COLAB_version_of_plt_refactor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g4aj12Wog8xg"
   },
   "outputs": [],
   "source": [
    "import sqlite3 as sl\n",
    "import pandas as pd  # type: ignore\n",
    "\n",
    "\n",
    "COLORS_by_TYPE = {\n",
    "    'fire': 'red',\n",
    "    'water': '#09E1FF',\n",
    "    'normal': '#1DFDA8',\n",
    "    'poison': '#B918FF',\n",
    "    'electric': 'yellow',\n",
    "    'ground': '#FF9C15',\n",
    "    'fairy': '#FF69B4',\n",
    "    'grass': '#34FF5C',\n",
    "    'bug': '#90EE38',\n",
    "    'psychic': '#B71ECF',\n",
    "    'rock': '#DCB883',\n",
    "    'fighting': '#FF3A17',\n",
    "    'ghost': '#6817ff',\n",
    "    'ice': '#52fffa',\n",
    "    'dragon': '#a533ff',\n",
    "    'dark': '#3D009C',\n",
    "    'flying': '#4da1ff',\n",
    "    'steel': '#bfbfbf'}\n",
    "\n",
    "\n",
    "def clean_lite_6(datf: pd.DataFrame) -> pd.DataFrame:\n",
    "    return (datf.fillna('')\n",
    "            .assign(Legendary=[1 if x else 0 for x in datf.Legendary],\n",
    "                    Sp_Attack=datf['Sp. Atk'],\n",
    "                    Sp_Defense=datf['Sp. Def'],\n",
    "                    Type1=datf['Type 1'],\n",
    "                    Type2=datf['Type 2'])\n",
    "            .drop(['Sp. Atk', 'Sp. Def', 'Type 1', 'Type 2'], axis=1)\n",
    "            .rename(lambda s: s.lower() + '_g6', axis='columns')\n",
    "            )\n",
    "\n",
    "\n",
    "def clean_7(datf: pd.DataFrame) -> pd.DataFrame:\n",
    "    '''we need to renamed `against_fight` to `against_fighting`'''\n",
    "    return datf\n",
    "\n",
    "\n",
    "df6 = pd.read_csv('https://raw.githubusercontent.com/pokepokepokedex/pokedex-ds-quinn/master/Pokemon.csv').pipe(clean_lite_6)\n",
    "\n",
    "df7 = pd.read_csv('https://raw.githubusercontent.com/pokepokepokedex/pokedex-ds-quinn/master/pokemon_w7.csv').pipe(clean_7)\n",
    "\n",
    "df = df7.merge(df6, how='outer', left_on='name', right_on='name' + '_g6')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import pandas as pd # type: ignore\n",
    "import numpy as np # type: ignore\n",
    "from scipy.stats import norm # type: ignore\n",
    "import altair as alt # type : ignore\n",
    "from typing import Optional\n",
    "from functools import reduce\n",
    "from itertools import chain\n",
    "Vcat = lambda R,S: R & S\n",
    "Ocat = lambda C,D: C + D\n",
    "\n",
    "#from models import df, COLORS_by_TYPE\n",
    "\n",
    "\n",
    "types = set(chain.from_iterable(df[['type1', 'type2']].values)) - {np.nan}\n",
    "\n",
    "ordering = pd.DataFrame(np.ones((len(types), len(types))), columns=types, index=types)\n",
    "\n",
    "\n",
    "\n",
    "class PokeDescribe: \n",
    "    def __init__(self, datf: pd.DataFrame): \n",
    "        self.TYPE_COLOR_MAPPING = COLORS_by_TYPE\n",
    "        self.HEIGHT = 30\n",
    "        self.WIDTH = 330\n",
    "        self.xlim = (0, 180)\n",
    "        self.stats = ['hp', 'attack', 'defense', \n",
    "                      'sp_attack', 'sp_defense', 'speed']\n",
    "        self.df = datf\n",
    "        self.x = np.linspace(self.xlim[0], self.xlim[1], 1000)\n",
    "        self.gaussians = {name: norm(loc=self.df[name].mean(), \n",
    "                                     scale=self.df[name].std()) \n",
    "                          for name in self.stats}\n",
    "        self.bells = pd.DataFrame({**{'x': self.x}, \n",
    "                                   **{name: self.gaussians[name].pdf(self.x) \n",
    "                                      for name in self.stats}})\n",
    "        self.C = alt.Chart(self.bells, \n",
    "                           height=self.HEIGHT, \n",
    "                           width=self.WIDTH\n",
    "                          ).mark_line(color='white').encode(\n",
    "            x=alt.X('x', title=None, axis=alt.Axis(labels=False)))\n",
    "        self.charts = {name: self.C.encode(y=alt.Y(name, title=None, axis=alt.Axis(labels=False))) for name in self.stats}\n",
    "        self.BellCurves = reduce(Vcat, [self.charts[name] for name in self.stats])\n",
    "\n",
    "class PokeDescribeNAME(PokeDescribe): \n",
    "    def __init__(self, datf: pd.DataFrame, Name: str): \n",
    "        super().__init__(datf)\n",
    "        self.PSI = 50\n",
    "        self.pokename = Name\n",
    "        self.typ = self.df[self.df.name==self.pokename].type1.values[0]\n",
    "        self.typ_color = self.TYPE_COLOR_MAPPING[self.typ]\n",
    "        self.y_max = 1.3 * max([max(ls) for ls in [self.gaussians[st].pdf(self.x) for st in self.stats]])\n",
    "        self.y = pd.DataFrame({'y': np.linspace(0, self.y_max, self.PSI)})\n",
