derevo package

Subpackages

derevo.models package

Submodules

derevo.adjacency module

Get adjency graph method is defined here.

derevo.adjacency.get_adjacency_graph(species_in_parks: pd.DataFrame, edge_key_value: int = 1, target_parks: list[str] | None = None) → nx.Graph[source]: Return adjacency graph where weight of edges equals to number of co-occurence cases.

derevo.adjacency.write_adjacency_graph_gexf(species_in_parks: pd.DataFrame, output_path: str | BinaryIO = 'adjacency_graph.gexf', edge_key_value: int = 1, target_parks: list[str] | None = None) → None[source]: Write adjacency graph where weight of edges equals to number of co-occurence cases to a given file (by name or a binary file-like object) in gexf format.

derevo.combined module

Get combined graph method is defined here.

derevo.combined.get_combined_graph(plants: pd.DataFrame, cohabitation_attributes: pd.DataFrame, species_in_parks: pd.DataFrame, target_parks: list[str] | None = None) → nx.Graph[source]: Returns combined graph with weights equal to number of co-occurence cases and compatability outcome in attributes.

derevo.combined.write_combined_graph_gexf(plants: pd.DataFrame, cohabitation_attributes: pd.DataFrame, species_in_parks: pd.DataFrame, target_parks: list[str] | None = None, output_path: str | BytesIO = 'combined_graph.gexf') → None[source]: Write combined graph with weights equal to number of co-occurence cases and compatability outcome in attributes to a given file (by name or a binary file-like object) in gexf format.

derevo.compatability module

Get cohabitation graph method is defined here

derevo.compatability.get_compatability_graph(plants: DataFrame, cohabitation_attributes: DataFrame) → Graph[source]: Return compatability graph where weights of edges equals to outcome of species interaction (1 for negative, 2 for neutral, 3 for positive).

derevo.compatability.write_compatability_graph_gexf(plants: pd.DataFrame, cohabitation_attributes: pd.DataFrame, output_path: str | BinaryIO = 'compatability_graph.gexf') → None[source]: Write compatability graph where weights of edges equals to outcome of species interaction (1 for negative, 2 for neutral, 3 for positive) to a given file (by name or a binary file-like object) in gexf format.

derevo.compatability.get_compatability_for_species(species_list: list[str], compatability_graph) → Graph[source]: Return compatability graph for a set of selected species.

derevo.compatability.write_compatability_for_species_gexf(species_list: list[str], compatability_graph, output_path: str | BinaryIO = 'compatability_graph.gexf') → None[source]: Return compatability graph for a set of selected species.

derevo.composition module

Composition-related methods are defined here.

derevo.composition.get_compositions(plants_available: list[Plant], territory: Territory, cohabitation_attributes: list[GeneraCohabitation], plants_present: list[Plant] | None = None) → list[list[Plant]][source]: Return plants composition variants list for the given parameters.

derevo.composition._intersection_check(greenery_polygon: GeoDataFrame, factor: GeoDataFrame)[source]: Determine if greenery polygon intersects with another polygon of light or limitation.

derevo.composition.get_updated_composition(plants: pd.DataFrame, plants_with_limitations_resistance: pd.DataFrame, plants_suitable_for_light: pd.DataFrame, cohabitation_attributes: pd.DataFrame, limitations: gpd.GeoDataFrame, light: gpd.GeoDataFrame, species_in_parks: pd.DataFrame, greenery_polygon: gpd.GeoDataFrame) → list[nx.Graph] | None[source]: Return list of graphs with variants of updated plants composition.

derevo.composition.write_updated_composition_gexf(plants: pd.DataFrame, plants_with_limitations_resistance: pd.DataFrame, plants_suitable_for_light: pd.DataFrame, cohabitation_attributes: pd.DataFrame, limitations: gpd.GeoDataFrame, light: gpd.GeoDataFrame, species_in_parks: pd.DataFrame, greenery_polygon: gpd.GeoDataFrame, output_path_prefix: str | Iterable[BytesIO] | Iterable[str])[source]: Write variants of updated plants composition to files with given prefix or names / file-like objects given in iterator.

derevo.composition.get_recommended_composition(plants: pd.DataFrame, plants_with_limitations_resistance: pd.DataFrame, plants_suitable_for_light: pd.DataFrame, limitations: pd.DataFrame, light: pd.DataFrame, cohabitation_attributes: pd.DataFrame, greenery_polygon: gpd.GeoDataFrame) → list[nx.Graph] | None[source]: Return list of graphs with variants of recommended composition with account for outer factors.

derevo.composition.write_recommended_composition_gexf(plants: pd.DataFrame, plants_with_limitations_resistance: pd.DataFrame, plants_suitable_for_light: pd.DataFrame, limitations: pd.DataFrame, light: pd.DataFrame, cohabitation_attributes: pd.DataFrame, greenery_polygon: gpd.GeoDataFrame, output_path_prefix: str | Iterable[BytesIO] | Iterable[str] = 'recommended') → None[source]: Write list of graphs with variants of recommended composition with account for outer factors to files with given prefix or names / file-like objects given in iterator.

derevo.composition.get_composition_unknown(plants: DataFrame, cohabitation_attributes: DataFrame) → list[Graph][source]: Return list of graphs with variants of recommended composition for a place with unknown outer factors.

derevo.composition.write_composition_unknown_gfsx(plants: pd.DataFrame, cohabitation_attributes: pd.DataFrame, output_path_prefix: str | Iterable[BytesIO] | Iterable[str] = 'new_graph') → list[nx.Graph][source]: Write graphs with variants of recommended composition for a place with unknown outer factors to files with given prefix or names / file-like objects given in iterator.

derevo.optimal_resolution module

Get optimal resolution method is defined here.

derevo.optimal_resolution.get_best_resolution(plants: pd.DataFrame, plants_with_limitations_resistance: pd.DataFrame, plants_suitable_for_light: pd.DataFrame, cohabitation_attributes: pd.DataFrame, graph_axes: Axes | None = None) → pd.DataFrame[source]

Return dataframe with calculated best resolutions for current collection of species and limitation factors / light variants.

If graph_axes is given, scatter plot will be drawn on it.

derevo.prepare_polygons module

Polygons preparation logic is defined here.

derevo.prepare_polygons.make_grid(polygon, edge_size: int, polygon_id: int, crs: int = 32636) → gpd.GeoDataFrame | None[source]: Return grid with given edge_size.

derevo.prepare_polygons.get_ids(grid_id: DataFrame) → DataFrame[source]: Unify identifiers in the given DataFrame.

derevo.prepare_polygons.green_lims(limitations: GeoDataFrame, greenery_polygons: GeoDataFrame) → GeoDataFrame[source]: Overlay given limitations with greenery polygons.

derevo.territories module

Territory-related methods are defined here.

derevo.territories._geom_func(gdf: GeoDataFrame, geom: BaseGeometry) → GeoDataFrame[source]: Return rows of GeoDataFrame which geometry is covered by, covers or intersect the given geometry.

derevo.territories.get_territory(greenery_polygon: BaseGeometry, global_territory: GlobalTerritory, territory_data: Territory | None = None) → Territory[source]: Get territory information based on its geometry, used-defined known data and other factors polygons.

Module contents

Derevo module helps to create optimal plants compositions taking into an account their cohabitations and possible limitation factors