{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import json\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "import plotly.io as pio\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "from unicodeit import replace as tex_to_unis\n",
    "# sys.path.append(\"C:/Users/rurur/Desktop/p/python/plotly\")\n",
    "# import plotly_setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython-3.11.txt CPython-3.11_full.txt CPython-3.9.txt CPython-3.9_full.txt Pyodide.txt PyPy-3.9-v7.3.13.txt PyPy-3.9-v7.3.13_full.txt Xeus.txt\n",
      "PyPy-3.9-v7.3.13 CPython-3.9 CPython-3.11 Pyodide Xeus\n"
     ]
    }
   ],
   "source": [
    "result_files = os.listdir(\"./results\")\n",
    "result_titles = sorted(set([f.rstrip(\"full_.txt\") for f in result_files]))\n",
    "result_titles[0], result_titles[2] = result_titles[2], result_titles[0]\n",
    "print(*result_files)\n",
    "print(*result_titles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame()\n",
    "funcs = [\"time_with_types\", \"time_no_types\", \"time_np\"]\n",
    "funcs_titles = [f.lstrip(\"time_\") for f in funcs]\n",
    "funcs_titles[-1] = \"numpy\"\n",
    "for rtitle in result_titles:\n",
    "    with open(f\"results/{rtitle}.txt\", \"r\", encoding=\"utf-8\") as f:\n",
    "        result = json.load(f)\n",
    "    result_df = pd.DataFrame(dict(\n",
    "        interpreter=result[\"interpreter\"],\n",
    "        time=[result[func] for func in funcs],\n",
    "        funcs=funcs_titles,\n",
    "        test_mode=\"func\"\n",
    "    ))\n",
    "    df = pd.concat([df, result_df], ignore_index=True)\n",
    "\n",
    "    fname_full_time = f\"{rtitle}_full.txt\"\n",
    "    if fname_full_time in result_files:\n",
    "        with open(f\"results/{fname_full_time}\", \"r\", encoding=\"utf-8\") as f:\n",
    "            times = [float(line) for line in f.readlines()]\n",
    "        result_df = pd.DataFrame(dict(\n",
    "            interpreter=result[\"interpreter\"],\n",
    "            time=times,\n",
    "            funcs=funcs_titles,\n",
    "            test_mode=\"full\"\n",
    "        ))\n",
    "        df = pd.concat([df, pd.DataFrame(result_df)], ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>interpreter</th>\n",
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       "      <th>test_mode</th>\n",
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       "3   PyPy-3.9-v7.3.13   5.319000  with_types      full\n",
       "4   PyPy-3.9-v7.3.13   3.638000    no_types      full\n",
       "5   PyPy-3.9-v7.3.13  14.639000       numpy      full\n",
       "6        CPython-3.9   1.445288  with_types      func\n",
       "7        CPython-3.9   1.476920    no_types      func\n",
       "8        CPython-3.9   1.407270       numpy      func\n",
       "9        CPython-3.9   2.242000  with_types      full\n",
       "10       CPython-3.9   1.502000    no_types      full\n",
       "11       CPython-3.9   1.986000       numpy      full\n",
       "12      CPython-3.11   0.820678  with_types      func\n",
       "13      CPython-3.11   0.856216    no_types      func\n",
       "14      CPython-3.11   1.202004       numpy      func\n",
       "15      CPython-3.11   1.630000  with_types      full\n",
       "16      CPython-3.11   1.166000    no_types      full\n",
       "17      CPython-3.11   1.675000       numpy      full\n",
       "18           piodide   1.785000  with_types      func\n",
       "19           piodide   1.681000    no_types      func\n",
       "20           piodide   2.833000       numpy      func\n",
       "21              Xeus   1.155519  with_types      func\n",
       "22              Xeus   1.038669    no_types      func\n",
       "23              Xeus   1.272793       numpy      func"
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     },
     "execution_count": 25,
     "metadata": {},
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         "showticklabels": true,
         "title": {}
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        "xaxis4": {
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         "showticklabels": true,
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         "showticklabels": true,
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         "showticklabels": true,
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        "yaxis": {
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        "yaxis2": {
         "anchor": "x2",
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         "range": [
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        "yaxis4": {
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         "range": [
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         "title": {
          "text": "Time, s"
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        },
        "yaxis5": {
         "anchor": "x5",
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         "range": [
          0,
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         "showticklabels": false
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        "yaxis6": {
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         "showticklabels": false
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       }
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     },
     "metadata": {},
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   ],
   "source": [
    "fig = px.bar(df,\n",
    "    x=\"interpreter\",\n",
    "    y=\"time\",\n",
    "    facet_col=\"funcs\",\n",
    "    facet_row=\"test_mode\",\n",
    "    facet_row_spacing=0.15,\n",
    "    facet_col_spacing=0.03,\n",
    "    labels=dict(time=\"Time, s\")\n",
    ").update_layout(\n",
    "    width=800, height=620,\n",
    "    margin=dict(b=10, t=20),\n",
    "    # title=dict(text=\"Results of Testing\"),\n",
    "    font=dict(size=13)\n",
    ").update_yaxes(\n",
    "    matches=None,\n",
    ").update_yaxes(\n",
    "    # showticklabels=True,\n",
    "    row=1,\n",
    "    range=(0, 16)\n",
    ").update_yaxes(\n",
    "    # showticklabels=True,\n",
    "    row=2,\n",
    "    range=(0, 4)\n",
    ").update_xaxes(\n",
    "    matches=None,\n",
    "    showticklabels=True,\n",
    "    title=None\n",
    ").for_each_annotation(\n",
    "    lambda a: a.update(text=a.text.split(\"=\")[-1])\n",
    ")\n",
    "fig.write_image(\"result.png\", scale=2.5)\n",
    "# fig.layout.yaxis.matches = 'y'\n",
    "# fig.layout.yaxis1.matches = 'y1'\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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