self.model.late(
time=None,
scripts=substitution_script,
comment="fixes mutations in haploid gen"
)
if __name__ == "__main__":
import shadie
with shadie.Model() as mod:
# define mutation types
m0 = shadie.mtype(0.5, 'n', 0, 0.4)
m1 = shadie.mtype(0.5, 'g', 0.8, 0.75)
#I suggest we add a checkpoint that calculates the average
#fitness of mutations input by the user. If fitness is too high
#the simuulation will lag tremendously.
# OK: a good use case for logger.warning('fitness is too high...')
# define elements types
e0 = shadie.etype([m0, m1], [1, 2])
e1 = shadie.etype([m1], [1])
# design chromosome of elements
chrom = shadie.chromosome.random(
genome_size=20000,
noncds=e0,
intron=e0,
'gam_pop_size': "NA",
'spo_mutation_rate': self.model.metadata['mutation_rate'],
'recombination_rate': self.model.metadata['recomb_rate']
}
self.model.map["initialize"][0]['constants']["K"] = self.pop_size
self.model.map["initialize"][0]['simglobals'][
"METADATA"] = metadata_dict
if __name__ == "__main__":
import shadie
# define mutation types
m0 = shadie.mtype(0.5, 'n', 0, 0.4)
m1 = shadie.mtype(0.5, 'g', 0.8, 0.75, diffexpr="diploid")
# define elements types
e0 = shadie.etype([m0, m1], [1, 2])
e1 = shadie.etype([m1], [1])
# design chromosome of elements
chrom = shadie.chromosome.random(
genome_size=20000,
noncds=e0,
intron=e0,
exon=e1,
)
print(m1._expr)
self.data.loc[start] = (
data[key].altname,
start,
end,
data[key].name,
data[key],
data[key].coding,
)
if __name__ == "__main__":
import shadie
# define mutation types
m0 = shadie.mtype(0.5, 'n', 2.0, 1.0)
m1 = shadie.mtype(0.5, 'g', 3.0, 1.0)
m2 = shadie.mtype(0.5, 'f', 0)
# define elements types
e0 = shadie.etype([m0, m1], [1, 2])
e1 = shadie.etype([m2], [1])
# # print(e0.mlist)
# chrom = shadie.chromosome.random(100000)
# #print(chrom.data.iloc[:50, :4])
# #chrom.review("chromosome")
# default = shadie.chromosome.default()
# print(default.data)
Returns a toyplot histogram of the selection coefficients.
"""
canvas, axes, marks = self[0].draw(show_mean=False, **kwargs)
for mut in self[1:]:
marks.append(mut._draw_hists(axes=axes))
return canvas, axes, marks
if __name__ == "__main__":
# generate random chromosome
import shadie
mlist = shadie.mlist(
#shadie.mtype(0.5, 'f', 0.0),
shadie.mtype(0.5, 'f', 0.1),
shadie.mtype(0.5, 'n', 0.5, 0.25),
shadie.mtype(0.5, 'g', 2.0, 0.1),
shadie.mtype(0.1, 'e', 2.5, diffexpr="diploid"),
)
for muta in mlist:
muta.summary()
#m2 = shadie.mtype(0.5, 'f', 0)
print(mlist)
test = []
expr = []
for mut in mlist:
test.append(mut.name)
expr.append(mut._expr)
print(test)
"min value in the 99% CI of all MutationType distributions"
return min([i.mlist.min for i in self])
@property
def max(self):
"max value in the 99% CI of all MutationType distributions"
return max([i.mlist.max for i in self])
if __name__ == "__main__":
import shadie
# create mutationlist
mlist = shadie.mlist(
shadie.mtype(0.5, 'f', 0.1),
shadie.mtype(0.5, 'n', 0.05, 0.02),
)
# create elements (a mutation list with frequencies)
ele1 = ElementType(
mlist,
(1, 1),
)
ele2 = ElementType(mlist, (0.5, 1))
print(ele1.mlist)
print(ele2)
print(ele2.to_slim())
print(ele2.coding)
for mut in ele1.mlist: