def GetProperties(self,
flist,
proppath='/parameters',
respath='/simulation/results',
verbose=False):
fs = self.GetFileNames(flist)
resultfiles = []
for f in fs:
try:
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if verbose: log("Loading from file" + f)
rfile = ResultFile(f)
rfile.props = self.ReadParameters(proppath)
try:
obs = self.GetObservableList(respath)
rfile.props["ObservableList"] = [
pt.hdf5_name_decode(x) for x in obs
]
except:
pass
resultfiles.append(rfile)
except Exception as e:
log(e)
log(traceback.format_exc())
return resultfiles
def loadTimeEvolution(flist,
globalproppath='/parameters',
resroot='/timesteps/',
localpropsuffix='/parameters',
measurements=None):
ll = Hdf5Loader()
data = []
#loop over files
for f in flist:
try:
#open the file and open the results root group
h5file = h5.archive(f, 'r')
#enumerate the subgroups
L = h5file.list_children(resroot)
#Create an iterator of length the number of subgroups
stepper = [i + 1 for i in range(len(L))]
#Read in global props
globalprops = ll.GetProperties([f], globalproppath)
for d in stepper:
#Get the measurements from the numbered subgroups
locdata=ll.ReadMeasurementFromFile([f],proppath=resroot+str(d)+localpropsuffix, \
respath=resroot+str(d)+'/results', measurements=measurements)
#Append the global props to the local props
for i in range(len(locdata[0])):
locdata[0][i].props.update(globalprops[0].props)
#Extend the total dataset with this data
data.extend(locdata)
except Exception as e:
log(e)
log(traceback.format_exc())
return data
def ReadDMFTIterations(self,
flist,
observable='G_tau',
measurements='0',
proppath='/parameters',
respath='/simulation/iteration',
verbose=False):
fs = self.GetFileNames(flist)
fileset = []
for f in fs:
try:
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if verbose: log("Loading from file " + f)
list_ = self.GetObservableList(respath + '/1/results/' +
observable + '/')
#grp = self.h5f.require_group(respath)
params = self.ReadParameters(proppath)
obslist = [
pt.hdf5_name_encode(obs) for obs in measurements
if pt.hdf5_name_encode(obs) in list_
]
iterationset = []
for it in self.h5f.list_children(respath):
obsset = []
for m in obslist:
try:
if verbose: log("Loading " + m)
d = DataSet()
size = 0
path = it + '/results/' + observable + '/' + m
if "mean" in self.h5f.list_children(respath + '/' +
path):
if self.h5f.is_scalar(respath + '/' + path +
'/mean/value'):
size = 1
obs = self.h5f[respath + '/' + path +
'/mean/value']
d.y = np.array([obs])
else:
obs = self.h5f[respath + '/' + path +
'/mean/value']
size = len(obs)
d.y = obs
d.x = np.arange(0, size)
d.props['hdf5_path'] = respath + "/" + path
d.props['observable'] = pt.hdf5_name_decode(m)
d.props['iteration'] = it
d.props.update(params)
except AttributeError:
log("Could not create DataSet")
pass
obsset.append(d)
iterationset.append(obsset)
fileset.append(iterationset)
except Exception as e:
log(e)
log(traceback.format_exc())
return fileset
def ReadSpectrumFromFile(self,
flist,
proppath='/parameters',
respath='/spectrum',
verbose=False):
fs = self.GetFileNames(flist)
sets = []
for f in fs:
try:
fileset = []
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if verbose: log("Loading from file " + f)
params = self.ReadParameters(proppath)
if 'energies' in self.h5f.list_children(respath):
try:
d = DataSet()
d.props['hdf5_path'] = respath
d.props['observable'] = 'spectrum'
d.y = self.h5f[respath + '/energies']
d.x = range(len(d.y))
d.props.update(params)
try:
d.props.update(
self.ReadParameters('quantumnumbers'))
except:
if verbose: log("no quantumnumbers stored ")
pass
fileset.append(d)
except AttributeError:
pass
if 'sectors' in self.h5f.list_children(respath):
for secnum in self.h5f.list_children(respath + '/sectors'):
try:
d = DataSet()
secpath = respath + '/sectors/' + secnum
d.props['hdf5_path'] = secpath
d.props['observable'] = 'spectrum'
