def __init__(self, spec_window, gauss, v_macro, line_file, line, SN,
**kwargs):
# Default optional parameters:
# Writes star's name at the plot
if ('star_name' in kwargs):
self.name = kwargs['star_name']
else:
self.name = 'Unnamed star'
# x_vel is the velocity shift to be applied to the x-axis
if ('x_vel' in kwargs):
self.vshift = kwargs['x_vel']
else:
self.vshift = 0.0
# x_wl is the wavelengths shift to be applied to the x-axis
if ('x_wl' in kwargs):
self.xshift = kwargs['x_wl']
else:
self.xshift = 0.0
# y_add is the additive shift to be applied to the spectrum
if ('y_add' in kwargs):
self.yadd = kwargs['y_add']
else:
self.yadd = 0.0
# y_mult is the multiplicative shift to be applied to the spectrum
if ('y_mult' in kwargs):
self.ymult = kwargs['y_mult']
else:
self.ymult = 0.0
# perf_radius is the number of points around the line center where
# to evaluate the performance of the synthetic spectrum
if ('perf_radius' in kwargs):
self.radius = kwargs['perf_radius']
else:
self.radius = 5
# bwing_w is the weight to be applied to the blue side of the line
# when evaluating the performance
if ('bwing_w' in kwargs):
self.bwing_w = kwargs['bwing_w']
else:
self.bwing_w = 5.0
# bwing_w is the weight to be applied to the red side of the line
# when evaluating the performance
if ('rwing_w' in kwargs):
self.rwing_w = kwargs['rwing_w']
else:
self.rwing_w = 1.0
# center_w is the weight to be applied to the line center when
# evaluating the performance
if ('center_w' in kwargs):
self.center_w = kwargs['center_w']
else:
self.center_w = 10.0
# Maximum number of points around the performance radius that are
# allowed to be a bad fit (1 S/N sigma lower than observed signal)
# If this limit is exceeded, the variable badfit_status will return
# True after running find()
# For high precision spectrum, set this to a very low number
if ('badfit_tol' in kwargs):
self.badfit_tol = kwargs['badfit_tol']
else:
self.badfit_tol = 10
self.c = 2.998E18
self.am = utils.arr_manage()
self.spec = spec_window
self.gauss = gauss
self.v_m = v_macro
self.lines = np.loadtxt(line_file, skiprows=1, usecols=(0, 1))
self.Z = self.lines[line, 1]
self.line_center = self.lines[line, 0]
self.spec_sigma = 1. / SN
* s_twins.csv: contains the information of the stars
* filenames.lis: contains the list of names of the spectrum .fits files
* lines.dat: contains information about the lines to be analyzed
* continuum.dat: contains the wavelengths to be used to calibrate the continuum
"""
# Getting star names
star_names = np.loadtxt('s_twins.csv',
skiprows=1,
usecols=(0,),
dtype=str,
delimiter=',')
N = len(star_names)
# This is used to manage data arrays
u = utils.arr_manage()
# The following function is used to set the input files to be used by MOOG
# for a specific star from the list star_names.
def set_star(choice, **kwargs):
if ('inverted_filelist' in kwargs):
invert = kwargs['inverted_filelist']
else:
invert = False
choice = int(choice)
print 'Creating the stellar atmosphere file.'
def __init__(self, spec_window, gauss, v_macro, line_file, line, SN,
**kwargs):
# Default optional parameters:
# Writes star's name at the plot
if ('star_name' in kwargs):
self.name = kwargs['star_name']
else:
self.name = 'Unnamed star'
# x_vel is the velocity shift to be applied to the x-axis
if ('x_vel' in kwargs):
self.vshift = kwargs['x_vel']
else:
self.vshift = 0.0
# x_wl is the wavelengths shift to be applied to the x-axis
if ('x_wl' in kwargs):
self.xshift = kwargs['x_wl']
else:
self.xshift = 0.0
# y_add is the additive shift to be applied to the spectrum
if ('y_add' in kwargs):
self.yadd = kwargs['y_add']
else:
self.yadd = 0.0
# y_mult is the multiplicative shift to be applied to the spectrum
if ('y_mult' in kwargs):
self.ymult = kwargs['y_mult']
else:
self.ymult = 0.0
# perf_radius is the number of points around the line center where
# to evaluate the performance of the synthetic spectrum
if ('perf_radius' in kwargs):
self.radius = kwargs['perf_radius']
else:
self.radius = 5
# bwing_w is the weight to be applied to the blue side of the line
# when evaluating the performance
if ('bwing_w' in kwargs):
self.bwing_w = kwargs['bwing_w']
else:
self.bwing_w = 5.0
# bwing_w is the weight to be applied to the red side of the line
# when evaluating the performance
if ('rwing_w' in kwargs):
self.rwing_w = kwargs['rwing_w']
else:
self.rwing_w = 1.0
# center_w is the weight to be applied to the line center when
# evaluating the performance
if ('center_w' in kwargs):
self.center_w = kwargs['center_w']
else:
self.center_w = 10.0
# Maximum number of points around the performance radius that are
# allowed to be a bad fit (1 S/N sigma lower than observed signal)
# If this limit is exceeded, the variable badfit_status will return
# True after running find()
# For high precision spectrum, set this to a very low number
if ('badfit_tol' in kwargs):
self.badfit_tol = kwargs['badfit_tol']
else:
self.badfit_tol = 10
self.c = 2.998E18
self.am = utils.arr_manage()
self.spec = spec_window
self.gauss = gauss
self.v_m = v_macro
self.lines = np.loadtxt(line_file, skiprows=1, usecols=(0,1))
self.Z = self.lines[line,1]
self.line_center = self.lines[line,0]
self.spec_sigma = 1./SN
