/usr/lib/python2.7/dist-packages/cogent/evolve/pairwise_distance.py is in python-cogent 1.9-9.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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from numpy import log, zeros, float64, int32, array, sqrt, dot, diag, eye
from numpy.linalg import det, norm, inv, LinAlgError
from cogent import DNA, RNA, LoadTable
from cogent.util.progress_display import display_wrap
__author__ = "Gavin Huttley, Yicheng Zhu and Ben Kaehler"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Gavin Huttley", "Yicheng Zhu", "Ben Kaehler"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Gavin Huttley"
__email__ = "gavin.huttley@anu.edu.au"
__status__ = "Alpha" # pending addition of protein distance metrics
def _same_moltype(ref, query):
"""if ref and query have the same states"""
return set(ref) == set(query)
def get_pyrimidine_indices(moltype):
"""returns pyrimidine indices for the moltype"""
states = list(moltype)
if _same_moltype(RNA, moltype):
return map(states.index, 'CU')
elif _same_moltype(DNA, moltype):
return map(states.index, 'CT')
else:
raise RuntimeError('Non-nucleic acid MolType')
def get_purine_indices(moltype):
"""returns purine indices for the moltype"""
states = list(moltype)
if not _same_moltype(RNA, moltype) and not _same_moltype(DNA, moltype):
raise RuntimeError('Non-nucleic acid MolType')
return map(states.index, 'AG')
def get_matrix_diff_coords(indices):
"""returns coordinates for off diagonal elements"""
return [(i,j) for i in indices for j in indices if i != j]
def get_moltype_index_array(moltype, invalid=-9):
"""returns the index array for a molecular type"""
canonical_chars = list(moltype)
# maximum ordinal for an allowed character, this defines the length of
# the required numpy array
max_ord = max(map(ord, moltype.All.keys()))
char_to_index = zeros(max_ord+1, int32)
# all non canonical_chars are ``invalid''
char_to_index.fill(invalid)
for i in range(len(canonical_chars)):
c = canonical_chars[i]
o = ord(c)
char_to_index[o] = i
return char_to_index
def seq_to_indices(seq, char_to_index):
"""returns an array with sequence characters replaced by their index"""
ords = map(ord, seq)
indices = char_to_index.take(ords)
return indices
def _fill_diversity_matrix(matrix, seq1, seq2):
"""fills the diversity matrix for valid positions.
Assumes the provided sequences have been converted to indices with
invalid characters being negative numbers (use get_moltype_index_array
plus seq_to_indices)."""
paired = array([seq1, seq2]).T
paired = paired[paired.min(axis=1) >= 0]
for i in range(len(paired)):
matrix[paired[i][0], paired[i][1]] += 1
def _jc69_from_matrix(matrix):
"""computes JC69 stats from a diversity matrix"""
invalid = None, None, None, None
total = matrix.sum()
diffs = total - sum(matrix[i,i] for i in range(matrix.shape[0]))
if total == 0:
return invalid
p = diffs / total
if p >= 0.75: # cannot take log
return invalid
factor = (1 - (4 / 3) * p)
dist = -3.0 * log(factor) / 4
var = p * (1 - p) / (factor * factor * total)
return total, p, dist, var
def _tn93_from_matrix(matrix, freqs, pur_indices, pyr_indices, pur_coords, pyr_coords, tv_coords):
invalid = None, None, None, None
total = matrix.sum()
freqs = matrix.sum(axis=0) + matrix.sum(axis=1)
freqs /= (2*total)
if total == 0:
return invalid
#
p = matrix.take(pur_coords + pyr_coords + tv_coords).sum() / total
freq_purs = freqs.take(pur_indices).sum()
prod_purs = freqs.take(pur_indices).prod()
freq_pyrs = freqs.take(pyr_indices).sum()
prod_pyrs = freqs.take(pyr_indices).prod()
# purine transition diffs
pur_ts_diffs = matrix.take(pur_coords).sum()
pur_ts_diffs /= total
# pyr transition diffs
pyr_ts_diffs = matrix.take(pyr_coords).sum()
pyr_ts_diffs /= total
# transversions
tv_diffs = matrix.take(tv_coords).sum() / total
coeff1 = 2 * prod_purs / freq_purs
coeff2 = 2 * prod_pyrs / freq_pyrs
coeff3 = 2 * (freq_purs * freq_pyrs - \
(prod_purs * freq_pyrs / freq_purs) -\
(prod_pyrs * freq_purs / freq_pyrs))
term1 = 1 - pur_ts_diffs / coeff1 - tv_diffs / (2*freq_purs)
term2 = 1 - pyr_ts_diffs / coeff2 - tv_diffs / (2*freq_pyrs)
term3 = 1 - tv_diffs / (2 * freq_purs * freq_pyrs)
if term1 <= 0 or term2 <= 0 or term3 <= 0: # log will fail
