def _map_filter_to_standard(self, filtername):
"""Maps a passed <filtername> to a standard Johnson/Cousins/Bessell
filter profile. Returns a SpectralElement, defaulting to V"""
band_mapping = {
'U': 'johnson_u',
'B': 'johnson_b',
'V': 'johnson_v',
'R': conf.bessell_R_file,
'Rc': 'cousins_r',
'I': conf.bessell_I_file,
'Ic': 'cousins_i',
'J': 'bessel_j',
'H': 'bessel_h',
'K': 'bessel_k',
'G': conf.gaiadr2_g_file
}
band_name = band_mapping.get(self._convert_filtername(filtername),
'johnson_v')
if band_name.startswith('http://svo'):
band = SpectralElement.from_file(band_name)
else:
band = SpectralElement.from_filter(band_name)
return band
def filter_vega_zp(filterfile, filterzero):
'''
######################################################
# Input #
# -------------------------------------------------- #
# filterfile: file containing filter function #
# filterzero: flux corresponding to mag=0 #
# -------------------------------------------------- #
# Output #
# -------------------------------------------------- #
# mag_0: magnitude of Vega in filter system. #
######################################################
'''
from synphot import SourceSpectrum, SpectralElement, Observation
#load Vega spectrum
spec = SourceSpectrum.from_vega()
#Load ascii filter function
if filterfile.split('/')[-1][:6] == 'Bessel':
filt = SpectralElement.from_file(filterfile, wave_unit='nm')
else:
filt = SpectralElement.from_file(filterfile, wave_unit='AA')
wave = filt.waveset
#Synthetic observation
obs = Observation(spec, filt)
flux = obs.effstim(flux_unit='jy', waverange=(wave[0],wave[-1]))
#Calibrate to zero point (zp)
mag_0 = -2.512*np.log10(flux/filterzero)
return mag_0
def __init__(self):
self.verbose = False
self.telescope_area = 25.326 * 10000. #JWST primary area in cm^2
self.wave_bins = np.arange(0.5, 5, 0.1) * u.micron
self.vega = SourceSpectrum.from_vega()
self.g2v = STS.grid_to_spec('ck04models', 5860, 0., 4.40)
self.kband = SpectralElement.from_filter('bessel_k')
self.bpdir = './'
self.bpfile = os.path.join(
self.bpdir, 'F335M_nircam_plus_ote_throughput_moda_sorted.txt')
self.bp = SpectralElement.from_file(self.bpfile, wave_unit=u.micron)
def filter_mag(filterfile, filterzero, syn_spec, syn_err=None, wrange=None):
'''
######################################################
# Input #
# -------------------------------------------------- #
# filterfile: file containing filter function #
# filterzero: flux [Jy] corresponding to mag=0 #
# syn_spec: synphot spectrum object of source #
# wrange: (start,end) observed wavelength range #
# -------------------------------------------------- #
# Output #
# -------------------------------------------------- #
# mag: magnitude of source in filter system. #
######################################################
'''
from synphot import SpectralElement, Observation, Empirical1D, SourceSpectrum
import astropy.units as u
#Load ascii filter function
if filterfile.split('/')[-1][:6] == 'Bessel':
filt = SpectralElement.from_file(filterfile, wave_unit='nm')
else:
filt = SpectralElement.from_file(filterfile, wave_unit='AA')
if wrange is None:
fwave = filt.waveset
swave = syn_spec.waveset
wrange = (max(fwave[0],swave[0]),min(fwave[-1],swave[-1]))
waves = np.linspace(wrange[0], wrange[-1], 10000)
#Synthetic observation
obs = Observation(syn_spec, filt, force='extrap')
flux = obs.effstim(flux_unit='jy', waverange=wrange).value
#flux = obs.effstim(flux_unit='jy', wavelengths=waves).value
#Calibrate magnitude with zero point
mag = -2.512*np.log10(flux/filterzero)
#Synthetic observation of error spectrum
if syn_err is not None:
#square filter and error spectrum
filt2 = SpectralElement(Empirical1D, points=fwave,
lookup_table=np.square(filt(fwave)))
pseudo_flux = np.square(syn_err(swave,flux_unit='jy')).value
syn_err2 = SourceSpectrum(Empirical1D, points=swave,
lookup_table=pseudo_flux)
#sum errors in quadrature
obs = Observation(syn_err2, filt2, force='extrap')
#flux_err = np.sqrt(obs.effstim(waverange=wrange).value)
flux_err = np.sqrt(obs.effstim(wavelengths=waves).value)
#convert to magnitude error
mag_err = (2.5/np.log(10))*(flux_err/flux)
return mag, mag_err
else:
return mag
def filter_flux(filterfile, syn_spec, syn_err=None, wrange=None):
'''
######################################################
