def test_aodm():
# Make fake spectrum
wave = np.linspace(1210, 1220, 100)*u.AA
npix = wave.size
fx = np.ones(npix)
fx[npix//2-5:npix//2+5] = 0.8
fx[npix//2] = 0.01
sig = np.ones(npix)*0.1
wrest = 1215.670*u.AA
fval = 0.4
velo = (wave-wrest)/wrest * 3e5*u.km/u.s
# Operate
N, sig_N, flag_sat = aodm((velo, fx, sig), (wrest, fval))
# Test
assert flag_sat is True
np.testing.assert_allclose((N.value, sig_N.value),
(96652191688169.72, 194151305045168.12))
assert N.unit == u.cm**-2
def test_aodm():
# Make fake spectrum
wave = np.linspace(1210, 1220, 100) * u.AA
npix = wave.size
fx = np.ones(npix)
fx[npix // 2 - 5:npix // 2 + 5] = 0.8
fx[npix // 2] = 0.01
sig = np.ones(npix) * 0.1
wrest = 1215.670 * u.AA
fval = 0.4
velo = (wave - wrest) / wrest * 3e5 * u.km / u.s
# Operate
N, sig_N, flag_sat = aodm((velo, fx, sig), (wrest, fval))
# Test
assert flag_sat is True
np.testing.assert_allclose((N.value, sig_N.value),
(96652191688169.72, 194151305045168.12))
assert N.unit == u.cm**-2
def measure_aodm(self, nsig=3., normalize=True):
""" AODM calculation
It sets these attributes:
* self.attrib[ 'N', 'sig_N', 'logN', 'sig_logN' ]:
Column densities and errors, linear and log
Parameters
----------
nsig : float, optional
Number of sigma significance required for a "detection"
normalize : bool, optional
Normalize first?
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=normalize)
velo = xdict['velo']
# Check that there is sufficient data
if len(fx) <= 1:
warnings.warn("Spectrum does not cover {:g}".format(self.wrest))
self.attrib['flag_N'] = 0
return
# Calculate
N, sig_N, flg_sat = laa.aodm((velo, fx, sig),
(self.wrest, self.data['f']))
# Flag
if flg_sat:
self.attrib['flag_N'] = 2
else:
if N > nsig * sig_N:
self.attrib['flag_N'] = 1
else:
self.attrib['flag_N'] = 3
# Values
self.attrib['N'] = N
self.attrib['sig_N'] = sig_N
# Log
laa.log_clm(self.attrib)
def measure_aodm(self, nsig=3., normalize=True):
""" AODM calculation
It sets these attributes:
* self.attrib[ 'N', 'sig_N', 'logN', 'sig_logN' ]:
Column densities and errors, linear and log
Parameters
----------
nsig : float, optional
Number of sigma significance required for a "detection"
normalize : bool, optional
Normalize first?
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=normalize)
velo = xdict['velo']
# Check that there is sufficient data
if len(fx) <= 1:
warnings.warn("Spectrum does not cover {:g}".format(self.wrest))
self.attrib['flag_N'] = 0
return
# Calculate
N, sig_N, flg_sat = laa.aodm((velo, fx, sig), (self.wrest,self.data['f']))
# Flag
if flg_sat:
self.attrib['flag_N'] = 2
else:
if N > nsig*sig_N:
self.attrib['flag_N'] = 1
else:
self.attrib['flag_N'] = 3
# Values
self.attrib['N'] = N
self.attrib['sig_N'] = sig_N
# Log
laa.log_clm(self.attrib)
def measure_aodm(self, nsig=3.):
""" AODM calculation
Parameters
----------
nsig: float, optional
Number of sigma significance required for a "detection"
Fills:
-------
self.attrib[ 'N', 'sigN', 'logN', 'sig_logN' ]
Column densities and errors, linear and log
"""
reload(laa)
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
velo = xdict['velo']
# Calculate
N,sigN,flg_sat = laa.aodm( (velo, fx, sig), (self.wrest,self.data['f']) )
# Flag
if flg_sat:
self.attrib['flagN'] = 2
else:
if N > nsig*sigN:
self.attrib['flagN'] = 1
else:
self.attrib['flagN'] = 3
# Values
self.attrib['N'] = N
self.attrib['sigN'] = sigN
# Log
logN = np.log10( self.attrib['N'].value )
lgvar = ((1. / (np.log(10.)*self.attrib['N'].value))**2) * self.attrib['sigN'].value**2
sig_logN = np.sqrt(lgvar)
self.attrib['logN'] = logN # Dimensionless
self.attrib['sig_logN'] = sig_logN # Dimensionless
def measure_aodm(self, nsig=3.):
""" AODM calculation
It sets these attributes:
* self.attrib[ 'N', 'sigN', 'logN', 'sig_logN' ]:
Column densities and errors, linear and log
Parameters
----------
nsig : float, optional
Number of sigma significance required for a "detection"
"""
# Cut spectrum
fx, sig, xdict = self.cut_spec(normalize=True)
velo = xdict['velo']
# Calculate
N,sigN,flg_sat = laa.aodm( (velo, fx, sig), (self.wrest,self.data['f']) )
# Flag
if flg_sat:
self.attrib['flagN'] = 2
else:
if N > nsig*sigN:
self.attrib['flagN'] = 1
else:
self.attrib['flagN'] = 3
# Values
self.attrib['N'] = N
self.attrib['sigN'] = sigN
# Log
logN, sig_logN = laa.log_clm(self.attrib)
self.attrib['logN'] = logN # Dimensionless
self.attrib['sig_logN'] = sig_logN # Dimensionless