Prediction

Classes to perform multidiversity index prediction.

BaseDataManager(*args, **kwargs)

Bases: niva.core.mixins.QgsLogicMixin

Base class for data management. Contain layers to extract features needed for computation.

Source code in niva/core/prediction/data_manager.py

def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)

crs property

Return CRS of data.

compute_metrics() abstractmethod

Compute metrics from data layers and output results as Sample. Sample will not have mdi, novelty & branch informations.

Source code in niva/core/prediction/data_manager.py

@abstractmethod
def compute_metrics(self) -> Sample:
    """Compute metrics from data layers and output results as Sample. Sample will not have mdi, novelty & branch informations."""

filter_iacs(layer)

Filter IACS on Niva.ID fields.

Parameters:

Name	Type	Description	Default
layer	qgis.core.QgsVectorLayer	IACS layer.	required

Returns:

Type	Description
qgis.core.QgsVectorLayer	Filtered IACS.

Source code in niva/core/prediction/data_manager.py

def filter_iacs(self, layer: QgsVectorLayer) -> QgsVectorLayer:
    """Filter IACS on Niva.ID fields.

    Args:
        layer (QgsVectorLayer): IACS layer.

    Returns:
        Filtered IACS.
    """
    filtered = layer.materialize(
        QgsFeatureRequest().setFilterExpression(f'"Niva.ID" < 41')
    )
    return filtered

GridDataManager(grid, *args, **kwargs)

Bases: niva.core.prediction.data_manager.BaseDataManager

DataManager for grid based predictor.

Source code in niva/core/prediction/data_manager.py

def __init__(self, grid: QgsVectorLayer, *args, **kwargs):
    self.grid = grid
    super().__init__(*args, **kwargs)

compute_metrics(cell) abstractmethod

Compute cell metrics from data layers and output results as Sample. Sample will not have mdi, novelty & branch informations.

Parameters:

Name	Type	Description	Default
cell	qgis.core.QgsGeometry	Cell to compute metrics on.	required

Returns:

Type	Description
niva.core.samples.sample.Sample	Sample of variables.

Source code in niva/core/prediction/data_manager.py

@abstractmethod
def compute_metrics(self, cell: QgsGeometry) -> Sample:
    """Compute cell metrics from data layers and output results as Sample. Sample will not have mdi, novelty & branch informations.

    Args:
        cell (QgsGeometry): Cell to compute metrics on.

    Returns:
        Sample of variables.
    """
    pass

__getitem__(x)

Get feature from self.grid or use passed feature to compute metrics on a Sample.

Parameters:

Name	Type	Description	Default
x	int	Grids feature id or QgsFeature.	required

Returns:

Type	Description
niva.core.samples.sample.Sample	Sample corresponding to cell geometry.

Source code in niva/core/prediction/data_manager.py

def __getitem__(self, x: Union[int, QgsFeature]) -> Sample:
    """Get feature from self.grid or use passed feature to compute metrics on a Sample.

    Args:
        x (int): Grids feature id or QgsFeature.

    Returns:
        Sample corresponding to cell geometry.
    """

    if not isinstance(x, QgsFeature):
        cell = self.grid.getFeature(x)
    sample = self.compute_metrics(cell.geometry())
    sample.geometry = cell.geometry()
    return sample

T1GridDataManager(grid, iacs, artificial, naa=None, *args, **kwargs)

Bases: niva.core.prediction.data_manager.GridDataManager

Data manager for Contra Tier 1 with grid based predictions.

Parameters:

Name	Type	Description	Default
iacs	qgis.core.QgsVectorLayer	Layer of IACS data. Warning: Provide IACS with Niva.ID field.	required
artificial	qgis.core.QgsVectorLayer	Layer of artificial data.	required
naa	qgis.core.QgsVectorLayer	Layer of NAA data.	None

Attributes:

Name	Type	Description
iacs	qgis.core.QgsVectorLayer	Layer of IACS data.
artificial	qgis.core.QgsVectorLayer	Layer of artificial data.
naa	typing.Union[qgis.core.QgsVectorLayer, None]	Layer of NAA data or None if not passed to constructor.
iacs_index	qgis.core.QgsSpatialIndex	Spatial index for layer iacs.
artificial_index	qgis.core.QgsSpatialIndex	Spatial index for layer artificial.
naa_index	qgis.core.QgsSpatialIndex	Spatial index for layer naa.

Source code in niva/core/prediction/data_manager.py

def __init__(
    self,
    grid: QgsVectorLayer,
    iacs: QgsVectorLayer,
    artificial: QgsVectorLayer,
    naa: QgsVectorLayer = None,
    *args,
    **kwargs,
):
    super().__init__(grid, *args, **kwargs)
    self.iacs = self.filter_iacs(iacs)
    self.naa = naa
    self.artificial = artificial

    layers = [self.iacs, self.artificial]
    self.iacs_index = QgsSpatialIndex(self.iacs.getFeatures())
    self.artificial_index = QgsSpatialIndex(self.artificial.getFeatures())
    if self.naa:
        layers.append(self.naa)
        self.naa_index = QgsSpatialIndex(self.naa.getFeatures())

compute_metrics(cell)

Compute metrics and build Sample for cell.

Parameters:

Name	Type	Description	Default
cell	qgis.core.QgsGeometry	Cell geometry to compute metric on.	required

Returns:

Type	Description
niva.core.samples.sample.T1Sample	Tier 1 sample for cell.

Source code in niva/core/prediction/data_manager.py

def compute_metrics(self, cell: QgsGeometry) -> T1Sample:
    """Compute metrics and build Sample for cell.

    Args:
        cell (QgsGeometry): Cell geometry to compute metric on.

    Returns:
        Tier 1 sample for cell.
    """
    # Gather features
    iacs_feats = self._get_cell_features(self.iacs, self.iacs_index, cell)
    artificial_feats = self._get_cell_features(
        self.artificial, self.artificial_index, cell, crop=False
    )
    # Group geometries
    iacs = QgsGeometry.unaryUnion([f.geometry() for f in iacs_feats])
    artificial = QgsGeometry.unaryUnion([f.geometry() for f in artificial_feats])
    # get buildings with aoe geometry
    buildings = self._get_buildings(artificial_feats)
    # if naa apply geometries transformations.
    if self.naa:
        naa_feats = self._get_cell_features(self.naa, self.naa_index, cell)
        naa_union = QgsGeometry.unaryUnion([f.geometry() for f in naa_feats])
        naa_union = naa_union.difference(artificial)
        iacs = iacs.difference(naa_union)
        buildings = buildings.difference(naa_union)
    else:
        naa_union = QgsGeometry.fromWkt("POLYGON EMPTY")

    artificial = QgsGeometry.unaryUnion([artificial, buildings])
    if not naa_union.isEmpty():
        artificial = artificial.difference(naa_union)
    artificial = artificial.intersection(cell)

    # compute metrics
    snc = semi_natural_cover(artificial, iacs, cell.area())
    mfs = mean_field_size(iacs_feats)
    rich, simpson = diversity(iacs_feats)

    return T1Sample(richness=rich, simpson=simpson, snc=snc, mfs=mfs, geometry=cell)