#!/usr/bin/env python

from cogent.util.unit_test import TestCase, main
from cogent import LoadSeqs, DNA
from cogent.evolve.best_likelihood import aligned_columns_to_rows, count_column_freqs, get_ML_probs, \
     get_G93_lnL_from_array, BestLogLikelihood, _transpose, _take
import math

__author__ = "Helen Lindsay"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Gavin Huttley", "Helen Lindsay"]
__license__ = "GPL"
__version__ = "1.5.3"
__maintainer__ = "Helen Lindsay"
__email__ = "helen.lindsay@anu.edu.au"
__status__ = "Production"

IUPAC_DNA_ambiguities = 'NRYWSKMBDHV'

def makeSampleAlignment(gaps = False, ambiguities = False):
    if gaps:
        seqs_list = ['AAA--CTTTGG-T','CCCCC-TATG-GT','-AACCCTTTGGGT']
    elif ambiguities:
        seqs_list = ['AARNCCTTTGGC','CCNYCCTTTGSG','CAACCCTGWGGG']
    else:
        seqs_list = ['AAACCCGGGTTTA','CCCGGGTTTAAAC','GGGTTTAAACCCG']
    seqs = zip('abc', seqs_list)
    return LoadSeqs(data = seqs)

class TestGoldman93(TestCase):
    def setUp(self):
        self.aln = makeSampleAlignment()
        self.gapped_aln = makeSampleAlignment(gaps = True)
        self.ambig_aln = makeSampleAlignment(ambiguities = True)
    
    def test_aligned_columns_to_rows(self):
        obs = aligned_columns_to_rows(self.aln[:-1], 3)
        expect = [['AAA','CCC','GGG'],['CCC','GGG','TTT'],
                  ['GGG','TTT','AAA'], ['TTT','AAA','CCC']]
        assert obs == expect, (obs, expect)
        
        obs = aligned_columns_to_rows(self.aln, 1)
        expect =  [['A','C','G'],['A','C','G'],['A','C','G'],
                   ['C','G','T'],['C','G','T'],['C','G','T'],
                   ['G','T','A'],['G','T','A'],['G','T','A'],
                   ['T','A','C'],['T','A','C'],['T','A','C'],
                   ['A','C','G']]
        self.assertEqual(obs, expect)
        obs = aligned_columns_to_rows(self.gapped_aln[:-1], 3, allowed_chars='ACGT')
        expect = [['TTT','TAT','TTT']]
        self.assertEqual(obs, expect)
        
        obs = aligned_columns_to_rows(self.ambig_aln, 2, exclude_chars=IUPAC_DNA_ambiguities)
        expect = [['AA','CC','CA'],['CC','CC','CC'],['TT','TT','TG']]
        self.assertEqual(obs, expect)
    
    def test_count_column_freqs(self):
        columns = aligned_columns_to_rows(self.aln, 1)
        obs = count_column_freqs(columns)
        expect = {'A C G' : 4, 'C G T' : 3, 'G T A' : 3, 'T A C' : 3}
        self.assertEqual(obs, expect)
        
        columns = aligned_columns_to_rows(self.aln[:-1], 2)
        obs = count_column_freqs(columns)
        expect = {'AA CC GG': 1, 'AC CG GT': 1, 'CC GG TT':1, 'GG TT AA':1,
                  'GT TA AC':1, 'TT AA CC':1}
        self.assertEqual(obs, expect)
    
    def test__transpose(self):
        """test transposing an array"""
        a = [[0,1,2],[3,4,5],[6,7,8],[9,10,11]]
        e = [[0,3,6,9],[1,4,7,10],[2,5,8,11]]
        self.assertEqual(_transpose(a), e)
    
    def test__take(self):
        """test taking selected rows from an array"""
        e = [[0,3,6,9],[1,4,7,10],[2,5,8,11]]
        self.assertEqual(_take(e, [0,1]), [[0,3,6,9],[1,4,7,10]])
        self.assertEqual(_take(e, [1,2]), [[1,4,7,10],[2,5,8,11]])
        self.assertEqual(_take(e, [0,2]), [[0,3,6,9],[2,5,8,11]])
    
    def test_get_ML_probs(self):
        columns = aligned_columns_to_rows(self.aln, 1)
        obs = get_ML_probs(columns, with_patterns=True)
        expect = {'A C G' : 4/13.0, 'C G T' : 3/13.0, 'G T A' : 3/13.0, 'T A C' : 3/13.0}
        sum = 0
        for pattern, lnL, freq in obs:
            self.assertFloatEqual(lnL, expect[pattern])
            sum += lnL
            self.assertTrue(lnL >= 0)
        self.assertFloatEqual(sum, 1)
    
    def test_get_G93_lnL_from_array(self):
        columns = aligned_columns_to_rows(self.aln, 1)
        obs = get_G93_lnL_from_array(columns)
        expect = math.log(math.pow(4/13.0, 4)) + 3*math.log(math.pow(3/13.0, 3))
        self.assertFloatEqual(obs, expect)
    
    def test_BestLogLikelihood(self):
        obs = BestLogLikelihood(self.aln, DNA.Alphabet)
        expect = math.log(math.pow(4/13.0, 4)) + 3*math.log(math.pow(3/13.0, 3))
        self.assertFloatEqual(obs,expect)
        lnL, l = BestLogLikelihood(self.aln, DNA.Alphabet, return_length=True)
        self.assertEqual(l, len(self.aln))

if __name__ == '__main__':
    main()