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+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.
+
+.. 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)
+
+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.
+
+.. code:: ipython3
+
+    workdir = Path("/ssd980/working/hybrid/steinmetz_imposed_motion")
+    workdir.mkdir(exist_ok=True)
+
+.. code:: ipython3
+
+    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(
+        recording_preproc, preset="nonrigid_fast_and_accurate", n_jobs=4, progress_bar=True, output_motion_info=True
+    )
+
+
+
+.. parsed-literal::
+
+    detect and localize:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+.. code:: ipython3
+
+    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)
+    )
+
+
+
+.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_9_0.png
+
+
+Retrieve templates from database
+--------------------------------
+
+.. code:: ipython3
+
+    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}")
+
+
+.. parsed-literal::
+
+    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
+
+    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']
+
+
+Let’s perform a query: templates from visual brain regions and at the
+“top” of the probe
+
+.. code:: ipython3
+
+    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 towards the bottom half of the probe,
+where the activity looks interesting!
+
+.. 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
+    )
+
+    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:
+
+.. 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_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_ignore_drift, sorting_hybrid = sgen.generate_hybrid_recording(
+        recording=recording_preproc, templates=templates_relocated, seed=2308
+    )
+    recording_hybrid_ignore_drift
+
+
+
+
+.. raw:: html
+
+    <div style='border:1px solid #ddd; padding:10px;'><strong>InjectTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - float64 dtype - 167.99 GiB</strong></div><details style='margin-left: 10px;'>  <summary><strong>Channel IDs</strong></summary><ul>['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'] </details><details style='margin-left: 10px;'>  <summary><strong>Annotations</strong></summary><ul><li> <strong> is_filtered </strong>: True</li><li> <strong> probe_0_planar_contour </strong>: [[ -11 9989]
+     [ -11  -11]
+     [  24 -186]
+     [  59  -11]
+     [  59 9989]]</li><li> <strong> probes_info </strong>: [{'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'}]</li></ul> </details><details style='margin-left: 10px;'><summary><strong>Channel Properties</strong></summary><ul><details><summary> <strong> gain_to_uV </strong> </summary>[2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375]</details><details><summary> <strong> offset_to_uV </strong> </summary>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0]</details><details><summary> <strong> channel_names </strong> </summary>['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']</details><details><summary> <strong> contact_vector </strong> </summary>[(0, 16.,    0., 'square', 12., '', 'e0',   0, 'um', 1., 0., 0., 1.,   0, 0, 0, 500, 250, 1)
+     (0, 48.,    0., 'square', 12., '', 'e1',   1, 'um', 1., 0., 0., 1.,   1, 0, 0, 500, 250, 1)
+     (0,  0.,   20., 'square', 12., '', 'e2',   2, 'um', 1., 0., 0., 1.,   2, 0, 0, 500, 250, 1)
+     (0, 32.,   20., 'square', 12., '', 'e3',   3, 'um', 1., 0., 0., 1.,   3, 0, 0, 500, 250, 1)
+     (0, 16.,   40., 'square', 12., '', 'e4',   4, 'um', 1., 0., 0., 1.,   4, 0, 0, 500, 250, 1)
+     (0, 48.,   40., 'square', 12., '', 'e5',   5, 'um', 1., 0., 0., 1.,   5, 0, 0, 500, 250, 1)
+     (0,  0.,   60., 'square', 12., '', 'e6',   6, 'um', 1., 0., 0., 1.,   6, 0, 0, 500, 250, 1)
+     (0, 32.,   60., 'square', 12., '', 'e7',   7, 'um', 1., 0., 0., 1.,   7, 0, 0, 500, 250, 1)
+     (0, 16.,   80., 'square', 12., '', 'e8',   8, 'um', 1., 0., 0., 1.,   8, 0, 0, 500, 250, 1)
+     (0, 48.,   80., 'square', 12., '', 'e9',   9, 'um', 1., 0., 0., 1.,   9, 0, 0, 500, 250, 1)
+     (0,  0.,  100., 'square', 12., '', 'e10',  10, 'um', 1., 0., 0., 1.,  10, 0, 0, 500, 250, 1)
+     (0, 32.,  100., 'square', 12., '', 'e11',  11, 'um', 1., 0., 0., 1.,  11, 0, 0, 500, 250, 1)
+     (0, 16.,  120., 'square', 12., '', 'e12',  12, 'um', 1., 0., 0., 1.,  12, 0, 0, 500, 250, 1)
+     (0, 48.,  120., 'square', 12., '', 'e13',  13, 'um', 1., 0., 0., 1.,  13, 0, 0, 500, 250, 1)
+     (0,  0.,  140., 'square', 12., '', 'e14',  14, 'um', 1., 0., 0., 1.,  14, 0, 0, 500, 250, 1)
+     (0, 32.,  140., 'square', 12., '', 'e15',  15, 'um', 1., 0., 0., 1.,  15, 0, 0, 500, 250, 1)
+     (0, 16.,  160., 'square', 12., '', 'e16',  16, 'um', 1., 0., 0., 1.,  16, 0, 0, 500, 250, 1)
+     (0, 48.,  160., 'square', 12., '', 'e17',  17, 'um', 1., 0., 0., 1.,  17, 0, 0, 500, 250, 1)
+     (0,  0.,  180., 'square', 12., '', 'e18',  18, 'um', 1., 0., 0., 1.,  18, 0, 0, 500, 250, 1)
+     (0, 32.,  180., 'square', 12., '', 'e19',  19, 'um', 1., 0., 0., 1.,  19, 0, 0, 500, 250, 1)
+     (0, 16.,  200., 'square', 12., '', 'e20',  20, 'um', 1., 0., 0., 1.,  20, 0, 0, 500, 250, 1)
+     (0, 48.,  200., 'square', 12., '', 'e21',  21, 'um', 1., 0., 0., 1.,  21, 0, 0, 500, 250, 1)
+     (0,  0.,  220., 'square', 12., '', 'e22',  22, 'um', 1., 0., 0., 1.,  22, 0, 0, 500, 250, 1)
+     (0, 32.,  220., 'square', 12., '', 'e23',  23, 'um', 1., 0., 0., 1.,  23, 0, 0, 500, 250, 1)
+     (0, 16.,  240., 'square', 12., '', 'e24',  24, 'um', 1., 0., 0., 1.,  24, 0, 0, 500, 250, 1)
+     (0, 48.,  240., 'square', 12., '', 'e25',  25, 'um', 1., 0., 0., 1.,  25, 0, 0, 500, 250, 1)
+     (0,  0.,  260., 'square', 12., '', 'e26',  26, 'um', 1., 0., 0., 1.,  26, 0, 0, 500, 250, 1)
+     (0, 32.,  260., 'square', 12., '', 'e27',  27, 'um', 1., 0., 0., 1.,  27, 0, 0, 500, 250, 1)
+     (0, 16.,  280., 'square', 12., '', 'e28',  28, 'um', 1., 0., 0., 1.,  28, 0, 0, 500, 250, 1)
+     (0, 48.,  280., 'square', 12., '', 'e29',  29, 'um', 1., 0., 0., 1.,  29, 0, 0, 500, 250, 1)
+     (0,  0.,  300., 'square', 12., '', 'e30',  30, 'um', 1., 0., 0., 1.,  30, 0, 0, 500, 250, 1)
+     (0, 32.,  300., 'square', 12., '', 'e31',  31, 'um', 1., 0., 0., 1.,  31, 0, 0, 500, 250, 1)
+     (0, 16.,  320., 'square', 12., '', 'e32',  32, 'um', 1., 0., 0., 1.,  32, 0, 0, 500, 250, 1)
+     (0, 48.,  320., 'square', 12., '', 'e33',  33, 'um', 1., 0., 0., 1.,  33, 0, 0, 500, 250, 1)
+     (0,  0.,  340., 'square', 12., '', 'e34',  34, 'um', 1., 0., 0., 1.,  34, 0, 0, 500, 250, 1)
+     (0, 32.,  340., 'square', 12., '', 'e35',  35, 'um', 1., 0., 0., 1.,  35, 0, 0, 500, 250, 1)
+     (0, 16.,  360., 'square', 12., '', 'e36',  36, 'um', 1., 0., 0., 1.,  36, 0, 0, 500, 250, 1)
+     (0, 48.,  360., 'square', 12., '', 'e37',  37, 'um', 1., 0., 0., 1.,  37, 0, 0, 500, 250, 1)
+     (0,  0.,  380., 'square', 12., '', 'e38',  38, 'um', 1., 0., 0., 1.,  38, 0, 0, 500, 250, 1)
+     (0, 32.,  380., 'square', 12., '', 'e39',  39, 'um', 1., 0., 0., 1.,  39, 0, 0, 500, 250, 1)
+     (0, 16.,  400., 'square', 12., '', 'e40',  40, 'um', 1., 0., 0., 1.,  40, 0, 0, 500, 250, 1)
+     (0, 48.,  400., 'square', 12., '', 'e41',  41, 'um', 1., 0., 0., 1.,  41, 0, 0, 500, 250, 1)
+     (0,  0.,  420., 'square', 12., '', 'e42',  42, 'um', 1., 0., 0., 1.,  42, 0, 0, 500, 250, 1)
+     (0, 32.,  420., 'square', 12., '', 'e43',  43, 'um', 1., 0., 0., 1.,  43, 0, 0, 500, 250, 1)
+     (0, 16.,  440., 'square', 12., '', 'e44',  44, 'um', 1., 0., 0., 1.,  44, 0, 0, 500, 250, 1)
+     (0, 48.,  440., 'square', 12., '', 'e45',  45, 'um', 1., 0., 0., 1.,  45, 0, 0, 500, 250, 1)
+     (0,  0.,  460., 'square', 12., '', 'e46',  46, 'um', 1., 0., 0., 1.,  46, 0, 0, 500, 250, 1)
+     (0, 32.,  460., 'square', 12., '', 'e47',  47, 'um', 1., 0., 0., 1.,  47, 0, 0, 500, 250, 1)
+     (0, 16.,  480., 'square', 12., '', 'e48',  48, 'um', 1., 0., 0., 1.,  48, 0, 0, 500, 250, 1)
+     (0, 48.,  480., 'square', 12., '', 'e49',  49, 'um', 1., 0., 0., 1.,  49, 0, 0, 500, 250, 1)
+     (0,  0.,  500., 'square', 12., '', 'e50',  50, 'um', 1., 0., 0., 1.,  50, 0, 0, 500, 250, 1)
+     (0, 32.,  500., 'square', 12., '', 'e51',  51, 'um', 1., 0., 0., 1.,  51, 0, 0, 500, 250, 1)
+     (0, 16.,  520., 'square', 12., '', 'e52',  52, 'um', 1., 0., 0., 1.,  52, 0, 0, 500, 250, 1)
+     (0, 48.,  520., 'square', 12., '', 'e53',  53, 'um', 1., 0., 0., 1.,  53, 0, 0, 500, 250, 1)
+     (0,  0.,  540., 'square', 12., '', 'e54',  54, 'um', 1., 0., 0., 1.,  54, 0, 0, 500, 250, 1)
+     (0, 32.,  540., 'square', 12., '', 'e55',  55, 'um', 1., 0., 0., 1.,  55, 0, 0, 500, 250, 1)
+     (0, 16.,  560., 'square', 12., '', 'e56',  56, 'um', 1., 0., 0., 1.,  56, 0, 0, 500, 250, 1)
