Merge branch 'main' into verbose_to_non_parallel_case

Author: h-mayorquin

pyproject.toml

@@ -95,7 +95,7 @@ full = [
     "scikit-learn",
     "networkx",
     "distinctipy",
-    "matplotlib<3.9",  # See https://github.com/SpikeInterface/spikeinterface/issues/2863
+    "matplotlib>=3.6", # matplotlib.colormaps
     "cuda-python; platform_system != 'Darwin'",
     "numba",
 ]
@@ -159,8 +159,8 @@ test = [
 ]
 
 docs = [
-    "Sphinx==5.1.1",
-    "sphinx_rtd_theme==1.0.0",
+    "Sphinx",
+    "sphinx_rtd_theme",
     "sphinx-gallery",
     "sphinx-design",
     "numpydoc",
@@ -173,6 +173,7 @@ docs = [
     "hdbscan>=0.8.33",   # For sorters spykingcircus2 + tridesclous
     "numba", # For many postprocessing functions
     "xarray", # For use of SortingAnalyzer zarr format
+    "networkx",
     # for release we need pypi, so this needs to be commented
     "probeinterface @ git+https://github.com/SpikeInterface/probeinterface.git",  # We always build from the latest version
     "neo @ git+https://github.com/NeuralEnsemble/python-neo.git",  # We always build from the latest version

src/spikeinterface/core/analyzer_extension_core.py

@@ -488,9 +488,9 @@ def get_templates(self, unit_ids=None, operator="average", percentile=None, save
                 self.params["operators"] += [(operator, percentile)]
             templates_array = self.data[key]
 
-        if save:
-            if not self.sorting_analyzer.is_read_only():
-                self.save()
+            if save:
+                if not self.sorting_analyzer.is_read_only():
+                    self.save()
 
         if unit_ids is not None:
             unit_indices = self.sorting_analyzer.sorting.ids_to_indices(unit_ids)

src/spikeinterface/core/baserecording.py

@@ -491,7 +491,7 @@ def time_to_sample_index(self, time_s, segment_index=None):
         rs = self._recording_segments[segment_index]
         return rs.time_to_sample_index(time_s)
 
-    def _save(self, format="binary", **save_kwargs):
+    def _save(self, format="binary", verbose: bool = False, **save_kwargs):
         # handle t_starts
         t_starts = []
         has_time_vectors = []
@@ -510,7 +510,7 @@ def _save(self, format="binary", **save_kwargs):
             file_paths = [folder / f"traces_cached_seg{i}.raw" for i in range(self.get_num_segments())]
             dtype = kwargs.get("dtype", None) or self.get_dtype()
 
-            write_binary_recording(self, file_paths=file_paths, dtype=dtype, **job_kwargs)
+            write_binary_recording(self, file_paths=file_paths, dtype=dtype, verbose=verbose, **job_kwargs)
 
             from .binaryrecordingextractor import BinaryRecordingExtractor
 
@@ -540,14 +540,18 @@ def _save(self, format="binary", **save_kwargs):
 
                 cached = SharedMemoryRecording.from_recording(self, **job_kwargs)
             else:
+                from spikeinterface.core import NumpyRecording
+
                 cached = NumpyRecording.from_recording(self, **job_kwargs)
 
         elif format == "zarr":
             from .zarrextractors import ZarrRecordingExtractor
 
             zarr_path = kwargs.pop("zarr_path")
             storage_options = kwargs.pop("storage_options")
-            ZarrRecordingExtractor.write_recording(self, zarr_path, storage_options, **kwargs, **job_kwargs)
+            ZarrRecordingExtractor.write_recording(
+                self, zarr_path, storage_options, verbose=verbose, **kwargs, **job_kwargs
+            )
             cached = ZarrRecordingExtractor(zarr_path, storage_options)
 
         elif format == "nwb":

src/spikeinterface/core/job_tools.py

@@ -46,7 +46,6 @@
     "chunk_duration",
     "progress_bar",
     "mp_context",
-    "verbose",
     "max_threads_per_process",
 )
 
@@ -131,6 +130,8 @@ def split_job_kwargs(mixed_kwargs):
 def divide_segment_into_chunks(num_frames, chunk_size):
     if chunk_size is None:
         chunks = [(0, num_frames)]
+    elif chunk_size > num_frames:
+        chunks = [(0, num_frames)]
     else:
         n = num_frames // chunk_size
 
