eradiate.experiments.CanopyExperiment#

class eradiate.experiments.CanopyExperiment(measures=_Nothing.NOTHING, illumination=_Nothing.NOTHING, canopy=None, padding=0, surface=_Nothing.NOTHING, integrator=_Nothing.NOTHING)[source]#

Bases: eradiate.experiments._core.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.
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.
integrator (Integrator or dict, default: PathIntegrator()) – Monte Carlo integration algorithm specification. This parameter can be specified as a dictionary which will be interpreted by integrator_factory.

Fields

measures (list of Measure) – List of measure specifications.
illumination (DirectionalIllumination or ConstantIllumination) – Illumination specification.
canopy (Canopy or None) – Canopy specification.
padding (int) – Padding level.
surface (BasicSurface or None) – Surface specification.

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].

classmethod from_dict(d)#

Instantiate from a dictionary. The default implementation raises an exception.

Parameters: d (dict) – Dictionary to be converted to an Experiment.
Returns: Experiment

kernel_dict(ctx)[source]#

Return a dictionary suitable for kernel scene configuration.

Parameters: ctx (KernelDictContext) – A context data structure containing parameters relevant for kernel dictionary generation.
Returns: KernelDict – Kernel dictionary which can be loaded as a Mitsuba object.

kernel_dicts(measure)#

A generator which returns kernel dictionaries (and the associated context) relevant to a given measure.

Parameters

measure (Measure or int) – Measure for which kernel dictionaries are to be generated. Alternatively, the index in the self.measure list can be passed.

Yields

kernel_dict (KernelDict) – Generated kernel dictionary.
ctx (KernelDictContext) – Context used to generate kernel_dict.

pipeline(*measures)#

Request post-processing pipeline for a given measure.

Parameters: *measures (Measure or int) – One or several measures for which to get a post-processing pipeline. If integer values are passed, they are used to query the measure list.
Returns: pipelines (Pipeline or tuple of Pipeline) – If a single measure is passed, a single Pipeline instance is returned; if multiple measures are passed, a tuple of pipelines is returned.

postprocess(*measures, pipeline_kwargs=None)#

Post-process raw results stored in a measure’s results field. This requires a successful execution of process(). Post-processed results are stored in self.results.

Parameters

*measures (Measure or int) – One or several measures for which to perform post-processing. Alternatively, indexes in the measure array can be passed. If no value is passed, all measures are processed.
pipeline_kwargs (dict, optional) – A dictionary of pipeline keyword arguments forwarded to Pipeline.transform().

Raises

ValueError – If measure.results is None, i.e. if process() has not been successfully run.