pandeia_io.PandeiaCalculation

pandeia_io.PandeiaCalculation(self, instrument, mode=None)

A class to interface with the pandeia.engine package.

Parameters

Name	Type	Description	Default
`instrument`		The JWST instrument: nircam, niriss, nirspec, miri.	required
`mode`		Observing mode. If not set, default to the first item for each instrument from this list below: instrument mode comments ———- —- ——– nircam: lw_tsgrism spectroscopy sw_tsgrism spectroscopy target_acq aquisition niriss: soss spectroscopy target_acq aquisition nirspec: bots spectroscopy target_acq aquisition miri: lrsslitless spectroscopy mrs_ts spectroscopy target_acq aquisition	`None`

Examples

>>> import gen_tso.pandeia_io as jwst

>>> pando = jwst.PandeiaCalculation('nircam')
>>> pando = jwst.PandeiaCalculation('nirspec')
>>> pando = jwst.PandeiaCalculation('nircam', 'target_acq')

Methods

Name	Description
calc_noise	Run a Pandeia calculation and extract the observed wavelength,
get_configs	Print out or return the list of available configurations.
get_saturation_values	Calculate the brightest-pixel rate (e-/s) and full_well (e-)
get_scene	Get a flattened copy of the scene containing the SED and
perform_calculation	Run pandeia’s perform_calculation() for the given configuration
saturation_fraction	Estimate the number of groups below a given saturation fraction or
set_scene	Set the stellar point-source scene to observe.
show_config	Display a summary of the instrumental and scene configuration
simulate_tso	Simulate a time-series observation spectrum with noise
tso_calculation	Run pandeia to simulate a transit/eclipse time-series observation
tso_print	Print to screen a summary of the latest tso_calculation() ran.
wl_ranges	Get wavelength range covered by the instrument/mode

calc_noise

pandeia_io.PandeiaCalculation.calc_noise(obs_dur=None, ngroup=None, disperser=None, filter=None, subarray=None, readout=None, aperture=None, nint=None)

Run a Pandeia calculation and extract the observed wavelength, flux, and variances.

Parameters

Name	Description	Default
`obs_dur`	Duration of the observation.	`None`
`ngroup`	Number of groups per integrations	`None`
`disperser`		`None`
`filter`		`None`
`subarray`		`None`
`readout`		`None`
`aperture`		`None`

Returns

Type	Description
TBD

Examples

>>> import gen_tso.pandeia_io as jwst

>>> instrument = 'nircam'
>>> mode = 'lw_tsgrism'
>>> pando = jwst.PandeiaCalculation(instrument, mode)
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.637,
>>> )
>>> # Example TBD

get_configs

pandeia_io.PandeiaCalculation.get_configs(output=None)

Print out or return the list of available configurations.

Parameters

Name	Type	Description	Default
`output`		The configuration variable to list. Select from: readouts, subarrays, filters, or dispersers.	`None`

Returns

Type	Description
outputs: 1D list of strings	The list of available inputs for the requested variable.

get_saturation_values

pandeia_io.PandeiaCalculation.get_saturation_values(disperser, filter, subarray, readout, ngroup=2, aperture=None, order=None, get_max=False)

Calculate the brightest-pixel rate (e-/s) and full_well (e-) for the current instrument and scene configuration, which once known, are sufficient to calculate the saturation level once the saturation time is known.

Examples

>>> import gen_tso.pandeia_io as jwst

>>> instrument = 'nircam'
>>> mode = 'lw_tsgrism'
>>> pando = jwst.PandeiaCalculation(instrument, mode)
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k2v',
>>>     norm_band='2mass,ks', norm_magnitude=8.351,
>>> )
>>> brightest_pixel_rate, full_well = pando.get_saturation_values(
>>>     disperser='grismr', filter='f444w',
>>>     readout='rapid', subarray='subgrism64',
>>> )

>>> # Also works for Target Acquisition:
>>> instrument = 'nircam'
>>> mode = 'target_acq'
>>> pando = jwst.PandeiaCalculation(instrument, mode)
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.351,
>>> )
>>> brightest_pixel_rate, full_well = pando.get_saturation_values(
>>>     disperser=None, filter='f335m',
>>>     readout='rapid', subarray='sub32tats', ngroup=3,
>>> )

get_scene

pandeia_io.PandeiaCalculation.get_scene()

Get a flattened copy of the scene containing the SED and normalization properties.

Returns

Type	Description
Dictionary	Scene arguments.

perform_calculation

pandeia_io.PandeiaCalculation.perform_calculation(ngroup, nint, disperser=None, filter=None, subarray=None, readout=None, aperture=None, order=None)

Run pandeia’s perform_calculation() for the given configuration (or set of configurations, see notes below).