# Input #
# -------------------------------------------------- #
# filterfile: file containing filter function #
# syn_spec: synphot spectrum object of source #
# wrange: (start,end) observed wavelength range #
# -------------------------------------------------- #
# Output #
# -------------------------------------------------- #
# flux: flux of source in filter system. #
######################################################
'''
from synphot import SpectralElement, Observation, Empirical1D, SourceSpectrum
import astropy.units as u
#Load ascii filter function
if filterfile.split('/')[-1][:6] == 'Bessel':
filt = SpectralElement.from_file(filterfile, wave_unit='nm')
else:
filt = SpectralElement.from_file(filterfile, wave_unit='AA')
if wrange is None:
fwave = filt.waveset
swave = syn_spec.waveset
wrange = (max(fwave[0],swave[0]),min(fwave[-1],swave[-1]))
wlengths = fwave[np.logical_and(fwave>wrange[0], fwave<wrange[1])]
else:
fwave = filt.waveset
wlengths = fwave[np.logical_and(fwave>wrange[0]*u.AA, fwave<wrange[1]*u.AA)]
#Synthetic observation
obs = Observation(syn_spec, filt, force='extrap')
flux = obs.effstim(flux_unit='jy', waverange=wrange).value
#flux = obs.effstim(flux_unit='jy', wavelengths=wlengths).value
#Synthetic observation of error spectrum
if syn_err is not None:
#square filter and error spectrum
filt2 = SpectralElement(Empirical1D, points=fwave,
lookup_table=np.square(filt(fwave)))
pseudo_flux = np.square(syn_err(swave,flux_unit='jy')).value
syn_err2 = SourceSpectrum(Empirical1D, points=swave,
lookup_table=pseudo_flux)
#sum errors in quadrature
obs = Observation(syn_err2, filt2, force='extrap')
flux_err = np.sqrt(obs.effstim(waverange=wrange).value)
return flux, flux_err
else:
return flux
def rebin_spectra(self, new_waves):
"""
Rebin a synphot spectra to a new wavelength grid conserving flux.
Grid does not need to be linear and can be at higher or lower resolution
Parameters
----------
new_waves: an array of the output wavelenghts in Angstroms but other units can be
specified
Returns
-------
a new Spextrum instance
"""
if isinstance(new_waves, u.Quantity):
new_waves = new_waves.to(u.AA).value
waves = self.waveset.value # else assumed to be angstroms
f = np.ones(len(waves))
filt = SpectralElement(Empirical1D, points=waves, lookup_table=f)
obs = Observation(self, filt, binset=new_waves, force='taper')
newflux = obs.binflux
rebin_spec = Empirical1D(points=new_waves,
lookup_table=newflux,
meta=self.meta)
sp = Spextrum(modelclass=rebin_spec)
sp = self._restore_attr(sp)
sp.meta.update({"rebinned_spectra": True})
return sp
def photons_in_range(self,
wmin=None,
wmax=None,
area=1 * u.cm**2,
filter_curve=None):
"""
Return the number of photons between wave_min and wave_max or within
a bandpass (filter)
Parameters
----------
wmin :
[Angstrom]
wmax :
[Angstrom]
area : u.Quantity
[cm2]
filter_curve : str
Name of a filter from
- a generic filter name (see ``DEFAULT_FILTERS``)
- a spanish-vo filter service reference (e.g. ``"Paranal/HAWKI.Ks"``)
- a filter in the spextra database
- the path to the file containing the filter (see ``Passband``)
- a ``Passband`` or ``synphot.SpectralElement`` object
Returns
-------
counts : u.Quantity array
"""
if isinstance(area, u.Quantity):
area = area.to(u.cm**2).value #
if isinstance(wmin, u.Quantity):
wmin = wmin.to(u.Angstrom).value
if isinstance(wmax, u.Quantity):
wmin = wmax.to(u.Angstrom).value
if filter_curve is None:
# this makes a bandpass out of wmin and wmax
try:
mid_point = 0.5 * (wmin + wmax)
width = abs(wmax - wmin)
filter_curve = SpectralElement(Box1D,
amplitude=1,
x_0=mid_point,
width=width)
except ValueError("Please specify wmin/wmax or a filter"):
raise
elif os.path.exists(filter_curve):
filter_curve = Passband.from_file(filename=filter_curve)
elif isinstance(filter_curve, (Passband, SpectralElement)):
filter_curve = filter_curve
else:
filter_curve = Passband(filter_curve)
obs = Observation(self, filter_curve)
counts = obs.countrate(area=area * u.cm**2)
return counts
def collapse(self, waveset):
throughput = self.radiometry_table.throughput(waveset)
self._throughput = SpectralElement(Empirical1D, points=waveset,
lookup_table=throughput)
emission = self.radiometry_table.emission(waveset)
self._emission = SourceSpectrum(Empirical1D, points=waveset,
lookup_table=emission)
def get_phot(spec, bands):
"""
Compute the fluxes in the requested bands.
