From 20bf64f41193b7ddeda360e7ecbca46a0d30ee6b Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Mon, 24 Jun 2024 18:40:30 +0100 Subject: [PATCH 01/12] Add rough unfinished first draft. --- doc/how_to/handle_times.rst | 134 ++++++++++++++++++++++++++++++++++++ 1 file changed, 134 insertions(+) create mode 100644 doc/how_to/handle_times.rst diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst new file mode 100644 index 0000000000..f101282031 --- /dev/null +++ b/doc/how_to/handle_times.rst @@ -0,0 +1,134 @@ +How SpikeInterface handles Time +================================= + +Extracellula electrophysiology commonly involves synchronisation of events +across many timestreams. For example, an experiment may involve +displaying a stimuli to an animal and recording the stimuli-evoked +neuronal responses. It is critical that timings is represented in +a clear way across data streams so they may be properly syncronised during +analysis. + +Below, we will explore the ways that SpikeInterface represents time +information and how you can use it in your analysis to ensure the timing +of your spike events is represented faithfully. The ways that will +be explored are **providing no times**, **providing start times** +and **providing the full time array**. + +A familiarity with the terms used in digital sampling (e.g. sampling +frequency) will be assumed below. If you are not familiar with these concepts, +please see the below dropdown for a refresher. + +.. dropdown:: Digital Sampling + + What we are fundamentally interested in is recording the electrical + waveforms found in the brain. In the real world, these are 'continuous' + signals changing through time. To represent these real signals on our + finite-memory computers, we must 'sample' the real-world continuous signals + at discrete time points. + + When sampling our signal the fundamental question we need to ask is - how fast? + A natural approach is to sample our signal every X seconds. For the sake of example, + let's say we decide to sample our signal 4 times per second. This is known as the + 'sampling frequency' (sometimes denoted $f_s$) and is expressed in Hertz (Hz) i.e. + 'samples-per-second'. + + Another way of thinking about the same idea is to ask - how often are we + sampling our signal? In our example, we are sampling the signal once every 0.25 seconds. + This is known as the 'sampling step' (sometimes denoted $t_s$) and is the inverse of + the sampling frequency. + + [PICTURE] + + In the real world, we will sample our signal much faster. For example, Neuropixels + samples at 30 kHz (30,000 samples per second), with a + sampling step of 1/30000 = 0.0003 seconds! + + On our computer, we typically repesent time as a long array of numbers. + For example, XXXX. Often, it is easier to access this array using 'indices' + rather than the raw time units. MORE ON THIS. It is useful to remember + these below quick conversion between time and indices that are often + used in code: + + ``time * sampling_frequency = index`` and + ``index * sampling_step = index * (1/sampling_frequency = time``. + +------------------------------------------------------------------ +An Overview of the possible Time representations in SpikeInterface +------------------------------------------------------------------ + +When loading a raw recording, SpikeInterface only has access to the +sampling frequency and raw data samples of the data. Only in rare +cases are the time-stamps of extracellular electrophysiological data +are stored alongside the recordings and can be directly loaded +(TODO: is any of that true?) + +In some applications, having the exact time is very important. +For example, NeuroPixels. In this case want to include the +exact times. How do you do this? where is this information stored? + +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Providing no times to spikeinterface +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In this case, all times used will be generated from the sample index +and sampling frequency. For example, if a spike peak is detected at +index 300 of the raw data, and the sampling frequency is 30 kHz, +the peak time will be 300 * (1/30000) = 0.01 s. + +Note that time is represented in spikeinterface in XXXX datatype, and so +resolution will be this. For example, a spike at index 1000 of the raw +data will be represented as 1000 * (1/30000) = 0.03333XXX seconds. + +For a multi-segment recording, each segment will be fixed to start at +0 seconds (TODO: is this true)? If there is a peak at index 300 in +segment one and index in the peak at index 300 in segment two, both +segments action potential peaks will be given a spike time of 0.1 s, +and their segment tracked to distinguish them. TODO: CHECK + +^^^^^^^^^^^^^^^^^^^^^^^^^ +Providing the start time +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Alternatively, the start-time of each segment can be provided to +spikeinterface. The same method (index * (1 / sampling_frequency)) +will be used to determine event spike, but now an initial offset +will be added using the `t_start` variable. TODO: reword, less confusing. + +For example, imagine you have two recording sessions, recorded at +30 kHz sampling frequency, both 30 minutes long. You record one session, +wait 10 minutes, then record another session. In this example the will imagine +these two sessions are loaded into spikeinterface as two segments on a +single recording (however, the process would be the same if you had loaded +the sessions separately into two recording objects). + +If you wanted to represent the sessions as starting relative to the +first session, and keeping the true timings of the recording, you can +do: + +``` +recording.select_segment(1).set_times(XXX) +``` + +Alternatively, if you wanted to ignore the 10 minute gap, you could do: + +``` +recording.select_segment(1).set_times(XXXX) +``` + +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Providing the full time array +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Finally, may want to get full time array. Where do you get this from? + +Note that for some extractors, e.g. read_openephys you can load the syncrhonized timestamps directly (load_sync_timestamps param). It would be important to mention! + + +-------------------------- +Accessing time information +-------------------------- + +Cover the The two time array functions. + +`time_to_sample_index` +`sample_index_to_time` From 6b01c8ef9fabd74cd8a589f3912af5d54809dd9d Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Thu, 27 Jun 2024 20:40:36 +0100 Subject: [PATCH 02/12] Add some rough code. --- doc/how_to/handle_times.rst | 24 +++++++++++++++++++++--- 1 file changed, 21 insertions(+), 3 deletions(-) diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst index f101282031..818ed64dbb 100644 --- a/doc/how_to/handle_times.rst +++ b/doc/how_to/handle_times.rst @@ -1,7 +1,7 @@ How SpikeInterface handles Time ================================= -Extracellula electrophysiology commonly involves synchronisation of events +Extracellular electrophysiology commonly involves synchronisation of events across many timestreams. For example, an experiment may involve displaying a stimuli to an animal and recording the stimuli-evoked neuronal responses. It is critical that timings is represented in @@ -85,10 +85,17 @@ segment one and index in the peak at index 300 in segment two, both segments action potential peaks will be given a spike time of 0.1 s, and their segment tracked to distinguish them. TODO: CHECK +# TODO: yes follow a code example! +```sorting.get_unit_spike_train(0, return_times=True, segment_index=1)``` + ^^^^^^^^^^^^^^^^^^^^^^^^^ Providing the start time ^^^^^^^^^^^^^^^^^^^^^^^^^ +### TODO :::: This section make no sense because you can't set +`t_start` directly as far as I can tell, it is only available on +a few extractors. + Alternatively, the start-time of each segment can be provided to spikeinterface. The same method (index * (1 / sampling_frequency)) will be used to determine event spike, but now an initial offset @@ -114,15 +121,27 @@ Alternatively, if you wanted to ignore the 10 minute gap, you could do: ``` recording.select_segment(1).set_times(XXXX) ``` +``` print spike times``` +sorting.get_unit_spike_train(0, return_times=True, segment_index=1) +# TODO: it says use time vector but this doesn't return times. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Providing the full time array ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Finally, may want to get full time array. Where do you get this from? +Loaded - some more information on where from + +`recording.has_time_vector()`. + +we can set times e.g. as above. -Note that for some extractors, e.g. read_openephys you can load the syncrhonized timestamps directly (load_sync_timestamps param). It would be important to mention! +1) set the times +2) print the times. Note how they are different to the above case! +Note that for some extractors, e.g. read_openephys you can load the +syncrhonized timestamps directly (load_sync_timestamps param). It would be +important to mention! Section on when files are loaded autoamticaly! -------------------------- Accessing time information @@ -130,5 +149,4 @@ Accessing time information Cover the The two time array functions. -`time_to_sample_index` `sample_index_to_time` From 0682bbe25ad44262e95d5c58d8cad840e3ded246 Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Mon, 28 Oct 2024 14:15:05 +0000 Subject: [PATCH 03/12] Add new version. --- doc/how_to/handle-times-sampling-image.png | Bin 0 -> 26044 bytes doc/how_to/handle_times.rst | 206 +++++++++------- doc/how_to_new/how_to_new_jupyter.zip | Bin 0 -> 5870 bytes doc/how_to_new/how_to_new_python.zip | Bin 0 -> 3972 bytes ...sphx_glr_plot_combine_recordings_thumb.png | Bin 0 -> 26794 bytes doc/how_to_new/index.rst | 65 +++++ doc/how_to_new/plot_combine_recordings.ipynb | 129 ++++++++++ doc/how_to_new/plot_combine_recordings.py | 92 +++++++ doc/how_to_new/plot_combine_recordings.py.md5 | 1 + doc/how_to_new/plot_combine_recordings.rst | 232 ++++++++++++++++++ .../plot_combine_recordings_codeobj.pickle | Bin 0 -> 4419 bytes doc/how_to_new/sg_execution_times.rst | 37 +++ doc/sg_execution_times.rst | 82 +++++++ 13 files changed, 751 insertions(+), 93 deletions(-) create mode 100644 doc/how_to/handle-times-sampling-image.png create mode 100644 doc/how_to_new/how_to_new_jupyter.zip create mode 100644 doc/how_to_new/how_to_new_python.zip create mode 100644 doc/how_to_new/images/thumb/sphx_glr_plot_combine_recordings_thumb.png create mode 100644 doc/how_to_new/index.rst create mode 100644 doc/how_to_new/plot_combine_recordings.ipynb create mode 100644 doc/how_to_new/plot_combine_recordings.py create mode 100644 doc/how_to_new/plot_combine_recordings.py.md5 create mode 100644 doc/how_to_new/plot_combine_recordings.rst create mode 100644 doc/how_to_new/plot_combine_recordings_codeobj.pickle create mode 100644 doc/how_to_new/sg_execution_times.rst create mode 100644 doc/sg_execution_times.rst diff --git a/doc/how_to/handle-times-sampling-image.png 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But, we do not use the gallery +page features for the How To and so do not need it. Instead, we have a custom .rst file +in the How To folder that points to the sphinx-gallery outputs. + + + +.. raw:: html + +
+ +.. thumbnail-parent-div-open + +.. raw:: html + +
+ +.. only:: html + + .. image:: /how_to_new/images/thumb/sphx_glr_plot_combine_recordings_thumb.png + :alt: + + :ref:`sphx_glr_how_to_new_plot_combine_recordings.py` + +.. raw:: html + +
Combine recordings in SpikeInterface
+
