On 02.07.2014 13:44, Mads Ipsen wrote:
>
>
> On 02/07/14 12:46, Julian Taylor wrote:
>> On Wed, Jul 2, 2014 at 12:15 PM, Mads Ipsen <mads.ip...@gmail.com> wrote:
>>> Hi,
>>>
>>> If you setup an M x N array like this
>>>
>>> a = 1.0*numpy.arange(24).reshape(8,3)
>>>
>>> you can access the data from a C function like this
>>>
>>> void foo(PyObject * numpy_data)
>>> {
>>> // Get dimension and data pointer
>>> int const m = static_cast<int>(PyArray_DIMS(numpy_data)[0]);
>>> int const n = static_cast<int>(PyArray_DIMS(numpy_data)[1]);
>>> double * const data = (double *) PyArray_DATA(numpy_data);
>>>
>>> // Access data
>>> ...
>>> }
>>>
>>> Now, suppose I have an irregular shaped numpy array like this
>>>
>>> a1 = numpy.array([ 1.0, 2.0, 3.0])
>>> a2 = numpy.array([-2.0, 4.0])
>>> a3 = numpy.array([5.0])
>>> b = numpy.array([a1,a2,a3])
>>>
>>> How can open up the doors to the array data of b on the C-side?
>>>
>>
>> numpy does not directly support irregular shaped arrays (or ragged arrays).
>> If you look at the result of your example you will see this:
>> In [5]: b
>> Out[5]: array([array([ 1., 2., 3.]), array([-2., 4.]), array([
>> 5.])], dtype=object)
>>
>> b has datatype object, this means it is a 1d array containing more
>> array objects. Numpy does not directly know about the shapes or types
>> the sub arrays. It is not necessarily homogeneous anymore, but
>> compared to a regular python list you still have elementwise
>> operations (if the contained python objects support them) and it can
>> have multiple dimensions.
>>
>> In C you would access such an array it like this:
>>
>> PyArrayObject * const data = (PyArrayObject *) PyArray_DATA(numpy_data);
>> for (i=0; i < PyArray_DIMS(numpy_data)[0]; i++) {
>> assert(PyArray_Check(data[i]));
>> double * const sub_data = (double *) PyArray_DATA(data[i]);
>> }
>> _______________________________________________
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>>
>
> Thanks - that'll get me going!
>
another thing, don't use int as the index to the array, use npy_intp
which is large enough to also index arrays > 4GB if the platform
supports it.
Also note that object arrays are not very well optimized in numpy, so
numerous operations can be slow.
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