def compareEpsilon(testfiles, reffiles, tol_factor='auto', whatlist=None):
""" Compare results from diagonalization applications
returns True if test succeeded"""
if tol_factor == 'auto':
tol_factor = 1.0
testdata = pyalps.loadEigenstateMeasurements(testfiles)
refdata = pyalps.loadEigenstateMeasurements(reffiles)
if not testdata or not refdata:
if not testdata:
print(
"loadEigenstateMeasurements of file %s returned an empty list"
% testfiles)
if not refdata:
print(
"loadEigenstateMeasurements of file %s returned an empty list"
% reffiles)
return
# File level
compare_list = []
for testtask, reftask in zip(testdata, refdata):
try:
# ALPS applications
testfile = testtask[0][0].props['filename']
reffile = reftask[0][0].props['filename']
except AttributeError:
# workaround for MAQUIS DMRG which doesn't have sectors
testtask = [testtask]
reftask = [reftask]
testfile = testtask[0][0].props['filename']
reffile = reftask[0][0].props['filename']
# Ensure we compare equivalent tasks
if len(testtask) != len(reftask):
raise Exception("Comparison Error: test and reference data have \
different number of sectors\n\
(Have both reference and test data been pyalps.evaluate'd?)"
)
# Sector level
#print("\ncomparing file " + testfile + " against file " + reffile)
compare_sector = []
for testsector, refsector in zip(testtask, reftask):
# Observables
# Select only observables from whatlist if specified
if whatlist:
notfoundtest = [
w for w in whatlist
if w not in [o.props['observable'] for o in testsector]
]
if notfoundtest:
print(
"The following observables specified for comparison\n\
have not been found in test results:")
print("File:", testfile)
print(notfoundtest)
sys.exit(1)
notfoundref = [
w for w in whatlist
if w not in [o.props['observable'] for o in refsector]
]
if notfoundref:
print(
"The following observables specified for comparison\n\
have not been found in reference results:")
print("File:", reffile)
print(notfoundref)
sys.exit(1)
testsector = [
o for o in testsector if o.props['observable'] in whatlist
]
refsector = [
o for o in refsector if o.props['observable'] in whatlist
]
for testobs, refobs in zip(testsector, refsector):
# Scalar observables
if pyalps.size(testobs.y[0]) == 1:
tol = max(10e-12,
np.abs(refobs.y[0]) * 10e-12) * tol_factor
diff = np.abs(testobs.y[0] - refobs.y[0])
compare_sector.append(obsdict(tol, diff, testobs.props))
# Array valued observables
else:
tol_list = []
diff_list = []
for (ty, ry) in zip(testobs.y[0], refobs.y[0]):
tol_list.append(max(10e-12, ry * 10e-12))
diff_list.append(np.abs(ty - ry))
maxdiff = max(diff_list)
tol = tol_list[diff_list.index(maxdiff)] * tol_factor
compare_sector.append(obsdict(tol, maxdiff, testobs.props))
compare_list.append(compare_sector)
#writeTest2stdout(compare_list) # or a file, if that has been specified
succeed_list = [
obs['passed'] for obs_list in compare_list for obs in obs_list
]
return False not in succeed_list, compare_list
def compareMC(testfiles, reffiles, tol_factor='auto', whatlist=None):
""" Compare results of Monte Carlo Simulations
returns True if test succeeded"""
if tol_factor == 'auto':
tol_factor = 2.0
testdata = pyalps.loadMeasurements(testfiles)
refdata = pyalps.loadMeasurements(reffiles)
if len(testdata) != len(refdata):
raise Exception(
"Comparison Error: test and reference data differ in number of tasks"
)
# File level
compare_list = []
for testtask, reftask in zip(testdata, refdata):
testfile = testtask[0].props['filename']
reffile = reftask[0].props['filename']
# Ensure we compare equivalent tasks
if len(testtask) != len(reftask):
raise Exception("Comparison Error: test and reference data have \
different number of observables\n\
(Have both reference and test data been evaluated?)")
