From 7f292a1e7a98bcf5dcb7687b7921bec329e97cc7 Mon Sep 17 00:00:00 2001
From: JoeZiminski <joseph.j.ziminski@gmail.com>
Date: Tue, 25 Jun 2024 20:02:10 +0100
Subject: [PATCH] Tidied up version with something approaching final parameters
 used for example.

---
 .../handle_drift/handle_drift_jupyter.zip     | Bin 17729 -> 18536 bytes
 .../handle_drift/handle_drift_python.zip      | Bin 14640 -> 15207 bytes
 .../images/sphx_glr_plot_handle_drift_001.png | Bin 102874 -> 171243 bytes
 .../images/sphx_glr_plot_handle_drift_002.png | Bin 121414 -> 211906 bytes
 .../images/sphx_glr_plot_handle_drift_003.png | Bin 140770 -> 202691 bytes
 .../images/sphx_glr_plot_handle_drift_004.png | Bin 70807 -> 96243 bytes
 .../images/sphx_glr_plot_handle_drift_005.png | Bin 85009 -> 161548 bytes
 .../images/sphx_glr_plot_handle_drift_006.png | Bin 131528 -> 264072 bytes
 .../sphx_glr_plot_handle_drift_thumb.png      | Bin 37699 -> 51826 bytes
 doc/long_tutorials/handle_drift/index.rst     |   4 +-
 .../handle_drift/plot_handle_drift.ipynb      |  55 +--
 .../handle_drift/plot_handle_drift.py         | 291 +++++++-------
 .../handle_drift/plot_handle_drift.py.md5     |   2 +-
 .../handle_drift/plot_handle_drift.rst        | 371 +++++++++---------
 .../plot_handle_drift_codeobj.pickle          | Bin 8397 -> 9077 bytes
 .../handle_drift/sg_execution_times.rst       |   4 +-
 doc/references.rst                            |   1 +
 .../handle_drift/plot_handle_drift.py         | 287 +++++++-------
 18 files changed, 539 insertions(+), 476 deletions(-)

diff --git a/doc/long_tutorials/handle_drift/handle_drift_jupyter.zip b/doc/long_tutorials/handle_drift/handle_drift_jupyter.zip
index 186717da3d9de733db74e10e7b0503eda0bc935d..783568e455ada17ebfbdea718ba337b9202cc38c 100644
GIT binary patch
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diff --git a/doc/long_tutorials/handle_drift/handle_drift_python.zip b/doc/long_tutorials/handle_drift/handle_drift_python.zip
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diff --git a/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_001.png b/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_001.png
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diff --git a/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_002.png b/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_002.png
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diff --git a/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_005.png b/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_005.png
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diff --git a/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_006.png b/doc/long_tutorials/handle_drift/images/sphx_glr_plot_handle_drift_006.png
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diff --git a/doc/long_tutorials/handle_drift/images/thumb/sphx_glr_plot_handle_drift_thumb.png b/doc/long_tutorials/handle_drift/images/thumb/sphx_glr_plot_handle_drift_thumb.png
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diff --git a/doc/long_tutorials/handle_drift/index.rst b/doc/long_tutorials/handle_drift/index.rst
index c2386dd76c..8247b44383 100644
--- a/doc/long_tutorials/handle_drift/index.rst
+++ b/doc/long_tutorials/handle_drift/index.rst
@@ -24,7 +24,7 @@ sphinx-gallery generates.
 
 .. raw:: html
 
-    <div class="sphx-glr-thumbcontainer" tooltip="When running in vivo electrophysiology recordings, movement of the probe is an inevitability, e...">
+    <div class="sphx-glr-thumbcontainer" tooltip="Probe movement is an inevitability when running in vivo electrophysiology recordings. Motion, c...">
 
 .. only:: html
 
@@ -35,7 +35,7 @@ sphinx-gallery generates.
 
 .. raw:: html
 
-      <div class="sphx-glr-thumbnail-title">Handle motion/drift with spikeinterface NEW</div>
+      <div class="sphx-glr-thumbnail-title">Handle probe drift with spikeinterface NEW</div>
     </div>
 
