def convergence_measure_all(filename,index,fits_loader=None):
"""
Measures all the statistical descriptors of a convergence map as indicated by the index instance
"""
logging.debug("Processing {0}".format(filename))
#Load the map
if fits_loader is not None:
conv_map = ConvergenceMap.load(filename,format=fits_loader)
else:
conv_map = ConvergenceMap.load(filename,format=load_fits_default_convergence)
#Allocate memory for observables
descriptors = index
observables = np.zeros(descriptors.size)
#Measure descriptors as directed by input
for n in range(descriptors.num_descriptors):
if type(descriptors[n]) == PowerSpectrum:
l,observables[descriptors[n].first:descriptors[n].last] = conv_map.powerSpectrum(descriptors[n].l_edges)
elif type(descriptors[n]) == Moments:
observables[descriptors[n].first:descriptors[n].last] = conv_map.moments(connected=descriptors[n].connected)
elif type(descriptors[n]) == Peaks:
v,observables[descriptors[n].first:descriptors[n].last] = conv_map.peakCount(descriptors[n].thresholds,norm=descriptors[n].norm)
elif type(descriptors[n]) == PDF:
v,observables[descriptors[n].first:descriptors[n].last] = conv_map.pdf(descriptors[n].thresholds,norm=descriptors[n].norm)
elif type(descriptors[n]) == MinkowskiAll:
v,V0,V1,V2 = conv_map.minkowskiFunctionals(descriptors[n].thresholds,norm=descriptors[n].norm)
observables[descriptors[n].first:descriptors[n].last] = np.hstack((V0,V1,V2))
elif type(descriptors[n]) == MinkowskiSingle:
raise ValueError("Due to computational performance you have to measure all Minkowski functionals at once!")
else:
raise ValueError("Measurement of this descriptor not implemented!!!")
#Return
return observables
def peaks_loader(filename,thresholds):
logging.debug("Processing {0} peaks".format(filename))
conv_map = ConvergenceMap.load(filename,format=load_fits_default_convergence)
v,pk = conv_map.peakCount(thresholds,norm=True)
return v
def peaks_loader(filename, thresholds):
logging.debug("Processing {0} peaks".format(filename))
conv_map = ConvergenceMap.load(filename,
format=load_fits_default_convergence)
v, pk = conv_map.peakCount(thresholds, norm=True)
return v
def default_callback_loader(filename,l_edges):
"""
Default ensemble loader: reads a FITS data file containing a convergence map and measures its power spectrum
:param args: A dictionary that contains all the relevant parameters as keys. Must have a "map_id" key
:type args: Dictionary
:returns: ndarray of the measured statistics
:raises: AssertionError if the input dictionary doesn't have the required keywords
"""
logging.debug("Processing {0} power".format(filename))
conv_map = ConvergenceMap.load(filename,format=load_fits_default_convergence)
l,Pl = conv_map.powerSpectrum(l_edges)
return Pl
def default_callback_loader(filename, l_edges):
"""
Default ensemble loader: reads a FITS data file containing a convergence map and measures its power spectrum
:param args: A dictionary that contains all the relevant parameters as keys. Must have a "map_id" key
:type args: Dictionary
:returns: ndarray of the measured statistics
:raises: AssertionError if the input dictionary doesn't have the required keywords
"""
logging.debug("Processing {0} power".format(filename))
conv_map = ConvergenceMap.load(filename,
format=load_fits_default_convergence)
l, Pl = conv_map.powerSpectrum(l_edges)
return Pl
def convergence_measure_all(filename, index, fits_loader=None):
"""
Measures all the statistical descriptors of a convergence map as indicated by the index instance
"""
logging.debug("Processing {0}".format(filename))
#Load the map
if fits_loader is not None:
conv_map = ConvergenceMap.load(filename, format=fits_loader)
else:
conv_map = ConvergenceMap.load(filename,
format=load_fits_default_convergence)
#Allocate memory for observables
descriptors = index
observables = np.zeros(descriptors.size)
#Measure descriptors as directed by input
for n in range(descriptors.num_descriptors):
if type(descriptors[n]) == PowerSpectrum:
l, observables[descriptors[n].first:descriptors[n].
last] = conv_map.powerSpectrum(
descriptors[n].l_edges)
elif type(descriptors[n]) == Moments:
observables[descriptors[n].first:descriptors[n].
last] = conv_map.moments(
connected=descriptors[n].connected)
elif type(descriptors[n]) == Peaks:
v, observables[descriptors[n].first:descriptors[n].
last] = conv_map.peakCount(
descriptors[n].thresholds,
norm=descriptors[n].norm)
elif type(descriptors[n]) == PDF:
v, observables[descriptors[n].first:descriptors[n].
last] = conv_map.pdf(descriptors[n].thresholds,
norm=descriptors[n].norm)
elif type(descriptors[n]) == MinkowskiAll:
v, V0, V1, V2 = conv_map.minkowskiFunctionals(
descriptors[n].thresholds, norm=descriptors[n].norm)
observables[descriptors[n].first:descriptors[n].last] = np.hstack(
(V0, V1, V2))
elif type(descriptors[n]) == MinkowskiSingle:
raise ValueError(
"Due to computational performance you have to measure all Minkowski functionals at once!"
)
else:
raise ValueError(
"Measurement of this descriptor not implemented!!!")
#Return
return observables
