/usr/lib/python2.7/dist-packages/cogent/phylo/util.py is in python-cogent 1.9-9.
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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 75 | #!/usr/bin/env python
import numpy
Float = numpy.core.numerictypes.sctype2char(float)
# Distance matricies are presently represented as simple dictionaries, which
# need to be converted into numpy arrays before being fed into phylogenetic
# reconstruction algorithms.
__author__ = "Peter Maxwell"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "pm67nz@gmail.com"
__email__ = "rob@spot.colorado.edu"
__status__ = "Production"
def namesFromDistanceDict(dists):
"""Unique names from within the tuples which make up the keys of 'dists'"""
names = []
for key in dists:
for name in key:
if name not in names:
names.append(name)
return names
def lookupSymmetricDict(dists, a, b):
"""dists[a,b] or dists[b,a], whichever is present, so long as they
don't contradict each other"""
v1 = dists.get((a, b), None)
v2 = dists.get((b, a), None)
if v1 is None and v2 is None:
raise KeyError((a,b))
elif v1 is None or v2 is None or v1 == v2:
return v1 or v2
else:
raise ValueError("d[%s,%s] != d[%s,%s]" % (a,b,b,a))
def distanceDictTo2D(dists):
"""(names, dists). Distances converted into a straightforward distance
matrix"""
names = namesFromDistanceDict(dists)
L = len(names)
d = numpy.zeros([L, L], Float)
for (i, a) in enumerate(names):
for (j, b) in enumerate(names):
if i != j:
d[i, j] = lookupSymmetricDict(dists, a, b)
return (names, d)
def triangularOrder(keys):
"""Indices for extracting a 1D representation of a triangular matrix
where j > i and i is the inner dimension:
Yields (0,1), (0,2), (1, 2), (0,3), (1,3), (2,3), (0,4)..."""
N = len(keys)
for j in range(1, N):
for i in range(0, j):
yield (keys[i], keys[j])
def distanceDictAndNamesTo1D(dists, names):
"""Distances converted into a triangular matrix implemented as a 1D array
where j > i and i is the inner dimension:
d[0,1], d[0, 2], d[1, 2], d[0, 3]..."""
d = []
for (name_i, name_j) in triangularOrder(names):
d.append(lookupSymmetricDict(dists, name_i, name_j))
return numpy.array(d)
def distanceDictTo1D(dists):
"""(names, dists). Distances converted into a triangular matrix
implemented as a 1D array where j > i and i is the inner dimension:
d[0,1], d[0, 2], d[1, 2], d[0, 3]..."""
names = namesFromDistanceDict(dists)
d = distanceDictAndNamesTo1D(dists, names)
return (names, d)
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