#!/usr/bin/env python
"""Tests of the AAIndex parser.
"""
from cogent.util.unit_test import TestCase, main
from cogent.parse.aaindex import AAIndex1Parser, AAIndex2Parser,\
AAIndexRecord, AAIndex1Record, AAIndex2Record, AAIndex1FromFiles,\
AAIndex2FromFiles
__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2007-2012, The Cogent Project"
__credits__ = ["Greg Caporaso", "Rob Knight"]
__license__ = "GPL"
__version__ = "1.5.3-dev"
__maintainer__ = "Greg Caporaso"
__email__ = "caporaso@colorado.edu"
__status__ = "Production"
class test_aaindex1_parser(TestCase):
""" Tests aindex1_parser class """
def setUp(self):
""" Setup some variables """
self._fake_file = list(fake_file_aaindex1.split('\n'))
self.AAIndexObjects = AAIndex1FromFiles(self._fake_file)
def test_init(self):
""" AAI1: Test that init run w/o error """
aa1p = AAIndex1Parser()
def test_read_file_as_list(self):
"""AAI1: Test that a file is correctly opened as a list """
aap = AAIndex1Parser()
AAIndexObjects = aap(self._fake_file)
def test_correct_num_of_records(self):
"""AAI1: Test that one object is created per record """
self.assertEqual(6, len(self.AAIndexObjects))
def test_ID_entries(self):
""" AAI1: Test ID Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].ID, 'ANDN920101')
self.assertEqual(self.AAIndexObjects['ARGP820103'].ID, 'ARGP820103')
self.assertEqual(self.AAIndexObjects['JURD980101'].ID, 'JURD980101')
def test_single_line_Description_entries(self):
""" AAI1: Test Single Line Description Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Description,\
'alpha-CH chemical shifts (Andersen et al., 1992)')
self.assertEqual(self.AAIndexObjects['ARGP820103'].Description,\
'Membrane-buried preference parameters (Argos et al., 1982)')
def test_multi_line_Description_entries(self):
""" AAI1: Test Multi Line Description Entries """
self.assertEqual(self.AAIndexObjects['JURD980101'].Description,\
'Modified Kyte-Doolittle hydrophobicity scale (Juretic et al., 1998)')
def test_LITDB_entries(self):
""" AAI1: Test LITDB Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].LITDBEntryNum,\
'LIT:1810048b PMID:1575719')
self.assertEqual(self.AAIndexObjects['ARGP820103'].LITDBEntryNum,\
'LIT:0901079b PMID:7151796')
self.assertEqual(self.AAIndexObjects['JURD980101'].LITDBEntryNum,\
'')
def test_Authors_entries(self):
""" AAI1: Test Authors Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Authors,\
'Andersen, N.H., Cao, B. and Chen, C.')
self.assertEqual(self.AAIndexObjects['ARGP820103'].Authors,\
'Argos, P., Rao, J.K.M. and Hargrave, P.A.')
self.assertEqual(self.AAIndexObjects['JURD980101'].Authors,\
'Juretic, D., Lucic, B., Zucic, D. and Trinajstic, N.')
def test_mult_line_Title_entries(self):
""" AAI1: Test Multi Line Title Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Title,\
'Peptide/protein structure analysis using the chemical shift index ' +\
'method: upfield alpha-CH values reveal dynamic helices and aL sites')
self.assertEqual(self.AAIndexObjects['JURD980101'].Title,\
'Protein transmembrane structure: recognition and prediction by ' +\
'using hydrophobicity scales through preference functions')
def test_sing_line_Title_entries(self):
""" AAI1: Test Single Line Title Entries """
self.assertEqual(self.AAIndexObjects['ARGP820103'].Title,\
'Structural prediction of membrane-bound proteins')
def test_Citation_entries(self):
""" AAI1: Test Citation Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Citation,\
'Biochem. and Biophys. Res. Comm. 184, 1008-1014 (1992)')
self.assertEqual(self.AAIndexObjects['ARGP820103'].Citation,\
'Eur. J. Biochem. 128, 565-575 (1982)')
self.assertEqual(self.AAIndexObjects['JURD980101'].Citation,\
'Theoretical and Computational Chemistry, 5, 405-445 (1998)')
def test_Comments_entries(self):
""" AAI1: Test Comments Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Comments,\
'')
self.assertEqual(self.AAIndexObjects['ARGP820103'].Comments,\
'')
self.assertEqual(self.AAIndexObjects['JURD980101'].Comments,\
'')
self.assertEqual(self.AAIndexObjects['TSAJ990102'].Comments,\
'(Cyh 113.7)')
def test_single_line_Correlating_entries(self):
""" AAI1: Test single line Correlating Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].\
Correlating['BUNA790102'], 0.949)
def test_empty_Correlating_entries(self):
""" AAI1: Test empty Correlating Entries """
self.assertEqual(self.AAIndexObjects['WILM950104'].Correlating, {})
def test_multi_line_Correlating_entries(self):
""" AAI1: Test multi line Correlating Entries """
self.assertEqual(self.AAIndexObjects['ARGP820103'].\
Correlating['ARGP820102'], 0.961)
self.assertEqual(self.AAIndexObjects['ARGP820103'].\
Correlating['MIYS850101'], 0.822)
self.assertEqual(self.AAIndexObjects['ARGP820103'].\
Correlating['JURD980101'], 0.800)
self.assertEqual(self.AAIndexObjects['JURD980101'].\
Correlating['KYTJ820101'], 0.996)
self.assertEqual(self.AAIndexObjects['JURD980101'].\
Correlating['NADH010101'], 0.925)
self.assertEqual(self.AAIndexObjects['JURD980101'].\
Correlating['OOBM770101'], -0.903)
def test_Data_entries(self):
""" AAI1: Test Data Entries """
self.assertEqual(self.AAIndexObjects['ANDN920101'].Data['A'],\
4.35)
self.assertEqual(self.AAIndexObjects['ANDN920101'].Data['Q'],\
4.37)
