def setup_interface_more_obs():
# have a look at one observation
ol = pyterpol.ObservedList()
obs = read_obs_from_list('spec.lis')
ol.add_observations(obs)
# define a starlist
sl = pyterpol.StarList()
sl.add_component(component='primary',
teff=16000.,
logg=3.9,
vrot=95.,
z=1.2,
lr=1.0)
# define regions
rl = pyterpol.RegionList()
rl.add_region(wmin=6340, wmax=6410, groups=dict(lr=0))
rl.add_region(wmin=6540, wmax=6595, groups=dict(lr=0))
rl.add_region(wmin=6670, wmax=6685, groups=dict(lr=0))
# create interfaces
itf = pyterpol.Interface(sl=sl, rl=rl, ol=ol, debug=False)
# set fit order = 2 to do it fast
itf.set_grid_properties(order=2, step=0.05)
itf.setup()
# save the session
itf.save('initial.itf')
def load_observations(f):
"""
:param f: file
:return:
"""
# load the observations
flist = np.loadtxt(f, usecols=[0], unpack=True, dtype=str)
hjd = np.loadtxt(f, usecols=[1], unpack=True).tolist()
hjd[0] = None
# create list of observations
obs = []
for i, sf in enumerate(flist):
# wrap the spectrum into observed spectrum class
# o = pyterpol.ObservedSpectrum(filename=sf, group=dict(rv=0))
# o = pyterpol.ObservedSpectrum(filename=sf, group=dict(rv=0), hjd=hjd[i])
o = pyterpol.ObservedSpectrum(filename=sf,
group=dict(rv=i),
hjd=hjd[i])
# estimate uncertainty from continuum
o.get_sigma_from_continuum(6620., 6630.)
obs.append(o)
# create ObservedList
ol = pyterpol.ObservedList()
ol.add_observations(obs)
return ol, flist, hjd
def setup_interface_single_obs():
# have a look at one observation
ol = pyterpol.ObservedList()
obs = pyterpol.ObservedSpectrum(filename='v7c00001.asc',
group=dict(rv=0),
error=0.01)
# two methods how to estimate the error
print obs.get_sigma_from_continuum(cmin=6665, cmax=6670)
print obs.get_sigma_from_fft()
ol.add_observations([obs])
# define a starlist
sl = pyterpol.StarList()
sl.add_component(component='primary',
teff=17000.,
logg=4.0,
vrot=180.,
z=1.0,
lr=1.0)
# define regions
rl = pyterpol.RegionList()
rl.add_region(wmin=6340, wmax=6410, groups=dict(lr=0))
rl.add_region(wmin=6520, wmax=6610, groups=dict(lr=0))
rl.add_region(wmin=6665, wmax=6690, groups=dict(lr=0))
# create interfaces
itf = pyterpol.Interface(sl=sl, rl=rl, ol=ol)
# set fit order = 2 to do it fast
itf.set_grid_properties(order=2)
itf.setup()
# review the result - one rv group, one lr group
print itf
# plot comparison
itf.plot_all_comparisons(figname='teff17000')
# try different temperatures - this way we can easilyt review
# several comparisons
itf.set_parameter(parname='teff', value=25000.)
itf.populate_comparisons()
itf.plot_all_comparisons(figname='teff25000')
itf.set_parameter(parname='teff', value=13000.)
itf.populate_comparisons()
itf.plot_all_comparisons(figname='teff13000')
itf.save('initial.itf')
vrot=50.0,
lr=0.3)
sl.add_component(component='secondary',
teff=25000.,
logg=4.5,
rv=10.,
z=1.0,
vrot=150.0,
lr=0.7)
obs = [
dict(filename='a', error=0.001, group=dict(rv=1)),
dict(filename='b', error=0.001, group=dict(rv=2)),
dict(filename='c', error=0.001, group=dict(rv=3))
]
ol = pyterpol.ObservedList()
ol.add_observations(obs)
itf = pyterpol.Interface(sl=sl, ol=ol, rl=rl)
itf.setup()
# this reduces the list of observed spectra
reduced = itf.get_comparisons(rv=1)
# setup fitted parameterss
itf.set_parameter(parname='rv', group='all', fitted=True)
itf.set_parameter(parname='rv', component='primary', group=3, value=-100.)