 
diff --git a/doc/long_tutorials/handle_drift/plot_handle_drift.ipynb b/doc/long_tutorials/handle_drift/plot_handle_drift.ipynb
index a650dcfb80..2381583a5e 100644
--- a/doc/long_tutorials/handle_drift/plot_handle_drift.ipynb
+++ b/doc/long_tutorials/handle_drift/plot_handle_drift.ipynb
@@ -4,14 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Handle motion/drift with spikeinterface NEW\n\nWhen running *in vivo* electrophysiology recordings, movement of the probe is\nan inevitability, especially when the subjects are not head-fixed. SpikeInterface\nincludes a number of popular methods to compensate for probe motion during the\npreprocessing step.\n\n## What is drift and where does it come from?\n\nMovement of the probe means that the spikes recorded on the probe 'drift' along it.\nTypically, this motion is vertical along the probe (along the 'y' axis) which\nmanifests as the units moving long the probe in space.\n\nAll common motion-correction methods address this vertical drift. Horizontal ('x')\nor forward/backwards ('z') motion, that would appear as the amplitude of a unit\nchanging over time, are much harder to model and not handled in available motion-correction algorithms.\nFortunately, vertical drift is the most common form of motion as the probe is\nmore likely to move along the path it was inserted, rather than in other directions\nwhere it is buffeted against the brain.\n\nVertical drift can come in two forms, 'rigid' and 'non-rigid'. Rigid drift\nis drift caused by movement of the entire probe and the motion is the\nsame for all points along the probe. Non-rigid drift is instead caused by\nlocal movement of parts of the brain along the probe, and can affect\nthe recording at only certain points along the probe.\n\n## How SpikeInterface handles drift\n\nSpikeinterface offers a very flexible framework to handle drift as a\npreprocessing step. In this tutorial we will cover the three main\ndrift-correction algorithms implemented in SpikeInterface with\na focus on running the methods and interpreting the output. For\nmore information on the theory and implementation of these methods,\nsee the `motion_correction` section of the documentation.\n\n## The drift correction steps\n\nThe easiest way to run drift correction in SpikeInterface is with the\nhigh-level :py:func:`~spikeinterface.preprocessing.correct_motion()` function.\nThis function takes a preprocessed recording as input and then internally runs\nseveral steps and returns a lazy recording that interpolates the traces on-the-fly\nto compensate for the motion.\n\nThe\n:py:func:`~spikeinterface.preprocessing.correct_motion()`\nfunction provides a convenient wrapper around a number of sub-functions\nthat together implement the full drift correction algorithm.\n\nInternally this function runs the following steps:\n\n| **1.** ``localize_peaks()``\n| **2.** ``select_peaks()`` (optional)\n| **3.** ``estimate_motion()``\n| **4.** ``interpolate_motion()``\n\nAll these sub-steps have many parameters which dictate the\nspeed and effectiveness of motion correction. As such, ``correct_motion``\nprovides three setting 'presets' which configure the motion correct\nto proceed either as:\n\n* **rigid_fast** - a fast, not particularly accurate correction assuming ridigt drift.\n* **kilosort-like** - Mimics what is done in Kilosort (REF)\n* **nonrigid_accurate** - A decentralised drift correction, introduced by the Paninski group (REF)\n\n**Now, let's dive into running motion correction with these three\nmethods on a simulated dataset and interpreting the output.**\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>The below code uses multiprocessing. If you are on Windows, you may\n    need to place the code within a  ``if __name__ == \"__main__\":`` block.</p></div>\n\n"
+        "\n# Handle probe drift with spikeinterface NEW\n\nProbe movement is an inevitability when running\n*in vivo* electrophysiology recordings. Motion, caused by physical\nmovement of the probe or the sliding of brain tissue\ndeforming across the probe, can complicate the sorting\nand analysis of units.\n\nSpikeinterface offers a flexible framework to handle motion correction\nas a preprocessing step. In this tutorial we will cover the three main\ndrift-correction algorithms implemented in SpikeInterface\n(**rigid_fast**, **kilosort_like** and **nonrigid_accurate**) with\na focus on running the methods and interpreting the output.\n\nFor more information on the theory and implementation of these methods,\nsee the `motion_correction` section of the documentation.\n\n## What is probe drift?\n\nThe inserted probe can move from side-to-side (*'x' direction*),\nup-or-down (*'y' direction*) or forwards-or-backwards (*'z' direction*).\nMovement in the 'x' and 'y' direction is harder to model than vertical\ndrift (i.e. along the probe depth), and are not handled by most motion\ncorrection algorithms. Fortunately, vertical drift which is most easily \nhandled is most pronounced as the probe is most likely to move along the path of insertion. \n\nVertical drift can come in two forms, *'rigid'* and *'non-rigid'*. Rigid drift\nis drift caused by movement of the entire probe, and the motion is\nsimilar across all channels along the probe depth. In contrast,\nnon-rigid drift is instead caused by local movements brain along the\nprobe, and can selectively affect subsets of channels..\n\n## The drift correction steps\n\nThe easiest way to run drift correction in SpikeInterface is with the\nhigh-level :py:func:`~spikeinterface.preprocessing.correct_motion()` function.\nThis function takes a preprocessed recording as input and returns a motion-corrected\nrecording object. As with all other preprocessing steps, the correction (in this\ncase interpolation of the data to correct the detected motion) is lazy and applied on-the-fly when data is needed).\n\nThe :py:func:`~spikeinterface.preprocessing.correct_motion()`\nfunction implements motion correction algorithms in a modular way\nwrapping a number of subfunctions that together implement the \nfull drift correction algorithm.\n\nThese drift-correction modules are:\n\n| **1.** ``localize_peaks()`` (detect spikes and localize their position on the probe)\n| **2.** ``select_peaks()`` (optional, select a subset of peaks to use to estimate motion)\n| **3.** ``estimate_motion()`` (estimate motion using the detected spikes)\n| **4.** ``interpolate_motion()`` (perform interpolation on the raw data to account for the estimated drift).\n\nAll these sub-steps have many parameters which dictate the\nspeed and effectiveness of motion correction. As such, ``correct_motion``\nprovides three setting 'presets' which configure the motion correct\nto proceed either as:\n\n* **rigid_fast** - a fast, not particularly accurate correction assuming ridigt drift.\n* **kilosort-like** - Mimics what is done in Kilosort.\n* **nonrigid_accurate** - A decentralised drift correction (DREDGE), introduced by the Paninski group.\n\nWhen using motion correction in your analysis, please make sure to\n`cite the appropriate paper for your chosen method<cite-motion-correction>`.\n\n\n**Now, let's dive into running motion correction with these three\nmethods on a simulated dataset.**\n"
       ]
     },
     {
@@ -36,7 +29,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Next, we will generate a synthetic drifting recording. This recording will\nhave 500 separate units with firing rates randomly distributed between\n15 and 25 Hz. The recording will be in total 1000 seconds long.\n\n"
+        "Next, we will generate a synthetic, drifting recording. This recording will\nhave 100 separate units with firing rates randomly distributed between\n15 and 25 Hz.\n\nWe will create a zigzag drift pattern on the recording, starting at\n100 seconds and with a peak-to-peak period of 100 seconds (so we will\nhave 9 zigzags through our recording). We also add some non-linearity\nto the imposed motion.\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "<div class=\"alert alert-info\"><h4>Note</h4><p>This tutorial can take a long time to run with the default arguments.\n    If you would like to run this locally, you may want to edit ``num_units``\n    and ``duration`` to smaller values (e.g. 25 and 100 respectively).\n\n    Also note, the below code uses multiprocessing. If you are on Windows, you may\n    need to place the code within a  ``if __name__ == \"__main__\":`` block.</p></div>\n\n"
       ]
     },
     {
@@ -47,7 +47,7 @@
       },
       "outputs": [],
       "source": [
-        "# We will create a zigzag drift pattern on the recording, starting at\n# 100 seconds and with a peak-to-peak period of 100 seconds (so we will\n# have 9 zigzags through our recording). We also add some nonlinear\n# drift in to the motion (i.e. is not the same across the entire probe).\n\nnum_units = 50 # 500,\nduration = 100  # 1000,\n\n_, raw_recording, _ = generate_drifting_recording(\n    num_units=num_units,\n    duration=duration,\n    generate_sorting_kwargs=dict(firing_rates=(15, 25), refractory_period_ms=4.0),\n    seed=42,\n    generate_displacement_vector_kwargs=dict(motion_list=[\n            dict(\n                drift_mode=\"zigzag\",\n                non_rigid_gradient=None, # 0.1,\n                t_start_drift=10.0,  # 100.0\n                t_end_drift=None,\n                period_s=10,  # 100\n            ),\n        ],\n    )\n)\nprint(raw_recording)"
+        "num_units = 100  # 250 still too many I think!\nduration = 1000\n\n_, raw_recording, _ = generate_drifting_recording(\n    num_units=num_units,\n    duration=duration,\n    generate_sorting_kwargs=dict(firing_rates=(15, 25), refractory_period_ms=4.0),\n    seed=42,\n    generate_displacement_vector_kwargs=dict(motion_list=[\n            dict(\n                drift_mode=\"zigzag\",\n                non_rigid_gradient=None, # 0.1,\n                t_start_drift=int(duration/10),\n                t_end_drift=None,\n                period_s=int(duration/10),\n            ),\n        ],\n    )\n)\nprint(raw_recording)"
       ]
     },
     {
@@ -79,7 +79,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Run motion correction with one function!\n\nCorrecting for drift is easy! You just need to run a single function.\nWe will now run motion correction on our recording using the three\npresets described above - **rigid_fast**, **kilosort_like** and\n**nonrigid_accurate**.\n\nUnder the hood, each step, peak localisation, selection, motion estimation\nand interpolation expose a lot of options, making them highly flexible.\nThe presets are simply a set of configurations which sets the motion\ncorrection steps to perform as described in the original methods.\nFor example, we can print the full set of **kilosort_like** preset options:\n\n"
+        "## Run motion correction with one function!\n\nCorrecting for drift is easy! You just need to run a single function.\nWe will now run motion correction on our recording using the three\npresets described above - **rigid_fast**, **kilosort_like** and\n**nonrigid_accurate**.\n\nWe can run these presents with the ``preset`` argument of\n:py:func:`~spikeinterface.preprocessing.correct_motion()`. Under the\nhood, the presets define a set of parameters by set how to run the\n4 submodules that make up motion correction (described above).\n\n"
       ]
     },
     {
@@ -97,7 +97,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now, lets run motion correction with our three presets. We will\nset the ``job_kwargs`` to parallelize the job over a number of CPU cores.\nMotion correction is quite computationally intensive and so it is\nvery useful to run it across a high numer of jobs to speed it up.\n\n"
+        "Now, lets run motion correction with our three presets. We will\nset the ``job_kwargs`` to parallelize the job over a number of CPU cores\u2014motion\ncorrection is computationally intensive and will run faster with parallelization.\n\n"
       ]
     },
     {
@@ -108,21 +108,21 @@
       },
       "outputs": [],
       "source": [
-        "presets_to_run = (\"rigid_fast\", \"kilosort_like\", \"nonrigid_accurate\")\n\njob_kwargs = dict(n_jobs=40, chunk_duration=\"1s\", progress_bar=True)\n\nresults = {preset: {} for preset in presets_to_run}\nfor preset in presets_to_run:\n\n    recording_corrected, motion_info = si.correct_motion(\n        preprocessed_recording, preset=preset,  output_motion_info=True, **job_kwargs\n    )\n    results[preset][\"motion_info\"] = motion_info"
+        "presets_to_run = (\"rigid_fast\", \"kilosort_like\", \"nonrigid_accurate\")\n\njob_kwargs = dict(n_jobs=40, chunk_duration=\"1s\", progress_bar=True)\n\nresults = {preset: {} for preset in presets_to_run}\nfor preset in presets_to_run:\n\n    corrected_recording, motion_info = si.correct_motion(\n        preprocessed_recording, preset=preset,  output_motion_info=True, **job_kwargs\n    )\n    results[preset][\"motion_info\"] = motion_info"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        ".. seealso::\n   It is often very useful to save the output of motion correction to\n   file, so they can be loaded later. This can be done by setting\n   the ``folder`` argument of ``correct_motion`` to a path to save in.\n   The ``motion_info`` can be loaded back with ``si.load_motion_info``.\n\n"
+        ".. seealso::\n   It is often very useful to save ``motion_info`` to\n   file, so it can be loaded and visualised later. This can be done by setting\n   the ``folder`` argument of\n   :py:func:`~spikeinterface.preprocessing.correct_motion()` to a path to write\n   the motion output to. The ``motion_info`` can be loaded back with\n   ``si.load_motion_info``.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Plotting the results\n\nFor all methods we have 4 plots. On the x-axis of all plots we have\nthe (binned time). The plots display:\n  * **top left:** The estimated peak depth for every detected peaks.\n  * **top right:** The estimated peak depths after motion correction.\n  * **bottom left:** The average motion vector across depths and all motion across spatial depths (for non-rigid estimation).\n  * **bottom right:** if motion correction is non rigid, the motion vector across depths is plotted as a map, with the color code representing the motion in micrometers.\n\nThese plots are quite complicated, so it is worth covering them in detail.\nFor every detected action potential in our recording, we first estimate\nits depth (first panel) using a method from :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.\nThen, the probe motion is estimated the location of the detected peaks are\nadjusted to account for this (second panel). The motion estimation produces\na measure of how much and in what direction the probe is moving at any given\ntime bin (third panel). For non-rigid motion correction, the probe is divided\ninto subsections - the motion vectors displayed are per subsection (i.e. per\n'binned spatial depth') as well as the average. On the fourth panel, we see a\nmore detailed representation of the motion vectors. We can see the motion plotted\nas a heatmap at each binned spatial depth across all time bins. We see it captures\nthe zigzag pattern (alternating light and dark colors).\n\n"
+        "## Plotting the results\n\nNext, let's plot the results of our motion estimation using the ``plot_motion_info()``\nfunction. The plot contains 4 panels, on the x-axis of all plots we have\nthe (binned time). The plots display:\n  * **top left:** The estimated peak depth for every detected peak.\n  * **top right:** The estimated peak depths after motion correction.\n  * **bottom left:** The average motion vector across depths and all motion across spatial depths (for non-rigid estimation).\n  * **bottom right:** if motion correction is non rigid, the motion vector across depths is plotted as a map, with the color code representing the motion in micrometers.\n\n"
       ]
     },
     {
@@ -133,42 +133,49 @@
       },
       "outputs": [],
       "source": [
-        "for preset in presets_to_run:\n    fig = plt.figure(figsize=(7, 7))\n    si.plot_motion_info(\n        results[preset][\"motion_info\"],\n        recording=recording_corrected,  # recording only used to get the real times\n        figure=fig,\n        depth_lim=(400, 600),\n        color_amplitude=True,\n        amplitude_cmap=\"inferno\",\n        scatter_decimate=10,\n    )\n    fig.suptitle(f\"{preset=}\")"
+        "for preset in presets_to_run:\n\n    fig = plt.figure(figsize=(7, 7))\n\n    si.plot_motion_info(\n        results[preset][\"motion_info\"],\n        recording=corrected_recording,  # the recording is only used to get the real times\n        figure=fig,\n        depth_lim=(400, 600),\n        color_amplitude=True,\n        amplitude_cmap=\"inferno\",\n        scatter_decimate=10,            # Only plot every 10th peak\n    )\n    fig.suptitle(f\"{preset=}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "These plots are quite complicated, so it is worth covering them in detail.\nFor every detected spike in our recording, we first estimate\nits depth (first panel) using a method from\n:py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.\n\nThen, the probe motion is estimated the location of the detected peaks are\nadjusted to account for this (second panel).\n\nThe motion estimation produces\na measure of how much and in what direction the probe is moving at any given\ntime bin (third panel). For non-rigid motion correction, the probe is divided\ninto subsections - the motion vectors displayed are per subsection (i.e. per\n'binned spatial depth') as well as the average.\n\nOn the fourth panel, we see a\nmore detailed representation of the motion vectors. We can see the motion plotted\nas a heatmap at each binned spatial depth across all time bins. It captures\nthe zigzag pattern (alternating light and dark colors) of the injected motion.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "A few comments on the figures:\n  * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a \"rigid\" case.\n    The motion amplitude is globally underestimated because it averages across depths.\n    However, the corrected peaks are flatter than the non-corrected ones, so the job is partially done.\n    The big jump at=600s when the probe start moving is recovered quite well.\n  * The preset **kilosort_like** gives better results because it is a non-rigid case.\n    The motion vector is computed for different depths.\n    The corrected peak locations are flatter than the rigid case.\n    The motion vector map is still be a bit noisy at some depths (e.g around 1000um).\n  * The preset **nonrigid_accurate** seems to give the best results on this recording.\n    The motion vector seems less noisy globally, but it is not \"perfect\" (see at the top of the probe 3200um to 3800um).\n    Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion:\n    the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable.\n    The method defined by this preset is able to capture this.\n\n"
+        "A few comments on the figures:\n  * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a 'rigid' case.\n    The motion amplitude is globally underestimated because it averages across depths.\n    However, the corrected peaks are flatter than the non-corrected ones, so the job is partially done.\n    The big jump at=600s when the probe start moving is recovered quite well.\n  * The preset **kilosort_like** gives better results because it is a non-rigid case.\n    The motion vector is computed for different depths.\n    The corrected peak locations are flatter than the rigid case.\n    The motion vector map is still be a bit noisy at some depths (e.g around 1000um).\n  * The preset **nonrigid_accurate** seems to give the best results on this recording.\n    The motion vector seems less noisy globally, but it is not 'perfect' (see at the top of the probe 3200um to 3800um).\n    Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion:\n    the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable.\n    The method defined by this preset is able to capture this.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Correcting Peak Locations after Motion Correction\n\nTo understand how motion correction is applied to our data, it is\nimportant to understand the spikeinterface `peak` and `peak_locations`\nobjects, explored further in the below dropdown.\n\n\n"
+        "## Correcting Peak Locations after Motion Correction\n\nThe result of motion correction can be applied to the data in two ways.\nThe first is by interpolating the raw traces to correct for the estimated drift.\nThis changes the data in the\nrecording by shifting the signal across channels, and is given in the\n`corrected_recording` output from :py:func:`~spikeinterface.preprocessing.correct_motion()`.\nThis is useful in most cases, for continuing\nwith preprocessing and sorting with the corrected recording.\n\nThe second way is to apply the results of motion correction directly\nto the ``peak_locations`` object. If you are not familiar with\nSpikeInterface's ``peak`` and ``peak_locations`` objects,\nthese are explored further in the below dropdown.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        ".. dropdown:: Dropdown title\n\n  Information about detected action potentials is represented in\n  SpikeInterface is ``peaks`` and ``peak_locations`` objects. The\n  ``peaks`` object is an array for every detected action potential in th\n   the dataset, containing its XXX, XX, XX, XX. it is created by the\n   XXXX function.\n\n  The ``peak_locations`` is a partner object to the ``peaks`` object\n  containing XXX. For every peak in ``peak`` there is a corresponding\n  location in ``peak_locations``. The peak locations is estimated\n  using the XXXX function. One way of correcting for motion is to\n  correct these peak locations directly, using the output of\n  ``si.correct_motion`` or ``si.estimate_motion``.\n\n"
+        ".. dropdown:: 'Peaks' and 'Peak Locations' in SpikeInterface\n\n  Information about detected spikes is represented in\n  SpikeInterface's ``peaks`` and ``peak_locations`` objects. The\n  ``peaks`` object is an array for containing the\n  sample index, channel index (where its signal\n  is strongest), amplitude and recording segment index for every detected spike\n  in the dataset. It is created by the\n  :py:func:`~spikeinterface.sortingcomponents.peak_detection.detect_peaks()`\n  function.\n\n  The ``peak_locations`` is a partner object to the ``peaks`` object,\n  and contains the estimated location (``\"x\"``, ``\"y\"``) of the spike. For every spike in\n  ``peaks`` there is a corresponding location in ``peak_locations``.\n  The peak locations is estimated using the\n  :py:func:`~spikeinterface.sortingcomponents.peak_localization.localise_peaks()`\n  function.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The result of motion correction can be applied by interpolating the\nraw data to correct for the drift. Essentially, this shifts the signal\nacross the probe depth by, at each channel, interpolating other channels\nXXXX. This is performed on the `corrected_recording` output from the\n`correct_motion` channel. This is useful for continuing with\npreprocessing and sorting with the corrected recording.\n\nThe other way to apply the motion correction is to the ``peaks`` and\n``peaks_location`` objects directly. This is done using the function\n``correct_motion_on_peaks()``. Given a set of peaks, peak locations and\nthe ``motion`` object output from ``correct_motion``, it will shift the\nlocation of the peaks according to the motion estimate, outputting a new\n``peak_locations`` object. This is done to plot the peak locations in\nthe next section.\n\n"
+        "The other way to apply the motion correction is to the ``peaks`` and\n``peaks_location`` objects directly. This is done using the function\n``correct_motion_on_peaks()``. Given a set of peaks, peak locations and\nthe ``motion`` object output from :py:func:`~spikeinterface.preprocessing.correct_motion()`,\nit will shift the location of the peaks according to the motion estimate, outputting a new\n``peak_locations`` object. This is done to plot the peak locations in\nthe next section.\n\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>Note that the `peak_locations` output by `correct_motion`'s\n   `motion_info` is the ORIGINAL (uncorrected) peak locations. To get the corrected\n   peak locations, `correct_motion_on_peaks()` must be used!</p></div>\n\n"
+        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>Note that the ``peak_locations`` output by\n   :py:func:`~spikeinterface.preprocessing.correct_motion()`\n   (in the ``motion_info`` object) is the original (uncorrected) peak locations.\n   To get the corrected peak locations, ``correct_motion_on_peaks()`` must be used!</p></div>\n\n"
       ]
     },
     {
@@ -179,14 +186,14 @@
       },
       "outputs": [],
       "source": [
-        "for preset in presets_to_run:\n\n    motion_info = results[preset][\"motion_info\"]\n\n    peaks = motion_info[\"peaks\"]\n\n    original_peak_locations = motion_info[\"peak_locations\"]\n\n    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], recording_corrected)  # TODO: what recording to use.\n\n    widget = plot_peaks_on_probe(recording_corrected, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))\n    widget.figure.suptitle(preset)"
+        "for preset in presets_to_run:\n\n    motion_info = results[preset][\"motion_info\"]\n\n    peaks = motion_info[\"peaks\"]\n\n    original_peak_locations = motion_info[\"peak_locations\"]\n\n    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], corrected_recording)\n\n    widget = plot_peaks_on_probe(corrected_recording, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))\n    widget.figure.suptitle(preset)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Comparing the  Run Times\n\nThe different methods also have different speeds, the 'nonrigid_accurate'\nrequires more computation time, particulary at the ``estimate_motion`` phase,\nas seen in the run times:\n\n"
+        "## Comparing the  Run Times\n\nThe different methods also have different speeds, the 'nonrigid_accurate'\nrequires more computation time, in particular at the ``estimate_motion`` phase,\nas seen in the run times:\n\n"
       ]
     },
     {
@@ -204,7 +211,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Summary\n\nThat's it for our overall tour of correcting motion in\nSpikeInterface. If you'd like to explore more, see the API docs\nfor `estimate_motion()`, `interpolate_motion`() (ay others?). Remember\nthat correcting motion makes some assumptions on your datatype - always\noutput and plot the motion correction information for your recordings,\nto make sure they are acting as expected!\n\n"
+        "## Summary\n\nThat's it for our tour of motion correction in\nSpikeInterface. Remember that correcting motion makes some\nassumptions on your data (e.g. number of channels, noise in the recording)\u2014always\nplot the motion correction information for your\nrecordings, to make sure the correction is behaviour as expected!\n\n"
       ]
     }
   ],
diff --git a/doc/long_tutorials/handle_drift/plot_handle_drift.py b/doc/long_tutorials/handle_drift/plot_handle_drift.py
index e3d779eb8e..0088487408 100644
--- a/doc/long_tutorials/handle_drift/plot_handle_drift.py
+++ b/doc/long_tutorials/handle_drift/plot_handle_drift.py
@@ -1,66 +1,61 @@
 """
 ===========================================
-Handle motion/drift with spikeinterface NEW
+Handle probe drift with spikeinterface NEW
 ===========================================
 