+ + +.. thumbnail-parent-div-close + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /how_to_new/plot_combine_recordings + + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-gallery + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download all examples in Python source code: how_to_new_python.zip ` + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download all examples in Jupyter notebooks: how_to_new_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/how_to_new/plot_combine_recordings.ipynb b/doc/how_to_new/plot_combine_recordings.ipynb new file mode 100644 index 0000000000..6deb20e059 --- /dev/null +++ b/doc/how_to_new/plot_combine_recordings.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Combine recordings in SpikeInterface\n\nIn this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware\nsettings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to\nsplit their recording into multiple files for different experimental conditions. If the probe has not been moved,\nhowever, then during sorting it would likely make sense to combine these individual recording objects into one\nrecording object.\n\n## Why Combine?\n\nCombining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording.\n\nSpike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the\nexact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will\nbe the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each\n`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between\neach `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units.\n\n## Combining recordings continuous in time\n\nSome file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user\ncontrolled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two\ntools for bringing together these files into one `Recording` object.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Concatenating Recordings\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# First, let's cover concatenating recordings together. This will generate a mono-segment\n# recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan\n\nimport spikeinterface as si # This is only core\nimport spikeinterface.extractors as se\n\nrecording_one, _ = si.generate_ground_truth_recording(durations=[25])\nrecording_two, _ = si.generate_ground_truth_recording(durations=[25])\n\nprint(recording_one)\n\nprint(recording_two)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we will concatenate these recordings together.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "concatenated_recording = si.concatenate_recordings([recording_one, recording_two])\n\nprint(concatenated_recording)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we know that we will deal with a lot of files, we can actually work our\nway through a series of them relatively quickly by doing\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)]\nlist_of_recordings = []\nfor rec in list_of_recs:\n list_of_recordings.append(rec)\nrecording = si.concatenate_recordings(list_of_recordings)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Append Recordings\n\nIf you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use\n`append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data\nseparate, but it is important to note that not all sorters can handle multi-segment objects.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording = si.append_recordings([recording_one, recording_two])\n\nprint(recording)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pitfalls\n\nIt's important to remember that these operations are directional. So:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "recording_forward = si.concatenate_recordings([recording_one, recording_two])\nrecording_backward = si.concatenate_recordings([recording_two, recording_one])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is important because your spike times will be relative to the start of your recording.\n\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/doc/how_to_new/plot_combine_recordings.py b/doc/how_to_new/plot_combine_recordings.py new file mode 100644 index 0000000000..e61880e769 --- /dev/null +++ b/doc/how_to_new/plot_combine_recordings.py @@ -0,0 +1,92 @@ +""" +==================================== +Combine recordings in SpikeInterface +==================================== + +In this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware +settings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to +split their recording into multiple files for different experimental conditions. If the probe has not been moved, +however, then during sorting it would likely make sense to combine these individual recording objects into one +recording object. + +------------ +Why Combine? +------------ + +Combining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording. + +Spike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the +exact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will +be the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each +`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between +each `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units. + +--------------------------------------- +Combining recordings continuous in time +--------------------------------------- + +Some file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user +controlled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two +tools for bringing together these files into one `Recording` object. +""" + +# %% +# ------------------------ +# Concatenating Recordings +# ------------------------ + +# First, let's cover concatenating recordings together. This will generate a mono-segment +# recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan + +import spikeinterface as si # This is only core +import spikeinterface.extractors as se + +recording_one, _ = si.generate_ground_truth_recording(durations=[25]) +recording_two, _ = si.generate_ground_truth_recording(durations=[25]) + +print(recording_one) + +print(recording_two) + +# %% +# Next, we will concatenate these recordings together. + +concatenated_recording = si.concatenate_recordings([recording_one, recording_two]) + +print(concatenated_recording) + +# %% +# If we know that we will deal with a lot of files, we can actually work our +# way through a series of them relatively quickly by doing + +list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)] +list_of_recordings = [] +for rec in list_of_recs: + list_of_recordings.append(rec) +recording = si.concatenate_recordings(list_of_recordings) + +# %% +# ----------------- +# Append Recordings +# ----------------- +# +# If you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use +# `append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data +# separate, but it is important to note that not all sorters can handle multi-segment objects. + +recording = si.append_recordings([recording_one, recording_two]) + +print(recording) + +# %% +# -------- +# Pitfalls +# -------- +# +# It's important to remember that these operations are directional. So: + +recording_forward = si.concatenate_recordings([recording_one, recording_two]) +recording_backward = si.concatenate_recordings([recording_two, recording_one]) + +# %% +# This is important because your spike times will be relative to the start of your recording. diff --git a/doc/how_to_new/plot_combine_recordings.py.md5 b/doc/how_to_new/plot_combine_recordings.py.md5 new file mode 100644 index 0000000000..ff3eece3cb --- /dev/null +++ b/doc/how_to_new/plot_combine_recordings.py.md5 @@ -0,0 +1 @@ +10e64ebfb85b2795b4e151146f63e213 diff --git a/doc/how_to_new/plot_combine_recordings.rst b/doc/how_to_new/plot_combine_recordings.rst new file mode 100644 index 0000000000..f3c72f41ff --- /dev/null +++ b/doc/how_to_new/plot_combine_recordings.rst @@ -0,0 +1,232 @@ + +.. DO NOT EDIT. +.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. +.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: +.. "how_to_new\plot_combine_recordings.py" +.. LINE NUMBERS ARE GIVEN BELOW. + +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + :ref:`Go to the end ` + to download the full example code. + +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_how_to_new_plot_combine_recordings.py: + + +==================================== +Combine recordings in SpikeInterface +==================================== + +In this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware +settings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to +split their recording into multiple files for different experimental conditions. If the probe has not been moved, +however, then during sorting it would likely make sense to combine these individual recording objects into one +recording object. + +------------ +Why Combine? +------------ + +Combining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording. + +Spike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the +exact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will +be the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each +`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between +each `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units. + +--------------------------------------- +Combining recordings continuous in time +--------------------------------------- + +Some file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user +controlled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two +tools for bringing together these files into one `Recording` object. + +.. GENERATED FROM PYTHON SOURCE LINES 34-37 + +------------------------ +Concatenating Recordings +------------------------ + +.. GENERATED FROM PYTHON SOURCE LINES 37-51 + +.. code-block:: Python + + + # First, let's cover concatenating recordings together. This will generate a mono-segment + # recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan + + import spikeinterface as si # This is only core + import spikeinterface.extractors as se + + recording_one, _ = si.generate_ground_truth_recording(durations=[25]) + recording_two, _ = si.generate_ground_truth_recording(durations=[25]) + + print(recording_one) + + print(recording_two) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + C:\fMRIData\git-repo\forks\spikeinterface\src\spikeinterface\core\generate.py:2255: UserWarning: generate_unit_locations(): no solution for minimum_distance=20 and max_iteration=100 + warnings.warn(f"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=}") + GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 625,000 samples - 25.00s - float32 dtype + 9.54 MiB + GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 625,000 samples - 25.00s - float32 dtype + 9.54 MiB + + + + +.. GENERATED FROM PYTHON SOURCE LINES 52-53 + +Next, we will concatenate these recordings together. + +.. GENERATED FROM PYTHON SOURCE LINES 53-58 + +.. code-block:: Python + + + concatenated_recording = si.concatenate_recordings([recording_one, recording_two]) + + print(concatenated_recording) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 1,250,000 samples - 50.00s + float32 dtype - 19.07 MiB + + + + +.. GENERATED FROM PYTHON SOURCE LINES 59-61 + +If we know that we will deal with a lot of files, we can actually work our +way through a series of them relatively quickly by doing + +.. GENERATED FROM PYTHON SOURCE LINES 61-68 + +.. code-block:: Python + + + list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)] + list_of_recordings = [] + for rec in list_of_recs: + list_of_recordings.append(rec) + recording = si.concatenate_recordings(list_of_recordings) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 69-76 + +----------------- +Append Recordings +----------------- + +If you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use +`append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data +separate, but it is important to note that not all sorters can handle multi-segment objects. + +.. GENERATED FROM PYTHON SOURCE LINES 76-81 + +.. code-block:: Python + + + recording = si.append_recordings([recording_one, recording_two]) + + print(recording) + + + + + +.. rst-class:: sphx-glr-script-out + + .. code-block:: none + + GroundTruthRecording: 4 channels - 25.0kHz - 2 segments - 1,250,000 samples - 50.00s + float32 dtype - 19.07 MiB + Segments: + Samples: 625,000 | 625,000 + Durations: 25.00s | 25.00s + Memory: 9.54 MiB | 9.54 MiB + + + + +.. GENERATED FROM PYTHON SOURCE LINES 82-87 + +-------- +Pitfalls +-------- + +It's important to remember that these operations are directional. So: + +.. GENERATED FROM PYTHON SOURCE LINES 87-91 + +.. code-block:: Python + + + recording_forward = si.concatenate_recordings([recording_one, recording_two]) + recording_backward = si.concatenate_recordings([recording_two, recording_one]) + + + + + + + + +.. GENERATED FROM PYTHON SOURCE LINES 92-93 + +This is important because your spike times will be relative to the start of your recording. + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** (0 minutes 0.203 seconds) + + +.. _sphx_glr_download_how_to_new_plot_combine_recordings.py: + +.. only:: html + + .. container:: sphx-glr-footer sphx-glr-footer-example + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_combine_recordings.ipynb ` + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_combine_recordings.py ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/how_to_new/plot_combine_recordings_codeobj.pickle b/doc/how_to_new/plot_combine_recordings_codeobj.pickle new file mode 100644 index 0000000000000000000000000000000000000000..a3564ff771cc95eeccdb66bb845f6d0db555ed31 GIT binary patch literal 4419 zcmd^D&2G~`5C%~oRgy|6v}yqfD3|s?