if 'noshow' in sys.argv:
matplotlib.use('Agg')
import matplotlib.pyplot as plt
if os.path.exists('../data'):
os.chdir('..')
moviedir = sys.argv[1]
frames = eval(sys.argv[2]) # a list of integers
Ss = []
labels = []
for f in frames:
print f
e, lndos = readnew.e_lndos('%s/%06d-lnw.dat' % (moviedir, f))
its = readnew.iterations('%s/%06d-lnw.dat' % (moviedir, f))
lndos = -lndos
Smin = lndos[lndos > lndos[0]].min()
lndos -= Smin
if len(Ss) > 0:
Ss.append(lndos - lastdos)
else:
Ss = [lndos]
lastdos = lndos * 1
labels.append('%d iterations' % its)
# plt.stackplot(e[lndos > lndos[0]], lndos[lndos>lndos[0]])#, label='%d iterations' % its)
plt.stackplot(e, Ss, labels=labels)
plt.plot(e, lndos, color='black', linewidth=2)
plt.title('Density of States for various iterations')
for i in range(len(T)):
boltz_arg = S - E / T[i]
P = np.exp(boltz_arg - boltz_arg.max())
P = P / P.sum()
U = (E * P).sum()
C[i] = ((E - U)**2 * P).sum() / T[i]**2
return C
T = np.arange(1.5, 5, 0.01)
# Compute the reference energy, entropy, and heat capacity once.
try:
eref, lndosref, Nrt_ref = readnew.e_lndos_ps(ref)
except:
eref, lndosref = readnew.e_lndos(ref)
#cvref = heat_capacity(T, eref, lndosref)
cvref = heat_capacity(T, eref[0:Emin - Emax + 1], lndosref[0:Emin - Emax + 1])
# form a dictionary to place into a pandas dataframe.
mydictionary = {
'Temperature': T,
'cvref': cvref,
}
df = pd.DataFrame(mydictionary)
for f in filename:
name = '%s.yaml' % (f)
for n in range(1, seed_avg + 1):
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('$E$')
mine = 1e100
maxe = -1e100
numframes = 0
dataformat = 'data/%s/%s-%%s-movie/%%06d' % (subdirname, filename)
lastframe = -1
alldone = False
for frame in range(1, 100000):
if alldone:
break
for suffix in suffixes:
basename = dataformat % (suffix, frame)
try:
e, lndos = readnew.e_lndos(basename)
except (KeyboardInterrupt, SystemExit):
raise
except:
alldone = True
break
numframes = frame + 1
if len(e[lndos != lndos[-1]]) > 1:
mine = min(mine, e[lndos != lndos[-1]].min() - 5)
if len(e[lndos != lndos[0]]):
maxe = max(maxe, e[lndos != lndos[0]].max() + 5)
if numframes % 25 == 0 and frame != lastframe:
print('counting %dth frame' % numframes)
lastframe = frame
bestframe = sorted(
#arg number = ['000100']
all_methods = ['-tmi3', '-tmi2', '-tmi', '-toe', '-tmmc']
methods = []
prettymethods = []
T = []
u = []
energy = []
lndos = []
ps = []
hist = []
fref = 'data/lv/ww%.2f-ff%.2f-%gx%g-tmi3-dos.dat' % (ww, ff, lenx, lenyz)
energy_ref, lndos_ref = readnew.e_lndos(fref)
max_entropy_state = readnew.max_entropy_state(fref)
for method in all_methods:
try:
fbase = 'data/lv/ww%.2f-ff%.2f-%gx%g%s-movie/%s' % (
ww, ff, lenx, lenyz, method, number)
myT, myu, mycv, mys, myminT = readnew.T_u_cv_s_minT(fbase)
myenergy, mylndos, myps = readnew.e_lndos_ps(fbase)
_, myhist = readnew.e_hist(fbase)
except:
continue
energy.append(myenergy[:len(lndos_ref)])
lndos.append(mylndos[:len(lndos_ref)])
ps.append(myps[:len(lndos_ref)])
data['movies']['entropy'] = np.array(data['movies']['entropy'])
lndos = data['movies']['entropy']
N_save_times = len(data['movies']['entropy'])
ref = reference
if ref[:len('data/')] != 'data/':
