## First, let's compute the SFS for a single population with a three-epoch history
n = 20 ## sample size
## specify the epochs backwards in time from the present
epochs = [
ExponentialTruncatedSizeHistory(
n_max=n, tau=0.25, N_top=1.0, N_bottom=10.0
), # an exponential growth period for 0.25 units of time. "top"=past, "bottom"=present
ConstantTruncatedSizeHistory(n_max=n, tau=0.1,
N=0.1), # a bottleneck for 0.1 units of time
ConstantTruncatedSizeHistory(n_max=n, tau=float('inf'),
N=1.0), # ancestral population size = 1
]
## turn the list of epochs into a single population history
demo1 = PiecewiseHistory(epochs)
## print the SFS entries
print "Printing SFS entries for three epoch history"
print[demo1.freq(i, n) for i in range(1, n)]
## Next, print out the "truncated SFS" for just the middle epoch
## i.e., the frequency spectrum for mutations that occur within the middle epoch
print "\nPrinting truncated SFS for middle epoch"
print[epochs[1].freq(i, n) for i in range(1, n)]
## Finally, let's compute SFS entries for a multipopulation demography
# For our demography, we'll have three leaf populations: 'a','b','c', with 10,5,8 sampled alleles respectively
## specify demography via a newick string
## to specify additional population parameters, follow branch length with [&&momi:...]
