Typed memoryview doesn't work with read-only buffers

Typed memoryviews don’t work with read-only buffers. A banal example:

cpdef getmax(double[:] x):
    cdef double max = -float('inf')
    for val in x:
        if val > max:
            max = val
    return max

Example session:

>>> import numpy as np
>>> values = np.random.random(8)
>>> values 
array([ 0.4574,  0.8468,  0.744 ,  0.3764,  0.7581,  0.8123,  0.8783,  0.9341])

>>> import getmax
>>> getmax.getmax(values)
0.9341213061235054

>>> values.setflags(write=False)
>>> assert values.flags.writeable == False
>>> getmax.getmax(values)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-669165de7389> in <module>()
----> 1 getmax.getmax(values)

/tmp/getmax.pyx in getmax.getmax (getmax.c:1617)()
----> 1 cpdef getmax(double[:] x):
      2     cdef:
      3         double max = -float('inf')
      4 
      5     for val in x:

/tmp/getmax.so in View.MemoryView.memoryview_cwrapper (getmax.c:7403)()
/tmp/getmax.so in View.MemoryView.memoryview.__cinit__ (getmax.c:3678)()

ValueError: buffer source array is read-only

There is no evident reason why above simple function wouldn’t work with a read-only buffer. About two years ago, this issue was raised on the mailing list along with a RFC patch: https://mail.python.org/pipermail/cython-devel/2015-February/004316.html Some discussion about a possible use for a const keyword: https://groups.google.com/forum/#!topic/cython-users/32CMgaLrNhc

[scoder]

I have an initial implementation up at https://github.com/scoder/cython/tree/readonly_buffers that automatically determines the need for a writable buffer, and otherwise sticks to a read-only one. It has some bugs and fails to detect some “read-only” vs. “needs writable” cases at compile time, but it mostly works.

It also implements explicit const memory views, e.g. cdef const int[:] a, which should always request a read-only view.

Feedback welcome.

[rainwoodman]

It appears this bug is keeping people away from the memoryview to use the older ndarray interface. for example, see this workaround in pandas https://github.com/pandas-dev/pandas/pull/12013