ref = 'data/' + ref
maxref = Smax #int(readnew.max_entropy_state(ref))
minref = Smin # int(readnew.min_important_energy(ref))
n_energies = int(minref - maxref+1)
print maxref, minref
try:
eref, lndosref, Nrt_ref = readnew.e_lndos_ps(ref)
except:
eref, lndosref = readnew.e_lndos(ref)
errorinentropy = np.zeros(N_save_times)
maxerror = np.zeros(N_save_times)
for i in range(0,N_save_times):
# below just set average S equal between lndos and lndosref
if yamlRef:
norm_factor = np.mean(lndos[i][maxyaml:minyaml+1]) - np.mean(lndosref[0:len(lndosref)])
doserror = lndos[i][maxyaml:minyaml+1][::-1] - lndosref[0:len(lndosref)] - norm_factor
else:
norm_factor = np.mean(lndos[i][maxyaml:minyaml+1]) - np.mean(lndosref[maxref:minref+1])
doserror = lndos[i][maxyaml:minyaml+1][::-1] - lndosref[maxref:minref+1] - norm_factor
errorinentropy[i] = np.sum(abs(doserror))/len(doserror)
maxerror[i] = np.amax(doserror) - np.amin(doserror)
mine = 1e100
maxe = -1e100
numframes = 0
dataformat = 'data/%s/%s-%%s-movie/%%06d' % (subdirname, filename)
lastframe = -1
alldone = False
for frame in range(1,100000):
if alldone:
break
for suffix in suffixes:
basename = dataformat % (suffix, frame)
try:
e, lndos = readnew.e_lndos(basename)
except (KeyboardInterrupt, SystemExit):
raise
except:
alldone = True
break
numframes = frame+1
if len(e[lndos != lndos[-1]]) > 1:
mine = min(mine, e[lndos != lndos[-1]].min() - 5)
if len(e[lndos != lndos[0]]):
maxe = max(maxe, e[lndos != lndos[0]].max()+5)
if numframes % 25 == 0 and frame != lastframe:
print('counting %dth frame' % numframes)
lastframe = frame
bestframe = sorted(glob.glob('data/%s/%s-%s-movie/*-lndos.dat'
#arg number = [000100]
all_methods = [ '-tmi3', '-tmi2', '-tmi', '-toe', '-tmmc']
methods = []
prettymethods = []
T = []
u = []
energy = []
lndos = []
ps = []
hist = []
fref = 'data/lv/ww%.2f-ff%.2f-%gx%g-tmi3-dos.dat' % (ww,ff,lenx,lenyz)
energy_ref,lndos_ref = readnew.e_lndos(fref)
max_entropy_state = readnew.max_entropy_state(fref)
for method in all_methods:
try:
fbase = 'data/lv/ww%.2f-ff%.2f-%gx%g%s-movie/%s' % (ww,ff,lenx,lenyz,method,number)
myT,myu,mycv,mys,myminT = readnew.T_u_cv_s_minT(fbase)
myenergy,mylndos,myps = readnew.e_lndos_ps(fbase)
_, myhist = readnew.e_hist(fbase)
except:
continue
energy.append(myenergy[:len(lndos_ref)])
lndos.append(mylndos[:len(lndos_ref)])
ps.append(myps[:len(lndos_ref)])
newhist = np.zeros_like(lndos_ref)
matplotlib.use('Agg')
import matplotlib.pyplot as plt
if os.path.exists('../data'):
os.chdir('..')
moviedir = sys.argv[1]
frames = eval(sys.argv[2]) # a list of integers
Ss = []
labels = []
for f in frames:
print f
e, lndos = readnew.e_lndos('%s/%06d-lnw.dat' % (moviedir, f))
its = readnew.iterations('%s/%06d-lnw.dat' % (moviedir, f))
lndos = -lndos
Smin = lndos[lndos>lndos[0]].min()
lndos -= Smin
if len(Ss) > 0:
Ss.append(lndos - lastdos)
else:
Ss = [lndos]
lastdos = lndos*1
labels.append('%d iterations' % its)
# plt.stackplot(e[lndos > lndos[0]], lndos[lndos>lndos[0]])#, label='%d iterations' % its)
plt.stackplot(e, Ss, labels=labels)
plt.plot(e, lndos, color = 'black', linewidth = 2)
plt.title('Density of States for various iterations')
