Speed measurement
Here we will compare the speed of plotting UnfoldMakie with MNE (Python) and EEGLAB (MATLAB).
Three cases are measured:
- Single topoplot
- Topoplot series with 50 topoplots
- Topoplott animation with 50 timestamps
Note that the results of benchmarking on your computer and on Github may differ.
using UnfoldMakie
using TopoPlots
using BenchmarkTools
using Observables
using CairoMakie
using Statistics
using PythonPlot;
using PyMNE; CondaPkg Found dependencies: /home/runner/.julia/packages/CondaPkg/8GjrP/CondaPkg.toml
CondaPkg Found dependencies: /home/runner/.julia/packages/PyMNE/cNGDN/CondaPkg.toml
CondaPkg Found dependencies: /home/runner/.julia/packages/PythonCall/83z4q/CondaPkg.toml
CondaPkg Found dependencies: /home/runner/.julia/packages/PythonPlot/oS8x4/CondaPkg.toml
CondaPkg Resolving changes
+ libstdcxx
+ libstdcxx-ng
+ matplotlib
+ mne (pip)
+ openssl
+ python
+ uv
CondaPkg Creating environment
│ /home/runner/.julia/artifacts/43005ee838d3b9ab9b3f1d790fa2e43bd62e6912/bin/micromamba
│ -r /home/runner/.julia/scratchspaces/0b3b1443-0f03-428d-bdfb-f27f9c1191ea/root
│ create
│ -y
│ -p /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env
│ --override-channels
│ libstdcxx[version='>=3.4,<15.0']
│ libstdcxx-ng[version='>=3.4,<15.0']
│ matplotlib[version='>=1']
│ openssl[version='>=3, <3.6']
│ python[version='>=3.10,!=3.14.0,!=3.14.1,<4, >=3.4,<4',channel='conda-forge',build='*cp*']
│ uv[version='>=0.4']
│ -c conda-forge
└ -c anaconda
Transaction
Prefix: /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env
Updating specs:
- libstdcxx[version=">=3.4,<15.0"]
- libstdcxx-ng[version=">=3.4,<15.0"]
- matplotlib>=1
- openssl[version=">=3,<3.6"]
- conda-forge::python[version=">=3.10,(!=3.14.0,(!=3.14.1,(<4,(>=3.4,<4))))",build="*cp*"]
- uv>=0.4
Package Version Build Channel Size
───────────────────────────────────────────────────────────────────────────────────────────
Install:
───────────────────────────────────────────────────────────────────────────────────────────
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Install: 134 packages
Total download: 1MB
───────────────────────────────────────────────────────────────────────────────────────────
Transaction starting
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Transaction finished
To activate this environment, use:
micromamba activate /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env
Or to execute a single command in this environment, use:
micromamba run -p /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env mycommand
CondaPkg Installing Pip packages
│ /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env/bin/uv
│ pip
│ install
└ mne >=1.4
Using Python 3.14.3 environment at: /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/env
Resolved 25 packages in 120ms
Downloading scipy (33.6MiB)
Downloading mne (7.1MiB)
Downloaded mne
Downloaded scipy
Prepared 14 packages in 561ms
Installed 14 packages in 37ms
+ certifi==2026.2.25
+ charset-normalizer==3.4.5
+ decorator==5.2.1
+ idna==3.11
+ jinja2==3.1.6
+ lazy-loader==0.5
+ markupsafe==3.0.3
+ mne==1.11.0
+ platformdirs==4.9.4
+ pooch==1.9.0
+ requests==2.32.5
+ scipy==1.17.1
+ tqdm==4.67.3
+ urllib3==2.6.3Data input
dat, positions = TopoPlots.example_data()
df = UnfoldMakie.eeg_array_to_dataframe(dat[:, :, 1], string.(1:length(positions)));Topoplots
UnfoldMakie.jl
@benchmark plot_topoplot(dat[:, 320, 1]; positions = positions)BenchmarkTools.Trial: 102 samples with 1 evaluation per sample.
Range (min … max): 44.132 ms … 310.265 ms ┊ GC (min … max): 0.00% … 83.43%
Time (median): 45.991 ms ┊ GC (median): 0.00%
Time (mean ± σ): 48.944 ms ± 26.180 ms ┊ GC (mean ± σ): 5.18% ± 8.26%
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44.1 ms Histogram: frequency by time 51.3 ms <
Memory estimate: 11.82 MiB, allocs estimate: 194614.UnfoldMakie.jl with DelaunayMesh
@benchmark plot_topoplot(
dat[:, 320, 1];
positions = positions,
topo_interpolation = (; interpolation = DelaunayMesh()),
)BenchmarkTools.Trial: 97 samples with 1 evaluation per sample.
