/usr/lib/python2.7/dist-packages/cogent/maths/matrix/distance.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 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 | #!/usr/bin/env python
"""Code supporting distance matrices with arbitrary row/column labels.
Currently used to support amino acid distance matrices and similar.
NOTE: This is _much_ slower than using a numpy array. It is primarily
convenient when you want to index into a matrix by keys (e.g. by amino acid
labels) and when you expect to have a lot of missing values. You will probably
want to use this for prototyping, then move to numpy arrays if and when
performance becomes an issue.
"""
from cogent.util.misc import Delegator
from cogent.util.dict2d import Dict2D
from copy import deepcopy
__author__ = "Greg Caporaso"
__copyright__ = "Copyright 2007-2016, The Cogent Project"
__credits__ = ["Greg Caporaso", "Rob Knight"]
__license__ = "GPL"
__version__ = "1.9"
__maintainer__ = "Greg Caporaso"
__email__ = "caporaso@colorado.edu"
__status__ = "Production"
class DistanceMatrix(Dict2D, Delegator):
""" 2D dict giving distances from A to B and vice versa """
# default set of amino acids
RowOrder = list('ACDEFGHIKLMNPQRSTVWY')
ColOrder = list('ACDEFGHIKLMNPQRSTVWY')
Pad = True
def __init__(self, data=None, RowOrder=None, ColOrder=None, Default=None,
Pad=None, RowConstructor=None, info=None):
""" Init dict with pre-exisitng data: dict of dicts
Usage:
data = distance matrix in form acceptable by Dict2D class
RowOrder = list of 'interesting keys', default is the set of
all amino acids
ColOrder = list of 'interesting keys', default is the set of
all amino acids
Default = value to set padded elements to
Pad = boolean describing whether to fill object to hold all
possible elements based on RowOrder and ColOrder
RowConstructor = constructor to use when building inner
objects, default dict
info = the AAIndexRecord object
Power = Power the original matrix has been raised to yield current
matrix
"""
if RowOrder is not None:
self.RowOrder = RowOrder
if ColOrder is not None:
self.ColOrder = ColOrder
if Pad is not None:
self.Pad = Pad
# Initialize super class attributes
Dict2D.__init__(self, data=data, RowOrder=self.RowOrder,\
ColOrder=self.ColOrder, Default=Default, Pad=self.Pad,\
RowConstructor=RowConstructor)
Delegator.__init__(self, info)
# The power to which the original data has been raised to give
# the current data, starts at 1., modified by elementPow()
# accessed as self.Power
self.__dict__['Power'] = 1.
def elementPow(self, power, ignore_invalid=True):
""" Raises all elements in matrix to power
power: the power to raise all elements in the matrix to,
must be a floatable value or a TypeError is raise
ignore_invalid: leaves invalid (not floatable)
matrix data untouched
"""
try:
n = float(power)
except ValueError:
raise TypeError, 'Must pass a floatable value to elementPow'
if ignore_invalid:
def Pow(x):
try:
return x**n
except TypeError:
return x
else:
def Pow(x):
return x**n
self.scale(Pow)
self.Power = self.Power * n
def copy(self):
""" Returns a deep copy of the DistanceMatrix object """
# Is there a better way to do this? It's tricky to keep the delegator
# part functioning
return deepcopy(self)
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