playground/first_light_vs_first_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MultipleLocator\n",
    "\n",
    "import emcee\n",
    "from fit_just_early_lc import prep_light_curve\n",
    "\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## See if first light is just a function of the time  of the first detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "info_path=\"../../forced_lightcurves/sample_lc_v2/\"\n",
    "salt_df = pd.read_csv(info_path + \"../../Nobs_cut_salt2_spec_subtype_pec.csv\")\n",
    "res = pd.read_csv('results_40percent.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "thin_by = 250\n",
    "rel_flux_cutoff = 0.4\n",
    "t_fl = 18\n",
    "\n",
    "# sn = 'ZTF18abaxlpi'\n",
    "sn = 'ZTF18abauprj'\n",
    "\n",
    "t_max = float(salt_df['t0_g_adopted'][salt_df['name'] == sn].values)\n",
    "z = float(salt_df['z_adopt'][salt_df['name'] == sn].values)\n",
    "g_max = float(salt_df['fratio_gmax_2adam'][salt_df['name'] == sn].values)\n",
    "r_max = float(salt_df['fratio_rmax_2adam'][salt_df['name'] == sn].values)\n",
    "\n",
    "t_data, f_data, f_unc_data, fcqfid_data = prep_light_curve(info_path+\"{}_force_phot.h5\".format(sn),\n",
    "                                                           t_max=t_max, \n",
    "                                                           z=z,\n",
    "                                                           g_max=g_max,\n",
    "                                                           r_max=r_max,\n",
    "                                                           rel_flux_cutoff=rel_flux_cutoff,\n",
    "                                                           flux_scale=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_varchange.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_varchange.h5 -0.42462560750643963 [-18.48548224   1.53335985   1.38205177]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.05.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.05.h5 -0.42462560750643963 [-17.61773702   1.05375862   0.87473791]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.075.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.075.h5 -0.42462560750643963 [-16.59663504   0.81113545   0.52188721]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.1.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.1.h5 -0.42462560750643963 [-14.38156308   0.35539189   0.29491077]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.15.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18aasdted_emcee_40_z0.15.h5 -0.42462560750643963 [-46.92682916   0.30179171   0.3687451 ]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_varchange.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_varchange.h5 -0.6083500000294801 [-19.21945314   2.3534985    2.23361637]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.05.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.05.h5 -0.6083500000294801 [-18.84785322   2.21963218   2.03500022]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.075.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.075.h5 -0.6083500000294801 [-18.4772356    1.97493151   1.91447437]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.1.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.1.h5 -0.6083500000294801 [-17.13252962   1.64592961   0.87707752]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.15.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abauprj_emcee_40_z0.15.h5 -0.6083500000294801 [-17.21815482   0.47472563   0.78291429]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_varchange.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_varchange.h5 -0.31116036011946296 [-19.73213753   2.7128341    2.4032466 ]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.05.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.05.h5 -0.31116036011946296 [-18.59155095   2.11130904   1.96714012]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.075.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.075.h5 -0.31116036011946296 [-18.89321908   2.20501042   1.93097686]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.1.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.1.h5 -0.31116036011946296 [-16.10247403   1.17641788   1.01398118]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.15.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abcflnz_emcee_40_z0.15.h5 -0.31116036011946296 [-14.91765306   0.59741002   0.41412195]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_varchange.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_varchange.h5 -0.2716541878350065 [-18.07046142   2.08495785   1.84451797]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.05.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.05.h5 -0.2716541878350065 [-17.44228637   1.39635856   1.48146529]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.075.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.075.h5 -0.2716541878350065 [-15.39493329   0.15509576   0.75362202]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.1.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.1.h5 -0.2716541878350065 [-25.20870125   0.20460025   0.21372638]\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.15.h5\n",
      "../../forced_lightcurves/sample_lc_v2//big_unc/ZTF18abgmcmv_emcee_40_z0.15.h5 -0.2716541878350065 [-46.1134084    0.27367585   0.32211454]\n"
     ]
    }
   ],
   "source": [
    "z_files  = glob.glob(info_path + '/big_unc/*z0*h5')\n",
    "\n",
    "for sn in ['ZTF18aasdted', 'ZTF18abauprj', 'ZTF18abcflnz', 'ZTF18abgmcmv']:\n",
    "    z_files.append(info_path + '/big_unc/{}_emcee_40_varchange.h5'.format(sn))\n",
    "\n",
    "z_array = np.zeros(len(z_files))\n",
    "t0_array = np.zeros_like(z_array)\n",
    "t0_05 = np.zeros_like(z_array)\n",
    "t0_95 = np.zeros_like(z_array)\n",
    "\n",
    "for znum, zfile in enumerate(np.sort(z_files)):\n",
    "    sn = zfile.split('/big_unc/')[1].split('_')[0]\n",
    "    if 'varchange' in zfile:\n",
    "        z_array[znum] = salt_df.z_adopt.iloc[np.where(salt_df.name == sn)]\n",
    "        thin_by = 250\n",
    "    else:\n",
    "        z_array[znum] = float(zfile.split('_z')[1].split('.h5')[0])\n",
    "        thin_by = 100\n",
    "    print(zfile)\n",
    "    reader = emcee.backends.HDFBackend(zfile)\n",
    "    nsteps = thin_by*np.shape(reader.get_chain())[0]\n",
    "    tau = reader.get_autocorr_time(tol=0)\n",
    "    \n",
    "    burnin = int(5*np.max(tau))\n",
    "    samples = reader.get_chain(discard=burnin, thin=np.max([int(np.max(tau)), 1]), flat=True)\n",
    "    lnpost = reader.get_log_prob(discard=burnin, thin=np.max([int(np.max(tau)), 1]), flat=True)\n",
    "\n",
    "    # correct to B band max from g_ztf max\n",
    "    t_B_max = salt_df.t0_B_salt2.iloc[np.where(salt_df.name == sn)].values[0]\n",
    "    t_g_max = salt_df.t0_g_adopted.iloc[np.where(salt_df.name == sn)].values[0]\n",
    "    z_sn = salt_df.z_adopt.iloc[np.where(salt_df.name == sn)].values[0]\n",
    "    t_offset = (t_g_max - t_B_max)/(1 + z_sn)\n",
    "\n",
    "    t0_05[znum], t0_array[znum], t0_95[znum] = np.percentile(samples[:,0], (5, 50, 95)) + t_offset\n",
    "    \n",
    "    print(zfile, t_offset, np.percentile(samples[:,[0,2,4]], 50, axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot  evolution with redshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_rise = res['t_rise_50'].values\n",
