

In [1]:

    
import sys
sys.path.append('../src')
from popgen import *
%matplotlib inline

One dimension



In [2]:

    
m = SteppingStone(gens=100)
m.mig = [0, 0.01, 0.05]
m.pop_size = 200
m.num_pops_x = 5
m.num_msats = 50
BasicView(m, [fst(), ExpHe()], ['mean'], with_model=True)
m.run()

peripheral populations



In [3]:

    
m = SteppingStone(gens=200)
m.mig = 0.02
m.pop_size = 300
m.num_pops_x = 10
m.num_msats = 5
MetaVsDemeView(m, ExpHe(), ExpHe(do_structured=True))
MetaVsDemeView(m, NumAlleles(), NumAlleles(do_structured=True))
m.run()

Two dimensions



In [4]:

    
m = SteppingStone(gens=100, two_d=True)
m.mig = [0, 0.01, 0.05]
m.pop_size = 200
m.num_pops_y = 2
m.num_pops_x = 5
m.num_msats = 50
BasicView(m, [fst(), ExpHe()], ['mean'], with_model=True)
m.run()

Princinpal Components Analysis over time!



In [5]:

    
m = SteppingStone(gens=60)
m.mig = [0.00, 0.01, 0.1]
m.pop_size = 100
m.num_pops_x = 10
m.num_msats = 50
IndividualView(m, PCA(), step=20)
m.run()



In [6]:

    
m = SteppingStone(gens=600)
m.mig = [0.0, 0.01, 0.1]
m.pop_size = 100
m.num_pops_x = 10
m.num_msats = 50
IndividualView(m, PCA(), step=150)
m.run()

PCA over time, 2D



In [7]:

    
m = SteppingStone(gens=1200, two_d=True)
m.mig = [0.001, 0.01]
m.pop_size = 100
m.num_pops_x = 4
m.num_pops_y = 4
m.num_msats = 50
IndividualView(m, PCA(), step=300)
m.run()



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