On 01/02/2012 05:51 PM, Tony Yu wrote:
>
>
> On Mon, Jan 2, 2012 at 3:33 PM, Eric Firing <[email protected]
> <mailto:[email protected]>> wrote:
>
> On 12/30/2011 01:57 PM, Paul Ivanov wrote:
>
> Eric Firing, on 2011-12-27 15:31, wrote:
>
> It looks like this is something I can fix by modifying
> ListedColormap.
> It is discarding the alpha values, and I don't think there
> is any reason
> it needs to do so.
>
>
> One of my first attempts at a contribution to matplotlib three
> years ago was related to this. It was in reply to a similar
> question on list, and I wrote a patch, but never saw it through
> to inclusion because it wasn't something I needed.
>
>
> http://www.mail-archive.com/__matplotlib-users@lists.__sourceforge.net/msg09216.html
>
> <http://www.mail-archive.com/[email protected]/msg09216.html>
>
> I think it's a helpful starting point, as I include a discussion
> on the limitation of mpl colormaps there.
>
>
> I'm switching this to the devel list.
>
> Please try
> https://github.com/efiring/__matplotlib/tree/colormap_alpha
> <https://github.com/efiring/matplotlib/tree/colormap_alpha>
> which has changes similar to yours so that alpha is fully changeable
> in colormaps.
>
> I think this is going to be OK as far as the colormap end of things
> is concerned, but it turns up a new problem related to alpha in
> images, and reminds us of an old problem with alpha in agg, at
> least. The problems are illustrated in the attached modification of
> the custom_cmap.py example. I added a fourth panel for testing
> alpha. Look at the comments on the code for that panel, and try
> switching between pcolormesh and imshow. Pcolormesh basically works
> as expected, except for the prominent artifacts on patch boundaries
> (visible also in the colorbar for that panel). These boundary
> artifacts are the old problem. The new problem is that imshow with
> alpha in the colormap is completely wonky with a white background,
> but looks more normal with a black background--which is not so good
> if what you really want is a white background showing through the
> transparency.
>
> Eric
>
>
> This is great! I had hacked together a custom colormap class and
> overrode its __call__ method to get a similar effect. This solution is
> much more elegant and general.
>
> As for the imshow issue, it seems to be an issue with the "nearest"
> interpolation method. The example copied below shows the result for
> three different interpolation methods. The weird behavior only occurs
> when interpolation is set to 'nearest' (I checked all other
> interpolation methods, not just the 3 below). What's really strange is
> that `interpolation='none'` gives the expected result, but in theory,
> 'none' maps to the same interpolation function as 'nearest'. A quick
> scan of matplotlib.image suggests that 'none' and 'nearest' share the
> same code path, but I'm obviously missing something.
It looks to me like 'none' is going through _draw_unsampled_image
instead of the path that all the other interpolations, including
'nearest' go through. I think that JJ put in this unsampled
functionality about two years ago. I've never dug into the guts of
image operations and rendering, so I don't even understand what sort of
"sampling" is referred to here.
Eric
>
> -Tony
>
> #~~~~
> import matplotlib.pyplot as plt
>
>
> cdict = {'red': ((0.0, 0.0, 0.0),
> (0.5, 0.8, 1.0),
> (1.0, 0.4, 1.0)),
>
> 'green': ((0.0, 0.0, 0.0),
> (0.5, 0.9, 0.9),
> (1.0, 0.0, 0.0)),
>
> 'blue': ((0.0, 0.0, 0.4),
> (0.5, 1.0, 0.8),
> (1.0, 0.0, 0.0)),
>
> 'alpha': ((0.0, 1.0, 1.0),
> (0.5, 0.3, 0.3),
> (1.0, 1.0, 1.0))}
>
> plt.register_cmap(name='BlueRedAlpha', data=cdict)
>
>
> if __name__ == '__main__':
> import numpy as np
>
> w = 10
> y = np.linspace(0, 2*np.pi, w+1)
> Z = np.tile(y, (w+1, 1))
>
> plt.rcParams['image.cmap'] = 'BlueRedAlpha'
>
> f, axes = plt.subplots(ncols=3)
> interp_method = ['none', 'bilinear', 'nearest']
> for interp, ax in zip(interp_method, axes):
> # Draw a line with low zorder so it will be behind the image.
> ax.plot([0, w], [0, w], color='c', lw=20, zorder=-1)
> ax.imshow(Z, interpolation=interp)
> ax.set_title(interp)
>
> plt.show()
> #~~~~
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