From 94ebc503f6cab42902e39b2016697df32e1cbe4d Mon Sep 17 00:00:00 2001 From: "Arthut M. Faria" Date: Tue, 21 Jun 2022 14:54:25 +0200 Subject: [PATCH] Delete FT_langevin.ipynb --- FT_langevin.ipynb | 400 ---------------------------------------------- 1 file changed, 400 deletions(-) delete mode 100644 FT_langevin.ipynb diff --git a/FT_langevin.ipynb b/FT_langevin.ipynb deleted file mode 100644 index 278f32b..0000000 --- a/FT_langevin.ipynb +++ /dev/null @@ -1,400 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Work Fluctuation Theorem for a gaussian Langevin eq." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pylab as plt\n", - "import numpy as np\n", - "import time as tm \n", - "from sys import argv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Definitions and functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# Gaussian estimator\n", - "\n", - "def gaussian_func(x, M, V):\n", - " denominator = np.sqrt(2*np.pi*V)\n", - " numerator = np.exp(-((x-M)**2)/(2*V))\n", - " return numerator/denominator" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#Harmonic force and internal energy\n", - "\n", - "def energy_force(x, t, *args):\n", - " v0 = args[0]\n", - " ks = args[1]\n", - " F = -ks*(x - v0*t)\n", - " U = (ks/2)*abs(x-v0*t)**2\n", - " \n", - " return F, U\n", - "\n", - "\n", - "# Return the time length\n", - "\n", - "def time_len(tMax, dt):\n", - "\n", - " t = 0\n", - " L = []\n", - " \n", - " while(t= tMax//2 :\n", - " v0 = 0\n", - " \n", - " _, Uf = energy_force(x, t, v0, ks)\n", - " \n", - " dU = (Uf - Ui)\n", - " \n", - " return time, dwork, dU #return: dif internal energy, elapsed time, dif work" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Time evolution for the work variable" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1796.0150220394135\n" - ] - } - ], - "source": [ - "###################### MAIN #############################\n", - "\n", - "# Parameters and time\n", - "\n", - "v0 = 0.2\n", - "ks = 2.0\n", - "gamma = 5.0 \n", - "kBT = 1.0\n", - "dt = 0.01\n", - "tMax = 100 \n", - "\n", - "\n", - "# Sample conditions\n", - "\n", - "N = 10**4 #int(argv[1])\n", - "x_init = 0.3\n", - "v_init = 0.\n", - "\n", - "\n", - "# Work vector definition and extras\n", - "\n", - "w = np.zeros(N)\n", - "\n", - "start = tm.time()\n", - "\n", - "########### Work ###########\n", - "\n", - "# Stochastic evolution and work for each trajectory in a sample\n", - "\n", - "for ii in range(N): \n", - "\n", - " time, dwork, dU = BAOAB_method(x_init, v_init, v0, tMax, dt, gamma, kBT, ks)\n", - "\n", - " w[ii] = (1/kBT)*np.trapz(dwork, time)\n", - " \n", - "\n", - "# Statistics\n", - "\n", - "mean_w = np.mean(w)\n", - "var_w = np.var(w)\n", - "\n", - "#########################################################\n", - "\n", - "end = tm.time()\n", - "print(end-start)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exporting or getting data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "############ Exporting data ################\n", - "\n", - "#output = np.array([w, q])\n", - "\n", - "#data_path = \"/home/fariaart/Dropbox/FT_data_%s.txt\" %N\n", - "#data_path = \"/home/des01/mbonanca/fariaart/Resultados/Doutorado/Lutz/FT-80_data_%s.txt\" %N\n", - "#with open(data_path , \"w+\") as data:\n", - " #np.savetxt(data, output.T, fmt='%f')\n", - " \n", - "\n", - "############ Getting data ################\n", - "\n", - "#data = np.loadtxt(\"/home/fariaart/Dropbox/Pesquisa/Doutorado/Lutz/dados/FT/FT_data_100000.txt\", float)\n", - "#w0 = data[:,0]\n", - "#q0 = data[:,1]\n", - "\n", - "#mean_w0 = np.mean(w0)\n", - "#var_w0 = np.var(w0)\n", - "\n", - "#mean_q0 = np.mean(q0)\n", - "#var_q0 = np.var(q0)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plots of \n", - "\n", - "* $\\rho\\,(W_{\\tau})$\n", - "* $\\left.\\rho\\,(W_{\\tau})\\, \\right /\\, \\rho\\,(-W_{\\tau})$" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_19048/246970912.py:26: RuntimeWarning: divide by zero encountered in true_divide\n", - " n_d = n/reverse_n\n", - "/tmp/ipykernel_19048/246970912.py:26: RuntimeWarning: invalid value encountered in true_divide\n", - " n_d = n/reverse_n\n", - "/tmp/ipykernel_19048/246970912.py:27: RuntimeWarning: divide by zero encountered in log\n", - " div = np.log(n_d)\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_bins = 100 \n", - "\n", - "########## Work Histogram and Analitical curve ##########\n", - "\n", - "n, bins, patches = plt.hist(w, color = (0.165, 0.52, 0.87), range = (-35.,35.), histtype='bar', linewidth=4, alpha= 0.4, ec=(0.165, 0.52, 0.87), bins= n_bins, density=True, label = 'num')\n", - "\n", - "x = np.linspace(bins[0], bins[n_bins], len(w))\n", - "\n", - "func = gaussian_func(x, mean_w, var_w)\n", - "plt.plot(x, func, linestyle = '--', color = 'darkblue', linewidth = 3.0, label = 'an')\n", - "\n", - "\n", - "# Settings\n", - "\n", - "plt.xlabel(r'$W_{\\tau}$', fontsize = 12, labelpad = 4)\n", - "plt.ylabel(r'$\\rho\\,(W_{\\tau})$', fontsize = 12, labelpad = 2)\n", - "plt.legend(loc='upper right')\n", - "plt.figure()\n", - "\n", - "\n", - "########### W Division Histogram ###########\n", - "\n", - "#reversing the freq vector\n", - "\n", - "reverse_n = n[::-1]\n", - "n_d = n/reverse_n\n", - "div = np.log(n_d)\n", - "\n", - "\n", - "plt.plot(bins[:-1], div, 'o', color = \"darkblue\", label = 'num')\n", - "plt.plot(bins[:-1],bins[:-1], color = \"red\", linestyle = '--', linewidth = 2.0, label = 'an')\n", - "\n", - "# Settings\n", - "\n", - "plt.xlabel(r'$W_{\\tau}$', fontsize = 12, labelpad = 4)\n", - "plt.ylabel(r'$\\rho\\,(W_{\\tau})\\, /\\, \\rho\\,(-W_{\\tau})$', fontsize = 12, labelpad = 4)\n", - "plt.xlim(-7.5,7.5)\n", - "\n", - "plt.legend(loc='upper left')\n", - "plt.figure()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}