Re: [Numpy-discussion] Numpy compilation error

2015-04-12 Thread Pauli Virtanen
12.04.2015, 17:15, Peter Kerpedjiev kirjoitti: [clip] > numpy/random/mtrand/distributions.c:892:1: internal compiler error: > Illegal instruction An internal compiler error means your compiler (in this case, gcc) is broken. The easiest solution is to use a newer version of the compiler, assuming t

[Numpy-discussion] Numpy compilation error

2015-04-12 Thread Peter Kerpedjiev
Dear all, Upon trying to install numpy using 'pip install numpy' in a virtualenv, I get the following error messages: creating build/temp.linux-x86_64-2.7/numpy/random/mtrand compile options: '-D_FILE_OFFSET_BITS=64 -D_LARGEFILE_SOURCE=1 -D_LARGEFILE64_SOURCE=1 -Inumpy/core/include -Ibuild/

Re: [Numpy-discussion] Automatic number of bins for numpy histograms

2015-04-12 Thread Ralf Gommers
On Sun, Apr 12, 2015 at 9:45 AM, Jaime Fernández del Río < jaime.f...@gmail.com> wrote: > On Sun, Apr 12, 2015 at 12:19 AM, Varun wrote: > >> >> http://nbviewer.ipython.org/github/nayyarv/matplotlib/blob/master/examples/sta >> tistics/A >>

Re: [Numpy-discussion] Automatic number of bins for numpy histograms

2015-04-12 Thread Varun
Using a URL shortener for the notebook to get around the 80 char width limit http://goo.gl/JmfTRJ ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Automatic number of bins for numpy histograms

2015-04-12 Thread Jaime Fernández del Río
On Sun, Apr 12, 2015 at 12:19 AM, Varun wrote: > > http://nbviewer.ipython.org/github/nayyarv/matplotlib/blob/master/examples/sta > tistics/A utomating%20Binwidth%20Choice%20for%20Histogram.ipynb > > Long story short, histogram visualisations that depend on numpy (such as > matplotlib, or nearly

[Numpy-discussion] Automatic number of bins for numpy histograms

2015-04-12 Thread Varun
http://nbviewer.ipython.org/github/nayyarv/matplotlib/blob/master/examples/sta tistics/A utomating%20Binwidth%20Choice%20for%20Histogram.ipynb Long story short, histogram visualisations that depend on numpy (such as matplotlib, or nearly all of them) have poor default behaviour as I have to const