tests_modelo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import modelo\n",
    "from importlib import reload\n",
    "reload(modelo)\n",
    "from variablesFijas import DistribucionesFijas, crear_archivos_distribuciones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ganancia semanal: 905.13857398294\n",
      "Promedio ocupación: 0.9683282970390302\n",
      "Promedio rechazo cupo: 65.4\n",
      "Promedio rechazo monto: 19.2\n",
      "Ganancia semanal: 943.679924283795\n",
      "Promedio ocupación: 0.9780004308161814\n",
      "Promedio rechazo cupo: 68.10000000000001\n",
      "Promedio rechazo monto: 16.3\n",
      "Ganancia semanal: 895.176640236881\n",
      "Promedio ocupación: 0.9675770251704167\n",
      "Promedio rechazo cupo: 62.4\n",
      "Promedio rechazo monto: 22.5\n",
      "Ganancia semanal: 878.1146310027086\n",
      "Promedio ocupación: 0.9514176420920325\n",
      "Promedio rechazo cupo: 60.099999999999994\n",
      "Promedio rechazo monto: 23.0\n",
      "Ganancia semanal: 906.530670118883\n",
      "Promedio ocupación: 0.9709746859994633\n",
      "Promedio rechazo cupo: 62.9\n",
      "Promedio rechazo monto: 22.900000000000002\n",
      "Ganancia semanal: 888.1757395194602\n",
      "Promedio ocupación: 0.9658179909876853\n",
      "Promedio rechazo cupo: 67.0\n",
      "Promedio rechazo monto: 18.8\n",
      "Ganancia semanal: 913.159863273369\n",
      "Promedio ocupación: 0.9587982598555098\n",
      "Promedio rechazo cupo: 58.599999999999994\n",
      "Promedio rechazo monto: 23.7\n",
      "Ganancia semanal: 916.7245336454138\n",
      "Promedio ocupación: 0.9710389047485352\n",
      "Promedio rechazo cupo: 67.10000000000001\n",
      "Promedio rechazo monto: 19.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 11\u001b[0m\n\u001b[0;32m      9\u001b[0m     model\u001b[38;5;241m.\u001b[39mreset()\n\u001b[0;32m     10\u001b[0m     model\u001b[38;5;241m.\u001b[39minit_random()\n\u001b[1;32m---> 11\u001b[0m     counter \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msimular\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     12\u001b[0m data\u001b[38;5;241m.\u001b[39mappend((i, counter\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m10\u001b[39m))\n",
      "File \u001b[1;32mc:\\Users\\Manu\\OneDrive\\Escritorio\\UTN\\Simulación\\TP6\\modelo.py:87\u001b[0m, in \u001b[0;36mModelo.simular\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msimular\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m     86\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloops):\n\u001b[1;32m---> 87\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     88\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcalcular_resultados()\n",
      "File \u001b[1;32mc:\\Users\\Manu\\OneDrive\\Escritorio\\UTN\\Simulación\\TP6\\modelo.py:66\u001b[0m, in \u001b[0;36mModelo.loop\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcontador_llegadas_totales \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     65\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtiempo_actual \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m dt\n\u001b[1;32m---> 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msumatoria_ganancias \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimum\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpacientes_actuales\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdt\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhonorarios_pacientes\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     67\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpacientes_actuales \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmaximum(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpacientes_actuales \u001b[38;5;241m-\u001b[39m dt, \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m     68\u001b[0m horas_ocupadas \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhoras_ocupadas()\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe Kernel crashed while executing code in the current cell or a previous cell. \n",
      "\u001b[1;31mPlease review the code in the cell(s) to identify a possible cause of the failure. \n",
      "\u001b[1;31mClick <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "b = 15\n",
    "m = 0.1\n",
    "data = []\n",
    "\n",
    "for i in range(100):\n",
    "    model = modelo.Modelo(b, m+0.01*i, 1000)\n",
    "    counter = 0\n",
    "    for j in range(10):\n",
    "        model.reset()\n",
    "        model.init_random()\n",
    "        counter += model.simular()[0]\n",
    "    data.append((i, counter/10))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = modelo.Modelo(, 0.1, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Example list of tuples\n",
    "\n",
    "# Extract x and y values from the list of tuples\n",
    "x_values = [item[0] for item in data]\n",
    "y_values = [item[1] for item in data]\n",
    "\n",
    "# Plot the data\n",
    "plt.plot(x_values, y_values, marker='o', linestyle='-')\n",
    "plt.xlabel('Number of Loops')\n",
    "plt.ylabel('Result')\n",
    "plt.title('Plot of Results vs. Number of Loops')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2, 592.3058775151359),\n",
       " (2, 606.7989633740627),\n",
       " (2, 605.6055898459784),\n",
       " (2, 618.799772717642),\n",
       " (2, 621.1737863454036),\n",
       " (2, 625.9151767059514),\n",
       " (2, 633.471235718469),\n",
       " (2, 644.0716231134643),\n",
       " (2, 656.2520544988433),\n",
       " (2, 643.5569042005556)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "DistribucionesFijas.__init__() missing 3 required positional arguments: 'llegadas', 'montos', and 'duracion'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[3], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m l \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m10\u001b[39m):\n\u001b[1;32m----> 3\u001b[0m     model \u001b[38;5;241m=\u001b[39m modelo\u001b[38;5;241m.\u001b[39mModelo(\u001b[38;5;241m15\u001b[39m, i\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;241m30_000\u001b[39m, aleatoriedad\u001b[38;5;241m=\u001b[39m \u001b[43mDistribucionesFijas\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m      4\u001b[0m     l\u001b[38;5;241m.\u001b[39mappend(model\u001b[38;5;241m.\u001b[39msimular()[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      6\u001b[0m l\n",
      "\u001b[1;31mTypeError\u001b[0m: DistribucionesFijas.__init__() missing 3 required positional arguments: 'llegadas', 'montos', and 'duracion'"
     ]
    }
   ],
   "source": [
    "\n",
    "l = []\n",
    "for i in range(10):\n",
    "    model = modelo.Modelo(15, i*0.1, 30_000, aleatoriedad=)\n",
    "    l.append(model.simular()[0])\n",
    "\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "crear_archivos_distribuciones(100_000, modelo.DistribucionesAleatorias())"
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}