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#!/usr/bin/env python
"""Wrapper for numpy arrays so that they can be indexed by name
>>> a = numpy.identity(3, int)
>>> b = DictArrayTemplate('abc', 'ABC').wrap(a)
>>> b[0]
===========
A B C
-----------
1 0 0
-----------
>>> b['a']
===========
A B C
-----------
1 0 0
-----------
>>> b.keys()
['a', 'b', 'c']
>>> b['a'].keys()
['A', 'B', 'C']
"""
import numpy
from cogent.format import table
__author__ = "Peter Maxwell"
__copyright__ = "Copyright 2007-2009, The Cogent Project"
__credits__ = ["Peter Maxwell", "Gavin Huttley"]
__license__ = "GPL"
__version__ = "1.4.1"
__maintainer__ = "Peter Maxwell"
__email__ = "pm67nz@gmail.com"
__status__ = "Production"
class DictArrayTemplate(object):
def __init__(self, *dimensions):
self.names = []
self.ordinals = []
for names in dimensions:
if isinstance(names, int):
names = range(names)
else:
names = list(names)[:]
self.names.append(names)
self.ordinals.append(dict((c,i) for (i,c) in enumerate(names)))
self._shape = tuple(len(keys) for keys in self.names)
def __eq__(self, other):
return self is other or (
isinstance(other, DictArrayTemplate) and self.names == other.names)
def unwrap(self, value):
"""Convert to a simple numpy array"""
if isinstance(value, DictArray):
if value.template == self:
return value.array
elif isinstance(value, dict):
# should handle dicts as well.
raise NotImplementedError
else:
value = numpy.asarray(value)
assert value.shape == self._shape, (value.shape, self._shape)
return value
def wrap(self, array, dtype = None):
# dtype is numpy
array = numpy.asarray(array, dtype=dtype)
for (dim, categories) in enumerate(self.names):
assert len(categories) == numpy.shape(array)[dim], "cats=%s; dim=%s" % (categories, dim)
return DictArray(array, self)
def interpretIndex(self, names):
if not isinstance(names, tuple):
names = (names,)
index = []
remaining = []
for (ordinals, allnames, name) in zip(self.ordinals, self.names, names):
if not isinstance(name, (int, slice)):
name = ordinals[name]
elif isinstance(name, slice):
start = name.start
stop = name.stop
if isinstance(name.start, str):
start = allnames.index(name.start)
if isinstance(name.stop, str):
stop = allnames.index(name.stop)
name = slice(start, stop, name.step)
remaining.append(allnames.__getitem__(name))
index.append(name)
remaining.extend(self.names[len(index):])
if remaining:
klass = type(self)(*remaining)
else:
klass = None
return (tuple(index), klass)
def array_repr(self, a):
if len(a.shape) == 1:
heading = [str(n) for n in self.names[0]]
a = a[numpy.newaxis, :]
elif len(a.shape) == 2:
heading = [''] + [str(n) for n in self.names[1]]
a = [[str(name)] + list(row) for (name, row) in zip(self.names[0], a)]
else:
return '%s dimensional %s' % (
len(self.names), type(self).__name__)
formatted = table.formattedCells(rows=a, header=heading)
return str(table.simpleFormat(formatted[0], formatted[1], space=4))
class DictArray(object):
"""Wraps a numpy array so that it can be indexed with strings like nested
dictionaries (only ordered), for things like substitution matrices and
bin probabilities."""
def __init__(self, *args, **kwargs):
"""allow alternate ways of creating for time being"""
if len(args) <= 2:
self.array = args[0]
self.template = args[1]
else:
if 'dtype' in kwargs or 'typecode' in kwargs:
dtype = kwargs['dtype']
kwargs.pop('dtype', None)
kwargs.pop('typecode', None)
else:
dtype = None
create_new = DictArrayTemplate(*args[1:]).wrap(args[0], dtype=dtype)
self.__dict__ = create_new.__dict__
self.Shape = self.array.shape
def asarray(self):
return self.array
def __array__(self):
return self.array
def asdict(self):
return dict(self.items())
def __getitem__(self, names):
(index, remaining) = self.template.interpretIndex(names)
result = self.array[index]
if remaining is not None:
result = self.__class__(result, remaining)
return result
def __iter__(self):
(index, remaining) = self.template.interpretIndex(0)
for elt in self.array:
if remaining is None:
yield elt
else:
yield remaining.wrap(elt)
def __len__(self):
return len(self.template.names[0])
def keys(self):
return self.template.names[0][:]
def items(self):
return [(n,self[n]) for n in self.keys()]
def __repr__(self):
return self.template.array_repr(self.array)
def __ne__(self, other):
return not self.__eq__(other)
def __eq__(self, other):
if self is other:
return True
elif isinstance(other, DictArray):
return self.template == other.template and numpy.all(
self.array == other.array)
elif type(other) is type(self.array):
return self.array == other
elif isinstance(other, dict):
return self.asdict() == other
else:
return False
|
