# Now need to make the gene samples and snp samples match up
desired_samples = gene_samples
num_haploids = len(desired_samples)
# 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)
# 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_ordered_subject_pairs(
sample_order_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)
same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(
gene_sample_idx_map, desired_same_subject_idxs)
diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(
snp_sample_idx_map, desired_diff_subject_idxs)
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
high_coverage_low_pi_same_sample_idxs, high_coverage_low_pi_same_subject_idxs, high_coverage_low_pi_diff_subject_idxs = parse_midas_data.calculate_subject_pairs(
subject_sample_map, high_coverage_low_pi_samples)
