def test_nframes_is_none(make_rampmodel):
"""Make sure step is skipped if nframes is None
"""
datmod = make_rampmodel(2, None, 4, 2048, 2048)
output = GroupScaleStep.call(datmod)
assert (output.meta.cal_step.group_scale == 'SKIPPED')
def test_nframes_equal_frame_divisor(make_rampmodel):
"""If nframes and frame_divisor are equal, skip correction
"""
datmod = make_rampmodel(2, 4, 4, 2048, 2048)
output = GroupScaleStep.call(datmod)
print(output.meta.exposure.frame_divisor, output.meta.exposure.nframes)
assert (output.meta.cal_step.group_scale == 'SKIPPED')
def run_early_pipeline(self,
filename,
odd_even_rows=False,
odd_even_columns=True,
use_side_ref_pixels=True,
group_scale=False):
"""Runs the early steps of the jwst pipeline (dq_init, saturation,
superbias, refpix) on uncalibrated files and outputs the result.
Parameters
----------
filename : str
File on which to run the pipeline steps
odd_even_rows : bool
Option to treat odd and even rows separately during refpix step
odd_even_columns : bools
Option to treat odd and even columns separately during refpix step
use_side_ref_pixels : bool
Option to perform the side refpix correction during refpix step
group_scale : bool
Option to rescale pixel values to correct for instances where
on-board frame averaging did not result in the proper values
Returns
-------
output_filename : str
The full path to the calibrated file
"""
output_filename = filename.replace('_uncal', '').replace(
'.fits', '_superbias_refpix.fits')
if not os.path.isfile(output_filename):
# Run the group_scale and dq_init steps on the input file
if group_scale:
model = GroupScaleStep.call(filename)
model = DQInitStep.call(model)
else:
model = DQInitStep.call(filename)
# Run the saturation and superbias steps
model = SaturationStep.call(model)
model = SuperBiasStep.call(model)
# Run the refpix step and save the output
model = RefPixStep.call(model,
odd_even_rows=odd_even_rows,
odd_even_columns=odd_even_columns,
use_side_ref_pixels=use_side_ref_pixels)
model.save(output_filename)
set_permissions(output_filename)
else:
logging.info('\t{} already exists'.format(output_filename))
return output_filename
def test_nframes_is_power_of_two(make_rampmodel):
"""When frame_divisor is None, the correction will skip if
nframes is a power of 2.
"""
datmod = make_rampmodel(2, 4, None, 2048, 2048)
output = GroupScaleStep.call(datmod)
assert (output.meta.cal_step.group_scale == 'SKIPPED')
def test_nframes_is_not_power_of_two(make_rampmodel):
"""When frame_divisor is None, then do_correction will be applied if
nframes is not a power of two but will be skip because if nframes or
frame_divisor is none.
"""
datmod = make_rampmodel(2, 3, None, 2048, 2048)
output = GroupScaleStep.call(datmod)
assert (output.meta.cal_step.group_scale == 'SKIPPED')
def test_nframes_not_equal_frame_divisor(make_rampmodel):
"""If nframes and frame_divisor are not equal, do correction
"""
datmod = make_rampmodel(2, 2, 4, 2048, 2048)
output = GroupScaleStep.call(datmod)
# Assert that the step completed
assert (output.meta.cal_step.group_scale == 'COMPLETE')
# This assertion doesn't verify for correct output,
# it just checks that the correction ran and that the data array
# outputs are different than the inputs as requested in the document.
assert not np.array_equal(output.data, datmod.data)
def test_scale_value(make_rampmodel):
"""Compare the ratio of the FRMDIVSR/NFRAMES from the data model input and
compare to the output of the pipeline.
"""
datmod = make_rampmodel(2, 2, 4, 2048, 2048)
# Calculate the scale based off of the input.
scale = datmod.meta.exposure.frame_divisor / datmod.meta.exposure.nframes
output = GroupScaleStep.call(datmod)
scale_from_data = np.unique(output.data / datmod.data)
# Since the scale value is applied uniformly to the array, if we divide the output
# by the input then we should get a single unique value (ie the scale) calculated
# by the pipeline.
assert (len(scale_from_data) == 1)
# Make sure the scale calculated manually from the data model aboved matched what the
# pipeline calculated.
assert (scale == scale_from_data[0])