{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tools for data and metadata handling\n", "## as used in [Brochier et al., 2018](https://www.nature.com/articles/sdata201855)\n", "Julia Sprenger | Institut de Neurosciences de la Timone | Brainhack Marseille 2020" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Content\n", "- [GIN](/home/julia/repositories/gin/multielectrode_grasp/code/presentation/figures/elephant_structure.png) -- for centralized data hosting and versioning\n", "- [open metadata Markup Language (odML)](https://g-node.github.io/python-odml) -- for metadata organization\n", "- [odMLtables](https://odmltables.readthedocs.io/en/latest/) -- for user-friendly interaction with odML\n", "- [Neo](https://neo.readthedocs.io/en/stable/) -- for conversion and representation of ephys data\n", "- [[Elephant]](https://elephant.readthedocs.io/en/latest/) -- for analysis of ephys data\n", "- [[Viziphant]](https://viziphant.readthedocs.io/en/latest/) -- for visualization of ephys data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### GIN ([gin.g-node.org](gin.g-node.org)) \"odml-logo\"\n", "\n", "- service for hosting & versioning data files\n", "- based on git & git-annex\n", "- public and private repositories\n", "- DOI service for data publication\n", "- open source; option of local setup\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### [Repository of Brochier et al.](https://gin.g-node.org/INT/multielectrode_grasp)\n", "\n", "
\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### [open metadata Markup Language (odML)](https://g-node.github.io/python-odml) \"odml-logo\"\n", "\n", "- framework for hierarchical structure of metadata including contextual information\n", "- generic metadata handling, not limited to ephys\n", "- can be exported to **xml**, json, yaml, rdf\n", "- implemented in **Python**, java, Matlab\n", "
\n", "\"odML_artistic_tree\"\n", "
\n", "\n", "_[Figure modified from Zehl et al. 2016]_" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Interacting with odML\n", "\n", "- offline html visualization\n", "- online visualization on the [GIN webservice](https://gin.g-node.org/), e.g. the published [metadata collection](https://gin.g-node.org/INT/multielectrode_grasp/src/enh/neo09/datasets/i140703-001.odml)\n", "- [odML-UI](https://pypi.org/project/odML-UI/) package (not recommended)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "### Example: Create a minimal metadata collection" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "import datetime\n", "import odml\n", "\n", "# Create odML objects\n", "doc = odml.Document(\n", " author='Julia Sprenger', \n", " date=datetime.date.today(),\n", " version=0.1, \n", " repository='/my/data/repository')\n", "\n", "section = odml.Section(\n", " name='Recording', \n", " type='online data',\n", " definition='Details about the recording procedure')\n", "\n", "property = odml.Property(\n", " name='Recording_Quality', \n", " values='good', \n", " definition='Subjective quality assessment by the experimenter (\"good\"/\"ok\"/\"bad\")')\n", "\n", "# Create links between objects\n", "doc.append(section)\n", "section.append(property)\n", "\n", "odml.save(doc, 'minimal_collection.odml')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### How to efficiently work with an odML metadata collection on a daily basis?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### odMLtables \"odml-logo\"\n", "- user-friendly interaction with odML\n", "- Python API & graphical user interface\n", "- main feature: conversion between hierarchical odML format & tabular representation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### odMLtables: hierarchical-tabular conversion\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Summary: metadata organization options\n", "- custom odML structure with comprehensive documentation\n", "- easy visualization\n", "- programmatically and tabular accessibility\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### But what to do about the primary data?\n", "- many diverse, proprietary formats\n", "- limited, tailored software solutions\n", "- difficult comparison across projects" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Neo: central interface for electrophysiology data \"odml-logo\"\n", "\n", "![neo-as-interface](figures/neo_as_interface.png)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Neo: standardized electrophysiology data representation \"odml-logo\"\n", "*Data Objects*: numpy array + essential metadata + custom annotations\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Neo: standardized electrophysiology data representation \"odml-logo\"\n", "*Container Objects*: provide structure and logical relations and custom annotations\n", "
