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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 PyMNE
using PythonPlot
using BenchmarkTools
using Observables
using CairoMakie

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: 135 samples with 1 evaluation per sample.
 Range (min … max):  32.779 ms … 379.535 ms  ┊ GC (min … max): 0.00% … 86.85%
 Time  (median):     34.253 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   37.059 ms ±  29.743 ms  ┊ GC (mean ± σ):  6.59% ±  7.47%

  ▃█▁▁▁ ▁ ▂▃▃ ▁                                                 
  █████▇█████▆█▃▁▄▁▃▃▅▃▆▅▃▁▃▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▃ ▃
  32.8 ms         Histogram: frequency by time         44.2 ms <

 Memory estimate: 9.12 MiB, allocs estimate: 138958.

UnfoldMakie.jl with DelaunayMesh

@benchmark plot_topoplot(
    dat[:, 320, 1];
    positions = positions,
    topo_interpolation = (; interpolation = DelaunayMesh()),
)
BenchmarkTools.Trial: 144 samples with 1 evaluation per sample.
 Range (min … max):  33.084 ms … 50.703 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     34.350 ms              ┊ GC (median):    0.00%
 Time  (mean ± σ):   34.873 ms ±  2.374 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▃▆▅▆▂█▂▂                                                     
  ████████▆▃▃▃▄▅▅▃▃▃▃▁▁▁▃▁▁▁▁▁▃▁▁▁▁▃▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▁▁▁▁▃ ▃
  33.1 ms         Histogram: frequency by time        47.8 ms <

 Memory estimate: 9.12 MiB, allocs estimate: 138965.

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()
end
BenchmarkTools.Trial: 307 samples with 1 evaluation per sample.
 Range (min … max):  11.866 ms … 644.853 ms  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     12.230 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   16.304 ms ±  47.602 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

  ▆█▅▄▅▅▃▂▁                                                     
  █████████▇▅▅▆▁▄▄▁▁▁▁▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▄▄▁▁▁▁▁▁▁▄ ▆
  11.9 ms       Histogram: log(frequency) by time      20.1 ms <

 Memory estimate: 4.22 KiB, allocs estimate: 130.

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]"),
    )
end
BenchmarkTools.Trial: 3 samples with 1 evaluation per sample.
 Range (min … max):  1.941 s …   2.053 s  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     1.975 s              ┊ GC (median):    0.00%
 Time  (mean ± σ):   1.989 s ± 57.503 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

  █                █                                      █  
  █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█ ▁
  1.94 s         Histogram: frequency by time        2.05 s <

 Memory estimate: 400.72 MiB, allocs estimate: 5811718.

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):  687.266 ms …    2.031 s  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     702.659 ms               ┊ GC (median):    0.00%
 Time  (mean ± σ):   920.627 ms ± 544.033 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

  █▃                                                             
  ██▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▇ ▁
  687 ms           Histogram: frequency by time          2.03 s <

 Memory estimate: 3.20 KiB, allocs estimate: 107.

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 = 50
50

UnfoldMakie 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
end
BenchmarkTools.Trial: 2 samples with 1 evaluation per sample.
 Range (min … max):  3.904 s …   3.971 s  ┊ GC (min … max): 0.00% … 0.00%
 Time  (median):     3.937 s              ┊ GC (median):    0.00%
 Time  (mean ± σ):   3.937 s ± 47.291 ms  ┊ GC (mean ± σ):  0.00% ± 0.00%

  █                                                       █  
  █▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁█ ▁
  3.9 s          Histogram: frequency by time        3.97 s <

 Memory estimate: 680.86 MiB, allocs estimate: 962744.

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)
end
BenchmarkTools.Trial: 1 sample with 1 evaluation per sample.
 Single result which took 7.806 s (0.00% GC) to evaluate,
 with a memory estimate of 4.13 KiB, over 154 allocations.

Note, that due to some bugs in (probably) PythonCall topoplot is black and white.

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