def test_grid_to_spec():
"""Test creating spectrum from grid, and related cache."""
sp = catalog.grid_to_spec('k93models', 6440, 0, 4.3)
w = sp.waveset
w_first_50 = w[:50]
y_first_50 = units.convert_flux(w_first_50, sp(w_first_50), units.FLAM)
w_last_50 = w[-50:]
y_last_50 = units.convert_flux(w_last_50, sp(w_last_50), units.FLAM)
assert 'k93' in sp.meta['expr']
np.testing.assert_allclose(w_first_50.value, [
90.90000153, 93.50000763, 96.09999847, 97.70000458, 99.59999847, 102,
103.80000305, 105.6000061, 107.70000458, 110.40000153, 114,
117.79999542, 121.30000305, 124.79999542, 127.09999847, 128.40000916,
130.5, 132.3999939, 133.90000916, 136.6000061, 139.80000305,
143.30000305, 147.19999695, 151, 155.20001221, 158.80000305,
162.00001526, 166, 170.30000305, 173.40000916, 176.80000305,
180.20001221, 181.69999695, 186.1000061, 191, 193.8999939,
198.40000916, 201.80000305, 205, 210.5, 216.20001221, 219.80000305,
223, 226.80000305, 230, 234, 240, 246.5, 252.3999939, 256.80001831
])
np.testing.assert_array_equal(y_first_50.value, 0)
np.testing.assert_allclose(w_last_50.value, [
83800, 84200, 84600, 85000, 85400, 85800, 86200, 86600, 87000, 87400,
87800, 88200, 88600, 89000, 89400, 89800, 90200, 90600, 91000, 91400,
91800, 92200, 92600, 93000, 93400, 93800, 94200, 94600, 95000, 95400,
95800, 96200, 96600, 97000, 97400, 97800, 98200, 98600, 99000, 99400,
99800, 100200, 200000, 400000, 600000, 800000, 1000000, 1200000,
1400000, 1600000
])
np.testing.assert_allclose(y_last_50.value, [
2.52510792e+03, 2.47883842e+03, 2.43311637e+03, 2.38843415e+03,
2.34455095e+03, 2.30190141e+03, 2.25982266e+03, 2.21930715e+03,
2.17950029e+03, 2.14031198e+03, 2.10216378e+03, 2.06411734e+03,
2.02789000e+03, 1.99191291e+03, 1.95752853e+03, 1.92259620e+03,
1.88976666e+03, 1.85768178e+03, 1.82475330e+03, 1.79369145e+03,
1.76356796e+03, 1.73377904e+03, 1.70432192e+03, 1.67572220e+03,
1.64739969e+03, 1.61997833e+03, 1.59299008e+03, 1.56657219e+03,
1.54066436e+03, 1.51508799e+03, 1.49065412e+03, 1.46606232e+03,
1.44255637e+03, 1.41922753e+03, 1.39555249e+03, 1.37360936e+03,
1.35179525e+03, 1.33041182e+03, 1.30944458e+03, 1.28851215e+03,
1.26828580e+03, 1.24841065e+03, 8.04744247e+01, 5.03657385e+00,
9.88851448e-01, 3.10885179e-01, 1.26599425e-01, 6.07383728e-02,
3.26344365e-02, 1.90505413e-02
])
# Test cache
key = list(catalog._CACHE.keys())[0]
assert key.endswith('grid/k93models/catalog.fits')
assert isinstance(catalog._CACHE[key], list)
# Reset cache
catalog.reset_cache()
assert catalog._CACHE == {}
def test_grid_to_spec():
"""Test creating spectrum from grid, and related cache."""