    "t_rise_unc = np.vstack((res['t_rise_50'] - res['t_rise_05'],\n",
    "                    res['t_rise_95'] - res['t_rise_50'])) \n",
    "alpha_g = res['flat_alpha_g_50'].values\n",
    "alpha_g_unc = np.vstack((res['flat_alpha_g_50'] - res['flat_alpha_g_05'],\n",
    "                         res['flat_alpha_g_95'] - res['flat_alpha_g_50']))\n",
    "alpha_r = res['flat_alpha_r_50'].values\n",
    "alpha_r_unc = np.vstack((res['flat_alpha_r_50'] - res['flat_alpha_r_05'],\n",
    "                         res['flat_alpha_r_95'] - res['flat_alpha_r_50']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = np.where((salt_df['Ia subtype'] != 'Ia-CSM') & \n",
    "                  (salt_df['Ia subtype'] != 'SC') & \n",
    "                  (salt_df['Ia subtype'] != 'SC*') & \n",
    "                  (salt_df['Ia subtype'] != '86G-like') & \n",
    "                  (salt_df['Ia subtype'] != '02cx-like'))\n",
    "good = np.where(res.final_selection.values == 1)\n",
    "has_host_z = np.where(salt_df.z_source != 'Msl')\n",
    "low_z_good = np.where((salt_df.z_source != 'Msl') & (salt_df.z_adopt < 0.03))\n",
    "\n",
    "normal_good = np.intersect1d(normal, good)\n",
    "normal_good_host = np.intersect1d(normal_good, has_host_z)\n",
    "normal_low_z = np.intersect1d(normal_good, low_z_good)\n",
    "\n",
    "normal_plot = np.setdiff1d(normal[0], normal_good)\n",
    "normal_good_plot = np.setdiff1d(normal_good, normal_good_host)\n",
    "normal_good_host_plot = np.setdiff1d(normal_good_host, normal_low_z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "        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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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ySfbBCvazTIZguX3+6HT6bC4uIjp6WkUFRWhqKgIy8vLso9BgpUY4V4/r9eL2dnZsJIU/PdYrxmZ/k5He1/4z//Y2FhYWSTByn5IsFYhwUovlIOVXLJJsOTcLCmCFdv+2SZYLpcLN2/eDDtZKfjYsUa9M/2djvS+BH7+Jycnw8oiCVb2Q4K1CglW+lHCLEIl9CEZZJNgRbtZxvqehBMsILM5WCRYyROsXIpgBX7+tVptWFkkwcp+SLBWIcHKDJn8kispipYo2SRYkW6W8bwnkQSLn0VoMplQXFxMghXD8ZQkWEDu5GBRBEu59AwZ0N6nR8+QISntkWCtQoKVGTL1JVdaHliiZJNgAdI3y3jfk2iCBQBWqzVuwXr35HmcOXEFzht6yX1IsMJzcLQd+4ev4+Bou+jv8QgWkDuzCCkHS5m89V4/Sg/dxFvv9SelvbwXrMrKSmzYsAHr168nwcoAmfqSK20mY6JkUrCsVmvYhOVYZhHG+56kWrBG9zTDUHQdxte7Jffx+XwoKytDZWUlCdYqbsYHL+PHuM2IAbMe4zYjvIwfbmbl9YlXsOT2K9PyIOd9oVmEyiNVgrV+/Xps2LABlZWVwmN5IVg8FMHKDBTBSg6ZFCyTyRT2ZhmtveCkdCVGsCIJFsMwsFgs6OvrQ2dnJ3Q6HaxWKxiGyUvB8jJ+GJcdqBrvwsttJ/F8a7Wwvdx2ElXjXTAuO+D2e9Hf35/XggVQHSylQRGsFEGClRkoBys55IJgAfG9J5kSLLvdjpaWFlRUVGDLli3YsmULSkpKUFFRAbVaDavVGrX9SOcSy/5KECwv48fl6SGRVIXbLk8PweVxCyUzkt2vTMsDCVZ2QoKVIkiwMkOmv+Q0izC0nVhJpmAByZ1FmCrBstvtqK6uRklJCUpKSkSCxW/V1dUxX0+yVbBikStBsqaG4PZJCzQJFglWJiDBShEkWJlBCV9yJfQhUXJJsGLtRzoFy+v1gmEYtLS0iGRKSrCKi4uhVqvBMExc5xLL/jFHm1Yjg8kSLOOyIya54jfjskOyvWwWLH7WKsuyJFhZBglWiiDBygxK+JIroQ+JEk2w5A5/kmCJCRYsp9MJq9WKiooKWYJVUVER01BhugRLzvPktulmfKga74pLsKrGu+FhQhfIzlbBCpwduLCwIIhWOEiwlAUJVoogwcoMSviSK6EPiRJNsOSeIwmWmGDBslqt0Ol0IpGKJFglJSWYmpqK61xi2T+TguVl/CEJ7XK3l9tOwsuEzhLNRsEKnKQxNjaG8fFxDA0NYWFhIewPHBIsZUGClSJIsDKDEr7kSuhDopBgST8vULB2796Nffv2JSRYJpMJY2NjMQnW+Ph4XOcSy/6ZFKzpJUtccsVv00uWmI6tVMHiy4yYzWYMDg5ifHwcw8PD0Ov1YWfDkmApCxKsFEGClRmU8CVXQh8SJV8FKzghXkqwGIaByWRCX18furq6hFIK8QjWwsICRbCCGLMZExKscZsxpmPLGQrPZARrdnYW/f39uHPnDsbGxmAymcLWcyPBUhYkWCmCBCszKOFLroQ+JEo+CpZUSYfgfCeLxYKWlhaUl5eHlFJobm6Gw+EQ2pJCSrDk5GAVFRWhsLAQe/bsgcUSGqGRIp4ZrU6nM+7nBf430j7RUFIEK17hTBZerxcLCwvo6+vD2NgYpqamItZzI8FSFiRYKYIEKzMo4UuuhD4kSr4JVriipPPz88I+drsdR48eFaJJwRJUXFwslFIIdzwpwYo2i7CoqEjYmpqaYLVao8qP1+uFwWBAf38/DAaD7EkJ/GLWsdZyy9UcrEwLFrDy2ZyenhainZHeFxKsxEh27UISrBRBgpUZlPAlV0IfEiXfBCvcsjoGw8oirbwE8cN04QSrpKQEarU6JsECwtfBKi4uRlFREYqLi1FVVSVruR9eFnU6HdRqtXBTjiZlLMtCr9fHtRpBsgSL5TjMu+wpnUUYfBNVumABK+IkJ7JIgpUYyTgHn/9eGRUpwQp8PFZIsFYhwcoMSviSK6EPiZIMwYq36KoSI1j8MJ4cwaqoqIDZbJY8TjjBAlauGWq1OqSSe3l5OZqamrCwsCAkPUd6TXlZnJ2dRWtrK2ZnZ2Wtwej3+6HT6eJaTzOaiHi93qjvgcPrRsVgE16/rY57mFBOHaxwExfiOa90wX9OovWDBCsxEj0Hv59FW68efv/K9zNYsIIfj5W8Fyxa7DmzKOFLroQ+JEqigpXIskHhBIsXNrfbHXO/nE4nfD6fIEOBiyhH6jN/w9LpdEIeFD9cFyhBxcXFgmCVlJRAp9NJ9iOSYAH31iLs7+8XEugtFotQzkHOa6nECFa0zxMA9Jtn8HxrNV5sPYbpJQsuTQ3GJFdyK7mTYKWGTL9GwcQz3JfIOfj9LE7Vj6H00E2cqh+D38+iTj2B0kM3UaeekHw8Vmix51UogpUZlPAlV0IfEiURwUp04WspwQqUH71eH/bimYhg8X0PjLrxQzPDw8OiXKjCwsKIgjU6OirZfjTBAgCfz4eysjLs3btX6Ges0UCv1wu9Xo/m5uaIr1cwqcrBkiNYAHBi4iZ+3XkaY9Z5eBk/rkzfliVXV6Zvy16LMFbBUsLyV7kkWF6vF7OzsylfqzWe8433NQqUJ347VT8Gt8ePtl493B6/5OOxSlbeR7B4SLAygxLkRgl9SJREBCtcPpOcoSYgVLCChS1SRCZRwQpuY2FhAX6/H5OTk6J8qGiCFW8EC4DQz+LiYlE/Y/1cLS0tob29HUtLS7Kfk6pZhFKfJ6fPC47jRH/zsQxs3nuS5GH8MC47UDXeHZL4/nLbSVSNd8PosmPZ50F/f7/ksHJwv2IRrHiFM9nkkmC5XK6wKQCZ6E+iz/H5GbT16kXyFChRFps7RK74ra1XH1NOFgnWKiRYmUEJcqOEPiSKkiJYwcIWKacoFYLFsiwWFhawZ88eWRGseHOweJIlWC6XCx0dHTHdyOIdEotVsIYss/h15ym0GSZkte/2++Bl/Bi3GaFZ1GPcZoSH8cOx7ITFYoFer8eNGzeSKliJDJkmGxKs1PUn0ecA0hGsaBtFsBKABCszKEFulNCHRMlUDhbDMBgZGUFDQwO0Wi0Yhsl4BAsAPB4PmpqaZOVgxTOLMJB0CZbUe5JqwWJYFmfu9glRqJfaTmDWKX9txXdG2rF/+DreHm6D1WoVIqRTU1Po7u5OqmAlkvSfbJQuWLFEPaMJVrKihOkULCA2yUo0B4sEiwQrIyhBbpTQh0TJxCxChmGg0WhQV1eHkydP4vLly9BoNGAYJq4crMB94hEslmVhMBiE/keqg8ULVjx1sIJJl2BJtZdKwZqxLKCg76pomK9isAkOb/hJC8G82nUGz7dW4zcdp0SRJYpgifcLJpV5ZLH+mIomWMm6fqZbsAB5khWvXAEkWAIkWJlBCXKjhD4kAn8x5quSSyH3HGN5LQwGAxoaGtDY2Ihz585BrVajoaFBqEUVa7+C/x2LYPE3jbGxMdFNw2KxQK1Wh63kzn/fIwnW5Lud8E7bYKrqzynBinQT79LfwS/a3xPE6sXrx3B1ZhhsUA5WNHjBerXzjOimbrVaZd+08y0HK5EZvdGIJx0gFwTLteyDVm9Dt2YOF5oncLF1UnjM4fRGFCyHM/7XnwRrFRKszKAEuVFCH+Il0dl68e4HrJRCqK+vh1qtxrlz59DR0YH6+npRwngskbV4BcvhcAg3jcnJSdFNw+m8txZhYCkFs9kc8SbO82rXGSxNmMDY3HBPLuaMYIUTER/L4MREjyhq9Vr3Wdy1m2Jqn0cQrK4zIqGL5aYdi2DFm/SfbOIVrETzIaMRz4SWbBIsjuNgc3gwrrOgrVePM9fG8ebJ/hBpqjh6CxzHUQQrXZBgZQYlyI0S+hAPych1inc/IHoEi28v1YJls9mEm4ZWqxXdNPg2rVYrysrKUFlZCZ/PF9KvcH08MNIGv8MDY2UXmCVPyFqHQPYJVrihtAWXA9t7L4vk6o3brXD54/8FHyhYgf1MpWBF2ycdxCtYic7ojUYuRbBYloPdIZ5xO66zyMqpOnNtDD7KwUofJFiZIdMXQqX0IR6SMVsv3v2AyDlYge1lOoIFrAhWYFtyBWtxyQHrhVEYiq7DemEUfneobCRTsK5fv55ywQqXDG5ddgpC9D/Xj+OqdjCkLEOsSAkWwzDQarWiiRHhzkvq3HJZsFIdwQKUn4Ml1R+fn8XcwhIGRhfQ0KFDdd0w9lT1oH9kVrSf1eEJkaPK6l6cvDKK5hvTuD1hhs3hoVmE6YYEKzNk+kKolD7EQ6YjWID0LMLg9lItWJFysBIRLI7l4JyzwVB0Xdi88w5wbFAtqCQKllqtzlgEi2VZjNuM2HTzAsYWZpPyvQgWLLvdDo1Gg8uXL4eV8uAhvnwSLCC1OVg8yZxFmGzBcjqdmDUuoWfIgEutd3Ho7BB2H5aWnmsdd0VtcByH880TaLulx5jOAqvDE1q7jepgpR8SrMyQqSTUQDJ9MU6ETOZg8URbizAdggWEziIMfCwuwfKzMB3qFQmW6XAvuKBfsdkmWMBKDtbwrA5Dk+MhN3GWYwVpTZRgwbp79y4aGhqEvL3gYWUpucg3wQJSO4uQR3aCeIoEi+M4LLm8mF1YErXjdDrx3pXRqBGld05r0HpTukBwNKiSe5ohwcpfMn0xTpRMzSLkSVSwpAQlHsECQm9YcgRL6mbG+Vm4BudFcsVvrqF5kWQlKli8yMQjWMGROjlwHIemqWH8rO0Einuvwh9liC4RggVrdHQU9fX16OjoCJkYEW54LPhznQ+CJee5iZJOweI4Dha7G2PaRVy/NYPTDWN440TfyhDesV5wHCd671puTAtiU3a4B4fPDeHy9bu4dXseMwYHPF4mpnOQIp1rEZJgkWDlLZm+GCeDWCJF0dqJhtfrxdTUlCAsc3NzWStY4YZjOB+D+b2dkoJlrOwC57snJYkKVmDid6yCFeux3H4f3hltFyWyt8yNx91eNGKJYIVL8LbZbLL7RoIln1QLls3hQVP3FE5cGsHe6lsRo1H2JY/ovZtbWIJmbAHzZif8THixSfQ18vtZtPXqBXl6672VGYdvvdcv+Xis5L1gVVZWYsOGDVi/fj0JVp6S6YtxMkinYLlcLnR1dQmV0LNVsAKT40W5SF4G9sZJSbniN3vjJNjVX9HZIljTSxb88eZ5kVwdvN0GLxP/pIhoxJKDRREsMdkgWD4/g8kpE/pHjJg3i9tbtC1HlKp9x3tRc3UULTen4XB643rvkvEaBeZUBQtW8OOxwgvW+vXrsWHDBlRWVgqP5YVg8VAEK3/J9MU4GZBgrRCLYAWWd+AjJjabDX6bG4aStoiCZShpA2NbqWiudMHiOA4ts+P46fXjooWXbxi1YZ+XKsFyOp1YXl4WZhHWtw6j7dYMeoYoBysYpQnWotWBqTk7bg4acFE9iXdrB0XJ52239KLnsSyH8qO3BGGpvTaO9j497kxZYF8KTT7PlGAFIiVYiZD3ESweEqz8JdMX42QQSWRiSZjNJ8EKjmDNz8+D8fqxeHIwslytbovvDYLzMYoWLJffizdvt4qiVtt7L0Frno/4vFQKltPpFG7ab57sC7mh5fssQh6lCFZztxaV1TJqSzWEDjXPLSxh2R3+uyvVHxKsHIQEK38J/pLKvbEriXAiE+uUb6UJVllZGfbt25ewYDmdzqg5WC6nC567i7Lkit88dy1gfH5FCtaSz4PXus+K5OrERA98LBP1ZpZJwQo+PglWanA6neA4Dos2N0buLkLdM4MLzRMh+11tm5QUqrIjPag6fxt1LePoHTbCYEqsv3ktWD6fD+3t7SgsLMRPf/pTPPvss3j66afxox/9CK+88gqOHz8OvV4f0ki2QIKVv+SqYMVTtDCTgrVlyxZBUpxOJywWC/r6+oTlbaxWa9SilOEEK/g9DewXHzHh/CzMxwZiEqzF4xpwfiZhweKLb9bV1ckqvin3sXfHOvF8azV+2VGDPtN0yP5KEaw3T0oLltYTAZsAACAASURBVFT0lQQrPhiGhdHsgmZ8Adc6dTh6YQgVR0OTz5dc4h9ht27PYs+Rm6iuG0Jj1xQGx00wLrrAMNIRxnjJS8FqamrC97//fTz88MNYu3Yt1q5dizVr1oRs/GNPPPEESktLYTLFt3ZVpiDByl9yVbDiWXYjnYLl9Xrh9XpFgrVnzx7YbDY0NzejoqIiZIFmtVodMek5HsHi9+MYFp7J9EewAhO/q6urJYtvRmsv3GMexo+Dox1YdEtHgZQjWH0hxw8XfSXBik5wfpPF7kbZkR5ZFcvv6u/N2mQYBpOTd1FfL6/qfiLknWA98cQTglDdf//9ePLJJ/HCCy9g8+bNqKiowIEDB1BYWIjf/va3+Jd/+Rf82Z/9mSBc73//+/Hyyy9H/AApCRKs/CVXBUvpEaxg+dm6dSs0Gg2OHj0qtB0oWPxWXV0t+p4mQ7CWlpbg7JsDs+RJew5WYOmCU6dOSa7pGK09p9OJcZsR/eYZWcdUumCFneEZsPxRPOeVLtIpWC63D1q9Dd2aOdS1TODgGQ06+8VLyDAsiz0SgnXg1ADONd1BZ/8sJqetcDi9gpzxS2FFqrofSz+jkXeC9YEPfAD//u//jrNnz8LtdstqcGJiAtu3b8cTTzyBNWvW4EMf+hDOnj0bc8ccDgdeeeUVbNy4EY8++ihUKhU2bdokua/P50NpaSm+9KUv4cEHH8S6devw9a9/He3t7bKPR4KVv+SqYAHKzsEKlp+qqirU1dUJshJOsEpKSqBWq4ULfaKCxdg9WKjqvbfeoMklaxahP0mzCAOLb546dUpUfFPqXIJhORa1d27hhdZj+Hn7e1hYXpLcT6otpQqW1AzP4AW84zmvdJEqwXI4vRjXWdDWq0fttXFBCIK3s413Qp5b1zKJi62TuDlkwNScHWZL5Hsdv5h7uKr7kfoZD3knWFIrx8dCU1MT/uEf/gFbt26N+blarRbr1q3DU089heeeey6sYDEMg29/+9tYt24dduzYgebmZtTV1WHLli2or6+XfTwSrPwllwULSN0swt27d2Pfvn0xCVag4AXLT29vLwoKCmQJVkVFBfjrU8I5WF4/5l/vEuTJb3bJq4PlS04drEQiWDbvMnZrrgUlst+MekylC1a+R7BYloPesBjy94YOXdQhvj1HeiQFK5hofdPpdGGr7t/rZ/KW9Mk7wUrKEYCIS3iEg+M4IVRpMpnCClZZWRnWrl2Lzs7OhPpIgpW/5Lpg8Y/LbScaLpcLU1NTQgL65OQkpqenZQmW1L/52YK9vb2CTEUTrJKSEkxNTYW0Ge8Qoe32LBb234RnakXaklnJ3ev1YnZ2Nmz00Gw2o6CgADt27EBVVZXsoZjbljn8uvO0IFYvtB7DBZ0GLCdfpJUqWPmQg8Wvnen1+WEwOTEwtoCGDh2q64ZRXrWSfB5c4mBgbEEkU5XVvTh5eQTN3VO4PWHCgsUFRqbsRHuNokWwkr0oNQlWhogkWI899hj+/u//PuFjkGDlLyRY8vez2+1obm7G7t27BfkpKirC7t270dzcHPJjyuFwhK1pFCg/+/btQ2dnJ7Zu3SpbsMbHx0PajCZYZcW7YW2agMNgCekLx6RmLcJoS43wNboKCwtx7ty5qMnEDMeiVtuPFwKiVr/pPI0x67xk+1Jkg2ABuTmL0Odj0D2gx9lrI9h/8paoWGfwppsVLxO0aHPj+q0ZjOkssDpCi3XGQrTXKFIOVjz5nXL7Q4KVZsIJ1vT0NFQqFV5++WW89tpr+MQnPoH77rsPX/ziF3Ho0KGYjkGClb+QYMnbz263o7q6GsXFxdi1a5dIsHbt2oXi4mJRArrX64Verw9blTswwjM4OIixsTHs2rULhYWFKCoqSnoEq6p4P8aLGmAouo6F4/1YWloK2S8Qzs/CdLhXJFemw70iueLbToZgFRcXo6mpCS6XSzIa4HQ6sehxorC/XjQkWDHYhHlr6HBSJLJFsKT6ki2C5Vz2YfjOPGaN4pw4r88fUapKD93E26c1qG0YxexC9Hy6eJHzGvHlQxoaxLMI45mhLLc/eSNYU1NTSGRLFuEEq7OzEyqVCh/60IfwxS9+Ee+99x6uXr2Kf/u3f4NKpcL+/fvDtunxeGC324VtZmaGBCtPIcGKvh/DMGhpaRFEIJxg8Qnofr9fGD4It66c0+mE3W5HS0sLCgoK0NvbK2p3586dKCgoSDgHi3X7Ybk0GpKkbp8yifYLhmM5OOdsoud5DQ5wrDhqkArBkkxwXlrCn25eEMTqxevHcGXmNliOi+lGExgVIsFKHI7jYLW7MaZdxPVbMzjdMIbXj/cKshRcvNPv9+Od0ys38t2HbuLg6QGcqb+Nm4NzmDE44PH603IOctuX+vxSBEsevGDNzMyIfMPj8UDF17aKZ7vvvvuS0kEgvGC1t7dDpVLhgQceECXecRyHJ598Ep/5zGfCtrlp0yaoVKqQjQQr/yDBir6f1WpFRUWFLMGqqKiAxWLB/Py8cPHlf+HabPeGPCwWC6qrq4VIVVVVFS5cuCC0u2XLFmzdulVIfI9nFqFVo4dxX7c4CnW0H/YZc8SbOM/ikgPWC6PCDEPfsidkn0DB8njuPZ5swXI6nRhcnMXzrdV4rfssJu2RBVGK4LwZi8UiuZ+SBStSrk86BWvk7iJOXB7B3ureiNGod05rRM9jWRZDozMYGp3C+J2JsHKiZMECKAdLDrxgBW+bNm2C6rHHHkPwdv/99wu1ru6//3586lOfwgMPPCD87YEHHhD2TRbhBGt0dBQqlQpf/vKXQ57z2muvQaVSwWg0SrZJESyCR0qw5CzRoiRSLVg6nU6Qm8LCQuzYsQObN2+WFCx++C5SBIthGDQ3Nwv7b9++HUVFRejv78eBAwcEwdq8eTN27NgRcx0sZskLy9lhkVhNF7XgXPERYahSjmAdGGmD3+GBcV8XmCWP5PXE6XSiqKgIhYWFK0nLQcnYyRQsAOgy3oXT55V8LBJSUQe9Xi8ZdUiVYPGvfbBgvXGiL2yeVTw33GT03+dnMLuwhP4RI+rbtXB7xcNfvcPGsFK1t/oW3rsyipYb0xiZNIe0zcvJ2NhYWDlRumABNIswGhEjWME7v/TSS3jggQfwi1/8AmNjY0KCHcdxGB8fxy9+8Qu8//3vx89+9rOkdI4nnGD5/X48/PDDkoL1u9/9DiqVSnZFecrByl8Cv6QMw8heokVJpFqwxsbGUFJSIsx227ZtGzZv3ozNmzdLCtb4+HjEHCw+IlZTUwONRoPe3l50dHSgt7cXMzMz6OzsRGFhoXCM4uJi7N27F2q1OuQ7GixY7lET5ivEMwDN7w3i9eKKiJXcpXi16wyWJkxgbG64JxcxPT0tejzSUEk8gtXS0iIIVs/CFN4d6xSus4m+v1J5MzqdTjJvJlWCxbfNvyZvnFiJ/rx+7FbYmYLpEKxljx9Tc3bcHDLgYuskDtUOhuRJTRvEnzu90SHcjGuvjaO9T487UxbYw4h4MPwswnBykg2CFUs7cvuTi4IVNcl93759WLt2LU6ePBmxwZMnT2Lt2rV4/fXXk9JBIPIswv/4j//A/fffD61WK/yN4zh85StfweOPPy77GCRY+Qv/JeXzgeQu0aIk0hHBKioqwo4dO7Bz505s375dkJ+CggLJCBYQfhbh1NQUNBoN6urqUFBQILTFt3flyhXMzs6iv78fnZ2dmJycjLoWIX/DWr5jFsRqvqITy7eN8Hq9UZfKkeLVrjOYPNgJ77QNpqp+3L17V/R4pGTfeATr+vXrsC45cGi4Xci1apodi9jHaI/xKCGCBawkebvdbty4cQOvH18pR/D68VswGAyw2+3w+RlRYetUChbLcnj7lEbWEjI9Q+K6ZAzDwuWWjnDLXcEk04s9y4EEK35kC9aXv/xl2cN+jz32GP7yL/8y4c5dunQJNTU1OHjwIFQqFZ599lnU1NSgpqZGeLMnJibw4Q9/GH/xF3+B48eP4+LFi3jmmWewZs0a1NTUyD4WCVb+widb8/lAcpdoURLpyMEqKyvDjh07sGvXLpFg7dq1KyQHKzABPVhkvF4v5ubmhKHAQLnihx23bNmCAwcOoL+/H8XFxUJJhnB9Dh5yWTw3DOuFUThM0sWSYxGs0T3NK/WvXu/GnTviAo7JjmDVtTZhc0Ai+/Ot1Tg01hmxj9EeCyTTOVh+P4u2Xj18fgYjIyMBgtUryBX/eDx9CbxJcxwHi82N0buLaO2Zwan6MTR2hU6+erd2MESmyo704Mj5IVxp06J32Ai90QGvT34UmwQr/v7kpWA9/PDD+NrXviar0a997Wt4+OGHE+7cZz/7WckEMZVKJYpYDQ4O4tvf/jYeeeQRPPjgg/ibv/kbXLhwIaZjkWDlL06nU5ghF0mwgpOrlUSqBYthGDQ1NcmKYAUnoAe26XA4YLVa0djYKMqzktq2bNmCuro6VFVVhSwbw+NbcMJ8dRQmkwkGgwGTk5MwGAwwLZiizpJLhmAB4ZN9YxWsP75bif9pPSaI1UttJ9BmmEjaECFPJmYRHhhpg9/P4lT9GEoP3cSp+jF4fX6cbx5H6aGbON88Dp+fET3u94fWTgsHw7Awml3oGdSjsWsK1ReGUHH0Vog4HaodDHlufYcWxy+NoLFrCoPjJhjNLjBMYjlFJFjx9ycvBesTn/gEHn74YURbPsdqteLhhx/Gxz/+8aR0MF2QYOUvZrNZmCEXTbACozNKItWCBax8Rw4fPhwxB0sqAT2wTZvNBqPRiD179sgSrF27dqG/vz8k0sL5WTjadPfWDOwYh9FoRG9vL4xGY9R165IpWEDkgpjRblDzZhNeqX1bFLX6443zmHWKP2fJEqzA/dMhWGWaRix7fYI88dup+jG4PX609erh9vglH/f72ZB1cL0+JqRaec+QQdYw35sn+2VXOk8EEqz4+xPcnpxZo8kiY4L1n//5n1izZg2+9a1vYXFRuqidxWLBt771LaxduxY/+MEPktLBdEGClb9otdoQkZJT4FJJpEOwgJXvSVNTE0pKSkSzCPlK7lIJ6MERrLt376KoqAg7d+4MO0QY+N/h4WHRRdY7a4fpnVuiJPb5Qz3QabVobGwUIkmpiGCNjo7KTkp2Op1hCzXy6Oxm/L6rViRX+zUtsDjEFbwj9THaY5H2T4dgGZ1LaOvVSwrPqfoxWGzuELnit7ZePZbdPtwYnENdyyQOnlnJlwou3jk1Zw957oGaAZxtvIOO/llMTFvhcHoTqnweCyRY8fdH6nsU7TnJImOCNTU1hY997GNYu3YtPvCBD+BHP/oRCgsLcfDgQRQWFuK//uu/8MEPfhBr1qzBo48+Gjakr1RIsPKXkZGRmAQrUj5QpkiXYAH31iLs7+9HV1cX7t69C6PRKKoBFa5fTqcTo6OjKCoqQlFREbZt2xYiWYGCtXXrVmg0Gvh8PrBeJnQR5pI2OFp18Ljc0Ov1uHbtGvR6fdR16+IRLENlJ/r6+mRPq19eXg671Aiwcs3ZqT5zby3Bpir8/sAelJeXSy47lK2CFS6CFW3jI1xSz+sbEc/Sc3v9uKieRPutKUzN2WG2ZPY6ToIVf3/yUrAAYHh4GE8++aRQ8yqwsCj/tyeffBK3b99OSufSCQlWdpHMYqAUwYptP5fLha6uLuzevRuVlZVRjxssMnxNreLiYhQXF2Pnzp2SSe58Jff+/n447yzA+OYNccHQI33wBUQylpaW0NLSEnUJnHD9kkIsWB0YGBiQXRgy0mK5drsdR48exa7SYvy0/jBeuvgO/li0Ezt37hRel0j1vqTOJxbSKVhSOVjxytWeIz04euE2bk9Il9+JJ48nFZBgxd+fvBUsnsbGRrz66qv47ne/i3/6p3/Cd7/7Xbz66qu4du1aUjqVCUiwsotkChblYMW2Hy9YfFJ7pNdfSmT4GliBZR34RHl+LcLCwkIUFxejsKAA2qNd4qjV7nY4b8yELFnjcrlw/fp10Y1ArmCFe0+DI1iDg4Nh110LPpZOp0N9fT06Ojpw7tw5dHR04FL9VRgMBrS0tAhRvO3lJdi8basglbxgRapYL3U+sZBuwQqcRRhNsni5Ott4B8fqbqP6bBd6b89iweKKmj9FgiUfEqxQFCFYuQgJVnaRTMFyOmkWYSz7JSpY/LqGgYIVOCzIL8VTXFyMixcvYmBfvSBXiyc08FuWw/aro6MjLsEKd6NLVgTr7LmzeFNdh5+rj2NcP4WKigoUFRWtSGRhoXD+wYIVbs1FqfOJhUwJFsuysC15IgqWbckD+9IyOI6TfE8TOa90EK2AaCAkWKHtkGDlICRYyidQqlwuV1IFi+pgyd8vUcECIPl6BwvW0aNHodFoUFlSDuP+G3ANGCImKadesGLLwWIYBhqNBmcvX8Tv6++VX9jWfQHFJSVCBIsXLKkIVuCQdLYLls/ng9fHyIpg8XWwbDYb6uvrRWtYhkNO+YlUI2cJnEBIsELbIcHKISorK7FhwwasX7+eBEvhpFKwgJUvg1qtDqnkHm6JFiWRbYIFrMw6Dny9N2/ejJPbD+BM2RE0NDRgcXER5eXlKCkpgdcTfSHZVAtWrLMIAWDUYsCv22tEswQP9DSiuHTlsytHsMbHx6Ou+aZ0wTow3CZLroJLNNhsNtTW1kZd8kxuAdVUElh0lq/HJhXpDIQEK7SdXBSs9evXY8OGDaisrBQeS4pgHT9+HNXV1cloKqVQBEv5pFqwgHtrEfIz5GgtwlCSJVj8cKHFYsFgey+0b63mWJV34PYtDWw2W0zvcayCFRhdkJODFakOVvCxWI7DxakhvBhQOPRXHTUYXJwVLZzNS1YkweKlIbiQabhjyyGdglWmaYTbKz0bUE4kq62tLaJgxbIEUCoJXDZJq9WGzdULhARL3E6kenKpPDZPWiNYP/7xj6FWq2NudGFhAXv37sUXvvAFrF27Ftu3b09GX1MKCZbySYdg8ccpKytDZWVlwm2ni2wVLI7lYO/QYaZILUpkHzzTlnLBivT/PPFEsOzeZZRpGkVRq01tZzBnNQO4t9A1f27FxcVhBauiogILCwuSS/EEEm0oKlxfUy1Yv22vhdnuSqgOlt3hjChYsSxinUoogpUYFosl4ooIqTw2T1oFiy/F8JnPfAY/+clPcODAAfT29sJgMAgnb7PZcPfuXVy8eBFbtmzBxo0bcf/992Pt2rX4+Mc/jvLy8qy4SZFgKR9esPgb0Pbt25MiQVKClSx5SxfZKFh23QJMh3tFYnWnqAGLQzPo6OhQnGDJycEasRrwm87T92pbtVbj9J0edN+4IfSLT/KXI1hqtXplbUWJxaQTIR2C5XB6UXC8PWzJBTmV3LMpggVQDla8sCwLvV4fcU3PVB07kLQKVmtrK773ve/h/e9/f0jdK6mNr4X1+c9/HkVFRSGF8pQMCZby8fl82LNnD86ePQuNRoPe3l50dnYmPIxHghXbfokKFudj4FBrYSi+J1azRa24urMau4tKYbPZFCpY4WcRer1eTE5OotswKcjVK11nMGqdl7xBBSb5hxMsflJFuMWkEyEdgjUz70DJoRsoPXQT7zWMhKw16PX5UdcygdJDN1HXMhGyVmG25WDx0CzC2PH7/dDpdJI/JHJWsPh/mEwm7N+/Hz/4wQ/wuc99Dvfff78gU2vWrMG6devwjW98A3/4wx/iGlJUAiRYyobP1RkfH0dvby/6+vrQ29uLqqoqlJaWoqKiAmq1Oi6pJ8GKbb9gwXI6nZidnQ0b1RFFraZMWDjQI4paLbzTg3eL3xBmECpJsA6OtmP24hAsp4awcKQ3bB2swGNX37mB8sEm2L1u4TGpGxQ/qaK8vFwkWBUVFaJlh8ItJp0I6Roi/NPVK9hzWI3zJ8rBjt2E38+irVcPn38l6vTmyf7VNQL7ACDg8ZUfSyaTSZZgAcqYRchDdbBiIy8jWJGeaLVaRUOF2Q4JlnKx2+1oaWlBeXk5tm7dKlT83rVrF+rq6qDRaITZZvGUUsgFwfJ6vRkTLJvNFvYCHCxYjgUr5is6hWVultqn4HV7hFINShEsN+ODl/Fj3GbEiHEWOvMCPF4fFu02IVeEZVlMLS2GHNvPMmADyklEukExDAOTySRMqhgdHcXi4mJIf6LNIoyVVAlWcBmNV7vOwFDxMzCl/w3mrV8DALy+e2LK39B4wQIAn58RFniORbAC+02CFR0lCRagzBwstucK2PazYHuuxNVe3pdp4CHBUiaBQymBS6ps27YNhYWF2LZtGw4cOACNRoPS0tK4ioHmgmAB8V2Ign8gpVqwnE4nloeMMFf1wz69ctMMzK3z+XyCrGRCsLyMH8ZlB6rGu/By20lRovrLbSdRNd4Fo8sOt9+H87oBPN9ajY75yYgFMaPdoKxWK8rKyrB3715cuXIFVqtVst/JvJkkW7BYlkPLjWm09+lFf3+16wxMFf8jEqzAtqUEi9+HYRiMjIzgxIkTGBkZkfWdJsGSj9IEK9ZZhMn+0QGEChbz1q9Fn91YIcFahQRLeQQmAxcXF2Pr1q2oqqpCb28vent70dHRIfy7q6sLp06dims5m3wWLLlDZYHIFSzW5YO5bhgO0733wul0guM4cCwnHEsJguVyueBl/Lg8PSSSqnDbxalBmN1OvNp1Bj9rO4EZiykhweLLNZw+fRomkymrBMvnY3C28Y6QnB64TmC8gmW326HRaFBXV4dDhw4JkepokqUUwZJ7/SHBCm1HzjUpFcPmAAlWyiDBUh6B09nLysrQ19eHCxcuYNeuXaLFgfmhwtnZWWGoMJYFmXNFsCJdZNIpWBzHYXnIiPm9K0OBptohyfalBMvtdkOr1eLKlSu4c+dO2gTL7fPKlqtAyZpesqBJPwqn05mXguVc9uHohduCXO0+fBOasXvSEK9g3b17Fw0NDWhsbERVVRUaGxuFhbITOa90Iff4JFih7US7JknNGk3GxA+ABCtlkGApD74gY2lpKQYGBrB//36RWAVvhw8fxuDgIEpLSzE+Pi77OLkiWJHgb+LB55RswWLsbizWDIkXZy7vALMUmk8hJVi9vb24fPkyjh8/jrq6OhQUFKRFsIzLjpjkit/ml+1hjx3YL7mCdfz4cczNzWWFYJksy9hfMyDI1d7qW9DqxUvaxCtYo6OjqK+vh1qtRlVVFdRqNerr66HT6RI6r3RBghV/f6Jdk6TqniWjdAlAgpUySLCUx9jYGEpKSlBTU4O6urqIcsVHsi5evIiampq8jGDxOG/o4WjTwXnjXj5MygXrxg1YO7WYL+sQyZXpzCAcC+IhwuB/8693YWEhrly5ArVajdraWqjVallRi8B+xSNYbsaHqvGuuASrarwbHsaXFMEqLCzE4cOHMT09rXjB0s3asbe6V5Cr/TUDWLCEnh9FsCJDghXaDkWwchASLOXBR7A0Gg0KCgqiClZBQQEKCgowODiYlzlYPMbXu4XK4zypFCyLdh66N9tEYmV8vRvuO2bJJPfgf/Ovd0FBAS5fvoyOjg7U1taio6NDVtQCWMnX44cWtVqtkKsjRyC8jD8koV3u9nLbSXgZP5aXl/NGsAbHTSg73CPI1dELt7Hkkh6eli9Y4mnx2Z6DRYIVf38oByuHoMWelYvVasW+ffvQ19cnLAYcTbC2bNmC0dHRvJxFyJNqwfJ6vULdK2ePHoYSsVzZrt4B6/EL7ckVrHgjWAzDQKPRCEOLly9fFm7GcgRieskSl1zx2/SSBW63O2cEK9LsrN7heVG19bONd+Dzhf+uyRWs4LpDTmd2zyIkwYq/P3KvSdk0izBliz1nCxTBUh4Ms7JMRldXl1CIMZxc7dixA9u3b8e2bdswMjIS03FIsBD2/6OxPDh/L2r11g14p8U5OLEIVnAOVqAoRcJgMKChoUFSzOQI1ph1PiHBGrcZ4fF4ckKwokUGbEsevHGiD6WHbqKpewosK657FdzWr9tr4hYsIHvrYMk9fqRIu1IEK/AHVSLtyO1PLNekZL9Gio1g+Xw+vPXWW/jxj3+MjRs3CknGNTU1GB0djatz6YQES5nY7XYMDg5GFKytW7eioKBAWGYklvwrgAQrkFhD7RzHwXRyAGMnOuC0L4U8HlwANZpgBc4iDBzqi4ROp0N9fb3k0GK4C7DBaoZ6bhy12n6M2RITrFyJYMnNbZlbWELvsDHqMVwuF97pqMfSvpdzRrDkfj/kHj+d8pCq9jMpWLGkgshBkYKl1Wrx+OOP46GHHsJf/dVfYe3atbh16xYA4MUXX8R///d/x9W5dEKCpVzMZjNKS0tDBGvLli3YsWMHioqKhHygWGtgASRYcvHorHCotSF/dzqdsocQRLlPqzercHWwwrUXjNwIlsXjQqN+FCUDDXhhVY5+f+MsXH4v5WBBenbWnckZLLvjy2/hz9v/5v/mjGAlQ5zk7keCFb19uQVd5aJIwfrXf/1XfOUrX4HBYIDf78eaNWsEwTpx4gT+/M//PK7OpRMSLOXCFxwtLi5GQUEBdu3aJfx3+/btgjjEU8UdiCxYLpcr62Qr2YLFuv2wXR4XhgI9k4uix2PJ0ZA6XqKCJScHy8v48VLbCUlJGrPOJzSL0O33RYy6RRtiUYpgBUew+m/rsLf6Fs413QlZAkcOJFiJ7UeCFb39vBCsD37wgzh79uxKhxhGJFhqtRoPPfRQXJ1LJyRYyoZfMocfKuSjWVu2bBHEIZ51CAESrHB4vV64x0ww7usSJbFbL4iH/DMtWIB4FmHP+G3cWpgKOd6+oRZBjP7QfRZntH3QORbBcVzcdbCMyw7odLqY88YCUYpgAfdysFq7RlF2+F4y+80heaUyAsmkYKVqjVwSrNS3Q4IVxCOPPILz58+vdChIsE6fPo2PfexjcXUunZBgKR+bzYaLFy8KswV5wdq7dy/UanXc7x0JVijMkhemU4MisZrf0wFX31xINCPTgsVxHGacFpy604NXW9/D863V+Hn7e/Cx4lmE8Y8koAAAIABJREFUmkU9zus00DutWFoS54vFU8n9yvRtuDxuXLt2Le7aXYCyBIvjOLT36kUzBU83jMHjjS0qDGRWsFIFCVbq2yHBCuKb3/wmnn766ZUOBQnWM888g2eeeSauzqUTEizl4/P5UFNTg4GBAfT396OzsxP9/f2wWCwxDwsGkmuCZT7aD++0DaaqezctuYLFcRxcGgPmy8UFQy2nh8A4PJLHS5Zg8X2TI1gcx0HrMOP03T78341zkgKkWdSHvTgH/51fi/DK9G3ZcuVl/JiZmQmbYC8X/r0pKCjAkSNHMDg4KHkdSrVgMQyLy9fviuSqoUMXcaZgJEiwEttPKeeS7nZIsILo7u7Ggw8+iL/+679GSUkJ1q5di9deew3f+c538IEPfAAajSauzqUTEizlExjp2L17N4qKirB79+6ExSfSFzgbE97dk4tgbG64A3Kl5AgW6/Fj8YRGXDC0sgvLwwsRc3DSKVhexo+Tkz14rftsWPnZeesy6meGYfMuy55azv+/h/HDuOxA1Xh3SOL7y20nUTXeDeOyAx5mpc5XpAR7uRgMBhQUFGDHjh04ePAg6urq0NPTE/KjIZWC5fb4cfLKqEiubg4Z4sq94iHBSmw/pZxLutsJzguMVOsqLwQLWJGsb3zjG3jf+96HNWvW4L777sNTTz2FmzdvxtWxdEOCpXwCBau4uBhbtmwRhpUSIdcEi1nywFjZBWbpXsRJjmBxHIfF9+4NC5rODoF1yVsDMF2CxXFciFy90HoMpQPXcFU7iGvtrbKGFqP9Snb7ffAyfozbjBhdmMWUeQEenw/WJYdov0gJ9nLRaDTYsWMHdu7cicOHD+PatWu4fPlyiKSlSrCsDg8OnhkUxKq86hY0Y3MJH4MEK7H9lHIu6WgnUKQC8+iiVWvPG8HicbvdmJ2djSlBVQmQYCmfVAlWpITYbBMs1sfAemFUSEZnV6tsyx0i9NvcWHinB57JRdkXzngEK9IwrH1pCe+1XcOBwVb86vw7IX2umbyFF68fw57BJrTO3YHD6xb6ITd3K5JgBX4e3hlpx+zFISzWDMFU1Y87d+6EtBVumR653Lp1Czt27MCOHTtw+PBh1NfXo66uLmSYMVWCFTgs+MaJPswtLCXlWCRYie2nlHNJdTvBImWxWGTXZMs7weKZnJzE5cuXZX8xMg0JlvJJlWDJOWY2CBbHcvDNL4mG+HzzS+BYLkSwOD8Lx3UdbMOhkQp+WEjOhZOXi4aGhrByIUewikpLsPntvTgwfB0/bxcPze2qEA8D271uOH2hUpwswQr896tdZzC6p1mYNCAlWLEeO5iBgQGRYF28eBHnzp3D7OxsxD4nQqBgeX0Mqs7fxru1g7CtRj2TJVg9PT3w7/8NmLKfgNn/Skjb+2v6UXa4B/tJsOJuI16UIFhSIqXX6+H1ekNqss3Pz8Pv94uenxeC9ctf/hI/+9nPhP+vra3F/fffjzVr1uCjH/0oenp64upcOiHByh74m3O6BKusrAz79u1TvmD5WZgO9YoEy3S4F5x/JfwuCI1uEQvv9KzMDHy9C6zXL9letAtn4PDYyZMnww6PhROsot2l2HSwEvsGm/FCc5VkPtULzVXYfGCvrNc+JYLVmXrBmpqaws6dO7F9+3YcPHgQtbW1aGlpgccjnlSQyhysJZcX7oDPQazH4nzSEyDkYjaFLrEEkGClEiUIllRxW51OJ0S1KIIF4HOf+xyOHDki/P8TTzyB73znO9BoNPjmN7+Jb33rW3F1Lp2QYGUP6RIshmFgsVjQ19eHrq4u6HQ6WK3WhGYspgrOz8IVsC5g4OYamofH5cbe3eW4vrtW/HjJdbgnFiXbjHbhDEzwPnfuXNgEbyl5cbqX8WIYqfp520kUXD+LtqlRFJXKjx6mQrBe6TydcsGamZlBUVERdu3ahWPHjqG7uxvT09MhN5Nk3BAZloW6ZwbzC9aIbcYsWH4f2KHrK5GqGDe/phXD4/OSx89nwUpVLS85x05XO+EiWCzLZiQHq2fIgI4+PXpW674x+18RRV9jJSmC9dBDD0GtVgNYWRdszZo16O5eqcFTV1eHj3/843F1Lh1UVlZiw4YNWL9+PQkWIWC329HS0oKKigqhsCm/FI9arYbD4YjeSBrhfAzm93ZKCpaxsgusxw/j8b6Q6JZdF/7GFe3CGbgG4Llz58KWKDA7HRi3GUVt+nw+/PLCQUGqXmyswm9OH0CfcQr2JQc6OjpEUbdcFqyFhQV4vV5MTEygtrYWExMTsFgsUfscKx6vH6eujqH00E28W6uBx8skTbCAFckSfvHL3d76NfxeL45euC15/HwWrFSjBMECpHOweNIxizDR6Guk5/OCtX79emzYsAGVlZXCY7IF69FHH8W5c+cAAAcPHsSHP/xhIY+jsbERDz/8cLx9TxsUwSJ4+Krx/M09ULASrRqfClgvA3vjpKRc8Zu9cRLeGRsMRdcxU9SCK7tPQN2ijrhuYyIRLKvHhabZMZQOXMMLrcfwvx2nwHCsSLD+dLASr9TsR59Bh2KJWYTJFqzAX8BKEywAMBqNqK2thdFolHztE7mR2Zc8OHR2SEhmLzvSg6k5e9IFi9WoYxIsX38LBobnKMk9AyhFsADpWYRy2k+KYCUQfWWH2sD5w1+bkhLBevrpp/H1r38dFy5cwJe+9CV8//vfFx7bv38/Pv/5z8d4yumHBIsA7q17GChTUoIV77qHqcBvc8NQ0hZRsAwlbfCbXJg814M3i/fKEsVYc7BOX6nDkRtNKOy7KiymHLiNWuclk9yD5SlVghWpXESmBYthGIyMjODEiRMYGRlJaqFRg8mJN0/2C3K173gf7ugWIrYZ77FiimK99WswXi/eeq+fBCsDKEmwAttJt2AB8UdfI8kVkCTBGhkZweOPP441a9bg//2//4fJyUnhsX/8x3/ED3/4Q/lnmiFIsAhgpZxBRUWFLMGqqKiIGAFKB5yPweLJwchytbotvjcIp30J5eXlwjkUFxeHFUU5F063z4tjA9fx25aTYQt//uHGOZy624uFZQcJlgTz8/PQaDSoq6vDoUOHklpo9M6UBeVVtwS5eueMBha7O+zNLJFjAbFFsViNGsPj85JlGrxeb4h0yvkxQ4IlHxKse8QTfWU16vQIFo/ZbA75m0ajSXoiWiogwSKAlbyiYJEKJ1glJSWYmprKSD85jgPr9sMzuShLrvhtacyI7s4ukWCFE0U5F06W4/CbztMhUrWppw7ntAOYXrKIqoGTYIUyPDyMhoYGNDY2oqqqCo2NjQkXGuU4Drduz4sqs5+4NIJlt1/UVrIFC5AZDVj99X/0wu2VvlU3gTPNgDPPgrMYwCwaMNLdhoba93Di4H5cPXsaQ7dugPG4Ix6bBEs+JFhiYo2+RpMrIIV1sLINEiwCAMbGxmISrPHx8ZT3ifUyWB4ywnFdB+v5EZgO92K+rAOsywfzsYGYBGvxuAYel1skWOFEkb+wcRwHnWMRZ7R9ODTWGbLf4ZEOPN9ajS0363BxahAGly1kn+A2lSRYwYm06RasgYEB1NfXQ61Wo6qqCmq1GpcuXYq70CjHcWjsmhLJ1UX1JPzMvUThVAsWOxA5GsD/+uenxVv3/ELWjc3aelZ8LIsBTOmPV3Ji9rwAf/mLYCpfArPvZTCv/wLMm79auWkeeAWcQzxblh3pAnN0C5jq7WCO7wRzYheYk4VgakrAnNoN5kwZmLMVYK8dDXld2FsNYOsPgW04ArbxKNimY2BbToBVvwf2+il4mk+C7TgL7m7oMnFsfxPY/mawAy1w91wDe7sd7HAn2NFucGM3wY3fAjfRB84lvhdxbhe42Tvg5ibBzWvBLUwHSOk8OOsCOLsJnGMxZIkjjvGD8/tW/suFfs4TIZcEK5boa0oF66GHHhKWwXnwwQfx0EMPhd0oyZ3IFjIRwWK9DHzzS1geWcBSxzQ8WvEMMtblk45GdU7DPW5OOIIlJYosx2Fofho1k7dES9O80HoMdq84ijBjWUBjd3tMs/eiCVZXVxdsNltMNcjiESypqeC5EMFq7ZkR5KqtVx9yw03ZEKFxCuz1U+A8y+GjAQG//nnBsu35uawbm6n5jPh45lnZQzqcXTzCwvZclffcd/8Q8rowp3fLuxE3VYe8Rkzpj+X1N0jOON2Q/HMNmnXHtpwI2ufH8Jc9B2bPC2Aq/ueelJ7fF9rf2nIw7/4BzKE/gjmyCUzVFjDV28Ac2wHmxC74TxSAqSkGN3ZD3F+3E2zdG2AvvgX28ttgr7wD9uq7K1J67SjYpmpBSrklq+gzyS1Mg71xCezNK3B31oHtvbYipgMtYAdbwQ61gR3uhPVWc+hncGEanO42uKkRcDNjYik1ToFbWJVSZ+gPwViir3KIW7A2b94sVBretGkTNm/eHHGLFYfDgVdeeQUbN27Eo48+CpVKhU2bNoXsx3Ec9u/fjyeffBKPPPIIPvrRj+Kpp55CXV1dTMcjwSKA1OZg+S3LcI+b4eyege3KOMzHB2B8vTt0xl/DhOh5HMdhvrzj3j7F17Hw1k0snhkGs+xLOAeLF0WWYzFqncexOzfxatcZ6cKfrcfQZ54R9S/aUjmByBWs0dFRmEymmGqQxSpY4ZbjCCzBka05WBzH4aJ6EkN3pJPDUyFYbM+Ve2LR1wi2r1FaOgZaQgSrd/++lZvvlXfAXjoA56lyzL69GfMHt2Dmjd9j4e0/wbj/DzDfahWfp9UI5ui2lZv+4T/Bd/APYN79A5h3fgfm7VfB7P8NmDf/F8wbvwS3ZA3q71Uwu5+LLiyH/i/kdWFqSuQJVssJcX85Vr4kaQfFz72rkf/cIKFmm4/Le+6p0pD3lXn3D/LOteeKuL+ORfn9Nc2IPpNyI0m+N34Z+jm88Lq8/l55J/S5Ay1hP7fC82RGrwAFDxFqtVqsW7cOTz31FJ577rmwgvXHP/4RKpUKL774Iurr63H+/Hls3LgRKpUKp0+fln08EiwCSGwWIcdy8Fvd8Ny1YHlwPqRty9lh2SIUzPKQEe47ZvjNLnCrQz1exo9+8wxcliV5swhty7h48aLoHPgcLOOiSTKX6vnWarzYegxlmkao58Zh9y6H9C3ZgmW1WtHQ0IDy8vKYapDFKlhSVaTn5+dhs937ZRuLYHV1dWVsFqHPFyqewTdZqbYSEazgKAm3OHfvRnTw9+C87tBoQNCv/3BrEca7iHZc5SU4DhzLrgyf+bzgvMsrw3HLS+BcdnCue583YdjcZlqJlBh14Ax3wc1OgNOPg5sZBTc1jOXRHnB3NeDMs0HHYleGBIeug9W0wn3j6oqM9l4De6se7M3LYLsvgu2qA2cTD39x5lmwzcfBNlaDvVYFtuGwSErZurfAXngD7IXXQ86R7WtcGf48sWsl8nR0G3yH/7QSkTr0f+ClVEo6mKPbwOx9CUz5i2D2PB9WStlb9eL+2kzyBWtxTixYA82ynud9839D+3uuUp5gXX039LkHfy87+iqHhAXL4/Fg3759uH37dvSdY4DjOOECYTKZwgrWpz/9afzt3/6t6G9utxvr1q3Dd77zHdnHI8EieCLWwSouwb7iclw8eAqLnXdhb74Ly5nbK0vPlLaJhIZjxTc4R6tWsgio+Wg/bJfGVob8Rk3wm6PfpL2MH5enh/B8azWmLCZYr01EFCxr4wSsziXMGudRvLsUO/btCZlFWNRfL0jVT68fx57+a2ifn4QzSiG+ZAqW3W7H0aNHsWvXLmG9Sbk1yDIdwZL7GgTD55EEliSIpQ7W3RkbXj/Rh1njkuxjxitYnM8DdrAVTPU2sDcuhTzONhwG298MzrMMn3sZTH9LyK9/n/uepIcTLCC+RbQpyV0+iR6b44KkNEg8OJZZEVGrEdyiYWVYbmFmZZjOoF0Ztpu9syKlPo94iNC2AG68B9zYDSz3q8He7lgZFhxsXYky9TeB7b0GW4fEZ3CkC2zbabCtp1Zy4pqPrwxHXjsqltKh6yHPZc5Vgr3TB7avKeHoFZCkCNaDDz6IlpYW2QeNlUiC9dhjj+Hb3/52yN8/+clP4nvf+57sY5BgrcBJ/BLONziOg21+Ed11auwvf110kz+x513Z+U5+izja4522wXFdh+XbRnjnHGDd0msAysG47BBkaHvvJXg93oiV3L0eL7bduoQZxyLeGGzGTxuOoDhIVtRz43jjdiu6jVos+32yL8DR5EKqwKeUYPHRw+Li4oiCFa4GWaZzsDIhWP2jRuw+vJJr9frxPtgc8qpSxypY3MI02GtVK5EM/obz9u+EhGkplpeXwfq8ovXcOL9P9BpFEiwg9qFXEiz5ZPLYUmQ6yT0QyVysGKNXQJIE66tf/SreeSc0tJgsIglWWVkZ7rvvPrz99tuwWCyYm5vDr371Kzz44INoa2uTfQwSrBW5YuyevJEs1utfSS4fXsBS+xSsdaMwV/VhvuKeqJjaJ9Df3y/kAVn0C9JCtbsdpoO3YKkdhkOthUtjALucmjUS3YwPVeNdomG8jtkJ2DWzkn2za+bQPjuB51urUTXejSHLHJ5vrcZpdb3kkiw8yRIsKbGRWkSbz3+TI1hS+W+ZnkWYTsHiOA4tN6ZFMwXPN0/A5w8vPFJtRRKslWjV9ZVZdlLDJUc2heQ18fDRJ/2UTshpYfuawAWJOwkWCRaP0gQrOA8s1ugVkCTBam5u/v/tnXlwHNWdx9v3BQFjFgcTEjCLQMCyW7shOEvACw6La6uWLAS2kspWyAGGJBwhJJAlZn2AjQ/JwrJky2BjG0m2ZfmSLduyZF22LkuyLsuWJeseybrnkDSaq7u/+8dMj+foGc093TO/T1VXyExP9+t+nnkf/fr3fg8PPPCAV0LjDe4ECwBSUlIwa9YsMAwDhmFwxx13IC8vz+0x9Xo9NBqNdVMoFFEvWNyE0bxuXZDEIBzwRhasyF/1npY3UJ27joSEBCQlJcFoNILneSiPXoEmvw3amhvQd6rAanRu810CjYE14Z0S+8KeH5Qfht5gwNC+Grv2D+2rgc5gwJ/KD2PF+XS8U5IBrcmA/dcq0D064vbHK1iC5WoRbWEGpyeCJTaDM9h1sBRby5BT5Jwf58k9cIe3gmU0scgquG4nV8XVCq/+DboTLF6rgT5nj320Sti2vgXu7B7z4x0X57PNnzpx4oQ5p2X/OvCW/D0SLO/3CwYkWO6xi2L5EL0CbgqWQqGw8w29Xu+5YD388MOYP38+pk6divnz5+Phhx9GbGysdXvkkUe8bpgt7gTr66+/xqxZs/DBBx/g3LlzOH36NH72s59h7ty5yMnJcT6YhVWrVlmFzHaLVsHijCxUJ6+ZpeLkNXAyimLxLAeTcgL6thGMV/VAk9uKkYzLGEipNEvGnktOn1EeveJSqgZSKjGScRma3FaMNw14VYspFHSPKUWT0TNaqzHWq7Ivy3BDhYzr1Xb7dY/drAc0Pj4uukq98J4neCNYSqVSdBHtL7/8ErW1tdi8ebPHguVYWiJYgrUn4wRytpfg1PZyJO+vFl14NlSCNT5hRHr2VatYbdlXhfpm7wcZt4Kl15rrSdmK1b7/A1ebD14/+fU5rlFprC82S1bDeadzkmCRYAlIUbAco6/eIgiW47Zq1Sp7wcrOzoZCoRA9yGuvvYZf/epXbjd/cCVYSqUSc+bMwR/+8AenzyxduhT33Xefy2NSBOsmPMfD2D9mNzAb+8eckrTDiXmmj317dNeHMfhV1aQz6PoTSp3+2h4r68bw/nqoz7RgrEIBXfMQjEPj4B0esbib7RYqeJ7H4MQYqge7UDHQgcsjveKz/S7sx9Coxk6UB0c1eOvCfrv9WtQDAG4+GnM1Ky/QgiUksItNHkhISEBtbS0+/fRTyUWwVpxPx6a0csTvrUJSepU1V8sWg8GA3t5el7LqDk8FS3FjBF9l1lvlalv6JXT0uC7q6g7h+NquFnBNFU7v6099aY5W5XztNlolRmdnJ3Jzc1FWVoasrCzo9qwE33kF+n2r0Nvba/dolwSLBEtAaoIFALzBPvrqLR5HsKZMmYJnnnkmII32FleCVV5eDoZhsHfvXqfPfPDBB2AYBmNjns2sieYcLN7EYWiv86MlR9kIejt4Huy4AQaFGtqGPowWdUB57CqGvr6Evi2lMCjs+0bfrnQvVlvLMJxaa47IGXyLyIkJli+DqLfc0KpxuL0GWxrO4Y9lmVY5SrxciGZ1v8t1/xIa8qEb12EguQK6cR0SGvKd9ukeU8JoNFqTu3t6ekSvKZCCZZvA7qr8RUNDAzZs2ID169djw4YNIcvBcveemGAJsw3FIlm+4IlgGY0sth+oscrVl5n1GFR6Hy0DzDMBdZfyzdP1438DNvF34PX2A8j4QK9H0SoxHCNY2sS3wcb/Bvrk91BVVWW3cHOoBMsfAfbk+MHeLxiE4nfMG6QoWFzjBfD6CdEZh57gcQ7WlClT8PTTT3t00FOnTuHixYs+NUgMV4LV1dVlrYFlC8/zeOqppzB//nyP//KKVsHiTRy0l/tFBUXb2B90yeJ5HqrsaxjaV4v+L8rcCpPWobaUSaVDf0IphvbUQHn8KkbPd0B7uR+GHg04rTEgeVFighWoH0VzZGoUVYOdGNHZH/OKJRHdcfu48ji0JoNTDpbtdnlAAaNah8sDCqf33inJgIE1QalUWssTCDPoHIUhkIJlm8DuSrAyMzORnZ2NNWvWYOPGjSGZRTjZe46Clby/2lovy2TyfRaoLZ5GsGqv9iJ+bxVST1zBmNb7wZEfUoDLTwOb5JxbxdUV2u3rz79x2xysjIwMjG81n8+Y8n7YBMufR7ieHD/Y+0UDUhQsNm0t+M4rYNPW+vT5oAjWj370I0ybNs2nBtly+vRpZGZm4uuvvwbDMHj11VeRmZmJzMxM6xfl5ZdfxtSpU/Hee+/h7NmzOHHiBH7605+CYRh8+umnHp8ragXLyLqd3u/rjELOwMI4MAbdtSGMlXdDfboZw2l1TlXKAWBwZ+WkRTIHd1U7Fe+0rZUWLAIlWIJMVQ524nB7DeLrz+GPZYes4lN8w3522phRb33vz+VHsK2xEFmd9agbVmDcqHeaRegoYe3KQXxcedzpvdSWi9Aa9XYFNnt7e0WFIZCCZZvA7kqw4uPj0dDQgK+++sqtYAWqDpYn7zlHsKrDEsES2nWtY0S0qKgreKMeXGPJzWiVYyXsPSvNuVU6+3vm78AvzCLMy8uDPtm8zqAp5U8kWA5ILYoUTiQpWDZJ7r7gVrAWL16Mn/70p1i7di2mTJmCJUuWeHTQf/3Xf8XUqVN9apAt3/ve90QTxBiGQUdHBwBzUdHNmzfj8ccfty6Vs2TJEqSlpXk1+EajYHEGFpr8Nrdyo8lv8+jxmr5DCXXOdYwcbBBd/kXYhr+pdfrsyCHzUi+DOysxcugyNHmtGL/UC327EiaVLqy5YP4KVk73FSeZEttSW5wjvo3KXij1WtF/x7Z1sLzZejQj1seDoYxgCYtouxOsuLg4bN26FQ0NDTh37pxTJfdt27ahuLjY5Xc0JBGs9GrRHCx/EBMstWYUzR32ixP7Ij1s2hrxmYA5u8H3tmLcRQpFICIrwr8LU8qfSLCISYk6wZoyZQqmTJmCqVOnWv933rx5ePLJJ7FixQokJyejpKTELknWYDDgrrvuwl133eXzRYWDaBQsk1rn2RIrKh2MA2MYv9QLzblWjGQ2gtM7RDsuKjwqezC023lGHztuCHm+l6dMJlgcz6N/QoPKgQ6c62ly+nzylWKXsvOXiqPY1liIE5311sRzT7Gt5O7pdrqzAX1DgzAajXYFNkORg+VJBMs2ktXb24vh4WG7GmSBXovQk/ccBWv7gZqARa4EHAVL0duPtJONiN9bZbeW4GT9wbPOjyy5i6duitXeT8DVnLOLVvk7g9Qd0SxYFJnynqgTrI6ODhw9ehQrV67ElClTMHPmTEybNg224iVs999/P5YvX46HHnoIU6dOFa2uLmWiTbB4I+vVIsHsqB79CaU3Zxn22c88010fvplcvq0cw2l1UJ1qxlhZNyaaBmHsH/M50Tyc2AqW3mBAv1aD893XkNl2CXH1eXi39GZk6vcXDoB1GHyzuxqw4nw6Pqw4iqTGIpzobED9cA/UPs5KscXAmpDTfcUjucrpvgLVqAYpKSlWUQzlLMKRkRF88cUX2LhxIzZt2uRRArtQjFSoQeZJO4ItWDsO1kzaDm+xFayMwyex81CtNZk9aX8NdAaTaJsF+OFecAXpYJPeBj/SZ//euBrcmV3mJUl8qJPlD9EsWIT3RJ1g2f4fIQdLp9OhqqoKu3btwjvvvINnnnkGt99+u1W6pkyZgkWLFqG+vt63KwoT0SRYPMtNOgPPcdO1DGOsrNv6/yeu2v/D5nQmGHpHI6pIKWAWrHVJX+C97K/xbqnrxHLbGXq2aAw6n2WqurEPpbU9qG7sc7mPnjVhYGIUqS0XnRLf3ynJQGrLRQxMjEJvMiIlJcWp3MT4+LjLH7BACdbo6ChUKhUKCgqwadMmbNq0CRs3bpw0gd3bEhn+CJZKpbKey3Z2YjAEy3FGmzBINDYrkLCvwipX2w/UQNF3sy22beaNBnBXSs2L99omqxcd9Lt9jufylXAIlqvIUSAEy7HiPxFYolqw0tPTsWvXLpcHEmqflJSUyNLyo0qwTJzHlcytUawDDeAMJhi61WDHDCGtXB5sOJ5Hn1aDiwMdONR2CVeUN6zvGY1GbNi6xaVQfVhxFMmNRTjZ2YCGkR7ofChG54rJBh9bdCYjDKwJLeoBXBvsRdfwIPRGI/Ss0XodYrISCsFSq9XWpPrU1FRs3rwZGzduxOrVq90msEeqYDner8HBQVxtG0bCNzcrs+86XIvWdoVdbtz4+LglWrUfbNLbzrlVX7wJrvCA3+0TzuUv4RCsydri63HE1qwkAktUC1akE1WC5UMES9+uBM/K/y83s0ypUTHQjoy2amyrnZZfAAAgAElEQVSuy3OKTB1orbLuLwzyvzv3jVmmrhTj6PVLaBjpgSYAj/nc4Y1gCexuKkXvqUaMZDZiKPXm58IlWBzHQalUYnBwEP39/RgcHERBQQESEhLsBEssgT0aBIvneZwrbbFb9ib9ZAO6Fb125SD4rqswuloTcO9KcJfywOsC94ctCdZNOI6zmxQS6FmkhBkSrAgmmgQL8D4HKxIWgM5sq8E7Hjzm21h31umzEyZ7KQkFvgjWhxVHsSfjBI4ezMF4ZY/1dW8Fy5vHIa4GL9u/+vv6+tDX12dNVh8YGHBai9AxgT2QguVuQWcgPII1OjaO0+fb7ORq3+EytLd3OA3kfHOVc7TqzC7wPS1BiSaTYN3EZDLZlTUJdB00woy3gsVxHPr6+oIquiRYASLaBAsAWA9nEbJqXbibOikcz+GGVo3ygXZktFYjoSEfnMPAc6yjTlSoPrp4DNuvFCO76zIuj/RCY3B/vVIXLOHRpS3eCJa3j0PEBi+xv/oHBwehVCqtsrNp0yZs27bNpTwFSrAcr0en01lli6vOAVd6HOMXjjsJFsuy+KA0M2iC1XNjCMk21dnPV7ajpLgIwyWncOPSBbt7z7MmmLa/dzNaNeHZChW+QoJ1E4pghQZvBEv4Tjc3Nwf1kS0Jlp8kJSUhNjYWMTExUSdYHtfBklj0iuM59I6rUd7fjoOt1dhUlyta2bxPa79O26WhbqtMnfJQpsSIZMHyZTARG7xc/dWvVpv7RK1WW9cbDKZgOV5Pd3c32tra0NHRgeHhYevgb9zxvp1gCdXI3y0yr+O4MbXMmnjuL1qtFpWVlejp6UHjtS4kplWjqrQWmpNfQbf1d+ZlZTLjneuS9StClvtIgmUP5WAFH08Fy/Y73dbWFlThDZRgxcTEIDY2FklJSdb3okKwBKIxggUEr5J7sFAbJtwuE2O7VQ/aLwYcqMEpkgXLl8chnkawhoeHrSUhQiVYttfT29uL9vZ2NDU1oaurC319fTDueF9UsIT19N4rPmgRrJuV3P1Fq9Xi4sWL6Gpvw2hlHpR7P3XKqzJt+S14hxmpoZw8JCXBqqioCLtgATSLMNh4Kli23+mOjo6gPrKlCFaAiFrBCvNahLaYI1MqlPW34WBrFTbW5SKr077cB8/zdosfC9v/XjyOHVfO41RXIxqVvRgzBu+xZiQLVqAiWID4X/3CuQYHB7F+/fqACpbBYEBbW5tddMH2erq7u9HU1IT29nb09pqTyA3b/ygqWMKs6D+edxSsKjctcE9zpxLZxW0YV7Si9+AXMG5znglo3PIGDCd2QHu93ukPgmgVrEDUr6I6WNInkiNYJFhRKFg61jy1nzOxGNpXY19xfV9NUOWK5Tn0WGTqQGsVNtadxdslB53EKb7+nNNn91wrQ8qV8zjd3YgryhtBlSkxgvkjazTdjBiKDT6274uRq7iKE531yFVctT9uiHOwBFwll9+4cQOfffYZNm3aFDDBsj2+2PW0t7ejra0NPT09VnkMRQSL53lUXu6z5loV7NjlJFaGXX/FeEkWLhYXQKvVulyLMFQEU7BsJxGQYBEClIMVwUSTYBmsxSkr8E5JBjJaqzHWq7ITLEP/GExuliXxBpbnnP4aP9xe49Fjvk8vnQ5IGwJJsH5kTSYOJTU9MFnE1nHwcXzfG4I5i9CxcKYjYuURQilYwM3r0el0dvIoloM1NDQEo9EYkBwsk16H3LIOu5mCp/YdgzH+tzB9sQLsqZ0Yri8Hy7J2jzgjWbBsJYcEixDwZRbh0NAQzSKUA9EiWGJr2L11YT+GRjVQnbyGvk0XoDp5DYOjGuR0N8Igsr6ZO4TIVElfK/Zfr8TntTn4Q8lBDOnsZz1VDnQ4ydTHlceRcvU8znRfwVVlH8aN+kBeesAIxo+sycThcG4z4vdW4XBuM3R6A7KLWhG/twrZxa1O73srWcGsgzUZUhAs2/ds5VH4ARUEa9OmTWhtbcXw8DAmJiZ8mkXIm4zgmiqgPRiHzL2n7OSqvK4XE5fL0XlsF7Qj5ho+Qi0fEizXx/BHjFiWRUdHB/Ly8tDR0eF2PcvJIMEKHr7UwQp2f5BgBYhoECx3CwQnNORDN67DQHIFdOM6JDTkW9ew07uQLI7noRhXOsmUJwnnQ7ox7Lx6QfIyJUagv9S28iRsZskyoaSmBzq9SfR9bySLBEu8HbaCJSzjIyTADw8Pe1UHix/pA1d0EGzyu1AlvIO9u3Ks/fXFN9W42jYsOtiTYAVPsITZoGfOnEFGRgbOnDmDhoYGnyWLBCt4kGBFMNEgWAMTo24fx10eUMCo1uHygMLu9YGJUbA8h1GHkgYcz3tUuPPjyixUDLSH6aoDTyC/1EYTi5KaHjt5spUopVrnJFfCVlLTM2lOlvU8LmRldHTU5aPAYAkWy7K4fPkyPvvsM2zYsAE6XeBy6LwWrC//AjbhDRh3/glxcXGIj49Hd3e3dWbSRxVH8eb5/S4FS4hWsRkbrflUvV98iJSvi6z9lJxWBUXfqMvBvr+/HwAJlrtj+CpYQi5dcXExsrKyUFxcjLy8PPT1uV7f0x0kWMGDBCuCiXTB0rFGpLZUTCJCx9GuHMTHlcftXk9tuYgryhtIvFzgdNxNdbl2+/6tMgs7r15AjjUyFXn1YkIRwZps8+UxoSMGgwE9PT0uk9mDIVgajQYNDQ3IysrCp59+inXr1qGmpsavxzauziXA+xkdVaj6RQWLKzkGdvu7dsnq3Vv/F1v3lFn7afeRBigthXpdDfZXr5onJNjOgiTBsj+Gr4IlzAYtKytDVlYWysrKkJubi87OTp+OR4IVPEiwIphIFywDa/K4fpTj9k5JBrQmA9bVnHFKVi/vb0eO4gqaVH3QmiJPpsQIdg5WKORKmOrc2dnpshxDMASrvb0deXl5yM3Nxaeffor169cjJyfH54iCu3MJ8CYjuMYLYBPe8HozNpzHeUWTk2AZjUY0xL1vPxvw6/+F7mIOUrMaEL+3CgdON2FCdzNa6Gqwr6+vd2o/CZb9MSiCFfmQYEUwkS5Y3WNKn+RK2K6rB3Cm+4rXSe+RRjCLDXoiWYdzm6E3OEd7ymp7UVLTg5qr/WhqH0FnrwaDI1qMaQ1gRRbpFor1CXIlVqwvGIJ17do15ObmoqCgwCpYZ86c8Tmi4O5ctvAm480fS0+3nR+ANRpwvOQkLqZsw9Y9ZXaCtT9uLfRxv4Xp5A7w3desf3yMaQ0ouNgFk8N9dzXYt7S0OLWfBMv+GJSDFfmQYEUwkS5YzeoBvwSrRT0Q7ksIO8FeLqN/eBxKjc6tYCnVOrR0Kp0+m3yg1u3nktJrsPtIA653mT8rRLBarrejpOo6LjV04FprL1SaCWteV6REsABLFKu+2HO5OhwP44QWrKLZ+tqZlG+sgmUymTCiVON45iEo1Z4N/q4Ge9vfHBIs8WPQLMLIR4qCJaxVylXn+PR5EiwLkS5Y/kawusecB/VoIhQLvhqMrEcRLIPD8kUcx3ucuyUIFmAWxtrGdtH9tqZdws5DdUg7eQVH8ppx5kK7U0L9mNYApUYHncHkdhmicOdgCXgcxTocD5Neb645pteDPRwPNv436Nr6sY1gcdYZnrklHaJRRTHEBnux5PtoEazqxj6U1vagulFcsKkOVvQgRcHyFxIsC5EuWP7mYEX7o0Ff1uhzhdHEoqF5EMOqCZvj+56DxfM8+obG0d6jxpXWYVQ39uF8tQJnSztwPP869p+6it1HGpCUXoOegVG7tjRc6/VYzjjOXqKKqrqt7yV8U42UjDrsy2pE5tlryC5qQ35FF8rrenG5+ebgKcwibGhoCPksQqNuAlzDJFEsi1zZ1hwz6vVoPHMG278+j5OF10VqlplQXtfrcRsdB3t/BWuyQq++EIhjCdepLzsJrvQ49GUnvZYcEqzogQQrAklKSkJsbCxiYmIiWrA8mUXoakttuQg961kdokglEBEspUaHwspuJO2vQfzeKuSWdgDwfxahP2Hs3n4lKuq6UVrbg3PlnThR2IqMM03Ye+wykg/UYMs+8/m2H6h1+uyZC+LRL6e2nm2yfkb4QfzyUC3idpciblcRDpy+ihOFrcgr63TKIxuf8O7fnbsfXK1W6z6K5SBXtvd6sppknpbLENoRSMEKlDwEGsd2+dJOEqzoIZIFKyYmBrGxsUhKSrK+FxWCJRDpESxg8jpYrraBidHJDx4F+JKDxXE8WrtVogK1Ne0SDEb/62D5k4g5WaFRnucxoTNBNepc6qC+eRDZxW3IPHsN32Q1IiWjDgnfVDu181SRcxJ3YprzfmLb5ZYhu3MqNTrsP3UVx861IKekA+erFahq7MOV1iG0K9To6OiDqkcBQ2sDuPoicBcOg8veCXb/Oph2/BFcbT64ukJRwTIqh0JSkywaBcvXPCgSrOghkgUraiNYAtEgWO4qubva3FVyj0Y8nUU4oTPiYsMNfHW43mkw/uKbapy50I6+IfOPg7+V3IMpWN7C8zz0BhNUGh16B8fQ2q1CR/dNSRKkbe/ROmzeVYy4r8vdClZrt8ru+N09So+jfONbfmcnUC2Ja1CwKxUVtd2o+yoZLYlrodj6vxhO+CO0W95yGcHy9pHtZESbYI2Ojvo8k48EK3ogwYpgokGwALNk5XRf8Viuoj33SozJvtSDI1p8IRLJ+epwPSov37CrjSQcT6832uf16AzIPNOI+L1VyDzT6PS+7YAuVCNnv/yLT9cSSMGa7DjCf9sulTMxoYdmTI/+4XG0dw3jSn0rqi/U4vyZUowUHLdGoNiU99GcuNYj6dmy5yJMDhGq/F3fuP1Mwp4KZJ9rgt7gLLSBkivAvWAJUdFIEiwhcuVLLSoSrOiBBCuCiRbBAgA9a8LAxChSWy46Jb6/U5KB1JaLGJgYpciVCyb7UvM8j12HG6yD8JG8ZrR1q5ySxAHzrLKioiJkZmZCpzeYI1Y6A1JTUxG3uwTxe6sQt7sEquEh68w1X4qMuqpmHnLBUivBDvWgvfAksj//EIkbPrNLcucaL0w6y88Q/zo0X7yLgZExdN3QoKl9BDVXB1ByshC5+w7jxP5cHD9UDK6+EHzHZfAjfeCNBmQVeCZNR89UQ6c3eDSjU6c3eD3RwZ1gid0zd6+JHU8qCO1qbm72uZo6CVb04Eqw3KViSL0/SLAsRJNgCehMRhhYE7rHlGhRD6B7TAkDa4r6hPbJEL7U6lE9iqu6cbKo1Wmf+muDKKrshlLjfoacSqVCYmIi4uLikJmZiRGlGqmpqVizZo2dYNXXXoKprR5G1RDYIwleVSPnGkvAm8T7NBiCxfMc+PYGcHWF4M5nwnA8CWz6Z2BT3neSpfTP/2ZXpoHvbnItVynvg92/Dlx2Crjzh8GLLMPkrs19gyoo+kfR0qlEdUM3SnNKkL9jN7J3piPzy6PYd7AC29IqEbfjGEpKSjCiHncrWCPqcZSUlHi8ELVAtAkWRbAIT3D3eHyyz0gVEiwL0ShYhPfwPI+r1/tx9FyLw2A7MfmHRejs7LQuwixsa9ascRKsnTt3wqTX+VSN3JVcAd4LFm80gB+5YSdQXGOJ0z1it77lUftObfjIrtAoP6YEl73TnJxeXwS+4zJ4ZZ/ba5iszWLvdXd329/PnR/ApNchISEBhw4doghWAKAcLMIbSLAiGBIswh0TehOqGvuw60iD00D7xTfVuNI6NPlBRGhubvZIsOLi4lBfewlsfZFXgsU1FPssWBNNVWaByt5hjkDt+KP4eY4nOn2W3fM3kQjUnzD29Sfo2bUG7WlbcGL9X7Dj89UhWSrH8b2mpibU114CV5tvvk+1BbhUeRGpqanQ6SaXq2DlYLm7DrkKlj+zCANV44sES/qQYEUwJFiEGP3D48gp6cAXqc5J619m1uNiww1ovazVZIunEay4uDjs3LkTvMngeRRLJHrFG/Xgh3stEagC6M+lw3BsG9gD68Hz9qKgz0vz7DzfrHK6Lq7xgjkC1dkIbW+7tR3hWirH8b2Ojg7z/dRPgN2/Drx+AkeOHPFKroIxi9DddchNsBzlKJztJMGSPiRYEQwJFiHGoZxrTgPqwdNXcb1LKZq07i22OViTCVZiYiL02vHJq5HbRq+0o+BO7gCb/qnrCJRl48fsSyLoKk47RaDYA+vBndoJ7sIRq0DxKvfrVNr+CIZzqRzb94aHh833s7YAvH4C+toCDI+owl4Hy911yE2wHCHBItxBghXBkGDJC6PRaBUSb5OMXaEZ0zutqdfSaa67tC29BgUXuzCi1gX0Sy3MIvREsC4UnAM70udZFMsSveLVQx4+Tvwt+L52u7aN93WbBUrZ73EOlBiOZRpYlkVTUxMyMjLQ1NQUMLlyPNdk7SgqKoJ650fgO69AvfMjl48HvalJ5gkkWCRYhDPCvfXmcbDU+4MEywIJlrwIlGDxPI+OHjWO51/Hln1VaFeo7d7nOB6XW4bsFlgO9Jdao1Li2De7kRa3Fsfj/g/7P/vISbD6E29Gn7jafGvu0GS5VzxrAhv/W/O28wOwBz8Hd/pLmM4fxpm4v+Hg5tVQd10HL1KSI1DXafuDKRxzaGgIWVlZGBryLXfNFd4IlkajwWiCuRipKm4F1qxZ4yRZh3ObYTSyyC5qRfzeKtG1CCkHyzNIsAh3+HJvpd4fJFgWSLDkhb+CpTOYcOlKP74+ap+0fvRcy6Sf9bl8QV8HuLIscLn7wB7bCjZ1teWx3W/t5Kh305t2gpWYehH6gxvto1P6CddRLIfcK1496CRQwv3bvHkzVCqVY1P9uk53SEmwAMC4w1w6Qh3/pjVyKMwiFB77NTU1ISWjFvF7q7DjYA0AWGuShXsWoa/J4+GABItwBwlWBBItiz1HGr4K1uCIFrllHdiadskpj2bnoTpU1Pc6PSZ0ZHx8HLxBZy4f0NUE7mo5uMrT4Ar2gz2RDPbAerBf/QX86Ijd5yaLOgmbPvEPqKiowPb9l6zt4i4cAZux0Zz/VHwIXH8HOBczCiebOWh7/6JdsARJNe74I+rq6lBRUYGOjg6MjY3BaGKh0+lQVlYmIlgmjCjVyMzMDFsdLJZlfS5/EA5IsAh3RLJg0WLPFMFyi6tK4KH6vCPeCtaNwTEcPN0kmqB8KOcamjuVYDkOPGsCPzoCvrcVfEs1+MFu++sYU8G07fceiRJ/o83+sy2X7PfZ8rp5gE//DGxWErj8NBjLTuD0lv9DUlISUjLqrIIlBm8yOkexJql75Xj/SLAEwXofCQkJSEpKsrsfWq1WVLD8iaAGSrD6+vp8LuAZDkiwCHdEsmBFbQRLgATLPbzJaF7GxIsK4p5UEvcVbwe4wRGtnVQlflOFc8eKMHjqgNvHddyFI/b3gTV5ljSe/C74zkb7z46rwbfWgu/vNP8375y7Y3tdngiW44xCT6JXtuchwbopWMJ9l4tgdXZ2+rwETTggwSLcQYIVwZBgTY5oxMSHWkyBwG6A046ZZ7p1N4G9Uo7OwnNoO51lLk9wYD3YIwkAgAOnm7D32GXUNg1g4nCiZ6UOzuxyPnfq2puP64oywF3KBXetEnxPiznfyY/rtb2ui/W9KK3tQXWj64iEXZ94ca9JsOQvWBTBCv25pT6gyxkSrAiGBGtyxCImHtVi8lE4eI41P6670Qa+5RJ43c0fR6PRiJNxn0Ab9zrY+N9Au+VNXEregj27ziJ+bxX27DoLkzWa9A4AYEJntOZXcWf3uHhc9ynY49vAnUsDV5ENvuOyU7uC+aX2duDmTUa7auQkWNEjWJSDFfpzS31AlzMkWBEMCZZneBXF8iCiwrMmcJfPgys/AS7vG/PjurQ1loWB7R/X8T03Z/gZjUYcjVuFgS8+QG7KHiTuKXXKrercuvLm4zqHWXR8X7vlcV0H+DEVeM7zqfZSEiwA4A2WauQGz9dDJMHyTbBSMmoB+CdYjhXOaRahfM4t9QFdzpBgRTAkWJ7hTRSLqy8C19cOrvCA+XFd4QEYbX7gqhv7UHqpCyZBpA7Hw6gcAns4XjxpvLkSAMByHK60DmLrV2dFk9YPHqlEU3UjTCMDQXk8KTXB4hovgNdPgGu84PV5okmwxOpx+SNYCQkJSE5O9rvQLdXBks+5pT6gyxkSrBCRn5+PX//613jooYcwd+5cLFq0CC+++CKqq6ud9r106RKWLVuGefPm4bbbbsNLL72EtrY2kaO6hwTLczyKYjnWajocD5Nej5KaHmthxp2HzEncmoR37N/X68Fm7wSbJjyuSzU/rhvuBctx2HW43kmqEtMuIa+sE4Mjwf/xlppgsWlrwXdeAZu21qvziM2asyXSBEtsP18Ei2VZKJVK1NbWoqKiAp2dnVCpVD5Hj0iw5HNuqQ/ocoYEK0S88sorePbZZ7F9+3YUFRUhMzMTS5YswfTp05Gfn2/dr6mpCbfeeiuefvppnDp1CkeOHMGjjz6KRYsWYXBw0KtzkmB5jidRLK624GbdJ4s8OVa/zi42V8euzj0Pk9HkcXXsU5bPxe+twvb9lThXchUDg8MhezwiOcGySXL3aH8HQWhvbxcVhGAKllqtRkFBAdRq9SSf8O34nu7nSw5WUVEREhMTby5pZFknsri4GKOjowFps9hrrpYQIcEK3bmlPqDLGRKsEDEw4Lx47NjYGBYuXIhly5ZZX3v11Vdx55132jW+s7MTM2bMwIcffujVOUmwvMNtFGvnB+CNevMjwu4mO3nyZn03g5FFYWWX3SK6Go0Gp86ex5avzuKzjUkBGeC8Rc6CpdFonARh8+bNovcvmIIVrME22IKVsLfMec1Im3Uk09PTvf4N8SZaJQYJ1uQ45r35itQHdDlDghVmnn32WcTExAAwV1KeM2cO3nzzTaf9/v3f/x0PPvigV8cmwfIOd1Es25mDRhOLkpoe0Vypw7nNUKp1TnIlbCU1PVCqdWhoMUcjNRoN0tPTAz7AeYtcBcvV/du8ebPo/SPBchYsYeFtV//+4uLiUFxc7FU0lQRLPkh9QJczJFhhRK1WW3OsAODatWtgGAbJyclO+/75z3/GlClToNPpPD4+CZb3eFpJ3HZxXE83IcJ1OLcZZ0vNs6OKiorsBrJADXDeIkfBcnf/bAXL9v6RYN0UrB0HazwWrMTERJd5bZ62mQRLmkh9QJczJFhh5Be/+AWmT59uTXQvLS0FwzA4cOCA077r168HwzC4ceOGy+Pp9XpoNBrrplAoSLC8xJtK4t5IliBX1Y196B82f4FUKhUSExM9EixvBzhvkaNgubt/joIl3D8SLHM7y8vLkWxZF9ITwYqLi0NXV5dfbSbBkiZSH9DlTCQLlkKhsPMNvV4vHcFauXIlGIbBtm3brK8JgnXw4EGn/QXBclfReNWqVWAYxmkjwfIObyqJeyJZQu6VwWAfgers7HQaxAI1wHmLHAXL3f1zFCzh/slRsDzNsRHawVXngCs9jvELx10KVmVlJZLTq70SrJaWFlendtmWyV5zhVzERS7tdIfUB3Q5E8mC5bitWrVKGoK1evVqMAyDdevW2b3u7yNCimAFBtsolidV20fHDW4Fa3RcfIBsbm72SrC8GeC8RWpfakESuOocl/v4cv/kKFjetkNApVJRBCvIyKWd7pDadz+SiGTBkmQES5Cr1atXO70nJLm/9dZbTu+98MILlOQeQniT0VxJfBK58jSCJVaiIVoiWMHCl/tHgkU5WIFELu10hxy/+3IhkgVLcjlYa9euBcMwWLlypct9/vu//xt33XWX3dTyrq4uzJw5Ex999JFX5yPB8h3eZARv0Pn9eNCdZEVLDlaw8OX+kWDRLMJAIpd2ukOO3325QIIVIuLi4sAwDJYvX47y8nKnTaCpqQm33HILnnnmGZw+fRpHjx7FY489RoVGwwBvcp374ussQlvJipZZhMHCl/tHgkV1sAKJXNrpDjl+9+UCCVaIWLp0qWhimLDZUl1djWXLlmHu3Ln41re+hf/6r/9Ca2ur1+ckwQoO/tbBciwyKoU6WP4WKwwX3t6/YFyn3AVrx8EaFBcXO1Vy37ZtG4qLi336t0eCJR+kPqDLGRKsCIYEK3iIRbA8qeQuloul0WgCPsBFE+G+f8IPYqAqa/vbDgFXgmUwGNDW1oaUjDqntQjr6upCuhahK+QiLnJppzukPqDLGRKsCIYEK7jYSpbjWoTZxa2i77si0ANctBHO+yeVH0R3guX43vj4uHVh8pSMWgD2i2V7Wk7D07a4es0VchEXubTTHVL59xuJkGBFMCRYwcdk4lBS02OVJ2HQ2nmoTvR9dwRygItGwnX/pPKD6E6wHO+HK8Fytb+/bXH1mivkIi5yaac7pPLvNxIhwYpgSLBCg21OlaNgOb7v9jgBHOCikXDdP6n8IHoqWBzHQa1Wk2AFALm00x1S+fcbiZBgRTAkWKFHTLA8hQTLP0iwJhcsg8GA4eFhdHZ2WutgSVGwwp3P5ikkWIQ7SLAiGBKs0FPd2IfS2h5UN7pe0sgVJFj+QYLlXrA4jsPw8DD6+vrQ29uL7QcsgnUw8IIlJkZSuU+BhASLcIcv91bqf1SQYFkgwZIXJFj+QYLlXrBMJhP6+/sxMjKCvr6+oEawPGlfJECCRbhD6rLkC1EvWElJSYiNjUVMTAwJlowgwfIPEizfIljC42wSLO8hwSKiDUGwYmJiEBsbi6SkJOt7USFYAhTBkhckWP5BguVbDhYJlu+QYBHRRtRHsARIsOQFCZZ/kGD5NouQBMt3SLCIaIMEywIJlrwgwfIPEizf6mCRYPkOCRYRbZBgWSDBkhckWP5BgkWCFWpIsIhogwTLAgmWvCDB8g8SLBKsUEOCRUQbJFgWSLDkBQmWf5BgkWCFmkgQrEgsJUAEDxIsCyRY8oIES55IRRxIsEJPJAgWQXgDCZYFEiyCCD5SEQcSrNBDgkVEGyRYFkiwCCL4SEUcSLBCDwkWEW2QYFkgwSKI4CMVcfBWsBzXzSTB8h65LEpNECtJPdgAABN0SURBVIGCBMsCCRZBBB+piIO3guVIsAWLJIQg5A8JlgUSLIIIPlIRB0dpcid+4RAsgiDkDwmWBRIsgogeSLAIggg2US9YSUlJiI2NRUxMDAkWQUQJJFgEQQQbQbBiYmIQGxuLpKQk63tRIVgCFMEiiOjBUZrcPbokwSIIwheiPoIlQIJFENGDN8n2JFgEQfgCCZYFEiyCiB5IsAiCCDYkWBZIsOQJDXSEL5BgEQQRbEiwLJBgyRMa6AhfIMEiCCLYkGBZIMGSJzTQEb7gTT0uEiyCIHyBBMsCCZY8oYGOCDYkWARB+AIJlgUSLHlCAx0RbEiwCILwBRIsCyRY8oQGOiLYkGARBOELJFgWSLDkCQ10RLAhwSIIwhdIsCyQYMkTGuiIYEOCRRCEL5BgWSDBkic00BHBhgSLIAhfiHrBosWe5Q0NdESwIcEiCMIXaLFnCxTBkic00BHBhgSLIAhfiPoIlgAJljyhgY4INiRYBEH4AgmWBRIseUIDHRFsSLAIgvAFEiwLJFjyhAY6ItiQYBEE4QskWBZIsOQJDXREsPFmYWiCIAgBEiwLJFjyhASLCDYkWARB+IIkBSs/Px+//vWv8dBDD2Hu3LlYtGgRXnzxRVRXV1v3YVkW8fHxeOGFF3DPPfdgzpw5ePjhh/HRRx9BpVJ5fU4SLHliNBqRkJCA5ORkEiwiKJBgEQThC5IUrFdeeQXPPvsstm/fjqKiImRmZmLJkiWYPn068vPzAQBjY2O49dZbsWLFCmRmZqKwsBDx8fGYP38+HnnkEUxMTHh1ThIs+cGyLJRKJWpra1FRUYHOzk6oVCqwLBvuphERBAkWQRC+IEnBGhgYcHptbGwMCxcuxLJlywCYB9fh4WGn/TIzM8EwDFJTU706JwmWvNBoNCgqKkJiYiLWrFmDNWvWIC4uDomJiSguLsbo6Gi4m0hECCRYBEH4giQFyxXPPvssYmJi3O7T1dUFhmGwfv16r45NgiUfNBoN0tPTrblXtoIlbOnp6dSXREAgwSIIwhdkI1hqtRq33XYbXnrpJbf77dmzBwzDICsry6vjk2DJA5ZlUVRUZCdTYoIVFxeH4uJielxI+A0JFkEQviAbwfrFL36B6dOn2yW6O9LT04OFCxfi+9//PjiOc3s8vV4PjUZj3RQKBQmWDFCpVEhMTPRIsBITE32a8EAQtpBgEQThC4JgKRQKO9/Q6/XSEayVK1eCYRhs27bN5T4jIyN4/PHHcdddd6GtrW3SY65atQoMwzhtJFjSprOz00mkXAlWXFwcurq6wt1kQuaQYBEE4QuCYDluq1atkoZgrV69GgzDYN26dS73USqV+Od//mcsWLAA9fX1Hh2XIljypLm52SvBamlpCXeTCZlDgkUQhC9IOoIlyNXq1atd7iPI1fz581FTU+PzuSgHSx5QBIsINSRYBEH4gmRzsNauXQuGYbBy5UqX+whydfvtt6Oqqsqv85FgyQPKwSJCDQkWQRC+IEnBiouLA8MwWL58OcrLy502AJiYmMATTzyBKVOmYOvWrU77tLa2enVOEix5QLMIiVBDgkUQhC9IUrCWLl0qmhgmbADQ0dHhdp/XXnvNq3OSYMkHqoNFhBISLIIgfEGSghUOSLDkhUajQXFxsVMl923btqG4uJj6kQgYJFgEQfgCCZYFEiz5IaxFWFdXR2sREkGDBIsgCF8gwbJAgiVPjEYjEhISkJSUBKPRGO7mEBEICRZBEL5AgmWBBEueGI1Ga94VCRYRDAwGQ7ibQBCEDCHBskCCJU9IsAiCIAgpQoJlgQRLnpBgEQRBEFKEBMsCCZY8IcEiCIIgpAgJlgUSLHlCgkUQBEFIkagXrKSkJMTGxiImJoYES4aQYBEEQRBSRBCsmJgYxMbGIikpyfpeVAiWAEWw5AkJFkEQBCFFoj6CJUCCJU9IsAiCIAgpQoJlgQRLnpBgEQRBEFKEBMsCCZY8IcEiCIIgpAgJlgUSLHlCgkUQBEFIERIsCyRY8oQEiyAIgpAiJFgWSLDkCQkWQRAEIUVIsCyQYMkTEiyCIAhCipBgWSDBkickWARBEIQUIcGyQIIlT0iwCIIgCClCgmWBBEuekGARBEEQUoQEywIJljwhwSIIgiCkSNQLFi32LG9IsAiCIAgpQos9W6AIFkEQBEEQgSLqI1gCJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLJFgEQRAEQQQKEiwLarUaDMNAoVBAo9HQRhtttNFGG220+bwpFAowDAO1Wu3kHFEhWMJiz4sXLwbDMLTRRhtttNFGG20B2x544IHoXuxZpVKBYRh0d3eH3XpjYmLC3gYptUX4K0Aq0UUp3BMptUNK/SOVeyKVtkipb6RyT6TSDuobabcjEP2jVquhUCjAcZyTc0SVYGk00snBio2NDXcTrEihLVLqG0Aa9wSQTjuk1D9SuSeANNoipb4BpHFPAGm0g/pGHKm0I9j9Q4IVJmzDiOFGCm2RUt8A0rgngHTaIaX+kco9AaTRFin1DSCNewJIox3UN+JIpR0kWAFEav/YiZtQ30gb6h/pQn0jXahvpA0JVgDR6/VYtWoV9Hp9uJtCOEB9I22of6QL9Y10ob6RNsHun6gSLIIgCIIgiFBAgkUQBEEQBBFgSLAIgiAIgiACDAkWQRAEQRBEgJGdYI2NjeG9997D3XffjVmzZuEf//EfceDAAY8+OzAwgNdeew0LFizAnDlzsGTJEpw7d05037y8PCxZsgRz5szBggUL8Nprr2FgYCCQlxJxhKJvPv74Y/zTP/0T5s+fj1mzZuH+++/HG2+8gc7OzkBfTkQRqu/N+Pg4PvnkEzz44IOYOXMm7rjjDvzbv/0bWlpaAnk5EUco+sdgMOCTTz7BfffdhxkzZuC73/0u/vrXv2JiYiLQlxNR+No3CoUC7733Hp555hncdtttYBgGe/bscdpPo9Hgs88+w9KlS7Fw4ULMmzcPjz32GDZs2ACdTheEK4ocgt03ALB06VLRyu0vvPDCpOeRnWA9//zzuP3225GSkoKCggK8/vrrYBgG6enpbj+n1+vx2GOP4Tvf+Q7S0tKQm5uLn/zkJ5g+fTqKiors9i0qKsL06dPxk5/8BLm5uUhLS8M999yDxx57jGaDuCEUffP73/8eGzduxIkTJ1BYWIjk5GTcfffdWLhwIYaHh4N5ebImFH0zNjaG73//+1i0aBESExNRVFSErKwsfPTRR6irqwvm5cmeUPTPyy+/jNmzZ2P9+vXIy8vD2rVrMXPmTPznf/5nMC9N9vjaN4WFhbjzzjvx4x//GD//+c9dDuKXL1/GnXfeiffffx9ZWVnIz8/H6tWrMXv2bCxbtgw8zwfpyuRPsPsGMAvW4sWLUV5ebrc1NTVN2j5ZCdapU6fAMAz2799v9/rzzz+PRYsWgWVZl59NTk4GwzAoKyuzvmYymfDII4/gBz/4gd2+TzzxBB555BGYTCbra6WlpWAYBtu3bw/Q1UQWoeobMU6fPg2GYbB7927fLyCCCVXfvPfee5g3bx7a2toCewERTij6p7y8HAzDID4+3u7z69evB8MwyM3NDdDVRBb+9I3t0ilVVVUuB/Hx8XGMj487vb5582YwDIMLFy74fgERTCj6BjAL1qOPPupTG2UlWK+//jpuueUWO/EBgP3794NhGJSWlrr87I9//GM89NBDTq8LPzA9PT0AgJ6eHjAMg88//9xp35iYGDz//PN+XkVkEoq+cYXwBdm3b59vjY9wQtE3Wq0W8+bNw69+9avANj4KCEX/xMXFgWEYp7+6GxoawDAM3nzzzQBcSeThT9/YMtkgLkZxcbGoQBBmQtU3USNYS5YswRNPPOH0emNjIxiGwc6dO11+9tvf/jZeffVVp9ezs7PBMAzOnj0LAMjJyQHDMDh16pTTvq+88gruvvtuP64gcglF39hiMpkwMTGBmpoaPPXUU4iJicHY2Jh/FxGhhKJvzp8/D4ZhsG7dOrz11lu4/fbbMWPGDPzLv/wLsrOzA3