From 1505e9aff7c331930a01d46264af7d526d8eb73a Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Thu, 29 Jun 2023 12:15:27 -0500 Subject: [PATCH 1/7] Add Docker image ID --- .devcontainer/Dockerfile | 2 +- CHANGELOG.md | 5 +++++ workflow_array_ephys/version.py | 2 +- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile index c63a49d..df0c4da 100644 --- a/.devcontainer/Dockerfile +++ b/.devcontainer/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.9-slim +FROM python:3.9-slim@sha256:5f0192a4f58a6ce99f732fe05e3b3d00f12ae62e183886bca3ebe3d202686c7f RUN \ adduser --system --disabled-password --shell /bin/bash vscode && \ diff --git a/CHANGELOG.md b/CHANGELOG.md index 52bdae1..8f194ac 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,10 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) convention. +## [0.3.3] - 2023-06-29 + ++ Add - Docker image ID + ## [0.3.2] - 2023-04-14 + Add - `quality_metrics.ipynb` for visualizing quality metrics. @@ -93,6 +97,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and + Add - Version +[0.3.3]: https://github.com/datajoint/workflow-array-ephys/releases/tag/0.3.3 [0.3.2]: https://github.com/datajoint/workflow-array-ephys/releases/tag/0.3.2 [0.3.1]: https://github.com/datajoint/workflow-array-ephys/releases/tag/0.3.1 [0.3.0]: https://github.com/datajoint/workflow-array-ephys/releases/tag/0.3.0 diff --git a/workflow_array_ephys/version.py b/workflow_array_ephys/version.py index 5b67f9e..ce8cfa6 100644 --- a/workflow_array_ephys/version.py +++ b/workflow_array_ephys/version.py @@ -2,4 +2,4 @@ Package metadata Update the Docker image tag in `docker-compose.yaml` to match """ -__version__ = "0.3.2" +__version__ = "0.3.3" From 4ba0f64f9635c3b9568285677c5790eea8cb4930 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Thu, 29 Jun 2023 12:33:45 -0500 Subject: [PATCH 2/7] Add VS Code extensions --- .devcontainer/devcontainer.json | 3 ++- .devcontainer/local/devcontainer.json | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 0768b84..c41fde9 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -22,7 +22,8 @@ "customizations": { "vscode": { "extensions": [ - "ms-python.python" + "ms-python.python", + "ms-toolsai.jupyter" ] } } diff --git a/.devcontainer/local/devcontainer.json b/.devcontainer/local/devcontainer.json index 68692fe..91346b8 100644 --- a/.devcontainer/local/devcontainer.json +++ b/.devcontainer/local/devcontainer.json @@ -22,7 +22,8 @@ "customizations": { "vscode": { "extensions": [ - "ms-python.python" + "ms-python.python", + "ms-toolsai.jupyter" ] } } From 1fbc9f773e2acb48c12372259f17957ac7366a98 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Thu, 29 Jun 2023 18:34:09 +0000 Subject: [PATCH 3/7] Update notebook --- notebooks/tutorial.ipynb | 1525 +++----------------------------------- 1 file changed, 120 insertions(+), 1405 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 376843d..8f52ba7 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -94,52 +94,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2023-03-20 21:44:02,597][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", - "[2023-03-20 21:44:02,607][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", - "[2023-03-20 21:44:02,614][INFO]: Connected root@fakeservices.datajoint.io:3306\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from workflow_array_ephys.pipeline import subject, session, probe, ephys" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " value = getattr(object, key)\n", - "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " value = getattr(object, key)\n", - "/usr/local/lib/python3.9/inspect.py:351: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n", - " value = getattr(object, key)\n" - ] - }, - { - "data": { - "image/svg+xml": "\n\n%3\n\n\n\nephys.ClusteringMethod\n\n\nephys.ClusteringMethod\n\n\n\n\n\nephys.ClusteringParamSet\n\n\nephys.ClusteringParamSet\n\n\n\n\n\nephys.ClusteringMethod->ephys.ClusteringParamSet\n\n\n\n\nephys.ClusteringTask\n\n\nephys.ClusteringTask\n\n\n\n\n\nephys.Clustering\n\n\nephys.Clustering\n\n\n\n\n\nephys.ClusteringTask->ephys.Clustering\n\n\n\n\nephys.EphysRecording.EphysFile\n\n\nephys.EphysRecording.EphysFile\n\n\n\n\n\nprobe.ElectrodeConfig\n\n\nprobe.ElectrodeConfig\n\n\n\n\n\nephys.EphysRecording\n\n\nephys.EphysRecording\n\n\n\n\n\nprobe.ElectrodeConfig->ephys.EphysRecording\n\n\n\n\nprobe.ElectrodeConfig.Electrode\n\n\nprobe.ElectrodeConfig.Electrode\n\n\n\n\n\nprobe.ElectrodeConfig->probe.ElectrodeConfig.Electrode\n\n\n\n\nephys.EphysRecording->ephys.ClusteringTask\n\n\n\n\nephys.EphysRecording->ephys.EphysRecording.EphysFile\n\n\n\n\nephys.LFP\n\n\nephys.LFP\n\n\n\n\n\nephys.EphysRecording->ephys.LFP\n\n\n\n\nsession.Session\n\n\nsession.Session\n\n\n\n\n\nephys.ProbeInsertion\n\n\nephys.ProbeInsertion\n\n\n\n\n\nsession.Session->ephys.ProbeInsertion\n\n\n\n\nephys.Curation\n\n\nephys.Curation\n\n\n\n\n\nephys.CuratedClustering\n\n\nephys.CuratedClustering\n\n\n\n\n\nephys.Curation->ephys.CuratedClustering\n\n\n\n\nephys.InsertionLocation\n\n\nephys.InsertionLocation\n\n\n\n\n\nprobe.ProbeType\n\n\nprobe.ProbeType\n\n\n\n\n\nprobe.ProbeType->probe.ElectrodeConfig\n\n\n\n\nprobe.Probe\n\n\nprobe.Probe\n\n\n\n\n\nprobe.ProbeType->probe.Probe\n\n\n\n\nprobe.ProbeType.Electrode\n\n\nprobe.ProbeType.Electrode\n\n\n\n\n\nprobe.ProbeType->probe.ProbeType.Electrode\n\n\n\n\nephys.CuratedClustering.Unit\n\n\nephys.CuratedClustering.Unit\n\n\n\n\n\nephys.WaveformSet.PeakWaveform\n\n\nephys.WaveformSet.PeakWaveform\n\n\n\n\n\nephys.CuratedClustering.Unit->ephys.WaveformSet.PeakWaveform\n\n\n\n\nephys.QualityMetrics.Waveform\n\n\nephys.QualityMetrics.Waveform\n\n\n\n\n\nephys.CuratedClustering.Unit->ephys.QualityMetrics.Waveform\n\n\n\n\nephys.QualityMetrics.Cluster\n\n\nephys.QualityMetrics.Cluster\n\n\n\n\n\nephys.