mnp.evaluation package

Submodules

class mnp.evaluation.qgis_templates.EnvironmentalFactorTemplate

Bases: QgisLayerTemplate

For reading the environmental factor qgis layer template

class mnp.evaluation.qgis_templates.HSITemplate

Bases: QgisLayerTemplate

For reading the HSI qgis layer template

class mnp.evaluation.qgis_templates.LandTypeMapTemplate(land_type_id_to_code_name: dict)

Bases: QgisLayerTemplate

Template for categorical land type map

class mnp.evaluation.qgis_templates.PopulationMapTemplate(species_model: SpeciesModel)

Bases: QgisLayerTemplate

Template for a species populations map, identifying individual populations, as well as keypopulation/non-keypopulation distinction

Parameters:

species_model (SpeciesModel) – Instance of SpeciesModel class

class mnp.evaluation.qgis_templates.QgisLayerTemplate

Bases: object

Class for qgis layer file templates. Holds location of template files and methods to find&replace text in a template and to write a template to file.

find_replace(key: str, value: str | int)

Find and replace in template lines. Used to modify a template towards a specific species

Parameters:

• key (str) – target text to replace

• value (str | int) – replacement text

read_template_lines(template_type: str)

Read a template type from file. Currently supported: * abiotic_factor template * hsi template * populations template * land_types

Parameters:

template_type (str) – key for reading a specific template from file. Currently supported choices are: [environmental_factor, fraction_key_populations_map, hsi, population_map]

write_to_file(destination)

Write template to file

Parameters:

destination (str) – full path, filename and extension for the template

mnp.evaluation.qgis_templates.get_land_type_color(land_type_code: str) → str

Return default colour for a SNL Natuurtype beheertype code.

Parameters:

land_type_code –

mnp.evaluation.qgis_templates.rand_web_color_hex() → str

” Generate random color. See: https://blog.stigok.com/2019/06/22/generate-random-color-codes-python.html

class mnp.evaluation.subselection_evaluation.SubselectionEvaluation(name: str, species_codes: set[str])

Bases: object

Class for holding classes that evaluate and generate output for a subselection of species. Creates tables and raster layer files in the cover output/<subselection_name> directory

Parameters:

• name (str) – name of the subselection

• species_codes (List[str]) – list of species codes for the species belonging to this subselection

mnp.evaluation.subselection_evaluation.create_output(evaluation: SubselectionEvaluation)

Create each of the given species subselection’s output tables, maps or other outputs.

Parameters:

evaluation (SubselectionEvaluation) – The subselection evaluation instance to create outputs for.

class mnp.evaluation.subselection_output.AssignedSpeciesMap(subselection_evaluation: SubselectionEvaluation, parameters: MNPParameters, land_types: dict, output_path: str = '')

Bases: SubselectionMap

Species GeoMap with the number of species assigned to the combined land types in each pixel location Ook wel bekend als de TOEKENNINGSKAART.

Parameters:

• species_codes (list[str]) – list with species codes

• geospatial_profile (DefaultGTiffProfile) – profile of geospatial output

• output_path (str) – output path

• land_types (dict) – output from read_aggregated_map()

content = 'assigned species based on land type'

create()

Calculate an array where each cell value identifies the number of distinct species from this subselection assigned to all land_types occuring in that cell.

Parameters:

• suitability_indexes (pd.DataFrame) – DataFrame with land type suitability indexes

• land_types (dict) – dictionary with land type NPZ files read from file as values, output from read_aggregated_map()

to_file()

write_explanatory_table()

Write CSV file with species count per land type

Parameters:

• parameters (MNPParameters) – output from make_parameters

• land_types (dict) – dictionary with land type NPZ files read from file as values, output from read_aggregated_map()

class mnp.evaluation.subselection_output.BiggestBottleneckMap(subselection_evaluation: SubselectionEvaluation, parameters: MNPParameters, output_path: str = '')

Bases: SubselectionMap

create()

save_qml()

Add QGIS QML template file to output for file formatting

to_file()

class mnp.evaluation.subselection_output.DetailedTable(subselection_evaluation: SubselectionEvaluation, species_models: ReferenceType, output_path: str)

Bases: Table

Output table for a species subselection, where rows are species and columns carry information regarding the performance of the species in the model run.

Parameters:

subselection_species_codes (list[str]) – list of species codes for which this table will be construed

add_to_table_data(master_dict: dict, species_obj: SpeciesModel)

Add data from a species model object to the table

Parameters:

• master_dict (dict) – dictionary with species model properties

• species_obj (SpeciesModel) – A speciesModel instance

create()

Create the detailed table.

Parameters:

species_models (list[SpeciesModel]) – list of SpeciesModel objects, from which information on the species performance in the model run is extracted.

class mnp.evaluation.subselection_output.EnvironmentalBottleneckMap(geospatial_profile: DefaultGTiffProfile, subselection_evaluation: SubselectionEvaluation, species_models: ReferenceType, output_path: str)

Bases: SubselectionMap

create()

class mnp.evaluation.subselection_output.EnvironmentalFactorHotspotMap(subselection_evaluation: SubselectionEvaluation, parameters: MNPParameters, land_types: dict, output_path: str = '')

Bases: SubselectionMap

Map with summarized environmental factor response for all species assigned to a location. Essentially a stack of the <cover>outputspecies_subselectionsfenvironmental_factor_maps<species_code>_environmental_factor.qlr maps, stacked for the species assigned to each Land-Type in the Landtypes map.