self.assertEqual(self.AAIndexObjects['ANDN920101'].Data['V'],\
3.95)
self.assertEqual(self.AAIndexObjects['ARGP820103'].Data['A'],\
1.56)
self.assertEqual(self.AAIndexObjects['ARGP820103'].Data['Q'],\
0.51)
self.assertEqual(self.AAIndexObjects['ARGP820103'].Data['V'],\
1.14)
self.assertEqual(self.AAIndexObjects['JURD980101'].Data['A'],\
1.10)
self.assertEqual(self.AAIndexObjects['JURD980101'].Data['Q'],\
-3.68)
self.assertEqual(self.AAIndexObjects['JURD980101'].Data['V'],\
4.2)
class test_aaindex2_parser(TestCase):
def setUp(self):
""" Setup some variables """
self._fake_file = list(fake_file_aaindex2.split('\n'))
self.AAIndexObjects = AAIndex2FromFiles(self._fake_file)
def test_init(self):
""" AAI2: Test that init run w/o error """
aa2p = AAIndex2Parser()
def test_read_file_as_list(self):
"""AAI1: Test that a file is correctly opened as a list """
aap = AAIndex2Parser()
AAIndexObjects = aap(self._fake_file)
def test_correct_num_of_records(self):
"""AAI2: Test that one object is created per record """
self.assertEqual(6, len(self.AAIndexObjects))
def test_ID_entries(self):
""" AAI2: Test ID Entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].ID, 'ALTS910101')
self.assertEqual(self.AAIndexObjects['BENS940103'].ID, 'BENS940103')
self.assertEqual(self.AAIndexObjects['QUIB020101'].ID, 'QUIB020101')
def test_Description_entries(self):
""" AAI2: Test Description Entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].Description,\
'The PAM-120 matrix (Altschul, 1991)')
self.assertEqual(self.AAIndexObjects['BENS940103'].Description,\
'Log-odds scoring matrix collected in 74-100 PAM (Benner et al., '+\
'1994)')
self.assertEqual(self.AAIndexObjects['QUIB020101'].Description,\
'STROMA score matrix for the alignment of known distant homologs ' +\
'(Qian-Goldstein, 2002)')
def test_LITDB_entries(self):
""" AAI2: Test LITDB Entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].LITDBEntryNum,\
'LIT:1713145 PMID:2051488')
self.assertEqual(self.AAIndexObjects['BENS940103'].LITDBEntryNum,\
'LIT:2023094 PMID:7700864')
self.assertEqual(self.AAIndexObjects['QUIB020101'].LITDBEntryNum,\
'PMID:12211027')
def test_Authors_entries(self):
""" AAI2: Test Atuthor Entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].Authors,\
'Altschul, S.F.')
self.assertEqual(self.AAIndexObjects['BENS940103'].Authors,\
'Benner, S.A., Cohen, M.A. and Gonnet, G.H.')
self.assertEqual(self.AAIndexObjects['QUIB020101'].Authors,\
'Qian, B. and Goldstein, R.A.')
def test_Title_entries(self):
""" AAI2: Test Title Entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].Title,\
'Amino acid substitution matrices from an information theoretic ' +\
'perspective')
self.assertEqual(self.AAIndexObjects['BENS940103'].Title,\
'Amino acid substitution during functionally constrained divergent ' +\
'evolution of protein sequences')
self.assertEqual(self.AAIndexObjects['QUIB020101'].Title,\
'Optimization of a new score function for the generation of '+\
'accurate alignments')
def test_Citation_entries(self):
""" AAI2: Test citation entries """
self.assertEqual(self.AAIndexObjects['ALTS910101'].Citation,\
'J. Mol. Biol. 219, 555-565 (1991)')
self.assertEqual(self.AAIndexObjects['BENS940103'].Citation,\
'Protein Engineering 7, 1323-1332 (1994)')
self.assertEqual(self.AAIndexObjects['QUIB020101'].Citation,\
'Proteins. 48, 605-610 (2002)')
def test_Comments_entries(self):
""" AAI2: Tests null, single line, multi line comments """
self.assertEqual(self.AAIndexObjects['ALTS910101'].Comments,\
'')
self.assertEqual(self.AAIndexObjects['BENS940103'].Comments,\
'extrapolated to 250 PAM')
self.assertEqual(self.AAIndexObjects['QUIB020101'].Comments,\
'')
self.assertEqual(self.AAIndexObjects['HENS920104'].Comments,\
'# Matrix made by matblas from blosum50.iij ' +
'* # BLOSUM Clustered Scoring Matrix in 1/3 Bit Units ' +
'* # Blocks Database = /data/blocks_5.0/blocks.dat ' +
'* # Cluster Percentage: >= 50 ' +
'* # Entropy = 0.4808, Expected = -0.3573')
def test_Data_entries_20x20_LTM(self):
""" AAI2: correct data entries when 20x20 LTM"""
self.assertEqual(self.AAIndexObjects['ALTS910101'].Data['A']['A'],\
3.)
self.assertEqual(self.AAIndexObjects['ALTS910101'].Data['Y']['R'],\
-6.)
self.assertEqual(self.AAIndexObjects['ALTS910101'].Data['V']['V'],\
5.)
self.assertEqual(self.AAIndexObjects['BENS940103'].Data['A']['A'],\
2.4)
self.assertEqual(self.AAIndexObjects['BENS940103'].Data['Y']['R'],\
-2.0)
self.assertEqual(self.AAIndexObjects['BENS940103'].Data['V']['V'],\
3.4)
self.assertEqual(self.AAIndexObjects['QUIB020101'].Data['A']['A'],\
2.5)
self.assertEqual(self.AAIndexObjects['QUIB020101'].Data['Y']['R'],\
-0.9)
self.assertEqual(self.AAIndexObjects['QUIB020101'].Data['V']['V'],\
4.2)
def test_Data_entries_20x20_Square(self):
""" AAI2: correct data entries when 20x20 squ matrix """
self.assertEqual(self.AAIndexObjects['HENS920104'].Data['V']['Y'],\
-1)
self.assertEqual(self.AAIndexObjects['HENS920104'].Data['Q']['A'],\
-1)
self.assertEqual(self.AAIndexObjects['HENS920104'].Data['N']['N'],\
7)
def test_Data_entries_with_abnormal_fields(self):
""" AAI2: test correct data entries when more than std fields present
Some entires in AAIndex2 have more that 20 fields, this tests that
that data is corrected parsed and identified.
