mirror of https://github.com/gsi-upm/soil
You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
106 lines
2.7 KiB
Python
106 lines
2.7 KiB
Python
import os
|
|
import yaml
|
|
import logging
|
|
from time import time
|
|
from glob import glob
|
|
from random import random
|
|
from copy import deepcopy
|
|
|
|
import networkx as nx
|
|
|
|
from contextlib import contextmanager
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
logger.setLevel(logging.INFO)
|
|
|
|
|
|
def load_network(network_params, dir_path=None):
|
|
if network_params is None:
|
|
return nx.Graph()
|
|
path = network_params.get('path', None)
|
|
if path:
|
|
if dir_path and not os.path.isabs(path):
|
|
path = os.path.join(dir_path, path)
|
|
extension = os.path.splitext(path)[1][1:]
|
|
kwargs = {}
|
|
if extension == 'gexf':
|
|
kwargs['version'] = '1.2draft'
|
|
kwargs['node_type'] = int
|
|
try:
|
|
method = getattr(nx.readwrite, 'read_' + extension)
|
|
except AttributeError:
|
|
raise AttributeError('Unknown format')
|
|
return method(path, **kwargs)
|
|
|
|
net_args = network_params.copy()
|
|
net_type = net_args.pop('generator')
|
|
|
|
method = getattr(nx.generators, net_type)
|
|
return method(**net_args)
|
|
|
|
|
|
def load_file(infile):
|
|
with open(infile, 'r') as f:
|
|
return list(yaml.load_all(f))
|
|
|
|
|
|
def load_files(*patterns):
|
|
for pattern in patterns:
|
|
for i in glob(pattern):
|
|
for config in load_file(i):
|
|
yield config, os.path.abspath(i)
|
|
|
|
|
|
def load_config(config):
|
|
if isinstance(config, dict):
|
|
yield config, None
|
|
else:
|
|
yield from load_files(config)
|
|
|
|
|
|
@contextmanager
|
|
def timer(name='task', pre="", function=logger.info, to_object=None):
|
|
start = time()
|
|
yield start
|
|
end = time()
|
|
function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
|
|
if to_object:
|
|
to_object.start = start
|
|
to_object.end = end
|
|
|
|
|
|
def agent_from_distribution(distribution, value=-1):
|
|
"""Find the agent """
|
|
if value < 0:
|
|
value = random()
|
|
for d in distribution:
|
|
threshold = d['threshold']
|
|
if value >= threshold[0] and value < threshold[1]:
|
|
state = {}
|
|
if 'state' in d:
|
|
state = deepcopy(d['state'])
|
|
return d['agent_type'], state
|
|
|
|
raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
|
|
|
|
|
|
def repr(v):
|
|
if isinstance(v, bool):
|
|
v = "true" if v else ""
|
|
return v, bool.__name__
|
|
return v, type(v).__name__
|
|
|
|
def convert(value, type_):
|
|
import importlib
|
|
try:
|
|
# Check if it's a builtin type
|
|
module = importlib.import_module('builtins')
|
|
cls = getattr(module, type_)
|
|
except AttributeError:
|
|
# if not, separate module and class
|
|
module, type_ = type_.rsplit(".", 1)
|
|
module = importlib.import_module(module)
|
|
cls = getattr(module, type_)
|
|
return cls(value)
|