""" For the input ELMs, take the JS

divergence for chicken/human H5N1

to human & chicken. For which ELMs

does the hypothesis holds. Sample equally

from flus to avoid biases. """

import sys, utils, os, utils_graph

from collections import defaultdict

elm_file = sys.argv[1]

working_elms = utils_graph.getNodes(elm_file)

flu_counts = {}

seen_seqs = {}

seen_seqs_ls = []

elm2seqs = defaultdict(dict)

flus = ('human', 'chicken')

for flu in flus:

# flu_elm_file = os.path.join('results',

# flu + '.H5N1.elms')

if 'human' in flu:

flu_elm_file = os.path.join('working/Jul1_year',

flu + '.H3N2.2008.elms')

else:

flu_elm_file = os.path.join('working/Jul1_year/',

flu + '.H5N1.2006.elms')

utils.count_flu_sampled(flu, flu_elm_file, flu_counts,

seen_seqs, {}, False)

for elmseq in seen_seqs[flu]:

elm, seq = elmseq.split(':')

elm2seqs[elm][elmseq] = True

counts = utils.count_host_elmSeqs(('Gallus_gallus','H_sapiens'),

False, {},

'working/Jun29/', working_elms,

'.init')

for elm in working_elms:

use_seqs = elm2seqs[elm]

host_vecs = utils.mk_count_vecs(counts, use_seqs)

host_dists = utils.mk_count_dists(host_vecs)

flu_vecs = utils.mk_count_vecs(flu_counts, use_seqs)

flu_dists = utils.mk_count_dists(flu_vecs)

flu = flu_dists['human']

human_score_H = utils.jensen_shannon_dists(host_dists['H_sapiens'],

flu)

chicken_score_H = utils.jensen_shannon_dists(host_dists['Gallus_gallus'],

flu)

flu = flu_dists['chicken']

human_score_C = utils.jensen_shannon_dists(host_dists['H_sapiens'],

flu)

chicken_score_C = utils.jensen_shannon_dists(host_dists['Gallus_gallus'],

flu)

if human_score_C > chicken_score_C and human_score_H < chicken_score_H:

print elm