Add option use_poisson (or not) in generate_random_noise #92
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…d use numpy.random.normal instead of numpy.random.poisson if False
| @@ -562,7 +562,7 @@ def simulate(self, sky_positions=None, focal_positions=None, | |||
| # Zero our random noise realization column. | |||
| output['random_noise_electrons'][:] = 0. | |||
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| def generate_random_noise(self, random_state=None): | |||
| def generate_random_noise(self, random_state=None, use_poisson=True): | |||
| """Generate a random noise realization for the most recent simulation. | |||
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| Fills the "random_noise_electrons" column in each camera's output | |||
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Missing a docstring description of the new parameter.
specsim/simulator.py
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| random_state.poisson(mean_electrons) - mean_electrons + | ||
| random_state.normal(scale=output['read_noise_electrons'])) | ||
| else : | ||
| output['random_noise_electrons'] = random_state.normal(scale=np.sqrt( mean_electrons*(mean_electrons>0) + output['read_noise_electrons']**2)) |
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Why did you add the (mean_electrons>0) since this is not necessary for the Poisson case? mean_electrons >= 0 should always be true here (or else there is a bug). Are you concerned about mean_electrons == 0?
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this was just to make sure we don't add a negative variance. but it is always positive or null I remove this test.
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I checked that |
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The failing travis test has been diagnosed and fixed on #91 so does not need to block this PR. |
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Looks good, thanks. |
numpy.random.poisson has the drawback of using a varying number of random numbers depending on the mean value (the argument of the function). this is problematic for some applications like testing the effect of DLA on the Lya correlation function where one would like to preserve the random numbers used to simulate the instrumental noise when adding DLAs or not. This PR fixes this by allowing the use of numpy.random.normal instead of numpy.random.poisson in specsim.simulator.Simulator.generate_random_noise.