Parameters
----------
spec : SpecData object
the spectrum
bands: list of strings
bands requested
Outputs
-------
band fluxes : numpy array
calculated band fluxes
"""
# create a SourceSpectrum object from the spectrum, excluding the bad regions
spectrum = SourceSpectrum(
Empirical1D,
points=spec.waves.to(u.Angstrom)[spec.npts != 0],
lookup_table=spec.fluxes[spec.npts != 0],
)
# path for band response curves
band_path = pkg_resources.resource_filename("measure_extinction",
"data/Band_RespCurves/")
# dictionary linking the bands to their response curves
bandnames = {
"J": "2MASSJ",
"H": "2MASSH",
"K": "2MASSKs",
"IRAC1": "IRAC1",
"IRAC2": "IRAC2",
"WISE1": "WISE1",
"WISE2": "WISE2",
"L": "AAOL",
"M": "AAOM",
}
# define the units of the output fluxes
funit = u.erg / (u.s * u.cm * u.cm * u.Angstrom)
# compute the flux in each band
fluxes = np.zeros(len(bands))
for k, band in enumerate(bands):
# create the bandpass (as a SpectralElement object)
bp = SpectralElement.from_file(
"%s%s.dat" %
(band_path,
bandnames[band])) # assumes wavelengths are in Angstrom!!
# integrate the spectrum over the bandpass, only if the bandpass fully overlaps with the spectrum (this actually excludes WISE2)
if bp.check_overlap(spectrum) == "full":
obs = Observation(spectrum, bp)
fluxes[k] = obs.effstim(funit).value
else:
fluxes[k] = np.nan
return fluxes
def get_phot(waves, exts, bands):
"""
Compute the extinction in the requested bands
Parameters
----------
waves : numpy.ndarray
The wavelengths
exts : numpy.ndarray
The extinction values at wavelengths "waves"
bands: list of strings
Bands requested
Outputs
-------
band extinctions : numpy array
Calculated band extinctions
"""
# create a SourceSpectrum object from the extinction curve
spectrum = SourceSpectrum(
Empirical1D,
points=waves * 1e4,
lookup_table=exts,
)
# path for band response curves
band_path = (
"/Users/mdecleir/measure_extinction/measure_extinction/data/Band_RespCurves/"
)
# dictionary linking the bands to their response curves
bandnames = {
"J": "2MASSJ",
"H": "2MASSH",
"K": "2MASSKs",
"IRAC1": "IRAC1",
"IRAC2": "IRAC2",
"WISE1": "WISE1",
"WISE2": "WISE2",
"L": "AAOL",
"M": "AAOM",
}
# compute the extinction value in each band
band_ext = np.zeros(len(bands))
for k, band in enumerate(bands):
# create the bandpass (as a SpectralElement object)
bp = SpectralElement.from_file(
"%s%s.dat" %
(band_path,
bandnames[band])) # assumes wavelengths are in Angstrom!!
