plt.figure(figsize=(5, 4))
inputs = [
#('vanilla_wang_landau',
# 'data/s000/periodic-ww1.30-ff0.30-N500-vanilla_wang_landau-minE-movie/002000-lndos.dat'),
('samc-1e+06',
'data/s000/periodic-ww1.50-ff0.17-N256-samc-100000-movie/000600-lndos.dat'
),
#('sad3', 'data/s000/periodic-ww1.30-ff0.30-N500-sad3-movie/002000-lndos.dat'),
#('sad3-test', 'data/s000/periodic-ww1.30-ff0.30-N500-sad3-test-movie/002000-lndos.dat')
]
for method, fname in inputs:
print('plotting', method)
e, lndos = readnew.e_lndos(fname)
colors.plot(e, lndos, method=method)
emax = -34
emin = -2000
eminimportant = -915
eSmax = -509
Smin = -1500
plt.axvline(eminimportant, linestyle=':', color='tab:gray')
plt.axvline(eSmax, linestyle=':', color='tab:gray')
#plt.axvline(emin)
#plt.axvline(eSmax)
plt.ylabel('$S/k_B$')
plt.ylim(Smin, 0)
plt.xlim(emin, emax)
plt.xlabel('$E$')
colors.legend()
print('only showing 1/%d of the frames' % skipby)
print('numframes', numframes)
for frame in range(1, numframes + 1):
if frame % 25 == 0:
print('working on frame %d/%d' % (frame, numframes))
plt.cla()
ax.plot(best_e, best_lndos, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame * skipby)
try:
e, lndos, ps, lndostm = readnew.e_lndos_ps_lndostm(basename)
colors.plot(e, lndos, method=suffix)
#if lndostm is not None and suffix[:2] != 'sa':
# colors.plot(e, lndostm, method=suffix+'-tm')
datname = basename + '-lndos.dat'
min_T = readnew.minT(datname)
too_lo, too_hi = readnew.too_low_high_energy(datname)
ax.axvline(-readnew.max_entropy_state(datname),
color='r',
linestyle=':')
min_important_energy = int(readnew.min_important_energy(datname))
ax.axvline(-min_important_energy, color='b', linestyle=':')
if too_lo is not None and suffix[:3] == 'sad':
ax.axvline(-too_lo, color='b', linestyle='--')
if too_lo is not None and suffix[:3] == 'sad':
ax.axvline(-too_hi, color='r', linestyle='--')
# Uncomment the following to plot a line at the
if system == 's000':
N = filebase.split('-N')[-1]
ff = filebase.split('-ff')[-1].split('-N')[0]
ff = float(ff)
# Get N directly from title.
moves = iterations * float(N)
if system == 'ising':
N = filebase.split('N')[-1]
moves = iterations * float(N)
max_time = max(max_time, moves.max())
if energy > 0:
plt.figure('error-at-energy-iterations')
colors.plot(moves, erroratenergy, method=method[1:])
plt.title('Error at energy %g %s' % (energy, filebase))
plt.xlabel('# moves')
plt.ylabel('error')
colors.legend()
plt.savefig('figs/%s-error-energy-%g.pdf' % (tex_filebase, energy))
plt.figure('round-trips-at-energy')
colors.plot(moves, Nrt_at_energy, method=method[1:])
plt.title('Round Trips at energy %g, %s' % (energy, filebase))
plt.xlabel('# moves')
plt.ylabel('Round Trips')
colors.legend()
plt.savefig('figs/%s-round-trips-%g.pdf' % (tex_filebase, energy))
plt.figure('error-at-energy-round-trips')
for frame in range(1, numframes + 1):
if frame % 25 == 0:
print(('working on frame %d/%d' % (frame, numframes)))
plt.cla()
ax.plot(best_e, best_hist, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame * skipby)
try:
datname = basename + '-transitions.dat'
#print(readnew.moves(datname))
e, hist = readnew.e_hist(basename)
colors.plot(e, hist, method=suffix)
except (KeyboardInterrupt, SystemExit):
raise
except Exception as e:
print(e)
pass
ax.set_xlabel(r'$E$')
ax.set_ylim(1.1 * minhist, maxhist + 5)
ax.set_xlim(mine, maxe)
ax.set_ylabel(r'$\textrm{Histogram}$')
colors.legend(loc='lower right')
fname = '%s/frame%06d.png' % (moviedir, frame)
plt.savefig(fname)
print('only showing 1/%d of the frames' % skipby)
print('numframes', numframes)
for frame in range(1, numframes+1):
if frame % 25 == 0:
print('working on frame %d/%d' % (frame, numframes))
plt.cla()
ax.plot(best_e, best_lndos, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame*skipby)
try:
e, lndos, ps, lndostm = readnew.e_lndos_ps_lndostm(basename)
colors.plot(e, lndos, method=suffix)
#if lndostm is not None and suffix[:2] != 'sa':
# colors.plot(e, lndostm, method=suffix+'-tm')
datname = basename+'-lndos.dat'
min_T = readnew.minT(datname)
too_lo, too_hi = readnew.too_low_high_energy(datname)
ax.axvline(-readnew.max_entropy_state(datname), color='r', linestyle=':')
min_important_energy = int(readnew.min_important_energy(datname))
ax.axvline(-min_important_energy, color='b', linestyle=':')
if too_lo is not None and suffix[:3] == 'sad':
