Using opacityscale with surface results in ordering by addition order, not depth
Dieses Issue hat noch niemand übernommen.
- Vorherrschende Sprache
- R
- Sterne
- 2.7k
- Forks
- 641
- PR-Merge-Kennzahlen
- Keine gemergten PRs in 30 T.
Beschreibung
Hi, been using plotly with R for a while now and really loving it - thanks for assembling such a phenomenal program! I'd like to report a bug found while plotting multiple surface objects, which causes them to layer in the order they're added (using %>%) rather than the "depth" at which they should really render in the figure. See below for code and examples.
The "opacityscale" function is a new one from the dev branch (and I don't think problems with it have been reported on here before) so this question may be a better fit for https://github.com/plotly/plotly.js instead, where there's a bit more discussion about it and the original implementation, but I don't know JavaScript and can't provide a reprex there.
This is great:

This is not great (same figure rotated to look from below):

It only seems to show up if the opacityscale parameter is added, even if the opacity is set to 1 for all values.
Renders fine:
# devtools::install_github("ropensci/plotly")
library(plotly)
df <- data.frame(x=1:10, y=1:10)
surface_data <- matrix(1:100, nrow=10, ncol=10)
z_mat <- matrix(0, nrow = 10, ncol = 10)
plot_ly(df, x=~x, y=~y) %>%
add_trace(type="surface",
z=z_mat,
surfacecolor=surface_data,
colorscale=list(list(0, 1), list("red", "orange"))) %>%
add_trace(type = "surface",
z = z_mat+2,
surfacecolor=surface_data,
colorscale = list(list(0, 1), list("yellow", "green"))) %>%
add_trace(type = "surface",
z = z_mat+1,
surfacecolor=surface_data,
colorscale = list(list(0, 1), list("blue", "purple")))

Does not render fine:
plot_ly(df, x=~x, y=~y) %>%
add_trace(type="surface",
z=z_mat,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale=list(list(0, 1), list("red", "orange"))) %>%
add_trace(type = "surface",
z = z_mat+1,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale = list(list(0, 1), list("yellow", "green"))) %>%
add_trace(type = "surface",
z = z_mat+2,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale = list(list(0, 1), list("blue", "purple")))

I've had a little trouble figuring out exactly how to best pass the opacityscale argument in R but I believe these settings should disable the opacity completely by setting every value between 0 and 1 to an opacity between 1 and 1 (i.e., always 1 and fully opaque).
Additionally, the surface that renders on "top" is the one that's added last in the piping order. If we alter the above code a little bit to render the purple/blue surface before the yellow/green one, we get some more interesting renders:
plot_ly(df, x=~x, y=~y) %>%
add_trace(type="surface",
z=z_mat,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale=list(list(0, 1), list("red", "orange"))) %>%
add_trace(type = "surface",
z = z_mat+2,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale = list(list(0, 1), list("blue", "purple"))) %>%
add_trace(type = "surface",
z = z_mat+1,
surfacecolor=surface_data,
opacityscale=list(list(0, 1), list(1, 1)),
colorscale = list(list(0, 1), list("yellow", "green")))


Beitragsleitfaden
Erste Schritte
- Lies das ganze Issue und danach den Beitragsleitfaden des Projekts.
- Schreib ins Issue, dass du es übernimmst — das erspart doppelte Arbeit.
- Forke das Repository und arbeite in einem Branch.
- Öffne einen Pull Request, der die Issue-Nummer nennt.
Rechercherichtung
Reproduziere das Problem mit den bereitgestellten R-Beispielen unter Verwendung von plot_ly(), add_trace(), surface-Traces und opacityscale und vergleiche anschließend die Darstellung mit und ohne opacityscale. Verfolge das Verhalten durch die in Issue 4331 referenzierte plotly.js-Implementierung; abgeschlossen ist die Aufgabe, wenn die surface-Reihenfolge bei vorhandenem opacityscale der Tiefenreihenfolge statt der Reihenfolge des Hinzufügens der Traces folgt.
Vom Indexierungsmodell aus dem Issue-Text verfasst.
Bewertung
- Tech-Stack
- javascript, r
- Bereich
- data-visualization
- Issue-Typ
- Bug
- Schwierigkeit
- 4/5
- Geschätzter Aufwand
- 3-5 Tage
- Aktivitätsstatus
- Veraltet
- Klarheit
- Größtenteils klar
- Anfängerfreundlichkeit
- 30/100