@@ -245,12 +246,12 @@ def ensure_chunk_size(
         else:
             raise ValueError("chunk_duration must be str or float")
     else:
+        # Edge case to define single chunk per segment for n_jobs=1.
+        # All chunking parameters equal None mean single chunk per segment
         if n_jobs == 1:
-            # not chunk computing
-            # TODO Discuss, Sam, is this something that we want to do?
-            # Even in single process mode, we should chunk the data to avoid loading the whole thing into memory I feel
-            # Am I wrong?
-            chunk_size = None
+            num_segments = recording.get_num_segments()
+            samples_in_larger_segment = max([recording.get_num_samples(segment) for segment in range(num_segments)])
+            chunk_size = samples_in_larger_segment
         else:
             raise ValueError("For n_jobs >1 you must specify total_memory or chunk_size or chunk_memory")
 

src/spikeinterface/core/recording_tools.py

@@ -73,6 +73,7 @@ def write_binary_recording(
     add_file_extension: bool = True,
     byte_offset: int = 0,
     auto_cast_uint: bool = True,
+    verbose: bool = False,
     **job_kwargs,
 ):
     """
@@ -98,6 +99,8 @@ def write_binary_recording(
     auto_cast_uint: bool, default: True
         If True, unsigned integers are automatically cast to int if the specified dtype is signed
         .. deprecated:: 0.103, use the `unsigned_to_signed` function instead.
+    verbose: bool
+        This is the verbosity of the ChunkRecordingExecutor
     {}
     """
     job_kwargs = fix_job_kwargs(job_kwargs)
@@ -138,7 +141,7 @@ def write_binary_recording(
     init_func = _init_binary_worker
     init_args = (recording, file_path_dict, dtype, byte_offset, cast_unsigned)
     executor = ChunkRecordingExecutor(
-        recording, func, init_func, init_args, job_name="write_binary_recording", **job_kwargs
+        recording, func, init_func, init_args, job_name="write_binary_recording", verbose=verbose, **job_kwargs
     )
     executor.run()
 
@@ -348,9 +351,6 @@ def write_memory_recording(recording, dtype=None, verbose=False, auto_cast_uint=
     else:
         init_args = (recording, arrays, None, None, dtype, cast_unsigned)
 
-    if "verbose" in job_kwargs:
-        del job_kwargs["verbose"]
-
     executor = ChunkRecordingExecutor(
         recording, func, init_func, init_args, verbose=verbose, job_name="write_memory_recording", **job_kwargs
     )

src/spikeinterface/core/sortinganalyzer.py

@@ -5,6 +5,7 @@
 from itertools import chain
 import os
 import json
+import math
 import pickle
 import weakref
 import shutil
@@ -237,7 +238,21 @@ def create(
         return_scaled=True,
     ):
         # some checks
-        assert sorting.sampling_frequency == recording.sampling_frequency
+        if sorting.sampling_frequency != recording.sampling_frequency:
+            if math.isclose(sorting.sampling_frequency, recording.sampling_frequency, abs_tol=1e-2, rel_tol=1e-5):
+                warnings.warn(
+                    "Sorting and Recording have a small difference in sampling frequency. "
+                    "This could be due to rounding of floats. Using the sampling frequency from the Recording."
+                )
+                # we make a copy here to change the smapling frequency
+                sorting = NumpySorting.from_sorting(sorting, with_metadata=True, copy_spike_vector=True)
+                sorting._sampling_frequency = recording.sampling_frequency
+            else:
+                raise ValueError(
+                    f"Sorting and Recording sampling frequencies are too different: "
+                    f"recording: {recording.sampling_frequency} - sorting: {sorting.sampling_frequency}. "
+                    "Ensure that you are associating the correct Recording and Sorting when creating a SortingAnalyzer."
+                )
         # check that multiple probes are non-overlapping
         all_probes = recording.get_probegroup().probes
         check_probe_do_not_overlap(all_probes)
@@ -570,9 +585,10 @@ def load_from_zarr(cls, folder, recording=None):
             rec_attributes["probegroup"] = None
 
         # sparsity
-        if "sparsity_mask" in zarr_root.attrs:
-            # sparsity = zarr_root.attrs["sparsity"]
-            sparsity = ChannelSparsity(zarr_root["sparsity_mask"], cls.unit_ids, rec_attributes["channel_ids"])
+        if "sparsity_mask" in zarr_root:
+            sparsity = ChannelSparsity(
+                np.array(zarr_root["sparsity_mask"]), sorting.unit_ids, rec_attributes["channel_ids"]
+            )
         else:
             sparsity = None
 
@@ -1581,10 +1597,6 @@ def load_data(self):
                 self.data[ext_data_name] = ext_data
 
         elif self.format == "zarr":
-            # Alessio
-            # TODO: we need decide if we make a copy to memory or keep the lazy loading. For binary_folder it used to be lazy with memmap
-            # but this make the garbage complicated when a data is hold by a plot but the o SortingAnalyzer is delete
-            # lets talk
             extension_group = self._get_zarr_extension_group(mode="r")
             for ext_data_name in extension_group.keys():
                 ext_data_ = extension_group[ext_data_name]
@@ -1600,7 +1612,8 @@ def load_data(self):
                 elif "object" in ext_data_.attrs:
                     ext_data = ext_data_[0]
                 else:
-                    ext_data = ext_data_
+                    # this load in memmory
+                    ext_data = np.array(ext_data_)
                 self.data[ext_data_name] = ext_data
 
     def copy(self, new_sorting_analyzer, unit_ids=None):

src/spikeinterface/core/sparsity.py

@@ -118,6 +118,14 @@ def __repr__(self):
         txt = f"ChannelSparsity - units: {self.num_units} - channels: {self.num_channels} - density, P(x=1): {density:0.2f}"
         return txt
 
