ascontiguousarray/asfortranarray take longer than flag check on matching order arrays

It appears to take ~1µs to call ascontiguousarray or asfortranarray on an array that already matches the expected order. Simply checking the order of the array and skipping if satisfied takes ~90ns. It’s a little surprising that ascontiguousarray or asfortranarray take a little over an order of magnitude longer. Any thoughts why?

Reproducing code example:

In [1]: import numpy as np

In [2]: a = np.random.random((200, 300)).copy(order='C')

In [3]: %%timeit
   ...: np.ascontiguousarray(a)
   ...: 
1.48 µs ± 93.1 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [4]: %%timeit
   ...: if not a.flags.c_contiguous:
   ...:     np.ascontiguousarray(a)
   ...: 
86.8 ns ± 1.72 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [5]: b = np.random.random((200, 300)).copy(order='F')

In [6]: %%timeit
   ...: np.asfortranarray(b)
   ...: 
1.38 µs ± 15.4 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [7]: %%timeit
   ...: if not b.flags.f_contiguous:
   ...:     np.asfortranarray(b)
   ...: 
87.8 ns ± 1.89 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

Numpy/Python version information:

1.15.1 3.6.6 | packaged by conda-forge | (default, Jul 26 2018, 09:55:02) 
[GCC 4.2.1 Compatible Apple LLVM 6.1.0 (clang-602.0.53)]

[seberg]

This has been fixed by my new argparsing code and moving these array “aliases” to C.

[jakirkham]

Oh sorry 🤦‍♂️

Yep looks great! 🎉

In [1]: import numpy as np

In [2]: a = np.random.random((200, 300)).copy(order='C')

In [3]: %%timeit
   ...: np.ascontiguousarray(a)
   ...: 
   ...: 
70.3 ns ± 0.192 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)

In [4]: b = np.random.random((200, 300)).copy(order='F')

In [5]: %%timeit
   ...: np.asfortranarray(b)
   ...: 
   ...: 
68.4 ns ± 0.427 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)