import sys
import numpy as np
import matplotlib.pyplot as plt
import re
import os
from scipy.optimize import curve_fit
from scipy.stats import sem
from matplotlib import rc
import commonAnalysis as ca
plt.rc('text', usetex=True)
plt.style.use('ggplot')
"""
Script to retrieve info about mc simulation of random graphs from graph evolution
Then calculates relevant info and produces various plots given these .txt files
"""
t1, m1, mags1 = ca.extractInfo(sys.argv[1])
abs_mags1 = np.abs(mags1)
abs_sterr = sem(abs_mags1, axis=1)
sus = np.zeros(len(t1))
sus_error = np.zeros(len(t1))
for i, mag in enumerate(abs_mags1):
sus[i] = np.var(mag, ddof=1)
sus_error[i] = ca.jackKnife(mag, np.var)
plt.errorbar(t1, sus, yerr=sus_error, fmt='bo', markersize='4')
plt.plot(t1[np.argmax(sus)],
max(sus),
'r^',
label=r"$T_C = {} \pm {}$".format(t1[np.argmax(sus)], t1[1] - t1[0]))
plt.xlabel(r"$T$")
#!/usr/bin/env python
import sys
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from commonAnalysis import extractInfo, spinspin, correlation_func_fit, tc_correlation_fit
plt.rc('text', usetex='true')
plt.style.use('ggplot')
t, means, mags = extractInfo(sys.argv[1])
ss, errors = spinspin(
sys.argv[1], compares=9) #set compares for no. of spins being correlated
#most of the parameters here should be set according to how the specific data has been generated
print len(ss), len(errors)
print len(ss[0])
sus = np.zeros(len(t))
sus_error = np.zeros(len(t))
for i, mag in enumerate(np.abs(mags)):
sus[i] = np.var(mag, ddof=1) / t[i]
spins = np.array(
ss[10]
) #10 index is arbitrary, corresponds to specific temp index being looked at
sterrs = np.array(errors[10])
distance = np.arange(1, len(spins) + 1)
#tstar = t.index(2.269)
#popt, pcov = curve_fit(correlation_func_fit, xdata = distance, ydata = spins)