d.y = self.h5f[secpath + '/energies']
d.x = range(len(d.y))
d.props.update(params)
try:
d.props.update(
self.ReadParameters(secpath +
'/quantumnumbers'))
except:
if verbose: log("no quantumnumbers stored ")
pass
fileset.append(d)
except AttributeError:
log("Could not create DataSet")
pass
sets.append(fileset)
except Exception as e:
log(e)
log(traceback.format_exc())
return sets
def compute(self):
globalproppath = self.getInputFromPort(
'GlobalPropertyPath') if self.hasInputFromPort(
'GlobalPropertyPath') else "/parameters"
localpropsuffix = self.getInputFromPort(
'LocalPropertySuffix') if self.hasInputFromPort(
'LocalPropertySuffix') else "/parameters"
resroot = self.getInputFromPort('ResultPath') if self.hasInputFromPort(
'ResultPath') else "/timesteps/"
loader = Hdf5Loader()
self.getInputFromPort('ResultFiles')
if self.hasInputFromPort('ResultFiles'):
files = [
f.props["filename"]
for f in self.getInputFromPort('ResultFiles')
]
datasets = []
if self.hasInputFromPort('Measurements'):
#loop over files
for f in files:
try:
#open the file and open the results root group
h5file = h5.archive(f, 'r')
#enumerate the subgroups
L = h5file.list_children(resroot)
#Create an iterator of length the number of subgroups
stepper = [i + 1 for i in range(len(L))]
#Read in global props
globalprops = loader.GetProperties([f], globalproppath)
for d in stepper:
#Get the measurements from the numbered subgroups
locdata=loader.ReadMeasurementFromFile([f],proppath=resroot+str(d)+localpropsuffix, \
respath=resroot+str(d)+'/results', measurements=self.getInputFromPort('Measurements'))
#Append the global props to the local props
for i in range(len(locdata[0])):
locdata[0][i].props.update(globalprops[0].props)
#Extend the total dataset with this data
datasets.extend(locdata)
except Exception, e:
debug.log(traceback.format_exc())
#
# You should have received a copy of the ALPS Library License along with
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also
# available from http://alps.comp-phys.org/.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# ****************************************************************************
import pyalps as alps
import pyalps.hdf5 as h5
import pyalps.alea as alea
iar = h5.archive('loadobs.h5', 'r')
for name in iar.list_children('/simulation/results'):
if iar.is_scalar('/simulation/results/' + alps.hdf5_name_encode(name) +
'/mean/value'):
obs = alea.MCScalarData()
else:
obs = alea.MCVectorData()
obs.load('loadobs.h5',
'/simulation/results/' + alps.hdf5_name_encode(name))
print name + ": " + str(obs)
def loadProperties(fname):
ar = h5.archive(fname)
return ar['/parameters']
#Get the measurements from the numbered subgroups
locdata=loader.ReadMeasurementFromFile([f],proppath=resroot+str(d)+localpropsuffix, \
respath=resroot+str(d)+'/results', measurements=self.getInputFromPort('Measurements'))
#Append the global props to the local props
for i in range(len(locdata[0])):
locdata[0][i].props.update(globalprops[0].props)
#Extend the total dataset with this data
datasets.extend(locdata)
except Exception, e:
debug.log(traceback.format_exc())
else:
#loop over files
for f in files:
try:
#open the file and open the results root group
h5file = h5.archive(f, 'r')
#enumerate the subgroups
L = h5file.list_children(resroot)
#Create an iterator of length the number of subgroups
stepper = [i + 1 for i in range(len(L))]
#Read in global props
globalprops = loader.GetProperties([f], globalproppath)
for d in stepper:
#Get the measurements from the numbered subgroups
locdata=loader.ReadMeasurementFromFile([f],proppath=resroot+str(d)+localpropsuffix, \
respath=resroot+str(d)+'/results', measurements=None)
#Append the global props to the local props
for i in range(len(locdata[0])):
locdata[0][i].props.update(globalprops[0].props)
#Extend the total dataset with this data
datasets.extend(locdata)
# the terms of the license, either version 1 or (at your option) any later
# version.
#
# You should have received a copy of the ALPS Library License along with
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also
# available from http://alps.comp-phys.org/.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
# DEALINGS IN THE SOFTWARE.
#
# ****************************************************************************
import pyalps as alps
import pyalps.hdf5 as h5
import pyalps.alea as alea
iar = h5.archive('loadobs.h5', 'r')
for name in iar.list_children('/simulation/results'):
if iar.is_scalar('/simulation/results/' + alps.hdf5_name_encode(name) + '/mean/value'):
obs = alea.MCScalarData()
else:
obs = alea.MCVectorData()
obs.load('loadobs.h5', '/simulation/results/' + alps.hdf5_name_encode(name))
print name + ": " + str(obs)
def ReadMeasurementFromFile(self,
flist,
proppath='/parameters',
respath='/simulation/results',
measurements=None,
verbose=False):
fs = self.GetFileNames(flist)
sets = []
for f in fs:
try:
fileset = []
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if verbose: log("Loading from file " + f)
list_ = self.GetObservableList(respath)
params = self.ReadParameters(proppath)
obslist = []
if measurements == None:
obslist = list_
else:
obslist = [
pt.hdf5_name_encode(obs) for obs in measurements
if pt.hdf5_name_encode(obs) in list_
]
for m in obslist:
if verbose: log("Loading " + m)
size = 0
xmin = 0
xstep = 1
x = None
if "histogram" in self.h5f.list_children(respath + '/' +
m):
obs = self.h5f[respath + '/' + m + '/histogram']
xmin = self.h5f[respath + '/' + m + '/@min']
xstep = self.h5f[respath + '/' + m + '/@stepsize']
size = len(obs)
x = np.arange(xmin, xmin + xstep * size, xstep)
elif "error" in self.h5f.list_children(respath + '/' + m +
'/mean'):
if self.h5f.is_scalar(respath + '/' + m +
'/mean/value'):
obs = pa.MCScalarData()
obs.load(self.h5fname, respath + '/' + m)
obs = np.array([obs])
size = 1
if obs[0].count == 0:
obs = None
else:
obs = None
if not self.h5f.is_group(
respath + '/' + m +
'/timeseries'): # check for simple binning
obs = np.array(self.h5f[respath + '/' + m +
'/mean/value'])
if 'L' in params: # ugly fix... really ugly
L = int(params['L'])
if L == obs.size:
params['origin'] = [(L - 1.) / 2.]
if L**2 == obs.size: # dimension 2
obs = obs.reshape([L, L])
params['origin'] = [(L - 1.) / 2.,
(L - 1.) / 2.]
elif L**3 == obs.size: # dimension 3
obs = obs.reshape([L, L, L])
params['origin'] = [(L - 1.) / 2.,
(L - 1.) / 2.,
(L - 1.) / 2.]
size = obs.size
else:
obs = pa.MCVectorData()
obs.load(self.h5fname, respath + '/' + m)
size = len(obs.mean)
if obs.count == 0:
obs = None
else:
if self.h5f.is_scalar(respath + '/' + m +
'/mean/value'):
obs = self.h5f[respath + '/' + m + '/mean/value']
obs = np.array([obs])
size = 1
else:
obs = self.h5f[respath + '/' + m + '/mean/value']
size = len(obs)
if "labels" in self.h5f.list_children(respath + '/' +
m) and x is None:
x = parse_labels(self.h5f[respath + '/' + m +
'/labels'])
elif x is None:
x = np.arange(xmin, xmin + xstep * size, xstep)
try:
if obs is not None:
d = DataSet()
d.y = obs
d.x = x
d.props['hdf5_path'] = respath + "/" + m
d.props['observable'] = pt.hdf5_name_decode(m)
d.props.update(params)
fileset.append(d)
except AttributeError:
log("Could not create DataSet")
sets.append(fileset)
except Exception as e:
log(e)
log(traceback.format_exc())
return sets
def impl_calculation(name, save_path, calculate):
usage = "Usage: %prog [options] FILE [FILE [...]]"
