load-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load all datasets from raw data files and pickle conveniently"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import seaborn as sns; sns.set()\n",
    "import pandas as pd\n",
    "import pickle\n",
    "from scipy import io\n",
    "import matplotlib\n",
    "from scipy import sparse\n",
    "\n",
    "# This file is from https://github.com/berenslab/rna-seq-tsne\n",
    "import rnaseqTools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scala et al. 2019 (L4 data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells: 118\n",
      "Number of genes: 26514\n",
      "\n",
      "(102, 17657)\n",
      "\n",
      "['ADP (mV)' 'AHP (mV)' 'AI' 'AP amplitude (mV)' 'AP threshold (mV)'\n",
      " 'AP width (ms)' 'Max number of spikes' 'Input R (MOhm)' 'Vm (mV)'\n",
      " 'Latency (ms)' 'Rheobase (pA)' 'Sag ratio' 'Tau (ms)']\n",
      "13\n",
      "\n",
      "Chosen offset: 7.08\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv('data/scala2019/patch-seq-counts.csv', sep='\\t')\n",
    "counts = data.values[:,1:].transpose().astype(float)\n",
    "genes = data.values[:,0]\n",
    "cells = np.array(data.columns[1:])\n",
    "\n",
    "print('Number of cells: {}\\nNumber of genes: {}'.format(counts.shape[0], counts.shape[1]))\n",
    "\n",
    "meta = pd.read_excel('data/scala2019/layer4-cells.xlsx', index_col=0)\n",
    "d = [d[6:10]+d[:2]+d[3:5] for d in meta.values[1:,1] if d is not np.nan]\n",
    "s = [s[7:].strip() if 'sample ' in s else s[6:].strip() for s in meta.values[1:,2] if s is not np.nan]\n",
    "names = [d[i]+'_sample_'+s[i] for i in range(len(d))]\n",
    "regions = [m for m in meta.values[1:,3] if m=='V1' or m=='S1']\n",
    "layers = [m for m in meta.values[1:,4] if ~np.isnan(m)]\n",
    "names2regions = dict(zip(names,regions))\n",
    "names2layers = dict(zip(names,layers))\n",
    "\n",
    "regions = np.array([names2regions['_'.join(c.split('_')[:3])] for c in cells])\n",
    "layers  = np.array([names2layers['_'.join(c.split('_')[:3])] for c in cells])\n",
    "\n",
    "ephysData = pd.read_csv('data/scala2019/patch-seq-ephys-features.csv')\n",
    "ephysNames = np.array(ephysData.columns[1:-2])\n",
    "ephysCells = np.array([s.split('sample')[0]+'_sample_'+s.split('sample')[1] for s in ephysData.values[:,0]])\n",
    "keep = ephysData['Good quality']\n",
    "ai0 = ephysData['AI']==0\n",
    "ephysData = ephysData.values[:, 1:-2].astype('float')\n",
    "ephysData[~keep,:] = np.nan\n",
    "ephysData[keep, ephysNames=='Latency (ms)'] = np.log(ephysData[keep, ephysNames=='Latency (ms)'])\n",
    "ephysData[keep & ~ai0, ephysNames=='AI'] = np.log(ephysData[keep & ~ai0, ephysNames=='AI']/100)\n",
    "ephysData[ai0, ephysNames=='AI'] = np.nan\n",
    "names2ephys = dict(zip(ephysCells, ephysData))\n",
    "ephys = np.array([names2ephys['_'.join(c.split('_')[:3])] if '_'.join(c.split('_')[:3]) in names2ephys\n",
    "          else np.ones(ephysNames.size)*np.nan for c in cells]).astype(float)\n",
    "\n",
    "# cells to exclude based on RNA (either low quality or maps to PV/excitatory\n",
    "exclude = ['20180605_sample_12_L4_S109', '20180626_sample_11_L4_S129',\n",
    "           '20180703_sample_6_L4_S157', '20180919_sample_21_L4_S136',\n",
    "           '20180605_sample_17_L4_S114', '20180626_sample_7_L4_S125',\n",
    "           '20180627_sample_1_L4_S137', '20180703_sample_4_L4_S154']\n",
    "\n",
