def measure_ew(self, flg=1, initial_guesses=None, nsig=3.):
""" Measures the observer frame equivalent width
Note this requires self.limits to be initialized
Default is simple boxcar integration.
Observer frame, not rest-frame (use measure_restew()
for rest-frame).
It sets these attributes:
* self.attrib['EW', 'sig_EW']:
The EW and error in observer frame
Parameters
----------
flg : int
* 1 -- Boxcar integration (default)
* 2 -- Gaussian fit
initial_guesses : tuple of floats [None]
If a model is chosen (e.g. flg=2, Gaussian) a tuple of
(amplitude, mean, stddev) can be specified.
nsig : float, optional
Number of sigma for flagging
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
wv = xdict['wave']
# Check that there is sufficient data
if len(fx) <= 1:
warnings.warn("Spectrum does not cover {:g}".format(self.wrest))
self.attrib['EW'] = 0.
self.attrib['sig_EW'] = -1
return
# Calculate
if flg == 1: # Boxcar
EW, sig_EW = lau.box_ew((wv, fx, sig))
elif flg == 2: #Gaussian
EW, sig_EW = lau.gaussian_ew((wv, fx, sig),
self.ltype,
initial_guesses=initial_guesses)
else:
raise ValueError(
'measure_ew: Not ready for this flag {:d}'.format(flg))
# Fill
self.attrib['EW'] = EW
self.attrib['sig_EW'] = sig_EW
# Flagging
if EW > (sig_EW * nsig):
self.attrib['flag_EW'] = 1
else:
self.attrib['flag_EW'] = 3
def measure_ew(self, flg=1, initial_guesses=None, nsig=3.):
""" Measures the observer frame equivalent width
Note this requires self.limits to be initialized
Default is simple boxcar integration.
Observer frame, not rest-frame (use measure_restew()
for rest-frame).
It sets these attributes:
* self.attrib['EW', 'sig_EW']:
The EW and error in observer frame
Parameters
----------
flg : int
* 1 -- Boxcar integration (default)
* 2 -- Gaussian fit
initial_guesses : tuple of floats [None]
If a model is chosen (e.g. flg=2, Gaussian) a tuple of
(amplitude, mean, stddev) can be specified.
nsig : float, optional
Number of sigma for flagging
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
wv = xdict['wave']
# Check that there is sufficient data
if len(fx) <= 1:
warnings.warn("Spectrum does not cover {:g}".format(self.wrest))
self.attrib['EW'] = 0.
self.attrib['sig_EW'] = -1
return
# Calculate
if flg == 1: # Boxcar
EW, sig_EW = lau.box_ew( (wv, fx, sig) )
elif flg == 2: #Gaussian
EW, sig_EW = lau.gaussian_ew( (wv, fx, sig), self.ltype, initial_guesses=initial_guesses)
else:
raise ValueError('measure_ew: Not ready for this flag {:d}'.format(flg))
# Fill
self.attrib['EW'] = EW
self.attrib['sig_EW'] = sig_EW
# Flagging
if EW > (sig_EW * nsig):
self.attrib['flag_EW'] = 1
else:
self.attrib['flag_EW'] = 3
def measure_ew(self, flg=1, initial_guesses=None):
""" EW calculation
Default is simple boxcar integration
Observer frame, not rest-frame (use measure_restew below)
wvlim must be set!
spec must be set!
Parameters
----------
flg: int, optional
1: Boxcar integration
2: Gaussian fit
initial_guesses, optional: tuple of floats
if a model is chosen (e.g. flg=2, Gaussian) a tuple of (amplitude, mean, stddev)
can be specified.
Fills:
-------
self.attrib[ 'EW', 'sigEW' ] :
EW and error in observer frame
"""
reload(lau)
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
wv = xdict['wave']
# Calculate
if flg == 1: # Boxcar
EW, sigEW = lau.box_ew( (wv, fx, sig) )
elif flg == 2: #Gaussian
EW, sigEW = lau.gaussian_ew( (wv, fx, sig), self.ltype, initial_guesses=initial_guesses)
else:
raise ValueError('measure_ew: Not ready for this flag {:d}'.format(flg))
# Fill
self.attrib['EW'] = EW
self.attrib['sigEW'] = sigEW
def measure_ew(self, flg=1, initial_guesses=None):
""" Measures the observer frame equivalent width
Note this requires the keys `wvlim` and `spec` in analy to
be set! Default is simple boxcar integration.
Observer frame, not rest-frame (use measure_restew()
for rest-frame).
It sets these attributes:
* self.attrib['EW', 'sig_EW']:
The EW and error in observer frame
Parameters
----------
flg : int
* 1 -- Boxcar integration (default)
* 2 -- Gaussian fit
initial_guesses : tuple of floats [None]
If a model is chosen (e.g. flg=2, Gaussian) a tuple of
(amplitude, mean, stddev) can be specified.
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
wv = xdict['wave']
# Calculate
if flg == 1: # Boxcar
EW, sig_EW = lau.box_ew( (wv, fx, sig) )
elif flg == 2: #Gaussian
EW, sig_EW = lau.gaussian_ew( (wv, fx, sig), self.ltype, initial_guesses=initial_guesses)
else:
raise ValueError('measure_ew: Not ready for this flag {:d}'.format(flg))
# Fill
self.attrib['EW'] = EW
self.attrib['sig_EW'] = sig_EW