+     (0, 48.,  560., 'square', 12., '', 'e57',  57, 'um', 1., 0., 0., 1.,  57, 0, 0, 500, 250, 1)
+     (0,  0.,  580., 'square', 12., '', 'e58',  58, 'um', 1., 0., 0., 1.,  58, 0, 0, 500, 250, 1)
+     (0, 32.,  580., 'square', 12., '', 'e59',  59, 'um', 1., 0., 0., 1.,  59, 0, 0, 500, 250, 1)
+     (0, 16.,  600., 'square', 12., '', 'e60',  60, 'um', 1., 0., 0., 1.,  60, 0, 0, 500, 250, 1)
+     (0, 48.,  600., 'square', 12., '', 'e61',  61, 'um', 1., 0., 0., 1.,  61, 0, 0, 500, 250, 1)
+     (0,  0.,  620., 'square', 12., '', 'e62',  62, 'um', 1., 0., 0., 1.,  62, 0, 0, 500, 250, 1)
+     (0, 32.,  620., 'square', 12., '', 'e63',  63, 'um', 1., 0., 0., 1.,  63, 0, 0, 500, 250, 1)
+     (0, 16.,  640., 'square', 12., '', 'e64',  64, 'um', 1., 0., 0., 1.,  64, 0, 0, 500, 250, 1)
+     (0, 48.,  640., 'square', 12., '', 'e65',  65, 'um', 1., 0., 0., 1.,  65, 0, 0, 500, 250, 1)
+     (0,  0.,  660., 'square', 12., '', 'e66',  66, 'um', 1., 0., 0., 1.,  66, 0, 0, 500, 250, 1)
+     (0, 32.,  660., 'square', 12., '', 'e67',  67, 'um', 1., 0., 0., 1.,  67, 0, 0, 500, 250, 1)
+     (0, 16.,  680., 'square', 12., '', 'e68',  68, 'um', 1., 0., 0., 1.,  68, 0, 0, 500, 250, 1)
+     (0, 48.,  680., 'square', 12., '', 'e69',  69, 'um', 1., 0., 0., 1.,  69, 0, 0, 500, 250, 1)
+     (0,  0.,  700., 'square', 12., '', 'e70',  70, 'um', 1., 0., 0., 1.,  70, 0, 0, 500, 250, 1)
+     (0, 32.,  700., 'square', 12., '', 'e71',  71, 'um', 1., 0., 0., 1.,  71, 0, 0, 500, 250, 1)
+     (0, 16.,  720., 'square', 12., '', 'e72',  72, 'um', 1., 0., 0., 1.,  72, 0, 0, 500, 250, 1)
+     (0, 48.,  720., 'square', 12., '', 'e73',  73, 'um', 1., 0., 0., 1.,  73, 0, 0, 500, 250, 1)
+     (0,  0.,  740., 'square', 12., '', 'e74',  74, 'um', 1., 0., 0., 1.,  74, 0, 0, 500, 250, 1)
+     (0, 32.,  740., 'square', 12., '', 'e75',  75, 'um', 1., 0., 0., 1.,  75, 0, 0, 500, 250, 1)
+     (0, 16.,  760., 'square', 12., '', 'e76',  76, 'um', 1., 0., 0., 1.,  76, 0, 0, 500, 250, 1)
+     (0, 48.,  760., 'square', 12., '', 'e77',  77, 'um', 1., 0., 0., 1.,  77, 0, 0, 500, 250, 1)
+     (0,  0.,  780., 'square', 12., '', 'e78',  78, 'um', 1., 0., 0., 1.,  78, 0, 0, 500, 250, 1)
+     (0, 32.,  780., 'square', 12., '', 'e79',  79, 'um', 1., 0., 0., 1.,  79, 0, 0, 500, 250, 1)
+     (0, 16.,  800., 'square', 12., '', 'e80',  80, 'um', 1., 0., 0., 1.,  80, 0, 0, 500, 250, 1)
+     (0, 48.,  800., 'square', 12., '', 'e81',  81, 'um', 1., 0., 0., 1.,  81, 0, 0, 500, 250, 1)
+     (0,  0.,  820., 'square', 12., '', 'e82',  82, 'um', 1., 0., 0., 1.,  82, 0, 0, 500, 250, 1)
+     (0, 32.,  820., 'square', 12., '', 'e83',  83, 'um', 1., 0., 0., 1.,  83, 0, 0, 500, 250, 1)
+     (0, 16.,  840., 'square', 12., '', 'e84',  84, 'um', 1., 0., 0., 1.,  84, 0, 0, 500, 250, 1)
+     (0, 48.,  840., 'square', 12., '', 'e85',  85, 'um', 1., 0., 0., 1.,  85, 0, 0, 500, 250, 1)
+     (0,  0.,  860., 'square', 12., '', 'e86',  86, 'um', 1., 0., 0., 1.,  86, 0, 0, 500, 250, 1)
+     (0, 32.,  860., 'square', 12., '', 'e87',  87, 'um', 1., 0., 0., 1.,  87, 0, 0, 500, 250, 1)
+     (0, 16.,  880., 'square', 12., '', 'e88',  88, 'um', 1., 0., 0., 1.,  88, 0, 0, 500, 250, 1)
+     (0, 48.,  880., 'square', 12., '', 'e89',  89, 'um', 1., 0., 0., 1.,  89, 0, 0, 500, 250, 1)
+     (0,  0.,  900., 'square', 12., '', 'e90',  90, 'um', 1., 0., 0., 1.,  90, 0, 0, 500, 250, 1)
+     (0, 32.,  900., 'square', 12., '', 'e91',  91, 'um', 1., 0., 0., 1.,  91, 0, 0, 500, 250, 1)
+     (0, 16.,  920., 'square', 12., '', 'e92',  92, 'um', 1., 0., 0., 1.,  92, 0, 0, 500, 250, 1)
+     (0, 48.,  920., 'square', 12., '', 'e93',  93, 'um', 1., 0., 0., 1.,  93, 0, 0, 500, 250, 1)
+     (0,  0.,  940., 'square', 12., '', 'e94',  94, 'um', 1., 0., 0., 1.,  94, 0, 0, 500, 250, 1)
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+     [   0. 2380.]
+     [  32. 2380.]
+     [  16. 2400.]
+     [  48. 2400.]
+     [   0. 2420.]
+     [  32. 2420.]
+     [  16. 2440.]
+     [  48. 2440.]
+     [   0. 2460.]
+     [  32. 2460.]
+     [  16. 2480.]
+     [  48. 2480.]
+     [   0. 2500.]
+     [  32. 2500.]
+     [  16. 2520.]
+     [  48. 2520.]
+     [   0. 2540.]
+     [  32. 2540.]
+     [  16. 2560.]
+     [  48. 2560.]
+     [   0. 2580.]
+     [  32. 2580.]
+     [  16. 2600.]
+     [  48. 2600.]
+     [   0. 2620.]
+     [  32. 2620.]
+     [  16. 2640.]
+     [  48. 2640.]
+     [   0. 2660.]
+     [  32. 2660.]
+     [  16. 2680.]
+     [  48. 2680.]
+     [   0. 2700.]
+     [  32. 2700.]
+     [  16. 2720.]
+     [  48. 2720.]
+     [   0. 2740.]
+     [  32. 2740.]
+     [  16. 2760.]
+     [  48. 2760.]
+     [   0. 2780.]
+     [  32. 2780.]
+     [  16. 2800.]
+     [  48. 2800.]
+     [   0. 2820.]
+     [  32. 2820.]
+     [  16. 2840.]
+     [  48. 2840.]
+     [   0. 2860.]
+     [  32. 2860.]
+     [  16. 2880.]
+     [  48. 2880.]
+     [   0. 2900.]
+     [  32. 2900.]
+     [  16. 2920.]
+     [  48. 2920.]
+     [   0. 2940.]
+     [  32. 2940.]
+     [  16. 2960.]
+     [  48. 2960.]
+     [   0. 2980.]
+     [  32. 2980.]
+     [  16. 3000.]
+     [  48. 3000.]
+     [   0. 3020.]
+     [  32. 3020.]
+     [  16. 3040.]
+     [  48. 3040.]
+     [   0. 3060.]
+     [  32. 3060.]
+     [  16. 3080.]
+     [  48. 3080.]
+     [   0. 3100.]
+     [  32. 3100.]
+     [  16. 3120.]
+     [  48. 3120.]
+     [   0. 3140.]
+     [  32. 3140.]
+     [  16. 3160.]
+     [  48. 3160.]
+     [   0. 3180.]
+     [  32. 3180.]
+     [  16. 3200.]
+     [  48. 3200.]
+     [   0. 3220.]
+     [  32. 3220.]
+     [  16. 3240.]
+     [  48. 3240.]
+     [   0. 3260.]
+     [  32. 3260.]
+     [  16. 3280.]
+     [  48. 3280.]
+     [   0. 3300.]
+     [  32. 3300.]
+     [  16. 3320.]
+     [  48. 3320.]
+     [   0. 3340.]
+     [  32. 3340.]
+     [  16. 3360.]
+     [  48. 3360.]
+     [   0. 3380.]
+     [  32. 3380.]
+     [  16. 3400.]
+     [  48. 3400.]
+     [   0. 3420.]
+     [  32. 3420.]
+     [  16. 3440.]
+     [  48. 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.]]</details><details><summary> <strong> group </strong> </summary>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0]</details><details><summary> <strong> inter_sample_shift </strong> </summary>[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]</details></ul></details>
+
+
+
+Note that the ``generate_hybrid_recording`` is warning us that we might
+want to account for drift!
+
+.. code:: ipython3
+
+    # 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
+
+
+
+
+.. raw:: html
+
+    <div style='border:1px solid #ddd; padding:10px;'><strong>InjectDriftingTemplatesRecording: 384 channels - 30.0kHz - 1 segments - 58,715,724 samples - 1,957.19s (32.62 minutes) - float64 dtype - 167.99 GiB</strong></div><details style='margin-left: 10px;'>  <summary><strong>Channel IDs</strong></summary><ul>['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'] </details><details style='margin-left: 10px;'>  <summary><strong>Annotations</strong></summary><ul><li> <strong> is_filtered </strong>: True</li><li> <strong> probe_0_planar_contour </strong>: [[ -11 9989]
+     [ -11  -11]
+     [  24 -186]
+     [  59  -11]
+     [  59 9989]]</li><li> <strong> probes_info </strong>: [{'manufacturer': 'IMEC', 'model_name': 'Neuropixels 1.0', 'serial_number': '18194814141'}]</li></ul> </details><details style='margin-left: 10px;'><summary><strong>Channel Properties</strong></summary><ul><details><summary> <strong> gain_to_uV </strong> </summary>[2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375 2.34375
+     2.34375 2.34375 2.34375 2.34375 2.34375 2.34375]</details><details><summary> <strong> offset_to_uV </strong> </summary>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0]</details><details><summary> <strong> channel_names </strong> </summary>['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']</details><details><summary> <strong> contact_vector </strong> </summary>[(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.)
+     (0, 16.,   40., 'circle', 1., '', '',   4, 'um', 1., 0., 0., 1.)
+     (0, 48.,   40., 'circle', 1., '', '',   5, 'um', 1., 0., 0., 1.)
+     (0,  0.,   60., 'circle', 1., '', '',   6, 'um', 1., 0., 0., 1.)
+     (0, 32.,   60., 'circle', 1., '', '',   7, 'um', 1., 0., 0., 1.)
+     (0, 16.,   80., 'circle', 1., '', '',   8, 'um', 1., 0., 0., 1.)
+     (0, 48.,   80., 'circle', 1., '', '',   9, 'um', 1., 0., 0., 1.)
+     (0,  0.,  100., 'circle', 1., '', '',  10, 'um', 1., 0., 0., 1.)
+     (0, 32.,  100., 'circle', 1., '', '',  11, 'um', 1., 0., 0., 1.)
+     (0, 16.,  120., 'circle', 1., '', '',  12, 'um', 1., 0., 0., 1.)
+     (0, 48.,  120., 'circle', 1., '', '',  13, 'um', 1., 0., 0., 1.)
+     (0,  0.,  140., 'circle', 1., '', '',  14, 'um', 1., 0., 0., 1.)
