From 26ac06f8f1e7c2c2699e0c634535d30332f88a0a Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Mon, 22 Jan 2024 15:55:49 +0100 Subject: [PATCH 01/90] WIP --- src/spikeinterface/core/template.py | 20 +++++++++++++++++++ src/spikeinterface/generation/drift_tools.py | 8 ++++++++ .../sorters/internal/spyking_circus2.py | 7 +++++++ 3 files changed, 35 insertions(+) diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py index 9a2a868f98..0a3e186940 100644 --- a/src/spikeinterface/core/template.py +++ b/src/spikeinterface/core/template.py @@ -165,6 +165,26 @@ def from_dict(cls, data): probe=data["probe"] if data["probe"] is None else Probe.from_dict(data["probe"]), ) + @classmethod + def from_waveform_extractor(cls, waveforms): + templates = waveforms.get_all_templates(mode='median') + sparsity_mask = waveforms.sparsity.mask + channel_ids = waveforms.recording.channel_ids + unit_ids = waveforms.unit_ids + sampling_frequency = waveforms.sampling_frequency + nbefore = waveforms.nbefore + probe = waveforms.recording.get_probe() + + return cls( + templates_array=templates, + sparsity_mask=sparsity_mask, + channel_ids=channel_ids, + unit_ids=unit_ids, + sampling_frequency=sampling_frequency, + nbefore=nbefore, + probe=probe, + ) + def to_json(self): from spikeinterface.core.core_tools import SIJsonEncoder diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index bb73e0fced..044f1d1f5f 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -513,3 +513,11 @@ def get_traces( def get_num_samples(self) -> int: return self.num_samples + + +def get_templates_from_recording(recording, num_templates=None, **sc2_params): + + from spikeinterface.sorters.runsorter import run_sorter + + templates = run_sorter('spykingcircus2', recording, templates_only=True, **sc2_params) + return templates \ No newline at end of file diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index c322d61230..70529e0f68 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -46,6 +46,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter): "multi_units_only": False, "job_kwargs": {"n_jobs": 0.8}, "debug": False, + "templates_only": False, } handle_multi_segment = True @@ -69,6 +70,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter): "cache_preprocessing": "How to cache the preprocessed recording. Mode can be memory, file, zarr, with extra arguments. In case of memory (default), \ memory_limit will control how much RAM can be used. In case of folder or zarr, delete_cache controls if cache is cleaned after sorting", "multi_units_only": "Boolean to get only multi units activity (i.e. one template per electrode)", + "templates_only" : "Boolean to return only the Templates after the clustering, without the matching step", "job_kwargs": "A dictionary to specify how many jobs and which parameters they should used", "debug": "Boolean to specify if the internal data structure should be kept for debugging", } @@ -226,6 +228,11 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): **waveforms_params, ) + if params["templates_only"]: + from spikeinterface.core.template import Templates + templates = Templates.from_waveform_extractor(we) + return templates + ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces matching_method = params["matching"]["method"] matching_params = params["matching"]["method_kwargs"].copy() From 4216503088bbda6a39819d07010c63655725a71c Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 23 Jan 2024 09:12:44 +0100 Subject: [PATCH 02/90] WIP --- src/spikeinterface/generation/drift_tools.py | 28 +++++++++++++++++--- 1 file changed, 25 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 044f1d1f5f..f4d05514ee 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -515,9 +515,31 @@ def get_num_samples(self) -> int: return self.num_samples -def get_templates_from_recording(recording, num_templates=None, **sc2_params): +def get_templates_from_recording(recording, num_templates=None, **sorter_params): from spikeinterface.sorters.runsorter import run_sorter - - templates = run_sorter('spykingcircus2', recording, templates_only=True, **sc2_params) + from spikeinterface.sorters.sorterlist import sorter_dict + from pathlib import Path + import json + + SorterClass = sorter_dict['spykingcircus2'] + output_folder = 'test' + verbose = True + remove_existing_folder = True + sorter_params['templates_only'] = True + + # only classmethod call not instance (stateless at instance level but state is in folder) + output_folder = SorterClass.initialize_folder(recording, output_folder, verbose, remove_existing_folder) + SorterClass.set_params_to_folder(recording, output_folder, sorter_params, verbose) + SorterClass.setup_recording(recording, output_folder, verbose=verbose) + + output_folder = Path(output_folder) + sorter_output_folder = output_folder / "sorter_output" + + # retrieve sorter_name and params + with (output_folder / "spikeinterface_params.json").open(mode="r") as f: + params = json.load(f) + sorter_params = params["sorter_params"] + + templates = SorterClass._run_from_folder(sorter_output_folder, sorter_params, verbose) return templates \ No newline at end of file From af485ea5dc74e5e73b020b9441a9ff0e5e4fbe8a Mon Sep 17 00:00:00 2001 From: Sebastien Date: Tue, 23 Jan 2024 11:47:20 +0100 Subject: [PATCH 03/90] Docstrings and cosmetics --- src/spikeinterface/generation/drift_tools.py | 30 +++++++++++++++++--- 1 file changed, 26 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index f4d05514ee..c09b82be03 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -515,17 +515,35 @@ def get_num_samples(self) -> int: return self.num_samples -def get_templates_from_recording(recording, num_templates=None, **sorter_params): +def get_templates_from_recording(recording, num_templates=None, output_folder=None, verbose=True, + remove_existing_folder=True, delete_output_folder=True, **sorter_params): + """ + Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) + with the only twist that the template matching step is not launch. Instead, a Template + object is returned based on the results of the clutering. + Parameters + ---------- + num_templates: int + The number of templates that should be kept + verbose: bool + **sorter_params: keyword arguments for `spyking_circus2` function + + Returns + ------- + templates: Templates + The found templates + """ from spikeinterface.sorters.runsorter import run_sorter from spikeinterface.sorters.sorterlist import sorter_dict + from spikeinterface.core.globals import get_global_tmp_folder from pathlib import Path import json SorterClass = sorter_dict['spykingcircus2'] - output_folder = 'test' - verbose = True - remove_existing_folder = True + if output_folder is None: + output_folder = get_global_tmp_folder() / 'templates' + sorter_params['templates_only'] = True # only classmethod call not instance (stateless at instance level but state is in folder) @@ -542,4 +560,8 @@ def get_templates_from_recording(recording, num_templates=None, **sorter_params) sorter_params = params["sorter_params"] templates = SorterClass._run_from_folder(sorter_output_folder, sorter_params, verbose) + + if delete_output_folder: + shutil.rmtree(sorter_output_folder) + return templates \ No newline at end of file From ddb1d512bef2dcdfd0cfab15d9add39827c64582 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Tue, 23 Jan 2024 11:48:06 +0100 Subject: [PATCH 04/90] WIP --- src/spikeinterface/generation/drift_tools.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index c09b82be03..f6e8252725 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -538,7 +538,7 @@ def get_templates_from_recording(recording, num_templates=None, output_folder=No from spikeinterface.sorters.sorterlist import sorter_dict from spikeinterface.core.globals import get_global_tmp_folder from pathlib import Path - import json + import json, shutil SorterClass = sorter_dict['spykingcircus2'] if output_folder is None: From 08ffeefd3967086b666042391b66c3a940186c8b Mon Sep 17 00:00:00 2001 From: Sebastien Date: Tue, 23 Jan 2024 11:50:55 +0100 Subject: [PATCH 05/90] WIP --- src/spikeinterface/generation/drift_tools.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index f6e8252725..eb6b64b877 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -526,6 +526,12 @@ def get_templates_from_recording(recording, num_templates=None, output_folder=No ---------- num_templates: int The number of templates that should be kept + output_folder: Path + The temporary folder where data are stored (None implies tmp folder is used) + remove_existing_folder: Path + Wheter or not the temporary folder should be overwritten (default True) + delete_output_folder: Path + Wheter or not the temporary folder should be deleted at the end (default True) verbose: bool **sorter_params: keyword arguments for `spyking_circus2` function From 0db15977f189136a66dd31fe6772c18edb6951e1 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 24 Jan 2024 14:31:14 +0000 Subject: [PATCH 06/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/core/template.py | 2 +- src/spikeinterface/generation/drift_tools.py | 63 ++++++++++--------- .../sorters/internal/spyking_circus2.py | 3 +- 3 files changed, 38 insertions(+), 30 deletions(-) diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py index 0a3e186940..0bb994b21d 100644 --- a/src/spikeinterface/core/template.py +++ b/src/spikeinterface/core/template.py @@ -167,7 +167,7 @@ def from_dict(cls, data): @classmethod def from_waveform_extractor(cls, waveforms): - templates = waveforms.get_all_templates(mode='median') + templates = waveforms.get_all_templates(mode="median") sparsity_mask = waveforms.sparsity.mask channel_ids = waveforms.recording.channel_ids unit_ids = waveforms.unit_ids diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index eb6b64b877..b451d1b8b5 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -515,48 +515,55 @@ def get_num_samples(self) -> int: return self.num_samples -def get_templates_from_recording(recording, num_templates=None, output_folder=None, verbose=True, - remove_existing_folder=True, delete_output_folder=True, **sorter_params): +def get_templates_from_recording( + recording, + num_templates=None, + output_folder=None, + verbose=True, + remove_existing_folder=True, + delete_output_folder=True, + **sorter_params, +): """ - Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) - with the only twist that the template matching step is not launch. Instead, a Template - object is returned based on the results of the clutering. + Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) + with the only twist that the template matching step is not launch. Instead, a Template + object is returned based on the results of the clutering. - Parameters - ---------- - num_templates: int - The number of templates that should be kept - output_folder: Path - The temporary folder where data are stored (None implies tmp folder is used) - remove_existing_folder: Path - Wheter or not the temporary folder should be overwritten (default True) - delete_output_folder: Path - Wheter or not the temporary folder should be deleted at the end (default True) - verbose: bool - **sorter_params: keyword arguments for `spyking_circus2` function + Parameters + ---------- + num_templates: int + The number of templates that should be kept + output_folder: Path + The temporary folder where data are stored (None implies tmp folder is used) + remove_existing_folder: Path + Wheter or not the temporary folder should be overwritten (default True) + delete_output_folder: Path + Wheter or not the temporary folder should be deleted at the end (default True) + verbose: bool + **sorter_params: keyword arguments for `spyking_circus2` function - Returns - ------- - templates: Templates - The found templates - """ + Returns + ------- + templates: Templates + The found templates + """ from spikeinterface.sorters.runsorter import run_sorter from spikeinterface.sorters.sorterlist import sorter_dict from spikeinterface.core.globals import get_global_tmp_folder from pathlib import Path import json, shutil - SorterClass = sorter_dict['spykingcircus2'] + SorterClass = sorter_dict["spykingcircus2"] if output_folder is None: - output_folder = get_global_tmp_folder() / 'templates' + output_folder = get_global_tmp_folder() / "templates" - sorter_params['templates_only'] = True + sorter_params["templates_only"] = True # only classmethod call not instance (stateless at instance level but state is in folder) output_folder = SorterClass.initialize_folder(recording, output_folder, verbose, remove_existing_folder) SorterClass.set_params_to_folder(recording, output_folder, sorter_params, verbose) SorterClass.setup_recording(recording, output_folder, verbose=verbose) - + output_folder = Path(output_folder) sorter_output_folder = output_folder / "sorter_output" @@ -564,10 +571,10 @@ def get_templates_from_recording(recording, num_templates=None, output_folder=No with (output_folder / "spikeinterface_params.json").open(mode="r") as f: params = json.load(f) sorter_params = params["sorter_params"] - + templates = SorterClass._run_from_folder(sorter_output_folder, sorter_params, verbose) if delete_output_folder: shutil.rmtree(sorter_output_folder) - return templates \ No newline at end of file + return templates diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 70529e0f68..aa7696535b 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -70,7 +70,7 @@ class Spykingcircus2Sorter(ComponentsBasedSorter): "cache_preprocessing": "How to cache the preprocessed recording. Mode can be memory, file, zarr, with extra arguments. In case of memory (default), \ memory_limit will control how much RAM can be used. In case of folder or zarr, delete_cache controls if cache is cleaned after sorting", "multi_units_only": "Boolean to get only multi units activity (i.e. one template per electrode)", - "templates_only" : "Boolean to return only the Templates after the clustering, without the matching step", + "templates_only": "Boolean to return only the Templates after the clustering, without the matching step", "job_kwargs": "A dictionary to specify how many jobs and which parameters they should used", "debug": "Boolean to specify if the internal data structure should be kept for debugging", } @@ -230,6 +230,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): if params["templates_only"]: from spikeinterface.core.template import Templates + templates = Templates.from_waveform_extractor(we) return templates From b89f6a3a002c4ba0c7d224ce173a7da7ea8a9ab6 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Thu, 25 Jan 2024 09:29:56 +0100 Subject: [PATCH 07/90] WIP --- src/spikeinterface/generation/drift_tools.py | 57 ----------- src/spikeinterface/generation/hybrid_tools.py | 54 +++++++++++ .../sorters/internal/spyking_circus2.py | 97 +++++++++---------- 3 files changed, 101 insertions(+), 107 deletions(-) create mode 100644 src/spikeinterface/generation/hybrid_tools.py diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index eb6b64b877..7a74dafec6 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -514,60 +514,3 @@ def get_traces( def get_num_samples(self) -> int: return self.num_samples - -def get_templates_from_recording(recording, num_templates=None, output_folder=None, verbose=True, - remove_existing_folder=True, delete_output_folder=True, **sorter_params): - """ - Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) - with the only twist that the template matching step is not launch. Instead, a Template - object is returned based on the results of the clutering. - - Parameters - ---------- - num_templates: int - The number of templates that should be kept - output_folder: Path - The temporary folder where data are stored (None implies tmp folder is used) - remove_existing_folder: Path - Wheter or not the temporary folder should be overwritten (default True) - delete_output_folder: Path - Wheter or not the temporary folder should be deleted at the end (default True) - verbose: bool - **sorter_params: keyword arguments for `spyking_circus2` function - - Returns - ------- - templates: Templates - The found templates - """ - from spikeinterface.sorters.runsorter import run_sorter - from spikeinterface.sorters.sorterlist import sorter_dict - from spikeinterface.core.globals import get_global_tmp_folder - from pathlib import Path - import json, shutil - - SorterClass = sorter_dict['spykingcircus2'] - if output_folder is None: - output_folder = get_global_tmp_folder() / 'templates' - - sorter_params['templates_only'] = True - - # only classmethod call not instance (stateless at instance level but state is in folder) - output_folder = SorterClass.initialize_folder(recording, output_folder, verbose, remove_existing_folder) - SorterClass.set_params_to_folder(recording, output_folder, sorter_params, verbose) - SorterClass.setup_recording(recording, output_folder, verbose=verbose) - - output_folder = Path(output_folder) - sorter_output_folder = output_folder / "sorter_output" - - # retrieve sorter_name and params - with (output_folder / "spikeinterface_params.json").open(mode="r") as f: - params = json.load(f) - sorter_params = params["sorter_params"] - - templates = SorterClass._run_from_folder(sorter_output_folder, sorter_params, verbose) - - if delete_output_folder: - shutil.rmtree(sorter_output_folder) - - return templates \ No newline at end of file diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py new file mode 100644 index 0000000000..0eb23266b8 --- /dev/null +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -0,0 +1,54 @@ +from __future__ import annotations +from typing import Optional + +import numpy as np +from spikeinterface.core import Templates, BaseRecording, BaseSorting, BaseRecordingSegment +import math + + + +def estimate_templates_from_recording(recording, + ms_before=2, + ms_after=2, + sorter_name='spykingcircus2', + sorter_params={'remove_existing_folder' : True, 'verbose' : False}, + run_sorter_params=None, + **job_kwargs): + """ + Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) + with the only twist that the template matching step is not launch. Instead, a Template + object is returned based on the results of the clutering. + + Parameters + ---------- + + + sorter_params: keyword arguments for `spyking_circus2` function + + Returns + ------- + templates: Templates + The found templates + """ + from spikeinterface.sorters.runsorter import run_sorter + from spikeinterface.sorters.sorterlist import sorter_dict + from spikeinterface.core.globals import get_global_tmp_folder + from pathlib import Path + import json, shutil + + if run_sorter_params is None: + run_sorter_params = {} + + sorter_params.update({'templates_only' : True}) + sorting = run_sorter(sorter_name, recording, **run_sorter_params, **sorter_params) + + from spikeinterface.core.waveform_tools import estimate_templates + sampling_frequency = recording.get_sampling_frequency() + nbefore = int(ms_before * sampling_frequency / 1000.) + nafter = int(ms_after * sampling_frequency / 1000.) + + print(job_kwargs) + spikes = sorting.to_spike_vector() + templates = estimate_templates(recording, spikes, np.unique(spikes["unit_index"]), nbefore, nafter, **job_kwargs) + + return templates \ No newline at end of file diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 70529e0f68..0f39c5dea6 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -218,57 +218,54 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): mode = "folder" waveforms_folder = sorter_output_folder / "waveforms" - we = extract_waveforms( - recording_f, - sorting, - waveforms_folder, - return_scaled=False, - precompute_template=["median"], - mode=mode, - **waveforms_params, - ) - - if params["templates_only"]: - from spikeinterface.core.template import Templates - templates = Templates.from_waveform_extractor(we) - return templates - - ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces - matching_method = params["matching"]["method"] - matching_params = params["matching"]["method_kwargs"].copy() - matching_job_params = {} - matching_job_params.update(job_kwargs) - if matching_method == "wobble": - matching_params["templates"] = we.get_all_templates(mode="median") - matching_params["nbefore"] = we.nbefore - matching_params["nafter"] = we.nafter - else: - matching_params["waveform_extractor"] = we + if not params["templates_only"]: + + we = extract_waveforms( + recording_f, + sorting, + waveforms_folder, + return_scaled=False, + precompute_template=["median"], + mode=mode, + **waveforms_params, + ) + + ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces + matching_method = params["matching"]["method"] + matching_params = params["matching"]["method_kwargs"].copy() + matching_job_params = {} + matching_job_params.update(job_kwargs) + if matching_method == "wobble": + matching_params["templates"] = we.get_all_templates(mode="median") + matching_params["nbefore"] = we.nbefore + matching_params["nafter"] = we.nafter + else: + matching_params["waveform_extractor"] = we + + if matching_method == "circus-omp-svd": + for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]: + if value in matching_job_params: + matching_job_params.pop(value) + matching_job_params["chunk_duration"] = "100ms" + + spikes = find_spikes_from_templates( + recording_f, matching_method, method_kwargs=matching_params, **matching_job_params + ) + + if params["debug"]: + fitting_folder = sorter_output_folder / "fitting" + fitting_folder.mkdir(parents=True, exist_ok=True) + np.save(fitting_folder / "spikes", spikes) - if matching_method == "circus-omp-svd": - for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]: - if value in matching_job_params: - matching_job_params.pop(value) - matching_job_params["chunk_duration"] = "100ms" - - spikes = find_spikes_from_templates( - recording_f, matching_method, method_kwargs=matching_params, **matching_job_params - ) - - if params["debug"]: - fitting_folder = sorter_output_folder / "fitting" - fitting_folder.mkdir(parents=True, exist_ok=True) - np.save(fitting_folder / "spikes", spikes) - - if verbose: - print("We found %d spikes" % len(spikes)) - - ## And this is it! We have a spyking circus - sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) - sorting["sample_index"] = spikes["sample_index"] - sorting["unit_index"] = spikes["cluster_index"] - sorting["segment_index"] = spikes["segment_index"] - sorting = NumpySorting(sorting, sampling_frequency, unit_ids) + if verbose: + print("We found %d spikes" % len(spikes)) + + ## And this is it! We have a spyking circus + sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) + sorting["sample_index"] = spikes["sample_index"] + sorting["unit_index"] = spikes["cluster_index"] + sorting["segment_index"] = spikes["segment_index"] + sorting = NumpySorting(sorting, sampling_frequency, unit_ids) sorting_folder = sorter_output_folder / "sorting" if sorting_folder.exists(): From 84b60acd214fe7b0326a8465a42e2f92bd19139e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 25 Jan 2024 08:31:24 +0000 Subject: [PATCH 08/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 54 ++++++++++--------- .../sorters/internal/spyking_circus2.py | 1 - 2 files changed, 28 insertions(+), 27 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 0eb23266b8..8ee4fb370f 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -6,30 +6,31 @@ import math - -def estimate_templates_from_recording(recording, - ms_before=2, - ms_after=2, - sorter_name='spykingcircus2', - sorter_params={'remove_existing_folder' : True, 'verbose' : False}, - run_sorter_params=None, - **job_kwargs): +def estimate_templates_from_recording( + recording, + ms_before=2, + ms_after=2, + sorter_name="spykingcircus2", + sorter_params={"remove_existing_folder": True, "verbose": False}, + run_sorter_params=None, + **job_kwargs, +): """ - Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) - with the only twist that the template matching step is not launch. Instead, a Template - object is returned based on the results of the clutering. + Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) + with the only twist that the template matching step is not launch. Instead, a Template + object is returned based on the results of the clutering. + + Parameters + ---------- - Parameters - ---------- - - sorter_params: keyword arguments for `spyking_circus2` function + sorter_params: keyword arguments for `spyking_circus2` function - Returns - ------- - templates: Templates - The found templates - """ + Returns + ------- + templates: Templates + The found templates + """ from spikeinterface.sorters.runsorter import run_sorter from spikeinterface.sorters.sorterlist import sorter_dict from spikeinterface.core.globals import get_global_tmp_folder @@ -39,16 +40,17 @@ def estimate_templates_from_recording(recording, if run_sorter_params is None: run_sorter_params = {} - sorter_params.update({'templates_only' : True}) + sorter_params.update({"templates_only": True}) sorting = run_sorter(sorter_name, recording, **run_sorter_params, **sorter_params) - + from spikeinterface.core.waveform_tools import estimate_templates + sampling_frequency = recording.get_sampling_frequency() - nbefore = int(ms_before * sampling_frequency / 1000.) - nafter = int(ms_after * sampling_frequency / 1000.) + nbefore = int(ms_before * sampling_frequency / 1000.0) + nafter = int(ms_after * sampling_frequency / 1000.0) print(job_kwargs) spikes = sorting.to_spike_vector() templates = estimate_templates(recording, spikes, np.unique(spikes["unit_index"]), nbefore, nafter, **job_kwargs) - - return templates \ No newline at end of file + + return templates diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 562cfa4492..0efa1c3562 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -219,7 +219,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): waveforms_folder = sorter_output_folder / "waveforms" if not params["templates_only"]: - we = extract_waveforms( recording_f, sorting, From f5f34355ceeb6de0ecf04a28e1a4b5dc70c9f231 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 25 Jan 2024 09:50:50 +0100 Subject: [PATCH 09/90] WIP --- src/spikeinterface/generation/hybrid_tools.py | 31 ++++++++++++------- .../sorters/internal/spyking_circus2.py | 6 ++-- 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 0eb23266b8..04c4b4b0c5 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -11,7 +11,7 @@ def estimate_templates_from_recording(recording, ms_before=2, ms_after=2, sorter_name='spykingcircus2', - sorter_params={'remove_existing_folder' : True, 'verbose' : False}, + sorter_params=None, run_sorter_params=None, **job_kwargs): """ @@ -31,24 +31,33 @@ def estimate_templates_from_recording(recording, The found templates """ from spikeinterface.sorters.runsorter import run_sorter - from spikeinterface.sorters.sorterlist import sorter_dict - from spikeinterface.core.globals import get_global_tmp_folder - from pathlib import Path - import json, shutil + from spikeinterface.core.template import Templates + from spikeinterface.core.waveform_tools import estimate_templates if run_sorter_params is None: - run_sorter_params = {} + run_sorter_params = {'remove_existing_folder' : True, 'verbose' : False} + + if sorter_params is None: + sorter_params = {} + + sorter_params.update({'matching' : {"method" : None}}) + - sorter_params.update({'templates_only' : True}) sorting = run_sorter(sorter_name, recording, **run_sorter_params, **sorter_params) - - from spikeinterface.core.waveform_tools import estimate_templates + sampling_frequency = recording.get_sampling_frequency() nbefore = int(ms_before * sampling_frequency / 1000.) nafter = int(ms_after * sampling_frequency / 1000.) - print(job_kwargs) spikes = sorting.to_spike_vector() - templates = estimate_templates(recording, spikes, np.unique(spikes["unit_index"]), nbefore, nafter, **job_kwargs) + unit_ids = np.unique(spikes["unit_index"]) + templates_array = estimate_templates(recording, spikes, unit_ids, nbefore, nafter, **job_kwargs) + sparsity_mask = None + channel_ids = recording.channel_ids + sampling_frequency = recording.get_sampling_frequency() + probe = recording.get_probe() + + templates = Templates(templates_array, sampling_frequency, nbefore, sparsity_mask, channel_ids, unit_ids, probe=probe) + return templates \ No newline at end of file diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 562cfa4492..926e6f410a 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -46,7 +46,6 @@ class Spykingcircus2Sorter(ComponentsBasedSorter): "multi_units_only": False, "job_kwargs": {"n_jobs": 0.8}, "debug": False, - "templates_only": False, } handle_multi_segment = True @@ -218,7 +217,9 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): mode = "folder" waveforms_folder = sorter_output_folder / "waveforms" - if not params["templates_only"]: + matching_method = params["matching"]["method"] + + if matching_method is not None: we = extract_waveforms( recording_f, @@ -231,7 +232,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): ) ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces - matching_method = params["matching"]["method"] matching_params = params["matching"]["method_kwargs"].copy() matching_job_params = {} matching_job_params.update(job_kwargs) From a5b12b0a59a180f2c85e82ce7c6f356df1e211dc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 25 Jan 2024 09:12:14 +0000 Subject: [PATCH 10/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 24 +++++++++---------- .../sorters/internal/spyking_circus2.py | 1 - 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 5757c15f95..f36c45e437 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -7,13 +7,8 @@ from spikeinterface.core.job_tools import split_job_kwargs - def estimate_templates_from_recording( - recording, - ms_before=2, - ms_after=2, - sorter_name="spykingcircus2", - **run_sorter_kwargs + recording, ms_before=2, ms_after=2, sorter_name="spykingcircus2", **run_sorter_kwargs ): """ Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) @@ -30,7 +25,7 @@ def estimate_templates_from_recording( The sorter to be used in order to get some fast clustering run_sorter_kwargs: dict The parameters to provide to the run_sorter function of spikeinterface - + sorter_params: keyword arguments for `spyking_circus2` function @@ -43,26 +38,29 @@ def estimate_templates_from_recording( from spikeinterface.core.template import Templates from spikeinterface.core.waveform_tools import estimate_templates - if sorter_name == 'spykingcircus2': + if sorter_name == "spykingcircus2": if "matching" not in run_sorter_kwargs: - run_sorter_kwargs['matching'] = {"method" : None} + run_sorter_kwargs["matching"] = {"method": None} sorting = run_sorter(sorter_name, recording, **run_sorter_kwargs) from spikeinterface.core.waveform_tools import estimate_templates + spikes = sorting.to_spike_vector() unit_ids = np.unique(spikes["unit_index"]) sampling_frequency = recording.get_sampling_frequency() - nbefore = int(ms_before * sampling_frequency / 1000.) - nafter = int(ms_after * sampling_frequency / 1000.) + nbefore = int(ms_before * sampling_frequency / 1000.0) + nafter = int(ms_after * sampling_frequency / 1000.0) _, job_kwargs = split_job_kwargs(run_sorter_kwargs) templates_array = estimate_templates(recording, spikes, unit_ids, nbefore, nafter, **job_kwargs) - + sparsity_mask = None channel_ids = recording.channel_ids probe = recording.get_probe() - templates = Templates(templates_array, sampling_frequency, nbefore, sparsity_mask, channel_ids, unit_ids, probe=probe) + templates = Templates( + templates_array, sampling_frequency, nbefore, sparsity_mask, channel_ids, unit_ids, probe=probe + ) return templates diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 926e6f410a..17027bc4cb 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -220,7 +220,6 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): matching_method = params["matching"]["method"] if matching_method is not None: - we = extract_waveforms( recording_f, sorting, From ae285a45c7e8d1ec2461b7fba2daba3ad31420fc Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 30 Jan 2024 12:53:59 +0100 Subject: [PATCH 11/90] WIP --- src/spikeinterface/sorters/internal/spyking_circus2.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 17027bc4cb..e661c7bda3 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -69,7 +69,6 @@ class Spykingcircus2Sorter(ComponentsBasedSorter): "cache_preprocessing": "How to cache the preprocessed recording. Mode can be memory, file, zarr, with extra arguments. In case of memory (default), \ memory_limit will control how much RAM can be used. In case of folder or zarr, delete_cache controls if cache is cleaned after sorting", "multi_units_only": "Boolean to get only multi units activity (i.e. one template per electrode)", - "templates_only": "Boolean to return only the Templates after the clustering, without the matching step", "job_kwargs": "A dictionary to specify how many jobs and which parameters they should used", "debug": "Boolean to specify if the internal data structure should be kept for debugging", } From 2b8656f63414d3a6fa4cc813a2f75079a5b89226 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 12 Mar 2024 14:07:25 +0100 Subject: [PATCH 12/90] WIP --- .../sorters/internal/spyking_circus2.py | 54 +++++++------------ 1 file changed, 18 insertions(+), 36 deletions(-) diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 164b956850..3b65e7b65c 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -230,47 +230,29 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): matching_job_params = job_kwargs.copy() if matching_method == "circus-omp-svd": - for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]: if value in matching_job_params: - matching_job_params[value] = None + matching_job_params.pop(value) matching_job_params["chunk_duration"] = "100ms" - ## We launch a OMP matching pursuit by full convolution of the templates and the raw traces - matching_params = params["matching"]["method_kwargs"].copy() - matching_job_params = {} - matching_job_params.update(job_kwargs) - if matching_method == "wobble": - matching_params["templates"] = we.get_all_templates(mode="median") - matching_params["nbefore"] = we.nbefore - matching_params["nafter"] = we.nafter - else: - matching_params["waveform_extractor"] = we - - if matching_method == "circus-omp-svd": - for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]: - if value in matching_job_params: - matching_job_params.pop(value) - matching_job_params["chunk_duration"] = "100ms" - - spikes = find_spikes_from_templates( - recording_f, matching_method, method_kwargs=matching_params, **matching_job_params - ) - - if params["debug"]: - fitting_folder = sorter_output_folder / "fitting" - fitting_folder.mkdir(parents=True, exist_ok=True) - np.save(fitting_folder / "spikes", spikes) + spikes = find_spikes_from_templates( + recording_f, matching_method, method_kwargs=matching_params, **matching_job_params + ) - if verbose: - print("We found %d spikes" % len(spikes)) - - ## And this is it! We have a spyking circus - sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) - sorting["sample_index"] = spikes["sample_index"] - sorting["unit_index"] = spikes["cluster_index"] - sorting["segment_index"] = spikes["segment_index"] - sorting = NumpySorting(sorting, sampling_frequency, unit_ids) + if params["debug"]: + fitting_folder = sorter_output_folder / "fitting" + fitting_folder.mkdir(parents=True, exist_ok=True) + np.save(fitting_folder / "spikes", spikes) + + if verbose: + print("We found %d spikes" % len(spikes)) + + ## And this is it! We have a spyking circus + sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) + sorting["sample_index"] = spikes["sample_index"] + sorting["unit_index"] = spikes["cluster_index"] + sorting["segment_index"] = spikes["segment_index"] + sorting = NumpySorting(sorting, sampling_frequency, unit_ids) sorting_folder = sorter_output_folder / "sorting" if sorting_folder.exists(): From b2365d4318407da556cab0c2c7a4ffecf9b81a06 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 12 Mar 2024 14:09:21 +0100 Subject: [PATCH 13/90] WIP --- src/spikeinterface/core/template.py | 20 ------------------- .../sorters/internal/spyking_circus2.py | 2 +- 2 files changed, 1 insertion(+), 21 deletions(-) diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py index af08c8ac9b..d85faa7513 100644 --- a/src/spikeinterface/core/template.py +++ b/src/spikeinterface/core/template.py @@ -191,26 +191,6 @@ def from_dict(cls, data): probe=data["probe"] if data["probe"] is None else Probe.from_dict(data["probe"]), ) - @classmethod - def from_waveform_extractor(cls, waveforms): - templates = waveforms.get_all_templates(mode="median") - sparsity_mask = waveforms.sparsity.mask - channel_ids = waveforms.recording.channel_ids - unit_ids = waveforms.unit_ids - sampling_frequency = waveforms.sampling_frequency - nbefore = waveforms.nbefore - probe = waveforms.recording.get_probe() - - return cls( - templates_array=templates, - sparsity_mask=sparsity_mask, - channel_ids=channel_ids, - unit_ids=unit_ids, - sampling_frequency=sampling_frequency, - nbefore=nbefore, - probe=probe, - ) - def add_templates_to_zarr_group(self, zarr_group: "zarr.Group") -> None: """ Adds a serialized version of the object to a given Zarr group. diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index 3b65e7b65c..11a65a46c0 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -232,7 +232,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): if matching_method == "circus-omp-svd": for value in ["chunk_size", "chunk_memory", "total_memory", "chunk_duration"]: if value in matching_job_params: - matching_job_params.pop(value) + matching_job_params[value] = None matching_job_params["chunk_duration"] = "100ms" spikes = find_spikes_from_templates( From 6a1673dcc35728f1b601adf01ae0a378e2665d27 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 09:42:33 +0100 Subject: [PATCH 14/90] WIP --- .../sorters/internal/spyking_circus2.py | 32 +++++++++---------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index a05df0e6a2..2fd086c053 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -235,24 +235,24 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): matching_job_params[value] = None matching_job_params["chunk_duration"] = "100ms" - spikes = find_spikes_from_templates( - recording_f, matching_method, method_kwargs=matching_params, **matching_job_params - ) + spikes = find_spikes_from_templates( + recording_f, matching_method, method_kwargs=matching_params, **matching_job_params + ) - if params["debug"]: - fitting_folder = sorter_output_folder / "fitting" - fitting_folder.mkdir(parents=True, exist_ok=True) - np.save(fitting_folder / "spikes", spikes) + if params["debug"]: + fitting_folder = sorter_output_folder / "fitting" + fitting_folder.mkdir(parents=True, exist_ok=True) + np.save(fitting_folder / "spikes", spikes) - if verbose: - print("We found %d spikes" % len(spikes)) - - ## And this is it! We have a spyking circus - sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) - sorting["sample_index"] = spikes["sample_index"] - sorting["unit_index"] = spikes["cluster_index"] - sorting["segment_index"] = spikes["segment_index"] - sorting = NumpySorting(sorting, sampling_frequency, unit_ids) + if verbose: + print("We found %d spikes" % len(spikes)) + + ## And this is it! We have a spyking circus + sorting = np.zeros(spikes.size, dtype=minimum_spike_dtype) + sorting["sample_index"] = spikes["sample_index"] + sorting["unit_index"] = spikes["cluster_index"] + sorting["segment_index"] = spikes["segment_index"] + sorting = NumpySorting(sorting, sampling_frequency, unit_ids) sorting_folder = sorter_output_folder / "sorting" if sorting_folder.exists(): From 774288207b928e46fa6ee00371ec9cd20869b86b Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 10:07:15 +0100 Subject: [PATCH 15/90] WIP --- src/spikeinterface/generation/hybrid_tools.py | 100 +++++++++++++++++- 1 file changed, 98 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index f36c45e437..420e8e9248 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -2,8 +2,9 @@ from typing import Optional import numpy as np -from spikeinterface.core import Templates, BaseRecording, BaseSorting, BaseRecordingSegment -import math +from spikeinterface.core.template import Templates +from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording +from spikeinterface.core.generate import generate_templates, generate_unit_locations, _ensure_seed, generate_sorting from spikeinterface.core.job_tools import split_job_kwargs @@ -64,3 +65,98 @@ def estimate_templates_from_recording( ) return templates + + +def generate_hybrid_recording(recording, motion=None, num_units=10, + sorting=None, + templates=None, + ms_before=1.0, + ms_after=3.0, + upsample_factor=None, + upsample_vector=None, + sorting_kwargs={'seed' : 2205}, + unit_locations_kwargs={'seed' : 2205}, + generate_templates_kwargs={'seed' : 2205}, + seed=None): + + # if None so the same seed will be used for all steps + seed = _ensure_seed(seed) + rng = np.random.default_rng(seed) + + fs = recording.sampling_frequency + probe = recording.get_probe() + + sorting = generate_sorting( + num_units=num_units, + sampling_frequency=fs, + durations = [recording.get_duration()], + **sorting_kwargs) + + num_spikes = sorting.to_spike_vector().size + + if templates is None: + channel_locations = probe.contact_positions + unit_locations = generate_unit_locations(num_units, channel_locations, **unit_locations_kwargs) + templates_array = generate_templates(channel_locations, unit_locations, **generate_templates_kwargs) + + nbefore = int(ms_before * sampling_frequency / 1000.0) + nafter = int(ms_after * sampling_frequency / 1000.0) + assert (nbefore + nafter) == templates.shape[1] + + + if templates.ndim == 3: + upsample_vector = None + else: + if upsample_vector is None: + upsample_factor = templates.shape[3] + upsample_vector = rng.integers(0, upsample_factor, size=num_spikes) + + if motion is not None: + num_displacement = displacements.shape[0] + templates_array_moved = np.zeros(shape=(num_displacement, ) + templates_array.shape, dtype=templates_array.dtype) + for i in range(num_displacement): + unit_locations_moved = unit_locations.copy() + unit_locations_moved[:, :2] += displacements[i, :][np.newaxis, :] + templates_array_moved[i, :, :, :] = generate_templates(channel_locations, unit_locations_moved, **generate_templates_kwargs) + + templates = Templates( + templates_array=templates_array, + sampling_frequency=fs, + nbefore=nbefore, + probe=probe, + ) + + # if drift_amplitude > 0: + # start = np.array([0, -drift_amplitude/2]) + # stop = np.array([0, drift_amplitude/2]) + # num_step = int(drift_amplitude) * 2 + 1 + # # print('num_step', num_step) + # displacements = make_linear_displacement(start, stop, num_step=num_step) + # else: + # displacements = np.zeros((1, 2)) + # start = np.array([0, 0]) + # stop = np.array([0, 0]) + + + hybrid_recording = InjectDriftingTemplatesRecording( + sorting=sorting, + parent_recording=recording, + drifting_templates=drifting_templates, + displacement_vectors=[displacement_vectors], + displacement_sampling_frequency=displacement_sampling_frequency, + displacement_unit_factor=displacement_unit_factor, + num_samples=[int(duration*sampling_frequency)], + amplitude_factor=None, + ) + + else: + + hybrid_recording = InjectTemplatesRecording( + sorting, + templates, + nbefore=nbefore, + parent_recording=recording, + upsample_vector=upsample_vector, + ) + + return hybrid_recording, sorting \ No newline at end of file From 36ec32d95922502fc227c1144f48d11af2d41475 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 10:41:49 +0100 Subject: [PATCH 16/90] WIP --- src/spikeinterface/generation/hybrid_tools.py | 154 +++++++++++++----- 1 file changed, 109 insertions(+), 45 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 420e8e9248..4f8f8fc4b6 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -3,8 +3,8 @@ import numpy as np from spikeinterface.core.template import Templates -from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording -from spikeinterface.core.generate import generate_templates, generate_unit_locations, _ensure_seed, generate_sorting +from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording, DriftingTemplates +from spikeinterface.core.generate import generate_templates, generate_unit_locations, _ensure_seed, generate_sorting, InjectTemplatesRecording from spikeinterface.core.job_tools import split_job_kwargs @@ -67,76 +67,140 @@ def estimate_templates_from_recording( return templates -def generate_hybrid_recording(recording, motion=None, num_units=10, +def generate_hybrid_recording(recording, + motion_info=None, + num_units=10, sorting=None, templates=None, ms_before=1.0, ms_after=3.0, upsample_factor=None, upsample_vector=None, - sorting_kwargs={'seed' : 2205}, - unit_locations_kwargs={'seed' : 2205}, - generate_templates_kwargs={'seed' : 2205}, + generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), + generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), + generate_templates_kwargs=dict(), seed=None): + """ + Generate an hybrid recording with spike given sorting+templates. + + Parameters + ---------- + recording: BaseRecording + The recording to inject units in + motion_info: All the information about the motion + The motion datastructure of the recording + num_units: int, default: 10 + Number of units, not used when sorting is given. + sorting: Sorting or None + An external sorting object. If not provide, one is genrated. + templates: np.array or None + The templates of units. + If None they are generated. + Shape can be: + * (num_units, num_samples, num_channels): standard case + * (num_units, num_samples, num_channels, upsample_factor): case with oversample template to introduce jitter. + ms_before: float, default: 1.5 + Cut out in ms before spike peak. + ms_after: float, default: 3 + Cut out in ms after spike peak. + upsample_factor: None or int, default: None + A upsampling factor used only when templates are not provided. + upsample_vector: np.array or None + Optional the upsample_vector can given. This has the same shape as spike_vector + generate_sorting_kwargs: dict + When sorting is not provide, this dict is used to generated a Sorting. + generate_unit_locations_kwargs: dict + Dict used to generated template when template not provided. + generate_templates_kwargs: dict + Dict used to generated template when template not provided. + seed: int or None + Seed for random initialization. + If None a diffrent Recording is generated at every call. + Note: even with None a generated recording keep internaly a seed to regenerate the same signal after dump/load. + + Returns + ------- + recording: Recording + The generated recording extractor. + sorting: Sorting + The generated sorting extractor. + """ # if None so the same seed will be used for all steps seed = _ensure_seed(seed) rng = np.random.default_rng(seed) - fs = recording.sampling_frequency + sampling_frequency = recording.sampling_frequency probe = recording.get_probe() - - sorting = generate_sorting( - num_units=num_units, - sampling_frequency=fs, - durations = [recording.get_duration()], - **sorting_kwargs) + num_segments = recording.get_num_segments() + dtype = recording.dtype + durations = np.array([recording.get_duration(segment_index) for segment_index in range(num_segments)]) + + if sorting is None: + generate_sorting_kwargs = generate_sorting_kwargs.copy() + generate_sorting_kwargs["durations"] = durations + generate_sorting_kwargs["num_units"] = num_units + generate_sorting_kwargs["sampling_frequency"] = sampling_frequency + generate_sorting_kwargs["seed"] = seed + sorting = generate_sorting(**generate_sorting_kwargs) + else: + num_units = sorting.get_num_units() + assert sorting.sampling_frequency == sampling_frequency num_spikes = sorting.to_spike_vector().size if templates is None: channel_locations = probe.contact_positions - unit_locations = generate_unit_locations(num_units, channel_locations, **unit_locations_kwargs) - templates_array = generate_templates(channel_locations, unit_locations, **generate_templates_kwargs) + unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) + templates_array = generate_templates(channel_locations, + unit_locations, + sampling_frequency, + ms_before, + ms_after, + upsample_factor=upsample_factor, + seed=seed, + dtype=dtype, + **generate_templates_kwargs) + sorting.set_property("gt_unit_locations", unit_locations) nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) - assert (nbefore + nafter) == templates.shape[1] - + assert (nbefore + nafter) == templates_array.shape[1] - if templates.ndim == 3: + if templates_array.ndim == 3: upsample_vector = None else: if upsample_vector is None: upsample_factor = templates.shape[3] upsample_vector = rng.integers(0, upsample_factor, size=num_spikes) - if motion is not None: - num_displacement = displacements.shape[0] - templates_array_moved = np.zeros(shape=(num_displacement, ) + templates_array.shape, dtype=templates_array.dtype) - for i in range(num_displacement): - unit_locations_moved = unit_locations.copy() - unit_locations_moved[:, :2] += displacements[i, :][np.newaxis, :] - templates_array_moved[i, :, :, :] = generate_templates(channel_locations, unit_locations_moved, **generate_templates_kwargs) - - templates = Templates( - templates_array=templates_array, - sampling_frequency=fs, - nbefore=nbefore, - probe=probe, - ) + if motion_info is not None: - # if drift_amplitude > 0: - # start = np.array([0, -drift_amplitude/2]) - # stop = np.array([0, drift_amplitude/2]) - # num_step = int(drift_amplitude) * 2 + 1 - # # print('num_step', num_step) - # displacements = make_linear_displacement(start, stop, num_step=num_step) - # else: - # displacements = np.zeros((1, 2)) - # start = np.array([0, 0]) - # stop = np.array([0, 0]) - + templates = Templates(templates_array, + sampling_frequency, + nbefore, + None, + None, + None, + probe) + + start = np.array([0, np.min(motion_info['motion'])]) + stop = np.array([0, np.max(motion_info['motion'])]) + displacements = make_linear_displacement(start, stop, num_step=int((stop-start)[1])) + drifting_templates = DriftingTemplates.from_static(templates) + drifting_templates.precompute_displacements(displacements) + + displacement_sampling_frequency = 1./np.diff(motion_info['temporal_bins'])[0] + displacement_vectors = np.zeros((len(motion_info['temporal_bins']), 2, len(motion_info['spatial_bins']))) + displacement_unit_factor = np.zeros((num_units, len(motion_info['spatial_bins']))) + + for count, i in enumerate(motion_info['spatial_bins']): + local_motion = motion_info['motion'][:, count] + displacement_vectors[:, 1, count] = local_motion + + for count in range(num_units): + a = 1/np.abs((unit_locations[count, 1] - motion_info['spatial_bins'])) + displacement_unit_factor[count] = a/a.sum() hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, @@ -145,7 +209,7 @@ def generate_hybrid_recording(recording, motion=None, num_units=10, displacement_vectors=[displacement_vectors], displacement_sampling_frequency=displacement_sampling_frequency, displacement_unit_factor=displacement_unit_factor, - num_samples=[int(duration*sampling_frequency)], + num_samples=durations*sampling_frequency, amplitude_factor=None, ) @@ -153,7 +217,7 @@ def generate_hybrid_recording(recording, motion=None, num_units=10, hybrid_recording = InjectTemplatesRecording( sorting, - templates, + templates_array, nbefore=nbefore, parent_recording=recording, upsample_vector=upsample_vector, From f6c4c4a83f7dfd97f06cdb6a47f95fd287d97b07 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 28 Mar 2024 09:46:01 +0000 Subject: [PATCH 17/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 94 ++++++++++--------- 1 file changed, 49 insertions(+), 45 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 4f8f8fc4b6..55c2963841 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -4,7 +4,13 @@ import numpy as np from spikeinterface.core.template import Templates from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording, DriftingTemplates -from spikeinterface.core.generate import generate_templates, generate_unit_locations, _ensure_seed, generate_sorting, InjectTemplatesRecording +from spikeinterface.core.generate import ( + generate_templates, + generate_unit_locations, + _ensure_seed, + generate_sorting, + InjectTemplatesRecording, +) from spikeinterface.core.job_tools import split_job_kwargs @@ -67,19 +73,21 @@ def estimate_templates_from_recording( return templates -def generate_hybrid_recording(recording, - motion_info=None, - num_units=10, - sorting=None, - templates=None, - ms_before=1.0, - ms_after=3.0, - upsample_factor=None, - upsample_vector=None, - generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), - generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), - generate_templates_kwargs=dict(), - seed=None): +def generate_hybrid_recording( + recording, + motion_info=None, + num_units=10, + sorting=None, + templates=None, + ms_before=1.0, + ms_after=3.0, + upsample_factor=None, + upsample_vector=None, + generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), + generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), + generate_templates_kwargs=dict(), + seed=None, +): """ Generate an hybrid recording with spike given sorting+templates. @@ -152,15 +160,17 @@ def generate_hybrid_recording(recording, if templates is None: channel_locations = probe.contact_positions unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) - templates_array = generate_templates(channel_locations, - unit_locations, - sampling_frequency, - ms_before, - ms_after, - upsample_factor=upsample_factor, - seed=seed, - dtype=dtype, - **generate_templates_kwargs) + templates_array = generate_templates( + channel_locations, + unit_locations, + sampling_frequency, + ms_before, + ms_after, + upsample_factor=upsample_factor, + seed=seed, + dtype=dtype, + **generate_templates_kwargs, + ) sorting.set_property("gt_unit_locations", unit_locations) nbefore = int(ms_before * sampling_frequency / 1000.0) @@ -176,31 +186,25 @@ def generate_hybrid_recording(recording, if motion_info is not None: - templates = Templates(templates_array, - sampling_frequency, - nbefore, - None, - None, - None, - probe) - - start = np.array([0, np.min(motion_info['motion'])]) - stop = np.array([0, np.max(motion_info['motion'])]) - displacements = make_linear_displacement(start, stop, num_step=int((stop-start)[1])) + templates = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) + + start = np.array([0, np.min(motion_info["motion"])]) + stop = np.array([0, np.max(motion_info["motion"])]) + displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) drifting_templates = DriftingTemplates.from_static(templates) drifting_templates.precompute_displacements(displacements) - displacement_sampling_frequency = 1./np.diff(motion_info['temporal_bins'])[0] - displacement_vectors = np.zeros((len(motion_info['temporal_bins']), 2, len(motion_info['spatial_bins']))) - displacement_unit_factor = np.zeros((num_units, len(motion_info['spatial_bins']))) + displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] + displacement_vectors = np.zeros((len(motion_info["temporal_bins"]), 2, len(motion_info["spatial_bins"]))) + displacement_unit_factor = np.zeros((num_units, len(motion_info["spatial_bins"]))) - for count, i in enumerate(motion_info['spatial_bins']): - local_motion = motion_info['motion'][:, count] + for count, i in enumerate(motion_info["spatial_bins"]): + local_motion = motion_info["motion"][:, count] displacement_vectors[:, 1, count] = local_motion for count in range(num_units): - a = 1/np.abs((unit_locations[count, 1] - motion_info['spatial_bins'])) - displacement_unit_factor[count] = a/a.sum() + a = 1 / np.abs((unit_locations[count, 1] - motion_info["spatial_bins"])) + displacement_unit_factor[count] = a / a.sum() hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, @@ -209,10 +213,10 @@ def generate_hybrid_recording(recording, displacement_vectors=[displacement_vectors], displacement_sampling_frequency=displacement_sampling_frequency, displacement_unit_factor=displacement_unit_factor, - num_samples=durations*sampling_frequency, + num_samples=durations * sampling_frequency, amplitude_factor=None, ) - + else: hybrid_recording = InjectTemplatesRecording( @@ -222,5 +226,5 @@ def generate_hybrid_recording(recording, parent_recording=recording, upsample_vector=upsample_vector, ) - - return hybrid_recording, sorting \ No newline at end of file + + return hybrid_recording, sorting From b2e87a0bd153df7c712a678e7927a610a1b63589 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 10:55:41 +0100 Subject: [PATCH 18/90] Docs --- src/spikeinterface/generation/hybrid_tools.