# Smoothing surface in GLMakie
**URL:** https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957
**Category:** Visualization
**Tags:** plotting, glmakie
**Created:** [February 29, 2024, 3:14pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957 "2024-02-29T15:14:15Z")
**Posts on this page:** 8
**Page:** 1
### Author:  [@Alicanter](https://discourse.julialang.org/u/Alicanter)
#### Post date: [February 29, 2024, 3:14pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/1 "2024-02-29T15:14:15Z")
I have data from a simulation ([test.csv.gz](https://paste.c-net.org/ZatarcFreddo)) and I am trying to plot it as a surface.
The equations have a singularity at a certain threshold, so there is a cutoff in the values of the surface.
The problem I am facing is that as the values are irregular near the threshold, there is a “sawtooth” at the top instead of being smooth.
Is there a way to change the algorithm used to compute the surface so it is smoother? Or some way to interpolate it before?
I could always compute more points, but I had similar problems in other occasions so I’d like to find a solution if possible.
Here is the script to reproduce it:
```julia
using CSV
using DataFrames
using GLMakie
df = DataFrame(CSV.File("test.csv.gz"))
fig = Figure()
ax = Axis3(fig[1, 1]; aspect = :equal, xlabel = "x", ylabel = "y", zlabel = "z")
cmap = categorical_colors([:blue], 1)
surface!(ax, df[!, :x], df[!, :y], df[!, :z], colormap = cmap)
scatter!(ax, df[!, :x], df[!, :y], df[!, :z], color = :black, markersize = 3)
save("test.png", fig, px_per_unit = 5)
```
Thanks!
---
### Author:  [@sdanisch](https://discourse.julialang.org/u/sdanisch)
#### Post date: [February 29, 2024, 4:49pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/2 "2024-02-29T16:49:16Z")
Maybe use filters from [ImageFiltering.jl · ImageFiltering](https://juliaimages.org/ImageFiltering.jl/stable/)?
They should work with generic arrays…
---
### Author:  [@Alicanter](https://discourse.julialang.org/u/Alicanter)
#### Post date: [February 29, 2024, 5:25pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/3 "2024-02-29T17:25:37Z")
I would prefer not smoothing by changing the values and instead computing a surface that is like a convex hull of the points, or adding points so the surface looks better.
---
### Author:  [@Nathan\_Boyer](https://discourse.julialang.org/u/Nathan_Boyer)
#### Post date: [February 29, 2024, 7:16pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/4 "2024-02-29T19:16:56Z")
Regardless of how you smooth the data, you don’t have to change the original data. Create a separate `df_smooth` object and plot that beside `df` until you are happy with the look of `df_smooth`.
---
### Author:  [@rocco\_sprmnt21](https://discourse.julialang.org/u/rocco_sprmnt21)
#### Post date: [February 29, 2024, 9:13pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/5 "2024-02-29T21:13:29Z")
I don’t understand what your end goal is. But perhaps, as a naive idea, the following scheme can give you some ideas.
```julia
julia> m,M= extrema(df.z)
(0.0, 0.1087877834426117)
# 80%M
df008=findall( >(0.08),df.z)
df008m1=df008.-1
df8m=(dfsmooth.z[df008].+dfsmooth.z[df008m1])/2
dfsmooth=copy(df)
dfsmooth.z[df008].=df8m
dfsmooth.z[df008n].=df8m
fig07 = Figure()
ax = Axis3(fig07[1, 1]; aspect = :equal, xlabel = "x", ylabel = "y", zlabel = "z")
cmap = categorical_colors([:blue], 1)
surface!(ax, dfsmooth[!, :x], dfsmooth[!, :y], dfsmooth[!, :z], colormap = cmap)
scatter!(ax, dfsmooth[!, :x], dfsmooth[!, :y], dfsmooth[!, :z], color = :black, markersize = 3)
save("test.png", fig, px_per_unit = 5)
```
---
### Author:  [@rocco\_sprmnt21](https://discourse.julialang.org/u/rocco_sprmnt21)
#### Post date: [March 1, 2024, 9:55am UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/6 "2024-03-01T09:55:11Z")
A different naive idea could be to smooth the saw tips between the different minima of the projections on the X plane (for a part) or on the Y plane (for a part) or perhaps it would be better (for this figure) to use a projection on a cylinder.
placed the part of the X plane as an illustrative example of the idea.
