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 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 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 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
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
def GetIterations(self,
current_path,
params={},
measurements=None,
index=None,
verbose=False):
iterationset = []
#iteration_grp = self.h5f.require_group(respath+'/iteration')
for it in self.h5f.list_children(current_path + '/iteration'):
obsset = []
iteration_props = {}
if 'parameters' in self.h5f.list_children(current_path +
'/iteration/' + it):
iteration_props = self.ReadParameters(current_path +
'/iteration/' + it +
'/parameters')
iteration_props['iteration'] = it
respath = current_path + '/iteration/' + it + '/results'
list_ = self.GetObservableList(respath)
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 m in self.h5f.list_children(respath):
if "mean" in self.h5f.list_children(respath + '/' + m):
try:
d = DataSet()
itresultspath = respath + '/' + m
if verbose: log("Loading " + m)
measurements_props = {}
measurements_props['hdf5_path'] = itresultspath
measurements_props[
'observable'] = pt.hdf5_name_decode(m)
if index == None:
d.y = self.h5f[itresultspath + '/mean/value']
d.x = np.arange(0, len(d.y))
else:
try:
d.y = self.h5f[itresultspath +
'/mean/value'][index]
except:
pass
if "labels" in self.h5f.list_children(
itresultspath):
d.x = parse_labels(self.h5f[itresultspath +
'/labels'])
else:
d.x = np.arange(0, len(d.y))
d.props.update(params)
d.props.update(iteration_props)
d.props.update(measurements_props)
except AttributeError:
log("Could not create DataSet")
obsset.append(d)
iterationset.append(obsset)
return iterationset