&b=WDJ#YajpKj2l=+zS<-Xi}1(z3yJ3unI`AA3Y zq5c37(NUqvl>znR5W?>82p=L$1Y$kcLu~*_W_jXVG9IXc}jmJ1$4MEP&r#%YV`C zLX@tvDuQ{?xp?JaEOhQ?=&WSu>=6uqqu>%~ZB^3ZD%w_fhGxY#nTuvIoQ3XohVDqA z-DAOAPf(W9Tq~lPwo}Q)Tuo$93wQ+&DzdQ<*vTAe<$DlFSdQ(g&Rte@=yYB0@W^Gp z?%Pt&1LQfI_XMs}AXT2zDXpxd1X%|wGX{d52Zo>hC0&fBsJg@Kx|{&iQYcQQzVa4w z#_a7NL#Nm({u6tPGj#v8y}hi+#!`N-Ge^ut&Fo9N$Guf8-P9gO3DWkq_Jkb3X80Tp zq7ZgJo*CS?Y}VgL1ThG$+@?yQXK!A^DN7M8(!E#omqW~jLwlxV`*OTWE~MAeNmuK} zI20ImWLWIvT(XtmSw#D!M7!u{!i}f#AbPO}+j{y~0Ak&< z)dop}&~$r9bu6D!In=rMg2DqaFT&PnNfSESdC(iy)W;FBj_RYx}_7V7k3GJ4Z?C7UQ2+n{ literal 0 HcmV?d00001 diff --git a/doc/how_to_new/sg_execution_times.rst b/doc/how_to_new/sg_execution_times.rst new file mode 100644 index 0000000000..2aa34e5c02 --- /dev/null +++ b/doc/how_to_new/sg_execution_times.rst @@ -0,0 +1,37 @@ + +:orphan: + +.. _sphx_glr_how_to_new_sg_execution_times: + + +Computation times +================= +**00:00.203** total execution time for 1 file **from how_to_new**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_how_to_new_plot_combine_recordings.py` (``plot_combine_recordings.py``) + - 00:00.203 + - 0.0 diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst new file mode 100644 index 0000000000..b3933a942c --- /dev/null +++ b/doc/sg_execution_times.rst @@ -0,0 +1,82 @@ + +:orphan: + +.. _sphx_glr_sg_execution_times: + + +Computation times +================= +**03:26.753** total execution time for 16 files **from all galleries**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``..\examples\tutorials\core\plot_4_sorting_analyzer.py``) + - 03:26.753 + - 0.0 + * - :ref:`sphx_glr_tutorials_comparison_generate_erroneous_sorting.py` (``..\examples\tutorials\comparison\generate_erroneous_sorting.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``..\examples\tutorials\comparison\plot_5_comparison_sorter_weaknesses.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``..\examples\tutorials\core\plot_1_recording_extractor.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_2_sorting_extractor.py` (``..\examples\tutorials\core\plot_2_sorting_extractor.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_3_handle_probe_info.py` (``..\examples\tutorials\core\plot_3_handle_probe_info.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_5_append_concatenate_segments.py` (``..\examples\tutorials\core\plot_5_append_concatenate_segments.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_6_handle_times.py` (``..\examples\tutorials\core\plot_6_handle_times.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_extractors_plot_1_read_various_formats.py` (``..\examples\tutorials\extractors\plot_1_read_various_formats.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_extractors_plot_2_working_with_unscaled_traces.py` (``..\examples\tutorials\extractors\plot_2_working_with_unscaled_traces.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.py` (``..\examples\tutorials\qualitymetrics\plot_3_quality_mertics.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_4_curation.py` (``..\examples\tutorials\qualitymetrics\plot_4_curation.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_1_rec_gallery.py` (``..\examples\tutorials\widgets\plot_1_rec_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_2_sort_gallery.py` (``..\examples\tutorials\widgets\plot_2_sort_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_3_waveforms_gallery.py` (``..\examples\tutorials\widgets\plot_3_waveforms_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_4_peaks_gallery.py` (``..\examples\tutorials\widgets\plot_4_peaks_gallery.py``) + - 00:00.000 + - 0.0 From 932c0b22fbddc5d73ca16f87b9921901a7555782 Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Mon, 28 Oct 2024 15:17:44 +0000 Subject: [PATCH 04/12] Reformat a bit. --- doc/how_to/handle_times.rst | 60 +++++++++--------- .../handle-times-sampling-image.png | Bin doc/how_to/index.rst | 1 + doc/sg_execution_times.rst | 4 +- 4 files changed, 34 insertions(+), 31 deletions(-) rename doc/how_to/{ => handle_times_files}/handle-times-sampling-image.png (100%) diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst index 7c9d7f3d64..47782eff81 100644 --- a/doc/how_to/handle_times.rst +++ b/doc/how_to/handle_times.rst @@ -37,7 +37,7 @@ please see the below dropdown for a refresher. This is known as the 'sampling step' (sometimes denoted $t_s$) and is the inverse of the sampling frequency. - .. image:: handle-times-sampling-image.png + .. image:: handle_times_files/handle-times-sampling-image.png :alt: Image of continuous signal (1 second) with dots indicating samples collected at 0, 0.25, 0.5 and 0.75 seconds. :width: 400px :align: center @@ -56,7 +56,9 @@ An Overview of the possible Time representations in SpikeInterface When you load a recording into SpikeInterface, it will be automatically associated with a time array. Depending on your data format, this might -be loaded from metadata on your raw recording. [TODO: concrete example of this?] +be loaded from metadata on your raw recording. + +**[TODO: concrete example of this?]** If there is no time metadata on your raw recording, the times will be generated based on your sampling rate and number of samples. @@ -64,20 +66,19 @@ generated based on your sampling rate and number of samples. You can use the `get_times()` method to inspect the time array associated with your recording. -``` -import spikeinterface.full as si +.. code-block:: python -# Generate a recording for this example -recording, _ = si.generate_ground_truth_recording(durations=[10]) + import spikeinterface.full as si -print(f"number of samples: {recording.get_num_samples()}") -print(f"sampling frequency: {recording.get_sampling_frequency()}" + # Generate a recording for this example + recording, _ = si.generate_ground_truth_recording(durations=[10]) -print( - recording.get_times() -) + print(f"number of samples: {recording.get_num_samples()}") + print(f"sampling frequency: {recording.get_sampling_frequency()}" -``` + print( + recording.get_times() + ) Here, we see that as no time metadata was associated with the loaded recording, the time array starts at 0 seconds and continues until 10 seconds @@ -106,15 +107,15 @@ the first timepoint of the recording. Shifting by a positive value will increase the start time, while shifting by a negative value will decrease the start time. -``` -recording.shift_start_time(100.15) +.. code-block:: python + + recording.shift_start_time(100.15) -print(recording.get_times()) # time now start at 100.15 seconds + print(recording.get_times()) # time now start at 100.15 seconds -recording.shift_start_time(-50.15) + recording.shift_start_time(-50.15) -print(recording.get_times()) # time now start at 50 seconds -``` + print(recording.get_times()) # time now start at 50 seconds ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -144,20 +145,21 @@ Retrieving timepoints from sample index SpikeInterface provides two convenience methods for obtaining the timepoint in seconds given an index of the time array: -``` -sample_index = recording.time_to_sample_index(5.0) +.. code-block:: python + + sample_index = recording.time_to_sample_index(5.0) + + print(sample_index) -print(sample_index) -``` Similarly, you can retrieve the time array index given a timepoint: -``` -timepoint = recording.sample_index_to_to_time(125000) +.. code-block:: python + + timepoint = recording.sample_index_to_to_time(125000) -print(timepoint) -``` + print(timepoint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Aligning events across timestreams @@ -167,6 +169,6 @@ The alignment of electrophysiology recording time to other data streams (e.g. be is an important step in ephys analysis. To acheive this,it is common to collect a synconrisation ('sync') pulse on an additional channel. At present SpikeInterface does not include features for time-alignment, but some useful articles can be found on the following pages, -[SpikeGLX](https://github.com/billkarsh/SpikeGLX/blob/master/Markdown/UserManual.md#procedure-to-calibrate-sample-rates), -[OpenEphys](https://open-ephys.github.io/gui-docs/Tutorials/Data-Synchronization.html), -[NWB](https://neuroconv.readthedocs.io/en/main/user_guide/temporal_alignment.html). +`SpikeGLX `_, +`OpenEphys `_, +`NWB `_ diff --git a/doc/how_to/handle-times-sampling-image.png b/doc/how_to/handle_times_files/handle-times-sampling-image.png similarity index 100% rename from doc/how_to/handle-times-sampling-image.png rename to doc/how_to/handle_times_files/handle-times-sampling-image.png diff --git a/doc/how_to/index.rst b/doc/how_to/index.rst index 5d7eae9003..dce0298bd1 100644 --- a/doc/how_to/index.rst +++ b/doc/how_to/index.rst @@ -8,6 +8,7 @@ Guides on how to solve specific, short problems in SpikeInterface. Learn how to. viewers handle_drift + handle_times analyze_neuropixels load_matlab_data combine_recordings diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst index b3933a942c..c7517194e3 100644 --- a/doc/sg_execution_times.rst +++ b/doc/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**03:26.753** total execution time for 16 files **from all galleries**: +**00:04.190** total execution time for 16 files **from all galleries**: .. container:: @@ -33,7 +33,7 @@ Computation times - Time - Mem (MB) * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``..\examples\tutorials\core\plot_4_sorting_analyzer.py``) - - 03:26.753 + - 00:04.190 - 0.0 * - :ref:`sphx_glr_tutorials_comparison_generate_erroneous_sorting.py` (``..\examples\tutorials\comparison\generate_erroneous_sorting.py``) - 00:00.000 From 9b3f10dcd053454490f675dea643cdf32eee7fc8 Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Thu, 31 Oct 2024 18:15:37 +0000 Subject: [PATCH 05/12] Remove acidentally pushed how to new directory. --- doc/how_to_new/how_to_new_jupyter.zip | Bin 5870 -> 0 bytes doc/how_to_new/how_to_new_python.zip | Bin 3972 -> 0 bytes ...sphx_glr_plot_combine_recordings_thumb.png | Bin 26794 -> 0 bytes doc/how_to_new/index.rst | 65 ----- doc/how_to_new/plot_combine_recordings.ipynb | 129 ---------- doc/how_to_new/plot_combine_recordings.py | 92 ------- doc/how_to_new/plot_combine_recordings.py.md5 | 1 - doc/how_to_new/plot_combine_recordings.rst | 232 ------------------ .../plot_combine_recordings_codeobj.pickle | Bin 4419 -> 0 bytes doc/how_to_new/sg_execution_times.rst | 37 --- 10 files changed, 556 deletions(-) delete mode 100644 doc/how_to_new/how_to_new_jupyter.zip delete mode 100644 doc/how_to_new/how_to_new_python.zip delete mode 100644 doc/how_to_new/images/thumb/sphx_glr_plot_combine_recordings_thumb.png delete mode 100644 doc/how_to_new/index.rst delete mode 100644 doc/how_to_new/plot_combine_recordings.ipynb delete mode 100644 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But, we do not use the gallery -page features for the How To and so do not need it. Instead, we have a custom .rst file -in the How To folder that points to the sphinx-gallery outputs. - - - -.. raw:: html - -
- -.. thumbnail-parent-div-open - -.. raw:: html - -
- -.. only:: html - - .. image:: /how_to_new/images/thumb/sphx_glr_plot_combine_recordings_thumb.png - :alt: - - :ref:`sphx_glr_how_to_new_plot_combine_recordings.py` - -.. raw:: html - -
Combine recordings in SpikeInterface
-
