Configuration

Configuration#

pyRadtran assembles its configuration from three YAML layers — later layers override earlier ones:

  1. Package defaults — sensible values shipped with pyRadtran

  2. Your master config — ~/.pyradtran/config.yaml: machine-specific paths, set once (see Installation)

  3. Simulation config — the YAML you pass as config_path=: only what is different for this experiment

A typical simulation config is short:

simulation_defaults:
  source: solar
  rte_solver: disort
  mol_abs_param: lowtran per_nm
  wavelength_nm: [400, 770]     # nm; {start: 400, end: 770} also accepted
  output_columns: [eglo, eup, edir]
  output_altitudes_km: [0.0]

execution:
  max_workers: 8              # parallel uvspec processes
  timeout_seconds: 60
  cleanup_temp_files: false   # keep .inp files for debugging

Paths (libradtran_bin, libradtran_data, atmosphere_profile, solar_spectrum) belong in the master config and are inherited by every simulation.

Building configs in Python#

import pyradtran

cfg = pyradtran.load_config()               # defaults + master config
cfg.simulation_defaults.albedo_value = 0.2
cfg.to_yaml('config/my_simulation.yaml')    # save for reuse

You can also override any config field per run without touching YAML, using a dotted key in params:

ds.pyradtran.run(
    config_path='config/my_simulation.yaml',
    params={'simulation_defaults.wavelength_nm': [400, 700]},
)

Which knob lives where?#

  • Config: everything that defines the experiment — solver, spectral range, output columns/altitudes, parallelism, paths

  • params: everything that varies at run time — per-point values, parameter sweeps, extra uvspec options (see Parameters & Validation)

Both end up in the same generated input file; ds.pyradtran.explain() shows exactly which layer produced each line.

Contents