Data format

All output files are HDF5. Each file contains one or more groups (one per statistic). The file root carries a git_hash attribute.

HDF5 group schema

smf/{sample_name} and lf/{sample_name}:

smf/{sample_name}/
├── attrs            (provenance — see table below)
├── cosmology/       H0, Om0, Ok0, Ob0
├── bin_edges        (N+1,)  [dex or mag]
├── log10mstar_centres | mag_centres  (N,)
├── phi              (N,)    [Mpc⁻³ dex⁻¹ or Mpc⁻³ mag⁻¹]
├── phi_err          (N,)
└── cov              (N, N)

twopcf/{stat}/{sample_name} (stat = wp · wtheta · xi):

├── bin_edges    (N+1,)  [Mpc or arcmin]
├── sep_centres  (N,)
├── xi           (N,)    [Mpc or dimensionless]
└── cov          (N, N)

esd/{sample_name}:

├── rp_bin_edges  (N+1,)  [Mpc]
├── rp_centres    (N,)
├── delta_sigma   (N,)    [M_sun/pc²]
└── cov           (N, N)

joint_covariance (joint files only):

├── data_vector    (N_total,)          [phi | wp | ΔΣ]
├── err_jackknife  (N_total,)
├── cov            (N_total, N_total)
├── jk_subsamples  (N_JK, N_total)
├── mstar_centres  (N_smf,)  [dex]
└── rp_centres     (N_wp,)   [Mpc]

Provenance attributes

Every group carries these attrs:

Attribute	Type	gga_model	Description
script_name	str	—	Script that produced this group
git_hash	str	—	Short git commit hash at run time
run_date	str	—	UTC ISO-8601 timestamp
survey	str	—	bgs · mock · cosmos · gama
statistic	str	—	smf · lf · wp · wtheta · esd · …
input_catalogue	str	—	Semicolon-separated absolute input paths
z_eff	float	yes	np.average(z, weights=w)
n_gal	int	—	Number of galaxies
n_rand	int	—	Number of randoms (-1 if not applicable)
estimator	str	yes	vmax · swml · cminus
z_min, z_max	float	yes	Redshift interval
area_deg2, area_sr	float	yes	Survey footprint
cov_method	str	yes	jackknife · diagonal_poisson
weight_variant	str	yes	uniform · sys-add · sys-comb

Reading example

import h5py

with h5py.File("data/lf_smf/BGS_Mstar9.00_z0.05_0.08-smf-vmax.h5") as f:
    grp = f["smf/BGS_Mstar9.00_z0.05_0.08"]
    phi     = grp["phi"][:]
    cov     = grp["cov"][:]
    z_eff   = grp.attrs["z_eff"]
    area_sr = grp.attrs["area_sr"]

gga_model required fields

Field	Notes
phi, cov	Measurement and covariance
log10mstar_centres	Must match model grid
z_eff	Evaluates model at correct epoch
area_sr	Normalises volume integral
estimator	Selects matching likelihood
z_min, z_max	Redshift window for selection function
weight_variant	Selects systematic branch
cov_method	Hartlap correction flag