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 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')
def main():
"""
:return:
"""
# parameters
niter = 2
# 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
ol = load_observations('prekor.lst')[0]
## 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()
print itf
## 5) write rvs
itf.save('test.sav')
itf.write_rvs('test.rv.dat')
# 6) try to load it
itf.load('test.sav')
# 7) and save it again
itf.save('test2.sav')
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
itf.compute_chi2([
-100.,
100.,
"""
Make a comparison with some data created with the old version.
KOREL format of the ouput data is used.
"""
import pyterpol
# 1) setup the model
sl = pyterpol.StarList()
sl.add_component('primary', teff=11257., logg=4.43, vrot=28.8, lr=0.744, rv=-17.94, z=1.000)
sl.add_component('secondary', teff=7714., logg=4.25, vrot=26.42, lr=0.256, rv=-16.73, z=1.000)
# setup the data
obs = [
dict(filename='output000', korel=True, component='primary',),
dict(filename='output001', korel=True, component='secondary')
]
ol = pyterpol.ObservedList()
ol.add_observations(obs)
# create interface
itf = pyterpol.Interface(ol=ol, sl=sl, debug=True)
itf.setup()
# populate the comparisons
itf.populate_comparisons()
# plot the comparisons
itf.plot_all_comparisons()
rl.add_region(wmin=6540, wmax=6600, groups={'lr':0})
sl = pyterpol.StarList()
sl.add_component(component='primary', teff=17000., logg=4.0, rv=-100.0, z=1.0, vrot=40.0, lr=0.35)
sl.add_component(component='secondary', teff=26000., logg=4.0, rv=100.0, z=1.0, vrot=140.0, lr=0.65)
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)
# setup the class
itf = pyterpol.Interface(sl=sl, ol=ol, rl=rl, debug=False)
itf.set_grid_properties(order=2)
itf.setup()
# setup fitted parameters
itf.set_parameter(parname='logg', component='secondary', fitted=True, vmin=3.5, vmax=4.5)
itf.set_parameter(parname='teff', component='secondary', fitted=True, vmin=20000., vmax=28000.)
itf.set_parameter(parname='vrot', component='secondary', fitted=True, vmin=100., vmax=170.)
itf.set_parameter(parname='lr', component='secondary', fitted=True, vmin=0.5, vmax=0.8)
itf.set_parameter(parname='logg', component='primary', fitted=True, vmin=3.5, vmax=4.5)
itf.set_parameter(parname='teff', component='primary', fitted=True, vmin=15000., vmax=20000.)
itf.set_parameter(parname='vrot', component='primary', fitted=True, vmin=40., vmax=80.)
itf.set_parameter(parname='lr', component='primary', fitted=True, vmin=0.2, vmax=0.5)
fitpars = itf.get_fitted_parameters()
import numpy as np
import matplotlib.pyplot as plt
import pyterpol
itf = pyterpol.Interface()
itf.load('hd81357.sav')
itf.populate_comparisons()
# print itf.get_degrees_of_freedom()
# print itf.compute_chi2_treshold()
# try to load the data
data = pyterpol.read_fitlog('fit.log')
# try to plot the convergence
itf.plot_convergence(parameter='all')
# try to plot covariances
itf.plot_covariances(nbin=10, parameters=['teff', 'vrot', 'lr'])
rl = pyterpol.RegionList(debug=debug)
rl.add_region(wmin=4300, wmax=4380)
rl.add_region(wmin=4460, wmax=4500)
# 3) define a star
sl = pyterpol.StarList(debug=debug)
sl.add_component(component='primary',
teff=20000.,
logg=4.5,
rv=0.,
vrot=150.,
lr=0.5,
z=1.0)
sl.add_component(component='secondary',
teff=10000.,
logg=4.5,
rv=0.,
vrot=10.,
lr=0.5,
z=1.0)
# 4) define interface
itf = pyterpol.Interface(ol=ol, rl=rl, sl=sl, debug=debug)
itf.setup()
print itf
print itf.rel_rvgroup_region
print itf.list_comparisons()
itf.populate_comparisons()
itf.plot_all_comparisons()
rv=6.685,
z=1.003)
sl.add_component('secondary',
teff=31205.,
logg=3.36,
vrot=213.0,
lr=0.551,
rv=6.685,
z=1.003)
# 2) setup regions - skip on this, we want to compare only
# rl = pyterpol.RegionList(wmin=4810, wmax=5090)
# 3) setup observed data
ol = pyterpol.ObservedList()
obs = [dict(filename='d', error=0.01)]
ol.add_observations(obs)
# get the interface
itf = pyterpol.Interface(ol=ol, sl=sl)
# set grid properties
itf.set_grid_properties(order=2)
# setut the grid
itf.setup()
# do the comparisons
itf.populate_comparisons()
itf.plot_all_comparisons()
z=1.0,
vrot=140.0,
lr=0.65)
obs = [
dict(filename='a', error=0.001),
dict(filename='b', error=0.001),
dict(filename='c', error=0.001)
]
ol = pyterpol.ObservedList()
ol.add_observations(obs)
# setup the class
itf = pyterpol.Interface(sl=sl,
ol=ol,
rl=rl,
debug=False,
spectrum_by_spectrum=['rv', 'vrot'])
itf.set_grid_properties(order=3)
itf.setup()
print itf
print itf.list_comparisons()
# setup fitted parameters
itf.set_parameter(parname='rv', vmin=-150., vmax=150.)
itf.set_parameter(parname='logg',
component='secondary',
fitted=True,
vmin=3.5,
vmax=4.5)
itf.set_parameter(parname='teff',
"""
Test of setting up the class to start fitting.
"""
import pyterpol
rl = pyterpol.RegionList()
rl.add_region(wmin=5300, wmax=5500)
rl.add_region(wmin=6500, wmax=6600)
sl = pyterpol.StarList()
sl.add_component(teff=10000., logg=4.5, rv=10., z=1.0, vrot=20.0)
itf = pyterpol.Interface(sl=sl, rl=rl, debug=True)
print itf
itf.set_parameter(parname='teff',
value=20000.,
vmin=25000.,
vmax=15000.,
fitted=True)
itf.set_parameter(parname='logg', value=3.5, vmin=3., vmax=4., fitted=True)
print itf
# have a look at the fitted parameters
parlist = itf.get_fitted_parameters()
print pyterpol.parlist_to_list(parlist)
# setup and plot
itf.setup()
itf.populate_comparisons()
itf.plot_all_comparisons()
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')
