def gaussian_spectrum(
wavel_range: Union[Tuple[float, float], Tuple[np.float32, np.float32]],
model_param: Dict[str, float],
spec_res: float = 100.0,
double_gaussian: bool = False,
) -> box.ModelBox:
"""
Function for calculating a Gaussian spectrum (i.e. for an emission
line).
Parameters
----------
wavel_range : tuple(float, float)
Tuple with the minimum and maximum wavelength (um).
model_param : dict
Dictionary with the model parameters. Should contain
``'gauss_amplitude'``, ``'gauss_mean'``, ``'gauss_sigma'``,
and optionally ``'gauss_offset'``.
spec_res : float
Spectral resolution (default: 100).
double_gaussian : bool
Set to ``True`` for returning a double Gaussian function.
In that case, ``model_param`` should also contain
``'gauss_amplitude_2'``, ``'gauss_mean_2'``, and
``'gauss_sigma_2'``.
Returns
-------
species.core.box.ModelBox
Box with the Gaussian spectrum.
"""
wavel = create_wavelengths((wavel_range[0], wavel_range[1]), spec_res)
gauss_exp = np.exp(-0.5 * (wavel - model_param["gauss_mean"])**2 /
model_param["gauss_sigma"]**2)
flux = model_param["gauss_amplitude"] * gauss_exp
if double_gaussian:
gauss_exp = np.exp(-0.5 * (wavel - model_param["gauss_mean_2"])**2 /
model_param["gauss_sigma_2"]**2)
flux += model_param["gauss_amplitude_2"] * gauss_exp
if "gauss_offset" in model_param:
flux += model_param["gauss_offset"]
model_box = box.create_box(
boxtype="model",
model="gaussian",
wavelength=wavel,
flux=flux,
parameters=model_param,
quantity="flux",
)
return model_box
def multi_photometry(datatype: str,
spectrum: str,
filters: List[str],
parameters: Dict[str, float]) -> box.SynphotBox:
"""
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Spectrum name (e.g., 'drift-phoenix', 'planck', 'powerlaw').
filters : list(str, )
List with the filter names.
parameters : dict
Dictionary with the model parameters.
Returns
-------
species.core.box.SynphotBox
Box with synthetic photometry.
"""
print('Calculating synthetic photometry...', end='', flush=True)
flux = {}
if datatype == 'model':
for item in filters:
if spectrum == 'planck':
readmodel = read_planck.ReadPlanck(filter_name=item)
elif spectrum == 'powerlaw':
synphot = photometry.SyntheticPhotometry(item)
synphot.zero_point() # Set the wavel_range attribute
powerl_box = read_util.powerlaw_spectrum(synphot.wavel_range, parameters)
flux[item] = synphot.spectrum_to_flux(powerl_box.wavelength, powerl_box.flux)[0]
else:
readmodel = read_model.ReadModel(spectrum, filter_name=item)
try:
flux[item] = readmodel.get_flux(parameters)[0]
except IndexError:
flux[item] = np.nan
warnings.warn(f'The wavelength range of the {item} filter does not match with '
f'the wavelength range of {spectrum}. The flux is set to NaN.')
elif datatype == 'calibration':
for item in filters:
readcalib = read_calibration.ReadCalibration(spectrum, filter_name=item)
flux[item] = readcalib.get_flux(parameters)[0]
print(' [DONE]')
return box.create_box('synphot', name='synphot', flux=flux)
def get_magnitude(self, sptypes: List[str] = None) -> box.PhotometryBox:
"""
Function for calculating the apparent magnitude for the ``filter_name``.
Parameters
----------
sptypes : list(str)
Spectral types to select from a library. The spectral types should be indicated with
two characters (e.g. 'M5', 'L2', 'T3'). All spectra are selected if set to ``None``.
Returns
-------
species.core.box.PhotometryBox
Box with the synthetic photometry.
"""
specbox = self.get_spectrum(sptypes=sptypes, exclude_nan=True)
n_spectra = len(specbox.wavelength)
filter_profile = read_filter.ReadFilter(filter_name=self.filter_name)
mean_wavel = filter_profile.mean_wavelength()
wavelengths = np.full(n_spectra, mean_wavel)
filters = np.full(n_spectra, self.filter_name)
synphot = photometry.SyntheticPhotometry(filter_name=self.filter_name)
app_mag = []
abs_mag = []
for i in range(n_spectra):
if np.isnan(specbox.distance[i][0]):
app_tmp = (np.nan, np.nan)
abs_tmp = (np.nan, np.nan)
else:
app_tmp, abs_tmp = synphot.spectrum_to_magnitude(
specbox.wavelength[i],
specbox.flux[i],
error=specbox.error[i],
distance=(float(specbox.distance[i][0]),
float(specbox.distance[i][1])))
app_mag.append(app_tmp)
abs_mag.append(abs_tmp)
return box.create_box(boxtype='photometry',
name=specbox.name,
sptype=specbox.sptype,
wavelength=wavelengths,
flux=None,
app_mag=np.asarray(app_mag),
abs_mag=np.asarray(abs_mag),
filter_name=filters)
def get_color_magnitude(
temperatures: np.ndarray,
radius: float,
filters_color: Tuple[str, str],
filter_mag: str,
) -> box.ColorMagBox:
"""
Function for calculating the colors and magnitudes in the range of 100-10000 K.
Parameters
----------
temperatures : np.ndarray
Temperatures (K) for which the colors and magnitude are
calculated.
radius : float
Radius (Rjup).
filters_color : tuple(str, str)
Filter names for the color.
filter_mag : str
Filter name for the absolute magnitudes.
Returns
-------
species.core.box.ColorMagBox
Box with the colors and magnitudes.
"""
list_color = []
list_mag = []
for item in temperatures:
model_param = {"teff": item, "radius": radius, "distance": 10.0}
read_planck_0 = ReadPlanck(filter_name=filters_color[0])
read_planck_1 = ReadPlanck(filter_name=filters_color[1])
read_planck_2 = ReadPlanck(filter_name=filter_mag)
app_mag_0, _ = read_planck_0.get_magnitude(model_param)
app_mag_1, _ = read_planck_1.get_magnitude(model_param)
app_mag_2, _ = read_planck_2.get_magnitude(model_param)
list_color.append(app_mag_0[0] - app_mag_1[0])
list_mag.append(app_mag_2[0])
return box.create_box(
boxtype="colormag",
library="planck",
object_type=None,
filters_color=filters_color,
filter_mag=filter_mag,
color=list_color,
magnitude=list_mag,
sptype=temperatures,
names=None,
)
def get_flux(self, sptypes: List[str] = None) -> box.PhotometryBox:
"""
Function for calculating the average flux density for the
``filter_name``.
Parameters
----------
sptypes : list(str), None
Spectral types to select from a library. The spectral types
should be indicated with two characters (e.g. 'M5', 'L2',
'T3'). All spectra are selected if set to ``None``.
Returns
-------
species.core.box.PhotometryBox
Box with the synthetic photometry.
"""
specbox = self.get_spectrum(sptypes=sptypes, exclude_nan=True)
n_spectra = len(specbox.wavelength)
filter_profile = read_filter.ReadFilter(filter_name=self.filter_name)
mean_wavel = filter_profile.mean_wavelength()
wavelengths = np.full(n_spectra, mean_wavel)
filters = np.full(n_spectra, self.filter_name)
synphot = photometry.SyntheticPhotometry(filter_name=self.filter_name)
phot_flux = []
for i in range(n_spectra):
flux = synphot.spectrum_to_flux(
wavelength=specbox.wavelength[i],
flux=specbox.flux[i],
error=specbox.error[i],
)
phot_flux.append(flux)
phot_flux = np.asarray(phot_flux)
return box.create_box(
boxtype="photometry",
name=specbox.name,
sptype=specbox.sptype,
wavelength=wavelengths,
flux=phot_flux,
app_mag=None,
abs_mag=None,
filter_name=filters,
)
def get_color_color(
temperatures: np.ndarray,
radius: float,
filters_colors: Tuple[Tuple[str, str], Tuple[str, str]],
) -> box.ColorColorBox:
"""
Function for calculating two colors in the range of
100-10000 K.
Parameters
----------
temperatures : np.ndarray
Temperatures (K) for which the colors are calculated.
radius : float
Radius (Rjup).
filters_colors : tuple(tuple(str, str), tuple(str, str))
Two tuples with the filter names for the colors.
Returns
-------
species.core.box.ColorColorBox
Box with the colors.
"""
list_color_1 = []
list_color_2 = []
for item in temperatures:
model_param = {"teff": item, "radius": radius, "distance": 10.0}
read_planck_0 = ReadPlanck(filter_name=filters_colors[0][0])
read_planck_1 = ReadPlanck(filter_name=filters_colors[0][1])
read_planck_2 = ReadPlanck(filter_name=filters_colors[1][0])
read_planck_3 = ReadPlanck(filter_name=filters_colors[1][1])
app_mag_0, _ = read_planck_0.get_magnitude(model_param)
app_mag_1, _ = read_planck_1.get_magnitude(model_param)
app_mag_2, _ = read_planck_2.get_magnitude(model_param)
app_mag_3, _ = read_planck_3.get_magnitude(model_param)
list_color_1.append(app_mag_0[0] - app_mag_1[0])
list_color_2.append(app_mag_2[0] - app_mag_3[0])
return box.create_box(
boxtype="colorcolor",
library="planck",
object_type=None,
filters=filters_colors,
color1=list_color_1,
color2=list_color_2,
sptype=temperatures,
names=None,
)
def resample_spectrum(self,
wavel_points: np.ndarray,
model_param: Optional[Dict[str, float]] = None,
apply_mask: bool = False) -> box.SpectrumBox:
"""
Function for resampling of a spectrum and uncertainties onto a new wavelength grid.
Parameters
----------
wavel_points : np.ndarray
Wavelength points (um).
model_param : dict, None
Model parameters. Should contain the 'scaling' value. Not used if set to ``None``.
apply_mask : bool
Exclude negative values and NaN values.
Returns
-------
species.core.box.SpectrumBox
Box with the resampled spectrum.