parser = OptionParser(usage=usage)
parser.add_option("-v",
"--verbose",
action="store_true",
dest="verbose",
help="print detailed information")
parser.add_option("-w",
"--write",
action="store_true",
dest="write",
help="write the result(s) back into the file(s)")
parser.add_option(
"-n",
"--name",
action="append",
metavar="VAR",
dest="variables",
help=
"variable name, can be specified multiple times [default: all variables]"
)
parser.add_option(
"-p",
"--path",
action="store",
metavar="HDF5-PATH",
dest="path",
help=
"hdf5-path where the data is stored [default: \"/simulation/results\"]"
)
parser.set_defaults(verbose=False,
write=False,
variables=[],
path="/simulation/results")
(options, args) = parser.parse_args()
if len(args) == 0:
parser.print_help()
exit()
variables = options.variables
for filestring in args:
ar = h5.archive(filestring, 1)
if len(options.variables) == 0:
variables = ar.list_children(options.path)
if options.verbose:
print("Variables in file " + filestring + ": " +
" ".join(variables))
for variablestring in variables:
if ar.dimensions(options.path + "/" +
pyalps.hdf5_name_encode(variablestring) +
"/timeseries/data") == 1:
obs = alea.MCScalarData()
#_save = mcanalyze.write_dim_0
#vector_save = mcanalyze.write_dim_1
else:
obs = alea.MCVectorData()
#scalar_save = mcanalyze.write_dim_1
#vector_save = mcanalyze.write_dim_2
obs.load(
filestring,
options.path + "/" + pyalps.hdf5_name_encode(variablestring))
result = calculate(obs)
if options.verbose:
print("The " + name + " of variable " + variablestring +
" in file " + filestring + " is: " + str(result))
if options.write:
ar.write(
options.path + "/" +
pyalps.hdf5_name_encode(variablestring) + "/" + save_path,
result)
print("Done")
if len(childs) != 7:
raise Exception('invalid length of \'/\'')
if len(ar.extent("/int")) != 1 or ar.extent("/int")[0] != 1 or len(ar.extent("/cplx")) != 1 or ar.extent("/cplx")[0] != 1:
raise Exception('invalid scalar extent')
if len(ar.extent("/np/int")) != 1 or ar.extent("/cplx")[0] != 1 or len(ar.extent("/np/cplx")) != 2 or ar.extent("/np/cplx")[0] != 2 or ar.extent("/np/cplx")[1] != 2:
raise Exception('invalid array extent')
if type(i) != int or type(d) != float or type(c) != complex or type(s) != str:
raise Exception('invalid type')
if i != 9 or d - 9.123 > 0.001 or s != "test" or np.any(n != np.array([1, 2, 3])):
raise Exception('invalid scalar value')
if np.any(x[0] != np.array([1 + 1j,2 +2j])) or np.any(x[1] != np.array([3 + 3j,4 + 4j])):
raise Exception('invalid array value')
try:
oar = h5.archive("py.h5", 'w')
write(oar)
del oar
iar = h5.archive("py.h5", 'r')
if iar.is_complex("/int") or not iar.is_complex("/cplx") or not iar.extent("/np/cplx"):
raise Exception('invalid complex detection')
read(iar)
del iar
ar = h5.archive("py.h5", 'w')
write(ar)
read(ar)
del ar
print "SUCCESS"
def ReadDiagDataFromFile(self,
flist,
proppath='/parameters',
respath='/spectrum',
measurements=None,
index=None,
loadIterations=False,
verbose=False):
fs = self.GetFileNames(flist)
sets = []
for f in fs:
try:
fileset = []
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if verbose: log("Loading from file" + f)
params = self.ReadParameters(proppath)
if 'results' in self.h5f.list_children(respath):
list_ = self.GetObservableList(respath + '/results')
if measurements == None:
obslist = list_
else:
obslist = [
pt.hdf5_name_encode(obs) for obs in measurements
if pt.hdf5_name_encode(obs) in list_
]
if loadIterations == True:
if "iteration" in self.h5f.list_children(respath +
'/results'):
fileset.append(
self.GetIterations(respath + '/results',
params, measurements, index,
verbose))
else:
for m in obslist:
if "mean" in self.h5f.list_children(respath +
'/results/' +
m):
try:
if verbose: log("Loading" + m)
d = DataSet()
secresultspath = respath + '/results/' + m
d.props['hdf5_path'] = secresultspath
d.props[
