Northstar_Pancreas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zanini, Berghuis et al. 2019\n",
    "### Pancreas datasets\n",
    "### Fig 3 and related supplementary figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import third party packages:\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "from umap import UMAP\n",
    "\n",
    "import matplotlib as mpl\n",
    "from matplotlib import gridspec\n",
    "import matplotlib.lines as mlines\n",
    "%matplotlib notebook\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Make sure you can import northstar by adding its parent folder to your PYTHONPATH aka sys.path\n",
    "sys.path.insert(0, os.path.abspath('..')+'/northstar/build/lib') # Fabio's laptop\n",
    "import northstar\n",
    "\n",
    "# Make sure you can import the modules in the 'modules' folder by adding it to your PYTHONPATH aka sys.path\n",
    "sys.path.append(os.path.abspath('.')+'/modules')\n",
    "import northstar_analysis as noa\n",
    "import northstar_sankey as nosa\n",
    "import northstar_dotplot as ndot\n",
    "import get_timestamp as time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare new dataset\n",
    "### 11 pancreatic tumors and 1 healthy pancreas\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fn_counts = 'data/PancreasTumors/CuratedTumorCountTable.csv'\n",
    "fn_metadata = 'data/PancreasTumors/CuratedTumorMetaData.csv'\n",
    "counts_all = pd.read_csv(fn_counts, sep=',', index_col=0).astype(np.float32)\n",
    "meta_all = pd.read_csv(fn_metadata, sep=',', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count number of cells per tumor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
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       "    );\n",
       "\n",
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       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
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       "\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",
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       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
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       "    if (!this.waiting) {\n",
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       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
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       "\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",
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       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
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       "\n",
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       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
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       "\n",
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       "\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",
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       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        break;\n",
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       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
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       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
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       "\n",
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       "    fig.image_mode = msg['mode'];\n",
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       "\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": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "y = meta_all['Tumor'].value_counts()\n",
    "n = len(y)\n",
    "x = np.arange(n)\n",
    "colors = sns.color_palette('husl', n_colors=n)\n",
    "colors = colors[::2] + colors[1::2]\n",
    "for i in range(n):\n",
    "    ax.bar([i], [y.iloc[i]], color=colors[i])\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(y.index, rotation=90)\n",
    "ax.set_yscale('log')\n",
    "ax.grid(True)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exclude patients with < 10 cells and normal\n",
    "ind = ~meta_all['Tumor'].isin(['NuPa22', 'TuPa20', 'TuPa25', 'TuPa26', 'TuPa18'])\n",