return invalid
dist = -coeff1 * log(term1) - coeff2 * log(term2) - coeff3 * log(term3)
v1 = 1 / term1
v2 = 1 / term2
v3 = 1 / term3
v4 = (coeff1 * v1 / (2 * freq_purs)) + \
(coeff2 * v2 / (2 * freq_pyrs)) + \
(coeff3 * v3 / (2 * freq_purs * freq_pyrs))
var = v1**2 * pur_ts_diffs + v2**2 * pyr_ts_diffs + v4**2 * tv_diffs - \
(v1 * pur_ts_diffs + v2 * pyr_ts_diffs + v4 * tv_diffs)**2
var /= total
return total, p, dist, var
def _logdetcommon(matrix):
invalid = (None,)*5
total = matrix.sum()
diffs = total - matrix.diagonal().sum()
if total == 0:
return invalid
p = diffs / total
if diffs == 0: # seqs indentical
return invalid
# we replace the missing diagonal states with a frequency of 0.5,
# then normalise
frequency = matrix.copy()
frequency[(frequency == 0) * eye(*matrix.shape, dtype=bool)] = 0.5
frequency /= frequency.sum()
if det(frequency) <= 0: #if the result is nan
return invalid
# the inverse matrix of frequency, every element is squared
M_matrix = inv(frequency)**2
freqs = [frequency.sum(axis=axis) for axis in (0, 1)]
var_term = dot(M_matrix, frequency).diagonal().sum()
return total, p, frequency, freqs, var_term
def _paralinear(matrix):
"""the paralinear distance from a diversity matrix"""
invalid = (None,)*4
total, p, frequency, freqs, var_term = _logdetcommon(matrix)
if frequency is None:
return invalid
r = matrix.shape[0]
d_xy = - log(det(frequency) / sqrt((freqs[0] * freqs[1]).prod())) / r
var = (var_term - (1 / sqrt(freqs[0]*freqs[1])).sum()) / (r**2 * total)
return total, p, d_xy, var
def _logdet(matrix, use_tk_adjustment=True):
"""returns the LogDet from a diversity matrix
Arguments:
- use_tk_adjustment: when True, unequal state frequencies are allowed
"""
invalid = (None,)*4
total, p, frequency, freqs, var_term = _logdetcommon(matrix)
if frequency is None:
return invalid
r = matrix.shape[0]
if use_tk_adjustment:
coeff = (sum(sum(freqs)**2)/4 - 1) / (r - 1)
d_xy = coeff * log(det(frequency)/sqrt((freqs[0] * freqs[1]).prod()))
var = None
else:
d_xy = - log(det(frequency)) / r - log(r)
var = ( var_term / r**2 - 1 ) / total
return total, p, d_xy, var
try:
from _pairwise_distance import \
_fill_diversity_matrix as fill_diversity_matrix
# raise ImportError # for testing
except ImportError:
fill_diversity_matrix = _fill_diversity_matrix
def _number_formatter(template):
"""flexible number formatter"""
def call(val):
try:
result = template % val
except TypeError:
result = val
return result
return call
class _PairwiseDistance(object):
"""base class for computing pairwise distances"""
def __init__(self, moltype, invalid=-9, alignment=None):
super(_PairwiseDistance, self).__init__()
self.moltype = moltype
self.char_to_indices = get_moltype_index_array(moltype)
self._dim = len(list(moltype))
self._dists = None
self.Names = None
self.IndexedSeqs = None
if alignment is not None:
self._convert_seqs_to_indices(alignment)
self._func_args = []
def _convert_seqs_to_indices(self, alignment):
assert type(alignment.MolType) == type(self.moltype), \
'Alignment does not have correct MolType'
self._dists = {}
self.Names = alignment.Names[:]
indexed_seqs = []
for name in self.Names:
seq = alignment.getGappedSeq(name)
indexed = seq_to_indices(str(seq), self.char_to_indices)
indexed_seqs.append(indexed)
self.IndexedSeqs = array(indexed_seqs)
@staticmethod
def func():
pass # over ride in subclasses
@display_wrap
def run(self, alignment=None, ui=None):
"""computes the pairwise distances"""
if alignment is not None:
self._convert_seqs_to_indices(alignment)
matrix = zeros((self._dim, self._dim), float64)
done = 0.0
to_do = (len(self.Names) * len(self.Names) - 1) / 2
for i in range(len(self.Names)-1):
name_1 = self.Names[i]
s1 = self.IndexedSeqs[i]
for j in range(i+1, len(self.Names)):
name_2 = self.Names[j]
ui.display('%s vs %s' % (name_1, name_2), done / to_do )
done += 1
matrix.fill(0)
s2 = self.IndexedSeqs[j]
fill_diversity_matrix(matrix, s1, s2)
total, p, dist, var = self.func(matrix, *self._func_args)
self._dists[(name_1, name_2)] = (total, p, dist, var)
self._dists[(name_2, name_1)] = (total, p, dist, var)
def getPairwiseDistances(self):
"""returns a 2D dictionary of pairwise distances."""