    "selection = ~np.isin(cells, exclude) & ~np.isnan(np.sum(ephys, axis=1))\n",
    "counts = counts[selection, :]\n",
    "cells = cells[selection]\n",
    "ephys = ephys[selection,:]\n",
    "regions = regions[selection]\n",
    "layers = layers[selection]\n",
    "\n",
    "g = np.sum(counts>0, axis=0)>0\n",
    "counts = counts[:, g]\n",
    "genes = genes[g]\n",
    "\n",
    "print('')\n",
    "print(counts.shape)\n",
    "\n",
    "print('')\n",
    "print(ephysNames)\n",
    "print(ephysNames.size)\n",
    "\n",
    "print('')\n",
    "mostVariableGenes = rnaseqTools.geneSelection(counts, n=1000, threshold=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "shortEphysLabels = {'ADP (mV)': 'ADP',\n",
    "                    'AHP (mV)': 'AHP',\n",
    "                    'AI': 'AI',\n",
    "                    'AP amplitude (mV)': 'AP amp',\n",
    "                    'AP threshold (mV)': 'AP thesh',\n",
    "                    'AP width (ms)': 'AP width',\n",
    "                    'Max number of spikes': 'Max AP num',\n",
    "                    'Input R (MOhm)': 'R_input',\n",
    "                    'Vm (mV)': 'V_rest',\n",
    "                    'Latency (ms)': 'Latency',\n",
    "                    'Rheobase (pA)': 'Rheobase',\n",
    "                    'Sag ratio': 'Sag',\n",
    "                    'Tau (ms)': 'Tau'}\n",
    "\n",
    "ephysNames = [shortEphysLabels[e] for e in ephysNames]\n",
    "\n",
    "patchseqdata = {'counts': sparse.csr_matrix(counts), \n",
    "                'cells': cells, \n",
    "                'genes': genes, \n",
    "                'regions': regions, \n",
    "                'layers': layers, \n",
    "                'ephys': ephys,\n",
    "                'ephysNames': ephysNames,\n",
    "                'mostVariableGenes': mostVariableGenes}\n",
    "\n",
    "pickle.dump(patchseqdata, open('data/scala2019.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cadwell et al. 2016 (L1 data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells: 62\n",
      "Number of genes: 21812\n",
      "\n",
      "Number of cells: 44\n",
      "Number of genes: 15074\n",
      "Number of detected genes: 15054\n",
      "\n",
      "['V_rest' 'R_input' 'AP tau' 'AP thresh' 'AP width' 'AP amp' 'AHP' 'ADP'\n",
      " 'Adapt' 'ifDelayed' 'ifBurst']\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_excel('data/cadwell2016/Data_CortexInterneurons.xlsx', sheet_name=\"readcounts\")\n",
    "cells  = np.array([int(c[5:-1]) for c in data.values[2,2:-5]])\n",
    "genes  = np.array([c[1:-1] for c in data.values[4:,1]])\n",
    "counts = data.values[4:,2:-5].T.astype('float')\n",
    "\n",
    "print('Number of cells: {}\\nNumber of genes: {}\\n'.format(counts.shape[0], counts.shape[1]))\n",
    "\n",
    "# https://media.nature.com/original/nature-assets/nbt/journal/v34/n2/extref/nbt.3445-S3.xlsx\n",
    "varGenes = pd.read_excel('data/cadwell2016/nbt.3445-S3.xlsx')\n",
    "varGenes = varGenes.values[3:,-1]\n",
    "varGenes = np.array([g[1:-1] for g in varGenes])\n",
    "lgenes = list(genes)\n",
    "counts = counts[:, [lgenes.index(g) for g in varGenes]]\n",
    "\n",
    "from scipy import io\n",
    "mat = io.loadmat('data/cadwell2016/IntPhysiologyFinal.mat')\n",
    "types = np.array([mat['cell'][0][n][3][0][0] for n in range(len(mat['cell'][0]))])\n",
    "idxs  = np.array([mat['cell'][0][n][0][0][0] for n in range(len(mat['cell'][0]))])\n",
    "\n",