-When running *in vivo* electrophysiology recordings, movement of the probe is
-an inevitability, especially when the subjects are not head-fixed. SpikeInterface
-includes a number of popular methods to compensate for probe motion during the
-preprocessing step.
-
-------------------------------------------
-What is drift and where does it come from?
-------------------------------------------
-
-Movement of the probe means that the spikes recorded on the probe 'drift' along it.
-Typically, this motion is vertical along the probe (along the 'y' axis) which
-manifests as the units moving long the probe in space.
-
-All common motion-correction methods address this vertical drift. Horizontal ('x')
-or forward/backwards ('z') motion, that would appear as the amplitude of a unit
-changing over time, are much harder to model and not handled in available motion-correction algorithms.
-Fortunately, vertical drift is the most common form of motion as the probe is
-more likely to move along the path it was inserted, rather than in other directions
-where it is buffeted against the brain.
-
-Vertical drift can come in two forms, 'rigid' and 'non-rigid'. Rigid drift
-is drift caused by movement of the entire probe and the motion is the
-same for all points along the probe. Non-rigid drift is instead caused by
-local movement of parts of the brain along the probe, and can affect
-the recording at only certain points along the probe.
-
-------------------------------------------
-How SpikeInterface handles drift
-------------------------------------------
-
-Spikeinterface offers a very flexible framework to handle drift as a
-preprocessing step. In this tutorial we will cover the three main
-drift-correction algorithms implemented in SpikeInterface with
-a focus on running the methods and interpreting the output. For
-more information on the theory and implementation of these methods,
+Probe movement is an inevitability when running
+*in vivo* electrophysiology recordings. Motion, caused by physical
+movement of the probe or the sliding of brain tissue
+deforming across the probe, can complicate the sorting
+and analysis of units.
+
+Spikeinterface offers a flexible framework to handle motion correction
+as a preprocessing step. In this tutorial we will cover the three main
+drift-correction algorithms implemented in SpikeInterface
+(**rigid_fast**, **kilosort_like** and **nonrigid_accurate**) with
+a focus on running the methods and interpreting the output.
+
+For more information on the theory and implementation of these methods,
 see the :ref:`motion_correction` section of the documentation.
 
-------------------------------------------
+---------------------
+What is probe drift?
+---------------------
+
+The inserted probe can move from side-to-side (*'x' direction*),
+up-or-down (*'y' direction*) or forwards-or-backwards (*'z' direction*).
+Movement in the 'x' and 'y' direction is harder to model than vertical
+drift (i.e. along the probe depth), and are not handled by most motion
+correction algorithms. Fortunately, vertical drift which is most easily
+handled is most pronounced as the probe is most likely to move along the path of insertion.
+
+Vertical drift can come in two forms, *'rigid'* and *'non-rigid'*. Rigid drift
+is drift caused by movement of the entire probe, and the motion is
+similar across all channels along the probe depth. In contrast,
+non-rigid drift is instead caused by local movements brain along the
+probe, and can selectively affect subsets of channels..
+
+--------------------------
 The drift correction steps
-------------------------------------------
+--------------------------
 
 The easiest way to run drift correction in SpikeInterface is with the
 high-level :py:func:`~spikeinterface.preprocessing.correct_motion()` function.
-This function takes a preprocessed recording as input and then internally runs
-several steps and returns a lazy recording that interpolates the traces on-the-fly
-to compensate for the motion.
+This function takes a preprocessed recording as input and returns a motion-corrected
+recording object. As with all other preprocessing steps, the correction (in this
+case interpolation of the data to correct the detected motion) is lazy and applied on-the-fly when data is needed).
 
-The
-:py:func:`~spikeinterface.preprocessing.correct_motion()`
-function provides a convenient wrapper around a number of sub-functions
-that together implement the full drift correction algorithm.
+The :py:func:`~spikeinterface.preprocessing.correct_motion()`
+function implements motion correction algorithms in a modular way
+wrapping a number of subfunctions that together implement the
+full drift correction algorithm.
 
-Internally this function runs the following steps:
+These drift-correction modules are:
 
-| **1.** ``localize_peaks()``
-| **2.** ``select_peaks()`` (optional)
-| **3.** ``estimate_motion()``
-| **4.** ``interpolate_motion()``
+| **1.** ``localize_peaks()`` (detect spikes and localize their position on the probe)
+| **2.** ``select_peaks()`` (optional, select a subset of peaks to use to estimate motion)
+| **3.** ``estimate_motion()`` (estimate motion using the detected spikes)
+| **4.** ``interpolate_motion()`` (perform interpolation on the raw data to account for the estimated drift).
 
 All these sub-steps have many parameters which dictate the
 speed and effectiveness of motion correction. As such, ``correct_motion``
@@ -68,17 +63,17 @@
 to proceed either as:
 
 * **rigid_fast** - a fast, not particularly accurate correction assuming ridigt drift.
-* **kilosort-like** - Mimics what is done in Kilosort (REF)
-* **nonrigid_accurate** - A decentralised drift correction, introduced by the Paninski group (REF)
+* **kilosort-like** - Mimics what is done in Kilosort.
+* **nonrigid_accurate** - A decentralised drift correction (DREDGE), introduced by the Paninski group.
+
+When using motion correction in your analysis, please make sure to
+:ref:`cite the appropriate paper for your chosen method<cite-motion-correction>`.
+
 
 **Now, let's dive into running motion correction with these three
-methods on a simulated dataset and interpreting the output.**
+methods on a simulated dataset.**
 
 """
-# %%
-#.. warning::
-#    The below code uses multiprocessing. If you are on Windows, you may
-#    need to place the code within a  ``if __name__ == "__main__":`` block.
 
