Final_Neuron_Layout_Source.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9df2013b",
   "metadata": {},
   "source": [
    "<font size=8 face=\"Courier\"><b>Final Neuron Layout Source Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7433803b",
   "metadata": {},
   "source": [
    "This is the code use to create the plots in the notebook, `Final_Neuron_Layout.ipynb`. The purpose of the notebook is to help you understand how the location of a neuron relates to it's firing pattern. The most important part of the code are the functions `neuronLayout` and `spikeRaster`, located inside of the **Functions->Neuron Page** section. This is the code use to create the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ceeb537",
   "metadata": {},
   "source": [
    "If you'd like to adapt this code for your own purposes, the key take home is that you can identify the x/y location of a neuron that you are looking at on the spikeraster by using the object, `sd.neuron_data[0]`. In this example. `sd` equals one of the datasets, `data_before, data_stim, data_after`. `sd.neuron_data[0][{neuron_number}][\"position\"]` provides the x/y location of a neuron, where the *\"neuron number\"* corresponds to the neuron's row number on the spike raster."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d54155",
   "metadata": {},
   "source": [
    "# <font color=\"gray\">Import Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a03b0a8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                                                    # Packages for data analysis\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Patch, Circle\n",
    "from scipy.ndimage import gaussian_filter1d\n",
    "import braingeneers                                                   # Braingeneers code\n",
    "from braingeneers.analysis.analysis import SpikeData, read_phy_files\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual  # package for interactive widgets \n",
    "import ipywidgets as ipw\n",
    "from IPython.display import HTML, display, Javascript, clear_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e1a5c4a",
   "metadata": {},
   "source": [
    "# <font color=\"green\">Widgets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8c6046a",
   "metadata": {},
   "source": [
    "This section creates all the little interactive tools used to create the plots. It's not that necessary that you understand this code. The most important thing is that you understand how the values of the widgets (literally called `.values` in the later code) relate to variables that you can manipulate to create the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7b98db",
   "metadata": {},
   "source": [
    "## <font color=\"green\">Start Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26f7de1",
   "metadata": {},
   "source": [
    "simple start message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "933defe8",
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   "outputs": [],
   "source": [
    "start_message= ipw.HTML(value=\"<h4>Select the <i>group</i> and <i>file</i> that you would like to analyze.</h4>\")\n",
    "start_message"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766edecf",
   "metadata": {},
   "source": [
    "`start_group_name` is used to set the `group_num` to ensure you download the right dataset with the `loadData` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1005b984",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_group_name= ipw.Dropdown( options=['2', '3', '4', '5', '6'], value='2', description='Group:')\n",
    "start_group_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "192f9aec",
   "metadata": {},
   "source": [
    "`start_data_name` selects the dataset you wish to analyze, `data_before`, `data_stim`, or `data_after`. This dataset is saved as the variable `sd`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe395314",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_data_name= ipw.Dropdown( options=['data_before', 'data_stim', 'data_after'], value='data_after', description='File:')\n",
    "start_data_name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa148b7f",
   "metadata": {},
   "source": [
    "`start_button` simply brings you to the next window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8b6b26e",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_button = ipw.Button(description=\"Submit\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e15de847",
   "metadata": {},
   "source": [
    "`start` contains all the code to create the Start Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af5329c8",
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   "outputs": [],
   "source": [
    "start = ipw.VBox([start_message, start_group_name, start_data_name, start_button])\n",
    "start"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "925d6ba3",
   "metadata": {},
   "source": [
    "## <font color=\"green\">Neuron Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fc99ad7",
   "metadata": {},
   "source": [
    "Simple message to user for neuron page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f511c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "neuron_message= ipw.HTML(value=\"<h4>Select the <i>neuron</i> you are interested in.</h4>\")\n",
    "neuron_message"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5849ca8",
   "metadata": {},
   "source": [
    "`neuron_num` selects the neuron you want to analyze. This corresponds to the row number of the spike raster. It also corresponds to the x/y position of the neuron, `sd.neuron_data[0][{neuron_number}][\"position\"]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaac585e",
