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sf_comparison_plots.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "sf_comparison_plots.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/magland/5c82306f20aa2a81ba9d429b5e1d3c23/sf_comparison_plots.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"metadata": {
"id": "B-fkj9wnsYRk",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"## Explore SpikeForest datasets and sorting results\n",
"\n",
"This notebook demonstrates how to use the SpikeForest python API to explore datasets and sorting results."
]
},
{
"metadata": {
"id": "cyP-yRDVsLmG",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"## Install the required python packages\n",
"## Important: Only run this cell if you are on a hosted runtime and these packages are not yet installed\n",
"\n",
"%%capture\n",
"!pip install spikeforest"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "KecYj3Khut4v",
"colab_type": "code",
"outputId": "0a7aada3-2302-4199-a87f-c4e341ede78d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
}
},
"cell_type": "code",
"source": [
"%%bash\n",
"## Display the versions of the installed software \n",
"pip list | grep spikeforest\n",
"pip list | grep spikeextractors\n",
"pip list | grep spikewidgets\n",
"pip list | grep spiketoolkit\n",
"pip list | grep kbucket"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"spikeforest 0.4.0 /home/magland/src/mldevel/components/spikeforest \n",
"spikeextractors 0.2.2 /home/magland/src/mldevel/components/spikeextractors \n",
"spikewidgets 0.1.21 /home/magland/miniconda3/envs/devel/lib/python3.6/site-packages/spikewidgets-0.1.21-py3.6.egg \n",
"spiketoolkit 0.1.6 /home/magland/miniconda3/envs/devel/lib/python3.6/site-packages/spiketoolkit-0.1.6-py3.6.egg \n",
"kbucket 0.12.5 /home/magland/miniconda3/envs/devel/lib/python3.6/site-packages/kbucket-0.12.5-py3.6.egg \n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "QcZOXiVZsAjP",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"# Imports\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from kbucket import client as kb\n",
"import spikeforest as sf\n",
"import spikewidgets as sw\n",
"import spiketoolkit as st"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "kJDiW7xvYqji",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"## Configure read access to kbucket\n",
"sf.setKBucketConfig(collection='spikeforest',key=dict(name='spikeforest1-readonly'))"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "yFNnnd-Qtj8P",
"colab_type": "code",
"outputId": "d5e553be-69de-4687-9dba-c7e9fbcfd2d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"cell_type": "code",
"source": [
"# Load the SpikeForest data\n",
"SF=sf.SFData()\n",
"SF.loadRecordings(key=dict(name='spikeforest_recordings'))\n",
"SF.loadProcessingBatch(key=dict(batch_name='summarize_recordings',name='job_results'))\n",
"SF.loadProcessingBatch(key=dict(batch_name='ms4_magland_synth_dev3',name=\"job_results\"))\n",
"SF.loadProcessingBatch(key=dict(batch_name='irc_magland_synth_dev3',name=\"job_results\"))\n",
"SF.loadProcessingBatch(key=dict(batch_name='sc_magland_synth_dev3',name=\"job_results\"))\n",
"\n",
"#SF.loadProcessingBatch(key=dict(batch_name='irc_bionet',name=\"job_results\"))"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Warning: study not found: testing\n",
