def miri_full_coverage():
""" Generate a IFU images SHORT, LONG for all three bands """
input_model1 = datamodels.IFUImageModel()
input_model1.meta.wcsinfo._instance.update(wcsinfo)
input_model1.meta.instrument._instance.update(mirifushort_short)
input_model1.meta.observation._instance.update(observation)
input_model1.meta.subarray._instance.update(subarray)
input_model1.meta.cal_step.assign_wcs = 'COMPLETE'
input_model2 = datamodels.IFUImageModel()
input_model2.meta.wcsinfo._instance.update(wcsinfo)
input_model2.meta.instrument._instance.update(mirifushort_medium)
input_model2.meta.observation._instance.update(observation)
input_model2.meta.subarray._instance.update(subarray)
input_model2.meta.cal_step.assign_wcs = 'COMPLETE'
input_model3 = datamodels.IFUImageModel()
input_model3.meta.wcsinfo._instance.update(wcsinfo)
input_model3.meta.instrument._instance.update(mirifushort_long)
input_model3.meta.observation._instance.update(observation)
input_model3.meta.subarray._instance.update(subarray)
input_model3.meta.cal_step.assign_wcs = 'COMPLETE'
input_model4 = datamodels.IFUImageModel()
input_model4.meta.wcsinfo._instance.update(wcsinfo)
input_model4.meta.instrument._instance.update(mirifulong_short)
input_model4.meta.observation._instance.update(observation)
input_model4.meta.subarray._instance.update(subarray)
input_model4.meta.cal_step.assign_wcs = 'COMPLETE'
input_model5 = datamodels.IFUImageModel()
input_model5.meta.wcsinfo._instance.update(wcsinfo)
input_model5.meta.instrument._instance.update(mirifulong_medium)
input_model5.meta.observation._instance.update(observation)
input_model5.meta.subarray._instance.update(subarray)
input_model5.meta.cal_step.assign_wcs = 'COMPLETE'
input_model6 = datamodels.IFUImageModel()
input_model6.meta.wcsinfo._instance.update(wcsinfo)
input_model6.meta.instrument._instance.update(mirifulong_long)
input_model6.meta.observation._instance.update(observation)
input_model6.meta.subarray._instance.update(subarray)
input_model6.meta.cal_step.assign_wcs = 'COMPLETE'
# full range and 2 dithers (12 files, 2 dithers of each band)
input_models = []
input_models.append(input_model1)
input_models.append(input_model1)
input_models.append(input_model2)
input_models.append(input_model2)
input_models.append(input_model3)
input_models.append(input_model3)
input_models.append(input_model4)
input_models.append(input_model4)
input_models.append(input_model5)
input_models.append(input_model5)
input_models.append(input_model6)
input_models.append(input_model6)
return input_models
def test_ifu_correction():
"""Test application of IFU corrections"""
data = np.ones((5, 5))
pl_ps = 2 * data
pl_un = data / 2
input = datamodels.IFUImageModel(data=data,
pathloss_point=pl_ps,
pathloss_uniform=pl_un)
corrected = input.data * pl_ps / pl_un
result = correct_nrs_ifu_bkg(input)
assert np.allclose(corrected, result.data, rtol=1.e-10)
def test_cubebuild_miri(self):
"""
Regression test of cube_build performed on MIRI MRS data.
"""
input_file = self.get_data(
self.test_dir, 'jw10001001001_01101_00001_mirifushort_cal.fits')
input_model = datamodels.IFUImageModel(input_file)
CubeBuildStep.call(input_model, output_type='multi', save_results=True)
outputs = [('jw10001001001_01101_00001_mirifushort_s3d.fits',
'jw10001001001_01101_00001_mirifushort_s3d_ref.fits',
['primary', 'sci', 'err', 'dq', 'wmap'])]
self.compare_outputs(outputs)
def nirspec_medium_coverage():
""" Generate a IFU images NIRSpec G140M, G235M """
input_model1 = datamodels.IFUImageModel()
input_model1.meta.wcsinfo._instance.update(wcsinfo)
input_model1.meta.instrument._instance.update(nirspec_G140M)
input_model1.meta.observation._instance.update(observation)
input_model1.meta.subarray._instance.update(subarray_nirspec)
input_model1.meta.cal_step.assign_wcs = 'COMPLETE'
input_model2 = datamodels.IFUImageModel()
input_model2.meta.wcsinfo._instance.update(wcsinfo)
input_model2.meta.instrument._instance.update(nirspec_G235M)
input_model2.meta.observation._instance.update(observation)
input_model2.meta.subarray._instance.update(subarray_nirspec)
input_model2.meta.cal_step.assign_wcs = 'COMPLETE'
input_models = []
input_models.append(input_model1)
input_models.append(input_model1)
input_models.append(input_model2)
input_models.append(input_model2)
return input_models
def test_cubebuild_miri(_bigdata):
"""
Regression test of cube_build performed on MIRI MRS data.
