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)]
##For each element in details,
##find it in the "mutation view" for that replicate
for i in range(len(samples)):
##Make sure that lengths match up
for key in details[i]:
self.assertEqual(len(samples[i][1]),len(details[i][key]))
for j in range(len(samples[i][1])):
mm=[X for X in mviews[i] if X['pos'] == samples[i][1][j][0]]
##Make sure each position is uniuqe
self.assertEqual(len(mm),1)
for mmi in mm:
##Make sure that the position is equal to what we expect
self.assertEqual(mmi['pos'],samples[i][1][j][0])
##Make sure selection coefficient matches up
self.assertEqual(mmi['s'],details[i]['s'][j])
EP=mmi['n']/float(2000) #"expected" frequency
self.assertEqual(EP,details[i]['p'][j])
for i in samples[:4]:
print ("A sample from a population is a ",type(i))
print(len(samples))
# ### Getting additional information about samples
# In[10]:
#Again, use list comprehension to get the 'details' of each sample
#Given that each object in samples is a tuple, and that the second
#item in each tuple represents selected mutations, i[1] in the line
#below means that we are getting the mutation information only for
#selected variants
details = [fp.get_sample_details(i[1],j) for i,j in zip(samples,pops)]
# In[11]:
#details is now a list of pandas DataFrame objects
#Each DataFrame has the following columns:
# a: mutation age (in generations)
# h: dominance of the mutation
# p: frequency of the mutation in the population
# s: selection coefficient of the mutation
# label: A label applied for mutations for each region. Here, I use 0 for all regions
for i in details[:4]:
print(i)
print "S per sample =",segsites
#number of singletons per sample
nsing = [countDerived(si[0]) for si in s]
print "No. singletons per sample =",nsing
## remove the non-singleton sites from each sample
sAllSing = [filter( lambda x: isSingleton(x) == True, j[0] ) for j in s]
print "No. singletons per sample =",[len(i) for i in sAllSing]
##Remove the singletons
sNoSing = [filter( lambda x: isSingleton(x) == False, j[0] ) for j in s]
print "No. non-singletons per sample =",[len(i) for i in sNoSing]
##Get xtra info for each mutation in the sample
sh = [fwdpy.get_sample_details(i[0],j) for i,j in zip(s,pop)]
##Add a column to each DataFrame specifying the mutation position, count of derived state, and a "replicate ID"
for i in range(len(sh)):
sh[i]['pos']=[x[0] for x in s[i][0]]
sh[i]['freq']=[ x[1].count('1') for x in s[i][0]]
sh[i]['id']=[i]*len(sh[i].index)
##Write all DataFrames to a file
pandas.concat(sh).to_csv("test.csv",sep="\t",index=False)
##Now, evolve them some more and end with a bottleneck + recent, partial recovery
fwdpy.evolve_pops_more_t(rng,pop,[1000]*int(1e4) + [500]*100 + [750]*10,50,50)
##Check that all is cool with the data structures...
for i in range(len(pop)):
#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)
#popsizes are NumPy arrays of 32bit unsigned integers
popsizes = np.array([1000],dtype=np.uint32)
popsizes = np.tile(popsizes,10*1000)
pops = fwdpy.evolve_regions(rng,3,1000,popsizes[0:],0.001,0.0001,0.001,nregions,sregions,rregions)
s = [fwdpy.get_samples(rng,i,[10,]) for i in pops]
for i in range(len(s)):
print s[i],"\n\n"
print [i.gen() for i in pops]
fwdpy.evolve_regions_more(rng,pops,popsizes[0:],0.001,0.0001,0.001,nregions,sregions,rregions)
s = [fwdpy.get_samples(rng,i,[10,]) for i in pops]
for i in range(len(s)):
print s[i],"\n\n"
print [i.gen() for i in pops]
print [i.sane() for i in pops]
print [i.popsize() for i in pops]
##Not get more info for selected mutations
info = [fwdpy.get_sample_details(i[1],j) for i,j in zip(s,pops)]
print info