Add in-place poisson random-number generation

[vincefn]

With this version, the input array can be used to supply the per-element lamba value. This is similar to what numpy.random.poisson() allows, either supplying a shape and one lambda value, or an array of lambda values.

This is very useful when simulating detector data for imaging, where each point has a different expected value.

Let me know if you want examples, there are currently none for the random-number generators.

[inducer]

CI: https://gitlab.tiker.net/inducer/pycuda/-/merge_requests/25

[inducer]

There were some failures in the CI. Could you take a look?

(Click the red X to see the logs.)

[vincefn]

Hi Andreas, I looked at the CI output but I don't know what going on:

• a lot of test are failing, including in test_driver
• the error always seems to be g++-7: No such file or directory
• I had ran the tests on my computer without a problem

To test in a cleaner (compared to my computer) environment, I just made a notebook on google colab:
https://colab.research.google.com/drive/1njtsz7Jxtsi7l7chEW02b5TvsVmwo4Ym?usp=sharing

the tests pass there, so could it be a problem in the CI setup ? I'm not sure which file is missing, could it be g++-7 itself ?
Let me know if I missed something.

[inducer]

The g++-7 thing was an issue with my CI setup (which should be fixed). I only see one failure:

 Traceback (most recent call last):
  File "/var/lib/gitlab-runner/builds/338cd43e/0/inducer/pycuda/pycuda/tools.py", line 463, in f
    inner_f(*args, **kwargs)
  File "/var/lib/gitlab-runner/builds/338cd43e/0/inducer/pycuda/test/test_gpuarray.py", line 309, in test_curand_wrappers
    assert np.isclose(0.12511, tmp, atol=0.002)
AssertionError: assert False
 +  where False = <function isclose at 0x7fe60455b350>(0.12511, 0, atol=0.002)
 +    where <function isclose at 0x7fe60455b350> = np.isclose

[inducer]

Here is a log with that problem.

[vincefn]

Hmm - I thought I had set a tolerance high enough that the ratio of 1000000 values with lambda=10 with value==9 would be close enough to 0.12511, but apparently that's not the case... I tested a few 100's of times with all generators and they got close enough every time.

I guess I'll remove that test, it's probably a bad idea to have a numerical test based on random values, the central limit theorem won't get us where we want us every time.

[inducer]

Thanks for your contribution!

[vincefn]

Thanks a lot Andreas for including this quickly - and a million thanks again for pyCUDA and pyOpenCL - these are really fantastic tools for my research work (synchrotron-based X-ray imaging). Vincent

…

Le 1 juin 2020 à 18:59, Andreas Klöckner ***@***.***> a écrit : Thanks for your contribution! — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <#238 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ABBNOX2ICE6KMPON3BBSPT3RUPM7HANCNFSM4NKCJH2A>.

-- Vincent Favre-Nicolin