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 4f8f8fc4b6..23d34ad8ba 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -121,9 +121,9 @@ def generate_hybrid_recording(recording, Returns ------- recording: Recording - The generated recording extractor. + The generated hybrid recording extractor. sorting: Sorting - The generated sorting extractor. + The generated sorting extractor for the injected units. """ # if None so the same seed will be used for all steps From 275f87a181eea6eec077aa01f92ab865bfce4878 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 12:07:24 +0100 Subject: [PATCH 19/90] Adding tests --- .../generation/tests/test_hybrid_tools.py | 35 +++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 src/spikeinterface/generation/tests/test_hybrid_tools.py diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py new file mode 100644 index 0000000000..3f198f4b37 --- /dev/null +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -0,0 +1,35 @@ +import pytest +import numpy as np +from pathlib import Path + +from spikeinterface.core.generate import generate_ground_truth_recording +from spikeinterface.core import Templates, BaseRecording +from spikeinterface.preprocessing.motion import correct_motion, load_motion_info +from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording + + +if hasattr(pytest, "global_test_folder"): + cache_folder = pytest.global_test_folder / "generation" +else: + cache_folder = Path("cache_folder") / "generation" + + +def test_generate_hybrid_no_motion(): + rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) + hybrid, sorting = generate_hybrid_recording(rec) + +def test_generate_hybrid_motion(): + rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) + correct_motion(rec, folder=cache_folder / "motion") + motion = load_motion_info(cache_folder / "motion") + hybrid, sorting = generate_hybrid_recording(rec, motion) + +def test_estimate_templates(): + rec, sorting = generate_ground_truth_recording(num_units=10, sampling_frequency=20000) + templates = estimate_templates_from_recording(rec, output_folder=cache_folder / 'sc', remove_existing_folder=True) + assert len(templates.templates_array) > 0 + +if __name__ == "__main__": + test_generate_hybrid_no_motion() + test_generate_hybrid_motion() + test_estimate_templates() \ No newline at end of file From 4e97eeb28a9a72f738476d1c04c6b5658a426cbf Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 28 Mar 2024 11:10:50 +0000 Subject: [PATCH 20/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 3f198f4b37..dcf1663a15 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -18,18 +18,21 @@ def test_generate_hybrid_no_motion(): rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) hybrid, sorting = generate_hybrid_recording(rec) + def test_generate_hybrid_motion(): rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) correct_motion(rec, folder=cache_folder / "motion") motion = load_motion_info(cache_folder / "motion") hybrid, sorting = generate_hybrid_recording(rec, motion) + def test_estimate_templates(): rec, sorting = generate_ground_truth_recording(num_units=10, sampling_frequency=20000) - templates = estimate_templates_from_recording(rec, output_folder=cache_folder / 'sc', remove_existing_folder=True) + templates = estimate_templates_from_recording(rec, output_folder=cache_folder / "sc", remove_existing_folder=True) assert len(templates.templates_array) > 0 + if __name__ == "__main__": test_generate_hybrid_no_motion() test_generate_hybrid_motion() - test_estimate_templates() \ No newline at end of file + test_estimate_templates() From 45653f639c68c33bd4638617c36fd1b60531fe85 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 28 Mar 2024 13:29:31 +0100 Subject: [PATCH 21/90] One more test --- .../generation/tests/test_hybrid_tools.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 3f198f4b37..5701e7dd71 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -2,7 +2,7 @@ import numpy as np from pathlib import Path -from spikeinterface.core.generate import generate_ground_truth_recording +from spikeinterface.core.generate import generate_ground_truth_recording, generate_sorting from spikeinterface.core import Templates, BaseRecording from spikeinterface.preprocessing.motion import correct_motion, load_motion_info from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording @@ -14,6 +14,11 @@ cache_folder = Path("cache_folder") / "generation" +def test_generate_hybrid_with_sorting(): + gt_sorting = generate_sorting(durations=[10], num_units=20, sampling_frequency=20000) + rec, _ = generate_ground_truth_recording(durations=[10], sampling_frequency=20000, sorting=gt_sorting) + hybrid, _ = generate_hybrid_recording(rec) + def test_generate_hybrid_no_motion(): rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) hybrid, sorting = generate_hybrid_recording(rec) @@ -32,4 +37,5 @@ def test_estimate_templates(): if __name__ == "__main__": test_generate_hybrid_no_motion() test_generate_hybrid_motion() - test_estimate_templates() \ No newline at end of file + test_estimate_templates() + test_generate_hybrid_with_sorting() \ No newline at end of file From d1ee5256d44c5c81a34f8cd2f033394ec9ee61bb Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 28 Mar 2024 12:47:17 +0000 Subject: [PATCH 22/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 2431ef0e4d..3757f55cb1 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -19,6 +19,7 @@ def test_generate_hybrid_with_sorting(): rec, _ = generate_ground_truth_recording(durations=[10], sampling_frequency=20000, sorting=gt_sorting) hybrid, _ = generate_hybrid_recording(rec) + def test_generate_hybrid_no_motion(): rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) hybrid, sorting = generate_hybrid_recording(rec) From c12c7466c4f89819dd3816facc864c052f299afa Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Fri, 29 Mar 2024 08:37:07 +0100 Subject: [PATCH 23/90] Adding imports --- src/spikeinterface/generation/__init__.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index 494c9ac00a..e029ea9ceb 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -5,3 +5,8 @@ InjectDriftingTemplatesRecording, make_linear_displacement, ) + +from .hybrid_tools import ( + generate_hybrid_recording, + estimate_templates_from_recording +) \ No newline at end of file From a8509506875241a89a1444b19dc81697a7d7347f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 29 Mar 2024 09:05:37 +0000 Subject: [PATCH 24/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/__init__.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index e029ea9ceb..f344c173f9 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -6,7 +6,4 @@ make_linear_displacement, ) -from .hybrid_tools import ( - generate_hybrid_recording, - estimate_templates_from_recording -) \ No newline at end of file +from .hybrid_tools import generate_hybrid_recording, estimate_templates_from_recording From 4bcbe5ca9876f510e3ed9b4a85a0af148f7bb677 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Fri, 29 Mar 2024 16:29:56 +0100 Subject: [PATCH 25/90] Moving functions in localization_tools --- .../postprocessing/localization_tools.py | 344 ++++++++++++++++++ .../postprocessing/unit_localization.py | 344 +----------------- 2 files changed, 347 insertions(+), 341 deletions(-) create mode 100644 src/spikeinterface/postprocessing/localization_tools.py diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py new file mode 100644 index 0000000000..6428fa224b --- /dev/null +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -0,0 +1,344 @@ +from __future__ import annotations + +import warnings + +import numpy as np + +try: + import numba + + HAVE_NUMBA = True +except ImportError: + HAVE_NUMBA = False + +from ..core.template_tools import get_template_extremum_channel, _get_nbefore, _get_dense_templates_array + + +def make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um, initial_z=20): + # constant for initial guess and bounds + ind_max = np.argmax(wf_data) + max_ptp = wf_data[ind_max] + max_alpha = max_ptp * max_distance_um + + # initial guess is the center of mass + com = np.sum(wf_data[:, np.newaxis] * local_contact_locations, axis=0) / np.sum(wf_data) + x0 = np.zeros(4, dtype="float32") + x0[:2] = com + x0[2] = initial_z + initial_alpha = np.sqrt(np.sum((com - local_contact_locations[ind_max, :]) ** 2) + initial_z**2) * max_ptp + x0[3] = initial_alpha + + # bounds depend on initial guess + bounds = ( + [x0[0] - max_distance_um, x0[1] - max_distance_um, 1, 0], + [x0[0] + max_distance_um, x0[1] + max_distance_um, max_distance_um * 10, max_alpha], + ) + + return x0, bounds + + +def solve_monopolar_triangulation(wf_data, local_contact_locations, max_distance_um, optimizer): + import scipy.optimize + + x0, bounds = make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um) + + if optimizer == "least_square": + args = (wf_data, local_contact_locations) + try: + output = scipy.optimize.least_squares(estimate_distance_error, x0=x0, bounds=bounds, args=args) + return tuple(output["x"]) + except Exception as e: + print(f"scipy.optimize.least_squares error: {e}") + return (np.nan, np.nan, np.nan, np.nan) + + if optimizer == "minimize_with_log_penality": + x0 = x0[:3] + bounds = [(bounds[0][0], bounds[1][0]), (bounds[0][1], bounds[1][1]), (bounds[0][2], bounds[1][2])] + max_data = wf_data.max() + args = (wf_data, local_contact_locations, max_data) + try: + output = scipy.optimize.minimize(estimate_distance_error_with_log, x0=x0, bounds=bounds, args=args) + # final alpha + q = data_at(*output["x"], 1.0, local_contact_locations) + alpha = (wf_data * q).sum() / np.square(q).sum() + return (*output["x"], alpha) + except Exception as e: + print(f"scipy.optimize.minimize error: {e}") + return (np.nan, np.nan, np.nan, np.nan) + + +# ---- +# optimizer "least_square" + + +def estimate_distance_error(vec, wf_data, local_contact_locations): + # vec dims ar (x, y, z amplitude_factor) + # given that for contact_location x=dim0 + z=dim1 and y is orthogonal to probe + dist = np.sqrt(((local_contact_locations - vec[np.newaxis, :2]) ** 2).sum(axis=1) + vec[2] ** 2) + data_estimated = vec[3] / dist + err = wf_data - data_estimated + return err + + +# ---- +# optimizer "minimize_with_log_penality" + + +def data_at(x, y, z, alpha, local_contact_locations): + return alpha / np.sqrt( + np.square(x - local_contact_locations[:, 0]) + np.square(y - local_contact_locations[:, 1]) + np.square(z) + ) + + +def estimate_distance_error_with_log(vec, wf_data, local_contact_locations, max_data): + x, y, z = vec + q = data_at(x, y, z, 1.0, local_contact_locations) + alpha = (q * wf_data / max_data).sum() / (q * q).sum() + err = ( + np.square(wf_data / max_data - data_at(x, y, z, alpha, local_contact_locations)).mean() + - np.log1p(10.0 * z) / 10000.0 + ) + return err + + + +# --- +# waveform cleaning for localization. could be moved to another file + + +def make_shell(channel, geom, n_jumps=1): + """See make_shells""" + from scipy.spatial.distance import cdist + + pt = geom[channel] + dists = cdist([pt], geom).ravel() + radius = np.unique(dists)[1 : n_jumps + 1][-1] + return np.setdiff1d(np.flatnonzero(dists <= radius + 1e-8), [channel]) + + +def make_shells(geom, n_jumps=1): + """Get the neighbors of a channel within a radius + + That radius is found by figuring out the distance to the closest channel, + then the channel which is the next closest (but farther than the closest), + etc... for n_jumps. + + So, if n_jumps is 1, it will return the indices of channels which are + as close as the closest channel. If n_jumps is 2, it will include those + and also the indices of the next-closest channels. And so on... + + Returns + ------- + shell_neighbors : list + List of length geom.shape[0] (aka, the number of channels) + The ith entry in the list is an array with the indices of the neighbors + of the ith channel. + i is not included in these arrays (a channel is not in its own shell). + """ + return [make_shell(c, geom, n_jumps=n_jumps) for c in range(geom.shape[0])] + + +def make_radial_order_parents(geom, neighbours_mask, n_jumps_per_growth=1, n_jumps_parent=3): + """Pre-computes a helper data structure for enforce_decrease_shells""" + n_channels = len(geom) + + # which channels should we consider as possible parents for each channel? + shells = make_shells(geom, n_jumps=n_jumps_parent) + + radial_parents = [] + for channel, neighbors in enumerate(neighbours_mask): + channel_parents = [] + + # convert from boolean mask to list of indices + neighbors = np.flatnonzero(neighbors) + + # the closest shell will do nothing + already_seen = [channel] + shell0 = make_shell(channel, geom, n_jumps=n_jumps_per_growth) + already_seen += sorted(c for c in shell0 if c not in already_seen) + + # so we start at the second jump + jumps = 2 + while len(already_seen) < (neighbors < n_channels).sum(): + # grow our search -- what are the next-closest channels? + new_shell = make_shell(channel, geom, n_jumps=jumps * n_jumps_per_growth) + new_shell = list(sorted(c for c in new_shell if (c not in already_seen) and (c in neighbors))) + + # for each new channel, find the intersection of the channels + # from previous shells and that channel's shell in `shells` + for new_chan in new_shell: + parents = np.intersect1d(shells[new_chan], already_seen) + parents_rel = np.flatnonzero(np.isin(neighbors, parents)) + if not len(parents_rel): + # this can happen for some strange geometries. in that case, bail. + continue + channel_parents.append((np.flatnonzero(neighbors == new_chan).item(), parents_rel)) + + # add this shell to what we have seen + already_seen += new_shell + jumps += 1 + + radial_parents.append(channel_parents) + + return radial_parents + + +def enforce_decrease_shells_data(wf_data, maxchan, radial_parents, in_place=False): + """Radial enforce decrease""" + (C,) = wf_data.shape + + # allocate storage for decreasing version of data + decreasing_data = wf_data if in_place else wf_data.copy() + + # loop to enforce data decrease from parent shells + for c, parents_rel in radial_parents[maxchan]: + if decreasing_data[c] > decreasing_data[parents_rel].max(): + decreasing_data[c] *= decreasing_data[parents_rel].max() / decreasing_data[c] + + return decreasing_data + + +def get_grid_convolution_templates_and_weights( + contact_locations, radius_um=40, upsampling_um=5, margin_um=50, weight_method={"mode": "exponential_3d"} +): + """Get a upsampled grid of artificial templates given a particular probe layout + + Parameters + ---------- + contact_locations: array + The positions of the channels + radius_um: float + Radius in um for channel sparsity. + upsampling_um: float + Upsampling resolution for the grid of templates + margin_um: float + The margin for the grid of fake templates + weight_method: dict + Parameter that should be provided to the get_convolution_weights() function + in order to know how to estimate the positions. One argument is mode that could + be either gaussian_2d (KS like) or exponential_3d (default) + + Returns + ------- + template_positions: array + The positions of the upsampled templates + weights: + The weights of the templates, on a per channel basis + nearest_template_mask: array + A sparsity mask to to know which template is close to the contact locations, given + the radius_um parameter + z_factors: array + The z_factors that have been used to generate the weights along the third dimension + """ + + import sklearn.metrics + + x_min, x_max = contact_locations[:, 0].min(), contact_locations[:, 0].max() + y_min, y_max = contact_locations[:, 1].min(), contact_locations[:, 1].max() + + x_min -= margin_um + x_max += margin_um + y_min -= margin_um + y_max += margin_um + + dx = np.abs(x_max - x_min) + dy = np.abs(y_max - y_min) + + eps = upsampling_um / 10 + + all_x, all_y = np.meshgrid( + np.arange(x_min, x_max + eps, upsampling_um), np.arange(y_min, y_max + eps, upsampling_um) + ) + + nb_templates = all_x.size + + template_positions = np.zeros((nb_templates, 2)) + template_positions[:, 0] = all_x.flatten() + template_positions[:, 1] = all_y.flatten() + + # mask to get nearest template given a channel + dist = sklearn.metrics.pairwise_distances(contact_locations, template_positions) + nearest_template_mask = dist <= radius_um + weights, z_factors = get_convolution_weights(dist, **weight_method) + + return template_positions, weights, nearest_template_mask, z_factors + + +def get_convolution_weights( + distances, + z_list_um=np.linspace(0, 120.0, 5), + sigma_list_um=np.linspace(5, 25, 5), + sparsity_threshold=None, + sigma_3d=2.5, + mode="exponential_3d", +): + """Get normalized weights for creating artificial templates, given some precomputed distances + + Parameters + ---------- + distances: 2D array + The distances between the source channels (real ones) and the upsampled one (virual ones) + sparsity_threshold: float, default None + The sparsity_threshold below which weights are set to 0 (speeding up computations). If None, + then a default value of 0.5/sqrt(distances.shape[0]) is set + mode: exponential_3d | gaussian_2d + The inference scheme to be used to get the convolution weights + Keyword arguments for the chosen method: + "gaussian_2d" (similar to KiloSort): + * sigma_list_um: array, default np.linspace(5, 25, 5) + The list of sigma to consider for decaying exponentials + "exponential_3d" (default): + * z_list_um: array, default np.linspace(0, 120.0, 5) + The list of z to consider for putative depth of the sources + * sigma_3d: float, default 2.5 + The scaling factor controling the decay of the exponential + + Returns + ------- + weights: + The weights of the templates, on a per channel basis + z_factors: array + The z_factors that have been used to generate the weights along the third dimension + """ + + if sparsity_threshold is not None: + assert 0 <= sparsity_threshold <= 1, "sparsity_threshold should be in [0, 1]" + + if mode == "exponential_3d": + weights = np.zeros((len(z_list_um), distances.shape[0], distances.shape[1]), dtype=np.float32) + for count, z in enumerate(z_list_um): + dist_3d = np.sqrt(distances**2 + z**2) + weights[count] = np.exp(-dist_3d / sigma_3d) + z_factors = z_list_um + elif mode == "gaussian_2d": + weights = np.zeros((len(sigma_list_um), distances.shape[0], distances.shape[1]), dtype=np.float32) + for count, sigma in enumerate(sigma_list_um): + alpha = 2 * (sigma**2) + weights[count] = np.exp(-(distances**2) / alpha) + z_factors = sigma_list_um + + # normalize to get normalized values in [0, 1] + with np.errstate(divide="ignore", invalid="ignore"): + norm = np.linalg.norm(weights, axis=1)[:, np.newaxis, :] + weights /= norm + + weights[~np.isfinite(weights)] = 0.0 + + # If sparsity is None or non zero, we are pruning weights that are below the + # sparsification factor. This will speed up furter computations + if sparsity_threshold is None: + sparsity_threshold = 0.5 / np.sqrt(distances.shape[0]) + weights[weights < sparsity_threshold] = 0 + + # re normalize to ensure we have unitary norms + with np.errstate(divide="ignore", invalid="ignore"): + norm = np.linalg.norm(weights, axis=1)[:, np.newaxis, :] + weights /= norm + + weights[~np.isfinite(weights)] = 0.0 + + return weights, z_factors + + +if HAVE_NUMBA: + enforce_decrease_shells = numba.jit(enforce_decrease_shells_data, nopython=True) diff --git a/src/spikeinterface/postprocessing/unit_localization.py b/src/spikeinterface/postprocessing/unit_localization.py index 7a391334c4..40abb99085 100644 --- a/src/spikeinterface/postprocessing/unit_localization.py +++ b/src/spikeinterface/postprocessing/unit_localization.py @@ -1,21 +1,12 @@ from __future__ import annotations -import warnings - import numpy as np - - -try: - import numba - - HAVE_NUMBA = True -except ImportError: - HAVE_NUMBA = False +import warnings from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension from ..core import compute_sparsity from ..core.template_tools import get_template_extremum_channel, _get_nbefore, _get_dense_templates_array - +from .localization_tools import make_radial_order_parents, solve_monopolar_triangulation, get_grid_convolution_templates_and_weights dtype_localize_by_method = { "center_of_mass": [("x", "float64"), ("y", "float64")], @@ -94,93 +85,6 @@ def get_data(self, outputs="numpy"): compute_unit_locations = ComputeUnitLocations.function_factory() -def make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um, initial_z=20): - # constant for initial guess and bounds - ind_max = np.argmax(wf_data) - max_ptp = wf_data[ind_max] - max_alpha = max_ptp * max_distance_um - - # initial guess is the center of mass - com = np.sum(wf_data[:, np.newaxis] * local_contact_locations, axis=0) / np.sum(wf_data) - x0 = np.zeros(4, dtype="float32") - x0[:2] = com - x0[2] = initial_z - initial_alpha = np.sqrt(np.sum((com - local_contact_locations[ind_max, :]) ** 2) + initial_z**2) * max_ptp - x0[3] = initial_alpha - - # bounds depend on initial guess - bounds = ( - [x0[0] - max_distance_um, x0[1] - max_distance_um, 1, 0], - [x0[0] + max_distance_um, x0[1] + max_distance_um, max_distance_um * 10, max_alpha], - ) - - return x0, bounds - - -def solve_monopolar_triangulation(wf_data, local_contact_locations, max_distance_um, optimizer): - import scipy.optimize - - x0, bounds = make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um) - - if optimizer == "least_square": - args = (wf_data, local_contact_locations) - try: - output = scipy.optimize.least_squares(estimate_distance_error, x0=x0, bounds=bounds, args=args) - return tuple(output["x"]) - except Exception as e: - print(f"scipy.optimize.least_squares error: {e}") - return (np.nan, np.nan, np.nan, np.nan) - - if optimizer == "minimize_with_log_penality": - x0 = x0[:3] - bounds = [(bounds[0][0], bounds[1][0]), (bounds[0][1], bounds[1][1]), (bounds[0][2], bounds[1][2])] - max_data = wf_data.max() - args = (wf_data, local_contact_locations, max_data) - try: - output = scipy.optimize.minimize(estimate_distance_error_with_log, x0=x0, bounds=bounds, args=args) - # final alpha - q = data_at(*output["x"], 1.0, local_contact_locations) - alpha = (wf_data * q).sum() / np.square(q).sum() - return (*output["x"], alpha) - except Exception as e: - print(f"scipy.optimize.minimize error: {e}") - return (np.nan, np.nan, np.nan, np.nan) - - -# ---- -# optimizer "least_square" - - -def estimate_distance_error(vec, wf_data, local_contact_locations): - # vec dims ar (x, y, z amplitude_factor) - # given that for contact_location x=dim0 + z=dim1 and y is orthogonal to probe - dist = np.sqrt(((local_contact_locations - vec[np.newaxis, :2]) ** 2).sum(axis=1) + vec[2] ** 2) - data_estimated = vec[3] / dist - err = wf_data - data_estimated - return err - - -# ---- -# optimizer "minimize_with_log_penality" - - -def data_at(x, y, z, alpha, local_contact_locations): - return alpha / np.sqrt( - np.square(x - local_contact_locations[:, 0]) + np.square(y - local_contact_locations[:, 1]) + np.square(z) - ) - - -def estimate_distance_error_with_log(vec, wf_data, local_contact_locations, max_data): - x, y, z = vec - q = data_at(x, y, z, 1.0, local_contact_locations) - alpha = (q * wf_data / max_data).sum() / (q * q).sum() - err = ( - np.square(wf_data / max_data - data_at(x, y, z, alpha, local_contact_locations)).mean() - - np.log1p(10.0 * z) / 10000.0 - ) - return err - - def compute_monopolar_triangulation( sorting_analyzer, optimizer="minimize_with_log_penality", @@ -438,246 +342,4 @@ def compute_grid_convolution( unit_location[i] /= scalar_products.sum() unit_location = np.nan_to_num(unit_location) - return unit_location - - -# --- -# waveform cleaning for localization. could be moved to another file - - -def make_shell(channel, geom, n_jumps=1): - """See make_shells""" - from scipy.spatial.distance import cdist - - pt = geom[channel] - dists = cdist([pt], geom).ravel() - radius = np.unique(dists)[1 : n_jumps + 1][-1] - return np.setdiff1d(np.flatnonzero(dists <= radius + 1e-8), [channel]) - - -def make_shells(geom, n_jumps=1): - """Get the neighbors of a channel within a radius - - That radius is found by figuring out the distance to the closest channel, - then the channel which is the next closest (but farther than the closest), - etc... for n_jumps. - - So, if n_jumps is 1, it will return the indices of channels which are - as close as the closest channel. If n_jumps is 2, it will include those - and also the indices of the next-closest channels. And so on... - - Returns - ------- - shell_neighbors : list - List of length geom.shape[0] (aka, the number of channels) - The ith entry in the list is an array with the indices of the neighbors - of the ith channel. - i is not included in these arrays (a channel is not in its own shell). - """ - return [make_shell(c, geom, n_jumps=n_jumps) for c in range(geom.shape[0])] - - -def make_radial_order_parents(geom, neighbours_mask, n_jumps_per_growth=1, n_jumps_parent=3): - """Pre-computes a helper data structure for enforce_decrease_shells""" - n_channels = len(geom) - - # which channels should we consider as possible parents for each channel? - shells = make_shells(geom, n_jumps=n_jumps_parent) - - radial_parents = [] - for channel, neighbors in enumerate(neighbours_mask): - channel_parents = [] - - # convert from boolean mask to list of indices - neighbors = np.flatnonzero(neighbors) - - # the closest shell will do nothing - already_seen = [channel] - shell0 = make_shell(channel, geom, n_jumps=n_jumps_per_growth) - already_seen += sorted(c for c in shell0 if c not in already_seen) - - # so we start at the second jump - jumps = 2 - while len(already_seen) < (neighbors < n_channels).sum(): - # grow our search -- what are the next-closest channels? - new_shell = make_shell(channel, geom, n_jumps=jumps * n_jumps_per_growth) - new_shell = list(sorted(c for c in new_shell if (c not in already_seen) and (c in neighbors))) - - # for each new channel, find the intersection of the channels - # from previous shells and that channel's shell in `shells` - for new_chan in new_shell: - parents = np.intersect1d(shells[new_chan], already_seen) - parents_rel = np.flatnonzero(np.isin(neighbors, parents)) - if not len(parents_rel): - # this can happen for some strange geometries. in that case, bail. - continue - channel_parents.append((np.flatnonzero(neighbors == new_chan).item(), parents_rel)) - - # add this shell to what we have seen - already_seen += new_shell - jumps += 1 - - radial_parents.append(channel_parents) - - return radial_parents - - -def enforce_decrease_shells_data(wf_data, maxchan, radial_parents, in_place=False): - """Radial enforce decrease""" - (C,) = wf_data.shape - - # allocate storage for decreasing version of data - decreasing_data = wf_data if in_place else wf_data.copy() - - # loop to enforce data decrease from parent shells - for c, parents_rel in radial_parents[maxchan]: - if decreasing_data[c] > decreasing_data[parents_rel].max(): - decreasing_data[c] *= decreasing_data[parents_rel].max() / decreasing_data[c] - - return decreasing_data - - -def get_grid_convolution_templates_and_weights( - contact_locations, radius_um=40, upsampling_um=5, margin_um=50, weight_method={"mode": "exponential_3d"} -): - """Get a upsampled grid of artificial templates given a particular probe layout - - Parameters - ---------- - contact_locations: array - The positions of the channels - radius_um: float - Radius in um for channel sparsity. - upsampling_um: float - Upsampling resolution for the grid of templates - margin_um: float - The margin for the grid of fake templates - weight_method: dict - Parameter that should be provided to the get_convolution_weights() function - in order to know how to estimate the positions. One argument is mode that could - be either gaussian_2d (KS like) or exponential_3d (default) - - Returns - ------- - template_positions: array - The positions of the upsampled templates - weights: - The weights of the templates, on a per channel basis - nearest_template_mask: array - A sparsity mask to to know which template is close to the contact locations, given - the radius_um parameter - z_factors: array - The z_factors that have been used to generate the weights along the third dimension - """ - - import sklearn.metrics - - x_min, x_max = contact_locations[:, 0].min(), contact_locations[:, 0].max() - y_min, y_max = contact_locations[:, 1].min(), contact_locations[:, 1].max() - - x_min -= margin_um - x_max += margin_um - y_min -= margin_um - y_max += margin_um - - dx = np.abs(x_max - x_min) - dy = np.abs(y_max - y_min) - - eps = upsampling_um / 10 - - all_x, all_y = np.meshgrid( - np.arange(x_min, x_max + eps, upsampling_um), np.arange(y_min, y_max + eps, upsampling_um) - ) - - nb_templates = all_x.size - - template_positions = np.zeros((nb_templates, 2)) - template_positions[:, 0] = all_x.flatten() - template_positions[:, 1] = all_y.flatten() - - # mask to get nearest template given a channel - dist = sklearn.metrics.pairwise_distances(contact_locations, template_positions) - nearest_template_mask = dist <= radius_um - weights, z_factors = get_convolution_weights(dist, **weight_method) - - return template_positions, weights, nearest_template_mask, z_factors - - -def get_convolution_weights( - distances, - z_list_um=np.linspace(0, 120.0, 5), - sigma_list_um=np.linspace(5, 25, 5), - sparsity_threshold=None, - sigma_3d=2.5, - mode="exponential_3d", -): - """Get normalized weights for creating artificial templates, given some precomputed distances - - Parameters - ---------- - distances: 2D array - The distances between the source channels (real ones) and the upsampled one (virual ones) - sparsity_threshold: float, default None - The sparsity_threshold below which weights are set to 0 (speeding up computations). If None, - then a default value of 0.5/sqrt(distances.shape[0]) is set - mode: exponential_3d | gaussian_2d - The inference scheme to be used to get the convolution weights - Keyword arguments for the chosen method: - "gaussian_2d" (similar to KiloSort): - * sigma_list_um: array, default np.linspace(5, 25, 5) - The list of sigma to consider for decaying exponentials - "exponential_3d" (default): - * z_list_um: array, default np.linspace(0, 120.0, 5) - The list of z to consider for putative depth of the sources - * sigma_3d: float, default 2.5 - The scaling factor controling the decay of the exponential - - Returns - ------- - weights: - The weights of the templates, on a per channel basis - z_factors: array - The z_factors that have been used to generate the weights along the third dimension - """ - - if sparsity_threshold is not None: - assert 0 <= sparsity_threshold <= 1, "sparsity_threshold should be in [0, 1]" - - if mode == "exponential_3d": - weights = np.zeros((len(z_list_um), distances.shape[0], distances.shape[1]), dtype=np.float32) - for count, z in enumerate(z_list_um): - dist_3d = np.sqrt(distances**2 + z**2) - weights[count] = np.exp(-dist_3d / sigma_3d) - z_factors = z_list_um - elif mode == "gaussian_2d": - weights = np.zeros((len(sigma_list_um), distances.shape[0], distances.shape[1]), dtype=np.float32) - for count, sigma in enumerate(sigma_list_um): - alpha = 2 * (sigma**2) - weights[count] = np.exp(-(distances**2) / alpha) - z_factors = sigma_list_um - - # normalize to get normalized values in [0, 1] - with np.errstate(divide="ignore", invalid="ignore"): - norm = np.linalg.norm(weights, axis=1)[:, np.newaxis, :] - weights /= norm - - weights[~np.isfinite(weights)] = 0.0 - - # If sparsity is None or non zero, we are pruning weights that are below the - # sparsification factor. This will speed up furter computations - if sparsity_threshold is None: - sparsity_threshold = 0.5 / np.sqrt(distances.shape[0]) - weights[weights < sparsity_threshold] = 0 - - # re normalize to ensure we have unitary norms - with np.errstate(divide="ignore", invalid="ignore"): - norm = np.linalg.norm(weights, axis=1)[:, np.newaxis, :] - weights /= norm - - weights[~np.isfinite(weights)] = 0.0 - - return weights, z_factors - - -if HAVE_NUMBA: - enforce_decrease_shells = numba.jit(enforce_decrease_shells_data, nopython=True) + return unit_location \ No newline at end of file From d974fe8771e4b47400e20d878c47749e862211c7 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 29 Mar 2024 15:30:32 +0000 Subject: [PATCH 26/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/postprocessing/localization_tools.py | 1 - src/spikeinterface/postprocessing/unit_localization.py | 8 ++++++-- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py index 6428fa224b..ad48f16d27 100644 --- a/src/spikeinterface/postprocessing/localization_tools.py +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -101,7 +101,6 @@ def estimate_distance_error_with_log(vec, wf_data, local_contact_locations, max_ return err - # --- # waveform cleaning for localization. could be moved to another file diff --git a/src/spikeinterface/postprocessing/unit_localization.py b/src/spikeinterface/postprocessing/unit_localization.py index 40abb99085..5bd7d1ca89 100644 --- a/src/spikeinterface/postprocessing/unit_localization.py +++ b/src/spikeinterface/postprocessing/unit_localization.py @@ -6,7 +6,11 @@ from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension from ..core import compute_sparsity from ..core.template_tools import get_template_extremum_channel, _get_nbefore, _get_dense_templates_array -from .localization_tools import make_radial_order_parents, solve_monopolar_triangulation, get_grid_convolution_templates_and_weights +from .localization_tools import ( + make_radial_order_parents, + solve_monopolar_triangulation, + get_grid_convolution_templates_and_weights, +) dtype_localize_by_method = { "center_of_mass": [("x", "float64"), ("y", "float64")], @@ -342,4 +346,4 @@ def compute_grid_convolution( unit_location[i] /= scalar_products.sum() unit_location = np.nan_to_num(unit_location) - return unit_location \ No newline at end of file + return unit_location From 3eedeb479f26316e902d30753067a2b6e0874635 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Fri, 29 Mar 2024 16:43:35 +0100 Subject: [PATCH 27/90] Imports --- .../postprocessing/localization_tools.py | 3 --- .../sortingcomponents/peak_localization.py | 10 ++++++---- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py index 6428fa224b..3fbc86e6a0 100644 --- a/src/spikeinterface/postprocessing/localization_tools.py +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -241,9 +241,6 @@ def get_grid_convolution_templates_and_weights( y_min -= margin_um y_max += margin_um - dx = np.abs(x_max - x_min) - dy = np.abs(y_max - y_min) - eps = upsampling_um / 10 all_x, all_y = np.meshgrid( diff --git a/src/spikeinterface/sortingcomponents/peak_localization.py b/src/spikeinterface/sortingcomponents/peak_localization.py index b06f6fac3e..1c44101686 100644 --- a/src/spikeinterface/sortingcomponents/peak_localization.py +++ b/src/spikeinterface/sortingcomponents/peak_localization.py @@ -24,12 +24,14 @@ from ..postprocessing.unit_localization import ( dtype_localize_by_method, possible_localization_methods, - solve_monopolar_triangulation, - make_radial_order_parents, - enforce_decrease_shells_data, - get_grid_convolution_templates_and_weights, ) +from ..postprocessing.localization_tools import ( + make_radial_order_parents, + solve_monopolar_triangulation, + enforce_decrease_shells_data, + get_grid_convolution_templates_and_weights) + from .tools import get_prototype_spike From 5f95fa0054a0406517a34c7e035858ee2003ed38 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 29 Mar 2024 15:44:06 +0000 Subject: [PATCH 28/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../sortingcomponents/peak_localization.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/sortingcomponents/peak_localization.py b/src/spikeinterface/sortingcomponents/peak_localization.py index 1c44101686..a6f6b75f03 100644 --- a/src/spikeinterface/sortingcomponents/peak_localization.py +++ b/src/spikeinterface/sortingcomponents/peak_localization.py @@ -27,10 +27,11 @@ ) from ..postprocessing.localization_tools import ( - make_radial_order_parents, - solve_monopolar_triangulation, - enforce_decrease_shells_data, - get_grid_convolution_templates_and_weights) + make_radial_order_parents, + solve_monopolar_triangulation, + enforce_decrease_shells_data, + get_grid_convolution_templates_and_weights, +) from .tools import get_prototype_spike From 23be2723ba6e08ca149beffc4c71672dfb2e7196 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Fri, 29 Mar 2024 16:47:29 +0100 Subject: [PATCH 29/90] Hybrid recordings also with given templates --- src/spikeinterface/generation/hybrid_tools.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 315b89c8c6..1d67f0f690 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -12,6 +12,7 @@ InjectTemplatesRecording, ) from spikeinterface.core.job_tools import split_job_kwargs +from spikeinterface.postprocessing.unit_localization import compute_monopolar_triangulation def estimate_templates_from_recording( @@ -171,7 +172,11 @@ def generate_hybrid_recording( dtype=dtype, **generate_templates_kwargs, ) - sorting.set_property("gt_unit_locations", unit_locations) + else: + unit_locations = compute_monopolar_triangulation(templates) + templates_array = templates.templates_array + + sorting.set_property("gt_unit_locations", unit_locations) nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) From 03fd84f03fe5ac2649b2094420e898b721544cb8 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Sat, 30 Mar 2024 06:53:22 +0100 Subject: [PATCH 30/90] Extension of relocalization to real templates --- src/spikeinterface/generation/hybrid_tools.py | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 1d67f0f690..fa4df0679f 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -3,7 +3,7 @@ import numpy as np from spikeinterface.core.template import Templates -from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording, DriftingTemplates +from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording, DriftingTemplates, interpolate_templates from spikeinterface.core.generate import ( generate_templates, generate_unit_locations, @@ -158,9 +158,10 @@ def generate_hybrid_recording( num_spikes = sorting.to_spike_vector().size + channel_locations = probe.contact_positions + unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) + if templates is None: - channel_locations = probe.contact_positions - unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) templates_array = generate_templates( channel_locations, unit_locations, @@ -173,8 +174,12 @@ def generate_hybrid_recording( **generate_templates_kwargs, ) else: - unit_locations = compute_monopolar_triangulation(templates) - templates_array = templates.templates_array + template_locations = compute_monopolar_triangulation(templates) + templates_array = np.zeros(templates.templates_array.shape, dtype=dtype) + for i in range(len(templates_array)): + src_template = templates.templates_array[i][np.newaxis, :, :] + deltas = (unit_locations[i] - template_locations[i]) + templates_array[i] = interpolate_templates(src_template, channel_locations, channel_locations + deltas[:2]) sorting.set_property("gt_unit_locations", unit_locations) @@ -186,7 +191,7 @@ def generate_hybrid_recording( upsample_vector = None else: if upsample_vector is None: - upsample_factor = templates.shape[3] + upsample_factor = templates_array.shape[3] upsample_vector = rng.integers(0, upsample_factor, size=num_spikes) if motion_info is not None: From c9b921572ec812a1109b968858e96b1c70a4dbd0 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 30 Mar 2024 05:53:44 +0000 Subject: [PATCH 31/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index fa4df0679f..ae63dd9c0c 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -3,7 +3,12 @@ import numpy as np from spikeinterface.core.template import Templates -from spikeinterface.generation import make_linear_displacement, InjectDriftingTemplatesRecording, DriftingTemplates, interpolate_templates +from spikeinterface.generation import ( + make_linear_displacement, + InjectDriftingTemplatesRecording, + DriftingTemplates, + interpolate_templates, +) from spikeinterface.core.generate import ( generate_templates, generate_unit_locations, @@ -178,7 +183,7 @@ def generate_hybrid_recording( templates_array = np.zeros(templates.templates_array.shape, dtype=dtype) for i in range(len(templates_array)): src_template = templates.templates_array[i][np.newaxis, :, :] - deltas = (unit_locations[i] - template_locations[i]) + deltas = unit_locations[i] - template_locations[i] templates_array[i] = interpolate_templates(src_template, channel_locations, channel_locations + deltas[:2]) sorting.set_property("gt_unit_locations", unit_locations) From 0d2d7f95dcdb59b0461ed058ee515c7e5c53955b Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Mon, 1 Apr 2024 22:40:39 +0200 Subject: [PATCH 32/90] Unit_locations could be given --- src/spikeinterface/generation/hybrid_tools.