def setUp( self ): '''Set up the parameters.''' # set lab and test parameters self._lab = epyc.Lab() self._lab[GFs.T] = [0.6, 0.9] self._lab[GFs.N] = 5000 self._lab[GFs.AVERAGE_K] = 5 # repetitions at each point in the parameter space self._repetitions = 1
def main():
e = NetworkVariabilityExperiment(params)
# TODO: Add capability to save study file in user-specified location
nb = epyc.JSONLabNotebook('network-variability-study.json')
lab = epyc.Lab(nb)
for key in asdict(params):
lab[key] = asdict(params)[key]
lab.runExperiment(epyc.RepeatedExperiment(e, 1))
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict(pInfect=0.1, pInfected=0.01, tInfected=1)
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab['pInfect'] = [0.1, 0.2, 0.3]
self._lab['pInfected'] = [0.01]
self._lab['tInfected'] = [0.5, 1, 2]
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict(pInfect=0.01, pInfected=0.001, pRecover=0.02)
self._er = networkx.erdos_renyi_graph(5000, 0.004)
# lab run
self._lab = epyc.Lab()
self._lab['pInfect'] = [0.01]
self._lab['pInfected'] = [0.001, 0.002, 0.005]
self._lab['pRecover'] = [0.02]
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict()
self._params[SIR_FixedRecovery.P_INFECT] = 0.1
self._params[SIR_FixedRecovery.P_INFECTED] = 0.01
self._params[SIR_FixedRecovery.T_INFECTED] = 1
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab[SIR_FixedRecovery.P_INFECT] = [0.1, 0.3]
self._lab[SIR_FixedRecovery.P_INFECTED] = [0.01]
self._lab[SIR_FixedRecovery.T_INFECTED] = [0.5, 1, 2]
def testEpidemic(self):
'''Test we get an epidemic'''
self._lab = epyc.Lab()
self._lab[SIR.P_INFECT] = 0.3
self._lab[SIR.P_INFECTED] = 0.01
self._lab[SIR.P_REMOVE] = 0.05
e = StochasticDynamics(self._model, self._network)
self._lab.runExperiment(e)
rc = (self._lab.results())[0]
six.assertCountEqual(self, rc[epyc.Experiment.RESULTS],
['S', 'I', 'R'])
self.assertTrue(rc[epyc.Experiment.RESULTS]['S'] > 0)
self.assertTrue(rc[epyc.Experiment.RESULTS]['I'] == 0)
self.assertTrue(rc[epyc.Experiment.RESULTS]['R'] > 0)
self.assertEqual(
rc[epyc.Experiment.RESULTS]['S'] +
rc[epyc.Experiment.RESULTS]['R'], self._network.order())
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single epidemic-causing experiment
self._params = dict()
self._params[SIR.P_INFECT] = 0.3
self._params[SIR.P_INFECTED] = 0.01
self._params[SIR.P_REMOVE] = 0.05
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab[SIR.P_INFECT] = [0.1, 0.3]
self._lab[SIR.P_INFECTED] = 0.01
self._lab[SIR.P_REMOVE] = [0.05, 1]
# model
self._model = SIR()
def testEpidemic(self):
'''Test we get an epidemic'''
self._lab = epyc.Lab()
self._lab[SIR.P_INFECT] = 0.3
self._lab[SIR.P_INFECTED] = 0.01
self._lab[SIR.P_REMOVE] = 0.05
e = StochasticDynamics(self._model, self._network)
self._lab.runExperiment(e)
rc = (self._lab.results())[0]
self.assertCountEqual(rc[epyc.Experiment.RESULTS],
[SIR.SUSCEPTIBLE, SIR.INFECTED, SIR.REMOVED])
self.assertTrue(rc[epyc.Experiment.RESULTS][SIR.SUSCEPTIBLE] > 0)
self.assertTrue(rc[epyc.Experiment.RESULTS][SIR.INFECTED] == 0)
self.assertTrue(rc[epyc.Experiment.RESULTS][SIR.REMOVED] > 0)
self.assertEqual(
rc[epyc.Experiment.RESULTS][SIR.SUSCEPTIBLE] +
rc[epyc.Experiment.RESULTS][SIR.REMOVED], self._network.order())
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict(pInfect=0.1, pInfected=0.01, pRemove=0.05)
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab['pInfect'] = [0.1, 0.2, 0.3]
self._lab['pInfected'] = [0.01]
self._lab['pRecover'] = [0.05, 0.1, 1]
# model
self._model = SIR()
# no maximum time needed
self._maxTime = None
def setUp(self):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict()
self._params[SIS.P_INFECT] = 0.1
self._params[SIS.P_INFECTED] = 0.01
self._params[SIS.P_REMOVE] = 0.05
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab[SIS.P_INFECT] = [0.1, 0.3]
self._lab[SIS.P_INFECTED] = 0.01
self._lab[SIS.P_REMOVE] = [0.05, 1]
# model
self._model = SIS()
# maximum time needed as disease may be endemic
self._maxTime = 2000
def testHDF5(self):
'''Test we can save and retrieve the time series as HDF5.'''