"""
calibbox = self.get_spectrum()
if apply_mask:
indices = np.where(calibbox.flux > 0.)[0]
calibbox.wavelength = calibbox.wavelength[indices]
calibbox.flux = calibbox.flux[indices]
calibbox.error = calibbox.error[indices]
flux_new, error_new = spectres.spectres(wavel_points,
calibbox.wavelength,
calibbox.flux,
spec_errs=calibbox.error,
fill=0.,
verbose=False)
if model_param is not None:
flux_new = model_param['scaling'] * flux_new
error_new = model_param['scaling'] * error_new
return box.create_box(boxtype='spectrum',
spectrum='calibration',
wavelength=wavel_points,
flux=flux_new,
error=error_new,
name=self.tag,
simbad=None,
sptype=None,
distance=None)
def multi_photometry(datatype,
spectrum,
filters,
parameters):
"""
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Spectrum name (e.g., 'drift-phoenix').
filters : tuple(str, )
Filter names.
parameters : dict
Parameters and values for the spectrum
Returns
-------
species.core.box.SynphotBox
Box with synthetic photometry.
"""
print('Calculating synthetic photometry...', end='', flush=True)
flux = {}
if datatype == 'model':
for item in filters:
if spectrum == 'planck':
readmodel = read_planck.ReadPlanck(filter_name=item)
else:
readmodel = read_model.ReadModel(spectrum, filter_name=item)
try:
flux[item] = readmodel.get_flux(parameters)[0]
except IndexError:
flux[item] = np.nan
warnings.warn(f'The wavelength range of the {item} filter does not match with '
f'the wavelength coverage of {spectrum}. The flux is set to NaN.')
elif datatype == 'calibration':
for item in filters:
readcalib = read_calibration.ReadCalibration(spectrum, filter_name=item)
flux[item] = readcalib.get_flux(parameters)[0]
print(' [DONE]')
return box.create_box('synphot', name='synphot', flux=flux)
def get_samples(self, tag, burnin=None, random=None):
"""
Parameters
----------
tag: str
Database tag with the samples.
burnin : int
Number of burnin samples to exclude. All samples are selected if set to None.
random : int
Number of random samples to select. All samples (with the burnin excluded) are
selected if set to None.
Returns
-------
species.core.box.SamplesBox
Box with the MCMC samples.
"""
h5_file = h5py.File(self.database, 'r')
dset = h5_file['results/mcmc/' + tag]
spectrum = dset.attrs['spectrum']
nparam = dset.attrs['nparam']
samples = np.asarray(dset)
if burnin:
samples = samples[:, burnin:, :]
if random:
ran_walker = np.random.randint(samples.shape[0], size=random)
ran_step = np.random.randint(samples.shape[1], size=random)
samples = samples[ran_walker, ran_step, :]
param = []
chisquare = []
for i in range(nparam):
param.append(dset.attrs['parameter' + str(i)])
chisquare.append(dset.attrs['chisquare' + str(i)])
h5_file.close()
return box.create_box('samples',
spectrum=spectrum,
parameters=param,
samples=samples,
chisquare=chisquare)
def powerlaw_spectrum(
wavel_range: Union[Tuple[float, float], Tuple[np.float32, np.float32]],
model_param: Dict[str, float],
spec_res: float = 100.0,
) -> box.ModelBox:
"""
Function for calculating a power-law spectrum. The power-law
function is calculated in log(wavelength)-log(flux) space but
stored in the :class:`~species.core.box.ModelBox` in linear
wavelength-flux space.
Parameters
----------
wavel_range : tuple(float, float)
Tuple with the minimum and maximum wavelength (um).
model_param : dict
Dictionary with the model parameters. Should contain
`'log_powerlaw_a'`, `'log_powerlaw_b'`, and `'log_powerlaw_c'`.
spec_res : float
Spectral resolution (default: 100).
Returns
-------
species.core.box.ModelBox
Box with the power-law spectrum.
"""
wavel = create_wavelengths((wavel_range[0], wavel_range[1]), spec_res)
log_flux = (model_param["log_powerlaw_a"] + model_param["log_powerlaw_b"] *
np.log10(wavel)**model_param["log_powerlaw_c"])
model_box = box.create_box(
boxtype="model",
model="powerlaw",
wavelength=wavel,
flux=10.0**log_flux,
parameters=model_param,
quantity="flux",
)
return model_box
def get_planck(temperature, radius, distance, wavelength, specres):
"""
Parameters
----------
temperature : float
Temperature (K).
radius : float
Radius (Rjup).
distance : float
Distance (pc).
wavelength : tuple(float, float)
Wavelength range (micron).
specres : float
Spectral resolution
Returns
-------
species.core.box.SpectrumBox
Box with the Planck spectrum.
"""
wl_points = [wavelength[0]]
while wl_points[-1] <= wavelength[1]:
wl_points.append(wl_points[-1] + wl_points[-1] / specres)
wl_points = np.asarray(wl_points) # [micron]
scaling = (radius * con.R_JUP / (distance * con.PARSEC))**2
flux = planck(np.copy(wl_points), temperature, scaling) # [W m-2 micron-1]
return box.create_box(boxtype='spectrum',
spectrum='planck',
wavelength=wl_points,
flux=flux,
error=None,
name=None,
simbad=None,
sptype=None,
distance=None)
def multi_photometry(datatype, spectrum, filters, parameters):
"""
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Spectrum name (e.g., 'drift-phoenix').
filters : tuple(str, )
Filter IDs.
parameters : dict
Parameters and values for the spectrum
Returns
-------
species.core.box.SynphotBox
Box with synthetic photometry.
"""
sys.stdout.write('Calculating synthetic photometry...')
sys.stdout.flush()
flux = {}
if datatype == 'model':
for item in filters:
readmodel = read_model.ReadModel(spectrum, item)
flux[item] = readmodel.get_photometry(parameters)
elif datatype == 'calibration':
for item in filters:
readcalib = read_calibration.ReadCalibration(spectrum, item)
flux[item] = readcalib.get_photometry(parameters)
sys.stdout.write(' [DONE]\n')
sys.stdout.flush()
return box.create_box('synphot', name='synphot', flux=flux)
def get_isochrone(self, age, mass, filters_color, filter_mag):
"""
Parameters
----------
age : str
Age (Myr) that is used to interpolate the isochrone data.
mass : numpy.ndarray
Masses (Mjup) for which the isochrone data is interpolated.
filters_color : tuple(str, str), None
Filter IDs for the color as listed in the file with the isochrone data. Not selected if
set to None or if only evolutionary tracks are available.
filter_mag : str, None
Filter ID for the absolute magnitude as listed in the file with the isochrone data. Not
selected if set to None or if only evolutionary tracks are available.
Returns
-------
species.core.box.IsochroneBox
Box with the isochrone data.
"""
age_points = np.repeat(age, mass.shape[0]) # [Myr]
color = None
mag_abs = None
index_teff = 2
index_logg = 4
with h5py.File(self.database, 'r') as h5_file:
model = h5_file['isochrones/' + self.tag +
'/evolution'].attrs['model']
evolution = np.asarray(h5_file['isochrones/' + self.tag +
'/evolution'])
if model == 'baraffe':
filters = list(h5_file['isochrones/' + self.tag + '/filters'])
magnitudes = np.asarray(h5_file['isochrones/' + self.tag +
'/magnitudes'])
if model == 'baraffe':
for i, item in enumerate(filters):
filters[i] = item.decode()
if filters_color is not None:
index_color_1 = filters.index(filters_color[0])
index_color_2 = filters.index(filters_color[1])
if filter_mag is not None:
index_mag = filters.index(filter_mag)
if filters_color is not None:
mag_color_1 = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_color_1],
xi=np.stack((age_points, mass), axis=1),
method='linear',
fill_value='nan',
rescale=False)
mag_color_2 = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_color_2],
xi=np.stack((age_points, mass), axis=1),
method='linear',
fill_value='nan',
rescale=False)
color = mag_color_1 - mag_color_2
if filter_mag is not None:
mag_abs = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_mag],
xi=np.stack((age_points, mass), axis=1),
method='linear',
fill_value='nan',
rescale=False)
teff = griddata(points=evolution[:, 0:2],
values=evolution[:, index_teff],
xi=np.stack((age_points, mass), axis=1),
method='linear',
fill_value='nan',
rescale=False)
logg = griddata(points=evolution[:, 0:2],
values=evolution[:, index_logg],
xi=np.stack((age_points, mass), axis=1),
method='linear',
fill_value='nan',
rescale=False)
return box.create_box(boxtype='isochrone',
model=self.tag,
filters_color=filters_color,
filter_mag=filter_mag,
color=color,
magnitude=mag_abs,
teff=teff,
logg=logg,
mass=mass)
def get_color_magnitude(self, age, mass, model, filters_color, filter_mag):
"""
Parameters
----------
age : str
Age (Myr) that is used to interpolate the isochrone data.
mass : numpy.ndarray
Masses (Mjup) for which the isochrone data is interpolated.
model : str
Atmospheric model used to compute the synthetic photometry.
filters_color : tuple(str, str), None
Filter IDs for the color as listed in the file with the isochrone data. Not selected if
set to None or if only evolutionary tracks are available.
filter_mag : str, None
Filter ID for the absolute magnitude as listed in the file with the isochrone data. Not
selected if set to None or if only evolutionary tracks are available.
Returns
-------
species.core.box.ColorMagBox
Box with the isochrone data.
"""
isochrone = self.get_isochrone(age=age,
mass=mass,
filters_color=None,
filter_mag=None)
model1 = read_model.ReadModel(model=model, wavelength=filters_color[0])
model2 = read_model.ReadModel(model=model, wavelength=filters_color[1])
mag1 = np.zeros(isochrone.mass.shape[0])
mag2 = np.zeros(isochrone.mass.shape[0])
for i, item in enumerate(isochrone.mass):
model_par = {
'teff': isochrone.teff[i],
'logg': isochrone.logg[i],
'feh': 0.,
'mass': item,
'distance': 10.
}
mag1[i], _ = model1.get_magnitude(model_par=model_par)
mag2[i], _ = model2.get_magnitude(model_par=model_par)
if filter_mag == filters_color[0]:
abs_mag = mag1
elif filter_mag == filters_color[1]:
abs_mag = mag2
else:
raise ValueError(
'The filter_mag argument should be equal to one of the two filter '
'values of filters_color.')
return box.create_box(boxtype='colormag',
library=model,
object_type='temperature',
filters_color=filters_color,
filter_mag=filter_mag,
color=mag1 - mag2,
magnitude=abs_mag,
sptype=isochrone.teff)
def multi_photometry(
datatype: str,
spectrum: str,
filters: List[str],
parameters: Dict[str, float],
radtrans: Optional[read_radtrans.ReadRadtrans] = None,
) -> box.SynphotBox:
"""
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Spectrum name (e.g., 'drift-phoenix', 'planck', 'powerlaw',
'petitradtrans').
filters : list(str)
List with the filter names.
parameters : dict
Dictionary with the model parameters.
radtrans : read_radtrans.ReadRadtrans, None
Instance of :class:`~species.read.read_radtrans.ReadRadtrans`.