# integrate the extinction curve over the bandpass, only if the bandpass fully overlaps with the extinction curve (this actually excludes WISE2)
if bp.check_overlap(spectrum) == "full":
obs = Observation(spectrum, bp)
band_ext[k] = obs.effstim().value
else:
band_ext[k] = np.nan
return band_ext
def _get_ter_property(self, ter_property):
"""
Looks for arrays for transmission, emissivity, or reflection
Parameters
----------
ter_property : str
``transmission``, ``emissivity``, ``reflection``
Returns
-------
response_curve : ``synphot.SpectralElement``
"""
compliment_names = ["transmission", "emissivity", "reflection"]
ii = np.where([ter_property == name
for name in compliment_names])[0][0]
compliment_names.pop(ii)
wave = self._get_array("wavelength")
value_arr = self._get_array(ter_property)
if value_arr is None:
value_arr = self._compliment_array(*compliment_names)
if value_arr is not None and wave is not None:
response_curve = SpectralElement(Empirical1D,
points=wave,
lookup_table=value_arr)
else:
response_curve = None
warnings.warn("Both wavelength and {} must be set"
"".format(ter_property))
return response_curve
def get_phot(mwave, mflux, band_names, band_resp_filenames):
"""
Compute the magnitudes in the requested bands.
Parameters
----------
mwave : vector
wavelengths of model flux
mflux : vector
model fluxes
band_names: list of strings
names of bands requested
band_resp_filename : list of strings
filenames of band response functions for the requested bands
Outputs
-------
bandinfo : BandData object
"""
# get a BandData object
bdata = BandData("BAND")
# path for non-HST band response curves
data_path = pkg_resources.resource_filename("measure_extinction",
"data/Band_RespCurves/")
# compute the fluxes in each band
for k, cband in enumerate(band_names):
if "HST" in cband:
bp_info = cband.split("_")
bp = STS.band("%s,%s,%s" % (bp_info[1], bp_info[2], bp_info[3]))
ncband = "%s_%s" % (bp_info[1], bp_info[3])
else:
bp = SpectralElement.from_file("%s%s" %
(data_path, band_resp_filenames[k]))
ncband = cband
# check if the wavelength units are in microns instead of Angstroms
# may not work
if max(bp.waveset) < 500 * u.Angstrom:
print("filter wavelengths not in angstroms")
exit()
# a.waveset *= 1e4
iresp = bp(mwave)
bflux = np.sum(iresp * mflux) / np.sum(iresp)
bflux_unc = 0.0
bdata.band_fluxes[ncband] = (bflux, bflux_unc)
# calculate the band magnitudes from the fluxes
bdata.get_band_mags_from_fluxes()
# get the band fluxes from the magnitudes
# partially redundant, but populates variables useful later
bdata.get_band_fluxes()
return bdata
def download_svo_filter(filter_name, return_style="synphot"):
"""
Query the SVO service for the true transmittance for a given filter
Copied 1 to 1 from tynt by Brett Morris
Parameters
----------
filt : str
Name of the filter as available on the spanish VO filter service
e.g: ``Paranal/HAWKI.Ks``
return_style : str, optional
Defines the format the data is returned
- synphot: synphot.SpectralElement
- table: astropy.table.Table
- quantity: astropy.unit.Quantity [wave, trans]
- array: np.ndarray [wave, trans], where wave is in Angstrom
- vo_table : astropy.table.Table - original output from SVO service
Returns
-------
filt_curve : See return_style
Astronomical filter object.
"""
path = download_file('http://svo2.cab.inta-csic.es/'
'theory/fps3/fps.php?ID={}'.format(filter_name),
cache=True)
tbl = Table.read(path, format='votable')
wave = u.Quantity(tbl['Wavelength'].data.data, u.Angstrom, copy=False)
trans = tbl['Transmission'].data.data
if return_style == "synphot":
filt = SpectralElement(Empirical1D, points=wave, lookup_table=trans)
elif return_style == "table":
filt = Table(data=[wave, trans], names=["wavelength", "transmission"])
filt.meta["wavelength_unit"] = "Angstrom"
elif return_style == "quantity":
filt = [wave, trans]
elif return_style == "array":
filt = [wave.value, trans]
elif return_style == "vo_table":
filt = tbl
return filt
def test_blackbody_maximum_agrees_with_wien(self, temp):
'''Check the maximum of emission against Wien's law for photon rate'''
emissivity = SpectralElement(Empirical1D, points=[1, 20],
lookup_table=[1., 1.])