Parameter

ngroup: Integer Number of groups per integration. Must be >= 2. nint: Integer Number of integrations. disperser: String Disperser/grating for the given instrument. filter: String Filter for the given instrument. subarray: String Subarray mode for the given instrument. readout: String Readout pattern mode for the given instrument. aperture: String Aperture configuration for the given instrument. order: Integer For NIRISS SOSS only, the spectral order. Other modes will ignore this argument.

Returns

Type	Description
dict	The Pandeia’s report output for the given configuration. If there’s more than one requested calculation, return a list of reports.

Notes

Provide a list of values for any of these arguments to compute a batch of calculations.
To leave a config parameter unmodified, leave the respective argument as None. To set a config parameter as None, set the argument to ’’.

saturation_fraction

pandeia_io.PandeiaCalculation.saturation_fraction(fraction=None, ngroup=None, flux_rate=None, full_well=None)

Estimate the number of groups below a given saturation fraction or the saturation level for a given number of groups.

Parameters

Name	Description	Default
`fraction`	If not None, estimate the maximum number of groups below the given saturation fraction (percentage units).	`None`
`ngroup`	If not None, estimate the saturation fraction (%) for ngroup.	`None`
`flux_rate`	e- per second rate at the brightest pixel.	`None`
`full_well`	Number of e- counts to saturate the detector.	`None`

Returns

Type	Description
(if fraction argument is not None)
Integer	Maximum number of groups to remain below the saturation fraction
(if ngroup argument is not None)
Float	Saturation level reached for given ngroup.

Examples

>>> import gen_tso.pandeia_io as jwst

>>> instrument = 'nircam'
>>> mode = 'lw_tsgrism'
>>> pando = jwst.PandeiaCalculation(instrument, mode)
>>> pando = jwst.PandeiaCalculation('niriss', 'soss')
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.351,
>>> )
>>>
>>> # Get number of groups below 80% saturation:
>>> ngroup = pando.saturation_fraction(fraction=80.0)
>>> print(ngroup)
104
>>>
>>> # Get saturation fraction (%) for 96 groups:
>>> fraction = pando.saturation_fraction(ngroup=91)
>>> print(fraction)
69.3642895274301

set_scene

pandeia_io.PandeiaCalculation.set_scene(sed_type, sed_model, norm_band, norm_magnitude, background='ecliptic_low')

Set the stellar point-source scene to observe.

Parameters

Name	Description	Default
`sed_type`	Type of model: ‘phoenix’, ‘k93models’, ‘blackbody’, or ‘flat’	required
`sed_model`	The SED model required for each sed_type: - phoenix or k93models: the model key (see load_sed_list) - blackbody: the effective temperature (K) - flat: the unit (‘flam’ or ‘fnu’)	required
`norm_band`	Band over which to normalize the spectrum.	required
`norm_magnitude`	Magnitude of the star at norm_band.	required
`background`	Set the background flux. Select from: ‘ecliptic_low’, ‘ecliptic_medium’, ‘ecliptic_high’, ‘minzodi_low’, ‘minzodi_medium’, ‘minzodi_high’	`'ecliptic_low'`

Examples

>>> import gen_tso.pandeia_io as jwst

>>> instrument = 'nircam'
>>> mode = 'lw_tsgrism'
>>> pando = jwst.PandeiaCalculation(instrument, mode)
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.637,
>>> )

show_config

pandeia_io.PandeiaCalculation.show_config()

Display a summary of the instrumental and scene configuration

simulate_tso

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

Simulate a time-series observation spectrum with noise for the given number of observations and spectral sampling.

Parameters

Name	Type	Description	Default
`TBD`			required

Returns

Type	Description
TBD

Examples

>>> TBD

tso_calculation

pandeia_io.PandeiaCalculation.tso_calculation(obs_type, transit_dur, obs_dur, depth_model, ngroup=None, disperser=None, filter=None, subarray=None, readout=None, aperture=None, order=None)

Run pandeia to simulate a transit/eclipse time-series observation

Parameters

Name	Description	Default
`obs_type`	The observing geometry ‘transit’ or ‘eclipse’.	required
`transit_dur`	Duration of the transit or eclipse event in hours.	required
`obs_dur`	Total duration of the observation (baseline plus transit or eclipse event) in hours.	required
`depth_model`	The transit or eclipse depth spectrum where the first item is the wavelength (um) and the second is the depth.	required
`ngroup`	Number of groups per integrations	`None`
`disperser`		`None`
`filter`		`None`
`subarray`		`None`
`readout`		`None`
`aperture`		`None`
`order`		`None`

