repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/konect.py | xflow/dataset/konect.py | import os
import networkx as nx
import requests
import random
import tarfile
import ndlib.models.ModelConfig as mc
def create_folder(folder_name):
if not os.path.exists(folder_name):
os.makedirs(folder_name)
def download_konect_dataset(url, filename):
response = requests.get(url)
if response.statu... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/eurostat.py | xflow/dataset/eurostat.py | import pandas as pd
import networkx as nx
import requests
from io import BytesIO, StringIO
import gzip
def eurostat_road_go_ta_tg():
# URL of the Eurostat TSV file (compressed)
eurostat_url = "https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/road_go_ta_tg/?format=TSV&compressed=true"
# Downlo... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/nx.py | xflow/dataset/nx.py | import networkx as nx
import random
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
def connSW(n, beta=None):
g = nx.connected_watts_strogatz_graph(n, 10, 0.1)
config = mc.Configuration()
for a, b in g.edges():
weight = random.randrange(40,80)
weight = round(wei... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/snap.py | xflow/dataset/snap.py | import os
import networkx as nx
import requests
import random
import ndlib.models.ModelConfig as mc
import gzip
# TODO add CAIDA
# https://snap.stanford.edu/data/as-caida.html
def create_folder(folder_name):
if not os.path.exists(folder_name):
os.makedirs(folder_name)
def download_snap_dataset(url, file... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/faf.py | xflow/dataset/faf.py | import pandas as pd
import networkx as nx
import requests
from zipfile import ZipFile
from io import BytesIO
def faf5_6():
# URL of the CSV file within the ZIP archive
zip_url = "https://faf.ornl.gov/faf5/data/download_files/FAF5.6.zip"
# Download the ZIP file
response = requests.get(zip_url)
zip_... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/pyg.py | xflow/dataset/pyg.py | import networkx as nx
import numpy as np
import torch_geometric.datasets as ds
import random
import ndlib
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
import torch_geometric
from torch_geometric.datasets import Planetoid, EmailEUCore, MyketDataset, BitcoinOTC, PolBlogs, KarateClub
from tor... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/dataset/__init__.py | xflow/dataset/__init__.py | __all__ = ['connSW', 'BA', 'ER', 'CiteSeer', 'PubMed', 'Cora', 'photo', 'coms']
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IBM/graph_generation.py | xflow/IBM/graph_generation.py | import networkx as nx
import torch_geometric.datasets as ds
import random
import ndlib
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
from torch_geometric.datasets import Planetoid
def connSW(n, beta=None):
g = nx.connected_watts_strogatz_graph(n, 10, 0.1)
config = mc.Configuration... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IBM/main.py | xflow/IBM/main.py | from graph_generation import *
from IBM_baselines import *
from evaluation import *
import time
print('exp 1')
g, config = connSW(1000, 0.1)
print('connSW is on.')
seeds = random.sample(list(g.nodes()), 10)
print('seeds: ', seeds)
beta = 0.1
for budget in [5, 10, 15, 20, 25, 30]:
print('budget: ', budget)
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IBM/evaluation.py | xflow/IBM/evaluation.py | import statistics as s
from IBM_baselines import IC, LT, SI
def blocking_effect_IC(g, config, seeds, selected_to_block):
g_block = g.__class__()
g_block.add_nodes_from(g)
g_block.add_edges_from(g.edges)
for a, b in g_block.edges():
weight = config.config["edges"]['threshold'][(a, b)]
g... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IBM/IBM_baselines.py | xflow/IBM/IBM_baselines.py | import networkx as nx
import numpy as np
import ndlib
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
import statistics as s
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
import random
# random
# baselines: simulation based
# greedy
def gre... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IM/graph_generation.py | xflow/IM/graph_generation.py | import networkx as nx
import torch_geometric.datasets as ds
import random
import ndlib
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
from torch_geometric.datasets import Planetoid
def connSW(n, beta=None):
