data/vn-vs-smp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load in the Python modules necessary to load the data (gzip and yaml), process the data (numpy and pandas), and plot the data (toyplot)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yaml version:     3.12\n",
      "numpy version:    1.13.3\n",
      "pandas version:   0.20.3\n",
      "toyplot version:  0.16.0\n"
     ]
    }
   ],
   "source": [
    "import gzip\n",
    "import yaml\n",
    "import numpy\n",
    "import pandas\n",
    "import toyplot.pdf\n",
    "\n",
    "print(\"yaml version:    \", yaml.__version__)\n",
    "print(\"numpy version:   \", numpy.__version__)\n",
    "print(\"pandas version:  \", pandas.__version__)\n",
    "print(\"toyplot version: \", toyplot.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ingest Data\n",
    "\n",
    "Load the data, which is output into a YAML files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'miniGraphics-skybridge-vn-scaling.yaml.gz'\n",
    "yaml_data = yaml.load(gzip.open(filename))\n",
    "data_vn = pandas.DataFrame(yaml_data)\n",
    "\n",
    "filename = 'miniGraphics-skybridge-smp-scaling.yaml.gz'\n",
    "yaml_data = yaml.load(gzip.open(filename))\n",
    "data_smp = pandas.DataFrame(yaml_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of the original runs gave suspect results where the runs took much longer than expected. These appear to be transient issues in the system since a repeat of the run is closer to what is expected. To get more realistic results, I re-ran these conditions. So jettison these runs from the original data and load up the re-runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove incorrect readings\n",
    "data_vn = data_vn.loc[\n",
    "    ((data_vn['composite-algorithm'] != '2-3-SwapBase') |\n",
    "     (data_vn['num-processes'] != 4096) |\n",
    "     (data_vn['image-height'] != 1080)) &\n",
    "    ((data_vn['composite-algorithm'] != 'BinarySwapFold') |\n",
    "     (data_vn['num-processes'] != 2896) |\n",
    "     (data_vn['image-height'] != 1080)) &\n",
    "    ((data_vn['composite-algorithm'] != 'BinarySwapRemainder') |\n",
    "     (data_vn['num-processes'] != 2048) |\n",
    "     (data_vn['image-height'] != 1080)) &\n",
    "    ((data_vn['composite-algorithm'] != 'BinarySwapTelescoping') |\n",
    "     (data_vn['num-processes'] != 1616) |\n",
    "     (data_vn['image-height'] != 1080))\n",
    "]\n",
    "# Load correct readings and add them to the data\n",
    "filename = 'miniGraphics-skybridge-vn-corrections.yaml.gz'\n",
    "yaml_data = yaml.load(gzip.open(filename))\n",
    "data_vn = pandas.concat([data_vn, pandas.DataFrame(yaml_data)], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The YAML data is hierarchical. The basic yaml reader to DataFrame just embeds dictionaries and lists in DataFrame columns. Fix that by expanding the data of these columns into new columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_single_column(original_data, column_to_expand):\n",
    "\n",
    "    expanded_data = pandas.DataFrame()\n",
    "    for index in original_data.index:\n",
    "        sub_table = pandas.DataFrame(original_data[column_to_expand][index])\n",
    "        for column in original_data.columns:\n",
    "            if column != column_to_expand:\n",
    "                sub_table[column] = numpy.full(sub_table.index.shape,\n",
    "                                               original_data[column][index],\n",
    "                                               dtype=original_data[column].dtype)\n",
    "        expanded_data = expanded_data.append(sub_table, ignore_index=True)\n",
    "    return expanded_data\n",
    "\n",
    "def flatten_table(original_data):\n",
    "    flat_data = original_data\n",
    "    for column_name in original_data.columns:\n",
    "        if isinstance(flat_data[column_name][0], list):\n",
    "            flat_data = expand_single_column(flat_data, column_name)\n",
    "    return flat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_vn = flatten_table(data_vn)\n",
    "data_smp = flatten_table(data_smp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add a column that gives a human-readable name to each image resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_height_names = {\n",
    "    500: 'Desktop Window',\n",
    "    1080: 'HDTV',\n",
    "    4320: '8K UHD',\n",
    "}\n",
    "\n",
    "data_vn['image-size'] = data_vn['image-height'].map(image_height_names)\n",
    "data_smp['image-size'] = data_smp['image-height'].map(image_height_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the algorithms from the identifiers the program wrote out to the strings used in the paper. Note that there are some extras in the data that we are ignoring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "algorithm_names = {\n",
    "    '2-3-SwapBase': '2-3 Swap',\n",
    "    'BinarySwapFold': 'Naive',\n",
    "    'BinarySwapTelescoping': 'Telescoping',\n",
    "    'BinarySwapRemainder': 'Remainder',\n",
    "    'IceTBase': 'IceT'\n",
    "}\n",
    "\n",
    "data_vn['composite-algorithm'] = data_vn['composite-algorithm'].map(algorithm_names)\n",
    "data_smp['composite-algorithm'] = data_smp['composite-algorithm'].map(algorithm_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print a summary of the table data. There are multiple ways that Jupyter and pandas will report a summary of a table, but I find this method the most effective. It prints out every column. Then for all columns with a \"small\" number of unique values, it gives those values. This latter information really helps identify the proper way to group values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "##### Virtual Node\n",
       "\n",
       "**color-buffer-format**: byte  \n",
       "**composite-algorithm**: Naive Remainder Telescoping 2-3 Swap IceT nan  \n",
       "**composite-seconds**: 0.00119348 &ndash; 3.49925  \n",
       "**compress-seconds**:  \n",
       "**construct-tree-seconds**: 0.000182052 &ndash; 0.0610641  \n",
       "**depth-buffer-format**: float  \n",
       "**gather-seconds**: 0.00071677 &ndash; 3.45673  \n",
       "**geometry**: box  \n",
       "**geometry-distribution**: duplicate  \n",
       "**geometry-overlap**: -0.05  \n",
       "**icet-copy-result-seconds**:  \n",
       "**image-compression**: True False  \n",
       "**image-height**: 288 1080 4320  \n",
       "**image-width**: 512 1920 7680  \n",
       "**num-processes**: 128 &ndash; 8192  \n",
       "**num-triangles**: 1536 &ndash; 98304  \n",
       "**paint-seconds**: 0.00011823 &ndash; 0.147793  \n",
       "**painter**: simple  \n",
       "**partial-composite-seconds**: 0.000293153 &ndash; 0.86969  \n",
       "**phi-rotation**: -178.406 &ndash; 136.756  \n",
       "**random-seed**: 17627  \n",
       "**rendering-order-dependent**: False  \n",
       "**start-time**: 2018-05-30T02:23:10.000000000 &ndash; 2018-06-08T12:10:45.000000000  \n",
       "**theta-rotation**: -177.945 &ndash; 179.418  \n",
       "**total-seconds**: 0.00146716 &ndash; 3.59942  \n",
       "**trial-num**: 0 &ndash; 19  \n",
       "**uncompress-seconds**:  \n",
       "**zoom**: 1.5  \n",
       "**image-size**: nan HDTV 8K UHD  \n",
       "\n",
       "##### SMP\n",
       "\n",
       "**color-buffer-format**: byte  \n",
       "**composite-algorithm**: Naive Remainder Telescoping 2-3 Swap IceT nan  \n",
       "**composite-seconds**: 0.000823865 &ndash; 0.99317  \n",
       "**compress-seconds**:  \n",
       "**construct-tree-seconds**: 1.1473e-05 &ndash; 0.00111429  \n",
       "**depth-buffer-format**: float  \n",
       "**gather-seconds**: 0.000536648 &ndash; 0.381332  \n",
       "**geometry**: box  \n",
       "**geometry-distribution**: duplicate  \n",
       "**geometry-overlap**: -0.05  \n",
       "**icet-copy-result-seconds**:  \n",
       "**image-compression**: True False  \n",
       "**image-height**: 288 1080 4320  \n",
       "**image-width**: 512 1920 7680  \n",
       "**num-processes**: 8 &ndash; 512  \n",
       "**num-triangles**: 96 &ndash; 6144  \n",
       "**paint-seconds**: 0.000120004 &ndash; 0.204059  \n",
       "**painter**: simple  \n",
       "**partial-composite-seconds**: 0.000219604 &ndash; 0.802574  \n",
       "**phi-rotation**: -178.406 &ndash; 136.756  \n",
       "**random-seed**: 17627  \n",
       "**rendering-order-dependent**: False  \n",
       "**start-time**: 2018-05-30T02:21:27.000000000 &ndash; 2018-06-08T12:10:16.000000000  \n",
       "**theta-rotation**: -177.945 &ndash; 179.418  \n",
       "**total-seconds**: 0.00107058 &ndash; 1.15555  \n",
       "**trial-num**: 0 &ndash; 19  \n",
       "**uncompress-seconds**:  \n",
       "**zoom**: 1.5  \n",
