NumPy-Discussion August 2014

numpy-discussion@python.org

55 participants
47 discussions

by Jurgens de Bruin

Hi, I am new to numpy so any help would be greatly appreciated. I have two arrays: array1 = np.arange(1,100+1) array2 = np.arange(1,50+1) How can I calculate/determine if array2 is a subset of array1 (falls within array 1) Something like : array2 in array1 = TRUE for the case above. Thank

7 years, 5 months

last call for numpy 1.8.2 bugfixes

by Julian Taylor

hi, as numpy 1.9 is going to be a relative hard upgrade as indexing changes expose a couple bugs in third party packages and the large amount of small little incompatibilities I will create a numpy 1.8.2 release tomorrow with a couple of important or hard to work around bugfixes. The most important bugfix is fixing the wrong result partition with multiple selections could produce if selections ended up in an equal range, see https://github.com/numpy/numpy/issues/4836 (if the crash is still unreproducable, help appreciated). the rest of the fixes are small ones listed below. If I have missed one or you consider one of the fixes to invasive for a bugfix release please speak up now. As the number of fixes is small I will skip a release candidate. Make fftpack._raw_fft threadsafe https://github.com/numpy/numpy/issues/4656 Prevent division by zero https://github.com/numpy/numpy/issues/650 Fix lack of NULL check in array_richcompare https://github.com/numpy/numpy/issues/4613 incorrect argument order to _copyto in in np.nanmax, np.nanmin https://github.com/numpy/numpy/issues/4628 Hold GIL for types with fields, fixes https://github.com/numpy/numpy/issues/4642 svd ufunc typo https://github.com/numpy/numpy/issues/4733 check alignment of strides for byteswap https://github.com/numpy/numpy/issues/4774 add missing elementsize alignment check for simd reductions https://github.com/numpy/numpy/issues/4853 ifort has issues with optimization flag /O2 https://github.com/numpy/numpy/issues/4602

7 years, 5 months

Requesting Code Review of weighted covariance ENH

by tpoole

Hi everyone, I've added the ability to handle weighted data in a covariance calculation, in a similar manner to that already implemented for the calculation of a weighted average. https://github.com/tpoole/numpy/compare/weighted_cov Could an experienced someone please look over my changes before I submit a pull request? Validation of the formula can be found at "Exponential smoothing weighted correlations", F. Pozzi, T. Matteo, and T. Aste, Eur. Phys. J. B. 85, 175 (2012). --- though it is unfortunately paywalled and "An Analysis of WinCross, SPSS, and Mentor Procedures for Estimating the Variance of a Weighted Mean", A. Madansky and H. G. B. Alexander, www.analyticalgroup.com/download/weighted_variance.pdf for the "effective number of samples". Thanks, Tom -- View this message in context: http://numpy-discussion.10968.n7.nabble.com/Requesting-Code-Review-of-weigh… Sent from the Numpy-discussion mailing list archive at Nabble.com.

7 years, 5 months

Class to experiment with '@'

by Charles R Harris

Hi All, I've attached a subclass of ndarray that implements the new '@' operator for experimentation and comment. It is only intended for playing with that operator and may not work for other things. You will need to install python 3.5.0a1 to play with it. Chuck

7 years, 5 months

It lives! Or at least is not undead

by Charles R Harris

charris@localhost [matmul (master)]$ python3.5 Python 3.5.0a0 (default:4425024f2e01, Aug 2 2014, 10:10:31) [GCC 4.8.3 20140624 (Red Hat 4.8.3-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as np >>> import testing >>> a = np.ones(3).view(testing.marray) >>> a@3 marray([ 3., 3., 3.]) >>> 3@a marray([ 3., 3., 3.]) >>> a(a)np.eye(3) marray([ 1., 1., 1.]) >>> np.eye(3)@a marray([ 1., 1., 1.]) This was just for quick experimentation. Chuck

7 years, 5 months

Remove numpy/compat/_inspect.py ?

by Charles R Harris

Hi All, The _inspect.py function looks like a numpy version of the python inspect function. ISTR that is was a work around for problems with the early python versions, but that would have been back in 2009. Thoughts? Chuck

7 years, 5 months

OSX wheels for older numpy versions on pypi

by Matthew Brett

Hi, I took the liberty of uploading OSX wheels for some older numpy versions to pypi. These can be useful for testing, or when building your own wheels to be compatible with earlier numpy versions - see: http://stackoverflow.com/questions/17709641/valueerror-numpy-dtype-has-the-… There are currently wheels for numpy 1.5.1 py27 numpy 1.6.0 py27 numpy 1.6.1 py27 numpy 1.7.1 py27, 32, 33, 34 These are all compiled against ATLAS: https://github.com/matthew-brett/numpy-atlas-binaries install with e.g. pip install numpy==1.6.1 If anyone needs other wheels compiled, let me know, I'll try and upload them, Cheers, Matthew

7 years, 5 months

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

NumPy-Discussion August 2014