swcho/webgl.ipynb

## webgl.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "webgl 성능 테스트",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOR8L0rPRv7NlksIA56Sm95",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/swcho/3808aa75d12289fe47416fa3353234dc/webgl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQf84llzBUbj",
        "outputId": "3e8f9183-d78f-40ba-e801-9ec85098d0f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "from io import StringIO\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "csv_str = \"\"\"\n",
        "from,to,len,fetchTime,name,reDrawTime\n",
        "1604642705073,1604642710456,14662,325,webgl,24\n",
        "1604642699678,1604642705072,14320,253,webgl,16\n",
        "1604642694290,1604642699677,14110,237,webgl,12\n",
        "1604642688893,1604642694289,14324,275,webgl,21\n",
        "1604642683493,1604642688892,14363,215,webgl,12\n",
        "1604642678094,1604642683492,14463,507,webgl,11\n",
        "1604642672701,1604642678093,14370,225,webgl,11\n",
        "1604642667297,1604642672700,14426,292,webgl,20\n",
        "1604642661870,1604642667296,14496,289,webgl,19\n",
        "1604642656497,1604642661869,14688,230,webgl,14\n",
        "1604642651147,1604642656496,14814,329,webgl,12\n",
        "1604642645791,1604642651146,14556,237,webgl,15\n",
        "1604642640390,1604642645790,14683,232,webgl,27\n",
        "1604642635009,1604642640389,14443,523,webgl,20\n",
        "1604642629599,1604642635008,14275,214,webgl,23\n",
        "1604642624196,1604642629598,14465,216,webgl,16\n",
        "1604642618799,1604642624195,14342,208,webgl,18\n",
        "1604642613370,1604642618798,14428,234,webgl,15\n",
        "1604642607978,1604642613369,14374,230,webgl,28\n",
        "1604642602549,1604642607977,14315,362,webgl,23\n",
        "1604642597104,1604642602548,14417,245,webgl,25\n",
        "1604642591704,1604642597103,14193,234,webgl,17\n",
        "1604642586285,1604642591703,14426,276,webgl,21\n",
        "1604642580885,1604642586284,14396,265,webgl,17\n",
        "1604642575485,1604642580884,14429,235,webgl,22\n",
        "1604642570085,1604642575484,14599,555,webgl,25\n",
        "1604642564707,1604642570084,14537,509,webgl,26\n",
        "1604642559294,1604642564706,14682,235,webgl,30\n",
        "1604642553944,1604642559292,14454,222,webgl,23\n",
        "1604642548576,1604642553943,14579,243,webgl,30\n",
        "1604642543203,1604642548575,14515,231,webgl,30\n",
        "1604642537835,1604642543202,14602,304,webgl,27\n",
        "1604642532433,1604642537834,14815,258,webgl,31\n",
        "1604642527094,1604642532432,14522,281,webgl,25\n",
        "1604642521677,1604642527093,14511,476,webgl,29\n",
        "1604642516303,1604642521676,14480,264,webgl,30\n",
        "1604642510930,1604642516302,14500,251,webgl,32\n",
        "1604642505584,1604642510929,14495,289,webgl,35\n",
        "1604642500216,1604642505583,14423,247,webgl,30\n",
        "1604642494896,1604642500215,14379,227,webgl,28\n",
        "1604642489521,1604642494895,14653,256,webgl,36\n",
        "1604642484176,1604642489520,14749,253,webgl,30\n",
        "1604642478789,1604642484175,14586,235,webgl,38\n",
        "1604642473380,1604642478788,14529,354,webgl,35\n",
        "1604642467947,1604642473379,14603,248,webgl,36\n",
        "1604642462565,1604642467945,14667,255,webgl,34\n",
        "1604642457168,1604642462564,14579,230,webgl,34\n",
        "1604642451797,1604642457167,14435,251,webgl,51\n",
        "1604642446439,1604642451796,14429,349,webgl,48\n",
        "1604642441070,1604642446438,14478,242,webgl,34\n",
        "1604642435690,1604642441069,14523,238,webgl,34\n",
        "1604642430288,1604642435688,14486,243,webgl,33\n",
        "1604642424877,1604642430287,14628,255,webgl,41\n",
        "1604642419498,1604642424876,14695,344,webgl,33\n",
        "1604642414128,1604642419497,14871,259,webgl,35\n",
        "1604642410457,1604642414127,10208,188,webgl,33\n",
        "1604642710457,1604642711456,2802,101,webgl,40\n",
        "1604642711457,1604642712456,2730,72,webgl,40\n",
        "1604642712457,1604642713456,2698,76,webgl,27\n",
        "1604642713457,1604642714456,2730,86,webgl,24\n",
        "1604642714457,1604642715456,2674,125,webgl,30\n",
        "1604642715457,1604642716456,2682,65,webgl,36\n",
        "1604642716458,1604642717456,2679,92,webgl,25\n",
        "1604642717457,1604642718456,2671,82,webgl,26\n",
        "1604642718457,1604642719454,2758,226,webgl,49\n",
        "1604642719457,1604642720456,2681,132,webgl,22\n",
        "1604642720457,1604642721456,2706,78,webgl,25\n",