"""
# There are no entries that fit this category that are square
# matrices, which is all we are concerned with at this point,
# so this method should just serve as a reminder to test this
# when we begin parsing data other than square matrices.
pass
def test_Data_entries_21x21_LTM(self):
""" AAI2: correct data entries when 21x21 LTM"""
self.assertEqual(self.AAIndexObjects['KOSJ950101'].Data['-']['-'],\
55.7)
self.assertEqual(self.AAIndexObjects['KOSJ950101'].Data['Y']['-'],\
0.3)
self.assertEqual(self.AAIndexObjects['KOSJ950101'].Data['N']['R'],\
3.0)
def test_Data_entries_22x21_square(self):
""" AAI2: correct data entries when 22x21 square matrix """
# It's not really a sqaure matrix, but it's fully populated ...
self.assertEqual(self.AAIndexObjects['OVEJ920102'].Data['J']['D'],\
0.001)
self.assertEqual(self.AAIndexObjects['OVEJ920102'].Data['-']['I'],\
0.022)
self.assertEqual(self.AAIndexObjects['OVEJ920102'].Data['D']['E'],\
0.109)
class AAIndexRecordTests(TestCase):
""" AAIR: Tests AAIndexRecord class """
def setUp(self):
self.id = "5"
self.description = "Some Info"
self.LITDB_entry_num = "25"
self.authors = "Greg"
self.title = "A test"
self.citation = "something"
self.comments = "This is a test, this is only a test"
self.data = {}
class AAIndex1RecordTests(AAIndexRecordTests):
""" AAIR1: Tests AAIndex1Records class """
def setUp(self):
AAIndexRecordTests.setUp(self)
self.correlating = [0.987, 0.783, 1., 0]
values = []
keys = 'ARNDCQEGHILKMFPSTWYV'
for i in range(20):
values += [float(i) + 0.15]
self.data = dict(zip(keys,values))
self.aar = AAIndex1Record(self.id, self.description,\
self.LITDB_entry_num, self.authors, self.title,\
self.citation, self.comments, self.correlating, self.data)
def test_init(self):
""" AAIR1: Tests init method returns with no errors"""
test_aar = AAIndex1Record(self.id, self.description,\
self.LITDB_entry_num, self.authors, self.title,\
self.citation, self.comments, self.correlating, self.data)
def test_general_init_data(self):
""" AAIR1: Tests init correctly initializes data"""
self.assertEqual(self.aar.ID, str(self.id))
self.assertEqual(self.aar.Description, str(self.description))
self.assertEqual(self.aar.LITDBEntryNum,\
str(self.LITDB_entry_num))
self.assertEqual(self.aar.Authors, str(self.authors))
self.assertEqual(self.aar.Title, str(self.title))
self.assertEqual(self.aar.Citation, str(self.citation))
self.assertEqual(self.aar.Comments, str(self.comments))
self.assertEqual(self.aar.Correlating, self.correlating)
self.assertEqual(self.aar.Data,self.data)
def test_toSquareDistanceMatrix(self):
""" AAIR1: Tests that _toSquareDistanceMatrix runs without returning an error """
square = self.aar._toSquareDistanceMatrix()
def test_toSquareDistanceMatrix_data_integrity_diagonal(self):
""" AAIR1: Tests that diag = 0 when square matrix is built """
square = self.aar._toSquareDistanceMatrix()
# Test diagonal
keys = 'ARNDCQEGHILKMFPSTWYV'
for k in keys:
self.assertEqual(square[k][k], 0.)
def test_toSquareDistanceMatrix_data_integrity(self):
""" AAIR1: Tests that _toSquareDistanceMatrix works right w/o stops """
square = self.aar._toSquareDistanceMatrix()
self.assertFloatEqualAbs(square['R']['A'], square['A']['R'])
self.assertFloatEqualAbs(square['A']['R'], 1.)
self.assertFloatEqualAbs(square['D']['N'], square['N']['D'])
self.assertFloatEqualAbs(square['D']['N'], 1.)
self.assertFloatEqualAbs(square['A']['C'], square['C']['A'])
self.assertFloatEqualAbs(square['A']['C'], 4.)
self.assertFloatEqualAbs(square['V']['A'], square['A']['V'])
self.assertFloatEqualAbs(square['V']['A'], 19.)
self.assertFloatEqualAbs(square['V']['Y'], square['Y']['V'])
self.assertFloatEqualAbs(square['V']['Y'], 1.)
def test_toSquareDistanceMatrix_data_integrity_w_stops(self):
""" AAIR1: Tests that _toSquareDistanceMatrix works right w/ stops """
square = self.aar._toSquareDistanceMatrix(include_stops=1)
self.assertFloatEqualAbs(square['R']['A'], square['A']['R'])
self.assertFloatEqualAbs(square['A']['R'], 1.)
self.assertFloatEqualAbs(square['D']['N'], square['N']['D'])
self.assertFloatEqualAbs(square['D']['N'], 1.)
self.assertFloatEqualAbs(square['A']['C'], square['C']['A'])
self.assertFloatEqualAbs(square['A']['C'], 4.)
self.assertFloatEqualAbs(square['V']['A'], square['A']['V'])
self.assertFloatEqualAbs(square['V']['A'], 19.)
self.assertFloatEqualAbs(square['V']['Y'], square['Y']['V'])
self.assertFloatEqualAbs(square['V']['Y'], 1.)
self.assertFloatEqualAbs(square['V']['*'], None)
self.assertFloatEqualAbs(square['*']['Y'], None)
self.assertFloatEqualAbs(square['*']['*'], None)
self.assertFloatEqualAbs(square['*']['R'], None)
def test_toDistanceMatrix(self):
""" AAIR1: Tests that toDistanceMatrix functions as expected """
dm = self.aar.toDistanceMatrix()
self.assertFloatEqualAbs(dm['R']['A'], dm['A']['R'])
self.assertFloatEqualAbs(dm['A']['R'], 1.)
self.assertFloatEqualAbs(dm['D']['N'], dm['N']['D'])
self.assertFloatEqualAbs(dm['D']['N'], 1.)
self.assertFloatEqualAbs(dm['A']['C'], dm['C']['A'])
self.assertFloatEqualAbs(dm['A']['C'], 4.)
self.assertFloatEqualAbs(dm['V']['A'], dm['A']['V'])
self.assertFloatEqualAbs(dm['V']['A'], 19.)
self.assertFloatEqualAbs(dm['V']['Y'], dm['Y']['V'])
self.assertFloatEqualAbs(dm['V']['Y'], 1.)
def test_toDistanceMatrix_w_stops(self):
""" AAIR1: Tests that toDistanceMatrix works right w/ stops """
square = self.aar.toDistanceMatrix(include_stops=1)
self.assertFloatEqualAbs(square['R']['A'], square['A']['R'])
self.assertFloatEqualAbs(square['A']['R'], 1.)
