index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(624, 106)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "#import string as st\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.image as mpimg\n",
    "from matplotlib import rcParams\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import widgets\n",
    "\n",
    "f='myData_rot.csv'\n",
    "ParamFit = pd.read_csv(f)\n",
    "ParamFit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "typeg=['ThorowgoodRbar','thorowgoodSbar','wintec','Wintec2']\n",
    "type=typeg[0]\n",
    "df=pd.read_csv(type+'_fitarcades.csv')\n",
    "dfd=pd.read_csv(type+'_divers.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_arcade(type,couleur,cheval,translate):\n",
    "    global Param_fit\n",
    "    df=pd.read_csv(type+'_fitarcades.csv')\n",
    "    dfd=pd.read_csv(type+'_divers.csv')\n",
    "    couleurW.options = dfd.nom\n",
    "    tutu=dfd.nom==couleur\n",
    "    num=np.argwhere(np.array(tutu))\n",
    "    nn=num[0][0]\n",
    "    param  = np.array(df.iloc[nn,2:]) # Je récupère les paramètres du fit\n",
    "    p      = np.poly1d(param) # Je construis un polynome\n",
    "    sc=dfd.sc[0]#cm/pixel\n",
    "    cut=20\n",
    "    x_lin = np.linspace(df.iloc[nn,0],df.iloc[nn,1],1000)\n",
    "\n",
    "    yf=p(x_lin)\n",
    "    x_lin*=sc\n",
    "    yf*=sc\n",
    "    yf+=translate\n",
    "    \n",
    "    #courbe cheval\n",
    "    scch=23\n",
    "    #i0 = 1#CL  >> ON ne trace pas CL avec ce programme\n",
    "    i1 = 14 #CT1\n",
    "    i2 = 27 #CT2\n",
    "    i3 = 40 #CT3\n",
    "   \n",
    "    idx_p1 = i1+2\n",
    "    idx_p11= i1+13\n",
    "    idx_x1 = i1\n",
    "    idx_x2 = i1+1\n",
    "    paramch  = np.array(ParamFit.iloc[cheval,idx_p1:idx_p11]) # Je récupère les paramètres du fit\n",
    "    pch=np.poly1d(paramch)\n",
    "    \n",
    "    cut=20\n",
    "    x_linch = np.linspace(ParamFit.iloc[cheval,idx_x1],ParamFit.iloc[cheval,idx_x2],1000)\n",
    "\n",
    "    yfch=pch(x_linch)\n",
    "    x_linch/=scch\n",
    "    yfch/=scch\n",
    "    mm=np.max(yfch)\n",
    "    pmin=np.argmax(yfch)\n",
    "    x_linch=x_linch-x_linch[pmin]\n",
    "    yfch=yfch-mm\n",
    "    #plot\n",
    "    plt.plot(x_lin,yf)\n",
    "    plt.plot(x_linch,yfch)\n",
    "    plt.legend([\"arcade\",\"dos\"])\n",
    "    titre = \"Profil CT1 pour le cheval n°\"+ str(ParamFit.iloc[cheval,0])\n",
    "    plt.title(titre)\n",
    "    plt.axis('scaled')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eb0731cc2b7c42fb83d0bd4f2c3e8a01",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Select(options=('ThorowgoodRbar', 'thorowgoodSbar', 'wintec', 'Wintec2'), value=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a491d275cd1466d981174e5d32aca16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mobserver\u001b[1;34m(change)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-e79b808dfda9>\u001b[0m in \u001b[0;36mplot_arcade\u001b[1;34m(type, couleur, cheval, translate)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtutu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnom\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcouleur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtutu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mnn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mparam\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je récupère les paramètres du fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp\u001b[0m      \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je construis un polynome\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mobserver\u001b[1;34m(change)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-e79b808dfda9>\u001b[0m in \u001b[0;36mplot_arcade\u001b[1;34m(type, couleur, cheval, translate)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtutu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnom\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcouleur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtutu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mnn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mparam\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je récupère les paramètres du fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp\u001b[0m      \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je construis un polynome\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mobserver\u001b[1;34m(change)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-e79b808dfda9>\u001b[0m in \u001b[0;36mplot_arcade\u001b[1;34m(type, couleur, cheval, translate)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtutu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnom\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcouleur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtutu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mnn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mparam\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je récupère les paramètres du fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp\u001b[0m      \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je construis un polynome\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mobserver\u001b[1;34m(change)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-e79b808dfda9>\u001b[0m in \u001b[0;36mplot_arcade\u001b[1;34m(type, couleur, cheval, translate)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtutu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnom\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcouleur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtutu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mnn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mparam\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je récupère les paramètres du fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp\u001b[0m      \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je construis un polynome\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mobserver\u001b[1;34m(change)\u001b[0m\n\u001b[0;32m     72\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m             \u001b[0mclear_output\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwait\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m             \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     75\u001b[0m             \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     76\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mw\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-3-e79b808dfda9>\u001b[0m in \u001b[0;36mplot_arcade\u001b[1;34m(type, couleur, cheval, translate)\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mtutu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnom\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mcouleur\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mnum\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margwhere\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtutu\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mnn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mparam\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je récupère les paramètres du fit\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mp\u001b[0m      \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoly1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# Je construis un polynome\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "typeW = widgets.Select(options=typeg)\n",
    "couleurW = widgets.Select(options=dfd.nom)\n",
    "\n",
    "chevalW=widgets.BoundedIntText(value=7,min=0,max=624,description='id_ch')\n",
    "translateW=widgets.IntSlider(min=0,max=10,description='translation_arcades')\n",
    "ui =widgets.VBox([widgets.HBox([typeW, couleurW]),widgets.HBox([chevalW,translateW])])\n",
    "#w=widgets.interactive_output(plot_arcade, type=typeW, couleur=couleurW,cheval=chevalW,translate=translateW)\n",
    "w=widgets.interactive_output(plot_arcade, {\"type\":typeW, \"couleur\":couleurW, \"cheval\":chevalW, \"translate\":translateW})\n",
    "display(ui,w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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 "nbformat": 4,
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