def test_addVAR(self):
ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104_ADUToElectrons.fits'))
p = NIRIImage([ad])
ad = p.addVAR(read_noise=True, poisson_noise=True)[0]
assert ad_compare(ad, os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104_varAdded.fits'))
def test_prepare(self):
ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104.fits'))
p = NIRIImage([ad])
ad = p.prepare()[0]
assert ad_compare(ad, os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104_prepared.fits'))
def test_nonlinearityCorrect(self):
ad = astrodata.open(os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104_varAdded.fits'))
p = NIRIImage([ad])
ad = p.nonlinearityCorrect()[0]
assert ad_compare(ad, os.path.join(TESTDATAPATH, 'NIRI',
'N20070819S0104_nonlinearityCorrected.fits'))
def test_writeOutputs(self):
filenames = ['N20070819S{:04d}_flatCorrected.fits'.format(i)
for i in range(104, 106)]
adinputs = [astrodata.open(os.path.join(TESTDATAPATH, 'NIRI', f))
for f in filenames]
p = NIRIImage(adinputs)
p.writeOutputs(prefix='test', suffix='_blah', strip=True)
# Check renamed files are on disk and the filenames have been
# changed for the adinputs
for f, ad in zip(filenames, p.streams['main']):
newfilename = 'test'+f.replace('flatCorrected', 'blah')
assert os.path.exists(newfilename)
os.remove(newfilename)
assert newfilename == ad.filename
def test_addToList(self):
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]
# Add one image twice, just for laughs; it should appear only once
adinputs.append(adinputs[0])
p = NIRIImage(adinputs)
p.stacks = {}
p.addToList(purpose='forTest')
for f in filenames:
newfilename = f.replace('flatCorrected', 'forTest')
assert os.path.exists(newfilename)
os.remove(newfilename)
# Check there's one stack of length 5
assert len(p.stacks) == 1
assert len(p.stacks[p.stacks.keys()[0]]) == 5
def niri_images():
"""Create two NIRI images, one all 1s, the other all 2s"""
try:
import AstroFaker
except ImportError:
pytest.skip("AstroFaker not installed")
adinputs = []
for i in (1, 2):
ad = AstroFaker.create('NIRI', 'IMAGE')
ad.init_default_extensions()
ad[0].data += i
adinputs.append(ad)
return NIRIImage(adinputs)
def niri_image():
try:
import AstroFaker
except ImportError:
pytest.skip("AstroFaker is not installed")
ad = AstroFaker.create('NIRI', 'IMAGE')
ad.init_default_extensions()
# SExtractor struggles if the background is noiseless
ad.add_read_noise()
for x, y in STAR_POSITIONS:
ad[0].add_star(amplitude=500, x=x, y=y)
return NIRIImage([ad])
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_adjust_wcs_to_reference(astrofaker, no_wcs, rotate, scale):
scale = False
mods = [models.Identity(2)]
for params in MODEL_PARMS:
m = models.Shift(params[0]) & models.Shift(params[1])
if rotate:
m |= models.Rotation2D(params[2])
if scale:
m |= models.Scale(params[3]) & models.Scale(params[3])
mods.append(m)
adinputs = make_images(astrofaker, mods=mods)
if no_wcs:
for ad in adinputs:
del ad[0].hdr['CD*']
ad[0].wcs = None
p = NIRIImage(adinputs)
p.detectSources()
p.adjustWCSToReference(first_pass=45 if no_wcs else 5,
rotate=rotate,
scale=scale)
# We're going to confirm that a grid of input points are transformed
# correctly, rather than comparing the components of the registration
# model with the original model
yin, xin = np.mgrid[:1000:100, :1000:100]
for i, (ad, m) in enumerate(zip(p.streams['main'], mods)):
if i == 0: # the Identity model: nothing to check
continue
m_regist = ad[0].wcs.forward_transform[:m.n_submodels]
print(m_regist)
xref, yref = m(xin, yin)
xout, yout = m_regist(xin, yin)
np.testing.assert_allclose(xref, xout, atol=0.1)
np.testing.assert_allclose(yref, yout, atol=0.1)
rms = np.sqrt(((xref - xout)**2 + (yref - yout)**2).mean())
assert rms < 0.05
def niriprim():
file_path = download_from_archive("N20190120S0287.fits")
ad = astrodata.open(file_path)
p = NIRIImage([ad])
p.addDQ()
return p
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
def test_not_prepared(adinputs):
p = NIRIImage(adinputs)
with pytest.raises(IOError):
p.stackFrames()
