{ "cells": [ { "cell_type": "markdown", "id": "a20f7712", "metadata": {}, "source": [ "# Hands-on session 1: Neo basics\n", "\n", "These exercises cover the basics introduced in Tutorial 1\n", "\n", "## Preparation: Download public ephys dataset\n", "On Linux you can download the publicly available dataset via the command below. On other systems, please download the files manually from [l101210-001.ns2](https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns2), [l101210-001.nev](https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.nev) and [l101210-001.ns5](https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns5) and save them in the same folder as this notebook.\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "f914945d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2021-08-24 21:51:53-- https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns2\n", "Resolving gin.g-node.org (gin.g-node.org)... 141.84.41.219\n", "Connecting to gin.g-node.org (gin.g-node.org)|141.84.41.219|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 43748 (43K) [application/octet-stream]\n", "Saving to: 'l101210-001.ns2’\n", "\n", "l101210-001.ns2 100%[===================>] 42,72K --.-KB/s in 0,1s \n", "\n", "2021-08-24 21:51:59 (327 KB/s) - 'l101210-001.ns2’ saved [43748/43748]\n", "\n", "--2021-08-24 21:51:59-- https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.nev\n", "Resolving gin.g-node.org (gin.g-node.org)... 141.84.41.219\n", "Connecting to gin.g-node.org (gin.g-node.org)|141.84.41.219|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1483616 (1,4M) [application/octet-stream]\n", "Saving to: 'l101210-001.nev’\n", "\n", "l101210-001.nev 100%[===================>] 1,41M 2,51MB/s in 0,6s \n", "\n", "2021-08-24 21:52:00 (2,51 MB/s) - 'l101210-001.nev’ saved [1483616/1483616]\n", "\n", "--2021-08-24 21:52:00-- https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns5\n", "Resolving gin.g-node.org (gin.g-node.org)... 141.84.41.219\n", "Connecting to gin.g-node.org (gin.g-node.org)|141.84.41.219|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 20971616 (20M) [application/octet-stream]\n", "Saving to: 'l101210-001.ns5’\n", "\n", "l101210-001.ns5 100%[===================>] 20,00M 5,22MB/s in 3,7s \n", "\n", "2021-08-24 21:52:04 (5,36 MB/s) - 'l101210-001.ns5’ saved [20971616/20971616]\n", "\n" ] } ], "source": [ "!wget -O l101210-001.ns2 https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns2\n", "!wget -O l101210-001.nev https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.nev\n", "!wget -O l101210-001.ns5 https://gin.g-node.org/NeuralEnsemble/ephy_testing_data/raw/master/blackrock/blackrock_2_1/l101210-001.ns5" ] }, { "cell_type": "markdown", "id": "95c26202", "metadata": {}, "source": [ "\n", "## Exercise 1: Exploring an ephys dataset\n", "\n", "1. Load the dataset you just downloaded with Neo. Which IO seems suitable for this dataset?\n", "2. How many continuous recording parts (segments) does this dataset contain?\n", "3. How many channels were recorded in this dataset and at what sampling rates?\n", "4. How many spiketrains does this dataset contain?\n", "\n", "### Your solution" ] }, { "cell_type": "code", "execution_count": 1, "id": "4fa3decf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of segments: 1\n", "Number of channels with sampling rate 1000.0 Hz: 6\n", "Number of channels with sampling rate 30000.0 Hz: 96\n", "Number of spiketrains: 218\n" ] } ], "source": [ "import neo\n", "io = neo.io.BlackrockIO('l101210-001')\n", "block = io.read_block()\n", "\n", "print(f'Number of segments: {len(block.segments)}')\n", "for anasig in block.segments[0].analogsignals:\n", " print(f'Number of channels with sampling rate {anasig.sampling_rate}: {anasig.shape[-1]}')\n", "print(f'Number of spiketrains: {len(block.segments[0].spiketrains)}')" ] }, { "cell_type": "markdown", "id": "31ba447a", "metadata": {}, "source": [ "## Exercise 2: Extracting data for visualization\n", "1. Visualize the channels 10 to 19 of the `AnalogSignal` with the highest temporal resolution. \n", "2. Add axis labels, title and legend based on metadata provided by the `AnalogSignal`. Check the `array_annotations` to label each channel.\n", "\n", "### Your solution" ] }, { "cell_type": "code", "execution_count": 69, "id": "feceba8f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'nsx5')" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "# identify analogsignal with 30kHz temporal resolution\n", "anasig = block.segments[0].analogsignals[1]\n", "fig = plt.figure()\n", "ax = plt.gca()\n", "ax.plot(anasig.times, anasig.magnitude[:,10:20])\n", "\n", "ax.set_ylabel(f'voltage [{anasig.units.dimensionality.latex}]')\n", "ax.set_xlabel(f'time [{anasig.times.dimensionality.latex}]')\n", "ax.legend(anasig.array_annotations['channel_names'][10:20], loc='best')\n", "ax.set_title(anasig.name)" ] }, { "cell_type": "markdown", "id": "766cb710", "metadata": {}, "source": [ "## Exercise 3: Saving the dataset using NIX\n", "- Save the complete dataset in a new file named `l101210-001.nix`\n", "- What is the size of the resulting nix file?\n", "\n", "### Your solution" ] }, { "cell_type": "code", "execution_count": 66, "id": "cc5336bb", "metadata": {}, "outputs": [], "source": [ "filename = 'l101210-001.nix'\n", "with neo.io.NixIO(filename, 'ow') as io:\n", " io.write_block(block)" ] }, { "cell_type": "code", "execution_count": 67, "id": "f9c5d03b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-rw-r-- 1 sprengerj sprengerj 47M août 25 10:42 l101210-001.nix\r\n" ] } ], "source": [ "ls -lh l101210-001.nix" ] }, { "cell_type": "markdown", "id": "81aa0351", "metadata": {}, "source": [ "## Bonus Exercise\n", "Did you bring your own data? Check if your format is supported by Neo and load your data!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }