def test_do_top_bottom_correction(setup_cube):
'''Test top/bottom correction for NIRCam data.'''
ngroups = 5
nrows = 2048
ncols = 2048
odd_even_columns = True
use_side_ref_pixels = True
side_smoothing_length = 11
side_gain = 1.0
input_model = setup_cube('NIRCAM','NRCALONG', ngroups, nrows, ncols)
input_model.meta.subarray.name = 'FULL'
init_dataset = NIRDataset(input_model,
odd_even_columns,
use_side_ref_pixels,
side_smoothing_length,
side_gain)
abounds = [0, 512, 1024, 1536, 2048]
top_even_amps = [12, 13, 14, 15]
top_odd_amps = [16, 17, 18, 19]
bottom_even_amps = [20, 21, 22, 23]
bottom_odd_amps = [24, 25, 26, 27]
dataval = [50, 51, 52, 53]
for i in np.arange(0, len(abounds)-1):
# bottom, odd
input_model.data[0, 0, :4, abounds[i]:abounds[i+1]:2] = bottom_even_amps[i]
# bottom, even
input_model.data[0, 0, :4, abounds[i]+1:abounds[i+1]-1:2] = bottom_odd_amps[i]
# top, odd
input_model.data[0, 0, -4:, abounds[i]:abounds[i+1]:2] = top_even_amps[i]
# top, even
input_model.data[0, 0, -4:, abounds[i]+1:abounds[i+1]-1:2] = top_odd_amps[i]
# data
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i+1]] = dataval[i]
refpix = init_dataset.get_refvalues(input_model.data[0, 0, :, :])
init_dataset.do_top_bottom_correction(input_model.data[0, 0, :, :], refpix)
for i in np.arange(0, len(abounds)-1):
even_rmean = np.mean([bottom_even_amps[i], top_even_amps[i]])
odd_rmean = np.mean([bottom_odd_amps[i], top_odd_amps[i]])
rmean = np.mean([even_rmean, odd_rmean])
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, :4, abounds[i]:abounds[i+1]:2]), bottom_even_amps[i] - even_rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, :4, abounds[i]+1:abounds[i+1]:2]), bottom_odd_amps[i] - odd_rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, -4:, abounds[i]:abounds[i+1]:2]), top_even_amps[i] - even_rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, -4:, abounds[i]+1:abounds[i+1]:2]), top_odd_amps[i] - odd_rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, 4:-4, abounds[i]:abounds[i+1]]), dataval[i] - rmean, decimal=1)
def test_do_top_bottom_correction_no_even_odd(setup_cube):
'''Test top/bottom correction with no even/odd.'''
ngroups = 3
nrows = 2048
ncols = 2048
odd_even_columns = False
use_side_ref_pixels = True
side_smoothing_length = 11
side_gain = 1.0
input_model = setup_cube('NIRCAM', 'NRCALONG', ngroups, nrows, ncols)
input_model.meta.subarray.name = 'FULL'
init_dataset = NIRDataset(input_model, odd_even_columns,
use_side_ref_pixels, side_smoothing_length,
side_gain)
abounds = [0, 512, 1024, 1536, 2048]
top_amps = [12, 13, 14, 15]
bottom_amps = [16, 17, 18, 19]
dataval = [50, 51, 52, 53]
for i in np.arange(0, len(abounds) - 1):
# bottom
input_model.data[0, 0, :4, abounds[i]:abounds[i + 1]] = bottom_amps[i]
# top
input_model.data[0, 0, -4:, abounds[i]:abounds[i + 1]] = top_amps[i]
# data
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i + 1]] = dataval[i]
refpix = init_dataset.get_refvalues(input_model.data[0, 0, :, :])
init_dataset.do_top_bottom_correction(input_model.data[0, 0, :, :], refpix)
for i in np.arange(0, len(abounds) - 1):
rmean = np.mean([top_amps[i], bottom_amps[i]])
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, :4, abounds[i]:abounds[i + 1]]),
bottom_amps[i] - rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, -4:, abounds[i]:abounds[i + 1]]),
top_amps[i] - rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i + 1]]),
dataval[i] - rmean,
decimal=1)
def test_do_top_bottom_correction_no_evenOdd(setup_cube):
'''Test top/bottom correction with no even/odd.'''
ngroups = 5
nrows = 2048
ncols = 2048
odd_even_columns = False
use_side_ref_pixels = True
side_smoothing_length = 11
side_gain = 1.0
input_model = setup_cube('NIRCAM','NRCALONG', ngroups, nrows, ncols)
input_model.meta.subarray.name = 'FULL'
init_dataset = NIRDataset(input_model,
odd_even_columns,
use_side_ref_pixels,
side_smoothing_length,
side_gain)
abounds = [0, 512, 1024, 1536, 2048]
top_amps = [12, 13, 14, 15]
bottom_amps = [16, 17, 18, 19]
dataval = [50, 51, 52, 53]
for i in np.arange(0, len(abounds)-1):
# bottom
input_model.data[0, 0, :4, abounds[i]:abounds[i+1]] = bottom_amps[i]
# top
input_model.data[0, 0, -4:, abounds[i]:abounds[i+1]] = top_amps[i]
# data
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i+1]] = dataval[i]
refpix = init_dataset.get_refvalues(input_model.data[0, 0, :, :])
init_dataset.do_top_bottom_correction(input_model.data[0, 0, :, :], refpix)
for i in np.arange(0, len(abounds)-1):
rmean = np.mean([top_amps[i], bottom_amps[i]])
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, :4, abounds[i]:abounds[i+1]]), bottom_amps[i] - rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, -4:, abounds[i]:abounds[i+1]]), top_amps[i] - rmean, decimal=1)
np.testing.assert_almost_equal(np.mean(input_model.data[0, 0, 4:-4, abounds[i]:abounds[i+1]]), dataval[i] - rmean, decimal=1)
def test_do_top_bottom_correction(setup_cube):
'''Test top/bottom correction for NIRCam data.'''
ngroups = 3
nrows = 2048
ncols = 2048
odd_even_columns = True
use_side_ref_pixels = True
side_smoothing_length = 11
side_gain = 1.0
input_model = setup_cube('NIRCAM', 'NRCALONG', ngroups, nrows, ncols)
input_model.meta.subarray.name = 'FULL'
init_dataset = NIRDataset(input_model, odd_even_columns,
use_side_ref_pixels, side_smoothing_length,
side_gain)
abounds = [0, 512, 1024, 1536, 2048]
top_even_amps = [12, 13, 14, 15]
top_odd_amps = [16, 17, 18, 19]
bottom_even_amps = [20, 21, 22, 23]
bottom_odd_amps = [24, 25, 26, 27]
dataval = [50, 51, 52, 53]
for i in np.arange(0, len(abounds) - 1):
# bottom, odd
input_model.data[0, 0, :4,
abounds[i]:abounds[i + 1]:2] = bottom_even_amps[i]
# bottom, even
input_model.data[0, 0, :4, abounds[i] + 1:abounds[i + 1] -
1:2] = bottom_odd_amps[i]
# top, odd
input_model.data[0, 0, -4:,
abounds[i]:abounds[i + 1]:2] = top_even_amps[i]
# top, even
input_model.data[0, 0, -4:,
abounds[i] + 1:abounds[i + 1] - 1:2] = top_odd_amps[i]
# data
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i + 1]] = dataval[i]
refpix = init_dataset.get_refvalues(input_model.data[0, 0, :, :])
init_dataset.do_top_bottom_correction(input_model.data[0, 0, :, :], refpix)
for i in np.arange(0, len(abounds) - 1):
even_rmean = np.mean([bottom_even_amps[i], top_even_amps[i]])
odd_rmean = np.mean([bottom_odd_amps[i], top_odd_amps[i]])
rmean = np.mean([even_rmean, odd_rmean])
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, :4, abounds[i]:abounds[i + 1]:2]),
bottom_even_amps[i] - even_rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, :4, abounds[i] + 1:abounds[i + 1]:2]),
bottom_odd_amps[i] - odd_rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, -4:, abounds[i]:abounds[i + 1]:2]),
top_even_amps[i] - even_rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, -4:, abounds[i] + 1:abounds[i + 1]:2]),
top_odd_amps[i] - odd_rmean,
decimal=1)
np.testing.assert_almost_equal(np.mean(
input_model.data[0, 0, 4:-4, abounds[i]:abounds[i + 1]]),
dataval[i] - rmean,
decimal=1)