- - -.. thumbnail-parent-div-close - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /how_to_new/plot_combine_recordings - - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-gallery - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: how_to_new_python.zip ` - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: how_to_new_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/how_to_new/plot_combine_recordings.ipynb b/doc/how_to_new/plot_combine_recordings.ipynb deleted file mode 100644 index 6deb20e059..0000000000 --- a/doc/how_to_new/plot_combine_recordings.ipynb +++ /dev/null @@ -1,129 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Combine recordings in SpikeInterface\n\nIn this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware\nsettings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to\nsplit their recording into multiple files for different experimental conditions. If the probe has not been moved,\nhowever, then during sorting it would likely make sense to combine these individual recording objects into one\nrecording object.\n\n## Why Combine?\n\nCombining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording.\n\nSpike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the\nexact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will\nbe the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each\n`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between\neach `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units.\n\n## Combining recordings continuous in time\n\nSome file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user\ncontrolled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two\ntools for bringing together these files into one `Recording` object.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Concatenating Recordings\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# First, let's cover concatenating recordings together. This will generate a mono-segment\n# recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan\n\nimport spikeinterface as si # This is only core\nimport spikeinterface.extractors as se\n\nrecording_one, _ = si.generate_ground_truth_recording(durations=[25])\nrecording_two, _ = si.generate_ground_truth_recording(durations=[25])\n\nprint(recording_one)\n\nprint(recording_two)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we will concatenate these recordings together.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "concatenated_recording = si.concatenate_recordings([recording_one, recording_two])\n\nprint(concatenated_recording)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we know that we will deal with a lot of files, we can actually work our\nway through a series of them relatively quickly by doing\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)]\nlist_of_recordings = []\nfor rec in list_of_recs:\n list_of_recordings.append(rec)\nrecording = si.concatenate_recordings(list_of_recordings)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Append Recordings\n\nIf you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use\n`append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data\nseparate, but it is important to note that not all sorters can handle multi-segment objects.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording = si.append_recordings([recording_one, recording_two])\n\nprint(recording)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Pitfalls\n\nIt's important to remember that these operations are directional. So:\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "recording_forward = si.concatenate_recordings([recording_one, recording_two])\nrecording_backward = si.concatenate_recordings([recording_two, recording_one])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is important because your spike times will be relative to the start of your recording.\n\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.14" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/doc/how_to_new/plot_combine_recordings.py b/doc/how_to_new/plot_combine_recordings.py deleted file mode 100644 index e61880e769..0000000000 --- a/doc/how_to_new/plot_combine_recordings.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -==================================== -Combine recordings in SpikeInterface -==================================== - -In this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware -settings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to -split their recording into multiple files for different experimental conditions. If the probe has not been moved, -however, then during sorting it would likely make sense to combine these individual recording objects into one -recording object. - ------------- -Why Combine? ------------- - -Combining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording. - -Spike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the -exact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will -be the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each -`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between -each `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units. - ---------------------------------------- -Combining recordings continuous in time ---------------------------------------- - -Some file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user -controlled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two -tools for bringing together these files into one `Recording` object. -""" - -# %% -# ------------------------ -# Concatenating Recordings -# ------------------------ - -# First, let's cover concatenating recordings together. This will generate a mono-segment -# recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan - -import spikeinterface as si # This is only core -import spikeinterface.extractors as se - -recording_one, _ = si.generate_ground_truth_recording(durations=[25]) -recording_two, _ = si.generate_ground_truth_recording(durations=[25]) - -print(recording_one) - -print(recording_two) - -# %% -# Next, we will concatenate these recordings together. - -concatenated_recording = si.concatenate_recordings([recording_one, recording_two]) - -print(concatenated_recording) - -# %% -# If we know that we will deal with a lot of files, we can actually work our -# way through a series of them relatively quickly by doing - -list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)] -list_of_recordings = [] -for rec in list_of_recs: - list_of_recordings.append(rec) -recording = si.concatenate_recordings(list_of_recordings) - -# %% -# ----------------- -# Append Recordings -# ----------------- -# -# If you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use -# `append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data -# separate, but it is important to note that not all sorters can handle multi-segment objects. - -recording = si.append_recordings([recording_one, recording_two]) - -print(recording) - -# %% -# -------- -# Pitfalls -# -------- -# -# It's important to remember that these operations are directional. So: - -recording_forward = si.concatenate_recordings([recording_one, recording_two]) -recording_backward = si.concatenate_recordings([recording_two, recording_one]) - -# %% -# This is important because your spike times will be relative to the start of your recording. diff --git a/doc/how_to_new/plot_combine_recordings.py.md5 b/doc/how_to_new/plot_combine_recordings.py.md5 deleted file mode 100644 index ff3eece3cb..0000000000 --- a/doc/how_to_new/plot_combine_recordings.py.md5 +++ /dev/null @@ -1 +0,0 @@ -10e64ebfb85b2795b4e151146f63e213 diff --git a/doc/how_to_new/plot_combine_recordings.rst b/doc/how_to_new/plot_combine_recordings.rst deleted file mode 100644 index f3c72f41ff..0000000000 --- a/doc/how_to_new/plot_combine_recordings.rst +++ /dev/null @@ -1,232 +0,0 @@ - -.. DO NOT EDIT. -.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. -.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: -.. "how_to_new\plot_combine_recordings.py" -.. LINE NUMBERS ARE GIVEN BELOW. - -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - :ref:`Go to the end ` - to download the full example code. - -.. rst-class:: sphx-glr-example-title - -.. _sphx_glr_how_to_new_plot_combine_recordings.py: - - -==================================== -Combine recordings in SpikeInterface -==================================== - -In this tutorial, we will walk through combining multiple recording objects. Sometimes this occurs due to hardware -settings (e.g., Intan software has a default setting of new files every 1 minute) or the experimenter decides to -split their recording into multiple files for different experimental conditions. If the probe has not been moved, -however, then during sorting it would likely make sense to combine these individual recording objects into one -recording object. - ------------- -Why Combine? ------------- - -Combining your data into a single recording allows you to have consistent labels (`unit_ids`) across the whole recording. - -Spike sorters seek to sort spikes within a recording into groups of units. Thus if multiple `Recording` objects have the -exact same probe location within some tissue and are occurring continuously in time, the units between the `Recordings` will -be the same. But if we sort each recording separately, the unit ids given by the sorter will not be the same between each -`Sorting`, and so we will need extensive post-processing to try to figure out which units are actually the same between -each `Sorting`. By combining everything into one `Recording`, all spikes will be sorted into the same pool of units. - ---------------------------------------- -Combining recordings continuous in time ---------------------------------------- - -Some file formats (e.g., Intan) automatically create new files every minute or few minutes (with a setting that can be user -controlled). Other times an experimenter separates their recording for experimental reasons. SpikeInterface provides two -tools for bringing together these files into one `Recording` object. - -.. GENERATED FROM PYTHON SOURCE LINES 34-37 - ------------------------- -Concatenating Recordings ------------------------- - -.. GENERATED FROM PYTHON SOURCE LINES 37-51 - -.. code-block:: Python - - - # First, let's cover concatenating recordings together. This will generate a mono-segment - # recording object. Let's load a set of Intan files. 0 is the amplifier data for Intan - - import spikeinterface as si # This is only core - import spikeinterface.extractors as se - - recording_one, _ = si.generate_ground_truth_recording(durations=[25]) - recording_two, _ = si.generate_ground_truth_recording(durations=[25]) - - print(recording_one) - - print(recording_two) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - C:\fMRIData\git-repo\forks\spikeinterface\src\spikeinterface\core\generate.py:2255: UserWarning: generate_unit_locations(): no solution for minimum_distance=20 and max_iteration=100 - warnings.warn(f"generate_unit_locations(): no solution for {minimum_distance=} and {max_iteration=}") - GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 625,000 samples - 25.00s - float32 dtype - 9.54 MiB - GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 625,000 samples - 25.00s - float32 dtype - 9.54 MiB - - - - -.. GENERATED FROM PYTHON SOURCE LINES 52-53 - -Next, we will concatenate these recordings together. - -.. GENERATED FROM PYTHON SOURCE LINES 53-58 - -.. code-block:: Python - - - concatenated_recording = si.concatenate_recordings([recording_one, recording_two]) - - print(concatenated_recording) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - GroundTruthRecording: 4 channels - 25.0kHz - 1 segments - 1,250,000 samples - 50.00s - float32 dtype - 19.07 MiB - - - - -.. GENERATED FROM PYTHON SOURCE LINES 59-61 - -If we know that we will deal with a lot of files, we can actually work our -way through a series of them relatively quickly by doing - -.. GENERATED FROM PYTHON SOURCE LINES 61-68 - -.. code-block:: Python - - - list_of_recs = [si.generate_ground_truth_recording(durations=[25])[0] for _ in range(4)] - list_of_recordings = [] - for rec in list_of_recs: - list_of_recordings.append(rec) - recording = si.concatenate_recordings(list_of_recordings) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 69-76 - ------------------ -Append Recordings ------------------ - -If you wish to keep each recording as a separate segment identity (e.g. if doing baseline, stim, poststim) you can use -`append` instead of `concatenate`. This has the benefit of allowing you to keep different parts of data -separate, but it is important to note that not all sorters can handle multi-segment objects. - -.. GENERATED FROM PYTHON SOURCE LINES 76-81 - -.. code-block:: Python - - - recording = si.append_recordings([recording_one, recording_two]) - - print(recording) - - - - - -.. rst-class:: sphx-glr-script-out - - .. code-block:: none - - GroundTruthRecording: 4 channels - 25.0kHz - 2 segments - 1,250,000 samples - 50.00s - float32 dtype - 19.07 MiB - Segments: - Samples: 625,000 | 625,000 - Durations: 25.00s | 25.00s - Memory: 9.54 MiB | 9.54 MiB - - - - -.. GENERATED FROM PYTHON SOURCE LINES 82-87 - --------- -Pitfalls --------- - -It's important to remember that these operations are directional. So: - -.. GENERATED FROM PYTHON SOURCE LINES 87-91 - -.. code-block:: Python - - - recording_forward = si.concatenate_recordings([recording_one, recording_two]) - recording_backward = si.concatenate_recordings([recording_two, recording_one]) - - - - - - - - -.. GENERATED FROM PYTHON SOURCE LINES 92-93 - -This is important because your spike times will be relative to the start of your recording. - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** (0 minutes 0.203 seconds) - - -.. _sphx_glr_download_how_to_new_plot_combine_recordings.py: - -.. only:: html - - .. container:: sphx-glr-footer sphx-glr-footer-example - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_combine_recordings.ipynb ` - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_combine_recordings.py ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/how_to_new/plot_combine_recordings_codeobj.pickle b/doc/how_to_new/plot_combine_recordings_codeobj.pickle deleted file mode 100644 index a3564ff771cc95eeccdb66bb845f6d0db555ed31..0000000000000000000000000000000000000000 GIT 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file **from how_to_new**: - -.. container:: - - .. raw:: html - - - - - - - - .. list-table:: - :header-rows: 1 - :class: table table-striped sg-datatable - - * - Example - - Time - - Mem (MB) - * - :ref:`sphx_glr_how_to_new_plot_combine_recordings.py` (``plot_combine_recordings.py``) - - 00:00.203 - - 0.0 From a7ac8ef854142e1ae514a13654fdc82db0eedf84 Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Thu, 31 Oct 2024 18:16:08 +0000 Subject: [PATCH 06/12] Remove execution times. --- doc/sg_execution_times.rst | 82 -------------------------------------- 1 file changed, 82 deletions(-) delete mode 100644 doc/sg_execution_times.rst diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst deleted file mode 100644 index c7517194e3..0000000000 --- a/doc/sg_execution_times.rst +++ /dev/null @@ -1,82 +0,0 @@ - -:orphan: - -.. _sphx_glr_sg_execution_times: - - -Computation times -================= -**00:04.190** total execution time for 16 files **from all galleries**: - -.. container:: - - .. raw:: html - - - - - - - - .. list-table:: - :header-rows: 1 - :class: table table-striped sg-datatable - - * - Example - - Time - - Mem (MB) - * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``..\examples\tutorials\core\plot_4_sorting_analyzer.py``) - - 00:04.190 - - 0.0 - * - :ref:`sphx_glr_tutorials_comparison_generate_erroneous_sorting.py` (``..\examples\tutorials\comparison\generate_erroneous_sorting.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``..\examples\tutorials\comparison\plot_5_comparison_sorter_weaknesses.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``..\examples\tutorials\core\plot_1_recording_extractor.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_2_sorting_extractor.py` (``..\examples\tutorials\core\plot_2_sorting_extractor.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_3_handle_probe_info.py` (``..\examples\tutorials\core\plot_3_handle_probe_info.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_5_append_concatenate_segments.py` (``..\examples\tutorials\core\plot_5_append_concatenate_segments.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_6_handle_times.py` (``..\examples\tutorials\core\plot_6_handle_times.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_extractors_plot_1_read_various_formats.py` (``..\examples\tutorials\extractors\plot_1_read_various_formats.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_extractors_plot_2_working_with_unscaled_traces.py` (``..\examples\tutorials\extractors\plot_2_working_with_unscaled_traces.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.py` (``..\examples\tutorials\qualitymetrics\plot_3_quality_mertics.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_4_curation.py` (``..\examples\tutorials\qualitymetrics\plot_4_curation.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_widgets_plot_1_rec_gallery.py` (``..\examples\tutorials\widgets\plot_1_rec_gallery.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_widgets_plot_2_sort_gallery.py` (``..\examples\tutorials\widgets\plot_2_sort_gallery.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_widgets_plot_3_waveforms_gallery.py` (``..\examples\tutorials\widgets\plot_3_waveforms_gallery.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_widgets_plot_4_peaks_gallery.py` (``..\examples\tutorials\widgets\plot_4_peaks_gallery.py``) - - 00:00.000 - - 0.0 From b3ccc310e949efd551fb6b0cfad5cdb55e1c79df Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Thu, 31 Oct 2024 19:28:43 +0000 Subject: [PATCH 07/12] Add page to (how to for now) index. --- doc/how_to/handle_times.rst | 2 +- doc/sg_execution_times.rst | 79 +++++++++++++++++++++++++++++++++++++ 2 files changed, 80 insertions(+), 1 deletion(-) create mode 100644 doc/sg_execution_times.rst diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst index 47782eff81..7288d8c8d9 100644 --- a/doc/how_to/handle_times.rst +++ b/doc/how_to/handle_times.rst @@ -1,4 +1,4 @@ -How SpikeInterface handles Time +How SpikeInterface handles time ================================= Extracellular electrophysiology commonly involves synchronisation of events diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst new file mode 100644 index 0000000000..d2de8e76d8 --- /dev/null +++ b/doc/sg_execution_times.rst @@ -0,0 +1,79 @@ + +:orphan: + +.. _sphx_glr_sg_execution_times: + + +Computation times +================= +**00:02.107** total execution time for 15 files **from all galleries**: + +.. container:: + + .. raw:: html + + + + + + + + .. list-table:: + :header-rows: 1 + :class: table table-striped sg-datatable + + * - Example + - Time + - Mem (MB) + * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``../examples/tutorials/core/plot_4_sorting_analyzer.py``) + - 00:02.107 + - 0.0 + * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``../examples/tutorials/comparison/plot_5_comparison_sorter_weaknesses.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``../examples/tutorials/core/plot_1_recording_extractor.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_2_sorting_extractor.py` (``../examples/tutorials/core/plot_2_sorting_extractor.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_3_handle_probe_info.py` (``../examples/tutorials/core/plot_3_handle_probe_info.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_5_append_concatenate_segments.py` (``../examples/tutorials/core/plot_5_append_concatenate_segments.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_core_plot_6_handle_times.py` (``../examples/tutorials/core/plot_6_handle_times.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_extractors_plot_1_read_various_formats.py` (``../examples/tutorials/extractors/plot_1_read_various_formats.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_extractors_plot_2_working_with_unscaled_traces.py` (``../examples/tutorials/extractors/plot_2_working_with_unscaled_traces.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.py` (``../examples/tutorials/qualitymetrics/plot_3_quality_mertics.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_4_curation.py` (``../examples/tutorials/qualitymetrics/plot_4_curation.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_1_rec_gallery.py` (``../examples/tutorials/widgets/plot_1_rec_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_2_sort_gallery.py` (``../examples/tutorials/widgets/plot_2_sort_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_3_waveforms_gallery.py` (``../examples/tutorials/widgets/plot_3_waveforms_gallery.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_widgets_plot_4_peaks_gallery.py` (``../examples/tutorials/widgets/plot_4_peaks_gallery.py``) + - 00:00.000 + - 0.0 From 47b2c37af20f893ec6217b8096f1408f3388897a Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Fri, 1 Nov 2024 13:31:18 +0000 Subject: [PATCH 08/12] COnvert to sphinx gallery. --- doc/how_to/handle_times.rst | 114 +- doc/sg_execution_times.rst | 40 +- .../core/handle-times-sampling-image.png | Bin 0 -> 26044 bytes .../tutorials/core/plot_7_handle_times_new.py | 142 + .../clean_analyzer.zarr/.zattrs | 10 + .../clean_analyzer.zarr/.zgroup | 3 + .../clean_analyzer.zarr/.zmetadata | 2581 +++++++++++++++++ .../clean_analyzer.zarr/extensions/.zgroup | 3 + .../extensions/noise_levels/.zattrs | 12 + 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examples/tutorials/qualitymetrics/clean_analyzer.zarr/sorting/unit_ids/.zarray create mode 100644 examples/tutorials/qualitymetrics/clean_analyzer.zarr/sorting/unit_ids/0 create mode 100644 examples/tutorials/qualitymetrics/clean_analyzer.zarr/sorting_provenance/.zarray create mode 100644 examples/tutorials/qualitymetrics/clean_analyzer.zarr/sorting_provenance/0 create mode 100644 examples/tutorials/qualitymetrics/clean_analyzer.zarr/sparsity_mask/.zarray create mode 100644 examples/tutorials/qualitymetrics/clean_analyzer.zarr/sparsity_mask/0.0 diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst index 7288d8c8d9..3bc4b8d40a 100644 --- a/doc/how_to/handle_times.rst +++ b/doc/how_to/handle_times.rst @@ -4,15 +4,13 @@ How SpikeInterface handles time Extracellular electrophysiology commonly involves synchronisation of events across many timestreams. For example, an experiment may involve displaying a stimuli to an animal and recording the stimuli-evoked -neuronal responses. It is critical that timings is represented in -a clear way across data streams so they may be properly synchronised during +neuronal responses. It is critical that timings are represented +accurately so they may be properly synchronised during analysis. -Below, we will explore the ways that SpikeInterface represents time +Below, we will explore the ways that SpikeInterface stores time information and how you can use it in your analysis to ensure the timing -of your spike events is represented faithfully. The ways that will -be explored are **providing no times**, **providing start times** -and **providing the full time array**. +of your spike events are accurate. A familiarity with the terms used in digital sampling (e.g. sampling frequency) will be assumed below. If you are not familiar with these concepts, @@ -56,22 +54,21 @@ An Overview of the possible Time representations in SpikeInterface When you load a recording into SpikeInterface, it will be automatically associated with a time array. Depending on your data format, this might -be loaded from metadata on your raw recording. +be loaded from metadata on your raw recording. If there is no time metadata +on your raw recording, the times will be generated based on your sampling +rate and number of samples. -**[TODO: concrete example of this?]