flux = surf_utils.make_emission_from_emissivity(temp, emissivity)
dlam = 0.1
wave = np.arange(3, 20, dlam) * u.um
wavemax = wave[np.argmax(flux(wave))]
if isinstance(temp, u.Quantity):
temp = temp.to(u.Kelvin, equivalencies=u.temperature()).value
wienmax = 3669.7 * u.um / temp
assert np.abs(wavemax - wienmax.to(u.um)) < dlam * u.um
def test_returns_correct_half_flux_with_bandpass(self):
flux = np.ones(11) * u.Unit("ph s-1 m-2 um-1")
wave = np.linspace(0.5, 2.5, 11) * u.um
spec = SourceSpectrum(Empirical1D, points=wave, lookup_table=flux)
bandpass = SpectralElement(Empirical1D,
points=np.linspace(1, 2, 13) * u.um,
lookup_table=0.5 * np.ones(13))
counts = source_utils.photons_in_range([spec],
1 * u.um,
2 * u.um,
bandpass=bandpass)
assert counts.value == approx(0.5)
def __init__(self, filter_name=None, modelclass=None, **kwargs):
if filter_name is not None:
if filter_name in DEFAULT_FILTERS:
filter_name = DEFAULT_FILTERS[filter_name]
try:
FilterContainer.__init__(self, filter_name)
meta, wave, trans = self._loader()
SpectralElement.__init__(self,
Empirical1D,
points=wave,
lookup_table=trans,
meta=meta)
except ValueError as e1:
try: # try to download it from SVO
meta, wave, trans = self._from_svo(filter_name)
SpectralElement.__init__(self,
Empirical1D,
points=wave,
lookup_table=trans,
meta=meta)
except ValueError as e2:
print("filter %s doesn't exist" % filter_name)
elif modelclass is not None:
SpectralElement.__init__(self, modelclass, **kwargs)
else:
raise ValueError("please define a filter")
def rebin_transmission(self, wavenew):
"""Rebins a potentially higher resolution atmospheric transmission
spectrum to a new wavelength grid <wavenew>.
Returns a new BaseUnitlessSpectrum constructed from the new passed
wavelength grid and the resampled transmission"""
wave = self.transmission.waveset
specin = self.transmission(wave)
# Have to pretend transmission is in a flux unit to make a SourceSpectrum
# so drop here and put back at the end
orig_unit = specin.unit
spec = SourceSpectrum(Empirical1D,
points=wave,
lookup_table=specin.value)
f = np.ones(len(wave))
filt = SpectralElement(Empirical1D, points=wave, lookup_table=f)
obs = Observation(spec, filt, binset=wavenew, force='taper')
new_trans = SpectralElement(Empirical1D,
points=wavenew,
lookup_table=obs.binflux.value * orig_unit)
return new_trans
def photons_from_source(self,
mag,
mag_filter,
filtername,
source_spec=None,
force_overlap='taper',
normalize=True):
"""Computes the photons coming from the source given by [source_spec] (Vega
is assumed if not given) of the given <mag> in <mag_filter> (e.g. for "V=20",
mag=20, mag_filter='V') when observed through the instrument's filter
given by <filtername>
"""
print("photons_from_source:", filtername)
standard_filter = self._convert_filtername(filtername)
band = self._map_filter_to_standard(mag_filter)
print(band.meta.get('expr', 'Unknown'), type(source_spec))
source_spec = source_spec or self._vega
if type(source_spec) != SourceSpectrum:
raise ETCError('Invalid sourcespec; must be a SourceSpectrum')
# XXX Todo: allow support for AB mags here
# test line below for comparison with SIGNAL. Difference in mag when
# using proper normalization is ~mag-0.0155 (for B=22.7)
# source_spec_norm = source_spec*10**(-0.4*mag)
if normalize is True:
source_spec_norm = source_spec.normalize(mag * units.VEGAMAG,
band,
vegaspec=self._vega)
else:
source_spec_norm = source_spec
self._create_combined()
filter_waves, filter_trans = self.instrument.filterset[
filtername]._get_arrays(None)
throughput = self._throughput_for_filter(filtername)
waves, thru = throughput._get_arrays(filter_waves)
spec_elements = SpectralElement(Empirical1D,
points=filter_waves,
lookup_table=filter_trans * thru)
# get the synphot observation object
synphot_obs = Observation(source_spec_norm,
spec_elements,
force=force_overlap)
if self.obs is None:
self.obs = synphot_obs
