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# -*- Mode: Python -*- Not really, but close enough
"""WARNING: this code is deprecated and slated for removal soon. See the
doc/cython directory for the replacement, which uses Cython (the actively
maintained version of Pyrex).
"""
cimport c_python
cimport c_numpy
import numpy
# Numpy must be initialized
c_numpy.import_array()
def print_array_info(c_numpy.ndarray arr):
cdef int i
print '-='*10
print 'printing array info for ndarray at 0x%0lx'%(<c_python.Py_intptr_t>arr,)
print 'print number of dimensions:',arr.nd
print 'address of strides: 0x%0lx'%(<c_python.Py_intptr_t>arr.strides,)
print 'strides:'
for i from 0<=i<arr.nd:
# print each stride
print ' stride %d:'%i,<c_python.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
cdef print_elements(char *data,
c_python.Py_intptr_t* strides,
c_python.Py_intptr_t* dimensions,
int nd,
int elsize,
object dtype):
cdef c_python.Py_intptr_t i,j
cdef void* elptr
if dtype not in [numpy.dtype(numpy.object_),
numpy.dtype(numpy.float64)]:
print ' print_elements() not (yet) implemented for dtype %s'%dtype.name
return
if nd ==0:
if dtype==numpy.dtype(numpy.object_):
elptr = (<void**>data)[0] #[0] dereferences pointer in Pyrex
print ' ',<object>elptr
elif dtype==numpy.dtype(numpy.float64):
print ' ',(<double*>data)[0]
elif nd == 1:
for i from 0<=i<dimensions[0]:
if dtype==numpy.dtype(numpy.object_):
elptr = (<void**>data)[0]
print ' ',<object>elptr
elif dtype==numpy.dtype(numpy.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(c_numpy.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 c_numpy.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 = numpy.array(-1e-30,dtype=numpy.float64)
arr2 = numpy.array([1.0,2.0,3.0],dtype=numpy.float64)
arr3 = numpy.arange(9,dtype=numpy.float64)
arr3.shape = 3,3
four = 4
arr4 = numpy.array(['one','two',3,four],dtype=numpy.object_)
arr5 = numpy.array([1,2,3]) # int types not (yet) supported by print_elements
for arr in [arr1,arr2,arr3,arr4,arr5]:
print_array_info(arr)
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