def test_make_short_var(): l = 6 var = "ABCDEFGH" hh = HtmlHandler(long_len=l) res = hh.make_short_var(var) expected = "ABC 2 FGH" eq_(expected, res) var = "ABCFGH" res = hh.make_short_var(var) expected = "ABCFGH" eq_(expected, res)
def test_find_residues_neighbouring_insertions(): gold_aln_cores = { "1ABCA": ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10'], "1ABCB": ['2', '3', '4', '5', '-', '7', '8', '9', '10', '11'], "1ABCC": ['-', '3', '4', '-', '-', '7', '8', '9', '10', '11'], } expected = { "1ABCA": [], "1ABCB": ['5', '7'], "1ABCC": ['3', '4', '7'], } hh = HtmlHandler() result = hh.find_residues_neighbouring_insertions(gold_aln_cores) eq_(result, expected)
def test_make_corvar(): aa_aln = {'1': "ABCDEFGHIJKLMNOP"} num_aln = { 'cores': { '1': [ [2], [6, 7, 8], [9, 10, 11] ], }, 'var': { '1': [[1], [3, 4, 5], [12, 13, 14, 15, 16]] } } expected = { '1': { 'cores': [ "B", "FGH", "IJK" ], 'var': [ "A", "CDE", "LMNOP" ]}} hh = HtmlHandler() res = hh.make_corvar(aa_aln, num_aln) eq_(res, expected) #num_aln = {'var': {'1NDDA': [], '3UF8A': [1, 2, 3, 4, 34, 35, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192]}, 'cores': {'1NDDA': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74], '3UF8A': [5, 6, 7, 8, 9, 10, 11, 12, 13, '-', 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, '-', '-', 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, '-', '-', '-']}} full_seq = { '1NDDA': 'MLIKVKTLTGKEIEIDIEPTDKVERIKERVEEKEGIPPQQQRLIYSGKQMNDEKTAADYKILGGSVLHLVLALR', '3UF8A': 'PETHINLKVSDGSSEIFFKIKKTTPLRRLMEAFAKRQGKEMDSLRFLYDGIRIQADQTPEDLDMEDNDIIEAHREQIGGSTVVTTESGLKYEDLTEGSGAEARAGQTVSVHYTGWLTDGQKFDSSKDRNDPFAFVLGGGMVIKGWDEGVQGMKVGGVRRLTIPPQLGYGARGAAGVIPPNATLVFEVELLDV' } res = hh.make_corvar(aa_aln, num_aln) print res
def test_get_full_seq_pos(): hh = HtmlHandler() merged_corvar_seq = "vhINLKVKGQDGNEVFFRIKRSTQMRKLMNAY----SVDMNSIAFLFDGRRLRAEQTPDELEMEEGDEIDAMLH--" corvar_index = 0 expected_pos = 2 pos = hh.get_full_seq_pos(merged_corvar_seq, corvar_index) eq_(pos, expected_pos) corvar_index = 67 expected_pos = 65 pos = hh.get_full_seq_pos(merged_corvar_seq, corvar_index) eq_(pos, expected_pos) merged_corvar_seq = "pethINLKVSDGS-SEI" corvar_index = 10 expected_pos = 13 pos = hh.get_full_seq_pos(merged_corvar_seq, corvar_index) eq_(pos, expected_pos) merged_corvar_seq = "pethINLKVSDGS-SEIFFKIKKTTPLRRLMEAF--RQGKEMDSLRFLYDGIRIQADQTPEDLDMEDNDIIEAHR---" corvar_index = 32 expected_pos = 35 pos = hh.get_full_seq_pos(merged_corvar_seq, corvar_index) eq_(pos, expected_pos)
def test_split_vars(): num_aln = { 'var': {'1': [1, 4, 9, 10]}, 'cores': {'1': [[2, 3], ['-'], ['-', 5, 6], [7, 8]]} } expected = { 'var': {'1': [[1], [], [4], [], [9, 10]]}, 'cores': {'1': [[2, 3], ['-'], ['-', 5, 6], [7, 8]]} } hh = HtmlHandler() res = hh.split_vars(num_aln) eq_(expected, res) num_aln = { 'var': {'1': [4]}, 'cores': {'1': [[1, 2, 3], [5, 6], [7, 8]]} } expected = { 'var': {'1': [[], [4], [], []]}, 'cores': {'1': [[1, 2, 3], [5, 6], [7, 8]]} } res = hh.split_vars(num_aln) eq_(expected, res)