self.assertFloatEqualAbs(square['D']['N'], square['N']['D'])
self.assertFloatEqualAbs(square['D']['N'], 1.)
self.assertFloatEqualAbs(square['A']['C'], square['C']['A'])
self.assertFloatEqualAbs(square['A']['C'], 4.)
self.assertFloatEqualAbs(square['V']['A'], square['A']['V'])
self.assertFloatEqualAbs(square['V']['A'], 19.)
self.assertFloatEqualAbs(square['V']['Y'], square['Y']['V'])
self.assertFloatEqualAbs(square['V']['Y'], 1.)
self.assertFloatEqualAbs(square['V']['*'], None)
self.assertFloatEqualAbs(square['*']['Y'], None)
self.assertFloatEqualAbs(square['*']['*'], None)
self.assertFloatEqualAbs(square['*']['R'], None)
class AAIndex2RecordTests(AAIndexRecordTests):
""" AAIR2: Tests AAIndex2Records class """
def setUp(self):
AAIndexRecordTests.setUp(self)
# Build LTM data
values = range(210)
keys = 'ARNDCQEGHILKMFPSTWYV'
self.LTMdata = dict.fromkeys(keys)
i = 0
for r in keys:
new_row = dict.fromkeys(keys)
for c in keys:
if keys.find(c) <= keys.find(r):
new_row[c] = values[i]
i +=1
self.LTMdata[r] = new_row
self.aarLTM = AAIndex2Record(self.id, self.description,\
self.LITDB_entry_num, self.authors, self.title,\
self.citation, self.comments, self.LTMdata)
# Build Square matrix data
values = range(400)
self.SQUdata = dict.fromkeys(keys)
i = 0
for r in keys:
new_row = dict.fromkeys(keys)
for c in keys:
new_row[c] = values[i]
i +=1
self.SQUdata[r] = new_row
self.aarSquare = AAIndex2Record(self.id, self.description,\
self.LITDB_entry_num, self.authors, self.title,\
self.citation, self.comments, self.SQUdata)
def test_init(self):
""" AAIR2: Tests init method returns with no errors"""
test_aar = AAIndex2Record(self.id, self.description,\
self.LITDB_entry_num, self.authors, self.title,\
self.citation, self.comments, self.SQUdata)
def test_init_data(self):
""" AAIR2: Tests init correctly initializes data"""
self.assertEqual(self.aarLTM.ID, str(self.id))
self.assertEqual(self.aarLTM.Description, str(self.description))
self.assertEqual(self.aarLTM.LITDBEntryNum,\
str(self.LITDB_entry_num))
self.assertEqual(self.aarLTM.Authors, str(self.authors))
self.assertEqual(self.aarLTM.Title, str(self.title))
self.assertEqual(self.aarLTM.Citation, str(self.citation))
self.assertEqual(self.aarLTM.Comments, str(self.comments))
# def test_matrix_values_col_by_row(self):
# """ Tests that keys and values correctly correspond in data LTM
#
#
# Also tests that reverse keys are same as forward keys.
#
# """
#
# data_matrix = self.aarLTM.Data
# self.assertEqual(data_matrix['A']['A'], 0)
# self.assertEqual(data_matrix['A']['R'], 1)
# self.assertEqual(data_matrix['R']['R'], 2)
# self.assertEqual(data_matrix['C']['H'], 40)
# self.assertEqual(data_matrix['I']['M'], 87)
# self.assertEqual(data_matrix['D']['P'], 108)
# self.assertEqual(data_matrix['W']['V'], 207)
# self.assertEqual(data_matrix['Y']['V'], 208)
# self.assertEqual(data_matrix['V']['V'], 209)
# def test_LTM_values_row_by_col(self):
# """ Tests that keys are correctly linked to values in a LTM
#
# This tests that some random places hold the correct values.
# These are some randomly selected keys with hand calculated
# values. Also included are the extreme values. Technically if
# the first and last three are correct all values should be
# correct.
#
# """
# data_matrix = self.aarLTM.Data
# self.assertEqual(data_matrix['R']['A'], 1)
# self.assertEqual(data_matrix['H']['C'], 40)
# self.assertEqual(data_matrix['M']['I'], 87)
# self.assertEqual(data_matrix['P']['D'], 108)
# self.assertEqual(data_matrix['V']['W'], 207)
# self.assertEqual(data_matrix['V']['Y'], 208)
# self.assertEqual(data_matrix['A']['A'], 0)
# self.assertEqual(data_matrix['R']['R'], 2)
# self.assertEqual(data_matrix['V']['V'], 209)
def test_Square_Matrix_values_row_by_col(self):
""" AAIR2: Tests that key -> value pair integrity in Square matrix
"""
data_matrix = self.aarSquare.Data
self.assertEqual(data_matrix['R']['A'], 20)
#self.assertEqual(data_matrix['H']['C'], 40)
#self.assertEqual(data_matrix['M']['I'], 87)
#self.assertEqual(data_matrix['P']['D'], 108)
#self.assertEqual(data_matrix['V']['W'], 207)
#self.assertEqual(data_matrix['V']['Y'], 208)
self.assertEqual(data_matrix['A']['A'], 0)
self.assertEqual(data_matrix['R']['R'], 21)
self.assertEqual(data_matrix['V']['V'], 399)
def test_toSquareDistanceMatrix_data_integrity(self):
""" AAIR2: Tests that _toSquareDistanceMatrix works right w/o stops
"""
square = self.aarSquare._toSquareDistanceMatrix()
self.assertEqual(square['R']['A'], 20)
self.assertEqual(square['A']['A'], 0)
self.assertEqual(square['R']['R'], 21)
self.assertEqual(square['V']['V'], 399)
def test_toSquareDistanceMatrix_data_integrity_w_stops(self):
""" AAIR2: Tests that _toSquareDistanceMatrix works right with stops
"""
square = self.aarSquare._toSquareDistanceMatrix(include_stops=1)
self.assertEqual(square['R']['A'], 20)
self.assertEqual(square['A']['A'], 0)
self.assertEqual(square['R']['R'], 21)
self.assertEqual(square['V']['V'], 399)
self.assertEqual(square['V']['*'], None)
self.assertEqual(square['*']['Y'], None)
self.assertEqual(square['*']['*'], None)
self.assertEqual(square['*']['R'], None)
# Data for parser tests
fake_file_aaindex1 =\
"""
H ANDN920101
D alpha-CH chemical shifts (Andersen et al., 1992)
R LIT:1810048b PMID:1575719
A Andersen, N.H., Cao, B. and Chen, C.
