It appears that you can't assign an ndarray to a memoryview slice? Is
this correct/expected behaviour?
Whilst there are a couple of possible workarounds it would be nice if
this just worked as it's a slightly surprising deviation from the
ndarray behaviour.
Broken example:
```
In [3]: %%cython
...: cimport cython
...: import numpy as np
...: cimport numpy as np
...:
...: cpdef np.ndarray[np.float64_t, ndim=2] f():
...: cdef np.ndarray[np.float64_t, ndim=2] x = np.ones([3, 4],
dtype=np.float64)
...: cdef double[:,:] y = np.zeros([3, 4], dtype=np.float64)
...: cdef np.ndarray[np.float64_t, ndim=1] row
...: cdef int idx
...: for idx, row in enumerate(x):
...: y[idx] = row
...: return np.asarray(y)
In [4]: f()
Traceback (most recent call last):
File "<ipython-input-4-0ec059b9bfe1>", line 1, in <module>
f()
File "_cython_magic_5f2586693ddbf044815dae01d800bc0c.pyx", line 5, in
_cython_magic_5f2586693ddbf044815dae01d800bc0c.f
(C:\Users\dhirschfeld\.ipython\cython\_cython_magic_5f2586693ddbf044815d
ae01d800bc0c.c:2086)
File "_cython_magic_5f2586693ddbf044815dae01d800bc0c.pyx", line 11, in
_cython_magic_5f2586693ddbf044815dae01d800bc0c.f
(C:\Users\dhirschfeld\.ipython\cython\_cython_magic_5f2586693ddbf044815d
ae01d800bc0c.c:1935)
```
Workarounds - either:
1. Type both lhs and rhs as ndarray
2. Create a 1D memoryview to use as a temporary container and assign
that to the memoryview slice
```
In [5]: %%cython
...: cimport cython
...: import numpy as np
...: cimport numpy as np
...:
...: cpdef np.ndarray[np.float64_t, ndim=2] f():
...: cdef np.ndarray[np.float64_t, ndim=2] x = np.ones([3, 4],
dtype=np.float64)
...: cdef np.ndarray[np.float64_t, ndim=2] y = np.zeros([3, 4],
dtype=np.float64)
...: cdef np.ndarray[np.float64_t, ndim=1] row
...: cdef int idx
...: for idx, row in enumerate(x):
...: y[idx] = row
...: return np.asarray(y)
In [6]: f()
Out[6]:
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]])
In [7]: %%cython
...: cimport cython
...: import numpy as np
...: cimport numpy as np
...:
...: cpdef np.ndarray[np.float64_t, ndim=2] f():
...: cdef np.ndarray[np.float64_t, ndim=2] x = np.ones([3, 4],
dtype=np.float64)
...: cdef double[:,:] y = np.zeros([3, 4], dtype=np.float64)
...: cdef np.ndarray[np.float64_t, ndim=1] row
...: cdef double[:] tmp
...: cdef int idx
...: for idx, row in enumerate(x):
...: tmp = row
...: y[idx] = tmp
...: return np.asarray(y)
In [8]: f()
Out[8]:
array([[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.],
[ 1., 1., 1., 1.]])
```
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