def run_chain_get_element_abund(moogfile, element):
run_path = 'running_dir/'
save_path = 'save_folder/'
teff, logg, feh, vtur = isp.read_parameters_moogfile(moogfile)
linelist_element = rp.get_install_dir()+rc.read_config_param('linelists', element+'_abund').replace("'", "")
rp.ares_make_mine_opt(run_path, linelist_element)
rp.run_ares(run_path)
filename_ares = rc.read_config_param('ares', 'fileout').replace("'", "")
filename_out = 'lines.' + filename_ares
isp.ares_to_lines(run_path+filename_ares, linelist_element, run_path+filename_out, 4000, 9999, 5, 150)
rp.create_abfind_par(run_path, filename_out)
rp.create_model_kurucz(run_path, teff, logg, feh, vtur)
rp.run_MOOG(run_path, 'abfind.par')
(ele1, ele1_sig, nele1, ele2, ele2_sig, nele2) = rmoog.read_moog_ele_sigma(run_path+'abund_plan_tspec.test', element, 2.)
return (ele1, ele1_sig, nele1, ele2, ele2_sig, nele2)
def error(filename, fix_logg=False):
"""
Computes the errors
"""
# Read the file
logout = readmoog(filename)
# First determine the set of variables that will be needed to
# determine the errors.
teff = logout[0]
logg = logout[1]
vt = logout[2]
metal = logout[3]
abundfe = logout[5]
# abundfe = (logout[5] + logout[8])/2.
sigmafe1 = logout[6]/np.sqrt(logout[4])
sigmafe2 = logout[9]/np.sqrt(logout[7])
# Do least squaresfits for FeI vs EW and EP
a1, b1, siga1, sigb1 = lsq(logout[19], logout[12])
a2, b2, siga2, sigb2 = lsq(logout[18], logout[12])
# Build new abfind.par file
linesfile = filename.replace('Out_moog_', '')
linesfile = linesfile.replace('b.', '')
rp.create_abfind_par('./', linesfile)
##############################
# VT: Run MOOG with +0.1 dex #
##############################
# Make intermod and transform file
run_moog(teff, logg, metal, vt + 0.10)
# Read the results
logoutvt = readmoog('abund_plan_tspec.test')
# Error on microturbulence
if logoutvt[11] == 0:
errormicro = abs(siga1/0.001) * 0.10
else:
errormicro = abs(siga1/logoutvt[11]) * 0.10
# Determine the variation of FeI
deltafe1micro = abs((errormicro/0.10) * (logoutvt[5]-abundfe))
#########
# TEFF: #
#########
# With these values, calculate the error on the slope of FeI with
# excitation potential
addslope = (errormicro/0.10) * logoutvt[10]
# Add this quadratically to the error on the original FeI-EP slope
errorslopeEP = np.hypot(addslope, siga2)
# Run MOOG with teff 100K extra
run_moog(teff + 100, logg, metal, vt)
# Read the results
logoutteff = readmoog('abund_plan_tspec.test')
# Error on temperature (assume the variation on the slope is linear
# with the error)
errorteff = abs(errorslopeEP/logoutteff[10]) * 100
# Determine the variation of FeI
deltafe1teff = abs((errorteff/100.) * (logoutteff[5]-abundfe))
#########
# logg: #
#########
if not fix_logg:
# Calculate the variation that the error in Teff caused in the
# abundances of FeII
addfe2error = abs((errorteff/100.) * (logoutteff[8]-abundfe))
# Quadratically add it to the original abundance error
sigmatotalfe2 = np.hypot(sigmafe2, addfe2error)
# Run MOOG with logg minus 0.20
run_moog(teff, logg - 0.20, metal, vt)
# Read the results
logoutlogg = readmoog('abund_plan_tspec.test')
# Error on logg
errorlogg = abs(sigmatotalfe2 / (logoutlogg[8]-abundfe) * 0.2)
else:
errorlogg = 0.0
###########
# [Fe/H]: #
###########
# Take into account the dispersion errors on FeI by teff and vt errors
# Add them quadratically
print sigmafe1, deltafe1teff, deltafe1micro
errormetal = math.sqrt(sigmafe1**2 + deltafe1teff**2 + deltafe1micro**2)
teff, errorteff = int(teff), int(errorteff)
logg, errorlogg = round(logg, 2), round(errorlogg, 2)
vt, errorvt = round(vt, 2), round(errormicro, 2)
metal, errormetal = round(metal, 2), round(errormetal, 2)
logg, errorlogg = round(logg, 2), round(errorlogg, 2)
result = (teff, errorteff, logg, errorlogg, vt, errormicro, metal,
errormetal, logout[4], logout[7], logout[6], logout[9])
return result