    "        self.D = alt.Chart(self.y).mark_line(color=self.typ_color).encode(y=alt.Y('y', title=None))\n",
    "        self.means = {st: self.df[self.df.name==self.pokename][st].mean() for st in self.stats}\n",
    "        self.Dcharts = {st: self.D.encode(x=alt.value(self.means[st]))\n",
    "                        for st in self.stats}\n",
    "        self.SHOW = reduce(Vcat, [self.charts[st] + self.Dcharts[st] \n",
    "                                  for st in self.stats]\n",
    "                          ).configure_text(color='white', angle=90)\n",
    "\n",
    "\n",
    "\n",
    "# from gaussians import PokeDescribeNAME\n",
    "\n",
    "# from models import df, COLORS_by_TYPE\n",
    "\n",
    "from typing import List\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "raichu = PokeDescribeNAME(df, 'Charizard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "colab_type": "code",
    "id": "eyDHTwYOg8x_",
    "outputId": "e5bc7c63-939c-42a5-ba50-48e4234daafb"
   },
   "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>attack</th>\n",
       "      <th>defense</th>\n",
       "      <th>hp</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "      <th>x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000662</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000992</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.180180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>0.360360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.001035</td>\n",
       "      <td>0.540541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000699</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.001050</td>\n",
       "      <td>0.720721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     attack   defense        hp  sp_attack  sp_defense     speed         x\n",
       "0  0.000662  0.000777  0.000518   0.001087    0.000570  0.000992  0.000000\n",
       "1  0.000671  0.000788  0.000527   0.001100    0.000580  0.001006  0.180180\n",
       "2  0.000680  0.000798  0.000537   0.001114    0.000589  0.001021  0.360360\n",
       "3  0.000689  0.000810  0.000546   0.001128    0.000599  0.001035  0.540541\n",
       "4  0.000699  0.000821  0.000556   0.001141    0.000609  0.001050  0.720721"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raichu.bells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 121
    },
    "colab_type": "code",
    "id": "83lACyutg8yS",
    "outputId": "df589d65-e0e9-421c-8b2b-eb1b32e4f03d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'attack': 104.0,\n",
       " 'defense': 78.0,\n",
       " 'hp': 78.0,\n",
       " 'sp_attack': 159.0,\n",
       " 'sp_defense': 115.0,\n",
       " 'speed': 100.0}"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raichu.means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "yFgEOybsg8yp",
    "outputId": "486b208e-fc67-402a-ac9b-a091f6ffe976"
   },
   "outputs": [
    {
     "data": {
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yclJVVVWqqamRJDU2NiqRSCidTi86BwAAoNQVvAcplUqpuro6dxwKhZRMJiVJyWRSoVDo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\n",
      "text/plain": [
       "<Figure size 576x396 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = ['hp', 'attack', 'defense', 'sp_attack', 'sp_defense', 'speed']\n",
    "\n",
    "fig, axes = plt.subplots(nrows=len(stats), ncols=1, sharex=True, constrained_layout=True)\n",
    "#axes.flatten()\n",
    "\n",
    "\n",
    "def bell(sts: List[str]): \n",
    "    for i in range(len(sts)): \n",
    "        c = COLORS_by_TYPE[raichu.typ]\n",
    "        axes[i].set_xlim(raichu.xlim)\n",
    "        axes[i].set_ylim(0, raichu.y_max)\n",
    "        axes[i].axvline(raichu.means[sts[i]], color=c)#, xmin=raichu.xlim[0], xmax=raichu.xlim[1])\n",
    "        axes[i].plot(x=raichu.bells.x, y=raichu.bells[sts[i]], color=c)\n",
    "\n",
    "\n",
    "# for i in range(len(stats)): \n",
    "#     (i)\n",
    "bell(stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "ZLXTKxE0g8y1",
    "outputId": "8f73b28d-0c8f-40cd-f466-6cec97948152"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PO/n6p6BPm4F32kzMvv0CqhOMQDuGdWkoxughyUi6l6rZj+JevQLXaaftgK1p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\nLmRMfPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats = ['hp', 'attack', 'defense', 'sp_attack', 'sp_defense', 'speed']\n",
    "sts = stats\n",
    "\n",
    "c = COLORS_by_TYPE[raichu.typ]\n",
    "\n",
    "plt.figure(1)\n",
    "\n",
    "plt.subplot(611)\n",
    "plt.plot(raichu.bells.x, raichu.bells.hp, color=c)\n",
    "plt.axvline(raichu.means[sts[0]], color=c)\n",
    "\n",
    "plt.subplot(612)\n",
    "plt.plot(raichu.bells.x, raichu.bells.attack, color=c)\n",
    "plt.axvline(raichu.means[sts[1]], color=c)\n",
    "\n",
