Source code for jwst.ramp_fitting.ramp_fit_step

#! /usr/bin/env python

from ..stpipe import Step
from .. import datamodels
from . import ramp_fit

import logging
log = logging.getLogger(__name__)

__all__ = ["RampFitStep"]

[docs]class RampFitStep (Step): """ This step fits a straight line to the value of counts vs. time to determine the mean count rate for each pixel. """ spec = """ int_name = string(default='') 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 """ # Prior to 04/26/17, the following were also in the spec above: # algorithm = option('OLS', 'GLS', default='OLS') # 'OLS' or 'GLS' # weighting = option('unweighted', 'optimal', default='unweighted') \ # # 'unweighted' or 'optimal' # As of 04/26/17, the only allowed algorithm is 'ols', and the # only allowed weighting is 'optimal'. algorithm = 'ols' # Only algorithm allowed for Build 7.1 # algorithm = 'gls' # 032520 weighting = 'optimal' # Only weighting allowed for Build 7.1 reference_file_types = ['readnoise', 'gain']
[docs] def process(self, input): with datamodels.RampModel(input) as input_model: max_cores = self.maximum_cores readnoise_filename = self.get_reference_file(input_model, 'readnoise') gain_filename = self.get_reference_file(input_model, 'gain')'Using READNOISE reference file: %s', readnoise_filename) readnoise_model = datamodels.ReadnoiseModel(readnoise_filename)'Using GAIN reference file: %s', gain_filename) gain_model = datamodels.GainModel(gain_filename) # Try to retrieve the gain factor from the gain reference file. # If found, store it in the science model meta data, so that it's # available later in the gain_scale step, which avoids having to # load the gain ref file again in that step. if gain_model.meta.exposure.gain_factor is not None: input_model.meta.exposure.gain_factor = \ gain_model.meta.exposure.gain_factor'Using algorithm = %s' % self.algorithm)'Using weighting = %s' % self.weighting) buffsize = ramp_fit.BUFSIZE if self.algorithm == "GLS": buffsize //= 10 out_model, int_model, opt_model, gls_opt_model = ramp_fit.ramp_fit( input_model, buffsize, self.save_opt, readnoise_model, gain_model, self.algorithm, self.weighting, max_cores ) readnoise_model.close() gain_model.close() # Save the OLS optional fit product, if it exists if opt_model is not None: self.save_model(opt_model, 'fitopt', output_file=self.opt_name) # Save the GLS optional fit product, if it exists if gls_opt_model is not None: self.save_model( gls_opt_model, 'fitoptgls', output_file=self.opt_name ) if out_model is not None: out_model.meta.cal_step.ramp_fit = 'COMPLETE' if (input_model.meta.exposure.type in ['NRS_IFU', 'MIR_MRS']) or ( input_model.meta.exposure.type in ['NRS_AUTOWAVE', 'NRS_LAMP'] and input_model.meta.instrument.lamp_mode == 'IFU'): out_model = datamodels.IFUImageModel(out_model) if int_model is not None: int_model.meta.cal_step.ramp_fit = 'COMPLETE' return out_model, int_model