def test_01_nonlinearity_task(self):
"""Run the NonlinearityTask"""
task = NonlinearityTask()
sensorid = 'RTM-022'
task.run(sensorid,
_detrespfile,
outputfile=self.fits_file,
plotfile=None)
nlc = NonlinearityCorrection.create_from_fits_file(self.fits_file)
ccd_1 = MaskedCCD(_flat_frame, bias_frame=_bias_frame)
ccd_2 = MaskedCCD(_flat_frame,
bias_frame=_bias_frame,
linearity_correction=nlc)
img_1 = ccd_1.unbiased_and_trimmed_image(1)
img_2 = ccd_2.unbiased_and_trimmed_image(1)
mean_1 = afwMath.makeStatistics(img_1, afwMath.MEAN,
ccd_1.stat_ctrl).getValue()
mean_2 = afwMath.makeStatistics(img_2, afwMath.MEAN,
ccd_2.stat_ctrl).getValue()
self.assertAlmostEqual(mean_1, 40.724625956352185)
self.assertAlmostEqual(mean_2, 40.724101151614555)
def test_find_pixel_defects(self):
ccd1 = MaskedCCD(self.flat_file1)
ccd2 = MaskedCCD(self.flat_file2)
for amp in ccd1:
xcorr, xcorr_err = crossCorrelate(
ccd1.unbiased_and_trimmed_image(amp),
ccd2.unbiased_and_trimmed_image(amp), self.maxLag,
self.nSigmaClip, self.backgroundBinSize)
self.assertTrue(1 > xcorr[0][1] and 1 > xcorr[1][0])
def test_bias_frame_subtraction(self):
bias_file = 'bias_image.fits'
def bias_func_x(x):
return 100 * (np.sin(x / 509. * 2. * np.pi) + 1)
bias_func_y = BiasFunc(self.bias_slope, self.bias_intercept)
bias_ccd = sim_tools.CCD(exptime=self.exptime, gain=self.gain)
for amp in bias_ccd.segments:
imarr = bias_ccd.segments[amp].image.getArray()
yvals = np.arange(0, imarr.shape[0], dtype=np.float)
for x in range(imarr.shape[1]):
imarr[:, x] += bias_func_x(x) + bias_func_y(yvals)
bias_ccd.writeto(bias_file)
image_file = 'image_file.fits'
signal_level = 1000.
image_ccd = sim_tools.CCD(exptime=self.exptime, gain=self.gain)
for amp in image_ccd.segments:
imarr = image_ccd.segments[amp].image.getArray()
yvals = np.arange(0, imarr.shape[0], dtype=np.float)
for x in range(imarr.shape[1]):
imarr[:, x] +=\
bias_func_x(x) + bias_func_y(yvals)
imaging_section = image_ccd.segments[amp].image.Factory(
image_ccd.segments[amp].image, self.amp_geom.imaging)
imaging_section += signal_level
image_ccd.writeto(image_file)
ccd = MaskedCCD(image_file, bias_frame=bias_file)
for amp in ccd:
image = ccd.unbiased_and_trimmed_image(amp)
self.assertAlmostEqual(
max(np.abs(image.getImage().getArray().ravel())) /
signal_level, 1, 6)
ccd = MaskedCCD(image_file)
for amp in ccd:
image = ccd.unbiased_and_trimmed_image(amp)
self.assertTrue(
max(np.abs(image.getImage().getArray().ravel())) /
signal_level > 1.01)
os.remove(bias_file)
os.remove(image_file)
def test_unbias_and_trim(self):
ccd = MaskedCCD(self.image_file)
for amp in ccd:
#
# Test of corresponding MaskedCCD method.
#
image = ccd.unbiased_and_trimmed_image(amp)
imarr = image.getImage().getArray()
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
def test_linearity_correction(self):
"""Linearity correction test."""
def linearity_correction(amp, flux):
"""Set the correction to be the amp number times the flux."""
return amp * flux
np.random.seed(1000)
ccd = MaskedCCD(self.image_file)
ccd_corr = MaskedCCD(self.image_file,
linearity_correction=linearity_correction)
for amp in ccd:
# Check that the ratio of the corrected to uncorrected
# flux is (almost) equal to the amp number.
ratio = np.median(
ccd_corr.unbiased_and_trimmed_image(
amp).getImage().array) / np.median(
ccd.unbiased_and_trimmed_image(amp).getImage().array)
self.assertAlmostEqual(amp, ratio, places=5)
def test_unbias_and_trim(self):
ccd = MaskedCCD(self.image_file)
for amp in ccd:
image = imutils.unbias_and_trim(ccd[amp],
self.amp_geom.serial_overscan,
self.amp_geom.imaging)
imarr = image.getImage().getArray()
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
#
# Test of corresponding MaskedCCD method.
#
image = ccd.unbiased_and_trimmed_image(amp)
imarr = image.getImage().getArray()
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
def test_unbias_and_trim(self):
ccd = MaskedCCD(self.image_file)
overscan = makeAmplifierGeometry(self.image_file)
for amp in ccd:
for method in ['mean', 'row', 'func', 'spline']:
image = imutils.unbias_and_trim(ccd[amp],
overscan.serial_overscan,
bias_method=method,
**self.kwargs)
imarr = image.getImage().getArray()
if method == 'mean':
self.assertTrue(max(np.abs(imarr.flat)) < 2)
else:
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
#
# Test of corresponding MaskedCCD method.
#
image = ccd.unbiased_and_trimmed_image(
amp, overscan.serial_overscan, **self.kwargs)
imarr = image.getImage().getArray()
self.assertTrue(max(np.abs(imarr.flat)) < 1e-6)
