diff --git a/Ejemplo.ipynb b/Ejemplo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..410058b5c05ebdd3170f2bc965de620e1d40a954 --- /dev/null +++ b/Ejemplo.ipynb @@ -0,0 +1,2048 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from main import *\n", + "import shutil #Para mover archivos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Codigo ejemplo y resultados\n", + "\n", + "El siguiente script muestra los resultados obtenidos a partir de la simulación, asi como también las lÃneas de código que se utilizan para generar dichos resultados; por cuestiones de tiempo de computación estas lÃneas se encuentran comentadas, si se desea correr alguna simulación existen dos maneras de hacerlo:\n", + "\n", + "1. (Como se encuentra indicado en el README.md) Corriendo el script run.py\n", + "2. Descomentando las lÃneas de código indicadas en este notebook, los datos generados se escribirán en la carpeta 'Archi'. Para esta opción se requiere tener una carpeta llamada 'Archi' en el directorio donde se tiene este notebook.\n", + "\n", + "**Cabe resaltar que** aunque se tengan las dos opciones se recomienda al usuario utilizar la primera opción." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Notas adicionales\n", + "\n", + "En este notebook de ejemplo se escriben algunas lÃneas de código adicionales para optimizar el uso de las funciones creadas en el archivo 'main.py'. A continuación se presentan:\n", + "\n", + "- **Bloque 1**: Lineas de código que permiten encontrar el valor del tiempo en el que se llega al estado estacionario, para poder correr las simulaciones.\n", + "- **Bloque 2**: la función 'leer_archivo(nombre)' que permite hacer gráficas de la distribución de espaciamientos y perfiles de concentración con mayor facilidad, a partir de los datos presentados en la carpeta 'Ejemplos'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estado en la iteración 1000:\n", + "[ 0. -1. 0. 0. -1. 0. 0. 1. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 2000:\n", + "[-1. 0. -1. 0. 0. 0. -1. 0. 0. 0. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 6000:\n", + "[-1. -1. -1. -1. 0. 1. 0. 1. 0. 1. 1.]\n", + " \n", + "Estado en la iteración 7000:\n", + "[-1. -1. 0. 0. -1. 1. 0. 0. 1. 1. 1.]\n", + " 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"---------------\n", + "Estado en la iteración 896000:\n", + "[-1. -1. 0. -1. -1. 0. 0. 1. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 897000:\n", + "[-1. -1. -1. -1. 0. 0. 0. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 901000:\n", + "[-1. 0. -1. -1. 0. 1. 0. 1. 0. 1. 1.]\n", + " \n", + "Estado en la iteración 902000:\n", + "[-1. -1. 0. 0. 0. 0. 1. 1. 0. 0. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 906000:\n", + "[-1. -1. 0. -1. -1. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 907000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 1. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 911000:\n", + "[-1. -1. -1. 0. -1. 0. 0. 1. 0. 0. 1.]\n", + " \n", + "Estado en la iteración 912000:\n", + "[-1. -1. -1. 0. 0. 0. -1. 0. 1. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 916000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 917000:\n", + "[-1. -1. -1. 0. 0. -1. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 921000:\n", + "[-1. -1. -1. 0. 0. 0. 0. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 922000:\n", + "[-1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 926000:\n", + "[-1. 0. -1. -1. 0. 0. -1. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 927000:\n", + "[-1. -1. 0. -1. 1. 0. 0. 0. 1. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 931000:\n", + "[-1. 0. -1. -1. 0. 0. 0. 0. 0. 1. 1.]\n", + " \n", + "Estado en la iteración 932000:\n", + "[-1. 0. 0. 0. 0. -1. 1. 0. 1. 1. 0.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 936000:\n", + "[ 0. -1. -1. -1. 1. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 937000:\n", + "[-1. -1. 0. 0. 0. 0. 1. 0. 1. 1. 0.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 941000:\n", + "[ 0. -1. -1. 0. -1. 0. 1. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 942000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 946000:\n", + "[ 0. -1. -1. -1. 0. 0. 0. 0. 0. 0. 1.]\n", + " \n", + "Estado en la iteración 947000:\n", + "[-1. -1. -1. 0. 0. 0. 1. 0. 1. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 951000:\n", + "[-1. -1. 0. 0. 0. -1. 0. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 952000:\n", + "[-1. 0. -1. 0. -1. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 956000:\n", + "[-1. -1. 0. 0. 0. 0. -1. 1. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 957000:\n", + "[ 0. -1. -1. 0. -1. 0. 0. 1. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 961000:\n", + "[-1. -1. -1. 0. -1. -1. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 962000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 0. 0. 1. 0.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 966000:\n", + "[-1. -1. 0. 0. 0. 0. 1. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 967000:\n", + "[-1. -1. -1. 0. 0. 0. 1. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 971000:\n", + "[ 0. 0. -1. 0. -1. 0. 0. 0. 0. 1. 1.]\n", + " \n", + "Estado en la iteración 972000:\n", + "[-1. -1. 0. -1. -1. 1. 0. 0. 0. 1. 0.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 976000:\n", + "[ 0. -1. -1. 0. -1. 1. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 977000:\n", + "[-1. -1. -1. 0. 0. 0. 0. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 981000:\n", + "[-1. -1. -1. 0. 0. 0. 0. 1. 1. 1. 0.]\n", + " \n", + "Estado en la iteración 982000:\n", + "[-1. 0. -1. -1. 0. 0. 0. 0. 1. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 986000:\n", + "[-1. -1. -1. -1. 0. 0. 0. 0. 1. 1. 1.]\n", + " \n", + "Estado en la iteración 987000:\n", + "[-1. -1. -1. 0. -1. 0. 0. 1. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 991000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 992000:\n", + "[-1. -1. -1. -1. 0. 0. 1. 0. 0. 1. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 996000:\n", + "[-1. -1. 0. -1. 0. 0. 1. 0. 1. 0. 1.]\n", + " \n", + "Estado en la iteración 997000:\n", + "[-1. -1. -1. 0. -1. 0. 0. 0. 1. 0. 1.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "Estado en la iteración 1001000:\n", + "[-1. -1. 0. -1. 0. 0. 0. 1. 1. 1. 0.]\n", + " \n", + "Estado en la iteración 1002000:\n", + "[-1. 0. -1. -1. 0. 0. 1. 1. 1. 1. 0.]\n", + " \n", + "Estado estacionario: False\n", + "---------------\n", + "break\n" + ] + } + ], + "source": [ + "#--------------------------\n", + "# BLOQUE 1\n", + "#--------------------------\n", + "\n", + "# LAS SIGUIENTES LINEAS DE CÓDIGO RESPONDEN A: ¿SE LLEGA AL ESTADO ESTACIONARIO?\n", + "\n", + "time_est=1000 # Tiempo estacionario estimado por el usuario\n", + "incremento=5000 # Incremento de las evaluaciones\n", + "limite = 1000000 # Limite de iteraciones impuesta por el usuario\n", + "\n", + "\n", + "# El funcionamiento de las siguientes lineas es el siguiente:\n", + "# La simulación para por dos razones:\n", + "# - si q = True, lo que indica, que se llegó al estado estacionario.\n", + "# - si i == limite, lo que indica que se alcanzó el lÃmite de iteraciones impuesta por el usuario\n", + "\n", + "q=False\n", + "i=time_est\n", + "\n", + "while q == False:\n", + " q=ANALISIS(L= 5, dx=1, dt=1, D=0.4, j=1, K=0.1, tiempo=i, opcion='estacionario')\n", + " print('---------------')\n", + " \n", + " if i >= limite:\n", + " i=i+incremento\n", + " print('break')\n", + " break\n", + " \n", + " i=i+incremento\n", + "\n", + "estacionario=i-incremento\n", + "\n", + "if i < limite:\n", + " print('Se llega al estado estacionario en: ', i-incremento, ' iteraciones')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#--------------------------\n", + "# BLOQUE 2\n", + "#--------------------------\n", + "\n", + "def leer_archivo(nombre):\n", + " \n", + " #LECTURA DE ARCHIVOS (Los que se encuentran en la carpeta Ejemplos)\n", + "\n", + " p=np.loadtxt('Ejemplos/'+str(nombre)+'A.txt')[:, 1] # Almacena concentraciones de A\n", + " a=np.loadtxt('Ejemplos/'+str(nombre)+'A.txt')[:, 0] # Almacena x\n", + "\n", + " p2=np.loadtxt('Ejemplos/'+str(nombre)+'B.txt')[:, 1] # Almacena concentraciones de A\n", + " a2=np.loadtxt('Ejemplos/'+str(nombre)+'B.txt')[:, 0] # Almacena x\n", + "\n", + " p3=np.loadtxt('Ejemplos/'+str(nombre)+'Desp.txt')[:, 1] # Almacena distribución de espaciamiento\n", + " a3=np.loadtxt('Ejemplos/'+str(nombre)+'Desp.txt')[:, 0] # Almacena x\n", + " \n", + " #GRAFICAS\n", + "\n", + " fig1=plt.figure(1)\n", + " plt.plot(a,p,c='b', alpha=0.5)\n", + " plt.plot(a2,p2,c='r', alpha=0.5)\n", + " plt.legend(['Particulas A', 'Particulas B'],loc='upper right',bbox_to_anchor=(1.35, 1))\n", + " plt.xlabel('x')\n", + " plt.ylabel('Perfil de densidad')\n", + " plt.show()\n", + "\n", + " fig2=plt.figure(2)\n", + " plt.plot(a3,p3,c='b', alpha=0.5)\n", + " plt.xlabel('x')\n", + " plt.ylabel('Distribución de espaciamientos')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Resultados" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estado dinámico" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A continuación se puede visualizar la evolución de un sistema de longitud 2L, con L=5 y Pr=Pj=Ps=1, es decir, la probabilidad de reacción, de incerción 1 de salto es igual a 1. Con el siguiente video se demuestra el correcto funcionamiento de la función 'Dinamica'.