    "meta = meta_all.loc[ind].copy()\n",
    "counts = counts_all.loc[:, ind]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53884, 1622)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify cell using atlases\n",
    "We reannotated the atlas to increase resolution for rare populations (stellate, epsilon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change names to forget atlas, else they are too long\n",
    "def sanitize_cell_type(x):\n",
    "    if 't_cell' in x:\n",
    "        out = 'T cell'\n",
    "    elif ('stellate' in x) and ('Enge' not in x):\n",
    "        out = ' '.join(x.split('_')[-2:])\n",
    "    else:\n",
    "        out = x.split('_')[-1]\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21    Baron_2016_acinar\n",
       "22    Baron_2016_acinar\n",
       "20    Baron_2016_acinar\n",
       "23    Baron_2016_acinar\n",
       "24    Baron_2016_acinar\n",
       "19    Baron_2016_acinar\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = northstar.Averages(\n",
    "    atlas=[\n",
    "        {'atlas_name': 'Baron_2016',\n",
    "         'cell_types': [\n",
    "        'acinar', 'beta', 'delta', 'activated_stellate',\n",
    "        'ductal', 'alpha',\n",
    "        'epsilon', 'gamma', 'endothelial', 'quiescent_stellate',\n",
    "        'macrophage',\n",
    "        'schwann', 'mast',\n",
    "        't_cell'],\n",
    "        },\n",
    "        {'atlas_name': 'Zanini_2018',\n",
    "         'cell_types': [\n",
    "             'B cell',\n",
    "             #'T cell',\n",
    "             'NK cell',\n",
    "             'classical monocyte', 'nonclassical monocyte',\n",
    "             'plasmablast',\n",
    "         ],\n",
    "        },\n",
    "    ],\n",
    "    n_pcs=35,\n",
    "    n_cells_per_type=20,\n",
    "    n_features_per_cell_type=40,\n",
    "    n_features_overdispersed=400,\n",
    "    n_neighbors=25,\n",
    "    resolution_parameter=0.02,\n",
    ")\n",
    "model.fit(counts)\n",
    "#meta['cellType'] = [sanitize_cell_type(x) for x in model.membership]\n",
    "model.estimate_closest_atlas_cell_type()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract principal components from northstar and calculate tSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cte = list(map(sanitize_cell_type, model.pca_data['cell_type']))\n",
    "ctypes = np.array(cte)\n",
    "ctypes[nae:] = meta['cellType'].values\n",
    "nae = model.pca_data['n_atlas']\n",
    "d = np.ones((len(cte), len(cte)))\n",
    "for i, nis in enumerate(model.neighbors):\n",
    "    for ni in nis:\n",
    "        d[i, ni] = 0\n",
    "\n",
    "vs = TSNE(perplexity=30, metric='precomputed').fit_transform(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export data for figure\n",
    "df = pd.DataFrame(vs, columns=['tsne1', 'tsne2'])\n",
    "df['cellType'] = ctypes.copy()\n",
    "df['cellID'] = ''\n",
    "df['cellID'].iloc[nae:] = counts.columns.values\n",
    "#df.to_csv('data_for_figures/pancreas_cell_type_tsne.tsv', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"459\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load data\n",
    "df = pd.read_csv('data_for_figures/pancreas_cell_type_tsne.tsv', sep='\\t')\n",
    "nae = (df['cellID'].astype(str) == 'nan').sum()\n",
    "ctypes_ext = df['cellType'].values\n",
    "meta['cellType'] = ctypes_ext[nae:]\n",
    "vs_ext = df[['tsne1', 'tsne2']].values\n",
    "\n",
    "# Atlas averages were replicated for tSNE, deduplicate here\n",
    "na = 19\n",
    "vs = np.zeros((vs_ext.shape[0] - nae + 19, vs_ext.shape[1]))\n",
    "ctypes = []\n",
    "for i in range(19):\n",
    "    vs[i] = vs_ext[i * 20: (i + 1) * 20].mean(axis=0)\n",
    "    ctypes.append(ctypes_ext[i * 20])\n",
    "vs[19:] = vs_ext[nae:]\n",
    "ctypes.extend(list(ctypes_ext[nae:]))\n",
    "ctypes = np.array(ctypes)\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4.6, 4.4))\n",
    "\n",
    "# Set colors\n",
    "ctypesu = np.unique(ctypes)\n",
    "lctu = len(ctypesu)\n",
    "colorsu = sns.color_palette('husl', n_colors=lctu)\n",
    "cmap = dict(zip(ctypesu, colorsu))\n",
    "colors = [cmap[x] for x in ctypes]\n",
    "\n",
    "# Plot atlas cells as squares, new cells as dots\n",
    "ax.scatter(vs[:na, 0], vs[:na,1], s=120, marker='*', edgecolor='k', lw=1, c=colors[:na], alpha=0.8)\n",
    "ax.scatter(vs[na:, 0], vs[na:,1], s=15, marker='o', c=colors[na:], alpha=0.3)\n",
    "\n",
    "for ct in ctypesu:\n",
    "    ind = ctypes == ct\n",
    "    if ct.isdigit():\n",
    "        x, y = vs[ind].mean(axis=0)\n",
    "    else:\n",
    "        ii = list(ctypes[:19]).index(ct)\n",
    "        x, y = vs[ii]\n",
    "    xt = x\n",
    "    yt = y + 2 * vs.max() / 50.\n",
    "    if ct == 'classical monocyte':\n",
    "        xt -= 10 * vs.max() / 50.\n",
    "        yt -= 6 * vs.max() / 50.\n",
    "    elif ct == 'quiescent stellate':\n",
    "        xt -= 0 * vs.max() / 50.\n",
    "        yt -= 0 * vs.max() / 50.\n",
    "    elif ct == 'activated stellate':\n",
    "        xt -= 0 * vs.max() / 50.\n",
    "        yt -= 13 * vs.max() / 50.\n",
    "    elif ct == 'nonclassical monocyte':\n",
    "        xt += 11 * vs.max() / 50.\n",
    "        yt -= 5 * vs.max() / 50.\n",
    "    elif ct == 'B cell':\n",
    "        xt -= 7 * vs.max() / 50.\n",
    "        yt -= 3 * vs.max() / 50.\n",
    "    elif ct == 'NK cell':\n",
    "        xt -= 9 * vs.max() / 50.\n",
    "        yt -= 3.5 * vs.max() / 50.\n",
    "    elif ct == 'plasmablast':\n",
    "        xt += 11.5 * vs.max() / 50.\n",
    "        yt -= 3.5 * vs.max() / 50.\n",
    "    elif ct == 'beta':\n",
    "        xt += 1 * vs.max() / 50.\n",
    "    elif ct == 'gamma':\n",
    "        yt -= 8 * vs.max() / 50.\n",
    "    elif ct == 'epsilon':\n",
    "        xt += 2 * vs.max() / 50.\n",
    "    elif ct == 'acinar':\n",
    "        xt -= 5 * vs.max() / 50.\n",
    "        yt -= 0 * vs.max() / 50.\n",
    "    elif ct == 'ductal':\n",
    "        xt += 5 * vs.max() / 50.\n",
    "        yt -= 0 * vs.max() / 50.\n",
    "    elif ct == 'endothelial':\n",
    "        yt += 0 * vs.max() / 50.\n",
    "        xt -= 9 * vs.max() / 50.\n",
    "    if len(ct) > 10:\n",
    "        ct = ct.replace(' ', '\\n')\n",
    "    ax.text(xt, yt, ct, ha='center', va='bottom', fontsize=9)\n",
    "    ax.scatter([x], [y], s=40, edgecolor='k', lw=1, alpha=0.1, facecolor='none')\n",
    "    \n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "ax.set_axis_off()\n",
    "fig.tight_layout()\n",
    "\n",
    "fig.savefig('figures/Fig3A.svg')\n",
    "fig.savefig('figures/Fig3A.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"450\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution of cells across cell types\n",
    "fig2, (ax, ax1) = plt.subplots(2, 1, figsize=(4.5, 2.6), gridspec_kw={'height_ratios': [1, 7]})\n",
    "df = meta[['Tumor', 'cellType']].copy()\n",
    "df['c'] = 1\n",
    "df = df.groupby(['Tumor', 'cellType']).sum()['c'].unstack().fillna(0).astype(int)\n",
    "dfn = 1.0 * (df.T / df.T.sum(axis=0)).T\n",
    "\n",
    "tumor_order = ['TuPa'+str(x) for x in [2, 4, 5, 1, 27, 6, 3, 31, 28, 29, 23]]\n",
    "dfn_sort = dfn.loc[tumor_order]\n",
    "cell_type_order = [\n",
    "    'acinar', 'activated stellate', 'nonclassical monocyte',\n",
    "    '21', '23', '20', '22', 'quiescent stellate', 'delta', '19']\n",
    "dfn_sort = dfn_sort.loc[:, cell_type_order]\n",
    "\n",
    "y = np.log10(meta['Tumor'].value_counts().loc[dfn_sort.index].values)\n",
    "ax.bar(0.5 + np.arange(len(y)), y, color='darkgrey')\n",
    "ax.set_ylim(1, 3)\n",
    "ax.set_yticks([1, 3])\n",
    "ax.set_yticklabels(['$10$', '$10^3$'], fontsize=9)\n",
    "ax.set_ylabel('# cells', ha='right', va='center', rotation=0, fontsize=10)\n",
    "sns.despine(ax=ax, top=True, right=True, bottom=True, left=False)\n",
    "\n",
    "#sns.heatmap(dfn_sort, ax=ax1, xticklabels=True, yticklabels=True, cbar=False, cmap='magma')\n",
    "def size_fun(fr):\n",
    "    return (8 * fr)**2\n",
    "    \n",
    "for i in range(dfn_sort.shape[0]):\n",
    "    for j in range(dfn_sort.shape[1]):\n",
    "        fr = dfn_sort.values[i, j]\n",
    "        r = size_fun(fr)\n",
    "        color = cmap[dfn_sort.columns[j]]\n",
    "        ax1.scatter([i + 0.5], [j + 0.5], color=color, s=[r])\n",
    "ax1.set_yticks(0.5 + np.arange(dfn_sort.shape[1]))\n",
    "ax1.set_xticks(0.5 + np.arange(dfn_sort.shape[0]))\n",
    "ax1.set_yticklabels(dfn_sort.columns)\n",
    "ax1.set_xticklabels(dfn_sort.index, rotation=90)\n",
    "\n",
    "for tk in ax1.get_xticklabels():\n",
    "    tk.set_fontsize(10)\n",
    "for tk in ax1.get_yticklabels():\n",
    "    tk.set_fontsize(10)\n",
    "ax1.set_ylim(dfn_sort.shape[1], 0)\n",
    "ax1.set_xlabel('')\n",
    "ax1.set_ylabel('')\n",
    "\n",
    "handles = []\n",
    "labels = []\n",
    "for fr in [0.25, 0.5, 0.75, 1.0]:\n",
    "    h = mlines.Line2D(\n",
    "        [], [], color=[0.2] * 3, marker='o', lw=0,\n",
    "        markersize=np.sqrt(size_fun(fr)),\n",
    "        )\n",
    "    handles.append(h)\n",
    "    labels.append(str(int(100 * fr))+'%')\n",
    "ax1.legend(\n",
    "    handles, labels,\n",
    "    loc='upper left',\n",
    "    title='Fraction\\nof tumor\\nin this\\ncluster:',\n",
    "    fontsize=8,\n",
    "    ncol=1,\n",
    "    bbox_to_anchor=(1.01, 1.04),\n",
    "    bbox_transform=ax1.transAxes,\n",
    ")\n",
    "sns.despine(ax=ax1)\n",
    "ax.set_xticks([])\n",
    "\n",
    "\n",
    "fig2.tight_layout(h_pad=0)\n",
    "fig2.savefig('figures/Fig3B.svg')\n",
    "fig2.savefig('figures/Fig3B.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Tumor</th>\n",
       "      <th>TuPa1</th>\n",
       "      <th>TuPa2</th>\n",
       "      <th>TuPa3</th>\n",
       "      <th>TuPa4</th>\n",
       "      <th>TuPa5</th>\n",
       "      <th>TuPa6</th>\n",
       "      <th>TuPa23</th>\n",
       "      <th>TuPa27</th>\n",
       "      <th>TuPa28</th>\n",
       "      <th>TuPa29</th>\n",
       "      <th>TuPa31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cellType</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>macrophage</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>19</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>schwann</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mast</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>T cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>154</td>\n",
       "      <td></td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>NK cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>B cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>plasmablast</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>classical monocyte</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nonclassical monocyte</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>58</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>endothelial</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>quiescent stellate</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>activated stellate</td>\n",
       "      <td>105</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ductal</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>acinar</td>\n",
       "      <td></td>\n",
       "      <td>46</td>\n",
       "      <td></td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>alpha</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>beta</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>gamma</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>delta</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>epsilon</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>356</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>295</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>18</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>14</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>121</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Tumor                 TuPa1 TuPa2 TuPa3 TuPa4 TuPa5 TuPa6 TuPa23 TuPa27  \\\n",
       "cellType                                                                  \n",
       "macrophage                                                     3          \n",
       "schwann                                                                   \n",
       "mast                                                           1          \n",
       "T cell                                                       154          \n",
       "NK cell                                                                   \n",
       "B cell                                                         1          \n",
       "plasmablast                                                    1          \n",
       "classical monocyte                                                    1   \n",
       "nonclassical monocyte                                                58   \n",
       "endothelial               3                                               \n",
       "quiescent stellate        2     1                                         \n",
       "activated stellate      105                                               \n",
       "ductal                                                                    \n",
       "acinar                         46          16    28     2             9   \n",
       "alpha                                                                     \n",
       "beta                                                                      \n",
       "gamma                                                   1                 \n",
       "delta                                                                     \n",
       "epsilon                                                                   \n",
       "19                                                           356          \n",
       "20                                                                        \n",
       "21                                   18                14                 \n",
       "22                              1                 1     1                 \n",
       "23                                    4           1            4          \n",
       "\n",
       "Tumor                 TuPa28 TuPa29 TuPa31  \n",
       "cellType                                    \n",
       "macrophage                19                \n",
       "schwann                                     \n",
       "mast                              1         \n",
       "T cell                    23      3      1  \n",
       "NK cell                                     \n",
       "B cell                                      \n",
       "plasmablast                                 \n",
       "classical monocyte                          \n",
       "nonclassical monocyte                       \n",
       "endothelial                                 \n",
       "quiescent stellate         3     22     15  \n",
       "activated stellate                          \n",
       "ductal                                      \n",
       "acinar                            3         \n",
       "alpha                      1                \n",
       "beta                                        \n",
       "gamma                      1                \n",
       "delta                      1     24      2  \n",
       "epsilon                    1                \n",
       "19                                3         \n",
       "20                       295                \n",
       "21                                     201  \n",
       "22                              121         \n",
       "23                         2     10     38  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = meta[['Tumor', 'cellType']].copy()\n",
    "df['c'] = 1\n",
    "abu = df.groupby(['Tumor', 'cellType']).sum()['c'].unstack().fillna('').T\n",
    "abu.loc[['macrophage', 'schwann', 'mast', 'T cell', 'NK cell', 'B cell', 'plasmablast', 'classical monocyte', 'nonclassical monocyte', 'endothelial', 'quiescent stellate', 'activated stellate', 'ductal', 'acinar', 'alpha', 'beta', 'gamma', 'delta', 'epsilon', '19', '20', '21', '22', '23'],\n",
    "        ['TuPa'+str(x) for x in [1, 2, 3, 4, 5, 6, 23, 27, 28, 29, 31]]].fillna('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot t-SNE of some markers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"700\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "genes = ['GCG', 'INS', 'PPY', 'SST', 'GHRL',\n",
    "         'PRSS1', 'PROM1', 'RGS5', 'KRT19',\n",
    "        'SPARC', 'PECAM1', 'PTPRC', 'GZMB', 'IL1A',\n",
    "         'GNLY', 'CD3E', 'MS4A1', 'TCL1A', 'COL6A2',\n",
    "        'THY1', 'LOXL4', 'VWF', 'CD2', 'CD68']\n",
    "\n",
    "L = len(model.features_all)\n",
    "N = len(model.pca_data['cell_type'])\n",
    "matrix = np.zeros((L, N))\n",
    "for i in range(20):\n",
    "    for j in range(20):\n",
    "        matrix[:, i * 20 + j] = model.matrix_all[:, i]\n",
    "matrix[:, nae:] = model.matrix_all[:, 19:]\n",
    "data = pd.DataFrame(matrix, index=model.features_all)\n",
    "\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(6, 4, figsize=(7, 10), sharex=True, sharey=True)\n",
    "axs = axs.ravel()\n",
    "\n",
    "cmap = plt.cm.get_cmap('viridis')\n",
    "for gene, ax in zip(genes, axs):\n",
    "    if gene not in data.index:\n",
    "        continue\n",
    "    count = np.log10(0.1 + data.loc[gene]).values\n",
    "    c = (count - count.min()) / (count.max() - count.min())\n",
    "    colors = cmap(c)\n",
    "    \n",
    "    # Plot atlas cells as squares, new cells as dots\n",
    "    ax.scatter(vs[:nae, 0], vs[:nae,1], s=25, marker='s', c=colors[:nae], alpha=0.3)\n",
    "    ax.scatter(vs[nae:, 0], vs[nae:,1], s=15, marker='o', c=colors[nae:], alpha=0.3)\n",
    "\n",
    "    ax.set_title(gene, fontsize=10)\n",
    "    ax.set_axis_off()\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig('figures/SupplFig4.png', dpi=300)\n",
    "fig.savefig('figures/SupplFig4.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find markers for each new cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DEGs for cluster: 19\n",
      "Candidate genes: 149\n",
      "DEGs for cluster: 20\n",
      "Candidate genes: 3301\n",
      "DEGs for cluster: 21\n",
      "Candidate genes: 1428\n",
      "DEGs for cluster: 22\n",
      "Candidate genes: 1151\n",
      "DEGs for cluster: 23\n",
      "Candidate genes: 15\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "arrays must all be same length",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-f188ff15050c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mkss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'statistic'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mmarkers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mct\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mmarkers_top\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmarkers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    409\u001b[0m             )\n\u001b[1;32m    410\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36minit_dict\u001b[0;34m(data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    255\u001b[0m             \u001b[0marr\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         ]\n\u001b[0;32m--> 257\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36marrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    366\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 368\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arrays must all be same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ks_2samp\n",
    "\n",
    "ct_new = meta['cellType'].unique()\n",
    "ct_new = [str(x) for x in range(50) if str(x) in ct_new]\n",
    "markers = {}\n",
    "for ct in ct_new:\n",
    "    print('DEGs for cluster:', ct)\n",
    "    ind = meta['cellType'] == ct\n",
    "    cp = counts.loc[:, ind]\n",
    "    cn = counts.loc[:, ~ind]\n",
    "    indg = ((cp > 10).mean(axis=1) >= 0.3) & ((cn < 10).mean(axis=1) >= 0.6)\n",
    "    genes = cp.index[indg]\n",
    "    print('Candidate genes: {:}'.format(len(genes)))\n",
    "    kss = []\n",
    "    for ig, gene in enumerate(genes):\n",
    "        cp1 = cp.loc[gene].values\n",
    "        cn1 = cn.loc[gene].values\n",
    "        res = ks_2samp(cp1, cn1)\n",
    "        kss.append([res.statistic, res.pvalue])\n",
    "    kss = pd.DataFrame(kss, columns=['statistic', 'pvalue'], index=genes)\n",
    "    kss.sort_values(by='statistic', ascending=False, inplace=True)\n",
    "    markers[ct] = kss.index[:50]\n",
    "markers_top = pd.DataFrame({k: v[:15] for k, v in markers.items()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>EGFR-AS1</td>\n",
       "      <td>APOH</td>\n",
       "      <td>PAX6</td>\n",
       "      <td>ANXA2</td>\n",