 # %%
 # -------------------------------------------
@@ -95,17 +90,27 @@
 from spikeinterface.widgets import plot_peaks_on_probe
 
 # %% read the file
-# Next, we will generate a synthetic drifting recording. This recording will
-# have 500 separate units with firing rates randomly distributed between
-# 15 and 25 Hz. The recording will be in total 1000 seconds long.
-
+# Next, we will generate a synthetic, drifting recording. This recording will
+# have 100 separate units with firing rates randomly distributed between
+# 15 and 25 Hz.
+#
 # We will create a zigzag drift pattern on the recording, starting at
 # 100 seconds and with a peak-to-peak period of 100 seconds (so we will
-# have 9 zigzags through our recording). We also add some nonlinear
-# drift in to the motion (i.e. is not the same across the entire probe).
+# have 9 zigzags through our recording). We also add some non-linearity
+# to the imposed motion.
+
+# %%
+#.. note::
+#    This tutorial can take a long time to run with the default arguments.
+#    If you would like to run this locally, you may want to edit ``num_units``
+#    and ``duration`` to smaller values (e.g. 25 and 100 respectively).
+#
+#    Also note, the below code uses multiprocessing. If you are on Windows, you may
+#    need to place the code within a  ``if __name__ == "__main__":`` block.
+
 
-num_units = 50 # 500,
-duration = 100  # 1000,
+num_units = 100  # 250 still too many I think!
+duration = 1000
 
 _, raw_recording, _ = generate_drifting_recording(
     num_units=num_units,
@@ -116,9 +121,9 @@
             dict(
                 drift_mode="zigzag",
                 non_rigid_gradient=None, # 0.1,
-                t_start_drift=10.0,  # 100.0
+                t_start_drift=int(duration/10),
                 t_end_drift=None,
-                period_s=10,  # 100
+                period_s=int(duration/10),
             ),
         ],
     )
@@ -148,18 +153,16 @@
 # presets described above - **rigid_fast**, **kilosort_like** and
 # **nonrigid_accurate**.
 #
-# Under the hood, each step, peak localisation, selection, motion estimation
-# and interpolation expose a lot of options, making them highly flexible.
-# The presets are simply a set of configurations which sets the motion
-# correction steps to perform as described in the original methods.
-# For example, we can print the full set of **kilosort_like** preset options:
+# We can run these presents with the ``preset`` argument of
+# :py:func:`~spikeinterface.preprocessing.correct_motion()`. Under the
+# hood, the presets define a set of parameters by set how to run the
+# 4 submodules that make up motion correction (described above).
 print(motion_options_preset["kilosort_like"])
 
 # %%
 # Now, lets run motion correction with our three presets. We will
-# set the ``job_kwargs`` to parallelize the job over a number of CPU cores.
-# Motion correction is quite computationally intensive and so it is
-# very useful to run it across a high numer of jobs to speed it up.
+# set the ``job_kwargs`` to parallelize the job over a number of CPU cores—motion
+# correction is computationally intensive and will run faster with parallelization.
 
 presets_to_run = ("rigid_fast", "kilosort_like", "nonrigid_accurate")
 
@@ -168,59 +171,71 @@
 results = {preset: {} for preset in presets_to_run}
 for preset in presets_to_run:
 
-    recording_corrected, motion_info = si.correct_motion(
+    corrected_recording, motion_info = si.correct_motion(
         preprocessed_recording, preset=preset,  output_motion_info=True, **job_kwargs
     )
     results[preset]["motion_info"] = motion_info
 
 # %%
 #.. seealso::
-#   It is often very useful to save the output of motion correction to
-#   file, so they can be loaded later. This can be done by setting
-#   the ``folder`` argument of ``correct_motion`` to a path to save in.
-#   The ``motion_info`` can be loaded back with ``si.load_motion_info``.
+#   It is often very useful to save ``motion_info`` to
+#   file, so it can be loaded and visualised later. This can be done by setting
+#   the ``folder`` argument of
+#   :py:func:`~spikeinterface.preprocessing.correct_motion()` to a path to write
+#   the motion output to. The ``motion_info`` can be loaded back with
+#   ``si.load_motion_info``.
 
 # %%
 # -------------------
 # Plotting the results
 # -------------------
 #
-# For all methods we have 4 plots. On the x-axis of all plots we have
+# Next, let's plot the results of our motion estimation using the ``plot_motion_info()``
+# function. The plot contains 4 panels, on the x-axis of all plots we have
 # the (binned time). The plots display:
-#   * **top left:** The estimated peak depth for every detected peaks.
+#   * **top left:** The estimated peak depth for every detected peak.
 #   * **top right:** The estimated peak depths after motion correction.
 #   * **bottom left:** The average motion vector across depths and all motion across spatial depths (for non-rigid estimation).
 #   * **bottom right:** if motion correction is non rigid, the motion vector across depths is plotted as a map, with the color code representing the motion in micrometers.
-#
-# These plots are quite complicated, so it is worth covering them in detail.
-# For every detected action potential in our recording, we first estimate
-# its depth (first panel) using a method from :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
-# Then, the probe motion is estimated the location of the detected peaks are
-# adjusted to account for this (second panel). The motion estimation produces
-# a measure of how much and in what direction the probe is moving at any given
-# time bin (third panel). For non-rigid motion correction, the probe is divided
-# into subsections - the motion vectors displayed are per subsection (i.e. per
-# 'binned spatial depth') as well as the average. On the fourth panel, we see a
-# more detailed representation of the motion vectors. We can see the motion plotted
-# as a heatmap at each binned spatial depth across all time bins. We see it captures
-# the zigzag pattern (alternating light and dark colors).
 
 for preset in presets_to_run:
+
     fig = plt.figure(figsize=(7, 7))
+
     si.plot_motion_info(
         results[preset]["motion_info"],
-        recording=recording_corrected,  # recording only used to get the real times
+        recording=corrected_recording,  # the recording is only used to get the real times
         figure=fig,
         depth_lim=(400, 600),
         color_amplitude=True,
         amplitude_cmap="inferno",
-        scatter_decimate=10,
+        scatter_decimate=10,            # Only plot every 10th peak
     )
     fig.suptitle(f"{preset=}")
 
+# %%
+# These plots are quite complicated, so it is worth covering them in detail.
+# For every detected spike in our recording, we first estimate
+# its depth (first panel) using a method from
+# :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
+#
+# Then, the probe motion is estimated the location of the detected peaks are
+# adjusted to account for this (second panel).
+#
+# The motion estimation produces
+# a measure of how much and in what direction the probe is moving at any given
+# time bin (third panel). For non-rigid motion correction, the probe is divided
+# into subsections - the motion vectors displayed are per subsection (i.e. per
+# 'binned spatial depth') as well as the average.
+#
+# On the fourth panel, we see a
+# more detailed representation of the motion vectors. We can see the motion plotted
+# as a heatmap at each binned spatial depth across all time bins. It captures
+# the zigzag pattern (alternating light and dark colors) of the injected motion.
+
 # %%
 # A few comments on the figures:
-#   * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a "rigid" case.
+#   * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a 'rigid' case.
 #     The motion amplitude is globally underestimated because it averages across depths.
 #     However, the corrected peaks are flatter than the non-corrected ones, so the job is partially done.
 #     The big jump at=600s when the probe start moving is recovered quite well.
@@ -229,7 +244,7 @@
 #     The corrected peak locations are flatter than the rigid case.
 #     The motion vector map is still be a bit noisy at some depths (e.g around 1000um).
 #   * The preset **nonrigid_accurate** seems to give the best results on this recording.
-#     The motion vector seems less noisy globally, but it is not "perfect" (see at the top of the probe 3200um to 3800um).
+#     The motion vector seems less noisy globally, but it is not 'perfect' (see at the top of the probe 3200um to 3800um).
 #     Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion:
 #     the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable.
 #     The method defined by this preset is able to capture this.
@@ -239,48 +254,55 @@
 # Correcting Peak Locations after Motion Correction
 # -------------------------------------------------
 #
-# To understand how motion correction is applied to our data, it is
-# important to understand the spikeinterface `peak` and `peak_locations`
-# objects, explored further in the below dropdown.
+# The result of motion correction can be applied to the data in two ways.
+# The first is by interpolating the raw traces to correct for the estimated drift.
+# This changes the data in the
+# recording by shifting the signal across channels, and is given in the
+# `corrected_recording` output from :py:func:`~spikeinterface.preprocessing.correct_motion()`.
+# This is useful in most cases, for continuing
+# with preprocessing and sorting with the corrected recording.
 #
+# The second way is to apply the results of motion correction directly
+# to the ``peak_locations`` object. If you are not familiar with
+# SpikeInterface's ``peak`` and ``peak_locations`` objects,
+# these are explored further in the below dropdown.
 
 # %%
-# .. dropdown:: Dropdown title
+# .. dropdown:: 'Peaks' and 'Peak Locations' in SpikeInterface
 #
-#   Information about detected action potentials is represented in
-#   SpikeInterface is ``peaks`` and ``peak_locations`` objects. The
-#   ``peaks`` object is an array for every detected action potential in th
-#    the dataset, containing its XXX, XX, XX, XX. it is created by the
-#    XXXX function.
+#   Information about detected spikes is represented in
+#   SpikeInterface's ``peaks`` and ``peak_locations`` objects. The
+#   ``peaks`` object is an array for containing the
+#   sample index, channel index (where its signal
+#   is strongest), amplitude and recording segment index for every detected spike
+#   in the dataset. It is created by the
+#   :py:func:`~spikeinterface.sortingcomponents.peak_detection.detect_peaks()`
+#   function.
 #
-#   The ``peak_locations`` is a partner object to the ``peaks`` object
-#   containing XXX. For every peak in ``peak`` there is a corresponding
-#   location in ``peak_locations``. The peak locations is estimated
-#   using the XXXX function. One way of correcting for motion is to
-#   correct these peak locations directly, using the output of
-#   ``si.correct_motion`` or ``si.estimate_motion``.
+#   The ``peak_locations`` is a partner object to the ``peaks`` object,
+#   and contains the estimated location (``"x"``, ``"y"``) of the spike. For every spike in
+#   ``peaks`` there is a corresponding location in ``peak_locations``.
+#   The peak locations is estimated using the
+#   :py:func:`~spikeinterface.sortingcomponents.peak_localization.localise_peaks()`
+#   function.
 
 # %%
-# The result of motion correction can be applied by interpolating the
-# raw data to correct for the drift. Essentially, this shifts the signal
-# across the probe depth by, at each channel, interpolating other channels
-# XXXX. This is performed on the `corrected_recording` output from the
-# `correct_motion` channel. This is useful for continuing with
-# preprocessing and sorting with the corrected recording.
-#
 # The other way to apply the motion correction is to the ``peaks`` and
 # ``peaks_location`` objects directly. This is done using the function
 # ``correct_motion_on_peaks()``. Given a set of peaks, peak locations and
-# the ``motion`` object output from ``correct_motion``, it will shift the
-# location of the peaks according to the motion estimate, outputting a new
+# the ``motion`` object output from :py:func:`~spikeinterface.preprocessing.correct_motion()`,
+# it will shift the location of the peaks according to the motion estimate, outputting a new
 # ``peak_locations`` object. This is done to plot the peak locations in
 # the next section.
+#
+
 
 # %%
 #.. warning::
-#   Note that the `peak_locations` output by `correct_motion`'s
-#   `motion_info` is the ORIGINAL (uncorrected) peak locations. To get the corrected
-#   peak locations, `correct_motion_on_peaks()` must be used!
+#   Note that the ``peak_locations`` output by
+#   :py:func:`~spikeinterface.preprocessing.correct_motion()`
+#   (in the ``motion_info`` object) is the original (uncorrected) peak locations.
+#   To get the corrected peak locations, ``correct_motion_on_peaks()`` must be used!
 
 for preset in presets_to_run:
 
@@ -290,9 +312,9 @@
 
     original_peak_locations = motion_info["peak_locations"]
 
-    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], recording_corrected)  # TODO: what recording to use.
+    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], corrected_recording)
 
-    widget = plot_peaks_on_probe(recording_corrected, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
+    widget = plot_peaks_on_probe(corrected_recording, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
     widget.figure.suptitle(preset)
 
