FIXED_COL_MAP,
c,
r,
m,
t,
prepare_matrices=zipHMM_prepare_matrices,
single_logL=zipHMM_single_logL)
current_model = 'I'
intervals_per_epoch = 10
from coalhmm.model import build_epoch_separated_model
if current_model == 'I':
modelI = build_epoch_separated_model(2, [[0, 0]], [1, intervals_per_epoch])
elif current_model == 'IM':
M_for_both = [[1], [0]]
modelIM = build_epoch_separated_model(
2, [[0, 1], [0, 0]], [1, intervals_per_epoch, intervals_per_epoch],
[None, M_for_both, None])
names = ["hg18", "pantro2"]
from coalhmm.fasta_parser import readAlignment
from scipy import zeros, int32
def read_observations(filename, names):
'''Reads in a single fasta file and converts it to a series of
# The migration is specified in a similar way, but instead of a single value
# each entry in the array is another array telling us what the branch can
# migrate to.
# If we have three branches and the middle one can migrate to either of its
# neighbours but not vice-versa it would look like this: [[],[0,2],[]].
# Finally coalhmm uses discretized time so we need to specify how finely each
# epoch is subdivided. The present won't benefit from more than 1 time interval
# since everything is separate.
intervals_per_epoch = 10
from coalhmm.model import build_epoch_separated_model
if current_model == 'I':
modelI = build_epoch_separated_model(
# With the isolation model we have two branches in the present:
2,
# We want to merge 0 and 1, so we map them both to 0:
[[0,0]],
[1,intervals_per_epoch])
elif current_model == 'IM':
# Creating an isolation-with-migration model is similar to the plain
# isolation model. We add an extra epoch (maintaining the two separate
# branches) and allow migration from 0 to 1 and from 1 to 0 in it.
M_for_both = [[1],[0]]
modelIM = build_epoch_separated_model(
2,
[[0,1], [0,0]],
[1,intervals_per_epoch,intervals_per_epoch],
# None for no migration in the first and last epoch
[None, M_for_both, None])
# Now we need some data to work on. 'example_data.fa' is a small chunk of an
# logLikelihood is changed to use our two new functions.
def logLikelihood(model, all_obs, c,r,m,t):
return logL_multiseq(model, all_obs, FIXED_COL_MAP, c,r,m,t,
prepare_matrices=zipHMM_prepare_matrices,
single_logL=zipHMM_single_logL)
current_model = 'I'
intervals_per_epoch = 10
from coalhmm.model import build_epoch_separated_model
if current_model == 'I':
modelI = build_epoch_separated_model(
2,
[[0,0]],
[1,intervals_per_epoch])
elif current_model == 'IM':
M_for_both = [[1],[0]]
modelIM = build_epoch_separated_model(
2,
[[0,1], [0,0]],
[1,intervals_per_epoch,intervals_per_epoch],
[None, M_for_both, None])
names = ["hg18", "pantro2"]
from coalhmm.fasta_parser import readAlignment
from scipy import zeros, int32
def read_observations(filename, names):
# The migration is specified in a similar way, but instead of a single value
# each entry in the array is another array telling us what the branch can
# migrate to.
# If we have three branches and the middle one can migrate to either of its
# neighbours but not vice-versa it would look like this: [[],[0,2],[]].
# Finally coalhmm uses discretized time so we need to specify how finely each
# epoch is subdivided. The present won't benefit from more than 1 time interval
# since everything is separate.
intervals_per_epoch = 10
from coalhmm.model import build_epoch_separated_model
if current_model == 'I':
modelI = build_epoch_separated_model(
# With the isolation model we have two branches in the present:
2,
# We want to merge 0 and 1, so we map them both to 0:
[[0, 0]],
[1, intervals_per_epoch])
elif current_model == 'IM':
# Creating an isolation-with-migration model is similar to the plain
# isolation model. We add an extra epoch (maintaining the two separate
# branches) and allow migration from 0 to 1 and from 1 to 0 in it.
M_for_both = [[1], [0]]
modelIM = build_epoch_separated_model(
2,
[[0, 1], [0, 0]],
[1, intervals_per_epoch, intervals_per_epoch],
# None for no migration in the first and last epoch
[None, M_for_both, None])
# Now we need some data to work on. 'example_data.fa' is a small chunk of an
