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astrofrog opened this issue Feb 5, 2018 · 5 comments
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Issue when setting scatter color in separate method call #10381

astrofrog opened this issue Feb 5, 2018 · 5 comments
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v3.1.0

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@astrofrog
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For an application I am developing, I need to call ax.scatter then change the colors of the points after the fact. However, if the input x/y values contain NaN values, then setting the colors after the fact doesn't work properly. The following example:

import matplotlib
matplotlib.use('Agg')

import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, aspect='auto')

x = np.array([1, 2, 3])
y = np.array([2, np.nan, 4])
c = np.array([3, 4, 5])

s = ax.scatter(x, y, c=c, vmin=3, vmax=5, s=320, zorder=1, edgecolor='none')

s = ax.scatter(x, y, s=160, zorder=2, edgecolor='none')
s.set_array(c)
s.set_clim(3, 5)

s = ax.scatter([], [], s=40, zorder=3, edgecolor='none')
s.set_offsets(np.vstack([x, y]).transpose())
s.set_array(c)
s.set_clim(3, 5)

fig.savefig('test.png')

produces the following output:

test

The top right point should be all yellow. However it isn't, because in the scatter call, invalid values are removed, so when I try and set the colors with set_array, the color values are offset from the x/y values. In the final case, things work since the 'bad' values are masked from neither the offsets nor the colors.

Is there a way to achieve something along the lines of the middle example (setting the colors after the fact)
@tacaswell
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This is because we create the initial artist and strip out any points that have nan in them. The new colors will then be offset from where you think they should be.

attn @efiring as you have been working around this code recently.

@tacaswell tacaswell added this to the v3.0 milestone Feb 5, 2018
@efiring
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efiring commented Feb 5, 2018

I think that adding the requested functionality will require writing a subclass of PathCollection that can keep track of the original inputs, and can repeat the mask- and nan-filtering operation every time a relevant property is updated.

@anntzer
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anntzer commented Feb 6, 2018

Do we actually need to filter out the nans (except for backcompatibility considerations)? Aren't the renderers supposed to be able to handle nan data to start with anyways?

@efiring
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efiring commented Feb 7, 2018

Good question; maybe they all can. If so, taking advantage of that would require some substantial changes, I think, because we support masked arrays, and use them internally. Going 100% with nans for missing values implies always using floating point, and switching from .max to .nanmax etc.

@efiring efiring mentioned this issue Mar 16, 2018
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@tacaswell tacaswell modified the milestones: v3.0, v3.1 Aug 11, 2018
@efiring efiring mentioned this issue Oct 6, 2018
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efiring added a commit to efiring/matplotlib that referenced this issue Oct 6, 2018
@efiring
Alternative approach; addresses matplotlib#10381
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anntzer pushed a commit to efiring/matplotlib that referenced this issue Nov 13, 2018
@efiring @anntzer
Alternative approach; addresses matplotlib#10381
2b8aaa6
anntzer pushed a commit to efiring/matplotlib that referenced this issue Nov 14, 2018
@efiring @anntzer
Alternative approach; addresses matplotlib#10381
3065de7
anntzer pushed a commit to efiring/matplotlib that referenced this issue Dec 12, 2018
@efiring @anntzer
Alternative approach; addresses matplotlib#10381
921100a
@tacaswell tacaswell modified the milestones: v3.1.0, v3.2.0 Mar 18, 2019
@efiring
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efiring commented Apr 15, 2019

I think this is closed by #12422. If not, please reopen.

@efiring efiring closed this as completed Apr 15, 2019
@QuLogic QuLogic modified the milestones: v3.2.0, v3.1.0 Apr 15, 2019
@astrofrog astrofrog mentioned this issue Apr 15, 2019
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@efiring @tacaswell @QuLogic @astrofrog @anntzer