Results & Post-Processing

Results & Post-Processing#

The result dataset#

Results come back as an xarray.Dataset with your input coordinates plus wavelength / altitude dimensions where applicable:

ds_sim = ds.pyradtran.run(config_path=cfg)

ds_sim.edir                                  # direct irradiance
ds_sim.eglo.sel(wavelength=550, method='nearest').plot()

Which variables appear is set by output_columns in your config (the uvspec output_user line). You never need to list lambda or zout yourself — pyRadtran adds them automatically for spectral and multi-altitude runs so the wavelength/altitude axes can always be reconstructed.

With save_to_file=True (default) the dataset is also written to a CF-style NetCDF file with the full configuration embedded as metadata.

Provenance: know what you did#

Every result records exactly what produced it, in its attrs (and thus in the saved NetCDF):

attr

contents

pyradtran_version, history

package version, CF-style history line

pyradtran_params

the params mapping as JSON (Var/Raw as their repr)

pyradtran_config

the full merged configuration as YAML

pyradtran_input_example

the annotated uvspec input of the first point — the explain() view, frozen into the result

pyradtran_libradtran_bin

which uvspec binary ran

pyradtran_channels

channel names, when channels= was used

 
print(res.attrs['pyradtran_input_example'])
# rte_solver twostr        # config
# albedo 0.4               # dataset-var
# ...

import yaml, json
cfg_used = yaml.safe_load(res.attrs['pyradtran_config'])
params_used = json.loads(res.attrs['pyradtran_params'])

Six months later, the NetCDF alone tells you the solver, spectral range, parameter sources, and the exact input file shape — no lab notebook required. Coordinates carry units too (wavelength in nm, altitude in km); jacobian() results additionally record jacobian_param, jacobian_delta, and jacobian_base_value.

Status codes & failure logs#

Every result carries a per-point status variable:

value

meaning

0

ok

1

uvspec failed

2

skipped (NaN coordinates, or missing/NaT time)

When at least one point fails, a failures_<timestamp>.log with the captured stderr is written to the working directory, and the failing .inp file is kept for post-mortem (see Debugging).

Instrument channels#

Convolve spectral results with instrument spectral response functions (SRFs) — either as post-processing or directly in run():

from pyradtran import convolve_channels, brightness_temperature

# srf: DataArray with dims (channel, wavelength), wavelength in nm
channel_ds = ds.pyradtran.run(config_path=cfg, channels=srf)

# thermal radiances -> brightness temperature (uvspec default units)
tb = brightness_temperature(channel_ds['uu'], wavelength_nm=10500.0)

keep_spectral=True retains the original spectral variables as <name>_spectral next to the channel-averaged ones.

Sensitivity kernels#

jacobian() runs the batch twice (base and perturbed) and returns the finite-difference kernel (perturbed - base) / delta:

jac = ds.pyradtran.jacobian('albedo', 0.01, config_path=cfg)
jac['eup']    # d(eup)/d(albedo), same dims as a normal result

The perturbed parameter must be a scalar (a params literal or config default) — perturbing a per-point Var is rejected. The status variable is not differentiated: the kernel carries the worst status of the two runs per point.

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