       "**image-size**: nan HDTV 8K UHD  \n"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import IPython.display\n",
    "\n",
    "data_description = '##### Virtual Node\\n\\n'\n",
    "\n",
    "for column_name in data_vn.columns:\n",
    "    data_description = data_description + '**' + column_name + '**: '\n",
    "    unique_values = data_vn[column_name].unique()\n",
    "    if (len(unique_values) < 10):\n",
    "        for value in unique_values:\n",
    "            data_description = data_description + str(value) + ' '\n",
    "    elif (numpy.issubdtype(unique_values.dtype, numpy.number)):\n",
    "        data_description = (\n",
    "            data_description +\n",
    "            str(numpy.nanmin(unique_values)) + ' &ndash; ' +\n",
    "            str(numpy.nanmax(unique_values)) + ' '\n",
    "        )\n",
    "    elif not pandas.isnull(unique_values).any():\n",
    "        data_description = (\n",
    "            data_description +\n",
    "            str(numpy.min(unique_values)) + ' &ndash; ' +\n",
    "            str(numpy.max(unique_values)) + ' '\n",
    "        )\n",
    "    data_description = data_description + ' \\n'\n",
    "    \n",
    "data_description = data_description + '\\n##### SMP\\n\\n'\n",
    "\n",
    "for column_name in data_smp.columns:\n",
    "    data_description = data_description + '**' + column_name + '**: '\n",
    "    unique_values = data_smp[column_name].unique()\n",
    "    if (len(unique_values) < 10):\n",
    "        for value in unique_values:\n",
    "            data_description = data_description + str(value) + ' '\n",
    "    elif (numpy.issubdtype(unique_values.dtype, numpy.number)):\n",
    "        data_description = (\n",
    "            data_description +\n",
    "            str(numpy.nanmin(unique_values)) + ' &ndash; ' +\n",
    "            str(numpy.nanmax(unique_values)) + ' '\n",
    "        )\n",
    "    elif not pandas.isnull(unique_values).any():\n",
    "        data_description = (\n",
    "            data_description +\n",
    "            str(numpy.min(unique_values)) + ' &ndash; ' +\n",
    "            str(numpy.max(unique_values)) + ' '\n",
    "        )\n",
    "    data_description = data_description + ' \\n'\n",
    "    \n",
    "IPython.display.display(IPython.display.Markdown(data_description))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Data\n",
    "\n",
    "We are plotting the time it takes to do a \"partial composite\" (that is the time to blend all the pixels, but the pixels are left distributed across all the processes).\n",
    "\n",
    "The first thing we want to do is to average the time it took over all trials. This is easily done with a pivot table. We also need the filter the data to those that have been run in both VN and SMP modes. These are those between 64 and 512 processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>image-size</th>\n",
       "      <th colspan=\"5\" halign=\"left\">8K UHD</th>\n",
       "      <th colspan=\"5\" halign=\"left\">HDTV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composite-algorithm</th>\n",
       "      <th>2-3 Swap</th>\n",
       "      <th>IceT</th>\n",
       "      <th>Naive</th>\n",
       "      <th>Remainder</th>\n",
       "      <th>Telescoping</th>\n",
       "      <th>2-3 Swap</th>\n",
       "      <th>IceT</th>\n",
       "      <th>Naive</th>\n",
       "      <th>Remainder</th>\n",
       "      <th>Telescoping</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num-processes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.424882</td>\n",
       "      <td>0.053204</td>\n",
       "      <td>0.450411</td>\n",
       "      <td>0.415041</td>\n",
       "      <td>0.415397</td>\n",
       "      <td>0.023590</td>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.022431</td>\n",
       "      <td>0.022514</td>\n",
       "      <td>0.022490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>0.440534</td>\n",
       "      <td>0.052022</td>\n",
       "      <td>0.685614</td>\n",
       "      <td>0.410063</td>\n",
       "      <td>0.412039</td>\n",
       "      <td>0.024490</td>\n",
       "      <td>0.004788</td>\n",
       "      <td>0.037664</td>\n",
       "      <td>0.022203</td>\n",
       "      <td>0.022332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>0.443978</td>\n",
       "      <td>0.053023</td>\n",
       "      <td>0.685982</td>\n",
       "      <td>0.412858</td>\n",
       "      <td>0.412648</td>\n",
       "      <td>0.023896</td>\n",
       "      <td>0.005028</td>\n",
       "      <td>0.037944</td>\n",
       "      <td>0.022328</td>\n",
       "      <td>0.022242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>0.438112</td>\n",
       "      <td>0.044091</td>\n",
       "      <td>0.679504</td>\n",
       "      <td>0.409229</td>\n",
       "      <td>0.409191</td>\n",
       "      <td>0.023896</td>\n",
       "      <td>0.004002</td>\n",
       "      <td>0.037442</td>\n",
       "      <td>0.022204</td>\n",
       "      <td>0.022245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>0.437661</td>\n",
       "      <td>0.041432</td>\n",
       "      <td>0.677950</td>\n",
       "      <td>0.406705</td>\n",
       "      <td>0.409481</td>\n",
       "      <td>0.024472</td>\n",
       "      <td>0.003871</td>\n",
       "      <td>0.037503</td>\n",
       "      <td>0.022121</td>\n",
       "      <td>0.022121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0.414052</td>\n",
       "      <td>0.037369</td>\n",
       "      <td>0.402548</td>\n",
       "      <td>0.430229</td>\n",
       "      <td>0.402594</td>\n",
       "      <td>0.023289</td>\n",
       "      <td>0.003264</td>\n",
       "      <td>0.022116</td>\n",
       "      <td>0.021993</td>\n",
       "      <td>0.022151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>0.428487</td>\n",
       "      <td>0.038341</td>\n",
       "      <td>0.665274</td>\n",
       "      <td>0.403638</td>\n",
       "      <td>0.403873</td>\n",
       "      <td>0.023900</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.036865</td>\n",
       "      <td>0.022126</td>\n",
       "      <td>0.022183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.426395</td>\n",
       "      <td>0.037734</td>\n",
       "      <td>0.668252</td>\n",
       "      <td>0.399939</td>\n",
       "      <td>0.400913</td>\n",
       "      <td>0.023573</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>0.036933</td>\n",
       "      <td>0.021988</td>\n",
       "      <td>0.022029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>0.427184</td>\n",
       "      <td>0.037632</td>\n",
       "      <td>0.663289</td>\n",
       "      <td>0.396885</td>\n",
       "      <td>0.398925</td>\n",
       "      <td>0.023456</td>\n",
       "      <td>0.003754</td>\n",
       "      <td>0.036448</td>\n",
       "      <td>0.021906</td>\n",
       "      <td>0.021776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.425750</td>\n",
       "      <td>0.033612</td>\n",
       "      <td>0.661835</td>\n",
       "      <td>0.397030</td>\n",
       "      <td>0.397765</td>\n",
       "      <td>0.023836</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.036566</td>\n",
       "      <td>0.021612</td>\n",
       "      <td>0.021997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>0.421872</td>\n",
       "      <td>0.031573</td>\n",
       "      <td>0.662567</td>\n",
       "      <td>0.402201</td>\n",
       "      <td>0.412633</td>\n",
       "      <td>0.023965</td>\n",
       "      <td>0.003078</td>\n",
       "      <td>0.036506</td>\n",
       "      <td>0.022167</td>\n",
       "      <td>0.021651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>0.407533</td>\n",
       "      <td>0.029525</td>\n",
       "      <td>0.395685</td>\n",
       "      <td>0.395133</td>\n",
       "      <td>0.394878</td>\n",
       "      <td>0.023806</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>0.021664</td>\n",
       "      <td>0.021638</td>\n",
       "      <td>0.022055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "image-size             8K UHD                                            \\\n",
       "composite-algorithm  2-3 Swap      IceT     Naive Remainder Telescoping   \n",
       "num-processes                                                             \n",
       "128                  0.424882  0.053204  0.450411  0.415041    0.415397   \n",
       "160                  0.440534  0.052022  0.685614  0.410063    0.412039   \n",
       "176                  0.443978  0.053023  0.685982  0.412858    0.412648   \n",
       "192                  0.438112  0.044091  0.679504  0.409229    0.409191   \n",
       "224                  0.437661  0.041432  0.677950  0.406705    0.409481   \n",
