import diversity_utils
import stats_utils
species_name = sys.argv[1]
min_change = 0.8
# load list of metaphlan2 genes
metaphlan2_genes = parse_midas_data.load_metaphlan2_genes(species_name)
# Load subject and sample metadata
sys.stderr.write("Loading HMP metadata...\n")
subject_sample_map = parse_midas_data.parse_subject_sample_map()
sys.stderr.write("Done!\n")
# Load genomic coverage distributions
sample_coverage_histograms, samples = parse_midas_data.parse_coverage_distribution(
species_name, combination_type="sample")
median_coverages = numpy.array([
stats_utils.calculate_median_from_histogram(sample_coverage_histogram)
for sample_coverage_histogram in sample_coverage_histograms
])
sample_coverage_map = {
samples[i]: median_coverages[i]
for i in xrange(0, len(samples))
}
# Load SNP information for species_name
sys.stderr.write("Loading %s...\n" % species_name)
samples, allele_counts_map, passed_sites_map = parse_midas_data.parse_snps(
species_name, combination_type="sample", debug=False)
sys.stderr.write("Done!\n")
import diversity_utils
import stats_utils
from numpy.random import choice
species_name = sys.argv[1]
debug = True
min_coverage = 20
# Load subject and sample metadata
sys.stderr.write("Loading HMP metadata...\n")
subject_sample_map = parse_midas_data.parse_subject_sample_map()
sys.stderr.write("Done!\n")
###
# Load genomic coverage distributions
sample_coverage_histograms, samples = parse_midas_data.parse_coverage_distribution(
species_name)
median_coverages = numpy.array([
stats_utils.calculate_nonzero_median_from_histogram(
sample_coverage_histogram)
for sample_coverage_histogram in sample_coverage_histograms
])
sample_coverage_map = {
samples[i]: median_coverages[i]
for i in xrange(0, len(samples))
}
# Load pi information for species_name
sys.stderr.write("Loading within-sample diversity for %s...\n" % species_name)
samples, total_pis, total_pi_opportunities = parse_midas_data.parse_within_sample_pi(
species_name, debug)
sys.stderr.write("Done!\n")