+     (0, 32.,  140., 'circle', 1., '', '',  15, 'um', 1., 0., 0., 1.)
+     (0, 16.,  160., 'circle', 1., '', '',  16, 'um', 1., 0., 0., 1.)
+     (0, 48.,  160., 'circle', 1., '', '',  17, 'um', 1., 0., 0., 1.)
+     (0,  0.,  180., 'circle', 1., '', '',  18, 'um', 1., 0., 0., 1.)
+     (0, 32.,  180., 'circle', 1., '', '',  19, 'um', 1., 0., 0., 1.)
+     (0, 16.,  200., 'circle', 1., '', '',  20, 'um', 1., 0., 0., 1.)
+     (0, 48.,  200., 'circle', 1., '', '',  21, 'um', 1., 0., 0., 1.)
+     (0,  0.,  220., 'circle', 1., '', '',  22, 'um', 1., 0., 0., 1.)
+     (0, 32.,  220., 'circle', 1., '', '',  23, 'um', 1., 0., 0., 1.)
+     (0, 16.,  240., 'circle', 1., '', '',  24, 'um', 1., 0., 0., 1.)
+     (0, 48.,  240., 'circle', 1., '', '',  25, 'um', 1., 0., 0., 1.)
+     (0,  0.,  260., 'circle', 1., '', '',  26, 'um', 1., 0., 0., 1.)
+     (0, 32.,  260., 'circle', 1., '', '',  27, 'um', 1., 0., 0., 1.)
+     (0, 16.,  280., 'circle', 1., '', '',  28, 'um', 1., 0., 0., 1.)
+     (0, 48.,  280., 'circle', 1., '', '',  29, 'um', 1., 0., 0., 1.)
+     (0,  0.,  300., 'circle', 1., '', '',  30, 'um', 1., 0., 0., 1.)
+     (0, 32.,  300., 'circle', 1., '', '',  31, 'um', 1., 0., 0., 1.)
+     (0, 16.,  320., 'circle', 1., '', '',  32, 'um', 1., 0., 0., 1.)
+     (0, 48.,  320., 'circle', 1., '', '',  33, 'um', 1., 0., 0., 1.)
+     (0,  0.,  340., 'circle', 1., '', '',  34, 'um', 1., 0., 0., 1.)
+     (0, 32.,  340., 'circle', 1., '', '',  35, 'um', 1., 0., 0., 1.)
+     (0, 16.,  360., 'circle', 1., '', '',  36, 'um', 1., 0., 0., 1.)
+     (0, 48.,  360., 'circle', 1., '', '',  37, 'um', 1., 0., 0., 1.)
+     (0,  0.,  380., 'circle', 1., '', '',  38, 'um', 1., 0., 0., 1.)
+     (0, 32.,  380., 'circle', 1., '', '',  39, 'um', 1., 0., 0., 1.)
+     (0, 16.,  400., 'circle', 1., '', '',  40, 'um', 1., 0., 0., 1.)
+     (0, 48.,  400., 'circle', 1., '', '',  41, 'um', 1., 0., 0., 1.)
+     (0,  0.,  420., 'circle', 1., '', '',  42, 'um', 1., 0., 0., 1.)
+     (0, 32.,  420., 'circle', 1., '', '',  43, 'um', 1., 0., 0., 1.)
+     (0, 16.,  440., 'circle', 1., '', '',  44, 'um', 1., 0., 0., 1.)
+     (0, 48.,  440., 'circle', 1., '', '',  45, 'um', 1., 0., 0., 1.)
+     (0,  0.,  460., 'circle', 1., '', '',  46, 'um', 1., 0., 0., 1.)
+     (0, 32.,  460., 'circle', 1., '', '',  47, 'um', 1., 0., 0., 1.)
+     (0, 16.,  480., 'circle', 1., '', '',  48, 'um', 1., 0., 0., 1.)
+     (0, 48.,  480., 'circle', 1., '', '',  49, 'um', 1., 0., 0., 1.)
+     (0,  0.,  500., 'circle', 1., '', '',  50, 'um', 1., 0., 0., 1.)
+     (0, 32.,  500., 'circle', 1., '', '',  51, 'um', 1., 0., 0., 1.)
+     (0, 16.,  520., 'circle', 1., '', '',  52, 'um', 1., 0., 0., 1.)
+     (0, 48.,  520., 'circle', 1., '', '',  53, 'um', 1., 0., 0., 1.)
+     (0,  0.,  540., 'circle', 1., '', '',  54, 'um', 1., 0., 0., 1.)
+     (0, 32.,  540., 'circle', 1., '', '',  55, 'um', 1., 0., 0., 1.)
+     (0, 16.,  560., 'circle', 1., '', '',  56, 'um', 1., 0., 0., 1.)
+     (0, 48.,  560., 'circle', 1., '', '',  57, 'um', 1., 0., 0., 1.)
+     (0,  0.,  580., 'circle', 1., '', '',  58, 'um', 1., 0., 0., 1.)
+     (0, 32.,  580., 'circle', 1., '', '',  59, 'um', 1., 0., 0., 1.)
+     (0, 16.,  600., 'circle', 1., '', '',  60, 'um', 1., 0., 0., 1.)
+     (0, 48.,  600., 'circle', 1., '', '',  61, 'um', 1., 0., 0., 1.)
+     (0,  0.,  620., 'circle', 1., '', '',  62, 'um', 1., 0., 0., 1.)
+     (0, 32.,  620., 'circle', 1., '', '',  63, 'um', 1., 0., 0., 1.)
+     (0, 16.,  640., 'circle', 1., '', '',  64, 'um', 1., 0., 0., 1.)
+     (0, 48.,  640., 'circle', 1., '', '',  65, 'um', 1., 0., 0., 1.)
+     (0,  0.,  660., 'circle', 1., '', '',  66, 'um', 1., 0., 0., 1.)
+     (0, 32.,  660., 'circle', 1., '', '',  67, 'um', 1., 0., 0., 1.)
+     (0, 16.,  680., 'circle', 1., '', '',  68, 'um', 1., 0., 0., 1.)
+     (0, 48.,  680., 'circle', 1., '', '',  69, 'um', 1., 0., 0., 1.)
+     (0,  0.,  700., 'circle', 1., '', '',  70, 'um', 1., 0., 0., 1.)
+     (0, 32.,  700., 'circle', 1., '', '',  71, 'um', 1., 0., 0., 1.)
+     (0, 16.,  720., 'circle', 1., '', '',  72, 'um', 1., 0., 0., 1.)
+     (0, 48.,  720., 'circle', 1., '', '',  73, 'um', 1., 0., 0., 1.)
+     (0,  0.,  740., 'circle', 1., '', '',  74, 'um', 1., 0., 0., 1.)
+     (0, 32.,  740., 'circle', 1., '', '',  75, 'um', 1., 0., 0., 1.)
+     (0, 16.,  760., 'circle', 1., '', '',  76, 'um', 1., 0., 0., 1.)
+     (0, 48.,  760., 'circle', 1., '', '',  77, 'um', 1., 0., 0., 1.)
+     (0,  0.,  780., 'circle', 1., '', '',  78, 'um', 1., 0., 0., 1.)
+     (0, 32.,  780., 'circle', 1., '', '',  79, 'um', 1., 0., 0., 1.)
+     (0, 16.,  800., 'circle', 1., '', '',  80, 'um', 1., 0., 0., 1.)
+     (0, 48.,  800., 'circle', 1., '', '',  81, 'um', 1., 0., 0., 1.)
+     (0,  0.,  820., 'circle', 1., '', '',  82, 'um', 1., 0., 0., 1.)
+     (0, 32.,  820., 'circle', 1., '', '',  83, 'um', 1., 0., 0., 1.)
+     (0, 16.,  840., 'circle', 1., '', '',  84, 'um', 1., 0., 0., 1.)
+     (0, 48.,  840., 'circle', 1., '', '',  85, 'um', 1., 0., 0., 1.)
+     (0,  0.,  860., 'circle', 1., '', '',  86, 'um', 1., 0., 0., 1.)
+     (0, 32.,  860., 'circle', 1., '', '',  87, 'um', 1., 0., 0., 1.)
+     (0, 16.,  880., 'circle', 1., '', '',  88, 'um', 1., 0., 0., 1.)
+     (0, 48.,  880., 'circle', 1., '', '',  89, 'um', 1., 0., 0., 1.)
+     (0,  0.,  900., 'circle', 1., '', '',  90, 'um', 1., 0., 0., 1.)
+     (0, 32.,  900., 'circle', 1., '', '',  91, 'um', 1., 0., 0., 1.)
+     (0, 16.,  920., 'circle', 1., '', '',  92, 'um', 1., 0., 0., 1.)
+     (0, 48.,  920., 'circle', 1., '', '',  93, 'um', 1., 0., 0., 1.)
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+     (0, 48., 3480., 'circle', 1., '', '', 349, 'um', 1., 0., 0., 1.)
+     (0,  0., 3500., 'circle', 1., '', '', 350, 'um', 1., 0., 0., 1.)
+     (0, 32., 3500., 'circle', 1., '', '', 351, 'um', 1., 0., 0., 1.)
+     (0, 16., 3520., 'circle', 1., '', '', 352, 'um', 1., 0., 0., 1.)
+     (0, 48., 3520., 'circle', 1., '', '', 353, 'um', 1., 0., 0., 1.)
+     (0,  0., 3540., 'circle', 1., '', '', 354, 'um', 1., 0., 0., 1.)
+     (0, 32., 3540., 'circle', 1., '', '', 355, 'um', 1., 0., 0., 1.)
+     (0, 16., 3560., 'circle', 1., '', '', 356, 'um', 1., 0., 0., 1.)
+     (0, 48., 3560., 'circle', 1., '', '', 357, 'um', 1., 0., 0., 1.)
+     (0,  0., 3580., 'circle', 1., '', '', 358, 'um', 1., 0., 0., 1.)
+     (0, 32., 3580., 'circle', 1., '', '', 359, 'um', 1., 0., 0., 1.)
+     (0, 16., 3600., 'circle', 1., '', '', 360, 'um', 1., 0., 0., 1.)
+     (0, 48., 3600., 'circle', 1., '', '', 361, 'um', 1., 0., 0., 1.)
+     (0,  0., 3620., 'circle', 1., '', '', 362, 'um', 1., 0., 0., 1.)
+     (0, 32., 3620., 'circle', 1., '', '', 363, 'um', 1., 0., 0., 1.)
+     (0, 16., 3640., 'circle', 1., '', '', 364, 'um', 1., 0., 0., 1.)
+     (0, 48., 3640., 'circle', 1., '', '', 365, 'um', 1., 0., 0., 1.)
+     (0,  0., 3660., 'circle', 1., '', '', 366, 'um', 1., 0., 0., 1.)
+     (0, 32., 3660., 'circle', 1., '', '', 367, 'um', 1., 0., 0., 1.)
+     (0, 16., 3680., 'circle', 1., '', '', 368, 'um', 1., 0., 0., 1.)
+     (0, 48., 3680., 'circle', 1., '', '', 369, 'um', 1., 0., 0., 1.)
+     (0,  0., 3700., 'circle', 1., '', '', 370, 'um', 1., 0., 0., 1.)
+     (0, 32., 3700., 'circle', 1., '', '', 371, 'um', 1., 0., 0., 1.)
+     (0, 16., 3720., 'circle', 1., '', '', 372, 'um', 1., 0., 0., 1.)
+     (0, 48., 3720., 'circle', 1., '', '', 373, 'um', 1., 0., 0., 1.)
+     (0,  0., 3740., 'circle', 1., '', '', 374, 'um', 1., 0., 0., 1.)
+     (0, 32., 3740., 'circle', 1., '', '', 375, 'um', 1., 0., 0., 1.)
+     (0, 16., 3760., 'circle', 1., '', '', 376, 'um', 1., 0., 0., 1.)
+     (0, 48., 3760., 'circle', 1., '', '', 377, 'um', 1., 0., 0., 1.)
+     (0,  0., 3780., 'circle', 1., '', '', 378, 'um', 1., 0., 0., 1.)
+     (0, 32., 3780., 'circle', 1., '', '', 379, 'um', 1., 0., 0., 1.)
+     (0, 16., 3800., 'circle', 1., '', '', 380, 'um', 1., 0., 0., 1.)
+     (0, 48., 3800., 'circle', 1., '', '', 381, 'um', 1., 0., 0., 1.)
+     (0,  0., 3820., 'circle', 1., '', '', 382, 'um', 1., 0., 0., 1.)
+     (0, 32., 3820., 'circle', 1., '', '', 383, 'um', 1., 0., 0., 1.)]</details><details><summary> <strong> location </strong> </summary>[[  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.  720.]
+     [  48.  720.]
+     [   0.  740.]
+     [  32.  740.]
+     [  16.  760.]
+     [  48.  760.]
+     [   0.  780.]
+     [  32.  780.]
+     [  16.  800.]
+     [  48.  800.]
+     [   0.  820.]
+     [  32.  820.]
+     [  16.  840.]
+     [  48.  840.]
+     [   0.  860.]
+     [  32.  860.]
+     [  16.  880.]
+     [  48.  880.]
+     [   0.  900.]
+     [  32.  900.]
+     [  16.  920.]
+     [  48.  920.]
+     [   0.  940.]
+     [  32.  940.]
+     [  16.  960.]
+     [  48.  960.]
+     [   0.  980.]