       "256                  0.414052  0.037369  0.402548  0.430229    0.402594   \n",
       "272                  0.428487  0.038341  0.665274  0.403638    0.403873   \n",
       "320                  0.426395  0.037734  0.668252  0.399939    0.400913   \n",
       "352                  0.427184  0.037632  0.663289  0.396885    0.398925   \n",
       "400                  0.425750  0.033612  0.661835  0.397030    0.397765   \n",
       "448                  0.421872  0.031573  0.662567  0.402201    0.412633   \n",
       "512                  0.407533  0.029525  0.395685  0.395133    0.394878   \n",
       "\n",
       "image-size               HDTV                                            \n",
       "composite-algorithm  2-3 Swap      IceT     Naive Remainder Telescoping  \n",
       "num-processes                                                            \n",
       "128                  0.023590  0.003565  0.022431  0.022514    0.022490  \n",
       "160                  0.024490  0.004788  0.037664  0.022203    0.022332  \n",
       "176                  0.023896  0.005028  0.037944  0.022328    0.022242  \n",
       "192                  0.023896  0.004002  0.037442  0.022204    0.022245  \n",
       "224                  0.024472  0.003871  0.037503  0.022121    0.022121  \n",
       "256                  0.023289  0.003264  0.022116  0.021993    0.022151  \n",
       "272                  0.023900  0.003878  0.036865  0.022126    0.022183  \n",
       "320                  0.023573  0.002853  0.036933  0.021988    0.022029  \n",
       "352                  0.023456  0.003754  0.036448  0.021906    0.021776  \n",
       "400                  0.023836  0.002649  0.036566  0.021612    0.021997  \n",
       "448                  0.023965  0.003078  0.036506  0.022167    0.021651  \n",
       "512                  0.023806  0.002864  0.021664  0.021638    0.022055  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_indices = (\n",
    "    (data_vn['num-processes'] >= 128) &\n",
    "    (data_vn['num-processes'] <= 512)\n",
    ")\n",
    "\n",
    "average_partial_composite_vn = data_vn[filtered_indices].pivot_table(\n",
    "    values='partial-composite-seconds',\n",
    "    index='num-processes',\n",
    "    columns=[\n",
    "        'image-size',\n",
    "        'composite-algorithm',\n",
    "    ],\n",
    "    aggfunc='mean',\n",
    ")\n",
    "\n",
    "average_partial_composite_vn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>image-size</th>\n",
       "      <th colspan=\"5\" halign=\"left\">8K UHD</th>\n",
       "      <th colspan=\"5\" halign=\"left\">HDTV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>composite-algorithm</th>\n",
       "      <th>2-3 Swap</th>\n",
       "      <th>IceT</th>\n",
       "      <th>Naive</th>\n",
       "      <th>Remainder</th>\n",
       "      <th>Telescoping</th>\n",
       "      <th>2-3 Swap</th>\n",
       "      <th>IceT</th>\n",
       "      <th>Naive</th>\n",
       "      <th>Remainder</th>\n",
       "      <th>Telescoping</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num-processes</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.346160</td>\n",
       "      <td>0.025752</td>\n",
       "      <td>0.324867</td>\n",
       "      <td>0.325992</td>\n",
       "      <td>0.325512</td>\n",
       "      <td>0.018828</td>\n",
       "      <td>0.001801</td>\n",
       "      <td>0.016950</td>\n",
       "      <td>0.017302</td>\n",
       "      <td>0.017308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.383349</td>\n",
       "      <td>0.035832</td>\n",
       "      <td>0.620365</td>\n",
       "      <td>0.329196</td>\n",
       "      <td>0.330120</td>\n",
       "      <td>0.020551</td>\n",
       "      <td>0.002746</td>\n",
       "      <td>0.031701</td>\n",
       "      <td>0.017456</td>\n",
       "      <td>0.017435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>0.362933</td>\n",
       "      <td>0.037592</td>\n",
       "      <td>0.609298</td>\n",
       "      <td>0.335417</td>\n",
       "      <td>0.322732</td>\n",
       "      <td>0.019846</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>0.031150</td>\n",
       "      <td>0.017175</td>\n",
       "      <td>0.017202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>0.350075</td>\n",
       "      <td>0.028211</td>\n",
       "      <td>0.601279</td>\n",
       "      <td>0.318414</td>\n",
       "      <td>0.318868</td>\n",
       "      <td>0.019369</td>\n",
       "      <td>0.002439</td>\n",
       "      <td>0.031606</td>\n",
       "      <td>0.017061</td>\n",
       "      <td>0.017088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.368279</td>\n",
       "      <td>0.025565</td>\n",
       "      <td>0.606522</td>\n",
       "      <td>0.320694</td>\n",
       "      <td>0.318968</td>\n",
       "      <td>0.020347</td>\n",
       "      <td>0.002287</td>\n",
       "      <td>0.032032</td>\n",
       "      <td>0.017223</td>\n",
       "      <td>0.017191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.023898</td>\n",
       "      <td>0.605073</td>\n",
       "      <td>0.314026</td>\n",
       "      <td>0.314605</td>\n",
       "      <td>0.020020</td>\n",
       "      <td>0.002169</td>\n",
       "      <td>0.031738</td>\n",
       "      <td>0.017156</td>\n",
       "      <td>0.016907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0.329589</td>\n",
       "      <td>0.017220</td>\n",
       "      <td>0.308654</td>\n",
       "      <td>0.308345</td>\n",
       "      <td>0.308671</td>\n",
       "      <td>0.018597</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>0.016653</td>\n",
       "      <td>0.016841</td>\n",
       "      <td>0.016982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>0.359717</td>\n",
       "      <td>0.028142</td>\n",
       "      <td>0.596548</td>\n",
       "      <td>0.312132</td>\n",
       "      <td>0.315081</td>\n",
       "      <td>0.020175</td>\n",
       "      <td>0.002269</td>\n",
       "      <td>0.031012</td>\n",
       "      <td>0.017163</td>\n",
       "      <td>0.017393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.349128</td>\n",
       "      <td>0.029455</td>\n",
       "      <td>0.593704</td>\n",
       "      <td>0.310503</td>\n",
       "      <td>0.308433</td>\n",
       "      <td>0.019464</td>\n",
       "      <td>0.002474</td>\n",
       "      <td>0.032058</td>\n",
       "      <td>0.016914</td>\n",
       "      <td>0.016995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>0.339380</td>\n",
       "      <td>0.021054</td>\n",
       "      <td>0.589601</td>\n",
       "      <td>0.306951</td>\n",
       "      <td>0.307453</td>\n",
       "      <td>0.019154</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.032794</td>\n",
       "      <td>0.017007</td>\n",
       "      <td>0.016998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>0.348666</td>\n",
       "      <td>0.018447</td>\n",
       "      <td>0.582892</td>\n",
       "      <td>0.304362</td>\n",
       "      <td>0.302856</td>\n",
       "      <td>0.020316</td>\n",
       "      <td>0.001912</td>\n",
       "      <td>0.032517</td>\n",
       "      <td>0.016765</td>\n",
       "      <td>0.016890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>0.343736</td>\n",
       "      <td>0.016096</td>\n",
       "      <td>0.583155</td>\n",
       "      <td>0.299269</td>\n",
       "      <td>0.300455</td>\n",
       "      <td>0.019635</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.032158</td>\n",
       "      <td>0.016752</td>\n",
       "      <td>0.016840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>0.323190</td>\n",
       "      <td>0.012963</td>\n",
       "      <td>0.300264</td>\n",
       "      <td>0.300909</td>\n",
       "      <td>0.301313</td>\n",
       "      <td>0.019172</td>\n",
       "      <td>0.001552</td>\n",
       "      <td>0.016727</td>\n",
       "      <td>0.016755</td>\n",
       "      <td>0.016832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "image-size             8K UHD                                            \\\n",
       "composite-algorithm  2-3 Swap      IceT     Naive Remainder Telescoping   \n",
       "num-processes                                                             \n",
       "128                  0.346160  0.025752  0.324867  0.325992    0.325512   \n",
       "143                  0.383349  0.035832  0.620365  0.329196    0.330120   \n",
       "161                  0.362933  0.037592  0.609298  0.335417    0.322732   \n",
       "181                  0.350075  0.028211  0.601279  0.318414    0.318868   \n",
       "203                  0.368279  0.025565  0.606522  0.320694    0.318968   \n",