\n", + "\n", + "En ocasiones, se puede apreciar, que en algunos fotogramas no hay salto, esto ocurre porque el algoritmo utilizado para generar la simulación de Monte Carlo, _no_ es _libre de rechazos_, provocando que puedan ocurrir iteraciones en las que el sistema tenga la misma configuración que en la iteración anterior, la razón de esto es principalmente porque, la escogencia del sitio en el que se realiza la dinámica en cada iteración es pseudo-aleatorio y ya que se deben cumplir ciertas condiciones para que la dinámica ocurra, no es posible satisfacer tales condiciones en todas las iteraciones." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Si se desea correr el código en este notebook, descomentar las siguientes lÃneas**<br>\n", + "Funcionamiento: las animaciones serán creadas y movidas a la carpeta Archi" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# %%time\n", + "# #Genera un gif llamado anim.gif y un video llamado video_dinamica.mp4 \n", + "# #en la misma carpeta donde se encuentra este notebook\n", + "# nombre = animacion(1000, 1, 1, 1)\n", + "\n", + "# #Mueve el video y el gif a la carpeta Archi\n", + "# shutil.move(nombre, 'Archi/'+ nombre)\n", + "# shutil.move('anim.gif', 'Archi/anim.gif.mp4')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<video src=\"Ejemplos/video_dinamica.mp4\" controls >\n", + " Your browser does not support the <code>video</code> element.\n", + " </video>" + ], + "text/plain": [ + "<IPython.core.display.Video object>" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Se proyecta el video\n", + "Video('Ejemplos/video_dinamica.mp4') " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estado estacionario\n", + "\n", + "A continuación se pueden ver las gráficas de la distribución de espaciamientos y el perfil de concentraciones para diferentes simulaciones. Se hacen simulaciones con los mismos parámetros salvo el número de repeticiones para hacer comparaciones." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Datos 1:\n", + "\n", + "_Parametros:_ <br>\n", + "L=5, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=146000, rep=1000\n", + "\n", + "_Tiempo de computo:_ <br>\n", + "CPU times: user 3h 36min 48s, sys: 4min 15s, total: 3h 41min 3s <br>\n", + "Wall time: 3h 40min 21s" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Si se desea correr el código en este notebook, descomentar las siguientes lÃneas**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# #CODIGO PARA CORRER LA SIMULACIÓN\n", + "# ANALISIS(L=5, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=146000, rep=1000, opcion='graficos', nombre = 'Datos1')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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" + } + ], + "source": [ + "# GRAFICAS A PARTIR DE TXT\n", + "leer_archivo('Datos1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Datos 2:\n", + "\n", + "_Parametros:_ <br>\n", + "L=5, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=146000, rep=5000" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# #CODIGO PARA CORRER LA SIMULACIÓN tiempo=146000\n", + "# ANALISIS(L=5, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=146000, rep=5000, opcion='graficos', nombre = 'Datos2')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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" + } + ], + "source": [ + "# GRAFICAS A PARTIR DE TXT\n", + "leer_archivo('Datos2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###### Datos 3:\n", + "\n", + "_Parametros:_ <br>\n", + "L=50, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=50000, rep=10000" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# #CODIGO PARA CORRER LA SIMULACIÓN\n", + "# ANALISIS(L=50, dx=1, dt=1, D=0.5, j=1, K=0.1, tiempo=50000, rep=10000, opcion='graficos', nombre = 'Datos3')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 432x288 with 1 Axes>" + ] + }, + "metadata": { + "needs_background": "light" + }, + "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" + } + ], + "source": [ + "# GRAFICAS A PARTIR DE TXT\n", + "leer_archivo('Datos3')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}