rho_per_locus=100.0
f=qtm.MlocusAdditiveTrait()
delmuts=[1e-3]*NLOCI
mmodels=[fp.GaussianS(0,1,1,0.25)]*len(delmuts)
for i in mmodels:
print i
nmuts=[theta_neutral/float(4*nlist[0])]*NLOCI
recrates=[theta_neutral/float(4*nlist[0])]*NLOCI
pfile = gzip.open("pickle_out.p","wb")
for i in range(1):
x = fp.MlocusPopVec(NREPS,nlist[0],NLOCI)
sampler=fp.NothingSampler(len(x))
qtm.evolve_qtraits_mloc_sample_fitness(rnge,x,sampler,f,
nlist[:5000],
nmuts,
delmuts,
mmodels,
recrates,
[0.0]*(NLOCI-1),sample=0,
VS=2,optimum=0.)
v = fp.view_mutations(x)
for i in v:
print i
for i in x:
d=fpio.serialize(i)
pickle.dump(d,pfile)
pfile.close()
# coding: utf-8
# # Example of taking 'views' from simulated populations
# In[1]:
from __future__ import print_function
import fwdpy as fp
import pandas as pd
from background_selection_setup import *
# Get the mutations that are segregating in each population:
# In[2]:
mutations = [fp.view_mutations(i) for i in pops]
# Look at the raw data in the first element of each list:
# In[3]:
for i in mutations:
print(i[0])
# Let's make that nicer, and convert each list of dictionaries to a Pandas DataFrame object:
# In[4]:
mutations2 = [pd.DataFrame(i) for i in mutations]
# In[5]:
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:
print (i[i.neutral==False])
#Now, lets use our custom temporal sampler
sampler=cts1.SelectedSFSSampler(len(pops))
#Evolve these pops for another 100 generations,
#tracking joint frequency,s of all selected mutations
fp.evolve_regions_sampler(rng,pops,sampler,
nlist[:100],
mutrate_neutral,
mutrate_sel,
recrate,
nregions,
# # Example of taking 'views' from simulated populations
# In[1]:
from __future__ import print_function
import fwdpy as fp
import pandas as pd
from background_selection_setup import *
# Get the mutations that are segregating in each population:
# In[2]:
mutations = [fp.view_mutations(i) for i in pops]
# Look at the raw data in the first element of each list:
# In[3]:
for i in mutations:
print(i[0])
# Let's make that nicer, and convert each list of dictionaries to a Pandas DataFrame object:
# In[4]:
mutations2 = [pd.DataFrame(i) for i in mutations]
rng = fp.GSLrng(101)
pops = fp.evolve_regions(rng, #The random number generator
4, #The number of pops to simulate = number of threads to use.
N, #Initial population size for each of the 4 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
##Get fixation views:
fixations = [fp.view_fixations(i) for i in pops]
##Get mutation views:
mviews = fp.view_mutations(pops)
class test_sample_details(unittest.TestCase):
def test_CompareUsingView(self):
"""
Evolve some population, take samples,
get details of those samples, make sure
those details match up what 'views' think should
be in the population.
"""
samples = [fp.get_samples(rng,i,100) for i in pops]
details = [fp.get_sample_details(i[1],j) for i,j in zip(samples,pops)]