plotly / plotly/plotly.py

incorrect representation of mixed rgb and rgba colors in mesh3d

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bug P3
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Description

Hi folks!

I encountered some unexpected behavior in Mesh3d. I hope you could have a look. It is hard for me to judge if these are issues, here or upstream, or I am missing something.

image

  1. Incorrect colors in plots with intensity and a discontinuous colorscale (see panel f2).
    a) Colors with opacity (rgba) are shown without opacity (rgb). This issue was reported before.
    b) Discontinuous transitions in the colorscale appear to be ignored. As a result, colors are introduced that are not found in the colorscale. In the example, panel f2: purple = black + magenta, raspberry = magenta + orange.
    c) I would expect the same colorscale behavior as in 'scatter' or 'heatmap' (panel f1).

  2. Render artifacts occur for facecolor or vertexcolor surfaces, when rgb and rgba colors are put in the same trace (see panel f3).
    a) Surfaces do look opaque when only rgb colors are present (panel f5).
    b) A partial workaround was to plot rgb and rgba vertices in separate traces (panel f6). Not sure what to do when vertices have a gradient between rgb and rgba...

  3. An additional trace is needed to add a colorbar when facecolor or vertexcolor is used instead of intensity.
    a) I do expect the colorbar to be dropped when colors are hardcoded. But the workaround to add a custom colorbar or coloraxis with a 'hidden' trace may not be obvious to everyone. Add an example to the documentation?

  4. The rgba colors in the colorbar appear mixed with layout paper_bgcolor, effectively the transparent magenta colors are too dark in the Working Example.
    a) Can a colorbar background be set to match the (default) plot background color?

  5. The image above is a screenshot and differs from the exported image, using f7.write_image(...) after the Working Example below.
    a) In my exported copy, some of the layout scene axes appeared 'ghost'-duplicated into the next subplot. Only visible if the scene axes (ranges) change between subplots. Issue on my system or in Orca?

Screenshot image vs Orca[1.3.0] image

Finally, for a large number of shapes (or animations) I noticed significant performance loss in looking up and hardcoding the colors of vertices. Ideally, I would get the intensity method of Mesh3d to give me the expected result (more like f6, less like f2). Any ideas? Your advise is appreciated!

import numpy as np
import itertools
import plotly as py
import plotly.graph_objects as go
from plotly.subplots import make_subplots

def flatten_dict(d):
    return({k: [y for x in [v[k] for v in d] for y in x] for k in d[0]})

def reindex_voxels(dl):
    m=0
    res = []
    for i,od in enumerate(dl):
        ix = len(dl[i]['x'])
        d = od.copy()
        for n in ['i','j','k']:
            d[n] = [ m + j for j in d[n] ]
        m += ix
        res.append(d)
    return(flatten_dict(res))

# some data
x = y = np.linspace(0,1,3)
x,y = np.meshgrid(x,y)
z = 1-(x+y)/2 

# custom colorscale, mixed rgb and rgba, discontinuous breaks
cs = [[0.0, 'rgb(64, 64, 64)'],
    [0.25, 'rgb(64, 64, 64)'],
    [0.25, 'rgba(255, 0, 255, 0.1)'],
    [0.5, 'rgba(255, 0, 255, 0.5)'],
    [0.5, 'rgb(255, 160, 33)'],
    [0.8, 'rgb(255, 255, 129)'],
    [1.0, 'rgb(255, 255, 225)']]

# list of shapes
vxl = \
[{'x': [-0.25, -0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25],
  'y': [-0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25, -0.25],
  'z': [0.875, 0.875, 0.875, 0.875, 1.125, 1.125, 1.125, 1.125],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]},
 {'x': [0.25, 0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75],
  'y': [-0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25, -0.25],
  'z': [0.625, 0.625, 0.625, 0.625, 0.875, 0.875, 0.875, 0.875],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75]},
 {'x': [0.75, 0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25],
  'y': [-0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25, -0.25],
  'z': [0.375, 0.375, 0.375, 0.375, 0.625, 0.625, 0.625, 0.625],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]},
 {'x': [-0.25, -0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25],
  'y': [0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75, 0.25],
  'z': [0.625, 0.625, 0.625, 0.625, 0.875, 0.875, 0.875, 0.875],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75, 0.75]},
 {'x': [0.25, 0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75],
  'y': [0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75, 0.25],
  'z': [0.375, 0.375, 0.375, 0.375, 0.625, 0.625, 0.625, 0.625],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]},
 {'x': [0.75, 0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25],
  'y': [0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75, 0.25],
  'z': [0.125, 0.125, 0.125, 0.125, 0.375, 0.375, 0.375, 0.375],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]},
 {'x': [-0.25, -0.25, 0.25, 0.25, -0.25, -0.25, 0.25, 0.25],
  'y': [0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25, 0.75],
  'z': [0.375, 0.375, 0.375, 0.375, 0.625, 0.625, 0.625, 0.625],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]},
 {'x': [0.25, 0.25, 0.75, 0.75, 0.25, 0.25, 0.75, 0.75],
  'y': [0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25, 0.75],
  'z': [0.125, 0.125, 0.125, 0.125, 0.375, 0.375, 0.375, 0.375],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]},
 {'x': [0.75, 0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25],
  'y': [0.75, 1.25, 1.25, 0.75, 0.75, 1.25, 1.25, 0.75],
  'z': [-0.125, -0.125, -0.125, -0.125, 0.125, 0.125, 0.125, 0.125],
  'i': [7, 0, 0, 0, 4, 4, 2, 6, 4, 0, 3, 7],
  'j': [3, 4, 1, 2, 5, 6, 5, 5, 0, 1, 2, 2],
  'k': [0, 7, 2, 3, 6, 7, 1, 2, 5, 5, 7, 6],
  'intensity': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}]

