{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison of Charting Libraries Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:24.230758Z",
     "iopub.status.busy": "2024-04-26T11:56:24.230758Z",
     "iopub.status.idle": "2024-04-26T11:56:25.157640Z",
     "shell.execute_reply": "2024-04-26T11:56:25.157640Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from lets_plot import *\n",
    "from lets_plot.mapping import as_discrete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.157640Z",
     "iopub.status.busy": "2024-04-26T11:56:25.157640Z",
     "iopub.status.idle": "2024-04-26T11:56:25.173766Z",
     "shell.execute_reply": "2024-04-26T11:56:25.173766Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div id=\"6Tg2z5\"></div>\n",
       "            <script type=\"text/javascript\" data-lets-plot-script=\"library\">\n",
       "                if(!window.letsPlotCallQueue) {\n",
       "                    window.letsPlotCallQueue = [];\n",
       "                }; \n",
       "                window.letsPlotCall = function(f) {\n",
       "                    window.letsPlotCallQueue.push(f);\n",
       "                };\n",
       "                (function() {\n",
       "                    var script = document.createElement(\"script\");\n",
       "                    script.type = \"text/javascript\";\n",
       "                    script.src = \"https://cdn.jsdelivr.net/gh/JetBrains/lets-plot@v4.3.2/js-package/distr/lets-plot.min.js\";\n",
       "                    script.onload = function() {\n",
       "                        window.letsPlotCall = function(f) {f();};\n",
       "                        window.letsPlotCallQueue.forEach(function(f) {f();});\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        \n",
       "                    };\n",
       "                    script.onerror = function(event) {\n",
       "                        window.letsPlotCall = function(f) {};    // noop\n",
       "                        window.letsPlotCallQueue = [];\n",
       "                        var div = document.createElement(\"div\");\n",
       "                        div.style.color = 'darkred';\n",
       "                        div.textContent = 'Error loading Lets-Plot JS';\n",
       "                        document.getElementById(\"6Tg2z5\").appendChild(div);\n",
       "                    };\n",
       "                    var e = document.getElementById(\"6Tg2z5\");\n",
       "                    e.appendChild(script);\n",
       "                })()\n",
       "            </script>\n",
       "            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LetsPlot.setup_html()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.189498Z",
     "iopub.status.busy": "2024-04-26T11:56:25.189498Z",
     "iopub.status.idle": "2024-04-26T11:56:25.220806Z",
     "shell.execute_reply": "2024-04-26T11:56:25.220806Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def get_json_data(file_path):\n",
    "    from requests import get\n",
    "\n",
    "    r = get(file_path, stream=True)\n",
    "    if r.status_code == 200:\n",
    "        return r.json()\n",
    "    else:\n",
    "        return {}\n",
    "\n",
    "def get_data(file_path):\n",
    "    colors = {\n",
    "        'LightningChart': '#e41a1c',\n",
    "        'Plotly.js': '#4daf4a',\n",
    "        'Canvas.js': '#ff7f00',\n",
    "        'eCharts': '#ffff33',\n",
    "        'Lets-Plot groups': '#377eb8',\n",
    "        'Lets-Plot layers': '#984ea3',\n",
    "    }\n",
    "    data = {\n",
    "        'test': [],\n",
    "        'lib': [],\n",
    "        'channels_count': [],\n",
    "        'channel_data_points_per_second': [],\n",
    "        'time_domain_interval': [],\n",
    "        'loadup_speed_ms': [],\n",
    "        'fps_avg': [],\n",
    "        'fps_median': [],\n",
    "        'frame_time_avg': [],\n",
    "        'frame_time_median': [],\n",
    "        'color': [],\n",
    "    }\n",
    "    for test, test_data in get_json_data(file_path).items():\n",
    "        for lib, test_lib_data in test_data.items():\n",
    "            data['test'].append(test)\n",
    "            data['lib'].append(lib)\n",
    "            data['channels_count'].append(test_lib_data['config']['channelsCount'])\n",
    "            data['channel_data_points_per_second'].append(test_lib_data['config']['channelDataPointsPerSecond'])\n",
    "            data['time_domain_interval'].append(test_lib_data['config']['timeDomainInterval'])\n",
    "            data['loadup_speed_ms'].append(test_lib_data['loadupSpeedMs'])\n",
    "            data['fps_avg'].append(test_lib_data['fps']['avg'])\n",
    "            data['fps_median'].append(test_lib_data['fps']['median'])\n",
    "            data['frame_time_avg'].append(test_lib_data['frameTime']['avg'])\n",
    "            data['frame_time_median'].append(test_lib_data['frameTime']['median'])\n",
    "            data['color'].append(colors[lib])\n",
    "    return pd.DataFrame.from_dict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.220806Z",
     "iopub.status.busy": "2024-04-26T11:56:25.220806Z",
     "iopub.status.idle": "2024-04-26T11:56:25.755221Z",
     "shell.execute_reply": "2024-04-26T11:56:25.755221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>test</th>\n",
