arr[:] = 0

src/spikeinterface/core/waveform_tools.py

@@ -483,7 +483,7 @@ def extract_waveforms_to_single_buffer(
     if sparsity_mask is None:
         num_chans = recording.get_num_channels()
     else:
-        num_chans = max(np.sum(sparsity_mask, axis=1))
+        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)
 
     if mode == "memmap":

src/spikeinterface/sorters/external/kilosort4.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
 from pathlib import Path
-import os
 from typing import Union
+from packaging.version import parse
 
 from ..basesorter import BaseSorter
 from .kilosortbase import KilosortBase
@@ -51,10 +51,11 @@ class Kilosort4Sorter(BaseSorter):
         "save_extra_kwargs": False,
         "skip_kilosort_preprocessing": False,
         "scaleproc": None,
+        "torch_device": "auto",
     }
 
     _params_description = {
-        "batch_size": "Number of samples per batch. Default value: 60000.",
+        "batch_size": "Number of samples included in each batch of data.",
         "nblocks": "Number of non-overlapping blocks for drift correction (additional nblocks-1 blocks are created in the overlaps). Default value: 1.",
         "Th_universal": "Spike detection threshold for universal templates. Th(1) in previous versions of Kilosort. Default value: 9.",
         "Th_learned": "Spike detection threshold for learned templates. Th(2) in previous versions of Kilosort. Default value: 8.",
@@ -87,6 +88,7 @@ class Kilosort4Sorter(BaseSorter):
         "save_extra_kwargs": "If True, additional kwargs are saved to the output",
         "skip_kilosort_preprocessing": "Can optionally skip the internal kilosort preprocessing",
         "scaleproc": "int16 scaling of whitened data, if None set to 200.",
+        "torch_device": "Select the torch device auto/cuda/cpu",
     }
 
     sorter_description = """Kilosort4 is a Python package for spike sorting on GPUs with template matching.
@@ -152,7 +154,10 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
 
         probe_filename = sorter_output_folder / "probe.prb"
 
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        torch_device = params["torch_device"]
+        if torch_device == "auto":
+            torch_device = "cuda" if torch.cuda.is_available() else "cpu"
+        device = torch.device(torch_device)
 
         # load probe
         recording = cls.load_recording_from_folder(sorter_output_folder.parent, with_warnings=False)
@@ -222,39 +227,14 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
         torch.cuda.manual_seed_all(1)
         torch.random.manual_seed(1)
         # if not params["skip_kilosort_preprocessing"]:
-        if params["do_correction"]:
-            # this function applies both preprocessing and drift correction
-            ops, bfile, st0 = compute_drift_correction(
-                ops, device, tic0=tic0, progress_bar=progress_bar, file_object=file_object
-            )
-        else:
+        if not params["do_correction"]:
             print("Skipping drift correction.")
-            hp_filter = ops["preprocessing"]["hp_filter"]
-            whiten_mat = ops["preprocessing"]["whiten_mat"]
-
-            bfile = BinaryFiltered(
-                ops["filename"],
-                n_chan_bin,
-                fs,
-                NT,
-                nt,
-                twav_min,
-                chan_map,
-                hp_filter=hp_filter,
-                whiten_mat=whiten_mat,
-                device=device,
-                do_CAR=do_CAR,
-                invert_sign=invert,
-                dtype=dtype,
-                tmin=tmin,
-                tmax=tmax,
-                artifact_threshold=artifact,
-                file_object=file_object,
-            )
+            ops["nblocks"] = 0
 
-        # TODO: don't think we need to do this actually
-        # Save intermediate `ops` for use by GUI plots
-        # io.save_ops(ops, results_dir)
+        # this function applies both preprocessing and drift correction
+        ops, bfile, st0 = compute_drift_correction(
+            ops, device, tic0=tic0, progress_bar=progress_bar, file_object=file_object
+        )
 
         # Sort spikes and save results
         st, tF, _, _ = detect_spikes(ops, device, bfile, tic0=tic0, progress_bar=progress_bar)
@@ -263,39 +243,8 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             ops["preprocessing"] = dict(
                 hp_filter=torch.as_tensor(np.zeros(1)), whiten_mat=torch.as_tensor(np.eye(recording.get_num_channels()))
             )
-        ops, similar_templates, is_ref, est_contam_rate = save_sorting(
-            ops, results_dir, st, clu, tF, Wall, bfile.imin, tic0, save_extra_vars=save_extra_vars
-        )
-
-        # # Clean-up temporary files
-        # if params["delete_recording_dat"] and (recording_file := sorter_output_folder / "recording.dat").exists():
-        #     recording_file.unlink()
-
-        # all_tmp_files = ("matlab_files", "temp_wh.dat")
-
-        # if isinstance(params["delete_tmp_files"], bool):
-        #     if params["delete_tmp_files"]:
-        #         tmp_files_to_remove = all_tmp_files
-        #     else:
-        #         tmp_files_to_remove = ()
-        # else:
-        #     assert isinstance(
-        #         params["delete_tmp_files"], (tuple, list)
-        #     ), "`delete_tmp_files` must be a `Bool`, `Tuple` or `List`."
-
-        #     for name in params["delete_tmp_files"]:
-        #         assert name in all_tmp_files, f"{name} is not a valid option, must be one of: {all_tmp_files}"
-
-        #     tmp_files_to_remove = params["delete_tmp_files"]
-
-        # if "temp_wh.dat" in tmp_files_to_remove:
-        #     if (temp_wh_file := sorter_output_folder / "temp_wh.dat").exists():
-        #         temp_wh_file.unlink()
 
-        # if "matlab_files" in tmp_files_to_remove:
-        #     for ext in ["*.m", "*.mat"]:
-        #         for temp_file in sorter_output_folder.glob(ext):
-        #             temp_file.unlink()
+        _ = save_sorting(ops, results_dir, st, clu, tF, Wall, bfile.imin, tic0, save_extra_vars=save_extra_vars)
 
     @classmethod
     def _get_result_from_folder(cls, sorter_output_folder):