def Load(s=0.1, experimentID=0, nu0=0.005, numReplicates=3, step=10, ModelName='TimeSeries', samplingWindow=50,
L=50000, depthRate=30):
if not s: nu0=0.005
sim = Simulation.load(s=s, experimentID=experimentID % 100, nu0=nu0, numReplicates=numReplicates, step=step,
ExperimentName=ModelName, All=True, L=L, replicates=range(numReplicates),
depthRate=depthRate)
sim.experimentID=experimentID
startGen=0
sim.setSamplingTimes(maxGeneration=min(startGen+samplingWindow,sim.getTrueGenerationTimes()[-1]),step=step,startGeneration=startGen)
sim.createDF()
return sim
def load(ExperimentName, s=0.1, L=50000, experimentID=0, nu0=0.005, isFolded=False, All=False, startGeneration=0,
maxGeneration=50, numReplicates=3, numSamples=5, step=10, replicates=None, depthRate=np.inf):
path='{}{}/simpop/'.format(utl.simoutpath, ExperimentName) + Simulation.getSimulationName(s=s, L=L, experimentID=experimentID, initialCarrierFreq=nu0, isFolded=isFolded) + '.pkl'
sim= pd.read_pickle(path)
sim.savedPath=path
if replicates is not None: sim.setReplicates(sorted(replicates))
elif numReplicates is not None: sim.setReplicates(range(numReplicates))
if depthRate != np.inf:
sim.Xi = sim.X
sim.X = sim.C.loc[depthRate] / sim.D.loc[depthRate].astype(float)
sim.X = np.array(map(lambda x: utl.roundto(x, 5), sim.X.reshape(-1) * 1e4)).reshape(sim.X.shape) / 1e4
if not All: sim.setSamplingTimes(maxGeneration=min(maxGeneration,sim.getGenerationTimes()[-1]),numSamples=numSamples,step=step,startGeneration=startGeneration)
return sim
