entropy = np.real(entropy)
Ls = np.array(Ls)
extrap_Ls = np.array([0, 1]+list(Ls))
fit_pts, params, R = analysis.curve_fit_main(entropy, analysis.linear, Ls, extrap_Ls)
# flip sign of gamma
params = [params[0], -1*params[1]]
print params
print fit_pts[0], fit_pts[1]
plt.plot(Ls, entropy, 'k.', label='Data')
plt.plot(extrap_Ls, fit_pts, 'b-', label='Fit')
text = text = plt.text(1, 5, r'$S = \alpha W-\gamma$')
pl2_txt = lambda params: (r'$\alpha={}$'+'\n'+r'\gamma={}$').format(IO.round_sig(params[0],2),IO.round_sig(params[1], 2))
text2 = plt.text(1, 3.5, pl2_txt(params))
plt.xlim(xmin=-1, xmax=11)
plt.ylim(ymin=-1)
plt.axhline(0, color='k', ls=':')
plt.axvline(0, color='k', ls=':')
#plt.title('Computing Topological Entanglement Entropy')
plt.xlabel(r'$W$')
plt.ylabel('Entanglement Entropy')
plt.legend(loc='best', numpoints=1, frameon=False)
plt.tight_layout()
plt.show()
colors = ['b', 'g', 'r', 'k', 'c']
plt.xlabel(r'$\frac{1}{W}$')
plt.ylabel('Entanglement Gap')
ax.set_xticks([0]+[1/L for L in xrange(4, 12, 2)])
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: r'$\frac{1}{'+str(int(round(1/x)))+'}$' if x>0 else r'$\frac{1}{\infty}$'))
for k, color in zip(keys, colors):
plt.plot([1/L for L in LL(*k)], E(*k), marker='.', ls='', color=color)
plt.xlim(-0.01, 0.26)
plt.ylim(-.1, 3)
plt.grid(axis='y')
plt.tight_layout()
pl3_txt = lambda params: 'B='+str(IO.round_sig(params[0],3))+'\nC='+str(IO.round_sig(params[1],3))
for k, color in zip(keys, colors):
if k[0]==0:
params, R, line, text = try_fit([1/L for L in LL(*k)], E(*k), a.quadratic, color, [1 for x in LL(*k)],(0, 0.3), [0, 0.1], pl3_txt)
else:
params, R, line, text = try_fit([1/L for L in LL(*k)], E(*k), a.quadratic, color, [1 for x in LL(*k)],(0, 0.3), [0, 0.1], pl3_txt)
#k = keys[1]
#params, R, line, text = try_fit([1/L for L in LL(*k)], E(*k), a.linear, 'g', [1 for x in LL(*k)],(0, 0.3), [0, 0.3], pl3_txt)
plt.show()
# plt.yscale('log')
# ax.set_yticks([0.1, 0.2, 0.5, 1, 2, 3])
# ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: str(IO.round_sig(x, 2))))
symdata = symmetric_inverse_corr2.view_props(['L', 'Correlation Bound'])
symdata = np.array([[L, val] for L, val in symdata])
symdata = np.array(sorted(symdata, key = lambda x:x[0]))
plt.sca(axs[1])
fit_data = symdata[:, 1][-4:]
extrapolate_pnts = np.arange(3, 10, 0.1)
fit_func = a.expic
fit_Ls = symdata[:, 0][-4:]
fit_label = 'Fit'
(fit_data, params, R) = a.curve_fit_main(fit_data,fit_func, fit_Ls, extrapolate_pnts)
symmetric_inverse_corr2.plot('L', 'Correlation Bound', ax = axs[1], xlabel = 'L', ylabel='Correlation Length', title = 'Symmetric Correlation Length', label = 'Data', marker = '*', color = 'k')
plt.plot(extrapolate_pnts, fit_data, label = fit_label, color='k')
txt_coords = (5, -0.3)
