Configuration#
Analyses use YAML configuration layered on top of a compact canonical default file. This keeps ordinary analyses reproducible while allowing a variation to change only the relevant options.
CM2026 analyses#
The observational defaults live in
configs/cm2026/cm2026_defaults.yaml. The standard baseline overlay is
configs/cm2026/cm2026_base.yaml and named variations live in
configs/cm2026/variations.
Create arguments for the baseline or a named variation with:
from cup1d import Analysis, Args
baseline_args = Args.from_baseline()
variation_args = Args.from_variation("zmin")
analysis = Analysis(variation_args)
Each variation YAML is sparse: its values override the baseline defaults. Redshift nodes, covariance grids, fiducial model values, priors, and emulator training-set choices are derived internally where appropriate.
Other analyses#
Configurations that are not CM2026 variations are grouped by analysis type:
configs/forecastsSynthetic forecasts, named by the covariance data set and emulator.
configs/mocks/<simulation>Simulation mocks, with separate files for each covariance and emulator combination.
configs/hiresJoint fits that add high-resolution P1D measurements to DESI DR1.
configs/tan2026Paper-specific configurations for the TAN2026 analysis.
Load an observational configuration explicitly with:
args = Args.from_yaml("configs/tan2026/DESIY1_FFT3_dir_DLA_TAN.yaml")
Synthetic configurations must request the synthetic defaults:
forecast_args = Args.from_yaml(
"configs/forecasts/DESIY1_QMLE3_CH24_mpgcen_gpr.yaml",
synthetic=True,
)
mock_args = Args.from_yaml(
"configs/mocks/mpg_central/DESIY1_QMLE3_CH24_mpgcen_gpr.yaml",
synthetic=True,
)
Multiple data sets are selected with a YAML list. For example,
configs/hires/hires.yaml combines DESI DR1 with Karacayli et al. (2022):
data_label:
- DESIY1_QMLE3
- Karacayli2022
scripts/data/data_sampler.py accepts either a CM2026 variation name or a
path to a YAML configuration file. Relative YAML paths are interpreted from
the repository root.