Reading GADGET data (experimental)

Mera's analysis layer is code-blind, so a reader only has to fill the standard structs. This page adds a frontend for the GADGET HDF5 snapshot format — also written by GIZMO, AREPO, SWIFT, EAGLE and IllustrisTNG — so getvar, projection, msum, center_of_mass and the rest run on its particles unchanged.

Scope

GADGET is particle-based (no Eulerian grid), so this is a particle reader: it loads the PartType* groups into a Mera PartDataType via getparticles. Gas SPH fields (Density/InternalEnergy/…) are not yet exposed; the gas particles load as particles like the rest. 3-D.

Usage

getinfo / getparticles auto-detect GADGET from the HDF5 Header group:

using Mera
info = getinfo(200, "/path/to/gadget/run")   # finds snap…_200.hdf5, simcode = "GADGET"
part = getparticles(info)                      # a PartDataType (:x,:y,:z,:vx,:vy,:vz,:mass,:id,:family)

msum(part); center_of_mass(part); getvar(part, :vx)

:family is the GADGET particle type — 0 gas, 1 halo/DM, 2 disk, 3 bulge, 4 stars, 5 boundary/BH. On a large snapshot, restrict to a subset with the frontend directly to keep RAM bounded:

stars = getparticles_gadget(info; families=[4])      # just the star particles
dm    = getparticles_gadget(info; families=[1])      # just the dark matter

Masses come from each type's Masses dataset, or from Header/MassTable for types that store a single per-type value (e.g. dark matter).

Loading a spatial sub-region

getparticles honours the RAMSES spatial-window arguments xrange/yrange/zrange (with center/range_unit). Particles outside the box are dropped per type as they are read, so a sub-region of a huge snapshot never accumulates in memory:

# the central 20 % box (fractions of the box, relative to its centre)
part = getparticles(info; xrange=[-0.1, 0.1], yrange=[-0.1, 0.1], zrange=[-0.1, 0.1],
                    center=[:bc], range_unit=:standard)

The result equals a full load filtered by getvar(:x), and the window is recorded in part.ranges. Combine with families= (on the frontend) to load, say, only the stars in a region.

Worked example: the yt GadgetDiskGalaxy sample

The yt GadgetDiskGalaxy sample is a z ≈ 1.9 galaxy with ~11.9M particles (4.3M gas, 4.8M DM, 2.3M disk, 451k stars). getinfo prints the overview:

julia> info = getinfo(200, "/data/gadget_diskgalaxy/GadgetDiskGalaxy");

Code: GADGET
output: 200  time: 0.34483  redshift: 1.9
boxlen = 64000.0
particles: 4334546 gas, 4786616 halo/DM, 2333848 disk, 450921 stars, 1149 bndry/BH  (total 11907080)
-------------------------------------------------------

and the particles plot directly — the dark-matter cosmic web and the star particles tracing the forming galaxy:

dm = getparticles_gadget(info; families=[1]); st = getparticles_gadget(info; families=[4])
# scatter getvar(dm,:x) vs getvar(dm,:y), and the stars — or project with a finer lmax/res

GADGET disk galaxy: the dark-matter halo and filaments (left, 4.8M particles) and the star particles tracing the forming galaxy (right, 451k) — read into a PartDataType and plotted with the usual getvar calls.

Units

GADGET data is in code units (commonly length kpc/h, mass 10¹⁰ M⊙/h, velocity km/s, with h the dimensionless Hubble parameter). The defaults treat the run as dimensionless; pass the run's CGS unit_length/unit_density/unit_velocity to getinfo_gadget for physical conversions (mind the h factors). Cosmological metadata (Time = scale factor, Redshift, HubbleParam, Omega0/OmegaLambda) is read from the Header.

How it maps onto Mera's structs

Each PartTypeN group has Coordinates/Velocities (3×N), ParticleIDs, and optionally Masses. The reader concatenates the requested types into one PartDataType with columns (:x,:y,:z, :vx,:vy,:vz, :mass, :id, :family) — positions in code units [0, boxlen], exactly the convention the RAMSES/PLUTO particle readers use, so the particle analysis works unchanged. The mapping is verified data-free in test/60_gadget_reader_tests.jl (a synthesised GADGET file with a MassTable fallback) and on the real GadgetDiskGalaxy sample.

Reference readers

The GADGET HDF5 layout is shared and well-documented; this frontend agrees with the origin tools:

The GADGET snapshot specification — the Header + PartType* group format (Coordinates/ Velocities/Masses/ParticleIDs, Header/MassTable/BoxSize/…), documented in the GADGET-4 guide and reused by GIZMO, AREPO, SWIFT, EAGLE and IllustrisTNG.
yt — its particle frontends read the same format and select sub-volumes lazily via data objects; Mera's load-time window mirrors that on the particle list. The GadgetDiskGalaxy sample used above comes from the yt sample-data collection.