diff options
Diffstat (limited to 'doc/cython')
| -rw-r--r-- | doc/cython/MANIFEST | 2 | ||||
| -rw-r--r-- | doc/cython/Makefile | 37 | ||||
| -rw-r--r-- | doc/cython/README.txt | 20 | ||||
| -rw-r--r-- | doc/cython/c_numpy.pxd | 137 | ||||
| -rw-r--r-- | doc/cython/c_python.pxd | 62 | ||||
| -rw-r--r-- | doc/cython/numpyx.pyx | 127 | ||||
| -rwxr-xr-x | doc/cython/run_test.py | 5 | ||||
| -rwxr-xr-x | doc/cython/setup.py | 51 |
8 files changed, 0 insertions, 441 deletions
diff --git a/doc/cython/MANIFEST b/doc/cython/MANIFEST deleted file mode 100644 index feb3ec22a..000000000 --- a/doc/cython/MANIFEST +++ /dev/null @@ -1,2 +0,0 @@ -numpyx.pyx -setup.py diff --git a/doc/cython/Makefile b/doc/cython/Makefile deleted file mode 100644 index 7c9c72981..000000000 --- a/doc/cython/Makefile +++ /dev/null @@ -1,37 +0,0 @@ -# Simple makefile to quickly access handy build commands for Cython extension -# code generation. Note that the actual code to produce the extension lives in -# the setup.py file, this Makefile is just meant as a command -# convenience/reminder while doing development. - -help: - @echo "Numpy/Cython tasks. Available tasks:" - @echo "ext -> build the Cython extension module." - @echo "html -> create annotated HTML from the .pyx sources" - @echo "test -> run a simple test demo." - @echo "all -> Call ext, html and finally test." - -all: ext html test - -ext: numpyx.so - -test: ext - python run_test.py - -html: numpyx.pyx.html - -numpyx.so: numpyx.pyx numpyx.c - python setup.py build_ext --inplace - -numpyx.pyx.html: numpyx.pyx - cython -a numpyx.pyx - @echo "Annotated HTML of the C code generated in numpyx.html" - -# Phony targets for cleanup and similar uses - -.PHONY: clean -clean: - rm -rf *~ *.so *.c *.o *.html build - -# Suffix rules -%.c : %.pyx - cython $< diff --git a/doc/cython/README.txt b/doc/cython/README.txt deleted file mode 100644 index f527e358b..000000000 --- a/doc/cython/README.txt +++ /dev/null @@ -1,20 +0,0 @@ -================== - NumPy and Cython -================== - -This directory contains a small example of how to use NumPy and Cython -together. While much work is planned for the Summer of 2008 as part of the -Google Summer of Code project to improve integration between the two, even -today Cython can be used effectively to write optimized code that accesses -NumPy arrays. - -The example provided is just a stub showing how to build an extension and -access the array objects; improvements to this to show more sophisticated tasks -are welcome. - -To run it locally, simply type:: - - make help - -which shows you the currently available targets (these are just handy -shorthands for common commands). diff --git a/doc/cython/c_numpy.pxd b/doc/cython/c_numpy.pxd deleted file mode 100644 index e178b8e33..000000000 --- a/doc/cython/c_numpy.pxd +++ /dev/null @@ -1,137 +0,0 @@ -# :Author: Travis Oliphant - -# API declaration section. This basically exposes the NumPy C API to -# Pyrex/Cython programs. - -cdef extern from "numpy/arrayobject.h": - - cdef enum NPY_TYPES: - NPY_BOOL - NPY_BYTE - NPY_UBYTE - NPY_SHORT - NPY_USHORT - NPY_INT - NPY_UINT - NPY_LONG - NPY_ULONG - NPY_LONGLONG - NPY_ULONGLONG - NPY_FLOAT - NPY_DOUBLE - NPY_LONGDOUBLE - NPY_CFLOAT - NPY_CDOUBLE - NPY_CLONGDOUBLE - NPY_OBJECT - NPY_STRING - NPY_UNICODE - NPY_VOID - NPY_NTYPES - NPY_NOTYPE - - cdef enum requirements: - NPY_CONTIGUOUS - NPY_FORTRAN - NPY_OWNDATA - NPY_FORCECAST - NPY_ENSURECOPY - NPY_ENSUREARRAY - NPY_ELEMENTSTRIDES - NPY_ALIGNED - NPY_NOTSWAPPED - NPY_WRITEABLE - NPY_UPDATEIFCOPY - NPY_ARR_HAS_DESCR - - NPY_BEHAVED - NPY_BEHAVED_NS - NPY_CARRAY - NPY_CARRAY_RO - NPY_FARRAY - NPY_FARRAY_RO - NPY_DEFAULT - - NPY_IN_ARRAY - NPY_OUT_ARRAY - NPY_INOUT_ARRAY - NPY_IN_FARRAY - NPY_OUT_FARRAY - NPY_INOUT_FARRAY - - NPY_UPDATE_ALL - - cdef enum defines: - NPY_MAXDIMS - - ctypedef struct npy_cdouble: - double real - double imag - - ctypedef struct npy_cfloat: - double real - double imag - - ctypedef int npy_intp - - ctypedef extern class numpy.dtype [object PyArray_Descr]: - cdef int type_num, elsize, alignment - cdef char type, kind, byteorder - cdef int flags - cdef object fields, typeobj - - ctypedef extern class numpy.ndarray [object PyArrayObject]: - cdef char *data - cdef int nd - cdef npy_intp *dimensions - cdef npy_intp *strides - cdef object base - cdef dtype descr - cdef int flags - - ctypedef extern class numpy.flatiter [object PyArrayIterObject]: - cdef int nd_m1 - cdef npy_intp index, size - cdef ndarray ao - cdef char *dataptr - - ctypedef extern class numpy.broadcast [object PyArrayMultiIterObject]: - cdef int numiter - cdef npy_intp size, index - cdef int nd - cdef npy_intp *dimensions - cdef void **iters - - object PyArray_ZEROS(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran) - object PyArray_EMPTY(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran) - dtype PyArray_DescrFromTypeNum(NPY_TYPES type_num) - object PyArray_SimpleNew(int ndims, npy_intp* dims, NPY_TYPES type_num) - int PyArray_Check(object obj) - object PyArray_ContiguousFromAny(object obj, NPY_TYPES type, - int mindim, int maxdim) - object PyArray_ContiguousFromObject(object obj, NPY_TYPES type, - int mindim, int maxdim) - npy_intp PyArray_SIZE(ndarray arr) - npy_intp PyArray_NBYTES(ndarray arr) - void *PyArray_DATA(ndarray arr) - object PyArray_FromAny(object obj, dtype newtype, int mindim, int maxdim, - int requirements, object context) - object PyArray_FROMANY(object obj, NPY_TYPES type_num, int min, - int max, int requirements) - object PyArray_NewFromDescr(object subtype, dtype newtype, int nd, - npy_intp* dims, npy_intp* strides, void* data, - int flags, object parent) - - object PyArray_FROM_OTF(object obj, NPY_TYPES type, int flags) - object PyArray_EnsureArray(object) - - object PyArray_MultiIterNew(int n, ...) - - char *PyArray_MultiIter_DATA(broadcast multi, int i) - void PyArray_MultiIter_NEXTi(broadcast multi, int i) - void PyArray_MultiIter_NEXT(broadcast multi) - - object PyArray_IterNew(object arr) - void PyArray_ITER_NEXT(flatiter it) - - void import_array() diff --git a/doc/cython/c_python.pxd b/doc/cython/c_python.pxd deleted file mode 100644 index 46d2fd1a7..000000000 --- a/doc/cython/c_python.pxd +++ /dev/null @@ -1,62 +0,0 @@ -# :Author: Robert Kern -# :Copyright: 2004, Enthought, Inc. -# :License: BSD Style - - -cdef extern from "Python.h": - # Not part of the Python API, but we might as well define it here. - # Note that the exact type doesn't actually matter for Pyrex. - ctypedef int size_t - - # Some type declarations we need - ctypedef int Py_intptr_t - - - # String API - char* PyString_AsString(object string) - char* PyString_AS_STRING(object string) - object PyString_FromString(char* c_string) - object