diff --git a/Fisica Estadistica/Quiz_3.ipynb b/Fisica Estadistica/Quiz_3.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d54f5755df48f77a423d12a05b20da5dd4cd63e9 --- /dev/null +++ b/Fisica Estadistica/Quiz_3.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quiz 3\n", + "\n", + "Integrantes: \n", + "- Nicolas Mantilla Molina - 2210707\n", + "- Brayan Amorocho Lizcano - 2210719\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Desarrollo" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Primero importamos las librerias necesarias para el desarrollo del quiz" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from tabulate import tabulate" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dos subsistemas que comparten energÃa, cada una con 2 partÃculas y 6 niveles de energÃa. Cada partÃcula puede ocupar un nivel de energÃa a la vez. El objetivo es encontrar el par de energÃas de los subsistemas A y B con más número de estados accesibles cuya energÃa total sea 10. \n", + "\n", + "Vamos a ver todos los estados posibles para el subsistema A y B y luego vamos a ver cuales son los pares de estados que suman 10. Posteriormente se contará el número de estados accesibles de la energÃa total como una función de la energÃa de alguno de los subsistemas, esto debido a que hay una ligadura entre las energÃas de los subsistemas A y B (EA+EB=ET)." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "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>EA</th>\n", + " <th>EB</th>\n", + " <th>OmegaA</th>\n", + " <th>OmegaB</th>\n", + " <th>OmegaT</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>0</th>\n", + " <td>2</td>\n", + " <td>8</td>\n", + " <td>1</td>\n", + " <td>5</td>\n", + " <td>5</td>\n", + " </tr>\n", + " <tr>\n", + " <th>1</th>\n", + " <td>3</td>\n", + " <td>7</td>\n", + " <td>2</td>\n", + " <td>6</td>\n", + " <td>12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>2</th>\n", + " <td>4</td>\n", + " <td>6</td>\n", + " <td>3</td>\n", + " <td>5</td>\n", + " <td>15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>3</th>\n", + " <td>5</td>\n", + " <td>5</td>\n", + " <td>4</td>\n", + " <td>4</td>\n", + " <td>16</td>\n", + " </tr>\n", + " <tr>\n", + " <th>4</th>\n", + " <td>6</td>\n", + " <td>4</td>\n", + " <td>5</td>\n", + " <td>3</td>\n", + " <td>15</td>\n", + " </tr>\n", + " <tr>\n", + " <th>5</th>\n", + " <td>7</td>\n", + " <td>3</td>\n", + " <td>6</td>\n", + " <td>2</td>\n", + " <td>12</td>\n", + " </tr>\n", + " <tr>\n", + " <th>6</th>\n", + " <td>8</td>\n", + " <td>2</td>\n", + " <td>5</td>\n", + " <td>1</td>\n", + " <td>5</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " EA EB OmegaA OmegaB OmegaT\n", + "0 2 8 1 5 5\n", + "1 3 7 2 6 12\n", + "2 4 6 3 5 15\n", + "3 5 5 4 4 16\n", + "4 6 4 5 3 15\n", + "5 7 3 6 2 12\n", + "6 8 2 5 1 5" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ET = 10 #Energia total\n", + "\n", + "NumPartA = 2 #Numero de particulas A\n", + "NumPartB = 2 #Numero de particulas B\n", + "\n", + "EnerPosible = 6 #Numero de energias posibles\n", + "\n", + "#Estados posibles de las particulas A y B con cualquier energÃa\n", + "EstadosA = [[i+1, j+1] for i in range(EnerPosible) for j in range(EnerPosible)]\n", + "EstadosB = [[i+1, j+1] for i in range(EnerPosible) for j in range(EnerPosible)]\n", + "\n", + "#Producto cartesiano de los estados posibles de las particulas A y B en un dataframe\n", + "TodosEstados = pd.DataFrame([(a, b) for a in EstadosA for b in EstadosB], columns=['A', 'B'])\n", + "\n", + "#EnergÃa del susbistema A, B y