diff --git a/doc/api.rst b/doc/api.rst index 4d8bd2f329..afc3e38ab2 100644 --- a/doc/api.rst +++ b/doc/api.rst @@ -159,6 +159,8 @@ spikeinterface.preprocessing .. autofunction:: common_reference .. autofunction:: correct_lsb .. autofunction:: correct_motion + .. autofunction:: get_motion_presets + .. autofunction:: get_motion_parameters_preset .. autofunction:: depth_order .. autofunction:: detect_bad_channels .. autofunction:: directional_derivative diff --git a/doc/how_to/handle_drift.rst b/doc/how_to/handle_drift.rst index fae4e8d2f6..eb19665874 100644 --- a/doc/how_to/handle_drift.rst +++ b/doc/how_to/handle_drift.rst @@ -1,4 +1,4 @@ -.. code:: ipython +.. code:: ipython3 %matplotlib inline %load_ext autoreload @@ -7,12 +7,12 @@ Handle motion/drift with spikeinterface ======================================= -SpikeInterface offers a very flexible framework to handle drift as a +Spikeinterface offers a very flexible framework to handle drift as a preprocessing step. If you want to know more, please read the -:ref:`motion_correction` section of the documentation. +``motion_correction`` section of the documentation. Here is a short demo on how to handle drift using the high-level -function :py:func:`~spikeinterface.preprocessing.correct_motion()`. +function ``spikeinterface.preprocessing.correct_motion()``. This function takes a preprocessed recording as input and then internally runs several steps (it can be slow!) and returns a lazy @@ -34,7 +34,7 @@ parameters. The high-level function suggests 3 predifined “presets” and we will explore them using a very well known public dataset recorded by Nick Steinmetz: `Imposed motion -datasets `_ +datasets `__ This dataset contains 3 recordings and each recording contains a Neuropixels 1 and a Neuropixels 2 probe. @@ -42,7 +42,7 @@ Neuropixels 1 and a Neuropixels 2 probe. Here we will use *dataset1* with *neuropixel1*. This dataset is the *“hello world”* for drift correction in the spike sorting community! -.. code:: ipython +.. code:: ipython3 from pathlib import Path import matplotlib.pyplot as plt @@ -52,12 +52,15 @@ Here we will use *dataset1* with *neuropixel1*. This dataset is the import spikeinterface.full as si -.. code:: ipython + from spikeinterface.preprocessing import get_motion_parameters_preset, get_motion_presets - base_folder = Path('/mnt/data/sam/DataSpikeSorting/imposed_motion_nick') - dataset_folder = base_folder / 'dataset1/NP1' -.. code:: ipython +.. code:: ipython3 + + base_folder = Path("/mnt/data/sam/DataSpikeSorting/imposed_motion_nick") + dataset_folder = base_folder / "dataset1/NP1" + +.. code:: ipython3 # read the file raw_rec = si.read_spikeglx(dataset_folder) @@ -66,10 +69,1045 @@ Here we will use *dataset1* with *neuropixel1*. This dataset is the -.. parsed-literal:: - - SpikeGLXRecordingExtractor: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - int16 dtype - 42.00 GiB +.. raw:: html + +
SpikeGLXRecordingExtractor: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - int16 dtype - 42.00 GiB
Channel IDs
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Annotations
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  • 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'}]
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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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@@ -77,89 +1115,245 @@ We preprocess the recording with bandpass filter and a common median reference. Note, that it is better to not whiten the recording before motion estimation to get a better estimate of peak locations! -.. code:: ipython +.. code:: ipython3 def preprocess_chain(rec): - rec = si.bandpass_filter(rec, freq_min=300., freq_max=6000.) - rec = si.common_reference(rec, reference='global', operator='median') + rec = rec.astype('float32') + rec = si.bandpass_filter(rec, freq_min=300.0, freq_max=6000.0) + rec = si.common_reference(rec, reference="global", operator="median") return rec - rec = preprocess_chain(raw_rec) -.. code:: ipython +.. code:: ipython3 - job_kwargs = dict(n_jobs=40, chunk_duration='1s', progress_bar=True) + rec = preprocess_chain(raw_rec) -Run motion correction with one function! -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code:: ipython3 + + job_kwargs = dict(n_jobs=40, chunk_duration="1s", progress_bar=True) Correcting for drift is easy! You just need to run a single function. We -will try this function with 3 presets. +will try this function with some presets. Internally a preset is a dictionary of dictionaries containing all -parameters for each step. +parameters for every steps. Here we also save the motion