pandeia_io.simulate_tso

pandeia_io.simulate_tso(
    tso,
    n_obs=1,
    resolution=None,
    bins=None,
    noiseless=False,
)

Given a TSO dict from a pandeia TSO run, simulate a transit/eclipse spectrum scaling the noise by the number of observations and resampling over wavelength.

Parameters

Name	Type	Description	Default
tso			required
n_obs		Number of transit/eclipse observations	1
resolution		If not None, resample the spectrum at the given resolution.	None
bins		If not None, bin the spectrum in between the edges given by this array.	None
noiseless		If True, do not add scatter noise to the spectrum.	False

Returns

Name	Type	Description
bin_wl	1D array	Wavelengths of binned transit/eclipse spectrum.
bin_spec	1D array	Binned simulated transit/eclipse spectrum.
bin_err	1D array	Uncertainties of bin_spec.
bin_widths	1D or 2D array	For spectra, the 1D bin widths of bin_wl. For photometry, an array of shape [1,2] with the (lower,upper) widths of the passband relative to bin_wl.

Examples

>>> import gen_tso.pandeia_io as jwst
>>> import matplotlib.pyplot as plt

>>> scene = jwst.make_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.351,
>>> )

>>> # NIRSpec BOTS spectra
>>> pando = jwst.PandeiaCalculation('nirspec', 'bots')
>>> pando.calc['scene'] = [scene]
>>> disperser='g395h'
>>> filter='f290lp'
>>> readout='nrsrapid'
>>> subarray='sub2048'
>>> ngroup = 16

>>> transit_dur = 2.71
>>> obs_dur = 6.0
>>> obs_type = 'transit'
>>> depth_model = np.loadtxt('WASP80b_transit.dat', unpack=True)

>>> tso = pando.tso_calculation(
>>>     obs_type, transit_dur, obs_dur, depth_model,
>>>     ngroup, disperser, filter, subarray, readout,
>>> )

>>> bin_wl, bin_spec, bin_err, bin_widths = jwst.simulate_tso(
>>>    tso, n_obs=1, resolution=300.0, noiseless=False,
>>> )

>>> plt.figure(10)
>>> plt.clf()
>>> plt.plot(depth_model[0], depth_model[1], c='orange')
>>> plt.plot(tso['wl'], tso['depth_spectrum'], c='orangered')
>>> plt.errorbar(bin_wl, bin_spec, bin_err, fmt='ok', ms=4, mfc='w')
>>> plt.xlim(2.8, 5.3)