/
plot_spec_en_results.py
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plot_spec_en_results.py
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#!/usr/bin/env python3
import os, sys
import matplotlib.pyplot as plt
import numpy as np
import gvar as gv
# if gv.__version__ != '11.5.2':
# print('your gvar is version ', gv.__version__)
# sys.exit('For now, you must have gvar version 11.5.2 to be able to read the results')
s_mev = 197.3 / 0.08730
s_gev = s_mev / 1000
# set the enery levels
mpi = 0.14073
mN = 0.4904
models = ['SqW', 'HO', '1/n', '1/n2']
models = ['HO', '1/n', '1/n2']
file_m = {'SqW':'sw_result', 'HO':'ho_result', '1/n':'n_result', '1/n2':'n2_result'}
fits = dict()
for m in models:
fits[m] = dict()
for k in ['logGBF','w','Q','E0','E1','E2','E3','E4','gA','z0','pdE1', 'pdE2', 'pdE3', 'pdE4']:
fits[m][k] = []
for t in ['3','4','5','6','7']:
for m in models:
f = gv.load('data/spec_results_pt2/'+file_m[m]+t) # [prior, posterior, Q, logGBF]
fits[m]['logGBF'].append(f[3])
fits[m]['Q'].append(f[2])
fits[m]['E0'].append(f[1]['E0'])
for n in [1,2,3,4]:
tmp = f[1]['E0']
for l in range(1,n+1):
tmp += f[1]['dE'+str(l)]
fits[m]['E'+str(n)].append(tmp)
fits[m]['pdE'+str(n)].append(f[0]['dE'+str(n)])
#print(f[0]['dE2'])
fits[m]['gA'].append(f[1]['A3_00'])
fits[m]['z0'].append(f[1]['z0'])
# make weights
for t in range(5):
lGBF_rel = []
for m in models:
lGBF_rel.append(fits[m]['logGBF'][t])
#print(lGBF_rel)
lGBF_rel = np.array(lGBF_rel) - max(lGBF_rel)
#print(lGBF_rel)
w = np.exp(lGBF_rel)
w = w / w.sum()
#print(w)
for i_m, m in enumerate(models):
fits[m]['w'].append(w[i_m])
# excited state energy gaps
def dE_mod(n,mod):
p = gv.BufferDict()
if mod == 'SqW':
p['log(dE_%d)' %n] = gv.gvar(np.log(2*mpi * (2*n-1)), 0.5 / (2*n-1) )
elif mod == 'HO':
p['log(dE_%d)' %n] = gv.gvar(np.log(2*mpi), .5)
elif mod == '1/n':
p['log(dE_%d)' %n] = gv.gvar(np.log(2*mpi / n), .5*n)
elif mod == '1/n2':
p['log(dE_%d)' %n] = gv.gvar(np.log(2*mpi / n**2), .5*n**2)
dEn = p['dE_%d' %n]
#print(mod, n, dEn)
return dEn
def Npi(mN,mpi,L):
E = dict()
n_max = 20
for px in range(n_max):
for py in range(n_max):
for pz in range(n_max):
p = np.array([px,py,pz])
psq = int(np.sum(p**2))
if not np.all(p==0) and psq not in E:
E[psq] = np.sqrt(mN**2 +(2*np.pi/L)**2 * psq) + np.sqrt(mpi**2 +(2*np.pi/L)**2 * psq)
return E
# plot fit results of En
fig_width = 6.75 # in inches, 2x as wide as APS column
gr = 1.618034333 # golden ratio
figsize = (2*fig_width, fig_width )
aspect=[0.1, 0.1, 0.895, 0.895]
textp = {"fontsize": 18}
labelp = {"labelsize": 14}
labelsize = {'size': 16}
plt.rcParams.update({"text.usetex": True})
red = "#FF6F6F"
peach = "#FF9E6F"
orange = "#FFBC6F"
sunkist = "#FFDF6F"
yellow = "#FFEE6F"
lime = "#CBF169"
green = "#5CD25C"
turquoise = "#4AAB89"
blue = "#508EAD"
grape = "#635BB1"
violet = "#7C5AB8"
fuschia = "#C3559F"
color_list = [violet, green, fuschia, yellow, blue, orange, turquoise, red]
plt.ion()
plt.rcParams['xtick.direction'] = 'in'
plt.rcParams['ytick.direction'] = 'in'
gridspec_tmin = {'height_ratios': [10, 2, 1.25, 1.25],
'left': 0.06, 'bottom': 0.1, 'right': 0.945, 'top': 0.99, 'hspace':0}
fig, (ax_es, ax_e0, ax_Q, ax_w) = plt.subplots(4, 1, sharex = True, gridspec_kw=gridspec_tmin, figsize=figsize)
# add N-pi levels
E_Npi = Npi(mN=mN, mpi=mpi, L=32)
ax_es.axhline(E_Npi[1]+10, linestyle=':', color='k', label=r'$N(\mathbf{q})\pi(\mathbf{-q})$')
for psq in E_Npi:
#ax_es.axhline(E_Npi[psq], linestyle=(0, (5, 10)), color='k',alpha=.3, label=lbl)
ax_es.axhline(E_Npi[psq], linestyle=':', color='k',alpha=.3)
# add lowest N-pipi level
ax_es.axhline(mN+2*mpi, linestyle=(0, (3, 5, 1, 5)), color='k',alpha=.2)