+     [  32.  980.]
+     [  16. 1000.]
+     [  48. 1000.]
+     [   0. 1020.]
+     [  32. 1020.]
+     [  16. 1040.]
+     [  48. 1040.]
+     [   0. 1060.]
+     [  32. 1060.]
+     [  16. 1080.]
+     [  48. 1080.]
+     [   0. 1100.]
+     [  32. 1100.]
+     [  16. 1120.]
+     [  48. 1120.]
+     [   0. 1140.]
+     [  32. 1140.]
+     [  16. 1160.]
+     [  48. 1160.]
+     [   0. 1180.]
+     [  32. 1180.]
+     [  16. 1200.]
+     [  48. 1200.]
+     [   0. 1220.]
+     [  32. 1220.]
+     [  16. 1240.]
+     [  48. 1240.]
+     [   0. 1260.]
+     [  32. 1260.]
+     [  16. 1280.]
+     [  48. 1280.]
+     [   0. 1300.]
+     [  32. 1300.]
+     [  16. 1320.]
+     [  48. 1320.]
+     [   0. 1340.]
+     [  32. 1340.]
+     [  16. 1360.]
+     [  48. 1360.]
+     [   0. 1380.]
+     [  32. 1380.]
+     [  16. 1400.]
+     [  48. 1400.]
+     [   0. 1420.]
+     [  32. 1420.]
+     [  16. 1440.]
+     [  48. 1440.]
+     [   0. 1460.]
+     [  32. 1460.]
+     [  16. 1480.]
+     [  48. 1480.]
+     [   0. 1500.]
+     [  32. 1500.]
+     [  16. 1520.]
+     [  48. 1520.]
+     [   0. 1540.]
+     [  32. 1540.]
+     [  16. 1560.]
+     [  48. 1560.]
+     [   0. 1580.]
+     [  32. 1580.]
+     [  16. 1600.]
+     [  48. 1600.]
+     [   0. 1620.]
+     [  32. 1620.]
+     [  16. 1640.]
+     [  48. 1640.]
+     [   0. 1660.]
+     [  32. 1660.]
+     [  16. 1680.]
+     [  48. 1680.]
+     [   0. 1700.]
+     [  32. 1700.]
+     [  16. 1720.]
+     [  48. 1720.]
+     [   0. 1740.]
+     [  32. 1740.]
+     [  16. 1760.]
+     [  48. 1760.]
+     [   0. 1780.]
+     [  32. 1780.]
+     [  16. 1800.]
+     [  48. 1800.]
+     [   0. 1820.]
+     [  32. 1820.]
+     [  16. 1840.]
+     [  48. 1840.]
+     [   0. 1860.]
+     [  32. 1860.]
+     [  16. 1880.]
+     [  48. 1880.]
+     [   0. 1900.]
+     [  32. 1900.]
+     [  16. 1920.]
+     [  48. 1920.]
+     [   0. 1940.]
+     [  32. 1940.]
+     [  16. 1960.]
+     [  48. 1960.]
+     [   0. 1980.]
+     [  32. 1980.]
+     [  16. 2000.]
+     [  48. 2000.]
+     [   0. 2020.]
+     [  32. 2020.]
+     [  16. 2040.]
+     [  48. 2040.]
+     [   0. 2060.]
+     [  32. 2060.]
+     [  16. 2080.]
+     [  48. 2080.]
+     [   0. 2100.]
+     [  32. 2100.]
+     [  16. 2120.]
+     [  48. 2120.]
+     [   0. 2140.]
+     [  32. 2140.]
+     [  16. 2160.]
+     [  48. 2160.]
+     [   0. 2180.]
+     [  32. 2180.]
+     [  16. 2200.]
+     [  48. 2200.]
+     [   0. 2220.]
+     [  32. 2220.]
+     [  16. 2240.]
+     [  48. 2240.]
+     [   0. 2260.]
+     [  32. 2260.]
+     [  16. 2280.]
+     [  48. 2280.]
+     [   0. 2300.]
+     [  32. 2300.]
+     [  16. 2320.]
+     [  48. 2320.]
+     [   0. 2340.]
+     [  32. 2340.]
+     [  16. 2360.]
+     [  48. 2360.]
+     [   0. 2380.]
+     [  32. 2380.]
+     [  16. 2400.]
+     [  48. 2400.]
+     [   0. 2420.]
+     [  32. 2420.]
+     [  16. 2440.]
+     [  48. 2440.]
+     [   0. 2460.]
+     [  32. 2460.]
+     [  16. 2480.]
+     [  48. 2480.]
+     [   0. 2500.]
+     [  32. 2500.]
+     [  16. 2520.]
+     [  48. 2520.]
+     [   0. 2540.]
+     [  32. 2540.]
+     [  16. 2560.]
+     [  48. 2560.]
+     [   0. 2580.]
+     [  32. 2580.]
+     [  16. 2600.]
+     [  48. 2600.]
+     [   0. 2620.]
+     [  32. 2620.]
+     [  16. 2640.]
+     [  48. 2640.]
+     [   0. 2660.]
+     [  32. 2660.]
+     [  16. 2680.]
+     [  48. 2680.]
+     [   0. 2700.]
+     [  32. 2700.]
+     [  16. 2720.]
+     [  48. 2720.]
+     [   0. 2740.]
+     [  32. 2740.]
+     [  16. 2760.]
+     [  48. 2760.]
+     [   0. 2780.]
+     [  32. 2780.]
+     [  16. 2800.]
+     [  48. 2800.]
+     [   0. 2820.]
+     [  32. 2820.]
+     [  16. 2840.]
+     [  48. 2840.]
+     [   0. 2860.]
+     [  32. 2860.]
+     [  16. 2880.]
+     [  48. 2880.]
+     [   0. 2900.]
+     [  32. 2900.]
+     [  16. 2920.]
+     [  48. 2920.]
+     [   0. 2940.]
+     [  32. 2940.]
+     [  16. 2960.]
+     [  48. 2960.]
+     [   0. 2980.]
+     [  32. 2980.]
+     [  16. 3000.]
+     [  48. 3000.]
+     [   0. 3020.]
+     [  32. 3020.]
+     [  16. 3040.]
+     [  48. 3040.]
+     [   0. 3060.]
+     [  32. 3060.]
+     [  16. 3080.]
+     [  48. 3080.]
+     [   0. 3100.]
+     [  32. 3100.]
+     [  16. 3120.]
+     [  48. 3120.]
+     [   0. 3140.]
+     [  32. 3140.]
+     [  16. 3160.]
+     [  48. 3160.]
+     [   0. 3180.]
+     [  32. 3180.]
+     [  16. 3200.]
+     [  48. 3200.]
+     [   0. 3220.]
+     [  32. 3220.]
+     [  16. 3240.]
+     [  48. 3240.]
+     [   0. 3260.]
+     [  32. 3260.]
+     [  16. 3280.]
+     [  48. 3280.]
+     [   0. 3300.]
+     [  32. 3300.]
+     [  16. 3320.]
+     [  48. 3320.]
+     [   0. 3340.]
+     [  32. 3340.]
+     [  16. 3360.]
+     [  48. 3360.]
+     [   0. 3380.]
+     [  32. 3380.]
+     [  16. 3400.]
+     [  48. 3400.]
+     [   0. 3420.]
+     [  32. 3420.]
+     [  16. 3440.]
+     [  48. 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.]]</details><details><summary> <strong> group </strong> </summary>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+     0 0 0 0 0 0 0 0 0 0 0 0 0 0]</details><details><summary> <strong> inter_sample_shift </strong> </summary>[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]</details></ul></details>
+
+
+
+We can use the ``SortingAnalyzer`` to estimate spike locations and plot
+them:
+
+.. code:: ipython3
+
+    # 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")
+
+
+
+.. parsed-literal::
+
+    estimate_sparsity:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    estimate_templates_with_accumulator:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    spike_locations:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    estimate_sparsity:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    estimate_templates_with_accumulator:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+.. parsed-literal::
+
+    spike_locations:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+
+Let’s plot the added hybrid spikes using the drift maps:
+
+.. code:: ipython3
+
+    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)
+
+
+
+.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_28_1.png
+
+
+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``
+
+.. code:: ipython3
+
+    # 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
+    )
+
+
+.. parsed-literal::
+
+    write_binary_recording
+    n_jobs=16 - samples_per_chunk=30,000 - chunk_memory=87.89 MiB - total_memory=1.37 GiB - chunk_duration=1.00s
+
+
+
+.. parsed-literal::
+
+    write_binary_recording:   0%|          | 0/1958 [00:00<?, ?it/s]
+
+
+.. code:: ipython3
+
+    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",
+            },
+        },
+    }
+
+.. code:: ipython3
+
+    study_folder = workdir / "gt_study"
+
+    gtstudy = sc.GroundTruthStudy(study_folder)
+
+.. code:: ipython3
+
+    # run the spike sorting jobs
+    gtstudy.run_sorters(verbose=False, keep=True)
+
+.. code:: ipython3
+
+    # 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.
+
+.. code:: ipython3
+
+    w_perf = sw.plot_study_performances(gtstudy, figsize=(12, 7))
+    w_perf.axes[0, 0].legend(loc=4)
+
+
+
+.. image:: benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_37_1.png
+
+
+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?**
diff --git a/doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_20_0.png b/doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_20_0.png
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diff --git a/doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_9_0.png b/doc/how_to/benchmark_with_hybrid_recordings_files/benchmark_with_hybrid_recordings_9_0.png
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diff --git a/doc/how_to/index.rst b/doc/how_to/index.rst
index 54fd404848..66dd9b417c 100644
--- a/doc/how_to/index.rst
+++ b/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
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`.
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..5507ab7a7f
--- /dev/null
+++ b/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?**
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
diff --git a/src/spikeinterface/core/core_tools.py b/src/spikeinterface/core/core_tools.py
index 066ab58d8c..4a40d8e425 100644
--- a/src/spikeinterface/core/core_tools.py
+++ b/src/spikeinterface/core/core_tools.py
@@ -75,6 +75,7 @@ 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):
@@ -98,6 +99,9 @@ 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/generate.py b/src/spikeinterface/core/generate.py
index 9924a22403..af6664b886 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
@@ -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)
 
