medis.Detector package¶
Submodules¶
medis.Detector.H2RG module¶
medis.Detector.MKIDs module¶
medis.Detector.analysis module¶
medis.Detector.distribution module¶
All credit for this function goes to Eelco Hoogendoom at stackoverflow.com/questions/21100716/fast-arbitrary-distribution-random-sampling
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class
medis.Detector.distribution.Distribution(pdf, sort=True, interpolation=True, transform=<function Distribution.<lambda>>)¶ Bases:
objectdraws samples from a one dimensional probability distribution, by means of inversion of a discrete inverstion of a cumulative density function
the pdf can be sorted first to prevent numerical error in the cumulative sum this is set as default; for big density functions with high contrast, it is absolutely necessary, and for small density functions, the overhead is minimal
a call to this distibution object returns indices into density array
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ndim¶
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sum¶ cached sum of all pdf values; the pdf need not sum to one, and is imlpicitly normalized
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medis.Detector.distribution.MR(I, Ic, Is)¶ modified rician distribution
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medis.Detector.distribution.bessel(k)¶ modified zero order besset=l
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medis.Detector.distribution.gaussian(mu, sig, x)¶
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medis.Detector.distribution.gaussian2(x, sig, mu)¶
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medis.Detector.distribution.lognorm(x, mu, sigma)¶
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medis.Detector.distribution.poisson(lamda, k)¶