def test_calc_alignment_quality_complex(): # testcase 1 - basic, all good gold_json_path = "gold_standard_src/tests/testdata/complex_scoring/gold_tauto.json" # gold_corvar_path = "gold_standard_src/tests/testdata/complex_scoring/gold_tauto.txt.Var" aln_path = "gold_standard_src/tests/testdata/complex_scoring/tautomerase_final_core.txt" paths = {"gold_path": gold_json_path, "aln_path": aln_path} # gold_in = parse_gold_json(gold_path, corvar_path) output = "gold_standard_src/tests/testdata/complex_scoring/tautomerase_final_core.txt_out" in_format = "3SSP" calc_result = calculate_aln_quality_complex(paths, output, in_format, write_json=True) # create html output hh = HtmlHandler() hh.write_html(calc_result, "tmp", complex_scoring=True) # test case 2 - two solutions for first residue of 3ABFA - can be aligned # either with 1 or 2 (each gets 0.5 score) # gold_json_path = "gold_standard_src/tests/testdata/complex_scoring/gold_tauto_m5.json" aln_path = "gold_standard_src/tests/testdata/complex_scoring/tautomerase_final_core_alternative.txt" paths = {"gold_path": gold_json_path, "aln_path": aln_path} # gold_in = parse_gold_json(gold_path, corvar_path) output = "gold_standard_src/tests/testdata/complex_scoring/tautomerase_final_core.txt_out" calc_result2 = calculate_aln_quality_complex(paths, output, in_format, write_json=True) # create html output hh = HtmlHandler() hh.write_html(calc_result2, "tmp2", complex_scoring=True) expected_overall = 0.82642 #ok_(abs(calc_result2["overall_score"] - expected_overall) < 0.0001) # check if all residues in 3M21A are incorrectcly aligned all_incorrect = [ not i[0] for k, i in calc_result2["per_residue_scores"]["3M21A"].iteritems() ] ok_(all_incorrect)
def run_comparison(aln1_path, aln2_path, outprefix, full_seq_path): aln_dict1 = parse_fasta(aln1_path) aln_dict2 = parse_fasta(aln2_path) full_seq = parse_fasta(full_seq_path) core_indexes = None num_aln_dict1 = core_aln_to_num(aln_dict1, full_seq, core_indexes)[0] num_aln_dict2 = core_aln_to_num(aln_dict2, full_seq, core_indexes)[0] comp_result = compare_alignments(num_aln_dict1, num_aln_dict2) hh = HtmlHandler() quality_data = { 'aa_aln': aln_dict1, 'wrong_cols': comp_result['diff_cols1'] } hh.write_html(quality_data, outprefix + '1') quality_data = { 'aa_aln': aln_dict2, 'wrong_cols': comp_result['diff_cols2'] } hh.write_html(quality_data, outprefix + '2')
def test_aln_to_html_var(): aa_aln = {'1': 'ACDEFGKLMNOP', '2': 'ABC---DEF---'} wrong = {'1': {0: 1, 10: 1, 11: 1}, '2': {}} full_seq = {'1': 'ABCDEFGHIJKLMNOP', '2': 'ABCDEF'} num_aln, core_indexes = core_aln_to_num(aa_aln, full_seq) expected = "1 <span class=featWRONG3>A</span> b <span class=featOK>C" \ "</span><span class=featOK>D</span><span class=featOK>E</span>" \ "<span class=featOK>F</span><span class=featOK>G</span> hij <s" \ "pan class=featOK>K</span><span class=featOK>L</span><span cla" \ "ss=featOK>M</span><span class=featOK>N</span><span class=feat" \ "WRONG3>O</span><span class=featWRONG3>P</span> \n2 <span" \ " class=featOK>A</span> <span class=featOK>B</span><span cla" \ "ss=featOK>C</span>--- <span class=featOK>D</span><span cl" \ "ass=featOK>E</span><span class=featOK>F</span>--- \n" hh = HtmlHandler() quality_data = {'num_aln': num_aln, 'aa_aln': aa_aln, 'wrong_cols': wrong, 'full': full_seq, 'core_indexes': core_indexes} res = hh.aln_to_html_var(quality_data) eq_(res, expected) expected_path = "gold_standard_src/tests/testdata/expected.html" with open(expected_path) as a: expected = a.read() expected_excerpt = expected.splitlines()[3:] res_path = "gold_standard_src/tests/testdata/test.html" hh.var = True hh.write_html(quality_data, "gold_standard_src/tests/testdata/test") ok_(os.path.exists(res_path)) with open(res_path) as a: res = a.read() result_excerpt = res.splitlines()[3:] os.remove(res_path) eq_(expected_excerpt, result_excerpt)