T Peptide/protein structure analysis using the chemical shift index method:
upfield alpha-CH values reveal dynamic helices and aL sites
J Biochem. and Biophys. Res. Comm. 184, 1008-1014 (1992)
C BUNA790102 0.949
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
4.35 4.38 4.75 4.76 4.65 4.37 4.29 3.97 4.63 3.95
4.17 4.36 4.52 4.66 4.44 4.50 4.35 4.70 4.60 3.95
//
H ARGP820101
D Hydrophobicity index (Argos et al., 1982)
R LIT:0901079b PMID:7151796
A Argos, P., Rao, J.K.M. and Hargrave, P.A.
T Structural prediction of membrane-bound proteins
J Eur. J. Biochem. 128, 565-575 (1982)
C JOND750101 1.000 SIMZ760101 0.967 GOLD730101 0.936
TAKK010101 0.906 MEEJ810101 0.891 CIDH920105 0.867
LEVM760106 0.865 CIDH920102 0.862 MEEJ800102 0.855
MEEJ810102 0.853 CIDH920103 0.827 PLIV810101 0.820
CIDH920104 0.819 LEVM760107 0.806 NOZY710101 0.800
PARJ860101 -0.835 WOLS870101 -0.838 BULH740101 -0.854
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
0.61 0.60 0.06 0.46 1.07 0. 0.47 0.07 0.61 2.22
1.53 1.15 1.18 2.02 1.95 0.05 0.05 2.65 1.88 1.32
//
H TSAJ990102
D Volumes not including the crystallographic waters using the ProtOr (Tsai et
al., 1999)
R PMID:10388571
A Tsai, J., Taylor, R., Chothia, C. and Gerstein, M.
T The packing density in proteins: standard radii and volumes
J J Mol Biol. 290, 253-266 (1999)
* (Cyh 113.7)
C TSAJ990101 1.000 CHOC750101 0.996 BIGC670101 0.992
GOLD730102 0.991 KRIW790103 0.987 FAUJ880103 0.985
GRAR740103 0.978 CHAM820101 0.978 CHOC760101 0.972
FASG760101 0.940 LEVM760105 0.928 LEVM760102 0.918
ROSG850101 0.909 DAWD720101 0.905 CHAM830106 0.896
FAUJ880106 0.882 RADA880106 0.864 LEVM760107 0.861
LEVM760106 0.841 RADA880103 -0.879
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
90.0 194.0 124.7 117.3 103.3 149.4 142.2 64.9 160.0 163.9
164.0 167.3 167.0 191.9 122.9 95.4 121.5 228.2 197.0 139.0
//
H JURD980101
D Modified Kyte-Doolittle hydrophobicity scale (Juretic et al., 1998)
R
A Juretic, D., Lucic, B., Zucic, D. and Trinajstic, N.
T Protein transmembrane structure: recognition and prediction by using
hydrophobicity scales through preference functions
J Theoretical and Computational Chemistry, 5, 405-445 (1998)
C KYTJ820101 0.996 CHOC760103 0.967 NADH010102 0.931
JANJ780102 0.928 NADH010101 0.925 EISD860103 0.901
DESM900102 0.900 NADH010103 0.900 EISD840101 0.895
RADA880101 0.893 MANP780101 0.887 WOLR810101 0.881
PONP800103 0.879 JANJ790102 0.879 NADH010104 0.873
CHOC760104 0.870 PONP800102 0.869 JANJ790101 0.868
MEIH800103 0.861 PONP800101 0.858 NAKH920108 0.858
RADA880108 0.857 PONP800108 0.856 ROSG850102 0.854
PONP930101 0.849 RADA880107 0.842 BIOV880101 0.840
MIYS850101 0.837 FAUJ830101 0.833 CIDH920104 0.832
DESM900101 0.829 WARP780101 0.827 KANM800104 0.826
LIFS790102 0.824 RADA880104 0.824 NADH010105 0.821
NISK800101 0.816 NISK860101 0.808 BIOV880102 0.805
ARGP820102 0.802 ARGP820103 0.800 VHEG790101 -0.814
KRIW790101 -0.824 CHOC760102 -0.851 ROSM880101 -0.851
MONM990101 -0.853 JANJ780103 -0.853 RACS770102 -0.855
PRAM900101 -0.862 JANJ780101 -0.862 GUYH850101 -0.864
GRAR740102 -0.864 MEIH800102 -0.879 KUHL950101 -0.884
ROSM880102 -0.894 OOBM770101 -0.903
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
1.10 -5.10 -3.50 -3.60 2.50 -3.68 -3.20 -0.64 -3.20 4.50
3.80 -4.11 1.90 2.80 -1.90 -0.50 -0.70 -0.46 -1.3 4.2
//
H WILM950104
D Hydrophobicity coefficient in RP-HPLC, C18 with 0.1%TFA/2-PrOH/MeCN/H2O
(Wilce et al. 1995)
R
A Wilce, M.C., Aguilar, M.I. and Hearn, M.T.
T Physicochemical basis of amino acid hydrophobicity scales: evaluation of four
new scales of amino acid hydrophobicity coefficients derived from RP-HPLC of
peptides
J Anal Chem. 67, 1210-1219 (1995)
C
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
-2.34 1.60 2.81 -0.48 5.03 0.16 1.30 -1.06 -3.00 7.26
1.09 1.56 0.62 2.57 -0.15 1.93 0.19 3.59 -2.58 2.06
//
H ARGP820103
D Membrane-buried preference parameters (Argos et al., 1982)
R LIT:0901079b PMID:7151796
A Argos, P., Rao, J.K.M. and Hargrave, P.A.
T Structural prediction of membrane-bound proteins
J Eur. J. Biochem. 128, 565-575 (1982)
C ARGP820102 0.961 MIYS850101 0.822 NAKH900106 0.810
EISD860103 0.810 KYTJ820101 0.806 JURD980101 0.800
I A/L R/K N/M D/F C/P Q/S E/T G/W H/Y I/V
1.56 0.45 0.27 0.14 1.23 0.51 0.23 0.62 0.29 1.67
2.93 0.15 2.96 2.03 0.76 0.81 0.91 1.08 0.68 1.14
//
"""
fake_file_aaindex2 =\
"""
H ALTS910101
D The PAM-120 matrix (Altschul, 1991)
R LIT:1713145 PMID:2051488
A Altschul, S.F.