Only required with ``spectrum='petitradtrans'`. Make sure that
the ``wavel_range`` of the ``ReadRadtrans`` instance is
sufficiently broad to cover all the ``filters``. Not used if
set to `None`.
Returns
-------
species.core.box.SynphotBox
Box with synthetic photometry.
"""
print("Calculating synthetic photometry...", end="", flush=True)
flux = {}
if datatype == "model":
if spectrum == "petitradtrans":
# Calculate the petitRADTRANS spectrum only once
radtrans_box = radtrans.get_model(parameters)
for item in filters:
if spectrum == "petitradtrans":
# Use an instance of SyntheticPhotometry instead
# of get_flux from ReadRadtrans in order to not
# recalculate the spectrum
syn_phot = photometry.SyntheticPhotometry(item)
flux[item], _ = syn_phot.spectrum_to_flux(
radtrans_box.wavelength, radtrans_box.flux)
elif spectrum == "powerlaw":
synphot = photometry.SyntheticPhotometry(item)
# Set the wavel_range attribute
synphot.zero_point()
powerl_box = read_util.powerlaw_spectrum(
synphot.wavel_range, parameters)
flux[item] = synphot.spectrum_to_flux(powerl_box.wavelength,
powerl_box.flux)[0]
else:
if spectrum == "planck":
readmodel = read_planck.ReadPlanck(filter_name=item)
else:
readmodel = read_model.ReadModel(spectrum,
filter_name=item)
try:
if "teff_0" in parameters and "teff_1" in parameters:
# Binary system
param_0 = read_util.binary_to_single(parameters, 0)
model_flux_0 = readmodel.get_flux(param_0)[0]
param_1 = read_util.binary_to_single(parameters, 1)
model_flux_1 = readmodel.get_flux(param_1)[0]
flux[item] = (
parameters["spec_weight"] * model_flux_0 +
(1.0 - parameters["spec_weight"]) * model_flux_1)
else:
# Single object
flux[item] = readmodel.get_flux(parameters)[0]
except IndexError:
flux[item] = np.nan
warnings.warn(
f"The wavelength range of the {item} filter does not "
f"match with the wavelength range of {spectrum}. The "
f"flux is set to NaN.")
elif datatype == "calibration":
for item in filters:
readcalib = read_calibration.ReadCalibration(spectrum,
filter_name=item)
flux[item] = readcalib.get_flux(parameters)[0]
print(" [DONE]")
return box.create_box("synphot", name="synphot", flux=flux)
def get_residuals(
datatype: str,
spectrum: str,
parameters: Dict[str, float],
objectbox: box.ObjectBox,
inc_phot: Union[bool, List[str]] = True,
inc_spec: Union[bool, List[str]] = True,
radtrans: Optional[read_radtrans.ReadRadtrans] = None,
) -> box.ResidualsBox:
"""
Function for calculating the residuals from fitting model or
calibration spectra to a set of spectra and/or photometry.
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Name of the atmospheric model or calibration spectrum.
parameters : dict
Parameters and values for the spectrum
objectbox : species.core.box.ObjectBox
Box with the photometry and/or spectra of an object. A scaling
and/or error inflation of the spectra should be applied with
:func:`~species.util.read_util.update_spectra` beforehand.
inc_phot : bool, list(str)
Include photometric data in the fit. If a boolean, either all
(``True``) or none (``False``) of the data are selected. If a
list, a subset of filter names (as stored in the database) can
be provided.
inc_spec : bool, list(str)
Include spectroscopic data in the fit. If a boolean, either all
(``True``) or none (``False``) of the data are selected. If a
list, a subset of spectrum names (as stored in the database
with :func:`~species.data.database.Database.add_object`) can be
provided.
radtrans : read_radtrans.ReadRadtrans, None
Instance of :class:`~species.read.read_radtrans.ReadRadtrans`.
Only required with ``spectrum='petitradtrans'`. Make sure that
the ``wavel_range`` of the ``ReadRadtrans`` instance is
sufficiently broad to cover all the photometric and
spectroscopic data of ``inc_phot`` and ``inc_spec``. Not used
if set to ``None``.
Returns
-------
species.core.box.ResidualsBox
Box with the residuals.
"""
if isinstance(inc_phot, bool) and inc_phot:
inc_phot = objectbox.filters
if inc_phot:
model_phot = multi_photometry(
datatype=datatype,
spectrum=spectrum,
filters=inc_phot,
parameters=parameters,
radtrans=radtrans,
)
res_phot = {}
for item in inc_phot:
transmission = read_filter.ReadFilter(item)
res_phot[item] = np.zeros(objectbox.flux[item].shape)
if objectbox.flux[item].ndim == 1:
res_phot[item][0] = transmission.mean_wavelength()
res_phot[item][1] = (
objectbox.flux[item][0] -
model_phot.flux[item]) / objectbox.flux[item][1]
elif objectbox.flux[item].ndim == 2:
for j in range(objectbox.flux[item].shape[1]):
res_phot[item][0, j] = transmission.mean_wavelength()
res_phot[item][1, j] = (
objectbox.flux[item][0, j] -
model_phot.flux[item]) / objectbox.flux[item][1, j]
else:
res_phot = None
if inc_spec:
res_spec = {}
if spectrum == "petitradtrans":
# Calculate the petitRADTRANS spectrum only once
model = radtrans.get_model(parameters)
for key in objectbox.spectrum:
if isinstance(inc_spec, bool) or key in inc_spec:
wavel_range = (
0.9 * objectbox.spectrum[key][0][0, 0],
1.1 * objectbox.spectrum[key][0][-1, 0],
)
wl_new = objectbox.spectrum[key][0][:, 0]
spec_res = objectbox.spectrum[key][3]
if spectrum == "planck":
readmodel = read_planck.ReadPlanck(wavel_range=wavel_range)
model = readmodel.get_spectrum(model_param=parameters,
spec_res=1000.0)
# Separate resampling to the new wavelength points
flux_new = spectres.spectres(
wl_new,
model.wavelength,
model.flux,
spec_errs=None,
fill=0.0,
verbose=True,
)
elif spectrum == "petitradtrans":
# Separate resampling to the new wavelength points
flux_new = spectres.spectres(
wl_new,
model.wavelength,
model.flux,
spec_errs=None,
fill=0.0,
verbose=True,
)
else:
# Resampling to the new wavelength points
# is done by the get_model method
readmodel = read_model.ReadModel(spectrum,
wavel_range=wavel_range)
if "teff_0" in parameters and "teff_1" in parameters:
# Binary system
param_0 = read_util.binary_to_single(parameters, 0)
model_spec_0 = readmodel.get_model(
param_0,
spec_res=spec_res,
wavel_resample=wl_new,
smooth=True,
)
param_1 = read_util.binary_to_single(parameters, 1)
model_spec_1 = readmodel.get_model(
param_1,
spec_res=spec_res,
wavel_resample=wl_new,
smooth=True,
)
flux_comb = (
parameters["spec_weight"] * model_spec_0.flux +
(1.0 - parameters["spec_weight"]) *
model_spec_1.flux)
model_spec = box.create_box(
boxtype="model",
model=spectrum,
wavelength=wl_new,
flux=flux_comb,
parameters=parameters,
quantity="flux",
)
else:
# Single object
model_spec = readmodel.get_model(
parameters,
spec_res=spec_res,
wavel_resample=wl_new,
smooth=True,
)
flux_new = model_spec.flux
data_spec = objectbox.spectrum[key][0]
res_tmp = (data_spec[:, 1] - flux_new) / data_spec[:, 2]
res_spec[key] = np.column_stack([wl_new, res_tmp])
else:
res_spec = None
print("Calculating residuals... [DONE]")
print("Residuals (sigma):")
if res_phot is not None:
for item in inc_phot:
if res_phot[item].ndim == 1:
print(f" - {item}: {res_phot[item][1]:.2f}")
elif res_phot[item].ndim == 2:
for j in range(res_phot[item].shape[1]):
print(f" - {item}: {res_phot[item][1, j]:.2f}")
if res_spec is not None:
for key in objectbox.spectrum:
if isinstance(inc_spec, bool) or key in inc_spec:
print(f" - {key}: min: {np.nanmin(res_spec[key]):.2f}, "
f"max: {np.nanmax(res_spec[key]):.2f}")
chi2_stat = 0
n_dof = 0
if res_phot is not None:
for key, value in res_phot.items():
chi2_stat += value[1]**2
n_dof += 1
if res_spec is not None:
for key, value in res_spec.items():
chi2_stat += np.sum(value[:, 1]**2)
n_dof += value.shape[0]
for item in parameters:
if item not in ["mass", "luminosity", "distance"]:
n_dof -= 1
chi2_red = chi2_stat / n_dof
print(f"Reduced chi2 = {chi2_red:.2f}")
print(f"Number of degrees of freedom = {n_dof}")
return box.create_box(
boxtype="residuals",
name=objectbox.name,
photometry=res_phot,
spectrum=res_spec,
chi2_red=chi2_red,
)
def get_color_magnitude(self,
object_type: Optional[str] = None
) -> box.ColorMagBox:
"""
Function for extracting color-magnitude data from the selected
library.
Parameters
----------
object_type : str, None
Object type for which the colors and magnitudes are
extracted. Either field dwarfs ('field') or
young/low-gravity objects ('young'). All objects are
selected if set to ``None``.
Returns
-------
species.core.box.ColorMagBox
Box with the colors and magnitudes.
