matplotlib-devel Mailing List for matplotlib

Pull request at <https://github.com/matplotlib/matplotlib/pull/616>
Christoph
On 12/9/2011 12:11 PM, Christoph Gohlke wrote:
> Hello,
>
> while working on the scikits-image io plugin system, I noticed some
> issues with matplotlib's imread function. I have a patch for all these
> issues and will submit a PR but wanted to check on the list first.
>
>
> 1) imread does not properly detect the file type if an open file handle
> is used.
>
>>>> lena = pylab.imread(matplotlib.cbook.get_sample_data('lena.jpg'))
> Traceback (most recent call last):
> File "<stdin>", line 1, in<module>
> File "X:\Python27\lib\site-packages\matplotlib\pyplot.py", line 1740,
> in imread
> return _imread(*args, **kwargs)
> File "X:\Python27\lib\site-packages\matplotlib\image.py", line 1207,
> in imread
> return handler(fname)
> RuntimeError: _image_module::readpng: file not recognized as a PNG file
>
>
> 2) imread does not properly convert uint16 images to uint8 as reported
> on the scikits-image ML
> <https://groups.google.com/forum/#!topic/scikits-image/O47tNU01kLA>.
>
>
> 3) Any non-PNG image loaded with imread is displayed upside-down in imshow:
>
>>>> imshow(imread("lena.jpg")); show()
>
> A solution is to pass `origin='lower'` to imshow for non-PNG images. But
> is there a reason for this asymmetry? Any image loaded with PIL is
> explicitly flipped upside-down at
> <https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/image.py#L1267>.
>
>
> Christoph
>
>
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>

Ben,
Thanks for the reply. I definitely like your idea. Seems like we could
include some logic in axes.errorbar to look at the shapes of xerr and
yerr in a similar fashion to what I propose for axes.boxplots,
allowing the user to have custom lower and upper errors for each data
point (in a time series as I would use it).
I'll try to bang that out this weekend while this is still fresh.
-p
On Fri, Dec 9, 2011 at 1:29 PM, Benjamin Root <ben...@ou...> wrote:
> On Thu, Dec 8, 2011 at 10:45 AM, Paul Hobson <pmh...@gm...> wrote:
>>
>> Matplotlib gurus:
>>
>> I took at stab at the git work flow and incorporated my personal
>> modifications to the boxplot function. Github's diff can be found
>> here:
>> https://github.com/phobson/matplotlib/compare/master...manual_boxplots
>>
>> In summary, if your data is MxN, you can manually specify medians and
>> the confidence intervals around the medians using Nx1 and Nx2 arrays,
>> respectively. Alternatively, you can use lists or tuples and use Nones
>> if you want to specify those values only for some columns in your MxN
>> data set. In other words, with an Mx5 data array, you can specify
>> conf_intervals=[(ci1a,ci2a), (ci1b,ci2b), (ci1c,ci2c), None,
>> (ci1e,ci2e)]. Within the conf_intervals "array", the CIs can be listed
>> in any order as I use np.max() and np.min() to pull the upper and
>> lower values, respectively.
>>
>> The motivation behind this is that sometimes I need the confidence
>> levels to be different than 95%, and also that I compute those
>> confidence intervals with a bootstrapping routine that is more robust
>> than mpl-compatible one I submitted some time ago.
>>
>> I hope y'all find this to be a useful contribution. I'm an avid
>> matplotlib user. It really is a wonderful tool.
>>
>> Cheers,
>> paul h
>>
>
> Paul,
>
> Interesting. I haven't had much time to really look over your changes, but
> I have been wondering if the errorbar() and boxplot() functions could be
> treated as two different ways to display similar information. Therefore,
> perhaps their call signatures could be made more similar to each other.
> What do you think?
>
> Ben Root
>

On Thu, Dec 8, 2011 at 10:45 AM, Paul Hobson <pmh...@gm...> wrote:
> Matplotlib gurus:
>
> I took at stab at the git work flow and incorporated my personal
> modifications to the boxplot function. Github's diff can be found
> here:
> https://github.com/phobson/matplotlib/compare/master...manual_boxplots
>
> In summary, if your data is MxN, you can manually specify medians and
> the confidence intervals around the medians using Nx1 and Nx2 arrays,
> respectively. Alternatively, you can use lists or tuples and use Nones
> if you want to specify those values only for some columns in your MxN
> data set. In other words, with an Mx5 data array, you can specify
> conf_intervals=[(ci1a,ci2a), (ci1b,ci2b), (ci1c,ci2c), None,
> (ci1e,ci2e)]. Within the conf_intervals "array", the CIs can be listed
> in any order as I use np.max() and np.min() to pull the upper and
> lower values, respectively.
>
> The motivation behind this is that sometimes I need the confidence
> levels to be different than 95%, and also that I compute those
> confidence intervals with a bootstrapping routine that is more robust
> than mpl-compatible one I submitted some time ago.
>
> I hope y'all find this to be a useful contribution. I'm an avid
> matplotlib user. It really is a wonderful tool.
>
> Cheers,
> paul h
>
>
Paul,
Interesting. I haven't had much time to really look over your changes, but
I have been wondering if the errorbar() and boxplot() functions could be
treated as two different ways to display similar information. Therefore,
perhaps their call signatures could be made more similar to each other.
What do you think?
Ben Root

Hello,
while working on the scikits-image io plugin system, I noticed some 
issues with matplotlib's imread function. I have a patch for all these 
issues and will submit a PR but wanted to check on the list first.
1) imread does not properly detect the file type if an open file handle 
is used.
>>> lena = pylab.imread(matplotlib.cbook.get_sample_data('lena.jpg'))
Traceback (most recent call last):
 File "<stdin>", line 1, in <module>
 File "X:\Python27\lib\site-packages\matplotlib\pyplot.py", line 1740, 
in imread
 return _imread(*args, **kwargs)
 File "X:\Python27\lib\site-packages\matplotlib\image.py", line 1207, 
in imread
 return handler(fname)
RuntimeError: _image_module::readpng: file not recognized as a PNG file
2) imread does not properly convert uint16 images to uint8 as reported 
on the scikits-image ML 
<https://groups.google.com/forum/#!topic/scikits-image/O47tNU01kLA>.
3) Any non-PNG image loaded with imread is displayed upside-down in imshow:
>>> imshow(imread("lena.jpg")); show()
A solution is to pass `origin='lower'` to imshow for non-PNG images. But 
is there a reason for this asymmetry? Any image loaded with PIL is 
explicitly flipped upside-down at 
<https://github.com/matplotlib/matplotlib/blob/master/lib/matplotlib/image.py#L1267>.
Christoph