Tools for Generation of Hybrid recordings

doc/how_to/index.rst

@@ -13,3 +13,4 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to.
     combine_recordings
     process_by_channel_group
     load_your_data_into_sorting
+    benchmark_with_hybrid_recordings

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`.

examples/how_to/analyse_neuropixels.py → 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.
 

examples/how_to/benchmark_with_hybrid_recordings.py

@@ -0,0 +1,293 @@
+# ---
+# jupyter:
+#   jupytext:
+#     cell_metadata_filter: -all
+#     formats: ipynb,py
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       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.
+#
+# 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
+
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+# -
+
+# %matplotlib inline
+
+si.set_global_job_kwargs(n_jobs=16)
+
+# 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.
+
+workdir = Path("/ssd980/working/hybrid/steinmetz_imposed_motion")
+workdir.mkdir(exist_ok=True)
+
+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(
+    recording_preproc, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True
+)
+
+ax = sw.plot_drift_raster_map(
+    peaks=motion_info["peaks"],
+    peak_locations=motion_info["peak_locations"],
+    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(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 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:
+
+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 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(
+    templates=templates_selected,
+    min_amplitude=min_amplitude,
+    max_amplitude=max_amplitude
+)
+
+min_displacement = 1000
+max_displacement = 3000
+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)
+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
+#
+# 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_ignore_drift, sorting_hybrid = sgen.generate_hybrid_recording(
+    recording=recording_preproc, templates=templates_relocated, seed=2308
+)
+recording_hybrid_ignore_drift
+
+# Note that the `generate_hybrid_recording` is warning us that we might want to account for drift!
+
+# 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_with_drift
+
+# We can use the `SortingAnalyzer` to estimate spike locations and plot them:
+
+# +
+# construct analyzers and compute spike locations
+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")
+# -
+
+# Let's plot the added hybrid spikes using the drift maps:
+
+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=recording_preproc,
+    cmap="Greys_r",
+    scatter_decimate=10,
+    ax=axs[0],
+)
+_ = 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=recording_preproc,
+    cmap="Greys_r",
+    scatter_decimate=10,
+    ax=axs[1],
+)
+_ = 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`
+
+# to speed up computations, let's first dump the recording to binary
+recording_hybrid_bin = recording_hybrid_with_drift.save(
+    folder=workdir / "hybrid_bin",
+    overwrite=True
+)
+
+# +
+datasets = {
+    "hybrid": (recording_hybrid_bin, 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"
+
+gtstudy = sc.GroundTruthStudy(study_folder)
+
+# -
+
+# run the spike sorting jobs
+gtstudy.run_sorters(verbose=False, keep=True)
+
+# run the comparisons
+gtstudy.run_comparisons(exhaustive_gt=False)
+
+# ## Plot performances
+#
+# Given that we know the exactly where we injected the hybrid spikes, we can now compute and plot performance metrics: accuracy, precision, and recall.
+#
+# In the following plot, the x axis is the unit index, while the y axis is the performance metric. The units are sorted by performance.
+
+w_perf = sw.plot_study_performances(gtstudy, figsize=(12, 7))
+w_perf.axes[0, 0].legend(loc=4)
+
+# From the performance plots, we can see that there is no clear "winner", but `Kilosort3` definitely performs worse than the other options.
+#
+# Although non of the sorters find all units perfectly, `Kilosort2.5`, `Kilosort4`, and `SpyKING CIRCUS 2` all find around 10-12 hybrid units with accuracy greater than 80%.
+# `Kilosort4` has a better overall curve, being able to find almost all units with an accuracy above 50%. `Kilosort2.5` performs well when looking at precision (finding all spikes in a hybrid unit), at the cost of lower recall (finding spikes when it shouldn't).
+#
+#
+# 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?**

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

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)
 

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
@@ -1858,7 +1857,7 @@ 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)
 

src/spikeinterface/core/node_pipeline.py

@@ -516,7 +516,6 @@ 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
 
 

src/spikeinterface/core/sortinganalyzer.py

@@ -970,7 +970,9 @@ def compute_one_extension(self, extension_name, save=True, verbose=False, **kwar
         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)