'observable'] = pt.hdf5_name_decode(m)
if index == None:
d.y = self.h5f[secresultspath +
'/mean/value']
d.x = np.arange(0, len(d.y))
else:
try:
d.y = self.h5f[
secresultspath +
'/mean/value'][index]
except:
pass
if "labels" in self.h5f.list_children(
secresultspath):
d.x = parse_labels(
self.h5f[secresultspath +
'/labels'])
else:
d.x = np.arange(0, len(d.y))
d.props.update(params)
fileset.append(d)
except AttributeError:
log("Could not create DataSet")
if loadIterations == True:
if "iteration" in self.h5f.list_children(respath):
fileset.append(
self.GetIterations(respath, params, measurements,
index, verbose))
if 'sectors' in self.h5f.list_children(respath):
list_ = self.GetObservableList(respath +
'/sectors/0/results')
if measurements == None:
obslist = list_
else:
obslist = [
pt.hdf5_name_encode(obs) for obs in measurements
if pt.hdf5_name_encode(obs) in list_
]
for secnum in self.h5f.list_children(respath + '/sectors'):
sector_sets = []
for m in obslist:
if "mean" in self.h5f.list_children(respath +
'/sectors/' +
secnum +
'/results/' +
m):
try:
if verbose: log("Loading" + m)
d = DataSet()
secpath = respath + '/sectors/' + secnum
secresultspath = respath + '/sectors/' + secnum + '/results/' + m
d.props['hdf5_path'] = secresultspath
d.props[
'observable'] = pt.hdf5_name_decode(m)
if index == None:
d.y = self.h5f[secresultspath +
'/mean/value']
d.x = np.arange(0, len(d.y))
else:
try:
d.y = self.h5f[
secresultspath +
'/mean/value'][index]
except:
pass
if "labels" in self.h5f.list_children(
secresultspath):
d.x = parse_labels(
self.h5f[secresultspath +
'/labels'])
else:
d.x = np.arange(0, len(d.y))
d.props.update(params)
try:
d.props.update(
self.ReadParameters(
secpath + '/quantumnumbers'))
except:
if verbose:
log("no quantumnumbers stored ")
pass
sector_sets.append(d)
except AttributeError:
log("Could not create DataSet")
pass
fileset.append(sector_sets)
sets.append(fileset)
except RuntimeError:
raise
except Exception as e:
log(e)
log(traceback.format_exc())
return sets
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import pyalps.hdf5 as hdf5
import pyalps.ngs as ngs
import sys
p = ngs.params({'val1': 42, 'val2': '42', 'a': 1, 'x': 2, 'b': 3})
print type(p["val1"]), type(p["val2"]), type(p["undefined"])
oar = hdf5.archive('parms1.h5', 'w')
p.save(oar) # does not use path '/parameters'
del oar
oar = hdf5.archive('parms2.h5', 'w')
print p.keys(), p.values()
for key in p.keys():
print key
oar['parameters/' + key] = p[key]
del oar
iar = hdf5.archive('parms2.h5', 'r')
p.load(iar)
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import numpy as np
import pyalps.hdf5 as hdf5
## Python 3 does not have `long` type anymore
import sys
if sys.version_info > (3,):
long = int
ar = hdf5.archive('pyngs.h5', 'w')
a = np.array([1, 2, 3]);
b = np.array([1.1, 2.0, 3.5]);
c = np.array([1.1 + 1j, 2.0j, 3.5]);
d = {"a": a, 2 + 3j: "foo"}
ar["/list"] = [1, 2, 3]
ar["/list2"] = [[[1, 2], [3, 4]], [[1, 2], [3, 4]], [[1, 2], [3, 4]], [[1, 2], [3, 4]]]
ar["/tuple"] = (1, 2, 3)
ar["/dict"] = {"scalar": 1, "numpy": a, "numpycpx": c, "list": [1, 2, 3], "string": "str", 1: 1, 4: d}
ar["/numpy"] = a
ar["/numpy2"] = b
ar["/numpy3"] = c
ar["/numpyel"] = a[0]
ar["/numpyel2"] = b[0]
ar["/numpyel3"] = c[0]
def load(self, filename):
with hdf5.archive(filename, 'r') as ar:
self = ar['/']
def save(self, filename):
with hdf5.archive(filename, 'w') as ar:
ar['/'] = self
for j in range(0, FLAVORS, 2):
if (j != i):
udata[i * FLAVORS +
j] = Up - J # Hubbard repulsion interband same spin
udata[(i + 1) * FLAVORS +
(j +
1)] = Up - J # Hubbard repulsion interband same spin
udata[i * FLAVORS +
(j +
1)] = Up # Hubbard repulsion interband opposite spin
udata[(i + 1) * FLAVORS +
j] = Up # Hubbard repulsion interband opposite spin
return udata.reshape(FLAVORS, FLAVORS)
ar = archive('hyb.param.out.h5',