total\n", + "TodosEstados['EA'] = TodosEstados['A'].apply(lambda x: sum(x))\n", + "TodosEstados['EB'] = TodosEstados['B'].apply(lambda x: sum(x))\n", + "TodosEstados['ET'] = TodosEstados['EA'] + TodosEstados['EB']\n", + "\n", + "#Ahora filtramos por ET\n", + "EstadosPermitidos = TodosEstados[TodosEstados['ET'] == ET]\n", + "\n", + "#Ahora, para cada energÃa de A y B, contamos los estados accesibles y, además, los estados accesibles para la energÃa total siendo el producto de las anteriores\n", + "def NumeroEstadosSub(DF, Esub, Sistema = 'A'):\n", + " EstadosEsub = DF[DF['E'+Sistema] == Esub]\n", + " Cuenta = 0\n", + " EstadosUnicos = []\n", + " for i in EstadosEsub[Sistema]:\n", + " if i not in EstadosUnicos:\n", + " EstadosUnicos.append(i)\n", + " Cuenta += 1\n", + " return Cuenta\n", + "\n", + "EstadosAccesibles = pd.DataFrame({'EA': EstadosPermitidos['EA'].unique(), 'EB': EstadosPermitidos['EB'].unique()})\n", + "EstadosAccesibles['OmegaA'] = [NumeroEstadosSub(EstadosPermitidos, i, 'A') for i in EstadosAccesibles['EA']]\n", + "EstadosAccesibles['OmegaB'] = [NumeroEstadosSub(EstadosPermitidos, i, 'B') for i in EstadosAccesibles['EB']]\n", + "EstadosAccesibles['OmegaT'] = EstadosAccesibles['OmegaA'] * EstadosAccesibles['OmegaB']\n", + "EstadosAccesibles" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ahora con el número de estados accesibles para cada energÃa EA, podemos verificar el comportamiento del número de estados accesibles $\\Omega_T(E_A,E_B) = \\Omega_T(E_A)$:" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 1000x600 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.plot(EstadosAccesibles['EA'], EstadosAccesibles['OmegaT'], 'o-', label='$\\Omega_T(EA)$', color='purple')\n", + "# plt.plot(EstadosAccesibles['EB'], EstadosAccesibles['OmegaT'], 'o-', label='$\\Omega_T(EB)$', color='green')\n", + "plt.xlabel('$E_A$', fontsize=15)\n", + "plt.ylabel('$\\Omega_T$', fontsize=15)\n", + "plt.title('Número de estados accesibles total $\\Omega_T$ para cada $E_A$', fontsize=17)\n", + "plt.grid()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tablas para $\\LaTeX$:" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{rllrrr}\n", + "\\hline\n", + " & A & B & EA & EB & ET \\\\\n", + "\\hline\n", + " 11 & [1, 1] & [2, 6] & 2 & 8 & 10 \\\\\n", + " 16 & [1, 1] & [3, 5] & 2 & 8 & 10 \\\\\n", + " 21 & [1, 1] & [4, 4] & 2 & 8 & 10 \\\\\n", + " 26 & [1, 1] & [5, 3] & 2 & 8 & 10 \\\\\n", + " 31 & [1, 1] & [6, 2] & 2 & 8 & 10 \\\\\n", + " 41 & [1, 2] & [1, 6] & 3 & 7 & 10 \\\\\n", + " 46 & [1, 2] & [2, 5] & 3 & 7 & 10 \\\\\n", + " 51 & [1, 2] & [3, 4] & 3 & 7 & 10 \\\\\n", + " 56 & [1, 2] & [4, 3] & 3 & 7 & 10 \\\\\n", + " 61 & [1, 2] & [5, 2] & 3 & 7 & 10 \\\\\n", + " 66 & [1, 2] & [6, 1] & 3 & 7 & 10 \\\\\n", + " 76 & [1, 3] & [1, 5] & 4 & 6 & 10 \\\\\n", + " 81 & [1, 3] & [2, 4] & 4 & 6 & 10 \\\\\n", + " 86 & [1, 3] & [3, 3] & 4 & 6 & 10 \\\\\n", + " 91 & [1, 3] & [4, 2] & 4 & 6 & 10 \\\\\n", + " 96 & [1, 3] & [5, 1] & 4 & 6 & 10 \\\\\n", + " 111 & [1, 4] & [1, 4] & 5 & 5 & 10 \\\\\n", + " 116 & [1, 4] & [2, 3] & 5 & 5 & 10 \\\\\n", + " 121 & [1, 4] & [3, 2] & 5 & 5 & 10 \\\\\n", + " 126 & [1, 4] & [4, 1] & 5 & 5 & 10 \\\\\n", + " 146 & [1, 5] & [1, 3] & 6 & 4 & 10 \\\\\n", + " 151 & [1, 5] & [2, 2] & 6 & 4 & 10 \\\\\n", + " 156 & [1, 5] & [3, 1] & 6 & 4 & 10 \\\\\n", + " 181 & [1, 6] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 186 & [1, 6] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 221 & [2, 1] & [1, 6] & 3 & 7 & 10 \\\\\n", + " 226 & [2, 1] & [2, 5] & 3 & 7 & 10 \\\\\n", + " 231 & [2, 1] & [3, 4] & 3 & 7 & 10 \\\\\n", + " 236 & [2, 1] & [4, 3] & 3 & 7 & 10 \\\\\n", + " 241 & [2, 1] & [5, 2] & 3 & 7 & 10 \\\\\n", + " 246 & [2, 1] & [6, 1] & 3 & 7 & 10 \\\\\n", + " 256 & [2, 2] & [1, 5] & 4 & 6 & 10 \\\\\n", + " 261 & [2, 2] & [2, 4] & 4 & 6 & 10 \\\\\n", + " 266 & [2, 2] & [3, 3] & 4 & 6 & 10 \\\\\n", + " 271 & [2, 2] & [4, 2] & 4 & 6 & 10 \\\\\n", + " 276 & [2, 2] & [5, 1] & 4 & 6 & 10 \\\\\n", + " 291 & [2, 3] & [1, 4] & 5 & 5 & 10 \\\\\n", + " 296 & [2, 3] & [2, 3] & 5 & 5 & 10 \\\\\n", + " 301 & [2, 3] & [3, 2] & 5 & 5 & 10 \\\\\n", + " 306 & [2, 3] & [4, 1] & 5 & 5 & 10 \\\\\n", + " 326 & [2, 4] & [1, 3] & 6 & 4 & 10 \\\\\n", + " 331 & [2, 4] & [2, 2] & 6 & 4 & 10 \\\\\n", + " 336 & [2, 4] & [3, 1] & 6 & 4 & 10 \\\\\n", + " 361 & [2, 5] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 366 & [2, 5] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 396 & [2, 6] & [1, 1] & 8 & 2 & 10 \\\\\n", + " 436 & [3, 1] & [1, 5] & 4 & 6 & 10 \\\\\n", + " 441 & [3, 1] & [2, 4] & 4 & 6 & 10 \\\\\n", + " 446 & [3, 1] & [3, 3] & 4 & 6 & 10 \\\\\n", + " 451 & [3, 1] & [4, 2] & 4 & 6 & 10 \\\\\n", + " 456 & [3, 1] & [5, 1] & 4 & 6 & 10 \\\\\n", + " 471 & [3, 2] & [1, 4] & 5 & 5 & 10 \\\\\n", + " 476 & [3, 2] & [2, 3] & 5 & 5 & 10 \\\\\n", + " 481 & [3, 2] & [3, 2] & 5 & 5 & 10 \\\\\n", + " 486 & [3, 2] & [4, 1] & 5 & 5 & 10 \\\\\n", + " 506 & [3, 3] & [1, 3] & 6 & 4 & 10 \\\\\n", + " 511 & [3, 3] & [2, 2] & 6 & 4 & 10 \\\\\n", + " 516 & [3, 3] & [3, 1] & 6 & 4 & 10 \\\\\n", + " 541 & [3, 4] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 546 & [3, 4] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 576 & [3, 5] & [1, 1] & 8 & 2 & 10 \\\\\n", + " 651 & [4, 1] & [1, 4] & 5 & 5 & 10 \\\\\n", + " 656 & [4, 1] & [2, 3] & 5 & 5 & 10 \\\\\n", + " 661 & [4, 1] & [3, 2] & 5 & 5 & 10 \\\\\n", + " 666 & [4, 1] & [4, 1] & 5 & 5 & 10 \\\\\n", + " 686 & [4, 2] & [1, 3] & 6 & 4 & 10 \\\\\n", + " 691 & [4, 2] & [2, 2] & 6 & 4 & 10 \\\\\n", + " 696 & [4, 2] & [3, 1] & 6 & 4 & 10 \\\\\n", + " 721 & [4, 3] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 726 & [4, 3] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 756 & [4, 4] & [1, 1] & 8 & 2 & 10 \\\\\n", + " 866 & [5, 1] & [1, 3] & 6 & 4 & 10 \\\\\n", + " 871 & [5, 1] & [2, 2] & 6 & 4 & 10 \\\\\n", + " 876 & [5, 1] & [3, 1] & 6 & 4 & 10 \\\\\n", + " 901 & [5, 2] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 906 & [5, 2] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 936 & [5, 3] & [1, 1] & 8 & 2 & 10 \\\\\n", + " 1081 & [6, 1] & [1, 2] & 7 & 3 & 10 \\\\\n", + " 1086 & [6, 1] & [2, 1] & 7 & 3 & 10 \\\\\n", + " 1116 & [6, 2] & [1, 1] & 8 & 2 & 10 \\\\\n", + "\\hline\n", + "\\end{tabular}\n" + ] + } + ], + "source": [ + "print(tabulate(EstadosPermitidos, headers='keys', tablefmt='latex'))" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\\begin{tabular}{rrrrrr}\n", + "\\hline\n", + " & EA & EB & OmegaA & OmegaB & OmegaT \\\\\n", + "\\hline\n", + " 0 & 2 & 8 & 1 & 5 & 5 \\\\\n", + " 1 & 3 & 7 & 2 & 6 & 12 \\\\\n", + " 2 & 4 & 6 & 3 & 5 & 15 \\\\\n", + " 3 & 5 & 5 & 4 & 4 & 16 \\\\\n", + " 4 & 6 & 4 & 5 & 3 & 15 \\\\\n", + " 5 & 7 & 3 & 6 & 2 & 12 \\\\\n", + " 6 & 8 & 2 & 5 & 1 & 5 \\\\\n", + "\\hline\n", + "\\end{tabular}\n" + ] + } + ], + "source": [ + "print(tabulate(EstadosAccesibles, headers='keys', tablefmt='latex'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "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": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}