def test_pixflat(self):
image = preproc(self.rawimage, self.header, primary_header = self.primary_header, pixflat=False)
pixflat = np.ones_like(image.pix)
image = preproc(self.rawimage, self.header, primary_header = self.primary_header, pixflat=pixflat)
fits.writeto(self.calibfile, pixflat)
image = preproc(self.rawimage, self.header, primary_header = self.primary_header, pixflat=self.calibfile)
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, primary_header = self.primary_header, pixflat=pixflat[0:10, 0:10])
def test_pixflat(self):
image = preproc(self.rawimage, self.header, pixflat=False)
pixflat = np.ones_like(image.pix)
image = preproc(self.rawimage, self.header, pixflat=pixflat)
fits.writeto(self.calibfile, pixflat)
image = preproc(self.rawimage, self.header, pixflat=self.calibfile)
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, pixflat=pixflat[0:10, 0:10])
def test_bias(self):
image = preproc(self.rawimage, self.header, bias=False)
bias = np.zeros(self.rawimage.shape)
image = preproc(self.rawimage, self.header, bias=bias)
fits.writeto(self.calibfile, bias)
image = preproc(self.rawimage, self.header, bias=self.calibfile)
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, bias=bias[0:10, 0:10])
def test_bias(self):
image = preproc(self.rawimage, self.header, bias=False)
bias = np.zeros(self.rawimage.shape)
image = preproc(self.rawimage, self.header, bias=bias)
fits.writeto(self.calibfile, bias)
image = preproc(self.rawimage, self.header, bias=self.calibfile)
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, bias=bias[0:10, 0:10])
def test_mask(self):
image = preproc(self.rawimage, self.header, mask=False)
mask = np.random.randint(0, 2, size=image.pix.shape)
image = preproc(self.rawimage, self.header, mask=mask)
self.assertTrue(np.all(image.mask == mask))
fits.writeto(self.calibfile, mask)
image = preproc(self.rawimage, self.header, mask=self.calibfile)
self.assertTrue(np.all(image.mask == mask))
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, mask=mask[0:10, 0:10])
def test_mask(self):
image = preproc(self.rawimage, self.header, mask=False)
mask = np.random.randint(0, 2, size=image.pix.shape)
image = preproc(self.rawimage, self.header, mask=mask)
self.assertTrue(np.all(image.mask == mask))
fits.writeto(self.calibfile, mask)
image = preproc(self.rawimage, self.header, mask=self.calibfile)
self.assertTrue(np.all(image.mask == mask))
with self.assertRaises(ValueError):
image = preproc(self.rawimage, self.header, mask=mask[0:10, 0:10])
def test_pixflat_mask(self):
from desispec.maskbits import ccdmask
pixflat = np.ones((2 * self.ny, 2 * self.nx))
pixflat[0:10, 0:10] = 0.0
pixflat[10:20, 10:20] = 0.05
image = preproc(self.rawimage, self.header, pixflat=pixflat)
self.assertTrue(np.all(image.mask[0:10, 0:10] & ccdmask.PIXFLATZERO))
self.assertTrue(np.all(image.mask[10:20, 10:20] & ccdmask.PIXFLATLOW))
def test_pixflat_mask(self):
from desispec.maskbits import ccdmask
pixflat = np.ones((2*self.ny, 2*self.nx))
pixflat[0:10, 0:10] = 0.0
pixflat[10:20, 10:20] = 0.05
image = preproc(self.rawimage, self.header, pixflat=pixflat)
self.assertTrue(np.all(image.mask[0:10,0:10] & ccdmask.PIXFLATZERO))
self.assertTrue(np.all(image.mask[10:20,10:20] & ccdmask.PIXFLATLOW))
def test_preproc1234(self):
"""Should also work with old amp names 1-4 instead of A-D"""
hdr = self.header.copy()
for prefix in ('ORSEC', 'BIASSEC', 'DATASEC', 'CCDSEC'):
for amp, ampnum in zip(('A','B','C','D'), ('1','2','3','4')):
if prefix+amp in hdr:
hdr[prefix+ampnum] = hdr[prefix+amp]
del hdr[prefix+amp]
image = preproc(self.rawimage, hdr, primary_header=self.primary_header)
def test_preproc(self):
image = preproc(self.rawimage, self.header)
self.assertEqual(image.pix.shape, (2*self.ny, 2*self.nx))
self.assertTrue(np.all(image.ivar <= 1/image.readnoise**2))
for amp in ('1', '2', '3', '4'):
pix = image.pix[self.quad[amp]]
rdnoise = np.median(image.readnoise[self.quad[amp]])
npixover = self.ny * self.noverscan
self.assertAlmostEqual(np.mean(pix), 0.0, delta=3*rdnoise/np.sqrt(npixover))
self.assertAlmostEqual(np.std(pix), self.rdnoise[amp], delta=0.2)
self.assertAlmostEqual(rdnoise, self.rdnoise[amp], delta=0.2)
def test_preproc(self):
