def test_tiny_pixel_offset(self):
pixel_noise = 0.0001
self.mygauss[self.xbar + 30, self.ybar] += pixel_noise
self.fit = fitgaussian(self.mygauss, self.moments)
for param in FIT_PARAMS:
self.assertAlmostEqual(getattr(self, param),
self.fit[param],
places=6)
def test_small_pixel_offset(self):
pixel_noise = 0.001
n_noisy_pix = 2
# Place noisy pix symmetrically about centre to avoid position offset
self.mygauss[self.xbar + 30, self.ybar] += pixel_noise
self.mygauss[self.xbar - 30, self.ybar] += pixel_noise
self.fit = fitgaussian(self.mygauss, self.moments)
for param in FIT_PARAMS:
self.assertAlmostEqual(getattr(self, param),
self.fit[param],
places=5)
def setUp(self):
Xin, Yin = numpy.indices((500, 500))
self.height = 10
self.x = 250
self.y = 250
self.maj = 40
self.min = 40
self.theta = 0
self.mygauss = numpy.ma.array(
gaussian(self.height, self.x, self.y, self.maj, self.min,
self.theta)(Xin, Yin))
self.moments = moments(self.mygauss, beam, 0)
self.fit = fitgaussian(self.mygauss, self.moments)
def test_noisy_background(self):
# Use a fixed random state seed, so unit-test is reproducible:
rstate = numpy.random.RandomState(42)
pixel_noise = 0.5
self.mygauss += rstate.normal(scale=pixel_noise,
size=len(self.mygauss.ravel())).reshape(
self.mygauss.shape)
self.fit = fitgaussian(self.mygauss, self.moments)
self.longMessage = True # Show assertion fail values + given message
# First, let's check we've converged to a reasonable fit in the
# presence of noise:
# print
for param in FIT_PARAMS:
# print param, getattr(self,param), self.fit[param]
self.assertAlmostEqual(
getattr(self, param),
self.fit[param],
places=1,
msg=param + " misfit (bad random noise seed?)",
)
# Now we run the full error-profiling routine and check the chi-sq
# calculations:
self.fit_w_errs, _ = source_profile_and_errors(
data=self.mygauss,
threshold=0.,
noise=pixel_noise,
beam=(self.semimajor, self.semiminor, self.theta),
)
npix = len(self.mygauss.ravel())
# print "CHISQ", npix, self.fit_w_errs.chisq
# NB: this is the calculation for reduced chisq in presence of
# independent pixels, i.e. uncorrelated noise.
# For real data, we try to take the noise-correlation into account.
# print "Reduced chisq", self.fit_w_errs.chisq / npix
self.assertTrue(0.9 < self.fit_w_errs.chisq / npix < 1.1)