# In[6]:
#Initalize a random number generator with seed value of 101
rng = fp.GSLrng(101)
# In[7]:
#Simulate 40 replicate populations. This uses C++11 threads behind the scenes:
pops = fp.evolve_regions(rng, #The random number generator
40, #The number of pops to simulate = number of threads to use.
N, #Initial population size for each of the 40 demes
nlist[0:], #List of population sizes over time.
0.005, #Neutral mutation rate (per gamete, per generation)
0.01, #Deleterious mutation rate (per gamete, per generation)
0.005, #Recombination rate (per diploid, per generation)
nregions, #Defined above
sregions, #Defined above
recregions)#Defined above
# In[8]:
#Now, pops is a Python list with len(pops) = 40
#Each element's type is fwdpy.singlepop
print(len(pops))
print(type(pops[0]))
#The first part is the same as the example for fwdpy.evolve_regions import fwdpy import numpy as np nregions = [fwdpy.Region(0,1,1),fwdpy.Region(2,3,1)] sregions = [fwdpy.ExpS(1,2,1,-0.001,0.0),fwdpy.ExpS(1,2,0.01,0.001)] rregions = [fwdpy.Region(0,3,1)] rng = fwdpy.GSLrng(100) popsizes = np.array([1000],dtype=np.uint32) # Evolve for 5N generations initially popsizes=np.tile(popsizes,10000) pops = fwdpy.evolve_regions(rng,1,1000,popsizes[0:],0.001,0.01,0.001,nregions,sregions,rregions) #Now, "bud" off a daughter population of same size, and evolve both for another 100 generations mpops = fwdpy.evolve_regions_split(rng,pops,popsizes[0:100],popsizes[0:100],0.001,0.0001,0.001,nregions,sregions,rregions) samples = [fwdpy.get_samples(rng,i,[10,10]) for i in mpops] details = [ fwdpy.get_sample_details(rng,i[1],j)
# In[27]: #set up our sim rng = fp.GSLrng(101) nregions = [fp.Region(0,1,1),fp.Region(2,3,1)] sregions = [fp.ExpS(1,2,1,-0.1),fp.ExpS(1,2,0.1,0.001)] rregions = [fp.Region(0,3,1)] popsizes = np.array([1000]*10000,dtype=np.uint32) # In[28]: #Run the sim pops = fp.evolve_regions(rng,4,1000,popsizes[0:],0.001,0.0001,0.001,nregions,sregions,rregions) # In[29]: #Take samples from the simulation samples = [fp.get_samples(rng,i,20) for i in pops] # ## Calculating sliding windows # # We are going to want non-overlapping widwos of size 0.1. # # One thing to keep track of is the total size of our region, which is the half-open interval $[0,3)$ # # ### Manual method
theta=100.0
rho=100.0
mutrate_neutral = theta/float(4*N)
mutrate_sel = 0.0
recrate = rho/float(4*N)
rng = fp.GSLrng(101)
#Evolve 20 populations to mutation-drift equilibrium
pops = fp.evolve_regions(rng,20,
nlist[0],
nlist,
mutrate_neutral,
mutrate_sel,
recrate,
nregions,
sregions,
recregions)
#Change a mutation's selection coefficient
for i in pops:
x=csp.change_selection_coeff(i,0.5,10,0.1,0.25)
#print out count and position of variant actually changed
print (x)
#Get the list of mutations in each population
views = [pd.DataFrame(i) for i in fp.view_mutations(pops)]
for i in views:
import fwdpy as fp
import numpy as np
rng = fp.GSLrng(101)
N=1000
nlist=np.array([N]*10*N,dtype=np.uint32)
p=fp.evolve_regions(rng,
64,N,
nlist,
0.25,
0.0,
0.25,
[fp.Region(0,1,1)],
[],
[fp.Region(0,1,1)])
#p.append(p2)
#print len(p)
#v = fp.view_diploids_pd(p,range(0,N,1))
#v=[fp.view_diploids(i,range(0,N,1)) for i in p]
#b = fp.view_diploids_pd(p,range(0,N,1))
#print b
#for i in v:
# print i
#g = [i.gen() for i in p]
