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 PythonPlot;
using PyMNE; CondaPkg Found dependencies: /home/runner/.julia/packages/PyMNE/cNGDN/CondaPkg.toml
CondaPkg Found dependencies: /home/runner/.julia/packages/PythonCall/L4cjh/CondaPkg.toml
CondaPkg Found dependencies: /home/runner/.julia/packages/PythonPlot/oS8x4/CondaPkg.toml
CondaPkg Resolving changes
+ libstdcxx-ng
+ matplotlib
+ mne (pip)
+ openssl
+ python
+ uv
CondaPkg Initialising pixi
│ /home/runner/.julia/artifacts/cefba4912c2b400756d043a2563ef77a0088866b/bin/pixi
│ init
│ --format pixi
└ /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg
✔ Created /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/pixi.toml
CondaPkg Wrote /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/pixi.toml
│ [dependencies]
│ openssl = ">=3, <3.6"
│ uv = ">=0.4"
│ libstdcxx-ng = ">=3.4,<15.0"
│ matplotlib = ">=1"
│
│ [dependencies.python]
│ channel = "conda-forge"
│ build = "*cpython*"
│ version = ">=3.8,<4, >=3.4,<4"
│
│ [project]
│ name = ".CondaPkg"
│ platforms = ["linux-64"]
│ channels = ["conda-forge", "anaconda"]
│ channel-priority = "strict"
│ description = "automatically generated by CondaPkg.jl"
│
│ [pypi-dependencies]
└ mne = ">=1.4"
CondaPkg Installing packages
│ /home/runner/.julia/artifacts/cefba4912c2b400756d043a2563ef77a0088866b/bin/pixi
│ install
└ --manifest-path /home/runner/work/UnfoldMakie.jl/UnfoldMakie.jl/docs/.CondaPkg/pixi.toml
✔ The default environment has been installed.Data 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: 142 samples with 1 evaluation per sample.
Range (min … max): 31.601 ms … 141.646 ms ┊ GC (min … max): 0.00% … 56.56%
Time (median): 32.249 ms ┊ GC (median): 0.00%
Time (mean ± σ): 35.243 ms ± 15.883 ms ┊ GC (mean ± σ): 5.56% ± 9.08%
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31.6 ms Histogram: log(frequency) by time 133 ms <
Memory estimate: 8.94 MiB, allocs estimate: 134979.UnfoldMakie.jl with DelaunayMesh
@benchmark plot_topoplot(
dat[:, 320, 1];
positions = positions,
topo_interpolation = (; interpolation = DelaunayMesh()),
)BenchmarkTools.Trial: 142 samples with 1 evaluation per sample.
Range (min … max): 31.482 ms … 153.396 ms ┊ GC (min … max): 0.00% … 57.10%
Time (median): 32.128 ms ┊ GC (median): 0.00%
Time (mean ± σ): 35.618 ms ± 18.275 ms ┊ GC (mean ± σ): 6.28% ± 9.28%
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31.5 ms Histogram: log(frequency) by time 146 ms <
Memory estimate: 8.94 MiB, allocs estimate: 134984.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: 286 samples with 1 evaluation per sample.
Range (min … max): 11.795 ms … 601.880 ms ┊ GC (min … max): 0.00% … 0.00%
Time (median): 12.111 ms ┊ GC (median): 0.00%
Time (mean ± σ): 17.489 ms ± 51.582 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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11.8 ms Histogram: frequency by time 13.5 ms <
Memory estimate: 4.12 KiB, allocs estimate: 124.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): 1.839 s … 1.861 s ┊ GC (min … max): 3.35% … 5.13%
Time (median): 1.846 s ┊ GC (median): 5.17%
Time (mean ± σ): 1.849 s ± 11.034 ms ┊ GC (mean ± σ): 4.62% ± 1.10%
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1.84 s Histogram: frequency by time 1.86 s <
Memory estimate: 386.48 MiB, allocs estimate: 5519586.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: 6 samples with 1 evaluation per sample.
Range (min … max): 689.554 ms … 1.929 s ┊ GC (min … max): 0.00% … 0.00%
Time (median): 690.743 ms ┊ GC (median): 0.00%
Time (mean ± σ): 897.430 ms ± 505.385 ms ┊ GC (mean ± σ): 0.00% ± 0.00%
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690 ms Histogram: frequency by time 1.93 s <
Memory estimate: 3.06 KiB, allocs estimate: 98.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
@benchmark begin
f = Makie.Figure()
dat_obs = Observable(dat[:, 1, 1])
plot_topoplot!(f[1, 1], dat_obs, positions = positions)
record(f, "topoplot_animation_UM.gif", timestamps; framerate = framerate) do t
dat_obs[] = @view(dat[:, t, 1])
end
endBenchmarkTools.Trial: 2 samples with 1 evaluation per sample.
Range (min … max): 3.907 s … 4.004 s ┊ GC (min … max): 0.44% … 1.61%
Time (median): 3.956 s ┊ GC (median): 1.03%
Time (mean ± σ): 3.956 s ± 68.735 ms ┊ GC (mean ± σ): 1.03% ± 0.83%
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3.91 s Histogram: frequency by time 4 s <
Memory estimate: 678.78 MiB, allocs estimate: 896288.
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 9.165 s (0.00% GC) to evaluate,
with a memory estimate of 4.07 KiB, over 150 allocations.Note, that due to some bugs in (probably) PythonCall topoplot is black and white.
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