RampFitStep

class jwst.ramp_fitting.RampFitStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)

Bases: JwstStep

Fit ramp data to determine the mean count rate.

Create a Step instance.

Parameters:

namestr

The name of the Step instance. Used in logging messages and in cache filenames. If not provided, one will be generated based on the class name.

parentStep

The parent step of this step. Used to determine a fully-qualified name for this step, and to determine the mode in which to run this step.

config_filestr or pathlib.Path

The path to the config file that this step was initialized with. Use to determine relative path names of other config files.

_validate_kwdsbool

Validate given kws against specs/config.

**kwsdict

Additional parameters to set. These will be set as member variables on the new Step instance.

Attributes Summary

class_alias
reference_file_types
spec
weighting

Methods Summary

process(step_input)

Fit ramps using the specified ramp fitting algorithm.

Attributes Documentation

class_alias = 'ramp_fit'

reference_file_types: ClassVar = ['readnoise', 'gain']

spec

algorithm = option('OLS_C', 'LIKELY', default='OLS_C')
int_name = string(default='')
save_opt = boolean(default=False) # Save optional output
opt_name = string(default='')
suppress_one_group = boolean(default=True)  # Suppress saturated ramps with good 0th group
firstgroup = integer(default=None)   # Ignore groups before this one (zero indexed)
lastgroup = integer(default=None)   # Ignore groups after this one (zero indexed)
maximum_cores = string(default='1') # cores for multiprocessing. Can be an integer, 'half', 'quarter', or 'all'

weighting = 'optimal'

Methods Documentation

process(step_input)

Fit ramps using the specified ramp fitting algorithm.

Parameters:

step_inputRampModel

The input ramp model to fit the ramps.

Returns:

out_modelImageModel

The output 2-D image model with the fit ramps.

int_modelCubeModel

The output 3-D image model with the fit ramps for each integration.