def introgression_prior(vector_lib,
Fsts_test,
angle,
range_windows,
target=[0, 1],
region=[0, 1],
passport=False):
'''
Use the same vector for two populations at a given range.
- region: span of differentiation pattern in prop to range provided.
'''
ID = 'introgression'
progress = (angle - range_windows[0]) / (range_windows[1] -
range_windows[0])
if progress >= region[0] and progress <= region[1]:
fst_wanted = 0
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
else:
fst_wanted = .1
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
if passport:
return new_freqs, ID
else:
return new_freqs
def alien_prior_III(vector_lib,
Fsts_test,
angle,
range_windows,
target=[0, 1],
fst_a=0.2,
fst_b=.2,
region=[-5, 5],
passport=False):
ID = 'alien III'
progress = (angle - range_windows[0]) / (range_windows[1] -
range_windows[0])
if progress >= region[0] and progress <= region[1]:
fst_wanted = fst_a
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
else:
fst_wanted = fst_b
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
if passport:
return new_freqs, ID
else:
return new_freqs
def sin_prior(vector_lib,
Fsts_test,
angle,
range_windows,
target=[0, 1],
freq=2,
range_fst=[0, 0.15],
passport=False):
'''
Sinusoid differentiation between targets.
- index of target pop vetors in Pops matrix. list.
- fst_max: range of divergence pattern in fsts.
- passport: return function ID. Boolean.
'''
ID = 'sinusoid'
fst_middle = sum(range_fst) / float(2)
progress = (angle - range_windows[0]) / (range_windows[1] -
range_windows[0])
fst_wanted = np.sin(progress * np.pi * freq) * (
(range_fst[1] - range_fst[0]) / 2) + fst_middle
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
if passport:
return new_freqs, ID
else:
return new_freqs
def linear_prior(vector_lib,
Fsts_test,
angle,
range_windows,
target=[0, 1],
region=[-5, 5],
slope=1,
range_fst=[0, 0.15],
passport=False):
'''
Linear differentiation between target populations.
- region: span of differentiation pattern in prop to range provided.
- fst_max: range of divergence pattern in fsts.
- passport: return function ID. Boolean.
'''
ID = 'linear'
fst_middle = sum(range_fst) / float(2)
progress = (angle - range_windows[0]) / (range_windows[1] -
range_windows[0])
fst_wanted = range_fst[0] + progress * (range_fst[1] -
range_fst[0]) * slope
who = fst_select(Fsts_test, fst_wanted, range_allow=0.01)
new_freqs = vector_lib[who, :]
if passport:
return new_freqs, ID
else:
return new_freqs
