def load_pds(fname):
"""Load PDS from a file."""
if get_file_format(fname) == 'pickle':
data = _load_data_pickle(fname)
elif get_file_format(fname) == 'nc':
data = _load_data_nc(fname)
type_string = data['__sr__class__type__']
if 'AveragedPowerspectrum' in type_string:
cpds = AveragedPowerspectrum()
elif 'Powerspectrum' in type_string:
cpds = Powerspectrum()
elif 'AveragedCrossspectrum' in type_string:
cpds = AveragedCrossspectrum()
elif 'Crossspectrum' in type_string:
cpds = Crossspectrum()
else:
raise ValueError('Unrecognized data type in file')
data.pop('__sr__class__type__')
for key in data.keys():
setattr(cpds, key, data[key])
lc1_name = fname.replace(HEN_FILE_EXTENSION,
'__lc1__' + HEN_FILE_EXTENSION)
lc2_name = fname.replace(HEN_FILE_EXTENSION,
'__lc2__' + HEN_FILE_EXTENSION)
pds1_name = fname.replace(HEN_FILE_EXTENSION,
'__pds1__' + HEN_FILE_EXTENSION)
pds2_name = fname.replace(HEN_FILE_EXTENSION,
'__pds2__' + HEN_FILE_EXTENSION)
cs_all_names = glob.glob(
fname.replace(HEN_FILE_EXTENSION,
'__cs__[0-9]__' + HEN_FILE_EXTENSION))
if os.path.exists(lc1_name):
cpds.lc1 = load_lcurve(lc1_name)
if os.path.exists(lc2_name):
cpds.lc2 = load_lcurve(lc2_name)
if os.path.exists(pds1_name):
cpds.pds1 = load_pds(pds1_name)
if os.path.exists(pds2_name):
cpds.pds2 = load_pds(pds2_name)
if len(cs_all_names) > 0:
cs_all = []
for c in cs_all_names:
cs_all.append(load_pds(c))
cpds.cs_all = cs_all
return cpds
def load_pds(fname, nosub=False):
"""Load PDS from a file."""
if get_file_format(fname) == 'pickle':
data = _load_data_pickle(fname)
elif get_file_format(fname) == 'nc':
data = _load_data_nc(fname)
type_string = data['__sr__class__type__']
if 'AveragedPowerspectrum' in type_string:
cpds = AveragedPowerspectrum()
elif 'Powerspectrum' in type_string:
cpds = Powerspectrum()
elif 'AveragedCrossspectrum' in type_string:
cpds = AveragedCrossspectrum()
elif 'Crossspectrum' in type_string:
cpds = Crossspectrum()
else:
raise ValueError('Unrecognized data type in file')
data.pop('__sr__class__type__')
for key in data.keys():
setattr(cpds, key, data[key])
if 'amplitude' in list(data.keys()):
cpds.amplitude = bool(data["amplitude"])
outdir = fname.replace(HEN_FILE_EXTENSION, "")
modelfiles = glob.glob(
os.path.join(outdir, fname.replace(HEN_FILE_EXTENSION, '__mod*__.p')))
cpds.best_fits = None
if len(modelfiles) >= 1:
bmodels = []
for mfile in modelfiles:
if os.path.exists(mfile):
bmodels.append(load_model(mfile)[0])
cpds.best_fits = bmodels
if nosub:
return cpds
lc1_name = os.path.join(outdir, '__lc1__' + HEN_FILE_EXTENSION)
lc2_name = os.path.join(outdir, '__lc2__' + HEN_FILE_EXTENSION)
pds1_name = os.path.join(outdir, '__pds1__' + HEN_FILE_EXTENSION)
pds2_name = os.path.join(outdir, '__pds2__' + HEN_FILE_EXTENSION)
cs_all_names = glob.glob(
os.path.join(outdir, '__cs__[0-9]__' + HEN_FILE_EXTENSION))
if os.path.exists(lc1_name):
cpds.lc1 = load_lcurve(lc1_name)
if os.path.exists(lc2_name):
cpds.lc2 = load_lcurve(lc2_name)
if os.path.exists(pds1_name):
cpds.pds1 = load_pds(pds1_name)
if os.path.exists(pds2_name):
cpds.pds2 = load_pds(pds2_name)
if len(cs_all_names) > 0:
cs_all = []
for c in cs_all_names:
cs_all.append(load_pds(c))
cpds.cs_all = cs_all
return cpds
