eradiate.experiments.CanopyExperiment

eradiate.experiments.CanopyExperiment#

class eradiate.experiments.CanopyExperiment(measures=NOTHING, integrator=AUTO, results=NOTHING, default_spectral_set=AUTO, quad_spec=NOTHING, extra_objects=None, illumination=NOTHING, canopy=None, padding=0, surface=NOTHING)[source]#

Bases: EarthObservationExperiment

Simulate radiation in a scene with an explicit canopy and no atmosphere. This experiment assumes that the surface is plane and accounts for ground unit cell padding.

Parameters:

  • measures (list of Measure or list of dict or Measure or dict, default: MultiDistantMeasure()) – List of measure specifications. The passed list may contain dictionaries, which will be interpreted by measure_factory. Optionally, a single Measure or dictionary specification may be passed and will automatically be wrapped into a list.

  • integrator (Integrator or dict or AUTO, default: AUTO) – Monte Carlo integration algorithm specification. This parameter can be specified as a dictionary which will be interpreted by integrator_factory.The integrator defaults to AUTO, which will choose the appropriate integrator depending on the experiment’s configuration.

  • default_spectral_set (BinSet or WavelengthSet or AUTO, default: AUTO) – Default spectral set. This attribute is used to set the default value for spectral_set.If the value is AUTO, the default spectral set is selected based on the active mode. Otherwise, the value must be a BinSet or WavelengthSet instance.

  • extra_objects (dict or None, default: None) – Dictionary of extra objects to be added to the scene. The keys of this dictionary are used to identify the objects in the kernel dictionary.

  • illumination (DirectionalIllumination or ConstantIllumination or dict, default: DirectionalIllumination()) – Illumination specification. This parameter can be specified as a dictionary which will be interpreted by illumination_factory.

  • canopy (Canopy or dict or None, default: None) – Canopy specification. This parameter can be specified as a dictionary which will be interpreted by biosphere_factory.

  • padding (int, default: 0) – Padding level. The scene will be padded with copies to account for adjacency effects. This, in practice, has effects similar to making the scene periodic.A value of 0 will yield only the defined scene. A value of 1 will add one copy in every direction, yielding a 3×3 patch. A value of 2 will yield a 5×5 patch, etc. The optimal padding level depends on the scene.

  • surface (BasicSurface or BSDF or dict, optional, default: BasicSurface(bsdf=LambertianBSDF())) – Surface specification. If set to None, no surface will be added. This parameter can be specified as a dictionary which will be interpreted by surface_factory and bsdf_factory. If relevant, the surface size will be adjusted automatically upon kernel. dictionary generation.

Fields:

Notes

A post-initialisation step will constrain the measure setup if a distant measure is used and no target is defined:

  • if a canopy is defined, the target will be set to the top of the canopy unit cell (i.e. without its padding);

  • if no canopy is defined, the target will be set to [0, 0, 0].

clear()#

Clear previous experiment results and reset internal state.

init()#

Generate kernel dictionary and initialise Mitsuba scene.

pipeline(measure)#

Return the post-processing pipeline for a given measure.

Parameters:

measure (Measure or int) – Measure for which the pipeline is generated.

Returns:

hamilton.driver.Driver

postprocess()#

Post-process raw results and store them in results.

process(spp=0, seed_state=None)#

Run simulation and collect raw results.

Parameters:

  • spp (int, optional) – Sample count. If set to 0, the value set in the original scene definition takes precedence.

  • seed_state (SeedState, optional) – Seed state used to generate seeds to initialize Mitsuba’s RNG at every iteration of the parametric loop. If unset, Eradiate’s root seed state is used.

spectral_indices(measure_index)#

Generate spectral indices for a given measure.

Parameters:

measure_index (int) – Measure index for which spectral indices are generated.

Yields:

SpectralIndex – Spectral index.

property context_init#

Return a single context used for scene initialization.

property contexts#

Return a list of contexts used for processing.

property results#

Post-processed simulation results.

Returns:

dict[str, Dataset] – Dictionary mapping measure IDs to xarray datasets.

property scene#

Return a scene object used for kernel dictionary template and parameter table generation.

property spectral_set#

A dictionary mapping measure index to the associated spectral set.

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