correction results into a folder to be able to load them later. -.. code:: ipython +preset and parameters +~~~~~~~~~~~~~~~~~~~~~ - # internally, we can explore a preset like this - # every parameter can be overwritten at runtime - from spikeinterface.preprocessing.motion import motion_options_preset - motion_options_preset['kilosort_like'] +Motion correction has some steps and eevry step can be controlled by a +method and related parameters. + +A preset is a nested dict that contains theses methods/parameters. + +.. code:: ipython3 + + preset_keys = get_motion_presets() + preset_keys .. parsed-literal:: - {'doc': 'Mimic the drift correction of kilosrt (grid_convolution + iterative_template)', - 'detect_kwargs': {'method': 'locally_exclusive', - 'peak_sign': 'neg', + ['dredge', + 'dredge_fast', + 'nonrigid_accurate', + 'nonrigid_fast_and_accurate', + 'rigid_fast', + 'kilosort_like'] + + + +.. code:: ipython3 + + one_preset_params = get_motion_parameters_preset("kilosort_like") + one_preset_params + + + + +.. parsed-literal:: + + {'doc': 'Mimic the drift correction of kilosort (grid_convolution + iterative_template)', + 'detect_kwargs': {'peak_sign': 'neg', 'detect_threshold': 8.0, 'exclude_sweep_ms': 0.1, - 'radius_um': 50}, - 'select_kwargs': None, - 'localize_peaks_kwargs': {'method': 'grid_convolution', - 'radius_um': 30.0, - 'upsampling_um': 3.0, - 'sigma_um': array([ 5. , 12.5, 20. ]), + 'radius_um': 50, + 'noise_levels': None, + 'random_chunk_kwargs': {}, + 'method': 'locally_exclusive'}, + 'select_kwargs': {}, + 'localize_peaks_kwargs': {'radius_um': 40.0, + 'upsampling_um': 5.0, 'sigma_ms': 0.25, - 'margin_um': 30.0, + 'margin_um': 50.0, 'prototype': None, - 'percentile': 5.0}, - 'estimate_motion_kwargs': {'method': 'iterative_template', - 'bin_duration_s': 2.0, + 'percentile': 5.0, + 'peak_sign': 'neg', + 'weight_method': {'mode': 'gaussian_2d', + 'sigma_list_um': array([ 5., 10., 15., 20., 25.])}, + 'method': 'grid_convolution'}, + 'estimate_motion_kwargs': {'direction': 'y', 'rigid': False, - 'win_step_um': 50.0, - 'win_sigma_um': 150.0, - 'margin_um': 0, - 'win_shape': 'rect'}, - 'interpolate_motion_kwargs': {'direction': 1, - 'border_mode': 'force_extrapolate', + 'win_shape': 'rect', + 'win_step_um': 200.0, + 'win_scale_um': 400.0, + 'win_margin_um': None, + 'bin_um': 10.0, + 'hist_margin_um': 0, + 'bin_s': 2.0, + 'num_amp_bins': 20, + 'num_shifts_global': 15, + 'num_iterations': 10, + 'num_shifts_block': 5, + 'smoothing_sigma': 0.5, + 'kriging_sigma': 1, + 'kriging_p': 2, + 'kriging_d': 2, + 'method': 'iterative_template'}, + 'interpolate_motion_kwargs': {'border_mode': 'force_extrapolate', 'spatial_interpolation_method': 'kriging', - 'sigma_um': [20.0, 30], - 'p': 1}} + 'sigma_um': 20.0, + 'p': 2}} + + + +Run motion correction with one function! +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Correcting for drift is easy! You just need to run a single function. We +will try this function with some presets. +Here we also save the motion correction results into a folder to be able +to load them later. -.. code:: ipython +.. code:: ipython3 - # lets try theses 3 presets - some_presets = ('rigid_fast', 'kilosort_like', 'nonrigid_accurate') - # some_presets = ('nonrigid_accurate', ) + # lets try theses presets + some_presets = ("rigid_fast", "kilosort_like", "nonrigid_accurate", "nonrigid_fast_and_accurate", "dredge", "dredge_fast") -.. code:: ipython +.. code:: ipython3 - # compute motion with 3 presets + # compute motion with theses presets for preset in some_presets: - print('Computing with', preset) - folder = base_folder / 'motion_folder_dataset1' / preset + print("Computing with", preset) + folder = base_folder / "motion_folder_dataset1" / preset if folder.exists(): shutil.rmtree(folder) - recording_corrected, motion_info = si.correct_motion(rec, preset=preset, - folder=folder, - output_motion_info=True, **job_kwargs) + recording_corrected, motion, motion_info = si.correct_motion( + rec, preset=preset, folder=folder, output_motion=True, output_motion_info=True, **job_kwargs + ) + + +.. parsed-literal:: + + Computing with rigid_fast + + + +.. parsed-literal:: + + detect and localize: 0%| | 0/1958 [00:00 int(sr * time_lim0)) & (peaks['sample_index'] < int(sr * time_lim1)) + time_lim0 = 750.0 + time_lim1 = 1500.0 + mask = (peaks["sample_index"] > int(sr * time_lim0)) & (peaks["sample_index"] < int(sr * time_lim1)) sl = slice(None, None, 5) - amps = np.abs(peaks['amplitude'][mask][sl]) + amps = np.abs(peaks["amplitude"][mask][sl]) amps /= np.quantile(amps, 0.95) - c = plt.get_cmap('inferno')(amps) + c = plt.get_cmap("inferno")(amps) color_kargs = dict(alpha=0.2, s=2, c=c) - loc = motion_info['peak_locations'] - #color='black', - ax.scatter(loc['x'][mask][sl], loc['y'][mask][sl], **color_kargs) + peak_locations = motion_info["peak_locations"] + # color='black', + ax.scatter(peak_locations["x"][mask][sl], peak_locations["y"][mask][sl], **color_kargs) - loc2 = correct_motion_on_peaks(motion_info['peaks'], motion_info['peak_locations'], rec.sampling_frequency, - motion_info['motion'], motion_info['temporal_bins'], motion_info['spatial_bins'], direction="y") + peak_locations2 = correct_motion_on_peaks(peaks, peak_locations, motion,rec) ax = axs[1] si.plot_probe_map(rec, ax=ax) # color='black', - ax.scatter(loc2['x'][mask][sl], loc2['y'][mask][sl], **color_kargs) + ax.scatter(peak_locations2["x"][mask][sl], peak_locations2["y"][mask][sl], **color_kargs) ax.set_ylim(400, 600) fig.suptitle(f"{preset=}") @@ -286,15 +1506,27 @@ to display the results. -.. image:: handle_drift_files/handle_drift_15_0.png +.. image:: handle_drift_files/handle_drift_19_0.png + + +.. image:: handle_drift_files/handle_drift_19_1.png -.. image:: handle_drift_files/handle_drift_15_1.png +.. image:: handle_drift_files/handle_drift_19_2.png -.. image:: handle_drift_files/handle_drift_15_2.png + +.. image:: handle_drift_files/handle_drift_19_3.png + + + +.. image:: handle_drift_files/handle_drift_19_4.png + + + +.. image:: handle_drift_files/handle_drift_19_5.png run times @@ -303,20 +1535,20 @@ run times Presets and related methods have differents accuracies but also computation speeds. It is good to have this in mind! -.. code:: ipython +.. code:: ipython3 run_times = [] for preset in some_presets: - folder = base_folder / 'motion_folder_dataset1' / preset + folder = base_folder / "motion_folder_dataset1" / preset motion_info = si.load_motion_info(folder) - run_times.append(motion_info['run_times']) + run_times.append(motion_info["run_times"]) keys = run_times[0].keys() bottom = np.zeros(len(run_times)) - fig, ax = plt.subplots() + fig, ax = plt.subplots(figsize=(14, 6)) for k in keys: rtimes = np.array([rt[k] for rt in run_times]) - if np.any(rtimes>0.): + if np.any(rtimes > 0.0): ax.bar(some_presets, rtimes, bottom=bottom, label=k) bottom += rtimes ax.legend() @@ -326,9 +1558,9 @@ computation speeds. 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a/doc/how_to/handle_drift_files/handle_drift_21_1.png b/doc/how_to/handle_drift_files/handle_drift_21_1.png new file mode 100644 index 0000000000..e8bd6b5ff1 Binary files /dev/null and b/doc/how_to/handle_drift_files/handle_drift_21_1.png differ diff --git a/examples/how_to/handle_drift.py b/examples/how_to/handle_drift.py index ecf17a1b1f..d324fd1f0f 100644 --- a/examples/how_to/handle_drift.py +++ b/examples/how_to/handle_drift.py @@ -2,12 +2,12 @@ # jupyter: # jupytext: # cell_metadata_filter: -all -# formats: py,ipynb +# formats: py:light,ipynb # text_representation: # extension: .py # format_name: light # format_version: '1.5' -# jupytext_version: 1.14.6 +# jupytext_version: 1.16.2 # kernelspec: # display_name: Python 3 (ipykernel) # language: python @@ -55,6 +55,8 @@ import spikeinterface.full as si +from spikeinterface.preprocessing import get_motion_parameters_preset, get_motion_presets + # - base_folder = Path("/mnt/data/sam/DataSpikeSorting/imposed_motion_nick") @@ -70,6 +72,7 @@ def preprocess_chain(rec): + rec = rec.astype('float32') rec = si.bandpass_filter(rec, freq_min=300.0, freq_max=6000.0) rec = si.common_reference(rec, reference="global", operator="median") return rec @@ -79,33 +82,46 @@ def preprocess_chain(rec): job_kwargs = dict(n_jobs=40, chunk_duration="1s", progress_bar=True) -# ### Run motion correction with one function! +# # # Correcting for drift is easy! You just need to run a single function. -# We will try this function with 3 presets. +# We will try this function with some presets. # # Internally a preset is a dictionary of dictionaries containing all parameters for every steps. # # Here we also save the motion correction results into a folder to be able to load them later. -# internally, we can explore a preset like this -# every parameter can be overwritten at runtime -from spikeinterface.preprocessing.motion import motion_options_preset +# ### preset and parameters +# +# Motion correction has some steps and eevry step can be controlled by a method and related parameters. +# +# A preset is a nested dict that contains theses methods/parameters. + +preset_keys = get_motion_presets() +preset_keys + +one_preset_params = get_motion_parameters_preset("kilosort_like") +one_preset_params + +# ### Run motion correction with one function! +# +# Correcting for