From 70367f69fa39250c4aabf18788268a55c193a313 Mon Sep 17 00:00:00 2001 From: hernandeza <annicolehd@gmail.com> Date: Sat, 15 May 2021 21:17:58 -0500 Subject: [PATCH] adding scripts --- .gitignore | 2 + analysis.ipynb | 259 +++++++++++++++++++++++++++++++++++++++++++++++++ main.py | 20 ++++ metropolis.py | 17 ++++ observables.py | 14 +++ parameters.py | 6 ++ sampling.py | 63 ++++++++++++ 7 files changed, 381 insertions(+) create mode 100644 .gitignore create mode 100644 analysis.ipynb create mode 100644 main.py create mode 100644 metropolis.py create mode 100644 observables.py create mode 100644 parameters.py create mode 100644 sampling.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f22a19e --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +.ipynb_checkpoints/ +__pycache__/ diff --git a/analysis.ipynb b/analysis.ipynb new file mode 100644 index 0000000..b155e66 --- /dev/null +++ b/analysis.ipynb @@ -0,0 +1,259 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy.optimize import leastsq \n", + "import os\n", + "from parameters import *\n", + "import re\n", + "import random #to shuffle the positions for the cummulant ratios" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "T = np.linspace(t_min, t_max, nt)\n", + "\n", + "owd=os.getcwd() \n", + "path=os.chdir('data') \n", + "datalist=[]\n", + "listOfEntries= os.scandir(path)\n", + "\n", + "for entry in listOfEntries:\n", + " if entry.path.endswith('csv'):\n", + " datalist.append(entry.name)\n", + " \n", + "os.chdir(owd)\n", + "datalist.sort()\n", + "\n", + "seed = [re.search('data_ising_(.*).csv', s) for s in datalist]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "scaling = []\n", + "\n", + "for file in datalist:\n", + " scaling.append(np.loadtxt('data/' + file, delimiter = ',' , unpack = True))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "<Figure size 1080x720 with 4 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Gráficas de observables \n", + "\n", + "fig, axs = plt.subplots(2,2, figsize=(15, 10), facecolor='w', edgecolor='k')\n", + "fig.subplots_adjust(hspace = .5, wspace=.2)\n", + "\n", + "labels = ['EnergÃa', 'Magnetización', 'Susceptibilidad magnética', 'Calor especÃfico']\n", + "\n", + "axs = axs.ravel()\n", + "\n", + "for i in range(1,5):\n", + " for j in range(len(datalist)):\n", + " \n", + " axs[i].plot(scaling[0][0], scaling[j][i], 'o-', label = 'N = ' + str(seed[j].group(1)))\n", + " axs[i].set_xlabel('Temperature')\n", + " axs[i].set_ylabel(labels[i])\n", + " axs[i].legend()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['data_ising_10.csv',\n", + " 'data_ising_20.csv',\n", + " 'data_ising_30.csv',\n", + " 'data_ising_5.csv']" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Cumulante de Binder\n", + "\n", + "for i in range(len(datalist)): \n", + " plt.figure (figsize = (15,10))\n", + " plt.plot(T, scaling[i][5], 'o-', label = 'N = ' + str(seed[j].group(1)))\n", + " plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Relación entre los cumulantes de Binder para el cálculo de la temperatura crÃtica\n", + "\n", + "numeros = random.shuffle(list(range(0,len(datalist))))\n", + "\n", + "ratio_1 = scaling[numeros[0]][5] / scaling[numeros[1]][5] \n", + "ratio_2 = scaling[numeros[2]][5] / scaling[numeros[3]][5]\n", + "\n", + "plt.plot(T, ratio_1, 'o-', label = 'U' + seed[numeros[0]].group(1) + '/U' + seed[numeros[1]].group(1))\n", + "plt.plot(T, ratio_2, 