if self._dists is None:
return None
dists = {}
for name_1 in self.Names:
for name_2 in self.Names:
if name_1 == name_2:
continue
val = self._dists[(name_1, name_2)][2]
dists[(name_1, name_2)] = val
dists[(name_2, name_1)] = val
return dists
def _get_stats(self, stat, transform=None, **kwargs):
"""returns a table for the indicated statistics"""
if self._dists is None:
return None
rows = []
for row_name in self.Names:
row = [row_name]
for col_name in self.Names:
if row_name == col_name:
row.append('')
continue
val = self._dists[(row_name, col_name)][stat]
if transform is not None:
val = transform(val)
row.append(val)
rows.append(row)
header = [r'Seq1 \ Seq2'] + self.Names
table = LoadTable(header=header, rows=rows, row_ids = True,
missing_data='*', **kwargs)
return table
@property
def Dists(self):
kwargs = dict(title='Pairwise Distances', digits=4)
return self._get_stats(2, **kwargs)
@property
def StdErr(self):
stderr = lambda x: sqrt(x)
kwargs = dict(title='Standard Error of Pairwise Distances', digits=4)
return self._get_stats(3, transform=stderr, **kwargs)
@property
def Variances(self):
kwargs = dict(title='Variances of Pairwise Distances', digits=4)
table = self._get_stats(3, **kwargs)
var_formatter = _number_formatter("%.2e")
if table is not None:
for name in self.Names:
table.setColumnFormat(name, var_formatter)
return table
@property
def Proportions(self):
kwargs = dict(title='Proportion variable sites', digits=4)
return self._get_stats(1, **kwargs)
@property
def Lengths(self):
kwargs = dict(title='Pairwise Aligned Lengths', digits=0)
return self._get_stats(0, **kwargs)
class _NucleicSeqPair(_PairwiseDistance):
"""docstring for _NucleicSeqPair"""
def __init__(self, *args, **kwargs):
super(_NucleicSeqPair, self).__init__(*args, **kwargs)
if not _same_moltype(DNA, self.moltype) and \
not _same_moltype(RNA, self.moltype):
raise RuntimeError('Invalid MolType for this metric')
class JC69Pair(_NucleicSeqPair):
"""calculator for pairwise alignments"""
def __init__(self, *args, **kwargs):
"""states: the valid sequence states"""
super(JC69Pair, self).__init__(*args, **kwargs)
self.func = _jc69_from_matrix
class TN93Pair(_NucleicSeqPair):
"""calculator for pairwise alignments"""
def __init__(self, *args, **kwargs):
"""states: the valid sequence states"""
super(TN93Pair, self).__init__(*args, **kwargs)
self._freqs = zeros(self._dim, float64)
self.pur_indices = get_purine_indices(self.moltype)
self.pyr_indices = get_pyrimidine_indices(self.moltype)
# matrix coordinates
self.pyr_coords = get_matrix_diff_coords(self.pyr_indices)
self.pur_coords = get_matrix_diff_coords(self.pur_indices)
self.tv_coords = get_matrix_diff_coords(range(self._dim))
for coord in self.pur_coords + self.pyr_coords:
self.tv_coords.remove(coord)
# flattened
self.pyr_coords = [i * 4 + j for i, j in self.pyr_coords]
self.pur_coords = [i * 4 + j for i, j in self.pur_coords]
self.tv_coords = [i * 4 + j for i, j in self.tv_coords]
self.func = _tn93_from_matrix
self._func_args = [self._freqs, self.pur_indices,
self.pyr_indices, self.pur_coords,
self.pyr_coords, self.tv_coords]
class LogDetPair(_PairwiseDistance):
"""computes logdet distance between sequence pairs"""
def __init__(self, use_tk_adjustment=True, *args, **kwargs):
"""Arguments:
- use_tk_adjustment: use the correction of Tamura and Kumar 2002
"""
super(LogDetPair, self).__init__(*args, **kwargs)
self.func = _logdet
self._func_args = [use_tk_adjustment]
def run(self, use_tk_adjustment=None, *args, **kwargs):
if use_tk_adjustment is not None:
self._func_args = [use_tk_adjustment]
super(LogDetPair, self).run(*args, **kwargs)
class ParalinearPair(_PairwiseDistance):
"""computes the paralinear distance (Lake 1994) between sequence pairs"""
def __init__(self, *args, **kwargs):
super(ParalinearPair, self).__init__(*args, **kwargs)
self.func = _paralinear
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