"Warning: study not found: testing\n",
"Warning: study not found: testing\n",
"Loaded 0 sorting results and 155 recording summary results\n",
"Loaded 40 sorting results and 0 recording summary results\n",
"Loaded 40 sorting results and 0 recording summary results\n",
"Loaded 40 sorting results and 0 recording summary results\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "syRd5nB4wLqL",
"colab_type": "code",
"outputId": "5a1bbc1f-0fdc-42be-eb41-055930b45e81",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"cell_type": "code",
"source": [
"# Display the study names\n",
"display(', '.join(SF.studyNames()))\n",
"\n",
"# Load a study and display the dataset names\n",
"study=SF.study('magland_synth_noise20_K20_C4')"
],
"execution_count": 5,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"'bionet_drift, bionet_shuffle, bionet_static, magland_synth_noise10_K10_C4, magland_synth_noise10_K10_C8, magland_synth_noise10_K20_C4, magland_synth_noise10_K20_C8, magland_synth_noise20_K10_C4, magland_synth_noise20_K10_C8, magland_synth_noise20_K20_C4, magland_synth_noise20_K20_C8, boyden----, kampff----, mea256yger----'"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "K_ttlQ6ZyKut",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import pandas as pd\n",
"import random\n",
"import altair as alt\n",
"alt.renderers.enable('colab')\n",
"\n",
"def show_accuracy_plot(study_name,sorter_name,title):\n",
"\n",
" # Accumulate the sorting results\n",
" def accumulate_comparison_with_ground_truth(*,studies,sorter_name,fieldnames):\n",
" ret=[]\n",
" for study in studies:\n",
" recordings=[study.recording(name) for name in study.recordingNames()]\n",
" for R in recordings:\n",
" result=R.sortingResult(sorter_name)\n",
" A=result.comparisonWithTruth(format='json')\n",
" B=R.trueUnitsInfo(format='json')\n",
" snr_by_true_unit=dict()\n",
" for b in B:\n",
" snr_by_true_unit[b['unit_id']]=b['snr']\n",
" for i in A:\n",
" a=A[i]\n",
" rec=dict()\n",
" rec['recording_name']=R.name()\n",
" rec['unit_id']=a['Unit ID']\n",
" rec['snr']=snr_by_true_unit[rec['unit_id']]\n",
" for fieldname in fieldnames:\n",
" rec[fieldname]=float(a[fieldname])\n",
" ret.append(rec)\n",
" return ret\n",
" \n",
" study=SF.study(study_name)\n",
" X=accumulate_comparison_with_ground_truth(\n",
" studies=[study],\n",
" sorter_name=sorter_name,\n",
" fieldnames=['Accuracy']\n",
" )\n",
" \n",
" # Display the accumulated sorting results\n",
" cc=alt.Chart(pd.DataFrame(X),title=title).mark_point().encode(\n",
" x='snr',\n",
" y='Accuracy',\n",
" color='recording_name',\n",
" tooltip='recording_name'\n",
" ).interactive()\n",
" display(cc)\n"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "yTun3KGELs7t",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"study_name='magland_synth_noise10_K10_C4'"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "Rz1KSeFF2h5N",
"colab_type": "code",
"outputId": "2d7c2c42-852a-4049-bfc1-af3d9fe023d8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
}
},
"cell_type": "code",
"source": [
"show_accuracy_plot(\n",
" study_name=study_name,\n",
" sorter_name='MountainSort4-thr3',\n",
" title='MountainSort4'\n",
")"
],
"execution_count": 8,