#print g
#d = fp.view_diploids(p,[0,1])
#print d
def test_nonFiniteNonNeutralMutationRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng,1,1000,popsizes[0:],0.001,np.nan,0.001,nregions,sregions,rregions)
def test_nonFiniteRecRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng,1,1000,popsizes[0:],0.001,0.001,np.inf,nregions,sregions,rregions)
import unittest
import fwdpy
import numpy as np
nregions = [fwdpy.Region(0, 1, 1), fwdpy.Region(2, 3, 1)]
sregions = [fwdpy.ExpS(1, 2, 1, -0.1), fwdpy.ExpS(1, 2, 0.01, 0.001)]
rregions = [fwdpy.Region(0, 3, 1)]
rng = fwdpy.GSLrng(100)
N = 1000
NGENS = 100
popsizes = np.array([N], dtype=np.uint32)
popsizes = np.tile(popsizes, NGENS)
pops = fwdpy.evolve_regions(rng, 1, N, popsizes[0:], 0.001, 0.0001, 0.001,
nregions, sregions, rregions)
#The sum of the gamete counts must be 2*(deme size):
#mpops = fwdpy.evolve_regions_split(rng,pops,popsizes[0:],popsizes[0:],0.001,0.0001,0.001,nregions,sregions,rregions,[0]*2)
class test_singlepop_views(unittest.TestCase):
def testNumGametes(self):
gams = fwdpy.view_gametes(pops[0])
nsingle = 0
for i in gams:
nsingle += i['n']
self.assertEqual(nsingle, 2000)
def testDipsize(self):
dips_single = fwdpy.view_diploids(pops[0], [0, 1, 2])
self.assertEqual(len(dips_single), 3)
def test_negativeNeutralMutationRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng,1,1000,popsizes[0:],-0.001,0.0001,0.001,nregions,sregions,rregions)
def test_nonFiniteRecRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng, 1, 1000, popsizes[0:], 0.001,
0.001, np.inf, nregions, sregions,
rregions)
def test_nonFiniteNonNeutralMutationRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng, 1, 1000, popsizes[0:], 0.001,
np.nan, 0.001, nregions, sregions,
rregions)
def test_negativeNeutralMutationRate(self):
with self.assertRaises(RuntimeError):
pops = fwdpy.evolve_regions(rng, 1, 1000, popsizes[0:], -0.001,
0.0001, 0.001, nregions, sregions,
rregions)
"/", dt.day, "/", dt.year)
print("The dependency versions are", fp.pkg_dependencies())
# In[27]:
#set up our sim
rng = fp.GSLrng(101)
nregions = [fp.Region(0, 1, 1), fp.Region(2, 3, 1)]
sregions = [fp.ExpS(1, 2, 1, -0.1), fp.ExpS(1, 2, 0.1, 0.001)]
rregions = [fp.Region(0, 3, 1)]
popsizes = np.array([1000] * 10000, dtype=np.uint32)
# In[28]:
#Run the sim
pops = fp.evolve_regions(rng, 4, 1000, popsizes[0:], 0.001, 0.0001, 0.001,
nregions, sregions, rregions)
# In[29]:
#Take samples from the simulation
samples = [fp.get_samples(rng, i, 20) for i in pops]
# ## Calculating sliding windows
#
# We are going to want non-overlapping widwos of size 0.1.
#
# One thing to keep track of is the total size of our region, which is the half-open interval $[0,3)$
#
# ### Manual method
#
# Let's just do it using pure Python:
import fwdpy as fp import numpy as np rng = fp.GSLrng(101) N = 1000 nlist = np.array([N] * 10 * N, dtype=np.uint32) p = fp.evolve_regions(rng, 64, N, nlist, 0.25, 0.0, 0.25, [fp.Region(0, 1, 1)], [], [fp.Region(0, 1, 1)]) # p.append(p2) # print len(p) # v = fp.view_diploids_pd(p,range(0,N,1)) # v=[fp.view_diploids(i,range(0,N,1)) for i in p] # b = fp.view_diploids_pd(p,range(0,N,1)) # print b # for i in v: # print i # g = [i.gen() for i in p] # print g # d = fp.view_diploids(p,[0,1]) # print d
