Proba-Roulette/roulette.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gain ( somme à addition en plus de la mise de départ si victoire ): \n",
    "\n",
    "    - Rouge, Noir, Pair, Impair, 1 à 18, 19 à 36 : mise * 1\n",
    "    - à cheval sur 2 colonnes ( moitié des nombres ), douze nombres à cheval ( 1/3 des nombres, 24 ) : mise * 0.5\n",
    "    - colonne, douzaine : mise * 2\n",
    "    - sixain ( six nombres ) : mise * 5\n",
    "    - à cheval sur deux cases : mise * 17\n",
    "    - un seul nombre : mise * 35\n",
    "    - 1/3 : mise * 3 ( pas à additionner à la mise de départ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(11, 22, 937), (13, 31, 941), (13, 24, 944), (15, 28, 933), (7, 26, 947), (4, 27, 947), (19, 24, 928), (18, 18, 933), (7, 20, 949), (13, 22, 945), (19, 21, 940), (18, 27, 943), (16, 16, 947), (9, 28, 938), (14, 21, 935), (18, 27, 937), (13, 29, 942), (12, 30, 929), (8, 20, 957), (10, 21, 956)]\n",
      "moyenne séances gagnée ( stop à 14€ ) : 0.1285\n",
      "moyenne séances perdue ( stop à 14€ ) : 0.24100000000000002\n",
      "moyenne séances finies à + de 0 mais moins de 14 ( stop à 14€ ) : 9.414\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import numpy.random as rd\n",
    "\n",
    "\n",
    "def tirage_num(n):\n",
    "    return rd.randint(0, 36, n)\n",
    "\n",
    "def rouge_noir(nb):\n",
    "    rouge = [1, 3, 5, 7, 9, 12, 14, 16, 18, 19, 21, 23, 25, 27, 30, 32, 34, 36]\n",
    "    noir = [2, 4, 6, 8, 10, 11, 13, 15, 17, 20, 22, 24, 26, 28, 29, 31, 33, 35]\n",
    "    if nb in rouge:\n",
    "        return 1\n",
    "    elif nb in noir:\n",
    "        return 2\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def tiers(nb):\n",
    "    tiers_1 = [3,6,9,12,15,18,21,24,27,30,33,36]\n",
    "    tiers_2 = [2,5,8,11,14,17,20,23,26,29,32,35]\n",
    "    tiers_3 = [1,4,7,10,13,16,19,22,25,28,31,34]\n",
    "    if nb in tiers_1:\n",
    "        return 1\n",
    "    elif nb in tiers_2:\n",
    "        return 2\n",
    "    elif nb in tiers_3:\n",
    "        return 3\n",
    "    else:\n",
    "        return 0\n",
    "\n",
    "def pair_impair(nb):\n",
    "    if nb % 2 == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "    \n",
    "def victoire_pair_rouge(nb, argent): # on considère que l'on joue à chaque partie 1€ sur rouge et 1€ sur pair\n",
    "    argent -= 2\n",
    "    if(rouge_noir(nb) == 1 and pair_impair(nb) == 0):\n",
    "        argent += 4\n",
    "    elif(rouge_noir(nb) == 1):\n",
    "        argent += 2\n",
    "    elif(pair_impair(nb) == 0):\n",
    "        argent += 2\n",
    "    if(pair_impair(nb) == 1 and rouge_noir(nb) == 2 ):\n",
    "        argent += 0\n",