"""
if self.lib_type == "phot_lib":
with h5py.File(self.database, "r") as h5_file:
sptype = np.asarray(
h5_file[f"photometry/{self.library}/sptype"])
parallax = np.asarray(
h5_file[f"photometry/{self.library}/parallax"])
parallax_error = np.asarray(
h5_file[f"photometry/{self.library}/parallax_error"])
flag = np.asarray(h5_file[f"photometry/{self.library}/flag"])
obj_names = np.asarray(
h5_file[f"photometry/{self.library}/name"])
for i in range(sptype.shape[0]):
if isinstance(sptype[i], bytes):
sptype[i] = sptype[i].decode("utf-8")
if isinstance(flag[i], bytes):
flag[i] = flag[i].decode("utf-8")
if isinstance(obj_names[i], bytes):
obj_names[i] = obj_names[i].decode("utf-8")
if object_type is None:
indices = np.arange(0, np.size(sptype), 1)
elif object_type == "field":
indices = np.where(flag == "null")[0]
elif object_type == "young":
indices = []
for j, object_flag in enumerate(flag):
if "young" in object_flag:
indices.append(j)
elif "lowg" in object_flag:
indices.append(j)
indices = np.array(indices)
if indices.size > 0:
with h5py.File(self.database, "r") as h5_file:
mag1 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_color[0]}"])
mag2 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_color[1]}"])
else:
raise ValueError(
f"There is not data available from '{self.library}' for "
f"'{object_type}' type objects with the chosen filters.")
color = mag1 - mag2
distance = phot_util.parallax_to_distance(
(parallax, parallax_error))
if self.filter_mag == self.filters_color[0]:
mag, _ = phot_util.apparent_to_absolute(
(mag1, None), (distance[0], distance[1]))
elif self.filter_mag == self.filters_color[1]:
mag, _ = phot_util.apparent_to_absolute(
(mag2, None), (distance[0], distance[1]))
color = color[indices]
mag = mag[indices]
sptype = sptype[indices]
obj_names = obj_names[indices]
indices = []
for i in range(color.size):
if not np.isnan(color[i]) and not np.isnan(mag[i]):
indices.append(i)
colormag_box = box.create_box(
boxtype="colormag",
library=self.library,
object_type=object_type,
filters_color=self.filters_color,
filter_mag=self.filter_mag,
color=color[indices],
magnitude=mag[indices],
sptype=sptype[indices],
names=obj_names[indices],
)
elif self.lib_type == "spec_lib":
read_spec_0 = read_spectrum.ReadSpectrum(
spec_library=self.library, filter_name=self.filters_color[0])
read_spec_1 = read_spectrum.ReadSpectrum(
spec_library=self.library, filter_name=self.filters_color[1])
read_spec_2 = read_spectrum.ReadSpectrum(
spec_library=self.library, filter_name=self.filter_mag)
phot_box_0 = read_spec_0.get_magnitude(sptypes=None)
phot_box_1 = read_spec_1.get_magnitude(sptypes=None)
phot_box_2 = read_spec_2.get_magnitude(sptypes=None)
colormag_box = box.create_box(
boxtype="colormag",
library=self.library,
object_type=object_type,
filters_color=self.filters_color,
filter_mag=self.filter_mag,
color=phot_box_0.app_mag[:, 0] - phot_box_1.app_mag[:, 0],
magnitude=phot_box_2.abs_mag[:, 0],
sptype=phot_box_0.sptype,
names=None,
)
return colormag_box
def get_model(self,
model_param: Dict[str, float],
spec_res: Optional[float] = None,
wavel_resample: Optional[np.ndarray] = None,
magnitude: bool = False,
smooth: bool = False) -> box.ModelBox:
"""
Function for extracting a model spectrum by linearly interpolating the model grid.
Parameters
----------
model_param : dict
Dictionary with the model parameters and values. The values should be within the
boundaries of the grid. The grid boundaries of the spectra in the database can be
obtained with :func:`~species.read.read_model.ReadModel.get_bounds()`.
spec_res : float, None
Spectral resolution that is used for smoothing the spectrum with a Gaussian kernel
when ``smooth=True`` and/or resampling the spectrum when ``wavel_range`` of
``FitModel`` is not ``None``. The original wavelength points are used if both
``spec_res`` and ``wavel_resample`` are set to ``None``, or if ``smooth`` is set to
``True``.
wavel_resample : np.ndarray, None
Wavelength points (um) to which the spectrum is resampled. In that case, ``spec_res``
can still be used for smoothing the spectrum with a Gaussian kernel.
magnitude : bool
Normalize the spectrum with a flux calibrated spectrum of Vega and return the magnitude
instead of flux density.
smooth : bool
If ``True``, the spectrum is smoothed with a Gaussian kernel to the spectral resolution
of ``spec_res``. This requires either a uniform spectral resolution of the input
spectra (fast) or a uniform wavelength spacing of the input spectra (slow).
Returns
-------
species.core.box.ModelBox
Box with the model spectrum.
"""
if smooth and spec_res is None:
warnings.warn('The \'spec_res\' argument is required for smoothing the spectrum when '
'\'smooth\' is set to True.')
grid_bounds = self.get_bounds()
extra_param = ['radius', 'distance', 'mass', 'luminosity', 'lognorm_radius',
'lognorm_sigma', 'lognorm_ext', 'ism_ext', 'ism_red', 'powerlaw_max',
'powerlaw_exp', 'powerlaw_ext']
for key in self.get_parameters():
if key not in model_param.keys():
raise ValueError(f'The \'{key}\' parameter is required by \'{self.model}\'. '
f'The mandatory parameters are {self.get_parameters()}.')
if model_param[key] < grid_bounds[key][0]:
raise ValueError(f'The input value of \'{key}\' is smaller than the lower '
f'boundary of the model grid ({model_param[key]} < '
f'{grid_bounds[key][0]}).')
if model_param[key] > grid_bounds[key][1]:
raise ValueError(f'The input value of \'{key}\' is larger than the upper '
f'boundary of the model grid ({model_param[key]} > '
f'{grid_bounds[key][1]}).')
for key in model_param.keys():
if key not in self.get_parameters() and key not in extra_param:
warnings.warn(f'The \'{key}\' parameter is not required by \'{self.model}\' so '
f'the parameter will be ignored. The mandatory parameters are '
f'{self.get_parameters()}.')
if 'mass' in model_param and 'radius' not in model_param:
mass = 1e3 * model_param['mass'] * constants.M_JUP # (g)
radius = math.sqrt(1e3 * constants.GRAVITY * mass / (10.**model_param['logg'])) # (cm)
model_param['radius'] = 1e-2 * radius / constants.R_JUP # (Rjup)
if self.spectrum_interp is None:
self.interpolate_model()
if self.wavel_range is None:
wl_points = self.get_wavelengths()
self.wavel_range = (wl_points[0], wl_points[-1])
parameters = []
if 'teff' in model_param:
parameters.append(model_param['teff'])
if 'logg' in model_param:
parameters.append(model_param['logg'])
if 'feh' in model_param:
parameters.append(model_param['feh'])
if 'co' in model_param:
parameters.append(model_param['co'])
if 'fsed' in model_param:
parameters.append(model_param['fsed'])
flux = self.spectrum_interp(parameters)[0]
if 'radius' in model_param:
model_param['mass'] = read_util.get_mass(model_param['logg'], model_param['radius'])
if 'distance' in model_param:
scaling = (model_param['radius']*constants.R_JUP)**2 / \
(model_param['distance']*constants.PARSEC)**2
flux *= scaling
if smooth:
flux = read_util.smooth_spectrum(wavelength=self.wl_points,
flux=flux,
spec_res=spec_res)
if wavel_resample is not None:
flux = spectres.spectres(wavel_resample,
self.wl_points,
flux,
spec_errs=None,
fill=np.nan,
verbose=True)
elif spec_res is not None and not smooth:
index = np.where(np.isnan(flux))[0]
if index.size > 0:
raise ValueError('Flux values should not contains NaNs. Please make sure that '
'the parameter values and the wavelength range are within '
'the grid boundaries as stored in the database.')
wavel_resample = read_util.create_wavelengths(
(self.wl_points[0], self.wl_points[-1]), spec_res)
indices = np.where((wavel_resample > self.wl_points[0]) &
(wavel_resample < self.wl_points[-2]))[0]
wavel_resample = wavel_resample[indices]
flux = spectres.spectres(wavel_resample,
self.wl_points,
flux,
spec_errs=None,
fill=np.nan,
verbose=True)
if magnitude:
quantity = 'magnitude'
with h5py.File(self.database, 'r') as h5_file:
try:
h5_file['spectra/calibration/vega']
except KeyError:
h5_file.close()
species_db = database.Database()
species_db.add_spectrum('vega')
h5_file = h5py.File(self.database, 'r')
readcalib = read_calibration.ReadCalibration('vega', filter_name=None)
calibbox = readcalib.get_spectrum()
if wavel_resample is not None:
new_spec_wavs = wavel_resample
else:
new_spec_wavs = self.wl_points
flux_vega, _ = spectres.spectres(new_spec_wavs,
calibbox.wavelength,
calibbox.flux,
spec_errs=calibbox.error,
fill=np.nan,
verbose=True)
flux = -2.5*np.log10(flux/flux_vega)
else:
quantity = 'flux'
if np.isnan(np.sum(flux)):
warnings.warn(f'The resampled spectrum contains {np.sum(np.isnan(flux))} NaNs, '
f'probably because the original wavelength range does not fully '
f'encompass the new wavelength range. The happened with the '
f'following parameters: {model_param}.')
if wavel_resample is None:
wavelength = self.wl_points
else:
wavelength = wavel_resample
# is_finite = np.where(np.isfinite(flux))[0]
#
# if wavel_resample is None:
# wavelength = self.wl_points[is_finite]
# else:
# wavelength = wavel_resample[is_finite]
#
# if wavelength.shape[0] == 0:
# raise ValueError(f'The model spectrum is empty. Perhaps the grid could not be '
# f'interpolated at {model_param} because zeros are stored in the '
# f'database.')
model_box = box.create_box(boxtype='model',
model=self.model,
wavelength=wavelength,
flux=flux,
parameters=model_param,
quantity=quantity)
if 'lognorm_radius' in model_param and 'lognorm_sigma' in model_param and \
'lognorm_ext' in model_param:
model_box.flux = self.apply_lognorm_ext(model_box.wavelength,
model_box.flux,
model_param['lognorm_radius'],
model_param['lognorm_sigma'],
model_param['lognorm_ext'])
if 'powerlaw_max' in model_param and 'powerlaw_exp' in model_param and \
'powerlaw_ext' in model_param:
model_box.flux = self.apply_powerlaw_ext(model_box.wavelength,
model_box.flux,
model_param['powerlaw_max'],
model_param['powerlaw_exp'],
model_param['powerlaw_ext'])
if 'ism_ext' in model_param and 'ism_red' in model_param:
model_box.flux = self.apply_ism_ext(model_box.wavelength,
model_box.flux,
model_param['ism_ext'],
model_param['ism_red'])
if 'radius' in model_box.parameters:
model_box.parameters['luminosity'] = 4. * np.pi * (
model_box.parameters['radius'] * constants.R_JUP)**2 * constants.SIGMA_SB * \
model_box.parameters['teff']**4. / constants.L_SUN # (Lsun)
return model_box
def get_color_magnitude(
self,
age: float,
masses: np.ndarray,
model: str,
filters_color: Tuple[str, str],
filter_mag: str,
adapt_logg: bool = False,
) -> box.ColorMagBox:
"""
Function for calculating color-magnitude
combinations from a selected isochrone.
