def __init__(self, topology, tags):
super().__init__()
# construct the underlying network generator
if topology == 'ER':
self._gen = ERNetwork()
elif topology == 'BA':
self._gen = BANetwork()
elif topology == 'PLC':
self._gen = PLCNetwork()
else:
raise Exception(f'Unrecognised topology {topology}')
self._generator = None
self._tags = tags
def testTopologyMarker(self):
'''Test we capture the topology of the network.'''
param = dict()
param[SIR.P_INFECT] = 0.1
param[SIR.P_REMOVE] = 0.01
param[SIR.P_INFECTED] = 0 # so no running time
gen = FixedNetwork(networkx.gnp_random_graph(1000, 0.001))
param_g = param.copy()
e = StochasticDynamics(SIR(), gen)
rc = e.set(param_g).run()
ps = rc[Experiment.PARAMETERS]
self.assertIn(NetworkGenerator.TOPOLOGY, ps)
self.assertEqual(ps[NetworkGenerator.TOPOLOGY], gen.topology())
gen = ERNetwork()
param_er = param.copy()
param_er[ERNetwork.N] = 1000
param_er[ERNetwork.KMEAN] = 20
e = StochasticDynamics(SIR(), gen)
rc = e.set(param_er).run()
ps = rc[Experiment.PARAMETERS]
self.assertIn(NetworkGenerator.TOPOLOGY, ps)
self.assertEqual(ps[NetworkGenerator.TOPOLOGY], gen.topology())
gen = BANetwork()
param_ba = param.copy()
param_ba[BANetwork.N] = 1000
param_ba[BANetwork.M] = 20
e = StochasticDynamics(SIR(), gen)
rc = e.set(param_ba).run()
ps = rc[Experiment.PARAMETERS]
self.assertIn(NetworkGenerator.TOPOLOGY, ps)
self.assertEqual(ps[NetworkGenerator.TOPOLOGY], gen.topology())
gen = PLCNetwork()
param_plc = param.copy()
param_plc[PLCNetwork.N] = 1000
param_plc[PLCNetwork.EXPONENT] = 2
param_plc[PLCNetwork.CUTOFF] = 5
e = StochasticDynamics(SIR(), gen)
rc = e.set(param_plc).run()
ps = rc[Experiment.PARAMETERS]
self.assertIn(NetworkGenerator.TOPOLOGY, ps)
self.assertEqual(ps[NetworkGenerator.TOPOLOGY], gen.topology())
# network topological parameters (for an ER network)
N = int(1e5)
kmean = 50
if not nb.already('er'):
# run the simulations
lab[ERNetwork.N] = N
lab[ERNetwork.KMEAN] = kmean
lab[SIR.P_INFECTED] = 0.001
lab[SIR.P_INFECT] = 0.0001
lab[SIR.P_REMOVE] = 0.001
lab[Monitor.DELTA] = 1
lab['repetitions'] = range(10)
e = StochasticDynamics(ProcessSequence([SIR(), Monitor()]), ERNetwork())
lab.runExperiment(e)
# figure 1: mean I size with error bars
fig = plt.figure(figsize=(7, 5))
# sub-sampling in time and space
maxt = 5000
step = 200
# wrangle the time series fore observations and infected component size
df = lab.dataframe()
ts = df.loc[(df[Monitor.OBSERVATIONS].apply(len) == df[
Monitor.OBSERVATIONS].apply(len).max())].iloc[0][Monitor.OBSERVATIONS]
infecteds = DataFrame(df[Monitor.timeSeriesForLocus(
SIR.INFECTED)].values.tolist()).rename(columns=lambda i: ts[i])
def testER(self):
'''Test we can generate ER networks with all parameter combinations.'''
param = dict()
param[ERNetwork.N] = 1000
# test using <k>
param[ERNetwork.KMEAN] = 20
gen = ERNetwork(param)
_ = gen.generate()
# test using phi
del param[ERNetwork.KMEAN]
param[ERNetwork.PHI] = 0.02
gen = ERNetwork(param)
__ = gen.generate()
# test working with both
param[ERNetwork.KMEAN] = 20
gen = ERNetwork(param)
_ = gen.generate()
# test failing with neither
del param[ERNetwork.KMEAN]
del param[ERNetwork.PHI]
gen = ERNetwork(param)
with self.assertRaises(AttributeError):
_ = gen.generate()
#
# epydemic is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with epydemic. If not, see <http://www.gnu.org/licenses/gpl.html>.