       "228                  0.359755  0.023898  0.605073  0.314026    0.314605   \n",
       "256                  0.329589  0.017220  0.308654  0.308345    0.308671   \n",
       "287                  0.359717  0.028142  0.596548  0.312132    0.315081   \n",
       "322                  0.349128  0.029455  0.593704  0.310503    0.308433   \n",
       "362                  0.339380  0.021054  0.589601  0.306951    0.307453   \n",
       "406                  0.348666  0.018447  0.582892  0.304362    0.302856   \n",
       "456                  0.343736  0.016096  0.583155  0.299269    0.300455   \n",
       "512                  0.323190  0.012963  0.300264  0.300909    0.301313   \n",
       "\n",
       "image-size               HDTV                                            \n",
       "composite-algorithm  2-3 Swap      IceT     Naive Remainder Telescoping  \n",
       "num-processes                                                            \n",
       "128                  0.018828  0.001801  0.016950  0.017302    0.017308  \n",
       "143                  0.020551  0.002746  0.031701  0.017456    0.017435  \n",
       "161                  0.019846  0.002704  0.031150  0.017175    0.017202  \n",
       "181                  0.019369  0.002439  0.031606  0.017061    0.017088  \n",
       "203                  0.020347  0.002287  0.032032  0.017223    0.017191  \n",
       "228                  0.020020  0.002169  0.031738  0.017156    0.016907  \n",
       "256                  0.018597  0.001812  0.016653  0.016841    0.016982  \n",
       "287                  0.020175  0.002269  0.031012  0.017163    0.017393  \n",
       "322                  0.019464  0.002474  0.032058  0.016914    0.016995  \n",
       "362                  0.019154  0.002034  0.032794  0.017007    0.016998  \n",
       "406                  0.020316  0.001912  0.032517  0.016765    0.016890  \n",
       "456                  0.019635  0.001807  0.032158  0.016752    0.016840  \n",
       "512                  0.019172  0.001552  0.016727  0.016755    0.016832  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_indices = (\n",
    "    (data_smp['num-processes'] >= 128) &\n",
    "    (data_smp['num-processes'] <= 512)\n",
    ")\n",
    "\n",
    "average_partial_composite_smp = data_smp[filtered_indices].pivot_table(\n",
    "    values='partial-composite-seconds',\n",
    "    index='num-processes',\n",
    "    columns=[\n",
    "        'image-size',\n",
    "        'composite-algorithm',\n",
    "    ],\n",
    "    aggfunc='mean',\n",
    ")\n",
    "\n",
    "average_partial_composite_smp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.037943829999999998"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.max(numpy.max(average_partial_composite_vn['HDTV']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a grouping structure of the data so we can pull out the actual data values for each trial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;opacity:1.0;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-123.372\" y=\"10.266\">Pure Distributed: Naive</text></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t68eea4921ee644f4b63f3929f78b0746\" transform=\"translate(44.4,244.8)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"248.64000000000001\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">128</text></g><g transform=\"translate(124.32000000000001,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">256</text></g><g transform=\"translate(248.64000000000001,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">512</text></g></g><g transform=\"translate(124.32000000000001,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-61.686\" y=\"10.266\">Number of Processes</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t8e0c7b8023f941b691b77b4d37e685a6\" transform=\"translate(44.4,244.8)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"95.92332479999999\" x2=\"218.55646079999997\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.00</text></g><g transform=\"translate(57.599999999999994,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.01</text></g><g transform=\"translate(115.19999999999999,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.02</text></g><g transform=\"translate(172.79999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.03</text></g><g transform=\"translate(230.39999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.04</text></g></g><g transform=\"translate(115.19999999999999,-22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-96.69\" y=\"0.0\">Partial Composite Time (seconds)</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"ted29cf7ea44242eb924635810d301d20\"><clipPath id=\"t78fb15473974493782d1fe2a7f6ba1a8\"><rect height=\"250.39999999999998\" width=\"268.64000000000004\" x=\"327.44\" y=\"4.4\"></rect></clipPath><g clip-path=\"url(#t78fb15473974493782d1fe2a7f6ba1a8)\"><g class=\"toyplot-mark-Plot\" id=\"t9f410a2ad13d4b4699c0aa7c06574f1e\" style=\"fill:none\"><g class=\"toyplot-Series\"><path d=\"M 337.44 108.92108159999999 L 377.46210075639681 103.73552639999998 L 394.55653882898883 107.15823360000003 L 410.16253808965416 107.15973120000001 L 437.81036391020132 103.8425184 L 461.75999999999999 110.65268159999997 L 472.6333804242422 107.1341568 L 501.78210075639686 109.01738879999998 L 518.87653882898894 109.6956864 L 541.80420151279384 107.50541759999999 L 562.13036391020137 106.7617152 L 586.08000000000004 107.67689280000003\" style=\"stroke:rgb(40%,76.1%,64.7%);stroke-opacity:1.0;stroke-width:2.0\"></path></g></g><g class=\"toyplot-mark-Plot\" id=\"t80cb845c14434d318cc73fe54e9add97\" style=\"fill:none\"><g class=\"toyplot-Series\"><path d=\"M 337.44 136.35207360000004 L 357.31520458828942 126.4254912 L 378.57958628720917 130.48493759999997 L 399.58084068218409 133.23453119999999 L 420.15456122446119 127.6016832 L 440.98488656096083 129.48652800000002 L 461.75999999999999 137.67966720000001 L 482.26122912432157 128.592288 L 502.89958628720916 132.6845088 L 523.90084068218414 134.47451520000001 L 544.47456122446113 127.78053119999996 L 565.30488656096054 131.700816 L 586.08000000000004 134.36746559999997\" style=\"stroke:rgb(98.8%,55.3%,38.4%);stroke-opacity:1.0;stroke-width:2.0\"></path></g></g><g class=\"toyplot-mark-Text\" id=\"t84db092c0e2c49edb109458d20d2da58\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" transform=\"translate(586.08000000000004,103.73552639999998)\"><text style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;opacity:1.0;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-123.384\" y=\"0.0\">Virtual Node: 2-3 Swap</text></g></g></g><g class=\"toyplot-mark-Text\" id=\"t64ffa414501942c3b86d3fb84f4f4f02\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" transform=\"translate(586.08000000000004,137.67966720000001)\"><text style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;opacity:1.0;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-143.388\" y=\"10.266\">Pure Distributed: 2-3 Swap</text></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t59f62195f6bc48eb8c85b10c4ac841f3\" transform=\"translate(337.44,244.8)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"248.64000000000004\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">128</text></g><g transform=\"translate(124.32000000000002,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">256</text></g><g transform=\"translate(248.64000000000004,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">512</text></g></g><g transform=\"translate(124.32000000000002,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-61.686\" y=\"10.266\">Number of Processes</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t09915808749b4efcb7131052806bcc81\" transform=\"translate(337.44,244.8)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"107.12033279999997\" x2=\"141.06447359999999\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.00</text></g><g transform=\"translate(57.599999999999994,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.01</text></g><g transform=\"translate(115.19999999999999,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.02</text></g><g transform=\"translate(172.79999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.03</text></g><g transform=\"translate(230.39999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.04</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g><g class=\"toyplot-coordinates-Cartesian\" id=\"t4665985b03c1464db114a439b1cf6628\"><clipPath id=\"teb552f24e36e4c76ba909cde08b6b224\"><rect height=\"250.39999999999998\" width=\"277.52\" x=\"611.6\" y=\"4.4\"></rect></clipPath><g clip-path=\"url(#teb552f24e36e4c76ba909cde08b6b224)\"><g class=\"toyplot-mark-Plot\" id=\"t4fcb5751f6324ccd943dccc5639c4e65\" style=\"fill:none\"><g class=\"toyplot-Series\"><path d=\"M 621.60000000000002 