CuratedClustering.Unit->ephys.QualityMetrics.Cluster\n\n\n\n\nephys.WaveformSet.Waveform\n\n\nephys.WaveformSet.Waveform\n\n\n\n\n\nephys.CuratedClustering.Unit->ephys.WaveformSet.Waveform\n\n\n\n\nprobe.Probe->ephys.ProbeInsertion\n\n\n\n\nephys.AcquisitionSoftware\n\n\nephys.AcquisitionSoftware\n\n\n\n\n\nephys.AcquisitionSoftware->ephys.EphysRecording\n\n\n\n\nephys.QualityMetrics\n\n\nephys.QualityMetrics\n\n\n\n\n\nephys.QualityMetrics->ephys.QualityMetrics.Waveform\n\n\n\n\nephys.QualityMetrics->ephys.QualityMetrics.Cluster\n\n\n\n\nprobe.ElectrodeConfig.Electrode->ephys.CuratedClustering.Unit\n\n\n\n\nephys.LFP.Electrode\n\n\nephys.LFP.Electrode\n\n\n\n\n\nprobe.ElectrodeConfig.Electrode->ephys.LFP.Electrode\n\n\n\n\nprobe.ElectrodeConfig.Electrode->ephys.WaveformSet.Waveform\n\n\n\n\nephys.ProbeInsertion->ephys.EphysRecording\n\n\n\n\nephys.ProbeInsertion->ephys.InsertionLocation\n\n\n\n\nephys.LFP->ephys.LFP.Electrode\n\n\n\n\nephys.CuratedClustering->ephys.CuratedClustering.Unit\n\n\n\n\nephys.CuratedClustering->ephys.QualityMetrics\n\n\n\n\nephys.WaveformSet\n\n\nephys.WaveformSet\n\n\n\n\n\nephys.CuratedClustering->ephys.WaveformSet\n\n\n\n\nephys.WaveformSet->ephys.WaveformSet.PeakWaveform\n\n\n\n\nephys.WaveformSet->ephys.WaveformSet.Waveform\n\n\n\n\nephys.ClusteringParamSet->ephys.ClusteringTask\n\n\n\n\nsubject.Subject\n\n\nsubject.Subject\n\n\n\n\n\nsubject.Subject->session.Session\n\n\n\n\nephys.Clustering->ephys.Curation\n\n\n\n\nephys.ClusterQualityLabel\n\n\nephys.ClusterQualityLabel\n\n\n\n\n\nephys.ClusterQualityLabel->ephys.CuratedClustering.Unit\n\n\n\n\nprobe.ProbeType.Electrode->probe.ElectrodeConfig.Electrode\n\n\n\n", - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "(\n", " dj.Diagram(subject.Subject)\n", @@ -204,46 +170,18 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'subject : varchar(8) \\n---\\nsubject_nickname=\"\" : varchar(64) \\nsex : enum(\\'M\\',\\'F\\',\\'U\\') \\nsubject_birth_date : date \\nsubject_description=\"\" : varchar(1024) \\n'" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "subject.Subject.describe()" + "print(subject.Subject.describe())" ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "subject : varchar(8) # \n", - "---\n", - "subject_nickname=\"\" : varchar(64) # \n", - "sex : enum('M','F','U') # \n", - "subject_birth_date : date # \n", - "subject_description=\"\" : varchar(1024) # " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "subject.Subject.heading" ] @@ -260,115 +198,24 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - " " - ], - "text/plain": [ - "*subject subject_nickna sex subject_birth_ subject_descri\n", - "+----------+ +------------+ +-----+ +------------+ +------------+\n", - "subject5 U 2023-01-01 \n", - " (Total: 1)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "subject.Subject.insert1(\n", " dict(\n", " subject='subject5',\n", + " subject_nickname='subject5',\n", " subject_birth_date='2023-01-01',\n", - " sex='U'\n", + " sex='U',\n", + " subject_description='Example subject'\n", " )\n", ")\n", "subject.Subject()" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -378,42 +225,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'-> subject.Subject\\nsession_datetime : datetime \\n'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "session.Session.describe()" + "print(session.Session.describe())" ] }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "subject : varchar(8) # \n", - "session_datetime : datetime # " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "session.Session.heading" ] @@ -437,91 +260,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - " " - ], - "text/plain": [ - "*subject *session_datet\n", - "+----------+ +------------+\n", - "subject5 2023-03-20 21:\n", - " (Total: 1)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "session_key = dict(subject='subject5', \n", " session_datetime=datetime.datetime.now())\n", @@ -543,139 +284,27 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'-> session.Session\\n---\\nsession_dir : varchar(256) # Path to the data directory for a session\\n'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "session.SessionDirectory.describe()" + "print(session.SessionDirectory.describe())" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# \n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "---\n", - "session_dir : varchar(256) # Path to the data directory for a session" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "session.SessionDirectory.heading" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - "
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\n", - " " - ], - "text/plain": [ - "*subject *session_datet session_dir \n", - "+----------+ +------------+ +------------+\n", - "subject5 2023-03-20 21: subject5/sessi\n", - " (Total: 1)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "session.SessionDirectory.insert1(\n", " dict(\n", @@ -699,143 +328,32 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'# Represent a physical probe with unique identification\\nprobe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\\n---\\n-> probe.ProbeType\\nprobe_comment=\"\" : varchar(1000) \\n'" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "probe.Probe.describe()" + "print(probe.Probe.describe())" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Represent a physical probe with unique identification\n", - "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\n", - "---\n", - "probe_type : varchar(32) # e.g. neuropixels_1.0\n", - "probe_comment=\"\" : varchar(1000) # " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "probe.Probe.heading" ] }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Represent a physical probe with unique identification\n", - "
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probe

