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model[closefilter]=rawmodel[0:orlength]
model=np.append(model,rawmodel[orlength:])
nexpp+=100
data['ptime']=np.append(data['ptime'],np.zeros(100)+np.mean(data['ptime'][closefilter]))
data['pflux']=np.append(data['pflux'],np.zeros(100))
data['perror']=np.append(data['perror'],np.zeros(100))
data['pdataset']=np.append(data['pdataset'],np.zeros(100)-1)
data['pfilter']=np.append(data['pfilter'],np.zeros(100)+j)
else:
model[closefilter]+=rawmodel
if args.gp:
if any('gpmodtypep' in s for s in data['gpmodtype']):
if data['gpmodtype']['gpmodtypep'] == 'Matern32':
pkern=gppack.kernels.Matern32Kernel(parstruc['gpppartau']**2)*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Cosine':
pkern=gppack.kernels.CosineKernel(parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'ExpSine2':
pkern=gppack.kernels.ExpSine2Kernel(parstruc['gpppartau'],parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Haywood14QP':
if instruc['gppackflag'] == 'celerite':
pkern=celeritekernel(np.log(parstruc['gppparamp']**2),np.log(parstruc['gppparGamma']),np.log(1./np.sqrt(2.)/parstruc['gpppartau']),np.log(parstruc['gppparP']*2.))
else:
pkern1=gppack.kernels.ExpSine2Kernel(parstruc['gppparGamma'],parstruc['gppparP'])
pkern2=gppack.kernels.ExpSquaredKernel(parstruc['gpppartau'])
pkern=pkern1*pkern2*parstruc['gppparamp']**2
gp=gppack.GP(pkern)
useforgp=np.where(data['gppuse'] == 1)
notforgp=np.where(data['gppuse'] == 0)
if args.binary and modelstruc['photmodflag'] == 'batman': modelstruc['aors'] = aors[i]*(1.+parstruc['rprs'+str(i+1)]) #PRIMARY eclipse, so PRIMARY in background, R*=R1, Rp=R2
oflux=np.array(data['pflux'])
mresid=np.array(data['pflux'])
if args.gp: mresidgp=np.array(data['pflux'])
if pnfilters > 1:
fig=pl.figure(figsize=(8.0,6.0+2.0*pnfilters))
if args.plotresids:
import matplotlib.gridspec as gridspec
gs=gridspec.GridSpec(2, 1, height_ratios=[4,1])
ax1=pl.subplot(gs[0])
if args.gp:
if any('gpmodtypep' in s for s in data['gpmodtype']):
if data['gpmodtype']['gpmodtypep'] == 'Matern32':
pkern=gppack.kernels.Matern32Kernel(parstruc['gpppartau']**2)*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Cosine':
pkern=gppack.kernels.CosineKernel(parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'ExpSine2':
pkern=gppack.kernels.ExpSine2Kernel(parstruc['gpppartau'],parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Haywood14QP':
if struc1['gppackflag'] == 'celerite':
pkern=celeritekernel(np.log(parstruc['gppparamp']**2),np.log(parstruc['gppparGamma']),np.log(1./np.sqrt(2.)/parstruc['gpppartau']),np.log(parstruc['gppparP']*2.))
else:
pkern1=gppack.kernels.ExpSine2Kernel(parstruc['gppparGamma'],parstruc['gppparP'])
pkern2=gppack.kernels.ExpSquaredKernel(parstruc['gpppartau'])
pkern=pkern1*pkern2*parstruc['gppparamp']**2
gp=gppack.GP(pkern)
useforgp=np.where(data['gppuse'] == 1)
notforgp=np.where(data['gppuse'] == 0)
useforgp, notforgp = useforgp[0], notforgp[0]
gp.compute(np.array(data['ptime'][useforgp]),np.array(data['perror'][useforgp]))
nexpp+=100
data['ptime']=np.append(data['ptime'],np.zeros(100)+np.mean(data['ptime'][closefilter]))
data['pflux']=np.append(data['pflux'],np.zeros(100))
data['perror']=np.append(data['perror'],np.zeros(100))
data['pdataset']=np.append(data['pdataset'],np.zeros(100)-1)
data['pfilter']=np.append(data['pfilter'],np.zeros(100)+j)
else:
model[closefilter]+=rawmodel
if args.gp:
if any('gpmodtypep' in s for s in data['gpmodtype']):
if data['gpmodtype']['gpmodtypep'] == 'Matern32':
pkern=gppack.kernels.Matern32Kernel(parstruc['gpppartau']**2)*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Cosine':
pkern=gppack.kernels.CosineKernel(parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'ExpSine2':
pkern=gppack.kernels.ExpSine2Kernel(parstruc['gpppartau'],parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Haywood14QP':
if instruc['gppackflag'] == 'celerite':
pkern=celeritekernel(np.log(parstruc['gppparamp']**2),np.log(parstruc['gppparGamma']),np.log(1./np.sqrt(2.)/parstruc['gpppartau']),np.log(parstruc['gppparP']*2.))
else:
pkern1=gppack.kernels.ExpSine2Kernel(parstruc['gppparGamma'],parstruc['gppparP'])
pkern2=gppack.kernels.ExpSquaredKernel(parstruc['gpppartau'])
pkern=pkern1*pkern2*parstruc['gppparamp']**2
gp=gppack.GP(pkern)
useforgp=np.where(data['gppuse'] == 1)
notforgp=np.where(data['gppuse'] == 0)
useforgp, notforgp = useforgp[0], notforgp[0]
gp.compute(np.array(data['ptime'][useforgp]),np.array(data['perror'][useforgp]))
else:
modelstruc['photmodflag']='batman'
if args.binary and modelstruc['photmodflag'] == 'batman': modelstruc['aors'] = aors[i]*(1.+parstruc['rprs'+str(i+1)]) #PRIMARY eclipse, so PRIMARY in background, R*=R1, Rp=R2
oflux=np.array(data['pflux'])
mresid=np.array(data['pflux'])
if args.gp: mresidgp=np.array(data['pflux'])
if pnfilters > 1:
fig=pl.figure(figsize=(8.0,6.0+2.0*pnfilters))
if args.plotresids:
import matplotlib.gridspec as gridspec
gs=gridspec.GridSpec(2, 1, height_ratios=[4,1])
ax1=pl.subplot(gs[0])
if args.gp:
if any('gpmodtypep' in s for s in data['gpmodtype']):
if data['gpmodtype']['gpmodtypep'] == 'Matern32':
pkern=gppack.kernels.Matern32Kernel(parstruc['gpppartau']**2)*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Cosine':
pkern=gppack.kernels.CosineKernel(parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'ExpSine2':
pkern=gppack.kernels.ExpSine2Kernel(parstruc['gpppartau'],parstruc['gppparP'])*parstruc['gppparamp']**2
elif data['gpmodtype']['gpmodtypep'] == 'Haywood14QP':
if struc1['gppackflag'] == 'celerite':
pkern=celeritekernel(np.log(parstruc['gppparamp']**2),np.log(parstruc['gppparGamma']),np.log(1./np.sqrt(2.)/parstruc['gpppartau']),np.log(parstruc['gppparP']*2.))
else:
pkern1=gppack.kernels.ExpSine2Kernel(parstruc['gppparGamma'],parstruc['gppparP'])
pkern2=gppack.kernels.ExpSquaredKernel(parstruc['gpppartau'])
pkern=pkern1*pkern2*parstruc['gppparamp']**2
gp=gppack.GP(pkern)
useforgp=np.where(data['gppuse'] == 1)
notforgp=np.where(data['gppuse'] == 0)