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index ae63dd9c0c..06d5a50ddd 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -87,6 +87,7 @@ def generate_hybrid_recording( templates=None, ms_before=1.0, ms_after=3.0, + unit_locations=None, upsample_factor=None, upsample_vector=None, generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), @@ -117,6 +118,9 @@ def generate_hybrid_recording( Cut out in ms before spike peak. ms_after: float, default: 3 Cut out in ms after spike peak. + unit_locations: np.array, default: None + The locations at which the templates should be injected. If not provided, generated (see + generate_unit_location_kwargs) upsample_factor: None or int, default: None A upsampling factor used only when templates are not provided. upsample_vector: np.array or None @@ -164,7 +168,10 @@ def generate_hybrid_recording( num_spikes = sorting.to_spike_vector().size channel_locations = probe.contact_positions - unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) + if unit_locations is None: + unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) + else: + assert len(unit_locations) == num_units, "unit_locations and num_units should have the same length" if templates is None: templates_array = generate_templates( From 761cef5b2dd9dbae86c402e6d73a4faf00079c3f Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 2 Apr 2024 16:20:37 +0200 Subject: [PATCH 33/90] Update src/spikeinterface/generation/tests/test_hybrid_tools.py Co-authored-by: Alessio Buccino --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 3757f55cb1..aae4b1c886 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -8,10 +8,6 @@ from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording -if hasattr(pytest, "global_test_folder"): - cache_folder = pytest.global_test_folder / "generation" -else: - cache_folder = Path("cache_folder") / "generation" def test_generate_hybrid_with_sorting(): From 04c66c6aa57f648109f8a056b0e0d86a727d9158 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 2 Apr 2024 16:20:49 +0200 Subject: [PATCH 34/90] Update src/spikeinterface/generation/tests/test_hybrid_tools.py Co-authored-by: Alessio Buccino --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index aae4b1c886..8f74c76002 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -28,9 +28,9 @@ def test_generate_hybrid_motion(): hybrid, sorting = generate_hybrid_recording(rec, motion) -def test_estimate_templates(): +def test_estimate_templates(tmp_path): rec, sorting = generate_ground_truth_recording(num_units=10, sampling_frequency=20000) - templates = estimate_templates_from_recording(rec, output_folder=cache_folder / "sc", remove_existing_folder=True) + templates = estimate_templates_from_recording(rec, output_folder=tmp_path / "sc", remove_existing_folder=True) assert len(templates.templates_array) > 0 From b39bea91b1e86c6db8d1a5ad6d2d08630d721810 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Apr 2024 14:20:57 +0000 Subject: [PATCH 35/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 8f74c76002..f54dafd09f 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -8,8 +8,6 @@ from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording - - def test_generate_hybrid_with_sorting(): gt_sorting = generate_sorting(durations=[10], num_units=20, sampling_frequency=20000) rec, _ = generate_ground_truth_recording(durations=[10], sampling_frequency=20000, sorting=gt_sorting) From 394ecec84b39e02ba630f8a8e3b4f03f02e344d9 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 2 Apr 2024 16:21:25 +0200 Subject: [PATCH 36/90] Update src/spikeinterface/generation/hybrid_tools.py Co-authored-by: Alessio Buccino --- src/spikeinterface/generation/hybrid_tools.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 06d5a50ddd..e9cd7eb107 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -24,9 +24,10 @@ def estimate_templates_from_recording( recording, ms_before=2, ms_after=2, sorter_name="spykingcircus2", **run_sorter_kwargs ): """ - Get templates from a recording. Internally, SpyKING CIRCUS 2 is used (see parameters) - with the only twist that the template matching step is not launch. Instead, a Template - object is returned based on the results of the clutering. + Get templates from a recording. Internally, SpyKING CIRCUS 2 is used by default + with the only twist that the template matching step is not launched. Instead, a Template + object is returned based on the results of the clustering. Other sorters can be invoked + with the `sorter_name` and `run_sorter_kwargs` parameters. Parameters ---------- From 396a0cac0374892edbead47f41070a91fa05d5d0 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 2 Apr 2024 16:21:30 +0200 Subject: [PATCH 37/90] Update src/spikeinterface/generation/tests/test_hybrid_tools.py Co-authored-by: Alessio Buccino --- src/spikeinterface/generation/tests/test_hybrid_tools.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index f54dafd09f..36b32e99d0 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -19,10 +19,10 @@ def test_generate_hybrid_no_motion(): hybrid, sorting = generate_hybrid_recording(rec) -def test_generate_hybrid_motion(): +def test_generate_hybrid_motion(tmp_path): rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) - correct_motion(rec, folder=cache_folder / "motion") - motion = load_motion_info(cache_folder / "motion") + correct_motion(rec, folder=tmp_path / "motion") + motion = load_motion_info(tmp_path / "motion") hybrid, sorting = generate_hybrid_recording(rec, motion) From bf9cf532e4786e884ab88eb1666e6333dad52e9e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 2 Apr 2024 14:21:50 +0000 Subject: [PATCH 38/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index e9cd7eb107..a752f2559a 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -26,7 +26,7 @@ def estimate_templates_from_recording( """ Get templates from a recording. Internally, SpyKING CIRCUS 2 is used by default with the only twist that the template matching step is not launched. Instead, a Template - object is returned based on the results of the clustering. Other sorters can be invoked + object is returned based on the results of the clustering. Other sorters can be invoked with the `sorter_name` and `run_sorter_kwargs` parameters. Parameters From a33049ab2676c2ff359f26c332bd1b55726ff14b Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 4 Apr 2024 14:46:03 +0200 Subject: [PATCH 39/90] WIP --- src/spikeinterface/core/node_pipeline.py | 1 - src/spikeinterface/generation/drift_tools.py | 10 +++++----- src/spikeinterface/generation/hybrid_tools.py | 2 +- src/spikeinterface/preprocessing/motion.py | 2 +- 4 files changed, 7 insertions(+), 8 deletions(-) diff --git a/src/spikeinterface/core/node_pipeline.py b/src/spikeinterface/core/node_pipeline.py index 78e9a82cf0..ae8bb1467c 100644 --- a/src/spikeinterface/core/node_pipeline.py +++ b/src/spikeinterface/core/node_pipeline.py @@ -514,7 +514,6 @@ def _init_peak_pipeline(recording, nodes): worker_ctx["recording"] = recording worker_ctx["nodes"] = nodes worker_ctx["max_margin"] = max(node.get_trace_margin() for node in nodes) - return worker_ctx diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index bb73e0fced..412f101ff8 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -263,10 +263,10 @@ def __init__( # upsample_vector: Union[list[int], None] = None, ): import scipy.spatial - - assert isinstance( - drifting_templates, DriftingTemplates - ), "drifting_templates must be a DriftingTemplates object" + + # assert isinstance( + # drifting_templates, DriftingTemplates + # ), "drifting_templates must be a DriftingTemplates object" self.drifting_templates = drifting_templates if parent_recording is None: @@ -467,7 +467,7 @@ def get_traces( n_channels = len(channel_indices) if self.parent_recording is not None: - traces = self.parent_recording.get_traces(start_frame, end_frame, channel_indices).copy() + traces = self.parent_recording.get_traces(int(start_frame), int(end_frame), channel_indices).copy() else: traces = np.zeros([end_frame - start_frame, n_channels], dtype=self.dtype) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index a752f2559a..b511058b9b 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -216,7 +216,7 @@ def generate_hybrid_recording( displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) drifting_templates = DriftingTemplates.from_static(templates) drifting_templates.precompute_displacements(displacements) - + displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] displacement_vectors = np.zeros((len(motion_info["temporal_bins"]), 2, len(motion_info["spatial_bins"]))) displacement_unit_factor = np.zeros((num_units, len(motion_info["spatial_bins"]))) diff --git a/src/spikeinterface/preprocessing/motion.py b/src/spikeinterface/preprocessing/motion.py index 1b182a6436..29f00f6b8a 100644 --- a/src/spikeinterface/preprocessing/motion.py +++ b/src/spikeinterface/preprocessing/motion.py @@ -347,7 +347,7 @@ def correct_motion( node1 = ExtractDenseWaveforms(recording, parents=[node0], ms_before=0.1, ms_after=0.3) - # node nolcalize + # node detect + localize method = localize_peaks_kwargs.pop("method", "center_of_mass") method_class = localize_peak_methods[method] node2 = method_class(recording, parents=[node0, node1], return_output=True, **localize_peaks_kwargs) From 4c81d1e7ca37a28d90d0bc290a28d5e2b2c4cec9 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 4 Apr 2024 12:49:05 +0000 Subject: [PATCH 40/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/drift_tools.py | 2 +- src/spikeinterface/generation/hybrid_tools.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 412f101ff8..31a2fff885 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -263,7 +263,7 @@ def __init__( # upsample_vector: Union[list[int], None] = None, ): import scipy.spatial - + # assert isinstance( # drifting_templates, DriftingTemplates # ), "drifting_templates must be a DriftingTemplates object" diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index b511058b9b..a752f2559a 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -216,7 +216,7 @@ def generate_hybrid_recording( displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) drifting_templates = DriftingTemplates.from_static(templates) drifting_templates.precompute_displacements(displacements) - + displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] displacement_vectors = np.zeros((len(motion_info["temporal_bins"]), 2, len(motion_info["spatial_bins"]))) displacement_unit_factor = np.zeros((num_units, len(motion_info["spatial_bins"]))) From eaba48fadd9c4038777d2e691af5f409cc715431 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Tue, 9 Apr 2024 15:11:17 +0200 Subject: [PATCH 41/90] WIP --- src/spikeinterface/postprocessing/unit_localization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/postprocessing/unit_localization.py b/src/spikeinterface/postprocessing/unit_localization.py index c8bf1c3029..154ab15acc 100644 --- a/src/spikeinterface/postprocessing/unit_localization.py +++ b/src/spikeinterface/postprocessing/unit_localization.py @@ -5,7 +5,7 @@ from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension from ..core import compute_sparsity -from ..core.template_tools import get_template_extremum_channel, _get_nbefore, _get_dense_templates_array +from ..core.template_tools import get_template_extremum_channel, _get_nbefore from .localization_tools import ( make_radial_order_parents, solve_monopolar_triangulation, From 01ec8ab1f3f008f0df387059fa3a946603308b2c Mon Sep 17 00:00:00 2001 From: Sebastien Date: Tue, 9 Apr 2024 15:46:07 +0200 Subject: [PATCH 42/90] Imports --- src/spikeinterface/postprocessing/localization_tools.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py index e28811ebee..b9d9a15540 100644 --- a/src/spikeinterface/postprocessing/localization_tools.py +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -11,9 +11,6 @@ except ImportError: HAVE_NUMBA = False -from ..core.template_tools import get_template_extremum_channel, _get_nbefore, _get_dense_templates_array - - def make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um, initial_z=20): # constant for initial guess and bounds ind_max = np.argmax(wf_data) From 15a9491ca92a67a271311f31f13e418360162002 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 9 Apr 2024 13:51:10 +0000 Subject: [PATCH 43/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/postprocessing/localization_tools.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py index b9d9a15540..3394190e5a 100644 --- a/src/spikeinterface/postprocessing/localization_tools.py +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -11,6 +11,7 @@ except ImportError: HAVE_NUMBA = False + def make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um, initial_z=20): # constant for initial guess and bounds ind_max = np.argmax(wf_data) From b0810919df68f26c332128430bfeaa3600f12e75 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Tue, 9 Apr 2024 21:09:06 +0200 Subject: [PATCH 44/90] Imports --- src/spikeinterface/postprocessing/unit_localization.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/postprocessing/unit_localization.py b/src/spikeinterface/postprocessing/unit_localization.py index 154ab15acc..d52766362b 100644 --- a/src/spikeinterface/postprocessing/unit_localization.py +++ b/src/spikeinterface/postprocessing/unit_localization.py @@ -5,7 +5,7 @@ from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension from ..core import compute_sparsity -from ..core.template_tools import get_template_extremum_channel, _get_nbefore +from ..core.template_tools import get_template_extremum_channel, _get_nbefore, get_dense_templates_array from .localization_tools import ( make_radial_order_parents, solve_monopolar_triangulation, From 40fd9ff3afe9f2004a369adcf8f36b79507ccec6 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 1 May 2024 12:57:25 +0200 Subject: [PATCH 45/90] Add amplitude scaling std --- src/spikeinterface/generation/hybrid_tools.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index a752f2559a..e826b4b5e2 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -91,6 +91,7 @@ def generate_hybrid_recording( unit_locations=None, upsample_factor=None, upsample_vector=None, + amplitude_std=0.05, generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), generate_templates_kwargs=dict(), @@ -126,6 +127,9 @@ def generate_hybrid_recording( A upsampling factor used only when templates are not provided. upsample_vector: np.array or None Optional the upsample_vector can given. This has the same shape as spike_vector + amplitude_std: float, default: 0.05 + The standard deviation of the modulation to apply to the spikes when injecting them + into the recording. generate_sorting_kwargs: dict When sorting is not provide, this dict is used to generated a Sorting. generate_unit_locations_kwargs: dict @@ -229,6 +233,12 @@ def generate_hybrid_recording( a = 1 / np.abs((unit_locations[count, 1] - motion_info["spatial_bins"])) displacement_unit_factor[count] = a / a.sum() + if amplitude_std is not None: + spike_vector = sorting.to_spike_vector() + amplitude_factor = 1 + rng.randn(spike_vector.size) * amplitude_std + else: + amplitude_factor = None + hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, parent_recording=recording, @@ -237,7 +247,7 @@ def generate_hybrid_recording( displacement_sampling_frequency=displacement_sampling_frequency, displacement_unit_factor=displacement_unit_factor, num_samples=durations * sampling_frequency, - amplitude_factor=None, + amplitude_factor=amplitude_factor, ) else: From 76ab76d9ef7df9957bbe4d8d3d205f3a393dc7b3 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 1 May 2024 17:15:14 +0200 Subject: [PATCH 46/90] Bunch of fixes and enable the use of external Templates objects --- src/spikeinterface/core/generate.py | 3 +- src/spikeinterface/core/template.py | 49 +++++++++ src/spikeinterface/core/template_tools.py | 2 +- src/spikeinterface/generation/hybrid_tools.py | 101 ++++++++++-------- .../postprocessing/unit_localization.py | 50 ++++----- src/spikeinterface/preprocessing/__init__.py | 2 +- src/spikeinterface/preprocessing/motion.py | 24 ++++- .../sortingcomponents/peak_detection.py | 3 +- 8 files changed, 161 insertions(+), 73 deletions(-) diff --git a/src/spikeinterface/core/generate.py b/src/spikeinterface/core/generate.py index ec76fcbaa9..5762647294 100644 --- a/src/spikeinterface/core/generate.py +++ b/src/spikeinterface/core/generate.py @@ -3,7 +3,6 @@ import warnings import numpy as np from typing import Union, Optional, List, Literal -import warnings from math import ceil from .basesorting import SpikeVectorSortingSegment @@ -1862,7 +1861,7 @@ def get_traces( wf = template[start_template:end_template] if self.amplitude_vector is not None: wf = wf * self.amplitude_vector[i] - traces[start_traces:end_traces] += wf + traces[start_traces:end_traces] += wf.astype(traces.dtype, copy=False) return traces.astype(self.dtype, copy=False) diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py index 51688709b2..84f9ad2a5a 100644 --- a/src/spikeinterface/core/template.py +++ b/src/spikeinterface/core/template.py @@ -342,6 +342,55 @@ def to_json(self): def from_json(cls, json_str): return cls.from_dict(json.loads(json_str)) + def select_units(self, unit_ids): + """ + Return a new Templates object with only the selected units. + + Parameters + ---------- + unit_ids : list + List of unit IDs to select. + """ + unit_ids_list = list(self.unit_ids) + unit_indices = np.array([unit_ids_list.index(unit_id) for unit_id in unit_ids]) + sliced_sparsity_mask = None if self.sparsity_mask is None else self.sparsity_mask[unit_indices] + return Templates( + templates_array=self.templates_array[unit_indices], + sampling_frequency=self.sampling_frequency, + nbefore=self.nbefore, + sparsity_mask=sliced_sparsity_mask, + channel_ids=self.channel_ids, + unit_ids=unit_ids, + probe=self.probe, + check_for_consistent_sparsity=False, + ) + + def select_channels(self, channel_ids): + """ + Return a new Templates object with only the selected channels. + This operation can be useful to remove bad channels for hybrid recording + generation. + + Parameters + ---------- + channel_ids : list + List of channel IDs to select. + """ + assert not self.are_templates_sparse(), "Cannot select channels on sparse templates" + channel_ids_list = list(self.channel_ids) + channel_indices = np.array([channel_ids_list.index(channel_id) for channel_id in channel_ids]) + sliced_sparsity_mask = None if self.sparsity_mask is None else self.sparsity_mask[:, channel_indices] + return Templates( + templates_array=self.templates_array[:, :, channel_indices], + sampling_frequency=self.sampling_frequency, + nbefore=self.nbefore, + sparsity_mask=sliced_sparsity_mask, + channel_ids=channel_ids, + unit_ids=self.unit_ids, + probe=self.probe, + check_for_consistent_sparsity=False, + ) + def __eq__(self, other): """ Necessary to compare templates because they naturally compare objects by equality of their fields diff --git a/src/spikeinterface/core/template_tools.py b/src/spikeinterface/core/template_tools.py index 11581a49ce..bec1363d57 100644 --- a/src/spikeinterface/core/template_tools.py +++ b/src/spikeinterface/core/template_tools.py @@ -50,7 +50,7 @@ def _get_nbefore(one_object): raise ValueError("SortingAnalyzer need extension 'templates' to be computed") return ext.nbefore else: - raise ValueError("Input should be Templates or SortingAnalyzer or SortingAnalyzer") + raise ValueError("Input should be Templates or SortingAnalyzer") def get_template_amplitudes( diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index e826b4b5e2..5ae73e0aca 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -1,5 +1,5 @@ from __future__ import annotations -from typing import Optional +import warnings import numpy as np from spikeinterface.core.template import Templates @@ -86,6 +86,7 @@ def generate_hybrid_recording( num_units=10, sorting=None, templates=None, + templates_in_uV=True, ms_before=1.0, ms_after=3.0, unit_locations=None, @@ -110,12 +111,12 @@ def generate_hybrid_recording( Number of units, not used when sorting is given. sorting: Sorting or None An external sorting object. If not provide, one is genrated. - templates: np.array or None + templates: Templates or None, default: None The templates of units. If None they are generated. - Shape can be: - * (num_units, num_samples, num_channels): standard case - * (num_units, num_samples, num_channels, upsample_factor): case with oversample template to introduce jitter. + templates_in_uV: bool, default: True + If True, the templates are in uV, otherwise they are in the same unit as the recording. + In case the recording has scaling, the templates are "unscaled" before injection. ms_before: float, default: 1.5 Cut out in ms before spike peak. ms_after: float, default: 3 @@ -158,27 +159,13 @@ def generate_hybrid_recording( num_segments = recording.get_num_segments() dtype = recording.dtype durations = np.array([recording.get_duration(segment_index) for segment_index in range(num_segments)]) - - if sorting is None: - generate_sorting_kwargs = generate_sorting_kwargs.copy() - generate_sorting_kwargs["durations"] = durations - generate_sorting_kwargs["num_units"] = num_units - generate_sorting_kwargs["sampling_frequency"] = sampling_frequency - generate_sorting_kwargs["seed"] = seed - sorting = generate_sorting(**generate_sorting_kwargs) - else: - num_units = sorting.get_num_units() - assert sorting.sampling_frequency == sampling_frequency - - num_spikes = sorting.to_spike_vector().size - channel_locations = probe.contact_positions - if unit_locations is None: - unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) - else: - assert len(unit_locations) == num_units, "unit_locations and num_units should have the same length" if templates is None: + if unit_locations is None: + unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) + else: + assert len(unit_locations) == num_units, "unit_locations and num_units should have the same length" templates_array = generate_templates( channel_locations, unit_locations, @@ -190,19 +177,54 @@ def generate_hybrid_recording( dtype=dtype, **generate_templates_kwargs, ) + nbefore = int(ms_before * sampling_frequency / 1000.0) + nafter = int(ms_after * sampling_frequency / 1000.0) + templates = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) else: - template_locations = compute_monopolar_triangulation(templates) - templates_array = np.zeros(templates.templates_array.shape, dtype=dtype) - for i in range(len(templates_array)): - src_template = templates.templates_array[i][np.newaxis, :, :] - deltas = unit_locations[i] - template_locations[i] - templates_array[i] = interpolate_templates(src_template, channel_locations, channel_locations + deltas[:2]) + num_units = templates.num_units + assert isinstance(templates, Templates), "templates should be a Templates object" + assert ( + templates.num_channels == recording.get_num_channels() + ), "templates and recording should have the same number of channels" + unit_locations = compute_monopolar_triangulation(templates) + + channel_locations_rel = channel_locations - channel_locations[0] + templates_locations = templates.get_channel_locations() + templates_locations_rel = templates_locations - templates_locations[0] + + if not np.allclose(channel_locations_rel, templates_locations_rel): + warnings.warn("Channel locations are different between recording and templates. Interpolating templates.") + templates_array = np.zeros(templates.templates_array.shape, dtype=dtype) + for i in range(len(templates_array)): + src_template = templates.templates_array[i][np.newaxis, :, :] + templates_array[i] = interpolate_templates(src_template, templates_locations_rel, channel_locations_rel) + else: + templates_array = templates.templates_array + + # manage scaling of templates + if recording.has_scaled(): + if templates_in_uV: + templates_array = (templates_array - recording.get_channel_offsets()) / recording.get_channel_gains() + nbefore = templates.nbefore + nafter = templates.nafter + if sorting is None: + generate_sorting_kwargs = generate_sorting_kwargs.copy() + generate_sorting_kwargs["durations"] = durations + generate_sorting_kwargs["sampling_frequency"] = sampling_frequency + generate_sorting_kwargs["seed"] = seed + generate_sorting_kwargs["num_units"] = num_units + sorting = generate_sorting(**generate_sorting_kwargs) + else: + num_units = sorting.get_num_units() + assert sorting.sampling_frequency == sampling_frequency + + num_spikes = sorting.to_spike_vector().size sorting.set_property("gt_unit_locations", unit_locations) - nbefore = int(ms_before * sampling_frequency / 1000.0) - nafter = int(ms_after * sampling_frequency / 1000.0) - assert (nbefore + nafter) == templates_array.shape[1] + assert (nbefore + nafter) == templates_array.shape[ + 1 + ], "templates and ms_before, ms_after should have the same length" if templates_array.ndim == 3: upsample_vector = None @@ -211,10 +233,12 @@ def generate_hybrid_recording( upsample_factor = templates_array.shape[3] upsample_vector = rng.integers(0, upsample_factor, size=num_spikes) - if motion_info is not None: - - templates = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) + if amplitude_std is not None: + amplitude_factor = rng.normal(loc=1, scale=amplitude_std, size=num_spikes) + else: + amplitude_factor = None + if motion_info is not None: start = np.array([0, np.min(motion_info["motion"])]) stop = np.array([0, np.max(motion_info["motion"])]) displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) @@ -233,12 +257,6 @@ def generate_hybrid_recording( a = 1 / np.abs((unit_locations[count, 1] - motion_info["spatial_bins"])) displacement_unit_factor[count] = a / a.sum() - if amplitude_std is not None: - spike_vector = sorting.to_spike_vector() - amplitude_factor = 1 + rng.randn(spike_vector.size) * amplitude_std - else: - amplitude_factor = None - hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, parent_recording=recording, @@ -251,7 +269,6 @@ def generate_hybrid_recording( ) else: - hybrid_recording = InjectTemplatesRecording( sorting, templates_array, diff --git a/src/spikeinterface/postprocessing/unit_localization.py b/src/spikeinterface/postprocessing/unit_localization.py index d52766362b..9bb0f9cbbc 100644 --- a/src/spikeinterface/postprocessing/unit_localization.py +++ b/src/spikeinterface/postprocessing/unit_localization.py @@ -88,7 +88,7 @@ def get_data(self, outputs="numpy"): def compute_monopolar_triangulation( - sorting_analyzer, + sorting_analyzer_or_templates, optimizer="minimize_with_log_penality", radius_um=75, max_distance_um=1000, @@ -115,8 +115,8 @@ def compute_monopolar_triangulation( Parameters ---------- - sorting_analyzer: SortingAnalyzer - A SortingAnalyzer object + sorting_analyzer_or_templates: SortingAnalyzer | Templates + A SortingAnalyzer or Templates object method: "least_square" | "minimize_with_log_penality", default: "least_square" The optimizer to use radius_um: float, default: 75 @@ -142,13 +142,13 @@ def compute_monopolar_triangulation( assert optimizer in ("least_square", "minimize_with_log_penality") assert feature in ["ptp", "energy", "peak_voltage"], f"{feature} is not a valid feature" - unit_ids = sorting_analyzer.unit_ids + unit_ids = sorting_analyzer_or_templates.unit_ids - contact_locations = sorting_analyzer.get_channel_locations() + contact_locations = sorting_analyzer_or_templates.get_channel_locations() - sparsity = compute_sparsity(sorting_analyzer, method="radius", radius_um=radius_um) - templates = get_dense_templates_array(sorting_analyzer) - nbefore = _get_nbefore(sorting_analyzer) + sparsity = compute_sparsity(sorting_analyzer_or_templates, method="radius", radius_um=radius_um) + templates = get_dense_templates_array(sorting_analyzer_or_templates) + nbefore = _get_nbefore(sorting_analyzer_or_templates) if enforce_decrease: neighbours_mask = np.zeros((templates.shape[0], templates.shape[2]), dtype=bool) @@ -156,7 +156,7 @@ def compute_monopolar_triangulation( chan_inds = sparsity.unit_id_to_channel_indices[unit_id] neighbours_mask[i, chan_inds] = True enforce_decrease_radial_parents = make_radial_order_parents(contact_locations, neighbours_mask) - best_channels = get_template_extremum_channel(sorting_analyzer, outputs="index") + best_channels = get_template_extremum_channel(sorting_analyzer_or_templates, outputs="index") unit_location = np.zeros((unit_ids.size, 4), dtype="float64") for i, unit_id in enumerate(unit_ids): @@ -185,13 +185,13 @@ def compute_monopolar_triangulation( return unit_location -def compute_center_of_mass(sorting_analyzer, peak_sign="neg", radius_um=75, feature="ptp"): +def compute_center_of_mass(sorting_analyzer_or_templates, peak_sign="neg", radius_um=75, feature="ptp"): """ Computes the center of mass (COM) of a unit based on the template amplitudes. Parameters ---------- - sorting_analyzer: SortingAnalyzer + sorting_analyzer_or_templates: SortingAnalyzer | Templates A SortingAnalyzer object peak_sign: "neg" | "pos" | "both", default: "neg" Sign of the template to compute best channels @@ -204,15 +204,17 @@ def compute_center_of_mass(sorting_analyzer, peak_sign="neg", radius_um=75, feat ------- unit_location: np.array """ - unit_ids = sorting_analyzer.unit_ids + unit_ids = sorting_analyzer_or_templates.unit_ids - contact_locations = sorting_analyzer.get_channel_locations() + contact_locations = sorting_analyzer_or_templates.get_channel_locations() assert feature in ["ptp", "mean", "energy", "peak_voltage"], f"{feature} is not a valid feature" - sparsity = compute_sparsity(sorting_analyzer, peak_sign=peak_sign, method="radius", radius_um=radius_um) - templates = get_dense_templates_array(sorting_analyzer) - nbefore = _get_nbefore(sorting_analyzer) + sparsity = compute_sparsity( + sorting_analyzer_or_templates, peak_sign=peak_sign, method="radius", radius_um=radius_um + ) + templates = get_dense_templates_array(sorting_analyzer_or_templates) + nbefore = _get_nbefore(sorting_analyzer_or_templates) unit_location = np.zeros((unit_ids.size, 2), dtype="float64") for i, unit_id in enumerate(unit_ids): @@ -238,7 +240,7 @@ def compute_center_of_mass(sorting_analyzer, peak_sign="neg", radius_um=75, feat def compute_grid_convolution( - sorting_analyzer, + sorting_analyzer_or_templates, peak_sign="neg", radius_um=40.0, upsampling_um=5, @@ -253,7 +255,7 @@ def compute_grid_convolution( Parameters ---------- - sorting_analyzer: SortingAnalyzer + sorting_analyzer_or_templates: SortingAnalyzer | Templates A SortingAnalyzer object peak_sign: "neg" | "pos" | "both", default: "neg" Sign of the template to compute best channels @@ -279,14 +281,14 @@ def compute_grid_convolution( unit_location: np.array """ - contact_locations = sorting_analyzer.get_channel_locations() - unit_ids = sorting_analyzer.unit_ids + contact_locations = sorting_analyzer_or_templates.get_channel_locations() + unit_ids = sorting_analyzer_or_templates.unit_ids - templates = get_dense_templates_array(sorting_analyzer) - nbefore = _get_nbefore(sorting_analyzer) + templates = get_dense_templates_array(sorting_analyzer_or_templates) + nbefore = _get_nbefore(sorting_analyzer_or_templates) nafter = templates.shape[1] - nbefore - fs = sorting_analyzer.sampling_frequency + fs = sorting_analyzer_or_templates.sampling_frequency percentile = 100 - percentile assert 0 <= percentile <= 100, "Percentile should be in [0, 100]" @@ -302,7 +304,7 @@ def compute_grid_convolution( contact_locations, radius_um, upsampling_um, margin_um, weight_method ) - peak_channels = get_template_extremum_channel(sorting_analyzer, peak_sign, outputs="index") + peak_channels = get_template_extremum_channel(sorting_analyzer_or_templates, peak_sign, outputs="index") weights_sparsity_mask = weights > 0 diff --git a/src/spikeinterface/preprocessing/__init__.py b/src/spikeinterface/preprocessing/__init__.py index 38343f8804..5f9ac046e1 100644 --- a/src/spikeinterface/preprocessing/__init__.py +++ b/src/spikeinterface/preprocessing/__init__.py @@ -1,6 +1,6 @@ from .preprocessinglist import * -from .motion import correct_motion, load_motion_info +from .motion import correct_motion, load_motion_info, save_motion_info from .preprocessing_tools import get_spatial_interpolation_kernel from .detect_bad_channels import detect_bad_channels diff --git a/src/spikeinterface/preprocessing/motion.py b/src/spikeinterface/preprocessing/motion.py index 1b2719ec4f..15011fe88b 100644 --- a/src/spikeinterface/preprocessing/motion.py +++ b/src/spikeinterface/preprocessing/motion.py @@ -5,7 +5,7 @@ import numpy as np import json -import copy +import shutil from spikeinterface.core import get_noise_levels, fix_job_kwargs from spikeinterface.core.job_tools import _shared_job_kwargs_doc @@ -419,6 +419,28 @@ def correct_motion( correct_motion.__doc__ = correct_motion.__doc__.format(_doc_presets, _shared_job_kwargs_doc) +def save_motion_info(motion_info, folder, overwrite=False): + folder = Path(folder) + if folder.is_dir(): + if not overwrite: + raise FileExistsError(f"Folder {folder} already exists. Use `overwrite=True` to overwrite.") + else: + shutil.rmtree(folder) + folder.mkdir(exist_ok=True, parents=True) + + (folder / "parameters.json").write_text( + json.dumps(motion_info["parameters"], indent=4, cls=SIJsonEncoder), encoding="utf8" + ) + (folder / "run_times.json").write_text(json.dumps(motion_info["run_times"], indent=4), encoding="utf8") + + np.save(folder / "peaks.npy", motion_info["peaks"]) + np.save(folder / "peak_locations.npy", motion_info["peak_locations"]) + np.save(folder / "motion.npy", motion_info["motion"]) + np.save(folder / "temporal_bins.npy", motion_info["temporal_bins"]) + if motion_info["spatial_bins"] is not None: + np.save(folder / "spatial_bins.npy", motion_info["spatial_bins"]) + + def load_motion_info(folder): folder = Path(folder) diff --git a/src/spikeinterface/sortingcomponents/peak_detection.py b/src/spikeinterface/sortingcomponents/peak_detection.py index 508a033c41..f26fc55264 100644 --- a/src/spikeinterface/sortingcomponents/peak_detection.py +++ b/src/spikeinterface/sortingcomponents/peak_detection.py @@ -25,8 +25,7 @@ base_peak_dtype, ) -from spikeinterface.postprocessing.unit_localization import get_convolution_weights -from ..core import get_chunk_with_margin +from spikeinterface.postprocessing.localization_tools import get_convolution_weights from .tools import make_multi_method_doc From 104357709d74d5394129447e190e80375cc8e660 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 1 May 2024 20:17:50 +0200 Subject: [PATCH 47/90] Fix scaling for drifting templates --- src/spikeinterface/generation/drift_tools.py | 2 +- src/spikeinterface/generation/hybrid_tools.py | 14 ++++++++++---- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 31a2fff885..9e47c76e63 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -507,7 +507,7 @@ def get_traces( wf = template[start_template:end_template] if self.amplitude_vector is not None: wf *= self.amplitude_vector[i] - traces[start_traces:end_traces] += wf + traces[start_traces:end_traces] += wf.astype(self.dtype, copy=False) return traces.astype(self.dtype) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 5ae73e0aca..5edff8fea3 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -181,11 +181,13 @@ def generate_hybrid_recording( nafter = int(ms_after * sampling_frequency / 1000.0) templates = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) else: - num_units = templates.num_units assert isinstance(templates, Templates), "templates should be a Templates object" assert ( templates.num_channels == recording.get_num_channels() ), "templates and recording should have the same number of channels" + num_units = templates.num_units + nbefore = templates.nbefore + nafter = templates.nafter unit_locations = compute_monopolar_triangulation(templates) channel_locations_rel = channel_locations - channel_locations[0] @@ -202,11 +204,13 @@ def generate_hybrid_recording( templates_array = templates.templates_array # manage scaling of templates + templates_ = templates if recording.has_scaled(): if templates_in_uV: templates_array = (templates_array - recording.get_channel_offsets()) / recording.get_channel_gains() - nbefore = templates.nbefore - nafter = templates.nafter + # make a copy of the templates and reset templates_array (might have scaled templates) + templates_ = templates.select_units(templates.unit_ids) + templates_.templates_array = templates_array if sorting is None: generate_sorting_kwargs = generate_sorting_kwargs.copy() @@ -242,7 +246,9 @@ def generate_hybrid_recording( start = np.array([0, np.min(motion_info["motion"])]) stop = np.array([0, np.max(motion_info["motion"])]) displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) - drifting_templates = DriftingTemplates.from_static(templates) + + # use templates_, because templates_array might have been scaled + drifting_templates = DriftingTemplates.from_static(templates_) drifting_templates.precompute_displacements(displacements) displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] From 836c2d4804c163572572b2f7a8ae87e601882acf Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 2 May 2024 10:08:41 +0200 Subject: [PATCH 48/90] fix tests --- src/spikeinterface/generation/hybrid_tools.py | 2 +- src/spikeinterface/generation/tests/test_mock.py | 3 --- 2 files changed, 1 insertion(+), 4 deletions(-) delete mode 100644 src/spikeinterface/generation/tests/test_mock.py diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 5edff8fea3..5bae7a5180 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -179,7 +179,7 @@ def generate_hybrid_recording( ) nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) - templates = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) + templates_ = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) else: assert isinstance(templates, Templates), "templates should be a Templates object" assert ( diff --git a/src/spikeinterface/generation/tests/test_mock.py b/src/spikeinterface/generation/tests/test_mock.py deleted file mode 100644 index 37c6bde47e..0000000000 --- a/src/spikeinterface/generation/tests/test_mock.py +++ /dev/null @@ -1,3 +0,0 @@ -def test_mock(): - # TODO: Add test logic here - pass From 007acf72eb1c963b7d6f82fec4a1b4c8072ef797 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 6 May 2024 11:56:08 +0200 Subject: [PATCH 49/90] Extend template database functionality --- src/spikeinterface/core/recording_tools.py | 2 +- src/spikeinterface/generation/__init__.py | 5 +- src/spikeinterface/generation/drift_tools.py | 3 +- .../generation/template_database.py | 116 +++++++++++++++++- .../generation/tests/test_template_fetch.py | 2 +- 5 files changed, 122 insertions(+), 6 deletions(-) diff --git a/src/spikeinterface/core/recording_tools.py b/src/spikeinterface/core/recording_tools.py index 7934a61429..919382dadc 100644 --- a/src/spikeinterface/core/recording_tools.py +++ b/src/spikeinterface/core/recording_tools.py @@ -697,7 +697,7 @@ def get_chunk_with_margin( case zero padding is used, in the second case np.pad is called with mod="reflect". """ - length = rec_segment.get_num_samples() + length = int(rec_segment.get_num_samples()) if channel_indices is None: channel_indices = slice(None) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index 16de944218..8f59c9e1ca 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -15,5 +15,8 @@ ) from .template_database import ( - fetch_templates_from_database, + fetch_template_dataset, + fetch_templates_info, + list_avaliabe_datasets, + get_templates_from_database, ) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 9e47c76e63..acc642cd19 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -442,7 +442,8 @@ def __init__( # TODO: self.upsample_vector = upsample_vector self.upsample_vector = None self.parent_recording = parent_recording_segment - self.num_samples = parent_recording_segment.get_num_frames() if num_samples is None else num_samples + self.num_samples = parent_recording_segment.get_num_samples() if num_samples is None else num_samples + self.num_samples = int(num_samples) self.displacement_indices = displacement_indices self.templates_array_moved = templates_array_moved diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index 2bfcc32245..ae5c181d69 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -1,8 +1,26 @@ -from spikeinterface.core.template import Templates import zarr +import numpy as np +import pandas as pd + +from spikeinterface.core.template import Templates -def fetch_templates_from_database(dataset="test_templates") -> Templates: +def fetch_template_dataset(dataset="test_templates") -> Templates: + """ + Fetch a template dataset from the spikeinterface template database. + A dataset is a collection of templates with associated metadata for one specific recording. + + Parameters + ---------- + dataset : str, default: "test_templates" + The name of the dataset to fetch. + The dataset must be available in the spikeinterface template database. + + Returns + ------- + Templates + _description_ + """ s3_path = f"s3://spikeinterface-template-database/{dataset}/" zarr_group = zarr.open_consolidated(s3_path, storage_options={"anon": True}) @@ -10,3 +28,97 @@ def fetch_templates_from_database(dataset="test_templates") -> Templates: templates_object = Templates.from_zarr_group(zarr_group) return templates_object + + +def fetch_templates_info() -> pd.DataFrame: + """ + Fetch the information about the templates in the spikeinterface template database. + + Returns + ------- + pd.DataFrame + Dataframe containing the template information. + """ + s3_path = "s3://spikeinterface-template-database/templates.csv" + df = pd.read_csv(s3_path, storage_options={"anon": True}) + + return df + + +def list_avaliabe_datasets() -> list: + """ + List all available datasets in the spikeinterface template database. + + Returns + ------- + list + List of available datasets. + """ + df = fetch_templates_info() + datasets = np.unique(df["dataset"]).tolist() + + return datasets + + +def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] | np.ndarray) -> Templates: + """ + Retrieve templates from the spikeinterface template database. + + Parameters + ---------- + template_info : pd.DataFrame or array-like + Dataframe containing the template information, obtained by slicing/querying the output of fetch_templates_info. + + Returns + ------- + Templates + The templates object. + """ + templates_array = [] + if isinstance(template_df_or_indices, pd.DataFrame): + template_info = template_df_or_indices + else: + template_info = fetch_templates_info().iloc[template_df_or_indices] + requested_datasets = np.unique(template_info["dataset"]).tolist() + + nbefore = None + sampling_frequency = None + channel_locations = None + + for dataset in requested_datasets: + templates = fetch_template_dataset(dataset) + + # check consisency across datasets + if nbefore is None: + nbefore = templates.nbefore + if channel_locations is None: + channel_locations = templates.get_channel_locations() + if sampling_frequency is None: + sampling_frequency = templates.sampling_frequency + current_nbefore = templates.nbefore + current_channel_locations = templates.get_channel_locations() + current_sampling_frequency = templates.sampling_frequency + + assert ( + current_nbefore == nbefore + ), f"Number of samples before the peak is not consistent across datasets: {current_nbefore} != {nbefore}" + assert ( + current_sampling_frequency == sampling_frequency + ), f"Sampling frequency is not consistent across datasets: {current_sampling_frequency} != {sampling_frequency}" + assert np.array_equal( + current_channel_locations - current_channel_locations[0], + channel_locations - channel_locations[0], + ), "Channel locations are not consistent across datasets" + + template_indices = template_info[template_info["dataset"] == dataset]["template_index"] + templates_array.append(templates.templates_array[template_indices, :, :]) + templates_array = np.concatenate(templates_array, axis=0) + templates = Templates( + templates_array, + sampling_frequency=templates.sampling_frequency, + channel_ids=templates.channel_ids, + nbefore=templates.nbefore, + probe=templates.probe, + ) + + return templates diff --git a/src/spikeinterface/generation/tests/test_template_fetch.py b/src/spikeinterface/generation/tests/test_template_fetch.py index a7cc31af44..ef219e3671 100644 --- a/src/spikeinterface/generation/tests/test_template_fetch.py +++ b/src/spikeinterface/generation/tests/test_template_fetch.py @@ -5,7 +5,7 @@ def test_basic_call(): - templates = fetch_templates_from_database() + templates = fetch_templates_from_database("test_templates") assert isinstance(templates, Templates) From 74bb13c2cdd23138e4d4b4ed49efa0177d1bcd08 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 6 May 2024 14:28:53 +0200 Subject: [PATCH 50/90] Final touches --- .../generation/template_database.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index ae5c181d69..bc61bfaf0e 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -80,10 +80,13 @@ def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] else: template_info = fetch_templates_info().iloc[template_df_or_indices] requested_datasets = np.unique(template_info["dataset"]).tolist() + print(f"Fetching templates from {len(requested_datasets)} datasets") nbefore = None sampling_frequency = None channel_locations = None + probe = None + channel_ids = None for dataset in requested_datasets: templates = fetch_template_dataset(dataset) @@ -95,6 +98,10 @@ def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] channel_locations = templates.get_channel_locations() if sampling_frequency is None: sampling_frequency = templates.sampling_frequency + if probe is None: + probe = templates.probe + if channel_ids is None: + channel_ids = templates.channel_ids current_nbefore = templates.nbefore current_channel_locations = templates.get_channel_locations() current_sampling_frequency = templates.sampling_frequency @@ -115,10 +122,10 @@ def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] templates_array = np.concatenate(templates_array, axis=0) templates = Templates( templates_array, - sampling_frequency=templates.sampling_frequency, - channel_ids=templates.channel_ids, - nbefore=templates.nbefore, - probe=templates.probe, + sampling_frequency=sampling_frequency, + channel_ids=channel_ids, + nbefore=nbefore, + probe=probe, ) return templates From a0179a1f9ddd8a37e89544eade5575317a343bcc Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 6 May 2024 14:40:01 +0200 Subject: [PATCH 51/90] pandas local import --- src/spikeinterface/generation/template_database.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index bc61bfaf0e..680efb19f6 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -1,6 +1,5 @@ import zarr import numpy as np -import pandas as pd from spikeinterface.core.template import Templates @@ -21,7 +20,6 @@ def fetch_template_dataset(dataset="test_templates") -> Templates: Templates _description_ """ - s3_path = f"s3://spikeinterface-template-database/{dataset}/" zarr_group = zarr.open_consolidated(s3_path, storage_options={"anon": True}) @@ -30,7 +28,7 @@ def fetch_template_dataset(dataset="test_templates") -> Templates: return templates_object -def fetch_templates_info() -> pd.DataFrame: +def fetch_templates_info() -> "pandas.DataFrame": """ Fetch the information about the templates in the spikeinterface template database. @@ -39,6 +37,8 @@ def fetch_templates_info() -> pd.DataFrame: pd.DataFrame Dataframe containing the template information. """ + import pandas as pd + s3_path = "s3://spikeinterface-template-database/templates.csv" df = pd.read_csv(s3_path, storage_options={"anon": True}) @@ -60,7 +60,7 @@ def list_avaliabe_datasets() -> list: return datasets -def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] | np.ndarray) -> Templates: +def get_templates_from_database(template_df_or_indices: "pandas.DataFrame" | list[int] | np.ndarray) -> Templates: """ Retrieve templates from the spikeinterface template database. @@ -74,6 +74,8 @@ def get_templates_from_database(template_df_or_indices: pd.DataFrame | list[int] Templates The templates object. """ + import pandas as pd + templates_array = [] if isinstance(template_df_or_indices, pd.DataFrame): template_info = template_df_or_indices From e0b1ee641a82cfcc4016df336309d45a53d4311b Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 6 May 2024 14:50:22 +0200 Subject: [PATCH 52/90] Fix typing and SC2 matching --- src/spikeinterface/generation/drift_tools.py | 2 +- src/spikeinterface/generation/template_database.py | 4 ++-- src/spikeinterface/sorters/internal/spyking_circus2.