** +**[TODO: concrete example of a datatype that loads time also on the neo side]** -If there is no time metadata on your raw recording, the times will be -generated based on your sampling rate and number of samples. - -You can use the `get_times()` method to inspect the time array associated -with your recording. +You can use the :meth:`get_times() ` +method to inspect the time array associated with your recording. .. code-block:: python import spikeinterface.full as si # Generate a recording for this example - recording, _ = si.generate_ground_truth_recording(durations=[10]) + recording, sorting = si.generate_ground_truth_recording(durations=[10]) print(f"number of samples: {recording.get_num_samples()}") print(f"sampling frequency: {recording.get_sampling_frequency()}" @@ -80,22 +77,25 @@ with your recording. recording.get_times() ) -Here, we see that as no time metadata was associated with the loaded recording, -the time array starts at 0 seconds and continues until 10 seconds -(`10 * sampling_frequency`) in steps of sampling step (`1 / sampling_frequency`). +Here, we see that as no time metadata is associated with the loaded recording, +a default time array is generated from the number of samples and sampling frequency. +The times starts at :math:`0` seconds and continues until :math:`10` seconds +(:math:`10 \cdot \text{sampling frequency}` samples) in steps of sampling step size, +:math:`\frac{1}{\text{sampling frequency}}`. -If timings were loaded from metadata, you may find that the first timepoint is -not zero, or the times may not be separated by exactly `1/sampling_frequency` but -may be irregular due to small drifts in sampling rate during acquisition. +If timings are obtained from metadata during file loading, you may find that the first timepoint is +not zero, or the times may not be separated by exactly :math:`\frac{1}{\text{sampling frequency}}` +but may be irregular due to small drifts in sampling rate during acquisition (so called 'clock drift'). ^^^^^^^^^^^^^^^^^^^^^^^ Shifting the start time ^^^^^^^^^^^^^^^^^^^^^^^ Having loaded your recording object and inspected the associated -time vector, you may want to change the start time of your recording. +times, you may want to change the start time of your recording. + For example, your recording may not have metadata attached and you -want to shift the default time vector (with zero start time) to the +want to shift the default times to start at the true (real world) start time of the recording, or relative to some other event (e.g. behavioural trial start time). @@ -117,19 +117,52 @@ the start time. print(recording.get_times()) # time now start at 50 seconds +**TODO: link to new function and test when other PR is merged** + + +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Setting time vector changes spike times +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If we sort out recording, the spike times will reflect the times +set on the recording. In our case, because we already have the +sorting object based on the default times, we will set the new +recording object on the sorting. + +.. code-block:: python + + unit_id_to_show = sorting.unid_ids[0] + + spike_times_orig = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) + + sorting.register_recording(recording) + + spike_times_new = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Manually setting a time vector ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Less commonly, you may want to manually set the time vector on a recording. -For example, maybe you have a known time vector with non-regularly spaced -samples due to sampling drift, and you want to associate it with your recording. +It is also possible to manualyl set an entire time vector on your recording. +This might be useful in case you have the true sample timestamps of your +recording but these were not automatically loaded from metadata. You can associate any time vector with your recording (as long as it contains -as many samples as the recording itself) using `recording.set_times()`. +as many samples as the recording itself) using +:meth:`set_times() ` -[TODO - an example?] +.. code-block:: python + + times = np.linspace(0, 10, recording.get_num_samples())) + offset = np.cumsum( + np.linspace(0, 0.1, recording.get_num_samples()) + ) + true_times = times + offset + + recording.set_times(true_times) + + recording.get_times() .. warning:: @@ -142,8 +175,12 @@ as many samples as the recording itself) using `recording.set_times()`. Retrieving timepoints from sample index ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -SpikeInterface provides two convenience methods for obtaining the timepoint in seconds -given an index of the time array: +SpikeInterface provides two convenience methods for obtaining the +timepoint in seconds given an index of the time array. + +Use +:meth:`time_to_sample_index() ` +to go from time to the sample index: .. code-block:: python @@ -152,7 +189,9 @@ given an index of the time array: print(sample_index) -Similarly, you can retrieve the time array index given a timepoint: +and +:meth:`sample_index_to_to_time() ` +to can retrieve the index given a timepoint: .. code-block:: python @@ -166,9 +205,12 @@ Aligning events across timestreams ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The alignment of electrophysiology recording time to other data streams (e.g. behaviour) -is an important step in ephys analysis. To acheive this,it is common to collect -a synconrisation ('sync') pulse on an additional channel. At present SpikeInterface does not include -features for time-alignment, but some useful articles can be found on the following pages, -`SpikeGLX `_, -`OpenEphys `_, -`NWB `_ +is an important step in electrophysiology analysis. To achieve this,it is common to acquire +a synchronisation ('sync') pulse on an additional channel. + +At present SpikeInterface does not include features for time-alignment, +but some useful articles on how to approach this can be found on the following pages: + +* `SpikeGLX `_, +* `OpenEphys `_, +* `NWB `_ diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst index d2de8e76d8..0deca3e106 100644 --- a/doc/sg_execution_times.rst +++ b/doc/sg_execution_times.rst @@ -6,7 +6,7 @@ Computation times ================= -**00:02.107** total execution time for 15 files **from all galleries**: +**00:01.439** total execution time for 17 files **from all galleries**: .. container:: @@ -32,48 +32,54 @@ Computation times * - Example - Time - Mem (MB) - * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``../examples/tutorials/core/plot_4_sorting_analyzer.py``) - - 00:02.107 + * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``..\examples\tutorials\core\plot_4_sorting_analyzer.py``) + - 00:01.425 - 0.0 - * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``../examples/tutorials/comparison/plot_5_comparison_sorter_weaknesses.py``) + * - :ref:`sphx_glr_tutorials_core_plot_7_handle_times_new.py` (``..\examples\tutorials\core\plot_7_handle_times_new.py``) + - 00:00.013 + - 0.0 + * - :ref:`sphx_glr_tutorials_comparison_generate_erroneous_sorting.py` (``..\examples\tutorials\comparison\generate_erroneous_sorting.py``) + - 00:00.000 + - 0.0 + * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``..\examples\tutorials\comparison\plot_5_comparison_sorter_weaknesses.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``../examples/tutorials/core/plot_1_recording_extractor.py``) + * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``..\examples\tutorials\core\plot_1_recording_extractor.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_2_sorting_extractor.py` (``../examples/tutorials/core/plot_2_sorting_extractor.py``) + * - :ref:`sphx_glr_tutorials_core_plot_2_sorting_extractor.py` (``..\examples\tutorials\core\plot_2_sorting_extractor.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_3_handle_probe_info.py` (``../examples/tutorials/core/plot_3_handle_probe_info.py``) + * - :ref:`sphx_glr_tutorials_core_plot_3_handle_probe_info.py` (``..\examples\tutorials\core\plot_3_handle_probe_info.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_5_append_concatenate_segments.py` (``../examples/tutorials/core/plot_5_append_concatenate_segments.py``) + * - :ref:`sphx_glr_tutorials_core_plot_5_append_concatenate_segments.py` (``..\examples\tutorials\core\plot_5_append_concatenate_segments.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_6_handle_times.py` (``../examples/tutorials/core/plot_6_handle_times.py``) + * - :ref:`sphx_glr_tutorials_core_plot_6_handle_times.py` (``..\examples\tutorials\core\plot_6_handle_times.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_extractors_plot_1_read_various_formats.py` (``../examples/tutorials/extractors/plot_1_read_various_formats.py``) + * - :ref:`sphx_glr_tutorials_extractors_plot_1_read_various_formats.py` (``..\examples\tutorials\extractors\plot_1_read_various_formats.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_extractors_plot_2_working_with_unscaled_traces.py` (``../examples/tutorials/extractors/plot_2_working_with_unscaled_traces.py``) + * - :ref:`sphx_glr_tutorials_extractors_plot_2_working_with_unscaled_traces.py` (``..\examples\tutorials\extractors\plot_2_working_with_unscaled_traces.py``) - 00:00.000 - 0.0 - * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.py` (``../examples/tutorials/qualitymetrics/plot_3_quality_mertics.py``) + * - :ref:`sphx_glr_tutorials_qualitymetrics_plot_3_quality_mertics.py` 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z69AcM(!-FWE^s(-13rm$gK9DbV2M{M9$@SZYnE2-svvRQ629IIe$Ztg$5nYUDJY)r&B`yF?Ohb`N65GM4MfvY@0lo9H%-xze~RY9n_IW|M_rXWMD4)_v$Ng3f_H!ym2pghCFONVGy$NbB+D&RL# z17*C4@W)r>;y@k1p10#oiG1UHA-5>6_GP^Y3>lFW_iuhKI>&Qw{6BXrB z7nL&HH`0Rt#rC|dF7nkn%$Q#PS$}b4!+M`P>+Z#)8kqh5!_3KOVD@*I#_@>wiG7bp z+~}Ol+mJEJ$Oy>@m+&HZb&?|9m;0OK6c!eh<=AgKNR2@xrBJa;S>#$!{-y`|_I-s_MG+4|x62yywU%@XpS< m6F$3q8NPdcV| Date: Fri, 1 Nov 2024 13:34:16 +0000 Subject: [PATCH 09/12] Remove original times tutorial and old rst version of the new version. --- doc/how_to/handle_times.rst | 216 ------------------ .../tutorials/core/plot_6_handle_times.py | 161 ++++++++++--- .../tutorials/core/plot_7_handle_times_new.py | 142 ------------ 3 files changed, 125 insertions(+), 394 deletions(-) delete mode 100644 doc/how_to/handle_times.rst delete mode 100644 examples/tutorials/core/plot_7_handle_times_new.py diff --git a/doc/how_to/handle_times.rst b/doc/how_to/handle_times.rst deleted file mode 100644 index 3bc4b8d40a..0000000000 --- a/doc/how_to/handle_times.rst +++ /dev/null @@ -1,216 +0,0 @@ -How SpikeInterface handles time -================================= - -Extracellular electrophysiology commonly involves synchronisation of events -across many timestreams. For example, an experiment may involve -displaying a stimuli to an animal and recording the stimuli-evoked -neuronal responses. It is critical that timings are represented -accurately so they may be properly synchronised during -analysis. - -Below, we will explore the ways that SpikeInterface stores time -information and how you can use it in your analysis to ensure the timing -of your spike events are accurate. - -A familiarity with the terms used in digital sampling (e.g. sampling -frequency) will be assumed below. If you are not familiar with these concepts, -please see the below dropdown for a refresher. - -.. dropdown:: Digital Sampling - - What we are fundamentally interested in is recording the electrical - waveforms found in the brain. In the real world, these are 'continuous' - signals changing through time. To represent these real signals on our - finite-memory computers, we must 'sample' the real-world continuous signals - at discrete time points. - - When sampling our signal the fundamental question we need to ask is - how fast should - we sample the continuous data? A natural approach is to sample our signal every $N$ seconds. - For example, let's say we decide to sample our signal 4 times per second. - This is known as the 'sampling frequency' (sometimes denoted $f_s$) and is expressed - in Hertz (Hz) i.e. 'samples-per-second'. - - Another way of thinking about the same idea is to ask - how often are we - sampling our signal? In our example, we are sampling the signal once every 0.25 seconds. - This is known as the 'sampling step' (sometimes denoted $t_s$) and is the inverse of - the sampling frequency. - - .. image:: handle_times_files/handle-times-sampling-image.png - :alt: Image of continuous signal (1 second) with dots indicating samples collected at 0, 0.25, 0.5 and 0.75 seconds. - :width: 400px - :align: center - - In the real world, we will sample our signal much faster. For example, Neuropixels - samples at 30 kHz (30,000 samples per second), with a - sampling step of 1/30000 = 0.0003 seconds! - - Using computers, we typically represent time as a long array of numbers, - here we refer to this as a 'time array' in seconds. - For example, `[0, 0.25, 0.5, 0.75, ...]