"outputs": [
{
"output_type": "display_data",
"data": {
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"<script src=\"https://cdn.jsdelivr.net/npm//vega@3.3.1\"></script>\n",
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"<body>\n",
" <div id=\"vis\"></div>\n",
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"Chart({\n",
" data: Accuracy recording_name snr unit_id\n",
" 0 1.00 001_synth 25.396784 1\n",
" 1 0.99 001_synth 16.557398 2\n",
" 2 0.98 001_synth 10.427755 3\n",
" 3 0.98 001_synth 12.307774 4\n",
" 4 0.98 001_synth 9.730707 5\n",
" 5 0.98 001_synth 12.170331 6\n",
" 6 0.98 001_synth 16.160870 7\n",
" 7 0.98 001_synth 10.293515 8\n",
" 8 0.99 001_synth 13.785120 9\n",
" 9 1.00 001_synth 17.224081 10\n",
" 10 1.00 002_synth 16.794793 1\n",
" 11 0.99 002_synth 14.845406 2\n",
" 12 0.66 002_synth 10.493050 3\n",
" 13 1.00 002_synth 23.182871 4\n",
" 14 0.97 002_synth 11.871269 5\n",
" 15 0.99 002_synth 15.022614 6\n",
" 16 0.98 002_synth 9.586446 7\n",
" 17 0.99 002_synth 12.094306 8\n",
" 18 0.98 002_synth 11.835524 9\n",
" 19 1.00 002_synth 19.116053 10\n",
" 20 1.00 003_synth 16.646966 1\n",
" 21 0.99 003_synth 13.077902 2\n",
" 22 0.99 003_synth 10.728689 3\n",
" 23 0.98 003_synth 7.979660 4\n",
" 24 0.99 003_synth 9.436042 5\n",
" 25 0.54 003_synth 8.531089 6\n",
" 26 0.98 003_synth 13.507234 7\n",
" 27 0.98 003_synth 10.986865 8\n",
" 28 0.37 003_synth 7.336661 9\n",
" 29 0.98 003_synth 11.206825 10\n",
" .. ... ... ... ...\n",
" 70 0.99 008_synth 16.424779 1\n",
" 71 0.97 008_synth 10.574518 2\n",
" 72 0.98 008_synth 12.044561 3\n",
" 73 1.00 008_synth 23.911969 4\n",
" 74 0.98 008_synth 11.232470 5\n",
" 75 0.99 008_synth 13.245688 6\n",
" 76 0.98 008_synth 11.545822 7\n",
" 77 0.99 008_synth 13.987158 8\n",
" 78 0.99 008_synth 15.565146 9\n",
" 79 1.00 008_synth 18.571303 10\n",
" 80 0.98 009_synth 14.740083 1\n",
" 81 0.98 009_synth 14.314710 2\n",
" 82 0.57 009_synth 9.176301 3\n",
" 83 1.00 009_synth 16.605223 4\n",
" 84 0.98 009_synth 10.488155 5\n",
" 85 0.62 009_synth 8.393864 6\n",
" 86 1.00 009_synth 16.734778 7\n",
" 87 0.97 009_synth 10.523270 8\n",
" 88 0.96 009_synth 8.600044 9\n",
" 89 1.00 009_synth 15.291506 10\n",
" 90 0.98 010_synth 8.664441 1\n",
" 91 0.99 010_synth 11.206878 2\n",
" 92 0.50 010_synth 8.228800 3\n",
" 93 0.99 010_synth 15.749484 4\n",
" 94 1.00 010_synth 14.587038 5\n",
" 95 0.98 010_synth 10.585971 6\n",
" 96 0.99 010_synth 10.595648 7\n",
" 97 0.50 010_synth 9.892953 8\n",
" 98 0.99 010_synth 10.162029 9\n",
" 99 1.00 010_synth 13.332445 10\n",
" \n",
" [100 rows x 4 columns],\n",
" encoding: EncodingWithFacet({\n",
" color: Color({\n",
" shorthand: 'recording_name'\n",
" }),\n",
" tooltip: Tooltip({\n",
" shorthand: 'recording_name'\n",
" }),\n",
" x: X({\n",
" shorthand: 'snr'\n",
" }),\n",
" y: Y({\n",
" shorthand: 'Accuracy'\n",
" })\n",
" }),\n",
" mark: 'point',\n",
" selection: SelectionMapping({\n",
" selector001: SelectionDef({\n",
" bind: 'scales',\n",
" encodings: ['x', 'y'],\n",
" type: 'interval'\n",
" })\n",
" }),\n",
" title: 'MountainSort4'\n",
"})"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "pLkA6R6lLvSq",
"colab_type": "code",
"outputId": "75465d54-92ef-425d-d7db-45381978b46e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