   "metadata": {},
   "outputs": [],
   "source": [
    "neuron_num=ipw.IntSlider(description=\"Neuron:\")\n",
    "neuron_num"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b0f8129",
   "metadata": {},
   "source": [
    "simple button to move you to the next page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2229f276",
   "metadata": {},
   "outputs": [],
   "source": [
    "neuron_button = ipw.Button(description=\"Submit\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45d742dc",
   "metadata": {},
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   "source": [
    "neuron = ipw.VBox([neuron_message, neuron_num, neuron_button])\n",
    "neuron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fee6b42",
   "metadata": {},
   "source": [
    "## <font color=\"green\">Plots page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579b3925",
   "metadata": {},
   "source": [
    "There is only one button on the page that presents the plot, this is `reset_button`. It brings you back to the start so that you can analyze other datasets and neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbfa5e54",
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   "source": [
    "reset_button = ipw.Button(description=\"Reset\")"
   ]
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   "cell_type": "markdown",
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   "source": [
    "# <font color=\"blue\">Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96020886",
   "metadata": {},
   "source": [
    "These are the functions used to move between pages and create the plots. Notice that these functions only run when one of the buttons is clicked.  It's less important that you understand the code fog moving between pages, instead, I would focus on the helper functions used to retreive the dataset and create the plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc34fb34",
   "metadata": {},
   "source": [
    "## <font color=\"blue\">Start Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c4b7fa",
   "metadata": {},
   "source": [
    "These are the functions that run when the `start_button` is clicked on the Start Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b0bcd7b",
   "metadata": {},
   "source": [
    "### <font color=\"blue\">Helper : Load Data <font color=\"gray\"><small>Same Code as before"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6a6b315",
   "metadata": {},
   "source": [
    "Note, this is the same code used in the `Final_Source_Code.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2b08539",
   "metadata": {},
   "source": [
    "<font color=\"blue\">loadData</font> loads all the necesary data for analyzing one group. It's input is the number of the groups whose data you wish to analyze. It's \"output\" are the following global variables containing datasets used by later functions.\n",
    "* `group_num` - the number of the group whose data we are looking at\n",
    "* `data_before` - data recorded before stimulation\n",
    "* `data_stim` - uncurated stimulation pattern\n",
    "* `data_stim_bad` - curated stimulation pattern, it's \"bad\" because you can't see the stim patterns\n",
    "* `data_after` - data recorded right after stimulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13bbd2b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loadData(group_number):\n",
    "    global group_num; global data_before; global data_stim; global data_stim_bad; global data_after \n",
    "    \n",
    "    print(f\"loading data for group {str(group_number)}\")        # Set global variables used by later functions\n",
    "    group_num = group_number\n",
    "    data_before = read_phy_files(f\"./data/curated/grp{str(group_num)}_before.zip\")\n",
    "    data_stim = read_phy_files(f\"./data/uncurated/grp{str(group_num)}_stim.zip\")\n",
    "    data_stim_bad = read_phy_files(f\"./data/curated/grp{str(group_num)}_stim.zip\")\n",
    "    data_after = read_phy_files(f\"./data/curated/grp{str(group_num)}_after.zip\")\n",
    "    print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7a460e",
   "metadata": {},
   "source": [
    "### <font color=\"blue\">main function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cef7a7fe",
   "metadata": {},
   "source": [
    "This is the function that runs when the `start_button` is clicked. It shows the next page, which is the Neuron Page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac1fd93d",
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   "outputs": [],
   "source": [
    "def showNeuronPage(b):\n",
    "    global sd\n",
    "    loadData(int(start_group_name.value))\n",
    "    sds = {\"data_before\":data_before, \"data_stim\":data_stim, \"data_after\":data_after}    # 3 datasets data_name is allowed to be\n",
    "    sd = sds[start_data_name.value]                                                      # set dataset we are interested in to sd                                                \n",
    "    \n",
    "    neuron_num.max = len(sd.neuron_data[0]) - 1                                          # set number of neurons to that of the dataset\n",
    "    clear_output()\n",
    "    display(neuron)\n",
    "start_button.on_click(showNeuronPage)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a59d10",
   "metadata": {},
   "source": [
    "## <font color=\"blue\">Neuron Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4506c913",
   "metadata": {},
   "source": [
    "This is the function that runs when the `neuron_button` is clicked. It shows the next page, which is the Plots Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2608bee",
   "metadata": {},
   "source": [