g = nx.connected_watts_strogatz_graph(n, 10, 0.1)
config = mc.Configuration... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IM/IM_baselines.py | xflow/IM/IM_baselines.py | import networkx as nx
import numpy as np
import ndlib
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
import statistics as s
import random
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
from random import uniform, seed
from collections import Counter
import... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IM/main.py | xflow/IM/main.py | import networkx as nx
from time import time
from graph_generation import Cora, CiteSeer, PubMed, connSW, ER, coms, photo
from IM_baselines import eigen, degree, pi, sigma, greedy, celf, celfpp, IMRank, RIS
from evaluation import effectSI
def analyze(seed, beta, size):
g, config = connSW(size, beta)
print('bet... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/IM/evaluation.py | xflow/IM/evaluation.py | import networkx as nx
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn.inits import reset
import random
import numpy as np
from torch_geometric import utils
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
import statistics as s
def effectIC(g, con... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/graph_generation.py | xflow/SL/graph_generation.py | import networkx as nx
import torch_geometric.datasets as ds
import ndlib.models.ModelConfig as mc
import numpy as np
import random
from torch_geometric.datasets import Planetoid
def CiteSeer():
dataset = Planetoid(root='./Planetoid', name='CiteSeer') # Cora, CiteSeer, PubMed
data = dataset[0]
edges = (da... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/Gaussian.py | xflow/SL/Gaussian.py | import networkx as nx
from time import time
from graphGeneration import Cora, CiteSeer, PubMed, connSW, ER, coms, photo
from IM import eigen, degree, pi, sigma, Netshield, Soboldeg, Soboleigen, SobolPi, SobolSigma, SobolNS, greedyIC, degreeDis,SoboldegreeDis
from score import effectIC
import ndlib.models.ModelConfig a... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/main.py | xflow/SL/main.py | import networkx as nx
import cosasi
import random
import numpy as np
from graph_generation import CiteSeer, PubMed, Cora, coms, photo, connSW, rand
from time import time
import tracemalloc
import logging
# from memory_profiler import profile
tracemalloc.start()
# Create a logger
logger = logging.getLogger()
logger.se... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/__init__.py | xflow/SL/cosasi/__init__.py | from .contagion import *
from .source_inference import *
from .benchmark import *
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/benchmark/benchmark.py | xflow/SL/cosasi/benchmark/benchmark.py | import random
import os, sys
import json
sys.path.insert(0, os.getcwd())
import numpy as np
import networkx as nx
import cosasi
MODULE_PATH = __file__[: -len("benchmark.py")]
MODULE_PATH = (
MODULE_PATH
if len(MODULE_PATH) > 0 and (MODULE_PATH[-1] == "/" or MODULE_PATH[-1] == "\\")
else MODULE_PATH + "/... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/benchmark/__init__.py | xflow/SL/cosasi/benchmark/__init__.py | from .benchmark import *
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/benchmark/tests/__init__.py | xflow/SL/cosasi/benchmark/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/benchmark/tests/test_benchmark.py | xflow/SL/cosasi/benchmark/tests/test_benchmark.py | import os, sys
sys.path.insert(0, os.getcwd())
import pytest
from unittest import TestCase
import networkx as nx
import numpy as np
import cosasi
class Test_BenchmarkFromSimulation(TestCase):
def setUp(self):
self.number_infected_init = 3
self.sim_steps = 100
self.G = nx.fast_gnp_random... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/estimators.py | xflow/SL/cosasi/utils/estimators.py | import math
import random
import warnings
import scipy
import numpy as np
import networkx as nx
from sklearn.cluster import SpectralClustering
from .helpers import attack_degree, attack_degree_partition
from ..source_inference.multiple_source import netsleuth
def source_subgraphs(I, number_sources=2):
"""Subdiv... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/helpers.py | xflow/SL/cosasi/utils/helpers.py | import operator
import functools
import numpy as np
import networkx as nx
def list_product(l):
"""Returns the product the elements of a list.
