Ionospheric Background Models

find_msis_file(; year=2018, month=12, day=7, hour=11, minute=15,
                lat=76, lon=5, height=85:1:700)

Find or create a MSIS model data file for the specified conditions.

It first searches for an existing MSIS file matching the given parameters. If no matching file is found, it calculates new MSIS data using the Python pymsis package and saves it to a file. The pymsis package will download, compile and run some fortran code under the hood.

Keyword Arguments

• year::Int=2018: Year
• month::Int=12: Month (1-12)
• day::Int=7: Day of month (1-31)
• hour::Int=11: Hour in Universal Time (0-23)
• minute::Int=15: Minute (0-59)
• lat::Real=76: Geographic latitude in degrees North
• lon::Real=5: Geographic longitude in degrees East
• height::AbstractRange=85:1:700: Altitude range in km

Returns

• String: Full path to the MSIS data file

Notes

• Default parameters correspond to the VISIONS-2 rocket launch conditions
• Files are stored in internal_data/data_neutrals/ directory

find_iri_file(; year=2018, month=12, day=7, hour=11, minute=15,
                lat=76, lon=5, height=85:1:700)

Find or create an IRI model data file for the specified conditions.

It first searches for an existing IRI file matching the given parameters. If no matching file is found, it calculates new IRI data using the Python iri2020 package and saves it to a file. The iri2020 package will compile and run some fortran code under the hood.

Keyword Arguments

• year::Int=2018: Year
• month::Int=12: Month (1-12)
• day::Int=7: Day of month (1-31)
• hour::Int=11: Hour in Universal Time (0-23)
• minute::Int=15: Minute (0-59)
• lat::Real=76: Geographic latitude in degrees North
• lon::Real=5: Geographic longitude in degrees East
• height::AbstractRange=85:1:700: Altitude range in km

Returns

• String: Full path to the IRI data file

Notes

• Default parameters correspond to the VISIONS-2 rocket launch conditions
• Files are stored in internal_data/data_electron/ directory

MSISDensity(msis_file, species)

Density profile backed by an MSIS file. Callable on an altitude grid (m), returns the density vector (m⁻³) for the requested species (:N2, :O2, or :O).

Example

profile = MSISDensity(find_msis_file(), :N2)
n = profile(altitude_grid.h)   # Vector{Float64}, length == length(altitude_grid.h)

VectorDensity(h, n)

Density profile defined by user-supplied altitude (h, m) and density (n, m⁻³) vectors. Callable on any altitude grid (m); evaluates via PCHIP interpolation in log-space, consistent with AURORA's MSIS interpolation convention.

Example

profile = VectorDensity(h_msis_m, n_N2)
n = profile(altitude_grid.h)

@law expr

Wrap a law expr (e.g. z -> exp(-z)) into an ExprLaw, capturing its source text so it can be saved and reloaded verbatim. Use this instead of a bare anonymous function for any density profile, cascading law, or phase-function generator.

ExprLaw

Serialization-friendly wrapper around a user-supplied physical law. This can be a density profile, a cascading secondary-distribution law, a phase-function generator, etc. It stores the law's source as a string so the model can round-trip through JLD2: only the string is written to disk, and the callable is rebuilt by parsing and evaluating that string on load.

Build one with the @law macro:

density         = @law z -> 1e18 .* exp.(-(z .- 100e3) ./ 30e3)
secondary_e_law = @law (E_s, E_p) -> 1 / (11.4^2 + E_s^2)

To ensure reproducibility, laws must be self-contained: closed-form expressions referencing only their arguments and names available in AURORA/Base. A law that needs to carry e.g. parameters should be constructed as a functor struct instead. To ensure this, trying to build a @law that captures local variables will throw an error.