 # %%
@@ -301,7 +323,7 @@
 # -------------------------
 #
 # The different methods also have different speeds, the 'nonrigid_accurate'
-# requires more computation time, particulary at the ``estimate_motion`` phase,
+# requires more computation time, in particular at the ``estimate_motion`` phase,
 # as seen in the run times:
 
 for preset in presets_to_run:
@@ -313,9 +335,8 @@
 # Summary
 # ------------------------
 #
-# That's it for our overall tour of correcting motion in
-# SpikeInterface. If you'd like to explore more, see the API docs
-# for `estimate_motion()`, `interpolate_motion`() (ay others?). Remember
-# that correcting motion makes some assumptions on your datatype - always
-# output and plot the motion correction information for your recordings,
-# to make sure they are acting as expected!
+# That's it for our tour of motion correction in
+# SpikeInterface. Remember that correcting motion makes some
+# assumptions on your data (e.g. number of channels, noise in the recording)—always
+# plot the motion correction information for your
+# recordings, to make sure the correction is behaviour as expected!
diff --git a/doc/long_tutorials/handle_drift/plot_handle_drift.py.md5 b/doc/long_tutorials/handle_drift/plot_handle_drift.py.md5
index 44f00abce7..84427b7c1f 100644
--- a/doc/long_tutorials/handle_drift/plot_handle_drift.py.md5
+++ b/doc/long_tutorials/handle_drift/plot_handle_drift.py.md5
@@ -1 +1 @@
-cd1c4127974a165746a9b0c262f77f10
+35eece93960ca929da80e5c0f8e7c683
diff --git a/doc/long_tutorials/handle_drift/plot_handle_drift.rst b/doc/long_tutorials/handle_drift/plot_handle_drift.rst
index 5c185618f3..712be3c268 100644
--- a/doc/long_tutorials/handle_drift/plot_handle_drift.rst
+++ b/doc/long_tutorials/handle_drift/plot_handle_drift.rst
@@ -19,67 +19,62 @@
 
 
 ===========================================
-Handle motion/drift with spikeinterface NEW
+Handle probe drift with spikeinterface NEW
 ===========================================
 
-When running *in vivo* electrophysiology recordings, movement of the probe is
-an inevitability, especially when the subjects are not head-fixed. SpikeInterface
-includes a number of popular methods to compensate for probe motion during the
-preprocessing step.
-
-------------------------------------------
-What is drift and where does it come from?
-------------------------------------------
-
-Movement of the probe means that the spikes recorded on the probe 'drift' along it.
-Typically, this motion is vertical along the probe (along the 'y' axis) which
-manifests as the units moving long the probe in space.
-
-All common motion-correction methods address this vertical drift. Horizontal ('x')
-or forward/backwards ('z') motion, that would appear as the amplitude of a unit
-changing over time, are much harder to model and not handled in available motion-correction algorithms.
-Fortunately, vertical drift is the most common form of motion as the probe is
-more likely to move along the path it was inserted, rather than in other directions
-where it is buffeted against the brain.
-
-Vertical drift can come in two forms, 'rigid' and 'non-rigid'. Rigid drift
-is drift caused by movement of the entire probe and the motion is the
-same for all points along the probe. Non-rigid drift is instead caused by
-local movement of parts of the brain along the probe, and can affect
-the recording at only certain points along the probe.
-
-------------------------------------------
-How SpikeInterface handles drift
-------------------------------------------
-
-Spikeinterface offers a very flexible framework to handle drift as a
-preprocessing step. In this tutorial we will cover the three main
-drift-correction algorithms implemented in SpikeInterface with
-a focus on running the methods and interpreting the output. For
-more information on the theory and implementation of these methods,
+Probe movement is an inevitability when running
+*in vivo* electrophysiology recordings. Motion, caused by physical
+movement of the probe or the sliding of brain tissue
+deforming across the probe, can complicate the sorting
+and analysis of units.
+
+Spikeinterface offers a flexible framework to handle motion correction
+as a preprocessing step. In this tutorial we will cover the three main
+drift-correction algorithms implemented in SpikeInterface
+(**rigid_fast**, **kilosort_like** and **nonrigid_accurate**) with
+a focus on running the methods and interpreting the output.
+
+For more information on the theory and implementation of these methods,
 see the :ref:`motion_correction` section of the documentation.
 
-------------------------------------------
+---------------------
+What is probe drift?
+---------------------
+
+The inserted probe can move from side-to-side (*'x' direction*),
+up-or-down (*'y' direction*) or forwards-or-backwards (*'z' direction*).
+Movement in the 'x' and 'y' direction is harder to model than vertical
+drift (i.e. along the probe depth), and are not handled by most motion
+correction algorithms. Fortunately, vertical drift which is most easily
+handled is most pronounced as the probe is most likely to move along the path of insertion.
+
+Vertical drift can come in two forms, *'rigid'* and *'non-rigid'*. Rigid drift
+is drift caused by movement of the entire probe, and the motion is
+similar across all channels along the probe depth. In contrast,
+non-rigid drift is instead caused by local movements brain along the
+probe, and can selectively affect subsets of channels..
+
+--------------------------
 The drift correction steps
-------------------------------------------
+--------------------------
 
 The easiest way to run drift correction in SpikeInterface is with the
 high-level :py:func:`~spikeinterface.preprocessing.correct_motion()` function.
-This function takes a preprocessed recording as input and then internally runs
-several steps and returns a lazy recording that interpolates the traces on-the-fly
-to compensate for the motion.
+This function takes a preprocessed recording as input and returns a motion-corrected
+recording object. As with all other preprocessing steps, the correction (in this
+case interpolation of the data to correct the detected motion) is lazy and applied on-the-fly when data is needed).
 
-The
-:py:func:`~spikeinterface.preprocessing.correct_motion()`
-function provides a convenient wrapper around a number of sub-functions
-that together implement the full drift correction algorithm.
+The :py:func:`~spikeinterface.preprocessing.correct_motion()`
+function implements motion correction algorithms in a modular way
+wrapping a number of subfunctions that together implement the
+full drift correction algorithm.
 
-Internally this function runs the following steps:
+These drift-correction modules are:
 
-| **1.** ``localize_peaks()``
-| **2.** ``select_peaks()`` (optional)
-| **3.** ``estimate_motion()``
-| **4.** ``interpolate_motion()``
+| **1.** ``localize_peaks()`` (detect spikes and localize their position on the probe)
+| **2.** ``select_peaks()`` (optional, select a subset of peaks to use to estimate motion)
+| **3.** ``estimate_motion()`` (estimate motion using the detected spikes)
+| **4.** ``interpolate_motion()`` (perform interpolation on the raw data to account for the estimated drift).
 
 All these sub-steps have many parameters which dictate the
 speed and effectiveness of motion correction. As such, ``correct_motion``
@@ -87,19 +82,17 @@ provides three setting 'presets' which configure the motion correct
 to proceed either as:
 
 * **rigid_fast** - a fast, not particularly accurate correction assuming ridigt drift.
-* **kilosort-like** - Mimics what is done in Kilosort (REF)
-* **nonrigid_accurate** - A decentralised drift correction, introduced by the Paninski group (REF)
+* **kilosort-like** - Mimics what is done in Kilosort.
+* **nonrigid_accurate** - A decentralised drift correction (DREDGE), introduced by the Paninski group.
 
-**Now, let's dive into running motion correction with these three
-methods on a simulated dataset and interpreting the output.**
+When using motion correction in your analysis, please make sure to
+:ref:`cite the appropriate paper for your chosen method<cite-motion-correction>`.
 
-.. GENERATED FROM PYTHON SOURCE LINES 79-82
 
-.. warning::
-    The below code uses multiprocessing. If you are on Windows, you may
-    need to place the code within a  ``if __name__ == "__main__":`` block.
+**Now, let's dive into running motion correction with these three
+methods on a simulated dataset.**
 
-.. GENERATED FROM PYTHON SOURCE LINES 84-89
+.. GENERATED FROM PYTHON SOURCE LINES 79-84
 
 -------------------------------------------
 Setting up and preprocessing the recording
@@ -107,7 +100,7 @@ Setting up and preprocessing the recording
 
 First, we will import the modules we will need for this tutorial:
 
-.. GENERATED FROM PYTHON SOURCE LINES 89-97
+.. GENERATED FROM PYTHON SOURCE LINES 84-92
 
 .. code-block:: Python
 
@@ -126,24 +119,35 @@ First, we will import the modules we will need for this tutorial:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 98-101
+.. GENERATED FROM PYTHON SOURCE LINES 93-101
+
+Next, we will generate a synthetic, drifting recording. This recording will
+have 100 separate units with firing rates randomly distributed between
+15 and 25 Hz.
+
+We will create a zigzag drift pattern on the recording, starting at
+100 seconds and with a peak-to-peak period of 100 seconds (so we will
+have 9 zigzags through our recording). We also add some non-linearity
+to the imposed motion.
 
-Next, we will generate a synthetic drifting recording. This recording will
-have 500 separate units with firing rates randomly distributed between
-15 and 25 Hz. The recording will be in total 1000 seconds long.
+.. GENERATED FROM PYTHON SOURCE LINES 103-110
 
-.. GENERATED FROM PYTHON SOURCE LINES 101-128
+.. note::
+    This tutorial can take a long time to run with the default arguments.
+    If you would like to run this locally, you may want to edit ``num_units``
+    and ``duration`` to smaller values (e.g. 25 and 100 respectively).
+
+    Also note, the below code uses multiprocessing. If you are on Windows, you may
+    need to place the code within a  ``if __name__ == "__main__":`` block.
+
+.. GENERATED FROM PYTHON SOURCE LINES 110-133
 
 .. code-block:: Python
 
 
-    # We will create a zigzag drift pattern on the recording, starting at
-    # 100 seconds and with a peak-to-peak period of 100 seconds (so we will
-    # have 9 zigzags through our recording). We also add some nonlinear
-    # drift in to the motion (i.e. is not the same across the entire probe).
 
-    num_units = 50 # 500,
-    duration = 100  # 1000,
+    num_units = 100  # 250 still too many I think!
+    duration = 1000
 
     _, raw_recording, _ = generate_drifting_recording(
         num_units=num_units,
@@ -154,9 +158,9 @@ have 500 separate units with firing rates randomly distributed between
                 dict(
                     drift_mode="zigzag",
                     non_rigid_gradient=None, # 0.1,
-                    t_start_drift=10.0,  # 100.0
+                    t_start_drift=int(duration/10),
                     t_end_drift=None,
-                    period_s=10,  # 100
+                    period_s=int(duration/10),
                 ),
             ],
         )
@@ -171,18 +175,18 @@ have 500 separate units with firing rates randomly distributed between
 
  .. code-block:: none
 
-    InjectDriftingTemplatesRecording: 128 channels - 30.0kHz - 1 segments - 3,000,000 samples
-                                      100.00s (1.67 minutes) - float32 dtype - 1.43 GiB
+    InjectDriftingTemplatesRecording: 128 channels - 30.0kHz - 1 segments - 30,000,000 samples
+                                      1,000.00s (16.67 minutes) - float32 dtype - 14.31 GiB
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 129-131
+.. GENERATED FROM PYTHON SOURCE LINES 134-136
 
 Before performing motion correction, we will **preprocess** the recording
 with a bandpass filter and a common median reference.
 
-.. GENERATED FROM PYTHON SOURCE LINES 131-135
+.. GENERATED FROM PYTHON SOURCE LINES 136-140
 
 .. code-block:: Python
 
@@ -197,14 +201,14 @@ with a bandpass filter and a common median reference.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 136-140
+.. GENERATED FROM PYTHON SOURCE LINES 141-145
 
 .. warning::
     It is better to not whiten the recording before motion estimation, as this
     will give a better estimate of the peak locations. Whitening should
     be performed after motion correction.
 
-.. GENERATED FROM PYTHON SOURCE LINES 142-156
+.. GENERATED FROM PYTHON SOURCE LINES 147-160
 
 ----------------------------------------
 Run motion correction with one function!
@@ -215,13 +219,12 @@ We will now run motion correction on our recording using the three
 presets described above - **rigid_fast**, **kilosort_like** and
 **nonrigid_accurate**.
 
-Under the hood, each step, peak localisation, selection, motion estimation
-and interpolation expose a lot of options, making them highly flexible.
-The presets are simply a set of configurations which sets the motion
-correction steps to perform as described in the original methods.
-For example, we can print the full set of **kilosort_like** preset options:
+We can run these presents with the ``preset`` argument of
+:py:func:`~spikeinterface.preprocessing.correct_motion()`. Under the
+hood, the presets define a set of parameters by set how to run the
+4 submodules that make up motion correction (described above).
 