ax.axvline(-too_lo, color='b', linestyle='--')
if too_lo is not None and suffix[:3] == 'sad':
ax.axvline(-too_hi, color='r', linestyle='--')
# Uncomment the following to plot a line at the
# min_important_energy with slope determined by min_T
# ax.plot(e, (e+min_important_energy)/min_T + lndos[min_important_energy], colors[suffix_index]+'--')
for frame in range(1, numframes+1):
if frame % 25 == 0:
print('working on frame %d/%d' % (frame, numframes))
plt.cla()
ax.plot(best_e, best_hist, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame*skipby)
try:
datname = basename+'-transitions.dat'
#print(readnew.moves(datname))
e, hist = readnew.e_hist(basename)
colors.plot(e, hist, method=suffix)
except (KeyboardInterrupt, SystemExit):
raise
except Exception as e:
print(e)
pass
ax.set_xlabel(r'$E$')
ax.set_ylim(1.1*minhist, maxhist+5)
ax.set_xlim(mine, maxe)
ax.set_ylabel(r'$\textrm{Histogram}$')
colors.legend(loc='lower right')
fname = '%s/frame%06d.png' % (moviedir, frame)
plt.savefig(fname)
print 'only showing 1/%d of the frames' % skipby
print 'numframes', numframes
for frame in xrange(numframes):
if frame % 25 == 0:
print 'working on frame %d/%d' % (frame, numframes)
plt.cla()
ax.plot(best_e, best_lndos, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame * skipby)
try:
e, lndos, lndostm = readandcompute.e_lndos_lndostm(basename)
colors.plot(e, lndos, method=suffix)
if lndostm is not None and suffix[:2] != 'sa':
colors.plot(e, lndostm, method=suffix + '-tm')
datname = basename + '-lndos.dat'
min_T = readandcompute.minT(datname)
ax.axvline(-readandcompute.max_entropy_state(datname),
color='r',
linestyle=':')
min_important_energy = int(
readandcompute.min_important_energy(datname))
ax.axvline(-min_important_energy, color='b', linestyle=':')
# Uncomment the following to plot a line at the
# min_important_energy with slope determined by min_T
# ax.plot(e, (e+min_important_energy)/min_T + lndos[min_important_energy], colors[suffix_index]+'--')
# ax.axvline(-readandcompute.converged_state(datname), color=colors.color(suffix), linestyle=':')
# Uncomment the following to plot the lnw along with the lndos
data = np.loadtxt(datadir + 'wide-cv.txt')
wideax.set_xlim(0, data[-1, 0])
wide_ymin = 89
wide_ymax = 190
wideax.set_ylim(wide_ymin, wide_ymax)
# colors.plot(data[:,0], data[:,1], method='bench', axes=wideax)
for method in files:
fname = files[method][0.01]
data = np.loadtxt(datadir + fname + '-cv.txt')
widedata = np.loadtxt(datadir + fname + '-wide-cv.txt')
if len(data) == 0:
continue
if 'sad' in method:
colors.plot(data[:, 0],
data[:, 1],
method=r'SAD $\Delta E=0.01\epsilon$',
axes=zoomax)
colors.plot(widedata[:, 0],
widedata[:, 1],
method=r'SAD $\Delta E=0.01\epsilon$',
axes=wideax)
# fname = files[method][0.1]
# if os.path.exists(datadir+fname+'-cv.txt'):
# data = np.loadtxt(datadir+fname+'-cv.txt')
# colors.plot(data[:,0], data[:,1], method=r'SAD $\Delta E=0.1\epsilon$', axes=zoomax)
# widedata = np.loadtxt(datadir+fname+'-wide-cv.txt')
# colors.plot(widedata[:,0], widedata[:,1], method=r'SAD $\Delta E=0.1\epsilon$', axes=wideax)
else:
colors.plot(data[:, 0], data[:, 1], method=method, axes=zoomax)
colors.plot(widedata[:, 0], widedata[:, 1], method=method, axes=wideax)
for method in extra_files:
# Formula to calculate N from title i.e. 100x10
# and use floor to always round up.
N = np.floor(0.25*0.20*float(NxN[0])*float(NxN[-1])*float(NxN[-1]))
ff = 1.0 # FIXME
moves = iterations * N
if filebase.startswith('s000'):
N = filebase.split('-N')[-1]
ff = filebase.split('-ff')[-1].split('-N')[0]
ff = float(ff)
# Get N directly from title.
moves = iterations * float(N)
max_time = max(max_time, moves.max())
if energy > 0:
plt.figure('error-at-energy-iterations')
colors.plot(moves, erroratenergy, method=method[1:])
plt.title('Error at energy %g %s' % (energy,filebase))
plt.xlabel('# moves')
plt.ylabel('error')
colors.legend()
plt.savefig('figs/%s-error-energy-%g.pdf' % (tex_filebase, energy))
plt.figure('round-trips-at-energy' )
colors.plot(moves, Nrt_at_energy, method = method[1:])
plt.title('Round Trips at energy %g, %s' % (energy,filebase))
plt.xlabel('# moves')
plt.ylabel('Round Trips')
colors.legend()
plt.savefig('figs/%s-round-trips-%g.pdf' % (tex_filebase, energy))
plt.figure('error-at-energy-round-trips')
matplotlib.use('Agg')
print('true?')
