except:
subprocess.call(['python3', 'run_mixing.py', f'{T}', f'{args.dir}', f'{args.graph}', \
'--maxcorrtime', '10000', \
'--maxmixing', '10000'])
mixingResults = IO.loadResults(targetDirectory, 'mixingResults')
corrTimeSettings = IO.loadResults(targetDirectory, 'corrTimeSettings')
burninSteps = mixingResults['burninSteps']
distSamples = mixingResults['distSamples']
systemSnapshotSettings = dict( \
nSnapshots = args.snapshots, \
burninSteps = int(burninSteps), \
distSamples = int(distSamples)
)
IO.saveSettings(targetDirectory, systemSnapshotSettings, 'systemSnapshots')
selected_nodes = []
remaining_nodes = list(graph)
nodes_array = np.array(remaining_nodes)
mi_greedy = {}
h_greedy = {}
mi_random = {}
h_random = {}
mi_brute_force = {}
h_brute_force = {}
if args.bruteForce:
temps = np.linspace(args.minT, args.maxT, args.numT)
nSamples = int(1e4)
burninSteps = int(1e4)
magSide = ''
updateType = 'async'
targetDirectory = f'{os.getcwd()}/{args.dir}'
os.makedirs(targetDirectory, exist_ok=True)
settings = dict(
nSamples = nSamples, \
burninSteps = burninSteps, \
updateMethod = updateType
)
IO.saveSettings(targetDirectory, settings)
for i, g in enumerate(ensemble):
graph = nx.read_gpickle(g)
filename = os.path.split(g)[-1].strip('.gpickle')
modelSettings = dict(\
graph = graph,\
updateType = updateType,\
magSide = magSide
)
model = fastIsing.Ising(**modelSettings)
Tc = Tc_idx = -1
while Tc < 0:
args = parser.parse_args()
T = args.T #float(sys.argv[1])
targetDirectory = args.dir #sys.argv[2]
os.makedirs(targetDirectory, exist_ok=True)
# load network
graph = nx.read_gpickle(args.graph)
N = len(graph)
networkSettings = dict( \
path = args.graph, \
nNodes = N
)
IO.saveSettings(targetDirectory, networkSettings, 'network')
# setup Ising model with nNodes spin flip attempts per simulation step
modelSettings = dict( \
temperature = T, \
updateType = 'async' ,\
magSide = ''
)
#IO.saveSettings(targetDirectory, modelSettings, 'model')
model = fastIsing.Ising(graph, **modelSettings)
#print(model.mapping)
#print(list(graph))
# determine mixing/correlation time
mixingTimeSettings = dict( \
snapshots.append(s)
MI, HX = computeMI_cond(model, node, minDist, maxDist,
allNeighbours_G, snapshots, nTrials,
nSamples, modelSettings)
MIs_cond[T] = MI
snapshotSettingsJoint = dict( \
nSamples = args.numSamplesJoint, \
repeats = args.repeats, \
burninSteps = args.burninSteps, \
distSamples = args.distSamples, \
maxDist = maxDist, \
nBins = args.bins
)
IO.saveSettings(targetDirectory, snapshotSettingsJoint,
'jointSnapshots')
allNeighbours_G_allNodes = model.neighboursAtDistAllNodes(
nodes, maxDist)
avgSnapshots, avgSystemSnapshots, fullSnapshots = simulation.getJointSnapshotsPerDistNodes(model, nodes, \
allNeighbours_G_allNodes, \
**snapshotSettingsJoint, threads=nthreads, \
initStateIdx=1, getFullSnapshots=1)
MI, corr = infoTheory.pairwiseMI_allNodes(
model,
nodes,
fullSnapshots.reshape(
(args.repeats * args.numSamplesJoint, -1)),
distMax=maxDist)