\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Loading data of the publication \"odml-logo\"\n", "Here we are using Neo to load the data and inspect the standard data representation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "import neo\n", "\n", "# storage location of the dataset\n", "session_path = '../../datasets/i140703-001'\n", "\n", "# Initializing IO for Blackrock recording session\n", "io = neo.BlackrockIO(session_path)\n", "\n", "# Creating the complete Neo structure without loading data into memory\n", "block = io.read_block(lazy=True)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "AnalogSignal with 1 channels of length 10000; units uV; datatype float32 \n", "name: 'chan1'\n", "sampling rate: 1000.0 Hz\n", "time: 0.0 s to 10.0 s" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import quantities as pq\n", "\n", "# Loading data of a single recording channel\n", "multi_trace = block.segments[0].analogsignals[1]\n", "\n", "single_trace = multi_trace.load(\n", " channel_indexes=[0],\n", " time_slice=(0*pq.s,10*pq.s))\n", "\n", "# inspect the AnalogSignal object\n", "single_trace" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[SpikeTrain\n", " name: 'ch1#0'\n", " description: 'SpikeTrain channel_id: 1, unit_id: 0'\n", " annotations: {'id': 'Unit 1000',\n", " 'channel_id': 1,\n", " 'unit_id': 0,\n", " 'unit_tag': 'unclassified'},\n", " SpikeTrain\n", " name: 'ch1#1'\n", " description: 'SpikeTrain channel_id: 1, unit_id: 1'\n", " annotations: {'id': 'Unit 1001',\n", " 'channel_id': 1,\n", " 'unit_id': 1,\n", " 'unit_tag': '1'},\n", " SpikeTrain\n", " name: 'ch1#2'\n", " description: 'SpikeTrain channel_id: 1, unit_id: 2'\n", " annotations: {'id': 'Unit 1002',\n", " 'channel_id': 1,\n", " 'unit_id': 2,\n", " 'unit_tag': '2'}]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Selecting spiketrains of the same channel as single recording trace\n", "channel_id = single_trace.array_annotations['channel_ids'][0]\n", "spiketrains = block.filter(channel_id=channel_id)\n", "\n", "# Loading spiketrain data\n", "spiketrains = [st.load(time_slice=(None, 10*pq.s)) for st in spiketrains]\n", "spiketrains" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.03293333 0.99996667 1.10386667 1.17866667 1.50043333 1.54853333\n", " 3.5233 3.96836667 4.64523333 6.35246667 6.5574 9.15593333\n", " 9.20743333] s\n" ] } ], "source": [ "print(spiketrains[0])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Visualizing the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Available Tools\n", "- common Python libraries: matplotlib, seaborn\n", "- custom visualization for data streams using neo: ephyviewer\n", "- custom visualization for neo objects: neo-view (online), viziphant (offline)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import viziphant\n", "viziphant.rasterplot.eventplot(spiketrains)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Automatic calculation of simple features, e.g. the inter-spike-interval distribution (ISI)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "viziphant.statistics.plot_isi_histogram(spiketrains, cutoff=0.5*pq.s)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "
\"elephant\"
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Elephant \"elephant-logo\"\n", "\n", "Calculating the instantaneous rate of spiketrains" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnalogSignal with 3 channels of length 99; units Hz; datatype float64 \n", "annotations: {'t_stop': array(10.) * s,\n", " 'kernel': {'type': 'GaussianKernel', 'sigma': '20.0 ms', 'invert': False}}\n", "sampling rate: 0.01 1/ms\n", "time: 0.0017 s to 9.9017 s" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import elephant\n", "kernel = elephant.kernels.GaussianKernel(20*pq.ms)\n", "rate = elephant.statistics.instantaneous_rate(spiketrains, sampling_period=100*pq.ms, kernel=kernel)\n", "rate" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "viziphant.statistics.plot_instantaneous_rates_colormesh(rate) " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Summary\n", "\n", "
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Thank your for listening!\n", "\n", "_The datasets used in this presentation are available at https://gin.g-node.org/INT/multielectrode_grasp/_\n", "\n", "_as well as the presentation (https://gin.g-node.org/sprenger/multielectrode_grasp/src/tool_intro/code/presentation)_\n", "\n", "\n", "

\n", "\n", "

\n", " \n", "*References*\n", " \n", "\n", " \n", "- **data pulication:** Brochier et al., 2018. Massively parallel recordings in macaque motor cortex during an instructed delayed reach-to-grasp task. Scientific Data 5, 180055. https://doi.org/10.1038/sdata.2018.55\n", "- **Neo:** https://pypi.org/project/neo/ and https://doi.org/10.3389/fninf.2014.00010\n", "- **odML:** https://pypi.org/project/odml and https://doi.org/10.3389/fninf.2011.00016\n", "- **NIX:** https://pypi.org/project/nixio and https://doi.org/10.3389/fninf.2014.00015\n", "- **odMLtables** https://pypi.org/project/odmltables and https://doi.org/10.3389/fninf.2019.00062\n", "- **elephant** https://pypi.org/project/elephant\n", "- **viziphant** https://pypi.org/project/viziphant\n", " \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Slideshow", "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }