4067 lines (4066 with data), 454.3 kB

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi'] = 100\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pysam\n",
    "import singlecellmultiomics.molecule\n",
    "import singlecellmultiomics.fragment\n",
    "import pysamiterators\n",
    "import pandas as pd\n",
    "\n",
    "nla_test_bam_path = '../data/mini_nla_test.bam'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "reference = pysamiterators.iterators.CachedFasta(pysam.FastaFile('/media/sf_data/references/GATK-Bundle/hg38/Homo_sapiens_assembly38.fasta'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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, fig.canvas.height);\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"
    },
    {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from singlecellmultiomics.molecule import NlaIIIMolecule, MoleculeIterator\n",
    "from singlecellmultiomics.fragment import NLAIIIFragment\n",
    "import collections\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "gc_distribution = collections.defaultdict(collections.Counter) #cell->GC_bin->count\n",
    "gc_bin_size = 2\n",
    "with pysam.AlignmentFile('/media/sf_H_DRIVE/data/nla/APKS1-P10-1-1/sorted.bam') as alignments:\n",
    "    for i,molecule in enumerate(\n",
    "            MoleculeIterator(alignments,\n",
    "                             moleculeClass=NlaIIIMolecule,\n",
    "                             fragmentClass=NLAIIIFragment,\n",
    "                             molecule_class_args={\n",
    "                                 'reference':reference,\n",
    "                                 'min_max_mapping_quality':20\n",
    "                             },                     \n",
    "                             fragment_class_args={\n",
    "                                'umi_hamming_distance':1\n",
    "                             },\n",
    "                          )):\n",
    "        try:\n",
    "            bf = collections.Counter( molecule.get_fragment_span_sequence() )\n",
    "            gc = (bf['G']+bf['C'])/sum(bf.values())\n",
    "        except ValueError: # cannot obtain gc\n",
    "            continue\n",
    "        \n",
    "        gc_bin = int(gc*100/gc_bin_size)*gc_bin_size\n",
    "        gc_distribution[molecule.sample][gc_bin]+=1\n",
    "        \n",
    "        if i>0 and i%10000==0: #update plot every 1k molecules\n",
    "            ax.clear()\n",
    "\n",
    "            cf = pd.DataFrame(gc_distribution).sort_index().fillna(0)\n",
    "            df = cf.T[cf.sum() > np.percentile(cf.sum(),40) ].T\n",
    "            nf = df/df.sum()\n",
    "            nf.plot(ax=ax,legend=False)\n",
    "            fig.canvas.draw()\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "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, fig.canvas.height);\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=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "df.plot(ax=ax,legend=False)\n",
    "fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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></th>\n",
       "      <th>APKS2-P18-1-1_366</th>\n",
       "      <th>APKS2-P18-1-1_177</th>\n",
       "      <th>APKS2-P18-1-1_279</th>\n",
       "      <th>APKS2-P18-1-1_173</th>\n",
       "      <th>APKS2-P18-1-1_301</th>\n",
       "      <th>APKS2-P18-1-1_253</th>\n",
       "      <th>APKS2-P18-1-1_29</th>\n",
       "      <th>APKS2-P18-1-1_353</th>\n",
       "      <th>APKS2-P18-1-1_140</th>\n",
       "      <th>APKS2-P18-1-1_283</th>\n",
       "      <th>...</th>\n",
       "      <th>APKS2-P18-1-1_260</th>\n",
       "      <th>APKS2-P18-1-1_217</th>\n",
       "      <th>APKS2-P18-1-1_19</th>\n",
       "      <th>APKS2-P18-1-1_188</th>\n",
       "      <th>APKS2-P18-1-1_354</th>\n",
       "      <th>APKS2-P18-1-1_218</th>\n",
       "      <th>APKS2-P18-1-1_258</th>\n",
       "      <th>APKS2-P18-1-1_74</th>\n",
       "      <th>APKS2-P18-1-1_380</th>\n",
       "      <th>APKS2-P18-1-1_375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>...</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004598</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.010076</td>\n",
       "      <td>0.001300</td>\n",
       "      <td>0.003049</td>\n",
       "      <td>0.001650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001770</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.020161</td>\n",
       "      <td>0.016092</td>\n",
       "      <td>0.016334</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.011704</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.009901</td>\n",
       "      <td>0.027211</td>\n",
       "      <td>0.010619</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.040323</td>\n",
       "      <td>0.029885</td>\n",
       "      <td>0.039927</td>\n",
       "      <td>0.052897</td>\n",
       "      <td>0.045514</td>\n",
       "      <td>0.051829</td>\n",
       "      <td>0.033003</td>\n",
       "      <td>0.040816</td>\n",
       "      <td>0.033628</td>\n",
       "      <td>0.038356</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.084677</td>\n",
       "      <td>0.059770</td>\n",
       "      <td>0.081670</td>\n",
       "      <td>0.085642</td>\n",
       "      <td>0.091027</td>\n",
       "      <td>0.088415</td>\n",
       "      <td>0.080858</td>\n",
       "      <td>0.057823</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>0.104110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.105747</td>\n",
       "      <td>0.130672</td>\n",
       "      <td>0.133501</td>\n",
       "      <td>0.102731</td>\n",
       "      <td>0.131098</td>\n",
       "      <td>0.125413</td>\n",
       "      <td>0.125850</td>\n",
       "      <td>0.107965</td>\n",
       "      <td>0.120548</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.173387</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.139746</td>\n",
       "      <td>0.158690</td>\n",
       "      <td>0.176853</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.183673</td>\n",
       "      <td>0.152212</td>\n",
       "      <td>0.153425</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.165323</td>\n",
       "      <td>0.179310</td>\n",
       "      <td>0.188748</td>\n",
       "      <td>0.158690</td>\n",
       "      <td>0.162549</td>\n",
       "      <td>0.176829</td>\n",
       "      <td>0.158416</td>\n",
       "      <td>0.156463</td>\n",
       "      <td>0.180531</td>\n",
       "      <td>0.158904</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>0.133065</td>\n",
       "      <td>0.154023</td>\n",
       "      <td>0.128857</td>\n",
       "      <td>0.138539</td>\n",
       "      <td>0.123537</td>\n",
       "      <td>0.146341</td>\n",
       "      <td>0.128713</td>\n",
       "      <td>0.136054</td>\n",
       "      <td>0.152212</td>\n",
       "      <td>0.115068</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>0.084677</td>\n",
       "      <td>0.082759</td>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.078086</td>\n",
       "      <td>0.107932</td>\n",
       "      <td>0.070122</td>\n",
       "      <td>0.095710</td>\n",
       "      <td>0.108844</td>\n",
       "      <td>0.081416</td>\n",
       "      <td>0.098630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>0.064516</td>\n",
       "      <td>0.096552</td>\n",
       "      <td>0.092559</td>\n",
       "      <td>0.073048</td>\n",
       "      <td>0.083225</td>\n",
       "      <td>0.100610</td>\n",
       "      <td>0.097360</td>\n",
       "      <td>0.098639</td>\n",
       "      <td>0.093805</td>\n",
       "      <td>0.087671</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.068548</td>\n",
       "      <td>0.073563</td>\n",
       "      <td>0.072595</td>\n",
       "      <td>0.083123</td>\n",
       "      <td>0.059818</td>\n",
       "      <td>0.054878</td>\n",
       "      <td>0.075908</td>\n",
       "      <td>0.044218</td>\n",
       "      <td>0.076106</td>\n",
       "      <td>0.079452</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0.024194</td>\n",
       "      <td>0.020690</td>\n",
       "      <td>0.012704</td>\n",
       "      <td>0.015113</td>\n",
       "      <td>0.022107</td>\n",
       "      <td>0.024390</td>\n",
       "      <td>0.019802</td>\n",
       "      <td>0.013605</td>\n",
       "      <td>0.012389</td>\n",
       "      <td>0.030137</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.012097</td>\n",
       "      <td>0.004598</td>\n",
       "      <td>0.007260</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009103</td>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>0.006803</td>\n",
       "      <td>0.008850</td>\n",
       "      <td>0.005479</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.004032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001815</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 251 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     APKS2-P18-1-1_366  APKS2-P18-1-1_177  APKS2-P18-1-1_279  \\\n",
       "0             0.000000           0.000000           0.000000   \n",
       "5             0.000000           0.000000           0.000000   \n",
       "10            0.000000           0.000000           0.000000   \n",
       "15            0.000000           0.004598           0.001815   \n",
       "20            0.020161           0.016092           0.016334   \n",
       "25            0.040323           0.029885           0.039927   \n",
       "30            0.084677           0.059770           0.081670   \n",
       "35            0.125000           0.105747           0.130672   \n",
       "40            0.173387           0.172414           0.139746   \n",
       "45            0.165323           0.179310           0.188748   \n",
       "50            0.133065           0.154023           0.128857   \n",
       "55            0.084677           0.082759           0.085299   \n",
       "60            0.064516           0.096552           0.092559   \n",
       "65            0.068548           0.073563           0.072595   \n",
       "70            0.024194           0.020690           0.012704   \n",
       "75            0.012097           0.004598           0.007260   \n",
       "80            0.004032           0.000000           0.001815   \n",
       "85            0.000000           0.000000           0.000000   \n",
       "90            0.000000           0.000000           0.000000   \n",
       "100           0.000000           0.000000           0.000000   \n",
       "\n",
       "     APKS2-P18-1-1_173  APKS2-P18-1-1_301  APKS2-P18-1-1_253  \\\n",
       "0             0.000000           0.001300           0.000000   \n",
       "5             0.000000           0.000000           0.000000   \n",
       "10            0.000000           0.001300           0.000000   \n",
       "15            0.010076           0.001300           0.003049   \n",
       "20            0.012594           0.011704           0.009146   \n",
       "25            0.052897           0.045514           0.051829   \n",
       "30            0.085642           0.091027           0.088415   \n",
       "35            0.133501           0.102731           0.131098   \n",
       "40            0.158690           0.176853           0.134146   \n",
       "45            0.158690           0.162549           0.176829   \n",
       "50            0.138539           0.123537           0.146341   \n",
       "55            0.078086           0.107932           0.070122   \n",
       "60            0.073048           0.083225           0.100610   \n",
       "65            0.083123           0.059818           0.054878   \n",
       "70            0.015113           0.022107           0.024390   \n",
       "75            0.000000           0.009103           0.009146   \n",
       "80            0.000000           0.000000           0.000000   \n",
       "85            0.000000           0.000000           0.000000   \n",
       "90            0.000000           0.000000           0.000000   \n",
       "100           0.000000           0.000000           0.000000   \n",
       "\n",
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       "[20 rows x 251 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/buysdb/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \n"
     ]
    },
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-a07907dfc6b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mdf\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[0mgc_distribution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfillna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\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~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2964\u001b[0m         \u001b[0;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2965\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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-> 2966\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2967\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2968\u001b[0m         \u001b[0;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_bool_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3016\u001b[0m         \u001b[0;31m# check_bool_indexer will throw exception if Series key cannot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3017\u001b[0m         \u001b[0;31m# be reindexed to match DataFrame rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3018\u001b[0;31m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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   3019\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3020\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(index, key)\u001b[0m\n\u001b[1;32m   2384\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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[1;32m   2385\u001b[0m             raise IndexingError(\n\u001b[0;32m-> 2386\u001b[0;31m                 \u001b[0;34m\"Unalignable boolean Series provided as \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2387\u001b[0m                 \u001b[0;34m\"indexer (index of the boolean Series and of \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2388\u001b[0m                 \u001b[0;34m\"the indexed object do not match).\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame(gc_distribution).sort_index().fillna(0)\n",
    "df = df[ df.sum() > np.percentile(df.sum(),10) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_353</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_140</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_234</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_30</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_352</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_246</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_116</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_71</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_107</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_117</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_64</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_348</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_118</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_59</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_252</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_231</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_202</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_195</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_340</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_299</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_201</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_147</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_104</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_311</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_241</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_261</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_144</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_274</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_378</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_238</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_222</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_248</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_60</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_374</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_88</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_302</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_191</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_229</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_216</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_34</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_203</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_146</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_183</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_55</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_293</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_332</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_338</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APKS2-P18-1-1_377</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    5    10   15   20    25    30    35    40    45    50  \\\n",
       "APKS2-P18-1-1_366  0.0  0.0  0.0  3.0   7.0  15.0  16.0  26.0  30.0   8.0   \n",
       "APKS2-P18-1-1_177  0.0  0.0  2.0  4.0   9.0  13.0  22.0  30.0  28.0  14.0   \n",
       "APKS2-P18-1-1_279  0.0  0.0  1.0  7.0  16.0  29.0  40.0  41.0  43.0  16.0   \n",
       "APKS2-P18-1-1_173  0.0  0.0  4.0  3.0  18.0  19.0  30.0  36.0  32.0  17.0   \n",
       "APKS2-P18-1-1_301  0.0  0.0  1.0  7.0  28.0  48.0  40.0  66.0  59.0  16.0   \n",
       "...                ...  ...  ...  ...   ...   ...   ...   ...   ...   ...   \n",
       "APKS2-P18-1-1_55   0.0  0.0  0.0  0.0   1.0   0.0   1.0   0.0   0.0   0.0   \n",
       "APKS2-P18-1-1_293  0.0  0.0  0.0  0.0   2.0   2.0   1.0   0.0   0.0   0.0   \n",
       "APKS2-P18-1-1_332  0.0  0.0  0.0  0.0   0.0   0.0   1.0   2.0   0.0   0.0   \n",
       "APKS2-P18-1-1_338  0.0  0.0  0.0  0.0   0.0   0.0   0.0   2.0   0.0   0.0   \n",
       "APKS2-P18-1-1_377  0.0  0.0  0.0  0.0   1.0   1.0   0.0   0.0   1.0   0.0   \n",
       "\n",
       "                     55    60   65   70   75   80  \n",
       "APKS2-P18-1-1_366   4.0   0.0  1.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_177   4.0   3.0  2.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_279   5.0   2.0  1.0  1.0  0.0  0.0  \n",
       "APKS2-P18-1-1_173   6.0   7.0  5.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_301  19.0  10.0  4.0  1.0  0.0  0.0  \n",
       "...                 ...   ...  ...  ...  ...  ...  \n",
       "APKS2-P18-1-1_55    0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_293   1.0   0.0  0.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_332   0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_338   0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "APKS2-P18-1-1_377   0.0   0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[168 rows x 16 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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></th>\n",
       "      <th>GC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.05</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.15</th>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.20</th>\n",
       "      <td>468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>1272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.30</th>\n",
       "      <td>2405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.35</th>\n",
       "      <td>2745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.40</th>\n",
       "      <td>4227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.45</th>\n",
       "      <td>4147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>1894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.55</th>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.65</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.70</th>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.80</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        GC\n",
       "0.05     2\n",
       "0.10     2\n",
       "0.15    96\n",
       "0.20   468\n",
       "0.25  1272\n",
       "0.30  2405\n",
       "0.35  2745\n",
       "0.40  4227\n",
       "0.45  4147\n",
       "0.50  1894\n",
       "0.55   740\n",
       "0.60   388\n",
       "0.65   160\n",
       "0.70    54\n",
       "0.75     5\n",
       "0.80     2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from singlecellmultiomics.molecule import TAPSNlaIIIMolecule, MoleculeIterator\n",
    "from singlecellmultiomics.fragment import NLAIIIFragment\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "methylation_distribution = collections.Counter()\n",
    "\n",
    "with pysam.AlignmentFile('/media/sf_H_DRIVE/data/TAPS/sorted.bam') as alignments:\n",
    "    for i,molecule in enumerate(\n",
    "            MoleculeIterator(alignments,\n",
    "                            \n",
    "                             moleculeClass=NlaIIIMolecule,\n",
    "                             fragmentClass=NLAIIIFragment,\n",
    "                             fragment_class_args={\n",
    "                                'umi_hamming_distance':1\n",
    "                             },\n",
    "                          )):\n",
    "        try:\n",
    "            gc = molecule.get_consensus_gc_ratio()\n",
    "        except Exception as e:\n",
    "            continue\n",
    "            \n",
    "        gc_bin = int(gc*100/gc_bin_size)*gc_bin_size\n",
    "        gc_distribution[gc_bin]+=1\n",
    "        \n",
    "        if i>0 and i%1000==0: #update plot every 1k molecules\n",
    "            ax.clear()\n",
    "            pd.DataFrame({'GC':gc_distribution}).plot.bar(ax=ax)\n",
    "            fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(RT_vs_gc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find a molecule with 4 fragments\n",
    "molecules_seen = [] # store all molecules, used in next examples\n",
    "with pysam.AlignmentFile(nla_test_bam_path) as alignments:\n",
    "    for i,molecule in enumerate(\n",
    "            singlecellmultiomics.molecule.MoleculeIterator(alignments,\n",
    "                                                            fragment_class_args={\n",
    "                                                                'umi_hamming_distance':1\n",
    "                                                            },\n",
    "                                                           moleculeClass=singlecellmultiomics.molecule.NlaIIIMolecule,\n",
    "                                                           fragmentClass=singlecellmultiomics.fragment.NLAIIIFragment\n",
    "\n",
    "                                                          )):\n",
    "        molecules_seen.append(molecule)\n",
    "        if len(molecule)==4 and i>0:\n",
    "            break\n",
    "molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain associated unique molecular identifiers\n",
    "molecule.umi_counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over all fragments in the molecule, obtain their R1 and print the read name:\n",
    "for fragment in molecule:\n",
    "    print(fragment.get_R1().query_name )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over all reverse transcription reactions:\n",
    "for (reverse_primer_start, reverse_primer_sequence), associated_fragments in molecule.get_rt_reactions().items():\n",
    "    print(reverse_primer_start,reverse_primer_sequence,associated_fragments)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equivalence testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing fragments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compare two fragments: (check if they should belong to the same molecule)\n",
    "fragment_A = molecule[0]\n",
    "fragment_B = molecule[1]\n",
    "fragment_A == fragment_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain a fragment not belonging to the molecule \n",
    "fragment_C = molecules_seen[0][0]\n",
    "fragment_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fragment_C == fragment_A"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing fragment to molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fragment A belongs to molecule, this comparison results in True\n",
    "fragment_A == molecule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fragment C does not belong to molecule\n",
    "fragment_C == molecule "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Consensus sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain the aligned base frequencies in a pandas dataframe\n",
    "pd.DataFrame( molecule.get_base_observation_dict() )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain the molecule consensus sequence as pandas df:\n",
    "pd.DataFrame({'base':molecule.get_consensus()}).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the molecule here in the notebook:\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML( molecule.get_html() ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display a single read:\n",
    "fragment = molecule[0]\n",
    "display(HTML(fragment.get_html(span_start=molecule.spanStart, span_end=molecule.spanEnd,show_read1=1,show_read2=0) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "str(fragment[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tensor = molecule.get_alignment_tensor(\n",
    "    max_reads=8,\n",
    "    centroid=molecule.spanStart,\n",
    "    window_radius=10)\n",
    "plt.imshow(tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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