'rw') #read the parameters from the output file
parms = ar['/parameters'] # returns a python dict
n_orb = parms['N_ORBITALS']
a = array(zeros(n_orb**2)).reshape(n_orb,
n_orb) # density-density correlation matrix
for i in range(1, n_orb): # i>j
for j in range(i):
a[i][j] = ar['/simulation/results/nn_%i_%i/mean/value' % (i, j)]
for i in range(n_orb): # i==j
a[i][i] = ar['/simulation/results/density_%i/mean/value' % i]
for j in range(1, n_orb): # the matrix is symmetric
for i in range(j): # i<j
def save(self, filename):
with hdf5.archive(filename, 'w') as ar:
ar['/simulation/realizations/0/clones/0'] = self
def load(self, filename):
with hdf5.archive(filename, 'r') as ar:
self = ar['/simulation/realizations/0/clones/0']
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also #
# available from http://alps.comp-phys.org/. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import numpy as np
import pyalps.hdf5 as hdf5
import os
ar=hdf5.archive('foo%d.h5', 'al')
s=2**10
while s < 2**29:
print s
a = np.empty(s)
ar[str(s)] = a
s *= 2
i = 0
while os.path.isfile('foo%d.h5'%i):
os.remove('foo%d.h5'%i)
i += 1
if len(childs) != 7:
raise Exception('invalid length of \'/\'')
if len(ar.extent("/int")) != 1 or ar.extent("/int")[0] != 1 or len(ar.extent("/cplx")) != 1 or ar.extent("/cplx")[0] != 1:
raise Exception('invalid scalar extent')
if len(ar.extent("/np/int")) != 1 or ar.extent("/cplx")[0] != 1 or len(ar.extent("/np/cplx")) != 2 or ar.extent("/np/cplx")[0] != 2 or ar.extent("/np/cplx")[1] != 2:
raise Exception('invalid array extent')
if type(i) != int or type(d) != float or type(c) != complex or type(s) != str:
raise Exception('invalid type')
if i != 9 or d - 9.123 > 0.001 or s != "test" or np.any(n != np.array([1, 2, 3])):
raise Exception('invalid scalar value')
if np.any(x[0] != np.array([1 + 1j,2 +2j])) or np.any(x[1] != np.array([3 + 3j,4 + 4j])):
raise Exception('invalid array value')
try:
oar = h5.archive("py.h5", 'w')
write(oar)
del oar
iar = h5.archive("py.h5", 'r')
if iar.is_complex("/int") or not iar.is_complex("/cplx") or not iar.extent("/np/cplx"):
raise Exception('invalid complex detection')
read(iar)
del iar
ar = h5.archive("py.h5", 'w')
write(ar)
read(ar)
del ar
print("SUCCESS")
def ReadBinningAnalysis(self,
flist,
measurements=None,
proppath='/parameters',
respath=None,
verbose=False):
fs = self.GetFileNames(flist)
sets = []
for f in fs:
try:
fileset = []
if verbose: log('loading from file ' + f)
self.h5f = h5.archive(f, 'r')
self.h5fname = f
if respath == None:
respath = "/simulation/results"
list_ = self.GetObservableList(respath)
# this is exception-safe in the sense that it's also required in the line above
#grp = self.h5f.require_group(respath)
params = self.ReadParameters(proppath)
obslist = []
if measurements == None:
obslist = list_
else:
obslist = [
pt.hdf5_name_encode(obs) for obs in measurements
if pt.hdf5_name_encode(obs) in list_
]
for m in obslist:
try:
d = DataSet()
if "timeseries" in self.h5f.list_children(respath +
'/' + m):
k = self.h5f.list_children(respath + '/' + m +
'/timeseries')
if "logbinning" in k and "logbinning2" in k and "logbinning_counts" in k:
if verbose: log("Loading" + m)
bins = self.h5f[respath + '/' + m +
'/timeseries/logbinning'][0:-7]
bins2 = self.h5f[
respath + '/' + m +
'/timeseries/logbinning2'][0:-7]
counts = self.h5f[
respath + '/' + m +
'/timeseries/logbinning_counts'][0:-7]
scale = 1
for i in range(len(counts)):
mean = bins[i] / (counts[i] * scale)
mean2 = bins2[i] / counts[i]
bins2[i] = np.sqrt(
(mean2 - mean * mean) / counts[i])
scale *= 2
d.y = bins2
d.x = np.arange(0, len(d.y))
d.props['hdf5_path'] = respath + m
d.props[
'observable'] = 'binning analysis of ' + pt.hdf5_name_decode(
m)
d.props.update(params)
if verbose:
log(' loaded binnig analysis for ' + m)
fileset.append(d)
except AttributeError:
log("Could not create DataSet")
sets.append(fileset)
