matplotlib-devel Mailing List for matplotlib

Hello,
To be available from the Debian main archive, we need to provide a
tarball containing all source code, something that later generates a
"binary" content or in a form that's what called "the preferred form
of modification" (f.e. in a sphinx doc, it's the rst files, not the
html files resulting from the building process, since the preferred
form of modification of that documentation is thru the rst files).
The question comes directly to the datafile: is the file format
datafiles are shipped the preferred form of modification? if yes, what
are the ways/tools to modify those files? Moreover, what are the
sources of those files (i.e. where are they downloaded from)?
I know that it might seem a bit picky, from a Debian POV, these
information are really important.
Thanks & Cheers,
-- 
Sandro Tosi (aka morph, morpheus, matrixhasu)
My website: http://matrixhasu.altervista.org/
Me at Debian: http://wiki.debian.org/SandroTosi

On 9/19/11 2:54 PM, Sandro Tosi wrote:
> Hello,
> it seems nad2bin is not installed, but only compiled. Is that
> expected? Should it be installed by hand instead of by setup.py
> install?
>
> Cheers,
It does not need to be installed.
-Jeff
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jef...@no...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

Hello,
it seems nad2bin is not installed, but only compiled. Is that
expected? Should it be installed by hand instead of by setup.py
install?
Cheers,
-- 
Sandro Tosi (aka morph, morpheus, matrixhasu)
My website: http://matrixhasu.altervista.org/
Me at Debian: http://wiki.debian.org/SandroTosi

Hello,
the doc build process is a lot of hand-crafting work and it also
requires the full source of matplotlib just to get a couple of
directory.
I know that code duplication sucks, but I'd suggest to include
directly into the basemap dir all the files (sphinxext & _static at
the very least) needed for the doc compilation. This would help a lot
the distributions (Debian of course :) ).
Thanks for considering,
-- 
Sandro Tosi (aka morph, morpheus, matrixhasu)
My website: http://matrixhasu.altervista.org/
Me at Debian: http://wiki.debian.org/SandroTosi

On 9/18/11 8:49 AM, Sandro Tosi wrote:
> Hi,
> when running
>
> python setup.py clean
>
> nad2bin is compiled. I've just worked around with the attached patch,
> so it would be nice if you can integrate it upstream or come up with a
> better solution.
>
> Regards,
Applied your patch to github master. Will be in 1.0.2, which I will 
release soon.
-Jeff
-- 
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jef...@no...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg

On 9/18/2011 2:30 PM, Eric Firing wrote:
> On 09/18/2011 09:30 AM, Christoph Gohlke wrote:
>> Hello,
>>
>> matplotlib uses int(x*255) or np.array(x*255, np.uint8) to quantize
>> normalized floating point numbers x in the range [0.0 to 1.0] to
>> integers in the range [0 to 255]. This way only 1.0 is mapped to 255,
>> not for example 0.999. Is this really intended or would not the largest
>> floating point number below 256.0 be a better scale factor than 255? The
>> exact factor depends on the floating point precision (~255.999992 for
>> np.float32, ~255.93 for np.float16).
>>
>> Christoph
>
> Christoph,
>
> It's a reasonable question; but do you have use cases in mind where it
> actually makes a difference?
>
> The simple scaling with truncation is used in many places, both in the
> python and the c++ code.
>
> Eric
>
Hi Eric,
visually it will be hardly noticeable in most cases. However, I'd expect 
the histogram of normalized intensity data to be the same as the 
histogram of a linear grayscale image of that data (neglecting gamma 
correction, image scaling/interpolation for now). Consider this code for 
example:
import numpy as np
a = np.random.rand(1024*1024)
a[0], a[-1] = 0.0, 1.0
h0 = np.histogram(a, bins=256, range=(0, 1))[0]
h1 = np.bincount(np.uint8(a * 255))
h2 = np.bincount(np.uint8(a * 255.9999999999999))
print (h0 - h1)
print (h0 - h2)
Christoph

John Hunter <jd...@gm...> writes:
> I confirmed this on linux and added an issue on github:
> https://github.com/matplotlib/matplotlib/issues/478. 
Pull request #479 fixes the immediate problem, but for the long term we
should fix the handling of redirection in get_sample_data.
-- 
Jouni K. Seppänen
http://www.iki.fi/jks