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714000838neuropixels 1.0 - 3B
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\n", - " " - ], - "text/plain": [ - "*probe probe_type probe_comment \n", - "+-----------+ +------------+ +------------+\n", - "714000838 neuropixels 1. \n", - " (Total: 1)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "probe.Probe.insert1(\n", " dict(probe=\"714000838\", \n", - " probe_type=\"neuropixels 1.0 - 3B\")\n", + " probe_type=\"neuropixels 1.0 - 3B\",\n", + " probe_comment=\"Example probe\")\n", ") # this info could be achieve from neuropixels meta file.\n", "probe.Probe()" ] @@ -851,144 +369,27 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'# Probe insertion implanted into an animal for a given session.\\n-> session.Session\\ninsertion_number : tinyint unsigned \\n---\\n-> probe.Probe\\n'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "ephys.ProbeInsertion.describe()" + "print(ephys.ProbeInsertion.describe())" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Probe insertion implanted into an animal for a given session.\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "---\n", - "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.ProbeInsertion.heading" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Probe insertion implanted into an animal for a given session.\n", - "
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\n", - " " - ], - "text/plain": [ - "*subject *session_datet *insertion_num probe \n", - "+----------+ +------------+ +------------+ +-----------+\n", - "subject5 2023-03-20 21: 1 714000838 \n", - " (Total: 1)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.ProbeInsertion.insert1(\n", " dict(\n", @@ -1023,274 +424,45 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Ephys recording from a probe insertion for a given session.\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "---\n", - "electrode_config_hash : uuid # \n", - "acq_software : varchar(24) # \n", - "sampling_rate : float # (Hz)\n", - "recording_datetime : datetime # datetime of the recording from this probe\n", - "recording_duration : float # (seconds) duration of the recording from this probe" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.EphysRecording.heading" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Paths of files of a given EphysRecording round.\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "file_path : varchar(255) # filepath relative to root data directory" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.EphysRecording.EphysFile.heading" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " Ephys recording from a probe insertion for a given session.\n", - "
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subject52023-03-20 21:44:0918d4cc6d8-a02d-42c8-bf27-7459c39ea0eeSpikeGLX30000.02018-07-03 20:32:28338.666
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0kilosort2Spike sorting using Kilosort2de78cee1-526f-319e-b6d5-8a2ba04963d8=BLOB=
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\\'trigger\\': trigger computation\\n'" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "ephys.ClusteringTask.describe()" + "print(ephys.ClusteringTask.describe())" ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Manual table for defining a clustering task ready to be run\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "paramset_idx : smallint # \n", - "---\n", - "clustering_output_dir=\"\" : varchar(255) # clustering output directory relative to the clustering root data directory\n", - "task_mode=\"load\" : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.ClusteringTask.heading" ] @@ -1773,7 +609,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1783,7 +619,7 @@ " insertion_number=1,\n", " paramset_idx=0,\n", " task_mode='load', # load or trigger\n", - " clustering_output_dir=\"subject5/session1/probe_1/ks2.1_01\"\n", + " clustering_output_dir=\"subject5/session1/probe_1/kilosort2-5_1\"\n", " )\n", ")" ] @@ -1799,17 +635,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Clustering: 100%|██████████| 1/1 [00:00<00:00, 15.88it/s]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.Clustering.populate(session_key, display_progress=True)" ] @@ -1826,38 +654,16 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "# Manual curation procedure\n", - "subject : varchar(8) # \n", - "session_datetime : datetime # \n", - "insertion_number : tinyint unsigned # \n", - "paramset_idx : smallint # \n", - "curation_id : int # \n", - "---\n", - "curation_time : datetime # time of generation of this set of curated clustering results\n", - "curation_output_dir : varchar(255) # output directory of the curated results, relative to root data directory\n", - "quality_control : tinyint # has this clustering result undergone quality control?\n", - "manual_curation : tinyint # has manual curation been performed on this clustering result?\n", - "curation_note=\"\" : varchar(2000) # " - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.Curation.heading" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1876,19 +682,9 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "CuratedClustering: 100%|██████████| 1/1 [00:02<00:00, 2.22s/it]\n", - "LFP: 100%|██████████| 1/1 [00:20<00:00, 20.91s/it]\n", - "WaveformSet: 100%|██████████| 1/1 [05:47<00:00, 347.33s/it]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ephys.CuratedClustering.populate(session_key, display_progress=True)\n", "ephys.LFP.populate(session_key, display_progress=True)\n", @@ -1940,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1960,20 +756,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plt.plot(lfp_average)\n", "plt.title(\"Average LFP Waveform for Insertion 1\")\n", @@ -2000,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2010,20 +795,9 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "x = np.hstack(unit_spiketimes)\n", "y = np.hstack([np.full_like(s, u) for u, s in zip(units, unit_spiketimes)])\n", @@ -2034,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2044,78 +818,18 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'subject': 'subject5',\n", - " 'session_datetime': datetime.datetime(2023, 3, 20, 21, 44, 9),\n", - " 'insertion_number': 1,\n", - " 'paramset_idx': 0,\n", - " 'curation_id': 1,\n", - " 'unit': 15,\n", - " 'electrode_config_hash': UUID('8d4cc6d8-a02d-42c8-bf27-7459c39ea0ee'),\n", - " 'probe_type': 'neuropixels 1.0 - 3A',\n", - " 'electrode': 186,\n", - " 'cluster_quality_label': 'good',\n", - " 'spike_count': 18,\n", - " 'spike_times': array([ 48.74336667, 67.63773333, 69.05666667, 83.11203333,\n", - " 94.1978 , 108.0454 , 110.36893333, 122.16836667,\n", - " 149.41143333, 152.55593333, 182.84816667, 236.8929 ,\n", - " 270.40613333, 292.4432 , 299.99216667, 321.74016667,\n", - " 326.5434 , 329.26373333]),\n", - " 'spike_sites': array([186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186, 186,\n", - " 186, 186, 186, 186, 186]),\n", - " 'spike_depths': array([2144.38644037, 2240.44959257, 2286.37081289, 2177.07452433,\n", - " 2227.42382471, 2143.86346649, 2235.74014979, 2236.04354982,\n", - " 2236.06676094, 2299.53408387, 2222.6567435 , 2165.88562393,\n", - " 2165.55087743, 2254.93847903, 2241.71132163, 2057.9714868 ,\n", - " 2272.82484323, 2229.42266297]),\n", - " 'peak_electrode_waveform': array([-343.61979167, -345.703125 , -344.79166667, -341.53645833,\n", - " -339.0625 , -340.88541667, -342.1875 , -339.97395833,\n", - " -338.02083333, -336.97916667, -338.41145833, -341.53645833,\n", - " -343.48958333, -344.53125 , -341.796875 , -337.36979167,\n", - " -339.32291667, -336.58854167, -338.80208333, -337.63020833,\n", - " -336.97916667, -338.02083333, -338.02083333, -333.984375 ,\n", - " -324.86979167, -317.05729167, -313.93229167, -309.765625 ,\n", - " -304.296875 , -300.13020833, -300.65104167, -301.43229167,\n", - " -306.11979167, -311.58854167, -316.40625 , -320.57291667,\n", - " -333.203125 , -339.58333333, -340.625 , -337.63020833,\n", - " -344.140625 , -350.91145833, -356.11979167, -363.80208333,\n", - " -375. , -379.42708333, -382.68229167, -389.97395833,\n", - " -391.796875 , -393.61979167, -390.625 , -395.96354167,\n", - " -400.52083333, -399.73958333, -395.18229167, -392.96875 ,\n", - " -395.05208333, -391.796875 , -392.31770833, -387.36979167,\n", - " -387.23958333, -387.890625 , -384.375 , -383.33333333])}" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "unit_data" ] }, { "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampling_rate = (ephys.EphysRecording & insert_key).fetch1(\n", " \"sampling_rate\"\n", @@ -2156,6 +870,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [] @@ -2177,7 +892,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.17" }, "orig_nbformat": 4, "vscode": { From 74d991a2c91c5d4981865a56933fba39223ca5aa Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Thu, 29 Jun 2023 21:15:47 +0000 Subject: [PATCH 4/7] Update requirements --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index d1a7ae3..b114b3e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ datajoint>=0.13.0 element-animal>=0.1.5 -element-array-ephys>=0.2.7 +element-array-ephys>=0.2.11 element-electrode-localization>=0.1.2 element-event>=0.1.2 element-interface>=0.5.0 From 75875f4fc534119dc356da7349693a5bac951211 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Thu, 29 Jun 2023 16:18:23 -0500 Subject: [PATCH 5/7] Update changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8f194ac..db1991b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,8 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ## [0.3.3] - 2023-06-29 + Add - Docker image ID ++ Update - `element-array-ephys` version ++ Update - Tutorial notebook to work around bug ## [0.3.2] - 2023-04-14 From c07c2dcf9bb40d01a04e1f7083e5e0c91556cfd8 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 30 Jun 2023 03:38:06 +0000 Subject: [PATCH 6/7] Add output --- notebooks/tutorial.ipynb | 2148 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 2037 insertions(+), 111 deletions(-) diff --git a/notebooks/tutorial.ipynb b/notebooks/tutorial.ipynb index 8f52ba7..f48012f 100644 --- a/notebooks/tutorial.ipynb +++ b/notebooks/tutorial.ipynb @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -94,18 +94,479 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2023-06-30 03:34:11,767][WARNING]: lab.Project and related tables will be removed in a future version of Element Lab. Please use the project schema.\n", + "[2023-06-30 03:34:11,871][INFO]: Connecting root@fakeservices.datajoint.io:3306\n", + "[2023-06-30 03:34:11,899][INFO]: Connected root@fakeservices.datajoint.io:3306\n" + ] + } + ], "source": [ "from workflow_array_ephys.pipeline import subject, session, probe, ephys" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "%3\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording\n", + "\n", + "\n", + "ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", + "\n", + "ephys.LFP\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.LFP\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringTask\n", + "\n", + "\n", + "ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.EphysRecording->ephys.EphysRecording.EphysFile\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics\n", + "\n", + "\n", + "ephys.QualityMetrics\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics->ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.QualityMetrics->ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringMethod\n", + "\n", + "\n", + "ephys.ClusteringMethod\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringParamSet\n", + "\n", + "\n", + "ephys.ClusteringParamSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringMethod->ephys.ClusteringParamSet\n", + "\n", + "\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "session.Session\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion\n", + "\n", + "\n", + "ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "session.Session->ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "ephys.InsertionLocation\n", + "\n", + "\n", + "ephys.InsertionLocation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP.Electrode\n", + "\n", + "\n", + "ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig.Electrode->ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.AcquisitionSoftware\n", + "\n", + "\n", + "ephys.AcquisitionSoftware\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.AcquisitionSoftware->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Cluster\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.QualityMetrics.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering.Unit->ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusterQualityLabel\n", + "\n", + "\n", + "ephys.ClusterQualityLabel\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusterQualityLabel->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig\n", + "\n", + "\n", + "probe.ElectrodeConfig\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "probe.ElectrodeConfig->probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.EphysRecording\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ProbeInsertion->ephys.InsertionLocation\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType\n", + "\n", + "\n", + "probe.ProbeType\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.ElectrodeConfig\n", + "\n", + "\n", + "\n", + "\n", + "probe.Probe\n", + "\n", + "\n", + "probe.Probe\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.Probe\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType.Electrode\n", + "\n", + "\n", + "probe.ProbeType.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType->probe.ProbeType.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "probe.Probe->ephys.ProbeInsertion\n", + "\n", + "\n", + "\n", + "\n", + "ephys.LFP->ephys.LFP.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringParamSet->ephys.ClusteringTask\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Curation\n", + "\n", + "\n", + "ephys.Curation\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering\n", + "\n", + "\n", + "ephys.CuratedClustering\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Curation->ephys.CuratedClustering\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Clustering\n", + "\n", + "\n", + "ephys.Clustering\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.Clustering->ephys.Curation\n", + "\n", + "\n", + "\n", + "\n", + "probe.ProbeType.Electrode->probe.ElectrodeConfig.Electrode\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet\n", + "\n", + "\n", + "ephys.WaveformSet\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet->ephys.WaveformSet.Waveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.WaveformSet->ephys.WaveformSet.PeakWaveform\n", + "\n", + "\n", + "\n", + "\n", + "ephys.ClusteringTask->ephys.Clustering\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.QualityMetrics\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.CuratedClustering.Unit\n", + "\n", + "\n", + "\n", + "\n", + "ephys.CuratedClustering->ephys.WaveformSet\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "subject.Subject\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "subject.Subject->session.Session\n", + "\n", + "\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(\n", " dj.Diagram(subject.Subject)\n", @@ -170,18 +631,49 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "subject : varchar(8) \n", + "---\n", + "subject_nickname : varchar(64) \n", + "sex : enum('M','F','U') \n", + "subject_birth_date : date \n", + "subject_description : varchar(1024) \n", + "\n" + ] + } + ], "source": [ "print(subject.Subject.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "---\n", + "subject_nickname : varchar(64) # \n", + "sex : enum('M','F','U') # \n", + "subject_birth_date : date # \n", + "subject_description : varchar(1024) # " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject.heading" ] @@ -198,9 +690,105 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