-.. GENERATED FROM PYTHON SOURCE LINES 156-158
+.. GENERATED FROM PYTHON SOURCE LINES 160-162
 
 .. code-block:: Python
 
@@ -240,14 +243,13 @@ For example, we can print the full set of **kilosort_like** preset options:
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 159-163
+.. GENERATED FROM PYTHON SOURCE LINES 163-166
 
 Now, lets run motion correction with our three presets. We will
-set the ``job_kwargs`` to parallelize the job over a number of CPU cores.
-Motion correction is quite computationally intensive and so it is
-very useful to run it across a high numer of jobs to speed it up.
+set the ``job_kwargs`` to parallelize the job over a number of CPU cores—motion
+correction is computationally intensive and will run faster with parallelization.
 
-.. GENERATED FROM PYTHON SOURCE LINES 163-176
+.. GENERATED FROM PYTHON SOURCE LINES 166-179
 
 .. code-block:: Python
 
@@ -259,7 +261,7 @@ very useful to run it across a high numer of jobs to speed it up.
     results = {preset: {} for preset in presets_to_run}
     for preset in presets_to_run:
 
-        recording_corrected, motion_info = si.correct_motion(
+        corrected_recording, motion_info = si.correct_motion(
             preprocessed_recording, preset=preset,  output_motion_info=True, **job_kwargs
         )
         results[preset]["motion_info"] = motion_info
@@ -272,62 +274,54 @@ very useful to run it across a high numer of jobs to speed it up.
 
  .. code-block:: none
 
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-.. GENERATED FROM PYTHON SOURCE LINES 177-182
+.. GENERATED FROM PYTHON SOURCE LINES 180-187
 
 .. seealso::
-   It is often very useful to save the output of motion correction to
-   file, so they can be loaded later. This can be done by setting
-   the ``folder`` argument of ``correct_motion`` to a path to save in.
-   The ``motion_info`` can be loaded back with ``si.load_motion_info``.
+   It is often very useful to save ``motion_info`` to
+   file, so it can be loaded and visualised later. This can be done by setting
+   the ``folder`` argument of
+   :py:func:`~spikeinterface.preprocessing.correct_motion()` to a path to write
+   the motion output to. The ``motion_info`` can be loaded back with
+   ``si.load_motion_info``.
 
-.. GENERATED FROM PYTHON SOURCE LINES 184-207
+.. GENERATED FROM PYTHON SOURCE LINES 189-200
 
 -------------------
 Plotting the results
 -------------------
 
-For all methods we have 4 plots. On the x-axis of all plots we have
+Next, let's plot the results of our motion estimation using the ``plot_motion_info()``
+function. The plot contains 4 panels, on the x-axis of all plots we have
 the (binned time). The plots display:
-  * **top left:** The estimated peak depth for every detected peaks.
+  * **top left:** The estimated peak depth for every detected peak.
   * **top right:** The estimated peak depths after motion correction.
   * **bottom left:** The average motion vector across depths and all motion across spatial depths (for non-rigid estimation).
   * **bottom right:** if motion correction is non rigid, the motion vector across depths is plotted as a map, with the color code representing the motion in micrometers.
 
-These plots are quite complicated, so it is worth covering them in detail.
-For every detected action potential in our recording, we first estimate
-its depth (first panel) using a method from :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
-Then, the probe motion is estimated the location of the detected peaks are
-adjusted to account for this (second panel). The motion estimation produces
-a measure of how much and in what direction the probe is moving at any given
-time bin (third panel). For non-rigid motion correction, the probe is divided
-into subsections - the motion vectors displayed are per subsection (i.e. per
-'binned spatial depth') as well as the average. On the fourth panel, we see a
-more detailed representation of the motion vectors. We can see the motion plotted
-as a heatmap at each binned spatial depth across all time bins. We see it captures
-the zigzag pattern (alternating light and dark colors).
-
-.. GENERATED FROM PYTHON SOURCE LINES 207-221
+.. GENERATED FROM PYTHON SOURCE LINES 200-216
 
 .. code-block:: Python
 
 
     for preset in presets_to_run:
+
         fig = plt.figure(figsize=(7, 7))
+
         si.plot_motion_info(
             results[preset]["motion_info"],
-            recording=recording_corrected,  # recording only used to get the real times
+            recording=corrected_recording,  # the recording is only used to get the real times
             figure=fig,
             depth_lim=(400, 600),
             color_amplitude=True,
             amplitude_cmap="inferno",
-            scatter_decimate=10,
+            scatter_decimate=10,            # Only plot every 10th peak
         )
         fig.suptitle(f"{preset=}")
 
@@ -359,20 +353,34 @@ the zigzag pattern (alternating light and dark colors).
          :class: sphx-glr-multi-img
 
 
-.. rst-class:: sphx-glr-script-out
 
- .. code-block:: none
 
-    /Users/joeziminski/git_repos/forks_/spikeinterface/src/spikeinterface/widgets/motion.py:276: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.
-      ax2.set_ylim(-motion_lim, motion_lim)
 
+.. GENERATED FROM PYTHON SOURCE LINES 217-235
 
+These plots are quite complicated, so it is worth covering them in detail.
+For every detected spike in our recording, we first estimate
+its depth (first panel) using a method from
+:py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
 
+Then, the probe motion is estimated the location of the detected peaks are
+adjusted to account for this (second panel).
 
-.. GENERATED FROM PYTHON SOURCE LINES 222-236
+The motion estimation produces
+a measure of how much and in what direction the probe is moving at any given
+time bin (third panel). For non-rigid motion correction, the probe is divided
+into subsections - the motion vectors displayed are per subsection (i.e. per
+'binned spatial depth') as well as the average.
+
+On the fourth panel, we see a
+more detailed representation of the motion vectors. We can see the motion plotted
+as a heatmap at each binned spatial depth across all time bins. It captures
+the zigzag pattern (alternating light and dark colors) of the injected motion.
+
+.. GENERATED FROM PYTHON SOURCE LINES 237-251
 
 A few comments on the figures:
-  * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a "rigid" case.
+  * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a 'rigid' case.
     The motion amplitude is globally underestimated because it averages across depths.
     However, the corrected peaks are flatter than the non-corrected ones, so the job is partially done.
     The big jump at=600s when the probe start moving is recovered quite well.
@@ -381,64 +389,70 @@ A few comments on the figures:
     The corrected peak locations are flatter than the rigid case.
     The motion vector map is still be a bit noisy at some depths (e.g around 1000um).
   * The preset **nonrigid_accurate** seems to give the best results on this recording.
-    The motion vector seems less noisy globally, but it is not "perfect" (see at the top of the probe 3200um to 3800um).
+    The motion vector seems less noisy globally, but it is not 'perfect' (see at the top of the probe 3200um to 3800um).
     Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion:
     the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable.
     The method defined by this preset is able to capture this.
 
-.. GENERATED FROM PYTHON SOURCE LINES 238-246
+.. GENERATED FROM PYTHON SOURCE LINES 253-269
 
 -------------------------------------------------
 Correcting Peak Locations after Motion Correction
 -------------------------------------------------
 
-To understand how motion correction is applied to our data, it is
-important to understand the spikeinterface `peak` and `peak_locations`
-objects, explored further in the below dropdown.
-
-
-.. GENERATED FROM PYTHON SOURCE LINES 248-262
-
-.. dropdown:: Dropdown title
-
-  Information about detected action potentials is represented in
-  SpikeInterface is ``peaks`` and ``peak_locations`` objects. The
-  ``peaks`` object is an array for every detected action potential in th
-   the dataset, containing its XXX, XX, XX, XX. it is created by the
-   XXXX function.
-
-  The ``peak_locations`` is a partner object to the ``peaks`` object
-  containing XXX. For every peak in ``peak`` there is a corresponding
-  location in ``peak_locations``. The peak locations is estimated
-  using the XXXX function. One way of correcting for motion is to
-  correct these peak locations directly, using the output of
-  ``si.correct_motion`` or ``si.estimate_motion``.
-
-.. GENERATED FROM PYTHON SOURCE LINES 264-278
-
-The result of motion correction can be applied by interpolating the
-raw data to correct for the drift. Essentially, this shifts the signal
-across the probe depth by, at each channel, interpolating other channels
-XXXX. This is performed on the `corrected_recording` output from the
-`correct_motion` channel. This is useful for continuing with
-preprocessing and sorting with the corrected recording.
+The result of motion correction can be applied to the data in two ways.
+The first is by interpolating the raw traces to correct for the estimated drift.
+This changes the data in the
+recording by shifting the signal across channels, and is given in the
+`corrected_recording` output from :py:func:`~spikeinterface.preprocessing.correct_motion()`.
+This is useful in most cases, for continuing
+with preprocessing and sorting with the corrected recording.
+
+The second way is to apply the results of motion correction directly
+to the ``peak_locations`` object. If you are not familiar with
+SpikeInterface's ``peak`` and ``peak_locations`` objects,
+these are explored further in the below dropdown.
+
+.. GENERATED FROM PYTHON SOURCE LINES 271-288
+
+.. dropdown:: 'Peaks' and 'Peak Locations' in SpikeInterface
+
+  Information about detected spikes is represented in
+  SpikeInterface's ``peaks`` and ``peak_locations`` objects. The
+  ``peaks`` object is an array for containing the
+  sample index, channel index (where its signal
+  is strongest), amplitude and recording segment index for every detected spike
+  in the dataset. It is created by the
+  :py:func:`~spikeinterface.sortingcomponents.peak_detection.detect_peaks()`
+  function.
+
+  The ``peak_locations`` is a partner object to the ``peaks`` object,
+  and contains the estimated location (``"x"``, ``"y"``) of the spike. For every spike in
+  ``peaks`` there is a corresponding location in ``peak_locations``.
+  The peak locations is estimated using the
+  :py:func:`~spikeinterface.sortingcomponents.peak_localization.localise_peaks()`
+  function.
+
+.. GENERATED FROM PYTHON SOURCE LINES 290-298
 
 The other way to apply the motion correction is to the ``peaks`` and
 ``peaks_location`` objects directly. This is done using the function
 ``correct_motion_on_peaks()``. Given a set of peaks, peak locations and
-the ``motion`` object output from ``correct_motion``, it will shift the
-location of the peaks according to the motion estimate, outputting a new
+the ``motion`` object output from :py:func:`~spikeinterface.preprocessing.correct_motion()`,
+it will shift the location of the peaks according to the motion estimate, outputting a new
 ``peak_locations`` object. This is done to plot the peak locations in
 the next section.
 
-.. GENERATED FROM PYTHON SOURCE LINES 280-284
+
+.. GENERATED FROM PYTHON SOURCE LINES 301-306
 
 .. warning::
-   Note that the `peak_locations` output by `correct_motion`'s
-   `motion_info` is the ORIGINAL (uncorrected) peak locations. To get the corrected
-   peak locations, `correct_motion_on_peaks()` must be used!
+   Note that the ``peak_locations`` output by
+   :py:func:`~spikeinterface.preprocessing.correct_motion()`
+   (in the ``motion_info`` object) is the original (uncorrected) peak locations.
+   To get the corrected peak locations, ``correct_motion_on_peaks()`` must be used!
 