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index acc642cd19..fc90f31792 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -468,7 +468,7 @@ def get_traces( n_channels = len(channel_indices) if self.parent_recording is not None: - traces = self.parent_recording.get_traces(int(start_frame), int(end_frame), channel_indices).copy() + traces = self.parent_recording.get_traces(start_frame, end_frame, channel_indices).copy() else: traces = np.zeros([end_frame - start_frame, n_channels], dtype=self.dtype) diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index 680efb19f6..022174c550 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -60,13 +60,13 @@ def list_avaliabe_datasets() -> list: return datasets -def get_templates_from_database(template_df_or_indices: "pandas.DataFrame" | list[int] | np.ndarray) -> Templates: +def get_templates_from_database(template_df_or_indices: "pandas.DataFrame | list[int] | np.ndarray") -> Templates: """ Retrieve templates from the spikeinterface template database. Parameters ---------- - template_info : pd.DataFrame or array-like + template_df_or_indices : pd.DataFrame or array-like Dataframe containing the template information, obtained by slicing/querying the output of fetch_templates_info. Returns diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py index d36728cbed..100fe27388 100644 --- a/src/spikeinterface/sorters/internal/spyking_circus2.py +++ b/src/spikeinterface/sorters/internal/spyking_circus2.py @@ -280,7 +280,7 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose): matching_job_params["chunk_duration"] = "100ms" spikes = find_spikes_from_templates( - recording_f, matching_method, method_kwargs=matching_params, **matching_job_params + recording_w, matching_method, method_kwargs=matching_params, **matching_job_params ) if params["debug"]: From 38a887ef76feabe0fa3c88e25c77d73fb2744496 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 6 May 2024 15:46:06 +0200 Subject: [PATCH 53/90] Fix tests --- src/spikeinterface/generation/__init__.py | 2 +- .../generation/template_database.py | 2 +- .../generation/tests/test_hybrid_tools.py | 3 +- .../generation/tests/test_template_fetch.py | 38 +++++++++++++++++-- 4 files changed, 39 insertions(+), 6 deletions(-) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index 8f59c9e1ca..0b2c08472a 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -17,6 +17,6 @@ from .template_database import ( fetch_template_dataset, fetch_templates_info, - list_avaliabe_datasets, + list_available_datasets, get_templates_from_database, ) diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index 022174c550..3dfe110022 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -45,7 +45,7 @@ def fetch_templates_info() -> "pandas.DataFrame": return df -def list_avaliabe_datasets() -> list: +def list_available_datasets() -> list: """ List all available datasets in the spikeinterface template database. diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 36b32e99d0..b315c8cc9c 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -3,7 +3,6 @@ from pathlib import Path from spikeinterface.core.generate import generate_ground_truth_recording, generate_sorting -from spikeinterface.core import Templates, BaseRecording from spikeinterface.preprocessing.motion import correct_motion, load_motion_info from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording @@ -30,6 +29,8 @@ def test_estimate_templates(tmp_path): rec, sorting = generate_ground_truth_recording(num_units=10, sampling_frequency=20000) templates = estimate_templates_from_recording(rec, output_folder=tmp_path / "sc", remove_existing_folder=True) assert len(templates.templates_array) > 0 + hybrid, _ = generate_hybrid_recording(rec, templates=templates) + assert np.array_equal(hybrid.templates, templates.templates_array) if __name__ == "__main__": diff --git a/src/spikeinterface/generation/tests/test_template_fetch.py b/src/spikeinterface/generation/tests/test_template_fetch.py index ef219e3671..077d7a7e87 100644 --- a/src/spikeinterface/generation/tests/test_template_fetch.py +++ b/src/spikeinterface/generation/tests/test_template_fetch.py @@ -1,13 +1,45 @@ import pytest -from spikeinterface.generation import fetch_templates_from_database + + +from spikeinterface.generation import ( + fetch_template_dataset, + fetch_templates_info, + list_available_datasets, + get_templates_from_database, +) from spikeinterface.core.template import Templates -def test_basic_call(): +def test_fetch_datasets(): - templates = fetch_templates_from_database("test_templates") + available_datasets = list_available_datasets() + assert len(available_datasets) > 0 + templates = fetch_template_dataset("test_templates") assert isinstance(templates, Templates) assert templates.num_units == 100 assert templates.num_channels == 384 + + +def test_fetch_templates_info(): + import pandas as pd + + templates_info = fetch_templates_info() + + assert isinstance(templates_info, pd.DataFrame) + + assert "dataset" in templates_info.columns + + +def test_get_templates_from_database(): + templates_info = fetch_templates_info() + + templates_info = templates_info.iloc[::15] + num_selected = len(templates_info) + + templates = get_templates_from_database(templates_info) + + assert isinstance(templates, Templates) + + assert templates.num_units == num_selected From 5dccdab89bba7875184857f087d1dfb2925b2458 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 10 May 2024 18:27:45 +0200 Subject: [PATCH 54/90] Remove unused import --- src/spikeinterface/widgets/unit_waveforms.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py index 13d654fb06..febfcb6020 100644 --- a/src/spikeinterface/widgets/unit_waveforms.py +++ b/src/spikeinterface/widgets/unit_waveforms.py @@ -1,6 +1,5 @@ from __future__ import annotations -from coverage import data import numpy as np from warnings import warn From 289f3374bad65cec559dee5d9b6450bc171c17d3 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 10 May 2024 18:29:08 +0200 Subject: [PATCH 55/90] Update src/spikeinterface/widgets/unit_waveforms.py --- src/spikeinterface/widgets/unit_waveforms.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py index 13d654fb06..febfcb6020 100644 --- a/src/spikeinterface/widgets/unit_waveforms.py +++ b/src/spikeinterface/widgets/unit_waveforms.py @@ -1,6 +1,5 @@ from __future__ import annotations -from coverage import data import numpy as np from warnings import warn From e25bac35933ed21ad260187fc8b33ad60877d65e Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 13 May 2024 16:51:11 +0200 Subject: [PATCH 56/90] Fix get_unit_colors tests and rename filter_templates to select_templates --- src/spikeinterface/exporters/report.py | 2 +- src/spikeinterface/generation/__init__.py | 2 +- src/spikeinterface/generation/hybrid_tools.py | 6 +++--- src/spikeinterface/widgets/spike_locations.py | 2 +- src/spikeinterface/widgets/spikes_on_traces.py | 2 +- src/spikeinterface/widgets/unit_depths.py | 2 +- src/spikeinterface/widgets/unit_locations.py | 2 +- src/spikeinterface/widgets/unit_summary.py | 2 +- src/spikeinterface/widgets/unit_templates.py | 6 +++++- src/spikeinterface/widgets/unit_waveforms.py | 2 +- src/spikeinterface/widgets/unit_waveforms_density_map.py | 2 +- 11 files changed, 17 insertions(+), 13 deletions(-) diff --git a/src/spikeinterface/exporters/report.py b/src/spikeinterface/exporters/report.py index ae3badaa6d..207b148cd8 100644 --- a/src/spikeinterface/exporters/report.py +++ b/src/spikeinterface/exporters/report.py @@ -107,7 +107,7 @@ def export_report( units["amplitude"] = pd.Series(get_template_extremum_amplitude(sorting_analyzer, peak_sign=peak_sign)) units.to_csv(output_folder / "unit list.csv", sep="\t") - unit_colors = sw.get_unit_colors(sorting) + unit_colors = sw.get_unit_colors(sorting.unit_ids) # global figures fig = plt.figure(figsize=(20, 10)) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index 230f4fe350..b868996563 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -9,7 +9,7 @@ from .hybrid_tools import ( generate_hybrid_recording, estimate_templates_from_recording, - filter_templates, + select_templates, scale_templates, shift_templates, ) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 29bae9fde4..6502bbf38d 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -86,7 +86,7 @@ def estimate_templates_from_recording( return templates -def filter_templates( +def select_templates( templates: Templates, min_amplitude: float | None = None, max_amplitude: float | None = None, @@ -96,7 +96,7 @@ def filter_templates( depth_direction: Literal["x", "y"] = "y", ): """ - Filter templates based on amplitude and depth. + Select templates from an existing Templates object based on amplitude and depth. Parameters ---------- @@ -118,7 +118,7 @@ def filter_templates( Returns ------- Templates - The filtered templates + The selected templates """ assert ( min_amplitude is not None or max_amplitude is not None or min_depth is not None or max_depth is not None diff --git a/src/spikeinterface/widgets/spike_locations.py b/src/spikeinterface/widgets/spike_locations.py index 94c9def630..c49bc3653e 100644 --- a/src/spikeinterface/widgets/spike_locations.py +++ b/src/spikeinterface/widgets/spike_locations.py @@ -70,7 +70,7 @@ def __init__( segment_index = 0 if unit_colors is None: - unit_colors = get_unit_colors(sorting) + unit_colors = get_unit_colors(sorting.unit_ids) if unit_ids is None: unit_ids = sorting.unit_ids diff --git a/src/spikeinterface/widgets/spikes_on_traces.py b/src/spikeinterface/widgets/spikes_on_traces.py index 9f1a3faeb2..9f69ccb7f0 100644 --- a/src/spikeinterface/widgets/spikes_on_traces.py +++ b/src/spikeinterface/widgets/spikes_on_traces.py @@ -99,7 +99,7 @@ def __init__( unit_ids = unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting) + unit_colors = get_unit_colors(sorting.unit_ids) # sparsity is done on all the units even if unit_ids is a few ones because some backend need then all if sorting_analyzer.is_sparse(): diff --git a/src/spikeinterface/widgets/unit_depths.py b/src/spikeinterface/widgets/unit_depths.py index c5fe3e05e8..136008e409 100644 --- a/src/spikeinterface/widgets/unit_depths.py +++ b/src/spikeinterface/widgets/unit_depths.py @@ -35,7 +35,7 @@ def __init__( unit_ids = sorting_analyzer.sorting.unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting) + unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) colors = [unit_colors[unit_id] for unit_id in unit_ids] diff --git a/src/spikeinterface/widgets/unit_locations.py b/src/spikeinterface/widgets/unit_locations.py index 3329c2183c..e4517ceddc 100644 --- a/src/spikeinterface/widgets/unit_locations.py +++ b/src/spikeinterface/widgets/unit_locations.py @@ -61,7 +61,7 @@ def __init__( probegroup = sorting_analyzer.get_probegroup() if unit_colors is None: - unit_colors = get_unit_colors(sorting) + unit_colors = get_unit_colors(sorting.unit_ids) if unit_ids is None: unit_ids = sorting.unit_ids diff --git a/src/spikeinterface/widgets/unit_summary.py b/src/spikeinterface/widgets/unit_summary.py index ea6476784e..50a186e981 100644 --- a/src/spikeinterface/widgets/unit_summary.py +++ b/src/spikeinterface/widgets/unit_summary.py @@ -48,7 +48,7 @@ def __init__( sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting) + unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) plot_data = dict( sorting_analyzer=sorting_analyzer, diff --git a/src/spikeinterface/widgets/unit_templates.py b/src/spikeinterface/widgets/unit_templates.py index 1350bb71a5..eb9a90d1d1 100644 --- a/src/spikeinterface/widgets/unit_templates.py +++ b/src/spikeinterface/widgets/unit_templates.py @@ -1,5 +1,6 @@ from __future__ import annotations +from ..core import SortingAnalyzer from .unit_waveforms import UnitWaveformsWidget from .base import to_attr @@ -17,6 +18,9 @@ def plot_sortingview(self, data_plot, **backend_kwargs): dp = to_attr(data_plot) + sorting_analyzer = dp.sorting_analyzer_or_templates + assert isinstance(sorting_analyzer, SortingAnalyzer), "This widget requires a SortingAnalyzer as input" + assert len(dp.templates_shading) <= 4, "Only 2 ans 4 templates shading are supported in sortingview" # ensure serializable for sortingview @@ -50,7 +54,7 @@ def plot_sortingview(self, data_plot, **backend_kwargs): v_average_waveforms = vv.AverageWaveforms(average_waveforms=aw_items, channel_locations=locations) if not dp.hide_unit_selector: - v_units_table = generate_unit_table_view(dp.sorting_analyzer.sorting) + v_units_table = generate_unit_table_view(sorting_analyzer.sorting) self.view = vv.Box( direction="horizontal", diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py index febfcb6020..19034f46ae 100644 --- a/src/spikeinterface/widgets/unit_waveforms.py +++ b/src/spikeinterface/widgets/unit_waveforms.py @@ -106,7 +106,7 @@ def __init__( **backend_kwargs, ): if not isinstance(sorting_analyzer_or_templates, Templates): - sorting_analyzer_or_templates = self.ensure_sorting_analyzer(sorting_analyzer) + sorting_analyzer_or_templates = self.ensure_sorting_analyzer(sorting_analyzer_or_templates) else: plot_waveforms = False shade_templates = False diff --git a/src/spikeinterface/widgets/unit_waveforms_density_map.py b/src/spikeinterface/widgets/unit_waveforms_density_map.py index 2e7ec883e6..1b5ea8e79a 100644 --- a/src/spikeinterface/widgets/unit_waveforms_density_map.py +++ b/src/spikeinterface/widgets/unit_waveforms_density_map.py @@ -57,7 +57,7 @@ def __init__( unit_ids = sorting_analyzer.unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting) + unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) if use_max_channel: assert len(unit_ids) == 1, " UnitWaveformDensity : use_max_channel=True works only with one unit" From b3816bdcdeed6666e184570506042a8c3388c093 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 14 May 2024 09:35:49 +0200 Subject: [PATCH 57/90] Fix test zarr path in tests --- src/spikeinterface/generation/tests/test_template_fetch.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/tests/test_template_fetch.py b/src/spikeinterface/generation/tests/test_template_fetch.py index 077d7a7e87..d99b5a16f1 100644 --- a/src/spikeinterface/generation/tests/test_template_fetch.py +++ b/src/spikeinterface/generation/tests/test_template_fetch.py @@ -15,7 +15,7 @@ def test_fetch_datasets(): available_datasets = list_available_datasets() assert len(available_datasets) > 0 - templates = fetch_template_dataset("test_templates") + templates = fetch_template_dataset("test_templates.zarr") assert isinstance(templates, Templates) assert templates.num_units == 100 From f034b39b1f0f6f9f1fc721d8ad29d54976bed67b Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 14 May 2024 11:26:24 +0200 Subject: [PATCH 58/90] Fix scaling and add temlate manipulation tests --- src/spikeinterface/generation/hybrid_tools.py | 9 ++- .../generation/tests/test_hybrid_tools.py | 5 +- .../generation/tests/test_template_fetch.py | 74 ++++++++++++++++++- 3 files changed, 83 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 6502bbf38d..3897618db4 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -200,8 +200,13 @@ def scale_templates( for i in range(templates.num_units): amplitudes[i] = amp_fun(templates_array[i, :, extremum_channel_indices[i]]) - scale_values = (max_amplitude - min_amplitude) / (amplitudes.max() - amplitudes.min()) - scaled_templates_array = templates_array * scale_values + # scale templates to meet min_amplitude and max_amplitude range + min_scale = np.min(amplitudes) / min_amplitude + max_scale = np.max(amplitudes) / max_amplitude + m = (max_scale - min_scale) / (np.max(amplitudes) - np.min(amplitudes)) + scales = m * (amplitudes - np.min(amplitudes)) + min_scale + + scaled_templates_array = templates.templates_array / scales[:, None, None] return Templates( templates_array=scaled_templates_array, diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index b315c8cc9c..95c791ad09 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -4,7 +4,10 @@ from spikeinterface.core.generate import generate_ground_truth_recording, generate_sorting from spikeinterface.preprocessing.motion import correct_motion, load_motion_info -from spikeinterface.generation.hybrid_tools import estimate_templates_from_recording, generate_hybrid_recording +from spikeinterface.generation.hybrid_tools import ( + estimate_templates_from_recording, + generate_hybrid_recording, +) def test_generate_hybrid_with_sorting(): diff --git a/src/spikeinterface/generation/tests/test_template_fetch.py b/src/spikeinterface/generation/tests/test_template_fetch.py index d99b5a16f1..9cac06f7a1 100644 --- a/src/spikeinterface/generation/tests/test_template_fetch.py +++ b/src/spikeinterface/generation/tests/test_template_fetch.py @@ -1,5 +1,7 @@ -import pytest +import numpy as np +from spikeinterface.core.template_tools import get_template_extremum_channel +from spikeinterface.core.template import Templates from spikeinterface.generation import ( fetch_template_dataset, @@ -7,7 +9,14 @@ list_available_datasets, get_templates_from_database, ) -from spikeinterface.core.template import Templates + +from spikeinterface.generation.hybrid_tools import ( + estimate_templates_from_recording, + generate_hybrid_recording, + select_templates, + scale_templates, + shift_templates, +) def test_fetch_datasets(): @@ -43,3 +52,64 @@ def test_get_templates_from_database(): assert isinstance(templates, Templates) assert templates.num_units == num_selected + + +def test_templates_manipulation(): + templates = fetch_template_dataset("test_templates.zarr") + + # select + min_amp = 100 + max_amp = 200 + min_depth = 0 + max_depth = 500 + templates_selected = select_templates( + templates, + min_amplitude=min_amp, + max_amplitude=max_amp, + min_depth=min_depth, + max_depth=max_depth, + amplitude_function="ptp", + ) + channel_locations = templates.get_channel_locations() + extremum_channel_indices = list(get_template_extremum_channel(templates_selected, outputs="index").values()) + extremum_channel_indices = np.array(extremum_channel_indices, dtype=int) + + for template_index in range(templates_selected.num_units): + template = templates_selected.templates_array[template_index] + template_amp = np.ptp(template[:, extremum_channel_indices[template_index]]) + template_depth = channel_locations[extremum_channel_indices[template_index], 1] + assert min_amp <= template_amp <= max_amp + assert min_depth <= template_depth <= max_depth + + # scale + min_amp_scaled = 10 + max_amp_scaled = 40 + templates_scaled = scale_templates( + templates_selected, min_amplitude=min_amp_scaled, max_amplitude=max_amp_scaled, amplitude_function="ptp" + ) + + for template_index in range(templates_scaled.num_units): + template = templates_scaled.templates_array[template_index] + template_amp = np.ptp(template[:, extremum_channel_indices[template_index]]) + assert min_amp_scaled <= template_amp <= max_amp_scaled + + # shift + min_shift = 2000 + max_shift = 3000 + templates_shifted = shift_templates(templates_scaled, min_displacement=min_shift, max_displacement=max_shift) + + extremum_channel_indices_shifted = list(get_template_extremum_channel(templates_shifted, outputs="index").values()) + extremum_channel_indices_shifted = np.array(extremum_channel_indices_shifted, dtype=int) + + for template_index in range(templates_shifted.num_units): + template = templates_shifted.templates_array[template_index] + template_depth = channel_locations[extremum_channel_indices_shifted[template_index], 1] + assert min_depth + min_shift <= template_depth <= max_depth + max_shift + + +if __name__ == "__main__": + test_fetch_datasets() + test_fetch_templates_info() + test_get_templates_from_database() + test_templates_manipulation() + print("All tests passed!") From e6d5cb389b4e5d1405864dbe3a4dd71b9edefeda Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 14 May 2024 13:00:23 +0200 Subject: [PATCH 59/90] Add widen/narrow button and scale bar to plot_unitwaveforms/templates --- src/spikeinterface/widgets/unit_waveforms.py | 111 ++++++++++++++---- .../widgets/utils_ipywidgets.py | 55 +++++++++ 2 files changed, 142 insertions(+), 24 deletions(-) diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py index 19034f46ae..0666c47b88 100644 --- a/src/spikeinterface/widgets/unit_waveforms.py +++ b/src/spikeinterface/widgets/unit_waveforms.py @@ -40,6 +40,8 @@ class UnitWaveformsWidget(BaseWidget): displayed per waveform, (matplotlib backend) scale : float, default: 1 Scale factor for the waveforms/templates (matplotlib backend) + widen_narrow_scale : float, default: 1 + Scale factor for the x-axis of the waveforms/templates (matplotlib backend) axis_equal : bool, default: False Equal aspect ratio for x and y axis, to visualize the array geometry to scale lw_waveforms : float, default: 1 @@ -65,6 +67,8 @@ class UnitWaveformsWidget(BaseWidget): are used for the lower bounds, and the second half for the upper bounds. Inner elements produce darker shadings. For sortingview backend only 2 or 4 elements are supported. + scalebar : bool, default: False + Display a scale bar on the waveforms plot (matplotlib backend) hide_unit_selector : bool, default: False For sortingview backend, if True the unit selector is not displayed same_axis : bool, default: False @@ -88,6 +92,7 @@ def __init__( sparsity=None, ncols=5, scale=1, + widen_narrow_scale=1, lw_waveforms=1, lw_templates=2, axis_equal=False, @@ -97,6 +102,7 @@ def __init__( same_axis=False, shade_templates=True, templates_percentile_shading=(1, 25, 75, 99), + scalebar=False, x_offset_units=False, alpha_waveforms=0.5, alpha_templates=1, @@ -168,10 +174,6 @@ def __init__( templates_percentile_shading = None templates_shading = self._get_template_shadings(unit_ids, templates_percentile_shading) - xvectors, y_scale, y_offset, delta_x = get_waveforms_scales( - templates, channel_locations, nbefore, x_offset_units - ) - wfs_by_ids = {} if plot_waveforms: # this must be a sorting_analyzer @@ -204,12 +206,14 @@ def __init__( plot_data = dict( sorting_analyzer_or_templates=sorting_analyzer_or_templates, sampling_frequency=sorting_analyzer_or_templates.sampling_frequency, + nbefore=nbefore, unit_ids=unit_ids, channel_ids=channel_ids, sparsity=sparsity, unit_colors=unit_colors, channel_locations=channel_locations, scale=scale, + widen_narrow_scale=widen_narrow_scale, templates=templates, templates_shading=templates_shading, do_shading=shade_templates, @@ -220,19 +224,16 @@ def __init__( unit_selected_waveforms=unit_selected_waveforms, axis_equal=axis_equal, max_spikes_per_unit=max_spikes_per_unit, - xvectors=xvectors, - y_scale=y_scale, - y_offset=y_offset, wfs_by_ids=wfs_by_ids, set_title=set_title, same_axis=same_axis, + scalebar=scalebar, templates_percentile_shading=templates_percentile_shading, x_offset_units=x_offset_units, lw_waveforms=lw_waveforms, lw_templates=lw_templates, alpha_waveforms=alpha_waveforms, alpha_templates=alpha_templates, - delta_x=delta_x, hide_unit_selector=hide_unit_selector, plot_legend=plot_legend, ) @@ -258,6 +259,10 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): self.figure, self.axes, self.ax = make_mpl_figure(**backend_kwargs) + xvectors, y_scale, y_offset, delta_x = get_waveforms_scales( + dp.templates, dp.channel_locations, dp.nbefore, dp.x_offset_units, dp.widen_narrow_scale + ) + for i, unit_id in enumerate(dp.unit_ids): if dp.same_axis: ax = self.ax @@ -266,7 +271,7 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): color = dp.unit_colors[unit_id] chan_inds = dp.sparsity.unit_id_to_channel_indices[unit_id] - xvectors_flat = dp.xvectors[:, chan_inds].T.flatten() + xvectors_flat = xvectors[:, chan_inds].T.flatten() # plot waveforms if dp.plot_waveforms: @@ -278,12 +283,12 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): random_idxs = np.random.permutation(len(wfs))[: dp.max_spikes_per_unit] wfs = wfs[random_idxs] - wfs = wfs * dp.y_scale + dp.y_offset[None, :, chan_inds] + wfs = wfs * y_scale + y_offset[None, :, chan_inds] wfs_flat = wfs.swapaxes(1, 2).reshape(wfs.shape[0], -1).T if dp.x_offset_units: # 0.7 is to match spacing in xvect - xvec = xvectors_flat + i * 0.7 * dp.delta_x + xvec = xvectors_flat + i * 0.7 * delta_x else: xvec = xvectors_flat @@ -291,14 +296,33 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): if not dp.plot_templates: ax.get_lines()[-1].set_label(f"{unit_id}") + if not dp.plot_templates and dp.scalebar and not dp.same_axis: + # xscale + min_wfs = np.min(wfs_flat) + wfs_for_scale = dp.wfs_by_ids[unit_id] * y_scale + offset = 0.1 * (np.max(wfs_flat) - np.min(wfs_flat)) + xargmin = np.nanargmin(xvec) + xscale_bar = [xvec[xargmin], xvec[xargmin + dp.nbefore]] + ax.plot(xscale_bar, [min_wfs - offset, min_wfs - offset], color="k") + nbefore_time = int(dp.nbefore / dp.sampling_frequency * 1000) + ax.text( + xscale_bar[0] + xscale_bar[1] // 3, min_wfs - 1.5 * offset, f"{nbefore_time} ms", fontsize=8 + ) + + # yscale + length = int(np.ptp(wfs_flat) // 5) + length_uv = int(np.ptp(wfs_for_scale) // 5) + x_offset = xscale_bar[0] - np.ptp(xscale_bar) // 2 + ax.plot([xscale_bar[0], xscale_bar[0]], [min_wfs - offset, min_wfs - offset + length], color="k") + ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\mu$V", fontsize=8, rotation=90) # plot template if dp.plot_templates: - template = dp.templates[i, :, :][:, chan_inds] * dp.scale * dp.y_scale + dp.y_offset[:, chan_inds] + template = dp.templates[i, :, :][:, chan_inds] * dp.scale * y_scale + y_offset[:, chan_inds] if dp.x_offset_units: # 0.7 is to match spacing in xvect - xvec = xvectors_flat + i * 0.7 * dp.delta_x + xvec = xvectors_flat + i * 0.7 * delta_x else: xvec = xvectors_flat # plot template shading if waveforms are not plotted @@ -310,12 +334,11 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): shading_alphas = np.linspace(lightest_gray_alpha, darkest_gray_alpha, n_shadings) for s in range(n_shadings): lower_bound = ( - dp.templates_shading[s][i, :, :][:, chan_inds] * dp.scale * dp.y_scale - + dp.y_offset[:, chan_inds] + dp.templates_shading[s][i, :, :][:, chan_inds] * dp.scale * y_scale + y_offset[:, chan_inds] ) upper_bound = ( - dp.templates_shading[n_percentiles - 1 - s][i, :, :][:, chan_inds] * dp.scale * dp.y_scale - + dp.y_offset[:, chan_inds] + dp.templates_shading[n_percentiles - 1 - s][i, :, :][:, chan_inds] * dp.scale * y_scale + + y_offset[:, chan_inds] ) ax.fill_between( xvec, @@ -345,6 +368,26 @@ def plot_matplotlib(self, data_plot, **backend_kwargs): if dp.set_title: ax.set_title(f"template {template_label}") + if not dp.plot_waveforms and dp.scalebar and not dp.same_axis: + # xscale + template_for_scale = dp.templates[i, :, :][:, chan_inds] * dp.scale + min_wfs = np.min(template) + offset = 0.1 * (np.max(template) - np.min(template)) + xargmin = np.nanargmin(xvec) + xscale_bar = [xvec[xargmin], xvec[xargmin + dp.nbefore]] + ax.plot(xscale_bar, [min_wfs - offset, min_wfs - offset], color="k") + nbefore_time = int(dp.nbefore / dp.sampling_frequency * 1000) + ax.text( + xscale_bar[0] + xscale_bar[1] // 3, min_wfs - 1.5 * offset, f"{nbefore_time} ms", fontsize=8 + ) + + # yscale + length = int(np.ptp(template) // 5) + length_uv = int(np.ptp(template_for_scale) // 5) + x_offset = xscale_bar[0] - np.ptp(xscale_bar) // 2 + ax.plot([xscale_bar[0], xscale_bar[0]], [min_wfs - offset, min_wfs - offset + length], color="k") + ax.text(x_offset, min_wfs - offset + length // 3, f"{length_uv} $\mu$V", fontsize=8, rotation=90) + # plot channels if dp.plot_channels: # TODO enhance this @@ -361,7 +404,7 @@ def plot_ipywidgets(self, data_plot, **backend_kwargs): import matplotlib.pyplot as plt import ipywidgets.widgets as widgets from IPython.display import display - from .utils_ipywidgets import check_ipywidget_backend, UnitSelector, ScaleWidget + from .utils_ipywidgets import check_ipywidget_backend, UnitSelector, ScaleWidget, WidenNarrowWidget check_ipywidget_backend() @@ -393,6 +436,7 @@ def plot_ipywidgets(self, data_plot, **backend_kwargs): self.unit_selector = UnitSelector(data_plot["unit_ids"], layout=widgets.Layout(height="80%")) self.unit_selector.value = list(data_plot["unit_ids"])[:1] self.scaler = ScaleWidget(value=data_plot["scale"], layout=widgets.Layout(height="20%")) + self.widen_narrow = WidenNarrowWidget(value=1.0, layout=widgets.Layout(height="20%")) self.same_axis_button = widgets.Checkbox( value=False, @@ -417,15 +461,21 @@ def plot_ipywidgets(self, data_plot, **backend_kwargs): description="hide axis", disabled=False, ) + + self.scalebar = widgets.Checkbox( + value=False, + description="scalebar", + disabled=False, + ) if self.sorting_analyzer is not None: - footer_list = [self.same_axis_button, self.template_shading_button, self.hide_axis_button] + footer_list = [self.same_axis_button, self.template_shading_button, self.hide_axis_button, self.scalebar] else: - footer_list = [self.same_axis_button, self.hide_axis_button] + footer_list = [self.same_axis_button, self.hide_axis_button, self.scalebar] if data_plot["plot_waveforms"]: footer_list.append(self.plot_templates_button) footer = widgets.HBox(footer_list) - left_sidebar = widgets.VBox([self.unit_selector, self.scaler]) + left_sidebar = widgets.VBox([self.unit_selector, self.scaler, self.widen_narrow]) self.widget = widgets.AppLayout( center=self.fig_wf.canvas, @@ -440,7 +490,14 @@ def plot_ipywidgets(self, data_plot, **backend_kwargs): self.unit_selector.observe(self._update_plot, names="value", type="change") self.scaler.observe(self._update_plot, names="value", type="change") - for w in self.same_axis_button, self.plot_templates_button, self.template_shading_button, self.hide_axis_button: + self.widen_narrow.observe(self._update_plot, names="value", type="change") + for w in ( + self.same_axis_button, + self.plot_templates_button, + self.template_shading_button, + self.hide_axis_button, + self.scalebar, + ): w.observe(self._update_plot, names="value", type="change") if backend_kwargs["display"]: @@ -502,6 +559,12 @@ def _update_plot(self, change): data_plot["plot_templates"] = plot_templates data_plot["do_shading"] = do_shading data_plot["scale"] = self.scaler.value + data_plot["widen_narrow_scale"] = self.widen_narrow.value + + if same_axis: + self.scalebar.value = False + data_plot["scalebar"] = self.scalebar.value + if data_plot["plot_waveforms"]: wf_ext = self.sorting_analyzer.get_extension("waveforms") data_plot["wfs_by_ids"] = { @@ -554,7 +617,7 @@ def _update_plot(self, change): fig_probe.canvas.flush_events() -def get_waveforms_scales(templates, channel_locations, nbefore, x_offset_units=False): +def get_waveforms_scales(templates, channel_locations, nbefore, x_offset_units=False, widen_narrow_scale=1.0): """ Return scales and x_vector for templates plotting """ @@ -582,7 +645,7 @@ def get_waveforms_scales(templates, channel_locations, nbefore, x_offset_units=F nsamples = templates.shape[1] - xvect = delta_x * (np.arange(nsamples) - nbefore) / nsamples * 0.7 + xvect = (delta_x * widen_narrow_scale) * (np.arange(nsamples) - nbefore) / nsamples * 0.7 if x_offset_units: ch_locs = channel_locations diff --git a/src/spikeinterface/widgets/utils_ipywidgets.py b/src/spikeinterface/widgets/utils_ipywidgets.py index 6d91140500..0fc7987b0c 100644 --- a/src/spikeinterface/widgets/utils_ipywidgets.py +++ b/src/spikeinterface/widgets/utils_ipywidgets.py @@ -329,6 +329,61 @@ def value_changed(self, change=None): self.update_label() +class WidenNarrowWidget(W.VBox): + value = traitlets.Float() + + def __init__(self, value=1.0, factor=1.2, **kwargs): + assert factor > 1.0 + self.factor = factor + + self.scale_label = W.Label("Widen/Narrow", layout=W.Layout(width="95%", justify_content="center")) + + self.right_selector = W.Button( + description="", + disabled=False, + button_style="", # 'success', 'info', 'warning', 'danger' or '' + tooltip="Increase horizontal scale", + icon="arrow-right", + # layout=W.Layout(width=f"{0.8 * width_cm}cm", height=f"{0.4 * height_cm}cm"), + layout=W.Layout(width="60%", align_self="center"), + ) + + self.left_selector = W.Button( + description="", + disabled=False, + button_style="", # 'success', 'info', 'warning', 'danger' or '' + tooltip="Decrease horizontal scale", + icon="arrow-left", + # layout=W.Layout(width=f"{0.8 * width_cm}cm", height=f"{0.4 * height_cm}cm"), + layout=W.Layout(width="60%", align_self="center"), + ) + + self.right_selector.on_click(self.left_clicked) + self.left_selector.on_click(self.right_clicked) + + self.value = value + super(W.VBox, self).__init__( + children=[self.scale_label, W.HBox([self.left_selector, self.right_selector])], + # layout=W.Layout(align_items="center", width="100%", height="100%"), + **kwargs, + ) + + # self.update_label() + # self.observe(self.value_changed, names=["value"], type="change") + + def update_label(self): + self.scale_label.value = f"Scale: {self.value:0.2f}" + + def left_clicked(self, change=None): + self.value = self.value / self.factor + + def right_clicked(self, change=None): + self.value = self.value * self.factor + + def value_changed(self, change=None): + self.update_label() + + class UnitSelector(W.VBox): value = traitlets.List() From 2835350f2f973794942578b31d6f29429bfcb48b Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Tue, 14 May 2024 13:07:16 +0200 Subject: [PATCH 60/90] cleanup ipywidgets utils --- src/spikeinterface/widgets/utils_ipywidgets.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/src/spikeinterface/widgets/utils_ipywidgets.py b/src/spikeinterface/widgets/utils_ipywidgets.py index 0fc7987b0c..0d81a8184f 100644 --- a/src/spikeinterface/widgets/utils_ipywidgets.py +++ b/src/spikeinterface/widgets/utils_ipywidgets.py @@ -364,25 +364,15 @@ def __init__(self, value=1.0, factor=1.2, **kwargs): self.value = value super(W.VBox, self).__init__( children=[self.scale_label, W.HBox([self.left_selector, self.right_selector])], - # layout=W.Layout(align_items="center", width="100%", height="100%"), **kwargs, ) - # self.update_label() - # self.observe(self.value_changed, names=["value"], type="change") - - def update_label(self): - self.scale_label.value = f"Scale: {self.value:0.2f}" - def left_clicked(self, change=None): self.value = self.value / self.factor def right_clicked(self, change=None): self.value = self.value * self.factor - def value_changed(self, change=None): - self.update_label() - class UnitSelector(W.VBox): value = traitlets.List() From ff8b29a287491c4f3275671bf9e35a5838b2068d Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 15 May 2024 13:41:46 +0200 Subject: [PATCH 61/90] Ramon's suggestions --- src/spikeinterface/generation/template_database.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/spikeinterface/generation/template_database.py b/src/spikeinterface/generation/template_database.py index 07c2927047..71a2534220 100644 --- a/src/spikeinterface/generation/template_database.py +++ b/src/spikeinterface/generation/template_database.py @@ -1,9 +1,11 @@ import zarr +import functools import numpy as np from spikeinterface.core.template import Templates +@functools.cache def fetch_template_dataset(dataset="test_templates.zarr") -> Templates: """ Fetch a template dataset from the spikeinterface template database. @@ -28,6 +30,7 @@ def fetch_template_dataset(dataset="test_templates.zarr") -> Templates: return templates_object +@functools.cache def fetch_templates_info() -> "pandas.DataFrame": """ Fetch the information about the templates in the spikeinterface template database. @@ -60,7 +63,9 @@ def list_available_datasets() -> list: return datasets -def get_templates_from_database(template_df_or_indices: "pandas.DataFrame | list[int] | np.ndarray") -> Templates: +def get_templates_from_database( + template_df_or_indices: "pandas.DataFrame | list[int] | np.ndarray", verbose: bool = False +) -> Templates: """ Retrieve templates from the spikeinterface template database. @@ -82,7 +87,8 @@ def get_templates_from_database(template_df_or_indices: "pandas.DataFrame | list else: template_info = fetch_templates_info().iloc[template_df_or_indices] requested_datasets = np.unique(template_info["dataset"]).tolist() - print(f"Fetching templates from {len(requested_datasets)} datasets") + if verbose: + print(f"Fetching templates from {len(requested_datasets)} datasets") nbefore = None sampling_frequency = None From 57af6acaea1e25c2bd843cab1e6b7292957bcbac Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 15 May 2024 17:57:01 +0200 Subject: [PATCH 62/90] Revert changes to geT_unit_colors --- src/spikeinterface/widgets/metrics.py | 2 +- src/spikeinterface/widgets/spike_locations.py | 2 +- src/spikeinterface/widgets/spikes_on_traces.py | 2 +- src/spikeinterface/widgets/unit_depths.py | 2 +- src/spikeinterface/widgets/unit_locations.py | 2 +- src/spikeinterface/widgets/unit_summary.py | 2 +- .../widgets/unit_waveforms_density_map.py | 2 +- src/spikeinterface/widgets/utils.py | 11 +++++++++-- 8 files changed, 16 insertions(+), 9 deletions(-) diff --git a/src/spikeinterface/widgets/metrics.py b/src/spikeinterface/widgets/metrics.py index a2287130ff..2fbd0e31eb 100644 --- a/src/spikeinterface/widgets/metrics.py +++ b/src/spikeinterface/widgets/metrics.py @@ -46,7 +46,7 @@ def __init__( **backend_kwargs, ): if unit_colors is None: - unit_colors = get_unit_colors(sorting.unit_ids) + unit_colors = get_unit_colors(sorting) if include_metrics is not None: selected_metrics = [m for m in metrics.columns if m in include_metrics] diff --git a/src/spikeinterface/widgets/spike_locations.py b/src/spikeinterface/widgets/spike_locations.py index c49bc3653e..94c9def630 100644 --- a/src/spikeinterface/widgets/spike_locations.py +++ b/src/spikeinterface/widgets/spike_locations.py @@ -70,7 +70,7 @@ def __init__( segment_index = 0 if unit_colors is None: - unit_colors = get_unit_colors(sorting.unit_ids) + unit_colors = get_unit_colors(sorting) if unit_ids is None: unit_ids = sorting.unit_ids diff --git a/src/spikeinterface/widgets/spikes_on_traces.py b/src/spikeinterface/widgets/spikes_on_traces.py index 9f69ccb7f0..9f1a3faeb2 100644 --- a/src/spikeinterface/widgets/spikes_on_traces.py +++ b/src/spikeinterface/widgets/spikes_on_traces.py @@ -99,7 +99,7 @@ def __init__( unit_ids = unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting.unit_ids) + unit_colors = get_unit_colors(sorting) # sparsity is done on all the units even if unit_ids is a few ones because some backend need then all if sorting_analyzer.is_sparse(): diff --git a/src/spikeinterface/widgets/unit_depths.py b/src/spikeinterface/widgets/unit_depths.py index 136008e409..c2e9c06863 100644 --- a/src/spikeinterface/widgets/unit_depths.py +++ b/src/spikeinterface/widgets/unit_depths.py @@ -35,7 +35,7 @@ def __init__( unit_ids = sorting_analyzer.sorting.unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) + unit_colors = get_unit_colors(sorting_analyzer) colors = [unit_colors[unit_id] for unit_id in unit_ids] diff --git a/src/spikeinterface/widgets/unit_locations.py b/src/spikeinterface/widgets/unit_locations.py index e4517ceddc..3329c2183c 100644 --- a/src/spikeinterface/widgets/unit_locations.py +++ b/src/spikeinterface/widgets/unit_locations.py @@ -61,7 +61,7 @@ def __init__( probegroup = sorting_analyzer.get_probegroup() if unit_colors is None: - unit_colors = get_unit_colors(sorting.unit_ids) + unit_colors = get_unit_colors(sorting) if unit_ids is None: unit_ids = sorting.unit_ids diff --git a/src/spikeinterface/widgets/unit_summary.py b/src/spikeinterface/widgets/unit_summary.py index 50a186e981..0b2a348edf 100644 --- a/src/spikeinterface/widgets/unit_summary.py +++ b/src/spikeinterface/widgets/unit_summary.py @@ -48,7 +48,7 @@ def __init__( sorting_analyzer = self.ensure_sorting_analyzer(sorting_analyzer) if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) + unit_colors = get_unit_colors(sorting_analyzer) plot_data = dict( sorting_analyzer=sorting_analyzer, diff --git a/src/spikeinterface/widgets/unit_waveforms_density_map.py b/src/spikeinterface/widgets/unit_waveforms_density_map.py index 1b5ea8e79a..6ef1a7a782 100644 --- a/src/spikeinterface/widgets/unit_waveforms_density_map.py +++ b/src/spikeinterface/widgets/unit_waveforms_density_map.py @@ -57,7 +57,7 @@ def __init__( unit_ids = sorting_analyzer.unit_ids if unit_colors is None: - unit_colors = get_unit_colors(sorting_analyzer.sorting.unit_ids) + unit_colors = get_unit_colors(sorting_analyzer) if use_max_channel: assert len(unit_ids) == 1, " UnitWaveformDensity : use_max_channel=True works only with one unit" diff --git a/src/spikeinterface/widgets/utils.py b/src/spikeinterface/widgets/utils.py index 81037ba5c7..3461fb179a 100644 --- a/src/spikeinterface/widgets/utils.py +++ b/src/spikeinterface/widgets/utils.py @@ -95,12 +95,19 @@ def get_some_colors(keys, color_engine="auto", map_name="gist_ncar", format="RGB return dict_colors -def get_unit_colors(unit_ids, color_engine="auto", map_name="gist_ncar", format="RGBA", shuffle=None, seed=None): +def get_unit_colors( + sorting_or_analyzer_or_templates, color_engine="auto", map_name="gist_ncar", format="RGBA", shuffle=None, seed=None +): """ Return a dict colors per units. """ colors = get_some_colors( - unit_ids, color_engine=color_engine, map_name=map_name, format=format, shuffle=shuffle, seed=seed + sorting_or_analyzer_or_templates.unit_ids, + color_engine=color_engine, + map_name=map_name, + format=format, + shuffle=shuffle, + seed=seed, ) return colors From 8317ba43aa8ebcdde9cef5d2ef0ade7a78e1e2dc Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 15 May 2024 19:09:51 +0200 Subject: [PATCH 63/90] fix tests --- src/spikeinterface/widgets/unit_waveforms.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py index 0666c47b88..2b3dc7ed34 100644 --- a/src/spikeinterface/widgets/unit_waveforms.py +++ b/src/spikeinterface/widgets/unit_waveforms.py @@ -122,7 +122,7 @@ def __init__( if channel_ids is None: channel_ids = sorting_analyzer_or_templates.channel_ids if unit_colors is None: - unit_colors = get_unit_colors(unit_ids) + unit_colors = get_unit_colors(sorting_analyzer_or_templates) channel_indices = [list(sorting_analyzer_or_templates.channel_ids).index(ch) for ch in channel_ids] channel_locations = sorting_analyzer_or_templates.get_channel_locations()[channel_indices] From bb1b48917ef23e9ba69ac106c7c92d958fa9b158 Mon Sep 17 00:00:00 2001 From: Pierre Yger Date: Fri, 31 May 2024 08:18:44 +0200 Subject: [PATCH 64/90] Fixing tests --- src/spikeinterface/exporters/report.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/exporters/report.py b/src/spikeinterface/exporters/report.py index 207b148cd8..ae3badaa6d 100644 --- a/src/spikeinterface/exporters/report.py +++ b/src/spikeinterface/exporters/report.py @@ -107,7 +107,7 @@ def export_report( units["amplitude"] = pd.Series(get_template_extremum_amplitude(sorting_analyzer, peak_sign=peak_sign)) units.to_csv(output_folder / "unit list.csv", sep="\t") - unit_colors = sw.get_unit_colors(sorting.unit_ids) + unit_colors = sw.get_unit_colors(sorting) # global figures fig = plt.figure(figsize=(20, 10)) From f5e81a0e269eb47552817acf7c7f6df06251b934 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Mon, 3 Jun 2024 14:12:41 +0200 Subject: [PATCH 65/90] Fixing tests --- src/spikeinterface/generation/hybrid_tools.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 3897618db4..30ec689f21 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -80,7 +80,14 @@ def estimate_templates_from_recording( probe = recording.get_probe() templates = Templates( - templates_array, sampling_frequency, nbefore, sparsity_mask, channel_ids, unit_ids, probe=probe + templates_array, + sampling_frequency, + nbefore, + True, + sparsity_mask, + channel_ids, + unit_ids, + probe=probe ) return templates @@ -411,7 +418,7 @@ def generate_hybrid_recording( ) nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) - templates_ = Templates(templates_array, sampling_frequency, nbefore, None, None, None, probe) + templates_ = Templates(templates_array, sampling_frequency, nbefore, True, None, None, None, probe) else: assert isinstance(templates, Templates), "templates should be a Templates object" assert ( @@ -437,7 +444,7 @@ def generate_hybrid_recording( # manage scaling of templates templates_ = templates - if recording.has_scaled(): + if recording.has_scaleable_traces(): if templates_in_uV: templates_array = (templates_array - recording.get_channel_offsets()) / recording.get_channel_gains() # make a copy of the templates and reset templates_array (might have scaled templates) From 8fe4efba60e23f1230acb357cc6d85361efa7727 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 3 Jun 2024 12:15:40 +0000 Subject: [PATCH 66/90] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/spikeinterface/generation/hybrid_tools.