itf.set_parameter(parname='rv', component='secondary', group=3, value=100.)
print itf
# this computes the models and chi-square
"""
Testing of RV group
"""
import pyterpol
import matplotlib.pyplot as plt
debug = False
# 1) define some observations
ol = pyterpol.ObservedList(debug=debug)
# obs = [
# dict(filename='o.asc', error=0.01, group=dict(rv=10)),
# dict(filename='o2.asc', error=0.01, group=dict(rv=10))
# ]
obs = [dict(filename='o.asc', error=0.01), dict(filename='o2.asc', error=0.01)]
ol.add_observations(obs)
# print ol
# 2) define fitted regions
rl = pyterpol.RegionList(debug=debug)
rl.add_region(wmin=4300, wmax=4380)
rl.add_region(wmin=4460, wmax=4500, groups={'teff': 1})
# 3) define a star
sl = pyterpol.StarList(debug=debug)
sl.add_component(component='primary',
teff=20000.,
logg=4.5,
rv=0.0,
vrot=0.0,
def main():
"""
:return:
"""
## 1) Generate region
rl = pyterpol.RegionList()
rl.add_region(wmin=6337., wmax=6410.0, groups=dict(lr=0))
rl.add_region(wmin=6530., wmax=6600.0, groups=dict(lr=0))
rl.add_region(wmin=6660., wmax=6690.0, groups=dict(lr=0))
## 2) Load observed data
# first wrap them into observed spectrum class
o1 = pyterpol.ObservedSpectrum(filename='blb00001.clean.asc',
instrumental_width=0.46,
group=dict(rv=0))
o2 = pyterpol.ObservedSpectrum(filename='blb00002.clean.asc',
instrumental_width=0.46,
group=dict(rv=1))
o1.get_sigma_from_continuum(6630., 6640.)
o2.get_sigma_from_continuum(6630., 6640.)
# create list of observed spectra
ol = pyterpol.ObservedList()
ol.add_observations([o1, o2])
# alternatively
ol = pyterpol.ObservedList()
obs = [
dict(filename='blb00001.clean.asc',
instrumental_width=0.46,
group=dict(rv=0),
error=0.01),
dict(filename='blb00002.clean.asc',
instrumental_width=0.46,
group=dict(rv=1),
error=0.01)
]
ol.add_observations(obs)
## 3) Generate components
sl = pyterpol.StarList()
sl.add_component('primary',
teff=16000.,
logg=4.285,
lr=1.0,
vrot=90.,
z=1.0)
## 4) construct the interface
itf = pyterpol.Interface(sl=sl, rl=rl, ol=ol)
itf.set_grid_properties(order=4, step=0.05)
itf.setup()
## 5) Set parameters for fitting
itf.set_parameter(parname='teff', vmin=14000., vmax=17000., fitted=True)
itf.set_parameter(parname='logg', vmin=3.5, vmax=4.5, fitted=True)
itf.set_parameter(parname='rv', vmin=-30., vmax=0., fitted=True)
itf.set_parameter(parname='vrot', vmin=70., vmax=110., fitted=True)
itf.set_parameter(parname='lr', vmin=0.99, vmax=1.01, fitted=True)
## 6) Choose fitting environment
itf.choose_fitter('nlopt_nelder_mead', ftol=1e-4)
## 7) Run the fitting
itf.run_fit()
# write down rvs
itf.write_rvs('hd.rvs')
## 8) plot and save results
itf.plot_all_comparisons(figname='final')
itf.save('test.final.sav')
