plt_refactor.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n",
    "raichu = PokeDescribeNAME(df, 'Charizard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "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>x</th>\n",
       "      <th>hp</th>\n",
       "      <th>attack</th>\n",
       "      <th>defense</th>\n",
       "      <th>sp_attack</th>\n",
       "      <th>sp_defense</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000518</td>\n",
       "      <td>0.000662</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.000570</td>\n",
       "      <td>0.000992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.180180</td>\n",
       "      <td>0.000527</td>\n",
       "      <td>0.000671</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>0.001006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.360360</td>\n",
       "      <td>0.000537</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.001114</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>0.001021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000689</td>\n",
       "      <td>0.000810</td>\n",
       "      <td>0.001128</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>0.001035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.720721</td>\n",
       "      <td>0.000556</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>0.000821</td>\n",
       "      <td>0.001141</td>\n",
       "      <td>0.000609</td>\n",
       "      <td>0.001050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x        hp    attack   defense  sp_attack  sp_defense     speed\n",
       "0  0.000000  0.000518  0.000662  0.000777   0.001087    0.000570  0.000992\n",
       "1  0.180180  0.000527  0.000671  0.000788   0.001100    0.000580  0.001006\n",
       "2  0.360360  0.000537  0.000680  0.000798   0.001114    0.000589  0.001021\n",
       "3  0.540541  0.000546  0.000689  0.000810   0.001128    0.000599  0.001035\n",
       "4  0.720721  0.000556  0.000699  0.000821   0.001141    0.000609  0.001050"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raichu.bells.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hp': 60.0,\n",
       " 'attack': 85.0,\n",
       " 'defense': 50.0,\n",
       " 'sp_attack': 95.0,\n",
       " 'sp_defense': 85.0,\n",
       " 'speed': 110.0}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raichu.means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I7E2yJ8lOYBFYXjdnGbilOb4ReLqqWr+CkyRp2i7Y8LSqziW5HXgK2AE8UlUvJ7kXGFbVMvAw8FiSVUav3BbPr0/yGnAJsDPJDcC1VXVy+k9FkqSPteroXVXHgGPrxu5Zc/w+cNOYtYMJ8kmStCleyUSS1EsWOElSL1ngJEm9ZIGTJPWSBU6S1EsWOElSL3XaLqe5765mfCXJddOLLknSeJ22y2nmLQJXAYeAh5rHkySpU522y2nGj1bV2ap6FVhtHk+SpE61uZLJRu1yPj9uTnNpr/Ptci4Dnlu3dn2rHZIcAY40p2eTvNQq/ewtAGdmHaIls3ZnO+WdfdZs1HxkQ7PP2p5Zu7FvksVtCtwk7XJatdGpqiVgCSDJsKoOtMg1c2btxnbKCtsrr1m7YdZuJBlOsr7rdjlt1kqSNHVdt8tZBhabT1nuAfYCL0wnuiRJ43XaLqeZ9wRwEjgH3FZVH17gSy5t/ulsObN2Yztlhe2V16zdMGs3Jsoa+5JKkvrIK5lIknrJAidJ6iULnCSplyxwkqRessBJknrJAidJ6iULnCSplyxwkqRessBJknrJAidJ6iULnCSpl9r0g9tSCwsLNRgMZh1D6p+VldHtvol6SEpb5sSJE2eqavdm189dgRsMBgyHE/W4k7SRgwdHt8ePzzKF1FqS1ydZ71uUkqRessBJknrJAidJ6iULnCSplyxwkqRessBJknrJAidJ6iULnCSplyxwkqRealXgkhxKspJkNcmdG9x/cZLHm/ufTzJoxncleSbJu0kemG50SZLGu2CBS7IDeBC4HtgP3Jxk/7pptwJvV9WVwP3Afc34+8DdwB1TSyxJUgttXsFdDaxW1StV9QFwFDi8bs5h4NHm+EngmiSpqveq6llGhU6SpC3TpsBdBryx5vxUM7bhnKo6B7wD7GobIsmRJMMkw9OnT7ddJknSWG0KXDYYq03MGauqlqrqQFUd2L17050RJEn6SJsCdwq4Ys355cCb4+YkuQi4FHhrGgElSdqMNgXuRWBvkj1JdgKLwPK6OcvALc3xjcDTVdX6FZwkSdN2wYanVXUuye3AU8AO4JGqejnJvcCwqpaBh4HHkqwyeuW2eH59kteAS4CdSW4Arq2qk9N/KpIkfaxVR++qOgYcWzd2z5rj94GbxqwdTJBPkqRN8UomkqRessBJknrJAidJ6iULnCSplyxwkqRessBJknqp03Y5zX13NeMrSa6bXnRJksbrtF1OM28RuAo4BDzUPJ4kSZ3qtF1OM360qs5W1avAavN4kiR1qut2OW3WSpI0dW0u1TVJu5xWbXSSHAGONKdnk7zUItc8WADOzDpES2btznbKu0CyfbJup301axf2TbK4TYH7JO1yTq1rl9NmLVW1BCwBJBlW1YG2T2CWzNqN7ZQVtldes3bDrN1IMpxkfdftcpaBxeZTlnuAvcALkwSWJKmNTtvlNPOeAE4C54DbqurDjp6LJEkf2Yp2OV8DvvYJMi19grmzZtZubKessL3ymrUbZu3GRFlj421JUh95qS5JUi/NVYG70CXBZinJFUmeSfLtJC8n+XvN+I8n+c9JvtPc/tiss56XZEeS/57k15vzPc2l1L7TXFpt56wzAiT5dJInk/xOs79/fl73NclXmv//LyX51SR/bF72NckjSb639p/ZjNvHjPyL5mftW0k+NwdZf7H5HvhWkv+Q5NNr7pvZJf82yrrmvjuSVJKF5nzu9rUZ/7vN3r2c5BfWjM/Vvib5qSTPJfmtJMMkVzfjm9vXqpqL/xh9gOV3gc8CO4FvAvtnnWtNvp8APtcc/wngfzC6dNkvAHc243cC980665rMfx/4t8CvN+dPAIvN8S8BX551xibLo8DfaY53Ap+ex31ldJGCV4EfWbOff2te9hX4K8DngJfWjG24j8AXgf/E6N+q/gzw/BxkvRa4qDm+b03W/c3vg4uBPc3viR2zzNqMX8How3evAwtzvK9/DfgvwMXN+WfmdV+B3wCuX7OXxyfZ13l6BdfmkmAzU1XfrapvNMf/B/g2o194ay9T9ihww2wSfr8klwN/A/jl5jzAFxhdSg3mJGuSSxh9oz8MUFUfVNUfMKf7yuiDWT+S0b/3/BTwXeZkX6vqvzH6FPNa4/bxMPCva+Q54NNJfmJrkm6ctap+o0ZXQgJ4jtG/mz2fdWaX/BuzrzC67u4/5PsvXjF3+wp8GfgnVXW2mfO9NVnnbV8LuKQ5vpSP/930pvZ1ngrctrmsV0bdEn4aeB74k1X1XRgVQeAzs0v2ff45ox++/9ec7wL+YM0vkHnZ388Cp4F/1byd+stJfpQ53Neq+j3gnwL/k1Fhewc4wXzu63nj9nHef97+NqM/scMcZk3yJeD3quqb6+6au6zATwJ/uXkb/b8m+XPN+Dxm/XngF5O8wehn7a5mfFNZ56nAtbqs16wl+ePAvwd+vqr+96zzbCTJzwLfq6oTa4c3mDoP+3sRo7cp/mVV/TTwHqO30uZO8/dXhxm9nfOngR9l1GVjvXnY1wuZ1+8HknyV0b+b/ZXzQxtMm1nWJJ8Cvgrcs9HdG4zNel8vAn6M0Vt7/wB4onlHZx6zfhn4SlVdAXyF5p0dNpl1ngpcq8t6zVKSP8qouP1KVX29Gf5f518qN7ffG7d+C/1F4EtJXmP0Vu8XGL2i+3Tz1hrMz/6eAk5V1fPN+ZOMCt487utfB16tqtNV9YfA14G/wHzu63nj9nEuf96S3AL8LPBz1fzlC/OX9c8w+kPON5ufscuBbyT5U8xfVhhl+nrz9t4LjN7VWWA+s97C6OcK4N/x8Vumm8o6TwWuzSXBZqb5E8/DwLer6p+tuWvtZcpuAf7jVmdbr6ruqqrLq2rAaB+frqqfA55hdCk1mJ+svw+8keT8RVWvYXTlm7nbV0ZvTf5Mkk813w/ns87dvq4xbh+Xgb/ZfDrtZ4B3zr+VOStJDgH/CPhSVf3fNXfN1SX/quq3q+ozVTVofsZOMfoA2u8zh/sK/BqjP+SS5CcZfZDrDHO2r403gb/aHH8B+E5zvLl93apPzLT8VM0XGX068XeBr846z7psf4nRS+JvAb/V/PdFRn+39ZvN/4jfBH581lnX5T7Ix5+i/Cyjb+BVRn86unjW+ZpcPwUMm739NUZvp8zlvgL/GPgd4CXgMUafQJuLfQV+ldHfDf4ho1+6t47bR0Zv+TzY/Kz9NnBgDrKuMvp7lvM/X7+0Zv5Xm6wrNJ+ym2XWdfe/xsefopzHfd0J/Jvme/YbwBfmdV+b37MnGH2683ngz06yr17JRJLUS/P0FqUkSVNjgZMk9ZIFTpLUSxY4SVIvWeAkSb1kgZMk9ZIFTpLUSxY4SVIv/X8RbNgJz9yiSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(611)\n",
    "\n",
    "ax.plot(x=raichu.bells.x, y=raichu.bells.hp)\n",
    "ax.plot(x=raichu.bells.x, y=raichu.bells.attack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=6, ncols=1)\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",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y76KaT7PBbz5PvYHX04OFa6sPAye651f/8xsAXx6p+0sWbkDOAX8x5jgbKRhWvWYW/iIr4EfAU93jI9Ne02us9c+An7DwzZXbu7G7gA902+ex8I2UOeD7wDtGXnt797rjnKPfvJrkmoG/Af575HN9Ctg+7fWs5Wc8cowNHwz+8lmS1PBbSZKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWoYDJKkhsEgSWr8L4G+I6VKUcyzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x=raichu.bells.x, y=raichu.bells.defense, color='black'); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f168e51b908>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.lineplot(x=raichu.bells.x, y=raichu.bells.defense, color=COLORS_by_TYPE[raichu.typ])\n",
    "sns.lineplot(x=raichu.bells.x, y=raichu.bells.sp_attack, color=COLORS_by_TYPE[raichu.typ])"
   ]
  }
 ],
 "metadata": {
  "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
}