def _fastpredict(self, jw, iw, set_threading=False):
""" jw : jwant
iw : iwant
"""
mw = np.zeros_like(jw)
vw = np.zeros_like(jw)
for i, (jwant, iwant) in enumerate(zip(jw, iw)):
if set_threading:
covar = spear_threading(jwant, self.jd, iwant + 1, self.id + 1,
**self.covparams)
else:
covar = spear(jwant, self.jd, iwant + 1, self.id + 1,
**self.covparams)
cplusninvdotcovar = chosolve_from_tri(self.U,
covar.T,
nugget=None,
inplace=False)
if set_threading:
vw[i] = spear_threading(jwant, jwant, iwant + 1, iwant + 1,
**self.covparams)
else:
vw[i] = spear(jwant, jwant, iwant + 1, iwant + 1,
**self.covparams)
mw[i] = np.dot(covar, self.cplusninvdoty)
vw[i] = vw[i] - np.dot(covar, cplusninvdotcovar)
return (mw, vw)
def _get_covmat(self, set_threading=False):
if set_threading:
self.cmatrix = spear_threading(self.jd, self.jd, self.id, self.id,
**self.covparams)
else:
self.cmatrix = spear(self.jd, self.jd, self.id, self.id,
**self.covparams)
print("covariance matrix calculated")
def generate(self, zylclist, set_threading=False) :
""" Presumably zylclist has our input j, and e, and the values in m
should be the mean.
Parameters
----------
zylclist: list of 3-list light curves
Pre-simulated light curves in zylclist, with the values in m-column as
the light curve mean, and those in e-column as the designated errorbar.
Returns
-------
zylclist_new: list of 3-list light curves
Simulated light curves in zylclist.
"""
nlc_obs = len(zylclist)
if nlc_obs != self.nlc_obs :
raise RuntimeError("zylclist has unmatched nlc_obs with spearmode %s" % self.spearmode)
jlist = []
mlist = []
elist = []
ilist = []
nptlist = []
npt = 0
for ilc, lclist in enumerate(zylclist):
if (len(lclist) == 3):
jsubarr, msubarr, esubarr = [np.array(l) for l in lclist]
if (np.min(msubarr) != np.max(msubarr)) :
print("WARNING: input zylclist has inequal m elements in light curve %d," +
"please make sure the m elements are filled with the desired mean" +
"of the mock light curves, now reset to zero"%ilc)
msubarr = msubarr * 0.0
nptlc = len(jsubarr)
# sort the date, safety
p = jsubarr.argsort()
jlist.append(jsubarr[p])
mlist.append(msubarr[p])
elist.append(esubarr[p])
ilist.append(np.zeros(nptlc, dtype="int")+ilc+1) # again, starting at 1.
npt += nptlc
nptlist.append(nptlc)
# collapse the list to one array
jarr, marr, earr, iarr = self._combineddataarr(npt, nptlist, jlist, mlist, elist, ilist)
# get covariance function
if set_threading :
if self.spearmode == "Rmap" :
cmatrix = spear_threading(jarr, jarr, iarr , iarr , self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=False)
elif self.spearmode == "Pmap" :
cmatrix = spear_threading(jarr, jarr, iarr , iarr , self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=True)
elif self.spearmode == "SPmap" :
cmatrix = spear_threading(jarr, jarr, iarr+1, iarr+1, self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=True)
else :
if self.spearmode == "Rmap" :
cmatrix = spear(jarr, jarr, iarr , iarr , self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=False)
elif self.spearmode == "Pmap" :
cmatrix = spear(jarr, jarr, iarr , iarr , self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=True)
elif self.spearmode == "SPmap" :
cmatrix = spear(jarr, jarr, iarr+1, iarr+1, self.sigma, self.tau, self.lags, self.wids, self.scales, set_pmap=True)
# cholesky decomposed cmatrix to L, for which a C array is desired.
L = np.empty(cmatrix.shape, order='C')
L[:] = cholesky2(cmatrix) # XXX without the error report.
# generate gaussian deviates y
y = multivariate_normal(np.zeros(npt), np.identity(npt))
# get x = L * y + u, where u is the mean of the light curve(s)
x = np.dot(L, y) + marr
# generate errors
# the way to get around peppering zeros is to generate deviates with unity std and multiply to earr.
# XXX no covariance implemented here.
e = earr * multivariate_normal(np.zeros(npt), np.identity(npt))
# add e
m = x + e
# unpack the data
_jlist, _mlist, _elist = self._unpackdataarr(npt, nptlist, jarr, m, earr, iarr)
zylclist_new = []
for ilc in xrange(self.nlc_obs) :
zylclist_new.append([_jlist[ilc], _mlist[ilc], _elist[ilc]])
return(zylclist_new)