-.. GENERATED FROM PYTHON SOURCE LINES 284-298
+.. GENERATED FROM PYTHON SOURCE LINES 306-320
 
 .. code-block:: Python
 
@@ -451,9 +465,9 @@ the next section.
 
         original_peak_locations = motion_info["peak_locations"]
 
-        corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], recording_corrected)  # TODO: what recording to use.
+        corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], corrected_recording)
 
-        widget = plot_peaks_on_probe(recording_corrected, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
+        widget = plot_peaks_on_probe(corrected_recording, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
         widget.figure.suptitle(preset)
 
 
@@ -487,17 +501,17 @@ the next section.
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 299-306
+.. GENERATED FROM PYTHON SOURCE LINES 321-328
 
 -------------------------
 Comparing the  Run Times
 -------------------------
 
 The different methods also have different speeds, the 'nonrigid_accurate'
-requires more computation time, particulary at the ``estimate_motion`` phase,
+requires more computation time, in particular at the ``estimate_motion`` phase,
 as seen in the run times:
 
-.. GENERATED FROM PYTHON SOURCE LINES 306-311
+.. GENERATED FROM PYTHON SOURCE LINES 328-333
 
 .. code-block:: Python
 
@@ -515,32 +529,31 @@ as seen in the run times:
  .. code-block:: none
 
     rigid_fast
-    {'detect_and_localize': 15.370884667150676, 'estimate_motion': 0.014450875110924244}
+    {'detect_and_localize': 147.8892037498299, 'estimate_motion': 0.23138420819304883}
     kilosort_like
-    {'detect_and_localize': 15.728250083047897, 'estimate_motion': 0.051764374831691384}
+    {'detect_and_localize': 136.35124741704203, 'estimate_motion': 0.9287026249803603}
     nonrigid_accurate
-    {'detect_and_localize': 28.611334583954886, 'estimate_motion': 55.025963000021875}
+    {'detect_and_localize': 618.623726791935, 'estimate_motion': 131.71972562512383}
 
 
 
 
-.. GENERATED FROM PYTHON SOURCE LINES 312-322
+.. GENERATED FROM PYTHON SOURCE LINES 334-343
 
 ------------------------
 Summary
 ------------------------
 
-That's it for our overall tour of correcting motion in
-SpikeInterface. If you'd like to explore more, see the API docs
-for `estimate_motion()`, `interpolate_motion`() (ay others?). Remember
-that correcting motion makes some assumptions on your datatype - always
-output and plot the motion correction information for your recordings,
-to make sure they are acting as expected!
+That's it for our tour of motion correction in
+SpikeInterface. Remember that correcting motion makes some
+assumptions on your data (e.g. number of channels, noise in the recording)—always
+plot the motion correction information for your
+recordings, to make sure the correction is behaviour as expected!
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (2 minutes 0.428 seconds)
+   **Total running time of the script:** (17 minutes 42.989 seconds)
 
 
 .. _sphx_glr_download_long_tutorials_handle_drift_plot_handle_drift.py:
diff --git a/doc/long_tutorials/handle_drift/plot_handle_drift_codeobj.pickle b/doc/long_tutorials/handle_drift/plot_handle_drift_codeobj.pickle
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diff --git a/doc/long_tutorials/handle_drift/sg_execution_times.rst b/doc/long_tutorials/handle_drift/sg_execution_times.rst
index 0ee5c11129..5126f45a1b 100644
--- a/doc/long_tutorials/handle_drift/sg_execution_times.rst
+++ b/doc/long_tutorials/handle_drift/sg_execution_times.rst
@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**02:00.428** total execution time for 1 file **from long_tutorials/handle_drift**:
+**17:42.989** total execution time for 1 file **from long_tutorials/handle_drift**:
 
 .. container::
 
@@ -33,5 +33,5 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_long_tutorials_handle_drift_plot_handle_drift.py` (``plot_handle_drift.py``)
-     - 02:00.428
+     - 17:42.989
      - 0.0
diff --git a/doc/references.rst b/doc/references.rst
index ace51db951..6193eced2f 100644
--- a/doc/references.rst
+++ b/doc/references.rst
@@ -19,6 +19,7 @@ If you use one of the following preprocessing methods, please cite the appropria
 - :code:`detect_bad_channels(method='coherence+psd')` [IBL]_
 - :code:`common_reference` [Rolston]_
 
+.. _cite-motion-correction:
 Motion Correction
 ^^^^^^^^^^^^^^^^^
 If you use the :code:`correct_motion` method in the preprocessing module, please cite [Garcia]_
diff --git a/examples/long_tutorials/handle_drift/plot_handle_drift.py b/examples/long_tutorials/handle_drift/plot_handle_drift.py
index 3133821804..0088487408 100644
--- a/examples/long_tutorials/handle_drift/plot_handle_drift.py
+++ b/examples/long_tutorials/handle_drift/plot_handle_drift.py
@@ -1,66 +1,61 @@
 """
 ===========================================
-Handle motion/drift with spikeinterface NEW
+Handle probe drift with spikeinterface NEW
 ===========================================
 
-When running *in vivo* electrophysiology recordings, movement of the probe is
-an inevitability, especially when the subjects are not head-fixed. SpikeInterface
-includes a number of popular methods to compensate for probe motion during the
-preprocessing step.
-
-------------------------------------------
-What is drift and where does it come from?
-------------------------------------------
-
-Movement of the probe means that the spikes recorded on the probe 'drift' along it.
-Typically, this motion is vertical along the probe (along the 'y' axis) which
-manifests as the units moving long the probe in space.
-
-All common motion-correction methods address this vertical drift. Horizontal ('x')
-or forward/backwards ('z') motion, that would appear as the amplitude of a unit
-changing over time, are much harder to model and not handled in available motion-correction algorithms.
-Fortunately, vertical drift is the most common form of motion as the probe is
-more likely to move along the path it was inserted, rather than in other directions
-where it is buffeted against the brain.
-
-Vertical drift can come in two forms, 'rigid' and 'non-rigid'. Rigid drift
-is drift caused by movement of the entire probe and the motion is the
-same for all points along the probe. Non-rigid drift is instead caused by
-local movement of parts of the brain along the probe, and can affect
-the recording at only certain points along the probe.
-
-------------------------------------------
-How SpikeInterface handles drift
-------------------------------------------
-
-Spikeinterface offers a very flexible framework to handle drift as a
-preprocessing step. In this tutorial we will cover the three main
-drift-correction algorithms implemented in SpikeInterface with
-a focus on running the methods and interpreting the output. For
-more information on the theory and implementation of these methods,
+Probe movement is an inevitability when running
+*in vivo* electrophysiology recordings. Motion, caused by physical
+movement of the probe or the sliding of brain tissue
+deforming across the probe, can complicate the sorting
+and analysis of units.
+
+Spikeinterface offers a flexible framework to handle motion correction
+as a preprocessing step. In this tutorial we will cover the three main
+drift-correction algorithms implemented in SpikeInterface
+(**rigid_fast**, **kilosort_like** and **nonrigid_accurate**) with
+a focus on running the methods and interpreting the output.
+
+For more information on the theory and implementation of these methods,
 see the :ref:`motion_correction` section of the documentation.
 
-------------------------------------------
+---------------------
+What is probe drift?
+---------------------
+
+The inserted probe can move from side-to-side (*'x' direction*),
+up-or-down (*'y' direction*) or forwards-or-backwards (*'z' direction*).
+Movement in the 'x' and 'y' direction is harder to model than vertical
+drift (i.e. along the probe depth), and are not handled by most motion
+correction algorithms. Fortunately, vertical drift which is most easily
+handled is most pronounced as the probe is most likely to move along the path of insertion.
+
+Vertical drift can come in two forms, *'rigid'* and *'non-rigid'*. Rigid drift
+is drift caused by movement of the entire probe, and the motion is
+similar across all channels along the probe depth. In contrast,
+non-rigid drift is instead caused by local movements brain along the
+probe, and can selectively affect subsets of channels..
+
+--------------------------
 The drift correction steps
-------------------------------------------
+--------------------------
 
 The easiest way to run drift correction in SpikeInterface is with the
 high-level :py:func:`~spikeinterface.preprocessing.correct_motion()` function.
-This function takes a preprocessed recording as input and then internally runs
-several steps and returns a lazy recording that interpolates the traces on-the-fly
-to compensate for the motion.
+This function takes a preprocessed recording as input and returns a motion-corrected
+recording object. As with all other preprocessing steps, the correction (in this
+case interpolation of the data to correct the detected motion) is lazy and applied on-the-fly when data is needed).
 
-The
-:py:func:`~spikeinterface.preprocessing.correct_motion()`
-function provides a convenient wrapper around a number of sub-functions
-that together implement the full drift correction algorithm.
+The :py:func:`~spikeinterface.preprocessing.correct_motion()`
+function implements motion correction algorithms in a modular way
+wrapping a number of subfunctions that together implement the
+full drift correction algorithm.
 
-Internally this function runs the following steps:
+These drift-correction modules are:
 
-| **1.** ``localize_peaks()``
-| **2.** ``select_peaks()`` (optional)
-| **3.** ``estimate_motion()``
-| **4.** ``interpolate_motion()``
+| **1.** ``localize_peaks()`` (detect spikes and localize their position on the probe)
+| **2.** ``select_peaks()`` (optional, select a subset of peaks to use to estimate motion)
+| **3.** ``estimate_motion()`` (estimate motion using the detected spikes)
+| **4.** ``interpolate_motion()`` (perform interpolation on the raw data to account for the estimated drift).
 
 All these sub-steps have many parameters which dictate the
 speed and effectiveness of motion correction. As such, ``correct_motion``
@@ -68,17 +63,17 @@
 to proceed either as:
 
 * **rigid_fast** - a fast, not particularly accurate correction assuming ridigt drift.
-* **kilosort-like** - Mimics what is done in Kilosort (REF)
-* **nonrigid_accurate** - A decentralised drift correction, introduced by the Paninski group (REF)
+* **kilosort-like** - Mimics what is done in Kilosort.
+* **nonrigid_accurate** - A decentralised drift correction (DREDGE), introduced by the Paninski group.
+
+When using motion correction in your analysis, please make sure to
+:ref:`cite the appropriate paper for your chosen method<cite-motion-correction>`.
+
 
 **Now, let's dive into running motion correction with these three
-methods on a simulated dataset and interpreting the output.**
+methods on a simulated dataset.**
 
 """
-# %%
-#.. warning::
-#    The below code uses multiprocessing. If you are on Windows, you may
-#    need to place the code within a  ``if __name__ == "__main__":`` block.
 
 # %%
 # -------------------------------------------
@@ -95,17 +90,27 @@
 from spikeinterface.widgets import plot_peaks_on_probe
 
 # %% read the file
-# Next, we will generate a synthetic drifting recording. This recording will
-# have 500 separate units with firing rates randomly distributed between
-# 15 and 25 Hz. The recording will be in total 1000 seconds long.
-
+# Next, we will generate a synthetic, drifting recording. This recording will
+# have 100 separate units with firing rates randomly distributed between
+# 15 and 25 Hz.
+#
 # We will create a zigzag drift pattern on the recording, starting at
 # 100 seconds and with a peak-to-peak period of 100 seconds (so we will
-# have 9 zigzags through our recording). We also add some nonlinear
-# drift in to the motion (i.e. is not the same across the entire probe).
+# have 9 zigzags through our recording). We also add some non-linearity
+# to the imposed motion.
+
+# %%
+#.. note::
+#    This tutorial can take a long time to run with the default arguments.
+#    If you would like to run this locally, you may want to edit ``num_units``
+#    and ``duration`` to smaller values (e.g. 25 and 100 respectively).
+#
+#    Also note, the below code uses multiprocessing. If you are on Windows, you may
+#    need to place the code within a  ``if __name__ == "__main__":`` block.
+
 
-num_units = 25  # 500,
-duration = 50   # 1000,
+num_units = 100  # 250 still too many I think!
+duration = 1000
 
