def mergeXY(sets, foreach=[]):
foreach_sets = {}
for iset in pyalps.flatten(sets):
fe_par_set = tuple((iset.props[m] for m in foreach))
if fe_par_set in foreach_sets:
foreach_sets[fe_par_set].append(iset)
else:
foreach_sets[fe_par_set] = [iset]
for k, v in foreach_sets.items():
common_props = pyalps.dict_intersect([q.props for q in v])
res = pyalps.DataSet()
res.props = common_props
for im in range(0, len(foreach)):
m = foreach[im]
res.props[m] = k[im]
for data in v:
if len(res.x) > 0 and len(res.y) > 0:
res.x = np.concatenate((res.x, data.x))
res.y = np.concatenate((res.y, data.y))
else:
res.x = data.x
res.y = data.y
order = np.argsort(res.x, kind='mergesort')
res.x = res.x[order]
res.y = res.y[order]
res.props['label'] = ''
for im in range(0, len(foreach)):
res.props['label'] += '%s = %s ' % (foreach[im], k[im])
foreach_sets[k] = res
return foreach_sets.values()
def __init__(self, sets, x, obs):
self.props = pyalps.dict_intersect( [d.props for d in pyalps.flatten(sets)] )
self.props['observable'] = str(obs)
self.xname = str(x)
self.obsx = []
self.ydata = np.empty((0,0))
self.xdata = np.empty(0)
self.bonddims = np.empty(0)
self.init_data(sets)
self.xdata = np.array(self.xdata)
self.ydata = np.array(self.ydata)
order = np.argsort(self.xdata)
self.bonddims = self.bonddims[order]
self.xdata = self.xdata[order]
for i in range(len(self.ydata)):
self.ydata[i] = self.ydata[i][order]
plotdata = pyalps.collectXY(data, 'T', '|Magnetization|')
plt.figure()
pyalps.plot.plot(plotdata)
plt.xlim(0, 3)
plt.ylim(0, 1)
plt.title('Ising model')
plt.show()
# convert the data to text file for plotting using another tool
print(pyalps.plot.convertToText(plotdata))
# convert the data to grace file for plotting using xmgrace
print(pyalps.plot.makeGracePlot(plotdata))
# convert the data to gnuplot file for plotting using gnuplot
print(pyalps.plot.makeGnuplotPlot(plotdata))
#calculate the Binder cumulants using jackknife-analysis
binder = pyalps.DataSet()
binder.props = pyalps.dict_intersect([d[0].props for d in data])
binder.x = [d[0].props['T'] for d in data]
binder.y = [d[1].y[0] / (d[0].y[0] * d[0].y[0]) for d in data]
print(binder)
# ... and plot them
plt.figure()
pyalps.plot.plot(binder)
plt.xlabel('T')
plt.ylabel('Binder cumulant')
plt.show()
import numpy as np
import matplotlib.pyplot as plt
import pyalps.plot
## Please run the tutorial5a.py before this one
listobs = ['0', '2'] # we look at convergence of a single flavor (=0)
## load all results
data = pyalps.loadDMFTIterations(pyalps.getResultFiles(pattern='parm_u_*.h5'), measurements=listobs, verbose=True)
## create a figure for each BETA
grouped = pyalps.groupSets(pyalps.flatten(data), ['U', 'observable'])
for sim in grouped:
common_props = pyalps.dict_intersect([ d.props for d in sim ])
## rescale x-axis and set label
for d in sim:
d.x = d.x * d.props['BETA']/float(d.props['N'])
d.y *= -1.
d.props['label'] = 'it'+d.props['iteration']
## plot all iterations for this BETA
plt.figure()
plt.xlabel(r'$\tau$')
plt.ylabel(r'$-G_{flavor=%8s}(\tau)$' % common_props['observable'])
plt.title('DMFT-05: Orbitally Selective Mott Transition on the Bethe lattice: ' + r'$U = %.4s$' % common_props['U'])
pyalps.plot.plot(sim)
plt.legend()
plt.yscale("log")
print "Your simulations has not yet ended, please run this command again later."
else:
if desc.failed():
print "Your submission has failed"
sys.exit(-1)
result_files = pyalps.getResultFiles(prefix='parm1')
print result_files
print pyalps.loadObservableList(result_files)
data = pyalps.loadMeasurements(result_files,['|Magnetization|','Magnetization^2'])
print data
plotdata = pyalps.collectXY(data,'T','|Magnetization|')
plt.figure()
pyalps.plot.plot(plotdata)
plt.xlim(0,3)
plt.ylim(0,1)
plt.title('Ising model')
plt.show()
print pyalps.plot.convertToText(plotdata)
print pyalps.plot.makeGracePlot(plotdata)
print pyalps.plot.makeGnuplotPlot(plotdata)
binder = pyalps.DataSet()
binder.props = pyalps.dict_intersect([d[0].props for d in data])
binder.x = [d[0].props['T'] for d in data]
binder.y = [d[1].y[0]/(d[0].y[0]*d[0].y[0]) for d in data]
print binder
plt.figure()
pyalps.plot.plot(binder)
plt.xlabel('T')
plt.ylabel('Binder cumulant')
plt.show()
data = pyalps.loadEigenstateMeasurements(pyalps.getResultFiles(prefix=prefix))
# join all momenta
grouped = pyalps.groupSets(pyalps.flatten(data), ['J1', 'L', 'Sz_total'])
nd = []
for group in grouped:
ally = []
allx = []
for q in group:
ally += list(q.y)
allx += list(q.x)
r = pyalps.DataSet()
sel = np.argsort(ally)
r.y = np.array(ally)[sel]
r.x = np.array(allx)[sel]
r.props = pyalps.dict_intersect([q.props for q in group])
nd.append( r )
data = nd
# remove states in the s=1 sector from the s=0 sector
grouped = pyalps.groupSets(pyalps.flatten(data), ['J1', 'L'])
nd = []
for group in grouped:
if group[0].props['Sz_total'] == 0:
s0 = group[0]
s1 = group[1]
else:
s0 = group[1]
s1 = group[0]
s0 = pyalps.subtract_spectrum(s0, s1)
nd.append(s0)
#print the observables stored in those files:
print("The files contain the following mesurements:", end=' ')
print(pyalps.loadObservableList(result_files))
#load a selection of measurements:
data = pyalps.loadMeasurements(result_files,
['|Magnetization|', 'Magnetization^2'])
obschoose = lambda d, o: np.array(d)[np.nonzero(
[xx.props['observable'] == o for xx in d])]
binder = []
for dd in data:
magn2 = obschoose(dd, 'Magnetization^2')[0]
magnabs = obschoose(dd, '|Magnetization|')[0]
res = pyalps.DataSet()
res.props = pyalps.dict_intersect([d.props for d in dd])
res.x = np.array([magnabs.props['T']])
res.y = np.array([magn2.y[0] / (magnabs.y[0] * magnabs.y[0])])
res.props['observable'] = 'Binder cumulant'
binder.append(res)
binder = pyalps.collectXY(binder, 'T', 'Binder cumulant')
# ... and plot them
plt.figure()
pyalps.plot.plot(binder)
plt.xlabel('T')
plt.ylabel('Binder cumulant')
plt.show()