       "      <th>lib</th>\n",
       "      <th>channels_count</th>\n",
       "      <th>channel_data_points_per_second</th>\n",
       "      <th>time_domain_interval</th>\n",
       "      <th>loadup_speed_ms</th>\n",
       "      <th>fps_avg</th>\n",
       "      <th>fps_median</th>\n",
       "      <th>frame_time_avg</th>\n",
       "      <th>frame_time_median</th>\n",
       "      <th>color</th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>LightningChart</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>101.7</td>\n",
       "      <td>58.412474</td>\n",
       "      <td>58.259538</td>\n",
       "      <td>17.258501</td>\n",
       "      <td>16.90</td>\n",
       "      <td>#e41a1c</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>Plotly.js</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>92.7</td>\n",
       "      <td>40.252554</td>\n",
       "      <td>40.922497</td>\n",
       "      <td>30.025803</td>\n",
       "      <td>24.25</td>\n",
       "      <td>#4daf4a</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>Canvas.js</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>101.5</td>\n",
       "      <td>23.045579</td>\n",
       "      <td>23.116954</td>\n",
       "      <td>43.772100</td>\n",
       "      <td>43.10</td>\n",
       "      <td>#ff7f00</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>eCharts</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>59.155815</td>\n",
       "      <td>59.053692</td>\n",
       "      <td>16.941370</td>\n",
       "      <td>17.00</td>\n",
       "      <td>#ffff33</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>Lets-Plot groups</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>68.1</td>\n",
       "      <td>49.928318</td>\n",
       "      <td>51.291719</td>\n",
       "      <td>19.021224</td>\n",
       "      <td>18.00</td>\n",
       "      <td>#377eb8</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A</td>\n",
       "      <td>Lets-Plot layers</td>\n",
       "      <td>1</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>104.9</td>\n",
       "      <td>57.002319</td>\n",
       "      <td>58.007307</td>\n",
       "      <td>17.057078</td>\n",
       "      <td>17.00</td>\n",
       "      <td>#984ea3</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  test               lib  channels_count  channel_data_points_per_second  \\\n",
       "0    A    LightningChart               1                            1000   \n",
       "1    A         Plotly.js               1                            1000   \n",
       "2    A         Canvas.js               1                            1000   \n",
       "3    A           eCharts               1                            1000   \n",
       "4    A  Lets-Plot groups               1                            1000   \n",
       "5    A  Lets-Plot layers               1                            1000   \n",
       "\n",
       "   time_domain_interval  loadup_speed_ms    fps_avg  fps_median  \\\n",
       "0                  1000            101.7  58.412474   58.259538   \n",
       "1                  1000             92.7  40.252554   40.922497   \n",
       "2                  1000            101.5  23.045579   23.116954   \n",
       "3                  1000             48.0  59.155815   59.053692   \n",
       "4                  1000             68.1  49.928318   51.291719   \n",
       "5                  1000            104.9  57.002319   58.007307   \n",
       "\n",
       "   frame_time_avg  frame_time_median    color  load  \n",
       "0       17.258501              16.90  #e41a1c  1000  \n",
       "1       30.025803              24.25  #4daf4a  1000  \n",
       "2       43.772100              43.10  #ff7f00  1000  \n",
       "3       16.941370              17.00  #ffff33  1000  \n",
       "4       19.021224              18.00  #377eb8  1000  \n",
       "5       17.057078              17.00  #984ea3  1000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = get_data(\"https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/lib_comparison.json\")\n",
    "df['load'] = df['channels_count'] * df['time_domain_interval']\n",
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here \"Lets-Plot groups\" is a realization of the benchmark in which each data channel corresponds to a group in aesthetics. In \"Lets-Plot layers\" each data channel corresponds to a plot layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.755221Z",
     "iopub.status.busy": "2024-04-26T11:56:25.755221Z",
     "iopub.status.idle": "2024-04-26T11:56:25.771365Z",
     "shell.execute_reply": "2024-04-26T11:56:25.771365Z"
    }
   },
   "outputs": [],
   "source": [
    "def comparison1(col):\n",
    "    return ggplot(df) + \\\n",
    "        geom_line(aes('load', col, color='color'), size=2, \\\n",
    "                  tooltips=layer_tooltips().line('@lib').format(col, '.2f').line('{0}|@{0}'.format(col))) + \\\n",
    "        scale_x_log10() + scale_color_identity() + \\\n",
    "        ggtitle('Comparison of {0} through load increasing'.format(col))\n",
    "\n",
    "def comparison2(col):\n",
    "    return ggplot(df) + \\\n",
    "        geom_bar(aes(as_discrete('lib', order_by=col, order=1), col, fill='color'), stat='identity', \\\n",