\n", + " \n", + "
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subject_nickname

\n", + " \n", + "
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sex

\n", + " \n", + "
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subject_birth_date

\n", + " \n", + "
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subject_description

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subject5subject5U2023-01-01Example subject
\n", + " \n", + "

Total: 1

\n", + " " + ], + "text/plain": [ + "*subject subject_nickna sex subject_birth_ subject_descri\n", + "+----------+ +------------+ +-----+ +------------+ +------------+\n", + "subject5 subject5 U 2023-01-01 Example subjec\n", + " (Total: 1)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "subject.Subject.insert1(\n", " dict(\n", @@ -225,18 +813,41 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> subject.Subject\n", + "session_datetime : datetime \n", + "\n" + ] + } + ], "source": [ "print(session.Session.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.Session.heading" ] @@ -260,9 +871,93 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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\n", + " \n", + "
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session_datetime

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subject52023-06-30 03:34:20
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet\n", + "+----------+ +------------+\n", + "subject5 2023-06-30 03:\n", + " (Total: 1)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session_key = dict(subject='subject5', \n", " session_datetime=datetime.datetime.now())\n", @@ -284,27 +979,141 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-> session.Session\n", + "---\n", + "session_dir : varchar(256) # Path to the data directory for a session\n", + "\n" + ] + } + ], "source": [ "print(session.SessionDirectory.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# \n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "---\n", + "session_dir : varchar(256) # Path to the data directory for a session" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.SessionDirectory.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
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subject

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session_datetime

\n", + " \n", + "
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session_dir

\n", + " Path to the data directory for a session\n", + "
subject52023-06-30 03:34:20subject5/session1
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\n", + " " + ], + "text/plain": [ + "*subject *session_datet session_dir \n", + "+----------+ +------------+ +------------+\n", + "subject5 2023-06-30 03: subject5/sessi\n", + " (Total: 1)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "session.SessionDirectory.insert1(\n", " dict(\n", @@ -328,27 +1137,143 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Represent a physical probe with unique identification\n", + "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\n", + "---\n", + "-> probe.ProbeType\n", + "probe_comment : varchar(1000) \n", + "\n" + ] + } + ], "source": [ "print(probe.Probe.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Represent a physical probe with unique identification\n", + "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)\n", + "---\n", + "probe_type : varchar(32) # e.g. neuropixels_1.0\n", + "probe_comment : varchar(1000) # " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "probe.Probe.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Represent a physical probe with unique identification\n", + "
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probe

\n", + " unique identifier for this model of probe (e.g. serial number)\n", + "
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probe_type

\n", + " e.g. neuropixels_1.0\n", + "
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probe_comment

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714000838neuropixels 1.0 - 3BExample probe
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Total: 1

\n", + " " + ], + "text/plain": [ + "*probe probe_type probe_comment \n", + "+-----------+ +------------+ +------------+\n", + "714000838 neuropixels 1. Example probe \n", + " (Total: 1)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "probe.Probe.insert1(\n", " dict(probe=\"714000838\", \n", @@ -369,27 +1294,148 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Probe insertion implanted into an animal for a given session.\n", + "-> session.Session\n", + "insertion_number : tinyint unsigned \n", + "---\n", + "-> probe.Probe\n", + "\n" + ] + } + ], "source": [ "print(ephys.ProbeInsertion.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Probe insertion implanted into an animal for a given session.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "---\n", + "probe : varchar(32) # unique identifier for this model of probe (e.g. serial number)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.ProbeInsertion.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Probe insertion implanted into an animal for a given session.\n", + "
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session_datetime

\n", + " \n", + "
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\n", + " \n", + "
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probe

\n", + " unique identifier for this model of probe (e.g. serial number)\n", + "
subject52023-06-30 03:34:201714000838
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num probe \n", + "+----------+ +------------+ +------------+ +-----------+\n", + "subject5 2023-06-30 03: 1 714000838 \n", + " (Total: 1)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.ProbeInsertion.insert1(\n", " dict(\n", @@ -424,45 +1470,285 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Ephys recording from a probe insertion for a given session.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "---\n", + "electrode_config_hash : uuid # \n", + "acq_software : varchar(24) # \n", + "sampling_rate : float # (Hz)\n", + "recording_datetime : datetime # datetime of the recording from this probe\n", + "recording_duration : float # (seconds) duration of the recording from this probe" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.EphysRecording.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Paths of files of a given EphysRecording round.\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "file_path : varchar(255) # filepath relative to root data directory" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.EphysRecording.EphysFile.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Ephys recording from a probe insertion for a given session.\n", + "
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sampling_rate