drift is easy! You just need to run a single function. +# We will try this function with some presets. +# +# Here we also save the motion correction results into a folder to be able to load them later. -motion_options_preset["kilosort_like"] +# lets try theses presets +some_presets = ("rigid_fast", "kilosort_like", "nonrigid_accurate", "nonrigid_fast_and_accurate", "dredge", "dredge_fast") -# lets try theses 3 presets -some_presets = ("rigid_fast", "kilosort_like", "nonrigid_accurate") -# some_presets = ('kilosort_like', ) -# compute motion with 3 presets +# compute motion with theses presets for preset in some_presets: print("Computing with", preset) folder = base_folder / "motion_folder_dataset1" / preset if folder.exists(): shutil.rmtree(folder) - recording_corrected, motion_info = si.correct_motion( - rec, preset=preset, folder=folder, output_motion_info=True, **job_kwargs + recording_corrected, motion, motion_info = si.correct_motion( + rec, preset=preset, folder=folder, output_motion=True, output_motion_info=True, **job_kwargs ) # ### Plot the results @@ -127,11 +143,20 @@ def preprocess_chain(rec): # The motion vector is computed for different depths. # The corrected peak locations are flatter than the rigid case. # The motion vector map is still be a bit noisy at some depths (e.g around 1000um). -# * The preset **nonrigid_accurate** seems to give the best results on this recording. -# The motion vector seems less noisy globally, but it is not "perfect" (see at the top of the probe 3200um to 3800um). -# Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion: +# * The preset **dredge** is offcial DREDge re-implementation in spikeinterface. +# It give the best result : very fast and smooth motion estimation. Very few noise. +# This method also capture very well the non rigid motion gradient along the probe. +# The best method on the market at the moement. +# An enormous thanks to the dream team : Charlie Windolf, Julien Boussard, Erdem Varol, Liam Paninski. +# Note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion: # the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable. # The method defined by this preset is able to capture this. +# * The preset **nonrigid_accurate** this is the ancestor of "dredge" before it was published. +# It seems to give the good results on this recording but with bit more noise. +# * The preset **dredge_fast** similar than dredge but faster (using grid_convolution). +# * The preset **nonrigid_fast_and_accurate** a variant of nonrigid_accurate but faster (using grid_convolution). +# +# for preset in some_presets: # load @@ -140,8 +165,8 @@ def preprocess_chain(rec): # and plot fig = plt.figure(figsize=(14, 8)) - si.plot_motion( - motion_info, + si.plot_motion_info( + motion_info, rec, figure=fig, depth_lim=(400, 600), color_amplitude=True, @@ -173,6 +198,8 @@ def preprocess_chain(rec): folder = base_folder / "motion_folder_dataset1" / preset motion_info = si.load_motion_info(folder) + motion = motion_info["motion"] + fig, axs = plt.subplots(ncols=2, figsize=(12, 8), sharey=True) ax = axs[0] @@ -190,24 +217,16 @@ def preprocess_chain(rec): color_kargs = dict(alpha=0.2, s=2, c=c) - loc = motion_info["peak_locations"] + peak_locations = motion_info["peak_locations"] # color='black', - ax.scatter(loc["x"][mask][sl], loc["y"][mask][sl], **color_kargs) - - loc2 = correct_motion_on_peaks( - motion_info["peaks"], - motion_info["peak_locations"], - rec.sampling_frequency, - motion_info["motion"], - motion_info["temporal_bins"], - motion_info["spatial_bins"], - direction="y", - ) + ax.scatter(peak_locations["x"][mask][sl], peak_locations["y"][mask][sl], **color_kargs) + + peak_locations2 = correct_motion_on_peaks(peaks, peak_locations, motion,rec) ax = axs[1] si.plot_probe_map(rec, ax=ax) # color='black', - ax.scatter(loc2["x"][mask][sl], loc2["y"][mask][sl], **color_kargs) + ax.scatter(peak_locations2["x"][mask][sl], peak_locations2["y"][mask][sl], **color_kargs) ax.set_ylim(400, 600) fig.suptitle(f"{preset=}") @@ -228,7 +247,7 @@ def preprocess_chain(rec): keys = run_times[0].keys() bottom = np.zeros(len(run_times)) -fig, ax = plt.subplots() +fig, ax = plt.subplots(figsize=(14, 6)) for k in keys: rtimes = np.array([rt[k] for rt in run_times]) if np.any(rtimes > 0.0): diff --git a/pyproject.toml b/pyproject.toml index d4a83bb7fe..8e90dc87fb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -139,6 +139,7 @@ test_extractors = [ test_preprocessing = [ "ibllib>=2.36.0", # for IBL + "torch", ] diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py index 70e13160f4..0e4f1985c6 100644 --- a/src/spikeinterface/generation/drift_tools.py +++ b/src/spikeinterface/generation/drift_tools.py @@ -262,9 +262,14 @@ def make_linear_displacement(start, stop, num_step=10): displacements : np.array The displacements with shape (num_step, 2) """ - displacements = (stop[np.newaxis, :] - start[np.newaxis, :]) / (num_step - 1) * np.arange(num_step)[ - :, np.newaxis - ] + start[np.newaxis, :] + if num_step < 1: + raise ValueError("make_linear_displacement needs num_step > 0") + if num_step == 1: + displacements = ((start + stop) / 2)[np.newaxis, :] + else: + displacements = (stop[np.newaxis, :] - start[np.newaxis, :]) / (num_step - 1) * np.arange(num_step)[ + :, np.newaxis + ] + start[np.newaxis, :] return displacements diff --git a/src/spikeinterface/generation/hybrid_tools.py b/src/spikeinterface/generation/hybrid_tools.py index 2806754c9d..0c82e496c0 100644 --- a/src/spikeinterface/generation/hybrid_tools.py +++ b/src/spikeinterface/generation/hybrid_tools.py @@ -517,6 +517,7 @@ def generate_hybrid_recording( elif dim == 2: raise NotImplementedError("3D motion not implemented yet") num_step = int((stop - start)[dim] / drift_step_um) + num_step = max(1, num_step) displacements = make_linear_displacement(start, stop, num_step=num_step) # use templates_, because templates_array might have been scaled diff --git a/src/spikeinterface/generation/tests/test_hybrid_tools.py b/src/spikeinterface/generation/tests/test_hybrid_tools.py index bdcd8dbb8f..535b3e63a1 100644 --- a/src/spikeinterface/generation/tests/test_hybrid_tools.py +++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py @@ -79,7 +79,7 @@ def test_estimate_templates(create_cache_folder): if __name__ == "__main__": - test_generate_hybrid_no_motion() + # test_generate_hybrid_no_motion() test_generate_hybrid_motion() - test_estimate_templates() - test_generate_hybrid_with_sorting() + # test_estimate_templates() + # test_generate_hybrid_with_sorting() diff --git a/src/spikeinterface/preprocessing/__init__.py b/src/spikeinterface/preprocessing/__init__.py index 5f9ac046e1..3343217090 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, save_motion_info +from .motion import correct_motion, load_motion_info, save_motion_info, get_motion_parameters_preset, get_motion_presets 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 8e9911b47e..8d1f9bc9f3 100644 --- a/src/spikeinterface/preprocessing/motion.py +++ b/src/spikeinterface/preprocessing/motion.py @@ -1,10 +1,12 @@ from __future__ import annotations +import copy import numpy as np import json import shutil from pathlib import Path import time +import inspect from spikeinterface.core import get_noise_levels, fix_job_kwargs from spikeinterface.core.job_tools import _shared_job_kwargs_doc @@ -12,9 +14,9 @@ from spikeinterface.core.job_tools import _shared_job_kwargs_doc motion_options_preset = { - # This preset should be the most acccurate - "nonrigid_accurate": { - "doc": "method by Paninski lab (monopolar_triangulation + decentralized)", + # dredge + "dredge": { + "doc": "Official Dredge preset", "detect_kwargs": dict( method="locally_exclusive", peak_sign="neg", @@ -25,52 +27,23 @@ "select_kwargs": dict(), "localize_peaks_kwargs": dict( method="monopolar_triangulation", - radius_um=75.0, - max_distance_um=150.0, - optimizer="minimize_with_log_penality", - enforce_decrease=True, - # feature="peak_voltage", - feature="ptp", ), "estimate_motion_kwargs": dict( - method="decentralized", + method="dredge_ap", direction="y", - bin_s=1.0, rigid=False, - bin_um=5.0, - hist_margin_um=20.0, win_shape="gaussian", - win_step_um=200.0, - win_scale_um=300.0, - histogram_depth_smooth_um=5.0, - histogram_time_smooth_s=None, - pairwise_displacement_method="conv", - max_displacement_um=100.0, - weight_scale="linear", - error_sigma=0.2, - conv_engine=None, - torch_device=None, - batch_size=1, - corr_threshold=0.0, - time_horizon_s=None, - convergence_method="lsmr", - soft_weights=False, - normalized_xcorr=True, - centered_xcorr=True, - temporal_prior=True, - spatial_prior=False, - force_spatial_median_continuity=False, - reference_displacement="median", - reference_displacement_time_s=0, - robust_regression_sigma=2, - weight_with_amplitude=False, + win_step_um=400.0, + win_scale_um=400.0, + win_margin_um=None, ), "interpolate_motion_kwargs": dict( - border_mode="remove_channels", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 + border_mode="force_extrapolate", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 ), }, - "nonrigid_fast_and_accurate": { - "doc": "mixed methods by KS & Paninski lab (grid_convolution + decentralized)", + # similar than dredge but faster + "dredge_fast": { + "doc": "Modified and