def cosmo_part(cosmo, zs):
x0 = cosmo.pars()
J = np.zeros((len(zs), len(x0)))
eps = np.sqrt(np.sqrt(np.finfo(float).eps))
f0 = cosmo.mu(zs) #- 10
for i in range(len(x0)):
h1 = eps * np.abs(x0[i])
if h1 == 0:
h1 = eps
pars = [p for p in x0]
pars[i] += h1
indexes = np.zeros(len(pars)).astype(bool)
indexes[i] = True
cosmo.update_pars(pars[i], indexes)
J[:, i] = (cosmo.mu(zs) - f0) / h1
return J
def construct_jacobian(self):
print 'Constructing the Jacobian ...'
d, p, cosmo, base = self.data, self.params, self.model_cosmo, self.spline_base
zs = d['z'].reshape((len(d), 1))
J = np.zeros((len(d), len(p.free)))
x0 = cosmo.pars()
eps = np.sqrt(np.sqrt(np.finfo(float).eps))
#eps = np.finfo(float).eps
f0 = cosmo.mu(zs)
c = d['c']
for par in self.free_cosmo_params:
h1 = 1e-4
p2 = self.params.copy()
p2[par] = self.params[par].free + h1
J[:,
p[par].indexof()] = (self.new_cosmo_model(p2).mu(zs) - f0) / h1
if 'theta_salt' not in fixed_pars:
J[:, p['theta_salt'].indexof()] = base.eval(np.array(
d['l_eff'])).toarray()
for j in np.unique(d['#SN']):
idx = d['#SN'] == j
snd = d[idx]
taille = len(snd)
if 'c' not in fixed_pars:
J[idx, p['c'].indexof(j)] = beta + np.polyval(
color_law_params, transfo(snd['l_eff']))
if 'mB' not in fixed_pars:
J[idx, p['mB'].indexof(j)] = np.ones(len(snd))
for band in 'grizy':
idxband = d['band'] == band
if 'zp' + band not in fixed_pars:
J[idxband, p['zp' + band].indexof()] = 1.
if 'dl' + band not in fixed_pars:
J[idxband, p['dl' + band].indexof()] = (
1. / (1 + d['z'][idxband]) *
(base.deriv(np.array(d['l_eff'][idxband])) *
params['theta_salt'].free + 1. /
(lv - lb) * np.polyval(np.polyder(color_law_params),
transfo(d['l_eff'][idxband])))).T
if 'beta' not in fixed_pars:
J[:, p['beta'].indexof()] = d['c'].reshape((len(d), 1))
kl = self.color_law_degree
if 'color_law' not in fixed_pars:
J[:, p['color_law'].indexof()] = np.array(
[c * transfo(d['l_eff'])**(kl - k) for k in range(kl + 1)]).T
self.J = J
self.Jr = J
Cm = (sparse.coo_matrix(np.diag(1. / d['snr']**2))).toarray()
self.C = Cm
self.Cr = Cm
print '... DONE !'
return J, Cm
def construct_jacobian(self):
pedestal = self.pedestal
print 'Constructing the Jacobian ...'
d, p, cosmo, base = self.data, self.params, self.model_cosmo, self.spline_base
zs = d['z'].reshape((len(d), 1))
x0 = cosmo.pars()
eps = np.sqrt(np.sqrt(np.finfo(float).eps))
#eps = np.finfo(float).eps
f0 = cosmo.mu(zs)
c = d['c']
all_lines = np.arange(len(d))
for par in self.free_cosmo_params:
h1 = 1e-4
p2 = self.params.copy()
p2[par] = self.params[par].free + h1
self.update_lines(all_lines, p[par].indexof(), ((self.new_cosmo_model(p2).mu(zs) - f0)/h1).flatten())
if 'theta_salt' not in self.fixed_pars:
bev = base.eval(np.array(d['l_eff']))
self.lines = np.hstack((self.lines, bev.row)); self.cols = np.hstack((self.cols, bev.col+ p['theta_salt'].indexof(0)))
self.J_dat = np.hstack((self.J_dat, bev.data))
#self.update_lines(bev.row, bev.col + p['theta_salt'].indexof(0), bev.data)
if 'c' not in self.fixed_pars:
self.update_lines_optimized(np.arange(len(d)), p['c'].indexof(d['#SN']), beta + np.polyval(color_law_params, transfo(d['l_eff'])))
if 'mB' not in self.fixed_pars:
self.update_lines_optimized(np.arange(len(d)), p['mB'].indexof(d['#SN']), np.ones(len(d)))
for band in self.bands:
idxband = d['band']==band
if 'zp'+band not in self.fixed_pars:
self.update_lines(all_lines[idxband], p['zp'+band].indexof(), 1.)