Parameters
----------
age : float
Age (Myr) at which the isochrone data is interpolated.
masses : np.ndarray
Masses (:math:`M_\\mathrm{J}`) at which the isochrone
data is interpolated.
model : str
Atmospheric model used to compute the synthetic photometry.
filters_color : tuple(str, str)
Filter names for the color as listed in the file with the
isochrone data. The filter names should be provided in the
format of the SVO Filter Profile Service.
filter_mag : str
Filter name for the absolute magnitude as listed in the
file with the isochrone data. The value should be equal
to one of the ``filters_color`` values.
adapt_logg : bool
Adapt :math:`\\log(g)` to the upper or lower boundary of
the atmospheric model grid whenever the :math:`\\log(g)`
that has been calculated from the isochrone mass and
radius lies outside the available range of the synthetic
spectra. Typically :math:`\\log(g)` has only a minor
impact on the broadband magnitudes and colors.
Returns
-------
species.core.box.ColorMagBox
Box with the color-magnitude data.
"""
isochrone = self.get_isochrone(age=age,
masses=masses,
filters_color=None,
filter_mag=None)
model1 = read_model.ReadModel(model=model,
filter_name=filters_color[0])
model2 = read_model.ReadModel(model=model,
filter_name=filters_color[1])
param_bounds = model1.get_bounds()
if model1.get_parameters() == ["teff", "logg", "feh"]:
if model == "sonora-bobcat":
iso_feh = float(self.tag[-4:])
else:
iso_feh = 0.0
elif model1.get_parameters() != ["teff", "logg"]:
raise ValueError(
"Creating synthetic colors and magnitudes from "
"isochrones is currently only implemented for "
"models with only Teff and log(g) as free parameters. "
"Please contact Tomas Stolker if additional "
"functionalities are required.")
else:
iso_feh = None
mag1 = np.zeros(isochrone.masses.shape[0])
mag2 = np.zeros(isochrone.masses.shape[0])
radius = np.zeros(isochrone.masses.shape[0])
for i, mass_item in enumerate(isochrone.masses):
model_param = {
"teff": isochrone.teff[i],
"logg": isochrone.logg[i],
"mass": mass_item,
"distance": 10.0,
}
if iso_feh is not None:
model_param["feh"] = iso_feh
radius[i] = read_util.get_radius(model_param["logg"],
model_param["mass"]) # (Rjup)
if np.isnan(isochrone.teff[i]):
mag1[i] = np.nan
mag2[i] = np.nan
warnings.warn(f"The value of Teff is NaN for the following "
f"isochrone sample: {model_param}. Setting "
f"the magnitudes to NaN.")
else:
for item_bounds in param_bounds:
if model_param[item_bounds] < param_bounds[item_bounds][0]:
if adapt_logg and item_bounds == "logg":
warnings.warn(
f"The log(g) is {model_param[item_bounds]} but the "
f"lower boundary of the model grid is "
f"{param_bounds[item_bounds][0]}. Adapting "
f"log(g) to {param_bounds[item_bounds][0]} since "
f"adapt_logg=True.")
model_param["logg"] = param_bounds["logg"][0]
else:
mag1[i] = np.nan
mag2[i] = np.nan
warnings.warn(
f"The value of {item_bounds} is "
f"{model_param[item_bounds]}, which is below "
f"the lower bound of the model grid "
f"({param_bounds[item_bounds][0]}). Setting the "
f"magnitudes to NaN for the following isochrone "
f"sample: {model_param}.")
elif model_param[item_bounds] > param_bounds[item_bounds][
1]:
if adapt_logg and item_bounds == "logg":
warnings.warn(
f"The log(g) is {model_param[item_bounds]} but "
f"the upper boundary of the model grid is "
f"{param_bounds[item_bounds][1]}. Adapting "
f"log(g) to {param_bounds[item_bounds][1]} "
f"since adapt_logg=True.")
model_param["logg"] = param_bounds["logg"][1]
else:
mag1[i] = np.nan
mag2[i] = np.nan
warnings.warn(
f"The value of {item_bounds} is "
f"{model_param[item_bounds]}, which is above "
f"the upper bound of the model grid "
f"({param_bounds[item_bounds][1]}). Setting the "
f"magnitudes to NaN for the following isochrone "
f"sample: {model_param}.")
if not np.isnan(mag1[i]):
mag1[i], _ = model1.get_magnitude(model_param)
mag2[i], _ = model2.get_magnitude(model_param)
if filter_mag == filters_color[0]:
abs_mag = mag1
elif filter_mag == filters_color[1]:
abs_mag = mag2
else:
raise ValueError("The argument of filter_mag should be equal to "
"one of the two filter values of filters_color.")
return box.create_box(
boxtype="colormag",
library=model,
object_type="model",
filters_color=filters_color,
filter_mag=filter_mag,
color=mag1 - mag2,
magnitude=abs_mag,
names=None,
sptype=masses,
mass=masses,
radius=radius,
iso_tag=self.tag,
)
def get_isochrone(
self,
age: float,
masses: np.ndarray,
filters_color: Optional[Tuple[str, str]] = None,
filter_mag: Optional[str] = None,
) -> box.IsochroneBox:
"""
Function for selecting an isochrone.
Parameters
----------
age : float
Age (Myr) at which the isochrone data is interpolated.
masses : np.ndarray
Masses (:math:`M_\\mathrm{J}`) at which the isochrone
data is interpolated.
filters_color : tuple(str, str), None
Filter names for the color as listed in the file with the
isochrone data. Not selected if set to ``None`` or if only
evolutionary tracks are available.
filter_mag : str, None
Filter name for the absolute magnitude as listed in the
file with the isochrone data. Not selected if set to
``None`` or if only evolutionary tracks are available.
Returns
-------
species.core.box.IsochroneBox
Box with the isochrone.
"""
age_points = np.full(masses.shape[0], age) # (Myr)
color = None
mag_abs = None
index_teff = 2
index_logg = 4
# Read isochrone data
with h5py.File(self.database, "r") as h5_file:
model = h5_file[f"isochrones/{self.tag}/evolution"].attrs["model"]
evolution = np.asarray(h5_file[f"isochrones/{self.tag}/evolution"])
if model == "baraffe":
filters = list(h5_file[f"isochrones/{self.tag}/filters"])
magnitudes = np.asarray(
h5_file[f"isochrones/{self.tag}/magnitudes"])
# Convert the h5py list of filters from bytes to strings
for i, item in enumerate(filters):
if isinstance(item, bytes):
filters[i] = item.decode("utf-8")
if model == "baraffe":
if filters_color is not None:
index_color_1 = filters.index(filters_color[0])
index_color_2 = filters.index(filters_color[1])
if filter_mag is not None:
index_mag = filters.index(filter_mag)
if filters_color is not None:
mag_color_1 = griddata(
points=evolution[:, 0:2],
values=magnitudes[:, index_color_1],
xi=np.stack((age_points, masses), axis=1),
method="linear",
fill_value="nan",
rescale=False,
)
mag_color_2 = griddata(
points=evolution[:, 0:2],
values=magnitudes[:, index_color_2],
xi=np.stack((age_points, masses), axis=1),
method="linear",
fill_value="nan",
rescale=False,
)
color = mag_color_1 - mag_color_2
if filter_mag is not None:
mag_abs = griddata(
points=evolution[:, 0:2],
values=magnitudes[:, index_mag],
xi=np.stack((age_points, masses), axis=1),
method="linear",
fill_value="nan",
rescale=False,
)
teff = griddata(
points=evolution[:, 0:2],
values=evolution[:, index_teff],
xi=np.stack((age_points, masses), axis=1),
method="linear",
fill_value="nan",
rescale=False,
)
logg = griddata(
points=evolution[:, 0:2],
values=evolution[:, index_logg],
xi=np.stack((age_points, masses), axis=1),
method="linear",
fill_value="nan",
rescale=False,
)
return box.create_box(
boxtype="isochrone",
model=self.tag,
filters_color=filters_color,
filter_mag=filter_mag,
color=color,
magnitude=mag_abs,
teff=teff,
logg=logg,
masses=masses,
)
def resample_spectrum(
self,
wavel_points: np.ndarray,
model_param: Optional[Dict[str, float]] = None,
spec_res: Optional[float] = None,
apply_mask: bool = False,
) -> box.SpectrumBox:
"""
Function for resampling the spectrum and optional uncertainties
onto a new wavelength grid.
Parameters
----------
wavel_points : np.ndarray
Wavelengths (um).
model_param : dict, None
Dictionary with the model parameters, which should only
contain the ``'scaling'`` keyword. No scaling is applied if
the argument of ``model_param`` is set to ``None``.
spec_res : float, None
Spectral resolution that is used for smoothing the spectrum
before resampling the wavelengths. No smoothing is applied
if the argument is set to ``None``. The smoothing can only
be applied ti spectra with a constant spectral resolution
(which is the case for all model spectra that are
compatible with ``species``) or a constant wavelength
spacing. The first smoothing approach is fastest.
apply_mask : bool
Exclude negative values and NaNs.
Returns
-------
species.core.box.SpectrumBox
Box with the resampled spectrum.
"""
calibbox = self.get_spectrum(apply_mask=apply_mask)
if spec_res is not None:
calibbox.flux = read_util.smooth_spectrum(
wavelength=calibbox.wavelength,
flux=calibbox.flux,
spec_res=spec_res)
flux_new, error_new = spectres.spectres(
wavel_points,
calibbox.wavelength,
calibbox.flux,
spec_errs=calibbox.error,
fill=0.0,
verbose=False,
)
if model_param is not None:
flux_new = model_param["scaling"] * flux_new
error_new = model_param["scaling"] * error_new
return box.create_box(
boxtype="spectrum",
spectrum="calibration",
wavelength=wavel_points,
flux=flux_new,
error=error_new,
name=self.tag,
)
def get_spectrum(
self,
model_param: Optional[Dict[str, float]] = None,
apply_mask: bool = False,
spec_res: Optional[float] = None,
extrapolate: bool = False,
min_wavelength: Optional[float] = None,
) -> box.SpectrumBox:
"""
Function for selecting the calibration spectrum.