countrate = synphot_obs.countrate(area=self.telescope.area)
return countrate
def test_with_bandpass_and_area_returns_correct_value(
self, flux, area, expected):
flux = flux * u.Unit("ph s-1 m-2 um-1")
spec = SourceSpectrum(Empirical1D,
points=np.linspace(0.5, 2.5, 11) * u.um,
lookup_table=flux)
bandpass = SpectralElement(Empirical1D,
points=np.linspace(1, 2, 13) * u.um,
lookup_table=0.5 * np.ones(13))
counts = source_utils.photons_in_range([spec],
1 * u.um,
2 * u.um,
bandpass=bandpass,
area=area)
assert counts.value == approx(expected)
def system_transmission(self):
wave_unit = u.Unit(rc.__currsys__["!SIM.spectral.wave_unit"])
dwave = rc.__currsys__["!SIM.spectral.spectral_bin_width"]
wave_min = rc.__currsys__["!SIM.spectral.wave_min"]
wave_max = rc.__currsys__["!SIM.spectral.wave_max"]
wave = np.arange(wave_min, wave_max, dwave)
trans = np.ones_like(wave)
sys_trans = SpectralElement(Empirical1D,
points=wave * u.Unit(wave_unit),
lookup_table=trans)
for effect in self.get_z_order_effects(100):
sys_trans *= effect.throughput
return sys_trans
def gaussian(cls, center, fwhm, peak, **kwargs):
"""
Creates a filter with a gaussian shape with given user parameters
Returns
-------
"""
if isinstance(center, u.Quantity) is False:
center = center*u.AA
if isinstance(fwhm, u.Quantity) is False:
fwhm = fwhm*u.AA
sigma = fwhm / (2.0 * np.sqrt(2.0 * np.log(2.0)))
modelclass = SpectralElement(Gaussian1D, amplitude=peak, mean=center, stddev=sigma, **kwargs)
return cls(modelclass=modelclass)
def square(cls, wmin, wmax, transmission):
"""
Creates a filter with a rectangular shape
Returns
-------
"""
if isinstance(wmin, u.Quantity) is False:
wmin = wmin*u.AA
if isinstance(wmax, u.Quantity) is False:
wmax = wmax*u.AA
center = (wmax + wmin) * 0.5
width = wmax - wmin
modelclass = SpectralElement(Box1D, amplitude=transmission, x_0=center, width=width)
return cls(modelclass=modelclass)
def plot_bandpasses(bands):
# create the figure
plt.figure(figsize=(15, 5))
# plot all response curves
for band in bands:
bp = SpectralElement.from_file("%s%s.dat" % (band_path, band))
if "MIPS" in band:
wavelengths = bp.waveset * 10**4
else:
wavelengths = bp.waveset
plt.plot(wavelengths, bp(bp.waveset), label=band)
plt.xlabel(r"$\lambda$ [$\AA$]", size=15)
plt.ylabel("transmission", size=15)
plt.legend(ncol=2, loc=1)
plt.show()
def set_bandpass_from_filter(self, filtername):
"""Loads the specified <filtername> from the transmission profile file
which is mapped via the etc.config.Conf() items.
Returns a SpectralElement instance for the filter profile
"""
if len(filtername) == 2 and filtername[1] == 'p':
filtername = filtername[0]
filename = conf.mapping.get(filtername, None)
if filename is None:
raise ETCError('Filter name {0} is invalid.'.format(filtername))
if 'LCO_' in filename().upper() and '.csv' in filename().lower():
file_path = pkg_resources.files('etc.data').joinpath(
os.path.expandvars(filename()))
source = "LCO iLab format"
header, wavelengths, throughput = self._read_lco_filter_csv(
file_path)
elif 'http://svo' in filename().lower():
source = "SVO filter service"
header, wavelengths, throughput = specio.read_remote_spec(
filename(), wave_unit=u.AA, flux_unit=units.THROUGHPUT)
else:
source = "local file"
file_path = pkg_resources.files('etc.data').joinpath(
os.path.expandvars(filename()))
warnings.simplefilter('ignore', category=AstropyUserWarning)
header, wavelengths, throughput = specio.read_ascii_spec(
file_path, wave_unit=u.nm, flux_unit=units.THROUGHPUT)
if throughput.mean() > 1.0:
throughput /= 100.0
header['notes'] = 'Divided by 100.0 to convert from percentage'
print("Reading from {} for {}".format(source, filtername))
header['filename'] = filename
header['descrip'] = filename.description
meta = {'header': header, 'expr': filtername}
return SpectralElement(Empirical1D,
points=wavelengths,
lookup_table=throughput,
meta=meta)
