from . import model_base
__all__ = ['RampFitOutputModel']
[docs]class RampFitOutputModel(model_base.DataModel):
"""
A data model for the optional output of the ramp fitting step.
In the parameter definitions below, `n_int` is the number of
integrations, `max_seg` is the maximum number of segments that
were fit, `nreads` is the number of reads in an integration, and
`ny` and `nx` are the height and width of the image.
Parameters
----------
init : any
Any of the initializers supported by `~jwst.datamodels.DataModel`.
slope : numpy array (n_int, max_seg, ny, nx)
sigslope : numpy array (n_int, max_seg, ny, nx)
var_poisson : numpy array (n_int, max_seg, ny, nx)
var_rnoise : numpy array (n_int, max_seg, ny, nx)
yint : numpy array (n_int, max_seg, ny, nx)
sigyint : numpy array (n_int, max_seg, ny, nx)
pedestal : numpy array (n_int, max_seg, ny, nx)
weights : numpy array (n_int, max_seg, ny, nx)
crmag : numpy array (n_int, max_seg, ny, nx)
"""
schema_url = "rampfitoutput.schema.yaml"
def __init__(self, init=None,
slope=None,
sigslope=None,
var_poisson=None,
var_rnoise=None,
yint=None,
sigyint=None,
pedestal=None,
weights=None,
crmag=None,
**kwargs):
super(RampFitOutputModel, self).__init__(init=init, **kwargs)
if slope is not None:
self.slope = slope
if sigslope is not None:
self.sigslope = sigslope
if var_poisson is not None:
self.var_poisson = var_poisson
if var_rnoise is not None:
self.var_rnoise = var_rnoise
if yint is not None:
self.yint = yint
if sigyint is not None:
self.sigyint = sigyint
if pedestal is not None:
self.pedestal = pedestal
if weights is not None:
self.weights = weights
if crmag is not None:
self.crmag = crmag