T Amino acid substitution matrices from an information theoretic perspective
J J. Mol. Biol. 219, 555-565 (1991)
M rows = ARNDCQEGHILKMFPSTWYV, cols = ARNDCQEGHILKMFPSTWYV
3.
-3. 6.
0. -1. 4.
0. -3. 2. 5.
-3. -4. -5. -7. 9.
-1. 1. 0. 1. -7. 6.
0. -3. 1. 3. -7. 2. 5.
1. -4. 0. 0. -5. -3. -1. 5.
-3. 1. 2. 0. -4. 3. -1. -4. 7.
-1. -2. -2. -3. -3. -3. -3. -4. -4. 6.
-3. -4. -4. -5. -7. -2. -4. -5. -3. 1. 5.
-2. 2. 1. -1. -7. 0. -1. -3. -2. -2. -4. 5.
-2. -1. -3. -4. -6. -1. -4. -4. -4. 1. 3. 0. 8.
-4. -4. -4. -7. -6. -6. -6. -5. -2. 0. 0. -6. -1. 8.
1. -1. -2. -2. -3. 0. -1. -2. -1. -3. -3. -2. -3. -5. 6.
1. -1. 1. 0. -1. -2. -1. 1. -2. -2. -4. -1. -2. -3. 1. 3.
1. -2. 0. -1. -3. -2. -2. -1. -3. 0. -3. -1. -1. -4. -1. 2. 4.
-7. 1. -5. -8. -8. -6. -8. -8. -5. -7. -5. -5. -7. -1. -7. -2. -6. 12.
-4. -6. -2. -5. -1. -5. -4. -6. -1. -2. -3. -6. -4. 4. -6. -3. -3. -1. 8.
0. -3. -3. -3. -2. -3. -3. -2. -3. 3. 1. -4. 1. -3. -2. -2. 0. -8. -3. 5.
//
H BENS940103
D Log-odds scoring matrix collected in 74-100 PAM (Benner et al., 1994)
R LIT:2023094 PMID:7700864
A Benner, S.A., Cohen, M.A. and Gonnet, G.H.
T Amino acid substitution during functionally constrained divergent
evolution of protein sequences
J Protein Engineering 7, 1323-1332 (1994)
* extrapolated to 250 PAM
M rows = ARNDCQEGHILKMFPSTWYV, cols = ARNDCQEGHILKMFPSTWYV
2.4
-0.8 4.8
-0.2 0.3 3.6
-0.3 -0.5 2.2 4.8
0.3 -2.2 -1.8 -3.2 11.8
-0.3 1.6 0.7 0.8 -2.6 3.0
-0.1 0.3 1.0 2.9 -3.2 1.7 3.7
0.6 -1.0 0.4 0.2 -2.0 -1.1 -0.5 6.6
-1.0 1.0 1.2 0.4 -1.3 1.4 0.2 -1.6 6.1
-0.8 -2.6 -2.8 -3.9 -1.2 -2.0 -2.9 -4.3 -2.3 4.0
-1.4 -2.4 -3.1 -4.2 -1.6 -1.7 -3.1 -4.6 -1.9 2.8 4.2
-0.4 2.9 0.9 0.4 -2.9 1.7 1.2 -1.1 0.6 -2.3 -2.4 3.4
-0.8 -1.8 -2.2 -3.2 -1.2 -1.0 -2.2 -3.5 -1.5 2.6 2.9 -1.5 4.5
-2.6 -3.5 -3.2 -4.7 -0.7 -2.8 -4.3 -5.4 0.0 0.9 2.1 -3.6 1.3 7.2
0.4 -1.0 -1.0 -1.0 -3.1 -0.2 -0.7 -1.7 -1.0 -2.6 -2.2 -0.8 -2.4 -3.8 7.5
1.1 -0.2 0.9 0.4 0.1 0.1 0.1 0.4 -0.3 -1.8 -2.2 0.0 -1.4 -2.6 0.5 2.1
0.7 -0.3 0.4 -0.2 -0.6 -0.1 -0.2 -1.0 -0.5 -0.3 -1.1 0.1 -0.4 -2.2 0.1 1.4 2.5
-4.1 -1.6 -4.0 -5.5 -0.9 -2.8 -4.7 -4.1 -1.0 -2.3 -0.9 -3.6 -1.3 3.0 -5.2 -3.4 -3.7 14.7
-2.6 -2.0 -1.4 -2.8 -0.4 -1.8 -3.0 -4.3 2.5 -1.0 -0.1 -2.4 -0.5 5.3 -3.4 -1.9 -2.1 3.6 8.1
0.1 -2.2 -2.2 -2.9 -0.2 -1.7 -2.1 -3.1 -2.1 3.2 1.9 -1.9 1.8 0.1 -1.9 -1.0 0.2 -2.9 -1.4 3.4
//
H QUIB020101
D STROMA score matrix for the alignment of known distant homologs
(Qian-Goldstein, 2002)
R PMID:12211027
A Qian, B. and Goldstein, R.A.