`. - ------------------------------------------------------------------- -An Overview of the possible Time representations in SpikeInterface ------------------------------------------------------------------- - -When you load a recording into SpikeInterface, it will be automatically -associated with a time array. Depending on your data format, this might -be loaded from metadata on your raw recording. If there is no time metadata -on your raw recording, the times will be generated based on your sampling -rate and number of samples. - -**[TODO: concrete example of a datatype that loads time also on the neo side]** - -You can use the :meth:`get_times() ` -method to inspect the time array associated with your recording. - -.. code-block:: python - - import spikeinterface.full as si - - # Generate a recording for this example - recording, sorting = si.generate_ground_truth_recording(durations=[10]) - - print(f"number of samples: {recording.get_num_samples()}") - print(f"sampling frequency: {recording.get_sampling_frequency()}" - - print( - recording.get_times() - ) - -Here, we see that as no time metadata is associated with the loaded recording, -a default time array is generated from the number of samples and sampling frequency. -The times starts at :math:`0` seconds and continues until :math:`10` seconds -(:math:`10 \cdot \text{sampling frequency}` samples) in steps of sampling step size, -:math:`\frac{1}{\text{sampling frequency}}`. - -If timings are obtained from metadata during file loading, you may find that the first timepoint is -not zero, or the times may not be separated by exactly :math:`\frac{1}{\text{sampling frequency}}` -but may be irregular due to small drifts in sampling rate during acquisition (so called 'clock drift'). - -^^^^^^^^^^^^^^^^^^^^^^^ -Shifting the start time -^^^^^^^^^^^^^^^^^^^^^^^ - -Having loaded your recording object and inspected the associated -times, you may want to change the start time of your recording. - -For example, your recording may not have metadata attached and you -want to shift the default times to start at the -true (real world) start time of the recording, or relative to some -other event (e.g. behavioural trial start time). - -Alternatively, you may want to change the start time of the metadata-loaded -recording for similar reasons. - -To do this, you can use the `shift_start_time()` function to shift -the first timepoint of the recording. Shifting by a positive value will -increase the start time, while shifting by a negative value will decrease -the start time. - -.. code-block:: python - - recording.shift_start_time(100.15) - - print(recording.get_times()) # time now start at 100.15 seconds - - recording.shift_start_time(-50.15) - - print(recording.get_times()) # time now start at 50 seconds - -**TODO: link to new function and test when other PR is merged** - - -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Setting time vector changes spike times -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -If we sort out recording, the spike times will reflect the times -set on the recording. In our case, because we already have the -sorting object based on the default times, we will set the new -recording object on the sorting. - -.. code-block:: python - - unit_id_to_show = sorting.unid_ids[0] - - spike_times_orig = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) - - sorting.register_recording(recording) - - spike_times_new = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) - - -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Manually setting a time vector -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -It is also possible to manualyl set an entire time vector on your recording. -This might be useful in case you have the true sample timestamps of your -recording but these were not automatically loaded from metadata. - -You can associate any time vector with your recording (as long as it contains -as many samples as the recording itself) using -:meth:`set_times() ` - -.. code-block:: python - - times = np.linspace(0, 10, recording.get_num_samples())) - offset = np.cumsum( - np.linspace(0, 0.1, recording.get_num_samples()) - ) - true_times = times + offset - - recording.set_times(true_times) - - recording.get_times() - -.. warning:: - - In the case of regularly spaced time vectors, it is recommended - to shift the default times rather than set your own time vector, - as this will require more memory under the hood. - - -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Retrieving timepoints from sample index -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -SpikeInterface provides two convenience methods for obtaining the -timepoint in seconds given an index of the time array. - -Use -:meth:`time_to_sample_index() ` -to go from time to the sample index: - -.. code-block:: python - - sample_index = recording.time_to_sample_index(5.0) - - print(sample_index) - - -and -:meth:`sample_index_to_to_time() ` -to can retrieve the index given a timepoint: - - -.. code-block:: python - - timepoint = recording.sample_index_to_to_time(125000) - - print(timepoint) - -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Aligning events across timestreams -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The alignment of electrophysiology recording time to other data streams (e.g. behaviour) -is an important step in electrophysiology analysis. To achieve this,it is common to acquire -a synchronisation ('sync') pulse on an additional channel. - -At present SpikeInterface does not include features for time-alignment, -but some useful articles on how to approach this can be found on the following pages: - -* `SpikeGLX `_, -* `OpenEphys `_, -* `NWB `_ diff --git a/examples/tutorials/core/plot_6_handle_times.py b/examples/tutorials/core/plot_6_handle_times.py index 28abf68d84..c8db2341b7 100644 --- a/examples/tutorials/core/plot_6_handle_times.py +++ b/examples/tutorials/core/plot_6_handle_times.py @@ -1,53 +1,142 @@ """ -Handle time information -======================= +How SpikeInterface Handles Time +================================ -By default, SpikeInterface assumes that a recording is uniformly sampled and it starts at 0 seconds. -However, in some cases there could be a different start time or even some missing frames in the recording. +Extracellular electrophysiology commonly involves synchronization of events across many timestreams. +For example, an experiment may involve displaying stimuli to an animal and recording the stimuli-evoked +neuronal responses. Accurate timing representation is critical for proper synchronization during analysis. + +This tutorial explores how SpikeInterface stores time information and how to use it in your analysis +to ensure spike event timing is accurate. + +Prerequisites: +-------------- +A basic understanding of digital sampling (e.g., sampling frequency) is assumed. +For a refresher, review the explanation below. -This notebook shows how to handle time information in SpikeInterface recording and sorting objects. """ -from spikeinterface.extractors import toy_example +# %% +# .. dropdown:: Digital Sampling +# +# What we are fundamentally interested in is recording the electrical waveforms found in the brain. +# These 'continuous' signals change over time. To represent them in finite-memory computers, we sample +# the continuous signals at discrete points in time. +# +# When sampling, a key question is: *How fast should we sample the continuous data?* We could sample +# the signal every :math:`N` seconds. For instance, if we sample 4 times per second, this is called the +# 'sampling frequency' (:math:`f_s`), expressed in Hertz (Hz), or samples per second. +# +# An alternative way to think of this is to ask: *How often do we sample the signal?* In this example, +# we sample every 0.25 seconds, which is called the 'sampling step' (:math:`t_s`), the inverse of the sampling frequency. +# +# .. image:: handle-times-sampling-image.png +# :alt: Image of continuous signal (1 second) with dots indicating samples collected at 0, 0.25, 0.5, and 0.75 seconds. +# :width: 400px +# :align: center +# +# In real-world applications, signals are sampled much faster. For example, Neuropixels samples at +# 30 kHz (30,000 samples per second) with a sampling step of :math:`\frac{1}{30000} = 0.0003` seconds. +# +# Computers typically represent time as a long array of numbers, referred to here as a +# 'time array' (e.g., ``[0, 0.25, 0.5, 0.75, ...]``). +# +# +# Overview of Time Representations in SpikeInterface +# --------------------------------------------------- +# When you load a recording into SpikeInterface, it is associated with a time array. +# Depending on the data format, this may be loaded from metadata. If no time metadata is available, +# times are generated based on the sampling rate and the number of samples. + +# %% +import spikeinterface.full as si -############################################################################## -# First let's generate a toy example with a single segment: +# Generate a recording for this example +recording, sorting = si.generate_ground_truth_recording(durations=[10]) -rec, sort = toy_example(num_segments=1) +# Print recording details +print(f"Number of samples: {recording.get_num_samples()}") +print(f"Sampling frequency: {recording.get_sampling_frequency()}") +print(f"Time vector: {recording.get_times()}") +# %% +# In this example, no time metadata was associated with the recording, so a default time array was +# generated based on the number of samples and sampling frequency. The time array starts at 0 seconds +# and continues to 10 seconds (10 * `sampling frequency` samples) in steps of the sampling step size, +# :math:`\dfrac{1}{\text{sampling frequency}}`. -############################################################################## -# Generally, the time information would be automatically loaded when reading a -# recording. -# However, sometimes we might need to add a time vector externally. -# For example, let's create a time vector by getting the default times and -# adding 5 s: +# %% +# Shifting the Start Time +# ----------------------- +# You may want to change the start time of your recording. This can be done using the `shift_start_time()` +# method, which adjusts the first time point of the recording. -default_times = rec.get_times() -print(default_times[:10]) -new_times = default_times + 5 +# %% +recording.shift_start_time(100.15) +print(recording.get_times()) # Time now starts at 100.15 seconds -############################################################################## -# We can now set the new time vector with the :code:`set_times()` function. -# Additionally, we can register to recording object to the sorting one so that -# time information can be accessed by the sorting object as well (note that this -# link is lost in case the sorting object is saved to disk!): +recording.shift_start_time(-50.15) +print(recording.get_times()) # Time now starts at 50 seconds -rec.set_times(new_times) -sort.register_recording(rec) +# %% +# Setting Time Vector Changes Spike Times +# --------------------------------------- +# If we use the sorting object with the default times, the spike times will reflect those times. +# You can register the recording to change the spike times accordingly. -# print new times -print(rec.get_times()[:10]) +# %% +unit_id_to_show = sorting.unit_ids[0] +spike_times_orig = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) -# print spike times (internally uses registered recording times) -spike_times0 = sort.get_unit_spike_train(sort.unit_ids[0], return_times=True) -print(spike_times0[:10]) +# Register the recording to adjust spike times +sorting.register_recording(recording) +spike_times_new = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) -############################################################################## -# While here we have shown how to set times only for a mono-segment recording, -# times can also be handled in multi-segment recordings (using the -# :code:`segment_index` argument when calling :code:`set_times()`). +# %% +# Manually Setting a Time Vector +# ------------------------------ +# It is also possible to manually set a time vector on your recording. This can be useful if you +# have true sample timestamps that were not loaded from metadata. + +# %% +import numpy as np + +times = np.linspace(0, 10, recording.get_num_samples()) +offset = np.cumsum(np.linspace(0, 0.1, recording.get_num_samples())) +true_times = times + offset + +recording.set_times(true_times) +print(recording.get_times()) + +# %% +# .. warning:: +# +# For regularly spaced time vectors, it is better to shift the