}
},
"cell_type": "code",
"source": [
"show_accuracy_plot(\n",
" study_name=study_name,\n",
" sorter_name='IronClust-tetrode',\n",
" title='IronClust'\n",
")"
],
"execution_count": 9,
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": [
"Chart({\n",
" data: Accuracy recording_name snr unit_id\n",
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" 7 0.98 001_synth 10.293515 8\n",
" 8 0.98 001_synth 13.785120 9\n",
" 9 0.98 001_synth 17.224081 10\n",
" 10 0.98 002_synth 16.794793 1\n",
" 11 0.98 002_synth 14.845406 2\n",
" 12 0.97 002_synth 10.493050 3\n",
" 13 1.00 002_synth 23.182871 4\n",
" 14 0.97 002_synth 11.871269 5\n",
" 15 0.98 002_synth 15.022614 6\n",
" 16 0.97 002_synth 9.586446 7\n",
" 17 0.98 002_synth 12.094306 8\n",
" 18 0.97 002_synth 11.835524 9\n",
" 19 0.99 002_synth 19.116053 10\n",
" 20 0.99 003_synth 16.646966 1\n",
" 21 0.97 003_synth 13.077902 2\n",
" 22 0.96 003_synth 10.728689 3\n",
" 23 0.97 003_synth 7.979660 4\n",
" 24 0.98 003_synth 9.436042 5\n",
" 25 0.96 003_synth 8.531089 6\n",
" 26 0.98 003_synth 13.507234 7\n",
" 27 0.98 003_synth 10.986865 8\n",
" 28 0.97 003_synth 7.336661 9\n",
" 29 0.98 003_synth 11.206825 10\n",
" .. ... ... ... ...\n",
" 70 0.99 008_synth 16.424779 1\n",
" 71 0.95 008_synth 10.574518 2\n",
" 72 0.97 008_synth 12.044561 3\n",
" 73 1.00 008_synth 23.911969 4\n",
" 74 0.98 008_synth 11.232470 5\n",
" 75 0.98 008_synth 13.245688 6\n",
" 76 0.97 008_synth 11.545822 7\n",
" 77 0.98 008_synth 13.987158 8\n",
" 78 0.98 008_synth 15.565146 9\n",
" 79 0.98 008_synth 18.571303 10\n",
" 80 0.98 009_synth 14.740083 1\n",
" 81 0.97 009_synth 14.314710 2\n",
" 82 0.98 009_synth 9.176301 3\n",
" 83 0.98 009_synth 16.605223 4\n",
" 84 0.97 009_synth 10.488155 5\n",
" 85 0.97 009_synth 8.393864 6\n",
" 86 0.99 009_synth 16.734778 7\n",
" 87 0.96 009_synth 10.523270 8\n",
" 88 0.96 009_synth 8.600044 9\n",
" 89 0.98 009_synth 15.291506 10\n",
" 90 0.97 010_synth 8.664441 1\n",
" 91 0.98 010_synth 11.206878 2\n",
" 92 0.97 010_synth 8.228800 3\n",
" 93 0.98 010_synth 15.749484 4\n",
" 94 0.99 010_synth 14.587038 5\n",
" 95 0.98 010_synth 10.585971 6\n",
" 96 0.98 010_synth 10.595648 7\n",
" 97 0.97 010_synth 9.892953 8\n",
" 98 0.96 010_synth 10.162029 9\n",
" 99 0.99 010_synth 13.332445 10\n",
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" bind: 'scales',\n",
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" }),\n",
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"})"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "SGX6yIfGL4KP",
"colab_type": "code",
"outputId": "0a416da0-c069-42cf-b5be-468e23eb6cf6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 407
}
},
"cell_type": "code",
"source": [
"show_accuracy_plot(\n",
" study_name=study_name,\n",
" sorter_name='SpykingCircus',\n",
" title='SpykingCircus'\n",
")"
],
"execution_count": 10,
"outputs": [
{
"output_type": "display_data",
"data": {
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" bind: 'scales',\n",
" encodings: ['x', 'y'],\n",
" type: 'interval'\n",
" })\n",
" }),\n",
" title: 'SpykingCircus'\n",
"})"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "lTDI8DCLMhTI",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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