From 5c86844c34674e3d580ac2cd12ef171e18130b13 Mon Sep 17 00:00:00 2001
From: Stefan van der Walt <stefan@sun.ac.za>
Date: Sat, 23 Aug 2008 23:17:23 +0000
Subject: Move documentation outside of source tree.  Remove `doc` import from
 __init__.

---
 doc/pyrex/numpyx.pyx | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 101 insertions(+)
 create mode 100644 doc/pyrex/numpyx.pyx

(limited to 'doc/pyrex/numpyx.pyx')

diff --git a/doc/pyrex/numpyx.pyx b/doc/pyrex/numpyx.pyx
new file mode 100644
index 000000000..068d251f6
--- /dev/null
+++ b/doc/pyrex/numpyx.pyx
@@ -0,0 +1,101 @@
+# -*- 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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