def setup_cube(ngroups, nrows, ncols):
''' Set up fake data to test.'''
nints = 1
data_model = RampModel()
data_model.data = np.zeros(shape=(nints, ngroups, nrows, ncols),
dtype=np.float32)
data_model.pixeldq = np.zeros(shape=(nrows, ncols), dtype=np.int32)
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.xsize = ncols
data_model.meta.subarray.ysize = nrows
data_model.meta.instrument.name = 'NIRCAM'
bias_model = SuperBiasModel()
bias_model.data = np.zeros(shape=(2048, 2048), dtype=np.float32)
bias_model.dq = np.zeros(shape=(2048, 2048), dtype=np.int32)
bias_model.meta.subarray.xstart = 1
bias_model.meta.subarray.ystart = 1
bias_model.meta.subarray.xsize = 2048
bias_model.meta.subarray.ysize = 2048
bias_model.meta.instrument.name = 'NIRCAM'
bias_model.meta.description = 'Fake data.'
bias_model.meta.telescope = 'JWST'
bias_model.meta.reftype = 'SuperBiasModel'
bias_model.meta.author = 'Alicia'
bias_model.meta.pedigree = 'Dummy'
bias_model.meta.useafter = '2015-10-01T00:00:00'
return data_model, bias_model
def insert_into_datamodel(self, subfile):
#read in a dummy/substitute file as a datamodel,
#and insert the data and self.header metadata
#into it
h = RampModel(subfile)
h.data = self.data
try:
h.zeroframe = self.zeroframe
except:
pass
h.err = np.zeros_like(self.data)
h.groupdq = np.zeros_like(self.data)
nint, ng, ny, nx = self.data.shape
h.pixeldq = np.zeros((ny, nx))
h.meta.exposure.readpatt = self.header['READPATT']
h.meta.exposure.nints = self.header['NINTS']
h.meta.exposure.ngroups = self.header['NGROUPS']
h.meta.exposure.nframes = self.header['NFRAMES']
h.meta.exposure.nskip = self.header['NSKIP']
h.meta.exposure.groupgap = self.header['GROUPGAP']
h.meta.exposure.type = self.header['EXP_TYPE']
h.meta.instrument.detector = self.header['DETECTOR']
h.meta.instrument.name = self.header['INSTRUME']
h.meta.subarray.fastaxis = self.header['FASTAXIS']
h.meta.subarray.slowaxis = self.header['SLOWAXIS']
return h
def setup_cube(ngroups, nrows, ncols):
''' Set up fake data to test.'''
nints = 1
data_model = RampModel()
data_model.data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)
data_model.pixeldq = np.zeros(shape=(nrows, ncols), dtype=np.int32)
data_model.groupdq = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.xsize = ncols
data_model.meta.subarray.ysize = nrows
data_model.meta.exposure.ngroups = ngroups
data_model.meta.instrument.name = 'NIRCAM'
saturation_model = SaturationModel()
saturation_model.data = np.zeros(shape=(2048, 2048), dtype=np.float32)
saturation_model.dq = np.zeros(shape=(2048, 2048), dtype=np.int32)
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.xsize = 2048
saturation_model.meta.subarray.ysize = 2048
saturation_model.meta.instrument.name = 'NIRCAM'
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.author = 'Alicia'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
return data_model, saturation_model
def _cube():
# create a JWST datamodel for NIRSPEC IRS2 data
data_model = RampModel((1, 5, 3200, 2048))
data_model.data = np.ones(((1, 5, 3200, 2048)))
data_model.groupdq = np.zeros(((1, 5, 3200, 2048)))
data_model.pixeldq = np.zeros(((3200, 2048)))
data_model.meta.instrument.name = 'NIRSPEC'
data_model.meta.instrument.detector = 'NRS1'
data_model.meta.instrument.filter = 'F100LP'
data_model.meta.observation.date = '2019-07-19'
data_model.meta.observation.time = '23:23:30.912'
data_model.meta.exposure.type = 'NRS_LAMP'
data_model.meta.subarray.name = 'FULL'
data_model.meta.subarray.xstart = 1
data_model.meta.subarray.xsize = 2048
data_model.meta.subarray.ystart = 1
data_model.meta.subarray.ysize = 2048
data_model.meta.exposure.nrs_normal = 16
data_model.meta.exposure.nrs_reference = 4
data_model.meta.exposure.readpatt = 'NRSIRS2RAPID'
# create a saturation model for the saturation step
saturation_model = SaturationModel((2048, 2048))
saturation_model.data = np.ones(
(2048, 2048)) * 60000 # saturation limit for every pixel is 60000
saturation_model.meta.description = 'Fake data.'
saturation_model.meta.telescope = 'JWST'
saturation_model.meta.reftype = 'SaturationModel'
saturation_model.meta.useafter = '2015-10-01T00:00:00'
saturation_model.meta.instrument.name = 'NIRSPEC'
saturation_model.meta.instrument.detector = 'NRS1'
saturation_model.meta.author = 'Clare'
saturation_model.meta.pedigree = 'Dummy'
saturation_model.meta.subarray.xstart = 1
saturation_model.meta.subarray.xsize = 2048
saturation_model.meta.subarray.ystart = 1
saturation_model.meta.subarray.ysize = 2048
return data_model, saturation_model