cxEUgo+kcQrvb2drv9mpubwTAMfvjDH/p5FZGJP31jiy+CtWrVKjAMg/r6eo8/E02Eqm+WLl2K2bNnY/78+Zg2bRoWL16Mjz/+2KPcRVkJ1oMPPiiaWHbjxg0wDIP169e7/OyMGTNE/0orKyuz+yshPT0dDMOgvLzcad8VK1Zg5syZflxB5BKKvhHo6+uzSzZ88skn0dvb6/9FRCih6JsDBw6AYRh861vfwlNPPYUTJ04gOzsbzz77LKZMmYKcnJzAXVCEEYr+OX78OBiGQWpqqt1+u3fvBsMwiImJ8fMqIhN/+sYWbwWrvr4ec+bMwUsvveRNc6OKUPXN3/72N2zfvh0FBQU4deoU3n77bUyfPh3PPPOM3aNGMWQnWMuXL3d6XbihYo/1BGbMmIG33nrL6XXhh0iYeSAIVkVFhdO+K1aswKxZs/y4gsglFH0jYDKZUFVVhZKSEnz11Vd48MEHERMTgxs3bvh/IRFIKL83d955J0ZHR637abVaLFq0CE899VQAriQyCUX/GAwG/P3f/z0WLVqE3NxcqFQqnDlzBgsXLsS0adPw8MMPB+6CIgh/+sYWbwSro6MD9957L2JiYjAyMuJtk6OGcPSNgPDI/ejRo273k5Vg0SNC6RLqR4S2KBQKTJ8+He+++673DY8CQvm9efHFF532/fnPf445c+b4cQWRTai+O9evX8eSJUuskd958+Zh69atuPPOO7Fs2bLAXEyEEepHhJ2dnbjvvvtw//33Q6FQ+NLkqCGcj2/7+/vBMAw+/PBDt/vJSrDeeOMN0aQ24fGEu6S2559/XvSvtM8//xwMw1gfMQlJ7hs2bHDa96GHHqIkdxeEom/c8d3vflf0rxkiNH0j/NUoJlg/+9nPMG/ePD+vInIJ9Xenp6cHDQ0N0Gq11n5bs2aN/xcSgfjTN7Z4MogLcvW9732P6vp5QCj7xhFBsP7617+63U9WgiVMxz948KDd68uXL590Wub27dudHv2ZTCY8+uijePLJJ+32/cEPfoDHHnvM7njCNOcdO3YE6Goii1D1jRjXr1/H1KlT8fbbb/t+ARFMqPrmhz/8IRYsWACNRmN9TavV4u6776YIiRvC+d15//33MW/evEln6kYr/vSNLZMN4l1dXbjvvvtw7733UpkTDwlV34ixceNGMAyD48ePu91PVoIFmP9imz9/Pr788ksUFBTgjTfeAMMwSEtLs+7zm9/8BtOmTbP7K0Cv1+PRRx/Fvffei/T0dOTl5eGll14SLchXWFiI6dOn46WXXkJeXh7S09Nx7733UqHRSQh239TX1+O5557D9u3bkZOTg9zcXMTHx+M73/kO/u7v/o7+6nNDKL43paWlmDlzJpYsWYJjx47h+PHjePrppzFjxgy7Ok2EM6Hon40bN2Lfvn0oLCzEwYMH8fLLL2Pq1KmTFmWMdnztGwDIzMxEZmamdUD+wx/+YH1NYGBgAIsXL8asWbOQlpbmVNCSHhW6Jth9c/78ebzwwgtISUlBbm4uTpw4gd/97neYNm0annvuuchKcgfM1aLfffddfPvb38bMmTPx+OOPOyVBv/baa2AYBh0dHXav9/f345e//CXuuOMOzJ49G0uWLEFeXp7oeXJzc7FkyRLMnj0bd9xxB375y1/SUjmTEOy+6e/vx//8z//ggQcewNy5czFz5kwsXrwYb731Frq7u4N9ebImVN+bCxcuYOnSpZg7dy7mzp2L5557zuNQfTQTiv5Zs2YNHnjgAcyaNQu33347li9fjvPnzwfzsiICf/rGdraz4yZQWFjodr9Vq1aF4CrlSbD75vr16/iP//gP3HPPPZg1axZmz56Nf/iHf8C6des8CrbITrAIgiAIgiCkDgkWQRAEQRBEgCHBIgiCIAiCCDAkWARBEARBEAGGBIsgCIIgCCLAkGARBEEQBEEEGBIsgiAIgiCIAEOCRRAEQRAEEWBIsAiCIAiCIAIMCRZBEARBEESAIcEiCIIgCIIIMCRYBEEQBEEQAYYEiyAIgiAIIsCQYBEEQQBYuXIlGIZxu82dOxccx4W7qQRByAASLIIgCACffPIJnnrqKdFtwYIFYBgGTz75ZLibSRCETCDBIgiCcENWVhZmzJiB2267DZWVleFuDkEQMoEEiyAIwgXZ2dmYOXMmbr31VpSXl4e7OQRByAgSLIIgCBFycnIwa9Ys3HLLLSgpKQl3cwiCkBkkWARBEA7k5eVh9uzZmDt3LoqLi8PdHIIgZAgJFkEQhA0FBQWYM2cO5syZg4KCgnA3hyAImUKCRRAEYaG4uBhz587F7NmzkZeXF+7mEAQhY0iwCIIgAJSUlOCWW27BrFmzkJOTE+7mEAQhc0iwCIKIesrLy3Hrrbdi5syZyM7ODndzCIKIAEiwCIKIaiorK3HbbbdhxowZyMrKCndzCIKIEEiwCIKIap588kkwDIMFCxa4rOT+ox/9CDqdLtxNJQhCRpBgEQQRtXAch7lz5066BuG9994b7qYSBCEzSLAIgiAIgiACzP8DwfcNOcJ8LroAAAAASUVORK5CYII=\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(23)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6,4))\n",
    "\n",
    "#  rise vs. redshift\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_plot], t_rise[normal_plot], \n",
    "             yerr=t_rise_unc[:,normal_plot], fmt='.', \n",
    "              color='0.7', lw=0.5, alpha=0.2)\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_good_plot], t_rise[normal_good_plot], \n",
    "             yerr=t_rise_unc[:,normal_good_plot], fmt='.',\n",
    "             color='0.6', lw=1, ms=7, alpha=0.5)\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_good_host_plot], t_rise[normal_good_host_plot], \n",
    "             yerr=t_rise_unc[:,normal_good_host_plot], fmt='o', ms=9,\n",
    "             color='0.5', mec='w', mew=0.5, lw=1.5, alpha=0.9, zorder=10)\n",
    "\n",
    "for j, col, sym in zip(range(4), \n",
    "                       ['#66c2a5','#fc8d62','#8da0cb','#e78ac3'], \n",
    "                       ['o', 'v', 'X', 'd']):\n",
    "    idx = np.arange(5*j,5*(j+1))\n",
    "    idx = idx[np.where(t0_array[idx] > -20.5)]\n",
    "    \n",
    "    z_data = z_array[idx] + np.random.uniform(-0.002,0.002,len(idx))\n",
    "    rise = -1*t0_array[idx]\n",
    "    rise_unc = np.vstack((t0_95[idx] - t0_array[idx], \n",
    "                          t0_array[idx] - t0_05[idx])) \n",
    "    \n",
    "    ax.errorbar(np.sort(z_data), rise[np.argsort(z_data)],\n",
    "                yerr=rise_unc[:,np.argsort(z_data)],\n",
    "                fmt='{}--'.format(sym), color=col, mec='w', mew=0.75,\n",
    "                ms=12, lw=2, zorder=20)\n",
    "\n",
    "\n",
    "ax.set_ylabel(r'$t_\\mathrm{rise}\\;(\\mathrm{d})$', fontsize=16)\n",
    "ax.set_ylim(23.5,10.5)\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.5))\n",
    "ax.yaxis.set_major_locator(MultipleLocator(2))\n",
    "ax.set_xlabel(r'$z$', fontsize=16)\n",
    "ax.set_xlim(0,0.155)\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.01))\n",
    "ax.xaxis.set_major_locator(MultipleLocator(0.03))\n",
    "ax.tick_params(top=True,right=True,which='both', labelsize=12)\n",
    "\n",
    "fig.subplots_adjust(top=0.986,right=0.99,left=0.1,bottom=0.122)\n",
    "fig.savefig('../paper/figures/high_z_systematic.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"1000\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(23)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "\n",
    "#  rise vs. redshift\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_plot], t_rise[normal_plot], \n",
    "             yerr=t_rise_unc[:,normal_plot], fmt='.', \n",
    "              color='0.7', lw=0.5, ms=15, alpha=0.2)\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_good_plot], t_rise[normal_good_plot], \n",
    "             yerr=t_rise_unc[:,normal_good_plot], fmt='.',\n",
    "             color='0.6', lw=1, ms=20, alpha=0.5)\n",
    "ax.errorbar(salt_df.z_adopt.values[normal_good_host_plot], t_rise[normal_good_host_plot], \n",
    "             yerr=t_rise_unc[:,normal_good_host_plot], fmt='o', ms=15,\n",
    "             color='0.5', mec='w', mew=0.5, lw=1.5, alpha=0.9, zorder=10)\n",
    "\n",
    "for j, col, sym in zip(range(4), \n",
    "                       ['#66c2a5','#fc8d62','#8da0cb','#e78ac3'], \n",
    "                       ['o', 'v', 'X', 'd']):\n",
    "    idx = np.arange(5*j,5*(j+1))\n",
    "    idx = idx[np.where(t0_array[idx] > -20)]\n",
    "    \n",
    "    z_data = z_array[idx] + np.random.uniform(-0.002,0.002,len(idx))\n",
    "    rise = -1*t0_array[idx]\n",
    "    rise_unc = np.vstack((t0_95[idx] - t0_array[idx], \n",
    "                          t0_array[idx] - t0_05[idx])) \n",
    "    \n",
    "    ax.errorbar(np.sort(z_data), rise[np.argsort(z_data)],\n",
    "                yerr=rise_unc[:,np.argsort(z_data)],\n",
    "                fmt='{}--'.format(sym), color=col, mec='w', mew=0.75,\n",
    "                ms=25, lw=2, zorder=20)\n",
    "\n",
    "\n",
    "ax.set_ylabel(r'$t_\\mathrm{rise}\\;(\\mathrm{d})$', fontsize=28)\n",
    "ax.set_ylim(23,11)\n",
    "ax.yaxis.set_minor_locator(MultipleLocator(0.5))\n",
    "ax.yaxis.set_major_locator(MultipleLocator(2))\n",
    "ax.set_xlabel(r'$z$', fontsize=28)\n",
    "ax.set_xlim(0.01,0.155)\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(0.005))\n",
    "ax.xaxis.set_major_locator(MultipleLocator(0.02))\n",
    "ax.tick_params(top=True,right=True,which='both', labelsize=20)\n",
    "\n",
    "fig.subplots_adjust(top=0.98,right=0.995,left=0.08,bottom=0.11)\n",
    "fig.savefig('poster_high_z_systematic.pdf')\n",
    "\n",
    "ax.axvspan(0,1e4, facecolor='white')\n",
    "fig.subplots_adjust(top=0.99,right=0.99,left=0.095,bottom=0.13)\n",
    "fig.savefig('poster_high_z_systematic.png', transparent=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}