def fix_lup_to_maggie(lup,
lup_sig,
photo_err=SDSS.photo_err,
abfix=SDSS.abfix):
"""
Convert SDSS database ugriz luptitude (asinh mag) to AB maggie
This function is different from lup_to_maggie in that it corrects
erratic entries. It also adds a photometric calibration
uncertainties and applies a correction to adjust to AB photometry.
REFERENCE
k_sdssfix.pro of kcorrect v4_1_4
"""
# the following line should set ivar to zero if sigma <= 0, which
# is basically what k_sdss_err2ivar.pro does.
lup_ivar = sigma2ivar(lup_sig)
mask = np.equal(lup, -9999.) * np.not_equal(lup_ivar, 0.)
lup_ivar = np.where(mask, 0., lup_ivar)
lup_sig = ivar2sigma(lup_ivar)
maggie, maggie_ivar = lup_to_maggie(lup, lup_sig)
mask = np.equal(lup_ivar, 0.)
maggie = np.where(mask, 0., maggie)
maggie_ivar = np.where(mask, 0., maggie_ivar)
# add photometric calibration errors
maggie_ivar = add_absigma_to_maggie_ivar(maggie, maggie_ivar, photo_err)
# correct for AB offsets
abfix = np.asarray(abfix, dtype=FTYPE)
maggie = maggie * 10**(-0.4 * abfix)
maggie_ivar = maggie_ivar * 10**(0.8 * abfix)
return maggie, maggie_ivar
def fix_lup_to_maggie(lup, lup_sig, photo_err=SDSS.photo_err, abfix=SDSS.abfix):
"""
Convert SDSS database ugriz luptitude (asinh mag) to AB maggie
This function is different from lup_to_maggie in that it corrects
erratic entries. It also adds a photometric calibration
uncertainties and applies a correction to adjust to AB photometry.
REFERENCE
k_sdssfix.pro of kcorrect v4_1_4
"""
# the following line should set ivar to zero if sigma <= 0, which
# is basically what k_sdss_err2ivar.pro does.
lup_ivar = sigma2ivar(lup_sig)
mask = np.equal(lup, -9999.) * np.not_equal(lup_ivar, 0.)
lup_ivar = np.where(mask, 0., lup_ivar)
lup_sig = ivar2sigma(lup_ivar)
maggie, maggie_ivar = lup_to_maggie(lup, lup_sig)
mask = np.equal(lup_ivar, 0.)
maggie = np.where(mask, 0., maggie)
maggie_ivar = np.where(mask, 0., maggie_ivar)
# add photometric calibration errors
maggie_ivar = add_absigma_to_maggie_ivar(maggie, maggie_ivar, photo_err)
# correct for AB offsets
abfix = np.asarray(abfix, dtype=FTYPE)
maggie = maggie * 10**(-0.4 * abfix)
maggie_ivar = maggie_ivar * 10**(0.8 * abfix)
return maggie, maggie_ivar
def lup_to_maggie(lup, lup_sig=[], sdss_filters='ugriz', bvalues=SDSS.bvalues):
"""
Convert luptitude (asinh mag) to AB maggie
Conversion from luptitude to maggie is:
maggie = 2 b sinh( - ln(b) - 0.4 ln(10) lup)
This is linear when maggie ~ b, identical to maggie for maggie >> b.
Conversion of the errors is:
maggie_err = 2 b cosh( -ln(b) - 0.4 ln(10) lup) (0.4 ln 10) lup_err
This function returns AB maggies for the given set of SDSS
filters. If lup_sig is also given, the function returns the
inverse variances of AB maggies. The size of each entry in the
input luptitude list must be the same as the number of SDSS
filters specified in sdss_filters as a string (e.g., 'ugriz',
'urz', etc.).
REFERENCE
k_lups2maggies.pro of kcorrect v4_1_4
INPUT
lup -- List of SDSS luptitudes
lup_sig -- 1 sigma uncertanties in luptitudes
sdss_filters -- SDSS filters as a string; defaults to 'ugriz'
bvalues -- Softening 'b' parameters; defaults to SDSS.bvalues
"""
# check input data
lup, lup_sig = np.atleast_2d(lup), np.atleast_2d(lup_sig)
if len(sdss_filters) != lup.shape[1]:
raise ValueError('Input lup size incompatible with sdss_filters.')
# get bvalues
bvalues = dict(zip(SDSS.names, bvalues))
b = np.zeros(len(sdss_filters), dtype=FTYPE)
for i in range(len(sdss_filters)):
b[i] = bvalues[sdss_filters[i]]
# compute maggie
maggie = 2. * b * np.sinh(-np.log(b) - 0.4 * np.log(10.) * lup)
if lup_sig.size != 0:
# uncertainties given, so compute the ivar of maggies
if lup.shape != lup_sig.shape:
raise ValueError('Array size different for lup and lup_sig.')
maggie_sigma = (2. * b *
np.cosh(-np.log(b) - 0.4 * np.log(10.) * lup) * 0.4 *
np.log(10.) * lup_sig)
return maggie, sigma2ivar(maggie_sigma)
return maggie
def lup_to_maggie(lup, lup_sig=[], sdss_filters='ugriz', bvalues=SDSS.bvalues):
"""
Convert luptitude (asinh mag) to AB maggie
Conversion from luptitude to maggie is:
maggie = 2 b sinh( - ln(b) - 0.4 ln(10) lup)
This is linear when maggie ~ b, identical to maggie for maggie >> b.
Conversion of the errors is:
maggie_err = 2 b cosh( -ln(b) - 0.4 ln(10) lup) (0.4 ln 10) lup_err
This function returns AB maggies for the given set of SDSS
filters. If lup_sig is also given, the function returns the
inverse variances of AB maggies. The size of each entry in the
input luptitude list must be the same as the number of SDSS
filters specified in sdss_filters as a string (e.g., 'ugriz',
'urz', etc.).
REFERENCE
k_lups2maggies.pro of kcorrect v4_1_4
INPUT
lup -- List of SDSS luptitudes
lup_sig -- 1 sigma uncertanties in luptitudes
sdss_filters -- SDSS filters as a string; defaults to 'ugriz'
bvalues -- Softening 'b' parameters; defaults to SDSS.bvalues
"""
# check input data
lup, lup_sig = np.atleast_2d(lup), np.atleast_2d(lup_sig)
if len(sdss_filters) != lup.shape[1]:
raise ValueError('Input lup size incompatible with sdss_filters.')
# get bvalues
bvalues = dict(zip(SDSS.names, bvalues))
b = np.zeros(len(sdss_filters), dtype=FTYPE)
for i in range(len(sdss_filters)):
b[i] = bvalues[sdss_filters[i]]
# compute maggie
maggie = 2. * b * np.sinh(-np.log(b) - 0.4 * np.log(10.) * lup)
if lup_sig.size != 0:
# uncertainties given, so compute the ivar of maggies
if lup.shape != lup_sig.shape:
raise ValueError('Array size different for lup and lup_sig.')
maggie_sigma = (2. * b * np.cosh(-np.log(b) - 0.4 * np.log(10.) * lup)
* 0.4 * np.log(10.) * lup_sig)
return maggie, sigma2ivar(maggie_sigma)
return maggie