Age | Commit message (Collapse) | Author |
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less than 10 elements
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entries to the table of contents.
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The sample_cdf() device function will draw random numbers from an arbitrary
disribution given a cumulative distribution function in the form of
a list of x,y points, beginning with y=0 and ending with y=1. For an
example of how to convert a ROOT histogram to this form, see the unit
test in test_sample_cdf.py
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independent classes.
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camera.EventViewer uses new fileio.root.RootReader class.
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the object in the center of the screen.
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coloring is now calculated using the new searching/sorting algorithms.
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color.
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to [number of bins] * [number of PMTs]. Instead we accumulate information
as the Monte Carlo runs in order to evaluate the PDFs only at the points
required for the likelihood calculation.
This new interface has been propagated all the way up from the GPU class
through the Simulation class to the Likelihood class. We have preserved
the full binned histogram implementation in case we need it in the future.
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a simple unit test for event reading and writing. There were several
minor I/O bugs that are now fixed! Always test your code, kids!
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imports properly. Also correct a units bug in the default time range
for the PDFs. We work in units of seconds, not nanoseconds!
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before.
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photon distributions to be propagated multiple times. Default nreps is 1.
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times before moving to the next element.
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photons when the parent particle is at (1,0,0)
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