def test_gradient_map():
image = np.zeros((6, 8))
grad = image_util.gradient_map(image)
npt.assert_almost_equal(grad, image, decimal=6)
image_ones = np.ones((6, 8))
grad = image_util.gradient_map(image_ones)
npt.assert_almost_equal(grad, image, decimal=6)
assert np.shape(grad) == np.shape(image)
def covariance_matrix(data,
background_rms,
exposure_map,
gradient_boost_factor=None):
"""
returns a diagonal matrix for the covariance estimation which describes the error
Notes:
- the exposure map must be positive definite. Values that deviate too much from the mean exposure time will be
given a lower limit to not under-predict the Poisson component of the noise.
- the data must be positive semi-definite for the Poisson noise estimate.
Values < 0 (Possible after mean subtraction) will not have a Poisson component in their noise estimate.
:param data: data array, eg in units of photons/second
:param background_rms: background noise rms, eg. in units (photons/second)^2
:param exposure_map: exposure time per pixel, e.g. in units of seconds
:param gradient_boost_factor: None or float, variance terms added in quadrature scaling with
gradient^2 * gradient_boost_factor
:return: len(d) x len(d) matrix that give the error of background and Poisson components; (photons/second)^2
"""
if gradient_boost_factor is not None:
gradient_map = image_util.gradient_map(data) * gradient_boost_factor
else:
gradient_map = 0
d_pos = np.zeros_like(data)
d_pos[data >= 0] = data[data >= 0]
sigma = d_pos / exposure_map + background_rms**2 + gradient_map**2
return sigma