--- /srv/rebuilderd/tmp/rebuilderd01YNG5/inputs/python-numpy-doc_2.2.4+ds-1_all.deb +++ /srv/rebuilderd/tmp/rebuilderd01YNG5/out/python-numpy-doc_2.2.4+ds-1_all.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2025-04-01 19:45:23.000000 debian-binary │ --rw-r--r-- 0 0 0 64868 2025-04-01 19:45:23.000000 control.tar.xz │ --rw-r--r-- 0 0 0 5748876 2025-04-01 19:45:23.000000 data.tar.xz │ +-rw-r--r-- 0 0 0 64884 2025-04-01 19:45:23.000000 control.tar.xz │ +-rw-r--r-- 0 0 0 5748848 2025-04-01 19:45:23.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── ./md5sums │ │ │ ├── ./md5sums │ │ │ │┄ Files differ ├── data.tar.xz │ ├── data.tar │ │ ├── file list │ │ │ @@ -2578,15 +2578,15 @@ │ │ │ -rw-r--r-- 0 root (0) root (0) 42758 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html │ │ │ -rw-r--r-- 0 root (0) root (0) 47423 2025-04-01 19:45:23.000000 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In [1]: import numpy.random
│ │ │  
│ │ │  In [2]: rng = np.random.default_rng()
│ │ │  
│ │ │  In [3]: %timeit -n 1 rng.standard_normal(100000)
│ │ │     ...: %timeit -n 1 numpy.random.standard_normal(100000)
│ │ │     ...: 
│ │ │ -1.28 ms +- 15.7 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -2.24 ms +- 23.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +1.12 ms +- 13.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +2.18 ms +- 34.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │  
│ │ │
│ │ │
In [4]: %timeit -n 1 rng.standard_exponential(100000)
│ │ │     ...: %timeit -n 1 numpy.random.standard_exponential(100000)
│ │ │     ...: 
│ │ │ -626 us +- 7.21 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -1.56 ms +- 15.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +537 us +- 8.08 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +1.54 ms +- 6.88 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │  
│ │ │
│ │ │
In [5]: %timeit -n 1 rng.standard_gamma(3.0, 100000)
│ │ │     ...: %timeit -n 1 numpy.random.standard_gamma(3.0, 100000)
│ │ │     ...: 
│ │ │ -2.3 ms +- 8.81 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -4.41 ms +- 9.56 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +2.16 ms +- 16.3 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +4.39 ms +- 11.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │  
│ │ │
│ │ │ │ │ │ │ │ │ │ │ │
In [6]: rng = np.random.default_rng()
│ │ │  
│ │ │  In [7]: rng.random(3, dtype=np.float64)
│ │ │ -Out[7]: array([0.20603939, 0.47061387, 0.86256701])
│ │ │ +Out[7]: array([0.81330308, 0.74177671, 0.84882046])
│ │ │  
│ │ │  In [8]: rng.random(3, dtype=np.float32)
│ │ │ -Out[8]: array([0.18358761, 0.15874952, 0.03037512], dtype=float32)
│ │ │ +Out[8]: array([0.06809193, 0.75169605, 0.85206586], dtype=float32)
│ │ │  
│ │ │  In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
│ │ │ -Out[9]: array([217,  76,  75], dtype=uint8)
│ │ │ +Out[9]: array([ 54, 230,   9], dtype=uint8)
│ │ │  
│ │ │
│ │ │