def from_vectors(cls, waves, trans, meta=None, wave_unit=u.AA):
"""
Create a ``Passband`` directly from from vectos (lists, numpy.arrays, etc)
Parameters
----------
waves: list-like
trans: list-like
meta: dictionary containing the metadata
wave_unit: u.Quantity, defaulted to angstroms
Returns
-------
Passband
"""
if isinstance(waves, u.Quantity) is False:
waves = waves*wave_unit
modelclass = SpectralElement(Empirical1D, points=waves, lookup_table=trans, meta=meta)
return cls(modelclass=modelclass)
def get_filter(filter_name):
# first check locally
# check generics
# check spanish_vo
path = find_file(filter_name, silent=True)
if path is not None:
tbl = ioascii.read(path)
wave = quantity_from_table("wavelength", tbl, u.um).to(u.um)
filt = SpectralElement(Empirical1D, points=wave,
lookup_table=tbl["transmission"])
elif filter_name in FILTER_DEFAULTS:
filt = download_svo_filter(FILTER_DEFAULTS[filter_name])
else:
try:
filt = download_svo_filter(filter_name)
except:
filt = None
return filt
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 __init__(self, name=None, camera_type="CCD", **kwargs):
_cam_types = [
"CCD",
]
self.camera_type = camera_type.upper() if camera_type.upper(
) in _cam_types else "CCD"
self.name = name if name is not None else self.camera_type + ' camera'
# Defaults assume no elements per channel on the assumption that it's
# in the common optics (populated in Instrument())
self.num_ar_coatings = kwargs.get('num_chan_ar_coatings', 0)
self.num_lenses = kwargs.get('num_chan_lenses', 0)
self.num_mirrors = kwargs.get('num_chan_mirrors', 0)
# Fused silica (common lens material) and fused quartz (common for CCD windows)
# turn out to have the same transmission...
self.lens_trans = kwargs.get('chan_lens_trans', 0.93)
self.mirror_refl = kwargs.get('chan_mirror_refl', 0.9925)
# Transmission/Reflection values of optical elements coating
self.ar_coating = kwargs.get('chan_ar_coating_refl', 0.99)
# Read common components first
trans_components = kwargs.get('trans_components', None)
wavelengths = np.arange(300, 1501, 1) * u.nm
if trans_components:
print("Computing channel transmission from components")
trans = len(wavelengths) * [
1.0,
]
for comp_name in trans_components.split(","):
print(comp_name)
element = read_element(comp_name)
trans *= element(wavelengths)
else:
print("Computing channel transmission from elements")
transmission = self._compute_transmission()
trans = len(wavelengths) * [
transmission,
]
header = {}
self.transmission = SpectralElement(Empirical1D,
points=wavelengths,
lookup_table=trans,
keep_neg=True,
meta={'header': header})
ccd_qe = kwargs.get('ccd_qe', 0.9)
if not isinstance(ccd_qe, (u.Quantity, numbers.Number)):
file_path = os.path.expandvars(ccd_qe)
if not os.path.exists(file_path):
file_path = str(
pkg_resources.files('etc.data').joinpath(ccd_qe))
header, wavelengths, throughput = specio.read_ascii_spec(
file_path, wave_unit=u.nm, flux_unit=units.THROUGHPUT)
if throughput.mean() > 1.0:
throughput /= 100.0
header['notes'] = 'Divided by 100.0 to convert from percentage'
header['filename'] = ccd_qe
self.ccd_qe = BaseUnitlessSpectrum(Empirical1D,
points=wavelengths,
lookup_table=throughput,
keep_neg=False,
meta={'header': header})
else:
self.ccd_qe = ccd_qe
self.ccd_gain = kwargs.get('ccd_gain', 1) * (u.electron / u.adu)
self.ccd_readnoise = kwargs.get('ccd_readnoise',
0) * (u.electron / u.pix)
ccd_pixsize = kwargs.get('ccd_pixsize', 0)
try:
ccd_pixsize_units = u.Unit(
kwargs.get('ccd_pixsize_units', 'micron'))
except ValueError:
ccd_pixsize_units = u.micron
self.ccd_pixsize = (ccd_pixsize * ccd_pixsize_units).to(u.micron)
self.ccd_xpixels = kwargs.get('ccd_xpixels', 0)
self.ccd_ypixels = kwargs.get('ccd_ypixels', 0)
self.ccd_xbinning = kwargs.get('ccd_xbinning', 1)
self.ccd_ybinning = kwargs.get('ccd_ybinning', 1)
def __init__(self, name=None, inst_type="IMAGER", **kwargs):