Range (min … max): 45.663 ms … 335.939 ms ┊ GC (min … max): 0.00% … 84.16%
Time (median): 49.047 ms ┊ GC (median): 0.00%
Time (mean ± σ): 51.650 ms ± 29.216 ms ┊ GC (mean ± σ): 5.64% ± 8.55%
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45.7 ms Histogram: frequency by time 51.3 ms <
Memory estimate: 11.82 MiB, allocs estimate: 194621.MNE
posmat = collect(reduce(hcat, [[p[1], p[2]] for p in positions])')
pypos = Py(posmat).to_numpy()
pydat = Py(dat[:, 320, 1])
@benchmark begin
f = PythonPlot.figure()
PyMNE.viz.plot_topomap(
pydat,
pypos,
sphere = 1.1,
extrapolate = "box",
cmap = "RdBu_r",
sensors = false,
contours = 6,
)
f.show()
endBenchmarkTools.Trial: 406 samples with 1 evaluation per sample.
Range (min … max): 11.620 ms … 174.932 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 11.775 ms ┊ GC (median): 0.00%
Time (mean ± σ): 12.323 ms ± 8.253 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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11.6 ms Histogram: log(frequency) by time 14.2 ms <
Memory estimate: 3.30 KiB, allocs estimate: 98.Topoplot series
Note that UnfoldMakie and MNE have different defaults for displaying topoplot series. UnfoldMakie in plot_topoplot averages over time samples. MNE in plot_topopmap displays single samples without averaging.
UnfoldMakie.jl
@benchmark begin
plot_topoplotseries(
df;
bin_num = 50,
positions = positions,
axis = (; xlabel = "Time windows [s]"),
)
endBenchmarkTools.Trial: 3 samples with 1 evaluation per sample.
Range (min … max): 2.106 s … 2.177 s ┊ GC (min … max): 0.00% … 0.00%
Time (median): 2.147 s ┊ GC (median): 0.00%
Time (mean ± σ): 2.143 s ± 35.930 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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2.11 s Histogram: frequency by time 2.18 s <
Memory estimate: 342.18 MiB, allocs estimate: 4920364.MNE
easycap_montage = PyMNE.channels.make_standard_montage("standard_1020")
ch_names = pyconvert(Vector{String}, easycap_montage.ch_names)[1:64]
info = PyMNE.create_info(PyList(ch_names), ch_types = "eeg", sfreq = 1)
info.set_montage(easycap_montage)
simulated_epochs = PyMNE.EvokedArray(Py(dat[:, :, 1]), info)
@benchmark simulated_epochs.plot_topomap(1:50)BenchmarkTools.Trial: 8 samples with 1 evaluation per sample.
Range (min … max): 671.754 ms … 696.650 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 676.848 ms ┊ GC (median): 0.00%
Time (mean ± σ): 678.959 ms ± 8.307 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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672 ms Histogram: frequency by time 697 ms <
Memory estimate: 2.39 KiB, allocs estimate: 69.MATLAB
Running MATLAB on a GitHub Action is not easy. So we benchmarked three consecutive executions (on a screenshot) on a server with an AMD EPYC 7452 32-core processor. Note that Github and the server we used for MATLAB benchmarking are two different computers, which can give different timing results.
Animation
The main advantage of Julia is the speed with which the figures are updated.
timestamps = range(1, 50, step = 1)
framerate = 5050UnfoldMakie with .gif
vals = vec(dat[:, :, 1])
p01, p99 = quantile(vals, [0.01, 0.99])
m = max(abs(p01), abs(p99))
cr = Float32.((-m, m))
@benchmark begin
f = Makie.Figure()
dat_obs = Observable(dat[:, 1, 1])
plot_topoplot!(f[1, 1], dat_obs, positions = positions, visual = (; contours = false, colorrange = cr),)
record(f, "topoplot_animation_UM.gif", timestamps; framerate = framerate) do t
dat_obs[] = @view(dat[:, t, 1])
end
endBenchmarkTools.Trial: 3 samples with 1 evaluation per sample.
Range (min … max): 1.958 s … 1.986 s ┊ GC (min … max): 0.00% … 0.00%
Time (median): 1.982 s ┊ GC (median): 0.00%
Time (mean ± σ): 1.975 s ± 15.399 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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1.96 s Histogram: frequency by time 1.99 s <
Memory estimate: 120.33 MiB, allocs estimate: 416766.
MNE with .gif
@benchmark begin
fig, anim = simulated_epochs.animate_topomap(
times = Py(timestamps),
frame_rate = framerate,
blit = false,
image_interp = "cubic", # same as CloughTocher
)
anim.save("topomap_animation_mne.gif", writer = "ffmpeg", fps = framerate)
endBenchmarkTools.Trial: 1 sample with 1 evaluation per sample.
Single result which took 9.221 s (0.00% GC) to evaluate,
with a memory estimate of 3.03 KiB, over 96 allocations.Note, that due to some bugs in (probably) PythonCall topoplot is black and white.
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