Parameters
----------
l : list
list of elements you want to multiply
"""
return functools.reduce(operator.mul, l, 1)
def longest_list(l):
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/__init__.py | xflow/SL/cosasi/utils/__init__.py | from .helpers import *
from . import estimators
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/tests/test_estimators.py | xflow/SL/cosasi/utils/tests/test_estimators.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import cosasi
class TestEstimators(TestCase):
def setUp(self):
self.G = self.G = nx.gnp_random_graph(50, 0.2)
contagion = cosasi.StaticNetworkContagion(
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/tests/__init__.py | xflow/SL/cosasi/utils/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/utils/tests/test_helpers.py | xflow/SL/cosasi/utils/tests/test_helpers.py | import os, sys
sys.path.insert(0, os.getcwd())
import pytest
import networkx as nx
import numpy as np
import random
from cosasi import utils
def test_list_product():
l = [1]
assert utils.list_product(l) == 1
l += [2]
assert utils.list_product(l) == 2
l += [-3]
assert utils.list_product(l) =... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/contagion/static_network_contagion.py | xflow/SL/cosasi/contagion/static_network_contagion.py | import random
import numpy as np
import operator
import networkx as nx
import ndlib.models.epidemics as ep
import ndlib.models.ModelConfig as mc
class StaticNetworkContagion:
"""A stochastic epidemic process defined on a static network.
Parameters
----------
G : NetworkX Graph
The network fo... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/contagion/__init__.py | xflow/SL/cosasi/contagion/__init__.py | from .static_network_contagion import *
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/contagion/tests/__init__.py | xflow/SL/cosasi/contagion/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/contagion/tests/test_static_network_contagion.py | xflow/SL/cosasi/contagion/tests/test_static_network_contagion.py | import os, sys
import collections
sys.path.insert(0, os.getcwd())
import pytest
from unittest import TestCase
import networkx as nx
import numpy as np
import cosasi
class Test_StaticNetworkContagion(TestCase):
def setUp(self):
self.number_infected_init = 10
self.sim_steps = 10
self.G = ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/__init__.py | xflow/SL/cosasi/source_inference/__init__.py | from . import single_source
from . import multiple_source
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/source_results.py | xflow/SL/cosasi/source_inference/source_results.py | """Generic objects for the result of single-source and multi-source localization.
All inference algorithms should return an instance of one of these classes.
"""
import json
from collections import Counter
from collections.abc import Iterable
import itertools
import numpy as np
import networkx as nx
MODULE_PATH = _... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/tests/test_source_results.py | xflow/SL/cosasi/source_inference/tests/test_source_results.py | import os, sys
import pytest
import itertools
import random
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import networkx as nx
import numpy as np
import cosasi
from ..source_results import SourceResult, SingleSourceResult, MultiSourceResult, node_set_distance
def test_node_set_distance():
G ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/tests/__init__.py | xflow/SL/cosasi/source_inference/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/lisn.py | xflow/SL/cosasi/source_inference/multiple_source/lisn.py | import itertools
import networkx as nx
import numpy as np
from ..source_results import MultiSourceResult
from ...utils import estimators
from .. import single_source
def fast_multisource_lisn(I, G, t, number_sources=None):
"""Greedily runs single-source LISN algorithm on each estimated infection
subgraph at... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/jordan.py | xflow/SL/cosasi/source_inference/multiple_source/jordan.py | import itertools
import networkx as nx
import numpy as np
from ..source_results import MultiSourceResult
from ...utils import estimators
from .. import single_source
def fast_multisource_jordan_centrality(I, G, number_sources=None):
"""Greedily runs single-source Jordan centrality on each estimated infection
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/netsleuth.py | xflow/SL/cosasi/source_inference/multiple_source/netsleuth.py | import itertools
import networkx as nx
import numpy as np
from ..source_results import MultiSourceResult
from ...utils import estimators
from .. import single_source
def netsleuth(I, G, hypotheses_per_step=1):
"""Implements the multi-source NETSLEUTH algorithm to score combinations
of nodes in G.