```julia
insertcols!(df,:nr=>1:nrow(df))
gdf=groupby(df,:x)
cdf=combine(gdf, [:y,:z,:nr]=>((y,z,n)->[(y[argmax(z)],z[argmax(z)],n[argmax(z)])])=>identity)
tcdf=transform(cdf,:z=>(z->[0;diff(z)])=>:zd)
sinks=findall(<(-0.01),tcdf.zd)
julia> for (i,s) in enumerate(sinks)
#cdf[tcdf.nr[s:s[i+1]]].=tcdf.z[s[i+1]]
tcdf.z[s:sinks[i+1]].=tcdf.z[sinks[i+1]]
end
ERROR: BoundsError: attempt to access 14-element Vector{Int64} at index [15]
Stacktrace:
# error but makes the wanted transformation
# It's not worth fixing this code just to exemplify the idea
dfsmooth=copy(df)
dfsmooth.z[tcdf.nr].=tcdf.z
figsm = Figure()
ax = Axis3(figsm[1, 1]; aspect = :equal, xlabel = "x", ylabel = "y", zlabel = "z")
cmap = categorical_colors([:blue], 1)
surface!(ax, dfsmooth[!, :x], dfsmooth[!, :y], dfsmooth[!, :z], colormap = cmap)
scatter!(ax, dfsmooth[!, :x], dfsmooth[!, :y], dfsmooth[!, :z], color = :black, markersize = 3)
save("byX_smooth.png", figsm, px_per_unit = 5)
```
to “cut” the tips projected onto a cylinder in a more graceful way you could use some properly done interpolation technique
Could the projection on a cylinder be simulated via a groupby done on the x/y ratio?
PS
Obviously, all this while waiting for someone who knows how to seriously do what the OP requests.
---
### Author:  [@rocco\_sprmnt21](https://discourse.julialang.org/u/rocco_sprmnt21)
#### Post date: [March 2, 2024, 9:25am UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/7 "2024-03-02T09:25:53Z")
Another smoothing exercise
> **smoother**
>
> ```julia
> using CSV,DataFrames,Plots
> df = DataFrame(CSV.File("test.csv.gz"))
> insertcols!(df,:nr=>1:nrow(df))
> gdf=groupby(df,:x) # proiezione sul piano X(x,0,0)
> cdf=combine(gdf, [:y,:z,:nr]=>((y,z,n)->[(y[argmax(z)],z[argmax(z)],n[argmax(z)])])=>identity) # prende i valori di cresta
>
> function findmin(cdf)
> insertcols!(cdf,1,:r=>1:nrow(cdf))
> insertcols!(cdf,4, :dz=> [0;diff(cdf.z)]) # serve a vedere dove "sale" e dove "scende"
> cdf.min.=true
> st=true
> for i in eachindex(cdf.dz) # cerca i punti di minimo da interpolare per tagliare via le creste
> if st
> cdf.min[i]= true
> if cdf.dz[i] < 0
> st=false
> end
> else
> cdf.min[i]= cdf.dz[i] < 0 ? true : false
> end
> end
> end
> function smoother(cdf)
> cdf.csm=cumsum(cdf.min)
> gcdf=groupby(cdf,:csm)
>
> function interpol(gr) #calcola i valori lineari tra due mimimi consecutivi e li sostituisce ai valori di "cresta"
> n=nrow(gr)+1
> zf=cdf.z[gr.r[n-1]+1]
> zi=cdf.z[gr.r[1]]
> for i in 2:n-1
> gr.z[i]=zi+(zf-zi)*i/n
> end
> gr
> end
> dfsm=combine(gr->nrow(gr)>1 ? interpol(gr) : gr, gcdf)
> Plots.plot!(dfsm.x, dfsm.z,xlims=(0, 10), ylims=(0,0.12))
> end
>
> using Plots
> Plots.plot(cdf.x, cdf.z,xlims=(0, 10), ylims=(0,0.12))
> ccdf=copy(cdf)
> findmin(ccdf)
> smoother(ccdf)
>
> ```
PS
Perhaps, in some cases, more passes should be made.
Like when you shave 😀
---
### Author:  [@Alicanter](https://discourse.julialang.org/u/Alicanter)
#### Post date: [March 2, 2024, 12:12pm UTC](https://discourse.julialang.org/t/smoothing-surface-in-glmakie/110957/8 "2024-03-02T12:12:55Z")
I am not looking for a way to change the points that are computed so the surface looks better. I am looking for a way for the surface to be more connected near the top.
A maybe better and simpler example is this code:
```julia
using GLMakie
# Gaussian with a maximum at (3, 3).
f(x, y) = 10*exp(-(x - 3)^2/2 - (y - 3)^2/2)
X = ones(7)' .* (0:6)
Y = ones(7) .* (0:6)'
Z = f.(X, Y)
# Convert to array and remove middle point to simulate singularity.
# An alternative would be to set Z[4, 4] = Inf but messes up with the colors.
X = reduce(vcat, X)
Y = reduce(vcat, Y)
Z = reduce(vcat, Z)
deleteat!(X, 25)
deleteat!(Y, 25)
deleteat!(Z, 25)
fig = Figure()
ax = Axis3(fig[1, 1]; aspect = :equal)
surface!(ax, X, Y, Z)
fig
```
This is a 2D Gaussian with a “singularity” at the middle (maximum).
The resulting surface has 4 triangles at the top, but I’d like it to also have egdes between the top points so they are connected and the surface looks like a Gaussian clipped at some threshold instead
In this case, `Plots.jl` works as I expect:
```julia
using Plots
Plots.surface(X, Y, Z)
```
