Parameter Sweeps API

Run the model over the Cartesian product of parameter values.

from biosnicar.drivers.sweep import parameter_sweep

parameter_sweep()

parameter_sweep(params, solver="adding-doubling", return_spectral=False,
                progress=True) -> SweepResult

Parameter	Type	Default	Description
`params`	dict	required	`{name: [values]}` for each parameter
`solver`	str	`"adding-doubling"`	`"adding-doubling"` or `"toon"`
`return_spectral`	bool	False	Include 480-band spectral albedo
`progress`	bool	True	Show tqdm progress bar

Supported parameter keys: solzen, direct, incoming, rds, rho, dz, layer_type, and impurity names (black_carbon, snow_algae, glacier_algae, dust).

Returns: SweepResult (behaves like a pandas DataFrame).

SweepResult

The result contains one row per parameter combination with columns for each input parameter plus output columns: BBA, BBAVIS, BBANIR.

.to_platform(*platforms)

Append satellite/GCM band columns:

df = parameter_sweep(params={...}).to_platform("sentinel2")
df = parameter_sweep(params={...}).to_platform("sentinel2", "modis")

With a single platform, columns are unprefixed (B3, NDSI). With multiple platforms, columns are prefixed (sentinel2_B3, modis_B1).

Returns: pandas.DataFrame

Example

from biosnicar.drivers.sweep import parameter_sweep
 
df = parameter_sweep(
    params={
        "solzen": [30, 40, 50, 60, 70],
        "rds": [100, 200, 500, 1000],
    }
)
 
# Pivot table
df.pivot_table(values="BBA", index="solzen", columns="rds")
 
# With satellite bands
df = parameter_sweep(
    params={"rds": [500, 1000], "solzen": [50, 60]},
).to_platform("sentinel2")
 
print(df[["rds", "solzen", "BBA", "B3", "NDSI"]])

Outputs Object Overview