diff --git a/src/spikeinterface/core/node_pipeline.py b/src/spikeinterface/core/node_pipeline.py
index 0722ede23f..ceff8577d3 100644
--- a/src/spikeinterface/core/node_pipeline.py
+++ b/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
 
 
diff --git a/src/spikeinterface/core/sortinganalyzer.py b/src/spikeinterface/core/sortinganalyzer.py
index d790308b76..fc20029ce6 100644
--- a/src/spikeinterface/core/sortinganalyzer.py
+++ b/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)
diff --git a/src/spikeinterface/core/template.py b/src/spikeinterface/core/template.py
index 066d79b6b4..b64f0610ea 100644
--- a/src/spikeinterface/core/template.py
+++ b/src/spikeinterface/core/template.py
@@ -353,9 +353,9 @@ def from_zarr_group(cls, zarr_group: "zarr.Group") -> "Templates":
         the `add_templates_to_zarr_group` method.
 
         """
-        templates_array = zarr_group["templates_array"]
-        channel_ids = zarr_group["channel_ids"]
-        unit_ids = zarr_group["unit_ids"]
+        templates_array = zarr_group["templates_array"][:]
+        channel_ids = zarr_group["channel_ids"][:]
+        unit_ids = zarr_group["unit_ids"][:]
         sampling_frequency = zarr_group.attrs["sampling_frequency"]
         nbefore = zarr_group.attrs["nbefore"]
 
