Pixel likelihood calculation¶
ctapipe.image.pixel_likelihood Module¶
Class for calculation of likelihood of a pixel expectation, given the pixel amplitude, the level of noise in the pixel and the photoelectron resolution. This calculation is taken from [denaurois2009].
The likelihood is essentially a poissonian convolved with a gaussian, at low signal a full possonian approach must be adopted, which requires the sum of contibutions over a number of potential contributing photoelectrons (which is slow). At high signal this simplifies to a gaussian approximation.
The full and gaussian approximations are implemented, in addition to a general purpose implementation, which tries to intellegently switch between the two. Speed tests are below:
neg_log_likelihood_approx(image, prediction, spe, ped) 29.8 µs per loop
neg_log_likelihood_numeric(image, prediction, spe, ped) 93.4 µs per loop
neg_log_likelihood(image, prediction, spe, ped) 59.9 µs per loop
TODO:¶
Need to implement more tests, particularly checking for error states
Additional terms may be useful to add to the likelihood
Functions¶
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Calculate negative log likelihood for telescope. |
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Calculate likelihood of prediction given the measured signal, full numerical integration from [denaurois2009]. |
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Safe implementation of the poissonian likelihood implementation, adaptively switches between the full solution and the gaussian approx depending on the prediction. |
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Calculation of the mean likelihood for a give expectation value of pixel intensity in the gaussian approximation. |
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Calculation of the mean likelihood for a give expectation value of pixel intensity using the full numerical integration. |
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Simple chi-squared statistic from Le Bohec et al 2008 |
Classes¶
Class Inheritance Diagram¶
