onstove.DataProcessor.add_layer#

DataProcessor.add_layer(path: str, layer_type: str, category: str = 'Other', name: str = None, query: str = None, postgres: bool = False, base_layer: bool = False, resample: str = 'nearest', normalization: str = 'MinMax', inverse: bool = False, distance_method: str | None = None, distance_limit: Callable[[ndarray], ndarray] | None = None, window: bool | None = False, rescale: bool = False)[source]#

Adds a new layer (type VectorLayer or RasterLayer) to the DataProcessor class

Parameters:

category: str: Category of the layer. This parameter is useful to group the data into logical categories such as “Demographics”, “Resources” and “Infrastructure” or “Demand”, “Supply” and “Others”. These categories are particularly relevant for the MCA analysis.
name: str: Name of the dataset.
path: str: Path to the layer.
layer_type: str: Layer type, vector or raster.
query: str, optional: A query string to filter the data. For more information refer to pandas.DataFrame.query.

Note

Only applicable for the vector layer_type.
postgres: bool, default False: Whether to use a PostgreSQL database connection to read the layer. The connection has to already exist and be stored in the conn attribute using the set_postgres() method.
base_layer: bool, default False: Whether the layer should be used as a base_layer for the data processing.
resample: str, default ‘nearest’: Sets the default method to use when resampling the dataset. Resampling occurs when changing the grid cell size of the raster, thus the values of the cells need to be readjusted to reflect the new cell size. Several sampling methods can be used, and which one to use is dependent on the nature of the data. For a list of the accepted methods refer to rasterio.enums.Resampling.

See also

RasterLayer
normalization: str, ‘MinMax’: Sets the default normalization method to use when calling the RasterLayer.normalize(). Currently, the only available option is ‘MinMax’. This is relevant to calculate the demand_index, supply_index, clean_cooking_index and assistance_need_index of the MCA model.

Note

If the layer_type is vector, this parameters will be passed to any raster dataset created from the vector layer, for example see VectorLayer.distance_raster.
inverse: bool, default False: Sets the default mode for the normalization algorithm (see RasterLayer.normalize()). If False, then the raster will be normalized (if normalize_rasters() is called) setting 1 as the high value and 0 as the low. If True, then the raster will be normalized setting the high value as 0 and the low values as 1.

Note

If the layer_type is vector, this parameters will be passed to any raster dataset created from the vector layer, for example see VectorLayer.distance_raster.
distance_method: str, optional: Sets the default distance algorithm to use when calling the get_distance_raster() method for the layer, see VectorLayer and RasterLayer.
distance_limit: Callable object (function or lambda function) with a numpy array as input, optional: Defines a distance limit or range to consider when calculating the distance raster, see VectorLayer and RasterLayer.
window: rasterio.windows.Window or gpd.GeoDataFrame: A window or bounding box to read in the data if layer_type is raster or vector respectively. See the window parameter of RasterLayer and the bbox parameter of VectorLayer.
rescale: bool, default False: Sets the default value for the rescale attribute. This attribute is used in the align() method to rescale the values of a cell proportionally to the change in size of the cell. This is useful when aligning rasters that have different cell sizes and their values can be scaled proportionally. See the rescale parameter of RasterLayer.