figure-fuzik.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure: the Fuzik et al. dataset"
   ]
  },
  {
   "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\n",
    "import pickle\n",
    "import matplotlib\n",
    "\n",
    "import sparseRRR\n",
    "\n",
    "def sns_styleset():\n",
    "    sns.set_context('paper')\n",
    "    sns.set_style('ticks')\n",
    "    matplotlib.rcParams['axes.linewidth']    = .5\n",
    "    matplotlib.rcParams['xtick.major.width'] = .5\n",
    "    matplotlib.rcParams['ytick.major.width'] = .5\n",
    "    matplotlib.rcParams['xtick.major.size'] = 2\n",
    "    matplotlib.rcParams['ytick.major.size'] = 2\n",
    "    matplotlib.rcParams['xtick.minor.size'] = 1\n",
    "    matplotlib.rcParams['ytick.minor.size'] = 1\n",
    "    matplotlib.rcParams['font.size']       = 6\n",
    "    matplotlib.rcParams['axes.titlesize']  = 7\n",
    "    matplotlib.rcParams['axes.labelsize']  = 6\n",
    "    matplotlib.rcParams['legend.fontsize'] = 6\n",
    "    matplotlib.rcParams['xtick.labelsize'] = 6\n",
    "    matplotlib.rcParams['ytick.labelsize'] = 6\n",
    "    matplotlib.rcParams['figure.dpi'] = 120     # only affects the notebook\n",
    "\n",
    "sns_styleset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(data, normalize='cpm'):\n",
    "    X = data['counts'][:,data['mostVariableGenes']] / np.sum(data['counts'], axis=1)\n",
    "    if normalize=='cpm':\n",
    "        X *= 1e+6\n",
    "    elif normalize=='median':\n",
    "        X *= np.median(np.array(np.sum(data['counts'],axis=1)))\n",
    "    X = np.array(X)\n",
    "    X = np.log2(X + 1)\n",
    "    X = X - np.mean(X, axis=0)\n",
    "    X = X / np.std(X, axis=0)\n",
    "\n",
    "    Y = data['ephys']\n",
    "    Y = Y - np.mean(Y, axis=0)\n",
    "    Y = Y / np.std(Y, axis=0)\n",
    "    \n",
    "    return (X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X: (80, 1313) \n",
      "Shape of Y: (80, 80)\n"
     ]
    }
   ],
   "source": [
    "data = pickle.load(open('data/fuzik2016.pickle', 'rb'))\n",
    "X,Y = preprocess(data, normalize='median')\n",
    "print('Shape of X:', X.shape, '\\nShape of Y:', Y.shape)\n",
    "\n",
    "cvresults = pickle.load(open('pickles/cvresults-fuzik2016.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
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       "            continue;\n",
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       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
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       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
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       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
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       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
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       "\n",
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       "    }\n",
       "};\n",
       "\n",
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       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
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       "    var size = msg['size'];\n",
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       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
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       "    y1 = Math.floor(y1) + 0.5;\n",
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       "\n",
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       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / mpl.ratio,\n",
       "        fig.canvas.height / mpl.ratio\n",
       "    );\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",
       "        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.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\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",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\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(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\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(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\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",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\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",
       "        }\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",
       "        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, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\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",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\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, 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\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"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\";/* global mpl */\n",
       "\n",
       "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 = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\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;\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",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / mpl.ratio;\n",
       "    fig.root.removeEventListener('remove', this._remove_fig_handler);\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.innerHTML =\n",
       "        '<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'] =\n",
       "        '<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 () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\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",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function () {\n",
       "    this.close_ws(this, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "    el.addEventListener('remove', this._remove_fig_handler);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('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",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\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",
       "\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",
       "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(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"803.9166666666667\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Genes selected: 10\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "fig = plt.figure(figsize=(6.7, 2))\n",
    "\n",
    "plt.subplot(131)\n",
    "cols = ['', [76/256, 114/256, 176/256], [196/256, 78/256, 82/256]]\n",
    "for rank in range(1,2+1):\n",
    "    # suppressing \"mean of empty slice\" warnings\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter('ignore', category=RuntimeWarning)\n",
    "        n = np.nanmean(cvresults[rank][2], axis=(0,1))\n",
    "        c = np.nanmean(cvresults[rank][0], axis=(0,1))\n",
    "        cr = np.nanmean(cvresults[rank][1], axis=(0,1))\n",
    "    \n",
    "        col = cols[rank]\n",
    "        plt.plot(n[:,0], cr[:,0], color=col, linewidth=.75,\n",
    "                 label='rank {}'.format(rank))\n",
    "        plt.plot(n[:,0], c[:,0], '--',  color=col, linewidth=.75)\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of genes')\n",
    "plt.ylabel(r'Cross-validated $R^2$')\n",
    "plt.title(r'sRRR with $\\alpha=0.5$')\n",
    "plt.ylim([0, .15])\n",
    "plt.xlim([1, 500])\n",
    "plt.text(1.2, .08, 'Relaxed', fontsize=6)\n",
    "plt.text(8,.02, 'Naive', fontsize=6)\n",
    "plt.plot([10,10], [0,.15], color=[.7,.7,.7], linewidth=.5, zorder=0)\n",
    "\n",
    "plt.subplot(132)\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n",
    "    n = np.nanmean(cvresults[2][2], axis=(0,1))\n",
    "    c = np.nanmean(cvresults[2][4], axis=(0,1))\n",
    "    \n",
    "plt.plot(n[:,0], c[:,0,0], color='k', linewidth=.75,\n",
    "                 label='rank {}'.format(rank))\n",
    "plt.plot(n[:,0], c[:,0,1], 'k--', linewidth=.75)\n",
    "\n",
    "# plt.legend(frameon=False, bbox_to_anchor=(1, 1.05))\n",
    "plt.xscale('log')\n",
    "plt.xlabel('Number of genes')\n",
    "plt.ylabel('Cross-validated correlation')\n",
    "plt.title(r'sRRR with $\\alpha=0.5$ and $r=2$')\n",
    "plt.ylim([0, 1])\n",
    "plt.xlim([1, 500])\n",
    "plt.text(7, .75, 'Component 1', fontsize=6)\n",
    "plt.text(7, .3, 'Component 2', fontsize=6)\n",
    "plt.plot([10,10], [0,1], color=[.7,.7,.7], linewidth=.5, zorder=0)\n",
    "\n",
    "\n",
    "#########\n",
    "w,v = sparseRRR.relaxed_elastic_rrr(X, Y, rank=1, alpha = .5, lambdau = 2)\n",
    "print('Genes selected:', np.sum(w!=0))\n",
    "\n",
    "plt.subplot(133, aspect='equal')\n",
    "ind = np.isin(data['cluster'], ['L5', 'L4', 'L2'])\n",
    "plt.scatter(X[ind,:]@w, Y[ind,:]@v, s=5)\n",
    "plt.scatter(X[~ind,:]@w, Y[~ind,:]@v, s=5)\n",
    "plt.text(2,-3,'Excitatory', fontsize=7)\n",
    "plt.text(-6,-8,'Inhibitory', fontsize=7)\n",
    "# plt.title(r'sRRR with $\\alpha=0.5$ and $r=1$')\n",
    "plt.xlabel('Transcriptomic comp. 1')\n",
    "plt.ylabel('Electrophysiological comp. 1')\n",
    "plt.xlim([-10,10])\n",
    "plt.ylim([-10,10])\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "\n",
    "fig.text(.01, .92, 'a', fontsize=8, fontweight='bold')\n",
    "fig.text(.33, .92, 'b', fontsize=8, fontweight='bold')\n",
    "fig.text(.70, .92, 'c', fontsize=8, fontweight='bold')\n",
    "\n",
    "plt.savefig('figures/fuzik.png', dpi=200)\n",
    "plt.savefig('figures/fuzik.pdf')"
   ]
  }
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