115.11881279999999 L 663.05146149769666 116.91264960000001 L 680.75641521573846 116.19135359999997 L 696.91977159285602 116.90297280000001 L 725.55501976413711 117.38488320000003 L 750.36000000000001 118.11821759999998 L 761.62171543939371 117.35611200000002 L 791.81146149769677 118.14995520000001 L 809.51641521573856 118.62408960000002 L 833.26292299539352 120.31752959999997 L 854.31501976413711 117.11744640000001 L 879.12 120.16670399999997\" style=\"stroke:rgb(40%,76.1%,64.7%);stroke-opacity:1.0;stroke-width:2.0\"></path></g></g><g class=\"toyplot-mark-Plot\" id=\"t14bfd397b4f9424592aa249b457dc746\" style=\"fill:none\"><g class=\"toyplot-Series\"><path d=\"M 621.60000000000002 145.14048 L 642.18503332358546 144.25096319999997 L 664.20885722603805 145.8695232 L 685.96015642083353 146.52711360000001 L 707.26865269676341 145.5949152 L 728.84291822385228 145.97982719999999 L 750.36000000000001 147.79503360000001 L 771.59341587876156 145.94088960000005 L 792.96885722603804 147.37726079999999 L 814.72015642083352 146.83887360000003 L 836.0286526967634 148.23253440000002 L 857.60291822385204 148.3075584 L 879.12 148.29393600000003\" style=\"stroke:rgb(98.8%,55.3%,38.4%);stroke-opacity:1.0;stroke-width:2.0\"></path></g></g><g class=\"toyplot-mark-Text\" id=\"t4dc1bbf391b44eedabf3153244415c80\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" transform=\"translate(879.12,115.11881279999999)\"><text style=\"fill:rgb(40%,76.1%,64.7%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;opacity:1.0;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-131.376\" y=\"0.0\">Virtual Node: Remainder</text></g></g></g><g class=\"toyplot-mark-Text\" id=\"tbf38d6c34e6c405da0944bc0c6fcdea8\"><g class=\"toyplot-Series\"><g class=\"toyplot-Datum\" transform=\"translate(879.12,148.3075584)\"><text style=\"fill:rgb(98.8%,55.3%,38.4%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:normal;opacity:1.0;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-151.38\" y=\"10.266\">Pure Distributed: Remainder</text></g></g></g></g><g class=\"toyplot-coordinates-Axis\" id=\"t36e7f0dd86014174af3355f88f12e141\" transform=\"translate(621.6,244.8)translate(0,10.0)\"><line style=\"\" x1=\"0\" x2=\"257.51999999999998\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">128</text></g><g transform=\"translate(128.76,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">256</text></g><g transform=\"translate(257.52,6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-8.34\" y=\"8.555\">512</text></g></g><g transform=\"translate(128.75999999999999,22)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:12.0px;font-weight:bold;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-61.686\" y=\"10.266\">Number of Processes</text></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"-3.0\" y2=\"4.5\"></line><text style=\"alignment-baseline:alphabetic;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"-6\"></text></g></g><g class=\"toyplot-coordinates-Axis\" id=\"td9229c721f87402cb75b64564ffb5876\" transform=\"translate(621.6,244.8)rotate(-90.0)translate(0,-10.0)\"><line style=\"\" x1=\"96.49244159999998\" x2=\"129.6811872\" y1=\"0\" y2=\"0\"></line><g><g transform=\"translate(0.0,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.00</text></g><g transform=\"translate(57.599999999999994,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.01</text></g><g transform=\"translate(115.19999999999999,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.02</text></g><g transform=\"translate(172.79999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.03</text></g><g transform=\"translate(230.39999999999998,-6)\"><text style=\"fill:rgb(16.1%,15.3%,14.1%);fill-opacity:1.0;font-family:helvetica;font-size:10.0px;font-weight:normal;stroke:none;vertical-align:baseline;white-space:pre\" x=\"-9.73\" y=\"-4.4408920985e-16\">0.04</text></g></g><g class=\"toyplot-coordinates-Axis-coordinates\" style=\"visibility:hidden\" transform=\"\"><line style=\"stroke:rgb(43.9%,50.2%,56.5%);stroke-opacity:1.0;stroke-width:1.0\" x1=\"0\" x2=\"0\" y1=\"3.0\" y2=\"-4.5\"></line><text style=\"alignment-baseline:hanging;fill:rgb(43.9%,50.2%,56.5%);fill-opacity:1.0;font-size:10px;font-weight:normal;stroke:none;text-anchor:middle\" x=\"0\" y=\"6\"></text></g></g></g></svg><div class=\"toyplot-behavior\"><script>(function()\n",
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       "        root.appendChild(menu);\n",
       "\n",
       "        var items = [];\n",
       "\n",
       "        var ignore_mouseup = null;\n",
       "        function open_menu(e)\n",
       "        {\n",
       "            var show_menu = false;\n",
       "            for(var index=0; index != items.length; ++index)\n",
       "            {\n",
       "                var item = items[index];\n",
       "                if(item.show(e))\n",
       "                {\n",
       "                    item.item.style.display = \"block\";\n",
       "                    show_menu = true;\n",
       "                }\n",
       "                else\n",
       "                {\n",
       "                    item.item.style.display = \"none\";\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if(show_menu)\n",
       "            {\n",
       "                ignore_mouseup = true;\n",
       "                menu.style.left = (e.clientX + 1) + \"px\";\n",
       "                menu.style.top = (e.clientY - 5) + \"px\";\n",
       "                menu.style.visibility = \"visible\";\n",
       "                e.stopPropagation();\n",
       "                e.preventDefault();\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function close_menu()\n",
       "        {\n",
       "            menu.style.visibility = \"hidden\";\n",
       "        }\n",
       "\n",
       "        function contextmenu(e)\n",
       "        {\n",
       "            open_menu(e);\n",
       "        }\n",
       "\n",
       "        function mousemove(e)\n",
       "        {\n",
       "            ignore_mouseup = false;\n",
       "        }\n",
       "\n",
       "        function mouseup(e)\n",
       "        {\n",
       "            if(ignore_mouseup)\n",
       "            {\n",
       "                ignore_mouseup = false;\n",
       "                return;\n",
       "            }\n",
       "            close_menu();\n",
       "        }\n",
       "\n",
       "        function keydown(e)\n",
       "        {\n",
       "            if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
       "            {\n",
       "                close_menu();\n",
       "            }\n",
       "        }\n",
       "\n",
       "        canvas.addEventListener(\"contextmenu\", contextmenu);\n",
       "        canvas.addEventListener(\"mousemove\", mousemove);\n",
       "        document.addEventListener(\"mouseup\", mouseup);\n",
       "        document.addEventListener(\"keydown\", keydown);\n",
       "\n",
       "        var module = {};\n",
       "        module.add_item = function(label, show, activate)\n",
       "        {\n",
       "            var wrapper = document.createElement(\"div\");\n",
       "            wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
       "            var item = wrapper.firstChild;\n",
       "\n",
       "            items.push({item: item, show: show});\n",
       "\n",
       "            function mouseover()\n",
       "            {\n",
       "                this.style.background = \"steelblue\";\n",
       "                this.style.color = \"white\";\n",
       "            }\n",
       "\n",
       "            function mouseout()\n",
       "            {\n",
       "                this.style.background = \"#eee\";\n",
       "                this.style.color = \"#333\";\n",
       "            }\n",
       "\n",
       "            function choose_item(e)\n",
       "            {\n",
       "                close_menu();\n",
       "                activate();\n",
       "\n",
       "                e.stopPropagation();\n",
       "                e.preventDefault();\n",
       "            }\n",
       "\n",
       "            item.addEventListener(\"mouseover\", mouseover);\n",
       "            item.addEventListener(\"mouseout\", mouseout);\n",
       "            item.addEventListener(\"mouseup\", choose_item);\n",
       "            item.addEventListener(\"contextmenu\", choose_item);\n",
       "\n",
       "            menu.appendChild(item);\n",
       "        };\n",
       "        return module;\n",
       "    })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
       "modules[\"toyplot/io\"] = (function()\n",
       "    {\n",
       "        var module = {};\n",
       "        module.save_file = function(mime_type, charset, data, filename)\n",