Different summary statistics are plausible for the stack: min/max/mean. Applicable summary statistic should be a class parameter

create()

save_qml()

Add QGIS QML template file to output for file formatting

to_file()

class mnp.evaluation.subselection_output.HotSpotsMap(geospatial_profile: DefaultGTiffProfile, assigned_species_map: AssignedSpeciesMap, key_population_count_map: KeyPopulationsCountMap, output_path: str = '')

Bases: SubselectionMap

Fraction key populations map.

Parameters:

• geospatial_profile (DefaultGTiffProfile) – profile of geospatial output

• output_path (str) – output path

content = 'fraction of assigned species with a population reaching at least a key population in size'

create()

Calculate Hotspots map, also known as fraction key populations map

Parameters:

• assigned_species_map (AssignedSpeciesMap) – Map with assigned species count per pixel

• key_population_count_map (KeyPopulationsCountMap) – Map with key population count per pixel

save_qml()

Add QGIS QML template file to output for file formatting

to_file()

class mnp.evaluation.subselection_output.KeyPopulationsCountMap(geospatial_profile: DefaultGTiffProfile, subselection_evaluation: SubselectionEvaluation, species_models: ReferenceType, output_path: str = '')

Bases: SubselectionMap

Species GeoMap with the number of keypopulations exisiting at each pixel location

Parameters:

• geospatial_profile (DefaultGTiffProfile) – profile of geospatial output

• output_path (str) – output path

content = 'key population count'

create()

class mnp.evaluation.subselection_output.LandTypeMap(subselection_evaluation: SubselectionEvaluation, land_types: dict, parameters: MNPParameters, output_path: str = '')

Bases: SubselectionOutput

QGIS qlr file pointing to the land-types map in <cover>inputland_typesland_types.tif QLR file is populated with land types only that are coupled to one or more species in the species subselection.

create()

save_qlr()

to_file()

class mnp.evaluation.subselection_output.QGisLayers(subselection_evaluation: SubselectionEvaluation, layer_type: str, species_models: ReferenceType, output_path: str)

Bases: SubselectionOutput

create()

Create QGIS QLR layer files for the tif files of the species in the subselection.

Concerns:

outputhsi<species_code>.tif –>

outputspecies_subselections<selection_name>hsi_maps<species_code>.qlr

outputenvironmental_factor<species_code>.tif –>

outputspecies_subselections<selection_name>environmental_factor_maps<species_code>.qlr

outputclustering<species_code>.tif –>

outputspecies_subselections<selection_name>population_maps<species_code>.qlr

Parameters:

• layer_type (str) – currently supported are [hsi, environmental_factor, habitats]

• species_models (list[SpeciesModel]) – list of SpeciesModel objects

Return type:

*.qlr on disk

to_file()

class mnp.evaluation.subselection_output.RegionalContributionTable(subselection_evaluation: SubselectionEvaluation, species_models: ReferenceType, output_path: str)

Bases: DetailedTable

Output table displaying contribution of sub-regions to a species’ habitat and populations.

Parameters:

subselection_species_codes (list[str]) – codes of subselection species to make the table for

create()

Create the regional contribution table.

Parameters:

species_models (list[SpeciesModel]) – list of SpeciesModel objects, from which information on the species performance in the model run is extracted.

name = 'regional_contribution_table.csv'

class mnp.evaluation.subselection_output.SubselectionMap(geospatial_profile: DefaultGTiffProfile, output_path: str = '')

Bases: SubselectionOutput

abstract create()

to_file()

class mnp.evaluation.subselection_output.SubselectionOutput(output_path: str)

Bases: ABC

Abstact base class for all classes that have to generate output for a subselection of species.

abstract create()

abstract to_file()

class mnp.evaluation.subselection_output.SummaryTable(detailed_table: DetailedTable, output_path: str)

Bases: Table

Output table for a species subselection. Table contains viability classes as rownames, species group names as columns and values are counts for each viability-class/species-group combination.

create()

Create the summary table.

Parameters:

detailed_table (pd.DataFrame) – table from the DetailedTable class

name = 'summary_table.csv'

class mnp.evaluation.subselection_output.Table(output_path)

Bases: SubselectionOutput

Basic properties of all output tables.

abstract create()

to_file()

Write the table to CSV file.

mnp.evaluation.subselection_output.add_output_to_subselection(evaluation: ~mnp.evaluation.subselection_evaluation.SubselectionEvaluation, output_pathway: ~mnp.preparation.io_pathways.OutputPathway, parameters: ~mnp.config.MNPParameters, species_models: list[~mnp.species_models.species_model.SpeciesModel], land_types: dict[slice(<class 'str'>, <class 'scipy.sparse._base.sparray'>, None)])

mnp.evaluation.subselection_output.identify_land_types_per_species(suitability_indexes: DataFrame, species_codes: set[str], land_types: dict)

Identify how many species are coupled to each land type, considering only the species in this particular subselection and only the land types available in this MNP run.

Parameters:

• species_codes (set[str]) – set of species code strings

• land_types (dict) – dictionary with land type NPZ files read from file as values, output from read_aggregated_map()

• suitability_indexes (pd.DataFrame) – DataFrame with land type suitability indexes

Return type:

Dictionary with species_codes as keys and list of land_types as values

mnp.evaluation.subselection_output.make_bottleneck_map(environmental_name: str, land_types: dict[str, sparray] | None, environmentals: dict[str, sparray] | None, parameters: MNPParameters, subselection_evaluations: list[SubselectionEvaluation])