--- /srv/rebuilderd/tmp/rebuilderdGn0Pxn/inputs/python-numpy-doc_2.2.4+ds-1.2_all.deb +++ /srv/rebuilderd/tmp/rebuilderdGn0Pxn/out/python-numpy-doc_2.2.4+ds-1.2_all.deb ├── file list │ @@ -1,3 +1,3 @@ │ -rw-r--r-- 0 0 0 4 2025-09-05 09:56:25.000000 debian-binary │ --rw-r--r-- 0 0 0 64948 2025-09-05 09:56:25.000000 control.tar.xz │ --rw-r--r-- 0 0 0 5743052 2025-09-05 09:56:25.000000 data.tar.xz │ +-rw-r--r-- 0 0 0 64944 2025-09-05 09:56:25.000000 control.tar.xz │ +-rw-r--r-- 0 0 0 5743140 2025-09-05 09:56:25.000000 data.tar.xz ├── control.tar.xz │ ├── control.tar │ │ ├── ./md5sums │ │ │ ├── ./md5sums │ │ │ │┄ Files differ ├── data.tar.xz │ ├── data.tar │ │ ├── file list │ │ │ @@ -2579,15 +2579,15 @@ │ │ │ -rw-r--r-- 0 root (0) root (0) 42758 2025-09-05 09:56:25.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.wald.html │ │ │ -rw-r--r-- 0 root (0) root (0) 47423 2025-09-05 09:56:25.000000 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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)
│ │ │     ...: 
│ │ │ -1.31 ms +- 15 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -2.11 ms +- 37.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +1.39 ms +- 60.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +2.54 ms +- 53.5 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)
│ │ │     ...: 
│ │ │ -616 us +- 14.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -1.5 ms +- 9.29 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +632 us +- 49.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +1.78 ms +- 68.5 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)
│ │ │     ...: 
│ │ │ -2.35 ms +- 16 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ -4.2 ms +- 29.1 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +2.9 ms +- 124 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ +4.73 ms +- 154 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([0.66537084, 0.12901083, 0.0876149 ])
│ │ │ +Out[7]: array([0.8465594 , 0.98310531, 0.85265632])
│ │ │  
│ │ │  In [8]: rng.random(3, dtype=np.float32)
│ │ │ -Out[8]: array([0.17078167, 0.42007202, 0.18308043], dtype=float32)
│ │ │ +Out[8]: array([0.80359584, 0.09596461, 0.28391868], dtype=float32)
│ │ │  
│ │ │  In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
│ │ │ -Out[9]: array([ 24, 123,  84], dtype=uint8)
│ │ │ +Out[9]: array([195,   0, 150], dtype=uint8)
│ │ │  
│ │ │
│ │ │