PyString_FromStringAndSize(char* c_string, int length) - object PyString_InternFromString(char *v) - - # Float API - object PyFloat_FromDouble(double v) - double PyFloat_AsDouble(object ob) - long PyInt_AsLong(object ob) - - - # Memory API - void* PyMem_Malloc(size_t n) - void* PyMem_Realloc(void* buf, size_t n) - void PyMem_Free(void* buf) - - void Py_DECREF(object obj) - void Py_XDECREF(object obj) - void Py_INCREF(object obj) - void Py_XINCREF(object obj) - - # CObject API - ctypedef void (*destructor1)(void* cobj) - ctypedef void (*destructor2)(void* cobj, void* desc) - int PyCObject_Check(object p) - object PyCObject_FromVoidPtr(void* cobj, destructor1 destr) - object PyCObject_FromVoidPtrAndDesc(void* cobj, void* desc, - destructor2 destr) - void* PyCObject_AsVoidPtr(object self) - void* PyCObject_GetDesc(object self) - int PyCObject_SetVoidPtr(object self, void* cobj) - - # TypeCheck API - int PyFloat_Check(object obj) - int PyInt_Check(object obj) - - # Error API - int PyErr_Occurred() - void PyErr_Clear() - int PyErr_CheckSignals() - -cdef extern from "string.h": - void *memcpy(void *s1, void *s2, int n) - -cdef extern from "math.h": - double fabs(double x) 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) - diff --git a/doc/cython/run_test.py b/doc/cython/run_test.py deleted file mode 100755 index 5ffd7e80f..000000000 --- a/doc/cython/run_test.py +++ /dev/null @@ -1,5 +0,0 @@ -#!/usr/bin/env python -from __future__ import division, absolute_import, print_function - -from numpyx import test -test() diff --git a/doc/cython/setup.py b/doc/cython/setup.py deleted file mode 100755 index fe122d4db..000000000 --- a/doc/cython/setup.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python -"""Install file for example on how to use Cython with Numpy. - -Note: Cython is the successor project to Pyrex. For more information, see -http://cython.org. - -""" -from __future__ import division, print_function - -from distutils.core import setup -from distutils.extension import Extension - -import numpy - -# We detect whether Cython is available, so that below, we can eventually ship -# pre-generated C for users to compile the extension without having Cython -# installed on their systems. -try: - from Cython.Distutils import build_ext - has_cython = True -except ImportError: - has_cython = False - -# Define a cython-based extension module, using the generated sources if cython -# is not available. -if has_cython: - pyx_sources = ['numpyx.pyx'] - cmdclass = {'build_ext': build_ext} -else: - # In production work, you can ship the auto-generated C source yourself to - # your users. In this case, we do NOT ship the .c file as part of numpy, - # so you'll need to actually have cython installed at least the first - # time. Since this is really just an example to show you how to use - # *Cython*, it makes more sense NOT to ship the C sources so you can edit - # the pyx at will with less chances for source update conflicts when you - # update numpy. - pyx_sources = ['numpyx.c'] - cmdclass = {} - - -# Declare the extension object -pyx_ext = Extension('numpyx', - pyx_sources, - include_dirs = [numpy.get_include()]) - -# Call the routine which does the real work -setup(name = 'numpyx', - description = 'Small example on using Cython to write a Numpy extension', - ext_modules = [pyx_ext], - cmdclass = cmdclass, - ) |