# * s = selection coefficient/effect size
# * label = The label assigned to a mutation. These labels can be associated with Regions and Sregions. Here, 1 is a mutation from the neutral region, 2 a selected mutation from the 'left' region and 3 a selected mutation from the 'right' regin.
#
# We can do all the usual subsetting, etc., using regular pandas tricks. For example, let's get the neutral mutations for each population:
# In[6]:
nmuts = [i[i.neutral == True] for i in mutations2]
for i in nmuts:
print(i.head())
# We can also take views of gametes:
# In[7]:
gametes = [fp.view_gametes(i) for i in pops]
# The format is really ugly. v Each gamete is a dict with two elements:
#
# * 'neutral' is a list of mutations _not_ affecting fitness. The format is the same as for the mutation views above.
# * 'selected' is a list of mutations that _do_ affect fitness. The format is the same as for the mutation views above.
# In[8]:
for i in gametes:
print(i[0])
# OK, let's clean that up. We'll focus on the selected mutations for each individual, and turn everything into a pd.DataFrame.
#
# We're only going to do this for the first simulated population.
def testNumGametes(self):
gams = fwdpy.view_gametes(pops[0])
nsingle=0
for i in gams: nsingle += i['n']
self.assertEqual(nsingle,2000)
# * label = The label assigned to a mutation. These labels can be associated with Regions and Sregions. Here, 1 is a mutation from the neutral region, 2 a selected mutation from the 'left' region and 3 a selected mutation from the 'right' regin.
#
# We can do all the usual subsetting, etc., using regular pandas tricks. For example, let's get the neutral mutations for each population:
# In[6]:
nmuts = [i[i.neutral == True] for i in mutations2]
for i in nmuts:
print(i.head())
# We can also take views of gametes:
# In[7]:
gametes = [fp.view_gametes(i) for i in pops]
# The format is really ugly. v Each gamete is a dict with two elements:
#
# * 'neutral' is a list of mutations _not_ affecting fitness. The format is the same as for the mutation views above.
# * 'selected' is a list of mutations that _do_ affect fitness. The format is the same as for the mutation views above.
# In[8]:
for i in gametes:
print(i[0])
# OK, let's clean that up. We'll focus on the selected mutations for each individual, and turn everything into a pd.DataFrame.
#
def testNumGametes(self):
gams = fwdpy.view_gametes(pops[0])
nsingle = 0
for i in gams:
nsingle += i['n']
self.assertEqual(nsingle, 2000)
