Validation with PySPEDAS

Performance Notes

Calling Python from Julia (via PythonCall.jl) introduces only a negligible overhead, typically within nanoseconds.
Memory allocations shown in Julia benchmarks do not include allocations that occur within Python. To measure Python-side allocations, profiling should be done directly in Python.
The documentation and benchmarks are generated using GitHub Actions. Running the code locally with multiple threads (e.g., by setting JULIA_NUM_THREADS) can yield even greater performance improvements for Julia.

using PySPEDAS
using SPEDAS
using PythonCall
using DimensionalData
using PySPEDAS: get_data
using CairoMakie, SpacePhysicsMakie

For minimum variance analysis, see MinimumVarianceAnalysis.jl.

Wave polarization

References: twavpol, test_twavpol.py - PySPEDAS, Wave polarization using SCM data - PySPEDAS

@py import pyspedas.analysis.tests.test_twavpol: TwavpolDataValidation
TwavpolDataValidation.setUpClass()

thc_scf_fac = get_data("thc_scf_fac") |> DimArray
py_tvars = [
    "thc_scf_fac_powspec",
    "thc_scf_fac_degpol",
    "thc_scf_fac_waveangle",
    "thc_scf_fac_elliptict",
    "thc_scf_fac_helict",
]
# PySPEDAS returns non valid values at the first and last frequency bin, the last time bin is also not valid
_subset_py(x) = x[1:end-1,2:end-1]
py_result = DimStack(_subset_py.(DimArray.(get_data.(py_tvars))))
py_result.thc_scf_fac_powspec.metadata[:colorscale] = log10
py_result.thc_scf_fac_helict.metadata[:colorscale] = identity
res = twavpol(thc_scf_fac)

f = Figure(; size=(1200, 800))
tplot(f[1,1], py_result)
tplot(f[1,2], res)
f

We can also use single value decomposition (SVD) technique to calculate the wave polarization.

res = twavpol_svd(thc_scf_fac)
tplot(res)

Benchmark

using Chairmarks

@b twavpol(thc_scf_fac), twavpol_svd(thc_scf_fac), pyspedas.twavpol("thc_scf_fac")

Julia is about 100 times faster than Python.