sp = catalog.grid_to_spec('k93models', 6440, 0, 4.3)
w = sp.waveset
w_first_50 = w[:50]
y_first_50 = units.convert_flux(w_first_50, sp(w_first_50), units.FLAM)
w_last_50 = w[-50:]
y_last_50 = units.convert_flux(w_last_50, sp(w_last_50), units.FLAM)
assert 'k93' in sp.meta['expr']
np.testing.assert_allclose(
w_first_50.value,
[90.90000153, 93.50000763, 96.09999847, 97.70000458, 99.59999847, 102,
103.80000305, 105.6000061, 107.70000458, 110.40000153, 114,
117.79999542, 121.30000305, 124.79999542, 127.09999847, 128.40000916,
130.5, 132.3999939, 133.90000916, 136.6000061, 139.80000305,
143.30000305, 147.19999695, 151, 155.20001221, 158.80000305,
162.00001526, 166, 170.30000305, 173.40000916, 176.80000305,
180.20001221, 181.69999695, 186.1000061, 191, 193.8999939,
198.40000916, 201.80000305, 205, 210.5, 216.20001221, 219.80000305,
223, 226.80000305, 230, 234, 240, 246.5, 252.3999939, 256.80001831])
np.testing.assert_array_equal(y_first_50.value, 0)
np.testing.assert_allclose(
w_last_50.value,
[83800, 84200, 84600, 85000, 85400, 85800, 86200, 86600, 87000, 87400,
87800, 88200, 88600, 89000, 89400, 89800, 90200, 90600, 91000, 91400,
91800, 92200, 92600, 93000, 93400, 93800, 94200, 94600, 95000, 95400,
95800, 96200, 96600, 97000, 97400, 97800, 98200, 98600, 99000, 99400,
99800, 100200, 200000, 400000, 600000, 800000, 1000000, 1200000,
1400000, 1600000])
np.testing.assert_allclose(
y_last_50.value,
[2.52510792e+03, 2.47883842e+03, 2.43311637e+03, 2.38843415e+03,
2.34455095e+03, 2.30190141e+03, 2.25982266e+03, 2.21930715e+03,
2.17950029e+03, 2.14031198e+03, 2.10216378e+03, 2.06411734e+03,
2.02789000e+03, 1.99191291e+03, 1.95752853e+03, 1.92259620e+03,
1.88976666e+03, 1.85768178e+03, 1.82475330e+03, 1.79369145e+03,
1.76356796e+03, 1.73377904e+03, 1.70432192e+03, 1.67572220e+03,
1.64739969e+03, 1.61997833e+03, 1.59299008e+03, 1.56657219e+03,
1.54066436e+03, 1.51508799e+03, 1.49065412e+03, 1.46606232e+03,
1.44255637e+03, 1.41922753e+03, 1.39555249e+03, 1.37360936e+03,
1.35179525e+03, 1.33041182e+03, 1.30944458e+03, 1.28851215e+03,
1.26828580e+03, 1.24841065e+03, 8.04744247e+01, 5.03657385e+00,
9.88851448e-01, 3.10885179e-01, 1.26599425e-01, 6.07383728e-02,
3.26344365e-02, 1.90505413e-02])
# Test cache
key = list(catalog._CACHE.keys())[0]
assert key.endswith('grid/k93models/catalog.fits')
assert isinstance(catalog._CACHE[key], list)
# Reset cache
catalog.reset_cache()
assert catalog._CACHE == {}
def add_abs_lines(self, center, ew, fwhm):
"""
TODO: accept different profiles (Lorentz1D, Voigt1D, etc)
Add a absorption line of to a spectrum with center, fwhm and equivalent width specified by the user
It also supports emission lines if ew is negative
Parameters
----------
center: float, list, np.ndarray, u.Quantity
The center of the line
fwhm: float, list, np.ndarray, u.Quantity
The FWHM of the line
ew: float, list, np.ndarray, u.Quantity
The Equivalent Width of the line
Returns
-------
Spextrum
"""
if isinstance(center, u.Quantity) is True:
center = center.to(u.AA).value
if isinstance(ew, u.Quantity) is True:
ew = ew.to(u.AA).value
if isinstance(fwhm, u.Quantity) is True:
fwhm = fwhm.to(u.AA).value
centers = np.array([center]).flatten()
ews = np.array([ew]).flatten()
fwhms = np.array([fwhm]).flatten()
sp = self # .__class__(modelclass=self.model)
sp.meta.update({"em_lines": {"center": list(centers),
"ew": list(ews),
"fwhm": list(fwhms)}})
for c, e, f in zip(centers, ews, fwhms):
sign = -1 * np.sign(e) # to keep the convention that EL are negative and ABS are positive
left, right = c - np.abs(e / 2), c + np.abs(e / 2)
wavelengths = self.waveset[(self.waveset.value >= left) & (self.waveset.value <= right)]
fluxes = units.convert_flux(wavelengths=wavelengths,
fluxes=self(wavelengths),
out_flux_unit=units.FLAM)
flux = np.trapz(fluxes.value, wavelengths.value)
line = GaussianFlux1D(mean=c, total_flux=sign * flux, fwhm=f)
lam = line.sampleset(factor_step=0.35) # bit better than Nyquist
g_abs = SourceSpectrum(Empirical1D, points=lam, lookup_table=line(lam))
sp = sp + g_abs
if (sp(wavelengths).value < 0).any():
warnings.warn("Warning: Flux<0 for specified EW and FHWM, setting it to Zero")
waves = sp.waveset[sp(sp.waveset) < 0]
zero_sp = SourceSpectrum(Empirical1D, points=waves, lookup_table=-1 * sp(waves).value)
sp = sp + zero_sp # Spextrum(modelclass=sp.model + zero_sp.model)
sp = self._restore_attr(Spextrum(modelclass=sp))
return sp
def __init__(self, mag, magsystem='VEGAMAG', filt_range=None, sed=None, temp=5778):
# Define the magnitude system.
if filt_range is None:
filt_range = [5000, 6000]
if magsystem.lower() == 'vegamag':
sys = units.VEGAMAG
elif magsystem.lower() == 'stmag':
sys = u.STmag
elif magsystem.lower() == 'abnu':
sys = u.ABmag
# Get Vega's spectrum.
vega = SourceSpectrum.from_vega()
# Set attributes.
self.mag = mag
self.SED = sed
self.temp = temp
self.inputFlux = units.convert_flux(filt_range, mag * sys, units.FLAM, vegaspec=vega)
self.range = filt_range
self.F_lambda = self.starF_lambda()
def synphot_calcs(self, bpfile):
"""
Calculate zeropoint for a given bandpass in several
photometric systems
Arguments:
----------
bpfile -- Text file containing the throuput table for
a single bandpass
Returns:
--------
Bandpass zeropoint value in Vega mags, AB mags, ST mags,
photflam, photfnu, and megajansky. Also returns pivot wavelength
"""
# Define wavelength list to use
#wave_bins = np.arange(0.5, 5, 0.1) * u.micron
# Use refactored synphot to calculate zeropoints
orig_bp = SpectralElement.from_file(bpfile)
# Now reduce the PCE curve by a factor equal to
# the gain, in order to get the output to translate
# from ADU/sec rather than e/sec
bp = orig_bp / self.gain
#bp.model.lookup_table = bp.model.lookup_table / self.gain
photflam = bp.unit_response(self.telescope_area)
photplam = bp.pivot()
st_zpt = -2.5 * np.log10(photflam.value) - 21.1
ab_zpt = (-2.5 * np.log10(photflam.value) - 21.1 -
5 * np.log10(photplam.value) + 18.6921)
#mjy_zpt = units.convert_flux(photplam,photflam, 'MJy')
mjy_zpt = photflam.to(u.MJy, u.spectral_density(photplam))
obs = Observation(self.vega, bp, binset=wave_bins)
vega_zpt = -obs.effstim(flux_unit='obmag', area=self.telescope_area)
photfnu = units.convert_flux(photplam, photflam, units.FNU)
return (vega_zpt.value, ab_zpt, st_zpt, photflam.value, photfnu.value,
mjy_zpt.value, photplam.value)
def grism_cal(self, throughput_files):
"""
Calculate flux cal outputs for grism mode. Input files should
contain the total system throughput for the grism plus crossing
filter. The input file list should contain throughputs for all
orders of just a single grism+crossing filter.
Arguments:
----------
throughput_files -- list of text files containing filter throughputs
Returns:
--------
Astropy table containing flux calibration information for grism mode
"""
print('need to update this for synphot')
allrows = []
filters = np.array([])
pupils = np.array([])
orders = np.array([])
fluxes = np.array([])
uncs = np.array([])
nelems = np.array([])
waves = np.array([])
resps = np.array([])
#waves = np.expand_dims(waves,axis=0)
#resps = np.expand_dims(resps,axis=0)
for file in throughput_files:
# Read in necessary file info
with fits.open(file) as h:
cname = h[0].header['COMPNAME']
junk, filter, mod = cname.split('_')
mod = mod[-1].upper()
pupil = 'GRISMR'
print('Eventually need to be smarter about pupil value!!!!!')
print("opening {}".format(file))
bp = SpectralElement.from_file(file)
# Now reduce the PCE curve by a factor equal to
# the gain, in order to get the output to translate
# from ADU/sec rather than from e/sec
bp.model.lookup_table = bp.model.lookup_table / self.gain
# Pivot wavelength in microns
pwave = bp.pivot()
#pwave = pwave.to(u.micron)
# Need to find the order here, so that the proper
# dispersion value is used.
ord = file.find('order')
order = file[ord + 5]
dispersion = self.disp[order]
#find pivot? mean? center? effective? wavelength
#denom = self.h * self.c / eff_lambda
#countratedensity = self.telescope_area * tp['Throughput'] * vegaflux / denom
#countratedensityflux,countratedensitywave,pwave = self.getcountsflux(bp)
#totalrate = self.integrate(countratedensity)
#Is the variable below used at all?
#grism_total_rate += totalrate
obs = self.getcountsflux(bp)
# From observation, get the flux in counts
countratedensityflux = obs(obs.binset,
flux_unit='count',
area=self.telescope_area)
print('countratedensityflux', countratedensityflux)
# Multiply by dispersion
countratedensityflux *= dispersion
# Countrate density at the pivot wavelength
print('pivot', pwave.value)
print('countratedensityflux*dispersion', countratedensityflux)
print('obs.binset', obs.binset)
#cnorm = np.interp(pwave.value,obs.binset,countratedensityflux)
cnorm = obs(pwave, flux_unit='count',
area=self.telescope_area) * dispersion
print('cnorm', cnorm)
# Vega flux value at pivot wavelength, convert to Jansky
vega_pivot = self.vega(pwave)
j0 = units.convert_flux(pwave, vega_pivot, 'MJy')
# Now we need to divide by the area of a pixel in
# sterradians, so we can eventually get MJy/str per ADU/sec
j0 /= self.pixel_area_sr
#vega_pivotorig = np.interp(pwave.value,self.vega.waveset,self.vega(self.vega.waveset))
#print("units of vega flux are: {}".format(self.vega(self.vega.waveset).unit))
#j0 = self.toJansky(vega_pivot.value,pwave.value) / 1.e6
# Ratio of Vega flux to the countrate density at pivot wavelength
ratio = j0 / cnorm
print('')
print('PHOTMJSR', ratio)
print(
'NIRISS values are 0.01 to 2.0. I would expect ours to be similar!'