       "        {\n",
       "            var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = filename;\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "        };\n",
       "        return module;\n",
       "    })();\n",
       "modules[\"toyplot.coordinates.Axis\"] = (\n",
       "        function(canvas)\n",
       "        {\n",
       "            function sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var axes = {};\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = canvas.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            canvas.addEventListener(\"click\", display_coordinates);\n",
       "\n",
       "            var module = {};\n",
       "            module.show_coordinates = function(axis_id, projection)\n",
       "            {\n",
       "                axes[axis_id] = projection;\n",
       "            }\n",
       "\n",
       "            return module;\n",
       "        })(modules[\"toyplot/canvas\"]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t272a8175b68f42ed9db7bd26c3e4c711\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.022430944999999997, 0.037663515, 0.03794383, 0.037441999999999996, 0.03750346, 0.022115580000000003, 0.036865094999999994, 0.036932610000000005, 0.03644792, 0.03656635, 0.036506109999999994, 0.021663965]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t1d2a5b1a318b43c89811ceaf7b345b7e\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.016949755, 0.03170137, 0.031150384999999996, 0.031605805, 0.03203233, 0.031737804999999994, 0.016653354999999998, 0.03101169, 0.032057985000000004, 0.032794265, 0.032517185, 0.032158025, 0.016727035000000005]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t68eea4921ee644f4b63f3929f78b0746\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -248.64000000000001}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 248.64000000000001, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t8e0c7b8023f941b691b77b4d37e685a6\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.04, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t9f410a2ad13d4b4699c0aa7c06574f1e\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.023590089999999998, 0.02449036, 0.023896139999999996, 0.023895879999999998, 0.024471785, 0.023289465000000002, 0.02390032, 0.023573370000000003, 0.023455610000000002, 0.023835865, 0.02396498, 0.023806094999999992]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t80cb845c14434d318cc73fe54e9add97\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.018827764999999996, 0.02055113, 0.019846365000000005, 0.019369005, 0.02034693, 0.020019699999999998, 0.018597279999999997, 0.020174949999999997, 0.019464495000000002, 0.01915373, 0.020315880000000005, 0.019635274999999997, 0.019172315000000002]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t59f62195f6bc48eb8c85b10c4ac841f3\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -248.64000000000004}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 248.64000000000004, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t09915808749b4efcb7131052806bcc81\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.04, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t4fcb5751f6324ccd943dccc5639c4e65\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.022514095, 0.022202664999999996, 0.022327890000000003, 0.022204344999999997, 0.022120679999999997, 0.021993365000000004, 0.022125674999999997, 0.021987854999999997, 0.021905539999999998, 0.021611540000000002, 0.022167109999999997, 0.021637725000000003]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t14bfd397b4f9424592aa249b457dc746\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.017302, 0.017456430000000002, 0.01717543, 0.017061265, 0.017223105, 0.017156280000000003, 0.016841139999999998, 0.017163039999999994, 0.01691367, 0.017007139999999997, 0.016765185, 0.01675216, 0.016754525]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t36e7f0dd86014174af3355f88f12e141\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -257.52}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 257.52, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"td9229c721f87402cb75b64564ffb5876\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.04, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "})();</script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_size = 'HDTV'\n",
    "\n",
    "canvas = toyplot.Canvas('9.25in', '3in',\n",
    "                        #style={'background-color': 'yellow'},\n",
    "                       )\n",
    "\n",
    "x_vn = average_partial_composite_vn.index\n",
    "x_smp = average_partial_composite_smp.index\n",
    "\n",
    "maxy = max(\n",
    "    numpy.max(numpy.max(average_partial_composite_vn[image_size])),\n",
    "    numpy.max(numpy.max(average_partial_composite_smp[image_size])),\n",
    ")\n",
    "\n",
    "xlocator = toyplot.locator.Log(base=2, format='{:.0f}')\n",
    "\n",
    "def doPlot(axes, algorithm):\n",
    "    axes.x.ticks.locator = xlocator\n",
    "    axes.y.domain.min = 0\n",
    "    axes.y.domain.max = maxy\n",
    "    \n",
    "    y_vn = numpy.array(average_partial_composite_vn[image_size, algorithm])\n",
    "    y_smp = numpy.array(average_partial_composite_smp[image_size, algorithm])\n",
    "\n",
    "    axes.plot(x_vn, y_vn)\n",
    "    axes.plot(x_smp, y_smp)\n",
    "\n",
    "    axes.text(\n",
    "        512, numpy.max(y_vn), 'Virtual Node: ' + algorithm,\n",
    "        style={\n",
    "            'text-anchor': 'end',\n",
    "            '-toyplot-vertical-align': 'last-baseline',\n",
    "        }\n",
    "    )\n",
    "    axes.text(\n",
    "        512, numpy.min(y_smp), 'Pure Distributed: ' + algorithm,\n",
    "        style={\n",
    "            'text-anchor': 'end',\n",
    "            '-toyplot-vertical-align': 'top',\n",
    "        }\n",
    "    )\n",
    "\n",
    "axesNaive = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('5%', '33%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axesNaive, 'Naive')\n",
    "\n",
    "axes23 = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    #ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('38%', '66%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axes23, '2-3 Swap')\n",
    "\n",
    "axesRemainder = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    #ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('70%', '99%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axesRemainder, 'Remainder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "toyplot.pdf.render(canvas, 'vn-vs-smp-hdtv.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "        module.get = function(owner, key)\n",
       "        {\n",
       "            for(var i = 0; i != tables.length; ++i)\n",
       "            {\n",
       "                var table = tables[i];\n",
       "                if(table.owner != owner)\n",
       "                    continue;\n",
       "                if(table.key != key)\n",
       "                    continue;\n",
       "                return {names: table.names, columns: table.columns};\n",
       "            }\n",
       "        }\n",
       "\n",
       "        module.get_csv = function(owner, key)\n",
       "        {\n",
       "            var table = module.get(owner, key);\n",
       "            if(table != undefined)\n",
       "            {\n",
       "                var csv = \"\";\n",
       "                csv += table.names.join(\",\") + \"\\n\";\n",
       "                for(var i = 0; i != table.columns[0].length; ++i)\n",
       "                {\n",
       "                  for(var j = 0; j != table.columns.length; ++j)\n",
       "                  {\n",
       "                    if(j)\n",
       "                      csv += \",\";\n",
       "                    csv += table.columns[j][i];\n",
       "                  }\n",
       "                  csv += \"\\n\";\n",
       "                }\n",
       "                return csv;\n",
       "            }\n",
       "        }\n",
       "\n",
       "        return module;\n",
       "    })();\n",
       "modules[\"toyplot/root/id\"] = \"tc31830c6add64a50aef789160293ece2\";\n",
       "modules[\"toyplot/root\"] = (function(root_id)\n",
       "    {\n",
       "        return document.querySelector(\"#\" + root_id);\n",
       "    })(modules[\"toyplot/root/id\"]);\n",
       "modules[\"toyplot/canvas/id\"] = \"t529c14dc20d44702b80beaeaa4eea305\";\n",
       "modules[\"toyplot/canvas\"] = (function(canvas_id)\n",
       "    {\n",
       "        return document.querySelector(\"#\" + canvas_id);\n",
       "    })(modules[\"toyplot/canvas/id\"]);\n",
       "modules[\"toyplot/menus/context\"] = (function(root, canvas)\n",
       "    {\n",
