#all_species_gene_changes_category={}
all_species_null = {}
all_data[species_name] = {}
#
####################
# Analyze the data #
####################
#
# Only plot samples above a certain depth threshold that are "haploids"
haploid_samples = diversity_utils.calculate_haploid_samples(species_name,
debug=debug)
#
if len(haploid_samples) < min_sample_size:
continue
#
same_sample_idxs, same_subject_idxs, diff_subject_idxs = parse_midas_data.calculate_ordered_subject_pairs(
sample_order_map, haploid_samples)
#
snp_samples = set()
sample_size = 0
for sample_pair_idx in xrange(0, len(same_subject_idxs[0])):
#
i = same_subject_idxs[0][sample_pair_idx]
j = same_subject_idxs[1][sample_pair_idx]
#
snp_samples.add(haploid_samples[i])
snp_samples.add(haploid_samples[j])
#
sample_size += 1
#
snp_samples = list(snp_samples)
allowed_sample_set = set(snp_samples)
sample_country_map = parse_HMP_data.parse_sample_country_map()
sample_order_map = parse_HMP_data.parse_sample_order_map()
sys.stderr.write("Done!\n")
temporal_samples = diversity_utils.calculate_temporal_samples(
species_name, min_coverage=config.min_median_coverage)
haploid_samples = set(
diversity_utils.calculate_haploid_samples(species_name, debug=debug))
import sfs_utils
sys.stderr.write("Loading SFSs for %s...\t" % species_name)
samples, sfs_map = parse_midas_data.parse_within_sample_sfs(
species_name, allowed_variant_types=set(['1D', '2D', '3D', '4D']))
sys.stderr.write("Done!\n")
same_sample_idxs, same_subject_idxs, diff_subject_idxs = parse_midas_data.calculate_ordered_subject_pairs(
sample_order_map, temporal_samples)
frequency_bins = numpy.linspace(0, 1, 21)
#fs = numpy.array([0.1,0.2,0.3,0.4,0.5,0.5,0.6,0.7,0.8,0.9])
dfs = numpy.array([0.6, 0.7, 0.8, 0.9, 0.98])
perrs = []
for sample_pair_idx in xrange(0, len(same_subject_idxs[0])):
i = same_subject_idxs[0][sample_pair_idx]
j = same_subject_idxs[1][sample_pair_idx]
sample_i = temporal_samples[i]
