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[math] Add vector filling overload for scalar distributions of TRandom #17077
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
| import unittest | ||
|
|
||
| import ROOT | ||
| import numpy as np | ||
|
|
||
| class TRandomNumpyArrays(unittest.TestCase): | ||
| """ | ||
| Test for pythonizations thst allow filling numpy arrays with TRandom scalar | ||
| RNGs. | ||
| We only cover the technical aspect, ensuring the array if filled. With the seed | ||
| set, all executions should be repeatable. | ||
| """ | ||
|
|
||
| # Tests | ||
| def test_binomial(self): | ||
| r = ROOT.TRandom2(123) | ||
| vi = np.zeros(100, dtype=np.int32) | ||
| r.BinomialN(100, vi, 123, 0.12345) | ||
| self.assertEqual(np.count_nonzero(vi), len(vi)) | ||
|
|
||
| def test_breit_wigner(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.BreitWignerN(100, vd, 123, 0.12345) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
|
|
||
| def test_exp(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.ExpN(100, vd, 1.5) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
|
|
||
| def test_gaus(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.GausN(100, vd, 5, 1.5) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
|
|
||
| def test_integer(self): | ||
| r = ROOT.TRandom2(123) | ||
| vui = np.zeros(100, dtype=np.uint32) | ||
| r.IntegerN(100, vui, 15) | ||
| self.assertGreater(np.count_nonzero(vui), 0.9 * len(vui)) | ||
|
|
||
| def test_landau(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.LandauN(100, vd, 5, 1.5) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
|
|
||
| def test_poisson(self): | ||
| r = ROOT.TRandom2(123) | ||
| vull = np.zeros(100, dtype=np.ulonglong) | ||
| r.PoissonN(100, vull, 15) | ||
| self.assertGreater(np.count_nonzero(vull), 0.9 * len(vull)) | ||
|
|
||
| def test_poissond_d(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.PoissonDN(100, vd, 15) | ||
| self.assertGreater(np.count_nonzero(vd), 0.9 * len(vd)) | ||
|
|
||
| def test_uniform(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.UniformN(100, vd, 5.0) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
|
|
||
| def test_uniform_2_params(self): | ||
| r = ROOT.TRandom2(123) | ||
| vd = np.zeros(100, dtype=np.float64) | ||
| r.UniformN(100, vd, 5, 1.5) | ||
| self.assertEqual(np.count_nonzero(vd), len(vd)) | ||
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Looks good! We can use something like:
To automatically forward to the loop wrappers when the user expects an array of random numbers, or to the original generation function when the input signatures follow the traditional format