    "                 tooltips=layer_tooltips().line('@lib').format(col, '.2f').line('{0}|@{0}'.format(col))) + \\\n",
    "        facet_grid(x='test') + \\\n",
    "        scale_fill_identity() + \\\n",
    "        coord_flip() + \\\n",
    "        ggtitle('Comparison of {0} through tests'.format(col)) + \\\n",
    "        theme_void()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison Charts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.771365Z",
     "iopub.status.busy": "2024-04-26T11:56:25.771365Z",
     "iopub.status.idle": "2024-04-26T11:56:25.865836Z",
     "shell.execute_reply": "2024-04-26T11:56:25.865836Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"Yd6q8W\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "\"lib\":[\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\",\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\",\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\"],\n",
       "\"loadup_speed_ms\":[101.70000000298023,92.69999999925494,101.5,48.0,68.09999999776483,104.90000000223517,77.39999999850988,123.19999999925494,179.5,120.19999999925494,283.5,225.29999999701977,99.40000000223517,235.69999999925494,886.5999999977648,301.30000000074506,688.0,548.6999999992549],\n",
       "\"color\":[\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\",\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\",\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\"],\n",
       "\"load\":[1000.0,1000.0,1000.0,1000.0,1000.0,1000.0,10000.0,10000.0,10000.0,10000.0,10000.0,10000.0,100000.0,100000.0,100000.0,100000.0,100000.0,100000.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Comparison of loadup_speed_ms through load increasing\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"trans\":\"log10\"\n",
       "},{\n",
       "\"aesthetic\":\"color\",\n",
       "\"guide\":\"none\",\n",
       "\"scale_mapper_kind\":\"identity\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "\"x\":\"load\",\n",
       "\"y\":\"loadup_speed_ms\",\n",
       "\"color\":\"color\"\n",
       "},\n",
       "\"tooltips\":{\n",
       "\"formats\":[{\n",
       "\"field\":\"loadup_speed_ms\",\n",
       "\"format\":\".2f\"\n",
       "}],\n",
       "\"lines\":[\"@lib\",\"loadup_speed_ms|@loadup_speed_ms\"]\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"size\":2.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
       "           var plotContainer = document.getElementById(\"Yd6q8W\");\n",
       "           window.letsPlotCall(function() {{\n",
       "               LetsPlot.buildPlotFromProcessedSpecs(plotSpec, -1, -1, plotContainer);\n",
       "           }});\n",
       "       })();\n",
       "   </script>"
      ],
      "text/plain": [
       "<lets_plot.plot.core.PlotSpec at 0x1fe17837910>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison1('loadup_speed_ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-04-26T11:56:25.867454Z",
     "iopub.status.busy": "2024-04-26T11:56:25.867454Z",
     "iopub.status.idle": "2024-04-26T11:56:25.882125Z",
     "shell.execute_reply": "2024-04-26T11:56:25.882125Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "   <div id=\"9eci0w\"></div>\n",
       "   <script type=\"text/javascript\" data-lets-plot-script=\"plot\">\n",
       "       (function() {\n",
       "           var plotSpec={\n",
       "\"data\":{\n",
       "\"lib\":[\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\",\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\",\"LightningChart\",\"Plotly.js\",\"Canvas.js\",\"eCharts\",\"Lets-Plot groups\",\"Lets-Plot layers\"],\n",
       "\"fps_avg\":[58.41247378739938,40.25255391220948,23.045579171890523,59.15581487332924,49.92831815567785,57.00231862477561,58.35075926529889,14.23815425768082,13.060580228003026,58.166643531097336,17.609254187195965,24.803138456444703,32.50563451904698,2.7291637199236027,2.7057610114803996,10.276482175570862,2.42716089923178,3.093920768933406],\n",
       "\"color\":[\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\",\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\",\"#e41a1c\",\"#4daf4a\",\"#ff7f00\",\"#ffff33\",\"#377eb8\",\"#984ea3\"],\n",
       "\"load\":[1000.0,1000.0,1000.0,1000.0,1000.0,1000.0,10000.0,10000.0,10000.0,10000.0,10000.0,10000.0,100000.0,100000.0,100000.0,100000.0,100000.0,100000.0]\n",
       "},\n",
       "\"mapping\":{\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"ggtitle\":{\n",
       "\"text\":\"Comparison of fps_avg through load increasing\"\n",
       "},\n",
       "\"kind\":\"plot\",\n",
       "\"scales\":[{\n",
       "\"aesthetic\":\"x\",\n",
       "\"trans\":\"log10\"\n",
       "},{\n",
       "\"aesthetic\":\"color\",\n",
       "\"guide\":\"none\",\n",
       "\"scale_mapper_kind\":\"identity\"\n",
       "}],\n",
       "\"layers\":[{\n",
       "\"geom\":\"line\",\n",
       "\"mapping\":{\n",
       "\"x\":\"load\",\n",
       "\"y\":\"fps_avg\",\n",
       "\"color\":\"color\"\n",
       "},\n",
       "\"tooltips\":{\n",
       "\"formats\":[{\n",
       "\"field\":\"fps_avg\",\n",
       "\"format\":\".2f\"\n",
       "}],\n",
       "\"lines\":[\"@lib\",\"fps_avg|@fps_avg\"]\n",
       "},\n",
       "\"data_meta\":{\n",
       "},\n",
       "\"size\":2.0,\n",
       "\"data\":{\n",
       "}\n",
       "}],\n",
       "\"metainfo_list\":[]\n",
       "};\n",
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