def get_mutation_fixation_trajectories(population):
mutations, depth_tuple = parse_annotated_timecourse(population)
population_avg_depth_times, population_avg_depths, clone_avg_depth_times, clone_avg_depths = depth_tuple
state_times, state_trajectories = parse_well_mixed_state_timecourse(population)
times = mutations[0][9]
Ms = numpy.zeros_like(times)*1.0
fixed_Ms = numpy.zeros_like(times)*1.0
#transit_times[population] = []
for mutation_idx in range(0,len(mutations)):
#location, gene_name, allele, var_type, test_statistic, pvalue, cutoff_idx, depth_fold_change, depth_change_pvalue, times, alts, depths, clone_times, clone_alts, clone_depths = mutations[mutation_idx]
location, gene_name, allele, var_type, codon, position_in_codon, AAs_count, test_statistic, pvalue, cutoff_idx, depth_fold_change, depth_change_pvalue, times, alts, depths, clone_times, clone_alts, clone_depths = mutations[mutation_idx]
state_Ls = state_trajectories[mutation_idx]
good_idxs, filtered_alts, filtered_depths = timecourse_utils.mask_timepoints(times, alts, depths, var_type, cutoff_idx, depth_fold_change, depth_change_pvalue)
freqs = timecourse_utils.estimate_frequencies(filtered_alts, filtered_depths)
masked_times = times[good_idxs]
masked_freqs = freqs[good_idxs]
masked_state_Ls = state_Ls[good_idxs]
t0,tf,transit_time = timecourse_utils.calculate_appearance_fixation_time_from_hmm(masked_times, masked_freqs, masked_state_Ls)
#print(t0,tf,transit_time)
if t0==tf==transit_time==None:
continue
#print(masked_times, masked_freqs)
interpolating_function = timecourse_utils.create_interpolation_function(masked_times, masked_freqs, tmax=100000)
fs = interpolating_function(times)
fs[fs<0]=0
# Record
Ms += fs
if masked_state_Ls[-1] in well_mixed_fixed_states:
fixed_Ms += (times>=tf)
return times, Ms, fixed_Ms
Ls = haplotype_trajectories[mutation_idx]
state_Ls = state_trajectories[mutation_idx]
good_idxs, filtered_alts, filtered_depths = timecourse_utils.mask_timepoints(times, alts, depths, var_type, cutoff_idx, depth_fold_change, depth_change_pvalue)
freqs = timecourse_utils.estimate_frequencies(filtered_alts, filtered_depths)
masked_times = times[good_idxs]
masked_freqs = freqs[good_idxs]
masked_state_Ls = state_Ls[good_idxs]
t0,tf,transit_time = timecourse_utils.calculate_appearance_fixation_time_from_hmm(masked_times, masked_freqs, masked_state_Ls)
transit_times[population].append(transit_time)
interpolating_function = timecourse_utils.create_interpolation_function(masked_times, masked_freqs, tmax=100000)
fs = interpolating_function(times)
fs[fs<0]=0
# Record
Ms += fs
if masked_state_Ls[-1] in parse_file.well_mixed_fixed_states:
fixed_Ms += (times>=tf)
fixed_mutation_trajectories[population] = (times, fixed_Ms)
mutation_trajectories[population] = (times, Ms)
sys.stderr.write("analyzed %d mutations!\n" % len(mutations))
if is_mutator and var_type == 'sv' or var_type == 'indel':
continue
Ls = haplotype_trajectories[mutation_idx]
good_idxs, filtered_alts, filtered_depths = timecourse_utils.mask_timepoints(
times, alts, depths, var_type, cutoff_idx, depth_fold_change,
depth_change_pvalue)
freqs = timecourse_utils.estimate_frequencies(filtered_alts,
filtered_depths)
masked_times = times[good_idxs]
masked_freqs = freqs[good_idxs]
interpolation_function = timecourse_utils.create_interpolation_function(
masked_times, masked_freqs, tmax=100000)
haplotype_interpolation_function = timecourse_utils.create_interpolation_function(
times, Ls, tmax=100000, kind='nearest')
clone_freqs = clone_alts * 1.0 / (clone_depths + (clone_depths == 0))
clone_depth_fold_changes = timecourse_utils.estimate_depth_fold_changes(
clone_avg_depths, clone_depths)
# classify clade
if (Ls[-1] == parse_file.ANCESTRAL_FIXED) or (
Ls[-1] == parse_file.ANCESTRAL_POLYMORPHIC):
clade = "ancestral"
elif (Ls[-1]
== parse_file.MAJOR_FIXED) or (Ls[-1]
== parse_file.MAJOR_POLYMORPHIC):
clade = "majority"