如何安全地将整数矩阵预分配为numpy中的索引矩阵

Question

I want to preallocate an integer matrix to store indices generated in iterations. In MATLAB this can be obtained by IXS = zeros(r,c) before for loops, where r and c are number of rows and columns. Thus all indices in subsequent for loops can be assigned into IXS to avoid dynamic assignment. If I accidentally select a 0 in my codes, for example, a wrong way to pick up these indices to select elements from a matrix, error can arise.

But in numpy, 0 or other minus values can also be used as indices. For example, if I preallocate IXS as IXS=np.zeros([r,c],dtype=int) in numpy. In a for loop, submatrix specified by the indices assigned into IXS previously can be obtained by X(:,IXS(IXS~=0)) in MATLAB, but the first row/column may be lost if I perform the selection in the same way in numpy.

Further, in a large program with operations of large matrices, preallocation is important in speeding up the computation, and it is easy to locate the error raised by wrong indexing as 0 may be selected in MATLAB. In numpy, if I select an array by for example X[:,IXS[:n]] with wrong n , no error occurs. I have to pay lots of times to check where the error is. More badly, if the final results are not so strange, I may ignore this bug. This always occurs in my program. Thus I have to debug my codes again and again.

I wonder is there a safe way to preallocate such index matrix in numpy?

Answer 1

How about filling the index array with values that are obviously too large:

In [156]: x=np.array([1,2,3,4,5])
In [157]: idx=np.full(6,999,dtype=int)
In [158]: idx[:3]=[1,0,4]
In [159]: idx
Out[159]: array([  1,   0,   4, 999, 999, 999])
In [160]: x[idx[:3]]
Out[160]: array([2, 1, 5])
In [161]: x[idx[:4]]
...
IndexError: index 999 is out of bounds for axis 1 with size 5

Answer 2

The equivalent of matlb zeros in numpy is numpy.zeros :

Return a new array of given shape and type, filled with zeros.

Answer 3

If you really really want to catch errors that way, initialize your indices with NaN.

IXS=np.full((r,c),np.nan, dtype=int)

That will always raise an IndexError .

Answer 4

Use a numpy.ma.masked_array

IXS=np.ma.masked_values(np.zeros((3,4),dtype=int),0)

masked_array(data =
 [[-- -- -- --]
 [-- -- -- --]
 [-- -- -- --]],
             mask =
 [[ True  True  True  True]
 [ True  True  True  True]
 [ True  True  True  True]],
       fill_value = 0)

Now if you set a value, you can use it as an index:

a=np.arange(10)
IXS[2,2]=5
a[IXS[2,2]]

5

But if you don't:

IXS[0,0]

masked

a[IXS[0,0]]

IndexError: arrays used as indices must be of integer (or boolean) type