    "        \n",
    "    return argent\n",
    "\n",
    "def victoire_tiers(nb, argent): # on considère que l'on joue à chaque partie 1€ sur le tiers 1 et 1€ sur le tiers 2\n",
    "    argent -= 2\n",
    "    if(tiers(nb) == 1 or tiers(nb) == 2):\n",
    "        argent += 3\n",
    "        \n",
    "    return argent\n",
    "\n",
    "\n",
    "def partie(cashin,tirage): # cashin = prix auquel on veut stopper la partie, tirage = nombre de tirage par partie\n",
    "    argent_base = 10\n",
    "    max = argent_base\n",
    "    for i in tirage_num(tirage):\n",
    "        if(argent_base > 0 and argent_base < cashin):\n",
    "            argent_base = victoire_tiers(i, argent_base)\n",
    "            if(argent_base > max):\n",
    "                max = argent_base\n",
    "        else:\n",
    "            break\n",
    "    return argent_base\n",
    "\n",
    "def npartie_return_argent(n,cashin, tirage): # comme au dessus mais le boucle n fois\n",
    "    list = [] \n",
    "    for i in range(n):\n",
    "        argent_base = 10\n",
    "        for i in tirage_num(tirage):\n",
    "            if(argent_base > 0 and argent_base < cashin):\n",
    "                argent_base = victoire_tiers(i, argent_base)\n",
    "            else:\n",
    "                break\n",
    "        list.append(argent_base)\n",
    "    return list\n",
    "    \n",
    "def npartie(n,cashin,tirage): # n = nombre de partie que l'on veut faire, cashin = prix auquel on veut stopper la partie, tirage = nombre de tirage par partie\n",
    "    nb = 0\n",
    "    gagnée = 0\n",
    "    perdue = 0\n",
    "    autre = 0\n",
    "    for i in range(n):\n",
    "        nb = partie(cashin,tirage)\n",
    "        if(nb == cashin):\n",
    "            gagnée += 1\n",
    "        elif(nb > 0 and nb < cashin):\n",
    "            autre += 1\n",
    "        elif(nb == 0):\n",
    "            perdue += 1\n",
    "    return gagnée, perdue, autre\n",
    "        \n",
    "list = []\n",
    "count = 20\n",
    "for i in range(count):\n",
    "    list.append(npartie(1000,20,10))\n",
    "\n",
    "print(list)\n",
    "\n",
    "gagnee_moy = 0\n",
    "perdue_moy = 0\n",
    "autre_moy = 0\n",
    "\n",
    "for i in list:\n",
    "    gagnee_moy += i[0]\n",
    "    perdue_moy += i[1]\n",
    "    autre_moy += i[2]\n",
    "\n",
    "\n",
    "print(\"moyenne séances gagnée ( stop à 14€ ) :\", (gagnee_moy/count)/100)\n",
    "print(\"moyenne séances perdue ( stop à 14€ ) :\", (perdue_moy/count)/100)\n",