except Exception as e:
log(e)
log(traceback.format_exc())
return sets
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also #
# available from http://alps.comp-phys.org/. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import numpy as np
import pyalps.hdf5 as hdf5
import os
ar = hdf5.archive('foo%d.h5', 'al')
s = 2**10
while s < 2**29:
print(s)
a = np.empty(s)
ar[str(s)] = a
s *= 2
i = 0
while os.path.isfile('foo%d.h5' % i):
os.remove('foo%d.h5' % i)
i += 1
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import pyalps.hdf5 as hdf5
import pyalps.ngs as ngs
import sys
p = ngs.params({
'val1' : 42,
'val2' : '42',
'a' : 1,
'x' : 2,
'b' : 3
})
print type(p["val1"]), type(p["val2"]), type(p["undefined"])
oar = hdf5.archive('parms1.h5', 'w')
p.save(oar) # does not use path '/parameters'
del oar
oar = hdf5.archive('parms2.h5', 'w')
print p.keys(), p.values()
for key in p.keys():
print key
oar['parameters/' + key] = p[key]
del oar
iar = hdf5.archive('parms2.h5', 'r')
p.load(iar)
for n in range(parms['N_MATSUBARA']):
w=(2*n+1)*pi/parms['BETA']
g.append(2.0/(I*w+mu+I*sqrt(4*parms['t']**2-(I*w+mu)**2))) # noninteracting Green's function on Bethe lattice
delta=[]
for i in range(parms['N_TAU']+1):
tau=i*parms['BETA']/parms['N_TAU']
g0tau=0.0;
for n in range(parms['N_MATSUBARA']):
iw=complex(0.0,(2*n+1)*pi/parms['BETA'])
g0tau+=((g[n]-1.0/iw)*exp(-iw*tau)).real # Fourier transform with tail subtracted
g0tau *= 2.0/parms['BETA']
g0tau += -1.0/2.0 # add back contribution of the tail
delta.append(parms['t']**2*g0tau) # delta=t**2 g
# write hybridization function to hdf5 archive (solver input)
ar=archive(parms['DELTA'],'w')
for m in range(parms['N_ORBITALS']):
ar['/Delta_%i'%m]=delta
del ar
print("generating retarded interaction...")
l =parms['lambda']
w0 =parms['w0']
beta=parms['BETA']
K = lambda tau: - (l**2)*(cosh(w0*(beta/2.0-tau))/sinh(w0*beta/2.0) - cosh(w0*beta/2.0)/sinh(w0*beta/2.0) )/(w0*w0)
Kp = lambda tau: + (l**2)*(sinh(w0*(beta/2.0-tau))/sinh(w0*beta/2.0))/w0
print("U_screened =", parms['U'] - 2*l**2/w0)
k_tau=[]
'N_HISTOGRAM_ORDERS':
50, # maximum order for the perturbation order histogram
'N_TAU':
N_TAU, # number of imaginary time points (tau_0=0, tau_N_TAU=BETA)
'N_MATSUBARA':
int(N_TAU / (2 * pi)), # number of Matsubara frequencies
'N_W':
1, # number of bosonic Matsubara frequencies for the local susceptibility
# additional parameters (used outside the solver only)
't': 1, # hopping
})
for parms in parameters:
if mpi.rank == 0:
ar = archive(parms['BASENAME'] + '.out.h5', 'a')
ar['/parameters'] = parms
del ar
print("creating initial hybridization...")
g = []
I = complex(0., 1.)
mu = 0.0
for n in range(parms['N_MATSUBARA']):
w = (2 * n + 1) * pi / parms['BETA']
g.append(2.0 / (I * w + mu + I * sqrt(4 * parms['t']**2 -
(I * w + mu)**2))
) # use GF with semielliptical DOS
delta = []
for i in range(parms['N_TAU'] + 1):
tau = i * parms['BETA'] / parms['N_TAU']
g0tau = 0.0
# available from http://alps.comp-phys.org/. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import numpy as np
import pyalps.hdf5 as hdf5
ar = hdf5.archive('pyngs.h5', 'w')
a = np.array([1, 2, 3]);
b = np.array([1.1, 2.0, 3.5]);
c = np.array([1.1 + 1j, 2.0j, 3.5]);
d = {"a": a, 2 + 3j: "foo"}
ar["/list"] = [1, 2, 3]
ar["/list2"] = [[[1, 2], [3, 4]], [[1, 2], [3, 4]], [[1, 2], [3, 4]], [[1, 2], [3, 4]]]
ar["/dict"] = {"scalar": 1, "numpy": a, "numpycpx": c, "list": [1, 2, 3], "string": "str", 1: 1, 4: d}
ar["/numpy"] = a
ar["/numpy2"] = b
ar["/numpy3"] = c
ar["/numpyel"] = a[0]
ar["/numpyel2"] = b[0]
ar["/numpyel3"] = c[0]
ar["/int"] = int(1)
def assert_type(p, k):
assert type(p[k]) == type(orig_dict[k])
## Create params
p = ngs.params({'val1': 42, 'val2': '42', 'a': 1, 'x': 2, 'b': 3})
## check content
for k in sorted(orig_dict.keys()):
assert p[k] == orig_dict[k]
assert_type(p, k)
print(k, 'ok!')
