# Copyright (c) 2022 AIRBUS and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from typing import Optional
from discrete_optimization.datasets import get_data_home
from discrete_optimization.facility.problem import (
Customer,
Facility,
Facility2DProblem,
Point,
)
[docs]
def get_data_available(
data_folder: Optional[str] = None, data_home: Optional[str] = None
) -> list[str]:
"""Get datasets available for facility.
Params:
data_folder: folder where datasets for facility whould be find.
If None, we look in "facility" subdirectory of `data_home`.
data_home: root directory for all datasets. Is None, set by
default to "~/discrete_optimization_data "
"""
if data_folder is None:
data_home = get_data_home(data_home=data_home)
data_folder = f"{data_home}/facility"
try:
datasets = [
os.path.abspath(os.path.join(data_folder, f))
for f in os.listdir(data_folder)
]
except FileNotFoundError:
datasets = []
return datasets
[docs]
def parse(input_data: str) -> Facility2DProblem:
# parse the input
lines = input_data.split("\n")
parts = lines[0].split()
facility_count = int(parts[0])
customer_count = int(parts[1])
facilities = []
for i in range(1, facility_count + 1):
parts = lines[i].split()
facilities.append(
Facility(
index=i - 1,
setup_cost=float(parts[0]),
capacity=int(parts[1]),
location=Point(x=float(parts[2]), y=float(parts[3])),
)
)
customers = []
for i in range(facility_count + 1, facility_count + 1 + customer_count):
parts = lines[i].split()
customers.append(
Customer(
index=i - 1 - facility_count,
demand=int(parts[0]),
location=Point(x=float(parts[1]), y=float(parts[2])),
)
)
problem = Facility2DProblem(facility_count, customer_count, facilities, customers)
return problem
[docs]
def parse_file(file_path: str) -> Facility2DProblem:
with open(file_path, "r", encoding="utf-8") as input_data_file:
input_data = input_data_file.read()
facility_model = parse(input_data)
return facility_model