    "print(\"moyenne séances finies à + de 0 mais moins de 14 ( stop à 14€ ) :\", (autre_moy/count)/100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([-1,  0,  2,  5,  8, 11, 14, 17, 20]), array([ 12,  30,  60, 164, 252, 244, 172,  50,  16], dtype=int64))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8Hn2eb0qvX3hxMo9At7Irxiyd/45JNShhEkd/KJWhob6k9C7gPoJY88yVwoeLvm2Z4reIlIH2a/hjA2lT08hbmIfhN5AeiSX9+PO4ji0dNP27ifHfHY9rp4vGvzYy+SeTaXyikawzskgpSKHxL42Mu3McTS81odk1cuf3nPag1OOte+rAwfmhvl+vtOyd6nls/Mnbf3LWfa+Pz2o4r69jfL6U99avu25mS0sL1dXVQKC716mnnsqCBQvYuHEjAHPmzEFKyXPPPcfOnTux2WxcddVVjBo1Cgj8snn88ce55pprcDgcHD58mFWrVmEYBtdceOXmH467cVao7yfK2oHPjlk6v87kOpQQqDvdIaS+pPRe4IfB7u/wMv1H1cban33Z0u9IKd2p07Wti9G3BNZv1Kxar5+K9nfbyTk3ByEEaVPS0J06vlYfwiIwvAaG10BYBHqXTvvmdiZUTOh1ne0ptrAGD1jRc0I9xu9L7QLIzc1l8eLFvV6fM+f4WppCCC6/vO+pboUQ3HDDDce+LywsDHQ7k8jPe+dlRzbAOSqyCHQpu2bM0vkRr+ysxIdq0x0i6ktKHyCEwD3q1L1y4QVbjLf7e9172Is108r+/9vPjp/sYP+K/RienkuT+Vv82PKON13acm34W/zkXZjH4X8epuGPDRReUcihNYco/GwhQut9E64LMWqbzbYr1PoFZGkndlkIgsebNviTsgikkbopT2ZMjOU1QuAAnm6orLvO7EKU4KjQTXD1JaWivqR0GYHhoWFZ/JwxtahV7u/zRQNce13klecx5WdT0FI1DtcEN0FXSn4Kk344icl3T0akCHwtPuyj7Oz7wz4+efATPAd79lxbnZm+r59T9UsIRC4dIU1eE+u10c7ynZJoo8RswF8bKuu+bnYhyuBU6Caw+pJSDfgjcHsk5xGQ88sVerNmyF5LmltzrdhybaRNDjw4y5qThWuvq9c+vubjN4++Fh/W3J5tEJ/+41NGfH4ETS81kbcwj+IvFnPo6UM99nk5Pc0RTv1ForV98L2OczmzYtajwCK1jycZI86I1fkjoAEPN1TW9R52pyQUFbqJ7WFgUTROlO6hrPIp4/WTt9tybNjybXgaA3elnR92Yh/Vc/bHrFlZtL7RipQS5w4nFocFW87xXOv6qAtbjo3U4tRArwUBCI73YOh20GIp9UOv4B/MCNHSGcr+LldWLIblAlCmj0v05dUfbKis+0+zi1D6p0I3QXU/NItK4B512m65cMH7xoaTt4+8fiT7/rCPj//fx7g/cVN4RSHNtc001zYDkHFaBimFKWz//nb2/2k/o24cdexYKSWH/nmIwisLAcg9P5fGvzay9zd7KbjspEURhMhc57B/GGrdxaLZNfhex7ndmbFZG01yZJZ/wpzBdzSVBvy5obLuP8wuROmb6r2QgOpLSr9JGA/NBiNA3F5jTP5wnGg8ki2Oda53jHcwpWpKj33zyvOOHydEj6DtcU4hmPi948+U7KPsTLlnSp/7AqzKzGiZ7wptqtiRoimkB2Nud3pMlsCZYBR+YMVyfizOHWU24O8NlXWXRroIphBiBfBZ4JCU8tSTXqsgsMR8oZTySCTXSSbqTjfB1JeUXkdg4vGY0CDvvhX64b7ad+NhvcOeP/hePRXTHHStUtKl6ynRv9OVeM/2TZse9fPGjgP4ZxQWwPwTcOnJG4UQY4HPAJ9EeP6ko0I3gdSXlF4E/JkgBj5EIsPNzLv+0bt9Nx46hSht00Tb4HseVyRag95XSi0md1y5MmNDOqlFsTh3DGURmChndLgnkFK+BjT38dJvgO8T4XJMyUiFboKoLyk9Bfg7MODosWg5Y4dceO5WY2M8rtWDEJYX0tPrQzmkULQF3Qym69bWkGsKwnm+kqG6am8xsLqhsi5qa+MJIa4C9kspt0TrnMlEhW4CqC8pzQCeBsJecjxUAsSda4wJ+e3yYLyuedSajPSQVvjNEZ1BdwHz+exdoVc0sFRp2zxCZve7ztsQcCaBnjARE0KkAT8CfhKN8yUjFbomqy8pFcBKIO7thRoU3LdCP6gZMqQRX5HamprSay6FgWTgCrp/r8cT/dFoZ/onh7UMfIK5IUrz8U4GJgJbhBB7gDHAO0KI4iicOymo0DVfJfB5sy6e6WLWd1YbcZ0wxS/E+L1Wa9Cj0xx4MgbfK8DtDnrXoGhS7J2mj0r0bmLB+mVDZd1nIjmBlPJ9KWWRlHKClHIC0ADMljL+n5iGKhW6JqovKb0E+C+z65i7XS6Y95ER1ykCn85M3xPsvjb0oHsjuFzRHY1Wqo/ZIxDD5d+JBXiyobJu0Ok+jxJCPAGsA6YJIRqEEFHtO56MhssP05BTX1JaRGDFXtP/HwjQvvO0MTa3Qx4afO/oeCE9LeiHYwKZC8HNQepyZaWHX9VJJG1z/JMScchvJHKBxxsq64JaS09K+SUp5UgppU1KOUZK+chJr09QfXRDY/o/+CT2f0Ch2UUcpUkK71uh7xdSGoPvHbkGq7XEgKCuJQSWbLqCmn/B7cqM2sPI0UbeZhvW6LZXJIYFwI/NLiJZqdA1QX1J6deBK8yu42TZTk7/5