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Diffstat (limited to 'doc/cython/numpyx.pyx')
| -rw-r--r-- | doc/cython/numpyx.pyx | 127 |
1 files changed, 0 insertions, 127 deletions
diff --git a/doc/cython/numpyx.pyx b/doc/cython/numpyx.pyx deleted file mode 100644 index cbc786ef0..000000000 --- a/doc/cython/numpyx.pyx +++ /dev/null @@ -1,127 +0,0 @@ -# -*- Mode: Python -*- Not really, but close enough -"""Cython access to Numpy arrays - simple example. -""" - -############################################################################# -# Load C APIs declared in .pxd files via cimport -# -# A 'cimport' is similar to a Python 'import' statement, but it provides access -# to the C part of a library instead of its Python-visible API. See the -# Pyrex/Cython documentation for details. - -cimport c_python as py - -cimport c_numpy as cnp - -# NOTE: numpy MUST be initialized before any other code is executed. -cnp.import_array() - -############################################################################# -# Load Python modules via normal import statements - -import numpy as np - -############################################################################# -# Regular code section begins - -# A 'def' function is visible in the Python-imported module -def print_array_info(cnp.ndarray arr): - """Simple information printer about an array. - - Code meant to illustrate Cython/NumPy integration only.""" - - cdef int i - - print '-='*10 - # Note: the double cast here (void * first, then py.Py_intptr_t) is needed - # in Cython but not in Pyrex, since the casting behavior of cython is - # slightly different (and generally safer) than that of Pyrex. In this - # case, we just want the memory address of the actual Array object, so we - # cast it to void before doing the py.Py_intptr_t cast: - print 'Printing array info for ndarray at 0x%0lx'% \ - (<py.Py_intptr_t><void *>arr,) - print 'number of dimensions:',arr.nd - print 'address of strides: 0x%0lx'%(<py.Py_intptr_t>arr.strides,) - print 'strides:' - for i from 0<=i<arr.nd: - # print each stride - print ' stride %d:'%i,<py.Py_intptr_t>arr.strides[i] - print 'memory dump:' - print_elements( arr.data, arr.strides, arr.dimensions, - arr.nd, sizeof(double), arr.dtype ) - print '-='*10 - print - -# A 'cdef' function is NOT visible to the python side, but it is accessible to -# the rest of this Cython module -cdef print_elements(char *data, - py.Py_intptr_t* strides, - py.Py_intptr_t* dimensions, - int nd, - int elsize, - object dtype): - cdef py.Py_intptr_t i,j - cdef void* elptr - - if dtype not in [np.dtype(np.object_), - np.dtype(np.float64)]: - print ' print_elements() not (yet) implemented for dtype %s'%dtype.name - return - - if nd ==0: - if dtype==np.dtype(np.object_): - elptr = (<void**>data)[0] #[0] dereferences pointer in Pyrex - print ' ',<object>elptr - elif dtype==np.dtype(np.float64): - print ' ',(<double*>data)[0] - elif nd == 1: - for i from 0<=i<dimensions[0]: - if dtype==np.dtype(np.object_): - elptr = (<void**>data)[0] - print ' ',<object>elptr - elif dtype==np.dtype(np.float64): - print ' ',(<double*>data)[0] - data = data + strides[0] - else: - for i from 0<=i<dimensions[0]: - print_elements(data, strides+1, dimensions+1, nd-1, elsize, dtype) - data = data + strides[0] - -def test_methods(cnp.ndarray arr): - """Test a few attribute accesses for an array. - - This illustrates how the pyrex-visible object is in practice a strange - hybrid of the C PyArrayObject struct and the python object. Some - properties (like .nd) are visible here but not in python, while others - like flags behave very differently: in python flags appears as a separate, - object while here we see the raw int holding the bit pattern. - - This makes sense when we think of how pyrex resolves arr.foo: if foo is - listed as a field in the ndarray struct description, it will be directly - accessed as a C variable without going through Python at all. This is why - for arr.flags, we see the actual int which holds all the flags as bit - fields. However, for any other attribute not listed in the struct, it - simply forwards the attribute lookup to python at runtime, just like python - would (which means that AttributeError can be raised for non-existent - attributes, for example).""" - - print 'arr.any() :',arr.any() - print 'arr.nd :',arr.nd - print 'arr.flags :',arr.flags - -def test(): - """this function is pure Python""" - arr1 = np.array(-1e-30,dtype=np.float64) - arr2 = np.array([1.0,2.0,3.0],dtype=np.float64) - - arr3 = np.arange(9,dtype=np.float64) - arr3.shape = 3,3 - - four = 4 - arr4 = np.array(['one','two',3,four],dtype=np.object_) - - arr5 = np.array([1,2,3]) # int types not (yet) supported by print_elements - - for arr in [arr1,arr2,arr3,arr4,arr5]: - print_array_info(arr) - |