Example #1
0
    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])
Example #2
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    def test_CountFixationsInSample2(self):
        #Repeat above test, but using sampler where neutral + selected variants are in different objects
        samples = [fp.get_samples(rng,i,100,False) for i in pops]

        for i in range(len(fixations)):
            FOUNDN=0
            FOUNDS=0
            for f in fixations[i]:
                if f['neutral'] is True:
                    mm=[X for X in samples[i][0] if X[0]==f['pos']]
                    FOUNDN+=len(mm)
                else:
                    mm=[X for X in samples[i][1] if X[0]==f['pos']]
                    FOUNDS+=len(mm)
                    self.assertEqual(FOUNDN+FOUNDS,len(fixations[i]))
Example #3
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 def test_CountFixationsInSample3(self):
     #Finally, make sure that sample w/o including fixations works,
     #meaning that no mutations are present in the sample with
     #derived count == nsam
     samples = [fp.get_samples(rng,i,100) for i in pops]
     ##Checks that no "true fixations" are in vector
     for i in range(len(fixations)):
         FOUNDN=0
         FOUNDS=0
         for f in fixations[i]:
             if f['neutral'] is True:
                 mm=[X for X in samples[i][0] if X[0]==f['pos']]
                 FOUNDN+=len(mm)
             else:
                 mm=[X for X in samples[i][1] if X[0]==f['pos']]
                 FOUNDS+=len(mm)
                 self.assertEqual(FOUNDN+FOUNDS,0)
     ##Checks that no polymorphisms are present as fixations in sample
     for i in samples:
         for j in i:
             x=j[0][1].count(b'1')
             self.assertTrue(x<100)
             x=j[1][1].count(b'1')
             self.assertTrue(x<100)
Example #4
0
# 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]))
                


# ## Taking samples from simulated populations

# In[9]:

#Use a list comprehension to get a random sample of size
#n = 20 from each replicate
samples = [fp.get_samples(rng,i,20) for i in pops]

#Samples is now a list of tuples of two lists.
#Each list contains tuples of mutation positions and genotypes.
#The first list represents neutral variants.
#The second list represents variants affecting fitness ('selected' variants)
#We will manipulate/analyze these genotypes, etc.,
#in a later example
for i in samples[:4]:
    print ("A sample from a population is a ",type(i))
    
print(len(samples))


# ### Getting additional information about samples
Example #5
0
import fwdpy
import pandas

rng = fwdpy.GSLrng(100)
pop = fwdpy.evolve_pops_t(rng,3,1000,[1000]*int(1e4),50,50)
s = [fwdpy.get_samples(rng,i,[10,]) for i in pop]

###fxn to ask if a site is a singleton
def isSingleton( site ):
    ones=site[1].count('1')
    if ones == 1:
        return True
    return False

##fxn to count derived singletonss in a sample
def countDerived( sample ):
    nsing=0
    for i in range(len(sample)):
        if isSingleton(sample[i]):
            nsing += 1
    return nsing

#list comprehension for automatic vectorizing
D = [fwdpy.TajimasD(si[0]) for si in s]
print "Tajima's D per sample =", D

#number of seg sites per sample
segsites = [len(si[0]) for si in s]
print "S per sample =",segsites

#number of singletons per sample
Example #6
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)
    )

#   evolve_regions() returns a 'popvec' object. It is an iterablet hat contains
#   a bunch of 'singlepop' objects.
#   Let's get some basic information about each one
for index, p in enumerate(pops):
    print "Population", index
    print "    Evolved to generation", p.gen()
    print "    Has", p.popsize(), "diploid individuals"

#   Now, to calculate some summary statistics
#   fwdpy has a handle into the libsequence library
import fwdpy.libseq

#   To calculate summary stats, we have to take a sample from our populations
#       get_samples(
#           RNG,
#           singlepop object,
#           nsam,
#           removeFixed=True, (True: keep only polymorphic sites)
#           deme=None (required for if there is pop structure)
#       )
samples = [fwdpy.get_samples(rng, p, 20) for p in pops]

#   Then we can calculate windowed summary statistics
#       windows(
windowed_stats = [
    fwdpp.libseq.windows(i[0], 0.1, 0.1, -2, 2) for i in samples
    ]