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import types
import numerix as _nx
__all__ = ['mgrid','ogrid','r_', 'row', 'c_', 'col', 'index_exp']
from type_check import ScalarType, asarray
import function_base
import matrix_base
makemat = _nx.Matrix
class nd_grid:
""" Construct a "meshgrid" in N-dimensions.
grid = nd_grid() creates an instance which will return a mesh-grid
when indexed. The dimension and number of the output arrays are equal
to the number of indexing dimensions. If the step length is not a
complex number, then the stop is not inclusive.
However, if the step length is a COMPLEX NUMBER (e.g. 5j), then the
integer part of it's magnitude is interpreted as specifying the
number of points to create between the start and stop values, where
the stop value IS INCLUSIVE.
If instantiated with an argument of 1, the mesh-grid is open or not
fleshed out so that only one-dimension of each returned argument is
greater than 1
Example:
>>> mgrid = nd_grid()
>>> mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
>>> mgrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
>>> ogrid = nd_grid(1)
>>> ogrid[0:5,0:5]
[array([[0],[1],[2],[3],[4]]), array([[0, 1, 2, 3, 4]])]
"""
def __init__(self, sparse=0):
self.sparse = sparse
def __getitem__(self,key):
try:
size = []
typecode = _nx.Int
for k in range(len(key)):
step = key[k].step
start = key[k].start
if start is None: start=0
if step is None: step=1
if type(step) is type(1j):
size.append(int(abs(step)))
typecode = _nx.Float
else:
size.append(int((key[k].stop - start)/(step*1.0)))
if isinstance(step,types.FloatType) or \
isinstance(start, types.FloatType) or \
isinstance(key[k].stop, types.FloatType):
typecode = _nx.Float
if self.sparse:
nn = map(lambda x,t: _nx.arange(x,typecode=t),size,(typecode,)*len(size))
else:
nn = _nx.indices(size,typecode)
for k in range(len(size)):
step = key[k].step
start = key[k].start
if start is None: start=0
if step is None: step=1
if type(step) is type(1j):
step = int(abs(step))
step = (key[k].stop - start)/float(step-1)
nn[k] = (nn[k]*step+start)
if self.sparse:
slobj = [_nx.NewAxis]*len(size)
for k in range(len(size)):
slobj[k] = slice(None,None)
nn[k] = nn[k][slobj]
slobj[k] = _nx.NewAxis
return nn
except (IndexError, TypeError):
step = key.step
stop = key.stop
start = key.start
if start is None: start = 0
if type(step) is type(1j):
step = abs(step)
length = int(step)
step = (key.stop-start)/float(step-1)
stop = key.stop+step
return _nx.arange(0,length,1,_nx.Float)*step + start
else:
return _nx.arange(start, stop, step)
def __getslice__(self,i,j):
return _nx.arange(i,j)
def __len__(self):
return 0
mgrid = nd_grid()
ogrid = nd_grid(1)
import sys
class concatenator:
""" Translates slice objects to concatenation along an axis.
"""
def _retval(self, res):
if not self.matrix:
return res
else:
if self.axis == 0:
return makemat(res)
else:
return makemat(res).T
def __init__(self, axis=0, matrix=0):
self.axis = axis
self.matrix = matrix
def __getitem__(self,key):
if isinstance(key,types.StringType):
frame = sys._getframe().f_back
mymat = matrix_base.bmat(key,frame.f_globals,frame.f_locals)
if self.matrix:
return mymat
else:
return asarray(mymat)
if type(key) is not types.TupleType:
key = (key,)
objs = []
for k in range(len(key)):
if type(key[k]) is types.SliceType:
typecode = _nx.Int
step = key[k].step
start = key[k].start
stop = key[k].stop
if start is None: start = 0
if step is None:
step = 1
if type(step) is type(1j):
size = int(abs(step))
typecode = _nx.Float
newobj = function_base.linspace(start, stop, num=size)
else:
newobj = _nx.arange(start, stop, step)
elif type(key[k]) in ScalarType:
newobj = asarray([key[k]])
else:
newobj = key[k]
objs.append(newobj)
res = _nx.concatenate(tuple(objs),axis=self.axis)
return self._retval(res)
def __getslice__(self,i,j):
res = _nx.arange(i,j)
return self._retval(res)
def __len__(self):
return 0
r_=concatenator(0)
c_=concatenator(-1)
row = concatenator(0,1)
col = concatenator(-1,1)
# A nicer way to build up index tuples for arrays.
#
# You can do all this with slice() plus a few special objects,
# but there's a lot to remember. This version is simpler because
# it uses the standard array indexing syntax.
#
# Written by Konrad Hinsen <hinsen@cnrs-orleans.fr>
# last revision: 1999-7-23
#
# Cosmetic changes by T. Oliphant 2001
#
#
# This module provides a convenient method for constructing
# array indices algorithmically. It provides one importable object,
# 'index_expression'.
#
# For any index combination, including slicing and axis insertion,
# 'a[indices]' is the same as 'a[index_expression[indices]]' for any
# array 'a'. However, 'index_expression[indices]' can be used anywhere
# in Python code and returns a tuple of slice objects that can be
# used in the construction of complex index expressions.
class _index_expression_class:
import sys
maxint = sys.maxint
def __getitem__(self, item):
if type(item) != type(()):
return (item,)
else:
return item
def __len__(self):
return self.maxint
def __getslice__(self, start, stop):
if stop == self.maxint:
stop = None
return self[start:stop:None]
index_exp = _index_expression_class()
# End contribution from Konrad.
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