    "cols = [2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]\n",
    "ephysNames = np.array(['V_rest', 'R_input', 'AP tau', 'AP thresh', 'AP width', 'AP amp', 'AHP', 'ADP', \n",
    "                   'Adapt', 'ifDelayed', 'ifBurst'])\n",
    "ephys = np.zeros((idxs.size, len(cols)))\n",
    "for j,c in enumerate(cols):\n",
    "    ephys[:,j] = [mat['cell'][0][i][c][0][0] for i in range(len(mat['cell'][0]))]\n",
    "\n",
    "# Int_44 and Int_45 were removed from the Cadwell et al. due to possible contamination\n",
    "keep = (idxs != 44) & (idxs != 45) & ~np.isnan(np.sum(ephys,axis=1))\n",
    "types = types[keep]\n",
    "idxs  =  idxs[keep]\n",
    "ephys = ephys[keep]\n",
    "counts = counts[[np.where(cells==i)[0][0] for i in idxs], :]\n",
    "\n",
    "print('Number of cells: {}\\nNumber of genes: {}'.format(counts.shape[0], counts.shape[1]))\n",
    "print('Number of detected genes: {}'.format(np.sum(np.sum(counts>0, axis=0)>0)))\n",
    "print('')\n",
    "print(ephysNames)\n",
    "print(ephysNames.size)\n",
    "\n",
    "mostVariableGenes = np.zeros(varGenes.size).astype(bool)\n",
    "mostVariableGenes[:3000] = True\n",
    "\n",
    "patchseqdata = {'counts': sparse.csr_matrix(counts),\n",
    "                'ephys': ephys, \n",
    "                'cellTypes': types,\n",
    "                'ephysNames': ephysNames, \n",
    "                'genes': varGenes,\n",
    "                'mostVariableGenes': mostVariableGenes}\n",
    "\n",
    "pickle.dump(patchseqdata, open('data/cadwell2016.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scala et al. 2020 (M1 data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# META DATA\n",
    "\n",
    "meta = pd.read_csv('data/scala2020/mini-atlas-meta-data.csv', sep='\\t')\n",
    "\n",
    "cells = meta['Cell'].values\n",
    "\n",
    "exclude = meta['Exclusion reasons'].values.astype(str)\n",
    "exclude[exclude=='nan'] = ''\n",
    "\n",
    "ttype = meta['RNA type'].values.astype(str)\n",
    "ttype[ttype=='nan'] = ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count matrix shape: (1320, 42184)\n"
     ]
    }
   ],
   "source": [
    "# TRANSCRIPTOMIC DATA: read counts\n",
    "\n",
    "data = pd.read_csv('data/scala2020/final_patch_seq_allcells_exon_allreads.txt.zip', \n",
    "                   sep='\\t', na_filter=False, index_col=0)\n",
    "d = data.values.transpose()\n",
    "counts = np.round(d.astype('float')).astype(int)\n",
    "counts = sparse.csr_matrix(counts)\n",
    "cellsRNA = np.array(data.columns)\n",
    "genes = np.array(data.index)\n",
    "\n",
    "print('Count matrix shape:', counts.shape)\n",
    "\n",
    "# Match the order to the spreadsheet\n",
    "reorder = [np.where(cellsRNA==c)[0][0] for c in cells]\n",
    "counts = counts[reorder,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cells with ephys data: 1319\n"
     ]
    }
   ],
   "source": [
    "# EPHYS DATA\n",
    "\n",
    "ephysData = pd.read_csv('data/scala2020/Cell_Features_M1.csv')\n",
    "ephysNames = np.array(ephysData.columns[2:]).astype(str)\n",
    "ephysCells = ephysData.values[:,1]\n",
    "ephysCells = [e.split('/')[-1][:-4] for e in ephysCells]\n",
    "ephysCells = [e[6:10]+e[:2]+e[3:5]+'_sample_'+e.split()[-1] for e in ephysCells]\n",
    "ephysData = ephysData.values[:,2:].astype('float')\n",
    "names2ephys = dict(zip(ephysCells, ephysData))\n",
    "ephysData = np.array([names2ephys[c] if c in names2ephys else ephysData[0]*np.nan for c in cells])\n",
