def real_spec(request):
band = request.param
wav, flux = load_aces_spectrum([3900, 4.5, 0.0, 0])
wav, flux = wav_selector(wav, flux, *band_limits(band))
flux = flux / snr_constant_band(wav, flux, 100, band)
atm = Atmosphere.from_band(band).at(wav)
return wav, flux, atm.transmission
def test_wav_selector(x, y, wav_min, wav_max):
"""Test some properties of wavelength selector."""
y = [xi + y for xi in x] # just to make y different
x1, y1 = utils.wav_selector(x, y, wav_min, wav_max)
assert all(x1 >= wav_min)
assert all(x1 <= wav_max)
assert len(x1) == len(y1)
assert isinstance(x1, np.ndarray)
assert isinstance(y1, np.ndarray)
def convolution(
wav: ndarray,
flux: ndarray,
vsini: float,
R: float,
band: str = "All",
*,
epsilon: float = 0.6,
fwhm_lim: float = 5.0,
num_procs: Optional[int] = None,
normalize: bool = True,
):
"""Perform convolution of spectrum.
Rotational convolution followed by a Gaussian of a specified resolution R.
Parameters
----------
wav: ndarray
Wavelength in microns
flux: ndarray
Photon flux
vsini: float
Rotational velocity in km/s.
R: int
Resolution of instrumental profile.
band: str
Wavelength band to choose, default="All"
epsilon: float (default = 0.6)
Limb darkening coefficient
fwhm_lim: float (default = 5.0)
FWHM limit for instrument broadening.
normalize: bool (default = True)
Area normalize the broadening kernels (corrects for unequal sampling of position).
num_procs: int, None
Number of processes to use with multiprocess.
If None it is assigned to 1 less then total number of cores.
If num_procs = 0, then multiprocess is not used.
Returns
-------
wav_band: ndarray
Wavelength for the selected band.
flux_band: ndarray
Original flux for the selected band.
flux_conv: ndarray
Convolved flux for the selected band.
"""
wav_band, flux_band = band_selector(wav, flux, band)
# We need to calculate the fwhm at this value in order to set the starting point for the convolution
fwhm_min = wav_band[0] / R # fwhm at the extremes of vector
fwhm_max = wav_band[-1] / R
# performing convolution with rotation kernel
print(
"Starting the Rotation convolution for vsini={0:.2f}...".format(vsini))
delta_lambda_min = wav_band[0] * vsini / c_kmps
delta_lambda_max = wav_band[-1] * vsini / c_kmps
# widest wavelength bin for the rotation convolution
lower_lim = wav_band[0] - delta_lambda_min - fwhm_lim * fwhm_min
upper_lim = wav_band[-1] + delta_lambda_max + fwhm_lim * fwhm_max
wav_ext_rotation, flux_ext_rotation = wav_selector(wav, flux, lower_lim,
upper_lim)
# wide wavelength bin for the resolution_convolution
lower_lim = wav_band[0] - fwhm_lim * fwhm_min
upper_lim = wav_band[-1] + fwhm_lim * fwhm_max
extended_wav, __ = wav_selector(wav, flux, lower_lim, upper_lim)
# rotational convolution
flux_conv_rot = rotational_convolution(
extended_wav,
wav_ext_rotation,
flux_ext_rotation,
vsini,
epsilon=epsilon,
num_procs=num_procs,
normalize=normalize,
)
print("Starting the Resolution convolution...")
flux_conv_res = resolution_convolution(
wav_band,
extended_wav,
flux_conv_rot,
R,
fwhm_lim=fwhm_lim,
num_procs=num_procs,
normalize=normalize,
)
return wav_band, flux_band, flux_conv_res
def convolution(
wav: ndarray,
flux: ndarray,
vsini: float,
R: float,
band: str = "All",
*,
epsilon: float = 0.6,
fwhm_lim: float = 5.0,
num_procs: Optional[Union[int, joblib.parallel.Parallel]] = None,
normalize: bool = True,
verbose: bool = True,
):
r"""Perform rotational then Instrumental broadening with convolutions.
Parameters
----------
wav: ndarray
Wavelength array.
flux: ndarray
Flux array.
vsini: float
Rotational velocity in km/s.
R: int
Resolution of instrumental profile.
band: str
Wavelength band to choose, default is "All".
epsilon: float
Limb darkening coefficient. Default is 0.6.
fwhm_lim: float
FWHM limit for instrument broadening. Default is 5.0.
normalize: bool
Area normalize the broadening kernel. This corrects for kernel area with unequal wavelength spacing.
Default is True.
num_procs: int, None or joblib.parallel.Parallel.
Number of processes to use, n_job parameter in joblib.
If num_procs = 1, then a single core is used.
Can also be a joblib.parallel.Parallel instance.
verbose: bool
Show the tqdm progress bar. Default is True.
Returns
-------
wav_band: ndarray
Wavelength for the selected band.
flux_band: ndarray
Original flux for the selected band.
flux_conv: ndarray
Convolved flux for the selected band.
"""
wav_band, flux_band = band_selector(wav, flux, band)
# Calculate FWHM at each end for the convolution
fwhm_min = wav_band[0] / R # fwhm at the extremes of vector
fwhm_max = wav_band[-1] / R
# performing convolution with rotation kernel
if verbose:
print("Starting the Rotation convolution for vsini={0:.2f}...".format(
vsini))
delta_lambda_min = wav_band[0] * vsini / c_kmps
delta_lambda_max = wav_band[-1] * vsini / c_kmps
# widest wavelength bin for the rotation convolution
lower_lim = wav_band[0] - delta_lambda_min - fwhm_lim * fwhm_min
upper_lim = wav_band[-1] + delta_lambda_max + fwhm_lim * fwhm_max
wav_ext_rotation, flux_ext_rotation = wav_selector(wav, flux, lower_lim,
upper_lim)
# wide wavelength bin for the resolution_convolution
lower_lim = wav_band[0] - fwhm_lim * fwhm_min
upper_lim = wav_band[-1] + fwhm_lim * fwhm_max
extended_wav, __ = wav_selector(wav, flux, lower_lim, upper_lim)
# rotational convolution
flux_conv_rot = rotational_convolution(
extended_wav,
wav_ext_rotation,
flux_ext_rotation,
vsini,
epsilon=epsilon,
num_procs=num_procs,
normalize=normalize,
verbose=verbose,
)
if verbose:
print("Starting the Resolution convolution...")
flux_conv_res = resolution_convolution(
wav_band,
extended_wav,
flux_conv_rot,
R,
fwhm_lim=fwhm_lim,
num_procs=num_procs,
normalize=normalize,
verbose=verbose,
)
return wav_band, flux_band, flux_conv_res