Provide more semantics around customization of the colorscale

Heatmap does not use all the colors specified in the colors argument when data passed to z value has skewed distribution. Here is a small example to illustrate the problem.

library(plotly)
library(RColorBrewer)

# create a dataframe where z has a skewed distribution
set.seed(1)
df = data.frame(x = rep(1:50, 20) , y = rep(1:20,each =50), z = rgamma(1000, 2, 0.5))

# check distribution of z 
plot_ly(data = df, x = ~z, type = "histogram")%>% 
    layout(title = "histogram of z")  

# original heatmap
# pass the column z with screwed distribution to z argument
plot_ly(data=df, x=~x, y=~y, z=~z, type="heatmap",  
        colors = "Spectral") %>% 
    layout(title = "original heatmap")

I currently work around the issue by creating a new variable according to the quantiles of the original variable, and pass it to the z argument.

# some data processing work 
# find unique quantiles of z 
quantiles = unique(quantile(df$z, seq(0,1,0.1)))

# create a dummy column z1 of discrete values using the quantiles as cut off
# the ideas is to arrage the data to subgroups of roughly the same size 
df$z1= cut(df$z, breaks =  c(quantiles[1]-1,quantiles[-1]), right = TRUE, labels = FALSE)

# check distribution of z1 
plot_ly(data = df, x = ~z1, type = "histogram")%>% 
    layout(title = "histogram of z1")

# new heatmap 
# passes the new column z1 to z argument 
plot_ly(data=df, x=~x, y=~y, z=~z1, type="heatmap",  
        # make sure hovering over displays original z
        text =~z, hoverinfo = "text", 
        # use the color palettes from RColorBrewer, 
        # or your customized colorscale
        colors = "Spectral",  
        # map the label of the colorbar back to the quantiles
        colorbar=list(tickmode="array", tickvals = 1:(length(quantiles)-1), ticktext = round(quantiles,2)[-1], title = "z")) %>% 
layout(title = "new heat map")

Here is the link to the heatmaps before and after data processing. The new heatmap picks up more colors from the "Spectral" palette to differentiate between smaller values.
https://i.stack.imgur.com/TymlR.png

I would like to see features in plotly to force heatmap use all the user specified colors.

Thanks!

Das bereitgestellte R-Heatmap-Beispiel mit den schiefen gamma-verteilten z-Werten reproduzieren und mit dem quantilbasierten Workaround vergleichen. Beginne damit, das colors-Argument von heatmap nachzuverfolgen und die beabsichtigte Semantik für die Verwendung aller angegebenen Farben zu bestimmen; zu den Abschlusskriterien sollte ein definiertes Verhalten für schiefe Verteilungen und ein verifiziertes Beispiel gehören.