    "\n",
    "print('Number of cells with ephys data:', np.sum(np.isin(cells, ephysCells)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_exclude = ['Afterdepolarization (mV)', 'AP Fano factor', 'ISI Fano factor', \n",
    "                    'Latency (ms)', 'Wildness', 'Spike frequency adaptation',\n",
    "                    'Sag area (mV*s)', 'Sag time (s)', 'Burstiness',\n",
    "                    'AP amplitude average adaptation index', 'ISI average adaptation index',\n",
    "                    'Rebound number of APs']\n",
    "features_log =     ['AP coefficient of variation', 'ISI coefficient of variation', 'ISI adaptation index']\n",
    "\n",
    "for e in features_log:\n",
    "    ephysData[:, ephysNames==e] = np.log(ephysData[:, ephysNames==e])\n",
    "ephysData = ephysData[:, ~np.isin(ephysNames, features_exclude)]\n",
    "ephysNames = ephysNames[~np.isin(ephysNames, features_exclude)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1213, 42184)\n",
      "(1213, 16)\n"
     ]
    }
   ],
   "source": [
    "selection = ~np.isnan(np.sum(ephysData, axis=1)) & (exclude=='') & (ttype!='')\n",
    "counts = counts[selection, :]\n",
    "cells = cells[selection]\n",
    "ephysData = ephysData[selection, :]\n",
    "ttype = ttype[selection]\n",
    "\n",
    "print(counts.shape)\n",
    "print(ephysData.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen offset: 7.59\n"
     ]
    },
    {
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       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mostVariableGenes = rnaseqTools.geneSelection(counts, n=1000, threshold=32, decay=1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterNames = ['Lamp5 Pax6', 'Lamp5 Egln3_1', 'Lamp5 Egln3_2', 'Lamp5 Egln3_3',\n",
    "       'Lamp5 Pdlim5_1', 'Lamp5 Pdlim5_2', 'Lamp5 Slc35d3', 'Lamp5 Lhx6',\n",
    "       'Sncg Col14a1', 'Sncg Slc17a8', 'Sncg Calb1_1', 'Sncg Calb1_2',\n",
    "       'Sncg Npy2r', 'Vip Sncg', 'Vip Serpinf1_1', 'Vip Serpinf1_2',\n",
    "       'Vip Serpinf1_3', 'Vip Htr1f', 'Vip Gpc3', 'Vip C1ql1',\n",
    "       'Vip Mybpc1_1', 'Vip Mybpc1_2', 'Vip Mybpc1_3', 'Vip Chat_1',\n",
    "       'Vip Chat_2', 'Vip Igfbp6_1', 'Vip Igfbp6_2', 'Sst Chodl',\n",
    "       'Sst Penk', 'Sst Myh8_1', 'Sst Myh8_2', 'Sst Myh8_3', 'Sst Htr1a',\n",
    "       'Sst Etv1', 'Sst Pvalb Etv1', 'Sst Crhr2_1', 'Sst Crhr2_2',\n",
    "       'Sst Hpse', 'Sst Calb2', 'Sst Pvalb Calb2', 'Sst Pappa',\n",
    "       'Sst C1ql3_1', 'Sst C1ql3_2', 'Sst Tac2', 'Sst Th_1', 'Sst Th_2',\n",
    "       'Sst Th_3', 'Pvalb Gabrg1', 'Pvalb Egfem1', 'Pvalb Gpr149',\n",
    "       'Pvalb Kank4', 'Pvalb Calb1_1', 'Pvalb Calb1_2', 'Pvalb Reln',\n",
    "       'Pvalb Il1rapl2', 'Pvalb Vipr2_1', 'Pvalb Vipr2_2', 'L2/3 IT_1',\n",
    "       'L2/3 IT_2', 'L2/3 IT_3', 'L4/5 IT_1', 'L4/5 IT_2', 'L5 IT_1',\n",
    "       'L5 IT_2', 'L5 IT_3', 'L5 IT_4', 'L6 IT_1', 'L6 IT_2',\n",
    "       'L6 IT Car3', 'L5 PT_1', 'L5 PT_2', 'L5 PT_3', 'L5 PT_4',\n",
    "       'L5/6 NP_1', 'L5/6 NP_2', 'L5/6 NP_3', 'L5/6 NP CT',\n",
    "       'L6 CT Gpr139', 'L6 CT Cpa6', 'L6 CT Grp', 'L6 CT Pou3f2',\n",
    "       'L6 CT Kit_1', 'L6 CT Kit_2', 'L6b Col6a1', 'L6b Shisa6_1',\n",
    "       'L6b Shisa6_2', 'L6b Ror1', 'L6b Kcnip1']\n",
    "\n",
    "clusterColors = ['#DDACC9', '#FF88AD', '#DD8091', '#F08E98', '#FF7290', '#FF8C97',\n",
    "       '#FFA388', '#C77963', '#7E0ACB', '#9440F3', '#9611B6', '#9900B3',\n",