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 30ec689f21..8af281fe66 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -80,14 +80,7 @@ def estimate_templates_from_recording( probe = recording.get_probe() templates = Templates( - templates_array, - sampling_frequency, - nbefore, - True, - sparsity_mask, - channel_ids, - unit_ids, - probe=probe + templates_array, sampling_frequency, nbefore, True, sparsity_mask, channel_ids, unit_ids, probe=probe ) return templates From 3047e30c74c241edb98a5bd0e378ca6e079d8fa5 Mon Sep 17 00:00:00 2001 From: Sebastien Date: Thu, 6 Jun 2024 16:19:42 +0200 Subject: [PATCH 67/90] Fixing colons --- src/spikeinterface/generation/hybrid_tools.py | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 8af281fe66..47df6b3a42 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -37,13 +37,13 @@ def estimate_templates_from_recording( Parameters ---------- - ms_before: float + ms_before : float The time before peaks of templates - ms_after: float + ms_after : float The time after peaks of templates - sorter_name: str + sorter_name : str The sorter to be used in order to get some fast clustering - run_sorter_kwargs: dict + run_sorter_kwargs : dict The parameters to provide to the run_sorter function of spikeinterface @@ -335,41 +335,41 @@ def generate_hybrid_recording( Parameters ---------- - recording: BaseRecording + recording : BaseRecording The recording to inject units in - motion_info: All the information about the motion + motion_info : All the information about the motion The motion datastructure of the recording - num_units: int, default: 10 + num_units : int, default: 10 Number of units, not used when sorting is given. - sorting: Sorting or None + sorting : Sorting or None An external sorting object. If not provide, one is genrated. - templates: Templates or None, default: None + templates : Templates or None, default: None The templates of units. If None they are generated. - templates_in_uV: bool, default: True + templates_in_uV : bool, default: True If True, the templates are in uV, otherwise they are in the same unit as the recording. In case the recording has scaling, the templates are "unscaled" before injection. - ms_before: float, default: 1.5 + ms_before : float, default: 1.5 Cut out in ms before spike peak. - ms_after: float, default: 3 + ms_after : float, default: 3 Cut out in ms after spike peak. - unit_locations: np.array, default: None + unit_locations : np.array, default: None The locations at which the templates should be injected. If not provided, generated (see generate_unit_location_kwargs) - upsample_factor: None or int, default: None + upsample_factor : None or int, default: None A upsampling factor used only when templates are not provided. - upsample_vector: np.array or None + upsample_vector : np.array or None Optional the upsample_vector can given. This has the same shape as spike_vector - amplitude_std: float, default: 0.05 + amplitude_std : float, default: 0.05 The standard deviation of the modulation to apply to the spikes when injecting them into the recording. - generate_sorting_kwargs: dict + generate_sorting_kwargs : dict When sorting is not provide, this dict is used to generated a Sorting. - generate_unit_locations_kwargs: dict + generate_unit_locations_kwargs : dict Dict used to generated template when template not provided. - generate_templates_kwargs: dict + generate_templates_kwargs : dict Dict used to generated template when template not provided. - seed: int or None + seed : int or None Seed for random initialization. If None a diffrent Recording is generated at every call. Note: even with None a generated recording keep internaly a seed to regenerate the same signal after dump/load. From 03fe4d9ada0b92af24d53227bdd152704ef41c96 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 12:49:21 +0200 Subject: [PATCH 68/90] from_static -> from_static_templates --- src/spikeinterface/generation/drift_tools.py | 2 +- src/spikeinterface/generation/drifting_generator.py | 2 +- src/spikeinterface/generation/hybrid_tools.py | 2 +- src/spikeinterface/generation/tests/test_drift_tools.py | 4 ++-- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 0ad3e4136b..43c0d5f48d 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -124,7 +124,7 @@ def __init__(self, **kwargs): self.displacements = None @classmethod - def from_static(cls, templates): + def from_static_templates(cls, templates): drifting_templates = cls( templates_array=templates.templates_array, sampling_frequency=templates.sampling_frequency, diff --git a/src/spikeinterface/generation/drifting_generator.py b/src/spikeinterface/generation/drifting_generator.py index 7f617c3ade..f712bfb41e 100644 --- a/src/spikeinterface/generation/drifting_generator.py +++ b/src/spikeinterface/generation/drifting_generator.py @@ -407,7 +407,7 @@ def generate_drifting_recording( is_scaled=True, ) - drifting_templates = DriftingTemplates.from_static(templates) + drifting_templates = DriftingTemplates.from_static_templates(templates) sorting = generate_sorting( num_units=num_units, diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 47df6b3a42..00ffae59b1 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -480,7 +480,7 @@ def generate_hybrid_recording( displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) # use templates_, because templates_array might have been scaled - drifting_templates = DriftingTemplates.from_static(templates_) + drifting_templates = DriftingTemplates.from_static_templates(templates_) drifting_templates.precompute_displacements(displacements) displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] diff --git a/src/spikeinterface/generation/tests/test_drift_tools.py b/src/spikeinterface/generation/tests/test_drift_tools.py index 8a4837100e..7783e29d63 100644 --- a/src/spikeinterface/generation/tests/test_drift_tools.py +++ b/src/spikeinterface/generation/tests/test_drift_tools.py @@ -94,7 +94,7 @@ def test_move_dense_templates(): def test_DriftingTemplates(): static_templates = make_some_templates() - drifting_templates = DriftingTemplates.from_static(static_templates) + drifting_templates = DriftingTemplates.from_static_templates(static_templates) displacement = np.array([[5.0, 10.0]]) unit_index = 0 @@ -119,7 +119,7 @@ def test_InjectDriftingTemplatesRecording(create_cache_folder): probe = templates.probe # drifting templates - drifting_templates = DriftingTemplates.from_static(templates) + drifting_templates = DriftingTemplates.from_static_templates(templates) channel_locations = probe.contact_positions num_units = templates.unit_ids.size From d023420f6e03a8bef058520d5c51a292a92c0644 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 12:51:18 +0200 Subject: [PATCH 69/90] Remove duplicate select_units/channels in Templates --- src/spikeinterface/core/template.py | 49 ----------------------------- 1 file changed, 49 deletions(-) diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py index 543fc88fee..b64f0610ea 100644 --- a/src/spikeinterface/core/template.py +++ b/src/spikeinterface/core/template.py @@ -411,55 +411,6 @@ def to_json(self): def from_json(cls, json_str): return cls.from_dict(json.loads(json_str)) - def select_units(self, unit_ids): - """ - Return a new Templates object with only the selected units. - - Parameters - ---------- - unit_ids : list - List of unit IDs to select. - """ - unit_ids_list = list(self.unit_ids) - unit_indices = np.array([unit_ids_list.index(unit_id) for unit_id in unit_ids], dtype=int) - sliced_sparsity_mask = None if self.sparsity_mask is None else self.sparsity_mask[unit_indices] - return Templates( - templates_array=self.templates_array[unit_indices], - sampling_frequency=self.sampling_frequency, - nbefore=self.nbefore, - sparsity_mask=sliced_sparsity_mask, - channel_ids=self.channel_ids, - unit_ids=unit_ids, - probe=self.probe, - check_for_consistent_sparsity=False, - ) - - def select_channels(self, channel_ids): - """ - Return a new Templates object with only the selected channels. - This operation can be useful to remove bad channels for hybrid recording - generation. - - Parameters - ---------- - channel_ids : list - List of channel IDs to select. - """ - assert not self.are_templates_sparse(), "Cannot select channels on sparse templates" - channel_ids_list = list(self.channel_ids) - channel_indices = np.array([channel_ids_list.index(channel_id) for channel_id in channel_ids]) - sliced_sparsity_mask = None if self.sparsity_mask is None else self.sparsity_mask[:, channel_indices] - return Templates( - templates_array=self.templates_array[:, :, channel_indices], - sampling_frequency=self.sampling_frequency, - nbefore=self.nbefore, - sparsity_mask=sliced_sparsity_mask, - channel_ids=channel_ids, - unit_ids=self.unit_ids, - probe=self.probe, - check_for_consistent_sparsity=False, - ) - def __eq__(self, other): """ Necessary to compare templates because they naturally compare objects by equality of their fields From 93eb5e3cfe6b594e7947d125430d7d045eadbb7a Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 13:29:42 +0200 Subject: [PATCH 70/90] Changes form code review --- src/spikeinterface/generation/drift_tools.py | 6 +- src/spikeinterface/generation/hybrid_tools.py | 199 ++++++++++-------- 2 files changed, 118 insertions(+), 87 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 43c0d5f48d..b3824c7261 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -264,9 +264,9 @@ def __init__( ): import scipy.spatial - # assert isinstance( - # drifting_templates, DriftingTemplates - # ), "drifting_templates must be a DriftingTemplates object" + assert isinstance( + drifting_templates, DriftingTemplates + ), "drifting_templates must be a DriftingTemplates object" self.drifting_templates = drifting_templates if parent_recording is None: diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 00ffae59b1..3e64fb9c47 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -4,20 +4,20 @@ from typing import Literal import numpy as np -from ..core.template import Templates +from spikeinterface.core import BaseRecording, BaseSorting, Templates -from ..core.generate import ( +from spikeinterface.core.generate import ( generate_templates, generate_unit_locations, generate_sorting, InjectTemplatesRecording, _ensure_seed, ) -from ..core.template_tools import get_template_extremum_channel -from ..core.job_tools import split_job_kwargs -from ..postprocessing.unit_localization import compute_monopolar_triangulation +from spikeinterface.core.template_tools import get_template_extremum_channel -from .drift_tools import ( +from spikeinterface.sortingcomponents.motion_utils import Motion + +from spikeinterface.generation.drift_tools import ( InjectDriftingTemplatesRecording, DriftingTemplates, make_linear_displacement, @@ -27,10 +27,15 @@ def estimate_templates_from_recording( - recording, ms_before=2, ms_after=2, sorter_name="spykingcircus2", **run_sorter_kwargs + recording: BaseRecording, + ms_before: float = 2, + ms_after: float = 2, + sorter_name: str = "spykingcircus2", + run_sorter_kwargs: dict | None = None, + job_kwargs: dict | None = None, ): """ - Get templates from a recording. Internally, SpyKING CIRCUS 2 is used by default + Get dense templates from a recording. Internally, SpyKING CIRCUS 2 is used by default with the only twist that the template matching step is not launched. Instead, a Template object is returned based on the results of the clustering. Other sorters can be invoked with the `sorter_name` and `run_sorter_kwargs` parameters. @@ -38,41 +43,38 @@ def estimate_templates_from_recording( Parameters ---------- ms_before : float - The time before peaks of templates + The time before peaks of templates. ms_after : float - The time after peaks of templates + The time after peaks of templates. sorter_name : str - The sorter to be used in order to get some fast clustering + The sorter to be used in order to get some fast clustering. run_sorter_kwargs : dict - The parameters to provide to the run_sorter function of spikeinterface - - - sorter_params: keyword arguments for `spyking_circus2` function + The parameters to provide to the run_sorter function of spikeinterface. + job_kwargs : dict + The jobe keyword arguments to be used in the estimation of the templates. Returns ------- templates: Templates - The found templates + The estimated templates """ - from spikeinterface.sorters.runsorter import run_sorter - from spikeinterface.core.template import Templates from spikeinterface.core.waveform_tools import estimate_templates + from spikeinterface.sorters.runsorter import run_sorter if sorter_name == "spykingcircus2": if "matching" not in run_sorter_kwargs: run_sorter_kwargs["matching"] = {"method": None} + run_sorter_kwargs = run_sorter_kwargs or {} sorting = run_sorter(sorter_name, recording, **run_sorter_kwargs) - from spikeinterface.core.waveform_tools import estimate_templates - spikes = sorting.to_spike_vector() - unit_ids = np.unique(spikes["unit_index"]) + unit_ids = sorting.unit_ids sampling_frequency = recording.get_sampling_frequency() nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) - _, job_kwargs = split_job_kwargs(run_sorter_kwargs) + job_kwargs = job_kwargs or {} templates_array = estimate_templates(recording, spikes, unit_ids, nbefore, nafter, **job_kwargs) sparsity_mask = None @@ -101,19 +103,19 @@ def select_templates( Parameters ---------- templates : Templates - The input templates + The input templates. min_amplitude : float | None, default: None - The minimum amplitude of the templates + The minimum amplitude of the templates. max_amplitude : float | None, default: None - The maximum amplitude of the templates + The maximum amplitude of the templates. min_depth : float | None, default: None - The minimum depth of the templates + The minimum depth of the templates. max_depth : float | None, default: None - The maximum depth of the templates + The maximum depth of the templates. amplitude_function : "ptp" | "min" | "max", default: "ptp" - The function to use to compute the amplitude of the templates. Can be "ptp", "min" or "max" + The function to use to compute the amplitude of the templates. Can be "ptp", "min" or "max". depth_direction : "x" | "y", default: "y" - The direction in which to move the templates. Can be "x" or "y" + The direction in which to move the templates. Can be "x" or "y". Returns ------- @@ -162,28 +164,28 @@ def select_templates( return filtered_templates -def scale_templates( +def scale_template_to_range( templates: Templates, min_amplitude: float, max_amplitude: float, amplitude_function: Literal["ptp", "min", "max"] = "ptp", ): """ - Scale templates to have a minimum and maximum amplitude. + Scale templates to have a range with the provided minimum and maximum amplitudes. Parameters ---------- templates : Templates - The input templates + The input templates. min_amplitude : float - The minimum amplitude of the output templates after scaling + The minimum amplitude of the output templates after scaling. max_amplitude : float - The maximum amplitude of the output templates after scaling + The maximum amplitude of the output templates after scaling. Returns ------- Templates - The scaled templates + The scaled templates. """ extremum_channel_indices = list(get_template_extremum_channel(templates, outputs="index").values()) extremum_channel_indices = np.array(extremum_channel_indices, dtype=int) @@ -219,7 +221,7 @@ def scale_templates( ) -def shift_templates( +def relocate_templates( templates: Templates, min_displacement: float, max_displacement: float, @@ -229,7 +231,7 @@ def shift_templates( seed: int | None = None, ): """ - Shift templates to have a minimum and maximum displacement. + Relocates templates to have a minimum and maximum displacement. Parameters ---------- @@ -255,7 +257,7 @@ def shift_templates( Returns ------- Templates - The moved templates + The relocated templates. """ seed = _ensure_seed(seed) @@ -313,37 +315,32 @@ def shift_templates( def generate_hybrid_recording( - recording, - motion_info=None, - num_units=10, - sorting=None, - templates=None, - templates_in_uV=True, - ms_before=1.0, - ms_after=3.0, - unit_locations=None, - upsample_factor=None, - upsample_vector=None, - amplitude_std=0.05, - generate_sorting_kwargs=dict(firing_rates=15, refractory_period_ms=4.0, seed=2205), - generate_unit_locations_kwargs=dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), - generate_templates_kwargs=dict(), - seed=None, -): + recording: BaseRecording, + motion: Motion | None = None, + sorting: BaseSorting | None = None, + templates: Templates | None = None, + templates_in_uV: bool = True, + unit_locations: np.ndarray | None = None, + upsample_factor: int | None = None, + upsample_vector: np.ndarray | None = None, + amplitude_std: float = 0.05, + generate_sorting_kwargs: dict = dict(num_units=10, firing_rates=15, refractory_period_ms=4.0, seed=2205), + generate_unit_locations_kwargs: dict = dict(margin_um=10.0, minimum_z=5.0, maximum_z=50.0, minimum_distance=20), + generate_templates_kwargs: dict = dict(ms_before=1.0, ms_after=3.0), + seed: int | None = None, +) -> tuple[BaseRecording, BaseSorting]: """ Generate an hybrid recording with spike given sorting+templates. Parameters ---------- recording : BaseRecording - The recording to inject units in - motion_info : All the information about the motion - The motion datastructure of the recording - num_units : int, default: 10 - Number of units, not used when sorting is given. - sorting : Sorting or None - An external sorting object. If not provide, one is genrated. - templates : Templates or None, default: None + The recording to inject units in. + motion : Motion | None, default: None + The motion object to use for the drifting templates. + sorting : Sorting | None, default: None + An external sorting object. If not provide, one is generated. + templates : Templates | None, default: None The templates of units. If None they are generated. templates_in_uV : bool, default: True @@ -376,7 +373,7 @@ def generate_hybrid_recording( Returns ------- - recording: Recording + recording: BaseRecording The generated hybrid recording extractor. sorting: Sorting The generated sorting extractor for the injected units. @@ -393,6 +390,23 @@ def generate_hybrid_recording( durations = np.array([recording.get_duration(segment_index) for segment_index in range(num_segments)]) channel_locations = probe.contact_positions + assert ( + templates is not None or sorting is not None or generate_sorting_kwargs is not None + ), "Provide templates or sorting or generate_sorting_kwargs" + + # check num_units + num_units = None + if templates is not None: + num_units = templates.num_units + if sorting is not None: + if num_units is not None: + assert num_units == sorting.get_num_units(), "num_units should be the same in templates and sorting" + else: + num_units = sorting.get_num_units() + if num_units is None: + assert "num_units" in generate_sorting_kwargs, "num_units should be provided in generate_sorting_kwargs" + num_units = generate_sorting_kwargs["num_units"] + if templates is None: if unit_locations is None: unit_locations = generate_unit_locations(num_units, channel_locations, **generate_unit_locations_kwargs) @@ -402,22 +416,23 @@ def generate_hybrid_recording( channel_locations, unit_locations, sampling_frequency, - ms_before, - ms_after, upsample_factor=upsample_factor, seed=seed, dtype=dtype, **generate_templates_kwargs, ) + ms_before = generate_templates_kwargs["ms_before"] + ms_after = generate_templates_kwargs["ms_after"] nbefore = int(ms_before * sampling_frequency / 1000.0) nafter = int(ms_after * sampling_frequency / 1000.0) templates_ = Templates(templates_array, sampling_frequency, nbefore, True, None, None, None, probe) else: + from spikeinterface.postprocessing.localization_tools import compute_monopolar_triangulation + assert isinstance(templates, Templates), "templates should be a Templates object" assert ( templates.num_channels == recording.get_num_channels() ), "templates and recording should have the same number of channels" - num_units = templates.num_units nbefore = templates.nbefore nafter = templates.nafter unit_locations = compute_monopolar_triangulation(templates) @@ -449,10 +464,8 @@ def generate_hybrid_recording( generate_sorting_kwargs["durations"] = durations generate_sorting_kwargs["sampling_frequency"] = sampling_frequency generate_sorting_kwargs["seed"] = seed - generate_sorting_kwargs["num_units"] = num_units sorting = generate_sorting(**generate_sorting_kwargs) else: - num_units = sorting.get_num_units() assert sorting.sampling_frequency == sampling_frequency num_spikes = sorting.to_spike_vector().size @@ -474,35 +487,53 @@ def generate_hybrid_recording( else: amplitude_factor = None - if motion_info is not None: - start = np.array([0, np.min(motion_info["motion"])]) - stop = np.array([0, np.max(motion_info["motion"])]) - displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[1])) + if motion is not None: + assert num_segments == motion.num_segments, "recording and motion should have the same number of segments" + dim = motion.dim + motion_array_concat = np.concatenat(motion.displacement) + if dim == 0: + start = np.array([np.min(motion_array_concat), 0]) + stop = np.array([np.max(motion_array_concat), 0]) + elif dim == 1: + start = np.array([0, np.min(motion_array_concat)]) + stop = np.array([0, np.max(motion_array_concat)]) + elif dim == 2: + raise NotImplementedError("3D motion not implemented yet") + displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[dim])) # use templates_, because templates_array might have been scaled drifting_templates = DriftingTemplates.from_static_templates(templates_) drifting_templates.precompute_displacements(displacements) - displacement_sampling_frequency = 1.0 / np.diff(motion_info["temporal_bins"])[0] - displacement_vectors = np.zeros((len(motion_info["temporal_bins"]), 2, len(motion_info["spatial_bins"]))) - displacement_unit_factor = np.zeros((num_units, len(motion_info["spatial_bins"]))) - - for count, i in enumerate(motion_info["spatial_bins"]): - local_motion = motion_info["motion"][:, count] - displacement_vectors[:, 1, count] = local_motion - + # calculate displacement vectors for each segment + displacement_sampling_frequency = 1.0 / np.diff(motion.temporal_bins_s[0])[0] + displacement_vectors = [] + spatial_bins_um = motion.spatial_bins_um + for segment_index in range(motion.num_segments): + temporal_bins_segment = motion.temporal_bins_s[segment_index] + displacement_segment = motion.displacement[segment_index] + displacement_vector = np.zeros((len(temporal_bins_segment, 2, len(spatial_bins_um)))) + displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) + + for count, i in enumerate(spatial_bins_um): + local_motion = displacement_segment[:, count] + displacement_vector[:, motion.dim, count] = local_motion + displacement_vectors.append(displacement_vector) + + # calculate displacement unit factor + displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) for count in range(num_units): - a = 1 / np.abs((unit_locations[count, 1] - motion_info["spatial_bins"])) + a = 1 / np.abs((unit_locations[count, motion.dim] - spatial_bins_um)) displacement_unit_factor[count] = a / a.sum() hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, parent_recording=recording, drifting_templates=drifting_templates, - displacement_vectors=[displacement_vectors], + displacement_vectors=displacement_vectors, displacement_sampling_frequency=displacement_sampling_frequency, displacement_unit_factor=displacement_unit_factor, - num_samples=durations * sampling_frequency, + num_samples=(np.array(durations) * sampling_frequency).astype("int64"), amplitude_factor=amplitude_factor, ) From c2df514d2a73cb59fc74669dec2301e66505056c Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 14:37:04 +0200 Subject: [PATCH 71/90] Fix imports and extend docstring --- src/spikeinterface/generation/__init__.py | 4 ++-- src/spikeinterface/generation/hybrid_tools.py | 15 ++++++++++++--- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/generation/__init__.py b/src/spikeinterface/generation/__init__.py index 9432ddb327..7a2291d932 100644 --- a/src/spikeinterface/generation/__init__.py +++ b/src/spikeinterface/generation/__init__.py @@ -10,8 +10,8 @@ generate_hybrid_recording, estimate_templates_from_recording, select_templates, - scale_templates, - shift_templates, + scale_template_to_range, + relocate_templates, ) from .noise_tools import generate_noise from .drifting_generator import ( diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 3e64fb9c47..d836d20c71 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -316,9 +316,9 @@ def relocate_templates( def generate_hybrid_recording( recording: BaseRecording, - motion: Motion | None = None, sorting: BaseSorting | None = None, templates: Templates | None = None, + motion: Motion | None = None, templates_in_uV: bool = True, unit_locations: np.ndarray | None = None, upsample_factor: int | None = None, @@ -332,17 +332,26 @@ def generate_hybrid_recording( """ Generate an hybrid recording with spike given sorting+templates. + The function starts from an existing recording and injects hybrid units in it. + The templates can be provided or generated. If the templates are not provided, + they are generated (using the `spikeinterface.core.generate.generate_templates()` function + and with arguments provided in `generate_templates_kwargs`). + The sorting can be provided or generated. If the sorting is not provided, it is generated + (using the `spikeinterface.core.generate.generate_sorting` function and with arguments + provided in `generate_sorting_kwargs`). + The injected spikes can optionally follow a motion pattern provided by a Motion object. + Parameters ---------- recording : BaseRecording The recording to inject units in. - motion : Motion | None, default: None - The motion object to use for the drifting templates. sorting : Sorting | None, default: None An external sorting object. If not provide, one is generated. templates : Templates | None, default: None The templates of units. If None they are generated. + motion : Motion | None, default: None + The motion object to use for the drifting templates. templates_in_uV : bool, default: True If True, the templates are in uV, otherwise they are in the same unit as the recording. In case the recording has scaling, the templates are "unscaled" before injection. From 534202edd32a5329614da65ace6ea904a00ea650 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 15:21:06 +0200 Subject: [PATCH 72/90] Move compute_* localization functions to localization_tools --- src/spikeinterface/core/template_tools.py | 1 - src/spikeinterface/generation/hybrid_tools.py | 6 +- .../generation/tests/test_hybrid_tools.py | 80 +++-- src/spikeinterface/postprocessing/__init__.py | 1 - .../postprocessing/localization_tools.py | 285 ++++++++++++++++++ .../postprocessing/unit_locations.py | 276 +---------------- .../benchmark/benchmark_peak_localization.py | 2 +- 7 files changed, 354 insertions(+), 297 deletions(-) diff --git a/src/spikeinterface/core/template_tools.py b/src/spikeinterface/core/template_tools.py index 4e4954f540..934b18ed49 100644 --- a/src/spikeinterface/core/template_tools.py +++ b/src/spikeinterface/core/template_tools.py @@ -3,7 +3,6 @@ import warnings from .template import Templates -from .sparsity import _sparsity_doc from .sortinganalyzer import SortingAnalyzer diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index d836d20c71..83d4690661 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -406,8 +406,10 @@ def generate_hybrid_recording( # check num_units num_units = None if templates is not None: + assert isinstance(templates, Templates), "templates should be a Templates object" num_units = templates.num_units if sorting is not None: + assert isinstance(sorting, BaseSorting), "sorting should be a Sorting object" if num_units is not None: assert num_units == sorting.get_num_units(), "num_units should be the same in templates and sorting" else: @@ -499,7 +501,7 @@ def generate_hybrid_recording( if motion is not None: assert num_segments == motion.num_segments, "recording and motion should have the same number of segments" dim = motion.dim - motion_array_concat = np.concatenat(motion.displacement) + motion_array_concat = np.concatenate(motion.displacement) if dim == 0: start = np.array([np.min(motion_array_concat), 0]) stop = np.array([np.max(motion_array_concat), 0]) @@ -521,7 +523,7 @@ def generate_hybrid_recording( for segment_index in range(motion.num_segments): temporal_bins_segment = motion.temporal_bins_s[segment_index] displacement_segment = motion.displacement[segment_index] - displacement_vector = np.zeros((len(temporal_bins_segment, 2, len(spatial_bins_um)))) + displacement_vector = np.zeros((len(temporal_bins_segment), 2, len(spatial_bins_um))) displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) for count, i in enumerate(spatial_bins_um): diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 95c791ad09..7939d0b70e 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -1,8 +1,12 @@ -import pytest import numpy as np -from pathlib import Path -from spikeinterface.core.generate import generate_ground_truth_recording, generate_sorting +from spikeinterface.core import Templates +from spikeinterface.core.generate import ( + generate_ground_truth_recording, + generate_sorting, + generate_templates, + generate_unit_locations, +) from spikeinterface.preprocessing.motion import correct_motion, load_motion_info from spikeinterface.generation.hybrid_tools import ( estimate_templates_from_recording, @@ -10,30 +14,68 @@ ) +def test_generate_hybrid_no_motion(): + rec, _ = generate_ground_truth_recording(sampling_frequency=20000, seed=0) + hybrid, _ = generate_hybrid_recording(rec, seed=0) + assert rec.get_num_channels() == hybrid.get_num_channels() + assert rec.get_num_frames() == hybrid.get_num_frames() + assert rec.get_num_segments() == hybrid.get_num_segments() + assert np.array_equal(rec.get_channel_locations(), hybrid.get_channel_locations()) + + def test_generate_hybrid_with_sorting(): - gt_sorting = generate_sorting(durations=[10], num_units=20, sampling_frequency=20000) - rec, _ = generate_ground_truth_recording(durations=[10], sampling_frequency=20000, sorting=gt_sorting) - hybrid, _ = generate_hybrid_recording(rec) + gt_sorting = generate_sorting(durations=[10], num_units=20, sampling_frequency=20000, seed=0) + rec, _ = generate_ground_truth_recording(durations=[10], sampling_frequency=20000, sorting=gt_sorting, seed=0) + hybrid, sorting_hybrid = generate_hybrid_recording(rec, sorting=gt_sorting) + assert rec.get_num_channels() == hybrid.get_num_channels() + assert rec.get_num_frames() == hybrid.get_num_frames() + assert rec.get_num_segments() == hybrid.get_num_segments() + assert np.array_equal(rec.get_channel_locations(), hybrid.get_channel_locations()) + assert sorting_hybrid.get_num_units() == len(hybrid.templates) -def test_generate_hybrid_no_motion(): - rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) - hybrid, sorting = generate_hybrid_recording(rec) +def test_generate_hybrid_motion(create_cache_folder): + cache_folder = create_cache_folder + rec, _ = generate_ground_truth_recording(sampling_frequency=20000, durations=[10], seed=0) + correct_motion(rec, folder=cache_folder / "motion") + motion_info = load_motion_info(cache_folder / "motion") + motion = motion_info["motion"] + hybrid, sorting_hybrid = generate_hybrid_recording(rec, motion=motion, seed=0) + assert rec.get_num_channels() == hybrid.get_num_channels() + assert rec.get_num_frames() == hybrid.get_num_frames() + assert rec.get_num_segments() == hybrid.get_num_segments() + assert np.array_equal(rec.get_channel_locations(), hybrid.get_channel_locations()) + assert sorting_hybrid.get_num_units() == len(hybrid.drifting_templates.unit_ids) -def test_generate_hybrid_motion(tmp_path): - rec, sorting = generate_ground_truth_recording(sampling_frequency=20000) - correct_motion(rec, folder=tmp_path / "motion") - motion = load_motion_info(tmp_path / "motion") - hybrid, sorting = generate_hybrid_recording(rec, motion) +def test_generate_hybrid_from_templates(): + num_units = 10 + ms_before = 2 + ms_after = 4 + rec, _ = generate_ground_truth_recording(sampling_frequency=20000, seed=0) + channel_locations = rec.get_channel_locations() + unit_locations = generate_unit_locations(num_units, channel_locations=channel_locations, seed=0) + templates_array = generate_templates( + channel_locations, unit_locations, rec.sampling_frequency, ms_before, ms_after, seed=0 + ) + nbefore = int(ms_before * rec.sampling_frequency / 1000) + templates = Templates(templates_array, rec.sampling_frequency, nbefore, True, None, None, None, rec.get_probe()) + hybrid, sorting_hybrid = generate_hybrid_recording(rec, templates=templates, seed=0) + assert np.array_equal(hybrid.templates, templates.templates_array) + assert rec.get_num_channels() == hybrid.get_num_channels() + assert rec.get_num_frames() == hybrid.get_num_frames() + assert rec.get_num_segments() == hybrid.get_num_segments() + assert np.array_equal(rec.get_channel_locations(), hybrid.get_channel_locations()) + assert sorting_hybrid.get_num_units() == num_units -def test_estimate_templates(tmp_path): - rec, sorting = generate_ground_truth_recording(num_units=10, sampling_frequency=20000) - templates = estimate_templates_from_recording(rec, output_folder=tmp_path / "sc", remove_existing_folder=True) +def test_estimate_templates(create_cache_folder): + cache_folder = create_cache_folder + rec, _ = generate_ground_truth_recording(num_units=10, sampling_frequency=20000, seed=0) + templates = estimate_templates_from_recording( + rec, run_sorter_kwargs=dict(folder=cache_folder / "sc", remove_existing_folder=True) + ) assert len(templates.templates_array) > 0 - hybrid, _ = generate_hybrid_recording(rec, templates=templates) - assert np.array_equal(hybrid.templates, templates.templates_array) if __name__ == "__main__": diff --git a/src/spikeinterface/postprocessing/__init__.py b/src/spikeinterface/postprocessing/__init__.py index ae071a55e0..34a0bfab9a 100644 --- a/src/spikeinterface/postprocessing/__init__.py +++ b/src/spikeinterface/postprocessing/__init__.py @@ -40,7 +40,6 @@ from .unit_locations import ( compute_unit_locations, ComputeUnitLocations, - compute_center_of_mass, ) from .amplitude_scalings import compute_amplitude_scalings, ComputeAmplitudeScalings diff --git a/src/spikeinterface/postprocessing/localization_tools.py b/src/spikeinterface/postprocessing/localization_tools.py index 3394190e5a..b7571a6f3e 100644 --- a/src/spikeinterface/postprocessing/localization_tools.py +++ b/src/spikeinterface/postprocessing/localization_tools.py @@ -12,6 +12,291 @@ HAVE_NUMBA = False +from spikeinterface.core import compute_sparsity, SortingAnalyzer, Templates +from spikeinterface.core.template_tools import get_template_extremum_channel, _get_nbefore, get_dense_templates_array + + +def compute_monopolar_triangulation( + sorting_analyzer_or_templates: SortingAnalyzer | Templates, + optimizer: str = "least_square", + radius_um: float = 75, + max_distance_um: float = 1000, + return_alpha: bool = False, + enforce_decrease: bool = False, + feature: str = "ptp", +) -> np.ndarray: + """ + Localize unit with monopolar triangulation. + This method is from Julien Boussard, Erdem Varol and Charlie Windolf + https://www.biorxiv.org/content/10.1101/2021.11.05.467503v1 + + There are 2 implementations of the 2 optimizer variants: + * https://github.com/int-brain-lab/spikes_localization_registration/blob/main/localization_pipeline/localizer.py + * https://github.com/cwindolf/spike-psvae/blob/main/spike_psvae/localization.py + + Important note about axis: + * x/y are dimmension on the probe plane (dim0, dim1) + * y is the depth by convention + * z it the orthogonal axis to the probe plan (dim2) + + Code from Erdem, Julien and Charlie do not use the same convention!!! + + + Parameters + ---------- + sorting_analyzer_or_templates : SortingAnalyzer | Templates + A SortingAnalyzer or Templates object + method : "least_square" | "minimize_with_log_penality", default: "least_square" + The optimizer to use + radius_um : float, default: 75 + For channel sparsity + max_distance_um : float, default: 1000 + to make bounddary in x, y, z and also for alpha + return_alpha : bool, default: False + Return or not the alpha value + enforce_decrease : bool, default: False + Enforce spatial decreasingness for PTP vectors + feature : "ptp" | "energy" | "peak_voltage", default: "ptp" + The available features to consider for estimating the position via + monopolar triangulation are peak-to-peak amplitudes ("ptp", default), + energy ("energy", as L2 norm) or voltages at the center of the waveform + ("peak_voltage") + + Returns + ------- + unit_location: np.ndarray + 3d or 4d, x, y, z, alpha + alpha is the amplitude at source estimation + """ + assert optimizer in ("least_square", "minimize_with_log_penality") + + assert feature in ["ptp", "energy", "peak_voltage"], f"{feature} is not a valid feature" + unit_ids = sorting_analyzer_or_templates.unit_ids + + contact_locations = sorting_analyzer_or_templates.get_channel_locations() + + sparsity = compute_sparsity(sorting_analyzer_or_templates, method="radius", radius_um=radius_um) + templates = get_dense_templates_array( + sorting_analyzer_or_templates, return_scaled=get_return_scaled(sorting_analyzer_or_templates) + ) + nbefore = _get_nbefore(sorting_analyzer_or_templates) + + if enforce_decrease: + neighbours_mask = np.zeros((templates.shape[0], templates.shape[2]), dtype=bool) + for i, unit_id in enumerate(unit_ids): + chan_inds = sparsity.unit_id_to_channel_indices[unit_id] + neighbours_mask[i, chan_inds] = True + enforce_decrease_radial_parents = make_radial_order_parents(contact_locations, neighbours_mask) + best_channels = get_template_extremum_channel(sorting_analyzer_or_templates, outputs="index") + + unit_location = np.zeros((unit_ids.size, 4), dtype="float64") + for i, unit_id in enumerate(unit_ids): + chan_inds = sparsity.unit_id_to_channel_indices[unit_id] + local_contact_locations = contact_locations[chan_inds, :] + + # wf is (nsample, nchan) - chann is only nieghboor + wf = templates[i, :, :][:, chan_inds] + if feature == "ptp": + wf_data = wf.ptp(axis=0) + elif feature == "energy": + wf_data = np.linalg.norm(wf, axis=0) + elif feature == "peak_voltage": + wf_data = np.abs(wf[nbefore]) + + # if enforce_decrease: + # enforce_decrease_shells_data( + # wf_data, best_channels[unit_id], enforce_decrease_radial_parents, in_place=True + # ) + + unit_location[i] = solve_monopolar_triangulation(wf_data, local_contact_locations, max_distance_um, optimizer) + + if not return_alpha: + unit_location = unit_location[:, :3] + + return unit_location + + +def compute_center_of_mass( + sorting_analyzer_or_templates: SortingAnalyzer | Templates, + peak_sign: str = "neg", + radius_um: float = 75, + feature: str = "ptp", +) -> np.ndarray: + """ + Computes the center of mass (COM) of a unit based on the template amplitudes. + + Parameters + ---------- + sorting_analyzer_or_templates : SortingAnalyzer | Templates + A SortingAnalyzer or Templates object + peak_sign : "neg" | "pos" | "both", default: "neg" + Sign of the template to compute best channels + radius_um : float + Radius to consider in order to estimate the COM + feature : "ptp" | "mean" | "energy" | "peak_voltage", default: "ptp" + Feature to consider for computation + + Returns + ------- + unit_location: np.array + """ + unit_ids = sorting_analyzer_or_templates.unit_ids + + contact_locations = sorting_analyzer_or_templates.get_channel_locations() + + assert feature in ["ptp", "mean", "energy", "peak_voltage"], f"{feature} is not a valid feature" + + sparsity = compute_sparsity( + sorting_analyzer_or_templates, peak_sign=peak_sign, method="radius", radius_um=radius_um + ) + templates = get_dense_templates_array( + sorting_analyzer_or_templates, return_scaled=get_return_scaled(sorting_analyzer_or_templates) + ) + nbefore = _get_nbefore(sorting_analyzer_or_templates) + + unit_location = np.zeros((unit_ids.size, 2), dtype="float64") + for i, unit_id in enumerate(unit_ids): + chan_inds = sparsity.unit_id_to_channel_indices[unit_id] + local_contact_locations = contact_locations[chan_inds, :] + + wf = templates[i, :, :] + + if feature == "ptp": + wf_data = (wf[:, chan_inds]).ptp(axis=0) + elif feature == "mean": + wf_data = (wf[:, chan_inds]).mean(axis=0) + elif feature == "energy": + wf_data = np.linalg.norm(wf[:, chan_inds], axis=0) + elif feature == "peak_voltage": + wf_data = wf[nbefore, chan_inds] + + # center of mass + com = np.sum(wf_data[:, np.newaxis] * local_contact_locations, axis=0) / np.sum(wf_data) + unit_location[i, :] = com + + return unit_location + + +def compute_grid_convolution( + sorting_analyzer_or_templates: SortingAnalyzer | Templates, + peak_sign: str = "neg", + radius_um: float = 40.0, + upsampling_um: float = 5, + sigma_ms: float = 0.25, + margin_um: float = 50, + prototype: np.ndarray | None = None, + percentile: float = 5, + weight_method: dict = {}, +) -> np.ndarray: + """ + Estimate the positions of the templates from a large grid of fake templates + + Parameters + ---------- + sorting_analyzer_or_templates : SortingAnalyzer | Templates + A SortingAnalyzer or Templates object + peak_sign : "neg" | "pos" | "both", default: "neg" + Sign of the template to compute best channels + radius_um : float, default: 40.0 + Radius to consider for the fake templates + upsampling_um : float, default: 5 + Upsampling resolution for the grid of templates + sigma_ms : float, default: 0.25 + The temporal decay of the fake templates + margin_um : float, default: 50 + The margin for the grid of fake templates + prototype : np.array or None, default: None + Fake waveforms for the templates. If None, generated as Gaussian + percentile : float, default: 5 + The percentage in [0, 100] of the best scalar products kept to + estimate the position + weight_method : dict + Parameter that should be provided to the get_convolution_weights() function + in order to know how to estimate the positions. One argument is mode that could + be either gaussian_2d (KS like) or exponential_3d (default) + Returns + ------- + unit_location: np.array + """ + + contact_locations = sorting_analyzer_or_templates.get_channel_locations() + unit_ids = sorting_analyzer_or_templates.unit_ids + + templates = get_dense_templates_array( + sorting_analyzer_or_templates, return_scaled=get_return_scaled(sorting_analyzer_or_templates) + ) + nbefore = _get_nbefore(sorting_analyzer_or_templates) + nafter = templates.shape[1] - nbefore + + fs = sorting_analyzer_or_templates.sampling_frequency + percentile = 100 - percentile + assert 0 <= percentile <= 100, "Percentile should be in [0, 100]" + + time_axis = np.arange(-nbefore, nafter) * 1000 / fs + if prototype is None: + prototype = np.exp(-(time_axis**2) / (2 * (sigma_ms**2))) + if peak_sign == "neg": + prototype *= -1 + + prototype = prototype[:, np.newaxis] + + template_positions, weights, nearest_template_mask, z_factors = get_grid_convolution_templates_and_weights( + contact_locations, radius_um, upsampling_um, margin_um, weight_method + ) + + peak_channels = get_template_extremum_channel(sorting_analyzer_or_templates, peak_sign, outputs="index") + + weights_sparsity_mask = weights > 0 + + nb_weights = weights.shape[0] + unit_location = np.zeros((unit_ids.size, 3), dtype="float64") + + for i, unit_id in enumerate(unit_ids): + main_chan = peak_channels[unit_id] + wf = templates[i, :, :] + nearest_mask = nearest_template_mask[main_chan, :] + channel_mask = np.sum(weights_sparsity_mask[:, :, nearest_mask], axis=(0, 2)) > 0 + num_templates = np.sum(nearest_mask) + sub_w = weights[:, channel_mask, :][:, :, nearest_mask] + global_products = (wf[:, channel_mask] * prototype).sum(axis=0) + + dot_products = np.zeros((nb_weights, num_templates), dtype=np.float32) + for count in range(nb_weights): + dot_products[count] = np.dot(global_products, sub_w[count]) + + mask = dot_products < 0 + if percentile > 0: + dot_products[mask] = np.nan + ## We need to catch warnings because some line can have only NaN, and + ## if so the nanpercentile function throws a warning + with warnings.catch_warnings(): + warnings.filterwarnings("ignore") + thresholds = np.nanpercentile(dot_products, percentile) + thresholds = np.nan_to_num(thresholds) + dot_products[dot_products < thresholds] = 0 + dot_products[mask] = 0 + + nearest_templates = template_positions[nearest_mask] + for count in range(nb_weights): + unit_location[i, :2] += np.dot(dot_products[count], nearest_templates) + + scalar_products = dot_products.sum(1) + unit_location[i, 2] = np.dot(z_factors, scalar_products) + with np.errstate(divide="ignore", invalid="ignore"): + unit_location[i] /= scalar_products.sum() + unit_location = np.nan_to_num(unit_location) + + return unit_location + + +def get_return_scaled(sorting_analyzer_or_templates): + if isinstance(sorting_analyzer_or_templates, Templates): + return_scaled = sorting_analyzer_or_templates.is_scaled + else: + return_scaled = sorting_analyzer_or_templates.return_scaled + return return_scaled + + def make_initial_guess_and_bounds(wf_data, local_contact_locations, max_distance_um, initial_z=20): # constant for initial guess and bounds ind_max = np.argmax(wf_data) diff --git a/src/spikeinterface/postprocessing/unit_locations.py b/src/spikeinterface/postprocessing/unit_locations.py index 641cbd82fa..9435030775 100644 --- a/src/spikeinterface/postprocessing/unit_locations.py +++ b/src/spikeinterface/postprocessing/unit_locations.py @@ -4,12 +4,10 @@ import warnings from ..core.sortinganalyzer import register_result_extension, AnalyzerExtension -from ..core import compute_sparsity -from ..core.template_tools import get_template_extremum_channel, _get_nbefore, get_dense_templates_array from .localization_tools import ( - make_radial_order_parents, - solve_monopolar_triangulation, - get_grid_convolution_templates_and_weights, + compute_center_of_mass, + compute_grid_convolution, + compute_monopolar_triangulation, ) dtype_localize_by_method = { @@ -85,271 +83,3 @@ def get_data(self, outputs="numpy"): register_result_extension(ComputeUnitLocations) compute_unit_locations = ComputeUnitLocations.function_factory() - - -def compute_monopolar_triangulation( - sorting_analyzer_or_templates, - optimizer="minimize_with_log_penality", - radius_um=75, - max_distance_um=1000, - return_alpha=False, - enforce_decrease=False, - feature="ptp", -): - """ - Localize unit with monopolar triangulation. - This method is from Julien Boussard, Erdem Varol and Charlie Windolf - https://www.biorxiv.org/content/10.1101/2021.11.05.467503v1 - - There are 2 implementations of the 2 optimizer variants: - * https://github.com/int-brain-lab/spikes_localization_registration/blob/main/localization_pipeline/localizer.py - * https://github.com/cwindolf/spike-psvae/blob/main/spike_psvae/localization.py - - Important note about axis: - * x/y are dimmension on the probe plane (dim0, dim1) - * y is the depth by convention - * z it the orthogonal axis to the probe plan (dim2) - - Code from Erdem, Julien and Charlie do not use the same convention!!! - - - Parameters - ---------- - sorting_analyzer_or_templates: SortingAnalyzer | Templates - A SortingAnalyzer or Templates object - method: "least_square" | "minimize_with_log_penality", default: "least_square" - The optimizer to use - radius_um: float, default: 75 - For channel sparsity - max_distance_um: float, default: 1000 - to make bounddary in x, y, z and also for alpha - return_alpha: bool, default: False - Return or not the alpha value - enforce_decrease : bool, default: False - Enforce spatial decreasingness for PTP vectors - feature: "ptp" | "energy" | "peak_voltage", default: "ptp" - The available features to consider for estimating the position via - monopolar triangulation are peak-to-peak amplitudes ("ptp", default), - energy ("energy", as L2 norm) or voltages at the center of the waveform - ("peak_voltage") - - Returns - ------- - unit_location: np.array - 3d or 4d, x, y, z, alpha - alpha is the amplitude at source estimation - """ - assert optimizer in ("least_square", "minimize_with_log_penality") - - assert feature in ["ptp", "energy", "peak_voltage"], f"{feature} is not a valid feature" - unit_ids = sorting_analyzer_or_templates.unit_ids - - contact_locations = sorting_analyzer_or_templates.get_channel_locations() - - sparsity = compute_sparsity(sorting_analyzer_or_templates, method="radius", radius_um=radius_um) - templates = get_dense_templates_array( - sorting_analyzer_or_templates, return_scaled=sorting_analyzer_or_templates.return_scaled - ) - nbefore = _get_nbefore(sorting_analyzer_or_templates) - - if enforce_decrease: - neighbours_mask = np.zeros((templates.shape[0], templates.shape[2]), dtype=bool) - for i, unit_id in enumerate(unit_ids): - chan_inds = sparsity.unit_id_to_channel_indices[unit_id] - neighbours_mask[i, chan_inds] = True - enforce_decrease_radial_parents = make_radial_order_parents(contact_locations, neighbours_mask) - best_channels = get_template_extremum_channel(sorting_analyzer_or_templates, outputs="index") - - unit_location = np.zeros((unit_ids.size, 4), dtype="float64") - for i, unit_id in enumerate(unit_ids): - chan_inds = sparsity.unit_id_to_channel_indices[unit_id] - local_contact_locations = contact_locations[chan_inds, :] - - # wf is (nsample, nchan) - chann is only nieghboor - wf = templates[i, :, :][:, chan_inds] - if feature == "ptp": - wf_data = wf.ptp(axis=0) - elif feature == "energy": - wf_data = np.linalg.norm(wf, axis=0) - elif feature == "peak_voltage": - wf_data = np.abs(wf[nbefore]) - - # if enforce_decrease: - # enforce_decrease_shells_data( - # wf_data, best_channels[unit_id], enforce_decrease_radial_parents, in_place=True - # ) - - unit_location[i] = solve_monopolar_triangulation(wf_data, local_contact_locations, max_distance_um, optimizer) - - if not return_alpha: - unit_location = unit_location[:, :3] - - return unit_location - - -def compute_center_of_mass(sorting_analyzer_or_templates, peak_sign="neg", radius_um=75, feature="ptp"): - """ - Computes the center of mass (COM) of a unit based on the template amplitudes. - - Parameters - ---------- - sorting_analyzer_or_templates: SortingAnalyzer | Templates - A SortingAnalyzer object - peak_sign: "neg" | "pos" | "both", default: "neg" - Sign of the template to compute best channels - radius_um: float - Radius to consider in order to estimate the COM - feature: "ptp" | "mean" | "energy" | "peak_voltage", default: "ptp" - Feature to consider for computation - - Returns - ------- - unit_location: np.array - """ - unit_ids = sorting_analyzer_or_templates.unit_ids - - contact_locations = sorting_analyzer_or_templates.get_channel_locations() - - assert feature in ["ptp", "mean", "energy", "peak_voltage"], f"{feature} is not a valid feature" - - sparsity = compute_sparsity( - sorting_analyzer_or_templates, peak_sign=peak_sign, method="radius", radius_um=radius_um - ) - templates = get_dense_templates_array( - sorting_analyzer_or_templates, return_scaled=sorting_analyzer_or_templates.return_scaled - ) - nbefore = _get_nbefore(sorting_analyzer_or_templates) - - unit_location = np.zeros((unit_ids.size, 2), dtype="float64") - for i, unit_id in enumerate(unit_ids): - chan_inds = sparsity.unit_id_to_channel_indices[unit_id] - local_contact_locations = contact_locations[chan_inds, :] - - wf = templates[i, :, :] - - if feature == "ptp": - wf_data = (wf[:, chan_inds]).ptp(axis=0) - elif feature == "mean": - wf_data = (wf[:, chan_inds]).mean(axis=0) - elif feature == "energy": - wf_data = np.linalg.norm(wf[:, chan_inds], axis=0) - elif feature == "peak_voltage": - wf_data = wf[nbefore, chan_inds] - - # center of mass - com = np.sum(wf_data[:, np.newaxis] * local_contact_locations, axis=0) / np.sum(wf_data) - unit_location[i, :] = com - - return unit_location - - -def compute_grid_convolution( - sorting_analyzer_or_templates, - peak_sign="neg", - radius_um=40.0, - upsampling_um=5, - sigma_ms=0.25, - margin_um=50, - prototype=None, - percentile=5, - weight_method={}, -): - """ - Estimate the positions of the templates from a large grid of fake templates - - Parameters - ---------- - sorting_analyzer_or_templates: SortingAnalyzer | Templates - A SortingAnalyzer object - peak_sign: "neg" | "pos" | "both", default: "neg" - Sign of the template to compute best channels - radius_um: float, default: 40.0 - Radius to consider for the fake templates - upsampling_um: float, default: 5 - Upsampling resolution for the grid of templates - sigma_ms: float, default: 0.25 - The temporal decay of the fake templates - margin_um: float, default: 50 - The margin for the grid of fake templates - prototype: np.array or None, default: None - Fake waveforms for the templates. If None, generated as Gaussian - percentile: float, default: 5 - The percentage in [0, 100] of the best scalar products kept to - estimate the position - weight_method: dict - Parameter that should be provided to the get_convolution_weights() function - in order to know how to estimate the positions. One argument is mode that could - be either gaussian_2d (KS like) or exponential_3d (default) - Returns - ------- - unit_location: np.array - """ - - contact_locations = sorting_analyzer_or_templates.get_channel_locations() - unit_ids = sorting_analyzer_or_templates.unit_ids - - templates = get_dense_templates_array( - sorting_analyzer_or_templates, return_scaled=sorting_analyzer_or_templates.return_scaled - ) - nbefore = _get_nbefore(sorting_analyzer_or_templates) - nafter = templates.shape[1] - nbefore - - fs = sorting_analyzer_or_templates.sampling_frequency - percentile = 100 - percentile - assert 0 <= percentile <= 100, "Percentile should be in [0, 100]" - - time_axis = np.arange(-nbefore, nafter) * 1000 / fs - if prototype is None: - prototype = np.exp(-(time_axis**2) / (2 * (sigma_ms**2))) - if peak_sign == "neg": - prototype *= -1 - - prototype = prototype[:, np.newaxis] - - template_positions, weights, nearest_template_mask, z_factors = get_grid_convolution_templates_and_weights( - contact_locations, radius_um, upsampling_um, margin_um, weight_method - ) - - peak_channels = get_template_extremum_channel(sorting_analyzer_or_templates, peak_sign, outputs="index") - - weights_sparsity_mask = weights > 0 - - nb_weights = weights.shape[0] - unit_location = np.zeros((unit_ids.size, 3), dtype="float64") - - for i, unit_id in enumerate(unit_ids): - main_chan = peak_channels[unit_id] - wf = templates[i, :, :] - nearest_mask = nearest_template_mask[main_chan, :] - channel_mask = np.sum(weights_sparsity_mask[:, :, nearest_mask], axis=(0, 2)) > 0 - num_templates = np.sum(nearest_mask) - sub_w = weights[:, channel_mask, :][:, :, nearest_mask] - global_products = (wf[:, channel_mask] * prototype).sum(axis=0) - - dot_products = np.zeros((nb_weights, num_templates), dtype=np.float32) - for count in range(nb_weights): - dot_products[count] = np.dot(global_products, sub_w[count]) - - mask = dot_products < 0 - if percentile > 0: - dot_products[mask] = np.nan - ## We need to catch warnings because some line can have only NaN, and - ## if so the nanpercentile function throws a warning - with warnings.catch_warnings(): - warnings.filterwarnings("ignore") - thresholds = np.nanpercentile(dot_products, percentile) - thresholds = np.nan_to_num(thresholds) - dot_products[dot_products < thresholds] = 0 - dot_products[mask] = 0 - - nearest_templates = template_positions[nearest_mask] - for count in range(nb_weights): - unit_location[i, :2] += np.dot(dot_products[count], nearest_templates) - - scalar_products = dot_products.sum(1) - unit_location[i, 2] = np.dot(z_factors, scalar_products) - with np.errstate(divide="ignore", invalid="ignore"): - unit_location[i] /= scalar_products.sum() - unit_location = np.nan_to_num(unit_location) - - return unit_location diff --git a/src/spikeinterface/sortingcomponents/benchmark/benchmark_peak_localization.py b/src/spikeinterface/sortingcomponents/benchmark/benchmark_peak_localization.py index 3eda5db3b6..05d142113b 100644 --- a/src/spikeinterface/sortingcomponents/benchmark/benchmark_peak_localization.py +++ b/src/spikeinterface/sortingcomponents/benchmark/benchmark_peak_localization.py @@ -1,6 +1,6 @@ from __future__ import annotations -from spikeinterface.postprocessing.unit_locations import ( +from spikeinterface.postprocessing.localization_tools import ( compute_center_of_mass, compute_monopolar_triangulation, compute_grid_convolution, From c86cad4e6561ab219a9b5b1d817c4b53ea28c632 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 15:25:20 +0200 Subject: [PATCH 73/90] Update save_motion_info and add tests --- src/spikeinterface/preprocessing/motion.py | 5 +---- .../preprocessing/tests/test_motion.py | 12 ++++++++---- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/src/spikeinterface/preprocessing/motion.py b/src/spikeinterface/preprocessing/motion.py index 9589dac059..b007d237a2 100644 --- a/src/spikeinterface/preprocessing/motion.py +++ b/src/spikeinterface/preprocessing/motion.py @@ -426,10 +426,7 @@ def save_motion_info(motion_info, folder, overwrite=False): np.save(folder / "peaks.npy", motion_info["peaks"]) np.save(folder / "peak_locations.npy", motion_info["peak_locations"]) - np.save(folder / "motion.npy", motion_info["motion"]) - np.save(folder / "temporal_bins.npy", motion_info["temporal_bins"]) - if motion_info["spatial_bins"] is not None: - np.save(folder / "spatial_bins.npy", motion_info["spatial_bins"]) + motion_info["motion"].save(folder / "motion") def load_motion_info(folder): diff --git a/src/spikeinterface/preprocessing/tests/test_motion.py b/src/spikeinterface/preprocessing/tests/test_motion.py index a298b41d8f..baa7235263 100644 --- a/src/spikeinterface/preprocessing/tests/test_motion.py +++ b/src/spikeinterface/preprocessing/tests/test_motion.py @@ -1,10 +1,7 @@ import shutil -from pathlib import Path -import numpy as np -import pytest from spikeinterface.core import generate_recording -from spikeinterface.preprocessing import correct_motion, load_motion_info +from spikeinterface.preprocessing import correct_motion, load_motion_info, save_motion_info def test_estimate_and_correct_motion(create_cache_folder): @@ -19,9 +16,16 @@ def test_estimate_and_correct_motion(create_cache_folder): rec_corrected = correct_motion(rec, folder=folder) print(rec_corrected) + # test reloading motion info motion_info = load_motion_info(folder) print(motion_info.keys()) + # test saving motion info + save_folder = folder / "motion_info" + save_motion_info(motion_info=motion_info, folder=save_folder) + motion_info_loaded = load_motion_info(save_folder) + assert motion_info_loaded["motion"] == motion_info["motion"] + if __name__ == "__main__": # print(correct_motion.