Returns

Type	Description
dict	A dictionary containing the time-series observation data: - wl: instrumental wavelength sampling (microns) - depth_spectrum: Transit/eclipse depth spectrum at instrumental wl - time_in: In-transit/eclipse measuring time (seconds) - flux_in: In-transit/eclipse flux (e-) - var_in: In-transit/eclipse variance - time_out: Out-of-transit/eclipse measuring time (seconds) - flux_out: Out-of-transit/eclipse flux (e-) - var_out: Out-of-transit/eclipse - report_in: In-transit/eclipse pandeia output report - report_out: Out-of-transit/eclipse pandeia output report

dict

A dictionary containing the time-series observation data: - wl: instrumental wavelength sampling (microns) - depth_spectrum: Transit/eclipse depth spectrum at instrumental wl - time_in: In-transit/eclipse measuring time (seconds) - flux_in: In-transit/eclipse flux (e-) - var_in: In-transit/eclipse variance - time_out: Out-of-transit/eclipse measuring time (seconds) - flux_out: Out-of-transit/eclipse flux (e-) - var_out: Out-of-transit/eclipse - report_in: In-transit/eclipse pandeia output report - report_out: Out-of-transit/eclipse pandeia output report

Examples

>>> import gen_tso.pandeia_io as jwst
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import pyratbay.constants as pc
>>>
>>> transit_dur = 2.131
>>> obs_dur = 6.01
>>> # Planet model: wl(um) and transit depth (no units):
>>> depth_model = np.loadtxt('WASP80b_transit.dat', unpack=True)

>>> # Set a NIRCam observation
>>> pando = jwst.PandeiaCalculation('nircam', 'lw_tsgrism')
>>> # The star:
>>> pando.set_scene(
>>>     sed_type='phoenix', sed_model='k5v',
>>>     norm_band='2mass,ks', norm_magnitude=8.351,
>>> )
>>> # Take a look at the default cofiguration:
>>> pando.calc['configuration']
>>>
>>> # Edit disperser, filter, readout, subarray, or aperture if needed
>>> # See options with pando.get_configs()
>>> ngroup = 90

>>> # Run TSO:
>>> obs_type = 'transit'
>>> tso = pando.tso_calculation(
>>>     obs_type, transit_dur, obs_dur, depth_model, ngroup,
>>> )
>>>
>>> # Draw a simulated transit spectrum at selected resolution
>>> obs_wl, obs_depth, obs_error, band_widths = jwst.simulate_tso(
>>>     tso, resolution=250.0,
>>> )
>>> plt.figure(4)
>>> plt.clf()
>>> plt.plot(
>>> tso['wl'], tso['depth_spectrum']/pc.percent,
>>>     c='salmon', label='depth at instrumental resolution',
>>> )
>>> plt.errorbar(
>>>     obs_wl, obs_depth/pc.percent, yerr=obs_error/pc.percent,
>>>     fmt='o', ms=5, color='xkcd:blue', mfc=(1,1,1,0.85),
>>>     label='simulated (noised up) transit spectrum',
>>> )
>>> plt.legend(loc='best')
>>> plt.xlim(3.6, 5.05)
>>> plt.ylim(2.88, 3.00)
>>> plt.xlabel('Wavelength (um)')
>>> plt.ylabel('Transit depth (%)')
>>> plt.title('WASP-80 b / NIRCam F444W')
>>>
>>> # Fluxes and Flux rates
>>> col1, col2 = plt.cm.viridis(0.8), plt.cm.viridis(0.25)
>>> plt.figure(0, (8.5, 4))
>>> plt.clf()
>>> plt.subplot(121)
>>> plt.plot(tso['wl'], tso['flux_out'], c=col2, label='out of transit')
>>> plt.plot(tso['wl'], tso['flux_in'], c=col1, label='in transit')
>>> plt.legend(loc='best')
>>> plt.xlabel('Wavelength (um)')
>>> plt.ylabel('Total collected flux (e-)')
>>> plt.subplot(122)
>>> plt.plot(tso['wl'], tso['flux_out']/tso['time_out'], c=col2, label='out of transit')
>>> plt.plot(tso['wl'], tso['flux_in']/tso['time_in'], c=col1, label='in transit')
>>> plt.legend(loc='best')
>>> plt.xlabel('Wavelength (um)')
>>> plt.ylabel('Flux rate (e-/s)')
>>> plt.tight_layout()

tso_print

pandeia_io.PandeiaCalculation.tso_print(format='rich')

Print to screen a summary of the latest tso_calculation() ran.

Parameters

Name	Type	Description	Default
`format`		If ‘rich’ print with colourful text when there are warnings or errors in values. If None, print as plain text.	`'rich'`

Examples

>>> import gen_tso.pandeia_io as jwst
>>> import numpy as np

>>> wl = np.logspace(0, 2, 1000)
>>> depth = [wl, np.tile(0.03, len(wl))]
>>> pando = jwst.PandeiaCalculation('nircam', 'lw_tsgrism')
>>> pando.set_scene('phoenix', 'k5v', '2mass,ks', 8.351)
>>> tso = pando.tso_calculation(
>>>     'transit', transit_dur=2.1, obs_dur=6.0, depth_model=depth,
>>>     ngroup=130, readout='rapid', filter='f444w',
>>> )
>>> pando.tso_print()

wl_ranges

pandeia_io.PandeiaCalculation.wl_ranges()

Get wavelength range covered by the instrument/mode