cv_data = pd.read_csv('%s/N%s-heat-capacity.csv' % (file_dir, N),
delimiter='\t',
encoding='utf-8',
engine='python')
#cv_data['cvref'] = cv_data['cvref'].astype(float)
print(cv_data.head(10))
cv_headers = list(cv_data)[1:]
print(cv_headers)
# Begin plotting the heat capacity
plt.figure('heat capacity plot')
Temp = np.array(pd.to_numeric(cv_data['Temperature'], errors='coerce'))
for name in cv_headers:
cv = pd.to_numeric(cv_data[name], errors='coerce')
colors.plot(1 / Temp, cv / N**2, method=name)
colors.legend(loc='best')
plt.xlabel(r'$\beta / J$')
plt.ylabel(r'$c_V$ / $ k_B$')
plt.xlim(0.3, 0.6)
if N == 32:
plt.ylim(0.3, 2.0)
plt.savefig('../ising/N%i-Cv.pdf' % N)
plt.show()
print 'trying method', method
for subdirectory in directory:
try:
basename = 'data/lv/%s%s-movie/%s' % (filebase, method, subdirectory)
e, hist = readandcompute.e_and_total_init_histogram(basename)
datname = basename + '-transitions.dat'
#min_T = readandcompute.minT(datname)
hist_max = np.amax(hist)
hist_norm = hist/hist_max # each method is normalized to itself.
plt.figure('Histogram evolution vs Energy')
plt.ylabel('Histogram')
plt.xlabel('Energy')
plt.subplot(len(directory),1,i)
colors.plot(e, hist_norm, method=method[1:])
colors.legend()
i = i + 1 # this is a hokey way to count through frames.
except:
continue
#plt.tight_layout(pad=2, w_pad=0.0, h_pad=0.0)
plt.suptitle('Maximum Entropy Error vs Iterations, %s' %filebase)
plt.show()
for method in methods:
i = 1
print('trying method', method)
for subdirectory in directory:
try:
basename = 'data/lv/%s%s-movie/%s' % (filebase, method,
subdirectory)
e, hist = readandcompute.e_and_total_init_histogram(basename)
datname = basename + '-transitions.dat'
#min_T = readandcompute.minT(datname)
hist_max = np.amax(hist)
hist_norm = hist / hist_max # each method is normalized to itself.
plt.figure('Histogram evolution vs Energy')
plt.ylabel('Histogram')
plt.xlabel('Energy')
plt.subplot(len(directory), 1, i)
colors.plot(e, hist_norm, method=method[1:])
colors.legend()
i = i + 1 # this is a hokey way to count through frames.
except:
continue
#plt.tight_layout(pad=2, w_pad=0.0, h_pad=0.0)
plt.suptitle('Maximum Entropy Error vs Iterations, %s' % filebase)
plt.show()
print 'only showing 1/%d of the frames' % skipby
print 'numframes', numframes
for frame in xrange(numframes):
if frame % 25 == 0:
print 'working on frame %d/%d' % (frame, numframes)
plt.cla()
ax.plot(best_e, best_lndos, ':', color='0.5')
for suffix_index in range(len(suffixes)):
suffix = suffixes[suffix_index]
basename = dataformat % (suffix, frame*skipby)
try:
e, lndos, lndostm = readandcompute.e_lndos_lndostm(basename)
colors.plot(e, lndos, method=suffix)
if lndostm is not None and suffix[:2] != 'sa':
colors.plot(e, lndostm, method=suffix+'-tm')
datname = basename+'-lndos.dat'
min_T = readandcompute.minT(datname)
ax.axvline(-readandcompute.max_entropy_state(datname), color='r', linestyle=':')
min_important_energy = int(readandcompute.min_important_energy(datname))
ax.axvline(-min_important_energy, color='b', linestyle=':')
# Uncomment the following to plot a line at the
# min_important_energy with slope determined by min_T
# ax.plot(e, (e+min_important_energy)/min_T + lndos[min_important_energy], colors[suffix_index]+'--')
# ax.axvline(-readandcompute.converged_state(datname), color=colors.color(suffix), linestyle=':')
# Uncomment the following to plot the lnw along with the lndos
# e, lnw = readandcompute.e_lnw(basename)
# ax.plot(e, -lnw, colors[suffix_index]+':')
except (KeyboardInterrupt, SystemExit):