Param... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/__init__.py | xflow/SL/cosasi/source_inference/multiple_source/__init__.py | from .netsleuth import *
from .jordan import *
from .lisn import *
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/tests/test_netsleuth.py | xflow/SL/cosasi/source_inference/multiple_source/tests/test_netsleuth.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import math
import cosasi
class TestNETSLEUTH(TestCase):
def setUp(self):
self.G = nx.complete_graph(n=100)
contagion = cosasi.StaticNetworkContagio... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/tests/test_lisn.py | xflow/SL/cosasi/source_inference/multiple_source/tests/test_lisn.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import cosasi
class TestLISN(TestCase):
def setUp(self):
self.G = nx.complete_graph(n=100)
contagion = cosasi.StaticNetworkContagion(
G=self.G, model="si", infection_rat... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/tests/__init__.py | xflow/SL/cosasi/source_inference/multiple_source/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/multiple_source/tests/test_jordan.py | xflow/SL/cosasi/source_inference/multiple_source/tests/test_jordan.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import cosasi
class TestJordan(TestCase):
def setUp(self):
self.G = nx.complete_graph(n=100)
contagion = cosasi.StaticNetworkContagion(
G=self.G, model="si", infection_r... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/lisn.py | xflow/SL/cosasi/source_inference/single_source/lisn.py | import math
import networkx as nx
import numpy as np
import scipy as sp
from ..source_results import SingleSourceResult
def lisn(I, G, t=None, infection_rate=0.1):
"""Implements the algorithm from Localizing the Information Source in a Network to
score all nodes in G [1]_.
Parameters
----------
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/jordan.py | xflow/SL/cosasi/source_inference/single_source/jordan.py | import networkx as nx
import numpy as np
from ...utils import soft_eccentricity
from ..source_results import SingleSourceResult
def jordan_centrality(I, G):
"""Computes the infection eccentricity of each node in the infection subgraph. To
produce a score with highest value corresponding to the Jordan center,... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/netsleuth.py | xflow/SL/cosasi/source_inference/single_source/netsleuth.py | import networkx as nx
import numpy as np
import warnings
from ..source_results import SingleSourceResult
def netsleuth(I, G):
"""Implements the single-source NETSLEUTH algorithm to score all nodes in G.
Parameters
----------
I : NetworkX Graph
The infection subgraph observed at a particular ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/__init__.py | xflow/SL/cosasi/source_inference/single_source/__init__.py | from .rumor_centrality import *
from .short_fat_tree import *
from .netsleuth import *
from .jordan import *
from .lisn import *
from .earliest_infection_first import *
| python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/short_fat_tree.py | xflow/SL/cosasi/source_inference/single_source/short_fat_tree.py | import math
import random
import networkx as nx
import numpy as np
from ...utils import longest_list_len
from ..source_results import SingleSourceResult
def short_fat_tree(I, G, infection_rate=0.1):
"""Implements the Short-Fat-Tree (SFT) algorithm to score all nodes in G.
Parameters
----------
I : ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/rumor_centrality.py | xflow/SL/cosasi/source_inference/single_source/rumor_centrality.py | import math
import random
import networkx as nx
from ...utils import list_product
from ..source_results import SingleSourceResult
def rumor_centrality_root(I, v, return_all_values=True):
"""Computes rumor centrality for all nodes, assuming a spanning tree rooted at v.
Parameters
----------
I : Netw... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/earliest_infection_first.py | xflow/SL/cosasi/source_inference/single_source/earliest_infection_first.py | import random
import networkx as nx
import numpy as np
from ...utils import soft_eccentricity
from ..source_results import SingleSourceResult
def earliest_infection_first(I, G, observer_dict):
"""Implements the Earliest Infection First algorithm to score all nodes in I.