import cProfile
from pstats import Stats, SortKey
from epydemic import SIR, ERNetwork, StochasticDynamics
profile = "profile.pstats"
N = int(1e5)
kmean = 10
params = dict()
params[SIR.P_INFECT] = 0.05
params[SIR.P_REMOVE] = 0.01
params[SIR.P_INFECTED] = 0.001
params[ERNetwork.N] = N
params[ERNetwork.KMEAN] = kmean
e = StochasticDynamics(SIR(), ERNetwork())
cProfile.run('e.set(params).run()', profile)
p = Stats(profile)
p.sort_stats(SortKey.TIME).print_stats(10)
# network topological parameters N = 10000 kmean = 20 alpha = 2.0 kappa = 20 params = dict() params[ERNetwork.N] = N params[ERNetwork.KMEAN] = kmean params[PLCNetwork.N] = N params[PLCNetwork.EXPONENT] = alpha params[PLCNetwork.CUTOFF] = kappa # construct networks g_er = ERNetwork().set(params).generate() g_plc = PLCNetwork().set(params).generate() # bond-percolate the networks e_er = BondPercolation(g_er) rc_er = e_er.set(params).run() e_plc = BondPercolation(g_plc) rc_plc = e_plc.set(params).run() # figure 1a: the bond percolation behaviour of an ER network fig = plt.figure(figsize=(5, 5)) xs = rc_er[Experiment.RESULTS][BondPercolation.P] ys = [gcc / N for gcc in rc_er[Experiment.RESULTS][BondPercolation.GCC]] plt.plot(xs, ys, 'g-')
lab = Lab(nb)
# network topological parameters (for an ER network)
N = int(1e5)
kmean = 50
# set up the simulations
lab[ERNetwork.N] = N
lab[ERNetwork.KMEAN] = kmean
lab[SIR.P_INFECTED] = 0.001
lab[SIR.P_INFECT] = 0.0001
lab[SIR.P_REMOVE] = 0.001
lab[Monitor.DELTA] = 50
# run the simulations
p = ProcessSequence([MonitoredSIR(), Monitor()])
if not nb.already('sto'):
e = StochasticDynamics(p, ERNetwork())
lab.runExperiment(e)
if not nb.already('syn'):
f = SynchronousDynamics(p, ERNetwork())
lab.runExperiment(f)
# figure 1: stochastic dynamics
doFigure('sto', 'stochastic dynamics',
'doc/implementation/sir-stochastic.png')
# figure 2: synchronous dynamics
doFigure('syn', 'synchronous dynamics',
'doc/implementation/sir-synchronous.png')
# network topological parameters (ER network)
N = 10000
kmean = 3
params = dict()
params[ERNetwork.N] = N
params[ERNetwork.KMEAN] = kmean
params[SIR.P_INFECT] = pInfect
params[SIR.P_REMOVE] = pRecover
params[SIR.P_INFECTED] = pInfected
params[Monitor.DELTA] = 10
e = StochasticDynamics(ProcessSequence([MonitoredSIR(),
Monitor()]), ERNetwork())
e.process().setMaximumTime(T)
rc = e.set(params).run()
res = rc[Experiment.RESULTS]
ts = res[Monitor.OBSERVATIONS]
er_sss = list(
map(lambda v: v / N, res[Monitor.timeSeriesForLocus(SIR.SUSCEPTIBLE)]))
er_iis = list(
map(lambda v: v / N, res[Monitor.timeSeriesForLocus(SIR.INFECTED)]))
er_rrs = list(
map(lambda v: v / N, res[Monitor.timeSeriesForLocus(SIR.REMOVED)]))
# figure 2: same epidemic on ER network
fig = plt.figure(figsize=(5, 5))
ax = fig.gca()