print('running craph graph')
graph = IO.loadPickle(loadGraph)
graphs.append(graph)
# w = nx.utils.powerlaw_sequence(N, 2)
# g = nx.expected_degree_graph(w)
# g = sorted(nx.connected_component_subgraphs(g), key = lambda x: len(x))[-1]
#for i, j in g.edges():
# g[i][j]['weight'] = np.random.rand() * 2 - 1
# graphs.append(g)
# graphs[0].add_edge(0,0)
# for j in np.int32(np.logspace(0, np.log10(N-1), 5)):
# graphs.append(nx.barabasi_albert_graph(N, j))
dataDir = 'Graphs' # relative path careful
df = IO.readCSV(f'{dataDir}/Graph_min1_1.csv', header=0, index_col=0)
h = IO.readCSV(f'{dataDir}/External_min1_1.csv', header=0, index_col=0)
graph = nx.from_pandas_adjacency(df)
attr = {}
for node, row in h.iterrows():
attr[node] = dict(H=row['externalField'], nudges=0)
nx.set_node_attributes(graph, attr)
graphs.append(graph)
if 'fs4' in os.uname().nodename or 'node' in os.uname().nodename:
now = datetime.datetime.now().isoformat()
rootDirectory = f'/var/scratch/cveltere/{now}' # data storage
else:
rootDirectory = f'{os.getcwd()}/Data/'
# # real = 1
# graphs += [nx.barabasi_albert_graph(n, i) for i in linspace(2, n - 1, 3, dtype = int)]
sr = IO.SimulationResult(**store)
IO.savePickle(fn, sr, verbose=1)
checkTime()
# init models
if __name__ == "__main__":
M = settings.get('model')
args = parser.parse_args()
file, PID = args.file, args.id
print(file, PID)
# this should only be run once per call
if not file:
g = nx.erdos_renyi_graph(3, np.random.uniform(0, 1))
PSYCHO = True
if PSYCHO:
df = IO.readCSV('Graphs/Graph_min1_1.csv', header = 0,\
index_col = 0)
h = IO.readCSV('Graphs/External_min1_1.csv', header = 0,\
index_col = 0)
g = nx.from_pandas_adjacency(df)
attr = {
node: dict(H=row['externalField'])
for node, row in h.iterrows()
}
nx.set_node_attributes(g, attr)
modelSettings['magSide'] = ''
settings['modelSettings'] = modelSettings
m = M(graph = g, \
**settings.get('modelSettings'), \
equilibrium = equilibrium)
matched = m.matched
