"""
Get cohabitation graph method is defined here
"""
from __future__ import annotations
from typing import BinaryIO
import networkx as nx
import numpy as np
import pandas as pd
[docs]
def get_compatability_graph(
plants: pd.DataFrame,
cohabitation_attributes: pd.DataFrame,
) -> nx.Graph:
"""
Return compatability graph where weights of edges equals to outcome of species interaction
(1 for negative, 2 for neutral, 3 for positive).
"""
plants = plants.copy()
cohabitation = cohabitation_attributes.copy()
df_comp = plants.copy()
df_comp = df_comp.join(df_comp, how="cross", rsuffix="_x")
df_comp = df_comp[df_comp["name_ru"] != df_comp["name_ru_x"]]
df_comp["genus_con"] = df_comp["genus"] + ":" + df_comp["genus_x"]
cohabitation["genus_con"] = cohabitation["genus_name_1"] + ":" + cohabitation["genus_name_2"]
df_comp = df_comp.merge(cohabitation[["cohabitation_type", "genus_con"]], on="genus_con", how="left")
df_comp = df_comp[~df_comp.filter(like="name_ru").apply(frozenset, axis=1).duplicated()].reset_index(drop=True)
df_comp = df_comp.rename(columns={"cohabitation_type": "weight"})
df_comp["weight"].fillna(2, inplace=True)
# df_comp["weight"].replace({"negative": 1, "neutral": 2, "positive": 3}, inplace=True)
df_comp["weight"].replace({"negative": -1, "neutral": 0, "positive": 1}, inplace=True)
df_comp = df_comp[["name_ru", "name_ru_x", "weight"]]
df_comp["is_compatability"] = 1
compatability_graph = nx.from_pandas_edgelist(
df_comp,
"name_ru",
"name_ru_x",
"weight",
create_using=nx.MultiGraph(),
edge_key="is_compatability",
)
plant_dict = plants[["name_ru", "name_latin", "is_invasive", "life_form"]].copy()
plant_dict["weights"] = [
", ".join(
[
str(x)
for x in list(
np.sort(df_comp[df_comp["name_ru"] == species]["weight"].unique()),
)
]
)
for species in plants["name_ru"]
]
plant_dict.index = plant_dict["name_ru"]
plant_dict = plant_dict.transpose()
plant_dict = plant_dict[plant_dict.index != "name_ru"].to_dict()
nx.set_node_attributes(compatability_graph, plant_dict)
return compatability_graph
[docs]
def write_compatability_graph_gexf(
plants: pd.DataFrame,
cohabitation_attributes: pd.DataFrame,
output_path: str | BinaryIO = "compatability_graph.gexf",
) -> None:
"""
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.
"""
compatability_graph = get_compatability_graph(plants, cohabitation_attributes)
nx.write_gexf(compatability_graph, f"{output_path}.gexf")
[docs]
def get_compatability_for_species(
species_list: list[str],
compatability_graph,
) -> nx.Graph:
"""
Return compatability graph for a set of selected species.
"""
current_graph = compatability_graph.subgraph(species_list)
return current_graph
[docs]
def write_compatability_for_species_gexf(
species_list: list[str],
compatability_graph,
output_path: str | BinaryIO = "compatability_graph.gexf",
) -> None:
"""
Return compatability graph for a set of selected species.
"""
current_graph = get_compatability_graph(species_list, compatability_graph)
nx.write_gexf(current_graph, output_path)