       "      <td>TTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>CTC-441N14-4</td>\n",
       "      <td>AGT</td>\n",
       "      <td>GC</td>\n",
       "      <td>C19orf33</td>\n",
       "      <td>GC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>C19orf57</td>\n",
       "      <td>CFC1B</td>\n",
       "      <td>GAD2</td>\n",
       "      <td>KRT19</td>\n",
       "      <td>SCG5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>CD74</td>\n",
       "      <td>SCGN</td>\n",
       "      <td>FAM159B</td>\n",
       "      <td>ANXA2P2</td>\n",
       "      <td>PPY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>TMEM229B</td>\n",
       "      <td>TM4SF5</td>\n",
       "      <td>KRT39</td>\n",
       "      <td>TM4SF1</td>\n",
       "      <td>RPL34P18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>HLA-DRA</td>\n",
       "      <td>CFC1</td>\n",
       "      <td>PPY</td>\n",
       "      <td>OCIAD2</td>\n",
       "      <td>CHGB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>DNAJC27-AS1</td>\n",
       "      <td>CALY</td>\n",
       "      <td>ERO1B</td>\n",
       "      <td>TMPRSS4</td>\n",
       "      <td>PAX6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>PCDH10</td>\n",
       "      <td>PCSK1N</td>\n",
       "      <td>CHGA</td>\n",
       "      <td>LGALS3</td>\n",
       "      <td>TM4SF4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>TNR</td>\n",
       "      <td>CCL15</td>\n",
       "      <td>SCG5</td>\n",
       "      <td>PLAT</td>\n",
       "      <td>CHGA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>GAREM2</td>\n",
       "      <td>MGST1</td>\n",
       "      <td>TTR</td>\n",
       "      <td>GPRC5A</td>\n",
       "      <td>ATP5EP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>RP11-253M7-1</td>\n",
       "      <td>SLC14A1</td>\n",
       "      <td>CHGB</td>\n",
       "      <td>S100P</td>\n",
       "      <td>C10orf10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>HLA-DPB1</td>\n",
       "      <td>RTBDN</td>\n",
       "      <td>PAPPA2</td>\n",
       "      <td>RPL7P1</td>\n",
       "      <td>HSP90AA2P</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>HLA-DRB1</td>\n",
       "      <td>SPTSSB</td>\n",
       "      <td>CPE</td>\n",
       "      <td>RPL7P23</td>\n",
       "      <td>ERO1B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>ST7L</td>\n",
       "      <td>TPPP3</td>\n",
       "      <td>SCG3</td>\n",
       "      <td>RPL7P32</td>\n",
       "      <td>PDK4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>HLA-DRB5</td>\n",
       "      <td>COL28A1</td>\n",
       "      <td>PDK4</td>\n",
       "      <td>RPL7P6</td>\n",
       "      <td>TTI2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              19       20       21        22         23\n",
       "0       EGFR-AS1     APOH     PAX6     ANXA2        TTR\n",
       "1   CTC-441N14-4      AGT       GC  C19orf33         GC\n",
       "2       C19orf57    CFC1B     GAD2     KRT19       SCG5\n",
       "3           CD74     SCGN  FAM159B   ANXA2P2        PPY\n",
       "4       TMEM229B   TM4SF5    KRT39    TM4SF1   RPL34P18\n",
       "5        HLA-DRA     CFC1      PPY    OCIAD2       CHGB\n",
       "6    DNAJC27-AS1     CALY    ERO1B   TMPRSS4       PAX6\n",
       "7         PCDH10   PCSK1N     CHGA    LGALS3     TM4SF4\n",
       "8            TNR    CCL15     SCG5      PLAT       CHGA\n",
       "9         GAREM2    MGST1      TTR    GPRC5A    ATP5EP2\n",
       "10  RP11-253M7-1  SLC14A1     CHGB     S100P   C10orf10\n",
       "11      HLA-DPB1    RTBDN   PAPPA2    RPL7P1  HSP90AA2P\n",
       "12      HLA-DRB1   SPTSSB      CPE   RPL7P23      ERO1B\n",
       "13          ST7L    TPPP3     SCG3   RPL7P32       PDK4\n",
       "14      HLA-DRB5  COL28A1     PDK4    RPL7P6       TTI2"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "markers_top = pd.DataFrame({k: v[:15] for k, v in markers.items()})\n",
    "markers_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "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=\"400\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ctype_order = ['21', '23', '20', '22', '19']\n",
    "genes1 = []\n",