__doc__) From 1ecda4a610a342cfc38fc14b1a09f3f0516561a4 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 19 Jun 2024 15:28:38 +0200 Subject: [PATCH 74/90] Use save_motion_info in correct_motion --- src/spikeinterface/preprocessing/motion.py | 31 ++++++++-------------- 1 file changed, 11 insertions(+), 20 deletions(-) diff --git a/src/spikeinterface/preprocessing/motion.py b/src/spikeinterface/preprocessing/motion.py index b007d237a2..0d65b1936a 100644 --- a/src/spikeinterface/preprocessing/motion.py +++ b/src/spikeinterface/preprocessing/motion.py @@ -206,6 +206,7 @@ def correct_motion( preset="nonrigid_accurate", folder=None, output_motion_info=False, + overwrite=False, detect_kwargs={}, select_kwargs={}, localize_peaks_kwargs={}, @@ -258,6 +259,8 @@ def correct_motion( If True, then the function returns a `motion_info` dictionary that contains variables to check intermediate steps (motion_histogram, non_rigid_windows, pairwise_displacement) This dictionary is the same when reloaded from the folder + overwrite : bool, default: False + If True and folder is given, overwrite the folder if it already exists detect_kwargs : dict Optional parameters to overwrite the ones in the preset for "detect" step. select_kwargs : dict @@ -315,14 +318,6 @@ def correct_motion( job_kwargs = fix_job_kwargs(job_kwargs) noise_levels = get_noise_levels(recording, return_scaled=False) - if folder is not None: - folder = Path(folder) - folder.mkdir(exist_ok=True, parents=True) - - (folder / "parameters.json").write_text(json.dumps(parameters, indent=4, cls=SIJsonEncoder), encoding="utf8") - if recording.check_serializability("json"): - recording.dump_to_json(folder / "recording.json") - if not do_selection: # maybe do this directly in the folder when not None, but might be slow on external storage gather_mode = "memory" @@ -372,9 +367,6 @@ def correct_motion( select_peaks=t2 - t1, localize_peaks=t3 - t2, ) - if folder is not None: - np.save(folder / "peaks.npy", peaks) - np.save(folder / "peak_locations.npy", peak_locations) t0 = time.perf_counter() motion = estimate_motion(recording, peaks, peak_locations, **estimate_motion_kwargs) @@ -383,18 +375,17 @@ def correct_motion( recording_corrected = InterpolateMotionRecording(recording, motion, **interpolate_motion_kwargs) + motion_info = dict( + parameters=parameters, + run_times=run_times, + peaks=peaks, + peak_locations=peak_locations, + motion=motion, + ) if folder is not None: - (folder / "run_times.json").write_text(json.dumps(run_times, indent=4), encoding="utf8") - motion.save(folder / "motion") + save_motion_info(motion_info, folder, overwrite=overwrite) if output_motion_info: - motion_info = dict( - parameters=parameters, - run_times=run_times, - peaks=peaks, - peak_locations=peak_locations, - motion=motion, - ) return recording_corrected, motion_info else: return recording_corrected From 28321d92118cc65536450db8cc282ec02ee1f5ee Mon Sep 17 00:00:00 2001 From: Charlie Windolf Date: Mon, 3 Jun 2024 14:55:48 -0400 Subject: [PATCH 75/90] User-specified precomputed templates in DriftingTemplates --- src/spikeinterface/generation/drift_tools.py | 54 ++++++++++++++++++-- 1 file changed, 49 insertions(+), 5 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index b3824c7261..4e6e1509ed 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -1,10 +1,13 @@ from __future__ import annotations + +import math from typing import Optional import numpy as np from numpy.typing import ArrayLike -from spikeinterface.core import Templates, BaseRecording, BaseSorting, BaseRecordingSegment -import math +from probeinterface import Probe +from spikeinterface.core import (BaseRecording, BaseRecordingSegment, + BaseSorting, Templates) def interpolate_templates(templates_array, source_locations, dest_locations, interpolation_method="cubic"): @@ -117,11 +120,20 @@ class DriftingTemplates(Templates): """ def __init__(self, **kwargs): - Templates.__init__(self, **kwargs) + super().__init__(self, **kwargs) assert self.probe is not None, "DriftingTemplates need a Probe in the init" - self.templates_array_moved = None - self.displacements = None + has_temps_moved = "templates_array_moved" in kwargs + has_disps = "displacements" in kwargs + precomputed = has_temps_moved or has_disps + if precomputed and not (has_temps_moved and has_disps): + raise ValueError( + "Please pass both template_array_moved and displacements to DriftingTemplates " + "if you are using precomputed displaceed templates." + ) + + self.templates_array_moved = kwargs.get("templates_array_moved", None) + self.displacements = kwargs.get("displacements", None) @classmethod def from_static_templates(cls, templates): @@ -133,6 +145,38 @@ def from_static_templates(cls, templates): ) return drifting_templates + @classmethod + def from_precomputed( + cls, + templates_array_moved: ArrayLike, + displacements: ArrayLike, + sampling_frequency: float, + nbefore: int, + probe: Probe, + ): + """Construct a DriftingTemplates object given precomputed drifting templates + + Arguments + --------- + templates_array_moved : np.array + Shape is (num_displacement, num_templates, num_samples, num_channels) + displacements : np.array + Shape is (num_displacement, 2). Last axis is xy, as in make_linear_displacement below. + sampling_frequency : float + nbefore : int + probe : probeinterface.Probe + """ + # take the central templates as representatives, just to make the super() + # constructor happy. they won't be used as drifting templates. + templates_static = templates_array_moved[templates_array_moved.shape[0] // 2] + return cls( + templates_array=templates_static, + displacements=displacements, + sampling_frequency=sampling_frequency, + nbefore=nbefore, + probe=probe, + ) + def move_one_template(self, unit_index, displacement, **interpolation_kwargs): """ Move on template given a displacement. From 643ea6a55383e003e9d168be7ad7c107b212f6cd Mon Sep 17 00:00:00 2001 From: Charlie Windolf Date: Mon, 3 Jun 2024 16:14:34 -0400 Subject: [PATCH 76/90] dataclasses are weird! --- src/spikeinterface/generation/drift_tools.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 4e6e1509ed..ff732525c5 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -120,7 +120,7 @@ class DriftingTemplates(Templates): """ def __init__(self, **kwargs): - super().__init__(self, **kwargs) + Templates.__init__(self, **kwargs) assert self.probe is not None, "DriftingTemplates need a Probe in the init" has_temps_moved = "templates_array_moved" in kwargs From 866da1e483aad58452616b95feff0b2757f88bbd Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 24 Jun 2024 16:37:00 +0200 Subject: [PATCH 77/90] Incorporate DriftingTemplates.from_precomputed_templates constructor from @cwindolf --- src/spikeinterface/generation/drift_tools.py | 52 +++++++++++++------ .../generation/tests/test_drift_tools.py | 20 +++++++ 2 files changed, 57 insertions(+), 15 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index ff732525c5..52861ae44a 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -6,8 +6,7 @@ import numpy as np from numpy.typing import ArrayLike from probeinterface import Probe -from spikeinterface.core import (BaseRecording, BaseRecordingSegment, - BaseSorting, Templates) +from spikeinterface.core import BaseRecording, BaseRecordingSegment, BaseSorting, Templates def interpolate_templates(templates_array, source_locations, dest_locations, interpolation_method="cubic"): @@ -120,23 +119,40 @@ class DriftingTemplates(Templates): """ def __init__(self, **kwargs): - Templates.__init__(self, **kwargs) - assert self.probe is not None, "DriftingTemplates need a Probe in the init" - - has_temps_moved = "templates_array_moved" in kwargs - has_disps = "displacements" in kwargs - precomputed = has_temps_moved or has_disps - if precomputed and not (has_temps_moved and has_disps): + has_templates_moved = "templates_array_moved" in kwargs + has_displacements = "displacements" in kwargs + precomputed = has_templates_moved or has_displacements + if precomputed and not (has_templates_moved and has_displacements): raise ValueError( "Please pass both template_array_moved and displacements to DriftingTemplates " "if you are using precomputed displaceed templates." ) + templates_array_moved = kwargs.pop("templates_array_moved", None) + displacements = kwargs.pop("displacements", None) - self.templates_array_moved = kwargs.get("templates_array_moved", None) - self.displacements = kwargs.get("displacements", None) + Templates.__init__(self, **kwargs) + assert self.probe is not None, "DriftingTemplates need a Probe in the init" + + self.templates_array_moved = templates_array_moved + self.displacements = displacements @classmethod - def from_static_templates(cls, templates): + def from_static_templates(cls, templates: Templates): + """ + Construct a DriftingTemplates object given static templates. + The drifting templates can be then computed using the `precompute_displacements` method. + + Parameters + ---------- + templates : Templates + The static templates. + + Returns + ------- + drifting_templates : DriftingTemplates + The drifting templates object. + + """ drifting_templates = cls( templates_array=templates.templates_array, sampling_frequency=templates.sampling_frequency, @@ -146,7 +162,7 @@ def from_static_templates(cls, templates): return drifting_templates @classmethod - def from_precomputed( + def from_precomputed_templates( cls, templates_array_moved: ArrayLike, displacements: ArrayLike, @@ -156,8 +172,8 @@ def from_precomputed( ): """Construct a DriftingTemplates object given precomputed drifting templates - Arguments - --------- + Parameters + ---------- templates_array_moved : np.array Shape is (num_displacement, num_templates, num_samples, num_channels) displacements : np.array @@ -165,12 +181,18 @@ def from_precomputed( sampling_frequency : float nbefore : int probe : probeinterface.Probe + + Returns + ------- + drifting_templates : DriftingTemplates + The drifting templates object. """ # take the central templates as representatives, just to make the super() # constructor happy. they won't be used as drifting templates. templates_static = templates_array_moved[templates_array_moved.shape[0] // 2] return cls( templates_array=templates_static, + templates_array_moved=templates_array_moved, displacements=displacements, sampling_frequency=sampling_frequency, nbefore=nbefore, diff --git a/src/spikeinterface/generation/tests/test_drift_tools.py b/src/spikeinterface/generation/tests/test_drift_tools.py index 7783e29d63..5647b33930 100644 --- a/src/spikeinterface/generation/tests/test_drift_tools.py +++ b/src/spikeinterface/generation/tests/test_drift_tools.py @@ -99,6 +99,7 @@ def test_DriftingTemplates(): displacement = np.array([[5.0, 10.0]]) unit_index = 0 moved_template_array = drifting_templates.move_one_template(unit_index, displacement) + assert not np.array_equal(moved_template_array, static_templates.templates_array[unit_index]) num_move = 5 amplitude_motion_um = 20 @@ -112,6 +113,25 @@ def test_DriftingTemplates(): static_templates.num_channels, ) + # test from precomputed + drifting_templates_from_precomputed = DriftingTemplates.from_precomputed_templates( + templates_array_moved=drifting_templates.templates_array_moved, + displacements=drifting_templates.displacements, + sampling_frequency=drifting_templates.sampling_frequency, + probe=drifting_templates.probe, + nbefore=drifting_templates.nbefore, + ) + assert drifting_templates_from_precomputed.templates_array_moved.shape == ( + num_move, + static_templates.num_units, + static_templates.num_samples, + static_templates.num_channels, + ) + assert np.array_equal( + drifting_templates_from_precomputed.templates_array_moved, drifting_templates.templates_array_moved + ) + assert np.array_equal(drifting_templates_from_precomputed.displacements, drifting_templates.displacements) + def test_InjectDriftingTemplatesRecording(create_cache_folder): cache_folder = create_cache_folder From 5c3e73bb16d3a566b9ef5b1a0fb89bc846e395a9 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Mon, 24 Jun 2024 17:23:11 +0200 Subject: [PATCH 78/90] Start hibrid benchmark docs and add Neuropixel-384 toy probe --- ..._neuropixels.py => analyze_neuropixels.py} | 2 +- .../benchmark_with_hybrid_recordings.py | 57 +++++++++++++++++++ .../generation/drifting_generator.py | 14 +++-- 3 files changed, 68 insertions(+), 5 deletions(-) rename examples/how_to/{analyse_neuropixels.py => analyze_neuropixels.py} (99%) create mode 100644 examples/how_to/benchmark_with_hybrid_recordings.py diff --git a/examples/how_to/analyse_neuropixels.py b/examples/how_to/analyze_neuropixels.py similarity index 99% rename from examples/how_to/analyse_neuropixels.py rename to examples/how_to/analyze_neuropixels.py index ce5bacdda0..aeee8b15b4 100644 --- a/examples/how_to/analyse_neuropixels.py +++ b/examples/how_to/analyze_neuropixels.py @@ -14,7 +14,7 @@ # name: python3 # --- -# # Analyse Neuropixels datasets +# # Analyze Neuropixels datasets # # This example shows how to perform Neuropixels-specific analysis, including custom pre- and post-processing. diff --git a/examples/how_to/benchmark_with_hybrid_recordings.py b/examples/how_to/benchmark_with_hybrid_recordings.py new file mode 100644 index 0000000000..4afb991c86 --- /dev/null +++ b/examples/how_to/benchmark_with_hybrid_recordings.py @@ -0,0 +1,57 @@ +# # Benchmark spike sorting with hybrid recordings +# +# This example shows how to use the SpikeInterface hybrid recordings framework to benchmark spike sorting results. +# +# Hybrid recordings are built from existing recordings by injecting units with known spiking activity. +# The template (aka average waveforms) of the injected units can be from previous spike sorted data. +# In this example, we will be using an open database of templates that we have constructed from the International Brain Laboratory - Brain Wide Map (available on [DANDI](https://dandiarchive.org/dandiset/000409?search=IBL&page=2&sortOption=0&sortDir=-1&showDrafts=true&showEmpty=false&pos=9)). +# +# Importantly, recordings from long-shank probes, such as Neuropixels, usually experience drifts. Such drifts have to be taken into account in order to smoothly inject spikes into the recording. + +# + +import spikeinterface as si +import spikeinterface.extractors as se +import spikeinterface.preprocessing as spre +import spikeinterface.comparison as sc +import spikeinterface.generation as sgen +import spikeinterface.widgets as sw + +from spikeinterface.sortingcomponents.motion_estimation import estimate_motion + +from pathlib import Path +# - + +# %matplotlib inline + +# To make this notebook self-contained, we will simulate a drifting recording similar to the one acquired by Nick Steinmetz and available [here](https://doi.org/10.6084/m9.figshare.14024495.v1), where an triangular motion was imposed to the recording by moving the probe up and down with a micro-manipulator. + +# + +zigzag_rigid = [ + { + 'drift_mode': 'zigzag', + 'non_rigid_gradient': None, + 't_start_drift': 30.0, + 't_end_drift': 210, + 'period_s': 60 + } +] + +# this generates a "static" and "drifting" recording version +static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( + probe_name="Neuropixel-128", #,'Neuropixel-384', + seed=23, + duration=240, + num_units=100, + generate_displacement_vector_kwargs={'motion_list' : zigzag_rigid} +) +# - + +# To visualize the + +_, motion_info = spre.correct_motion( + drifting_recording, + preset="nonrigid_fast_and_accurate", + n_jobs=4, + progress_bar=True, + output_motion_info=True +) diff --git a/src/spikeinterface/generation/drifting_generator.py b/src/spikeinterface/generation/drifting_generator.py index f712bfb41e..b811551287 100644 --- a/src/spikeinterface/generation/drifting_generator.py +++ b/src/spikeinterface/generation/drifting_generator.py @@ -25,12 +25,18 @@ # this should be moved in probeinterface but later _toy_probes = { + "Neuropixel-384": dict( + num_columns=4, + num_contact_per_column=[96] * 4, + xpitch=16, + ypitch=40, + y_shift_per_column=[20, 0, 20, 0], + contact_shapes="square", + contact_shape_params={"width": 12}, + ), "Neuropixel-128": dict( num_columns=4, - num_contact_per_column=[ - 32, - ] - * 4, + num_contact_per_column=[32] * 4, xpitch=16, ypitch=40, y_shift_per_column=[20, 0, 20, 0], From c45f283a955bdddf448706b0af498620cf0a7b3a Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 26 Jun 2024 09:31:45 +0200 Subject: [PATCH 79/90] wip example --- .../benchmark_with_hybrid_recordings.py | 68 ++++++++++++++++++- src/spikeinterface/generation/hybrid_tools.py | 2 + 2 files changed, 68 insertions(+), 2 deletions(-) diff --git a/examples/how_to/benchmark_with_hybrid_recordings.py b/examples/how_to/benchmark_with_hybrid_recordings.py index 4afb991c86..f99f75dac6 100644 --- a/examples/how_to/benchmark_with_hybrid_recordings.py +++ b/examples/how_to/benchmark_with_hybrid_recordings.py @@ -18,6 +18,7 @@ from spikeinterface.sortingcomponents.motion_estimation import estimate_motion +import numpy as np from pathlib import Path # - @@ -38,7 +39,7 @@ # this generates a "static" and "drifting" recording version static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( - probe_name="Neuropixel-128", #,'Neuropixel-384', + probe_name="Neuropixel-384", #,'Neuropixel-384', seed=23, duration=240, num_units=100, @@ -46,7 +47,7 @@ ) # - -# To visualize the +# To visualize the drift, we can estimate the motion and plot it: _, motion_info = spre.correct_motion( drifting_recording, @@ -55,3 +56,66 @@ progress_bar=True, output_motion_info=True ) + +# + +# +# sw.plot_drift_map(...) +# +# - + +# ## Retrieve templates from database + +templates_info = sgen.fetch_templates_database_info() + +templates_info.head() + +available_brain_areas = np.unique(templates_info.brain_area) +print(f"Available brain areas: {available_brain_areas}") + +# let's perform a query: templates from brain region VISp5 and at the "top" of the probe +target_area = "VISp5" +minimum_depth = 1500 +templates_selected_info = templates_info.query( + f"brain_area == '{target_area}' and depth_along_probe > {minimum_depth}" +) +display(templates_selected_info) + +# We can now retrieve the selected templates as a `Templates` object +# + +templates_selected = sgen.query_templates_from_database(templates_selected_info, verbose=True) +print(templates_selected) + +# While we selected templates from a target aread and at certain depths, we can see that the template amplitudes are quite large. This will make spike sorting easy... we can further manipulate the `Templates` by rescaling, relocating, or further selections with the `sgen.scale_template_to_range`, `sgen.relocate_templates`, and `sgen.select_templates` functions. +# +# In our case, let's rescale the amplitudes between 30 and 50 $\mu$V. + +templates_scaled = sgen.scale_template_to_range(templates=templates_selected, min_amplitude=30, max_amplitude=50) + +# Let's plot the selected templates: + +w.figure.su + +sparsity_plot = si.compute_sparsity(templates_selected) +w = sw.plot_unit_templates(templates_scaled, sparsity=sparsity_plot, ncols=4) +w.figure.subplots_adjust(wspace=0.5, hspace=0.7) + +# ## Constructing hybrid recordings + +drifting_recording = spre.depth_order(drifting_recording) + +drifting_recording.get_channel_locations()[:10] + +templates_scaled.get_channel_locations()[:10] + +recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( + recording=drifting_recording, + templates=templates_scaled, + motion=motion_info["motion"], + seed=2308 +) + +recording_hybrid + +# + +# show spike locations on top of original rastermap diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 83d4690661..456682d6f3 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -417,6 +417,8 @@ def generate_hybrid_recording( if num_units is None: assert "num_units" in generate_sorting_kwargs, "num_units should be provided in generate_sorting_kwargs" num_units = generate_sorting_kwargs["num_units"] + else: + generate_sorting_kwargs["num_units"] = num_units if templates is None: if unit_locations is None: From bc834852c8f3b7e0690dd554a8493104bfecfe6b Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Wed, 26 Jun 2024 14:53:17 +0200 Subject: [PATCH 80/90] continue how to an lint --- .../benchmark_with_hybrid_recordings.py | 261 +++++++++++++++--- src/spikeinterface/core/sortinganalyzer.py | 5 +- .../generation/drifting_generator.py | 86 +++--- .../sortingcomponents/peak_localization.py | 2 + src/spikeinterface/sortingcomponents/tools.py | 1 + 5 files changed, 280 insertions(+), 75 deletions(-) diff --git a/examples/how_to/benchmark_with_hybrid_recordings.py b/examples/how_to/benchmark_with_hybrid_recordings.py index f99f75dac6..5ec7519f32 100644 --- a/examples/how_to/benchmark_with_hybrid_recordings.py +++ b/examples/how_to/benchmark_with_hybrid_recordings.py @@ -19,42 +19,153 @@ from spikeinterface.sortingcomponents.motion_estimation import estimate_motion import numpy as np +import matplotlib.pyplot as plt from pathlib import Path + # - # %matplotlib inline +si.set_global_job_kwargs(n_jobs=4) + # To make this notebook self-contained, we will simulate a drifting recording similar to the one acquired by Nick Steinmetz and available [here](https://doi.org/10.6084/m9.figshare.14024495.v1), where an triangular motion was imposed to the recording by moving the probe up and down with a micro-manipulator. # + -zigzag_rigid = [ - { - 'drift_mode': 'zigzag', - 'non_rigid_gradient': None, - 't_start_drift': 30.0, - 't_end_drift': 210, - 'period_s': 60 - } -] +# sgen.generate_displacement_vector? + +# + +generate_displacement_vector_kwargs = { + "motion_list": [ + { + "drift_mode": "zigzag", + "non_rigid_gradient": None, + "t_start_drift": 30.0, + "t_end_drift": 210, + "period_s": 60, + } + ], + "drift_start_um": [0, 100], + "drift_stop_um": [0, -100], +} # this generates a "static" and "drifting" recording version static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( - probe_name="Neuropixel-384", #,'Neuropixel-384', + probe_name="Neuropixel-384", # ,'Neuropixel-384', seed=23, duration=240, num_units=100, - generate_displacement_vector_kwargs={'motion_list' : zigzag_rigid} + generate_displacement_vector_kwargs=generate_displacement_vector_kwargs, ) + +# we sort the channels by depth, to match the hybrid templates +drifting_recording = spre.depth_order(drifting_recording) # - # To visualize the drift, we can estimate the motion and plot it: _, motion_info = spre.correct_motion( - drifting_recording, - preset="nonrigid_fast_and_accurate", - n_jobs=4, - progress_bar=True, - output_motion_info=True + drifting_recording, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True +) + +sw.plot_motion_info(motion_info, recording=drifting_recording) + + +def plot_drift_map( + peaks=None, + peak_locations=None, + recording=None, + analyzer=None, + direction="y", + sampling_frequency=None, + segment_index=0, + depth_lim=None, + color_amplitude=True, + scatter_decimate=None, + color="k", + cmap="inferno", + clim=None, + alpha=1, + ax=None, +): + import matplotlib.pyplot as plt + from matplotlib.colors import Normalize + + if ax is None: + fig, ax = plt.subplots() + + assert peaks is not None or analyzer is not None + if peaks is not None: + assert peak_locations is not None + if recording is None: + assert sampling_frequency is not None + else: + sampling_frequency = recording.sampling_frequency + peak_amplitudes = peaks["amplitude"] + if analyzer is not None: + if analyzer.has_recording(): + recording = analyzer.recording + else: + recording = None + sampling_frequency = analyzer.sampling_frequency + peaks = analyzer.sorting.to_spike_vector() + assert analyzer.has_extension("spike_locations") + peak_locations = analyzer.get_extension("spike_locations").get_data() + if analyzer.has_extension("spike_amplitudes"): + peak_amplitudes = analyzer.get_extension("spike_amplitudes").get_data() + else: + peak_amplitudes = None + times = recording.get_times(segment_index=segment_index) if recording is not None else None + + if times is None: + x = peaks["sample_index"] / sampling_frequency + else: + # use real times and adjust temporal bins with t_start + x = times[peaks["sample_index"]] + + y = peak_locations[direction] + if scatter_decimate is not None: + x = x[::scatter_decimate] + y = y[::scatter_decimate] + y2 = y2[::scatter_decimate] + + if color_amplitude: + assert peak_amplitudes is not None, "To color by amplitudes the 'spike_amplitude' extension is needed" + amps = peak_amplitudes + amps_abs = np.abs(amps) + q_95 = np.quantile(amps_abs, 0.95) + if scatter_decimate is not None: + amps = amps[::scatter_decimate] + amps_abs = amps_abs[::scatter_decimate] + cmap = plt.colormaps[cmap] + if clim is None: + amps = amps_abs + amps /= q_95 + c = cmap(amps) + else: + norm_function = Normalize(vmin=clim[0], vmax=clim[1], clip=True) + c = cmap(norm_function(amps)) + color_kwargs = dict( + color=None, + c=c, + alpha=alpha, + ) + else: + color_kwargs = dict(color=color, c=None, alpha=alpha) + + ax.scatter(x, y, s=1, **color_kwargs) + if depth_lim is not None: + ax.set_ylim(*depth_lim) + ax.set_title("Peak depth") + ax.set_xlabel("Times [s]") + ax.set_ylabel("Depth [$\\mu$m]") + return ax + + +ax = plot_drift_map( + peaks=motion_info["peaks"], + peak_locations=motion_info["peak_locations"], + recording=drifting_recording, + cmap="Greys_r", ) # + @@ -67,17 +178,17 @@ templates_info = sgen.fetch_templates_database_info() +print(len(templates_info)) + templates_info.head() available_brain_areas = np.unique(templates_info.brain_area) print(f"Available brain areas: {available_brain_areas}") # let's perform a query: templates from brain region VISp5 and at the "top" of the probe -target_area = "VISp5" -minimum_depth = 1500 -templates_selected_info = templates_info.query( - f"brain_area == '{target_area}' and depth_along_probe > {minimum_depth}" -) +target_area = ["VISa5", "VISa6a", "VISp5", "VISp6a", "VISrl6b"] +minimum_depth = 1000 +templates_selected_info = templates_info.query(f"brain_area in {target_area} and depth_along_probe > {minimum_depth}") display(templates_selected_info) # We can now retrieve the selected templates as a `Templates` object @@ -92,30 +203,116 @@ templates_scaled = sgen.scale_template_to_range(templates=templates_selected, min_amplitude=30, max_amplitude=50) -# Let's plot the selected templates: +# + +# sgen.relocate_templates? +# - + +templates_relocated = sgen.relocate_templates(templates=templates_scaled, min_displacement=200, max_displacement=2000) -w.figure.su +# Let's plot the selected templates: -sparsity_plot = si.compute_sparsity(templates_selected) -w = sw.plot_unit_templates(templates_scaled, sparsity=sparsity_plot, ncols=4) +sparsity_plot = si.compute_sparsity(templates_relocated) +w = sw.plot_unit_templates(templates_relocated, sparsity=sparsity_plot, ncols=4) w.figure.subplots_adjust(wspace=0.5, hspace=0.7) # ## Constructing hybrid recordings -drifting_recording = spre.depth_order(drifting_recording) - -drifting_recording.get_channel_locations()[:10] +recording_hybrid_no_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=drifting_recording, templates=templates_relocated, seed=2308 +) -templates_scaled.get_channel_locations()[:10] +recording_hybrid_no_drift recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( recording=drifting_recording, - templates=templates_scaled, + templates=templates_relocated, motion=motion_info["motion"], - seed=2308 + sorting=sorting_hybrid, + seed=2308, ) recording_hybrid -# + # show spike locations on top of original rastermap +# construct hybrid analyzer for spike locations +analyzer_hybrid = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid) +analyzer_hybrid.compute(["random_spikes", "templates"]) +analyzer_hybrid.compute("spike_locations", method="grid_convolution") + +# construct hybrid analyzer for spike locations +analyzer_hybrid_no_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_no_drift) +analyzer_hybrid_no_drift.compute(["random_spikes", "templates"]) +analyzer_hybrid_no_drift.compute("spike_locations", method="grid_convolution") + +fig, axs = plt.subplots(ncols=2, figsize=(10, 7)) +_ = plot_drift_map( + peaks=motion_info["peaks"], + peak_locations=motion_info["peak_locations"], + recording=drifting_recording, + cmap="Greys_r", + ax=axs[0], +) +_ = plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) +_ = plot_drift_map( + peaks=motion_info["peaks"], + peak_locations=motion_info["peak_locations"], + recording=drifting_recording, + cmap="Greys_r", + ax=axs[1], +) +_ = plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) +axs[0].set_title("Ignoring drift") +axs[1].set_title("Accounting for drift") + +# ## Ground-truth study +# +# In this section ... + +# + +datasets = { + "hybrid": (recording_hybrid, sorting_hybrid), +} + +cases = { + ("kilosort2.5", "hybrid"): { + "label": "KS2.5", + "dataset": "hybrid", + "run_sorter_params": { + "sorter_name": "kilosort2_5", + }, + }, + ("kilosort3", "hybrid"): { + "label": "KS3", + "dataset": "hybrid", + "run_sorter_params": { + "sorter_name": "kilosort3", + }, + }, + ("kilosort4", "hybrid"): { + "label": "KS4", + "dataset": "hybrid", + "run_sorter_params": {"sorter_name": "kilosort4", "nblocks": 5}, + }, + ("sc2", "hybrid"): { + "label": "spykingcircus2", + "dataset": "hybrid", + "run_sorter_params": { + "sorter_name": "spykingcircus2", + }, + }, +} +# - + +study_folder = workdir / "gt_study" +if (workdir / "gt_study").is_dir(): + gtstudy = sc.GroundTruthStudy(study_folder) +else: + gtstudy = sc.GroundTruthStudy.create(study_folder=study_folder, datasets=datasets, cases=cases) + +gtstudy.run_sorters(verbose=True, keep=True) + +gtstudy.run_comparisons(exhaustive_gt=False) + +w_run_times = sw.plot_study_run_times(gtstudy) +w_perf = sw.plot_study_performances(gtstudy, figsize=(12, 7)) +w_perf.axes[0, 0].legend(loc=4) diff --git a/src/spikeinterface/core/sortinganalyzer.py b/src/spikeinterface/core/sortinganalyzer.py index e439ddf1ed..cfe312b6ef 100644 --- a/src/spikeinterface/core/sortinganalyzer.py +++ b/src/spikeinterface/core/sortinganalyzer.py @@ -967,10 +967,13 @@ def compute_one_extension(self, extension_name, save=True, verbose=False, **kwar >>> wfs = compute_waveforms(sorting_analyzer, **some_params) """ + print(extension_name) extension_class = get_extension_class(extension_name) for child in _get_children_dependencies(extension_name): - self.delete_extension(child) + if self.has_extension(child): + print(f"Deleting {child}") + self.delete_extension(child) if extension_class.need_job_kwargs: params, job_kwargs = split_job_kwargs(kwargs) diff --git a/src/spikeinterface/generation/drifting_generator.py b/src/spikeinterface/generation/drifting_generator.py index b811551287..a0e8ece37e 100644 --- a/src/spikeinterface/generation/drifting_generator.py +++ b/src/spikeinterface/generation/drifting_generator.py @@ -72,22 +72,24 @@ def make_one_displacement_vector( Parameters ---------- - drift_mode: "zigzag" | "bumps", default: "zigzag" - The drift mode - duration: float, default: 600 + drift_mode : "zigzag" | "bumps", default: "zigzag" + The drift mode. + duration : float, default: 600 Duration in seconds - displacement_sampling_frequency: float, default: 5 - Sample rate of the vector - t_start_drift: float | None, default: None - Time in s when drift starts - t_end_drift: float | None, default: None - Time in s when drift ends - period_s: float, default: 200. + amplitude_factor : float, default: 1 + The amplitude factor of the drift. + displacement_sampling_frequency : float, default: 5 + Sample rate of the vector. + t_start_drift : float | None, default: None + Time in s when drift starts. + t_end_drift : float | None, default: None + Time in s when drift ends. + period_s : float, default: 200. Period of the zigzag in seconds - bump_interval_s: tuple, default: (30, 90.) - Range interval between random bumps in seconds - seed: None | int - The seed for the random bumps + bump_interval_s : tuple, default: (30, 90.) + Range interval between random bumps in seconds. + seed : None | int + The seed for the random bumps. Returns ------- @@ -176,34 +178,34 @@ def generate_displacement_vector( Parameters ---------- - duration: float + duration : float Duration of the displacement vector in seconds - unit_locations: np.array + unit_locations : np.array The unit location with shape (num_units, 3) - displacement_sampling_frequency: float, default: 5. + displacement_sampling_frequency : float, default: 5. The sampling frequency of the displacement vector - drift_start_um: list of float, default: [0, 20.] - The start boundary of the motion - drift_stop_um: list of float, default: [0, -20.] - The stop boundary of the motion - drift_step_um: float, default: 1 + drift_start_um : list of float, default: [0, 20.] + The start boundary of the motion in the x and y direction. + drift_stop_um : list of float, default: [0, -20.] + The stop boundary of the motion in the x and y direction. + drift_step_um : float, default: 1 Use to create the displacements_steps array. This ensures an odd number of steps - motion_list: list of dict + motion_list : list of dict List of dicts containing individual motion vector parameters. len(motion_list) == displacement_vectors.shape[2] Returns ------- - displacement_vectors: numpy.ndarray + displacement_vectors : numpy.ndarray The drift vector is a numpy array with shape (num_times, 2, num_motions) num_motions is generally 1, but can be > 1 in case of combining several drift vectors - displacement_unit_factor: numpy array | None, default: None + displacement_unit_factor : numpy array | None, default: None A array containing the factor per unit of each drift (num_units, num_motions). This is used to create non-rigid drift with a factor gradient of depending on the unit positions - displacement_sampling_frequency: float + displacement_sampling_frequency : float The sampling frequency of drift vector - displacements_steps: numpy array + displacements_steps : numpy array Position of the motion steps (from start to step) with shape (num_step, 2) """ @@ -301,38 +303,38 @@ def generate_drifting_recording( Parameters ---------- - num_units: int, default: 250 + num_units : int, default: 250 Number of units. - duration: float, default: 600. + duration : float, default: 600. The duration in seconds. - sampling_frequency: float, dfault: 30000. + sampling_frequency : float, dfault: 30000. The sampling frequency. - probe_name: str, default: "Neuropixel-128" + probe_name : str, default: "Neuropixel-128" The probe type if generate_probe_kwargs is None. - generate_probe_kwargs: None or dict + generate_probe_kwargs : None or dict A dict to generate the probe, this supersede probe_name when not None. - generate_unit_locations_kwargs: dict + generate_unit_locations_kwargs : dict Parameters given to generate_unit_locations(). - generate_displacement_vector_kwargs: dict + generate_displacement_vector_kwargs : dict Parameters given to generate_displacement_vector(). - generate_templates_kwargs: dict + generate_templates_kwargs : dict Parameters given to generate_templates() - generate_sorting_kwargs: dict + generate_sorting_kwargs : dict Parameters given to generate_sorting(). - generate_noise_kwargs: dict + generate_noise_kwargs : dict Parameters given to generate_noise(). - extra_outputs: bool, default False + extra_outputs : bool, default False Return optionaly a dict with more variables. - seed: None ot int + seed : None ot int A unique seed for all steps. Returns ------- - static_recording: Recording + static_recording : Recording A generated recording with no motion. - drifting_recording: Recording + drifting_recording : Recording A generated recording with motion. - sorting: Sorting + sorting : Sorting The ground trith soring object. Same for both recordings. extra_infos: diff --git a/src/spikeinterface/sortingcomponents/peak_localization.py b/src/spikeinterface/sortingcomponents/peak_localization.py index d8cbecea5b..6d2ad09239 100644 --- a/src/spikeinterface/sortingcomponents/peak_localization.py +++ b/src/spikeinterface/sortingcomponents/peak_localization.py @@ -69,6 +69,8 @@ def get_localization_pipeline_nodes( elif method == "grid_convolution": if "prototype" not in method_kwargs: assert isinstance(peak_source, (PeakRetriever, SpikeRetriever)) + # extract prototypes silently + job_kwargs["progress_bar"] = False method_kwargs["prototype"] = get_prototype_spike( recording, peak_source.peaks, ms_before=ms_before, ms_after=ms_after, **job_kwargs ) diff --git a/src/spikeinterface/sortingcomponents/tools.py b/src/spikeinterface/sortingcomponents/tools.py index cc45dd3e40..8ee36cc9e5 100644 --- a/src/spikeinterface/sortingcomponents/tools.py +++ b/src/spikeinterface/sortingcomponents/tools.py @@ -62,6 +62,7 @@ def extract_waveform_at_max_channel(rec, peaks, ms_before=0.5, ms_after=1.5, **j return_scaled=False, sparsity_mask=sparsity_mask, copy=True, + verbose=False, **job_kwargs, ) From 397151299876ffb8d429b63b9a46447aa5387dcc Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 27 Jun 2024 09:20:55 +0200 Subject: [PATCH 81/90] Make Motion JSON serializable and add hybrit How to --- .../benchmark_with_hybrid_recordings.rst | 2230 +++++++++++++++++ .../benchmark_with_hybrid_recordings_22_0.png | Bin 0 -> 167387 bytes .../benchmark_with_hybrid_recordings_29_1.png | Bin 0 -> 263465 bytes .../benchmark_with_hybrid_recordings_38_1.png | Bin 0 -> 15370 bytes .../benchmark_with_hybrid_recordings_38_2.png | Bin 0 -> 63183 bytes .../benchmark_with_hybrid_recordings_9_0.png | Bin 0 -> 142577 bytes doc/how_to/index.rst | 1 + .../benchmark_with_hybrid_recordings.py | 79 +- src/spikeinterface/core/core_tools.py | 4 + src/spikeinterface/core/sortinganalyzer.py | 1 - .../sortingcomponents/motion_utils.py | 20 +- 11 files changed, 2299 insertions(+), 36 deletions(-) create mode 100644 doc/how_to/benchmark_with_hybrid_recordings.rst create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_22_0.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_29_1.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_38_1.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_38_2.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_9_0.png diff --git a/doc/how_to/benchmark_with_hybrid_recordings.rst b/doc/how_to/benchmark_with_hybrid_recordings.rst new file mode 100644 index 0000000000..5d78bc280b --- /dev/null +++ b/doc/how_to/benchmark_with_hybrid_recordings.rst @@ -0,0 +1,2230 @@ +Benchmark spike sorting with hybrid recordings +============================================== + +This example shows how to use the SpikeInterface hybrid recordings +framework to benchmark spike sorting results. + +Hybrid recordings are built from existing recordings by injecting units +with known spiking activity. The template (aka average waveforms) of the +injected units can be from previous spike sorted data. In this example, +we will be using an open database of templates that we have constructed +from the International Brain Laboratory - Brain Wide Map (available on +`DANDI `__). + +Importantly, recordings from long-shank probes, such as Neuropixels, +usually experience drifts. Such drifts have to be taken into account in +order to smoothly inject spikes into the recording. + +.. code:: ipython3 + + import spikeinterface as si + import spikeinterface.extractors as se + import spikeinterface.preprocessing as spre + import spikeinterface.comparison as sc + import spikeinterface.generation as sgen + import spikeinterface.widgets as sw + + from spikeinterface.sortingcomponents.motion_estimation import estimate_motion + + import numpy as np + import matplotlib.pyplot as plt + from pathlib import Path + + +.. code:: ipython3 + + %matplotlib inline + +.. code:: ipython3 + + si.set_global_job_kwargs(n_jobs=16) + +To make this notebook self-contained, we will simulate a drifting +recording similar to the one acquired by Nick Steinmetz and available +`here `__, where an +triangular motion was imposed to the recording by moving the probe up +and down with a micro-manipulator. + +.. code:: ipython3 + + generate_displacement_vector_kwargs = { + "motion_list": [ + { + "drift_mode": "zigzag", + "non_rigid_gradient": None, + "t_start_drift": 30.0, + "t_end_drift": 210, + "period_s": 60, + } + ], + "drift_start_um": [0, 60], + "drift_stop_um": [0, -60], + } + + # this generates a "static" and "drifting" recording version + static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( + probe_name="Neuropixel-384", + seed=23, + duration=240, + num_units=100, + generate_displacement_vector_kwargs=generate_displacement_vector_kwargs, + ) + + # we sort the channels by depth, to match the hybrid templates + drifting_recording = spre.depth_order(drifting_recording) + +To visualize the drift, we can estimate the motion and plot it: + +.. code:: ipython3 + + _, motion_info = spre.correct_motion( + drifting_recording, + preset="nonrigid_fast_and_accurate", + n_jobs=4, + progress_bar=True, + output_motion_info=True + ) + + + +.. parsed-literal:: + + detect and localize: 0%| | 0/240 [00:00 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
probeprobe_manufacturerbrain_areadepth_along_probeamplitude_uvnoise_level_uvsignal_to_noise_ratiotemplate_indexbest_channel_indexspikes_per_unitdatasetdataset_path
0Neuropixels 1.0IMECPVT60.0197.25844017.95249410.987802066293000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
1Neuropixels 1.0IMECPVT80.0111.84829020.3290105.501905198379000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
2Neuropixels 1.0IMECPVT80.0212.14151017.18937312.341434282940000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
3Neuropixels 1.0IMECPVT120.0118.65069619.8153135.9878283123674000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
4Neuropixels 1.0IMECPVT140.0111.78581022.4703064.9748244155834000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
+ + + + +.. code:: ipython3 + + available_brain_areas = np.unique(templates_info.brain_area) + print(f"Available brain areas: {available_brain_areas}") + + +.. parsed-literal:: + + Available brain areas: ['ACAd6a' 'ACAv5' 'ACAv6a' 'AIp6a' 'AIp6b' 'AUDp6a' 'AUDv6a' 'CA1' 'CA3' + 'CENT2' 'CENT3' 'CLA' 'COApm' 'CP' 'DG-sg' 'ECT5' 'ENTl3' 'ICd' 'ICe' + 'IRN' 'LGd-sh' 'LP' 'LSc' 'MB' 'MEA' 'MGm' 'MOs5' 'MRN' 'MV' 'MY' 'NLL' + 'P' 'PA' 'PAG' 'PARN' 'PB' 'PERI2/3' 'PERI5' 'PIR' 'PSV' 'PVT' 'Pa4' + 'PoT' 'RN' 'RSPagl5' 'RSPagl6a' 'RSPd5' 'RSPd6a' 'RSPv5' 'SCdg' 'SCig' + 'SCiw' 'SCsg' 'SF' 'SGN' 'SPFp' 'SSp-n5' 'SSp-n6a' 'SSs6a' 'SSs6b' 'SUT' + 'TEa5' 'TEa6a' 'TRS' 'VII' 'VISa5' 'VISa6a' 'VISp5' 'VISp6a' 'VISrl6b' + 'VPL' 'VPM' 'arb' 'ccb' 'ec' 'ee' 'fiber tracts' 'int' 'll' 'opt' 'scp'] + + +.. code:: ipython3 + + # let's perform a query: templates from visual brain regions and at the "top" of the probe + target_area = ["VISa5", "VISa6a", "VISp5", "VISp6a", "VISrl6b"] + minimum_depth = 1500 + templates_selected_info = templates_info.query(f"brain_area in {target_area} and depth_along_probe > {minimum_depth}") + len(templates_selected_info) + + + + +.. parsed-literal:: + + 31 + + + +We can now retrieve the selected templates as a ``Templates`` object + +.. code:: ipython3 + + templates_selected = sgen.query_templates_from_database(templates_selected_info, verbose=True) + print(templates_selected) + + +.. parsed-literal:: + + Fetching templates from 2 datasets + Templates: 31 units - 240 samples - 384 channels + sampling_frequency=30.00 kHz - ms_before=3.00 ms - ms_after=5.00 ms + Probe - IMEC - Neuropixels 1.0 - 18194814141 - 384ch - 1shanks + + +While we selected templates from a target aread and at certain depths, +we can see that the template amplitudes are quite large. This will make +spike sorting easy… we can further manipulate the ``Templates`` by +rescaling, relocating, or further selections with the +``sgen.scale_template_to_range``, ``sgen.relocate_templates``, and +``sgen.select_templates`` functions. + +In our case, let’s rescale the amplitudes between 50 and 150 +:math:`\mu`\ V and relocate them throughout the entire depth of the +probe. + +.. code:: ipython3 + + min_amplitude = 50 + max_amplitude = 150 + templates_scaled = sgen.scale_template_to_range( + templates=templates_selected, + min_amplitude=min_amplitude, + max_amplitude=max_amplitude + ) + +.. code:: ipython3 + + min_displacement = 200 + max_displacement = 4000 + templates_relocated = sgen.relocate_templates( + templates=templates_scaled, + min_displacement=min_displacement, + max_displacement=max_displacement + ) + +Let’s plot the selected templates: + +.. code:: ipython3 + + sparsity_plot = si.compute_sparsity(templates_relocated) + fig = plt.figure(figsize=(10, 10)) + w = sw.plot_unit_templates(templates_relocated, sparsity=sparsity_plot, ncols=4, figure=fig) + w.figure.subplots_adjust(wspace=0.5, hspace=0.7) + + + +.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_22_0.png + + +Constructing hybrid recordings +------------------------------ + +.. code:: ipython3 + + recording_hybrid_no_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=drifting_recording, templates=templates_relocated, seed=2308 + ) + recording_hybrid_no_drift + + +.. raw:: html + +
InjectTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 7,200,000 samples - 240.00s (4.00 minutes) - float32 dtype - 10.30 GiB
Channel IDs
    [ 96 288 0 192 97 289 1 193 98 290 2 194 99 291 3 195 100 292 + 4 196 101 293 5 197 102 294 6 198 103 295 7 199 104 296 8 200 + 105 297 9 201 106 298 10 202 107 299 11 203 108 300 12 204 109 301 + 13 205 110 302 14 206 111 303 15 207 112 304 16 208 113 305 17 209 + 114 306 18 210 115 307 19 211 116 308 20 212 117 309 21 213 118 310 + 22 214 119 311 23 215 120 312 24 216 121 313 25 217 122 314 26 218 + 123 315 27 219 124 316 28 220 125 317 29 221 126 318 30 222 127 319 + 31 223 128 320 32 224 129 321 33 225 130 322 34 226 131 323 35 227 + 132 324 36 228 133 325 37 229 134 326 38 230 135 327 39 231 136 328 + 40 232 137 329 41 233 138 330 42 234 139 331 43 235 140 332 44 236 + 141 333 45 237 142 334 46 238 143 335 47 239 144 336 48 240 145 337 + 49 241 146 338 50 242 147 339 51 243 148 340 52 244 149 341 53 245 + 150 342 54 246 151 343 55 247 152 344 56 248 153 345 57 249 154 346 + 58 250 155 347 59 251 156 348 60 252 157 349 61 253 158 350 62 254 + 159 351 63 255 160 352 64 256 161 353 65 257 162 354 66 258 163 355 + 67 259 164 356 68 260 165 357 69 261 166 358 70 262 167 359 71 263 + 168 360 72 264 169 361 73 265 170 362 74 266 171 363 75 267 172 364 + 76 268 173 365 77 269 174 366 78 270 175 367 79 271 176 368 80 272 + 177 369 81 273 178 370 82 274 179 371 83 275 180 372 84 276 181 373 + 85 277 182 374 86 278 183 375 87 279 184 376 88 280 185 377 89 281 + 186 378 90 282 187 379 91 283 188 380 92 284 189 381 93 285 190 382 + 94 286 191 383 95 287]
Annotations
  • is_filtered : False
  • probe_0_planar_contour : [[ -25. 3845.] + [ -25. -25.] + [ 24. -125.] + [ 73. -25.] + [ 73. 3845.]]