Parameters
----------
model_param : dict, None
Model parameters. Should contain the 'scaling' value. Not
used if set to ``None``.
apply_mask : bool
Exclude negative values and NaN values.
spec_res : float, None
Spectral resolution. Original wavelength points are used if
set to ``None``.
extrapolate : bool
Extrapolate to 6 um by fitting a power law function.
min_wavelength : float, None
Minimum wavelength used for fitting the power law function.
All data is used if set to ``None``.
Returns
-------
species.core.box.SpectrumBox
Box with the spectrum.
"""
with h5py.File(self.database, "r") as h5_file:
data = np.asarray(h5_file[f"spectra/calibration/{self.tag}"])
wavelength = np.asarray(data[0, ])
flux = np.asarray(data[1, ])
error = np.asarray(data[2, ])
if apply_mask:
indices = np.where(flux > 0.0)[0]
wavelength = wavelength[indices]
flux = flux[indices]
error = error[indices]
if model_param is not None:
flux = model_param["scaling"] * flux
error = model_param["scaling"] * error
if self.wavel_range is None:
wl_index = np.ones(wavelength.size, dtype=bool)
else:
wl_index = ((flux > 0.0)
& (wavelength > self.wavel_range[0])
& (wavelength < self.wavel_range[1]))
count = np.count_nonzero(wl_index)
if count > 0:
index = np.where(wl_index)[0]
if index[0] > 0:
wl_index[index[0] - 1] = True
if index[-1] < len(wl_index) - 1:
wl_index[index[-1] + 1] = True
wavelength = wavelength[wl_index]
flux = flux[wl_index]
error = error[wl_index]
if extrapolate:
def _power_law(wavelength, offset, scaling, power_index):
return offset + scaling * wavelength**power_index
if min_wavelength:
indices = np.where(wavelength > min_wavelength)[0]
else:
indices = np.arange(0, wavelength.size, 1)
popt, pcov = curve_fit(
f=_power_law,
xdata=wavelength[indices],
ydata=flux[indices],
p0=(0.0, np.mean(flux[indices]), -1.0),
sigma=error[indices],
)
sigma = np.sqrt(np.diag(pcov))
print("Fit result for f(x) = a + b*x^c:")
print(f"a = {popt[0]} +/- {sigma[0]}")
print(f"b = {popt[1]} +/- {sigma[1]}")
print(f"c = {popt[2]} +/- {sigma[2]}")
while wavelength[-1] <= 6.0:
wl_add = wavelength[-1] + wavelength[-1] / 1000.0
wavelength = np.append(wavelength, wl_add)
flux = np.append(flux,
_power_law(wl_add, popt[0], popt[1], popt[2]))
error = np.append(error, 0.0)
if spec_res is not None:
wavelength_new = read_util.create_wavelengths(
(wavelength[0], wavelength[-1]), spec_res)
flux_new, error_new = spectres.spectres(
wavelength_new,
wavelength,
flux,
spec_errs=error,
fill=0.0,
verbose=True,
)
wavelength = wavelength_new
flux = flux_new
error = error_new
return box.create_box(
boxtype="spectrum",
spectrum="calibration",
wavelength=wavelength,
flux=flux,
error=error,
name=self.tag,
)
def get_data(self,
model_param: Dict[str, float]) -> box.ModelBox:
"""
Function for selecting a model spectrum (without interpolation) for a set of parameter
values that coincide with the grid points. The stored grid points can be inspected with
:func:`~species.read.read_model.ReadModel.get_points`.
Parameters
----------
model_param : dict
Model parameters and values. Only discrete values from the original grid are possible.
Else, the nearest grid values are selected.
Returns
-------
species.core.box.ModelBox
Box with the model spectrum.
"""
for key in self.get_parameters():
if key not in model_param.keys():
raise ValueError(f'The \'{key}\' parameter is required by \'{self.model}\'. '
f'The mandatory parameters are {self.get_parameters()}.')
extra_param = ['radius', 'distance', 'mass', 'luminosity']
for key in model_param.keys():
if key not in self.get_parameters() and key not in extra_param:
warnings.warn(f'The \'{key}\' parameter is not required by \'{self.model}\' so '
f'the parameter will be ignored. The mandatory parameters are '
f'{self.get_parameters()}.')
h5_file = self.open_database()
param_key = []
param_val = []
if 'teff' in model_param:
param_key.append('teff')
param_val.append(model_param['teff'])
if 'logg' in model_param:
param_key.append('logg')
param_val.append(model_param['logg'])
if 'feh' in model_param:
param_key.append('feh')
param_val.append(model_param['feh'])
if 'co' in model_param:
param_key.append('co')
param_val.append(model_param['co'])
if 'fsed' in model_param:
param_key.append('fsed')
param_val.append(model_param['fsed'])
flux = np.asarray(h5_file[f'models/{self.model}/flux'])
indices = []
for item in param_key:
data = np.asarray(h5_file[f'models/{self.model}/{item}'])
data_index = np.argwhere(np.round(data, 4) == np.round(model_param[item], 4))[0]
if len(data_index) == 0:
raise ValueError('The parameter {item}={model_val[i]} is not found.')
indices.append(data_index[0])
wl_points, wl_index = self.wavelength_points(h5_file)
indices.append(wl_index)
flux = flux[tuple(indices)]
if 'radius' in model_param and 'distance' in model_param:
scaling = (model_param['radius']*constants.R_JUP)**2 / \
(model_param['distance']*constants.PARSEC)**2
flux *= scaling
h5_file.close()
model_box = box.create_box(boxtype='model',
model=self.model,
wavelength=wl_points,
flux=flux,
parameters=model_param,
quantity='flux')
if 'lognorm_radius' in model_param and 'lognorm_sigma' in model_param and \
'lognorm_ext' in model_param:
model_box.flux = self.apply_lognorm_ext(model_box.wavelength,
model_box.flux,
model_param['lognorm_radius'],
model_param['lognorm_sigma'],
model_param['lognorm_ext'])
if 'powerlaw_max' in model_param and 'powerlaw_exp' in model_param and \
'powerlaw_ext' in model_param:
model_box.flux = self.apply_powerlaw_ext(model_box.wavelength,
model_box.flux,
model_param['powerlaw_max'],
model_param['powerlaw_exp'],
model_param['powerlaw_ext'])
if 'ism_ext' in model_param and 'ism_red' in model_param:
model_box.flux = self.apply_ism_ext(model_box.wavelength,
model_box.flux,
model_param['ism_ext'],
model_param['ism_red'])
if 'radius' in model_box.parameters:
model_box.parameters['luminosity'] = 4. * np.pi * (
model_box.parameters['radius'] * constants.R_JUP)**2 * constants.SIGMA_SB * \
model_box.parameters['teff']**4. / constants.L_SUN # (Lsun)
return model_box
def get_color_color(self,
age: float,
masses: np.ndarray,
model: str,
filters_colors: Tuple[Tuple[str, str],
Tuple[str, str]]) -> box.ColorColorBox:
"""
Function for calculating color-magnitude combinations from a selected isochrone.
Parameters
----------
age : float
Age (Myr) at which the isochrone data is interpolated.
masses : np.ndarray
Masses (Mjup) at which the isochrone data is interpolated.
model : str
Atmospheric model used to compute the synthetic photometry.
filters_colors : tuple(tuple(str, str), tuple(str, str))
Filter names for the colors as listed in the file with the isochrone data. The filter
names should be provided in the format of the SVO Filter Profile Service.
Returns
-------
species.core.box.ColorColorBox
Box with the color-color data.
"""
isochrone = self.get_isochrone(age=age,
masses=masses,
filters_color=None,
filter_mag=None)
model1 = read_model.ReadModel(model=model, filter_name=filters_colors[0][0])
model2 = read_model.ReadModel(model=model, filter_name=filters_colors[0][1])
model3 = read_model.ReadModel(model=model, filter_name=filters_colors[1][0])
model4 = read_model.ReadModel(model=model, filter_name=filters_colors[1][1])
if model1.get_parameters() != ['teff', 'logg']:
raise ValueError('Creating synthetic colors and magnitudes from isochrones is '
'currently only implemented for models with only Teff and log(g) '
'as free parameters. Please contact Tomas Stolker if additional '
'functionalities are required.')
mag1 = np.zeros(isochrone.masses.shape[0])
mag2 = np.zeros(isochrone.masses.shape[0])
mag3 = np.zeros(isochrone.masses.shape[0])
mag4 = np.zeros(isochrone.masses.shape[0])
for i, mass_item in enumerate(isochrone.masses):
model_param = {'teff': isochrone.teff[i],
'logg': isochrone.logg[i],
'mass': mass_item,
'distance': 10.}
if np.isnan(isochrone.teff[i]):
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(f'The value of Teff is NaN for the following isochrone sample: '
f'{model_param}. Setting the magnitudes to NaN.')
else:
for item_bounds in model1.get_bounds():
if model_param[item_bounds] <= model1.get_bounds()[item_bounds][0]:
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(f'The value of {item_bounds} is {model_param[item_bounds]}, '
f'which is below the lower bound of the model grid '
f'({model1.get_bounds()[item_bounds][0]}). Setting the '
f'magnitudes to NaN for the following isochrone sample: '
f'{model_param}.')
elif model_param[item_bounds] >= model1.get_bounds()[item_bounds][1]:
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(f'The value of {item_bounds} is {model_param[item_bounds]}, '
f'which is above the upper bound of the model grid '
f'({model1.get_bounds()[item_bounds][1]}). Setting the '
f'magnitudes to NaN for the following isochrone sample: '
f'{model_param}.')
if not np.isnan(mag1[i]) and not np.isnan(mag2[i]) and\
not np.isnan(mag3[i]) and not np.isnan(mag4[i]):
mag1[i], _ = model1.get_magnitude(model_param)
mag2[i], _ = model2.get_magnitude(model_param)
mag3[i], _ = model3.get_magnitude(model_param)
mag4[i], _ = model4.get_magnitude(model_param)
return box.create_box(boxtype='colorcolor',
library=model,
object_type='model',
filters=filters_colors,
color1=mag1-mag2,
color2=mag3-mag4,
sptype=masses,
names=None)
def get_isochrone(self,
age: float,
masses: np.ndarray,
filters_color: Optional[Tuple[str, str]] = None,
filter_mag: Optional[str] = None) -> box.IsochroneBox:
"""
Function for selecting an isochrone.
Parameters
----------
age : float
Age (Myr) at which the isochrone data is interpolated.
masses : np.ndarray
Masses (Mjup) at which the isochrone data is interpolated.
filters_color : tuple(str, str), None
Filter names for the color as listed in the file with the isochrone data. Not selected
if set to ``None`` or if only evolutionary tracks are available.
filter_mag : str, None
Filter name for the absolute magnitude as listed in the file with the isochrone data.
