# Copyright (c) 2024 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.jsp.problem import JobShopProblem, Subjob
[docs]
def get_data_available(
data_folder: Optional[str] = None, data_home: Optional[str] = None
) -> list[str]:
"""Get datasets available for jobshop.
Params:
data_folder: folder where datasets for jobshop whould be find.
If None, we look in "jobshop" 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}/jobshop"
try:
files = [f for f in os.listdir(data_folder)]
except FileNotFoundError:
files = []
return [os.path.abspath(os.path.join(data_folder, f)) for f in files]
[docs]
def parse_file(file_path: str):
with open(file_path, "r") as file:
lines = file.readlines()
processed_line = 0
problem = []
for line in lines:
if not (line.startswith("#")):
split_line = line.split()
job = []
if processed_line == 0:
nb_jobs = int(split_line[0])
nb_machines = int(split_line[1])
else:
for num, n in enumerate(split_line):
if num % 2 == 0:
machine = int(n)
else:
job.append(
{"machine_id": machine, "processing_time": int(n)}
)
problem.append(job)
processed_line += 1
return JobShopProblem(list_jobs=[[Subjob(**x) for x in y] for y in problem])