@@ -364,7 +364,7 @@ def from_zarr_group(cls, zarr_group: "zarr.Group") -> "Templates":
 
         sparsity_mask = None
         if "sparsity_mask" in zarr_group:
-            sparsity_mask = zarr_group["sparsity_mask"]
+            sparsity_mask = zarr_group["sparsity_mask"][:]
 
         probe = None
         if "probe" in zarr_group:
@@ -449,7 +449,7 @@ def __eq__(self, other):
 
         return True
 
-    def get_channel_locations(self):
+    def get_channel_locations(self) -> np.ndarray:
         assert self.probe is not None, "Templates.get_channel_locations() needs a probe to be set"
         channel_locations = self.probe.contact_positions
         return channel_locations
diff --git a/src/spikeinterface/core/template_tools.py b/src/spikeinterface/core/template_tools.py
index 1ba9372322..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
 
 
@@ -50,7 +49,7 @@ 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/__init__.py b/src/spikeinterface/generation/__init__.py
index eae6320e8d..7a2291d932 100644
--- a/src/spikeinterface/generation/__init__.py
+++ b/src/spikeinterface/generation/__init__.py
@@ -5,6 +5,14 @@
     InjectDriftingTemplatesRecording,
     make_linear_displacement,
 )
+
+from .hybrid_tools import (
+    generate_hybrid_recording,
+    estimate_templates_from_recording,
+    select_templates,
+    scale_template_to_range,
+    relocate_templates,
+)
 from .noise_tools import generate_noise
 from .drifting_generator import (
     make_one_displacement_vector,
diff --git a/src/spikeinterface/generation/drift_tools.py b/src/spikeinterface/generation/drift_tools.py
index 99e4f4d36e..1f410f4330 100644
--- a/src/spikeinterface/generation/drift_tools.py
+++ b/src/spikeinterface/generation/drift_tools.py
@@ -1,10 +1,12 @@
 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"):
@@ -116,22 +118,80 @@ class DriftingTemplates(Templates):
         This is the same strategy used by MEArec.
     """
 
-    def __init__(self, **kwargs):
-        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"
-
-        self.templates_array_moved = None
-        self.displacements = None
+        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
 
     @classmethod
-    def from_static(cls, templates):
-        drifting_teplates = cls(
+    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,
             nbefore=templates.nbefore,
             probe=templates.probe,
         )
-        return drifting_teplates
+        return drifting_templates
+
+    @classmethod
+    def from_precomputed_templates(
+        cls,
+        templates_array_moved: ArrayLike,
+        displacements: ArrayLike,
+        sampling_frequency: float,
+        nbefore: int,
+        probe: Probe,
+    ):
+        """Construct a DriftingTemplates object given precomputed drifting templates
+
+        Parameters
+        ----------
+        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
+
+        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,
+            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):
         """
@@ -442,7 +502,8 @@ 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
@@ -507,7 +568,7 @@ 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/drifting_generator.py b/src/spikeinterface/generation/drifting_generator.py
index 7f617c3ade..a0e8ece37e 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],
@@ -66,22 +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
     -------
@@ -170,34 +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)
     """
 