  • probes_info : [{}]
Channel Properties
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    location [[ 16. 0.] + [ 48. 0.] + [ 0. 20.] + [ 32. 20.] + [ 16. 40.] + [ 48. 40.] + [ 0. 60.] + [ 32. 60.] + [ 16. 80.] + [ 48. 80.] + [ 0. 100.] + [ 32. 100.] + [ 16. 120.] + [ 48. 120.] + [ 0. 140.] + [ 32. 140.] + [ 16. 160.] + [ 48. 160.] + [ 0. 180.] + [ 32. 180.] + [ 16. 200.] + [ 48. 200.] + [ 0. 220.] + [ 32. 220.] + [ 16. 240.] + [ 48. 240.] + [ 0. 260.] + [ 32. 260.] + [ 16. 280.] + [ 48. 280.] + [ 0. 300.] + [ 32. 300.] + [ 16. 320.] + [ 48. 320.] + [ 0. 340.] + [ 32. 340.] + [ 16. 360.] + [ 48. 360.] + [ 0. 380.] + [ 32. 380.] + [ 16. 400.] + [ 48. 400.] + [ 0. 420.] + [ 32. 420.] + [ 16. 440.] + [ 48. 440.] + [ 0. 460.] + [ 32. 460.] + [ 16. 480.] + [ 48. 480.] + [ 0. 500.] + [ 32. 500.] + [ 16. 520.] + [ 48. 520.] + [ 0. 540.] + [ 32. 540.] + [ 16. 560.] + [ 48. 560.] + [ 0. 580.] + [ 32. 580.] + [ 16. 600.] + [ 48. 600.] + [ 0. 620.] + [ 32. 620.] + [ 16. 640.] + [ 48. 640.] + [ 0. 660.] + [ 32. 660.] + [ 16. 680.] + [ 48. 680.] + [ 0. 700.] + [ 32. 700.] + [ 16. 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3440.] + [ 0. 3460.] + [ 32. 3460.] + [ 16. 3480.] + [ 48. 3480.] + [ 0. 3500.] + [ 32. 3500.] + [ 16. 3520.] + [ 48. 3520.] + [ 0. 3540.] + [ 32. 3540.] + [ 16. 3560.] + [ 48. 3560.] + [ 0. 3580.] + [ 32. 3580.] + [ 16. 3600.] + [ 48. 3600.] + [ 0. 3620.] + [ 32. 3620.] + [ 16. 3640.] + [ 48. 3640.] + [ 0. 3660.] + [ 32. 3660.] + [ 16. 3680.] + [ 48. 3680.] + [ 0. 3700.] + [ 32. 3700.] + [ 16. 3720.] + [ 48. 3720.] + [ 0. 3740.] + [ 32. 3740.] + [ 16. 3760.] + [ 48. 3760.] + [ 0. 3780.] + [ 32. 3780.] + [ 16. 3800.] + [ 48. 3800.] + [ 0. 3820.] + [ 32. 3820.]]
    group [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
+ + + +.. code:: ipython3 + + recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( + recording=drifting_recording, + templates=templates_relocated, + motion=motion_info["motion"], + sorting=sorting_hybrid, + seed=2308, + ) + recording_hybrid + + +.. raw:: html + +
InjectDriftingTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 7,200,000 samples - 240.00s (4.00 minutes) - float32 dtype - 10.30 GiB
Channel IDs
    [ 96 288 0 192 97 289 1 193 98 290 2 194 99 291 3 195 100 292 + 4 196 101 293 5 197 102 294 6 198 103 295 7 199 104 296 8 200 + 105 297 9 201 106 298 10 202 107 299 11 203 108 300 12 204 109 301 + 13 205 110 302 14 206 111 303 15 207 112 304 16 208 113 305 17 209 + 114 306 18 210 115 307 19 211 116 308 20 212 117 309 21 213 118 310 + 22 214 119 311 23 215 120 312 24 216 121 313 25 217 122 314 26 218 + 123 315 27 219 124 316 28 220 125 317 29 221 126 318 30 222 127 319 + 31 223 128 320 32 224 129 321 33 225 130 322 34 226 131 323 35 227 + 132 324 36 228 133 325 37 229 134 326 38 230 135 327 39 231 136 328 + 40 232 137 329 41 233 138 330 42 234 139 331 43 235 140 332 44 236 + 141 333 45 237 142 334 46 238 143 335 47 239 144 336 48 240 145 337 + 49 241 146 338 50 242 147 339 51 243 148 340 52 244 149 341 53 245 + 150 342 54 246 151 343 55 247 152 344 56 248 153 345 57 249 154 346 + 58 250 155 347 59 251 156 348 60 252 157 349 61 253 158 350 62 254 + 159 351 63 255 160 352 64 256 161 353 65 257 162 354 66 258 163 355 + 67 259 164 356 68 260 165 357 69 261 166 358 70 262 167 359 71 263 + 168 360 72 264 169 361 73 265 170 362 74 266 171 363 75 267 172 364 + 76 268 173 365 77 269 174 366 78 270 175 367 79 271 176 368 80 272 + 177 369 81 273 178 370 82 274 179 371 83 275 180 372 84 276 181 373 + 85 277 182 374 86 278 183 375 87 279 184 376 88 280 185 377 89 281 + 186 378 90 282 187 379 91 283 188 380 92 284 189 381 93 285 190 382 + 94 286 191 383 95 287]
Annotations
  • is_filtered : False
  • probe_0_planar_contour : [[ -25. 3845.] + [ -25. -25.] + [ 24. -125.] + [ 73. -25.] + [ 73. 3845.]]
  • probes_info : [{'manufacturer': 'IMEC', 'model_name': 'Neuropixels 1.0', 'serial_number': '18194814141'}]
Channel Properties
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+ + + +.. code:: ipython3 + + # construct analyzers and compute spike locations + analyzer_hybrid = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid) + analyzer_hybrid.compute(["random_spikes", "templates"]) + analyzer_hybrid.compute("spike_locations", method="grid_convolution") + + + +.. parsed-literal:: + + estimate_sparsity: 0%| | 0/240 [00:001o+5)vXE0@6r#iz40K-QC?vcXtTVA>G{_(%mHuhwgXtyZ61{egF9w zN@r>u}z1LcE%{f=FjFd1E0xkj+6cm!Eh=3dv6pS!<2*AAsuej4G(1IsU2SF7F zd21sFXI(o(C`nxh8w+a(3sXHJCqp}XQ)??`Iu1Gp8X^-12OE1XdV0(M`2d}@oiROY zMNKid2)vDmsy!4Gitft;x_~d=6bc#&N>t#Rf=k+Ay0Ztm;B((FCSfB_mK%lTzJ8s zkD6;Yq^HkQ)vYc*9uIp61qWr0o3P-93uS|+d=4Q^tgkQs{nrZ~&`0>D|MyK?qy^52 z|N9OL?pJQ%|Na(uf-%_@`TxEK_5XY;Go9qWuKx0I=xY0%|G7Mik5k0|Ts{2l-_da@ z(Sw;k4NrFm2aVXck}blEF+%d5Chij^MX;9Qir=|~r`uP(pGT@qCq*PB{~Y|DqJ%{u zFj%NIEm5t?6B>Pfx<8q0us42qd^BBc3*P?#yd;vd=uh_rebkEv%Z)gc=5Rl)=kZzv#|+mU984OOud}6^6>5K`y}TqJzb(p7 z-&`$PyY3_#o6MGel;$~m9mxZM!@| 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-display(templates_selected_info) +len(templates_selected_info) # We can now retrieve the selected templates as a `Templates` object # @@ -199,20 +189,29 @@ def plot_drift_map( # While we selected templates from a target aread and at certain depths, we can see that the template amplitudes are quite large. This will make spike sorting easy... we can further manipulate the `Templates` by rescaling, relocating, or further selections with the `sgen.scale_template_to_range`, `sgen.relocate_templates`, and `sgen.select_templates` functions. # -# In our case, let's rescale the amplitudes between 30 and 50 $\mu$V. - -templates_scaled = sgen.scale_template_to_range(templates=templates_selected, min_amplitude=30, max_amplitude=50) - -# + -# sgen.relocate_templates? -# - +# In our case, let's rescale the amplitudes between 50 and 150 $\mu$V and relocate them throughout the entire depth of the probe. + +min_amplitude = 50 +max_amplitude = 150 +templates_scaled = sgen.scale_template_to_range( + templates=templates_selected, + min_amplitude=min_amplitude, + max_amplitude=max_amplitude +) -templates_relocated = sgen.relocate_templates(templates=templates_scaled, min_displacement=200, max_displacement=2000) +min_displacement = 200 +max_displacement = 4000 +templates_relocated = sgen.relocate_templates( + templates=templates_scaled, + min_displacement=min_displacement, + max_displacement=max_displacement +) # Let's plot the selected templates: sparsity_plot = si.compute_sparsity(templates_relocated) -w = sw.plot_unit_templates(templates_relocated, sparsity=sparsity_plot, ncols=4) +fig = plt.figure(figsize=(10, 10)) +w = sw.plot_unit_templates(templates_relocated, sparsity=sparsity_plot, ncols=4, figure=fig) w.figure.subplots_adjust(wspace=0.5, hspace=0.7) # ## Constructing hybrid recordings @@ -220,7 +219,6 @@ def plot_drift_map( recording_hybrid_no_drift, sorting_hybrid = sgen.generate_hybrid_recording( recording=drifting_recording, templates=templates_relocated, seed=2308 ) - recording_hybrid_no_drift recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( @@ -230,11 +228,9 @@ def plot_drift_map( sorting=sorting_hybrid, seed=2308, ) - recording_hybrid -# show spike locations on top of original rastermap -# construct hybrid analyzer for spike locations +# construct analyzers and compute spike locations analyzer_hybrid = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid) analyzer_hybrid.compute(["random_spikes", "templates"]) analyzer_hybrid.compute("spike_locations", method="grid_convolution") @@ -244,6 +240,8 @@ def plot_drift_map( analyzer_hybrid_no_drift.compute(["random_spikes", "templates"]) analyzer_hybrid_no_drift.compute("spike_locations", method="grid_convolution") +# Let's plot the added hybrid spikes using the drift maps: + fig, axs = plt.subplots(ncols=2, figsize=(10, 7)) _ = plot_drift_map( peaks=motion_info["peaks"], @@ -264,13 +262,30 @@ def plot_drift_map( axs[0].set_title("Ignoring drift") axs[1].set_title("Accounting for drift") +# We can see that clearly following drift is essential in order to properly blend the hybrid spikes into the recording! + # ## Ground-truth study # -# In this section ... +# In this section we will use the hybrid recording to benchmark a few spike sorters: +# - `Kilosort2.5` +# - `Kilosort3` +# - `Kilosort4` +# - `Spyking-CIRCUS 2` + +# + +# import shutil +# shutil.rmtree(study_folder) +# - + +workdir = Path("/ssd980/working/hybrid/drift") +workdir.mkdir(exist_ok=True) + +# to speed up computations, let's first dump the recording to binary +recording_hybrid_bin = recording_hybrid.save(folder=workdir / "hybrid_bin", overwrite=True) # + datasets = { - "hybrid": (recording_hybrid, sorting_hybrid), + "hybrid": (recording_hybrid_bin, sorting_hybrid), } cases = { @@ -309,7 +324,7 @@ def plot_drift_map( else: gtstudy = sc.GroundTruthStudy.create(study_folder=study_folder, datasets=datasets, cases=cases) -gtstudy.run_sorters(verbose=True, keep=True) +gtstudy.run_sorters(verbose=True, keep=False) gtstudy.run_comparisons(exhaustive_gt=False) diff --git a/src/spikeinterface/core/core_tools.py b/src/spikeinterface/core/core_tools.py index f3d8b3df7f..9ad0365f0d 100644 --- a/src/spikeinterface/core/core_tools.py +++ b/src/spikeinterface/core/core_tools.py @@ -74,6 +74,7 @@ class SIJsonEncoder(json.JSONEncoder): def default(self, obj): from spikeinterface.core.base import BaseExtractor + from spikeinterface.sortingcomponents.motion_utils import Motion # Over-write behaviors for datetime object if isinstance(obj, datetime.datetime): @@ -97,6 +98,9 @@ def default(self, obj): if isinstance(obj, BaseExtractor): return obj.to_dict() + if isinstance(obj, Motion): + return obj.to_dict() + # The base-class handles the assertion return super().default(obj) diff --git a/src/spikeinterface/core/sortinganalyzer.py b/src/spikeinterface/core/sortinganalyzer.py index cfe312b6ef..cf67df8a4f 100644 --- a/src/spikeinterface/core/sortinganalyzer.py +++ b/src/spikeinterface/core/sortinganalyzer.py @@ -967,7 +967,6 @@ def compute_one_extension(self, extension_name, save=True, verbose=False, **kwar >>> wfs = compute_waveforms(sorting_analyzer, **some_params) """ - print(extension_name) extension_class = get_extension_class(extension_name) for child in _get_children_dependencies(extension_name): diff --git a/src/spikeinterface/sortingcomponents/motion_utils.py b/src/spikeinterface/sortingcomponents/motion_utils.py index 26d4b35b1a..eeecdd3131 100644 --- a/src/spikeinterface/sortingcomponents/motion_utils.py +++ b/src/spikeinterface/sortingcomponents/motion_utils.py @@ -148,14 +148,27 @@ def get_displacement_at_time_and_depth(self, times_s, locations_um, segment_inde return displacement + @staticmethod + def from_dict(d): + return Motion(**d) + def to_dict(self): return dict( displacement=self.displacement, temporal_bins_s=self.temporal_bins_s, spatial_bins_um=self.spatial_bins_um, + direction=self.direction, interpolation_method=self.interpolation_method, ) + def __reduce__(self): + """ + This function is used by pickle to serialize the object. + """ + instance_constructor = self.from_dict + intialization_args = (self.to_dict(),) + return (instance_constructor, intialization_args) + def save(self, folder): folder = Path(folder) folder.mkdir(exist_ok=False, parents=True) @@ -223,8 +236,9 @@ def __eq__(self, other): def copy(self): return Motion( - self.displacement.copy(), - self.temporal_bins_s.copy(), - self.spatial_bins_um.copy(), + [d.copy() for d in self.displacement], + [t.copy() for t in self.temporal_bins_s], + [s.copy() for s in self.spatial_bins_um], + direction=self.direction, interpolation_method=self.interpolation_method, ) From e6da491fd3e01125138700f1f36d5a049f4bb4a7 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 27 Jun 2024 12:46:30 +0200 Subject: [PATCH 82/90] Remove custom pickle --- src/spikeinterface/sortingcomponents/motion_utils.py | 12 ------------ 1 file changed, 12 deletions(-) diff --git a/src/spikeinterface/sortingcomponents/motion_utils.py b/src/spikeinterface/sortingcomponents/motion_utils.py index eeecdd3131..cb8241d310 100644 --- a/src/spikeinterface/sortingcomponents/motion_utils.py +++ b/src/spikeinterface/sortingcomponents/motion_utils.py @@ -148,10 +148,6 @@ def get_displacement_at_time_and_depth(self, times_s, locations_um, segment_inde return displacement - @staticmethod - def from_dict(d): - return Motion(**d) - def to_dict(self): return dict( displacement=self.displacement, @@ -161,14 +157,6 @@ def to_dict(self): interpolation_method=self.interpolation_method, ) - def __reduce__(self): - """ - This function is used by pickle to serialize the object. - """ - instance_constructor = self.from_dict - intialization_args = (self.to_dict(),) - return (instance_constructor, intialization_args) - def save(self, folder): folder = Path(folder) folder.mkdir(exist_ok=False, parents=True) From 7f8f194c4ddcc37f863405c0d28bae787514cadd Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 27 Jun 2024 14:32:27 +0200 Subject: [PATCH 83/90] Remove duplicated line --- src/spikeinterface/generation/hybrid_tools.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 456682d6f3..a8427ca06d 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -526,7 +526,6 @@ def generate_hybrid_recording( temporal_bins_segment = motion.temporal_bins_s[segment_index] displacement_segment = motion.displacement[segment_index] displacement_vector = np.zeros((len(temporal_bins_segment), 2, len(spatial_bins_um))) - displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) for count, i in enumerate(spatial_bins_um): local_motion = displacement_segment[:, count] From d9c36a16ae5091e86bf0a95c0ae073831bac80ac Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 27 Jun 2024 14:56:33 +0200 Subject: [PATCH 84/90] Use official prot_drift_map --- .../benchmark_with_hybrid_recordings.rst | 103 +-------------- .../benchmark_with_hybrid_recordings.py | 119 ++++-------------- 2 files changed, 27 insertions(+), 195 deletions(-) diff --git a/doc/how_to/benchmark_with_hybrid_recordings.rst b/doc/how_to/benchmark_with_hybrid_recordings.rst index 5d78bc280b..3c699104ad 100644 --- a/doc/how_to/benchmark_with_hybrid_recordings.rst +++ b/doc/how_to/benchmark_with_hybrid_recordings.rst @@ -94,100 +94,7 @@ To visualize the drift, we can estimate the motion and plot it: .. code:: ipython3 - # substitute with "official" plot_drift map - def plot_drift_map( - peaks=None, - peak_locations=None, - recording=None, - analyzer=None, - direction="y", - sampling_frequency=None, - segment_index=0, - depth_lim=None, - color_amplitude=True, - scatter_decimate=None, - color="k", - cmap="inferno", - clim=None, - alpha=1, - ax=None, - ): - import matplotlib.pyplot as plt - from matplotlib.colors import Normalize - - if ax is None: - fig, ax = plt.subplots() - - assert peaks is not None or analyzer is not None - if peaks is not None: - assert peak_locations is not None - if recording is None: - assert sampling_frequency is not None - else: - sampling_frequency = recording.sampling_frequency - peak_amplitudes = peaks["amplitude"] - if analyzer is not None: - if analyzer.has_recording(): - recording = analyzer.recording - else: - recording = None - sampling_frequency = analyzer.sampling_frequency - peaks = analyzer.sorting.to_spike_vector() - assert analyzer.has_extension("spike_locations") - peak_locations = analyzer.get_extension("spike_locations").get_data() - if analyzer.has_extension("spike_amplitudes"): - peak_amplitudes = analyzer.get_extension("spike_amplitudes").get_data() - else: - peak_amplitudes = None - times = recording.get_times(segment_index=segment_index) if recording is not None else None - - if times is None: - x = peaks["sample_index"] / sampling_frequency - else: - # use real times and adjust temporal bins with t_start - x = times[peaks["sample_index"]] - - y = peak_locations[direction] - if scatter_decimate is not None: - x = x[::scatter_decimate] - y = y[::scatter_decimate] - y2 = y2[::scatter_decimate] - - if color_amplitude: - assert peak_amplitudes is not None, "To color by amplitudes the 'spike_amplitude' extension is needed" - amps = peak_amplitudes - amps_abs = np.abs(amps) - q_95 = np.quantile(amps_abs, 0.95) - if scatter_decimate is not None: - amps = amps[::scatter_decimate] - amps_abs = amps_abs[::scatter_decimate] - cmap = plt.colormaps[cmap] - if clim is None: - amps = amps_abs - amps /= q_95 - c = cmap(amps) - else: - norm_function = Normalize(vmin=clim[0], vmax=clim[1], clip=True) - c = cmap(norm_function(amps)) - color_kwargs = dict( - color=None, - c=c, - alpha=alpha, - ) - else: - color_kwargs = dict(color=color, c=None, alpha=alpha) - - ax.scatter(x, y, s=1, **color_kwargs) - if depth_lim is not None: - ax.set_ylim(*depth_lim) - ax.set_title("Peak depth") - ax.set_xlabel("Times [s]") - ax.set_ylabel("Depth [$\\mu$m]") - return ax - -.. code:: ipython3 - - ax = plot_drift_map( + ax = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, @@ -2098,22 +2005,22 @@ Let’s plot the added hybrid spikes using the drift maps: .. code:: ipython3 fig, axs = plt.subplots(ncols=2, figsize=(10, 7)) - _ = plot_drift_map( + _ = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, cmap="Greys_r", ax=axs[0], ) - _ = plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) - _ = plot_drift_map( + _ = sw.plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) + _ = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, cmap="Greys_r", ax=axs[1], ) - _ = plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) + _ = sw.plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) axs[0].set_title("Ignoring drift") axs[1].set_title("Accounting for drift") diff --git a/examples/how_to/benchmark_with_hybrid_recordings.py b/examples/how_to/benchmark_with_hybrid_recordings.py index 81e2705510..3c4efe629c 100644 --- a/examples/how_to/benchmark_with_hybrid_recordings.py +++ b/examples/how_to/benchmark_with_hybrid_recordings.py @@ -1,3 +1,20 @@ +# --- +# jupyter: +# jupytext: +# cell_metadata_filter: -all +# formats: ipynb,py:percent +# text_representation: +# extension: .py +# format_name: percent +# format_version: '1.3' +# jupytext_version: 1.16.2 +# kernelspec: +# display_name: Python 3 (ipykernel) +# language: python +# name: python3 +# --- + +# %% # # Benchmark spike sorting with hybrid recordings # # This example shows how to use the SpikeInterface hybrid recordings framework to benchmark spike sorting results. @@ -65,99 +82,7 @@ ) -# substitute with "official" plot_drift map -def plot_drift_map( - peaks=None, - peak_locations=None, - recording=None, - analyzer=None, - direction="y", - sampling_frequency=None, - segment_index=0, - depth_lim=None, - color_amplitude=True, - scatter_decimate=None, - color="k", - cmap="inferno", - clim=None, - alpha=1, - ax=None, -): - import matplotlib.pyplot as plt - from matplotlib.colors import Normalize - - if ax is None: - fig, ax = plt.subplots() - - assert peaks is not None or analyzer is not None - if peaks is not None: - assert peak_locations is not None - if recording is None: - assert sampling_frequency is not None - else: - sampling_frequency = recording.sampling_frequency - peak_amplitudes = peaks["amplitude"] - if analyzer is not None: - if analyzer.has_recording(): - recording = analyzer.recording - else: - recording = None - sampling_frequency = analyzer.sampling_frequency - peaks = analyzer.sorting.to_spike_vector() - assert analyzer.has_extension("spike_locations") - peak_locations = analyzer.get_extension("spike_locations").get_data() - if analyzer.has_extension("spike_amplitudes"): - peak_amplitudes = analyzer.get_extension("spike_amplitudes").get_data() - else: - peak_amplitudes = None - times = recording.get_times(segment_index=segment_index) if recording is not None else None - - if times is None: - x = peaks["sample_index"] / sampling_frequency - else: - # use real times and adjust temporal bins with t_start - x = times[peaks["sample_index"]] - - y = peak_locations[direction] - if scatter_decimate is not None: - x = x[::scatter_decimate] - y = y[::scatter_decimate] - y2 = y2[::scatter_decimate] - - if color_amplitude: - assert peak_amplitudes is not None, "To color by amplitudes the 'spike_amplitude' extension is needed" - amps = peak_amplitudes - amps_abs = np.abs(amps) - q_95 = np.quantile(amps_abs, 0.95) - if scatter_decimate is not None: - amps = amps[::scatter_decimate] - amps_abs = amps_abs[::scatter_decimate] - cmap = plt.colormaps[cmap] - if clim is None: - amps = amps_abs - amps /= q_95 - c = cmap(amps) - else: - norm_function = Normalize(vmin=clim[0], vmax=clim[1], clip=True) - c = cmap(norm_function(amps)) - color_kwargs = dict( - color=None, - c=c, - alpha=alpha, - ) - else: - color_kwargs = dict(color=color, c=None, alpha=alpha) - - ax.scatter(x, y, s=1, **color_kwargs) - if depth_lim is not None: - ax.set_ylim(*depth_lim) - ax.set_title("Peak depth") - ax.set_xlabel("Times [s]") - ax.set_ylabel("Depth [$\\mu$m]") - return ax - - -ax = plot_drift_map( +ax = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, @@ -243,22 +168,22 @@ def plot_drift_map( # Let's plot the added hybrid spikes using the drift maps: fig, axs = plt.subplots(ncols=2, figsize=(10, 7)) -_ = plot_drift_map( +_ = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, cmap="Greys_r", ax=axs[0], ) -_ = plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) -_ = plot_drift_map( +_ = sw.plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) +_ = sw.plot_drift_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], recording=drifting_recording, cmap="Greys_r", ax=axs[1], ) -_ = plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) +_ = sw.plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) axs[0].set_title("Ignoring drift") axs[1].set_title("Accounting for drift") From 8ffdd984f921278ec3e3ef6c5f1bed8d7c052925 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Thu, 27 Jun 2024 14:58:44 +0200 Subject: [PATCH 85/90] Add s3fs to test dependencies --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 69f4067d13..644d52608e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -139,6 +139,9 @@ test = [ # preprocessing "ibllib>=2.36.0", # for IBL + # streaming templates + "s3fs", + # tridesclous "numba", "hdbscan>=0.8.33", # Previous version had a broken wheel From 5186f74a1bfdafc30749a9a2ec1f99c5a28ea4d0 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 28 Jun 2024 11:09:05 +0200 Subject: [PATCH 86/90] Sam+Charlie's suggestions: estimate one motion vector per unit (plus other suggestions) --- src/spikeinterface/generation/drift_tools.py | 26 ++++----- src/spikeinterface/generation/hybrid_tools.py | 56 +++++++++++++------ src/spikeinterface/generation/noise_tools.py | 15 +++-- .../generation/tests/test_hybrid_tools.py | 6 +- .../sortingcomponents/motion_utils.py | 4 +- 5 files changed, 61 insertions(+), 46 deletions(-) diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 52861ae44a..1f410f4330 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -118,21 +118,15 @@ class DriftingTemplates(Templates): This is the same strategy used by MEArec. """ - def __init__(self, **kwargs): - has_templates_moved = "templates_array_moved" in kwargs - has_displacements = "displacements" in kwargs - precomputed = has_templates_moved or has_displacements - if precomputed and not (has_templates_moved and has_displacements): - raise ValueError( - "Please pass both template_array_moved and displacements to DriftingTemplates " - "if you are using precomputed displaceed templates." - ) - templates_array_moved = kwargs.pop("templates_array_moved", None) - displacements = kwargs.pop("displacements", None) - - Templates.__init__(self, **kwargs) + def __init__(self, templates_array_moved=None, displacements=None, **static_kwargs): + Templates.__init__(self, **static_kwargs) assert self.probe is not None, "DriftingTemplates need a Probe in the init" - + if templates_array_moved is not None: + if displacements is None: + raise ValueError( + "Please pass both template_array_moved and displacements to DriftingTemplates " + "if you are using precomputed displaced templates." + ) self.templates_array_moved = templates_array_moved self.displacements = displacements @@ -192,11 +186,11 @@ def from_precomputed_templates( templates_static = templates_array_moved[templates_array_moved.shape[0] // 2] return cls( templates_array=templates_static, - templates_array_moved=templates_array_moved, - displacements=displacements, sampling_frequency=sampling_frequency, nbefore=nbefore, probe=probe, + templates_array_moved=templates_array_moved, + displacements=displacements, ) def move_one_template(self, unit_index, displacement, **interpolation_kwargs): diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index a8427ca06d..6a0c702001 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -321,6 +321,7 @@ def generate_hybrid_recording( motion: Motion | None = None, templates_in_uV: bool = True, unit_locations: np.ndarray | None = None, + drift_step_um: float = 1.0, upsample_factor: int | None = None, upsample_vector: np.ndarray | None = None, amplitude_std: float = 0.05, @@ -361,7 +362,9 @@ def generate_hybrid_recording( Cut out in ms after spike peak. unit_locations : np.array, default: None The locations at which the templates should be injected. If not provided, generated (see - generate_unit_location_kwargs) + generate_unit_location_kwargs). + drift_step_um : float, default: 1.0 + The step in um to use for the drifting templates. upsample_factor : None or int, default: None A upsampling factor used only when templates are not provided. upsample_vector : np.array or None @@ -512,31 +515,46 @@ def generate_hybrid_recording( stop = np.array([0, np.max(motion_array_concat)]) elif dim == 2: raise NotImplementedError("3D motion not implemented yet") - displacements = make_linear_displacement(start, stop, num_step=int((stop - start)[dim])) + num_step = int((stop - start)[dim] / drift_step_um) + displacements = make_linear_displacement(start, stop, num_step=num_step) # use templates_, because templates_array might have been scaled drifting_templates = DriftingTemplates.from_static_templates(templates_) drifting_templates.precompute_displacements(displacements) - # calculate displacement vectors for each segment + # calculate displacement vectors for each segment and unit + # for each unit, we interpolate the motion at its location displacement_sampling_frequency = 1.0 / np.diff(motion.temporal_bins_s[0])[0] displacement_vectors = [] - spatial_bins_um = motion.spatial_bins_um for segment_index in range(motion.num_segments): temporal_bins_segment = motion.temporal_bins_s[segment_index] - displacement_segment = motion.displacement[segment_index] - displacement_vector = np.zeros((len(temporal_bins_segment), 2, len(spatial_bins_um))) - - for count, i in enumerate(spatial_bins_um): - local_motion = displacement_segment[:, count] - displacement_vector[:, motion.dim, count] = local_motion - displacement_vectors.append(displacement_vector) - - # calculate displacement unit factor - displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) - for count in range(num_units): - a = 1 / np.abs((unit_locations[count, motion.dim] - spatial_bins_um)) - displacement_unit_factor[count] = a / a.sum() + displacement_vector = np.zeros((len(temporal_bins_segment), 2, num_units)) + for unit_index in range(num_units): + motion_for_unit = motion.get_displacement_at_time_and_depth( + times=temporal_bins_segment, + locations_um=unit_locations[unit_index], + segment_index=segment_index, + ) + displacement_vector[:, motion.dim, unit_index] = motion_for_unit + # since displacement is estimated by interpolation for each unit, the unit factor is an eye + displacement_unit_factor = np.eye(num_units) + + # spatial_bins_um = motion.spatial_bins_um + # for segment_index in range(motion.num_segments): + # temporal_bins_segment = motion.temporal_bins_s[segment_index] + # displacement_segment = motion.displacement[segment_index] + # displacement_vector = np.zeros((len(temporal_bins_segment), 2, len(spatial_bins_um))) + + # for count, i in enumerate(spatial_bins_um): + # local_motion = displacement_segment[:, count] + # displacement_vector[:, motion.dim, count] = local_motion + # displacement_vectors.append(displacement_vector) + + # # calculate displacement unit factor + # displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) + # for count in range(num_units): + # a = 1 / np.abs((unit_locations[count, motion.dim] - spatial_bins_um)) + # displacement_unit_factor[count] = a / a.sum() hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, @@ -550,6 +568,10 @@ def generate_hybrid_recording( ) else: + warnings.warn( + "No Motion is provided! Please check that your recording is drift-free, otherwise the hybrid recording " + "will have stationary units over a drifting recording..." + ) hybrid_recording = InjectTemplatesRecording( sorting, templates_array, diff --git a/src/spikeinterface/generation/noise_tools.py b/src/spikeinterface/generation/noise_tools.py index 48555b3062..11f30e352f 100644 --- a/src/spikeinterface/generation/noise_tools.py +++ b/src/spikeinterface/generation/noise_tools.py @@ -10,25 +10,24 @@ def generate_noise( Parameters ---------- - probe: Probe + probe : Probe A probe object. - sampling_frequency: float + sampling_frequency : float Sampling frequency - durations: list of float + durations : list of float Durations - dtype: np.dtype + dtype : np.dtype Dtype - noise_levels: float | np.array | tuple + noise_levels : float | np.array | tuple If scalar same noises on all channels. If array then per channels noise level. If tuple, then this represent the range. - - seed: None | int + seed : None | int The seed for random generator. Returns ------- - noise: NoiseGeneratorRecording + noise : NoiseGeneratorRecording A lazy noise generator recording. """ diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index 7939d0b70e..d31a0ec81d 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -34,11 +34,9 @@ def test_generate_hybrid_with_sorting(): assert sorting_hybrid.get_num_units() == len(hybrid.templates) -def test_generate_hybrid_motion(create_cache_folder): - cache_folder = create_cache_folder +def test_generate_hybrid_motion(): rec, _ = generate_ground_truth_recording(sampling_frequency=20000, durations=[10], seed=0) - correct_motion(rec, folder=cache_folder / "motion") - motion_info = load_motion_info(cache_folder / "motion") + _, motion_info = correct_motion(rec, output_motion_info=True) motion = motion_info["motion"] hybrid, sorting_hybrid = generate_hybrid_recording(rec, motion=motion, seed=0) assert rec.get_num_channels() == hybrid.get_num_channels() diff --git a/src/spikeinterface/sortingcomponents/motion_utils.py b/src/spikeinterface/sortingcomponents/motion_utils.py index cb8241d310..39991f4e52 100644 --- a/src/spikeinterface/sortingcomponents/motion_utils.py +++ b/src/spikeinterface/sortingcomponents/motion_utils.py @@ -90,10 +90,12 @@ def get_displacement_at_time_and_depth(self, times_s, locations_um, segment_inde Parameters ---------- times_s: np.array + The time points at which to evaluate the displacement. locations_um: np.array Either this is a one-dimensional array (a vector of positions along self.dimension), or else a 2d array with the 2 or 3 spatial dimensions indexed along axis=1. - segment_index: int, optional + segment_index: int, default: None + The index of the segment to evaluate. If None, and there is only one segment, then that segment is used. grid : bool If grid=False, the default, then times_s and locations_um should have the same one-dimensional shape, and the returned displacement[i] is the displacement at time times_s[i] and location From 8b16006a1706909f7e92657f9510f984db0ea8cd Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 28 Jun 2024 15:30:40 +0200 Subject: [PATCH 87/90] Update how to and fix displacement_vectors --- .../benchmark_with_hybrid_recordings.py | 179 ++++++++++-------- src/spikeinterface/generation/hybrid_tools.py | 6 +- .../sortingcomponents/motion_utils.py | 2 +- 3 files changed, 108 insertions(+), 79 deletions(-) diff --git a/examples/how_to/benchmark_with_hybrid_recordings.py b/examples/how_to/benchmark_with_hybrid_recordings.py index 3c4efe629c..a7eb2c467a 100644 --- a/examples/how_to/benchmark_with_hybrid_recordings.py +++ b/examples/how_to/benchmark_with_hybrid_recordings.py @@ -2,11 +2,11 @@ # jupyter: # jupytext: # cell_metadata_filter: -all -# formats: ipynb,py:percent +# formats: ipynb,py # text_representation: # extension: .py -# format_name: percent -# format_version: '1.3' +# format_name: light +# format_version: '1.5' # jupytext_version: 1.16.2 # kernelspec: # display_name: Python 3 (ipykernel) @@ -14,7 +14,6 @@ # name: python3 # --- -# %% # # Benchmark spike sorting with hybrid recordings # # This example shows how to use the SpikeInterface hybrid recordings framework to benchmark spike sorting results. @@ -38,84 +37,67 @@ import numpy as np import matplotlib.pyplot as plt from pathlib import Path - # - # %matplotlib inline si.set_global_job_kwargs(n_jobs=16) -# To make this notebook self-contained, we will simulate a drifting recording similar to the one acquired by Nick Steinmetz and available [here](https://doi.org/10.6084/m9.figshare.14024495.v1), where an triangular motion was imposed to the recording by moving the probe up and down with a micro-manipulator. +# For this notebook, we will use a drifting recording similar to the one acquired by Nick Steinmetz and available [here](https://doi.org/10.6084/m9.figshare.14024495.v1), where an triangular motion was imposed to the recording by moving the probe up and down with a micro-manipulator. -# + -generate_displacement_vector_kwargs = { - "motion_list": [ - { - "drift_mode": "zigzag", - "non_rigid_gradient": None, - "t_start_drift": 30.0, - "t_end_drift": 210, - "period_s": 60, - } - ], - "drift_start_um": [0, 60], - "drift_stop_um": [0, -60], -} - -# this generates a "static" and "drifting" recording version -static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( - probe_name="Neuropixel-384", # ,'Neuropixel-384', - seed=23, - duration=240, - num_units=100, - generate_displacement_vector_kwargs=generate_displacement_vector_kwargs, -) +workdir = Path("/ssd980/working/hybrid/steinmetz_imposed_motion") +workdir.mkdir(exist_ok=True) -# we sort the channels by depth, to match the hybrid templates -drifting_recording = spre.depth_order(drifting_recording) -# - +recording_np1_imposed = se.read_spikeglx("/hdd1/data/spikeglx/nick-steinmetz/dataset1/p1_g0_t0/") +recording_preproc = spre.highpass_filter(recording_np1_imposed) +recording_preproc = spre.common_reference(recording_preproc) # To visualize the drift, we can estimate the motion and plot it: +# to correct for drift, we need a float dtype +recording_preproc = spre.astype(recording_preproc, "float") _, motion_info = spre.correct_motion( - drifting_recording, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True + recording_preproc, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True ) - -ax = sw.plot_drift_map( +ax = sw.plot_drift_raster_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], - recording=drifting_recording, + recording=recording_preproc, cmap="Greys_r", + scatter_decimate=10, + depth_lim=(-10, 3000) ) # ## Retrieve templates from database +# + templates_info = sgen.fetch_templates_database_info() -print(len(templates_info)) - -templates_info.head() +print(f"Number of templates in database: {len(templates_info)}") +print(f"Template database columns: {templates_info.columns}") +# - available_brain_areas = np.unique(templates_info.brain_area) print(f"Available brain areas: {available_brain_areas}") -# let's perform a query: templates from brain region VISp5 and at the "top" of the probe +# Let's perform a query: templates from visual brain regions and at the "top" of the probe + target_area = ["VISa5", "VISa6a", "VISp5", "VISp6a", "VISrl6b"] minimum_depth = 1500 templates_selected_info = templates_info.query(f"brain_area in {target_area} and depth_along_probe > {minimum_depth}") len(templates_selected_info) -# We can now retrieve the selected templates as a `Templates` object -# +# We can now retrieve the selected templates as a `Templates` object: templates_selected = sgen.query_templates_from_database(templates_selected_info, verbose=True) print(templates_selected) # While we selected templates from a target aread and at certain depths, we can see that the template amplitudes are quite large. This will make spike sorting easy... we can further manipulate the `Templates` by rescaling, relocating, or further selections with the `sgen.scale_template_to_range`, `sgen.relocate_templates`, and `sgen.select_templates` functions. # -# In our case, let's rescale the amplitudes between 50 and 150 $\mu$V and relocate them throughout the entire depth of the probe. +# In our case, let's rescale the amplitudes between 50 and 150 $\mu$V and relocate them towards the bottom half of the probe, where the activity looks interesting! +# + min_amplitude = 50 max_amplitude = 150 templates_scaled = sgen.scale_template_to_range( @@ -124,13 +106,14 @@ max_amplitude=max_amplitude ) -min_displacement = 200 +min_displacement = 1000 max_displacement = 4000 templates_relocated = sgen.relocate_templates( templates=templates_scaled, min_displacement=min_displacement, max_displacement=max_displacement ) +# - # Let's plot the selected templates: @@ -140,73 +123,99 @@ w.figure.subplots_adjust(wspace=0.5, hspace=0.7) # ## Constructing hybrid recordings +# +# We can construct now hybrid recordings with the selected templates. +# +# We will do this in two ways to show how important it is to account for drifts when injecting hybrid spikes. +# +# - For the first recording we will not pass the estimated motion (`recording_hybrid_ignore_drift`). +# - For the second recording, we will pass and account for the estimated motion (`recording_hybrid_with_drift`). -recording_hybrid_no_drift, sorting_hybrid = sgen.generate_hybrid_recording( - recording=drifting_recording, templates=templates_relocated, seed=2308 +recording_hybrid_ignore_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=recording_preproc, templates=templates_relocated, seed=2308 ) -recording_hybrid_no_drift +recording_hybrid_ignore_drift + +# Note that the `generate_hybrid_recording` is warning us that we might want to account for drift! -recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( - recording=drifting_recording, +# by passing the `sorting_hybrid` object, we make sure that injected spikes are the same +# this will take a bit more time because it's interpolating the templates to account for drifts +recording_hybrid_with_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=recording_preproc, templates=templates_relocated, motion=motion_info["motion"], sorting=sorting_hybrid, seed=2308, ) -recording_hybrid +recording_hybrid_with_drift +# We can use the `SortingAnalyzer` to estimate spike locations and plot them: + +# + # construct analyzers and compute spike locations -analyzer_hybrid = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid) -analyzer_hybrid.compute(["random_spikes", "templates"]) -analyzer_hybrid.compute("spike_locations", method="grid_convolution") +analyzer_hybrid_ignore_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_ignore_drift) +analyzer_hybrid_ignore_drift.compute(["random_spikes", "templates"]) +analyzer_hybrid_ignore_drift.compute("spike_locations", method="grid_convolution") -# construct hybrid analyzer for spike locations -analyzer_hybrid_no_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_no_drift) -analyzer_hybrid_no_drift.compute(["random_spikes", "templates"]) -analyzer_hybrid_no_drift.compute("spike_locations", method="grid_convolution") +analyzer_hybrid_with_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_with_drift) +analyzer_hybrid_with_drift.compute(["random_spikes", "templates"]) +analyzer_hybrid_with_drift.compute("spike_locations", method="grid_convolution") +# - # Let's plot the added hybrid spikes using the drift maps: -fig, axs = plt.subplots(ncols=2, figsize=(10, 7)) -_ = sw.plot_drift_map( +fig, axs = plt.subplots(ncols=2, figsize=(10, 7), sharex=True, sharey=True) +_ = sw.plot_drift_raster_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], - recording=drifting_recording, + recording=recording_preproc, cmap="Greys_r", + scatter_decimate=10, ax=axs[0], ) -_ = sw.plot_drift_map(analyzer=analyzer_hybrid_no_drift, color_amplitude=False, color="r", ax=axs[0]) -_ = sw.plot_drift_map( +_ = sw.plot_drift_raster_map( + sorting_analyzer=analyzer_hybrid_ignore_drift, + color_amplitude=False, + color="r", + scatter_decimate=10, + ax=axs[0] +) +_ = sw.plot_drift_raster_map( peaks=motion_info["peaks"], peak_locations=motion_info["peak_locations"], - recording=drifting_recording, + recording=recording_preproc, cmap="Greys_r", + scatter_decimate=10, ax=axs[1], ) -_ = sw.plot_drift_map(analyzer=analyzer_hybrid, color_amplitude=False, color="b", ax=axs[1]) -axs[0].set_title("Ignoring drift") -axs[1].set_title("Accounting for drift") +_ = sw.plot_drift_raster_map( + sorting_analyzer=analyzer_hybrid_with_drift, + color_amplitude=False, + color="b", + scatter_decimate=10, + ax=axs[1] +) +axs[0].set_title("Hybrid spikes\nIgnoring drift") +axs[1].set_title("Hybrid spikes\nAccounting for drift") +axs[0].set_xlim(1000, 1500) +axs[0].set_ylim(500, 2500) # We can see that clearly following drift is essential in order to properly blend the hybrid spikes into the recording! # ## Ground-truth study # # In this section we will use the hybrid recording to benchmark a few spike sorters: +# # - `Kilosort2.5` # - `Kilosort3` # - `Kilosort4` # - `Spyking-CIRCUS 2` -# + -# import shutil -# shutil.rmtree(study_folder) -# - - -workdir = Path("/ssd980/working/hybrid/drift") -workdir.mkdir(exist_ok=True) - # to speed up computations, let's first dump the recording to binary -recording_hybrid_bin = recording_hybrid.save(folder=workdir / "hybrid_bin", overwrite=True) +recording_hybrid_bin = recording_hybrid_with_drift.save( + folder=workdir / "hybrid_bin", + overwrite=True +) # + datasets = { @@ -249,10 +258,28 @@ else: gtstudy = sc.GroundTruthStudy.create(study_folder=study_folder, datasets=datasets, cases=cases) +# run the spike sorting jobs gtstudy.run_sorters(verbose=True, keep=False) +# run the comparisons gtstudy.run_comparisons(exhaustive_gt=False) -w_run_times = sw.plot_study_run_times(gtstudy) +# ## Plot performances + w_perf = sw.plot_study_performances(gtstudy, figsize=(12, 7)) w_perf.axes[0, 0].legend(loc=4) + +# And the winner of the hybrid study challenge is...`SpyKING-CIRCUS 2` 🎉🎉🎉 +# +# In this example, we showed how to: +# +# - Access and fetch templates from the SpikeInterface template database +# - Manipulate templates (scaling/relocating) +# - Construct hybrid recordings accounting for drifts +# - Use the `GroundTruthStudy` to benchmark different sorters +# +# The hybrid framework can be extended to target multiple recordings from different brain regions and species and creating recordings of increasing complexity to challenge the existing sorters! +# +# In addition, hybrid studies can also be used to fine-tune spike sorting parameters on specific datasets. +# +# **Are you ready to try it on your data?** diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 6a0c702001..9dc0cf2310 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -531,11 +531,13 @@ def generate_hybrid_recording( displacement_vector = np.zeros((len(temporal_bins_segment), 2, num_units)) for unit_index in range(num_units): motion_for_unit = motion.get_displacement_at_time_and_depth( - times=temporal_bins_segment, + times_s=temporal_bins_segment, locations_um=unit_locations[unit_index], segment_index=segment_index, + grid=True, ) - displacement_vector[:, motion.dim, unit_index] = motion_for_unit + displacement_vector[:, motion.dim, unit_index] = motion_for_unit[motion.dim, :] + displacement_vectors.append(displacement_vector) # since displacement is estimated by interpolation for each unit, the unit factor is an eye displacement_unit_factor = np.eye(num_units) diff --git a/src/spikeinterface/sortingcomponents/motion_utils.py b/src/spikeinterface/sortingcomponents/motion_utils.py index 39991f4e52..a8de3f6d13 100644 --- a/src/spikeinterface/sortingcomponents/motion_utils.py +++ b/src/spikeinterface/sortingcomponents/motion_utils.py @@ -96,7 +96,7 @@ def get_displacement_at_time_and_depth(self, times_s, locations_um, segment_inde else a 2d array with the 2 or 3 spatial dimensions indexed along axis=1. segment_index: int, default: None The index of the segment to evaluate. If None, and there is only one segment, then that segment is used. - grid : bool + grid : bool, default: False If grid=False, the default, then times_s and locations_um should have the same one-dimensional shape, and the returned displacement[i] is the displacement at time times_s[i] and location locations_um[i]. From b2d3d3b5d581969b3bfddd7e85a45cf4aa4fa9a0 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 28 Jun 2024 15:33:53 +0200 Subject: [PATCH 88/90] Update jupytext readme --- examples/how_to/README.md | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/examples/how_to/README.md b/examples/how_to/README.md index af17859ca7..01f11a7a28 100644 --- a/examples/how_to/README.md +++ b/examples/how_to/README.md @@ -14,17 +14,21 @@ with `nbconvert`. Here are the steps (in this example for the `get_started`): ``` >>> jupytext --to notebook get_started.py +>>> jupytext --set-formats ipynb,py get_started.ipynb ``` 2. Run the notebook +3. Sync the run notebook to the .py file: -3. Convert the notebook to .rst +``` +>>> jupytext --sync get_started.ipynb +``` + +4. Convert the notebook to .rst ``` >>> jupyter nbconvert get_started.ipynb --to rst ->>> jupyter nbconvert analyse_neuropixels.ipynb --to rst ``` - -4. Move the .rst and associated folder (e.g. `get_started.rst` and `get_started_files` folder) to the `doc/how_to`. +5. Move the .rst and associated folder (e.g. `get_started.rst` and `get_started_files` folder) to the `doc/how_to`. From efd5ecfdc58c2f6998e45d007e39461d2ac113c4 Mon Sep 17 00:00:00 2001 From: Alessio Buccino Date: Fri, 28 Jun 2024 21:00:37 +0200 Subject: [PATCH 89/90] Update how to benchmark with real data --- .../benchmark_with_hybrid_recordings.rst | 1735 ++++++++++------- .../benchmark_with_hybrid_recordings_20_0.png | Bin 0 -> 183377 bytes .../benchmark_with_hybrid_recordings_22_0.png | Bin 167387 -> 0 bytes .../benchmark_with_hybrid_recordings_28_1.png | Bin 0 -> 393421 bytes .../benchmark_with_hybrid_recordings_29_1.png | Bin 263465 -> 0 bytes .../benchmark_with_hybrid_recordings_37_1.png | Bin 0 -> 81662 bytes .../benchmark_with_hybrid_recordings_38_1.png | Bin 15370 -> 0 bytes .../benchmark_with_hybrid_recordings_38_2.png | Bin 63183 -> 0 bytes .../benchmark_with_hybrid_recordings_9_0.png | Bin 142577 -> 212689 bytes .../benchmark_with_hybrid_recordings.py | 24 +- src/spikeinterface/generation/hybrid_tools.py | 8 +- 11 files changed, 1095 insertions(+), 672 deletions(-) create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_20_0.png delete mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_22_0.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_28_1.png delete mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_29_1.png create mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_37_1.png delete mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_38_1.png delete mode 100644 doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_38_2.png diff --git a/doc/how_to/benchmark_with_hybrid_recordings.rst b/doc/how_to/benchmark_with_hybrid_recordings.rst index 3c699104ad..9e8c6c7d65 100644 --- a/doc/how_to/benchmark_with_hybrid_recordings.rst +++ b/doc/how_to/benchmark_with_hybrid_recordings.rst @@ -30,7 +30,6 @@ order to smoothly inject spikes into the recording. import matplotlib.pyplot as plt from pathlib import Path - .. code:: ipython3 %matplotlib inline @@ -39,66 +38,49 @@ order to smoothly inject spikes into the recording. si.set_global_job_kwargs(n_jobs=16) -To make this notebook self-contained, we will simulate a drifting -recording similar to the one acquired by Nick Steinmetz and available +For this notebook, we will use a drifting recording similar to the one +acquired by Nick Steinmetz and available `here `__, where an triangular motion was imposed to the recording by moving the probe up and down with a micro-manipulator. .. code:: ipython3 - generate_displacement_vector_kwargs = { - "motion_list": [ - { - "drift_mode": "zigzag", - "non_rigid_gradient": None, - "t_start_drift": 30.0, - "t_end_drift": 210, - "period_s": 60, - } - ], - "drift_start_um": [0, 60], - "drift_stop_um": [0, -60], - } + workdir = Path("/ssd980/working/hybrid/steinmetz_imposed_motion") + workdir.mkdir(exist_ok=True) - # this generates a "static" and "drifting" recording version - static_recording, drifting_recording, sorting = sgen.generate_drifting_recording( - probe_name="Neuropixel-384", - seed=23, - duration=240, - num_units=100, - generate_displacement_vector_kwargs=generate_displacement_vector_kwargs, - ) +.. code:: ipython3 - # we sort the channels by depth, to match the hybrid templates - drifting_recording = spre.depth_order(drifting_recording) + recording_np1_imposed = se.read_spikeglx("/hdd1/data/spikeglx/nick-steinmetz/dataset1/p1_g0_t0/") + recording_preproc = spre.highpass_filter(recording_np1_imposed) + recording_preproc = spre.common_reference(recording_preproc) To visualize the drift, we can estimate the motion and plot it: .. code:: ipython3 + # to correct for drift, we need a float dtype + recording_preproc = spre.astype(recording_preproc, "float") _, motion_info = spre.correct_motion( - drifting_recording, - preset="nonrigid_fast_and_accurate", - n_jobs=4, - progress_bar=True, - output_motion_info=True + recording_preproc, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True ) .. parsed-literal:: - detect and localize: 0%| | 0/240 [00:00 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
probeprobe_manufacturerbrain_areadepth_along_probeamplitude_uvnoise_level_uvsignal_to_noise_ratiotemplate_indexbest_channel_indexspikes_per_unitdatasetdataset_path
0Neuropixels 1.0IMECPVT60.0197.25844017.95249410.987802066293000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
1Neuropixels 1.0IMECPVT80.0111.84829020.3290105.501905198379000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
2Neuropixels 1.0IMECPVT80.0212.14151017.18937312.341434282940000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
3Neuropixels 1.0IMECPVT120.0118.65069619.8153135.9878283123674000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
4Neuropixels 1.0IMECPVT140.0111.78581022.4703064.9748244155834000409_sub-KS043_ses-9468fa93-21ae-4984-955c-e...s3://spikeinterface-template-database/000409_s...
