src.eval.raster_grid_evaluatorpp

Classes

RasterGridEvaluatorPP(**kwargs)

class src.eval.raster_grid_evaluatorpp.RasterGridEvaluatorPP(**kwargs)
Author:

Alberto M. Esmoris Pena

C++-accelerated raster-grid evaluator drop-in replacement for RasterGridEvaluator.

Generates one raster-like 2D grid per requested specification by delegating the heavy reducer sweep to the typed C++ entry points exposed by pyvl3dpp (one of evaluate_raster_grid_dd, _df, _fd, _ff depending on the structure / feature precision selectors) and the chained per-grid post-processors to the C++ kernels behind src.eval.raster.raster_postproc. The legacy JSON schema is preserved as much as possible.

  1. The hillshading post-processor is computed by the same C++ Horn kernel as the new hillshade key (the legacy earthpy code path is NOT used by PP).

  2. The four non-relational reducers (mean, median, min, max) accept a grid spec that omits target_relational; the six relational-using reducers (binary_mask, binary_mask_nofeats, recount, recount_nofeats, relative_recount, relative_recount_nofeats) still raise a byte-identical PP-prefixed legacy message.

Variables:

  • plot_path (str) – The path to write the raster.

  • conditions (list of dict or None) – The list of global conditional filters applied before any grid digestion.

  • grids (list of dict) – The many grid specifications.

  • crs (str) – The coordinate reference system (CRS).

  • xres (float) – The cell size along the x-axis.

  • yres (float) – The cell size along the y-axis.

  • grid_iter_step (int) – How many rows at most must be considered per iteration when generating the raster-like grid.

  • reverse_rows (bool) – Whether to reverse the rows of the grid (True) or not (False).

  • radius_expr (str) – An expression defining the radius of the neighborhood centered on each cell. In this expression l represents the greatest cell size, i.e., \(\max \; \{\mathrm{xres}, \mathrm{yres}\}\).

  • nthreads (int) – How many threads must be used by the C++ sweep. -1 means as many threads as available cores.

  • tree_type (str) – Selector consumed by the underlying NeighborhoodComputer ("auto" by default).

RELATIONAL_MISSING_MSG = 'RasterGridEvaluatorPP failed because no target relational specification was given.'
static extract_eval_args(spec)

Extract the arguments to initialize/instantiate a RasterGridEvaluatorPP from a key-word specification (mirrors the legacy RasterGridEvaluator.extract_eval_args() plus the new key tree_type).

Parameters:

spec (dict) – The key-word specification containing the arguments.

Returns:

The arguments to initialize/instantiate a RasterGridEvaluatorPP.

Return type:

dict

__init__(**kwargs)

Initialize/instantiate a RasterGridEvaluatorPP.

Parameters:

kwargs – The attributes for the RasterGridEvaluatorPP.

eval(pcloud)

Evaluate the point cloud as a raster-like 2D grid using the C++-accelerated reducer sweep and post-processor chain.

Parameters:

pcloud (PointCloud) – The point cloud to be evaluated.

Returns:

The raster grid obtained after computing the evaluation.

Return type:

RasterGridEvaluation

__call__(pcloud, **kwargs)

Evaluate with extra logic that is convenient for pipeline- based execution; mirrors the legacy RasterGridEvaluator.__call__() (lines 174-196).

See evaluator.Evaluator.eval().

Parameters:

  • pcloud (PointCloud) – The point cloud that must be evaluated through raster-like grid analysis.

  • kwargs – Extra arguments such as out_prefix used by the plotting step.

Returns:

Nothing.

eval_args_from_state(state)

Obtain the arguments to call the RasterGridEvaluatorPP from the current pipeline’s state (mirrors the legacy implementation).

Parameters:

state (SimplePipelineState) – The pipeline’s state.

Returns:

The dictionary of arguments for calling RasterGridEvaluatorPP.

Return type:

dict

apply_global_filter(X, F, pcloud)

Filter the given structure and feature spaces at a global level (mirrors legacy lines 846-876 but routes the per- condition filter through the extracted apply_condition_filter() free function).

Parameters:

  • X (np.ndarray) – The structure space matrix of point-wise coordinates.

  • F (np.ndarray or None) – The feature space matrix, or None.

  • pcloud (PointCloud) – The point cloud being filtered; its features are queried to resolve per-condition value names.

Returns:

The filtered structure and feature spaces.

Return type:

tuple of np.ndarray