high_cov_pis = clipped_pis[(median_coverages >= min_coverage)]
# get the time info for the snp_samples
time_pair_idxs, visno, day = parse_midas_data.calculate_time_pairs(
subject_sample_time_map, high_cov_samples)
#### time pair idxs where patients can have exactly 1 time point (so that points plotted are iid)
time_pair_idxs_unique, visno_snps_genes_unique, day_snps_genes_unique = parse_midas_data.calculate_unique_time_pairs(
subject_sample_time_map, high_cov_samples)
### different patient idx:
# to compare results to time_pair idxs, we want different patient pair idxs. This helps us to contextualize if we are seeing events within patients that resemble replacements or modifications.
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
snp_same_sample_idxs, snp_same_subject_idxs, snp_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, high_cov_samples)
##########
# Plot: #
##########
pylab.figure()
pylab.xlabel('First time point')
pylab.ylabel('Subsequent time point')
pylab.xlim([1e-5, 1e-1])
pylab.ylim([1e-5, 1e-1])
pylab.title(species_name)
pylab.loglog(high_cov_pis[time_pair_idxs[0]],
high_cov_pis[time_pair_idxs[1]],
'go',
if second_snp_difference_matrix.shape[0]==0:
second_snp_difference_matrix = numpy.zeros_like(chunk_snp_difference_matrix)*1.0
second_snp_opportunity_matrix = numpy.zeros_like(second_snp_difference_matrix)*1.0
second_snp_difference_matrix += chunk_snp_difference_matrix
second_snp_opportunity_matrix += chunk_snp_opportunity_matrix
sys.stderr.write("Done!\n")
# Now calculate rates
first_divergence_matrix = first_snp_difference_matrix / first_snp_opportunity_matrix
second_divergence_matrix = second_snp_difference_matrix / second_snp_opportunity_matrix
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
same_sample_idxs, same_subject_idxs, diff_subject_idxs = parse_midas_data.calculate_subject_pairs(subject_sample_map, joint_snp_samples)
# Set up figure
fig = plt.figure(figsize=(3, 2.5))
# Set up grids to hold figure panels
outer_grid = gridspec.GridSpec(1, 1)
###################
#
# SNP Panel
#
###################
divergence_axis = plt.Subplot(fig, outer_grid[0])
fig.add_subplot(divergence_axis)
# Now need to make the gene samples and snp samples match up
desired_samples = gene_samples[marker_coverages > min_coverage]
prevalence_idxs = (parse_midas_data.calculate_unique_samples(
subject_sample_map, gene_samples)) * (marker_coverages >= min_coverage)
prevalences = gene_diversity_utils.calculate_fractional_gene_prevalences(
gene_depth_matrix[:, prevalence_idxs], marker_coverages[prevalence_idxs])
pangenome_prevalences = numpy.array(prevalences, copy=True)
pangenome_prevalences.sort()
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, desired_samples)
snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(
desired_samples, snp_samples)
gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(
desired_samples, gene_samples)
same_sample_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_same_sample_idxs)
same_sample_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(
gene_sample_idx_map, desired_same_sample_idxs)
same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_same_subject_idxs)
fixation_matrix_non, persite_fixation_matrix_non = diversity_utils.calculate_fixation_matrix(
allele_counts_map,
passed_sites_map,
variant_type='1D',
min_change=min_change)
sys.stderr.write("Done!\n")
# Only plot samples above a certain depth threshold
high_coverage_samples = samples[median_coverages >= min_coverage]
high_coverage_low_pi_samples = samples[(median_coverages >= min_coverage) *
(pis <= 1e-03)]
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
high_coverage_same_sample_idxs, high_coverage_same_subject_idxs, high_coverage_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, high_coverage_samples)
sample_idx_map = parse_midas_data.calculate_sample_idx_map(
high_coverage_samples, samples)
same_sample_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_same_sample_idxs)
#
same_subject_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_same_subject_idxs)
#
diff_subject_idxs = parse_midas_data.apply_sample_index_map_to_indices(
sample_idx_map, high_coverage_diff_subject_idxs)
# Calculate which pairs of idxs belong to the same sample, which to the same subject
# and which to different subjects