Not selected if set to ``None`` or if only evolutionary tracks are available.
Returns
-------
species.core.box.IsochroneBox
Box with the isochrone.
"""
age_points = np.repeat(age, masses.shape[0]) # (Myr)
color = None
mag_abs = None
index_teff = 2
index_logg = 4
with h5py.File(self.database, 'r') as h5_file:
model = h5_file[f'isochrones/{self.tag}/evolution'].attrs['model']
evolution = np.asarray(h5_file[f'isochrones/{self.tag}/evolution'])
if model == 'baraffe':
filters = list(h5_file[f'isochrones/{self.tag}/filters'])
magnitudes = np.asarray(h5_file[f'isochrones/{self.tag}/magnitudes'])
if model == 'baraffe':
if filters_color is not None:
index_color_1 = filters.index(filters_color[0])
index_color_2 = filters.index(filters_color[1])
if filter_mag is not None:
index_mag = filters.index(filter_mag)
if filters_color is not None:
mag_color_1 = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_color_1],
xi=np.stack((age_points, masses), axis=1),
method='linear',
fill_value='nan',
rescale=False)
mag_color_2 = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_color_2],
xi=np.stack((age_points, masses), axis=1),
method='linear',
fill_value='nan',
rescale=False)
color = mag_color_1-mag_color_2
if filter_mag is not None:
mag_abs = griddata(points=evolution[:, 0:2],
values=magnitudes[:, index_mag],
xi=np.stack((age_points, masses), axis=1),
method='linear',
fill_value='nan',
rescale=False)
teff = griddata(points=evolution[:, 0:2],
values=evolution[:, index_teff],
xi=np.stack((age_points, masses), axis=1),
method='linear',
fill_value='nan',
rescale=False)
logg = griddata(points=evolution[:, 0:2],
values=evolution[:, index_logg],
xi=np.stack((age_points, masses), axis=1),
method='linear',
fill_value='nan',
rescale=False)
return box.create_box(boxtype='isochrone',
model=self.tag,
filters_color=filters_color,
filter_mag=filter_mag,
color=color,
magnitude=mag_abs,
teff=teff,
logg=logg,
masses=masses)
def get_color_color(self,
object_type: Optional[str] = None
) -> box.ColorColorBox:
"""
Function for extracting color-color data from the selected
library.
Parameters
----------
object_type : str, None
Object type for which the colors and magnitudes are
extracted. Either field dwarfs ('field') or
young/low-gravity objects ('young'). All objects are
selected if set to ``None``.
Returns
-------
species.core.box.ColorColorBox
Box with the colors.
"""
if self.lib_type == "phot_lib":
h5_file = h5py.File(self.database, "r")
sptype = np.asarray(h5_file[f"photometry/{self.library}/sptype"])
flag = np.asarray(h5_file[f"photometry/{self.library}/flag"])
obj_names = np.asarray(h5_file[f"photometry/{self.library}/name"])
if object_type is None:
indices = np.arange(0, np.size(sptype), 1)
elif object_type == "field":
indices = np.where(flag == "null")[0]
elif object_type == "young":
indices = []
for j, object_flag in enumerate(flag):
if "young" in object_flag:
indices.append(j)
elif "lowg" in object_flag:
indices.append(j)
indices = np.array(indices)
mag1 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_colors[0][0]}"])
mag2 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_colors[0][1]}"])
mag3 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_colors[1][0]}"])
mag4 = np.asarray(h5_file[
f"photometry/{self.library}/{self.filters_colors[1][1]}"])
color1 = mag1 - mag2
color2 = mag3 - mag4
color1 = color1[indices]
color2 = color2[indices]
sptype = sptype[indices]
obj_names = obj_names[indices]
indices = []
for i in range(color1.size):
if not np.isnan(color1[i]) and not np.isnan(color2[i]):
indices.append(i)
colorbox = box.create_box(
boxtype="colorcolor",
library=self.library,
object_type=object_type,
filters=self.filters_colors,
color1=color1[indices],
color2=color2[indices],
sptype=sptype[indices],
names=obj_names[indices],
)
h5_file.close()
elif self.lib_type == "spec_lib":
read_spec_0 = read_spectrum.ReadSpectrum(
spec_library=self.library,
filter_name=self.filters_colors[0][0])
read_spec_1 = read_spectrum.ReadSpectrum(
spec_library=self.library,
filter_name=self.filters_colors[0][1])
read_spec_2 = read_spectrum.ReadSpectrum(
spec_library=self.library,
filter_name=self.filters_colors[1][0])
read_spec_3 = read_spectrum.ReadSpectrum(
spec_library=self.library,
filter_name=self.filters_colors[1][1])
phot_box_0 = read_spec_0.get_magnitude(sptypes=None)
phot_box_1 = read_spec_1.get_magnitude(sptypes=None)
phot_box_2 = read_spec_2.get_magnitude(sptypes=None)
phot_box_3 = read_spec_3.get_magnitude(sptypes=None)
colorbox = box.create_box(
boxtype="colorcolor",
library=self.library,
object_type=object_type,
filters=self.filters_colors,
color1=phot_box_0.app_mag[:, 0] - phot_box_1.app_mag[:, 0],
color2=phot_box_2.app_mag[:, 0] - phot_box_3.app_mag[:, 0],
sptype=phot_box_0.sptype,
names=None,
)
return colorbox
def get_color_color(
self,
age: float,
masses: np.ndarray,
model: str,
filters_colors: Tuple[Tuple[str, str], Tuple[str, str]],
) -> box.ColorColorBox:
"""
Function for calculating color-magnitude combinations from a
selected isochrone.
Parameters
----------
age : float
Age (Myr) at which the isochrone data is interpolated.
masses : np.ndarray
Masses (:math:`M_\\mathrm{J}`) at which the isochrone
data is interpolated.
model : str
Atmospheric model used to compute the synthetic photometry.
filters_colors : tuple(tuple(str, str), tuple(str, str))
Filter names for the colors as listed in the file with the
isochrone data. The filter names should be provided in the
format of the SVO Filter Profile Service.
Returns
-------
species.core.box.ColorColorBox
Box with the color-color data.
"""
isochrone = self.get_isochrone(age=age,
masses=masses,
filters_color=None,
filter_mag=None)
model1 = read_model.ReadModel(model=model,
filter_name=filters_colors[0][0])
model2 = read_model.ReadModel(model=model,
filter_name=filters_colors[0][1])
model3 = read_model.ReadModel(model=model,
filter_name=filters_colors[1][0])
model4 = read_model.ReadModel(model=model,
filter_name=filters_colors[1][1])
if model1.get_parameters() == ["teff", "logg", "feh"]:
if model == "sonora-bobcat":
iso_feh = float(self.tag[-4:])
else:
iso_feh = 0.0
elif model1.get_parameters() != ["teff", "logg"]:
raise ValueError(
"Creating synthetic colors and magnitudes from "
"isochrones is currently only implemented for "
"models with only Teff and log(g) as free parameters. "
"Please contact Tomas Stolker if additional "
"functionalities are required.")
else:
iso_feh = None
mag1 = np.zeros(isochrone.masses.shape[0])
mag2 = np.zeros(isochrone.masses.shape[0])
mag3 = np.zeros(isochrone.masses.shape[0])
mag4 = np.zeros(isochrone.masses.shape[0])
radius = np.zeros(isochrone.masses.shape[0])
for i, mass_item in enumerate(isochrone.masses):
model_param = {
"teff": isochrone.teff[i],
"logg": isochrone.logg[i],
"mass": mass_item,
"distance": 10.0,
}
if iso_feh is not None:
model_param["feh"] = iso_feh
radius[i] = read_util.get_radius(model_param["logg"],
model_param["mass"]) # (Rjup)
if np.isnan(isochrone.teff[i]):
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(
f"The value of Teff is NaN for the following isochrone "
f"sample: {model_param}. Setting the magnitudes to NaN.")
else:
for item_bounds in model1.get_bounds():
if model_param[item_bounds] < model1.get_bounds(
)[item_bounds][0]:
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(
f"The value of {item_bounds} is "
f"{model_param[item_bounds]}, which is "
f"below the lower bound of the model grid "
f" ({model1.get_bounds()[item_bounds][0]}). "
f"Setting the magnitudes to NaN for the "
f"following isochrone sample: {model_param}.")
elif model_param[item_bounds] > model1.get_bounds(
)[item_bounds][1]:
mag1[i] = np.nan
mag2[i] = np.nan
mag3[i] = np.nan
mag4[i] = np.nan
warnings.warn(
f"The value of {item_bounds} is "
f"{model_param[item_bounds]}, which is above "
f"the upper bound of the model grid "
f"({model1.get_bounds()[item_bounds][1]}). "
f"Setting the magnitudes to NaN for the "
f"following isochrone sample: {model_param}.")
if (not np.isnan(mag1[i]) and not np.isnan(mag2[i])
and not np.isnan(mag3[i]) and not np.isnan(mag4[i])):
mag1[i], _ = model1.get_magnitude(model_param)
mag2[i], _ = model2.get_magnitude(model_param)
mag3[i], _ = model3.get_magnitude(model_param)
mag4[i], _ = model4.get_magnitude(model_param)
return box.create_box(
boxtype="colorcolor",
library=model,
object_type="model",
filters=filters_colors,
color1=mag1 - mag2,
color2=mag3 - mag4,
names=None,
sptype=masses,
mass=masses,
radius=radius,
iso_tag=self.tag,
)
def get_color_magnitude(self,
object_type: Optional[str] = None) -> box.ColorMagBox:
"""
Function for extracting color-magnitude data from the selected library.
Parameters
----------
object_type : str, None
Object type for which the colors and magnitudes are extracted. Either field dwarfs
('field') or young/low-gravity objects ('young'). All objects are selected if set
to ``None``.
Returns
-------
species.core.box.ColorMagBox
Box with the colors and magnitudes.