This algorithm is useful if some inf... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_netsleuth.py | xflow/SL/cosasi/source_inference/single_source/tests/test_netsleuth.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import math
import cosasi
class TestNETSLEUTH(TestCase):
def setUp(self):
self.G = nx.random_tree(n=500, seed=0)
contagion = cosasi.StaticNetworkCon... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_lisn.py | xflow/SL/cosasi/source_inference/single_source/tests/test_lisn.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import cosasi
class TestLISN(TestCase):
def setUp(self):
self.G = nx.random_tree(n=500, seed=0)
contagion = cosasi.StaticNetworkContagion(
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_earliest_infection_first.py | xflow/SL/cosasi/source_inference/single_source/tests/test_earliest_infection_first.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import cosasi
class TestEarliestInfectionFirst(TestCase):
def setUp(self):
self.G = nx.fast_gnp_random_graph(100, 0.25)
contagion = cosasi.StaticNet... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_short_fat_tree.py | xflow/SL/cosasi/source_inference/single_source/tests/test_short_fat_tree.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import math
import cosasi
class TestShortFatTree(TestCase):
def setUp(self):
self.G = nx.random_tree(n=500, seed=0)
contagion = cosasi.StaticNetwork... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_rumor_centrality.py | xflow/SL/cosasi/source_inference/single_source/tests/test_rumor_centrality.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import random
import cosasi
class TestRumorCentrality(TestCase):
def setUp(self):
self.G = nx.random_tree(n=500, seed=0)
contagion = cosasi.StaticNetworkContagion... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/__init__.py | xflow/SL/cosasi/source_inference/single_source/tests/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/xflow/SL/cosasi/source_inference/single_source/tests/test_jordan.py | xflow/SL/cosasi/source_inference/single_source/tests/test_jordan.py | import os, sys
sys.path.insert(0, os.getcwd())
from unittest import TestCase
import pytest
import networkx as nx
import numpy as np
import cosasi
class TestJordan(TestCase):
def setUp(self):
self.G = nx.random_tree(n=500, seed=0)
contagion = cosasi.StaticNetworkContagion(
G=self.G,... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/create_safe_traffic_lights.py | SUMOxPyPSA/create_safe_traffic_lights.py | #!/usr/bin/env python3
"""
Create safe traffic light logic with proper coordination and random offsets
"""
import gzip
import xml.etree.ElementTree as ET
import os
import random
def create_safe_traffic_light_logic(signal_count, tl_id, junction_offset=0):
"""Create safe traffic light logic with proper coordination... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/fix_intersection_timing.py | SUMOxPyPSA/fix_intersection_timing.py | #!/usr/bin/env python3
"""
Script to fix synchronized traffic lights at intersections by creating opposing logic
"""
import gzip
import xml.etree.ElementTree as ET
import os
import re
def analyze_intersection_traffic_lights(netfile):
"""Analyze traffic lights to find which ones are at the same intersection"""
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/compress_net.py | SUMOxPyPSA/compress_net.py | import gzip
import shutil
import os
import sys
def compress_file(input_file):
"""Compress a file using gzip compression"""
if not os.path.exists(input_file):
print(f"Error: File {input_file} does not exist")
return False
output_file = input_file + '.gz'
try:
with open(input... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/fix_traffic_synchronization.py | SUMOxPyPSA/fix_traffic_synchronization.py | #!/usr/bin/env python3
"""
Script to fix traffic light synchronization by adding random offsets and varying phase durations
"""
import gzip
import xml.etree.ElementTree as ET
import os
import random
def create_desynchronized_traffic_light_logic(signal_count, tl_id):
"""Create traffic light logic with random offse... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/build.py | SUMOxPyPSA/build.py | #!/usr/bin/env python3
"""
Unified build script for SUMO network generation
"""
import os
import sys
import subprocess
from sumo_config import SUMO_COMMON_CONFIG, CITY_CONFIGS
def run_command(cmd, cwd=None):
"""Run a shell command and print its output"""
print(f"Running: {' '.join(cmd)}")
process = subpro... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/desynchronize_traffic_lights.py | SUMOxPyPSA/desynchronize_traffic_lights.py | #!/usr/bin/env python3
