def getMock(zydata, confile, topfile, doufile, phofile, set_plot=False, mode="test") :
""" downsample the truth to get more realistic light curves
"""
if mode == "test" :
return(None)
else :
_c, _y, _z, _yb = zydata.split()
_zydata = _c + _y + _z
zydata_dou = True2Mock(_zydata, sparse=[8, 8, 8], errfac=[0.01, 0.01, 0.01], hasgap=[True, True, True], errcov=0.0)
zylclist_top = zydata_dou.zylclist[:2]
zydata_top = LightCurve(zylclist_top, names=names[0:2])
_zydata = _c + _yb
zydata_pho = True2Mock(_zydata, sparse=[8, 8], errfac=[0.01, 0.01], hasgap=[True, True], errcov=0.0)
if mode == "run" :
confile = ".".join([confile, "myrun"])
doufile = ".".join([doufile, "myrun"])
topfile = ".".join([topfile, "myrun"])
phofile = ".".join([phofile, "myrun"])
zydata_dou.save_continuum(confile)
zydata_dou.save(doufile)
zydata_top.save(topfile)
zydata_pho.save(phofile)
if set_plot :
print("plot mock light curves for continuum, yelm, zing, and yelm band lines")
_c, _yb = zydata_pho.split()
zymock = zydata_dou + _yb
zymock.plot(figout="mocklc", figext=figext)
def getMock(zydata, confile, topfile, doufile, phofile, set_plot=False, mode="test") :
""" downsample the truth to get more realistic light curves
"""
if mode == "test" :
return(None)
else :
_c, _y, _z, _yb = zydata.split()
_zydata = _c + _y + _z
zydata_dou = True2Mock(_zydata, sparse=[8, 8, 8], errfac=[0.01, 0.01, 0.01], hasgap=[True, True, True], errcov=0.0)
zylclist_top = zydata_dou.zylclist[:2]
zydata_top = LightCurve(zylclist_top, names=names[0:2])
_zydata = _c + _yb
zydata_pho = True2Mock(_zydata, sparse=[8, 8], errfac=[0.01, 0.01], hasgap=[True, True], errcov=0.0)
if mode == "run" :
confile = ".".join([confile, "myrun"])
doufile = ".".join([doufile, "myrun"])
topfile = ".".join([topfile, "myrun"])
phofile = ".".join([phofile, "myrun"])
zydata_dou.save_continuum(confile)
zydata_dou.save(doufile)
zydata_top.save(topfile)
zydata_pho.save(phofile)
if set_plot :
print("plot mock light curves for continuum, yelm, zing, and yelm band lines")
_c, _yb = zydata_pho.split()
zymock = zydata_dou + _yb
zymock.plot(figout="mocklc", figext=figext)
def generateTrueLC2(covfunc="drw"):
""" Generate RMap light curves first, with the sampling designed to allow a post-processing into the line band light curve. The simlest solution is to build light curves on dense regular time axis. The only downside here is that, only 'drw' covariance is allowed.
covfunc : str, optional
Name of the covariance funtion (default: drw).
"""
if covfunc != "drw":
raise RuntimeError(
"current no such covfunc implemented for generateTrueLC2, see demo_covfunc.py for details on how to generate LCs using non-DRW models %s"
% covfunc)
ps = PredictSpear(sigma, tau, llags, lwids, lscales, spearmode="Rmap")
mdense = np.zeros_like(jdense)
edense = np.zeros_like(jdense)
zylistold = [
[jdense, mdense + lcmeans[0], edense],
[jdense, mdense + lcmeans[1], edense],
[jdense, mdense + lcmeans[2], edense],
]
# this is for handling the prediction for Continuum, Yelm, and Zing.
zylistnew = ps.generate(zylistold)
# this is for handling the prediction for YelmBand.
phlc = [jdense, mdense, edense]
phlc[1] = zylistnew[0][1] + zylistnew[1][1]
zylistnew.append(phlc)
# this is for handling the prediction for YelmZingBand.
dplc = [jdense, mdense, edense]
dplc[1] = zylistnew[1][1] + zylistnew[2][1]
# combine into a single LightCurve
zylistnew.append(dplc)
zydata = LightCurve(zylistnew, names=names)
return (zydata)
def generateTrueLC(covfunc="kepler2_exp"):
"""generateTrueLC
covfunc : str, optional
Name of the covariance funtion, "drw" or "kepler2_exp" (default: kepler2_exp).