T Optimization of a new score function for the generation of accurate
alignments
J Proteins. 48, 605-610 (2002)
M rows = ARNDCQEGHILKMFPSTWYV, cols = ARNDCQEGHILKMFPSTWYV
2.5
0.2 5.2
1.1 0.7 2.5
1 0.1 3.3 5.3
1.2 -1.3 -1.9 -3.1 11.5
-0.1 2 1.9 1.1 -2.5 3.6
1.2 1.9 2.3 3.2 -2.4 1.7 3.7
1.4 -0.2 0.7 0.9 -1.3 -0.3 0.5 7.5
-1.4 1.5 1.4 0.5 -1.7 1.4 0.3 -1.7 6.8
0.3 -1.9 -2.4 -2.9 -3.2 -0.9 -3.1 -3.7 -1.8 4.5
-0.2 -1.5 -2.4 -3.4 -1.6 -1.2 -1.5 -3.8 -2.4 3.4 5.2
-0.2 3.4 1.6 1.4 -3 2.2 1.2 0.4 1.1 -1.5 -2 3.9
-0.2 -1.4 -2.1 -2.8 -1.3 -0.6 -2 -3.8 -0.8 2.2 3.1 -0.5 5.4
-1.6 -3.2 -2.5 -3.7 -0.8 -1.7 -13.7 -4.7 -0.9 2.2 3.7 -2.8 1.7 7
0.7 -0.6 -0.1 -0.2 -3.6 1 0 -0.8 -2.1 -2.4 -1.4 0.2 -1.9 -4.1 8.1
1.7 0.2 1.4 1.7 0.7 0.9 1.1 1.6 -0.1 -1.1 -0.8 1.4 -1.1 -2.5 2 2.8
1.7 0.2 1.4 0.1 0.3 -0.1 1.6 -0.6 -0.2 0 0.3 1 -0.3 -0.8 1.1 2.6 0.4
-3.3 -1.5 -4 -5.7 -0.5 -2.9 -4.7 -4.2 -1.2 -1.8 -1.2 -3 -0.6 3.7 -5 -2.8 -2.9 14.9
-1.8 -0.9 -0.8 -2.9 -0.3 -1.5 -2.2 -4.8 2.9 0.2 0.8 -1.5 0.5 5.2 -3.3 -0.9 -0.8 4.9 8.1
1.9 -2.8 -0.9 -2.5 0.7 -1.5 -1.3 -1.4 -2.5 4.5 3.4 -1 1.7 0.9 -1.1 -3 1.5 -2.5 0.3 4.2
//
H HENS920104
D BLOSUM50 substitution matrix (Henikoff-Henikoff, 1992)
R LIT:1902106 PMID:1438297
A Henikoff, S. and Henikoff, J.G.
T Amino acid substitution matrices from protein blocks
J Proc. Natl. Acad. Sci. USA 89, 10915-10919 (1992)
* # Matrix made by matblas from blosum50.iij
* # BLOSUM Clustered Scoring Matrix in 1/3 Bit Units
* # Blocks Database = /data/blocks_5.0/blocks.dat
* # Cluster Percentage: >= 50
* # Entropy = 0.4808, Expected = -0.3573
M rows = ARNDCQEGHILKMFPSTWYV, cols = ARNDCQEGHILKMFPSTWYV
5 -2 -1 -2 -1 -1 -1 0 -2 -1 -2 -1 -1 -3 -1 1 0 -3 -2 0
-2 7 -1 -2 -4 1 0 -3 0 -4 -3 3 -2 -3 -3 -1 -1 -3 -1 -3
-1 -1 7 2 -2 0 0 0 1 -3 -4 0 -2 -4 -2 1 0 -4 -2 -3
-2 -2 2 8 -4 0 2 -1 -1 -4 -4 -1 -4 -5 -1 0 -1 -5 -3 -4
-1 -4 -2 -4 13 -3 -3 -3 -3 -2 -2 -3 -2 -2 -4 -1 -1 -5 -3 -1
-1 1 0 0 -3 7 2 -2 1 -3 -2 2 0 -4 -1 0 -1 -1 -1 -3
-1 0 0 2 -3 2 6 -3 0 -4 -3 1 -2 -3 -1 -1 -1 -3 -2 -3
0 -3 0 -1 -3 -2 -3 8 -2 -4 -4 -2 -3 -4 -2 0 -2 -3 -3 -4
-2 0 1 -1 -3 1 0 -2 10 -4 -3 0 -1 -1 -2 -1 -2 -3 2 -4
-1 -4 -3 -4 -2 -3 -4 -4 -4 5 2 -3 2 0 -3 -3 -1 -3 -1 4
-2 -3 -4 -4 -2 -2 -3 -4 -3 2 5 -3 3 1 -4 -3 -1 -2 -1 1
-1 3 0 -1 -3 2 1 -2 0 -3 -3 6 -2 -4 -1 0 -1 -3 -2 -3
-1 -2 -2 -4 -2 0 -2 -3 -1 2 3 -2 7 0 -3 -2 -1 -1 0 1
-3 -3 -4 -5 -2 -4 -3 -4 -1 0 1 -4 0 8 -4 -3 -2 1 4 -1
-1 -3 -2 -1 -4 -1 -1 -2 -2 -3 -4 -1 -3 -4 10 -1 -1 -4 -3 -3
1 -1 1 0 -1 0 -1 0 -1 -3 -3 0 -2 -3 -1 5 2 -4 -2 -2
0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 2 5 -3 -2 0
-3 -3 -4 -5 -5 -1 -3 -3 -3 -3 -2 -3 -1 1 -4 -4 -3 15 2 -3
-2 -1 -2 -3 -3 -1 -2 -3 2 -1 -1 -2 0 4 -3 -2 -2 2 8 -1
0 -3 -3 -4 -1 -3 -3 -4 -4 4 1 -3 1 -1 -3 -2 0 -3 -1 5
//
H KOSJ950101
D Context-dependent optimal substitution matrices for exposed helix
(Koshi-Goldstein, 1995)
R LIT:2124140 PMID:8577693
A Koshi, J.M. and Goldstein, R.A.
T Context-dependent optimal substitution matrices.