"""
Script to desynchronize traffic lights by adding random phase offsets
"""
import gzip
import xml.etree.ElementTree as ET
import os
import random
def extract_and_desynchronize_traffic_lights(netfile):
"""Extract traffic lights and add random offsets to desynchronize them"""
traffic_l... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/generate_traffic_lights.py | SUMOxPyPSA/generate_traffic_lights.py | #!/usr/bin/env python3
"""
Script to extract all traffic light IDs from network file and generate matching traffic_lights.add.xml
"""
import gzip
import xml.etree.ElementTree as ET
import os
def extract_traffic_light_info(netfile):
"""Extract all traffic light IDs and their current phases from network file"""
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/modify_traffic_lights.py | SUMOxPyPSA/modify_traffic_lights.py | #!/usr/bin/env python3
"""
Script to modify existing traffic light logic to separate straight and left-turn signals
"""
import gzip
import xml.etree.ElementTree as ET
import os
import copy
def analyze_traffic_light_structure(netfile):
"""Analyze current traffic light structure"""
traffic_lights = {}
conne... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/randomize_traffic_lights.py | SUMOxPyPSA/randomize_traffic_lights.py | #!/usr/bin/env python3
"""
Script to randomize traffic light timing to break synchronization
"""
import gzip
import xml.etree.ElementTree as ET
import os
import random
def randomize_traffic_lights(netfile):
"""Extract traffic lights and randomize their timing"""
traffic_lights = {}
with gzip.open(net... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/regenerate_networks.py | SUMOxPyPSA/regenerate_networks.py | #!/usr/bin/env python3
"""
Script to regenerate SUMO networks with updated traffic light configuration
"""
import os
import subprocess
import sys
# Get SUMO binary path from config
try:
from config import SUMO_PATH
NETCONVERT_BINARY = os.path.join(SUMO_PATH, "bin/netconvert")
except ImportError:
print("Er... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/config.py | SUMOxPyPSA/config.py | import os
# SUMO Configuration
# Modify these paths according to your system
SUMO_PATH = "/usr/share/sumo" # Default for Linux
# Alternative paths for different systems:
# Windows: "C:\\Program Files (x86)\\Eclipse\\Sumo"
# macOS: "/opt/homebrew/Cellar/sumo/1.20.0/share/sumo"
# Web Server Configuration
HOST = "0.0.0... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/fix_traffic_lights.py | SUMOxPyPSA/fix_traffic_lights.py | #!/usr/bin/env python3
"""
Script to fix traffic light logic by adding all-red phases and ensuring proper cycling
"""
import gzip
import xml.etree.ElementTree as ET
import re
import os
def fix_traffic_light_logic(input_file, output_file):
"""
Fix traffic light logic by adding all-red phases between direction ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/fix_miami_traffic_lights.py | SUMOxPyPSA/fix_miami_traffic_lights.py | #!/usr/bin/env python3
"""
Script to fix Miami traffic lights with proper logic for different signal group counts
"""
import gzip
import xml.etree.ElementTree as ET
import os
def create_fixed_traffic_lights(netfile):
"""Create fixed traffic light logic that works for all signal group counts"""
traffic_lights ... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/map_to_power.py | SUMOxPyPSA/map_to_power.py | import os
import subprocess
import pandas as pd
import sys
def find_python_executable():
"""Find the appropriate Python executable to use."""
# Try different possible Python executables
python_candidates = [
'/bin/python',
'/usr/bin/python',
'/usr/bin/python3',
'python3',
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/app.py | SUMOxPyPSA/app.py | from flask import Flask, render_template, send_from_directory
from flask_socketio import SocketIO, emit
import traci
import time
import threading
import os
import sys
import tempfile
from config import *
from sumo_config import SUMO_COMMON_CONFIG, CITY_CONFIGS as SUMO_CITY_CONFIGS
app = Flask(__name__, static_url_path... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/sumo_config.py | SUMOxPyPSA/sumo_config.py | """
Unified SUMO configuration settings for all cities
"""
import os
# Base directory is where this file is located
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
# Common configuration settings for all cities
SUMO_COMMON_CONFIG = {
'processing': {
'time-to-teleport': '300',
'collision.act... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/gridkit.py | SUMOxPyPSA/tools/gridkit.py | #!/usr/bin/env python
"""GridKit is a power grid extraction toolkit.