tf = NamedTemporaryFile()
tf.close()
fn = tf.name
#fn = 'test.h5'
try:
nb = epyc.HDF5LabNotebook(fn, create=True)
lab = epyc.Lab(nb)
# run the experiment
m = MonitoredSIR()
m.setMaximumTime(100)
e = StochasticDynamics(
m, networkx.erdos_renyi_graph(1000, 5.0 / 1000))
lab[SIR.P_INFECTED] = 0.01
lab[SIR.P_INFECT] = 0.002
lab[SIR.P_REMOVE] = 0.002
lab[Monitor.DELTA] = 1.0
rc = lab.runExperiment(e)
df = lab.dataframe()
# check we read back in correctly
with epyc.HDF5LabNotebook(fn).open() as nb1:
df1 = nb1.dataframe()
r = df.iloc[0]
r1 = df1.iloc[0]
for f in [
Monitor.OBSERVATIONS,
Monitor.timeSeriesForLocus(SIR.SI),
Monitor.timeSeriesForLocus(SIR.INFECTED)
]:
self.assertCountEqual(r[f], r1[f])
finally:
try:
os.remove(fn)
#pass
except OSError:
pass
def setUp( self ):
'''Set up the experimental parameters and experiment.'''
# single experiment
self._params = dict()
self._params[SEIR.P_INFECT_SYMPTOMATIC] = 0.3
self._params[SEIR.P_INFECT_ASYMPTOMATIC] = 0.3
self._params[SEIR.P_EXPOSED] = 0.01
self._params[SEIR.P_SYMPTOMS] = 0.05
self._params[SEIR.P_REMOVE] = 0.05
self._network = networkx.erdos_renyi_graph(1000, 0.005)
# lab run
self._lab = epyc.Lab()
self._lab[SEIR.P_INFECT_SYMPTOMATIC] = 0.1
self._lab[SEIR.P_INFECT_ASYMPTOMATIC] = [ 0.1, 0.3 ]
self._lab[SEIR.P_EXPOSED] = 0.01
self._lab[SEIR.P_SYMPTOMS] = [ 0.05, 1 ]
self._lab[SEIR.P_REMOVE] = 0.05
# model
self._model = SEIR()
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
import seaborn
class CurveExperiment(epyc.Experiment):
def do(self, params):
x = params['x']
y = params['y']
r = numpy.sin(numpy.sqrt(x ** 2 + y ** 2))
return dict(result=r)
lab = epyc.Lab(notebook=epyc.LabNotebook(description="A point cloud of $sin \sqrt{x^2 + y^2}$"))
lab['x'] = numpy.linspace(-2 * numpy.pi, 2 * numpy.pi)
lab['y'] = numpy.linspace(-2 * numpy.pi, 2 * numpy.pi)
lab.runExperiment(CurveExperiment())
df = lab.notebook().dataframe()
fig = plt.figure(figsize = (8, 8))
ax = fig.gca(projection = '3d')
ax.scatter(df['x'], df['y'], df['result'], c = df['result'], depthshade = False, cmap = cm.coolwarm)
ax.set_xlim(numpy.floor(df['x'].min()), numpy.ceil(df['x'].max()))
ax.set_ylim(numpy.floor(df['y'].min()), numpy.ceil(df['y'].max()))
ax.set_zlim(numpy.floor(df['result'].min()), numpy.ceil(df['result'].max()))
plt.title(lab.notebook().description())
ax.set_xlabel('$x$')