       "        var wrapper = document.createElement(\"div\");\n",
       "        wrapper.innerHTML = \"<ul class='toyplot-context-menu' style='background:#eee; border:1px solid #b8b8b8; border-radius:5px; box-shadow: 0px 0px 8px rgba(0%,0%,0%,0.25); margin:0; padding:3px 0; position:fixed; visibility:hidden;'></ul>\"\n",
       "        var menu = wrapper.firstChild;\n",
       "\n",
       "        root.appendChild(menu);\n",
       "\n",
       "        var items = [];\n",
       "\n",
       "        var ignore_mouseup = null;\n",
       "        function open_menu(e)\n",
       "        {\n",
       "            var show_menu = false;\n",
       "            for(var index=0; index != items.length; ++index)\n",
       "            {\n",
       "                var item = items[index];\n",
       "                if(item.show(e))\n",
       "                {\n",
       "                    item.item.style.display = \"block\";\n",
       "                    show_menu = true;\n",
       "                }\n",
       "                else\n",
       "                {\n",
       "                    item.item.style.display = \"none\";\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if(show_menu)\n",
       "            {\n",
       "                ignore_mouseup = true;\n",
       "                menu.style.left = (e.clientX + 1) + \"px\";\n",
       "                menu.style.top = (e.clientY - 5) + \"px\";\n",
       "                menu.style.visibility = \"visible\";\n",
       "                e.stopPropagation();\n",
       "                e.preventDefault();\n",
       "            }\n",
       "        }\n",
       "\n",
       "        function close_menu()\n",
       "        {\n",
       "            menu.style.visibility = \"hidden\";\n",
       "        }\n",
       "\n",
       "        function contextmenu(e)\n",
       "        {\n",
       "            open_menu(e);\n",
       "        }\n",
       "\n",
       "        function mousemove(e)\n",
       "        {\n",
       "            ignore_mouseup = false;\n",
       "        }\n",
       "\n",
       "        function mouseup(e)\n",
       "        {\n",
       "            if(ignore_mouseup)\n",
       "            {\n",
       "                ignore_mouseup = false;\n",
       "                return;\n",
       "            }\n",
       "            close_menu();\n",
       "        }\n",
       "\n",
       "        function keydown(e)\n",
       "        {\n",
       "            if(e.key == \"Escape\" || e.key == \"Esc\" || e.keyCode == 27)\n",
       "            {\n",
       "                close_menu();\n",
       "            }\n",
       "        }\n",
       "\n",
       "        canvas.addEventListener(\"contextmenu\", contextmenu);\n",
       "        canvas.addEventListener(\"mousemove\", mousemove);\n",
       "        document.addEventListener(\"mouseup\", mouseup);\n",
       "        document.addEventListener(\"keydown\", keydown);\n",
       "\n",
       "        var module = {};\n",
       "        module.add_item = function(label, show, activate)\n",
       "        {\n",
       "            var wrapper = document.createElement(\"div\");\n",
       "            wrapper.innerHTML = \"<li class='toyplot-context-menu-item' style='background:#eee; color:#333; padding:2px 20px; list-style:none; margin:0; text-align:left;'>\" + label + \"</li>\"\n",
       "            var item = wrapper.firstChild;\n",
       "\n",
       "            items.push({item: item, show: show});\n",
       "\n",
       "            function mouseover()\n",
       "            {\n",
       "                this.style.background = \"steelblue\";\n",
       "                this.style.color = \"white\";\n",
       "            }\n",
       "\n",
       "            function mouseout()\n",
       "            {\n",
       "                this.style.background = \"#eee\";\n",
       "                this.style.color = \"#333\";\n",
       "            }\n",
       "\n",
       "            function choose_item(e)\n",
       "            {\n",
       "                close_menu();\n",
       "                activate();\n",
       "\n",
       "                e.stopPropagation();\n",
       "                e.preventDefault();\n",
       "            }\n",
       "\n",
       "            item.addEventListener(\"mouseover\", mouseover);\n",
       "            item.addEventListener(\"mouseout\", mouseout);\n",
       "            item.addEventListener(\"mouseup\", choose_item);\n",
       "            item.addEventListener(\"contextmenu\", choose_item);\n",
       "\n",
       "            menu.appendChild(item);\n",
       "        };\n",
       "        return module;\n",
       "    })(modules[\"toyplot/root\"],modules[\"toyplot/canvas\"]);\n",
       "modules[\"toyplot/io\"] = (function()\n",
       "    {\n",
       "        var module = {};\n",
       "        module.save_file = function(mime_type, charset, data, filename)\n",
       "        {\n",
       "            var uri = \"data:\" + mime_type + \";charset=\" + charset + \",\" + data;\n",
       "            uri = encodeURI(uri);\n",
       "\n",
       "            var link = document.createElement(\"a\");\n",
       "            if(typeof link.download != \"undefined\")\n",
       "            {\n",
       "              link.href = uri;\n",
       "              link.style = \"visibility:hidden\";\n",
       "              link.download = filename;\n",
       "\n",
       "              document.body.appendChild(link);\n",
       "              link.click();\n",
       "              document.body.removeChild(link);\n",
       "            }\n",
       "            else\n",
       "            {\n",
       "              window.open(uri);\n",
       "            }\n",
       "        };\n",
       "        return module;\n",
       "    })();\n",
       "modules[\"toyplot.coordinates.Axis\"] = (\n",
       "        function(canvas)\n",
       "        {\n",
       "            function sign(x)\n",
       "            {\n",
       "                return x < 0 ? -1 : x > 0 ? 1 : 0;\n",
       "            }\n",
       "\n",
       "            function mix(a, b, amount)\n",
       "            {\n",
       "                return ((1.0 - amount) * a) + (amount * b);\n",
       "            }\n",
       "\n",
       "            function log(x, base)\n",
       "            {\n",
       "                return Math.log(Math.abs(x)) / Math.log(base);\n",
       "            }\n",
       "\n",
       "            function in_range(a, x, b)\n",
       "            {\n",
       "                var left = Math.min(a, b);\n",
       "                var right = Math.max(a, b);\n",
       "                return left <= x && x <= right;\n",
       "            }\n",
       "\n",
       "            function inside(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(in_range(segment.range.min, range, segment.range.max))\n",
       "                        return true;\n",
       "                }\n",
       "                return false;\n",
       "            }\n",
       "\n",
       "            function to_domain(range, projection)\n",
       "            {\n",
       "                for(var i = 0; i != projection.length; ++i)\n",
       "                {\n",
       "                    var segment = projection[i];\n",
       "                    if(in_range(segment.range.bounds.min, range, segment.range.bounds.max))\n",
       "                    {\n",
       "                        if(segment.scale == \"linear\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            return mix(segment.domain.min, segment.domain.max, amount)\n",
       "                        }\n",
       "                        else if(segment.scale[0] == \"log\")\n",
       "                        {\n",
       "                            var amount = (range - segment.range.min) / (segment.range.max - segment.range.min);\n",
       "                            var base = segment.scale[1];\n",
       "                            return sign(segment.domain.min) * Math.pow(base, mix(log(segment.domain.min, base), log(segment.domain.max, base), amount));\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            var axes = {};\n",
       "\n",
       "            function display_coordinates(e)\n",
       "            {\n",
       "                var current = canvas.createSVGPoint();\n",
       "                current.x = e.clientX;\n",
       "                current.y = e.clientY;\n",
       "\n",
       "                for(var axis_id in axes)\n",
       "                {\n",
       "                    var axis = document.querySelector(\"#\" + axis_id);\n",
       "                    var coordinates = axis.querySelector(\".toyplot-coordinates-Axis-coordinates\");\n",