)
print('')
# Define a set of wavelengths to evaluate relative fluxcal
goodpts = bp(bp.waveset) > 0.0001
minwave = np.min(bp.waveset[goodpts])
maxwave = np.max(bp.waveset[goodpts])
w = minwave.value
allwaves = np.array([w])
while (w < maxwave.value):
delt = w / (np.absolute(np.int(order)) * self.resolving_power)
allwaves = np.append(allwaves, w + delt)
w += delt
# Calculate Vega flux at each wavelength
nelem = len(allwaves)
allfluxes = self.vega(allwaves)
alljansky = units.convert_flux(allwaves, allfluxes, 'MJy')
allcounts = np.interp(allwaves, obs.binset, countratedensityflux)
#allfluxes = np.interp(allwaves,self.vega.waveset,self.vega(self.vega.waveset))
#alljansky = self.toJansky(allfluxes,allwaves) / 1.e6
# Normalize the Vega counts at all wavelengths by the value at the pivot
# wavelength
allratio = alljansky.value / allcounts / ratio / self.pixel_area_sr
#print(np.min(allwaves),np.max(allwaves),allwaves[0],allwaves[-1])
#f,a = plt.subplots()
#a.plot(allwaves,allratio,color='red')
#a.set_xlabel('Wavelength ({})'.format(bp.waveset.unit))
#a.set_ylabel('Normalized Ratio (MJy/str per count/sec)')
#a.set_title("{}, Order {}".format(filter.upper(),order))
#f.savefig(os.path.split(file)[-1]+'_allratios.pdf')
#f,a = plt.subplots()
#a.plot(allwaves,alljansky,color='red')
#a.set_xlabel('Wavelength ({})'.format(bp.waveset.unit))
#a.set_ylabel('Vega Flux (MJy)')
#a.set_title("{}, Order {}".format(filter.upper(),order))
#f.savefig(os.path.split(file)[-1]+'_alljansky.pdf')
#f,a = plt.subplots()
#a.plot(allwaves,allcounts,color='red')
#a.set_xlabel('Wavelength ({})'.format(bp.waveset.unit))
#a.set_ylabel('Counts (count/sec)')
#a.set_title("{}, Order {}".format(filter.upper(),order))
#f.savefig(os.path.split(file)[-1]+'_allcounts.pdf')
if np.min(allcounts) < 0:
print('WARNING: counts<0 for {},{}'.format(
filter.upper(), order))
stop
if '444' in filter:
print("")
print('444!!!!!!')
print(allratio.value)
print(len(allratio.value))
print(type(allratio.value))
#bad = allratio.value > 1e5
bad = np.where(allratio.value > 1e5)
print(len(bad[0]))
print(type(bad[0]))
print(alljansky.value[bad[0][0:10]])
print(allcounts[bad[0][0:10]])
print(ratio)
print(self.str_per_detector)
print(allratio.value[bad[0][0:10]])
print(allwaves)
stop
# Pad allwaves and allratio to be the length needed by the
# photom reference file. Also convert wavelengths to microns.
allwaves = np.pad(allwaves / 1.e4, (0, self.maxlen - nelem),
'constant')
allratio = np.pad(allratio, (0, self.maxlen - nelem), 'constant')
print(allwaves[100:105])
print(allcounts[100:105])
print(alljansky[100:105])
print(alljansky[100:105] / allcounts[100:105])
print(ratio)
print(allratio[100:105])
print("******************")
print("******************")
print("******************")
print("need real conversion factor and uncertainty!!!")
conversion_factor = 1000.
uncertainty = ratio * .1
#row = [filter,pupil,np.int(order),ratio*conversion_factor,uncertainty,nelem,allwaves,allratio]
# Populate lists that will be used to create the final table
filters = np.append(filters, filter)
pupils = np.append(pupils, pupil)
orders = np.append(orders, np.int(order))
fluxes = np.append(fluxes, ratio * conversion_factor)
uncs = np.append(uncs, uncertainty)
nelems = np.append(nelems, nelem)
print(allwaves.shape)
if len(waves) == 0:
waves = allwaves
waves = np.expand_dims(waves, axis=0)
resps = allratio
resps = np.expand_dims(resps, axis=0)
else:
waves = np.append(waves,
np.expand_dims(allwaves, axis=0),
axis=0)
resps = np.append(resps,
np.expand_dims(allratio, axis=0),
axis=0)
print('waves.shape', waves.shape)
print('resps.shape', resps.shape)
print(filters)
print(pupils)
print(orders)
print(fluxes)
print(uncs)
print(nelems)
print(waves[0, 40:45])
print(resps[0, 40:45])
# Zip all data together
alldata = np.array(zip(np.array(filters), np.array(pupils),
np.array(fluxes), np.array(uncs),
np.array(orders), np.array(nelems),
np.array(waves), np.array(resps)),
dtype=self.model.phot_table.dtype)
return alldata
def p_functioncall(self, tree):
# Where all the real interpreter action is.
# Note that things that should only be done at the top level
# are performed in :func:`interpret` defined below.
""" V ::= function_call ( V LPAREN V RPAREN ) """
if not isinstance(tree[2].value, list):
args = [tree[2].value]
else:
args = tree[2].value
fname = tree[0].value
metadata = {'expr': '{0}{1}'.format(fname, tuple(args))}
if fname not in _SYFUNCTIONS:
log.error('Unknown function: {0}'.format(fname))
self.error(fname)
else:
# Constant spectrum
if fname == 'unit':
if args[1] not in _SYFORMS:
log.error('Unrecognized unit: {0}'.format(args[1]))
self.error(fname)
try:
fluxunit = units.validate_unit(args[1])
tree.value = SourceSpectrum(ConstFlux1D,
amplitude=args[0] * fluxunit,
meta=metadata)
except NotImplementedError as e:
log.error(str(e))
self.error(fname)
# Black body
elif fname == 'bb':
tree.value = SourceSpectrum(BlackBodyNorm1D,
temperature=args[0])
# Power law
elif fname == 'pl':
if args[2] not in _SYFORMS:
log.error('Unrecognized unit: {0}'.format(args[2]))
self.error(fname)
try:
fluxunit = units.validate_unit(args[2])
tree.value = SourceSpectrum(PowerLawFlux1D,
amplitude=1 * fluxunit,
x_0=args[0],
alpha=-args[1],
meta=metadata)
except (synexceptions.SynphotError, NotImplementedError) as e:
log.error(str(e))
self.error(fname)
# Box throughput
elif fname == 'box':
tree.value = SpectralElement(Box1D,
amplitude=1,
x_0=args[0],
width=args[1],
meta=metadata)
# Source spectrum from file
elif fname == 'spec':
tree.value = SourceSpectrum.from_file(irafconvert(args[0]))
tree.value.meta.update(metadata)
# Passband
elif fname == 'band':
tree.value = spectrum.band(tree[2].svalue)
tree.value.meta.update(metadata)
# Gaussian emission line
elif fname == 'em':
if args[3] not in _SYFORMS:
log.error('Unrecognized unit: {0}'.format(args[3]))
self.error(fname)
x0 = args[0]
fluxunit = units.validate_unit(args[3])
totflux = units.convert_flux(x0, args[2] * fluxunit,
units.PHOTLAM).value
tree.value = SourceSpectrum(GaussianFlux1D,
total_flux=totflux,
mean=x0,
fwhm=args[1])
# Catalog interpolation
elif fname == 'icat':
tree.value = grid_to_spec(*args)
# Renormalize source spectrum
elif fname == 'rn':
sp = args[0]
bp = args[1]
fluxunit = units.validate_unit(args[3])
rnval = args[2] * fluxunit
if not isinstance(sp, SourceSpectrum):
sp = SourceSpectrum.from_file(irafconvert(sp))
if not isinstance(bp, SpectralElement):
bp = SpectralElement.from_file(irafconvert(bp))
# Always force the renormalization to occur: prevent exceptions
# in case of partial overlap. Less robust but duplicates
# IRAF SYNPHOT. Force the renormalization in the case of
# partial overlap, but raise an exception if the spectrum and
# bandpass are entirely disjoint.
try:
tree.value = sp.normalize(rnval,
band=bp,
area=conf.area,
vegaspec=spectrum.Vega)
except synexceptions.PartialOverlap:
tree.value = sp.normalize(rnval,
band=bp,
area=conf.area,
vegaspec=spectrum.Vega,
force=True)
tree.value.warnings = {
'force_renorm': ('Renormalization exceeds the limit '
'of the specified passband.')
}
tree.value.meta.update(metadata)
# Redshift source spectrum (flat spectrum if fails)
elif fname == 'z':
sp = args[0]
# ETC generates junk (i.e., 'null') sometimes
if isinstance(sp, str) and sp != 'null':
sp = SourceSpectrum.from_file(irafconvert(sp))
if isinstance(sp, SourceSpectrum):
tree.value = sp
tree.value.z = args[1]
else:
tree.value = SourceSpectrum(ConstFlux1D, amplitude=1)
tree.value.meta.update(metadata)
# Extinction
elif fname == 'ebmvx':
try:
tree.value = spectrum.ebmvx(args[1], args[0])
except synexceptions.SynphotError as e:
log.error(str(e))
self.error(fname)
tree.value.meta.update(metadata)
# Default
else:
tree.value = ('would call {0} with the following args: '
'{1}'.format(fname, repr(args)))
def flux2ABmag(wave, flux, band, mag, plot=False):
# "band" should be either "sdss_g" or "V" for the moment
# The input "wave" should be in nm
# Define bandpass:
if band == "sdss_g":
bp = SpectralElement.from_file('../utility/photometry/g.dat')
magvega = -0.08
elif band == "V":
bp = SpectralElement.from_filter('johnson_v')
magvega = 0.03
# SDSS g-band magnitude of Vega
#gmagvega=-0.08
# Read the spectrum of Vega
sp_vega = SourceSpectrum.from_vega()
wv_vega = sp_vega.waveset
fl_vega = sp_vega(wv_vega, flux_unit=units.FLAM)
## Convolve with the bandpass
obs_vega = Observation(sp_vega, bp)
## Integrated flux
fluxtot_vega = obs_vega.integrate()
# Read the synthetic spectrum
sp = SourceSpectrum(Empirical1D, points = wave * 10., \
lookup_table=flux*units.FLAM)
wv = sp.waveset
fl = sp(wv, flux_unit=units.FLAM)
## Convolve with the bandpass
obs = Observation(sp, bp, force='extrap')
## Integrated g-band flux
fluxtot = obs.integrate()
# Scaling factor to make the flux compatible with the desired magnitude
dm = mag - magvega
const = fluxtot_vega * 10**(-0.4 * dm) / fluxtot
# Scale the original flux by const
fl_scale = const * fl
# Convert to ABmag
fl_scale_mag = units.convert_flux(wv, fl_scale, out_flux_unit='abmag')
sp_scaled_mag = SourceSpectrum(Empirical1D,
points=wv,
lookup_table=fl_scale_mag)
# Plot
if plot == True:
fig, ax = plt.subplots(2, 1, sharex=True)
ax[0].plot(wave * 10., flux, linestyle="-", marker="")
ax[1].plot(wv[1:-1],
sp_scaled_mag(wv, flux_unit=u.ABmag)[1:-1],
linestyle="-",
marker="")
ax[1].set_xlabel("Angstrom")
ax[0].set_ylabel("$F_{\lambda}$")
ax[1].set_ylabel("ABmag")
ax[1].set_ylim(mag + 3., mag - 2.0)
#plt.show()
return (wv[1:-1] / 10., sp_scaled_mag(wv, flux_unit=u.ABmag)[1:-1])