 _, raw_recording, _ = generate_drifting_recording(
     num_units=num_units,
@@ -116,9 +121,9 @@
             dict(
                 drift_mode="zigzag",
                 non_rigid_gradient=None, # 0.1,
-                t_start_drift=10.0,  # 100.0
+                t_start_drift=int(duration/10),
                 t_end_drift=None,
-                period_s=10,  # 100
+                period_s=int(duration/10),
             ),
         ],
     )
@@ -148,18 +153,16 @@
 # presets described above - **rigid_fast**, **kilosort_like** and
 # **nonrigid_accurate**.
 #
-# Under the hood, each step, peak localisation, selection, motion estimation
-# and interpolation expose a lot of options, making them highly flexible.
-# The presets are simply a set of configurations which sets the motion
-# correction steps to perform as described in the original methods.
-# For example, we can print the full set of **kilosort_like** preset options:
+# We can run these presents with the ``preset`` argument of
+# :py:func:`~spikeinterface.preprocessing.correct_motion()`. Under the
+# hood, the presets define a set of parameters by set how to run the
+# 4 submodules that make up motion correction (described above).
 print(motion_options_preset["kilosort_like"])
 
 # %%
 # Now, lets run motion correction with our three presets. We will
-# set the ``job_kwargs`` to parallelize the job over a number of CPU cores.
-# Motion correction is quite computationally intensive and so it is
-# very useful to run it across a high numer of jobs to speed it up.
+# set the ``job_kwargs`` to parallelize the job over a number of CPU cores—motion
+# correction is computationally intensive and will run faster with parallelization.
 
 presets_to_run = ("rigid_fast", "kilosort_like", "nonrigid_accurate")
 
@@ -168,59 +171,71 @@
 results = {preset: {} for preset in presets_to_run}
 for preset in presets_to_run:
 
-    recording_corrected, motion_info = si.correct_motion(
+    corrected_recording, motion_info = si.correct_motion(
         preprocessed_recording, preset=preset,  output_motion_info=True, **job_kwargs
     )
     results[preset]["motion_info"] = motion_info
 
 # %%
 #.. seealso::
-#   It is often very useful to save the output of motion correction to
-#   file, so they can be loaded later. This can be done by setting
-#   the ``folder`` argument of ``correct_motion`` to a path to save in.
-#   The ``motion_info`` can be loaded back with ``si.load_motion_info``.
+#   It is often very useful to save ``motion_info`` to
+#   file, so it can be loaded and visualised later. This can be done by setting
+#   the ``folder`` argument of
+#   :py:func:`~spikeinterface.preprocessing.correct_motion()` to a path to write
+#   the motion output to. The ``motion_info`` can be loaded back with
+#   ``si.load_motion_info``.
 
 # %%
 # -------------------
 # Plotting the results
 # -------------------
 #
-# For all methods we have 4 plots. On the x-axis of all plots we have
+# Next, let's plot the results of our motion estimation using the ``plot_motion_info()``
+# function. The plot contains 4 panels, on the x-axis of all plots we have
 # the (binned time). The plots display:
-#   * **top left:** The estimated peak depth for every detected peaks.
+#   * **top left:** The estimated peak depth for every detected peak.
 #   * **top right:** The estimated peak depths after motion correction.
 #   * **bottom left:** The average motion vector across depths and all motion across spatial depths (for non-rigid estimation).
 #   * **bottom right:** if motion correction is non rigid, the motion vector across depths is plotted as a map, with the color code representing the motion in micrometers.
-#
-# These plots are quite complicated, so it is worth covering them in detail.
-# For every detected action potential in our recording, we first estimate
-# its depth (first panel) using a method from :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
-# Then, the probe motion is estimated the location of the detected peaks are
-# adjusted to account for this (second panel). The motion estimation produces
-# a measure of how much and in what direction the probe is moving at any given
-# time bin (third panel). For non-rigid motion correction, the probe is divided
-# into subsections - the motion vectors displayed are per subsection (i.e. per
-# 'binned spatial depth') as well as the average. On the fourth panel, we see a
-# more detailed representation of the motion vectors. We can see the motion plotted
-# as a heatmap at each binned spatial depth across all time bins. We see it captures
-# the zigzag pattern (alternating light and dark colors).
 
 for preset in presets_to_run:
+
     fig = plt.figure(figsize=(7, 7))
+
     si.plot_motion_info(
         results[preset]["motion_info"],
-        recording=recording_corrected,  # recording only used to get the real times
+        recording=corrected_recording,  # the recording is only used to get the real times
         figure=fig,
         depth_lim=(400, 600),
         color_amplitude=True,
         amplitude_cmap="inferno",
-        scatter_decimate=10,
+        scatter_decimate=10,            # Only plot every 10th peak
     )
     fig.suptitle(f"{preset=}")
 
+# %%
+# These plots are quite complicated, so it is worth covering them in detail.
+# For every detected spike in our recording, we first estimate
+# its depth (first panel) using a method from
+# :py:func:`~spikeinterface.postprocessing.compute_unit_locations()`.
+#
+# Then, the probe motion is estimated the location of the detected peaks are
+# adjusted to account for this (second panel).
+#
+# The motion estimation produces
+# a measure of how much and in what direction the probe is moving at any given
+# time bin (third panel). For non-rigid motion correction, the probe is divided
+# into subsections - the motion vectors displayed are per subsection (i.e. per
+# 'binned spatial depth') as well as the average.
+#
+# On the fourth panel, we see a
+# more detailed representation of the motion vectors. We can see the motion plotted
+# as a heatmap at each binned spatial depth across all time bins. It captures
+# the zigzag pattern (alternating light and dark colors) of the injected motion.
+
 # %%
 # A few comments on the figures:
-#   * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a "rigid" case.
+#   * The preset **'rigid_fast'** has only one motion vector for the entire probe because it is a 'rigid' case.
 #     The motion amplitude is globally underestimated because it averages across depths.
 #     However, the corrected peaks are flatter than the non-corrected ones, so the job is partially done.
 #     The big jump at=600s when the probe start moving is recovered quite well.
@@ -229,7 +244,7 @@
 #     The corrected peak locations are flatter than the rigid case.
 #     The motion vector map is still be a bit noisy at some depths (e.g around 1000um).
 #   * The preset **nonrigid_accurate** seems to give the best results on this recording.
-#     The motion vector seems less noisy globally, but it is not "perfect" (see at the top of the probe 3200um to 3800um).
+#     The motion vector seems less noisy globally, but it is not 'perfect' (see at the top of the probe 3200um to 3800um).
 #     Also note that in the first part of the recording before the imposed motion (0-600s) we clearly have a non-rigid motion:
 #     the upper part of the probe (2000-3000um) experience some drifts, but the lower part (0-1000um) is relatively stable.
 #     The method defined by this preset is able to capture this.
@@ -239,47 +254,54 @@
 # Correcting Peak Locations after Motion Correction
 # -------------------------------------------------
 #
-# To understand how motion correction is applied to our data, it is
-# important to understand the spikeinterface `peak` and `peak_locations`
-# objects, explored further in the below dropdown.
+# The result of motion correction can be applied to the data in two ways.
+# The first is by interpolating the raw traces to correct for the estimated drift.
+# This changes the data in the
+# recording by shifting the signal across channels, and is given in the
+# `corrected_recording` output from :py:func:`~spikeinterface.preprocessing.correct_motion()`.
+# This is useful in most cases, for continuing
+# with preprocessing and sorting with the corrected recording.
 #
+# The second way is to apply the results of motion correction directly
+# to the ``peak_locations`` object. If you are not familiar with
+# SpikeInterface's ``peak`` and ``peak_locations`` objects,
+# these are explored further in the below dropdown.
 
 # %%
 # .. dropdown:: 'Peaks' and 'Peak Locations' in SpikeInterface
 #
-#   Information about detected action potentials is represented in
-#   SpikeInterface is ``peaks`` and ``peak_locations`` objects. The
-#   ``peaks`` object is an array for every detected action potential in th
-#    the dataset, containing its XXX, XX, XX, XX. it is created by the
-#    XXXX function.
+#   Information about detected spikes is represented in
+#   SpikeInterface's ``peaks`` and ``peak_locations`` objects. The
+#   ``peaks`` object is an array for containing the
+#   sample index, channel index (where its signal
+#   is strongest), amplitude and recording segment index for every detected spike
+#   in the dataset. It is created by the
+#   :py:func:`~spikeinterface.sortingcomponents.peak_detection.detect_peaks()`
+#   function.
 #
-#   The ``peak_locations`` is a partner object to the ``peaks`` object
-#   containing XXX. For every peak in ``peak`` there is a corresponding
-#   location in ``peak_locations``. The peak locations is estimated
-#   using the XXXX function. One way of correcting for motion is to
-#   correct these peak locations directly, using the output of
-#   ``si.correct_motion`` or ``si.estimate_motion``.
+#   The ``peak_locations`` is a partner object to the ``peaks`` object,
+#   and contains the estimated location (``"x"``, ``"y"``) of the spike. For every spike in
+#   ``peaks`` there is a corresponding location in ``peak_locations``.
+#   The peak locations is estimated using the
+#   :py:func:`~spikeinterface.sortingcomponents.peak_localization.localise_peaks()`
+#   function.
 
 # %%
-# The result of motion correction can be applied by interpolating the
-# raw data to correct for the drift. Essentially, this shifts the signal
-# across the probe depth by, at each channel, interpolating other channels
-# XXXX. This is performed on the `corrected_recording` output from the
-# `correct_motion` channel. This is useful for continuing with
-# preprocessing and sorting with the corrected recording.
-#
 # The other way to apply the motion correction is to the ``peaks`` and
 # ``peaks_location`` objects directly. This is done using the function
 # ``correct_motion_on_peaks()``. Given a set of peaks, peak locations and
-# the ``motion`` object output from ``correct_motion``, it will shift the
-# location of the peaks according to the motion estimate, outputting a new
+# the ``motion`` object output from :py:func:`~spikeinterface.preprocessing.correct_motion()`,
+# it will shift the location of the peaks according to the motion estimate, outputting a new
 # ``peak_locations`` object. This is done to plot the peak locations in
 # the next section.
+#
+
 
 # %%
 #.. warning::
-#   Note that the ``peak_locations`` output by ``correct_motion``s
-#   ``motion_info`` is the ORIGINAL (uncorrected) peak locations.
+#   Note that the ``peak_locations`` output by
+#   :py:func:`~spikeinterface.preprocessing.correct_motion()`
+#   (in the ``motion_info`` object) is the original (uncorrected) peak locations.
 #   To get the corrected peak locations, ``correct_motion_on_peaks()`` must be used!
 
 for preset in presets_to_run:
@@ -290,9 +312,9 @@
 
     original_peak_locations = motion_info["peak_locations"]
 
-    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], recording_corrected)  # TODO: what recording to use.
+    corrected_peak_locations = correct_motion_on_peaks(peaks, original_peak_locations, motion_info['motion'], corrected_recording)
 
-    widget = plot_peaks_on_probe(recording_corrected, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
+    widget = plot_peaks_on_probe(corrected_recording, [peaks, peaks], [original_peak_locations, corrected_peak_locations], ylim=(300,600))
     widget.figure.suptitle(preset)
 
 # %%
@@ -301,7 +323,7 @@
 # -------------------------
 #
 # The different methods also have different speeds, the 'nonrigid_accurate'
-# requires more computation time, particulary at the ``estimate_motion`` phase,
+# requires more computation time, in particular at the ``estimate_motion`` phase,
 # as seen in the run times:
 
 for preset in presets_to_run:
@@ -313,9 +335,8 @@
 # Summary
 # ------------------------
 #
-# That's it for our overall tour of correcting motion in
-# SpikeInterface. If you'd like to explore more, see the API docs
-# for `estimate_motion()`, `interpolate_motion`() (ay others?). Remember
-# that correcting motion makes some assumptions on your datatype - always
-# output and plot the motion correction information for your recordings,
-# to make sure they are acting as expected!
+# That's it for our tour of motion correction in
+# SpikeInterface. Remember that correcting motion makes some
+# assumptions on your data (e.g. number of channels, noise in the recording)—always
+# plot the motion correction information for your
+# recordings, to make sure the correction is behaviour as expected!