       "                    if(coordinates)\n",
       "                    {\n",
       "                        var projection = axes[axis_id];\n",
       "                        var local = current.matrixTransform(axis.getScreenCTM().inverse());\n",
       "                        if(inside(local.x, projection))\n",
       "                        {\n",
       "                            var domain = to_domain(local.x, projection);\n",
       "                            coordinates.style.visibility = \"visible\";\n",
       "                            coordinates.setAttribute(\"transform\", \"translate(\" + local.x + \")\");\n",
       "                            var text = coordinates.querySelector(\"text\");\n",
       "                            text.textContent = domain.toFixed(2);\n",
       "                        }\n",
       "                        else\n",
       "                        {\n",
       "                            coordinates.style.visibility= \"hidden\";\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            canvas.addEventListener(\"click\", display_coordinates);\n",
       "\n",
       "            var module = {};\n",
       "            module.show_coordinates = function(axis_id, projection)\n",
       "            {\n",
       "                axes[axis_id] = projection;\n",
       "            }\n",
       "\n",
       "            return module;\n",
       "        })(modules[\"toyplot/canvas\"]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t906cdb79fcef456d86931aae1520a680\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.4504112, 0.68561415, 0.6859822999999999, 0.67950375, 0.6779495000000001, 0.4025482, 0.6652737500000001, 0.6682515500000001, 0.66328875, 0.6618349999999998, 0.6625669000000001, 0.3956849]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"teef62ec3c6a44a9d9c249fab3f6c0748\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.32486725, 0.6203645499999999, 0.6092978500000001, 0.6012791, 0.6065217500000001, 0.6050731499999998, 0.30865375, 0.5965483500000001, 0.5937039499999999, 0.58960065, 0.5828919, 0.58315525, 0.3002635]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t9af302f7950b45d49a2e7b9aa425b719\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -248.64000000000001}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 248.64000000000001, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"td324a53145784ed4a00325651a5aef4b\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.75, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t37c82d90ceac4b3392d50df9e419f7c5\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.4248817500000001, 0.44053419999999993, 0.44397780000000003, 0.43811195, 0.4376614999999999, 0.41405185000000017, 0.42848685, 0.4263952000000001, 0.42718435, 0.42575035000000006, 0.42187215, 0.40753325]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"tf53f0dc202fa495cae2fb8300a7889b9\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.34616030000000003, 0.38334874999999996, 0.36293275, 0.35007455000000004, 0.36827909999999997, 0.359755, 0.3295891999999999, 0.35971695000000004, 0.34912820000000006, 0.3393795, 0.34866595000000006, 0.34373624999999997, 0.3231899]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"t16cdc95078ab4497a9caae95028ac638\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -248.64000000000004}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 248.64000000000004, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"ta5adcabeaf2e4ca283940674434adf12\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.75, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t6c2d7a38cc194963a8b17f3acbe9cfc8\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 160.0, 176.0, 192.0, 224.0, 256.0, 272.0, 320.0, 352.0, 400.0, 448.0, 512.0], [0.41504149999999995, 0.4100630999999999, 0.4128579, 0.4092290500000001, 0.4067054499999999, 0.43022945, 0.4036375999999999, 0.39993875000000007, 0.39688515, 0.3970298, 0.4022009, 0.39513265000000003]],\"toyplot\");\n",
       "(function(tables, context_menu, io, owner_id, key, label, names, columns, filename)\n",
       "        {\n",
       "            tables.set(owner_id, key, names, columns);\n",
       "\n",
       "            var owner = document.querySelector(\"#\" + owner_id);\n",
       "            function show_item(e)\n",
       "            {\n",
       "                return owner.contains(e.target);\n",
       "            }\n",
       "\n",
       "            function choose_item()\n",
       "            {\n",
       "                io.save_file(\"text/csv\", \"utf-8\", tables.get_csv(owner_id, key), filename + \".csv\");\n",
       "            }\n",
       "\n",
       "            context_menu.add_item(\"Save \" + label + \" as CSV\", show_item, choose_item);\n",
       "        })(modules[\"toyplot/tables\"],modules[\"toyplot/menus/context\"],modules[\"toyplot/io\"],\"t397f160e8f0047afa06b13d6050c8d55\",\"data\",\"plot data\",[\"x\", \"y0\"],[[128.0, 143.0, 161.0, 181.0, 203.0, 228.0, 256.0, 287.0, 322.0, 362.0, 406.0, 456.0, 512.0], [0.32599184999999997, 0.32919615000000013, 0.33541705, 0.3184139999999999, 0.32069449999999994, 0.314026, 0.3083451999999999, 0.31213155000000004, 0.31050339999999993, 0.30695145000000007, 0.30436240000000003, 0.2992693, 0.30090855000000005]],\"toyplot\");\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"tdf236b66989041289d5ba8dbff36127f\",[{\"domain\": {\"bounds\": {\"max\": 128.0, \"min\": -Infinity}, \"max\": 128.0, \"min\": -256.0000000000001}, \"range\": {\"bounds\": {\"max\": 0.0, \"min\": -Infinity}, \"max\": 0.0, \"min\": -257.52}, \"scale\": \"linear\"}, {\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": 128.0}, \"max\": 512.0000000000001, \"min\": 128.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": 0.0}, \"max\": 257.52, \"min\": 0.0}, \"scale\": [\"log\", 10]}]);\n",
       "(function(axis, axis_id, projection)\n",
       "        {\n",
       "            axis.show_coordinates(axis_id, projection);\n",
       "        })(modules[\"toyplot.coordinates.Axis\"],\"tbee786f591804c4180a281869eef940e\",[{\"domain\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 0.75, \"min\": 0.0}, \"range\": {\"bounds\": {\"max\": Infinity, \"min\": -Infinity}, \"max\": 230.39999999999998, \"min\": 0.0}, \"scale\": \"linear\"}]);\n",
       "})();</script></div></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_size = '8K UHD'\n",
    "\n",
    "canvas = toyplot.Canvas('9.25in', '3in',\n",
    "                        #style={'background-color': 'yellow'},\n",
    "                       )\n",
    "\n",
    "x_vn = average_partial_composite_vn.index\n",
    "x_smp = average_partial_composite_smp.index\n",
    "\n",
    "maxy = max(\n",
    "    numpy.max(numpy.max(average_partial_composite_vn[image_size])),\n",
    "    numpy.max(numpy.max(average_partial_composite_smp[image_size])),\n",
    ")\n",
    "\n",
    "xlocator = toyplot.locator.Log(base=2, format='{:.0f}')\n",
    "\n",
    "def doPlot(axes, algorithm):\n",
    "    axes.x.ticks.locator = xlocator\n",
    "    axes.y.domain.min = 0\n",
    "    axes.y.domain.max = maxy\n",
    "    \n",
    "    y_vn = numpy.array(average_partial_composite_vn[image_size, algorithm])\n",
    "    y_smp = numpy.array(average_partial_composite_smp[image_size, algorithm])\n",
    "\n",
    "    axes.plot(x_vn, y_vn)\n",
    "    axes.plot(x_smp, y_smp)\n",
    "\n",
    "    axes.text(\n",
    "        512, numpy.max(y_vn), 'Virtual Node: ' + algorithm,\n",
    "        style={\n",
    "            'text-anchor': 'end',\n",
    "            '-toyplot-vertical-align': 'last-baseline',\n",
    "        }\n",
    "    )\n",
    "    axes.text(\n",
    "        512, numpy.min(y_smp), 'Pure Distributed: ' + algorithm,\n",
    "        style={\n",
    "            'text-anchor': 'end',\n",
    "            '-toyplot-vertical-align': 'top',\n",
    "        }\n",
    "    )\n",
    "\n",
    "axesNaive = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('5%', '33%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axesNaive, 'Naive')\n",
    "\n",
    "axes23 = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    #ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('38%', '66%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axes23, '2-3 Swap')\n",
    "\n",
    "axesRemainder = canvas.cartesian(\n",
    "    xlabel = 'Number of Processes',\n",
    "    #ylabel = 'Partial Composite Time (seconds)',\n",
    "    xscale='log',\n",
    "    bounds=('70%', '99%', '5%', '85%'),\n",
    ")\n",
    "doPlot(axesRemainder, 'Remainder')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "toyplot.pdf.render(canvas, 'vn-vs-smp-8k.pdf')"
   ]
  }
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