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recording_datetime

\n", + " datetime of the recording from this probe\n", + "
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recording_duration

\n", + " (seconds) duration of the recording from this probe\n", + "
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electrode_config_hash

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acq_software

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sampling_rate

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recording_datetime

\n", + " datetime of the recording from this probe\n", + "
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recording_duration

\n", + " (seconds) duration of the recording from this probe\n", + "
subject52023-06-30 03:34:2018d4cc6d8-a02d-42c8-bf27-7459c39ea0eeSpikeGLX30000.02018-07-03 20:32:28338.666
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file_path

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subject52023-06-30 03:34:201subject5/session1/probe_1/npx_g0_t0.imec.ap.meta
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Total: 1

\n", + " " + ], + "text/plain": [ + "*subject *session_datet *insertion_num *file_path \n", + "+----------+ +------------+ +------------+ +------------+\n", + "subject5 2023-06-30 03: 1 subject5/sessi\n", + " (Total: 1)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.EphysRecording.EphysFile()" ] @@ -509,18 +1995,131 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Parameter set to be used in a clustering procedure\n", + "paramset_idx : smallint # \n", + "---\n", + "clustering_method : varchar(16) # \n", + "paramset_desc : varchar(128) # \n", + "param_set_hash : uuid # \n", + "params : longblob # dictionary of all applicable parameters" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.ClusteringParamSet.heading" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " Parameter set to be used in a clustering procedure\n", + "
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paramset_desc

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param_set_hash

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params

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0kilosort2Spike sorting using Kilosort2de78cee1-526f-319e-b6d5-8a2ba04963d8=BLOB=
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Total: 1