"""
input_model = datamodels.IFUImageModel(_bigdata+'/miri/test_cube_build/jw10001001001_01101_00001_mirifushort_cal.fits')
CubeBuildStep.call(input_model, output_type='multi', save_results=True)
h = fits.open('jw10001001001_01101_00001_mirifushort_s3d.fits')
href = fits.open(_bigdata+'/miri/test_cube_build/jw10001001001_01101_00001_mirifushort_s3d_ref.fits')
newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['dq'],h['wmap']])
newhref = fits.HDUList([href['primary'],href['sci'],href['err'],href['dq'],href['wmap']])
result = fits.diff.FITSDiff(newh,
newhref,
ignore_keywords = ['DATE','CAL_VER','CAL_VCS','CRDS_VER','CRDS_CTX'],
rtol = 0.000001
)
assert result.identical, result.report()
def science_image():
"""Generate science image"""
image = datamodels.IFUImageModel((10, 10))
image.data[:, :] = 100
image.meta.instrument.name = 'NIRSPEC'
image.meta.instrument.detector = 'NRS1'
image.meta.instrument.filter = 'CLEAR'
image.meta.instrument.grating = 'PRISM'
image.meta.exposure.type = 'NRS_IFU'
image.meta.observation.date = '2019-02-27'
image.meta.observation.time = '13:37:18.548'
image.meta.date = '2019-02-27T13:37:18.548'
image.meta.subarray.xstart = 1
image.meta.subarray.ystart = 1
image.meta.instrument.gwa_xtilt = 0.0001
image.meta.instrument.gwa_ytilt = 0.0001
image.meta.instrument.gwa_tilt = 37.0610
return image
def test_get_2d_pixgrid():
""" Test if x,y grid for channel is formed correctly """
# test if channel 1 then left side of detector
# test shape of x (1/2 detector) and min,max (0,511) + 4 reference pixels
input_model12 = datamodels.IFUImageModel((20, 20))
input_model12.meta.instrument._instance.update(mirifushort_short)
x, y = _get_2d_pixgrid(input_model12, '1')
xsize0 = x.shape[0]
xsize1 = x.shape[1]
assert xsize0 == 1024
assert xsize1 == 512
xmin = np.amin(x)
xmax = np.amax(x)
assert xmin == 4
assert xmax == 515
# test if channel 2 then right side of detector
# test shape of x (1/2 detector) and min,max 516, 1027 (no reference pixels)
input_model12 = datamodels.IFUImageModel((20, 20))
input_model12.meta.instrument._instance.update(mirifushort_short)
x, y = _get_2d_pixgrid(input_model12, '2')
xsize0 = x.shape[0]
xsize1 = x.shape[1]
assert xsize0 == 1024
assert xsize1 == 512
xmin = np.amin(x)
xmax = np.amax(x)
assert xmin == 516
assert xmax == 1027
# test if channel 3 then right side of detector
# test shape of x (1/2 detector) and min,max 516, 1027 (no reference pixels)
input_model34 = datamodels.IFUImageModel((20, 20))
input_model34.meta.instrument._instance.update(mirifulong_long)
x, y = _get_2d_pixgrid(input_model34, '3')
xsize0 = x.shape[0]
xsize1 = x.shape[1]
assert xsize0 == 1024
assert xsize1 == 512
xmin = np.amin(x)
xmax = np.amax(x)
assert xmin == 516
assert xmax == 1027
# test if channel 4 then left side of detector
# test shape of x (1/2 detector) and min,max 4, 515 (includes reference pixels)
input_model34 = datamodels.IFUImageModel((20, 20))
input_model34.meta.instrument._instance.update(mirifulong_long)
x, y = _get_2d_pixgrid(input_model34, '4')
xsize0 = x.shape[0]
xsize1 = x.shape[1]
assert xsize0 == 1024
assert xsize1 == 512
xmin = np.amin(x)
xmax = np.amax(x)
assert xmin == 4
assert xmax == 515
def test_find_channel_index(_jail, miri_dither_ch12):
""" Test if correct channel index is returned """
# channel 1 - model only has 1 bckground polynomial
input_model12 = datamodels.IFUImageModel((20, 20))
input_model12.meta.instrument._instance.update(mirifushort_short)
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '1'
input_model12.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model12, '1')
assert index == 0
# channel 2 background only has 1 background polynomial
input_model12 = datamodels.IFUImageModel((20, 20))
input_model12.meta.instrument._instance.update(mirifushort_short)
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '2'
input_model12.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model12, '2')
assert index == 0
# add background polynomial for channel 1 and test index
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '1'
input_model12.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model12, '1')
assert index == 1
# set up a new models only channel 3
# channel 3
input_model34 = datamodels.IFUImageModel((20, 20))
input_model34.meta.instrument._instance.update(mirifulong_long)
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '3'
input_model34.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model34, '3')