default times rather +# than set your own time vector to save memory. + +# %% +# Retrieving Timepoints from Sample Index +# --------------------------------------- +# SpikeInterface provides methods to convert between time points and sample indices. + +# %% +sample_index = recording.time_to_sample_index(5.0) +print(sample_index) + +timepoint = recording.sample_index_to_time(125000) +print(timepoint) + +# %% +# Aligning Events Across Timestreams +# ----------------------------------- +# Aligning electrophysiology recordings with other data streams (e.g., behavioral data) +# is an important step in analysis. This is often done by acquiring a synchronization +# pulse on an additional channel. +# +# While SpikeInterface does not include built-in features for time-alignment, +# these resources may be helpful: # -# Finally, you you run spike sorting through :code:`spikeinterface`, the recording -# is automatically registered to the output sorting object! +# * `SpikeGLX User Manual `_ +# * `OpenEphys Synchronization Guide `_ +# * `NWB Temporal Alignment `_ diff --git a/examples/tutorials/core/plot_7_handle_times_new.py b/examples/tutorials/core/plot_7_handle_times_new.py deleted file mode 100644 index c8db2341b7..0000000000 --- a/examples/tutorials/core/plot_7_handle_times_new.py +++ /dev/null @@ -1,142 +0,0 @@ -""" -How SpikeInterface Handles Time -================================ - -Extracellular electrophysiology commonly involves synchronization of events across many timestreams. -For example, an experiment may involve displaying stimuli to an animal and recording the stimuli-evoked -neuronal responses. Accurate timing representation is critical for proper synchronization during analysis. - -This tutorial explores how SpikeInterface stores time information and how to use it in your analysis -to ensure spike event timing is accurate. - -Prerequisites: --------------- -A basic understanding of digital sampling (e.g., sampling frequency) is assumed. -For a refresher, review the explanation below. - -""" - -# %% -# .. dropdown:: Digital Sampling -# -# What we are fundamentally interested in is recording the electrical waveforms found in the brain. -# These 'continuous' signals change over time. To represent them in finite-memory computers, we sample -# the continuous signals at discrete points in time. -# -# When sampling, a key question is: *How fast should we sample the continuous data?* We could sample -# the signal every :math:`N` seconds. For instance, if we sample 4 times per second, this is called the -# 'sampling frequency' (:math:`f_s`), expressed in Hertz (Hz), or samples per second. -# -# An alternative way to think of this is to ask: *How often do we sample the signal?* In this example, -# we sample every 0.25 seconds, which is called the 'sampling step' (:math:`t_s`), the inverse of the sampling frequency. -# -# .. image:: handle-times-sampling-image.png -# :alt: Image of continuous signal (1 second) with dots indicating samples collected at 0, 0.25, 0.5, and 0.75 seconds. -# :width: 400px -# :align: center -# -# In real-world applications, signals are sampled much faster. For example, Neuropixels samples at -# 30 kHz (30,000 samples per second) with a sampling step of :math:`\frac{1}{30000} = 0.0003` seconds. -# -# Computers typically represent time as a long array of numbers, referred to here as a -# 'time array' (e.g., ``[0, 0.25, 0.5, 0.75, ...]``). -# -# -# Overview of Time Representations in SpikeInterface -# --------------------------------------------------- -# When you load a recording into SpikeInterface, it is associated with a time array. -# Depending on the data format, this may be loaded from metadata. If no time metadata is available, -# times are generated based on the sampling rate and the number of samples. - -# %% -import spikeinterface.full as si - -# Generate a recording for this example -recording, sorting = si.generate_ground_truth_recording(durations=[10]) - -# Print recording details -print(f"Number of samples: {recording.get_num_samples()}") -print(f"Sampling frequency: {recording.get_sampling_frequency()}") -print(f"Time vector: {recording.get_times()}") - -# %% -# In this example, no time metadata was associated with the recording, so a default time array was -# generated based on the number of samples and sampling frequency. The time array starts at 0 seconds -# and continues to 10 seconds (10 * `sampling frequency` samples) in steps of the sampling step size, -# :math:`\dfrac{1}{\text{sampling frequency}}`. - -# %% -# Shifting the Start Time -# ----------------------- -# You may want to change the start time of your recording. This can be done using the `shift_start_time()` -# method, which adjusts the first time point of the recording. - -# %% -recording.shift_start_time(100.15) -print(recording.get_times()) # Time now starts at 100.15 seconds - -recording.shift_start_time(-50.15) -print(recording.get_times()) # Time now starts at 50 seconds - -# %% -# Setting Time Vector Changes Spike Times -# --------------------------------------- -# If we use the sorting object with the default times, the spike times will reflect those times. -# You can register the recording to change the spike times accordingly. - -# %% -unit_id_to_show = sorting.unit_ids[0] -spike_times_orig = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) - -# Register the recording to adjust spike times -sorting.register_recording(recording) - -spike_times_new = sorting.get_unit_spike_train(unit_id_to_show, return_times=True) - -# %% -# Manually Setting a Time Vector -# ------------------------------ -# It is also possible to manually set a time vector on your recording. This can be useful if you -# have true sample timestamps that were not loaded from metadata. - -# %% -import numpy as np - -times = np.linspace(0, 10, recording.get_num_samples()) -offset = np.cumsum(np.linspace(0, 0.1, recording.get_num_samples())) -true_times = times + offset - -recording.set_times(true_times) -print(recording.get_times()) - -# %% -# .. warning:: -# -# For regularly spaced time vectors, it is better to shift the default times rather -# than set your own time vector to save memory. - -# %% -# Retrieving Timepoints from Sample Index -# --------------------------------------- -# SpikeInterface provides methods to convert between time points and sample indices. - -# %% -sample_index = recording.time_to_sample_index(5.0) -print(sample_index) - -timepoint = recording.sample_index_to_time(125000) -print(timepoint) - -# %% -# Aligning Events Across Timestreams -# ----------------------------------- -# Aligning electrophysiology recordings with other data streams (e.g., behavioral data) -# is an important step in analysis. This is often done by acquiring a synchronization -# pulse on an additional channel. -# -# While SpikeInterface does not include built-in features for time-alignment, -# these resources may be helpful: -# -# * `SpikeGLX User Manual `_ -# * `OpenEphys Synchronization Guide `_ -# * `NWB Temporal Alignment `_ From 4ef2b69ff709f2f1ede0ed3e673d5e63e975c572 Mon Sep 17 00:00:00 2001 From: JoeZiminski Date: Fri, 1 Nov 2024 13:59:49 +0000 Subject: [PATCH 10/12] Remove execution times. --- doc/sg_execution_times.rst | 85 -------------------------------------- 1 file changed, 85 deletions(-) delete mode 100644 doc/sg_execution_times.rst diff --git a/doc/sg_execution_times.rst b/doc/sg_execution_times.rst deleted file mode 100644 index 0deca3e106..0000000000 --- a/doc/sg_execution_times.rst +++ /dev/null @@ -1,85 +0,0 @@ - -:orphan: - -.. _sphx_glr_sg_execution_times: - - -Computation times -================= -**00:01.439** total execution time for 17 files **from all galleries**: - -.. container:: - - .. raw:: html - - - - - - - - .. list-table:: - :header-rows: 1 - :class: table table-striped sg-datatable - - * - Example - - Time - - Mem (MB) - * - :ref:`sphx_glr_tutorials_core_plot_4_sorting_analyzer.py` (``..\examples\tutorials\core\plot_4_sorting_analyzer.py``) - - 00:01.425 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_7_handle_times_new.py` (``..\examples\tutorials\core\plot_7_handle_times_new.py``) - - 00:00.013 - - 0.0 - * - :ref:`sphx_glr_tutorials_comparison_generate_erroneous_sorting.py` (``..\examples\tutorials\comparison\generate_erroneous_sorting.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_comparison_plot_5_comparison_sorter_weaknesses.py` (``..\examples\tutorials\comparison\plot_5_comparison_sorter_weaknesses.py``) - - 00:00.000 - - 0.0 - * - :ref:`sphx_glr_tutorials_core_plot_1_recording_extractor.py` (``..\examples\tutorials\core\plot_1_recording_extractor.py``) - - 00:00.000 - - 0.0 - * - 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a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/.zattrs +++ /dev/null @@ -1,108 +0,0 @@ -{ - "info": { - "class": "spikeinterface.qualitymetrics.quality_metric_calculator.ComputeQualityMetrics", - "module": "spikeinterface", - "version": "0.101.1" - }, - "params": { - "metric_names": [ - "isi_violation", - "nearest_neighbor", - "snr" - ], - "peak_sign": null, - "qm_params": { - "amplitude_cutoff": { - "amplitudes_bins_min_ratio": 5, - "histogram_smoothing_value": 3, - "num_histogram_bins": 100, - "peak_sign": "neg" - }, - "amplitude_cv": { - "amplitude_extension": "spike_amplitudes", - "average_num_spikes_per_bin": 50, - "min_num_bins": 10, - "percentiles": [ - 5, - 95 - ] - }, - "amplitude_median": { - "peak_sign": "neg" - }, - "drift": { - "direction": "y", - "interval_s": 60, - "min_num_bins": 2, - "min_spikes_per_interval": 100 - }, - "firing_range": { - "bin_size_s": 5, - "percentiles": [ - 5, - 95 - ] - }, - "isi_violation": { - "isi_threshold_ms": 1.5, - "min_isi_ms": 0 - }, - "nearest_neighbor": { - "max_spikes": 10000, - "n_neighbors": 5 - }, - "nn_isolation": { - "max_spikes": 10000, - "min_fr": 0.0, - "min_spikes": 10, - "n_components": 10, - "n_neighbors": 4, - "peak_sign": "neg", - "radius_um": 100 - }, - "nn_noise_overlap": { - "max_spikes": 10000, - "min_fr": 0.0, - "min_spikes": 10, - "n_components": 10, - "n_neighbors": 4, - "peak_sign": "neg", - "radius_um": 100 - }, - "presence_ratio": { - "bin_duration_s": 60, - "mean_fr_ratio_thresh": 0.0 - }, - "rp_violation": { - "censored_period_ms": 0.0, - "refractory_period_ms": 1.0 - }, - "silhouette": { - "method": [ - "simplified" - ] - }, - "sliding_rp_violation": { - "bin_size_ms": 0.25, - "contamination_values": null, - "exclude_ref_period_below_ms": 0.5, - "max_ref_period_ms": 10, - "min_spikes": 0, - "window_size_s": 1 - }, - "snr": { - "peak_mode": "extremum", - "peak_sign": "neg" - }, - "synchrony": { - "synchrony_sizes": [ - 2, - 4, - 8 - ] - } - }, - "seed": null, - "skip_pc_metrics": false - } -} diff --git a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/.zgroup b/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/.zgroup deleted file mode 100644 index 3f3fad2d17..0000000000 --- a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/.zgroup +++ /dev/null @@ -1,3 +0,0 @@ -{ - "zarr_format": 2 -} diff --git a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zattrs b/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zattrs deleted file mode 100644 index 12c837b2ab..0000000000 --- a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zattrs +++ /dev/null @@ -1,3 +0,0 @@ -{ - "dataframe": true -} diff --git a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zgroup b/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zgroup deleted file mode 100644 index 3f3fad2d17..0000000000 --- a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/.zgroup +++ /dev/null @@ -1,3 +0,0 @@ -{ - "zarr_format": 2 -} diff --git a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/index/.zarray b/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/index/.zarray deleted file mode 100644 index c415e5573a..0000000000 --- a/examples/tutorials/qualitymetrics/clean_analyzer.zarr/extensions/quality_metrics/metrics/index/.zarray +++ /dev/null @@ -1,24 +0,0 @@ -{ - "chunks": [ - 7 - ], - "compressor": { - "blocksize": 0, - "clevel": 5, - "cname": "lz4", - "id": "blosc", - "shuffle": 1 - }, - "dtype": "|O", - "fill_value": null, - "filters": [ - { - "id": "vlen-utf8" - } - ], - "order": "C", - "shape": [ - 7 - ], - "zarr_format": 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