\n", + " " + ], + "text/plain": [ + "*paramset_idx clustering_met paramset_desc param_set_hash params \n", + "+------------+ +------------+ +------------+ +------------+ +--------+\n", + "0 kilosort2 Spike sorting de78cee1-526f- =BLOB= \n", + " (Total: 1)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# insert clustering task manually\n", "params_ks = {\n", @@ -571,18 +2170,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "# Manual table for defining a clustering task ready to be run\n", + "-> ephys.EphysRecording\n", + "-> ephys.ClusteringParamSet\n", + "---\n", + "clustering_output_dir : varchar(255) # clustering output directory relative to the clustering root data directory\n", + "task_mode : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation\n", + "\n" + ] + } + ], "source": [ "print(ephys.ClusteringTask.describe())" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# Manual table for defining a clustering task ready to be run\n", + "subject : varchar(8) # \n", + "session_datetime : datetime # \n", + "insertion_number : tinyint unsigned # \n", + "paramset_idx : smallint # \n", + "---\n", + "clustering_output_dir : varchar(255) # clustering output directory relative to the clustering root data directory\n", + "task_mode : enum('load','trigger') # 'load': load computed analysis results, 'trigger': trigger computation" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ephys.ClusteringTask.heading" ] @@ -609,7 +2240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -635,9 +2266,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clustering: 0%| | 0/1 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(lfp_average)\n", "plt.title(\"Average LFP Waveform for Insertion 1\")\n", @@ -785,7 +2483,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -795,9 +2493,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x = np.hstack(unit_spiketimes)\n", "y = np.hstack([np.full_like(s, u) for u, s in zip(units, unit_spiketimes)])\n", @@ -808,7 +2517,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -818,18 +2527,235 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'subject': 'subject5',\n", + " 'session_datetime': datetime.datetime(2023, 6, 30, 3, 34, 20),\n", + " 'insertion_number': 1,\n", + " 'paramset_idx': 0,\n", + " 'curation_id': 1,\n", + " 'unit': 15,\n", + " 'electrode_config_hash': UUID('8d4cc6d8-a02d-42c8-bf27-7459c39ea0ee'),\n", + " 'probe_type': 'neuropixels 1.0 - 3A',\n", + " 'electrode': 92,\n", + " 'cluster_quality_label': 'noise',\n", + " 'spike_count': 292,\n", + " 'spike_times': array([ 1.02606667, 1.19973333, 1.5044 , 1.52283333,\n", + " 1.86786667, 1.8688 , 1.8806 , 1.88553333,\n", + " 2.0581 , 2.76826667, 2.89186667, 2.9311 ,\n", + " 4.8753 , 5.2964 , 7.02266667, 9.65273333,\n", + " 9.81373333, 10.22443333, 11.96146667, 15.07173333,\n", + " 15.08983333, 15.78326667, 21.30936667, 22.7549 ,\n", + " 23.5582 , 23.6582 , 23.87043333, 24.16726667,\n", + " 24.25456667, 24.2671 , 25.0249 , 27.89116667,\n", + " 35.3036 , 36.0171 , 40.00396667, 40.0873 ,\n", + " 41.015 , 42.70086667, 45.8682 , 47.9291 ,\n", + " 48.90843333, 49.37996667, 49.39596667, 49.4058 ,\n", + " 49.65926667, 49.68203333, 49.74273333, 51.52213333,\n", + " 52.41486667, 55.27623333, 55.54576667, 55.81213333,\n", + " 56.0544 , 56.29426667, 56.36896667, 56.3743 ,\n", + " 56.47403333, 56.7147 , 56.71683333, 60.764 ,\n", + " 61.5317 , 61.54046667, 61.7721 , 62.10233333,\n", + " 62.10726667, 62.2118 , 62.81173333, 63.15896667,\n", + " 65.11126667, 65.495 , 67.18373333, 77.59256667,\n", + " 79.2709 , 80.46186667, 82.1178 , 85.6568 ,\n", + " 86.52613333, 89.12126667, 89.46963333, 89.64663333,\n", + " 