mnp.MNP
Module Contents
Functions
Subflow to prepare input for processing. - creates directories - copies all input files - reads land type map and environmental factor maps - determines overlap between all provided rasters - creates geospatial profile when running with precalculated HSI rasters - makes the parameter database |
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Generate all additional output aside from the standard tables. |
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Run and evaluate species models and evaluate species subselections. |
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Run the Model for Nature Policy on the given configuration. |
Data
API
- mnp.MNP.DATA_COVERAGE_TIF = 'spatial_data_coverage.tif'
- mnp.MNP.prepare_input(parameters: mnp.config.MNPParameters, input_pathway: mnp.preparation.io_pathways.InputPathway)
Subflow to prepare input for processing. - creates directories - copies all input files - reads land type map and environmental factor maps - determines overlap between all provided rasters - creates geospatial profile when running with precalculated HSI rasters - makes the parameter database
- config: ConfigParser
Configuration for current run
- input_pathway: InputPathway
the input pathway describing the operations to be done on the provided input
- land_types:dict
The land type map as a dictionary with land type codes as key and arrays as values
- environmentals
Dictionary with the name of the environmental factor as keys and their corresponding arrays as values
- complying_species: set
which species have all the needed information required for running
- parameters:dict
databases containing all domain parameters, like species traits and group traits
- mnp.MNP.evaluate_models(output_pathway: mnp.preparation.io_pathways.OutputPathway, parameters: mnp.config.MNPParameters, species_models: weakref.ReferenceType, land_types: dict[str, scipy.sparse.sparray])
Generate all additional output aside from the standard tables.
- output_pathway: OutputPathway
class describing which output to generate
- species_models: list[SpeciesModel]
list with a model object for each species in this run
- subselection_evaluation: list[SubselectionEvaluation]
list with an evaluation object for each species in this run
- parameters:dict
databases containing all domain parameters, eg species traits and group traits
- land_types:dict
The land type map as a dictionary with land type codes as key and arrays as values
- mnp.MNP.run_and_evaluate(land_types: dict[str, scipy.sparse.sparray] | None, environmentals: dict[str, scipy.sparse.sparray] | None, parameters: mnp.config.MNPParameters, output_pathway: mnp.preparation.io_pathways.OutputPathway)
Run and evaluate species models and evaluate species subselections.
Running a model is: 1. make HSI map 2. do clustering (make populations) 3. evaluate clusters
- land_types:dict
The land type map as a dictionary with land type codes as key and arrays as values
- environmentals
Dictionary with the name of the environmental factor as keys and their corresponding arrays as values
parameters
- species_models: list[SpeciesModel]
list with a model object for each species in this run
- subselection_evaluation: list[SubselectionEvaluation]
list with an evaluation object for each species in this run
- mnp.MNP.save_config_and_log(parameters: mnp.config.MNPParameters, config: configparser.ConfigParser)
- mnp.MNP.mnp(config: configparser.ConfigParser)
Run the Model for Nature Policy on the given configuration.
- config: ConfigParser
Configuration for current run