if 'dl'+band not in self.fixed_pars:
#if 'theta_salt' not in self.fixed_pars:
self.update_lines(all_lines[idxband], p['dl'+band].indexof(),
(1./(1+d['z'][idxband])*(base.deriv(np.array(d['l_eff'][idxband])) * self.params['theta_salt'].full + d['c'][idxband]*1./(lv-lb)*np.polyval(np.polyder(color_law_params), transfo(d['l_eff'][idxband])))).flatten())
if 'beta' not in self.fixed_pars:
self.update_lines(all_lines, p['beta'].indexof(), c)
kl = self.color_law_degree
if 'color_law' not in self.fixed_pars:
self.update_lines(all_lines, p['color_law'].indexof(), np.array([c*transfo(d['l_eff'])**(kl-k) for k in range(kl+1)]).flatten())
self.Clines = all_lines
self.Ccols = all_lines
self.C_dat = 1./d['snr']**2 + pedestal**2
print '... DONE !'
self.W = sparse.coo_matrix((1./(1./np.array(d['snr']**2)+pedestal**2), (all_lines, all_lines)))
def construct_jacobian(self):
print 'Constructing the Jacobian ...'
d, p, cosmo, base = self.data, self.params, self.model_cosmo, self.spline_base
zs = d['z'].reshape((len(d), 1))
x0 = cosmo.pars()
eps = np.sqrt(np.sqrt(np.finfo(float).eps))
#eps = np.finfo(float).eps
f0 = cosmo.mu(zs)
c = d['c']
all_lines = np.arange(len(d))
for par in self.free_cosmo_params:
h1 = 1e-4
p2 = self.params.copy()
p2[par] = self.params[par].free + h1
self.update_lines(all_lines, p[par].indexof(),
((self.new_cosmo_model(p2).mu(zs) - f0) /
h1).flatten())
if 'theta_salt' not in fixed_pars:
bev = base.eval(np.array(d['l_eff']))
self.lines = np.hstack((self.lines, bev.row))
self.cols = np.hstack(
(self.cols, bev.col + p['theta_salt'].indexof(0)))
self.J_dat = np.hstack((self.J_dat, bev.data))
#self.update_lines(bev.row, bev.col + p['theta_salt'].indexof(0), bev.data)
for j in np.unique(d['#SN']):
idx = d['#SN'] == j
snd = d[idx]
taille = len(snd)
if 'c' not in fixed_pars:
self.update_lines(
all_lines[idx], p['c'].indexof(j),
beta + np.polyval(color_law_params, transfo(snd['l_eff'])))
if 'mB' not in fixed_pars:
self.update_lines(all_lines[idx], p['mB'].indexof(j),
np.ones(len(snd)))
for band1 in np.unique(d['band']):
idxband = d['band'] == band1
###
### ATTENTION CA CHANGE ICI EN FONCTION DE L UNITE DE ZP ET DL
###
band = band1.replace('::', '_')
if 'ZP_' + band not in fixed_pars:
self.update_lines(all_lines[idxband],
p['ZP_' + band].indexof(), 1.)
if 'DL_' + band not in fixed_pars:
derder = base.deriv(np.array(
d['l_eff'][idxband])) * params['theta_salt'].full
if np.isnan(derder).sum() != 0:
self.jambon += list(d['band'][idxband][np.isnan(derder)])
self.update_lines(
all_lines[idxband], p['DL_' + band].indexof(),
(1. / (1 + d['z'][idxband]) *
(derder + d['c'][idxband] * 1. /
(lv - lb) * np.polyval(np.polyder(color_law_params),
transfo(d['l_eff'][idxband])))
).flatten())
if 'beta' not in fixed_pars:
self.update_lines(all_lines, p['beta'].indexof(), c)
kl = self.color_law_degree
if 'color_law' not in fixed_pars:
self.update_lines(
all_lines, p['color_law'].indexof(),
np.array([
c * transfo(d['l_eff'])**(kl - k) for k in range(kl + 1)
]).flatten())
self.Clines = np.arange(len(d))
self.Ccols = np.arange(len(d))
self.C_dat = 1. / d['snr']**2 + color_disp_func(d['l_eff'])**2
print '... DONE !'
self.W = sparse.coo_matrix(
((1. /
(1. / np.array(d['snr']**2) + color_disp_func(d['l_eff'])**2)),
(all_lines, all_lines)))