90.19103333, 92.0923 , 92.99573333, 93.36923333,\n", + " 93.68086667, 95.2097 , 97.96296667, 98.1067 ,\n", + " 98.69713333, 99.26963333, 99.28013333, 101.04216667,\n", + " 101.2002 , 101.3843 , 101.3975 , 101.40656667,\n", + " 102.00996667, 102.07066667, 102.17033333, 103.5861 ,\n", + " 104.68523333, 104.71643333, 105.2595 , 105.5166 ,\n", + " 105.54723333, 107.0848 , 109.82746667, 110.14853333,\n", + " 110.92203333, 111.2481 , 112.26 , 113.09466667,\n", + " 113.09846667, 113.1005 , 113.33513333, 118.842 ,\n", + " 118.96803333, 119.32606667, 119.64796667, 119.96313333,\n", + " 119.97343333, 120.19573333, 120.19693333, 120.21163333,\n", + " 121.1879 , 121.233 , 121.9611 , 122.40203333,\n", + " 123.1745 , 124.6798 , 124.68196667, 124.97396667,\n", + " 125.2205 , 125.87786667, 125.957 , 125.95803333,\n", + " 127.7149 , 136.8558 , 136.86413333, 138.49966667,\n", + " 139.49553333, 139.93163333, 139.9819 , 142.57013333,\n", + " 142.70053333, 143.343 , 143.40796667, 148.46066667,\n", + " 148.47023333, 148.89893333, 152.79766667, 153.2198 ,\n", + " 153.39096667, 153.62456667, 153.82253333, 153.82693333,\n", + " 153.8364 , 153.98863333, 155.4047 , 157.52706667,\n", + " 157.53233333, 163.1177 , 163.12516667, 163.46916667,\n", + " 163.69406667, 163.7119 , 166.6946 , 166.7006 ,\n", + " 170.78053333, 170.7891 , 170.79653333, 180.36596667,\n", + " 180.43416667, 182.72556667, 182.73706667, 182.76576667,\n", + " 184.24713333, 185.5131 , 185.7329 , 186.4198 ,\n", + " 186.48443333, 186.72036667, 186.95926667, 187.1402 ,\n", + " 187.16683333, 189.4036 , 193.27583333, 195.40946667,\n", + " 200.2427 , 203.03376667, 203.04076667, 205.84043333,\n", + " 206.1151 , 207.3402 , 210.95773333, 217.73583333,\n", + " 220.35 , 221.1235 , 227.7719 , 227.946 ,\n", + " 228.02456667, 231.2108 , 231.80003333, 233.48253333,\n", + " 236.8342 , 241.78993333, 243.6285 , 245.61546667,\n", + " 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786.18136435,\n", + " 864.98026608, 950.29718422, 930.39820918, 983.57154574,\n", + " 909.55498598, 925.76591071, 906.07808244, 964.41923255,\n", + " 911.17276099, 919.34859876, 906.11721418, 905.96844161,\n", + " 911.53683431, 958.97512493, 953.15452691, 889.09240389,\n", + " 957.98473324, 993.29170991, 928.99175736, 908.10563408]),\n", + " 'peak_electrode_waveform': array([-503.33636558, -502.734375 , -501.32973031, -500.34246575,\n", + " -499.73244863, -498.66491866, -494.41887842, -491.6015625 ,\n", + " -486.59300086, -481.55233305, -474.36055223, -466.79152397,\n", + " -458.98169949, -451.10766267, -442.41491866, -431.03328339,\n", + " -420.81549658, -410.91074486, -402.25010702, -395.59610445,\n", + " -389.25513699, -384.02985873, -381.67005565, -379.81592466,\n", + " -378.79655394, -380.26541096, -384.54355736, -392.41759418,\n", + " -401.11033818, -410.67797517, -420.13324058, -430.6640625 ,\n", + " -442.34267979, -452.71297089, -461.42979452, -469.67305223,\n", + " 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"execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sampling_rate = (ephys.EphysRecording & insert_key).fetch1(\n", " \"sampling_rate\"\n", From 59d076ccf9e3537032066220a071572f2087bc97 Mon Sep 17 00:00:00 2001 From: Kabilar Gunalan Date: Fri, 30 Jun 2023 03:51:17 +0000 Subject: [PATCH 7/7] Update changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index db1991b..d27aeb4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ Observes [Semantic Versioning](https://semver.org/spec/v2.0.0.html) standard and ## [0.3.3] - 2023-06-29 + Add - Docker image ID ++ Add - VS Code extensions + Update - `element-array-ephys` version + Update - Tutorial notebook to work around bug