exotic_miri.reference

class exotic_miri.reference.GetIntegrationTimes(*args: Any, **kwargs: Any)[source]

Bases: Step

Get the integration times.

process(input)[source]

Get and save the integration times for a data segment, and compute the integration duration in seconds.

Parameters:

input (jwst.datamodels.RampModel) – This is an uncal.fits loaded data segment.

Returns:

timing_data and integration_duration – Table of integration times data and duration of an integration in seconds.

Return type:

tuple(fits.table, float)

class exotic_miri.reference.SetCustomGain(*args: Any, **kwargs: Any)[source]

Bases: Step

Set a custom gain.

process(input)[source]

Set a custom gain value. This step creates a gain model which can be passed to other steps for processing. It should be passed to all steps that make use of the gain, and passed via the arg ‘override_gain’. This includes stage 1 steps such as jwst.calwebb_detector1.jump_step, jwst.calwebb_detector1.ramp_fit_step, and jwst.calwebb_detector1.gain_scale_step.

Parameters:

  • input (jwst.datamodels.RampModel) – This is an uncal.fits loaded data segment.

  • gain_value (float) – The gain value to set. Default is 3.1.

Returns:

gain – The gain model which can be passed to other steps.

Return type:

jwst.datamodels.GainModel

class exotic_miri.reference.SetCustomLinearity(*args: Any, **kwargs: Any)[source]

Bases: Step

Set a custom linearity correction.

process(input)[source]

Make self-calibrated linearity corrections per amplifier. This step uses the uncal.fits data to create a new linearity model. This model can then be passed to the jwst.calwebb_detector1.linearity_step via the arg ‘override_linearity’.

The correction involves extrapolating a linear fit to an assumed linear /“well-behaved” section of the ramps, and then fitting a polynomial to the residuals. The polynomial has the constant- and linear-term coefficients fixed at 0 and 1 respectively. Recommended usage requires a large number of groups, >~40, although this is still experimental.

Parameters:

  • input (jwst.datamodels.RampModel) – This is an uncal.fits loaded data segment.

  • group_idx_start_fit (integer) – The first group index included in the linear fit. This corresponds to the start of the section of the ramp which is assumed to be well behaved. Default is 10.

  • group_idx_end_fit (integer) – The last group index included in the linear fit. This corresponds to the end of the section of the ramp which is assumed to be well behaved. Default is 40.

  • group_idx_start_derive (integer) – The first group index included in the derived linearity correction. Default is 10.

  • group_idx_end_derive (integer) – The last group index included in the derived linearity correction. Default is -1.

  • row_idx_start_used (integer) – The first row index included in the derived linearity correction. Default is 350.

  • row_idx_end_used (integer) – The last row index included in the derived linearity correction. Default is 386.

  • draw_corrections (boolean) – Plot the derived linearity correction.

Returns:

linearity – The linearity model which can be passed to other steps.

Return type:

jwst.datamodels.LinearityModel

class exotic_miri.reference.GetWavelengthMap(*args: Any, **kwargs: Any)[source]

Bases: Step

Get the wavelength map.

process(input)[source]

Get and save the wavelength map data. This is the mapping from the detector pixels (row_idx, col_idx) to wavelength (lambda). To run this step the input must first have had the jwst.calwebb_spec2.assign_wcs_step and jwst.calwebb_spec2.srctype_step already run.

Parameters:

input (jwst.datamodels.CubeModel) – This is an rateints.fits loaded data segment.

Returns:

wavelength_map – The wavelength map with shape (n_rows, n_cols).

Return type:

np.ndarray

class exotic_miri.reference.GetDefaultGain(*args: Any, **kwargs: Any)[source]

Bases: Step

Get the default gain.

process(input)[source]

Get and save gain data from the default CRDS files.

Parameters:

  • input (jwst.datamodels.RampModel or jwst.datamodels.CubeModel) – This is either an uncal.fits or rateints.fits loaded data segment. The gain will be the same no matter which data segment you pass in.

  • median_value (boolean) – If True only return the median value rather than the gain model. Default is False.

  • save (boolean) – If True save the gain model to disc. Default is False.

  • save_path (string) – If save==True save the gain model to this path. Default is None.

Returns:

  • if median_value == False –

    gainjwst.datamodels.GainModel

    The gain model which can be passed to other steps.

  • elif median_value == True –

    gainfloat

    The median gain value on the entire detector.

class exotic_miri.reference.GetDefaultReadNoise(*args: Any, **kwargs: Any)[source]

Bases: Step

Get the default readnoise.

process(input)[source]

Get and save readnoise data from the default CRDS files.

Parameters:

  • input (jwst.datamodels.RampModel or jwst.datamodels.CubeModel) – This is either an uncal.fits or rateints.fits loaded data segment. The readnoise will be the same no matter which data segment you pass in.

  • median_value (boolean) – If True only return the median value rather than the readnoise model. Default is False.

  • save (boolean) – If True save the readnoise model to disc. Default is False.

  • save_path (string) – If save==True save the readnoise model to this path. Default is None.

Returns:

  • if median_value == False –

    gainjwst.datamodels.ReadnoiseModel

    The readnoise model which can be passed to other steps.

  • elif median_value == True –

    gainfloat

    The median readnoise value on the entire detector.