J Protein Engineering 8, 641-645 (1995)
M rows = -ARNDCQEGHILKMFPSTWYV, cols = -ARNDCQEGHILKMFPSTWYV
55.7
3.0 3.0
3.0 3.0 0.4
0.1 3.0 3.0 2.1
3.0 3.0 3.0 0.1 1.9
2.2 2.4 3.0 0.8 1.3 3.0
25.6 47.2 1.5 1.0 0.7 0.3 1.9
2.3 4.3 0.6 0.2 2.0 0.8 0.1 0.3
3.1 2.8 3.7 0.4 0.1 2.0 14.8 0.9 62.7
1.3 0.4 0.3 4.6 0.3 0.1 1.9 0.5 2.2 5.1
0.6 0.2 0.4 1.9 1.5 0.4 0.2 0.3 15.2 0.2 0.5
48.2 3.3 0.1 3.2 4.9 0.1 1.7 1.7 1.4 3.0 0.6 1.0
0.1 9.7 2.7 0.7 1.1 1.5 15.9 3.9 1.4 7.3 52.1 0.3 0.9
11.0 2.0 0.4 0.6 0.1 0.6 0.5 0.1 0.6 2.9 0.1 0.1 0.1 0.1
9.4 1.5 0.1 1.5 1.6 73.6 0.1 2.6 0.1 0.1 2.1 4.0 0.1 0.1 0.8
0.7 0.3 2.2 0.1 0.1 0.1 0.1 8.4 5.7 2.0 4.5 0.3 47.5 8.2 0.9 1.6
0.1 3.4 7.8 0.5 0.1 0.7 5.3 2.2 0.2 0.7 0.5 5.2 5.3 1.0 1.5 8.6 0.1
4.9 56.8 1.5 1.0 0.3 0.9 5.8 0.1 0.2 1.6 2.1 2.4 0.2 0.1 1.1 20.2 2.0 1.2
2.3 3.3 0.1 0.4 0.1 6 4.8 0.8 0.1 0.1 1.4 0.3 0.6 0.1 1.2 0.6 0.1 0.5 13.3
0.3 4.7 7.5 1.8 0.1 4.4 0.7 0.1 56.9 0.6 0.1 2.3 1.2 2.2 0.1 0.1 0.1 0.1 4.4 0.1
18.4 0.1 0.1 0.1 0.1 0.1 0.4 0.1 0.1 0.1 5 2.6 10.8 1.2 3.5 1.3 0.1 0.1 3.4 0.1 0.1
//
H OVEJ920102
D Environment-specific amino acid substitution matrix for alpha residues
(Overington et al., 1992)
R LIT:1811128 PMID:1304904
A Overington, J., Donnelly, D., Johnson, M.S., Sali, A. and Blundell, T.L.
T Environment-specific amino acid substitution tables: tertiary templates
and prediction of protein folds
J Protein Science 1, 216-226 (1992)
M rows = ACDEFGHIKLMNPQRSTVWYJ-, cols = ACDEFGHIKLMNPQRSTVWYJ
0.355 0.007 0.090 0.100 0.050 0.177 0.037 0.077 0.096 0.056 0.081 0.103 0.106 0.090 0.088 0.163 0.120 0.098 0.065 0.036 0.252
0.001 0.901 0.000 0.000 0.000 0.000 0.000 0.004 0.001 0.000 0.000 0.003 0.000 0.006 0.006 0.004 0.002 0.000 0.007 0.000 0.000
0.038 0.000 0.315 0.109 0.006 0.041 0.027 0.009 0.033 0.004 0.009 0.088 0.051 0.089 0.023 0.065 0.048 0.013 0.012 0.011 0.009
0.044 0.011 0.111 0.305 0.011 0.048 0.026 0.011 0.059 0.013 0.009 0.068 0.069 0.086 0.053 0.033 0.045 0.017 0.012 0.018 0.000
0.017 0.000 0.005 0.007 0.415 0.004 0.009 0.039 0.025 0.097 0.042 0.013 0.006 0.011 0.009 0.009 0.014 0.041 0.053 0.085 0.009
0.065 0.000 0.070 0.042 0.006 0.370 0.017 0.022 0.029 0.013 0.015 0.036 0.043 0.031 0.013 0.068 0.049 0.014 0.009 0.021 0.045
0.010 0.000 0.012 0.011 0.010 0.007 0.571 0.003 0.022 0.005 0.015 0.043 0.006 0.035 0.021 0.016 0.008 0.017 0.009 0.037 0.009
0.029 0.014 0.009 0.008 0.048 0.021 0.004 0.325 0.017 0.076 0.107 0.018 0.007 0.007 0.015 0.014 0.033 0.112 0.016 0.030 0.018
0.053 0.007 0.044 0.081 0.020 0.041 0.044 0.026 0.336 0.029 0.059 0.073 0.045 0.094 0.163 0.041 0.054 0.026 0.041 0.028 0.036
0.038 0.000 0.006 0.018 0.210 0.019 0.004 0.139 0.033 0.415 0.225 0.033 0.016 0.041 0.028 0.029 0.026 0.133 0.037 0.057 0.036
0.013 0.000 0.004 0.003 0.016 0.007 0.000 0.043 0.014 0.053 0.197 0.010 0.000 0.018 0.004 0.003 0.010 0.018 0.021 0.021 0.018
0.031 0.007 0.057 0.035 0.010 0.026 0.054 0.012 0.034 0.012 0.013 0.195 0.015 0.066 0.026 0.037 0.046 0.012 0.002 0.048 0.000
0.022 0.000 0.036 0.035 0.005 0.026 0.011 0.009 0.020 0.006 0.000 0.013 0.424 0.013 0.016 0.039 0.011 0.009 0.002 0.000 0.000
0.025 0.011 0.045 0.039 0.011 0.021 0.031 0.004 0.045 0.015 0.035 0.059 0.015 0.183 0.029 0.030 0.030 0.008 0.007 0.025 0.009
0.019 0.011 0.012 0.023 0.005 0.008 0.019 0.010 0.069 0.009 0.004 0.018 0.013 0.028 0.348 0.030 0.019 0.005 0.007 0.018 0.018
0.086 0.021 0.075 0.047 0.012 0.079 0.033 0.020 0.041 0.020 0.009 0.089 0.082 0.069 0.063 0.264 0.096 0.028 0.005 0.020 0.054
0.043 0.007 0.039 0.033 0.020 0.038 0.014 0.026 0.032 0.015 0.026 0.057 0.028 0.046 0.035 0.065 0.266 0.037 0.016 0.034 0.000
0.055 0.000 0.018 0.021 0.069 0.022 0.044 0.178 0.025 0.111 0.016 0.018 0.025 0.017 0.015 0.129 0.060 0.350 0.012 0.043 0.162
0.009 0.000 0.003 0.004 0.022 0.004 0.007 0.006 0.012 0.006 0.020 0.001 0.001 0.006 0.004 0.002 0.007 0.003 0.588 0.064 0.000
0.009 0.000 0.006 0.006 0.046 0.006 0.029 0.014 0.007 0.013 0.031 0.033 0.003 0.020 0.010 0.007 0.017 0.016 0.078 0.377 0.027
0.009 0.000 0.001 0.000 0.001 0.004 0.001 0.002 0.002 0.002 0.004 0.000 0.000 0.004 0.003 0.006 0.004 0.010 0.000 0.005 0.297
0.028 0.004 0.041 0.074 0.010 0.029 0.017 0.022 0.050 0.031 0.033 0.031 0.045 0.039 0.028 0.047 0.034 0.032 0.002 0.021 0.000
//
"""
# Run tests if called from the command line
if __name__ == '__main__':
main()