faster Dredge preset", "detect_kwargs": dict( method="locally_exclusive", peak_sign="neg", @@ -81,48 +54,49 @@ "select_kwargs": dict(), "localize_peaks_kwargs": dict( method="grid_convolution", - # radius_um=40.0, - radius_um=80.0, - upsampling_um=5.0, - sigma_ms=0.25, - margin_um=30.0, - prototype=None, - percentile=5.0, ), "estimate_motion_kwargs": dict( - method="decentralized", + method="dredge_ap", direction="y", - bin_s=2.0, rigid=False, - bin_um=5.0, - hist_margin_um=0.0, win_shape="gaussian", - win_step_um=100.0, - win_scale_um=200.0, + win_step_um=400.0, + win_scale_um=400.0, win_margin_um=None, - histogram_depth_smooth_um=5.0, - histogram_time_smooth_s=None, - pairwise_displacement_method="conv", - max_displacement_um=100.0, - weight_scale="linear", - error_sigma=0.2, - conv_engine=None, - torch_device=None, - batch_size=1, - corr_threshold=0.0, - time_horizon_s=None, - convergence_method="lsmr", - soft_weights=False, - normalized_xcorr=True, - centered_xcorr=True, - temporal_prior=True, - spatial_prior=False, - force_spatial_median_continuity=False, - reference_displacement="median", - reference_displacement_time_s=0, - robust_regression_sigma=2, - weight_with_amplitude=False, ), + "interpolate_motion_kwargs": dict( + border_mode="force_extrapolate", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 + ), + }, + # This preset is the encestor of dredge + "nonrigid_accurate": { + "doc": "method by Paninski lab (monopolar_triangulation + decentralized)", + "detect_kwargs": dict( + method="locally_exclusive", + peak_sign="neg", + detect_threshold=8.0, + exclude_sweep_ms=0.8, + radius_um=80.0, + ), + "select_kwargs": dict(), + "localize_peaks_kwargs": dict(method="monopolar_triangulation"), + "estimate_motion_kwargs": dict(method="decentralized", direction="y", rigid=False), + "interpolate_motion_kwargs": dict( + border_mode="remove_channels", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 + ), + }, + "nonrigid_fast_and_accurate": { + "doc": "mixed methods by KS & Paninski lab (grid_convolution + decentralized)", + "detect_kwargs": dict( + method="locally_exclusive", + peak_sign="neg", + detect_threshold=8.0, + exclude_sweep_ms=0.8, + radius_um=80.0, + ), + "select_kwargs": dict(), + "localize_peaks_kwargs": dict(method="grid_convolution"), + "estimate_motion_kwargs": dict(method="decentralized", direction="y", rigid=False), "interpolate_motion_kwargs": dict( border_mode="remove_channels", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 ), @@ -135,19 +109,12 @@ peak_sign="neg", detect_threshold=8.0, exclude_sweep_ms=0.1, - radius_um=50, - ), - "select_kwargs": dict(), - "localize_peaks_kwargs": dict( - method="center_of_mass", radius_um=75.0, - feature="ptp", - ), - "estimate_motion_kwargs": dict( - method="decentralized", - bin_s=10.0, - rigid=True, ), + "select_kwargs": dict(), + # "localize_peaks_kwargs": dict(method="grid_convolution"), + "localize_peaks_kwargs": dict(method="center_of_mass"), + "estimate_motion_kwargs": dict(method="dredge_ap", bin_s=5.0, rigid=True), "interpolate_motion_kwargs": dict( border_mode="remove_channels", spatial_interpolation_method="kriging", sigma_um=20.0, p=2 ), @@ -165,20 +132,14 @@ "select_kwargs": dict(), "localize_peaks_kwargs": dict( method="grid_convolution", - radius_um=40.0, - upsampling_um=5.0, weight_method={"mode": "gaussian_2d", "sigma_list_um": np.linspace(5, 25, 5)}, - sigma_ms=0.25, - margin_um=30.0, - prototype=None, - percentile=5.0, ), "estimate_motion_kwargs": dict( method="iterative_template", bin_s=2.0, rigid=False, - win_step_um=50.0, - win_scale_um=150.0, + win_step_um=200.0, + win_scale_um=400.0, hist_margin_um=0, win_shape="rect", ), @@ -197,9 +158,93 @@ } +def _get_default_motion_params(): + # dirty code that inspect class to get parameters + # when multi method for detect_peak/localize_peak/estimate_motion + + params = dict() + + from spikeinterface.sortingcomponents.peak_detection import detect_peak_methods + from spikeinterface.sortingcomponents.peak_localization import localize_peak_methods + from spikeinterface.sortingcomponents.motion.motion_estimation import estimate_motion_methods, estimate_motion + + params["detect_kwargs"] = dict() + for method_name, method_class in detect_peak_methods.items(): + if hasattr(method_class, "check_params"): + sig = inspect.signature(method_class.check_params) + params["detect_kwargs"][method_name] = { + k: v.default for k, v in sig.parameters.items() if k != "self" and v.default != inspect.Parameter.empty + } + + # no design by subclass + params["select_kwargs"] = dict() + + params["localize_peaks_kwargs"] = dict() + for method_name, method_class in localize_peak_methods.items(): + sig = inspect.signature(method_class.