[SciPy-Dev] Scipy Windows wheels

[Pauli Virtanen]

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Dear all,

Prerelease binary wheels for Scipy on Windows 32-bit & 64-bit are now
available in case you would like to test them.

Currently, the plans are that binary wheels will also be provided for
future releases on PyPi, so that you will be able to do simply "pip
install scipy" also on Windows. At least, assuming we manage to test
these wheels well enough for which help would be useful.

You can install the scipy prerelease packages as shown below. Note that
they are meant for testing only, and correspond to the current Scipy
development version. Please report issues found on the Scipy issue
tracker on github (be sure to mention how you installed scipy and
python).

The wheels are meant to be used with the Python obtained from https://p
ython.org --- these are not meant to be used with e.g. Conda, although
it may be they work.

The work leading to a viable automatized compilation approach was done
in
https://github.com/scipy/scipy/pull/7616
https://github.com/numpy/numpy/pull/9431

Example:

C:\Users\pauli\src\env2\Scripts>pip install -f https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com/ --pre scipy
Collecting scipy
  Downloading https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com/scipy-1.0.0.dev0+20170824221943_2a1fdcf-cp36-none-win32.whl (26.0MB)
    100% |████████████████████████████████| 26.0MB 47kB/s
Collecting numpy>=1.8.2 (from scipy)
  Downloading https://7933911d6844c6c53a7d-47bd50c35cd79bd838daf386af554a83.ssl.cf2.rackcdn.com/numpy-1.14.0.dev0+20170824081646_707f33f-cp36-none-win32.whl (6.8MB)
    100% |████████████████████████████████| 6.9MB 168kB/s
Installing collected packages: numpy, scipy
Successfully installed numpy-1.14.0.dev0+707f33f scipy-1.0.0.dev0+2a1fdcf

C:\Users\pauli\src\env2\Scripts>python
Python 3.6.2 (v3.6.2:5fd33b5, Jul  8 2017, 04:14:34) [MSC v.1900 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import scipy.integrate, scipy.linalg, numpy as np
>>> scipy.integrate.quad(lambda x: 1/(1 + x**2), -np.inf, np.inf)
(3.141592653589793, 5.155583041103855e-10)
>>> scipy.linalg.eigvals([[1,0],[0,2]])
array([ 1.+0.j,  2.+0.j])
>>> exit()

- -- 
Pauli Virtanen
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