valiante_lab_raw_ephys_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/stripathy/valiante_lab_abf_process/blob/master/valiante_lab_raw_ephys_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UV3b2SI8JTJJ"
   },
   "source": [
    "# Illustrating download and plotting of valiante lab raw ephys datasets\n",
    "\n",
    "Notebook written by [Shreejoy Tripathy](https://triplab.org), with datasets collected and compiled by Homeira Moradi-Chameh in the lab of [Dr. Taufik Valiante](http://www.neurontobrainlaboratory.ca/). A scientific description and reference to the datasets is available on [bioRxiv](https://www.biorxiv.org/content/10.1101/748988v2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f80Mf6g-FDJQ"
   },
   "source": [
    "# Using Allen Institute's feature extraction library, IPFX\n",
    "I've been using the allen institute intracellular ephys feature extraction toolbox, [IPFX](https://https://github.com/AllenInstitute/ipfx). It has some nice, straightforward data structures for intracellular ephys data and I've structured Homeira's data files from ABF into this structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "maisCp9TBfxD"
   },
   "outputs": [],
   "source": [
    "# this downloads and installs the IPFX repo from github\n",
    "!pip install git+https://github.com/AllenInstitute/ipfx.git"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Nw3CjYMoGE2d"
   },
   "source": [
    "# Downloading Valiante Lab raw ephys data from Github\n",
    "I've organized the abf-file based intrinsic ephys data into IPFX sweep set objects, located [here](https://github.com/stripathy/valiante_lab_abf_process/tree/master/sweep_sets). The lines below show how to download these files directly from github. They are stored on github as python pickle objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "oZBy1LVzCqL3",
    "outputId": "2f49f5cd-95f7-48ab-aa62-7a726487d1ef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://github.com/stripathy/valiante_lab_abf_process/blob/master/sweep_sets/13d03005.abf.p?raw=true\n"
     ]
    }
   ],
   "source": [
    "# download file from github repo\n",
    "\n",
    "intrinsic_file_name = '13d03005.abf'\n",
    "url = \"https://github.com/stripathy/valiante_lab_abf_process/blob/master/sweep_sets/%s.p?raw=true\" % (intrinsic_file_name) # dl=1 is important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hC-S8Kq-EIuQ"
   },
   "outputs": [],
   "source": [
    "# download the file, write it to collab disk, and then load it into memory using python pickle library\n",
    "import urllib.request\n",
    "u = urllib.request.urlopen(url)\n",
    "data = u.read()\n",
    "u.close()\n",
    "filename = intrinsic_file_name + '.p'\n",
    "with open(filename, \"wb\") as f :\n",
    "    f.write(data)\n",
    "\n",
    "import pickle\n",
    "#print(data)\n",
    "\n",
    "# load the file into a python object called sweep_set\n",
    "with open(filename, 'rb') as fp:\n",
    "    sweep_set = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "rcLE1KEgIag2"
   },
   "source": [
    "# Load and plot intrinsic ephys data from IPFX SweepSet objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "X1c0p5gKEUXI",
    "outputId": "6abd541a-2ca2-4604-ef51-ddc1fee189f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<ipfx.sweep.Sweep at 0x7f86a1294940>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bdb4e0>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0b4bf98>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0b6d748>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0cfe7b8>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0b5eb38>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9908>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9cf8>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9a90>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9e80>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9978>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9278>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd9da0>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd96d8>,\n",
       " <ipfx.sweep.Sweep at 0x7f86a0bd92b0>]"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sweep_set is the ipfx object that stores the intrinsic ephys data from a particular cell. \n",
    "# It's organized into a list of Sweep objects and by increasing step current injection\n",
    "sweep_set.sweeps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 265
    },
    "colab_type": "code",
    "id": "qq56S5fYEo9J",
    "outputId": "e83a32c4-91f8-4cc9-f253-0d7f75d0d7a1"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from ipfx.sweep import Sweep, SweepSet\n",
    "\n",
    "sweep_plot_index = 13\n",
    "curr_sweep = sweep_set.sweeps[sweep_plot_index]\n",
    "t = curr_sweep.t\n",
    "v = curr_sweep.v\n",
    "i = curr_sweep.i\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharex=True)\n",
    "\n",
    "axes[0].plot(t, v)\n",
    "axes[0].set_xlim(0, 1)\n",
    "axes[0].set_ylabel(\"mV\")\n",
    "\n",
    "axes[1].plot(t, i, c=\"orange\")\n",
    "axes[1].set_ylabel(\"pA\")\n",
    "\n",
    "sns.despine()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPi9VYBB4x6wNs4DHrJgZ2o",
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "valiante_lab_raw_ephys_example.ipynb",
   "provenance": []
  },
  "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}