@@ -295,38 +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:
@@ -407,7 +415,7 @@ 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
new file mode 100644
index 0000000000..a57e090f5f
--- /dev/null
+++ b/src/spikeinterface/generation/hybrid_tools.py
@@ -0,0 +1,568 @@
+from __future__ import annotations
+
+import warnings
+from typing import Literal
+import numpy as np
+
+from spikeinterface.core import BaseRecording, BaseSorting, Templates
+
+from spikeinterface.core.generate import (
+    generate_templates,
+    generate_unit_locations,
+    generate_sorting,
+    InjectTemplatesRecording,
+    _ensure_seed,
+)
+from spikeinterface.core.template_tools import get_template_extremum_channel
+
+from spikeinterface.sortingcomponents.motion_utils import Motion
+
+from spikeinterface.generation.drift_tools import (
+    InjectDriftingTemplatesRecording,
+    DriftingTemplates,
+    make_linear_displacement,
+    interpolate_templates,
+    move_dense_templates,
+)
+
+
+def estimate_templates_from_recording(
+    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 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.
+
+    Parameters
+    ----------
+    ms_before : float
+        The time before peaks of templates.
+    ms_after : float
+        The time after peaks of templates.
+    sorter_name : str
+        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.
+    job_kwargs : dict
+        The jobe keyword arguments to be used in the estimation of the templates.
+
+    Returns
+    -------
+    templates: Templates
+        The estimated 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)
+
+    spikes = sorting.to_spike_vector()
+    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 = job_kwargs or {}
+    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, True, sparsity_mask, channel_ids, unit_ids, probe=probe
+    )
+
+    return templates
+
+
+def select_templates(
+    templates: Templates,
+    min_amplitude: float | None = None,
+    max_amplitude: float | None = None,
+    min_depth: float | None = None,
+    max_depth: float | None = None,
+    amplitude_function: Literal["ptp", "min", "max"] = "ptp",
+    depth_direction: Literal["x", "y"] = "y",
+):
+    """
+    Select templates from an existing Templates object based on amplitude and depth.
+
+    Parameters
+    ----------
+    templates : Templates
+        The input templates.
+    min_amplitude : float | None, default: None
+        The minimum amplitude of the templates.
+    max_amplitude : float | None, default: None
+        The maximum amplitude of the templates.
+    min_depth : float | None, default: None
+        The minimum depth of the templates.
+    max_depth : float | None, default: None
+        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".
+    depth_direction : "x" | "y", default: "y"
+        The direction in which to move the templates. Can be "x" or "y".
+
+    Returns
+    -------
+    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
+    ), "At least one of min_amplitude, max_amplitude, min_depth, max_depth should be provided"
+    # get template amplitudes and depth
+    extremum_channel_indices = list(get_template_extremum_channel(templates, outputs="index").values())
+    extremum_channel_indices = np.array(extremum_channel_indices, dtype=int)
+
+    mask = np.ones(templates.num_units, dtype=bool)
+    if min_amplitude is not None or max_amplitude is not None:
+        # filter amplitudes
+        if amplitude_function == "ptp":
+            amp_fun = np.ptp
+        elif amplitude_function == "min":
+            amp_fun = np.min
+        elif amplitude_function == "max":
+            amp_fun = np.max
+        amplitudes = np.zeros(templates.num_units)
+        templates_array = templates.templates_array
+        for i in range(templates.num_units):
+            amplitudes[i] = amp_fun(templates_array[i, :, extremum_channel_indices[i]])
+        if min_amplitude is not None:
+            mask &= amplitudes >= min_amplitude
+        if max_amplitude is not None:
+            mask &= amplitudes <= max_amplitude
+    if min_depth is not None or max_depth is not None:
+        assert templates.probe is not None, "Templates should have a probe to filter based on depth"
+        depth_dimension = ["x", "y"].index(depth_direction)
+        channel_depths = templates.get_channel_locations()[:, depth_dimension]
+        unit_depths = channel_depths[extremum_channel_indices]
+        if min_depth is not None:
+            mask &= unit_depths >= min_depth
+        if max_depth is not None:
+            mask &= unit_depths <= max_depth
+    if np.sum(mask) == 0:
+        warnings.warn("No templates left after filtering")
+        return None
+    filtered_unit_ids = templates.unit_ids[mask]
+    filtered_templates = templates.select_units(filtered_unit_ids)
+
+    return filtered_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 range with the provided minimum and maximum amplitudes.
+
+    Parameters
+    ----------
+    templates : Templates
+        The input templates.
+    min_amplitude : float
+        The minimum amplitude of the output templates after scaling.
+    max_amplitude : float
+        The maximum amplitude of the output templates after scaling.
+
+    Returns
+    -------
+    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)
+
+    # get amplitudes
+    if amplitude_function == "ptp":
+        amp_fun = np.ptp
+    elif amplitude_function == "min":
+        amp_fun = np.min
+    elif amplitude_function == "max":
+        amp_fun = np.max
+    amplitudes = np.zeros(templates.num_units)
+    templates_array = templates.templates_array
+    for i in range(templates.num_units):
+        amplitudes[i] = amp_fun(templates_array[i, :, extremum_channel_indices[i]])
+
+    # 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,
+        sampling_frequency=templates.sampling_frequency,
+        nbefore=templates.nbefore,
+        sparsity_mask=templates.sparsity_mask,
+        channel_ids=templates.channel_ids,
+        unit_ids=templates.unit_ids,
+        probe=templates.probe,
+    )
+
+
+def relocate_templates(
+    templates: Templates,
+    min_displacement: float,
+    max_displacement: float,
+    margin: float = 0.0,
+    favor_borders: bool = True,
+    depth_direction: Literal["x", "y"] = "y",
+    seed: int | None = None,
+):
+    """
+    Relocates templates to have a minimum and maximum displacement.
+
+    Parameters
+    ----------
+    templates : Templates
+        The input templates
+    min_displacement : float
+        The minimum displacement of the templates
+    max_displacement : float
+        The maximum displacement of the templates
+    margin : float, default: 0.0
+        The margin to keep between the templates and the borders of the probe.
+        If greater than 0, the templates are allowed to go beyond the borders of the probe.
+    favor_borders : bool, default: True
+        If True, the templates are always moved to the borders of the probe if this is
+        possoble based on the min_displacement and max_displacement constraints.
+        This avoids a bias in moving templates towards the center of the probe.
+    depth_direction : "x" | "y", default: "y"
+        The direction in which to move the templates. Can be "x" or "y"
+    seed : int or None, default: None
+        Seed for random initialization.
+
+
+    Returns
+    -------
+    Templates
+        The relocated templates.
+    """
+    seed = _ensure_seed(seed)
+
+    extremum_channel_indices = list(get_template_extremum_channel(templates, outputs="index").values())
+    extremum_channel_indices = np.array(extremum_channel_indices, dtype=int)
+    depth_dimension = ["x", "y"].index(depth_direction)
+    channel_depths = templates.get_channel_locations()[:, depth_dimension]
+    unit_depths = channel_depths[extremum_channel_indices]
+
+    assert margin >= 0, "margin should be positive"
+    top_margin = np.max(channel_depths) + margin
+    bottom_margin = np.min(channel_depths) - margin
+
+    templates_array_moved = np.zeros_like(templates.templates_array, dtype=templates.templates_array.dtype)
+
+    rng = np.random.default_rng(seed)
+    displacements = rng.uniform(low=min_displacement, high=max_displacement, size=templates.num_units)
+    for i in range(templates.num_units):
+        # by default, displacement is positive
+        displacement = displacements[i]
+        unit_depth = unit_depths[i]
+        if not favor_borders:
+            displacement *= rng.choice([-1.0, 1.0])
+            if unit_depth + displacement > top_margin:
+                displacement = -displacement
+            elif unit_depth - displacement < bottom_margin:
+                displacement = -displacement
+        else:
+            # check if depth is closer to top or bottom
+            if unit_depth > (top_margin - bottom_margin) / 2:
+                # if over top margin, move down
+                if unit_depth + displacement > top_margin:
+                    displacement = -displacement
+            else:
+                # if within bottom margin, move down
+                if unit_depth - displacement >= bottom_margin:
+                    displacement = -displacement
+        displacement_vector = np.zeros(2)
+        displacement_vector[depth_dimension] = displacement
+        templates_array_moved[i] = move_dense_templates(
+            templates.templates_array[i][None],
+            displacements=displacement_vector[None],
+            source_probe=templates.probe,
+        )[0]
+
+    return Templates(
+        templates_array=templates_array_moved,
+        sampling_frequency=templates.sampling_frequency,
+        nbefore=templates.nbefore,
+        sparsity_mask=templates.sparsity_mask,
+        channel_ids=templates.channel_ids,
+        unit_ids=templates.unit_ids,
+        probe=templates.probe,
+    )
+
+
+def generate_hybrid_recording(
+    recording: BaseRecording,
+    sorting: BaseSorting | None = None,
+    templates: Templates | None = None,
+    motion: Motion | None = None,
+    are_templates_scaled: 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,
+    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.
+
+    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.
+    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.
+    are_templates_scaled : bool, default: True
+        If True, the templates are assumed to be in uV, otherwise 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
+        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).
+    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
+        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
+        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: BaseRecording
+        The generated hybrid recording extractor.
+    sorting: Sorting
+        The generated sorting extractor for the injected units.
+    """
+
+    # if None so the same seed will be used for all steps
+    seed = _ensure_seed(seed)
+    rng = np.random.default_rng(seed)
+
+    sampling_frequency = recording.sampling_frequency
+    probe = recording.get_probe()
+    num_segments = recording.get_num_segments()
+    dtype = recording.dtype
+    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:
+        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:
+            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"]
+    else:
+        generate_sorting_kwargs["num_units"] = num_units
+
+    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,
+            sampling_frequency,
+            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"
+        nbefore = templates.nbefore
+        nafter = templates.nafter
+        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
+        templates_ = templates
+        if recording.has_scaleable_traces():
+            if are_templates_scaled:
+                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)
+                templates_ = templates.select_units(templates.unit_ids)
+                templates_.templates_array = templates_array
+
+    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
+        sorting = generate_sorting(**generate_sorting_kwargs)
+    else:
+        assert sorting.sampling_frequency == sampling_frequency
+
+    num_spikes = sorting.to_spike_vector().size
+    sorting.set_property("gt_unit_locations", unit_locations)
+
+    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
+    else:
+        if upsample_vector is None:
+            upsample_factor = templates_array.shape[3]
+            upsample_vector = rng.integers(0, upsample_factor, size=num_spikes)
+
+    if amplitude_std is not None:
+        amplitude_factor = rng.normal(loc=1, scale=amplitude_std, size=num_spikes)
+    else:
+        amplitude_factor = None
+
+    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.concatenate(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")
+        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 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 = []
+        for segment_index in range(motion.num_segments):
+            temporal_bins_segment = motion.temporal_bins_s[segment_index]
+            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_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[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)
+
+        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=(np.array(durations) * sampling_frequency).astype("int64"),
+            amplitude_factor=amplitude_factor,
+        )
+
+    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,
+            nbefore=nbefore,
+            parent_recording=recording,
+            upsample_vector=upsample_vector,
+        )
+
+    return hybrid_recording, sorting
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_drift_tools.py b/src/spikeinterface/generation/tests/test_drift_tools.py
index 8a4837100e..5647b33930 100644
--- a/src/spikeinterface/generation/tests/test_drift_tools.py
+++ b/src/spikeinterface/generation/tests/test_drift_tools.py
@@ -94,11 +94,12 @@ 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
     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
@@ -119,7 +139,7 @@ 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
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..d31a0ec81d
--- /dev/null
+++ b/src/spikeinterface/generation/tests/test_hybrid_tools.py
@@ -0,0 +1,83 @@
+import numpy as np
+
+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,
+    generate_hybrid_recording,
+)
+
+
+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, 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_motion():
+    rec, _ = generate_ground_truth_recording(sampling_frequency=20000, durations=[10], seed=0)
+    _, 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()
+    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_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(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
+
+
+if __name__ == "__main__":
+    test_generate_hybrid_no_motion()
+    test_generate_hybrid_motion()
+    test_estimate_templates()
+    test_generate_hybrid_with_sorting()
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
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
new file mode 100644
index 0000000000..b7571a6f3e
--- /dev/null
+++ b/src/spikeinterface/postprocessing/localization_tools.py
@@ -0,0 +1,623 @@
+from __future__ import annotations
+
+import warnings
+
+import numpy as np
+
+try:
+    import numba
+
+    HAVE_NUMBA = True
+except ImportError:
+    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)
+    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
+
+    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_locations.py b/src/spikeinterface/postprocessing/unit_locations.py
index 16d9955e58..9435030775 100644
--- a/src/spikeinterface/postprocessing/unit_locations.py
+++ b/src/spikeinterface/postprocessing/unit_locations.py
@@ -1,21 +1,14 @@
 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 (
+    compute_center_of_mass,
+    compute_grid_convolution,
+    compute_monopolar_triangulation,
+)
 