_ins_types = ["IMAGER", "SPECTROGRAPH"]
self.name = name if name is not None else "Undefined"
self.inst_type = inst_type.upper() if inst_type.upper(
) in _ins_types else "IMAGER"
self.fiber_diameter = kwargs.get('fiber_diameter', None)
if self.fiber_diameter is not None:
self.fiber_diameter = self.fiber_diameter * u.arcsec
self.grating_linespermm = kwargs.get('grating_linespermm', None)
if self.grating_linespermm:
self.grating_spacing = 1.0 / (self.grating_linespermm * (1 / u.mm))
self.grating_blaze = kwargs.get('grating_blaze', 0.0)
self.grating_gamma = kwargs.get('grating_gamma', 0.0)
self.cam_focallength = kwargs.get('cam_focallength', None)
if self.cam_focallength:
self.cam_focallength = self.cam_focallength * u.mm
self.dispersion_along_x = kwargs.get('dispersion_along_x', True)
self._echelle_constant = None
# Defaults assume a "standard" imager with a single lens, 2 AR coatings
# on the front and back surfaces and no mirrors
self.num_ar_coatings = kwargs.get('num_ar_coatings', 2)
self.num_lenses = kwargs.get('num_inst_lenses', 1)
self.num_mirrors = kwargs.get('num_inst_mirrors', 0)
# Fused silica (common lens material) and fused quartz (common for CCD windows)
# turn out to have the same transmission...
self.lens_trans = kwargs.get('inst_lens_trans', 0.93)
self.mirror_refl = kwargs.get('inst_mirror_refl', 0.9925)
# Transmission/Reflection values of optical elements coating
self.ar_coating = kwargs.get('inst_ar_coating_refl', 0.99)
# Read common components first
trans_components = kwargs.get('trans_components', None)
wavelengths = np.arange(300, 1501, 1) * u.nm
if trans_components:
trans = len(wavelengths) * [
1.0,
]
for comp_name in trans_components.split(","):
print(comp_name)
element = read_element(comp_name.strip())
trans *= element(wavelengths)
else:
print("Computing transmission from elements")
transmission = self._compute_transmission()
trans = len(wavelengths) * [
transmission,
]
header = {}
self.transmission = SpectralElement(Empirical1D, points=wavelengths, lookup_table=trans,\
keep_neg=True, meta={'header': header})
fwhm = kwargs.get('fwhm', 1)
try:
fwhm_units = u.Unit(kwargs.get('fwhm_units', 'arcsec'))
except ValueError:
fwhm_units = u.arcsec
try:
# Already a Quantity
self.fwhm = fwhm.to(u.arcsec)
except AttributeError:
self.fwhm = fwhm * fwhm_units
focal_scale = kwargs.get('focal_scale', 0)
try:
focal_scale_units = u.Unit(
kwargs.get('focal_scale_units', 'arcsec/mm'))
except ValueError:
focal_scale_units = u.arcsec / u.mm
self.focal_scale = focal_scale * focal_scale_units
channels = kwargs.get('channels', {'default': {}})
self._num_channels = max(len(channels), 1)
self.channelset = OrderedDict()
self.filter2channel_map = OrderedDict()
self.filterlist = kwargs.get('filterlist', [])
self.filterset = OrderedDict()
for filtername in self.filterlist:
if filtername not in self.filterset:
self.filterset[filtername] = self.set_bandpass_from_filter(
filtername)
self.filter2channel_map[filtername] = 'default'
for channel in channels:
# Make copy of common params defined at the Instrument level and then
# overwrite with channel/camera-specific versions
camera_kwargs = kwargs.copy()
# Delete any trans_components
if 'trans_components' in camera_kwargs:
del (camera_kwargs['trans_components'])
for k, v in channels[channel].items():
camera_kwargs[k] = v
self.channelset[channel] = Camera(**camera_kwargs)
if self.channelset[
channel].ccd_pixsize != 0 and self.focal_scale != 0:
self.channelset[channel].ccd_pixscale = self.focal_scale.to(
u.arcsec / u.mm) * self.channelset[channel].ccd_pixsize.to(
u.mm)
cam_filterlist = camera_kwargs.get('filterlist', [])
for filtername in cam_filterlist:
if filtername not in self.filterset:
self.filterlist.append(filtername)
self.filterset[filtername] = self.set_bandpass_from_filter(
filtername)
self.filter2channel_map[filtername] = channel
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