- - + Number of templates in database: 601 + Template database columns: Index(['probe', 'probe_manufacturer', 'brain_area', 'depth_along_probe', + 'amplitude_uv', 'noise_level_uv', 'signal_to_noise_ratio', + 'template_index', 'best_channel_index', 'spikes_per_unit', 'dataset', + 'dataset_path'], + dtype='object') .. code:: ipython3 @@ -264,9 +127,11 @@ Retrieve templates from database 'VPL' 'VPM' 'arb' 'ccb' 'ec' 'ee' 'fiber tracts' 'int' 'll' 'opt' 'scp'] +Let’s perform a query: templates from visual brain regions and at the +“top” of the probe + .. code:: ipython3 - # let's perform a query: templates from visual brain regions and at the "top" of the probe target_area = ["VISa5", "VISa6a", "VISp5", "VISp6a", "VISrl6b"] minimum_depth = 1500 templates_selected_info = templates_info.query(f"brain_area in {target_area} and depth_along_probe > {minimum_depth}") @@ -281,7 +146,7 @@ Retrieve templates from database -We can now retrieve the selected templates as a ``Templates`` object +We can now retrieve the selected templates as a ``Templates`` object: .. code:: ipython3 @@ -305,8 +170,8 @@ rescaling, relocating, or further selections with the ``sgen.select_templates`` functions. In our case, let’s rescale the amplitudes between 50 and 150 -:math:`\mu`\ V and relocate them throughout the entire depth of the -probe. +:math:`\mu`\ V and relocate them towards the bottom half of the probe, +where the activity looks interesting! .. code:: ipython3 @@ -318,10 +183,8 @@ probe. max_amplitude=max_amplitude ) -.. code:: ipython3 - - min_displacement = 200 - max_displacement = 4000 + min_displacement = 1000 + max_displacement = 3000 templates_relocated = sgen.relocate_templates( templates=templates_scaled, min_displacement=min_displacement, @@ -339,431 +202,610 @@ Let’s plot the selected templates: -.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_22_0.png +.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_20_0.png Constructing hybrid recordings ------------------------------ +We can construct now hybrid recordings with the selected templates. + +We will do this in two ways to show how important it is to account for +drifts when injecting hybrid spikes. + +- For the first recording we will not pass the estimated motion + (``recording_hybrid_ignore_drift``). +- For the second recording, we will pass and account for the estimated + motion (``recording_hybrid_with_drift``). + .. code:: ipython3 - recording_hybrid_no_drift, sorting_hybrid = sgen.generate_hybrid_recording( - recording=drifting_recording, templates=templates_relocated, seed=2308 + recording_hybrid_ignore_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=recording_preproc, templates=templates_relocated, seed=2308 ) - recording_hybrid_no_drift + recording_hybrid_ignore_drift + + .. raw:: html -
InjectTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 7,200,000 samples - 240.00s (4.00 minutes) - float32 dtype - 10.30 GiB
Channel IDs
    [ 96 288 0 192 97 289 1 193 98 290 2 194 99 291 3 195 100 292 - 4 196 101 293 5 197 102 294 6 198 103 295 7 199 104 296 8 200 - 105 297 9 201 106 298 10 202 107 299 11 203 108 300 12 204 109 301 - 13 205 110 302 14 206 111 303 15 207 112 304 16 208 113 305 17 209 - 114 306 18 210 115 307 19 211 116 308 20 212 117 309 21 213 118 310 - 22 214 119 311 23 215 120 312 24 216 121 313 25 217 122 314 26 218 - 123 315 27 219 124 316 28 220 125 317 29 221 126 318 30 222 127 319 - 31 223 128 320 32 224 129 321 33 225 130 322 34 226 131 323 35 227 - 132 324 36 228 133 325 37 229 134 326 38 230 135 327 39 231 136 328 - 40 232 137 329 41 233 138 330 42 234 139 331 43 235 140 332 44 236 - 141 333 45 237 142 334 46 238 143 335 47 239 144 336 48 240 145 337 - 49 241 146 338 50 242 147 339 51 243 148 340 52 244 149 341 53 245 - 150 342 54 246 151 343 55 247 152 344 56 248 153 345 57 249 154 346 - 58 250 155 347 59 251 156 348 60 252 157 349 61 253 158 350 62 254 - 159 351 63 255 160 352 64 256 161 353 65 257 162 354 66 258 163 355 - 67 259 164 356 68 260 165 357 69 261 166 358 70 262 167 359 71 263 - 168 360 72 264 169 361 73 265 170 362 74 266 171 363 75 267 172 364 - 76 268 173 365 77 269 174 366 78 270 175 367 79 271 176 368 80 272 - 177 369 81 273 178 370 82 274 179 371 83 275 180 372 84 276 181 373 - 85 277 182 374 86 278 183 375 87 279 184 376 88 280 185 377 89 281 - 186 378 90 282 187 379 91 283 188 380 92 284 189 381 93 285 190 382 - 94 286 191 383 95 287]
Annotations
  • is_filtered : False
  • probe_0_planar_contour : [[ -25. 3845.] - [ -25. -25.] - [ 24. -125.] - [ 73. -25.] - [ 73. 3845.]]
  • probes_info : [{}]
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    location [[ 16. 0.] +
    InjectTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - float64 dtype - 167.99 GiB
    Channel IDs
      ['imec0.ap#AP0' 'imec0.ap#AP1' 'imec0.ap#AP2' 'imec0.ap#AP3' + 'imec0.ap#AP4' 'imec0.ap#AP5' 'imec0.ap#AP6' 'imec0.ap#AP7' + 'imec0.ap#AP8' 'imec0.ap#AP9' 'imec0.ap#AP10' 'imec0.ap#AP11' + 'imec0.ap#AP12' 'imec0.ap#AP13' 'imec0.ap#AP14' 'imec0.ap#AP15' + 'imec0.ap#AP16' 'imec0.ap#AP17' 'imec0.ap#AP18' 'imec0.ap#AP19' + 'imec0.ap#AP20' 'imec0.ap#AP21' 'imec0.ap#AP22' 'imec0.ap#AP23' + 'imec0.ap#AP24' 'imec0.ap#AP25' 'imec0.ap#AP26' 'imec0.ap#AP27' + 'imec0.ap#AP28' 'imec0.ap#AP29' 'imec0.ap#AP30' 'imec0.ap#AP31' + 'imec0.ap#AP32' 'imec0.ap#AP33' 'imec0.ap#AP34' 'imec0.ap#AP35' + 'imec0.ap#AP36' 'imec0.ap#AP37' 'imec0.ap#AP38' 'imec0.ap#AP39' + 'imec0.ap#AP40' 'imec0.ap#AP41' 'imec0.ap#AP42' 'imec0.ap#AP43' + 'imec0.ap#AP44' 'imec0.ap#AP45' 'imec0.ap#AP46' 'imec0.ap#AP47' + 'imec0.ap#AP48' 'imec0.ap#AP49' 'imec0.ap#AP50' 'imec0.ap#AP51' + 'imec0.ap#AP52' 'imec0.ap#AP53' 'imec0.ap#AP54' 'imec0.ap#AP55' + 'imec0.ap#AP56' 'imec0.ap#AP57' 'imec0.ap#AP58' 'imec0.ap#AP59' + 'imec0.ap#AP60' 'imec0.ap#AP61' 'imec0.ap#AP62' 'imec0.ap#AP63' + 'imec0.ap#AP64' 'imec0.ap#AP65' 'imec0.ap#AP66' 'imec0.ap#AP67' + 'imec0.ap#AP68' 'imec0.ap#AP69' 'imec0.ap#AP70' 'imec0.ap#AP71' + 'imec0.ap#AP72' 'imec0.ap#AP73' 'imec0.ap#AP74' 'imec0.ap#AP75' + 'imec0.ap#AP76' 'imec0.ap#AP77' 'imec0.ap#AP78' 'imec0.ap#AP79' + 'imec0.ap#AP80' 'imec0.ap#AP81' 'imec0.ap#AP82' 'imec0.ap#AP83' + 'imec0.ap#AP84' 'imec0.ap#AP85' 'imec0.ap#AP86' 'imec0.ap#AP87' + 'imec0.ap#AP88' 'imec0.ap#AP89' 'imec0.ap#AP90' 'imec0.ap#AP91' + 'imec0.ap#AP92' 'imec0.ap#AP93' 'imec0.ap#AP94' 'imec0.ap#AP95' + 'imec0.ap#AP96' 'imec0.ap#AP97' 'imec0.ap#AP98' 'imec0.ap#AP99' + 'imec0.ap#AP100' 'imec0.ap#AP101' 'imec0.ap#AP102' 'imec0.ap#AP103' + 'imec0.ap#AP104' 'imec0.ap#AP105' 'imec0.ap#AP106' 'imec0.ap#AP107' + 'imec0.ap#AP108' 'imec0.ap#AP109' 'imec0.ap#AP110' 'imec0.ap#AP111' + 'imec0.ap#AP112' 'imec0.ap#AP113' 'imec0.ap#AP114' 'imec0.ap#AP115' + 'imec0.ap#AP116' 'imec0.ap#AP117' 'imec0.ap#AP118' 'imec0.ap#AP119' + 'imec0.ap#AP120' 'imec0.ap#AP121' 'imec0.ap#AP122' 'imec0.ap#AP123' + 'imec0.ap#AP124' 'imec0.ap#AP125' 'imec0.ap#AP126' 'imec0.ap#AP127' + 'imec0.ap#AP128' 'imec0.ap#AP129' 'imec0.ap#AP130' 'imec0.ap#AP131' + 'imec0.ap#AP132' 'imec0.ap#AP133' 'imec0.ap#AP134' 'imec0.ap#AP135' + 'imec0.ap#AP136' 'imec0.ap#AP137' 'imec0.ap#AP138' 'imec0.ap#AP139' + 'imec0.ap#AP140' 'imec0.ap#AP141' 'imec0.ap#AP142' 'imec0.ap#AP143' + 'imec0.ap#AP144' 'imec0.ap#AP145' 'imec0.ap#AP146' 'imec0.ap#AP147' + 'imec0.ap#AP148' 'imec0.ap#AP149' 'imec0.ap#AP150' 'imec0.ap#AP151' + 'imec0.ap#AP152' 'imec0.ap#AP153' 'imec0.ap#AP154' 'imec0.ap#AP155' + 'imec0.ap#AP156' 'imec0.ap#AP157' 'imec0.ap#AP158' 'imec0.ap#AP159' + 'imec0.ap#AP160' 'imec0.ap#AP161' 'imec0.ap#AP162' 'imec0.ap#AP163' + 'imec0.ap#AP164' 'imec0.ap#AP165' 'imec0.ap#AP166' 'imec0.ap#AP167' + 'imec0.ap#AP168' 'imec0.ap#AP169' 'imec0.ap#AP170' 'imec0.ap#AP171' + 'imec0.ap#AP172' 'imec0.ap#AP173' 'imec0.ap#AP174' 'imec0.ap#AP175' + 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'imec0.ap#AP290' 'imec0.ap#AP291' + 'imec0.ap#AP292' 'imec0.ap#AP293' 'imec0.ap#AP294' 'imec0.ap#AP295' + 'imec0.ap#AP296' 'imec0.ap#AP297' 'imec0.ap#AP298' 'imec0.ap#AP299' + 'imec0.ap#AP300' 'imec0.ap#AP301' 'imec0.ap#AP302' 'imec0.ap#AP303' + 'imec0.ap#AP304' 'imec0.ap#AP305' 'imec0.ap#AP306' 'imec0.ap#AP307' + 'imec0.ap#AP308' 'imec0.ap#AP309' 'imec0.ap#AP310' 'imec0.ap#AP311' + 'imec0.ap#AP312' 'imec0.ap#AP313' 'imec0.ap#AP314' 'imec0.ap#AP315' + 'imec0.ap#AP316' 'imec0.ap#AP317' 'imec0.ap#AP318' 'imec0.ap#AP319' + 'imec0.ap#AP320' 'imec0.ap#AP321' 'imec0.ap#AP322' 'imec0.ap#AP323' + 'imec0.ap#AP324' 'imec0.ap#AP325' 'imec0.ap#AP326' 'imec0.ap#AP327' + 'imec0.ap#AP328' 'imec0.ap#AP329' 'imec0.ap#AP330' 'imec0.ap#AP331' + 'imec0.ap#AP332' 'imec0.ap#AP333' 'imec0.ap#AP334' 'imec0.ap#AP335' + 'imec0.ap#AP336' 'imec0.ap#AP337' 'imec0.ap#AP338' 'imec0.ap#AP339' + 'imec0.ap#AP340' 'imec0.ap#AP341' 'imec0.ap#AP342' 'imec0.ap#AP343' + 'imec0.ap#AP344' 'imec0.ap#AP345' 'imec0.ap#AP346' 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    Annotations
    • is_filtered : True
    • probe_0_planar_contour : [[ -11 9989] + [ -11 -11] + [ 24 -186] + [ 59 -11] + [ 59 9989]]
    • probes_info : [{'model_name': 'Neuropixels 1.0', 'manufacturer': 'IMEC', 'probe_type': '0', 'serial_number': '18408406612', 'part_number': 'PRB_1_4_0480_1_C', 'port': '1', 'slot': '2'}]
    Channel Properties
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      channel_names ['AP0' 'AP1' 'AP2' 'AP3' 'AP4' 'AP5' 'AP6' 'AP7' 'AP8' 'AP9' 'AP10' 'AP11' + 'AP12' 'AP13' 'AP14' 'AP15' 'AP16' 'AP17' 'AP18' 'AP19' 'AP20' 'AP21' + 'AP22' 'AP23' 'AP24' 'AP25' 'AP26' 'AP27' 'AP28' 'AP29' 'AP30' 'AP31' + 'AP32' 'AP33' 'AP34' 'AP35' 'AP36' 'AP37' 'AP38' 'AP39' 'AP40' 'AP41' + 'AP42' 'AP43' 'AP44' 'AP45' 'AP46' 'AP47' 'AP48' 'AP49' 'AP50' 'AP51' + 'AP52' 'AP53' 'AP54' 'AP55' 'AP56' 'AP57' 'AP58' 'AP59' 'AP60' 'AP61' + 'AP62' 'AP63' 'AP64' 'AP65' 'AP66' 'AP67' 'AP68' 'AP69' 'AP70' 'AP71' + 'AP72' 'AP73' 'AP74' 'AP75' 'AP76' 'AP77' 'AP78' 'AP79' 'AP80' 'AP81' + 'AP82' 'AP83' 'AP84' 'AP85' 'AP86' 'AP87' 'AP88' 'AP89' 'AP90' 'AP91' + 'AP92' 'AP93' 'AP94' 'AP95' 'AP96' 'AP97' 'AP98' 'AP99' 'AP100' 'AP101' + 'AP102' 'AP103' 'AP104' 'AP105' 'AP106' 'AP107' 'AP108' 'AP109' 'AP110' + 'AP111' 'AP112' 'AP113' 'AP114' 'AP115' 'AP116' 'AP117' 'AP118' 'AP119' + 'AP120' 'AP121' 'AP122' 'AP123' 'AP124' 'AP125' 'AP126' 'AP127' 'AP128' + 'AP129' 'AP130' 'AP131' 'AP132' 'AP133' 'AP134' 'AP135' 'AP136' 'AP137' + 'AP138' 'AP139' 'AP140' 'AP141' 'AP142' 'AP143' 'AP144' 'AP145' 'AP146' + 'AP147' 'AP148' 'AP149' 'AP150' 'AP151' 'AP152' 'AP153' 'AP154' 'AP155' + 'AP156' 'AP157' 'AP158' 'AP159' 'AP160' 'AP161' 'AP162' 'AP163' 'AP164' + 'AP165' 'AP166' 'AP167' 'AP168' 'AP169' 'AP170' 'AP171' 'AP172' 'AP173' + 'AP174' 'AP175' 'AP176' 'AP177' 'AP178' 'AP179' 'AP180' 'AP181' 'AP182' + 'AP183' 'AP184' 'AP185' 'AP186' 'AP187' 'AP188' 'AP189' 'AP190' 'AP191' + 'AP192' 'AP193' 'AP194' 'AP195' 'AP196' 'AP197' 'AP198' 'AP199' 'AP200' + 'AP201' 'AP202' 'AP203' 'AP204' 'AP205' 'AP206' 'AP207' 'AP208' 'AP209' + 'AP210' 'AP211' 'AP212' 'AP213' 'AP214' 'AP215' 'AP216' 'AP217' 'AP218' + 'AP219' 'AP220' 'AP221' 'AP222' 'AP223' 'AP224' 'AP225' 'AP226' 'AP227' + 'AP228' 'AP229' 'AP230' 'AP231' 'AP232' 'AP233' 'AP234' 'AP235' 'AP236' + 'AP237' 'AP238' 'AP239' 'AP240' 'AP241' 'AP242' 'AP243' 'AP244' 'AP245' + 'AP246' 'AP247' 'AP248' 'AP249' 'AP250' 'AP251' 'AP252' 'AP253' 'AP254' + 'AP255' 'AP256' 'AP257' 'AP258' 'AP259' 'AP260' 'AP261' 'AP262' 'AP263' + 'AP264' 'AP265' 'AP266' 'AP267' 'AP268' 'AP269' 'AP270' 'AP271' 'AP272' + 'AP273' 'AP274' 'AP275' 'AP276' 'AP277' 'AP278' 'AP279' 'AP280' 'AP281' + 'AP282' 'AP283' 'AP284' 'AP285' 'AP286' 'AP287' 'AP288' 'AP289' 'AP290' + 'AP291' 'AP292' 'AP293' 'AP294' 'AP295' 'AP296' 'AP297' 'AP298' 'AP299' + 'AP300' 'AP301' 'AP302' 'AP303' 'AP304' 'AP305' 'AP306' 'AP307' 'AP308' + 'AP309' 'AP310' 'AP311' 'AP312' 'AP313' 'AP314' 'AP315' 'AP316' 'AP317' + 'AP318' 'AP319' 'AP320' 'AP321' 'AP322' 'AP323' 'AP324' 'AP325' 'AP326' + 'AP327' 'AP328' 'AP329' 'AP330' 'AP331' 'AP332' 'AP333' 'AP334' 'AP335' + 'AP336' 'AP337' 'AP338' 'AP339' 'AP340' 'AP341' 'AP342' 'AP343' 'AP344' + 'AP345' 'AP346' 'AP347' 'AP348' 'AP349' 'AP350' 'AP351' 'AP352' 'AP353' + 'AP354' 'AP355' 'AP356' 'AP357' 'AP358' 'AP359' 'AP360' 'AP361' 'AP362' + 'AP363' 'AP364' 'AP365' 'AP366' 'AP367' 'AP368' 'AP369' 'AP370' 'AP371' + 'AP372' 'AP373' 'AP374' 'AP375' 'AP376' 'AP377' 'AP378' 'AP379' 'AP380' + 'AP381' 'AP382' 'AP383']
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      location [[ 16. 0.] [ 48. 0.] [ 0. 20.] [ 32. 20.] @@ -1156,50 +1198,287 @@ Constructing hybrid recordings 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
    - - + 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
    inter_sample_shift [0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385]
+ + + +Note that the ``generate_hybrid_recording`` is warning us that we might +want to account for drift! .. code:: ipython3 - recording_hybrid, sorting_hybrid = sgen.generate_hybrid_recording( - recording=drifting_recording, + # by passing the `sorting_hybrid` object, we make sure that injected spikes are the same + # this will take a bit more time because it's interpolating the templates to account for drifts + recording_hybrid_with_drift, sorting_hybrid = sgen.generate_hybrid_recording( + recording=recording_preproc, templates=templates_relocated, motion=motion_info["motion"], sorting=sorting_hybrid, seed=2308, ) - recording_hybrid + recording_hybrid_with_drift + + .. raw:: html -
InjectDriftingTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 7,200,000 samples - 240.00s (4.00 minutes) - float32 dtype - 10.30 GiB
Channel IDs
    [ 96 288 0 192 97 289 1 193 98 290 2 194 99 291 3 195 100 292 - 4 196 101 293 5 197 102 294 6 198 103 295 7 199 104 296 8 200 - 105 297 9 201 106 298 10 202 107 299 11 203 108 300 12 204 109 301 - 13 205 110 302 14 206 111 303 15 207 112 304 16 208 113 305 17 209 - 114 306 18 210 115 307 19 211 116 308 20 212 117 309 21 213 118 310 - 22 214 119 311 23 215 120 312 24 216 121 313 25 217 122 314 26 218 - 123 315 27 219 124 316 28 220 125 317 29 221 126 318 30 222 127 319 - 31 223 128 320 32 224 129 321 33 225 130 322 34 226 131 323 35 227 - 132 324 36 228 133 325 37 229 134 326 38 230 135 327 39 231 136 328 - 40 232 137 329 41 233 138 330 42 234 139 331 43 235 140 332 44 236 - 141 333 45 237 142 334 46 238 143 335 47 239 144 336 48 240 145 337 - 49 241 146 338 50 242 147 339 51 243 148 340 52 244 149 341 53 245 - 150 342 54 246 151 343 55 247 152 344 56 248 153 345 57 249 154 346 - 58 250 155 347 59 251 156 348 60 252 157 349 61 253 158 350 62 254 - 159 351 63 255 160 352 64 256 161 353 65 257 162 354 66 258 163 355 - 67 259 164 356 68 260 165 357 69 261 166 358 70 262 167 359 71 263 - 168 360 72 264 169 361 73 265 170 362 74 266 171 363 75 267 172 364 - 76 268 173 365 77 269 174 366 78 270 175 367 79 271 176 368 80 272 - 177 369 81 273 178 370 82 274 179 371 83 275 180 372 84 276 181 373 - 85 277 182 374 86 278 183 375 87 279 184 376 88 280 185 377 89 281 - 186 378 90 282 187 379 91 283 188 380 92 284 189 381 93 285 190 382 - 94 286 191 383 95 287]
Annotations
  • is_filtered : False
  • probe_0_planar_contour : [[ -25. 3845.] - [ -25. -25.] - [ 24. -125.] - [ 73. -25.] - [ 73. 3845.]]
  • probes_info : [{'manufacturer': 'IMEC', 'model_name': 'Neuropixels 1.0', 'serial_number': '18194814141'}]
Channel Properties
    contact_vector [(0, 16., 0., 'circle', 1., '', '', 0, 'um', 1., 0., 0., 1.) +
    InjectDriftingTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - float64 dtype - 167.99 GiB
    Channel IDs
      ['imec0.ap#AP0' 'imec0.ap#AP1' 'imec0.ap#AP2' 'imec0.ap#AP3' + 'imec0.ap#AP4' 'imec0.ap#AP5' 'imec0.ap#AP6' 'imec0.ap#AP7' + 'imec0.ap#AP8' 'imec0.ap#AP9' 'imec0.ap#AP10' 'imec0.ap#AP11' + 'imec0.ap#AP12' 'imec0.ap#AP13' 'imec0.ap#AP14' 'imec0.ap#AP15' + 'imec0.ap#AP16' 'imec0.ap#AP17' 'imec0.ap#AP18' 'imec0.ap#AP19' + 'imec0.ap#AP20' 'imec0.ap#AP21' 'imec0.ap#AP22' 'imec0.ap#AP23' + 'imec0.ap#AP24' 'imec0.ap#AP25' 'imec0.ap#AP26' 'imec0.ap#AP27' + 'imec0.ap#AP28' 'imec0.ap#AP29' 'imec0.ap#AP30' 'imec0.ap#AP31' + 'imec0.ap#AP32' 'imec0.ap#AP33' 'imec0.ap#AP34' 'imec0.ap#AP35' + 'imec0.ap#AP36' 'imec0.ap#AP37' 'imec0.ap#AP38' 'imec0.ap#AP39' + 'imec0.ap#AP40' 'imec0.ap#AP41' 'imec0.ap#AP42' 'imec0.ap#AP43' + 'imec0.ap#AP44' 'imec0.ap#AP45' 'imec0.ap#AP46' 'imec0.ap#AP47' + 'imec0.ap#AP48' 'imec0.ap#AP49' 'imec0.ap#AP50' 'imec0.ap#AP51' + 'imec0.ap#AP52' 'imec0.ap#AP53' 'imec0.ap#AP54' 'imec0.ap#AP55' + 'imec0.ap#AP56' 'imec0.ap#AP57' 'imec0.ap#AP58' 'imec0.ap#AP59' + 'imec0.ap#AP60' 'imec0.ap#AP61' 'imec0.ap#AP62' 'imec0.ap#AP63' + 'imec0.ap#AP64' 'imec0.ap#AP65' 'imec0.ap#AP66' 'imec0.ap#AP67' + 'imec0.ap#AP68' 'imec0.ap#AP69' 'imec0.ap#AP70' 'imec0.ap#AP71' + 'imec0.ap#AP72' 'imec0.ap#AP73' 'imec0.ap#AP74' 'imec0.ap#AP75' + 'imec0.ap#AP76' 'imec0.ap#AP77' 'imec0.ap#AP78' 'imec0.ap#AP79' + 'imec0.ap#AP80' 'imec0.ap#AP81' 'imec0.ap#AP82' 'imec0.ap#AP83' + 'imec0.ap#AP84' 'imec0.ap#AP85' 'imec0.ap#AP86' 'imec0.ap#AP87' + 'imec0.ap#AP88' 'imec0.ap#AP89' 'imec0.ap#AP90' 'imec0.ap#AP91' + 'imec0.ap#AP92' 'imec0.ap#AP93' 'imec0.ap#AP94' 'imec0.ap#AP95' + 'imec0.ap#AP96' 'imec0.ap#AP97' 'imec0.ap#AP98' 'imec0.ap#AP99' + 'imec0.ap#AP100' 'imec0.ap#AP101' 'imec0.ap#AP102' 'imec0.ap#AP103' + 'imec0.ap#AP104' 'imec0.ap#AP105' 'imec0.ap#AP106' 'imec0.ap#AP107' + 'imec0.ap#AP108' 'imec0.ap#AP109' 'imec0.ap#AP110' 'imec0.ap#AP111' + 'imec0.ap#AP112' 'imec0.ap#AP113' 'imec0.ap#AP114' 'imec0.ap#AP115' + 'imec0.ap#AP116' 'imec0.ap#AP117' 'imec0.ap#AP118' 'imec0.ap#AP119' + 'imec0.ap#AP120' 'imec0.ap#AP121' 'imec0.ap#AP122' 'imec0.ap#AP123' + 'imec0.ap#AP124' 'imec0.ap#AP125' 'imec0.ap#AP126' 'imec0.ap#AP127' + 'imec0.ap#AP128' 'imec0.ap#AP129' 'imec0.ap#AP130' 'imec0.ap#AP131' + 'imec0.ap#AP132' 'imec0.ap#AP133' 'imec0.ap#AP134' 'imec0.ap#AP135' + 'imec0.ap#AP136' 'imec0.ap#AP137' 'imec0.ap#AP138' 'imec0.ap#AP139' + 'imec0.ap#AP140' 'imec0.ap#AP141' 'imec0.ap#AP142' 'imec0.ap#AP143' + 'imec0.ap#AP144' 'imec0.ap#AP145' 'imec0.ap#AP146' 'imec0.ap#AP147' + 'imec0.ap#AP148' 'imec0.ap#AP149' 'imec0.ap#AP150' 'imec0.ap#AP151' + 'imec0.ap#AP152' 'imec0.ap#AP153' 'imec0.ap#AP154' 'imec0.ap#AP155' + 'imec0.ap#AP156' 'imec0.ap#AP157' 'imec0.ap#AP158' 'imec0.ap#AP159' + 'imec0.ap#AP160' 'imec0.ap#AP161' 'imec0.ap#AP162' 'imec0.ap#AP163' + 'imec0.ap#AP164' 'imec0.ap#AP165' 'imec0.ap#AP166' 'imec0.ap#AP167' + 'imec0.ap#AP168' 'imec0.ap#AP169' 'imec0.ap#AP170' 'imec0.ap#AP171' + 'imec0.ap#AP172' 'imec0.ap#AP173' 'imec0.ap#AP174' 'imec0.ap#AP175' + 'imec0.ap#AP176' 'imec0.ap#AP177' 'imec0.ap#AP178' 'imec0.ap#AP179' + 'imec0.ap#AP180' 'imec0.ap#AP181' 'imec0.ap#AP182' 'imec0.ap#AP183' + 'imec0.ap#AP184' 'imec0.ap#AP185' 'imec0.ap#AP186' 'imec0.ap#AP187' + 'imec0.ap#AP188' 'imec0.ap#AP189' 'imec0.ap#AP190' 'imec0.ap#AP191' + 'imec0.ap#AP192' 'imec0.ap#AP193' 'imec0.ap#AP194' 'imec0.ap#AP195' + 'imec0.ap#AP196' 'imec0.ap#AP197' 'imec0.ap#AP198' 'imec0.ap#AP199' + 'imec0.ap#AP200' 'imec0.ap#AP201' 'imec0.ap#AP202' 'imec0.ap#AP203' + 'imec0.ap#AP204' 'imec0.ap#AP205' 'imec0.ap#AP206' 'imec0.ap#AP207' + 'imec0.ap#AP208' 'imec0.ap#AP209' 'imec0.ap#AP210' 'imec0.ap#AP211' + 'imec0.ap#AP212' 'imec0.ap#AP213' 'imec0.ap#AP214' 'imec0.ap#AP215' + 'imec0.ap#AP216' 'imec0.ap#AP217' 'imec0.ap#AP218' 'imec0.ap#AP219' + 'imec0.ap#AP220' 'imec0.ap#AP221' 'imec0.ap#AP222' 'imec0.ap#AP223' + 'imec0.ap#AP224' 'imec0.ap#AP225' 'imec0.ap#AP226' 'imec0.ap#AP227' + 'imec0.ap#AP228' 'imec0.ap#AP229' 'imec0.ap#AP230' 'imec0.ap#AP231' + 'imec0.ap#AP232' 'imec0.ap#AP233' 'imec0.ap#AP234' 'imec0.ap#AP235' + 'imec0.ap#AP236' 'imec0.ap#AP237' 'imec0.ap#AP238' 'imec0.ap#AP239' + 'imec0.ap#AP240' 'imec0.ap#AP241' 'imec0.ap#AP242' 'imec0.ap#AP243' + 'imec0.ap#AP244' 'imec0.ap#AP245' 'imec0.ap#AP246' 'imec0.ap#AP247' + 'imec0.ap#AP248' 'imec0.ap#AP249' 'imec0.ap#AP250' 'imec0.ap#AP251' + 'imec0.ap#AP252' 'imec0.ap#AP253' 'imec0.ap#AP254' 'imec0.ap#AP255' + 'imec0.ap#AP256' 'imec0.ap#AP257' 'imec0.ap#AP258' 'imec0.ap#AP259' + 'imec0.ap#AP260' 'imec0.ap#AP261' 'imec0.ap#AP262' 'imec0.ap#AP263' + 'imec0.ap#AP264' 'imec0.ap#AP265' 'imec0.ap#AP266' 'imec0.ap#AP267' + 'imec0.ap#AP268' 'imec0.ap#AP269' 'imec0.ap#AP270' 'imec0.ap#AP271' + 'imec0.ap#AP272' 'imec0.ap#AP273' 'imec0.ap#AP274' 'imec0.ap#AP275' + 'imec0.ap#AP276' 'imec0.ap#AP277' 'imec0.ap#AP278' 'imec0.ap#AP279' + 'imec0.ap#AP280' 'imec0.ap#AP281' 'imec0.ap#AP282' 'imec0.ap#AP283' + 'imec0.ap#AP284' 'imec0.ap#AP285' 'imec0.ap#AP286' 'imec0.ap#AP287' + 'imec0.ap#AP288' 'imec0.ap#AP289' 'imec0.ap#AP290' 'imec0.ap#AP291' + 'imec0.ap#AP292' 'imec0.ap#AP293' 'imec0.ap#AP294' 'imec0.ap#AP295' + 'imec0.ap#AP296' 'imec0.ap#AP297' 'imec0.ap#AP298' 'imec0.ap#AP299' + 'imec0.ap#AP300' 'imec0.ap#AP301' 'imec0.ap#AP302' 'imec0.ap#AP303' + 'imec0.ap#AP304' 'imec0.ap#AP305' 'imec0.ap#AP306' 'imec0.ap#AP307' + 'imec0.ap#AP308' 'imec0.ap#AP309' 'imec0.ap#AP310' 'imec0.ap#AP311' + 'imec0.ap#AP312' 'imec0.ap#AP313' 'imec0.ap#AP314' 'imec0.ap#AP315' + 'imec0.ap#AP316' 'imec0.ap#AP317' 'imec0.ap#AP318' 'imec0.ap#AP319' + 'imec0.ap#AP320' 'imec0.ap#AP321' 'imec0.ap#AP322' 'imec0.ap#AP323' + 'imec0.ap#AP324' 'imec0.ap#AP325' 'imec0.ap#AP326' 'imec0.ap#AP327' + 'imec0.ap#AP328' 'imec0.ap#AP329' 'imec0.ap#AP330' 'imec0.ap#AP331' + 'imec0.ap#AP332' 'imec0.ap#AP333' 'imec0.ap#AP334' 'imec0.ap#AP335' + 'imec0.ap#AP336' 'imec0.ap#AP337' 'imec0.ap#AP338' 'imec0.ap#AP339' + 'imec0.ap#AP340' 'imec0.ap#AP341' 'imec0.ap#AP342' 'imec0.ap#AP343' + 'imec0.ap#AP344' 'imec0.ap#AP345' 'imec0.ap#AP346' 'imec0.ap#AP347' + 'imec0.ap#AP348' 'imec0.ap#AP349' 'imec0.ap#AP350' 'imec0.ap#AP351' + 'imec0.ap#AP352' 'imec0.ap#AP353' 'imec0.ap#AP354' 'imec0.ap#AP355' + 'imec0.ap#AP356' 'imec0.ap#AP357' 'imec0.ap#AP358' 'imec0.ap#AP359' + 'imec0.ap#AP360' 'imec0.ap#AP361' 'imec0.ap#AP362' 'imec0.ap#AP363' + 'imec0.ap#AP364' 'imec0.ap#AP365' 'imec0.ap#AP366' 'imec0.ap#AP367' + 'imec0.ap#AP368' 'imec0.ap#AP369' 'imec0.ap#AP370' 'imec0.ap#AP371' + 'imec0.ap#AP372' 'imec0.ap#AP373' 'imec0.ap#AP374' 'imec0.ap#AP375' + 'imec0.ap#AP376' 'imec0.ap#AP377' 'imec0.ap#AP378' 'imec0.ap#AP379' + 'imec0.ap#AP380' 'imec0.ap#AP381' 'imec0.ap#AP382' 'imec0.ap#AP383']
    Annotations
    • is_filtered : True
    • probe_0_planar_contour : [[ -11 9989] + [ -11 -11] + [ 24 -186] + [ 59 -11] + [ 59 9989]]
    • probes_info : [{'manufacturer': 'IMEC', 'model_name': 'Neuropixels 1.0', 'serial_number': '18194814141'}]
    Channel Properties
      gain_to_uV [2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 + 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375]
      offset_to_uV [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
      channel_names ['AP0' 'AP1' 'AP2' 'AP3' 'AP4' 'AP5' 'AP6' 'AP7' 'AP8' 'AP9' 'AP10' 'AP11' + 'AP12' 'AP13' 'AP14' 'AP15' 'AP16' 'AP17' 'AP18' 'AP19' 'AP20' 'AP21' + 'AP22' 'AP23' 'AP24' 'AP25' 'AP26' 'AP27' 'AP28' 'AP29' 'AP30' 'AP31' + 'AP32' 'AP33' 'AP34' 'AP35' 'AP36' 'AP37' 'AP38' 'AP39' 'AP40' 'AP41' + 'AP42' 'AP43' 'AP44' 'AP45' 'AP46' 'AP47' 'AP48' 'AP49' 'AP50' 'AP51' + 'AP52' 'AP53' 'AP54' 'AP55' 'AP56' 'AP57' 'AP58' 'AP59' 'AP60' 'AP61' + 'AP62' 'AP63' 'AP64' 'AP65' 'AP66' 'AP67' 'AP68' 'AP69' 'AP70' 'AP71' + 'AP72' 'AP73' 'AP74' 'AP75' 'AP76' 'AP77' 'AP78' 'AP79' 'AP80' 'AP81' + 'AP82' 'AP83' 'AP84' 'AP85' 'AP86' 'AP87' 'AP88' 'AP89' 'AP90' 'AP91' + 'AP92' 'AP93' 'AP94' 'AP95' 'AP96' 'AP97' 'AP98' 'AP99' 'AP100' 'AP101' + 'AP102' 'AP103' 'AP104' 'AP105' 'AP106' 'AP107' 'AP108' 'AP109' 'AP110' + 'AP111' 'AP112' 'AP113' 'AP114' 'AP115' 'AP116' 'AP117' 'AP118' 'AP119' + 'AP120' 'AP121' 'AP122' 'AP123' 'AP124' 'AP125' 'AP126' 'AP127' 'AP128' + 'AP129' 'AP130' 'AP131' 'AP132' 'AP133' 'AP134' 'AP135' 'AP136' 'AP137' + 'AP138' 'AP139' 'AP140' 'AP141' 'AP142' 'AP143' 'AP144' 'AP145' 'AP146' + 'AP147' 'AP148' 'AP149' 'AP150' 'AP151' 'AP152' 'AP153' 'AP154' 'AP155' + 'AP156' 'AP157' 'AP158' 'AP159' 'AP160' 'AP161' 'AP162' 'AP163' 'AP164' + 'AP165' 'AP166' 'AP167' 'AP168' 'AP169' 'AP170' 'AP171' 'AP172' 'AP173' + 'AP174' 'AP175' 'AP176' 'AP177' 'AP178' 'AP179' 'AP180' 'AP181' 'AP182' + 'AP183' 'AP184' 'AP185' 'AP186' 'AP187' 'AP188' 'AP189' 'AP190' 'AP191' + 'AP192' 'AP193' 'AP194' 'AP195' 'AP196' 'AP197' 'AP198' 'AP199' 'AP200' + 'AP201' 'AP202' 'AP203' 'AP204' 'AP205' 'AP206' 'AP207' 'AP208' 'AP209' + 'AP210' 'AP211' 'AP212' 'AP213' 'AP214' 'AP215' 'AP216' 'AP217' 'AP218' + 'AP219' 'AP220' 'AP221' 'AP222' 'AP223' 'AP224' 'AP225' 'AP226' 'AP227' + 'AP228' 'AP229' 'AP230' 'AP231' 'AP232' 'AP233' 'AP234' 'AP235' 'AP236' + 'AP237' 'AP238' 'AP239' 'AP240' 'AP241' 'AP242' 'AP243' 'AP244' 'AP245' + 'AP246' 'AP247' 'AP248' 'AP249' 'AP250' 'AP251' 'AP252' 'AP253' 'AP254' + 'AP255' 'AP256' 'AP257' 'AP258' 'AP259' 'AP260' 'AP261' 'AP262' 'AP263' + 'AP264' 'AP265' 'AP266' 'AP267' 'AP268' 'AP269' 'AP270' 'AP271' 'AP272' + 'AP273' 'AP274' 'AP275' 'AP276' 'AP277' 'AP278' 'AP279' 'AP280' 'AP281' + 'AP282' 'AP283' 'AP284' 'AP285' 'AP286' 'AP287' 'AP288' 'AP289' 'AP290' + 'AP291' 'AP292' 'AP293' 'AP294' 'AP295' 'AP296' 'AP297' 'AP298' 'AP299' + 'AP300' 'AP301' 'AP302' 'AP303' 'AP304' 'AP305' 'AP306' 'AP307' 'AP308' + 'AP309' 'AP310' 'AP311' 'AP312' 'AP313' 'AP314' 'AP315' 'AP316' 'AP317' + 'AP318' 'AP319' 'AP320' 'AP321' 'AP322' 'AP323' 'AP324' 'AP325' 'AP326' + 'AP327' 'AP328' 'AP329' 'AP330' 'AP331' 'AP332' 'AP333' 'AP334' 'AP335' + 'AP336' 'AP337' 'AP338' 'AP339' 'AP340' 'AP341' 'AP342' 'AP343' 'AP344' + 'AP345' 'AP346' 'AP347' 'AP348' 'AP349' 'AP350' 'AP351' 'AP352' 'AP353' + 'AP354' 'AP355' 'AP356' 'AP357' 'AP358' 'AP359' 'AP360' 'AP361' 'AP362' + 'AP363' 'AP364' 'AP365' 'AP366' 'AP367' 'AP368' 'AP369' 'AP370' 'AP371' + 'AP372' 'AP373' 'AP374' 'AP375' 'AP376' 'AP377' 'AP378' 'AP379' 'AP380' + 'AP381' 'AP382' 'AP383']
      contact_vector [(0, 16., 0., 'circle', 1., '', '', 0, 'um', 1., 0., 0., 1.) (0, 48., 0., 'circle', 1., '', '', 1, 'um', 1., 0., 0., 1.) (0, 0., 20., 'circle', 1., '', '', 2, 'um', 1., 0., 0., 1.) (0, 32., 20., 'circle', 1., '', '', 3, 'um', 1., 0., 0., 1.) @@ -1975,93 +2254,203 @@ Constructing hybrid recordings 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 - 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
    - - + 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
    inter_sample_shift [0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385 + 0. 0. 0.07692308 0.07692308 0.15384615 0.15384615 + 0.23076923 0.23076923 0.30769231 0.30769231 0.38461538 0.38461538 + 0.46153846 0.46153846 0.53846154 0.53846154 0.61538462 0.61538462 + 0.69230769 0.69230769 0.76923077 0.76923077 0.84615385 0.84615385]
+ + + +We can use the ``SortingAnalyzer`` to estimate spike locations and plot +them: .. code:: ipython3 # construct analyzers and compute spike locations - analyzer_hybrid = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid) - analyzer_hybrid.compute(["random_spikes", "templates"]) - analyzer_hybrid.compute("spike_locations", method="grid_convolution") + analyzer_hybrid_ignore_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_ignore_drift) + analyzer_hybrid_ignore_drift.compute(["random_spikes", "templates"]) + analyzer_hybrid_ignore_drift.compute("spike_locations", method="grid_convolution") + + analyzer_hybrid_with_drift = si.create_sorting_analyzer(sorting_hybrid, recording_hybrid_with_drift) + analyzer_hybrid_with_drift.compute(["random_spikes", "templates"]) + analyzer_hybrid_with_drift.compute("spike_locations", method="grid_convolution") .. parsed-literal:: - estimate_sparsity: 0%| | 0/240 [00:006Adgp|@DNOyOObi<*$ySqb5y1PWWySp2tM7lw`MfzRmfA4dj`1XD{ 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21:01:26 +0200 Subject: [PATCH 90/90] Remove old displacement_unit_factor --- src/spikeinterface/generation/hybrid_tools.py | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index bed3fd6829..a57e090f5f 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -541,23 +541,6 @@ def generate_hybrid_recording( # since displacement is estimated by interpolation for each unit, the unit factor is an eye displacement_unit_factor = np.eye(num_units) - # spatial_bins_um = motion.spatial_bins_um - # for segment_index in range(motion.num_segments): - # temporal_bins_segment = motion.temporal_bins_s[segment_index] - # displacement_segment = motion.displacement[segment_index] - # displacement_vector = np.zeros((len(temporal_bins_segment), 2, len(spatial_bins_um))) - - # for count, i in enumerate(spatial_bins_um): - # local_motion = displacement_segment[:, count] - # displacement_vector[:, motion.dim, count] = local_motion - # displacement_vectors.append(displacement_vector) - - # # calculate displacement unit factor - # displacement_unit_factor = np.zeros((num_units, len(spatial_bins_um))) - # for count in range(num_units): - # a = 1 / np.abs((unit_locations[count, motion.dim] - spatial_bins_um)) - # displacement_unit_factor[count] = a / a.sum() - hybrid_recording = InjectDriftingTemplatesRecording( sorting=sorting, parent_recording=recording,

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