        "1604642721457,1604642722456,2713,76,webgl,62\n",
        "1604642722457,1604642723456,2710,77,webgl,57\n",
        "1604642723457,1604642724456,2708,71,webgl,60\n",
        "1604642724457,1604642725456,2760,73,webgl,53\n",
        "1604642725457,1604642726456,2737,94,webgl,62\n",
        "1604642726458,1604642727455,2699,77,webgl,58\n",
        "1604642727457,1604642728456,2705,67,webgl,61\n",
        "1604642728457,1604642729456,2764,92,webgl,74\n",
        "1604642729458,1604642730456,2710,89,webgl,96\n",
        "1604642730457,1604642731456,2734,78,webgl,58\n",
        "1604642731457,1604642732456,2708,157,webgl,79\n",
        "1604642732457,1604642733456,2671,120,webgl,56\n",
        "1604642733457,1604642734456,2663,72,webgl,57\n",
        "1604642734457,1604642735456,2687,161,webgl,103\n",
        "1604642735457,1604642736456,2722,69,webgl,59\n",
        "1604642736457,1604642737456,2713,73,webgl,82\n",
        "1604642737457,1604642738456,2708,78,webgl,60\n",
        "1604642738457,1604642739456,2655,75,webgl,64\n",
        "1604642739457,1604642740456,2681,107,webgl,85\n",
        "\"\"\"\n",
        "\n",
        "csv = StringIO(csv_str)\n",
        "df = pd.read_csv(csv)\n",
        "df.head()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>from</th>\n",
              "      <th>to</th>\n",
              "      <th>len</th>\n",
              "      <th>fetchTime</th>\n",
              "      <th>name</th>\n",
              "      <th>reDrawTime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1604642705073</td>\n",
              "      <td>1604642710456</td>\n",
              "      <td>14662</td>\n",
              "      <td>325</td>\n",
              "      <td>webgl</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1604642699678</td>\n",
              "      <td>1604642705072</td>\n",
              "      <td>14320</td>\n",
              "      <td>253</td>\n",
              "      <td>webgl</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1604642694290</td>\n",
              "      <td>1604642699677</td>\n",
              "      <td>14110</td>\n",
              "      <td>237</td>\n",
              "      <td>webgl</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1604642688893</td>\n",
              "      <td>1604642694289</td>\n",
              "      <td>14324</td>\n",
              "      <td>275</td>\n",
              "      <td>webgl</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1604642683493</td>\n",
              "      <td>1604642688892</td>\n",
              "      <td>14363</td>\n",
              "      <td>215</td>\n",
              "      <td>webgl</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            from             to    len  fetchTime   name  reDrawTime\n",
              "0  1604642705073  1604642710456  14662        325  webgl          24\n",
              "1  1604642699678  1604642705072  14320        253  webgl          16\n",
              "2  1604642694290  1604642699677  14110        237  webgl          12\n",
              "3  1604642688893  1604642694289  14324        275  webgl          21\n",
              "4  1604642683493  1604642688892  14363        215  webgl          12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6IvldisL9j-",
        "outputId": "eb99f729-11c3-43b3-fd58-cd2beaa02ebe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "# 초기 드로잉 구간\n",
        "df_initial = df.where(14000 < df['len'])\n",
        "print('fetchTime:', df_initial['fetchTime'].mean())\n",
        "print('reDrawTimee:', df_initial['reDrawTime'].mean())\n",
        "pd.concat([df_initial['fetchTime'], df_initial['reDrawTime']]).plot()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fetchTime: 281.6727272727273\n",
            "reDrawTimee: 26.21818181818182\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd130d4c5c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tng-oxIUN2Ta",
        "outputId": "e5021c71-a310-4471-bc9f-3c9c1567a322",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        }
      },
      "source": [
        "# 2초당 업데이트 구간\n",
        "df_update = df.where(df['len'] < 3000)\n",
        "print('fetchTime:', df_update['fetchTime'].mean())\n",
        "print('reDrawTimee:', df_update['reDrawTime'].mean())\n",
        "pd.concat([df_update['fetchTime'], df_update['reDrawTime']]).plot()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fetchTime: 94.7\n",
            "reDrawTimee: 54.333333333333336\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd130d2cef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}