jNGRCOYgiWFyN1kT/0o2P0LRFtrMPt5PFEajSbRz/VPC2sO4CHi7obKurlmF5GMVOjGWX1J6WTgAbPr6M859XLBnO3G5nhca1VmRtDNGcFMeiMlnbpui8qdaZZ0vJ0l08ZE41wJygo81lBZF9bMb0r4VOjGUX1JqYXAiLOE/cgqQLtrlTEyu1MGN6luBF532HMH3yugmGbnYPtIqUWt5nP80zIH32vImwosNbuIZKNCN77uBM4xu4jBaJIR963QPyHGC+i1alqpU4igBjOMFM2ewfbRdVtIw4v7Y5OWrWOM/FMH33NYuKOhsm64PSxMaCp046S+pHQkcI/ZdQQrt4sz7vinsTamFxEi5d/pjqCmeiwWzYMO4PB57YPeDQdjtn9SVMJ7iNCAhxoq61QWxIn6i46fXxOYgGTImL9Vzj99hxHT8fXPZGQEFZTFYvAVe6IxGk1IGmfoY5LtAdOZgFpxIk5U6MZBfUlpOfAls+sIlQDL9/9uFGV1yaZYXWNLakpQM2AViNbBl5x3R94MO0UfuU1DS8Z5pu9tqKwbarOoDUnJ+MMVV/UlpTbgf82uI1wWychfrtA3fOMOSx5CRH3KSY+mTTlosRws1vUBx+7nMvikN25XZmQztEm65vlPmRXROUJU8dxSXt75Jvlpuby8aCUA33jmp+xqDoySbnd3kmXP4IWbV/Q47kD7p3z72Xs50tWMQPDlWVewaM61ANz76kO8sustZhSdwm8/G+iOu2rrizQ7W7nlzC/2V0oOgQnJb4z+u1ROpO50Y++bQKnZRUQiv5Mzb/tX7Np3n8lM3zHYPlnCOWjXpkhHoxXJ7E12bDmRnCNU15ZdymPX/qrHtoeuuocXbl7BCzev4LJpC7hs6oJex1k0C3dfcDu1tzzGMzcsZ+U7q9l+ZA/tnk4+OPgxL33tT9gsVuoP78Tl8/DU+8/x1dmDTvFxQ0Nl3VnRe3dKX1ToDkAIsUcI8b4QYrMQIuS5Z+tLSrMITIM35JVvkeeV7Tbej8W5/5WePugdtAPPoIHqcmXmhF2ERJ7nKwl6ToJoOWvsLHIcfTf1Symp+egVriq9sNdrIzIKKCsOzDaZkZrGlPzxHOw4jIaGz/AjpcTl82DTrPzh7Se5afYXsFmC+mD7iwjejhIEFbqDu0BKOUtKGc5MU98F8gbdawgQYP1RtZGX4ZSDP9EK0S6bdaocZAWCFHyDNthGMhotndSNeTJj4uB7xs9bDVsoSM9jYt7Avwv2tTWy9dOPOX3UdDJS0yiffBaX/mkRRRl5ZKams7nxQy6dOj/Yy5Y3VNb1TnklalToxkh9SWk+gdAdNiyS0b98VP842ueVQhR+kJIy4Hk15IADKaSkzTCsYS+9fpbvlIR7vvHMhy/3eZd7oi6vk9tW303VhXeSmRr4MPCNeV/mhZtX8JPyO7i/7hEqzvsaT2yp4RtP/5T/eXNlMJdWd7sxpEJ3YBJ4UQixSQhxa4jH/gAYdqOaCtuZu+h5Pertu6sy0xsHel0IUtNx9TsUWEot7B4WFql9PNEYcXq4x8eC3/Dz/PbXuLKkvN99fLqfW1ffzdXTL+ayaQt7vf7Bp9uRUjI5bxw1H73CQ1ffw96WA+xuHnQq43kNlXVXRvYOlP6o0B3YeVLK2cBlwBIhRO8nGn3oHghxR0wrM9Fn3pXnzNhrbI3mOV9NSxt0aHS+aG/t7zW/P/zRaDP18Qk3AXfdnk1Mzh/HyKy+e3FJKfnev37JKfnjuXXudX3uc3/dI9w1/xZ8hh+je/I4TQhc/kEH9wH8vKGyLqYLpCYrFboDkFLu7/7vIWA1EGyn+bsILIE9LAmw/fhJIzvdJaM2cuuIRZvuhQHTYAT9T3rj84U5Gk1yeJZ/gmmDIZasuYerH/sGu5o/4cxlX+DJLTUArKl/matKL+qx78GOI9z4t+8BsGH/+/xj6wu88ck7XPLo17jk0a9Ru3PdsX2f317HzOJpFGcWkG3PZHrRFC565Kt4dC/Ti6YEU9pM4OrovEvlRCLGw+uHLCFEOqBJKTu6//wS8DMp5fMDHVdfUpoN7GMYNi2c7NNs1t95uzVqXYwe+PTwOxc7XbP7e/0O752baoyzzwD4yVn3vT4+q+G8o681N49au/WDC3t/xh7EBL1w7UW+mSEflyReG7N0vvq7iTJ1p9u/EcDrQogtwNvAs4MFbrfbSILABRjRxlk3vRS99t3VmRkDTlQ+0KQ3bldm6B+FJZ5zfNPiviDoELKgobKu31+CSnhU6PZDSrlLSnla99cMKeWgT3S7p24ctm25fblsozy7ZJ+sj8a5NtpTRwz0+kjR5O/vtXDWRsuTGRvSSE2Y1TsS1LfNLmC4UaEbXZ8D4t7B3kwCUn7yVz3N4ZZBLaczEJcQJc1a/70QRoiWfpf3cbkyQ56j+DxfiQrcwV3XUFmnllePIhW60XWn2QWYwWowfumf9Mh7MwghajLSt/f3cuEAk9643aGNRrNL27tFMntaKMckqRTgdrOLGE5U6EZJfUnpVAIL/iWlkS2c/ZVaPeL11Woy0vttQsijvd8mBLcnLaS71jP9UyKeBjKJ3Brs6sHK4FToRs8NfW38cWMj5+34mCt37+qx/fGWZi7fvYsrdu/i/kP9LxWmS8nn9+zmGw3HO7R/78ABrt69m98cPr46zfKmI/y7oyPS9xCRK96S807ZL7dFco7tKbZ+F4PMFs7UvrZLSas0rPZgr6FJsXeqPvLMcOpLUiOAz5hdxHChQjcK6ktKBfCVvl77XHY2D4/p2cz7lrOL2s5OVo+fwD8nTuLmvP6nZ3ispYXJKcezZpvbjV0TPD1xIh+4XXToOof9ft5zubgo09xOEwJSqx7XUx0eGXb660KM2m6z7e7rtTTcfU56YxiWI6FcY7o+Zo8g+tNUDnN9/nwroVOhGx3zgQl9vTAnLY1sS8+/5idbW7klL58ULbA939r3sP+DPh9ruzr5Qnb2sW1WIXAbEkNK/FKiCcHvjxzmjoLEeCZkM5hw70o9otnIVmemf9LX9tR+Jr3RdVvwD/EkbWf4J4UzeVGyu7qhsi5hF1QdSlToRkdIEz/v8XrZ5HJy3d493PjJXt53ufrcb+mhQ9xVWIR2wj3Z5NRU8qwWvrB3D+dnZPCJ14sBTLcH/ek65kY3cc5/rtXrwj3+5fS0PkfzWTBy+tru89r7/gvswxgj710b1ojm3U1SaQR65ygRUqEbofqSUjtwTSjH6FLSpus8OW48dxUW8d3GA70W3n21s5M8q4UZfYTpD4tGsHrCRG7Oy+f3Rw7zzYICljcd4TsH9vO31tZI3k7UfO5NOWdyowxrRrJGi6XUD70eqAlBeipe98nb3Z7+H771IPGf6y+ZGk5NCqCaGKJChW7kLgSyB93rBMVWGxdnZiKEYKbDgQa06D0Xu33H5eSVzk4u2rmDigMHeMvp5PsHDvTY5+WODqbb7TgNyT6vj9+MGs2LHR24jH67s8aNAMfPHtMtqV4Z1BLrPQ8Wmesc9j5XCc6nvdd8vsGORsuSjg2Z0jEq5HqUoy5U66hFToVu5D4b6gHlmRm87QzMz7LH68UnJbmWnj1yvltYxCuTp/DvyVP49ahRzEtL475Rx/PCJyWPtbSwKC8ft2EceyykI/ElyHwaNp1Jv/izvjmcY1dlZjT3tb1QtPZ6SOdyZQW1Ntq5/pKkGJ4dQxbgUrOLGOpU6Ebu8oFevOvAfr60dy97vF4u2LmDf7S28vnsHPZ5fVy5excVB/Zzb/FIhBAc8vu4rWHQuU4BeKKlhauys3BoGtNSU3EbBlft3s0Mu50sS+J0qRx3mHOvqTNeD/W49XZ7fl/bi0VLr9nEXK7MQdtobdKydbSRd2qodSi9XGZ2AUNdws2WP5TUl5SWMciw3/tH9b3C+Il3rUcVWW38YUzv081NS2duWs9cufGEbmZCiH6vkwiufd2YvekUsXN3sei3D+7JOjUxvV0TbVmG7NF0Uyyaej00c7szBl0S6Qz/pKhNQ5nkPtNQWWcZs3S+PviuSl/UnW5kBrzLVQIEpP38zzopPhn8nLdCWJ5PT//o5M0jRXOvkWQeT/qA/eWE5MB0fYxpc+YOM3kEP6+00gcVupFRoRukFJ3JP39MfyeUY9ZkpPeayrFYNPdosJaSZiktA7bpnqKP/FhDU5/qokc1MURAhW6Y6ktK04CoTeCdDCZ+ynlXrTPeCHb/rakp40/eVkRrj+8NwzLw2miSrnn+U2YFe00lKCp0I6BCN3xnotrEQ/blV43Txh6SfQ7zPZlfiPGfWK0NJ27LF+09/s51f8qAbbUjZPamVGwhdelTBnV6Q2WdGmASJhW64Tvb7AKGIgEZ967UfTa/7DXIoS9PZ6b3COhs0dVj0huvz97/eSTyPF/JuLAKVQZiAdRQ6jCp0A3fOWYXMFSl+pl6z+P6hmD2fSE9rcedbTruHkOEPe70fp+ip5O6IVdmTAirSGUw88wuYKhSoRs+1Z4bgSmNzL/8bePNwfbbZ7WWGHBsiJ0db49JV1zu/kejne2bGvISPkrQVA+GMKnQDUN9SekpQGJM6zWE3fiyUTbmsNwz0D5SiNxN9tRjXces6Lknvu5yZfU5x65VatsmGEWnR6VQpS/qTjdMKnTDc4bZBQwHAjLvXal7rH7Z7yq/AKsyM47N8i4EWRb0YxPc9Lc22kz/+MN9bVeiZkxDZZ2axyIMKnTDU2p2AcOF3ce0n/5Vf2ugfV532HNO/D6XjmOT3rhdmbm9DpAcPk2foFaGiD118xEGFbrhUaEbRdP2s+DSjca6/l5v1bTpTiGOzVZWJFrbAaREer2910abaBRttaD12eygRNUpZhcwFKnQDU+J2QUMNze/ZMwY2ST7XDECIVJq0xz1R78dIVo6u//YJKXW82GZxHO2b+qMmBWqnEiFbhhU6IaovqRUA9RE2FEmIGvpo3qXVZfevl5fnZlx7E63WDS7AQzD0mv6x3yZsSGNVPWQMz5U6IZBhW7oJgLqo2sMOHyU/vgJvc9mhi2pKcemURspmnwAfn9Kr7XRzvWVqEm240eFbhhU6IZO/aDF0Ix9LLzoHWP9yds9mjbloMVyEKCYZj+A1+voMc2jXdreLZLZ6lNI/IxtqKxLnMX5hggVuqFT3WRi7OsvGKUjmmXDydvXZKTvABghWgHweNJ7rEs01z+l17SPSkwJIOg5kpUAFbqhKza7gOFOQPYvH9XbLLrsEaLPZaQJgALRZgVwuzKP/fxqUuw+RR+puonF3xizCxhqVOiGToVuHKR5mfHDp3oOE95ts50CkCM6UwCcJ4xGm6GP3SeOrRSnxNEIswsYalTohk6FbpzM3CMXnr/FePvo94YQRe+npHycgcsO4D46Gk3SOts/UXXUN4d6cBkiNR9s6FToxtE3njNO+XC82H8oR4wGWJWZfqDS0zEOaHe7M/MAxhj5m21YzzezzuFGSmkAbQZ6q274O/3S6/boLo/HcOoufycuvUNz+jtsXf62lOuZb3a5Q4oK3dCp3+xxJCD3lyv0hkXftowwNGF9NS0t/W5aswwp9nk8jkIk/nN906aZXWcik1L6QbYY0mj3S1+nz/C4vIbL59a7dJe/U7j0DovT35Hi1Dscbn9nhkvvyvYarhwgt/trIFti/w6GFxW6oetzghUldtI9lP3gb8ar/32d5fwjFm2GDym9PocHNEu2TFuXiSNpJpSXUrolstWQeptfep0+w+P26C6fW++SLr0Dp7/D6vJ3pDr1jjS3vzPDbThzfIYni8CseLEYNKJW5QiRCt3QqYERJpi1Sy5c8L6x4bUy7cy6dMe7wmPxAJzrn5Zldm3hklJ2SIw2XeodfsPb5TXcHo/u1N16l3H047vT35Hq0jvT3HpnllvvytGlP41AE1eiNHOp0A2RCt3Qqc7gJhAgbq8xJm0dJxpXZ2a0zXJb/SnS+v4oI6/M7NqklBJoMzBadenr9Btel1d3e9yGU3f7O3HqHZrL32Fz6h0Ol78jza13ZXl0Z66BkQlkml1/hFTohkiFbujUna5JNMi/b4W+5Vt3phTM7kppne2f1Dn4UaE5uf3Tb3jcHsPtc+tdfrfeidPfYXX6O1Jceofd5e/MdOtdWV7DlSOROUBOtOsZAlLMLmCoUaEbgvqSUgGoJWBMlOnmtG+slq94FmTq0/UxAy4ZI6X0SGSLIfV2v/Q5fYbbdbz9sxOXv8Pq1DtSXf4Oh0vvzHDrXbk+w5NJ7No/h6M+JyhS+qdCNzTqLtdEfktqp9uef3h8a2F2a8Okg01awxsuf6fh0jtF98f3VJe/I82ld3W3f/rSSaz2z+FIDb0OkQpdxXS6luJ02/MOuxwFrU5HkdPpKPK6HQW4U3NtPltGmt9qz5bCUogQGQR6j0z0Nj/dvsO3boHZtSvqTjdUKnRDUPpRvbu+pFQSmOhDGYSuWd2e1LxDLkdBm9NR1OlMK/K57AXSY8+zeW0ZjkCYWgsQIgsY3/0VFKHlWWBX7IpXgqXudEOkQjd0LiDN7CLMZAiL152ae9jlKGhxBcLU63IUGO7UPJs3JdOhWxzZhmYtQIhsYFwsatAsBXY9FidWQqXudEOkQjd0wzZ0DaH5PKm5h132ghZnWlGny1HocTkKpTs1z+JNyXT4rY4sQ7MVIEQuMLr7yxTCkpdj1rWVHtSdbohU6IbONfguicUQmt+bknPE5chvdjkKO5yOIq/TUWi47XkWb0qW3W9NyzI0ax6IfIQYxRCYM1hoOWo4dmJQd7ohUqEbuoQJXYnQPanZR9z2gmano6DD5SjyOB1Fhtuep3lTs+w+a1qmodnyu8N0WD3FF5o9G+hEDcs2m7rTDZEK3dBFvUP+ySTC8KZkNbnseU0uR1GHM63I7XIUGm57vuZJyUr1W9MydUtKHogChBhB0s5pavkUdBW65nKaXcBQo0I3dIfCPVCC9Nkym1z2/GaXo7C9O0x1lz1feFOz7T5resYJYao66A9GpLYi1b95k+03u4ChRoVu6A72tdFrS29x2/OPuByF7U5HkcvpKPS77fmaJzUn1WdLzzAsKbkSrRAhCoCCONc8LAktwyl1Fbom+8TsAoYaFboh2jHp6q3tWRNe86TmpPhs6Rm6lporhXb0if5gc48qUSS0HF3qYX/wUKJDhW6IVOiG6JNxFzsBNRIqAQhLnkU9xjGdCt0QqTXSQqd+yBKEZskflv2lhxADaDC7iKFGhW7oVOgmCKHlqeYccx2oqK7xm13EUKNCN3QqdBOEsGQnaVe5hKH+LYRBhW6IliwvbwFazK5DASFS0oFWs+tIYip0w6BCNzzvml2AcpRVdV8wz16zCxiKVOiGZ6PZBSjdhL3V7BKSmFp+PQwqdMOjQjdBCC3TbXYNSewtswsYilTohkeFboIQllzD7BqS1JGK6ho1i3wYVOiGYcny8t1As9l1KKBZ8tRCoeZQd7lhUqEbvk1mF6CA0PLTza4hSa03u4ChSoVu+FQTQwIQltw8s2tIUupON0wqdMOnQjcBCC27GJBm15FkJPC22UUMVSp0w6dCNwEIYUkBccTsOpLMtorqmjazixiqVOiGacny8k+AHWbXoQBYD5tdQZJR7bkRUKEbmWfMLkABoTnaza4hyfzb7AKGMhW6kVGhmwi0LI/ZJSQRH1BjdhFDmQrdyLwJqPZEk2mamuExjmpVe25kVOhGYMnych141uw6kp2w5KeYXUMSWW12AUOdCt3IqSYGkwlLvlqGPT4M4GmzixjqVOhG7kVATbpiIk3LVasrx8e6iuqaT80uYqhToRuhJcvLu1BPc82lZRQButllJAHVtBAFKnSjY43ZBSQzITQLCHUHFnurzC5gOFChGx2rAdVtyUwipcnsEoa5LRXVNbvNLmI4UKEbBUuWlx8B/mZ2HclMiLQOs2sY5laYXcBwoUI3ev7X7AKSmdCyfGbXMIx1An8yu4jhQoVulCxZXv4Wao5d0wiLGiARQysrqmvUUOsoUaEbXcvMLiBZCUt+qtk1DFMS9SkuqlToRtcTgHqgYwJNy88yu4Zh6t8V1TUfmV3EcKJCN4qWLC93ox44mEJYcgrNrmGY+r3ZBQw3KnSj7yECwyWVeBLpBYDX7DKGmd2ouUWiToVulHWvFPyc2XUkGyGEAO2g2XUMM8sqqmvUDUSUqdCNjfvNLiApiZQWs0sYRjqAR8wuYjhSoRsDS5aXr0XNxxB3QqR3ml3DMHJfRXVNq9lFDEcqdGPnR2YXkGyEJVsNkIiORuABs4sYrlToxsiS5eUbUHPtxpXQ8tTPc3T8tKK6xml2EcOV1ewChrn/B1yB+uUWF5ol35Ho8zu2Ol088dZmOjxeBHDWpHHMnzrx2OuvbttFzZZ67rnqYtJT+14Qw+3z8avnX2PG6BF8fvap+HWdR9/YRKvTxTlTxnPulAkA/G3je5w9eTxjcrNDKbEe1e0xplQYxNCS5eUfoH6A40ZY8kJKFzNoQnDFrOl8/9KF3HnhubyxYy8H2wJz9bQ6XWw/eJicNMeA53j+g+1MKsw79v22g0eYWJBLxSULeGfvfgAOtLYjpQw1cAEqK6prEv1315CmQjf2/h+BCUOUGBNaTpHZNQwmy2E/FoR2m5URWRm0uwILjzyz+UM+e1opYoDjG5rb6HR7mDri+GIZmibw+nUMw0DKwLbnP9jGJadOC7W8uorqGjU3dIyp0I2xJcvLPwWWml1HMhCaIwfoMruOYDV3Odnf2sa4/Bw+2H+QbIedUTn9j2Y2pGTNlkAwn2jqiAJanC5+9/KbnHfKBLbu/5TROdlkO+yhlvT90N+FEirVphsfvwZuBcaZXcjwZ/kU9ElmVzEYj8/Pyjc3cdWs6WhC4+X6ndy6YO6Ax7y5Yy+lI4t6NT9YNI3rzzodAN0wePi1t7n53Dms2fwhLU4Xc8aPYcboEYOV9I+K6pr1EbwlJUjqTjcOuudk+LrZdSQFkZrwAyR0w2Dlm5uYPW40ZWNG0tTZRXOXkwderOMXNbW0udz85qW6Y80OR+1tauGNHXv4RU0t/9xSz6Y9+3n2vZ5z0by5Yy9zxo/mk6YW7DYrN5w1m7Xbdw1WUivwrWi+R6V/6k43TpYsL39x2eLaPwC3mV3LcCa0DKfUE7e3k5SSpza8x4isDBZOC9yQj8zJ4p6rLj62zy9qavn2xef16r1w9G4WYMPufexraePymSXHtjm9Pj5sPMTXF8zlwwOfIhAgwKcP+lzsmxXVNfsjfnNKUNSdbnzdRWASESVGhJad0E/e9xxpYdPe/ew41MQDL9bxwIt11Dce6nf/fc2tPLXhvaDO/dLWj7mwdAqaEEwrLmT3kWZ+/cJrnDF+9ECHPVNRXfNYaO9CiYSQRx93KnGxbHHtQuAVGPAhtRImn+vNOt29fr7ZdQwRTcCMiuoatZJyHKk73Tjrnpfhd2bXMVxplvx0s2sYQm5XgRt/KnTN8UNgu9lFDEdCy8sxu4Yh4qmK6pqnzC4iGakHaSZYsrzctWxx7VeB1wFLOOdo6TzEn19ZSoezBYTg3NLLuaDsCzy7cSVv1j9LhiMHgCvnLmLGuHlBHQvw9PqH+XDf24zJn8KN5ZUAvL39Jbrc7Vww8wthv+d4EZbsQftGKXwK3G52EclKha5JliwvX79sce2vgMpwjteEhc+ftZixhVNxe538ctViSsacAcAFM6/hotO+GPKxOWkF7DvyMT+69v/4y9r72d+0i8Ls0azf9gJL/mNojO8QIiWdQBeoHHMrSWi3VlTXqLX8TKKaF8x1N2HOu5udns/YwqkA2FPSKM4ZT2vXkYiOFUJDN3SklHj9HiyalZe3PMXCU6/GYhlKv5+t/XcHUJaqob7mUqFroiXLy/3AF4GPIzlPU8dBGpp2MKEoMDz0tQ+e5t6/3cLjr/4Kp6cj6GPtKWnMGDeXpf+4jey0PBwp6ew5VM9pE8+LpLz4E/Y2s0tIUM8DPza7iGSnuowlgGWLa6cB6wnjI7HH5+K3a77DJadfz6xJ82l3NpNhzwYhqNnwKO3OZr5y/veCOvZkf1l7PwumX8m+Ix9T37CJ0fmTuHT2V0ItMe487U+8JvXGBWbXkWB2AmdWVNck/Ii94U7d6SaAJcvLtwHXAv5QjtN1P398sYo5p1x4LDSz0vLQNAua0Di39HL2Hvoo6GNPtO/IxyAlRTljeWfXayy6+CccbjvAobaGkN9fvAlLjlpMsad24CoVuIlBhW6CWLK8/N/At4PdX0rJX9beT3HOOC6cee2x7W1dx5+PbNn9OiPzJgR97IlqNjzK5Wfe3N3GG8gwTQi8fk+wJZpG0/JsZteQQHTgixXVNVvNLkQJUM0LCWbZ4toHgW8Mtt/Oxvf5zZpvMypvIkIEfndeOXcRG3fU0tC0EwHkZRbzpfnfITs9n9auI/x17a+5/T/+u99jj3Yt27L7dRqadnL5nK8CsGrdcuobNjI6bxI3XZj4S7/p3h2bfV1rZpldR4K4vaK65qFgdhRCrAA+CxySUp7ave1aoAooBeZKKTfGqtBkoUI3wSxbXGsl8MDjQrNrGaoMvXmvt/1P482uIwH8pqK65rvB7iyEWEBgwv0/nxC6pYAB/AG4S4Vu5FTzQoLp7tFwLbDZ5FKGLKFlFQPJfjfxKIEJloImpXwNaD5pW72Ucls0C0t2KnQT0JLl5S0E7nTfMbuWoUgIayoQXKfl4WkFsKiiukY9UExAKnQT1JLl5c0EgneD2bUMTbZkDd1HgFsqqmuS/U4/YanQTWBLlpe3AhcT6MOrhEBojnazazDB/wFfV4Gb2FToJrgly8vbgM8Ab5hdy5CiZbkH32lY+SOBORVU4CY4FbpDwJLl5R3ApUCd2bUMFZqWa3YJ8fQwcFukgSuEeAJYB0wTQjQIIRYJIT4nhGgAzgaeFUK8EIV6k5rqMjaELFtcmw7UAOebXErC87s3vel3rT3H7Dri4EHgDnWHO3So0B1ili2udQB/Bq4xu5ZEpvv2vO/rXFVmdh0xpAMVFdU1/2N2IUpoVPPCELNkebmLwMxk/2V2LYlMaLn5ZtcQQy3AZSpwhyZ1pzuELVtc+yUCfTLtZteSaKQ0dE/rbyHMlTkSWD1wZUV1zQ6zC1HCo+50h7Aly8ufABYC+8yuJdEIoVlADLfJzJ8FzlKBO7Sp0B3iliwvfxuYDbxsdi0JRwyrARL3EbjDTcb+x8OKal4YJpYtrrUAPyew5powuZyE4GlbsU4arWebXUeEuoDFFdU1j5tdiBIdKnSHmWWLa/+DQL/N0WbXYjZvx9/XGv5PFppdRwTWAjdXVNfsNrsQJXpU80KCEkJ8RwixVQjxgRDiCSFEUA/Lliwvfw6YQWBIaFITljyzSwiXE/gWcIEK3OFH3ekmICHEaOB1YLqU0iWEeAp4Tkr5p1DOs2xx7UUEhodOiHqRQ4Dfs2W93/nyWWbXEaLXCdzdqodlw5S6001cVsAhhLACacCBUE/QvQTQqcDvScL5ZTUtL8vsGkLgAiqAhSpwhzd1p5ughBDfAn5B4B/ji1LK6yM537LFtecSmPZvWhTKGxKk0XnY0/Zwodl1BGEdgbtbNVl4ElB3uglICJELXAVMBEYB6UKIiNY+X7K8/A1gFnAvkBwzcIn0AsBrdhkD2A1cD5yrAjd5qDvdBNS9GOClUspF3d/fCJwlpbw9Gudftrh2FHA3sAgY1ivnult++wkY48yu4yRHCAzjfqiiuiaRfykoMaBCNwEJIeYRGN57JoHmhT8BG6WUv4/mdZYtrp1EYKXX6xmmn3rcrQ9uRrpnmV1HNyfwG+A+NcgheanQTVBCiHuA6wA/8C5wi5TSE4trLVtcO53AwIrPx+L8ZvK0rXxDGk3nmlyGTqA9vaqiuqbR5FoUk6nQVY5Ztrj2DAIP7y4xu5Zo8XY+/arh23W+SZd3AU8Av6qorvnIpBqUBKNCV+ll2eLaOcBi4D+BdJPLiYjP+dprumfjgjhfdg/wEPBIRXVNU5yvrSQ4FbpKv5Ytrs0i0N57G3CayeWExe/Z+rbf+cLcOF3uZQJ9ov+plj9X+qNCVwnKssW18wiE73UEBmsMCYa/cZu344lY9k3uJLCSx/9WVNfUx/A6yjChQlcJybLFtdnAV4CbgDnmVjM4abhaPG0PRXuVylYCc9uuBp6vqK7pivL5lWFMha4StmWLa8cAVxIYyHEBCdrn193ygJPI784PAM8QCNpXK6prfBEXpiQlFbpKVHS3/14EfKb7a6K5FR3nbvmf3aCHWo8EPgLWAE8Db6kVd5VoUKGrxMSyxbVTCITwHALDj2dg0lpu7tbl7yCdswfZ7TDwdvfXW8DbFdU1LTEvTkk6KnSVuOhe2aKEQC+IWSf8tyjW1/a0P/a61A+fd8KmJgJ3scdCVs1bq8SLCl3FVMsW1xYTCOOiPr4KT/hzTh+HGwRG7Ond//UDPgJ3rQeBRqDR53x1m+55pwnYBeyuqK7piOFbUpQBqdBVhoRli2ttQArdAbtkebnf5JIUJSwqdBVFUeJoWM4spSiKkqhU6CqKosSRCl1FUZQ4UqGrKIoSRyp0FUVR4kiFrqIoShyp0FUURYkjFbqKoihxpEJXURQljlToKoqixJEKXUVRlDhSoasoihJHKnQVRVHiSIWuoihKHKnQVRRFiSMVuoqiKHH0/wEgLPydCKc7NwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Tab = []\n",
    "Tab.append(npartie_return_argent(1000,20,10))\n",
    "res = np.unique(Tab, return_counts = True)\n",
    "print(res)\n",
    "\n",
    "plt.bar(res[0], res[1])\n",
    "plt.ylabel('Nb de séances de 10 tours de roulette')\n",
    "plt.xlabel('Argent à la fin des 10 tours (ou après avoir tout perdu / cashout ) ')\n",
    "plt.title('Histogramme des séances de 10 tours de roulette avec stop à 20€ et argent initial de 10€')\n",
    "\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(res[1], labels=res[0], autopct='%1.1f%%', startangle=90, pctdistance=0.8)\n",
    "ax1.axis('equal') \n",
    "\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "eb33fcf88a50c80f630477f0a4bcd2b0c7dbd10b6c8bd5cc26f5d1fa87076e37"
  },
  "kernelspec": {
   "display_name": "Python 3.10.4 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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