Usage:
python gridkit.py path/to/data-file.osm --filter \\
--poly path/to/area.poly \\
--pg user=gridkit database=gridkit
GridKit will create a database, import the power data, run the
extraction procedures, and write CSV's wit... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/geojson-to-postgis.py | SUMOxPyPSA/tools/util/geojson-to-postgis.py | #!/usr/bin/env python
from __future__ import print_function, unicode_literals
import operator
import psycopg2
import psycopg2.extras
import io
import json
import sys
import logging
CREATE_TABLES = '''
CREATE EXTENSION IF NOT EXISTS hstore;
CREATE EXTENSION IF NOT EXISTS postgis;
DROP TABLE IF EXISTS feature_points;
D... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/network.py | SUMOxPyPSA/tools/util/network.py | from __future__ import unicode_literals, division, print_function
import io
import csv
import random
import itertools
import heapq
import math
import warnings
try:
from recordclass import recordclass
except ImportError:
from collections import namedtuple as recordclass
warnings.warn("recordclass is necessar... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/load_polyfile.py | SUMOxPyPSA/tools/util/load_polyfile.py | #!/usr/bin/env python
from __future__ import print_function, unicode_literals, division
import argparse
import sys
import os
import io
from polyfile import PolyfileParser
from geometry import Polygon
ap = argparse.ArgumentParser()
ap.add_argument('file', nargs='+', type=str)
ap.add_argument('--table', type=str, defaul... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/postgres.py | SUMOxPyPSA/tools/util/postgres.py | from .which import which
try:
import psycopg2
import psycopg2.extensions
except ImportError:
psycopg2 = False
PSQL = which('psql')
class QueryError(Exception):
def __init__(self, error, query):
super(QueryError, self).__init__(error)
self.query = query
def make_copy_query(subque... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/polyfile.py | SUMOxPyPSA/tools/util/polyfile.py | import re
# syntax of poly files;
# name
# number
# indented list of longitude, latitude
# end
# possibly another number
# another end
class PolyfileParser(object):
newline = re.compile(r'\s*\n')
whitespace = re.compile(r'\s+')
end = re.compile(r'END')
word = re.compile(r'\w+')
... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/__init__.py | SUMOxPyPSA/tools/util/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/geometry.py | SUMOxPyPSA/tools/util/geometry.py | from __future__ import print_function, division
import collections
def cross_vertical(line_d, line_v):
pt_d1, pt_d2 = line_d
pt_v1, pt_v2 = line_v
x_d1, y_d1 = pt_d1
x_d2, y_d2 = pt_d2
dy_d = y_d2 - y_d1
dx_d = x_d2 - x_d1
if dx_d == 0:
# parallel (vertical) line
return No... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/which.py | SUMOxPyPSA/tools/util/which.py | import os
def which(program):
'''Find executable for a given name by PATH, or None if no executable could be found'''
if os.name == 'nt':
return _nt_which(program)
elif os.name == 'posix':
return _posix_which(program)
raise NotImplementedError(os.platform)
def _nt_which(program):
P... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/tools/util/hstore.py | SUMOxPyPSA/tools/util/hstore.py | import re
class hstore(dict):
class parser(object):
word = re.compile(r'"([^"]+)"')
arrow = re.compile(r'\s*=>\s*')
comma = re.compile(r',\s*')
def __init__(self, text):
self.position = 0
self.text = text
def __iter__(self):
whil... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/miami/check_tls_id_mismatches.py | SUMOxPyPSA/miami/check_tls_id_mismatches.py | import gzip
import xml.etree.ElementTree as ET
def get_tllogic_ids_from_net(netfile):
ids = set()
with gzip.open(netfile, 'rt', encoding='utf-8') as f:
tree = ET.parse(f)
root = tree.getroot()
for tl in root.findall('tlLogic'):
tid = tl.get('id')
if tid is not No... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/SUMOxPyPSA/miami/extract_tllogic.py | SUMOxPyPSA/miami/extract_tllogic.py | import gzip
import xml.etree.ElementTree as ET
def extract_tllogics(filename):
with gzip.open(filename, 'rt', encoding='utf-8') as f:
tree = ET.parse(f)
root = tree.getroot()
for tl in root.findall('tlLogic'):
print(f"\n<tlLogic id=\"{tl.get('id')}\" type=\"{tl.get('type')}\" pr... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/examples/xflow_loader.py | examples/xflow_loader.py | import sys
import os
current_script_directory = os.path.dirname(os.path.abspath(__file__))
xflow_path = os.path.join(current_script_directory, '..', '..', 'xflow')
sys.path.insert(1, xflow_path) | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/examples/main.py | examples/main.py | import xflow_loader
