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-2578,15 +2578,15 @@\n -rw-r--r-- 0 root (0) root (0) 42758 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html\n -rw-r--r-- 0 root (0) root (0) 47423 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html\n -rw-r--r-- 0 root (0) root (0) 45546 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html\n -rw-r--r-- 0 root (0) root (0) 82403 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/generator.html\n -rw-r--r-- 0 root (0) root (0) 45982 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/index.html\n -rw-r--r-- 0 root (0) root (0) 89078 2025-03-09 20:14:24.000000 ./usr/share/doc/python-numpy/html/reference/random/legacy.html\n -rw-r--r-- 0 root (0) root (0) 35540 2025-03-09 20:14:24.000000 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In [1]: import numpy.random\n \n In [2]: rng = np.random.default_rng()\n \n In [3]: %timeit -n 1 rng.standard_normal(100000)\n    ...: %timeit -n 1 numpy.random.standard_normal(100000)\n    ...: \n-3.49 ms +- 721 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-7.84 ms +- 1.33 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.36 ms +- 33.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.61 ms +- 17.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [4]: %timeit -n 1 rng.standard_exponential(100000)\n    ...: %timeit -n 1 numpy.random.standard_exponential(100000)\n    ...: \n-1.93 ms +- 109 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-5.43 ms +- 1.18 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+724 us +- 6.86 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+1.94 ms +- 10.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)\n    ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)\n    ...: \n-8.56 ms +- 1.23 ms per loop (mean +- std. dev. of 7 runs, 1 loop each)\n-13.7 ms +- 265 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+2.98 ms +- 21.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n+5.05 ms +- 12.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)\n 
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In [6]: rng = np.random.default_rng()\n \n In [7]: rng.random(3, dtype=np.float64)\n-Out[7]: array([0.09837248, 0.04465839, 0.2084051 ])\n+Out[7]: array([0.55420097, 0.45697908, 0.69487323])\n \n In [8]: rng.random(3, dtype=np.float32)\n-Out[8]: array([0.13149399, 0.6899817 , 0.9949636 ], dtype=float32)\n+Out[8]: array([0.5988971 , 0.18808335, 0.86917627], dtype=float32)\n \n In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)\n-Out[9]: array([119, 176, 212], dtype=uint8)\n+Out[9]: array([252,  61,  65], dtype=uint8)\n 
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