    "       '#7A0099', '#AA4DB5', '#A720FF', '#AB1DFF', '#FF5FCD', '#FF4DC1',\n",
    "       '#B09FFF', '#BD3D9A', '#F70CF3', '#992E81', '#AB379C', '#FF00FF',\n",
    "       '#B3128A', '#7779BF', '#626EB8', '#FFFF00', '#FF8011', '#FF9F2C',\n",
    "       '#FFB307', '#FFBF09', '#BFAF00', '#FFB22B', '#D9C566', '#BE8652',\n",
    "       '#B0993C', '#CDB115', '#D2A328', '#784F14', '#635821', '#802600',\n",
    "       '#8A2B1A', '#804600', '#8C6012', '#A81111', '#9B211B', '#ED4C50',\n",
    "       '#C05661', '#E62A5D', '#BC4B11', '#B6411E', '#BC2D71', '#9C4165',\n",
    "       '#BC2C41', '#FF197F', '#F4358B', '#00FF34', '#07D945', '#2EB934',\n",
    "       '#09CCC6', '#52B8AA', '#58D2C1', '#4A9F93', '#4EAC9C', '#52B4B8',\n",
    "       '#B2AD23', '#81791F', '#5100FF', '#0D5D7E', '#0B77A5', '#0B8AA5',\n",
    "       '#0A75B1', '#48CB80', '#3C78BC', '#47867A', '#1AAB99', '#168577',\n",
    "       '#338C5E', '#2FBCE5', '#3E766C', '#516B78', '#557361', '#69419D',\n",
    "       '#46306A', '#464576', '#7044AA', '#573D90']\n",
    "\n",
    "colors = dict(zip(clusterNames, clusterColors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "shortEphysLabels = {'AP Fano factor': 'AP FF',\n",
    "                    'AP amplitude (mV)': 'AP amp',\n",
    "                    'AP amplitude adaptation index': 'AP amp AI',\n",
    "                    'AP amplitude average adaptation index': 'AP amp AI_m',\n",
    "                    'AP coefficient of variation': 'AP CV',\n",
    "                    'AP threshold (mV)': 'AP thresh',\n",
    "                    'AP width (ms)': 'AP width',\n",
    "                    'Afterdepolarization (mV)': 'ADP',\n",
    "                    'Afterhyperpolarization (mV)': 'AHP',\n",
    "                    'Burstiness': 'Burst',\n",
    "                    'ISI Fano factor': 'ISI FF',\n",
    "                    'ISI adaptation index': 'ISI AI',\n",
    "                    'ISI average adaptation index': 'ISI AI_m',\n",
    "                    'ISI coefficient of variation': 'ISI CV',\n",
    "                    'Input resistance (MOhm)': 'R_input',\n",
    "                    'Latency (ms)': 'Latency',\n",
    "                    'Max number of APs': 'Max AP num',\n",
    "                    'Membrane time constant (ms)': 'Tau',\n",
    "                    'Rebound (mV)': 'Rebound',\n",
    "                    'Resting membrane potential (mV)': 'V_rest',\n",
    "                    'Rheobase (pA)': 'Rheobase',\n",
    "                    'Sag area (mV*s)': 'Sag area',\n",
    "                    'Sag ratio': 'Sag',\n",
    "                    'Sag time (s)': 'Sag time',\n",
    "                    'Spike frequency adaptation': 'SFA',\n",
    "                    'Upstroke-to-downstroke ratio': 'UDR',\n",
    "                    'Wildness': 'Wildness'}\n",
    "\n",
    "ephysNames = [shortEphysLabels[e] for e in ephysNames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "patchseqdata = {'counts': counts.astype(np.int32), \n",
    "                'cells': cells, \n",
    "                'genes': genes, \n",
    "                'mostVariableGenes': mostVariableGenes,\n",
    "                'ephys': ephysData, \n",
    "                'ephysNames': ephysNames,\n",
    "                'ttype': ttype,\n",
    "                'colors': colors}\n",
    "\n",
