Copying populations in memory

It is possible to make in-memory copies of populations, and then evolve the two populations independently. Doing so makes use of the serialization features in the fwdpy.fwdpyio module. You do not need to import that module. The routines used below do so transparently wihtout polluting the namespace for your session.

import fwdpy as fp
import numpy as np
#Let's set up a simulation and evolve some populations...
nregions = [fp.Region(0,1,1),fp.Region(2,3,1)]
sregions = [fp.ExpS(1,2,1,-0.1),fp.ExpS(1,2,0.01,0.001)]
rregions = [fp.Region(0,3,1)]
rng = fp.GSLrng(201)
popsizes = np.array([1000],dtype=np.uint32)
popsizes=np.tile(popsizes,10000)
pops = fp.evolve_regions(rng,1,1000,popsizes[0:],0.001,0.001,0.001,nregions,sregions,rregions)

All of the above is pretty standard. Let’s make a copy of pops:

pops2 = fp.copypops(pops)

pops

<fwdpy.fwdpy.popvec at 0x7faaace9e2b8>

pops2

<fwdpy.fwdpy.popvec at 0x7faaace9e310>

We see above that the two pops are occuppying different spaces in memory.

Let’s now evolve them further.

#Evolve "pops"
fp.evolve_regions_more(rng,pops,popsizes[0:],0.001,0.001,0.001,nregions,sregions,rregions)

#Now, let's evolve "pops2", and end with a short bottleneck (100 gens at N=500)
popsizes = np.array([1000]*10000 + [500]*100,dtype=np.uint32)
fp.evolve_regions_more(rng,pops2,popsizes[0:],0.001,0.001,0.001,nregions,sregions,rregions)

for i in range(len(pops)):
    print pops[i].popsize(),' ',pops2[i].popsize()
    print pops[i].gen(),' ',pops2[i].gen()

1000   500
20000   20100

It is really that easy. Now, you can evolve a population to equilibrium and then evolve copies of it within a for loop to get multiple quasi-independent replicates of bottlenecks, etc.