txt = plt.text(txt_coords[0], txt_coords[1], fit_label+': \n$a-ce^{-L/b}$'+'\n a ={} \n b={} \nc={}'.format(*[IO.round_sig(p) for p in params]))
plt.xlim(3, 10)
plt.ylim(-0.4, 1.5)
notsymdata = notsymmetric_inverse_corr.view_props(['L', 'Correlation Bound'])
notsymdata = np.array([[L, val] for L, val in notsymdata])
notsymdata = np.array(sorted(notsymdata, key = lambda x:x[0]))
plt.sca(axs[1])
fit_data = notsymdata[:, 1][-4:]
extrapolate_pnts = np.arange(3, 10, 0.1)
fit_func = a.expic
fit_Ls = notsymdata[:, 0][-4:]
fit_label = 'Fit'
(fit_data, params, R) = a.curve_fit_main(fit_data,fit_func, fit_Ls, extrapolate_pnts)
notsymmetric_inverse_corr.plot('L', 'Correlation Bound', ax = axs[0], xlabel = 'L', ylabel='Correlation Length', title = 'Overall Correlation Length', label = 'Data', marker = '*', color = 'b')
# plt.plot(xdata, ydata, ls='', marker='.', markersize=4)
line = plt.plot(extrap, interp, ls="-", color=color)
if show_fit_details:
text = plt.text(txt_coords[0], txt_coords[1], txt_func(params))
else:
text = 0
return params, R, line, text
# set up the plot
fig = plt.plot()
ax = plt.gca()
plt.xscale("log")
ax.set_xticks(range(2, 12, 2))
ax.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: str(int(IO.round_sig(x, 2)))))
plt.yscale("log")
ax.set_yticks([0.1, 0.2, 0.5, 1, 2, 3])
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: str(IO.round_sig(x, 2))))
# labels = ['$\\vert 1, 0 \\rangle$', u'$\\vert 2, 0 \\rangle$', u'$\\vert 3, 0 #\\rangle$',
# u'$j_{-1} \\vert 0, 0 \\rangle$', '$j_{-1}\\vert 1, 0 \\rangle$']
labels = ["e=1, K=0", "e=2, K=0", "e=3, K=0", "e=0, K=1", "e=1, K=1"]
pl = lambda x, a, b: b * x ** (-a)
pl3_txt = lambda params: "B=" + str(IO.round_sig(params[0], 3)) + "\nC=" + str(IO.round_sig(params[1], 3))
pl_txt = lambda params: "B=" + str(IO.round_sig(params[0])) + "\nC=" + str(IO.round_sig(params[1]))
text_x = 11.5
max_x_in_fit = 11.1
from __future__ import division
from matplotlib import pyplot as plt
import numpy as np
from DataAnalysis import PEPS_plots as pp
from PEPS import IO_helper as IO
from PEPS.CylinderPEPS import run
import sfig
ev_to_corr = lambda e: -1/np.log(np.abs(e))
phase = lambda e: 'e^(i pi {})'.format(IO.round_sig(np.angle(e)/np.pi, 2))
def corrbound_data(state, Ls):
ans = {}
for L in Ls:
tmspec = run.load_transfer_matrix_spectrum(state, L)
if tmspec:
norm = tmspec[(0, 0)][0]
ev1 = tmspec[(0, 1)][0]/norm
if L>1:
symev00 = tmspec[(0, 0)][1]/norm
symev10 = tmspec[(1, 0)][0]/norm #K=+-1, N=0 ev is bigger than K=0, N=0 sometimes
print abs(symev00), abs(symev10)
symev = symev00 if abs(symev00)>abs(symev10) else symev10
#symev = symev00
else:
symev00 = tmspec[(0, 0)][1]/norm
symev = symev00
ans[L] = ev_to_corr(ev1), phase(ev1), ev_to_corr(symev), phase(symev)
return ans
IO.go_to_data_parent('softcoreboson', parent='Data//FBI-TM')
