def pysynth(x0, id_el, elements, dw, g_fwhm, selected_model, region, dwin,
b_win):
'''
Function: Wrtie a parameter file for synth module called batch.par
Variables: x0 = abundance values
id_el = element numbers which are allowed to vary
elements = full array of tuples, [(element number,abundance)]
dw = wavelength offset between data and model
g_fwhm = convolve model with gaussian
selected_model = MOOG capable stelalr model
region = wavelength in region of interest
dwin = resolution of model in Angstoms, should be higher than data resolution
b_win = bounds of plotting window
Expectations: None
'''
from SRP import create_moog_file_synth
from SRP import pymoogsilent
from SRP import moog_synth_chisqr
for i, e in enumerate(elements):
t1, t2 = e
s = np.where(t1 == id_el)
elements[i] = (t1, x0[s])
create_moog_file_synth('../DATS/' + selected_model, region - 6, region + 6,
dwin, 1.00, b_win, 1.1, elements, g_fwhm)
pymoogsilent()
#wave_m,flux_m = SRP.read_out3('out3')
#plt.plot(wave_m,flux_m,'--')
return moog_synth_chisqr('out3', 'star.xy', dw)
def pysynth_rot_metal_mcmc(x0, args):
'''
Function: Wrtie a parameter file for synth module called batch.par, amended to use rotational broadening parameter which can varry in fit, metals a fixed to singular value from solar
Variables: x0 = abundance values
id_el = element numbers which are allowed to vary
elements = full array of tuples, [(element number,abundance)]
dw = wavelength offset between data and model
rot = convolve model with rotationally brodened profile
selected_model = MOOG capable stelalr model
region = wavelength in region of interest
dwin = resolution of model in Angstoms, should be higher than data resolution
b_win = bounds of plotting window
'''
#print args," : args"
id_el, elements, dw, selected_model, region, dwin, b_win = args
#print x0," : x0"
from SRP import create_moog_file_synth
from SRP import pymoogsilent
from SRP import moog_synth_chisqr
#print x0," : x0"
rot = x0[0]
x = x0[1]
if (rot > 300.0 / 100.0) | (rot < 0.0):
return -np.inf
if (x > 2.0) | (x < -2.0):
return -np.inf
for i, e in enumerate(elements):
t1, t2 = e
elements[i] = (t1, x)
#print elements," : elements"
create_moog_file_synth_rot('../DATS/' + selected_model, region - 6,
region + 6, dwin, 1.00, b_win, 1.1, elements,
rot * 100.0)
fyiwdwytm = pymoogsilent()
#wave_m,flux_m = SRP.read_out3('out3')
#plt.plot(wave_m,flux_m,'--')
if fyiwdwytm:
#jack up chisqr if moog failed
prob = -np.inf
else:
chisqr = moog_synth_chisqr('out3', 'star.xy', dw)
prob = 1 / chisqr
return np.log(prob)
def pysynth_rot(x0, id_el, elements, dw, selected_model, region, dwin, b_win):
'''
Function: Wrtie a parameter file for synth module called batch.par, amended to use rotational broadening parameter which can varry in fit
Variables: x0 = abundance values
id_el = element numbers which are allowed to vary
elements = full array of tuples, [(element number,abundance)]
dw = wavelength offset between data and model
rot = convolve model with rotationally brodened profile
selected_model = MOOG capable stelalr model
region = wavelength in region of interest
dwin = resolution of model in Angstoms, should be higher than data resolution
b_win = bounds of plotting window
'''
from SRP import create_moog_file_synth
from SRP import pymoogsilent
from SRP import moog_synth_chisqr
#print x0," : x0 rot"
rot = x0[0]
x0 = x0[1:]
if (rot > 300) | (rot < 0):
return 10
if (x0 > 2) | (x0 < -2):
return 10
for i, e in enumerate(elements):
t1, t2 = e
s = np.where(t1 == id_el)
elements[i] = (t1, x0[s[0][0]])
#print elements," : elements"
create_moog_file_synth_rot('../DATS/' + selected_model, region - 6,
region + 6, dwin, 1.00, b_win, 1.1, elements,
rot)
fyiwdwytm = pymoogsilent()
#wave_m,flux_m = SRP.read_out3('out3')
#plt.plot(wave_m,flux_m,'--')
if fyiwdwytm:
#jack up chisqr if moog failed
chisqr = 1E6
else:
chisqr = moog_synth_chisqr('out3', 'star.xy', dw)
#print chisqr," : chisqr"
return chisqr
def pysynth_rot_fxd_mcmc(x0, id_el, elements, dw, rot, selected_model, region,
dwin, b_win):
'''
Function: Wrtie a parameter file for synth module called batch.par, amended to use rotational broadening parameter (fixed)
Variables: x0 = abundance values
id_el = element numbers which are allowed to vary
elements = full array of tuples, [(element number,abundance)]
dw = wavelength offset between data and model
rot = convolve model with rotationally brodened profile
selected_model = MOOG capable stelalr model
region = wavelength in region of interest
dwin = resolution of model in Angstoms, should be higher than data resolution
b_win = bounds of plotting window
Expectation: selected models are in a local directory './DATS/' (probably should make that a var)
'''
print x0, " : x0"
from SRP import create_moog_file_synth
from SRP import pymoogsilent
from SRP import moog_synth_chisqr
#sanity check
if (np.max(np.abs(x0)) > 5):
return 1E3
#print x0," : x0",rot
for i, e in enumerate(elements):
t1, t2 = e
s = np.where(t1 == id_el)
#print list(s),id_el,t1
elements[i] = (t1, x0[s[0][0]])
create_moog_file_synth_rot('../DATS/' + selected_model, region - 6,
region + 6, dwin, 1.00, b_win, 1.1, elements,
rot)
fyiwdwytm = pymoogsilent()
#wave_m,flux_m = SRP.read_out3('out3')
#plt.plot(wave_m,flux_m,'--')
if fyiwdwytm:
#jack up chisqr if moog failed
prob = -np.inf
else:
chisqr = moog_synth_chisqr('out3', 'star.xy', dw)
prob = 1 / chisqr
return np.log(prob)