# Observables
# Select only observables from whatlist if specified
if whatlist:
notfoundtest = [
w for w in whatlist
if w not in [o.props['observable'] for o in testtask]
]
if notfoundtest:
print(
"The following observables specified for comparison\nhave not been found in test results:"
)
print("File:", testfile)
print(notfoundtest)
sys.exit(1)
notfoundref = [
w for w in whatlist
if w not in [o.props['observable'] for o in reftask]
]
if notfoundref:
print(
"The following observables specified for comparison\nhave not been found in reference results:"
)
print("File:", reffile)
print(notfoundref)
sys.exit(1)
testtask = [
o for o in testtask if o.props['observable'] in whatlist
]
reftask = [o for o in reftask if o.props['observable'] in whatlist]
#print("\ncomparing file " + testfile + " against file " + reffile)
compare_obs = []
for testobs, refobs in zip(testtask, reftask):
# Scalar observables
if pyalps.size(testobs.y[0]) == 1:
testerr = testobs.y[0].error
referr = refobs.y[0].error
tol = np.sqrt(testerr**2 + referr**2) * tol_factor
diff = np.abs(testobs.y[0].mean - refobs.y[0].mean)
compare_obs.append(obsdict(tol, diff, testobs.props))
# Array valued observables
else:
tol_list = []
diff_list = []
for (ty, ry) in zip(testobs.y[0], refobs.y[0]):
tol_list.append(
np.sqrt(ty.error**2 + ry.error**2) * tol_factor)
diff_list.append(np.abs(ty - ry))
maxdiff = max(diff_list)
tol = tol_list[diff_list.index(maxdiff)] * tol_factor
compare_obs.append(obsdict(tol, maxdiff, testobs.props))
compare_list.append(compare_obs)
#writeTest2stdout(compare_list) # or a file, if that has been specified
succeed_list = [
obs['passed'] for obs_list in compare_list for obs in obs_list
]
return False not in succeed_list, compare_list
def compareMixed(testfiles, reffiles, tol_factor='auto', whatlist=None):
""" Compare results of QWL, DMRG (ALPS)
returns True if test succeeded"""
if tol_factor == 'auto':
tol_factor = 2.0
testdata = pyalps.loadMeasurements(testfiles)
refdata = pyalps.loadMeasurements(reffiles)
if len(testdata) != len(refdata):
raise Exception(
"Comparison Error: test and reference data differ in number of tasks"
)
# This is needed by the dmrg example
try:
testeig = pyalps.loadEigenstateMeasurements(testfiles)
refeig = pyalps.loadEigenstateMeasurements(reffiles)
for ttask, rtask, teig, reig in zip(testdata, refdata, testeig,
refeig):
ttask += teig
rtask += reig
except RuntimeError:
pass
# File level
compare_list = []
for testtask, reftask in zip(testdata, refdata):
testfile = testtask[0].props['filename']
reffile = reftask[0].props['filename']
# Ensure we compare equivalent tasks
if len(testtask) != len(reftask):
raise Exception("Comparison Error: test and reference data have \
different number of observables\n")
# Observables
# Select only observables from whatlist if specified
if whatlist:
notfoundtest = [
w for w in whatlist
if w not in [o.props['observable'] for o in testtask]
]
if notfoundtest:
print(
"The following observables specified for comparison\nhave not been found in test results:"
)
print("File:", testfile)
print(notfoundtest)
sys.exit(1)
notfoundref = [
w for w in whatlist
if w not in [o.props['observable'] for o in reftask]
]
if notfoundref:
print(
"The following observables specified for comparison\nhave not been found in reference results:"
)
print("File:", reffile)
print(notfoundref)
sys.exit(1)
testtask = [
o for o in testtask if o.props['observable'] in whatlist
]
reftask = [o for o in reftask if o.props['observable'] in whatlist]
#print("\ncomparing file " + testfile + " against file " + reffile)
compare_obs = []
for testobs, refobs in zip(testtask, reftask):
# MC if it succeeds
try:
# Scalar observables
if pyalps.size(testobs.y) == 1:
testerr = testobs.y[0].error
referr = refobs.y[0].error
tol = np.sqrt(testerr**2 + referr**2) * tol_factor
diff = np.abs(testobs.y[0].mean - refobs.y[0].mean)
compare_obs.append(obsdict(tol, diff, testobs.props))
# Array valued observables
else:
tol_list = []
diff_list = []
for (ty, ry) in zip(testobs.y, refobs.y):
tol_list.append(
np.sqrt(ty.error**2 + ry.error**2) * tol_factor)
diff_list.append(np.abs(ty - ry))
maxdiff = max(diff_list)
tol = tol_list[diff_list.index(maxdiff)] * tol_factor
compare_obs.append(obsdict(tol, maxdiff, testobs.props))
# Epsilon otherwise
except AttributeError:
# Scalar observables
if pyalps.size(testobs.y) == 1:
tol = max(10e-12,
np.abs(refobs.y[0]) * 10e-12) * tol_factor
diff = np.abs(testobs.y[0] - refobs.y[0])
compare_obs.append(obsdict(tol, diff, testobs.props))
# Array valued observables
else:
tol_list = []
diff_list = []
for (ty, ry) in zip(testobs.y, refobs.y):
tol_list.append(max(10e-12, ry * 10e-12))
diff_list.append(np.abs(ty - ry))
maxdiff = max(diff_list)
tol = tol_list[diff_list.index(maxdiff)] * tol_factor
compare_obs.append(obsdict(tol, maxdiff, testobs.props))
compare_list.append(compare_obs)
#writeTest2stdout(compare_list) # or a file, if that has been specified
succeed_list = [
obs['passed'] for obs_list in compare_list for obs in obs_list
]
return False not in succeed_list, compare_list
#prepare the input parameters
parms = [{
'LATTICE': "chain lattice",
'MODEL': "spin",
'local_S': 1,
'J': 1,
'L': 4,
'CONSERVED_QUANTUMNUMBERS': 'Sz',
'MEASURE_STRUCTURE_FACTOR[Structure Factor S]': 'Sz',
'MEASURE_CORRELATIONS[Diagonal spin correlations]=': 'Sz',
'MEASURE_CORRELATIONS[Offdiagonal spin correlations]': 'Splus:Sminus'
}]
#write the input file and run the simulation
input_file = pyalps.writeInputFiles('ed01a', parms)
res = pyalps.runApplication('sparsediag', input_file)
#load all measurements for all states
data = pyalps.loadEigenstateMeasurements(pyalps.getResultFiles(prefix='ed01a'))
# print properties of ground states in all sectors:
for sector in data[0]:
print '\nSector with Sz =', sector[0].props['Sz'],
print 'and k =', sector[0].props['TOTAL_MOMENTUM']
for s in sector:
if pyalps.size(s.y[0]) == 1:
print s.props['observable'], ' : ', s.y[0]
else:
for (x, y) in zip(s.x, s.y[0]):
print s.props['observable'], '(', x, ') : ', y