"""
if self.lib_type == 'phot_lib':
with h5py.File(self.database, 'r') as h5_file:
sptype = np.asarray(h5_file[f'photometry/{self.library}/sptype'])
dist = np.asarray(h5_file[f'photometry/{self.library}/distance'])
dist_error = np.asarray(h5_file[f'photometry/{self.library}/distance_error'])
flag = np.asarray(h5_file[f'photometry/{self.library}/flag'])
obj_names = np.asarray(h5_file[f'photometry/{self.library}/name'])
if object_type is None:
indices = np.arange(0, np.size(sptype), 1)
elif object_type == 'field':
indices = np.where(flag == 'null')[0]
elif object_type == 'young':
indices = []
for j, object_flag in enumerate(flag):
if 'young' in object_flag:
indices.append(j)
elif 'lowg' in object_flag:
indices.append(j)
indices = np.array(indices)
if indices.size > 0:
with h5py.File(self.database, 'r') as h5_file:
mag1 = np.asarray(h5_file[f'photometry/{self.library}/{self.filters_color[0]}'])
mag2 = np.asarray(h5_file[f'photometry/{self.library}/{self.filters_color[1]}'])
else:
raise ValueError(f'There is not data available from \'{self.library}\' for '
f'\'{object_type}\' type objects with the chosen filters.')
color = mag1 - mag2
if self.filter_mag == self.filters_color[0]:
mag, _ = phot_util.apparent_to_absolute((mag1, None), (dist, dist_error))
elif self.filter_mag == self.filters_color[1]:
mag, _ = phot_util.apparent_to_absolute((mag2, None), (dist, dist_error))
color = color[indices]
mag = mag[indices]
sptype = sptype[indices]
obj_names = obj_names[indices]
indices = []
for i in range(color.size):
if not np.isnan(color[i]) and not np.isnan(mag[i]):
indices.append(i)
colormag_box = box.create_box(boxtype='colormag',
library=self.library,
object_type=object_type,
filters_color=self.filters_color,
filter_mag=self.filter_mag,
color=color[indices],
magnitude=mag[indices],
sptype=sptype[indices],
names=obj_names[indices])
elif self.lib_type == 'spec_lib':
read_spec_0 = read_spectrum.ReadSpectrum(spec_library=self.library,
filter_name=self.filters_color[0])
read_spec_1 = read_spectrum.ReadSpectrum(spec_library=self.library,
filter_name=self.filters_color[1])
read_spec_2 = read_spectrum.ReadSpectrum(spec_library=self.library,
filter_name=self.filter_mag)
phot_box_0 = read_spec_0.get_magnitude(sptypes=None)
phot_box_1 = read_spec_1.get_magnitude(sptypes=None)
phot_box_2 = read_spec_2.get_magnitude(sptypes=None)
colormag_box = box.create_box(boxtype='colormag',
library=self.library,
object_type=object_type,
filters_color=self.filters_color,
filter_mag=self.filter_mag,
color=phot_box_0.app_mag[:, 0]-phot_box_1.app_mag[:, 0],
magnitude=phot_box_2.abs_mag[:, 0],
sptype=phot_box_0.sptype,
names=None)
return colormag_box
def get_spectrum(self,
model_param: Dict[str, Union[float, List[float]]],
spec_res: float,
smooth: bool = False) -> box.ModelBox:
"""
Function for calculating a Planck spectrum or a combination of multiple Planck spectra.
Parameters
----------
model_param : dict
Dictionary with the 'teff' (K), 'radius' (Rjup), and 'distance' (pc). The values
of 'teff' and 'radius' can be a single float, or a list with floats for a combination
of multiple Planck functions, e.g.
{'teff': [1500., 1000.], 'radius': [1., 2.], 'distance': 10.}.
spec_res : float
Spectral resolution.
smooth : bool
Returns
-------
species.core.box.ModelBox
Box with the Planck spectrum.
"""
if 'teff' in model_param and isinstance(model_param['teff'], list):
model_param = self.update_parameters(model_param)
wavel_points = read_util.create_wavelengths(self.wavel_range, spec_res)
n_planck = 0
for item in model_param:
if item[:4] == 'teff':
n_planck += 1
if n_planck == 1:
if 'radius' in model_param and 'distance' in model_param:
scaling = ((model_param['radius']*constants.R_JUP) /
(model_param['distance']*constants.PARSEC))**2
else:
scaling = 1.
flux = self.planck(wavel_points,
model_param['teff'],
scaling) # (W m-2 um-1)
else:
flux = np.zeros(wavel_points.shape)
for i in range(n_planck):
if f'radius_{i}' in model_param and 'distance' in model_param:
scaling = ((model_param[f'radius_{i}']*constants.R_JUP) /
(model_param['distance']*constants.PARSEC))**2
else:
scaling = 1.
flux += self.planck(wavel_points,
model_param[f'teff_{i}'],
scaling) # (W m-2 um-1)
if smooth:
flux = read_util.smooth_spectrum(wavel_points, flux, spec_res)
model_box = box.create_box(boxtype='model',
model='planck',
wavelength=wavel_points,
flux=flux,
parameters=model_param,
quantity='flux')
if n_planck == 1 and 'radius' in model_param:
model_box.parameters['luminosity'] = 4. * np.pi * (
model_box.parameters['radius'] * constants.R_JUP)**2 * constants.SIGMA_SB * \
model_box.parameters['teff']**4. / constants.L_SUN # (Lsun)
elif n_planck > 1:
lum_total = 0.
for i in range(n_planck):
if f'radius_{i}' in model_box.parameters:
# Add up the luminosity of the blackbody components (Lsun)
surface = 4. * np.pi * (model_box.parameters[f'radius_{i}']*constants.R_JUP)**2
lum_total += surface * constants.SIGMA_SB * \
model_box.parameters[f'teff_{i}']**4. / constants.L_SUN
if lum_total > 0.:
model_box.parameters['luminosity'] = lum_total
return model_box
def get_residuals(datatype: str,
spectrum: str,
parameters: Dict[str, float],
objectbox: box.ObjectBox,
inc_phot: Union[bool, List[str]] = True,
inc_spec: Union[bool, List[str]] = True,
**kwargs_radtrans: Optional[dict]) -> box.ResidualsBox:
"""
Parameters
----------
datatype : str
Data type ('model' or 'calibration').
spectrum : str
Name of the atmospheric model or calibration spectrum.
parameters : dict
Parameters and values for the spectrum
objectbox : species.core.box.ObjectBox
Box with the photometry and/or spectra of an object. A scaling and/or error inflation of
the spectra should be applied with :func:`~species.util.read_util.update_spectra`
beforehand.
inc_phot : bool, list(str)
Include photometric data in the fit. If a boolean, either all (``True``) or none
(``False``) of the data are selected. If a list, a subset of filter names (as stored in
the database) can be provided.
inc_spec : bool, list(str)
Include spectroscopic data in the fit. If a boolean, either all (``True``) or none
(``False``) of the data are selected. If a list, a subset of spectrum names (as stored
in the database with :func:`~species.data.database.Database.add_object`) can be
provided.
Keyword arguments
-----------------
kwargs_radtrans : dict
Dictionary with the keyword arguments for the ``ReadRadtrans`` object, containing
``line_species``, ``cloud_species``, and ``scattering``.
Returns
-------
species.core.box.ResidualsBox
Box with the residuals.
"""
if 'filters' in kwargs_radtrans:
warnings.warn('The \'filters\' parameter has been deprecated. Please use the \'inc_phot\' '
'parameter instead. The \'filters\' parameter is ignored.')
if isinstance(inc_phot, bool) and inc_phot:
inc_phot = objectbox.filters
if inc_phot:
model_phot = multi_photometry(datatype=datatype,
spectrum=spectrum,
filters=inc_phot,
parameters=parameters)
res_phot = {}
for item in inc_phot:
transmission = read_filter.ReadFilter(item)
res_phot[item] = np.zeros(objectbox.flux[item].shape)
if objectbox.flux[item].ndim == 1:
res_phot[item][0] = transmission.mean_wavelength()
res_phot[item][1] = (objectbox.flux[item][0]-model_phot.flux[item]) / \
objectbox.flux[item][1]
elif objectbox.flux[item].ndim == 2:
for j in range(objectbox.flux[item].shape[1]):
res_phot[item][0, j] = transmission.mean_wavelength()
res_phot[item][1, j] = (objectbox.flux[item][0, j]-model_phot.flux[item]) / \
objectbox.flux[item][1, j]
else:
res_phot = None
if inc_spec:
res_spec = {}
readmodel = None
for key in objectbox.spectrum:
if isinstance(inc_spec, bool) or key in inc_spec:
wavel_range = (0.9*objectbox.spectrum[key][0][0, 0],
1.1*objectbox.spectrum[key][0][-1, 0])
wl_new = objectbox.spectrum[key][0][:, 0]
spec_res = objectbox.spectrum[key][3]
if spectrum == 'planck':
readmodel = read_planck.ReadPlanck(wavel_range=wavel_range)
model = readmodel.get_spectrum(model_param=parameters, spec_res=1000.)
flux_new = spectres.spectres(wl_new,
model.wavelength,
model.flux,
spec_errs=None,
fill=0.,
verbose=True)
else:
if spectrum == 'petitradtrans':
# TODO change back
pass
# radtrans = read_radtrans.ReadRadtrans(line_species=kwargs_radtrans['line_species'],
# cloud_species=kwargs_radtrans['cloud_species'],
# scattering=kwargs_radtrans['scattering'],
# wavel_range=wavel_range)
#
# model = radtrans.get_model(parameters, spec_res=None)
#
# # separate resampling to the new wavelength points
#
# flux_new = spectres.spectres(wl_new,
# model.wavelength,
# model.flux,
# spec_errs=None,
# fill=0.,
# verbose=True)
else:
readmodel = read_model.ReadModel(spectrum, wavel_range=wavel_range)
# resampling to the new wavelength points is done in teh get_model function
model_spec = readmodel.get_model(parameters,
spec_res=spec_res,
wavel_resample=wl_new,
smooth=True)
flux_new = model_spec.flux
data_spec = objectbox.spectrum[key][0]
res_tmp = (data_spec[:, 1]-flux_new) / data_spec[:, 2]
res_spec[key] = np.column_stack([wl_new, res_tmp])
else:
res_spec = None
print('Calculating residuals... [DONE]')
print('Residuals (sigma):')
if res_phot is not None:
for item in inc_phot:
if res_phot[item].ndim == 1:
print(f' - {item}: {res_phot[item][1]:.2f}')
elif res_phot[item].ndim == 2:
for j in range(res_phot[item].shape[1]):
print(f' - {item}: {res_phot[item][1, j]:.2f}')
if res_spec is not None:
for key in objectbox.spectrum:
if isinstance(inc_spec, bool) or key in inc_spec:
print(f' - {key}: min: {np.nanmin(res_spec[key]):.2f}, '
f'max: {np.nanmax(res_spec[key]):.2f}')
return box.create_box(boxtype='residuals',
name=objectbox.name,
photometry=res_phot,
spectrum=res_spec)