"""
# create a `truth' mode light curve set with one continuum and two lines
# object name: loopdeloop
# line1 : yelm
# line2 : zing
jmin = np.max(lags)
jmax = np.max(jdense)-1.0
zylist = []
# this is for handling the prediction for Continuum.
if covfunc == "drw" :
PS = PredictSignal(lcmean=0.0, covfunc=covfunc, sigma=sigma, tau=tau)
elif covfunc == "kepler2_exp" :
PS = PredictSignal(lcmean=0.0, covfunc=covfunc, sigma=sigma, tau=tau,
nu=tau_cut, rank="NearlyFull")
else :
raise RuntimeError("current no such covfunc implemented %s"%covfunc)
# generate signal with no error
edense = np.zeros_like(jdense)
sdense = PS.generate(jdense, ewant=edense)
imin = np.searchsorted(jdense, jmin)
imax = np.searchsorted(jdense, jmax)
zylist.append([jdense[imin: imax], sdense[imin: imax]+lcmeans[0], edense[imin: imax]])
# this is for handling the prediction for Yelm, and Zing.
for i in xrange(1, 3) :
lag = lags[i]
wid = wids[i]
scale= scales[i]
jl, sl = generateLine(jdense, sdense, lag, wid, scale, mc_mean=0.0, ml_mean=0.0)
imin = np.searchsorted(jl, jmin)
imax = np.searchsorted(jl, jmax)
# print np.mean(sl)
zylist.append([jl[imin: imax], sl[imin: imax]+lcmeans[i], edense[imin: imax]])
if i == 1:
# special continuum prediction for YelmBand at the observed epochs of the YelmLine
jdense_yb = jl[imin: imax]
edense_yb = np.zeros_like(jdense_yb)
sdense_yb = PS.generate(jdense_yb, ewant=edense_yb)
phlc = [jdense_yb, sdense_yb + lcmeans[i] + zylist[1][1], edense_yb]
# this is for handling the prediction for YelmBand.
# combine into a single LightCurve
zylist.append(phlc)
zydata = LightCurve(zylist, names=names)
return(zydata)
def True2Mock(zydata,
sparse=[2, 4, 4],
errfac=[0.01, 0.01, 0.01],
hasgap=[True, True, True],
errcov=0.0):
""" Postprocess true light curves to observed light curves.
Parameters
----------
zydata: LightCurve
Input true LightCurve.
"""
test = np.array([len(sparse), len(errfac), len(hasgap)]) == zydata.nlc
if not np.all(test):
raise RuntimeError("input dimensions do not match")
zylclist = zydata.zylclist
names = zydata.names
zylclist_new = []
if any(hasgap):
# some may have gaps, to make sure the sun is synchronized in all light curves, we have to do this globally.
rj = zydata.rj
j0 = zydata.jarr[0]
j1 = zydata.jarr[-1]
ng = np.floor(rj / 180.0)
for i in xrange(zydata.nlc):
ispa = np.arange(0, zydata.nptlist[i], sparse[i])
j = zydata.jlist[i][ispa]
# strip off gaps
if hasgap[i]:
dj = np.floor((j - j0) / 180.0)
igap = np.where(np.mod(dj, 2) == 0)
indx = ispa[igap]
else:
indx = ispa
j = zydata.jlist[i][indx]
m = zydata.mlist[i][indx] + zydata.blist[i]
e = zydata.elist[i][indx]
# adding errors
e = e * 0.0 + m * errfac[i]
et = generateError(e, errcov=errcov)
m = m + et
zylclist_new.append([j, m, e])
zymock = LightCurve(zylclist_new, names=names)
return (zymock)