RampFitStep¶
- class romancal.ramp_fitting.RampFitStep(name=None, parent=None, config_file=None, _validate_kwds=True, **kws)[source]¶
Bases:
RomanStep
This step fits a straight line to the value of counts vs. time to determine the mean count rate for each pixel.
Create a
Step
instance.- Parameters:
name (str, optional) – 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.
parent (Step instance, optional) – 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_file (str or pathlib.Path, optional) – The path to the config file that this step was initialized with. Use to determine relative path names of other config files.
**kws (dict) – Additional parameters to set. These will be set as member variables on the new Step instance.
Attributes Summary
Methods Summary
ols
(input_model, readnoise_model, gain_model)Perform Optimal Linear Fitting on evenly-spaced resultants
ols_cas22
(input_model, readnoise_model, ...)Peform Optimal Linear Fitting on arbitrarily space resulants
process
(input)This is where real work happens.
Attributes Documentation
- reference_file_types: ClassVar = ['readnoise', 'gain', 'dark']¶
- spec¶
algorithm = option('ols','ols_cas22', default='ols_cas22') # Algorithm to use to fit. save_opt = boolean(default=False) # Save optional output opt_name = string(default='') maximum_cores = option('none','quarter','half','all',default='none') # max number of processes to create suffix = string(default='rampfit') # Default suffix of results use_ramp_jump_detection = boolean(default=True) # Use jump detection during ramp fitting threshold_intercept = float(default=None) # Override the intercept parameter for the threshold function in the jump detection algorithm. threshold_constant = float(default=None) # Override the constant parameter for the threshold function in the jump detection algorithm.
- weighting = 'optimal'¶
Methods Documentation
- ols(input_model, readnoise_model, gain_model)[source]¶
Perform Optimal Linear Fitting on evenly-spaced resultants
The OLS algorithm used is the same used by JWST for it’s ramp fitting.
- Parameters:
input_model (RampModel) – Model containing ramps.
readnoise_model (ReadnoiseRefModel) – Model with the read noise reference information.
gain_model (GainRefModel) – Model with the gain reference information.
- Returns:
out_model – Model containing a count-rate image.
- Return type:
ImageModel
- ols_cas22(input_model, readnoise_model, gain_model, dark_model)[source]¶
Peform Optimal Linear Fitting on arbitrarily space resulants
- Parameters:
input_model (RampModel) – Model containing ramps.
readnoise_model (ReadnoiseRefModel) – Model with the read noise reference information.
gain_model (GainRefModel) – Model with the gain reference information.
dark_model (DarkRefModel) – Model with the dark reference information
- Returns:
out_model – Model containing a count-rate image.
- Return type:
ImageModel