def initialize(self, key):
# Run through the standard initialization
super().initialize(key)
# for now, start with white noise
self.data_mat = self.sigma_mat.copy()
self.data_mat_last = self.data_mat.copy()
#Set up p0 and the independent sampler
fname = Starfish.specfmt.format(self.spectrum_id, self.order) + "phi.json"
phi = PhiParam.load(fname)
# print("Phi.regions", phi.regions)
# import sys
# sys.exit()
# Get the regions matrix
region_func = make_k_func_region(phi)
max_r = 4.0 * np.max(phi.regions, axis=0)[2]
self.region_mat = get_dense_C(self.wl, k_func=region_func, max_r=max_r)
print(self.region_mat)
# Then set phi to None
phi.regions = None
#Loading file that was previously output
# Convert PhiParam object to an array
self.p0 = phi.toarray()
jump = Starfish.config["Phi_jump"]
cheb_len = (self.npoly - 1) if self.chebyshevSpectrum.fix_c0 else self.npoly
cov_arr = np.concatenate((Starfish.config["cheb_jump"]**2 * np.ones((cheb_len,)), np.array([jump["sigAmp"], jump["logAmp"], jump["l"]])**2 ))
cov = np.diag(cov_arr)
def lnfunc(p):
# Convert p array into a PhiParam object
ind = self.npoly
if self.chebyshevSpectrum.fix_c0:
ind -= 1
cheb = p[0:ind]
sigAmp = p[ind]
ind+=1
logAmp = p[ind]
ind+=1
l = p[ind]
phi = PhiParam(self.spectrum_id, self.order, self.chebyshevSpectrum.fix_c0, cheb, sigAmp, logAmp, l)
self.update_Phi(phi)
lnp = self.evaluate()
self.logger.debug("Evaluated Phi parameters: {} {}".format(phi, lnp))
return lnp
def rejectfn():
self.logger.debug("Calling Phi revertfn.")
self.revert_Phi()
self.sampler = StateSampler(lnfunc, self.p0, cov, query_lnprob=self.get_lnprob, rejectfn=rejectfn, debug=True)
fname = Starfish.specfmt.format(spectrum_id, order) + "regions.json"
f = open(fname, "r")
read = json.load(f) # read is a dictionary
f.close()
mus = np.array(read["mus"])
if args.noise:
sigma_mat = sigma**2 * np.eye(len(sigma))
fname = Starfish.specfmt.format(spectrum_id, order) + "phi.json"
phi = PhiParam.load(fname)
if phi.regions is not None:
region_func = make_k_func_region(phi)
max_r = 4.0 * np.max(phi.regions, axis=0)[2]
region_mat = get_dense_C(wl, k_func=region_func, max_r=max_r)
else:
region_mat = 0.0
max_r = 6.0 * phi.l # [km/s]
# Create a partial function which returns the proper element.
k_func = make_k_func(phi)
data_mat = get_dense_C(wl, k_func=k_func,
max_r=max_r) + phi.sigAmp * sigma_mat + region_mat
# Get many random draws from data_mat
draws = random_draws(data_mat, num=4)
min_spec, max_spec = std_envelope(draws)
def initialize(self, key):
# Run through the standard initialization
super().initialize(key)
# for now, start with white noise
self.data_mat = self.sigma_mat.copy()
self.data_mat_last = self.data_mat.copy()
#Set up p0 and the independent sampler
fname = Starfish.specfmt.format(self.spectrum_id,
self.order) + "phi.json"
phi = PhiParam.load(fname)
# print("Phi.regions", phi.regions)
# import sys
# sys.exit()
# Get the regions matrix
region_func = make_k_func_region(phi)
max_r = 4.0 * np.max(phi.regions, axis=0)[2]
self.region_mat = get_dense_C(self.wl, k_func=region_func, max_r=max_r)
print(self.region_mat)
# Then set phi to None
phi.regions = None
#Loading file that was previously output
# Convert PhiParam object to an array
self.p0 = phi.toarray()
jump = Starfish.config["Phi_jump"]
cheb_len = (self.npoly -
1) if self.chebyshevSpectrum.fix_c0 else self.npoly
cov_arr = np.concatenate(
(Starfish.config["cheb_jump"]**2 * np.ones((cheb_len, )),
np.array([jump["sigAmp"], jump["logAmp"], jump["l"]])**2))
cov = np.diag(cov_arr)
def lnfunc(p):
# Convert p array into a PhiParam object
ind = self.npoly
if self.chebyshevSpectrum.fix_c0:
ind -= 1
cheb = p[0:ind]
sigAmp = p[ind]
ind += 1
logAmp = p[ind]
ind += 1
l = p[ind]
phi = PhiParam(self.spectrum_id, self.order,
self.chebyshevSpectrum.fix_c0, cheb, sigAmp, logAmp,
l)
self.update_Phi(phi)
lnp = self.evaluate()
self.logger.debug("Evaluated Phi parameters: {} {}".format(
phi, lnp))
return lnp
def rejectfn():
self.logger.debug("Calling Phi revertfn.")
self.revert_Phi()
self.sampler = StateSampler(lnfunc,
self.p0,
cov,
query_lnprob=self.get_lnprob,
rejectfn=rejectfn,
debug=True)
fname = Starfish.specfmt.format(spectrum_id, order) + "regions.json"
f = open(fname, "r")
read = json.load(f) # read is a dictionary
f.close()
mus = np.array(read["mus"])
if args.noise:
sigma_mat = sigma**2 * np.eye(len(sigma))
fname = Starfish.specfmt.format(spectrum_id, order) + "phi.json"
phi = PhiParam.load(fname)
if phi.regions is not None:
region_func = make_k_func_region(phi)
max_r = 4.0 * np.max(phi.regions, axis=0)[2]
region_mat = get_dense_C(wl, k_func=region_func, max_r=max_r)
else:
region_mat = 0.0
max_r = 6.0 * phi.l # [km/s]
# Create a partial function which returns the proper element.
k_func = make_k_func(phi)
data_mat = get_dense_C(wl, k_func=k_func, max_r=max_r) + phi.sigAmp*sigma_mat + region_mat
# Get many random draws from data_mat
draws = random_draws(data_mat, num=4)
min_spec, max_spec = std_envelope(draws)