# shape colors
cid = ['rgb(255, 255, 220)','rgb(255, 239, 113)', 
       'rgb(255, 160, 33)','rgb(255, 239, 113)',
       'rgb(255, 160, 33)','rgba(255, 0, 255, 0.2)',
       'rgb(255, 160, 33)','rgba(255, 0, 255, 0.2)', 
       'rgb(64, 64, 64)']

# concatenate shapes
vxa = reindex_voxels(vxl)

# concatenate and replace 'intensity' with 'vertexcolor'
vxb = reindex_voxels([{**{i:j for i,j in v.items() if i in ['x','y','z','i','j','k']},
                       **{'vertexcolor': [k]*len(v['x'])}} for k,v in zip(cid,vxl)])

# split vertices with opacity (rgba) and without opacity (rgb) , and replace 'intensity' by 'vertexcolor'
vxc0 = reindex_voxels([{**{i:j for i,j in v.items() if i in ['x','y','z','i','j','k']},
                        **{'vertexcolor': [k]*len(v['x'])}} for k,v in zip(cid,vxl) if k.startswith('rgba(')])
vxc1 = reindex_voxels([{**{i:j for i,j in v.items() if i in ['x','y','z','i','j','k']},
                        **{'vertexcolor': [k]*len(v['x'])}} for k,v in zip(cid,vxl) if k.startswith('rgb(')])

# figures
f1 = go.Figure(data=[go.Heatmap(x=x.ravel(),y=y.ravel(),z=z.ravel(),colorscale=cs)])
f2 = go.Figure(data=[go.Mesh3d(**vxa,colorscale=cs)])
f3 = go.Figure(data=[go.Mesh3d(**vxb,colorscale=cs)])
f4 = go.Figure(data=[go.Mesh3d(**vxc0)])
f5 = go.Figure(data=[go.Mesh3d(**vxc1)])

# multiple traces; 'vertexcolor' for rgb and rgba _and_ a transparent trace with 'intensity' to add a 'colorscale'
f6 = go.Figure(data=[go.Mesh3d(**vxc0),
                     go.Mesh3d(**vxc1),
                     go.Mesh3d(**vxa,opacity=0,colorscale=cs)])

#f6.show()  

# merge figures
ncol=2
nrow=3
fig = make_subplots(cols=ncol,rows=nrow,vertical_spacing=0.05,horizontal_spacing=0.02,
                    specs= ( np.array([{'type':'heatmap'},{'type':'scatter3d'}]+[{'type':'scatter3d'}]*4)
                            .reshape(nrow,ncol).tolist() ),
                    subplot_titles=['f1: <b>Heatmap</b>; correct',
                                    'f2: <b>Mesh3d</b> intensity; incorrect rgb & rgba',
                                    'f3: <b>Mesh3d</b> vertexcolor; render artifacts in rgb',
                                    'f4: <b>Mesh3d</b> vertexcolor; correct for only rgba',
                                    'f5: <b>Mesh3d</b> vertexcolor; correct for only rgb',
                                    'f6: <b>Mesh3d</b> vertexcolor; separate traces for rgb & rgba'])
fig.update_layout(height=1200,width=1000,title_text='3D representation of mixed rgb and rgba colors [plotly.py={}]'.format(py.__version__))

f7 = go.Figure(fig)
for loc,f in zip(list(itertools.product(*[range(1,nrow+1),range(1,ncol+1)])),[f1,f2,f3,f4,f5,f6]):
    if f is not None:
        for d in f['data']:
            f7.add_trace(d,row=loc[0],col=loc[1])              

f7.show()

Sorry, not a very short MWE...

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Par où commencer

  1. Lisez l'issue en entier, puis le guide de contribution du projet.
  2. Signalez en commentaire que vous la prenez — cela évite que deux personnes fassent le même travail.
  3. Forkez le dépôt et travaillez sur une branche.
  4. Ouvrez une pull request qui référence le numéro de l'issue.

Piste de recherche

Aucun fichier du dépôt ni aucun test n’est indiqué. Commencez par exécuter la reproduction Python fournie et séparez les rapports concernant Mesh3d intensity, vertexcolor, colorbar et export-rendering ; déterminez ensuite quel comportement relève de plotly.py plutôt que de Plotly.js ou d’Orca. La tâche serait considérée comme terminée lorsqu’un périmètre approuvé par un maintainer, une reproduction ciblée et la couverture de régression correspondante seraient disponibles.

Rédigé par le modèle d'indexation à partir du texte de l'issue.

Évaluation

Stack technique
python
Domaine
data-visualization
Type d'issue
Bug
Difficulté
5/5
Temps estimé
Plus d'une semaine
Activité
À l'abandon
Clarté
À clarifier
Accessibilité débutants
18/100

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