+    def __eq__(self, other):
+        return (
+            isinstance(other, ChannelSparsity)
+            and np.array_equal(self.channel_ids, other.channel_ids)
+            and np.array_equal(self.unit_ids, other.unit_ids)
+            and np.array_equal(self.mask, other.mask)
+        )
+
     @property
     def unit_id_to_channel_ids(self):
         if self._unit_id_to_channel_ids is None:

src/spikeinterface/core/tests/test_job_tools.py

@@ -42,11 +42,9 @@ def test_ensure_n_jobs():
 
 
 def test_ensure_chunk_size():
-    recording = generate_recording(num_channels=2)
+    recording = generate_recording(num_channels=2, durations=[5.0, 2.5])  # This is the default value for two semgents
     dtype = recording.get_dtype()
     assert dtype == "float32"
-    # make serializable
-    recording = recording.save()
 
     chunk_size = ensure_chunk_size(recording, total_memory="512M", chunk_size=None, chunk_memory=None, n_jobs=2)
     assert chunk_size == 32000000
@@ -69,6 +67,15 @@ def test_ensure_chunk_size():
     chunk_size = ensure_chunk_size(recording, chunk_duration="500ms")
     assert chunk_size == 15000
 
+    # Test edge case to define single chunk for n_jobs=1
+    chunk_size = ensure_chunk_size(recording, n_jobs=1, chunk_size=None)
+    chunks = divide_recording_into_chunks(recording, chunk_size)
+    assert len(chunks) == recording.get_num_segments()
+    for chunk in chunks:
+        segment_index, start_frame, end_frame = chunk
+        assert start_frame == 0
+        assert end_frame == recording.get_num_frames(segment_index=segment_index)
+
 
 def func(segment_index, start_frame, end_frame, worker_ctx):
     import os

src/spikeinterface/core/tests/test_recording_tools.py

@@ -37,8 +37,8 @@ def test_write_binary_recording(tmp_path):
     file_paths = [tmp_path / "binary01.raw"]
 
     # Write binary recording
-    job_kwargs = dict(verbose=False, n_jobs=1)
-    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, **job_kwargs)
+    job_kwargs = dict(n_jobs=1)
+    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, verbose=False, **job_kwargs)
 
     # Check if written data matches original data
     recorder_binary = BinaryRecordingExtractor(
@@ -64,9 +64,11 @@ def test_write_binary_recording_offset(tmp_path):
     file_paths = [tmp_path / "binary01.raw"]
 
     # Write binary recording
-    job_kwargs = dict(verbose=False, n_jobs=1)
+    job_kwargs = dict(n_jobs=1)
     byte_offset = 125
-    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, byte_offset=byte_offset, **job_kwargs)
+    write_binary_recording(
+        recording, file_paths=file_paths, dtype=dtype, byte_offset=byte_offset, verbose=False, **job_kwargs
+    )
 
     # Check if written data matches original data
     recorder_binary = BinaryRecordingExtractor(
@@ -97,8 +99,8 @@ def test_write_binary_recording_parallel(tmp_path):
     file_paths = [tmp_path / "binary01.raw", tmp_path / "binary02.raw"]
 
     # Write binary recording
-    job_kwargs = dict(verbose=False, n_jobs=2, chunk_memory="100k", mp_context="spawn")
-    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, **job_kwargs)
+    job_kwargs = dict(n_jobs=2, chunk_memory="100k", mp_context="spawn")
+    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, verbose=False, **job_kwargs)
 
     # Check if written data matches original data
     recorder_binary = BinaryRecordingExtractor(
@@ -127,8 +129,8 @@ def test_write_binary_recording_multiple_segment(tmp_path):
     file_paths = [tmp_path / "binary01.raw", tmp_path / "binary02.raw"]
 
     # Write binary recording
-    job_kwargs = dict(verbose=False, n_jobs=2, chunk_memory="100k", mp_context="spawn")
-    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, **job_kwargs)
+    job_kwargs = dict(n_jobs=2, chunk_memory="100k", mp_context="spawn")
+    write_binary_recording(recording, file_paths=file_paths, dtype=dtype, verbose=False, **job_kwargs)
 
     # Check if written data matches original data
     recorder_binary = BinaryRecordingExtractor(

src/spikeinterface/core/tests/test_sortinganalyzer.py

@@ -155,10 +155,15 @@ def _check_sorting_analyzers(sorting_analyzer, original_sorting, cache_folder):
 
         data = sorting_analyzer2.get_extension("dummy").data
         assert "result_one" in data
+        assert isinstance(data["result_one"], str)
+        assert isinstance(data["result_two"], np.ndarray)
         assert data["result_two"].size == original_sorting.to_spike_vector().size
+        assert np.array_equal(data["result_two"], sorting_analyzer.get_extension("dummy").data["result_two"])
 
         assert sorting_analyzer2.return_scaled == sorting_analyzer.return_scaled
 
+        assert sorting_analyzer2.sparsity == sorting_analyzer.sparsity
+
     # select unit_ids to several format
     for format in ("memory", "binary_folder", "zarr"):
         if format != "memory":