    "### <font color=\"blue\">Helper: neuron layout"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d487705e",
   "metadata": {},
   "source": [
    "This is the code used to create the 2D layout of the organoids, electrodes, neurons, and stimulation locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52576309",
   "metadata": {},
   "outputs": [],
   "source": [
    "def neuronLayout():\n",
    "    \n",
    "    # Stim electrodes for each group, and electrodes x/y locations\n",
    "    grp_stim_electrodes = {2:[15569], 3:[15569, 13810], 4:[15569, 13810], 5:[15569, 6705], 6:[15569, 13810, 19971]}\n",
    "    stim_xy = { 19971:[2992.5,1575.0], 13810:[2975.0,1085.0], 15569:[2957.5,1225.0], 6705:[1837.5,525.0] }\n",
    "                                                                 \n",
    "    # Add background image\n",
    "    plt.figure(figsize=(15,10))\n",
    "    img = plt.imread(\"data/Organoids_Closeup.jpeg\")\n",
    "    plt.imshow(img,  extent=[0, 3850, 0, 2100] ) #, #interpolation='nearest', aspect='auto')\n",
    "    \n",
    "    # Plot electrodes\n",
    "    electrode_mapping = pd.DataFrame.from_dict( sd.metadata[0], orient=\"index\", columns=['x','y']  ) \n",
    "    x = electrode_mapping.x.values                                                       # create electrode mapping from saved metadata\n",
    "    y = electrode_mapping.y.values\n",
    "    plt.scatter(x,y,s=4, c='darkorange')                                                                 # plot all electrodes\n",
    "    \n",
    "    # Plot neurons\n",
    "    neuron_x = []\n",
    "    neuron_y = []\n",
    "    for neuron in sd.neuron_data[0].values():                                            # Get X/Y locations of neurons\n",
    "        neuron_x.append(neuron['position'][0])\n",
    "        neuron_y.append(neuron['position'][1])\n",
    "    plt.scatter(neuron_x,neuron_y, c='magenta', s=100 )                                                # plot all neurons\n",
    "    \n",
    "    # plot stim electrodes\n",
    "    stim_x = []\n",
    "    stim_y = []\n",
    "    for electrode in grp_stim_electrodes[ group_num ]:\n",
    "        stim_x.append( stim_xy[electrode][0] )\n",
    "        stim_y.append( stim_xy[electrode][1] ) \n",
    "    plt.scatter(stim_x,stim_y, c='turquoise', alpha=.8, s=450 ) \n",
    "    \n",
    "    # Plot selected neuron\n",
    "    plt.scatter( sd.neuron_data[0][neuron_num.value][\"position\"][0], sd.neuron_data[0][neuron_num.value][\"position\"][1], c='blue', s=150 )   \n",
    "    \n",
    "    #add legend, axises limits, labels,  and title\n",
    "    legend_elements = [Patch(\"darkorange\",\"darkorange\"), Patch(\"magenta\",\"magenta\"), Patch(\"turquoise\",\"turquoise\"), Patch(\"blue\",\"blue\") ]   # Create colors in legend\n",
    "    plt.legend(legend_elements, [\"Electrode\",\"Other Neuron\", \"Stimulation\", \"Selected Neuron\"])       # Add legend\n",
    "    plt.xlim(0, 3850)\n",
    "    plt.ylim(0, 2100)\n",
    "    plt.xlabel('um')                                                                     # add axises and title\n",
    "    plt.ylabel('um')\n",
    "    plt.title(f\"Neuron & Electrode Layout\")\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8e28238",
   "metadata": {},
   "source": [
    "### <font color=\"blue\">Helper: Spike Raster"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8953b613",
   "metadata": {},
   "source": [
    "This is the code used to create the spike raster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c6a342e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def spikeRaster():\n",
    "    # Basic Scatter Plot\n",
    "    plt.figure(figsize=(15,10))\n",
    "    idces, times = sd.idces_times()\n",
    "    plt.scatter(times/1000,idces,marker='|',s=1, c=\"r\")   # Creates spike raster\n",
    "\n",
    "    # Plot Neuron of interest\n",
    "    neuron_vals = np.where(idces == neuron_num.value )[0]\n",
    "    plt.scatter( times[neuron_vals]/1000, idces[neuron_vals], marker='|',s=2, c=\"b\")  \n",
    "    \n",
    "    # Add labels to plot\n",
    "    plt.title( \"Spike Raster\" )\n",
    "    plt.xlabel(\"Time(s)\")\n",
    "    plt.ylabel('Unit #')\n",
    "    plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c05c08",
   "metadata": {},
   "source": [
    "### <font color=\"blue\">main function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "507e524d",
   "metadata": {},
   "source": [
    "This is the function that runs when the `neuron_button` is clicked. It shows the next page, which is the Plots of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f49b305",
   "metadata": {},
   "outputs": [],
   "source": [
    "def showPlots(b):\n",
    "    clear_output()\n",
    "    display(ipw.HTML(f\"<h4>Displaying Neuron {str(neuron_num.value)} for Group {start_group_name.value}- {start_data_name.value}</h4>\"))\n",
    "    display(reset_button)\n",
    "    neuronLayout()\n",
    "    spikeRaster()\n",
    "neuron_button.on_click(showPlots)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4aa44cd",
   "metadata": {},
   "source": [
    "## <font color=\"blue\">Plots Page"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b259364",
   "metadata": {},
   "source": [
    "There is a `reset_button` on the Plots Page. When it is clicked, we bring the user back to the beginning so that they can analyze other datasets and neurons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcccbd02",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reset(b):\n",
    "    clear_output()\n",
    "    display(start)\n",
    "reset_button.on_click(reset)"
   ]
  },
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