assert index == 0
# set up a new model only have channe 4
# channel 4
input_model34 = datamodels.IFUImageModel((20, 20))
input_model34.meta.instrument._instance.update(mirifulong_long)
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '4'
input_model34.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model34, '4')
assert index == 0
# set up a new model with both channel 3 and 4 background polynomials
# channel 3 & 4
input_model34 = datamodels.IFUImageModel((20, 20))
input_model34.meta.instrument._instance.update(mirifulong_long)
degree = (
1,
1,
1,
)
center = (
5,
5,
5,
)
poly = np.ndarray(9)
poly[:] = 1.3
channel = '4'
input_model34.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
channel = '3'
input_model34.meta.background.polynomial_info.append({
'degree':
degree,
'refpoint':
center,
'coefficients':
poly.ravel().tolist(),
'channel':
channel
})
index = _find_channel_bkg_index(input_model34, '4')
assert index == 0
index = _find_channel_bkg_index(input_model34, '3')
assert index == 1
def test_ifu_bbox():
bbox = {
0: ((122.0908542999878, 1586.2584665188083), (773.5411133037417,
825.1150258966278)),
1: ((140.3793485788431, 1606.8904629423566), (1190.353197027459,
1243.0853605832503)),
2: ((120.0139534379125, 1583.9271768905855), (724.3249534782219,
775.8104288584977)),
3: ((142.50252648927454, 1609.3106221382388), (1239.4122720740888,
1292.288713688988)),
4: ((117.88884113088403, 1581.5517394150106), (674.9787657901347,
726.3752061973377)),
5: ((144.57465414462143, 1611.688447569682), (1288.4808318659427,
1341.5035313084197)),
6: ((115.8602297714846, 1579.27471654949), (625.7982466386104,
677.1147840452901)),
7: ((146.7944728147906, 1614.2161842198498), (1337.531525654835,
1390.7050687363856)),
8: ((113.86384530944383, 1577.0293086386203), (576.5344359685643,
627.777022204828)),
9: ((149.0259581360621, 1616.7687282225652), (1386.5118806905086,
1439.843598490326)),
10: ((111.91564190274217, 1574.8351095461135), (527.229828693075,
578.402894851317)),
11: ((151.3053466801954, 1619.3720722471498), (1435.423685040875,
1488.917203728964)),
12: ((109.8957204607345, 1572.570246400894), (477.9699083444277,
529.0782087498488)),
13: ((153.5023503173659, 1621.9005029476564), (1484.38405923062,
1538.0443479389924)),
14: ((107.98320121613297, 1570.411787034636), (428.6704834494425,
479.7217241891257)),
15: ((155.77991404913857, 1624.5184927460925), (1533.169633314481,
1586.9984359105376)),
16: ((106.10212081215678, 1568.286103827344), (379.3860245240618,
430.3780648366697)),
17: ((158.23149941845386, 1627.305849064835), (1582.0496119714928,
1636.0513450787032)),
18: ((104.09366374413436, 1566.030231370944), (330.0822744105267,
381.01974582564395)),
19: ((160.4511021152353, 1629.888830991371), (1630.7797743277185,
1684.9592727079018)),
20: ((102.25220592881234, 1563.9475099032868), (280.7233309522168,
331.6093009077988)),
21: ((162.72784286205734, 1632.5257403739463), (1679.6815760587567,
1734.03692957156)),
22: ((100.40115742738622, 1561.8476640376036), (231.35443588323855,
282.19575854747006)),
23: ((165.05939163941662, 1635.2270773628682), (1728.511467615387,
1783.0485841263735)),
24: ((98.45723949658425, 1559.6499479349648), (182.0417295679079,
232.83530870639865)),
25: ((167.44628840053574, 1637.9923229870349), (1777.2512197664128,
1831.971115503598)),
26: ((96.56508092457855, 1557.5079027818058), (132.5285162704088,
183.27350269292484)),
27: ((169.8529496136358, 1640.778485168005), (1826.028691168028,
1880.9336718824313)),
28: ((94.71390837793813, 1555.4048050512263), (82.94691422559131,
133.63901517357235)),
29: ((172.3681094850081, 1643.685604697228), (1874.8184744639657,
1929.9072657798927))
}
hdul = create_nirspec_ifu_file("F290LP", "G140M")
im = datamodels.IFUImageModel(hdul)
im.meta.filename = "test_ifu.fits"
refs = create_reference_files(im)
pipe = nirspec.create_pipeline(im, refs, slit_y_range=[-.5, .5])
w = wcs.WCS(pipe)
im.meta.wcs = w
_, wrange = nirspec.spectral_order_wrange_from_model(im)
pipe = im.meta.wcs.pipeline
g2s = pipe[2].transform
transforms = [pipe[0].transform]
transforms.append(pipe[1].transform[1:])
transforms.append(astmodels.Identity(1))
transforms.append(astmodels.Identity(1))
transforms.extend([step.transform for step in pipe[4:-1]])
for sl in range(30):
transforms[2] = g2s.get_model(sl)
m = functools.reduce(lambda x, y: x | y,
[tr.inverse for tr in transforms[:3][::-1]])
bbox_sl = nirspec.compute_bounding_box(m, wrange)
assert_allclose(bbox[sl], bbox_sl)
