mnp.species_models.species_evaluation

Module Contents

Classes

SpeciesEvaluationParameters

Dataclass for holding parameters for species evaluation.

SpeciesEvaluation

Data

ID_COLNAME

AREA_M_COLNAME

EFF_AREA_M_COLNAME

EFF_AREA_KP_COLNAME

IS_KP_COLNAME

EFF_AREA_KP_NORM_COLNAME

API

mnp.species_models.species_evaluation.ID_COLNAME = 'id'
mnp.species_models.species_evaluation.AREA_M_COLNAME = 'area_m'
mnp.species_models.species_evaluation.EFF_AREA_M_COLNAME = 'effective_area_m'
mnp.species_models.species_evaluation.EFF_AREA_KP_COLNAME = 'effective_area_kp'
mnp.species_models.species_evaluation.IS_KP_COLNAME = 'is_key_population'
mnp.species_models.species_evaluation.EFF_AREA_KP_NORM_COLNAME = 'effective_area_kp_norm'
class mnp.species_models.species_evaluation.SpeciesEvaluationParameters

Dataclass for holding parameters for species evaluation.

key_population_area: float = 1
possibly_viable_threshold: float = 1
viable_threshold: float = 1
small_pop_threshold_area: float = 500
small_pop_slope: float = 2
pxl_area: float = 0
class mnp.species_models.species_evaluation.SpeciesEvaluation(mnp_parameters: MNPParameters or None, species_code: str, hsi: mnp.species_models.habitat_suitability.HSI, clustering: mnp.species_models.clustering.ClusteringProcedure)

Initialization

Species evaluation class. Is the species viable or not?

mnp_parameters: MNPParameters or None

Parameters for the MNP model.

species_codestr

The code for the species to evaluate.

hsiHSI or SubRegion

The HSI array or sub-region to evaluate.

clusteringClusteringProcedure or SubRegion

The clustering procedure or sub-region to use.

population_array(array_type: str, only_keypopulations=False) → scipy.sparse.sparray | int

Make an array for this species evaluation, with some value in the cells of each population.

array_type{‘binary’,’nkeys’}

The type of population array to make

only_keypopulationsbool

retain only population of at least a key population in size

arraynp.ndarray

array of this species’ populations

trait_info() → dict[str, float]

Return the values of this species traits

Dictionary with trait info

evaluate_metapopulations() → None

Evaluate metapopulations for the species

calculate()

Calculate evaluation for this SpeciesModel.

The following results are calculated at species level :

  • populations = number of populations

  • populations_key_population = amount of populations that reach a key population in size

  • total_area_m = total area in meters

  • total_effective_area_m = total area in meters, corrected for suitability

  • total_effective_area_kp = total area in key populations, corrected for suitability

  • total_effective_area_kp_norm = total area in key populations, corrected for suitability, after normalisation

  • viability_class = viability class for this species: None, 1, 2 or 3

  • viability_class_description = label for the corresponding viability class

update_viability_class()

Check if this species is viable and update the result dictionary.

  • sum_norm_keys = 0 –> 0

  • 0 < sum_norm_keys < possibly_viable_threshold –> 1

  • possibly_viable_threshold <= sum_norm_keys < viable_threshold –> 2

  • sum_norm_keys >= viable_threshold –> 3

update_results_dictionary()

Updates results dictionary.

results() → dict

Return result-dictionary.

dict

Dictionary with species evaluation specs

summary_table_to_file(output_path)

Write summary table with specifications of each population to .tif.vat.dbf file, as sidecar file to the <cover>/output/clustering/<species_code>.tif files