Python lazy evaluation numpy ndarray

In this case, it does not make sens to bind a transformation function, to every index of your array.

Instead, a more efficient approach would be to define a transformation, as a function, together with a subset of the array it applies to. Here is a basic implementation,

import numpy as npclass LazyEvaluation(object):    def __init__(self):        self.transforms = []    def add_transform(self, function, selection=slice(None), args={}):        self.transforms.append( (function, selection, args))    def __call__(self, x):        y = x.copy()         for function, selection, args in self.transforms:            y[selection] = function(y[selection], **args)        return y

that can be used as follows:

x = np.ones((6, 6))*2le = LazyEvaluation()le.add_transform(lambda x: 0, [[3], [0]]) # equivalent to x[3,0]le.add_transform(lambda x: x**2, (slice(4), slice(4,6)))  # equivalent to x[4,4:6]le.add_transform(lambda x: -1,  np.diag_indices(x.shape[0], x.ndim), ) # setting the diagonal result =  le(x)print(result)

which prints,

array([[-1.,  2.,  2.,  2.,  4.,  4.],       [ 2., -1.,  2.,  2.,  4.,  4.],       [ 2.,  2., -1.,  2.,  4.,  4.],       [ 0.,  2.,  2., -1.,  4.,  4.],       [ 2.,  2.,  2.,  2., -1.,  2.],       [ 2.,  2.,  2.,  2.,  2., -1.]])

This way you can easily support all advanced Numpy indexing (element by element access, slicing, fancy indexing etc.), while at the same time keeping your data in an array with a native data type (float, int, etc) which is much more efficient than using dtype='object'.