def test_associateSky():
filenames = [
'N20070819S{:04d}_flatCorrected.fits'.format(i)
for i in range(104, 109)
]
adinputs = [
astrodata.open(os.path.join(TESTDATAPATH, 'NIRI', f))
for f in filenames
]
p = NIRIImage(adinputs)
p.separateSky() # Difficult to construct this by hand
p.associateSky()
filename_set = {ad.phu['ORIGNAME'] for ad in adinputs}
# Test here is that each science frame has all other frames as skies
for k, v in p.sky_dict.items():
v = [ad.phu['ORIGNAME'] for ad in v]
assert len(v) == len(filenames) - 1
assert set([k] + v) == filename_set
def test_associateSky():
filenames = [
'N20070819S{:04d}_flatCorrected.fits'.format(i)
for i in range(104, 109)
]
adinputs = [
astrodata.open(os.path.join(TESTDATAPATH, 'NIRI', f))
for f in filenames
]
p = NIRIImage(adinputs)
p.separateSky() # Difficult to construct this by hand
p.associateSky()
filename_set = set([ad.phu['ORIGNAME'] for ad in adinputs])
# Test here is that each science frame has all other frames as skies
for k, v in p.sky_dict.items():
v = [ad.phu['ORIGNAME'] for ad in v]
assert len(v) == len(filenames) - 1
assert set([k] + v) == filename_set
# def test_correctBackgroundToReference(self):
# pass
# def test_darkCorrect(self):
# ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070819S0104_nonlinearityCorrected.fits'))
# p = NIRIImage([ad])
# ad = p.darkCorrect()[0]
# assert ad_compare(ad, os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070819S0104_darkCorrected.fits'))
# def test_darkCorrect_with_af(self):
# science = AstroFaker.create('NIRI', 'IMAGE')
# dark = AstroFaker.create('NIRI', 'IMAGE')
# p = NIRIImage([science])
# p.darkCorrect([science], dark=dark)
# science.subtract(dark)
# assert ad_compare(science, dark)
# af.init_default_extensions()
# af[0].mask = np.zeros_like(af[0].data, dtype=np.uint16)
# def test_flatCorrect(self):
# ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070819S0104_darkCorrected.fits'))
# p = NIRIImage([ad])
# ad = p.flatCorrect()[0]
# assert ad_compare(ad, os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070819S0104_flatCorrected.fits'))
#
# def test_makeSky(self):
# pass
#
# def test_nonlinearityCorrect(self):
# # Don't use NIRI data; NIRI has its own primitive
# pass
#
# def test_normalizeFlat(self):
# flat_file = os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070913S0220_flat.fits')
# ad = astrodata.open(flat_file)
# ad.multiply(10.0)
# del ad.phu['NORMLIZE'] # Delete timestamp of previous processing
# p = NIRIImage([ad])
# ad = p.normalizeFlat(suffix='_flat', strip=True)[0]
# assert ad_compare(ad, flat_file)
#
# def test_separateSky(self):
# pass
#
# def test_skyCorrect(self):
# pass
#
# def test_subtractSky(self):
# pass
#
# def test_subtractSkyBackground(self):
# ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070819S0104_flatCorrected.fits'))
# ad.hdr['SKYLEVEL'] = 1000.0
# orig_data = ad[0].data.copy()
# p = NIRIImage([ad])
# ad = p.subtractSkyBackground()[0]
# assert (orig_data - ad[0].data).min() > 999.99
# assert (orig_data - ad[0].data).max() < 1000.01
#
# def test_thresholdFlatfield(self):
# ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
# 'N20070913S0220_flat.fits'))
# del ad.phu['TRHFLAT'] # Delete timestamp of previous processing
# ad[0].data[100, 100] = 20.0
# p = NIRIImage([ad])
# ad = p.thresholdFlatfield()[0]
# assert ad[0].mask[100, 100] == 64