    "pickle.dump(patchseqdata, open('data/scala2020.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fuzik et al. 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count table size: (83, 24378)\n",
      "Ephys data size: (82, 89)\n",
      "Cells with sequence data but not matched to ephys: ['Run283_L4_C01', 'Run291_L7_E10', 'Run291_L7_D12']\n",
      "Number of matched cells: 80\n",
      "Number of ephys properties available for all cells: 80\n",
      "Counts shape: (80, 13089)\n",
      "Ephys shape: (80, 80)\n"
     ]
    }
   ],
   "source": [
    "# Load gene counts from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70844\n",
    "# from urllib.request import urlopen\n",
    "# url = 'ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70844/suppl/GSE70844_Fuzik_et_al_molcounts.xls.gz'\n",
    "# molcounts = pd.read_excel(GzipFile(fileobj=urlopen(url)))\n",
    "\n",
    "from gzip import GzipFile\n",
    "molcounts = pd.read_excel(GzipFile('data/fuzik2016/GSE70844_Fuzik_et_al_molcounts.xls.gz'), index_col=0)\n",
    "geneNames = np.array(list(molcounts.index))\n",
    "cells = np.array(list(molcounts))\n",
    "counts = molcounts.values.astype(np.float).T\n",
    "ind = [i for i,g in enumerate(geneNames) if g[:5] != 'ERCC-']    # excluding spike ins\n",
    "geneNames = geneNames[ind]\n",
    "counts = counts[:,ind]\n",
    "print('Count table size:', counts.shape)\n",
    "\n",
    "# Load ephys data\n",
    "ephys = pd.read_excel('data/fuzik2016/Patch-Seq e-phys ALL.xlsx', header=1)\n",
    "ephysProperties = list(ephys['run_label'][2:])\n",
    "ephysProperties[18:35] = [a+' OneAP' for a in ephysProperties[18:35]]  # renaming repeated properties\n",
    "ephysProperties[36:52] = [a+' CRamp' for a in ephysProperties[36:52]]\n",
    "ephysProperties[53:66] = [a+' THR1' for a in ephysProperties[53:66]]\n",
    "ephysProperties[67:80] = [a+' THR2' for a in ephysProperties[67:80]]\n",
    "ephysProperties[81:] = [a+' THR3' for a in ephysProperties[81:]]\n",
    "rowsExclude = [17, 35, 52, 66, 80]    # empty rows\n",
    "rowsInclude = np.setdiff1d(np.arange(len(ephysProperties)), rowsExclude)\n",
    "ephysProperties = np.array(ephysProperties)[rowsInclude]\n",
    "\n",
    "cellTypes = np.array(ephys.values[1,1:])\n",
    "\n",
    "ephys[ephys == '-'] = None\n",
    "ephysData = ephys.values[2:,1:].astype('float')[rowsInclude,:].T\n",
    "print('Ephys data size:', ephysData.shape)\n",
    "\n",
    "# Matching gene counts to ephys data (thanks to Shreejoy Tripathy)\n",
    "matching = pd.read_csv('data/fuzik2016/ephys_expr_matching.csv')\n",
    "cellsOrder = list(matching['expr_ids'])\n",
    "\n",
    "print('Cells with sequence data but not matched to ephys:', \n",
    "      [c for i,c in enumerate(cells) if c not in cellsOrder])\n",
    "ind = [i for i,c in enumerate(cells) if c in cellsOrder]\n",
    "cells = cells[ind]\n",
    "counts = counts[ind,:]\n",
    "print('Number of matched cells:', len(ind))\n",
    "\n",
    "ind = [cellsOrder.index(c) for c in cells]\n",
    "ephysData = ephysData[ind,:]\n",
    "cellTypes = cellTypes[ind]\n",
    "\n",
    "ephysComplete = ~np.isnan(np.sum(ephysData, axis=0))\n",
    "ephysData = ephysData[:,ephysComplete]\n",
    "ephysProperties = ephysProperties[ephysComplete]\n",
    "print('Number of ephys properties available for all cells:', np.sum(ephysComplete))\n",
    "\n",
    "# Sort out genes that are constant zero for the remaining cells\n",
    "detected = np.sum(counts, axis=0)>0\n",
    "counts = counts[:, detected]\n",
