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	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
bin_spec	1D array
bin_err	1D array
bin_widths	1D array

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=8.637,
>>> )

>>> # With NIRSpec
>>> 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(
>>>     '../planet_spectra/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)