m_lbl = {'SqW':r'SqW', 'HO':r'HO', '1/n':r'$1/n$', '1/n2':r'$1/n^2$'}
shift = {'SqW':-.3, 'HO':-.1, '1/n':.1, '1/n2':.3}
n_clr = {'SqW':red, 'HO':orange, '1/n':green, '1/n2':blue}
mrkr = {'SqW':'s', 'HO':'o', '1/n':'X', '1/n2':'D'}
p_width = 0.025
shift = {'SqW':-.3, 'HO':-.25, '1/n':.0, '1/n2':.25}
n_clr = {'SqW':red, 'HO':red, '1/n':green, '1/n2':blue}
models = ['HO', '1/n', '1/n2']
p_width = 0.0625/2
for i_t,t in enumerate([3,4,5,6,7]):
for m in models:
mfc = n_clr[m]
clr = 'k'#n_clr[m]
e0 = fits[m]['E0'][i_t]
if t==3 and m == 'HO':
clr = 'k'
mfc = 'white'#n_clr[m]
t_0 = t + shift[m]
ax_e0.errorbar(t_0, e0.mean, yerr=e0.sdev,
color=clr, marker=mrkr[m], mfc=mfc, capsize=3)
#gA = fits[m]['gA'][i_t]
#ax_gA.errorbar(t_0, gA.mean, yerr=gA.sdev,
# color=clr, marker=mrkr[m], mfc=mfc)
ax_Q.plot(t_0, fits[m]['Q'][i_t], color=n_clr[m], marker=mrkr[m], mfc=mfc)
ax_w.plot(t_0, fits[m]['w'][i_t], color=n_clr[m], marker=mrkr[m], mfc=mfc)
if t==3 and m == 'HO':
ax_e0.axhspan(e0.mean-e0.sdev, e0.mean+e0.sdev, color=n_clr[m], alpha=.3)
#ax_gA.axhspan(gA.mean-gA.sdev, gA.mean+gA.sdev, color=n_clr[m], alpha=.3)
for n in [1,2,3,4]:
# plot prior
#En = fits[m]['E'+str(n-1)][i_t] + dE_mod(n, m)
if n == 2 and False:
print(En)
print(fits[m]['E1'][i_t])
print(fits[m]['pdE2'][i_t])
En = fits[m]['E'+str(n-1)][i_t] + fits[m]['pdE'+str(n)][i_t]
n_base = t_0 + 2*p_width*(n-1)
p_range = np.arange(n_base-p_width, n_base+p_width+.001, .001)
if n == 2 and False:
print(En)
ax_es.fill_between(p_range, En.mean-En.sdev, En.mean+En.sdev,
color=n_clr[m], alpha=.5)
# plot posterior
lbl = ""
if i_t == 1 and n == 1:
lbl = m_lbl[m]
en = fits[m]['E'+str(n)][i_t]
if t==3 and m == 'HO':
ax_es.axhspan(en.mean-en.sdev, en.mean+en.sdev, color=n_clr[m], alpha=.3)
ax_es.errorbar(t_0+2*p_width*(n-1), en.mean, yerr=en.sdev,
color=clr, marker=mrkr[m], mfc=mfc, label=lbl, linestyle='None', capsize=3)
ax_e0.set_ylim(0.481, 0.499)
ax_e0.set_ylabel(r'$a_{09} E_0$',**textp)
ax_e0.tick_params(axis='both', which='major', **labelp)
ax_e0r = ax_e0.twinx()
ax_e0r.set_ylim(ax_e0.get_ylim()[0]*s_gev, ax_e0.get_ylim()[1]*s_gev)
ax_e0r.set_yticks([s_gev*t for t in ax_e0.get_yticks()[1:-1]])
ax_e0r.tick_params(axis='both', which='major', **labelp)
ax_e0r.set_yticklabels(["%.2f" %t for t in ax_e0r.get_yticks()])
ax_e0r.set_ylabel(r'$E_0 ({\rm GeV})$', **textp)
#ax_gA.set_ylim(1.16, 1.34)
#ax_gA.set_ylabel(r'$\mathring{g}_A$', **textp)
#ax_gA.tick_params(axis='both', which='major', **labelp)
#ax_gA.set_yticks([1.19, fits['HO']['gA'][0].mean, 1.31])
#ax_gA.set_yticklabels(["%.2f" %e for e in ax_gA.get_yticks()])
ax_w.set_ylim(0,1)
ax_w.set_yticks([0.1,0.75])
ax_w.set_ylabel(r'$w$', **textp)
ax_w.set_xlabel(r'$t_{\rm sep}^{\rm min} : C_2$', **textp)
ax_w.tick_params(axis='both', which='major', **labelp)
ax_Q.set_ylim(0,1)
ax_Q.set_yticks([0.1,0.75])
ax_Q.set_ylabel(r'$Q$', **textp)
ax_Q.tick_params(axis='both', which='major', **labelp)
ax_es.set_ylim(0.51, 3.8)
ax_es.legend(loc=1,ncol=5,**textp)
ax_es.tick_params(axis='both', which='major', **labelp)
ax_es.set_ylabel(r'$a_{09}E_n$', **textp)
ax_es.set_yticks([fits['HO']['E%d' %n][0].mean for n in range(1,5)])
ax_es.set_yticklabels(["%.3f" %e for e in ax_es.get_yticks()])
ax_esr = ax_es.twinx()
ax_esr.set_ylim(ax_es.get_ylim()[0]*s_gev, ax_es.get_ylim()[1]*s_gev)
ax_esr.tick_params(axis='both', which='major', **labelp)
ax_esr.set_yticks([s_gev*t for t in ax_es.get_yticks()])
ax_esr.set_yticklabels(["%.2f" %t for t in ax_esr.get_yticks()])
ax_esr.set_ylabel(r'$E_n ({\rm GeV})$', **textp)
if not os.path.exists('new_plots'):
os.makedirs('new_plots')
plt.savefig('new_plots/es_model_sensitivity_2pt.pdf', transparent=True)
plt.ioff()
plt.show()