    "for col in ctype_order:\n",
    "    for ig, gene in enumerate(markers_top[col].values):\n",
    "        if ig > 4:\n",
    "            continue\n",
    "        if gene.startswith('RPL'):\n",
    "            continue\n",
    "        if 'orf' in gene:\n",
    "            continue\n",
    "        if gene not in genes1:\n",
    "            genes1.append(gene)\n",
    "\n",
    "genes2 = [\n",
    "    'QPCT', 'SERPINA1',\n",
    "    'SCG2', 'PCSK2', 'PTPRN2',\n",
    "    'ARF3', 'FGB', 'GPX2',\n",
    "    'ITGA2', 'TSPAN8',\n",
    "    'LAMC2', 'CTSE', 'LGALS4', 'GPRC5A', 'AGR2',\n",
    "    'PTPRC', 'IGKC',\n",
    "    ]\n",
    "\n",
    "genes = list(set(genes1) | set(genes2))\n",
    "\n",
    "cov = counts.sum(axis=0)\n",
    "ind = meta['cellType'].isin([str(x) for x in range(50)])\n",
    "pmdata = meta.loc[ind, ['Tumor', 'cellType']]\n",
    "pdata = np.log10(0.1 + (1e6 * counts.loc[genes] / cov)[pmdata.index])\n",
    "\n",
    "# Sort new cell types\n",
    "pmdata['cellTypeIdx'] = -1\n",
    "for ill, ct in enumerate(ctype_order):\n",
    "    pmdata.loc[pmdata['cellType'] == ct, 'cellTypeIdx'] = ill\n",
    "pmdata.sort_values(['cellTypeIdx', 'Tumor'], inplace=True)\n",
    "pdata = pdata[pmdata.index]\n",
    "\n",
    "# Set colors\n",
    "ctypesu = np.unique(ctypes)\n",
    "lctu = len(ctypesu)\n",
    "colorsu = sns.color_palette('husl', n_colors=lctu)\n",
    "cmap = dict(zip(ctypesu, colorsu))\n",
    "colors = [cmap[x] for x in ctypes]\n",
    "\n",
    "fig, axs = plt.subplots(4, 1, figsize=(4, 6.2), gridspec_kw={'height_ratios': [17, 17, 1, 1]}, sharex=True)\n",
    "ax, ax2, ax0, ax1 = axs\n",
    "\n",
    "# Heatmap of the DEGs\n",
    "sns.heatmap(pdata.loc[genes1], ax=ax, xticklabels=False, yticklabels=True, cbar=False)\n",
    "for tk in ax.get_yticklabels():\n",
    "    tk.set_fontsize(9)\n",
    "ax.set_ylim(len(genes1), 0)\n",
    "\n",
    "# Heatmap of the known markers\n",
    "sns.heatmap(pdata.loc[genes2], ax=ax2, xticklabels=False, yticklabels=True, cbar=False)\n",
    "for tk in ax2.get_yticklabels():\n",
    "    tk.set_fontsize(9)\n",
    "ax2.set_ylim(len(genes2), 0)\n",
    "\n",
    "ctu = pmdata['cellType'].unique()\n",
    "lctu = len(ctu)\n",
    "iold = 0\n",
    "ct = pmdata['cellType']\n",
    "l = len(ct)\n",
    "tum = pmdata['Tumor'].values\n",
    "for i in range(1, l + 1):\n",
    "    if (i == l) or (ct[i] != ct[iold]):\n",
    "        r = plt.Rectangle((iold, 0), i - iold, 1, color=cmap[ct[iold]])\n",
    "        ax0.add_patch(r)\n",
    "        xm = 0.5 * (i + iold)\n",
    "        ym = 0.5\n",
    "        ax0.text(xm, ym, ct[i-1], ha='center', va='center', fontsize=8)\n",
    "        iold = i\n",
    "ax0.set_yticks([])\n",
    "\n",
    "ctu = meta['Tumor'].unique()\n",
    "lctu = len(ctu)\n",
    "colorsu = sns.color_palette('husl', n_colors=lctu)\n",
    "cmap = dict(zip(ctu, colorsu))\n",
    "iold = 0\n",
    "for i in range(1, len(pmdata['Tumor']) + 1):\n",
    "    if (i >= len(pmdata['Tumor'])) or (pmdata['Tumor'].iloc[i] != pmdata['Tumor'].iloc[iold]):\n",
    "        r = plt.Rectangle((iold, 0), i - iold, 1, color=cmap[pmdata['Tumor'].iloc[iold]])\n",
    "        ax1.add_patch(r)\n",
    "        iold = i\n",
    "    if (i < len(pmdata['Tumor'])) and (pmdata['cellType'].iloc[i] != pmdata['cellType'].iloc[i-1]):\n",
    "        ax1.plot([i-0.5]*2, [0, 1], lw=0.5, color='k')\n",
    "ax1.set_yticks([])\n",
    "\n",
    "handles = []\n",
    "labels = []\n",
    "for key, color in cmap.items():\n",
    "    h = mlines.Line2D(\n",
    "        [], [], color=color, marker='o', lw=0,\n",
    "        markersize=5,\n",
    "        )\n",
    "    handles.append(h)\n",
    "    labels.append(key.upper())\n",
    "ax1.legend(\n",
    "    handles, labels,\n",
    "    loc='upper left',\n",
    "    fontsize=8,\n",
    "    ncol=3,\n",
    "    bbox_to_anchor=(-0.03, -0.01),\n",
    "    bbox_transform=ax1.transAxes,\n",
    ")\n",
    "\n",
    "fig.tight_layout(h_pad=0.01)\n",
    "fig.savefig('figures/Fig3C.svg')\n",
    "fig.savefig('figures/Fig3C.png', dpi=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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