 dtype_localize_by_method = {
     "center_of_mass": [("x", "float64"), ("y", "float64")],
@@ -90,592 +83,3 @@ def get_data(self, outputs="numpy"):
 
 register_result_extension(ComputeUnitLocations)
 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",
-    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: SortingAnalyzer
-        A SortingAnalyzer 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.unit_ids
-
-    contact_locations = sorting_analyzer.get_channel_locations()
-
-    sparsity = compute_sparsity(sorting_analyzer, method="radius", radius_um=radius_um)
-    templates = get_dense_templates_array(sorting_analyzer, return_scaled=sorting_analyzer.return_scaled)
-    nbefore = _get_nbefore(sorting_analyzer)
-
-    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, 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, 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
-        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.unit_ids
-
-    contact_locations = sorting_analyzer.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, return_scaled=sorting_analyzer.return_scaled)
-    nbefore = _get_nbefore(sorting_analyzer)
-
-    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,
-    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: SortingAnalyzer
-        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.get_channel_locations()
-    unit_ids = sorting_analyzer.unit_ids
-
-    templates = get_dense_templates_array(sorting_analyzer, return_scaled=sorting_analyzer.return_scaled)
-    nbefore = _get_nbefore(sorting_analyzer)
-    nafter = templates.shape[1] - nbefore
-
-    fs = sorting_analyzer.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, 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
-
-
-# ---
-# 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/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 8023bd4367..0d65b1936a 100644
--- a/src/spikeinterface/preprocessing/motion.py
+++ b/src/spikeinterface/preprocessing/motion.py
@@ -2,6 +2,7 @@
 
 import numpy as np
 import json
+import shutil
 from pathlib import Path
 import time
 
@@ -205,6 +206,7 @@ def correct_motion(
     preset="nonrigid_accurate",
     folder=None,
     output_motion_info=False,
+    overwrite=False,
     detect_kwargs={},
     select_kwargs={},
     localize_peaks_kwargs={},
@@ -257,6 +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
@@ -314,14 +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"
@@ -332,7 +328,7 @@ 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)
@@ -371,9 +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)
@@ -382,18 +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
@@ -409,6 +401,25 @@ 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"])
+    motion_info["motion"].save(folder / "motion")
+
+
 def load_motion_info(folder):
     from spikeinterface.sortingcomponents.motion_utils import Motion
 
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__)
diff --git a/src/spikeinterface/sorters/internal/spyking_circus2.py b/src/spikeinterface/sorters/internal/spyking_circus2.py
index b5df0f1059..45cc93d0b6 100644
--- a/src/spikeinterface/sorters/internal/spyking_circus2.py
+++ b/src/spikeinterface/sorters/internal/spyking_circus2.py
@@ -278,29 +278,30 @@ def _run_from_folder(cls, sorter_output_folder, params, verbose):
             matching_params["templates"] = templates
             matching_job_params = job_kwargs.copy()
 
-            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["chunk_duration"] = "100ms"
+            if matching_method is not None:
+                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["chunk_duration"] = "100ms"
+
+                spikes = find_spikes_from_templates(
+                    recording_w, matching_method, method_kwargs=matching_params, **matching_job_params
+                )
 
-            spikes = find_spikes_from_templates(
-                recording_w, 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():
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,
diff --git a/src/spikeinterface/sortingcomponents/motion_utils.py b/src/spikeinterface/sortingcomponents/motion_utils.py
index 26d4b35b1a..a8de3f6d13 100644
--- a/src/spikeinterface/sortingcomponents/motion_utils.py
+++ b/src/spikeinterface/sortingcomponents/motion_utils.py
@@ -90,11 +90,13 @@ 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
-        grid : bool
+        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, 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].
@@ -153,6 +155,7 @@ def to_dict(self):
             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,
         )
 
@@ -223,8 +226,9 @@ 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,
         )
diff --git a/src/spikeinterface/sortingcomponents/peak_detection.py b/src/spikeinterface/sortingcomponents/peak_detection.py
index b6f7709d27..0d5c92ff28 100644
--- a/src/spikeinterface/sortingcomponents/peak_detection.py
+++ b/src/spikeinterface/sortingcomponents/peak_detection.py
@@ -23,7 +23,7 @@
     base_peak_dtype,
 )
 
-from spikeinterface.postprocessing.unit_locations import get_convolution_weights
+from spikeinterface.postprocessing.localization_tools import get_convolution_weights
 
 from .tools import make_multi_method_doc
 
diff --git a/src/spikeinterface/sortingcomponents/peak_localization.py b/src/spikeinterface/sortingcomponents/peak_localization.py
index 23faea2d79..6d2ad09239 100644
--- a/src/spikeinterface/sortingcomponents/peak_localization.py
+++ b/src/spikeinterface/sortingcomponents/peak_localization.py
@@ -24,8 +24,11 @@
 from ..postprocessing.unit_locations import (
     dtype_localize_by_method,
     possible_localization_methods,
-    solve_monopolar_triangulation,
+)
+
+from ..postprocessing.localization_tools import (
     make_radial_order_parents,
+    solve_monopolar_triangulation,
     enforce_decrease_shells_data,
     get_grid_convolution_templates_and_weights,
 )
@@ -66,6 +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,
     )
 
diff --git a/src/spikeinterface/widgets/unit_waveforms.py b/src/spikeinterface/widgets/unit_waveforms.py
index b046e55fbf..59f91306ea 100644
--- a/src/spikeinterface/widgets/unit_waveforms.py
+++ b/src/spikeinterface/widgets/unit_waveforms.py
@@ -540,9 +540,7 @@ def _update_plot(self, change):
 
         if self.sorting_analyzer is not None:
             templates = self.templates_ext.get_templates(unit_ids=unit_ids, operator="average")
-            templates_shadings = self._get_template_shadings(
-                unit_ids, self.next_data_plot["templates_percentile_shading"]
-            )
+            templates_shadings = self._get_template_shadings(unit_ids, data_plot["templates_percentile_shading"])
             channel_locations = self.sorting_analyzer.get_channel_locations()
         else:
             unit_indices = [list(self.templates.unit_ids).index(unit_id) for unit_id in unit_ids]