[Matplotlib-users] odd behavior with 'nearest' interpolation

[Jens Nielsen]

It seem like this is a genuine bug but I am not sure how to fix it. Can you
submit a bug report at Github so we are sure that this is captured? At
github you can attach pictures

Best Jens


On Thu, 19 Nov 2015 at 14:25 Smit, Christine E. (GSFC-610.2)[TELOPHASE
CORP] <christine.e.smit at nasa.gov> wrote:

> Yes. It works with 'none.' The problem is that sometimes we need to create
> images with low inflation factors. So, our data is nxm data points and we
> want a 2nx2m image or a 3nx3m image. We're currently getting around this
> bug by using 'none' to create an nxm image and then using imagemagick's
> convert to resize.
>
> From: Jens Nielsen <jenshnielsen at gmail.com>
> Date: Wednesday, November 18, 2015 at 6:28 AM
> To: Nathan Goldbaum <nathan12343 at gmail.com>, csmit <
> christine.e.smit at nasa.gov>
> Cc: "matplotlib-users at python.org" <matplotlib-users at python.org>
> Subject: Re: [Matplotlib-users] odd behavior with 'nearest' interpolation
>
> I can confirm this. The issue is notable with a dpi lower than 10 or so
> and seems to get worse as it is lowered towards 1.
> Can you try plotting the image with interpolation='none' If I do that I
> get the correct behaviour. 'none' is probably the correct setting if you
> wish to match
> image matrix 1to1 to png coords anyway.
>
> @nathan The image in the notebook is plotted with a different dpi and
> works correctly.
>
> best Jens
>
> On Tue, 17 Nov 2015 at 22:38 Nathan Goldbaum <nathan12343 at gmail.com>
> wrote:
>
>> This seems to be working ok for me:
>> https://gist.github.com/faa6b4008a8e3db68f46
>>
>> On Tue, Nov 17, 2015 at 4:22 PM, Smit, Christine E.
>> (GSFC-610.2)[TELOPHASE CORP] <christine.e.smit at nasa.gov> wrote:
>>
>>> Hi! I am using matplotlib v 1.4.3 with Python 2.7.10 :: Anaconda 2.4.0
>>> (64-bit).
>>>
>>> What I am doing here is creating a 7x7 pixel image from a 7x7 matrix. I
>>> expect to see one pixel per data point, but that's not what I'm seeing.
>>> Instead of a diagonal make up of single pixels, I get an odd 2x2 pixel blob
>>> in the middle of the correct one pixel diagonal.
>>>
>>>
>>> ---------------------------------------------------------------------------
>>> import numpy as np
>>> import matplotlib.pylab as plt
>>>
>>>
>>> if __name__ == "__main__":
>>>     n = 7
>>>     data = np.identity(n, float)
>>>
>>>     # Create an nxn size figure with no frame
>>>     fig = plt.figure(figsize=(n, n), frameon=False)
>>>
>>>     # make the axes to the edge of the figure
>>>     ax = plt.Axes(fig, [0.0, 0.0, 1.0, 1.0])
>>>     # turn the axes off
>>>     ax.set_axis_off()
>>>     # add the axes to this figure
>>>     fig.add_axes(ax)
>>>     # show the data. Don't do any interpolation.
>>>     ax.imshow(data, interpolation='nearest',
>>> origin='lower',aspect='auto')
>>>     # Save the figure at 1 dot per inch, which should mean 1 data point
>>> per
>>>     # pixel
>>>     fig.savefig("image.png", dpi=1)
>>>
>>>
>>> ---------------------------------------------------------------------------
>>>
>>> Since I'm not sure that if I can attach the png image I get, here is a
>>> ppm version of the image I get (between the ------). Save this image.ppm
>>> minus the dashes and you should be able to open it in something like gimp.
>>>
>>>
>>> ---------------------------------------------------------------------------
>>> P3
>>> # CREATOR: GIMP PNM Filter Version 1.1
>>> 7 7
>>> 255
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 127
>>> 0
>>> 0
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>> 0
>>> 0
>>> 127
>>>
>>> ---------------------------------------------------------------------------
>>>
>>> Thanks.
>>> Christine
>>>
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>>> Matplotlib-users at python.org
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