def get_divergence_diversity_sliding(aft, block_length, VERBOSE=0):
'''Get local divergence and diversity in a sliding window'''
cons_ind = Patient.get_initial_consensus_noinsertions(aft, return_ind=True)
ind_N = cons_ind == 5
cons_ind[ind_N] = 0
aft_nonanc = 1.0 - aft[:, cons_ind, np.arange(aft.shape[2])]
aft_nonanc[:, ind_N] = 0
aft_var = (aft * (1 - aft)).sum(axis=1)
struct = np.ones(block_length)
dg = np.ma.array(np.apply_along_axis(lambda x: np.convolve(x, struct, mode='valid'),
axis=1, arr=aft_nonanc), hard_mask=True)
ds = np.ma.array(np.apply_along_axis(lambda x: np.convolve(x, struct, mode='valid'),
axis=1, arr=aft_var), hard_mask=True)
# NOTE: normalization happens based on actual coverage
norm = np.apply_along_axis(lambda x: np.convolve(x, struct, mode='valid'),
axis=1, arr=(-aft[:, 0].mask))
dg.mask = norm < block_length
dg /= norm
ds.mask = norm < block_length
ds /= norm
x = np.arange(dg.shape[1]) + (block_length - 1) / 2.0
return (x, dg, ds)
def get_divergence_diversity_sliding(aft, block_length, VERBOSE=0):
'''Get local divergence and diversity in a sliding window'''
cons_ind = Patient.get_initial_consensus_noinsertions(aft, return_ind=True)
ind_N = cons_ind == 5
cons_ind[ind_N] = 0
aft_nonanc = 1.0 - aft[:, cons_ind, np.arange(aft.shape[2])]
aft_nonanc[:, ind_N] = 0
aft_var = (aft * (1 - aft)).sum(axis=1)
struct = np.ones(block_length)
dg = np.ma.array(
np.apply_along_axis(lambda x: np.convolve(x, struct, mode='valid'),
axis=1,
arr=aft_nonanc),
hard_mask=True)
ds = np.ma.array(np.apply_along_axis(
lambda x: np.convolve(x, struct, mode='valid'), axis=1, arr=aft_var),
hard_mask=True)
# NOTE: normalization happens based on actual coverage
norm = np.apply_along_axis(lambda x: np.convolve(x, struct, mode='valid'),
axis=1,
arr=(-aft[:, 0].mask))
dg.mask = norm < block_length
dg /= norm
ds.mask = norm < block_length
ds /= norm
x = np.arange(dg.shape[1]) + (block_length - 1) / 2.0
return (x, dg, ds)
def get_divergence(aft):
'''Get divergence from allele frequency trajectories'''
cons_ind = Patient.get_initial_consensus_noinsertions(aft, return_ind=True)
dg = 1 - aft[:, cons_ind, np.arange(aft.shape[2])].mean(axis=1)
return dg
