--- /srv/reproducible-results/rbuild-debian/r-b-build.zv0zGDGB/b1/numpy_2.2.4+ds-1_amd64.changes +++ /srv/reproducible-results/rbuild-debian/r-b-build.zv0zGDGB/b2/numpy_2.2.4+ds-1_amd64.changes ├── Files │ @@ -1,5 +1,5 @@ │ │ - c35ed55b01dd29be96d90852710caec7 5813716 doc optional python-numpy-doc_2.2.4+ds-1_all.deb │ + 0a12412148f1c2ce0de727bf9e7176bb 5813704 doc optional python-numpy-doc_2.2.4+ds-1_all.deb │ 5a54d7696adae46a1a8a9d77650829a3 30547976 debug optional python3-numpy-dbgsym_2.2.4+ds-1_amd64.deb │ 0da7207383b6ebbcbe9edbf5799379ed 138728 python optional python3-numpy-dev_2.2.4+ds-1_amd64.deb │ 11f487ebe5931c76367cbca87f910faf 5095276 python optional python3-numpy_2.2.4+ds-1_amd64.deb ├── 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 64876 2025-04-01 19:45:23.000000 control.tar.xz │ │ --rw-r--r-- 0 0 0 5748648 2025-04-01 19:45:23.000000 data.tar.xz │ │ +-rw-r--r-- 0 0 0 5748636 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 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.weibull.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 45546 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/random/generated/numpy.random.zipf.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 82403 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/random/generator.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 45982 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/random/index.html │ │ │ │ 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-rw-r--r-- 0 root (0) root (0) 45998 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.array-creation.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 50957 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.array-manipulation.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 27535 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.bitwise.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 54450 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/reference/routines.char.html │ │ │ │ @@ -2755,15 +2755,15 @@ │ │ │ │ -rw-r--r-- 0 root (0) root (0) 31655 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.1-notes.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 32348 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.2-notes.html │ │ │ │ -rw-r--r-- 0 root (0) root (0) 32865 2025-04-01 19:45:23.000000 ./usr/share/doc/python-numpy/html/release/2.2.3-notes.html │ │ │ │ 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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)
│ │ │ │     ...: 
│ │ │ │ -1.36 ms +- 32.2 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ -2.59 ms +- 21.8 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +1.36 ms +- 23 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +2.6 ms +- 16.8 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)
│ │ │ │     ...: 
│ │ │ │ -725 us +- 6.87 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ -1.92 ms +- 9.03 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +730 us +- 10.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +1.91 ms +- 8.68 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)
│ │ │ │     ...: 
│ │ │ │ -2.68 ms +- 16.5 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ -5.03 ms +- 12 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +2.67 ms +- 8.9 us per loop (mean +- std. dev. of 7 runs, 1 loop each)
│ │ │ │ +5.04 ms +- 4.78 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([0.57953394, 0.70265076, 0.73784974])
│ │ │ │ +Out[7]: array([0.32847107, 0.72205218, 0.28660008])
│ │ │ │  
│ │ │ │  In [8]: rng.random(3, dtype=np.float32)
│ │ │ │ -Out[8]: array([0.38022113, 0.21040004, 0.7271973 ], dtype=float32)
│ │ │ │ +Out[8]: array([0.86483556, 0.96224266, 0.9081517 ], dtype=float32)
│ │ │ │  
│ │ │ │  In [9]: rng.integers(0, 256, size=3, dtype=np.uint8)
│ │ │ │ -Out[9]: array([233,  25, 245], dtype=uint8)
│ │ │ │ +Out[9]: array([169, 179, 147], dtype=uint8)
│ │ │ │  
│ │ │ │
│ │ │ │