    "genesNames = geneNames[detected]\n",
    "\n",
    "print('Counts shape:', counts.shape)\n",
    "print('Ephys shape:', ephysData.shape)\n",
    "\n",
    "# Selection of genes\n",
    "mostVariableGenes = np.mean(counts,axis=0)>1\n",
    "\n",
    "patchseqdata = {'counts': sparse.csr_matrix(counts).astype(np.int32), \n",
    "                'cells': cells, \n",
    "                'genes': geneNames, \n",
    "                'mostVariableGenes': mostVariableGenes,\n",
    "                'ephys': ephysData, \n",
    "                'ephysNames': ephysProperties,\n",
    "                'cluster': cellTypes}\n",
    "\n",
    "pickle.dump(patchseqdata, open('data/fuzik2016.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gouwens et al. 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After kicking out Nan ephys neurons and certain ephys properties, the dataset contains  3395 neurons. We have 55 ephys features and 1252 different genes.\n"
     ]
    }
   ],
   "source": [
    "from data.gouwens2020.dataset import load_bioarxiv_dataset, partitions, Datagen\n",
    "D = load_bioarxiv_dataset('./data/gouwens2020/')\n",
    "X = D['T_dat']\n",
    "Y = D['E_pcipfx']\n",
    "\n",
    "# Sort out cells with nans in the ephys data\n",
    "ind_non_nan = ~np.isnan(np.sum(Y, axis=1))\n",
    "X = X[ind_non_nan, :]\n",
    "Y = Y[ind_non_nan, :]\n",
    "\n",
    "# We drop certain features that showed too high correlation with some other feature already or for which there was one\n",
    "# consistent outlier cell\n",
    "features_to_drop=['spc0 spk. fast trough v',\n",
    " 'long sq. stim. amp.',\n",
    " 'spc1 step subthr.',\n",
    " 'spc1 subthr. norm',\n",
    " 'long sq. AP1 up-down ratio',\n",
    " 'long sq. AP1 peak v',\n",
    " 'long sq. AP1 threshold v',\n",
    " 'long sq. AP1 width',\n",
    " 'short sq. AP1 width',\n",
    " 'spc1 inst. freq.',\n",
    " 'spc3 inst. freq.',\n",
    " 'long sq. AP1 downstroke',\n",
    " 'spc2 inst. freq.']\n",
    "\n",
    "features_to_drop_indices=[]\n",
    "for el in features_to_drop:\n",
    "    features_to_drop_indices.append(np.where(D['pcipfx_names']==el)[0][0])\n",
    "\n",
    "# Z-score\n",
    "Y = np.delete(Y, features_to_drop_indices, axis=1)\n",
    "Y = Y-np.mean(Y, axis=0)\n",
    "Y = Y/np.std(Y, axis=0)\n",
    "\n",
    "print('After kicking out Nan ephys neurons and certain ephys properties, the dataset contains ', X.shape[0], \\\n",
    "      'neurons. We have', Y.shape[1], 'ephys features and', X.shape[1], 'different genes.')\n",
    "\n",
    "cells=D['cluster_id']\n",
    "geneNames=D['gene_id']\n",
    "mostVariableGenes=np.ones_like(D['gene_id'], dtype='bool')\n",
    "ephysData = Y.copy()\n",
    "ephysNames = np.delete(D['pcipfx_names'], features_to_drop_indices)\n",
    "ttype = D['cluster'][ind_non_nan]\n",
    "colors={celltype:D['cluster_color'][D['cluster']==celltype][0] for celltype in np.unique(D['cluster'])}\n",
    "patchseqdata = {'counts': sparse.csr_matrix(X), \n",
    "                'cells': cells, \n",
    "                'genes': geneNames, \n",
    "                'mostVariableGenes': mostVariableGenes,\n",
    "                'ephys': ephysData, \n",
    "                'ephysNames': ephysNames,\n",
    "                'ttype': ttype,\n",
    "                'colors': colors}\n",
    "\n",
    "pickle.dump(patchseqdata, open('data/gouwens2020.pickle', 'wb'))"
   ]
  }
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