from xflow.dataset.nx import BA, connSW
from xflow.dataset.pyg import Cora
from xflow.diffusion import SI, IC, LT
from xflow.seed import random as seed_random, degree as seed_degree, eigen as seed_eigen
from xflow.util import run
# graphs to test
fn = lambda: connSW(n=1000, beta=0.1)
fn.__name__ =... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/examples/__init__.py | examples/__init__.py | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false | |
XGraph-Team/XFlow | https://github.com/XGraph-Team/XFlow/blob/1efc1844d3940fb726324c2a72c5f4325690910a/examples/FlowTaskEx1.py | examples/FlowTaskEx1.py | from FlowTasks import forward, backward, graph_eval
# ### Testing / Examples
# a FW1 dataset, with the observations stored as attributes to a networkx graph
# In[23]:
output = forward(1, obs_type = 'networkx', num_results=5)
#print observation type
print('Observations are of type:', type(output[0]['observations... | python | MIT | 1efc1844d3940fb726324c2a72c5f4325690910a | 2026-01-05T07:14:40.788228Z | false |
androidtrackers/certified-android-devices | https://github.com/androidtrackers/certified-android-devices/blob/47d470ee2a5633a17c31c22c53641c62fcb18519/sync.py | sync.py | #!/usr/bin/env -S uv run --script
# /// script
# dependencies = [
# "requests<3",
# ]
# ///
"""Google certified android devices tracker"""
import difflib
import json
import sys
from datetime import date
from os import environ, system
from pathlib import Path
from time import sleep
from requests import get, post
GI... | python | MIT | 47d470ee2a5633a17c31c22c53641c62fcb18519 | 2026-01-05T07:03:50.578463Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/setup.py | setup.py | """A setuptools based setup module for ITUR-py."""
# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open as open_codecs
from os import path
import itur
here = path.abspath(path.dirname(__file__))
# Get the long description from t... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/plotting.py | itur/plotting.py | # -*- coding: utf-8 -*-
"""``itur.plotting`` provides convenient function to plot maps in ITU-Rpy.
This submodule uses ``matplotlib`` and ``cartopy`` as the default library to
plot maps. Alternatively, the user can use ``basemap`` (if installed).
The example below shows the use of ``plot_in_map`` to display the mean ... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/utils.py | itur/utils.py | # -*- coding: utf-8 -*-
"""
``itur.utils`` is a utilities library for ITU-Rpy.
This utility library for ITU-Rpy contains methods to:
* Load data and build an interpolator object.
* Prepare the input and output arrays, and handle unit transformations.
* Compute distances and elevation angles between two points on Eart... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/__init__.py | itur/__init__.py | # -*- coding: utf-8 -*-
"""
ITU-RPy is a python implementation of the ITU-P R Recommendations.
ITU-Rpy can be used to compute atmospheric attenuation for Earth-to-space
and horizontal paths, for frequencies in the GHz range.
The propagation loss on an Earth-space path and a horizontal-path, relative to
the free-space... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/__version__.py | itur/__version__.py | # -*- coding: utf-8 -*-
"""Version information."""
# The following line *must* be the last in the module, exactly as formatted:
__version__ = "0.4.0"
| python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/models/itu838.py | itur/models/itu838.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from astropy import units as u
from itur.utils import prepare_quantity
class __ITU838__():
"""Specific attenuation model for rain for use in prediction methods
... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/models/itu1144.py | itur/models/itu1144.py | # -*- coding: utf-8 -*-
"""
Interpolation methods for the geophysical properties used to compute
propagation effects. These methods are based on those in Recommendation
ITU-R P.1144-7.
References
--------
[1] Guide to the application of the propagation methods of Radiocommunication
Study Group 3: https://www.itu.int/r... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
inigodelportillo/ITU-Rpy | https://github.com/inigodelportillo/ITU-Rpy/blob/e69587f75bdb7f8b1049259f36eb31a36ca5c570/itur/models/itu837.py | itur/models/itu837.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from astropy import units as u
from scipy.optimize import bisect
import scipy.stats as stats
from itur.models.itu1510 import surface_month_mean_temperature
from itur... | python | MIT | e69587f75bdb7f8b1049259f36eb31a36ca5c570 | 2026-01-05T07:12:38.084174Z | false |
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