aura - 1 year ago 206

Python Question

I have a np.array with dtype as object. Each element here is a np.array with dtype as float and shape as (2,2) --- in maths, it is a 2-by-2 matrix. My aim is to obtain one 2-dimenional matrix by converting all the object-type element into float-type element. This can be better presented by the following example.

`dA = 2 # dA is the dimension of the following A, here use 2 as example only`

A = np.empty((dA,dA), dtype=object) # A is a np.array with dtype as object

A[0,0] = np.array([[1,1],[1,1]]) # each element in A is a 2-by-2 matrix

A[0,1] = A[0,0]*2

A[1,0] = A[0,0]*3

A[1,1] = A[0,0]*4

My aim is to have one matrix B (the dimension of B is 2*dA-by-2*dA). The form of B in maths should be

`B =`

1 1 2 2

1 1 2 2

3 3 4 4

3 3 4 4

If dA is fixed at 2, then things can be easier, because I can hard-code

`a00 = A[0,0]`

a01 = A[0,1]

a10 = A[1,0]

a11 = A[1,1]

B0 = np.hstack((a00,a01))

B1 = np.hstack((a10,a11))

B = np.vstack((B0,B1))

But in reality, dA is a variable, it can be 2 or any other integer. Then I don't know how to do it. I think nested for loops can help but maybe you have brilliant ideas. It would be great if there is something like cell2mat function in MATLAB. Because here you can see A[i,j] as a cell in MATLAB.

Thanks in advance.

Recommended for you: Get network issues from **WhatsUp Gold**. **Not end users.**

Answer Source

Here's a quick way.

Your `A`

:

```
In [137]: A
Out[137]:
array([[array([[1, 1],
[1, 1]]), array([[2, 2],
[2, 2]])],
[array([[3, 3],
[3, 3]]), array([[4, 4],
[4, 4]])]], dtype=object)
```

Use `numpy.bmat`

, but convert `A`

to a python list first, so `bmat`

does what we want:

```
In [138]: B = np.bmat(A.tolist())
In [139]: B
Out[139]:
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])
```

The result is actually a `numpy.matrix`

. If you need a regular numpy array, use the `.A`

attribute of the `matrix`

object:

```
In [140]: B = np.bmat(A.tolist()).A
In [141]: B
Out[141]:
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])
```

Here's an alternative. (It still uses `A.tolist()`

.)

```
In [164]: np.swapaxes(A.tolist(), 1, 2).reshape(4, 4)
Out[164]:
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])
```

In the general case, you would need something like:

```
In [165]: np.swapaxes(A.tolist(), 1, 2).reshape(A.shape[0]*dA, A.shape[1]*dA)
Out[165]:
array([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 3, 4, 4],
[3, 3, 4, 4]])
```