__init__) + p = {k: v.default for k, v in sig.parameters.items() if k != "self" and v.default != inspect.Parameter.empty} + p.pop("parents", None) + p.pop("return_output", None) + p.pop("return_tensor", None) + params["localize_peaks_kwargs"][method_name] = p + + params["estimate_motion_kwargs"] = dict() + for method_name, method_class in estimate_motion_methods.items(): + sig = inspect.signature(estimate_motion) + p = {k: v.default for k, v in sig.parameters.items() if k != "self" and v.default != inspect.Parameter.empty} + for k in ("peaks", "peak_locations", "method", "extra_outputs", "verbose", "progress_bar", "margin_um"): + p.pop(k) + + sig = inspect.signature(method_class.run) + p.update( + {k: v.default for k, v in sig.parameters.items() if k != "self" and v.default != inspect.Parameter.empty} + ) + params["estimate_motion_kwargs"][method_name] = p + + # no design by subclass + params["interpolate_motion_kwargs"] = dict() + + return params + + +def get_motion_presets(): + preset_keys = list(motion_options_preset.keys()) + preset_keys.remove("") + return preset_keys + + +def get_motion_parameters_preset(preset): + """ + Get the parameters tree for a given preset for motion correction. + """ + preset_params = copy.deepcopy(motion_options_preset[preset]) + all_default_params = _get_default_motion_params() + params = dict() + for step, step_params in preset_params.items(): + if isinstance(step_params, str): + # the doc key + params[step] = step_params + + elif len(step_params) == 0: + # empty dict with no methods = skip the step (select_peaks for instance) + params[step] = dict() + + elif isinstance(step_params, dict): + if "method" in step_params: + method = step_params["method"] + params[step] = all_default_params[step][method] + else: + params[step] = dict() + params[step].update(step_params) + else: + raise ValueError(f"Preset {preset} is wrong") + + return params + + def correct_motion( recording, - preset="nonrigid_accurate", + preset="dredge_fast", folder=None, output_motion=False, output_motion_info=False, @@ -318,6 +363,7 @@ def correct_motion( job_kwargs = fix_job_kwargs(job_kwargs) noise_levels = get_noise_levels(recording, return_scaled=False) + progress_bar = job_kwargs.get("progress_bar", False) if folder is not None: folder = Path(folder) @@ -380,7 +426,7 @@ def correct_motion( ) t0 = time.perf_counter() - motion = estimate_motion(recording, peaks, peak_locations, **estimate_motion_kwargs) + motion = estimate_motion(recording, peaks, peak_locations, progress_bar=progress_bar, **estimate_motion_kwargs) t1 = time.perf_counter() run_times["estimate_motion"] = t1 - t0 diff --git a/src/spikeinterface/preprocessing/tests/test_motion.py b/src/spikeinterface/preprocessing/tests/test_motion.py index a1ad3766a9..e37d947abc 100644 --- a/src/spikeinterface/preprocessing/tests/test_motion.py +++ b/src/spikeinterface/preprocessing/tests/test_motion.py @@ -1,7 +1,13 @@ import shutil from spikeinterface.core import generate_recording -from spikeinterface.preprocessing import correct_motion, load_motion_info, save_motion_info +from spikeinterface.preprocessing import ( + correct_motion, + load_motion_info, + save_motion_info, + get_motion_parameters_preset, +) +from spikeinterface.preprocessing.motion import _get_default_motion_params def test_estimate_and_correct_motion(create_cache_folder): @@ -27,6 +33,19 @@ def test_estimate_and_correct_motion(create_cache_folder): assert motion_info_loaded["motion"] == motion_info["motion"] +def test_get_motion_parameters_preset(): + from pprint import pprint + + p = _get_default_motion_params() + # pprint(p) + + params = get_motion_parameters_preset("nonrigid_accurate") + params = get_motion_parameters_preset("dredge") + params = get_motion_parameters_preset("rigid_fast") + pprint(params) + + if __name__ == "__main__": # print(correct_motion.__doc__) - test_estimate_and_correct_motion() + # test_estimate_and_correct_motion() + test_get_motion_parameters_preset() diff --git a/src/spikeinterface/sortingcomponents/motion/decentralized.py b/src/spikeinterface/sortingcomponents/motion/decentralized.py index 41b03b1c43..a6bb9a5145 100644 --- a/src/spikeinterface/sortingcomponents/motion/decentralized.py +++ b/src/spikeinterface/sortingcomponents/motion/decentralized.py @@ -72,7 +72,7 @@ class DecentralizedRegistration: When not None the parwise discplament matrix is computed in a small time horizon. In short only pair of bins close in time. So the pariwaise matrix is super sparse and have values only the diagonal. - convergence_method: "lsmr" | "lsqr_robust" | "gradient_descent", default: "lsqr_robust" + convergence_method: "lsmr" | "lsqr_robust" | "gradient_descent", default: "lsmr" Which method to use to compute the global displacement vector from the pairwise matrix. robust_regression_sigma: float Use for convergence_method="lsqr_robust" for iterative selection of the regression. @@ -108,9 +108,9 @@ def run( verbose, progress_bar, extra, - bin_um=1.0, + bin_um=5.0, hist_margin_um=20.0, - bin_s=1.0, + bin_s=2.0, histogram_depth_smooth_um=1.0, histogram_time_smooth_s=1.0, pairwise_displacement_method="conv", @@ -122,7 +122,7 @@ def run( batch_size=1, corr_threshold=0.0, time_horizon_s=None, - convergence_method="lsqr_robust", + convergence_method="lsmr", soft_weights=False, normalized_xcorr=True, centered_xcorr=True, @@ -183,7 +183,7 @@ def run( motion_array = np.zeros((temporal_bins.size, len(non_rigid_windows)), dtype=np.float64) windows_iter = non_rigid_windows if progress_bar: - windows_iter = tqdm(windows_iter, desc="windows") + windows_iter = tqdm(windows_iter, desc="pairwise displacement") if spatial_prior: all_pairwise_displacements = np.empty( (len(non_rigid_windows), temporal_bins.size, temporal_bins.size), dtype=np.float64 @@ -299,7 +299,7 @@ def compute_pairwise_displacement( method="conv", weight_scale="linear", error_sigma=0.2, - conv_engine="numpy", + conv_engine=None, torch_device=None, batch_size=1, max_displacement_um=1500, @@ -741,7 +741,10 @@ def jac(p): # corr : tensor # """ # if conv_engine == "torch": -# assert HAVE_TORCH +# import torch +# import torch.nn.functional as F + +# # assert HAVE_TORCH # conv1d = F.conv1d # npx = torch # elif conv_engine == "numpy": diff --git a/src/spikeinterface/sortingcomponents/motion/dredge.py b/src/spikeinterface/sortingcomponents/motion/dredge.py index a0dde6d52b..6cc3a8d8d0 100644 --- a/src/spikeinterface/sortingcomponents/motion/dredge.py +++ b/src/spikeinterface/sortingcomponents/motion/dredge.py @@ -108,6 +108,11 @@ def run( **method_kwargs, ): + try: + import torch + except ImportError: + raise ImportError("The dredge method require torch: pip install torch") + outs = dredge_ap( recording, peaks, diff --git a/src/spikeinterface/sortingcomponents/motion/motion_estimation.py b/src/spikeinterface/sortingcomponents/motion/motion_estimation.py index 2d8564fc54..0d425c98da 100644 --- a/src/spikeinterface/sortingcomponents/motion/motion_estimation.py +++ b/src/spikeinterface/sortingcomponents/motion/motion_estimation.py @@ -21,8 +21,8 @@ def estimate_motion( direction="y", rigid=False, win_shape="gaussian", - win_step_um=50.0, # @alessio charlie is proposing here instead 400 - win_scale_um=150.0, # @alessio charlie is proposing here instead 400 + win_step_um=200.0, + win_scale_um=300.0, win_margin_um=None, method="decentralized", extra_outputs=False, diff --git a/src/spikeinterface/sortingcomponents/motion/tests/test_motion_estimation.py b/src/spikeinterface/sortingcomponents/motion/tests/test_motion_estimation.py index 0726ca5a87..c05109aaca 100644 --- a/src/spikeinterface/sortingcomponents/motion/tests/test_motion_estimation.py +++ b/src/spikeinterface/sortingcomponents/motion/tests/test_motion_estimation.py @@ -160,6 +160,8 @@ def test_estimate_motion(dataset): bin_um=10.0, margin_um=5, extra_outputs=True, + win_step_um=100.0, + win_scale_um=100.0, ) kwargs.update(cases_kwargs) diff --git a/src/spikeinterface/sortingcomponents/peak_detection.py b/src/spikeinterface/sortingcomponents/peak_detection.py index 0d5c92ff28..b984853123 100644 --- a/src/spikeinterface/sortingcomponents/peak_detection.py +++ b/src/spikeinterface/sortingcomponents/peak_detection.py @@ -50,7 +50,7 @@ def detect_peaks( - recording, method="by_channel", pipeline_nodes=None, gather_mode="memory", folder=None, names=None, **kwargs + recording, method="locally_exclusive", pipeline_nodes=None, gather_mode="memory", folder=None, names=None, **kwargs ): """Peak detection based on threshold crossing in term of k x MAD. diff --git a/src/spikeinterface/widgets/tests/test_widgets.py b/src/spikeinterface/widgets/tests/test_widgets.py index a09304dc86..e06c79ad2f 100644 --- a/src/spikeinterface/widgets/tests/test_widgets.py +++ b/src/spikeinterface/widgets/tests/test_widgets.py @@ -57,7 +57,12 @@ def setUpClass(cls): cls.sorting = sorting # estimate motion for motion widgets - _, cls.motion_info = correct_motion(recording, preset="kilosort_like", output_motion_info=True) + _, cls.motion_info = correct_motion( + recording, + preset="kilosort_like", + output_motion_info=True, + estimate_motion_kwargs={"win_step_um": 50, "win_scale_um": 100}, + ) cls.num_units = len(cls.sorting.get_unit_ids())