    "plt.subplot(613)\n",
    "plt.plot(raichu.bells.x, raichu.bells.defense, color=c)\n",
    "plt.axvline(raichu.means[sts[2]], color=c)\n",
    "\n",
    "plt.subplot(614)\n",
    "plt.plot(raichu.bells.x, raichu.bells.sp_attack, color=c)\n",
    "plt.axvline(raichu.means[sts[3]], color=c)\n",
    "\n",
    "plt.subplot(615)\n",
    "plt.plot(raichu.bells.x, raichu.bells.sp_defense, color=c)\n",
    "plt.axvline(raichu.means[sts[4]], color=c)\n",
    "\n",
    "plt.subplot(616)\n",
    "plt.plot(raichu.bells.x, raichu.bells.speed, color=c)\n",
    "plt.axvline(raichu.means[sts[5]], color=c)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 421
    },
    "colab_type": "code",
    "id": "YzWichSmg8zI",
    "outputId": "5c058b24-5438-4f1e-a0f1-39ea52128194"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ra3Xnzh2Fw2E5HA61tbXp4MGDevHihc6cOaNUKqVkMqlQKKTq6upP8bkAAAAs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\nABMKEgAAgAkFCQAAwOT/AXENk+JF0nN8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 6 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=6, ncols=1, constrained_layout=True, sharex=True)\n",
    "\n",
    "# for i in range(6): \n",
    "#     ax[i].set_xlim(raichu.xlim)\n",
    "\n",
    "ax[0].plot(x=raichu.bells.x, y=raichu.bells.hp, color='black')\n",
    "ax[1].plot(x=raichu.bells.x, y=raichu.bells.attack, color='black')\n",
    "ax[2].plot(x=raichu.bells.x, y=raichu.bells.defense, color='black')\n",
    "ax[3].plot(x=raichu.bells.x, y=raichu.bells.sp_attack, color='black')\n",
    "ax[4].plot(x=raichu.bells.x, y=raichu.bells.sp_defense, color='black')\n",
    "ax[5].plot(x=raichu.bells.x, y=raichu.bells.speed, color='black')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "QiS9-unXg8zU",
    "outputId": "450e9bd9-56bf-429b-8ff9-86fa839f86f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SpNraWo2MjGSsj46Oas2aNQoEAlqyZImqq6vTr4nH43ryySf13HPPedw6AAD5xVW0U6mU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\n7qYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x=raichu.bells.x, y=raichu.bells.defense, color='black'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220
    },
    "colab_type": "code",
    "id": "0bdH6PFrg8zn",
    "outputId": "c6206708-e1d3-42c6-c7d7-fef3e49e1752"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-b7f91dac5284>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbells\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbells\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefense\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCOLORS_by_TYPE\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbells\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbells\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msp_attack\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCOLORS_by_TYPE\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mraichu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'line'"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.line(x=raichu.bells.x, y=raichu.bells.defense, color=COLORS_by_TYPE[raichu.typ])\n",
    "sns.line(x=raichu.bells.x, y=raichu.bells.sp_attack, color=COLORS_by_TYPE[raichu.typ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6CQxvNEQkOCQ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5xTWsQxlkOAf"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "colab_type": "code",
    "id": "QiacHuX7kN-V",
    "outputId": "e93a7111-c64a-4c18-ec6c-ffec5ee8fb02"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+0CPTow0Dkc8Oawej6OgKNGjQiM2bN2K1WnXf5KPQvHk/ANpqaH8xZUoI8AGw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\nQqGojP8HmjYDkoA2+DcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def f(t):\n",
    "    return np.exp(-t) * np.cos(2*np.pi*t)\n",
    "\n",
    "t1 = np.arange(0.0, 5.0, 0.1)\n",
    "t2 = np.arange(0.0, 5.0, 0.02)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.subplot(211)\n",
    "plt.plot(t1, f(t1), 'bo', t2, f(t2), 'k')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(t2, np.cos(2*np.pi*t2), 'r--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 142
    },
    "colab_type": "code",
    "id": "enDvN7LUkN6q",
    "outputId": "09ea28cc-71db-4b53-d131-ecd32534d8a3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l+Q7K8h18d0MFPaHUwdvh0wGazoVpOhfm3Q9OU7HImbq8scSLL8c838UW4qaQ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\njCRnIYQQIsNIchZCCCEyzP8HAg7Dl8Z/PgsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(1)\n",
    "plt.subplot(611)\n",
    "plt.plot(raichu.bells.x, raichu.bells.hp)\n",
    "plt.subplot(612)\n",
    "plt.plot(raichu.bells.x, raichu.bells.attack)\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "COLAB version of plt_refactor.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}