Hi,
I've discovered that numpy array printing is slightly different for different platforms (might be related to platform-dependent types somehow - https://www.ibm.com/support/knowledgecenter/en/SSFKSJ_9.0.0/com.ibm.mq.ref.dev.doc/q104610_.htm).
This inconsistency makes the testing of docstring examples challenging (scikit-image/scikit-image#3873) across the operating systems.
Reproducing code example:
Notice the dtype part:
import numpy as np
### Windows 32bit
np.set_printoptions(legacy='1.13')
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0], dtype=int32)
np.set_printoptions(legacy=False)
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0], dtype=int32)
### Windows 64bit
np.set_printoptions(legacy='1.13')
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0], dtype=int64)
np.set_printoptions(legacy=False)
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0], dtype=int32)
### Linux / MacOS 64bit
np.set_printoptions(legacy='1.13')
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0])
np.set_printoptions(legacy=False)
np.argmin([[0,2,-1,3], [1,2,3,4]], axis=1)
# array([2, 0])
Numpy/Python version information:
(for Windows VM)
1.16.3, wide range of Python 3 versions
(for MacOS PC)
1.16.2 3.6.8 |Anaconda, Inc.| (default, Dec 29 2018, 19:04:46)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
Hi,
I've discovered that
numpyarray printing is slightly different for different platforms (might be related to platform-dependent types somehow - https://www.ibm.com/support/knowledgecenter/en/SSFKSJ_9.0.0/com.ibm.mq.ref.dev.doc/q104610_.htm).This inconsistency makes the testing of docstring examples challenging (scikit-image/scikit-image#3873) across the operating systems.
Reproducing code example:
Notice the
dtypepart:Numpy/Python version information:
(for Windows VM)
1.16.3, wide range of Python 3 versions
(for MacOS PC)
1.16.2 3.6.8 |Anaconda, Inc.| (default, Dec 29 2018, 19:04:46)
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]