def load(self):
"""Save this fit, so it be reloaded quickly next time."""
self.speak('attempting to load from {0}'.format(self.directory))
#self.speak(' the PDF')
self.pdf = PDF.load(self.directory + 'pdf.npy')
#self.speak(' the fitting notes')
self.notes = np.load(self.directory + 'fitting_notes.npy')[()]
#self.speak(' the best-fit model')
self.model.load(self.directory)
def load(self):
"""load a fit from a file, including parameters and LC modifcations"""
self.speak("attempting to load from {0}".format(self.directory))
# self.speak(' the PDF')
self.pdf = PDF.load(self.directory + "pdf.npy")
# self.speak(' the fitting notes')
# pick up the covariance matrix
self.covariance = self.pdf.covariance
for i in range(self.m):
match = np.array(self.pdf.names) == self.parameters[i]["code"]
for p in self.parameters[i]["parameter"]:
loaded = np.array(self.pdf.parameters)[match]
assert len(loaded) == 1
loaded = loaded[0]
p.value = loaded.value
p.uncertainty = loaded.uncertainty
# incorporate all modifications that were made to the light curves
# KLUDGE!
if self.__class__.__name__ == "MCMC":
filename = self.directory + "lm/modifications.npy"
else:
filename = self.directory + "modifications.npy"
modifications = np.load(filename)
self.speak("loaded TLC modifications from {0}".format(filename))
assert len(modifications) == len(self.tlcs)
# loop over the modications, and apply them
for i in range(len(self.tlcs)):
this = modifications[i]
tlc = self.tlcs[i]
assert tlc.name == this["name"]
tlc.rescaling = this["rescaling"]
tlc.bad = this["ok"] == False
assert tlc.bad.shape == tlc.flux.shape
self.speak("applied the outlier clipping and rescaling")