## Check nonetype
assert type(p["undefined"]) == type(None)
## Write to hdf5
with hdf5.archive('parms1.h5', 'w') as oar:
p.save(oar) # does not use path '/parameters'
with hdf5.archive('parms2.h5', 'w') as oar:
for key in sorted(p.keys()):
print(key)
oar['parameters/' + key] = p[key]
## Load from hdf5
with hdf5.archive('parms2.h5', 'r') as oar:
iar = hdf5.archive('parms2.h5', 'r')
p.load(iar)
for k in sorted(orig_dict.keys()):
assert p[k] == orig_dict[k]
assert_type(p, k)
print(k, 'ok!')
resume = True if 'c' in args else False
outfile = positional[0]
except (IndexError, getopt.GetoptError):
print 'usage: [-T timelimit] [-c] outputfile'
exit()
sim = ising.sim(ngs.params({
'L': 100,
'THERMALIZATION': 1000,
'SWEEPS': 10000,
'T': 2
}))
if resume:
try:
with hdf5.archive(outfile[0:outfile.rfind('.h5')] + '.clone0.h5', 'r') as ar:
sim.load(ar['/'])
except ArchiveNotFound: pass
if limit == 0:
sim.run(lambda: False)
else:
start = time.time()
sim.run(lambda: time.time() > start + float(limit))
with hdf5.archive(outfile[0:outfile.rfind('.h5')] + '.clone0.h5', 'w') as ar:
ar['/'] = sim
results = sim.collectResults() # TODO: how should we do that?
print results
outfile = positional[0]
except (IndexError, getopt.GetoptError):
print('usage: [-T timelimit] [-c] outputfile')
exit()
sim = ising.sim(
ngs.params({
'L': 100,
'THERMALIZATION': 1000,
'SWEEPS': 10000,
'T': 2
}))
if resume:
try:
with hdf5.archive(outfile[0:outfile.rfind('.h5')] + '.clone0.h5',
'r') as ar:
sim.load(ar['/'])
except ArchiveNotFound:
pass
if limit == 0:
sim.run(lambda: False)
else:
start = time.time()
sim.run(lambda: time.time() > start + float(limit))
with hdf5.archive(outfile[0:outfile.rfind('.h5')] + '.clone0.h5',
'w') as ar:
ar['/'] = sim
results = sim.collectResults() # TODO: how should we do that?
"""
import pyalps.hdf5 as hdf5
import sys, time, traceback, getopt
import ising
if __name__ == '__main__':
sim = ising.sim({'L': 100, 'THERMALIZATION': 100, 'SWEEPS': 1000, 'T': 2})
outfile = 'test'
limit = 1000
if limit == 0:
sim.run(lambda: False)
else:
start = time.time()
sim.run(lambda: time.time() > start + float(limit))
with hdf5.archive(outfile[0:outfile.rfind('.h5')] + '.clone0.h5',
'w') as ar:
ar['/'] = sim
results = sim.collectResults() # TODO: how should we do that?
for key, value in results.iteritems():
print "{}: {}".format(key, value)
with hdf5.archive(outfile, 'w') as ar:
ar['/parameters'] = sim.parameters
ar['/simulation/results'] = results
# version. #
# #
# You should have received a copy of the ALPS Library License along with #
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also #
# available from http://alps.comp-phys.org/. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import pyalps.hdf5 as h5
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
i = h5.archive('blubb', 'r')
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
del i
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
# version. #
# #
# You should have received a copy of the ALPS Library License along with #
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also #
# available from http://alps.comp-phys.org/. #
# #
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR #
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, #
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT #
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE #
# FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE, #
# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER #
# DEALINGS IN THE SOFTWARE. #
# #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import pyalps.hdf5 as h5
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
i = h5.archive('blubb', 'r')
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
del i
o = h5.archive('blubb', 'w')
o['/a'] = 0
del o