'o-', label = 'U' + seed[numeros[2]].group(1) + '/U' + seed[numeros[3]].group(1))\n", + "plt.legend" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Hallando las posiciones de las temperaturas crÃticas\n", + "\n", + "index_max_suscept = [np.argmax(scaling[i][3]) for i in range(0,len(datalist))]\n", + "\n", + "# Temperaturas de Curie\n", + "\n", + "curie_temps = T[index_max_suscept]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Extrayemdo valores correspondientes a las temperaturas de curie\n", + "\n", + "c_energy = [scaling[i][1][index_max_suscept[i]] for i in len(datalist)]\n", + "c_magnet = [scaling[i][2][index_max_suscept[i]] for i in len(datalist)]\n", + "c_suscept = [scaling[i][3][index_max_suscept[i]] for i in len(datalist)]\n", + "c_sheat = [scaling[i][4][index_max_suscept[i]] for i in len(datalist)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Ajuste lineal para el cálculo de los exponentes crÃticos (pendientes)\n", + "\n", + "def funcline(params,x):\n", + " return params[0]*x+params[1]\n", + "\n", + "def Errorlineal(tpl,x,y):\n", + " return funcline(tpl,x)-y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Estimación del exponente crÃtico para magnetización \n", + "p0_m = [0.5,-2]\n", + "best_m, suss_m = leastsq(Errorlineal, p0_m, args=(np.log(Ns),np.log(c_magnet)))\n", + "\n", + "# Estimación del exponente crÃtico para susceptibilidad \n", + "p0_s = [0.5,-2]\n", + "best_s, suss_s = leastsq(Errorlineal, p0_s, args=(np.log(Ns),np.log(c_suscept)))\n", + "\n", + "# Estimación del exponente crÃtico para el calor especÃfico\n", + "p0_c = [0.5,-2]\n", + "best_c, suss_c = leastsq(Errorlineal, p0_c, args=(np.log(Ns),np.log(c_sheat)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gráficos de los ajustes para los exponentes crÃticos\n", + "\n", + "fig, axs = plt.subplots(3, 1, figsize=(20, 10), facecolor='w', edgecolor='k')\n", + "\n", + "axs[0,0].scatter(np.log(Ns), np.log(c_magnet), label= 'datos')\n", + "axs[0,0].plot(np.log(Ns), funcline(best_m,np.log(Ns)), label = 'ajuste')\n", + "axs[0,0].set_xlabel('$\\ln(N)$')\n", + "axs[0,0].set_ylabel('$\\ln(M_c)')\n", + "axs[0,0].legend()\n", + "\n", + "axs[1,0].scatter(np.log(Ns), np.log(c_suscept), label= 'datos')\n", + "axs[1,0].plot(np.log(Ns), funcline(best_s,np.log(Ns)), label = 'ajuste')\n", + "axs[1,0].set_xlabel('$\\ln(N)$')\n", + "axs[1,0].set_ylabel('$\\ln(\\chi_c)')\n", + "axs[1,0].legend()\n", + "\n", + "axs[2,0].scatter(np.log(Ns), np.log(c_sheat), label= 'datos')\n", + "axs[2,0].plot(np.log(Ns), funcline(best_c,np.log(Ns)), label = 'ajuste')\n", + "axs[2,0].set_xlabel('$\\ln(N)$')\n", + "axs[2,0].set_ylabel('$\\ln(C_c)')\n", + "axs[2,0].legend()" + ] + } + ], + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/main.py b/main.py new file mode 100644 index 0000000..f01821e --- /dev/null +++ b/main.py @@ -0,0 +1,20 @@ +import numpy as np +from timeit import default_timer as timer + +#from parameters import * +from observables import * +#from metropolis import * +from sampling import * + +T = np.linspace(t_min, t_max, nt) +Ns = np.array(Ns) + +for N in Ns: + + start = timer() + result = finite_ising(N,mcSteps,eqSteps,T) + end = timer() + + print('Time the code takes to run for a lattice sice of ' + str(N) + ': ' + str((end - start) / 60) + ' minutes.') + + np.savetxt("data/data_ising_" + str(N) + ".csv" , result.T, delimiter=",") \ No newline at end of file diff --git a/metropolis.py b/metropolis.py new file mode 100644 index 0000000..14167e1 --- /dev/null +++ b/metropolis.py @@ -0,0 +1,17 @@ +import numpy as np + +def pi_0(N): + Pi_0 = 2*np.random.randint(2 , size = (N,N)) - 1 + return Pi_0 + +def MC_metropolis(arreglo, beta, N): + for i in range(0, 2 * N**2): + a, b = np.random.randint(0,N), np.random.randint(0,N) + rand_spin = arreglo[a, b] + vecinos = arreglo[(a+1)%N, b] + arreglo[a, (b+1)%N] + arreglo[(a-1)%N, b] + arreglo[a, (b-1)%N] + delta_energia = 2 * rand_spin * vecinos + if delta_energia < 0: + arreglo[a,b] *= -1 + elif np.random.uniform(0,1) < np.exp(-delta_energia*beta): + arreglo[a,b] *= -1 + return arreglo \ No newline at end of file diff --git a/observables.py b/observables.py new file mode 100644 index 0000000..8185e9f --- /dev/null +++ b/observables.py @@ -0,0 +1,14 @@ +import numpy as np + +def energia(arreglo, N): + energia = 0 + for i in range(0,N): + for j in range(0,N): + spin = arreglo[i,j] + vecinos = arreglo[(i+1)%N, j] + arreglo[i, (j+1)%N] + arreglo[(i-1)%N, j] + arreglo[i, (j-1)%N] + energia += - spin * vecinos + return energia / 2 + +def magnetizacion(arreglo): + magnetizacion = np.sum(arreglo) + return abs(magnetizacion) \ No newline at end of file diff --git a/parameters.py b/parameters.py new file mode 100644 index 0000000..92465cc --- /dev/null +++ b/parameters.py @@ -0,0 +1,6 @@ +t_min = 1.0 +t_max = 7.0 +nt = 100 +eqSteps = 100000 +mcSteps = 10000 +Ns = [5, 10, 20, 30] \ No newline at end of file diff --git a/sampling.py b/sampling.py new file mode 100644 index 0000000..fd5f887 --- /dev/null +++ b/sampling.py @@ -0,0 +1,63 @@ +import numpy as np + +from parameters import * +from observables import * +from metropolis import * + +def finite_ising(N,mcSteps,eqSteps,T): + + nt = len(T) + E,M,C,X,U = np.zeros(nt), np.zeros(nt), np.zeros(nt), np.zeros(nt), np.zeros(nt)#, np.zeros(nt) + N2 = N*N + + for t in range(nt): + E1 = M1 = E2 = M2 = M4 = 0 + beta = 1.0/T[t] + arreglo = pi_0(N) + + if t > 1.5 and t < 4.5: + eqSteps_p = 5*eqSteps + for i in range(eqSteps_p): # esto es supuestamente para llevarlo al equilibrio + arreglo = MC_metropolis(arreglo, beta, N) + #magn = magnetizacion(arreglo) + #energ = energia(arreglo) + #full_M.append(magn/N2) + #full_E.append(energ) + else: + for i in range(eqSteps): # esto es supuestamente para llevarlo al equilibrio + arreglo = MC_metropolis(arreglo, beta, N) + #magn = magnetizacion(arreglo) + #energ = energia(arreglo) + #full_M.append(magn/N2) + #full_E.append(energ) + + for i in range(mcSteps): # y aquà supuestamente el sistema se está desenvolviendo en el equilibrio + arreglo = MC_metropolis(arreglo,beta, N) + energ = energia(arreglo, N) + magn = magnetizacion(arreglo) + + E1 += energ + E2 += energ*energ + M1 += magn + M2 += magn*magn + M4 += magn*magn*magn*magn + + #calculo de las cantidades promedio + + E1_mean = E1 / mcSteps + E2_mean = E2 / mcSteps + M1_mean = M1 / mcSteps + M2_mean = M2 / mcSteps + M4_mean = M4 / mcSteps + + # cálculo de las cantidades especÃficas + + E[t] = E1_mean / N2 + M[t] = M1_mean / N2 + X[t] = beta * (M2_mean - M1_mean**2) / N2 + C[t] = beta**2 * (E2_mean - E1_mean**2) / N2 + + # cálculo del cumulante + + U[t] = 1 - M4_mean / (3 * M2_mean**2) + return np.array([T,E,M,X,C,U]) \ No newline at end of file -- GitLab