image = preproc(self.rawimage, self.header, primary_header = self.primary_header)
self.assertEqual(image.pix.shape, (2*self.ny, 2*self.nx))
self.assertTrue(np.all(image.ivar <= 1/image.readnoise**2))
for amp in ('A', 'B', 'C', 'D'):
pix = image.pix[self.quad[amp]]
rdnoise = np.median(image.readnoise[self.quad[amp]])
npixover = self.ny * self.noverscan
self.assertAlmostEqual(np.mean(pix), 0.0, delta=5*rdnoise/np.sqrt(npixover)) # JXP increased this
self.assertAlmostEqual(np.std(pix), self.rdnoise[amp], delta=0.2)
self.assertAlmostEqual(rdnoise, self.rdnoise[amp], delta=0.2)
def test_pedantic(self):
with self.assertRaises(ValueError):
_parse_sec_keyword('blat')
#- should log a warning about large readnoise
rawimage = self.rawimage + np.random.normal(scale=2,
size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log an error about huge readnoise
rawimage = self.rawimage + np.random.normal(scale=10,
size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log a warning about small readnoise
rdnoise = 0.7 * np.mean(self.rdnoise.values())
rawimage = np.random.normal(scale=rdnoise, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log a warning about tiny readnoise
rdnoise = 0.01 * np.mean(self.rdnoise.values())
rawimage = np.random.normal(scale=rdnoise, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- Missing expected RDNOISE keywords shouldn't be fatal
hdr = self.header.copy()
del hdr['RDNOISE1']
del hdr['RDNOISE2']
del hdr['RDNOISE3']
del hdr['RDNOISE4']
image = preproc(self.rawimage, hdr)
#- Missing expected GAIN keywords should log error but not crash
hdr = self.header.copy()
del hdr['GAIN1']
del hdr['GAIN2']
del hdr['GAIN3']
del hdr['GAIN4']
image = preproc(self.rawimage, hdr)
def test_pedantic(self):
with self.assertRaises(ValueError):
_parse_sec_keyword('blat')
#- should log a warning about large readnoise
rawimage = self.rawimage + np.random.normal(scale=2, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log an error about huge readnoise
rawimage = self.rawimage + np.random.normal(scale=10, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log a warning about small readnoise
rdnoise = 0.7 * np.mean(self.rdnoise.values())
rawimage = np.random.normal(scale=rdnoise, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- should log a warning about tiny readnoise
rdnoise = 0.01 * np.mean(self.rdnoise.values())
rawimage = np.random.normal(scale=rdnoise, size=self.rawimage.shape)
image = preproc(rawimage, self.header)
#- Missing expected RDNOISE keywords shouldn't be fatal
hdr = self.header.copy()
del hdr['RDNOISE1']
del hdr['RDNOISE2']
del hdr['RDNOISE3']
del hdr['RDNOISE4']
image = preproc(self.rawimage, hdr)
#- Missing expected GAIN keywords should log error but not crash
hdr = self.header.copy()
del hdr['GAIN1']
del hdr['GAIN2']
del hdr['GAIN3']
del hdr['GAIN4']
image = preproc(self.rawimage, hdr)
def test_preproc_no_orsec(self):
# Strip out ORSEC
old_header = self.header.copy()
old_image = self.rawimage.copy()
for amp in ('A', 'B', 'C', 'D'):
old_header.pop('ORSEC{}'.format(amp))
xy = parse_sec_keyword(self.header['DATASEC'+amp])
#old_image[xy] -= np.int32(self.offset_row[amp]) -- OR_SEC now zero
#
image = preproc(old_image, old_header, primary_header = self.primary_header)
self.assertEqual(image.pix.shape, (2*self.ny, 2*self.nx))
self.assertTrue(np.all(image.ivar <= 1/image.readnoise**2))
for amp in ('A', 'B', 'C', 'D'):
pix = image.pix[self.quad[amp]]
rdnoise = np.median(image.readnoise[self.quad[amp]])
npixover = self.ny * self.noverscan
self.assertAlmostEqual(np.mean(pix), 0.0, delta=1) # Using np.int32 pushed this to 1
self.assertAlmostEqual(np.std(pix), self.rdnoise[amp], delta=0.2)
self.assertAlmostEqual(rdnoise, self.rdnoise[amp], delta=0.2)
def test_default_mask(self):
image = preproc(self.rawimage, self.header, mask=True)
def test_default_pixflat(self):
image = preproc(self.rawimage, self.header, pixflat=True)
def test_default_bias(self):
image = preproc(self.rawimage, self.header, bias=True)
def test_default_bias(self):
image = preproc(self.rawimage, self.header, bias=True)
def test_default_pixflat(self):
image = preproc(self.rawimage, self.header, pixflat=True)
def test_default_mask(self):
image = preproc(self.rawimage, self.header, mask=True)