Commit 4d0bb3bc authored by Mark's avatar Mark

Merge pull request #112 from robertwb/release

Revert Numpy 1.7 hacks
parents ff7a7984 fc2d1d82
......@@ -34,8 +34,6 @@ import Symtab
import Options
from Cython import Utils
from Annotate import AnnotationItem
from NumpySupport import numpy_transform_attribute_node, \
should_apply_numpy_hack
from Cython.Debugging import print_call_chain
from DebugFlags import debug_disposal_code, debug_temp_alloc, \
......@@ -4460,16 +4458,6 @@ class AttributeNode(ExprNode):
# method of an extension type, so we treat it like a Python
# attribute.
pass
# NumPy hack
if (getattr(self.obj, 'type', None) and obj_type.is_extension_type
and should_apply_numpy_hack(obj_type)):
replacement_node = numpy_transform_attribute_node(self)
# Since we can't actually replace our node yet, we only grasp its
# type, and then the replacement happens in
# AnalyseExpresssionsTransform...
self.type = replacement_node.type
if replacement_node is not self:
return
# If we get here, the base object is not a struct/union/extension
# type, or it is an extension type and the attribute is either not
# declared or is declared as a Python method. Treat it as a Python
......
......@@ -24,10 +24,6 @@ module_name_pattern = re.compile(r"[A-Za-z_][A-Za-z0-9_]*(\.[A-Za-z_][A-Za-z0-9_
verbose = 0
standard_include_path = os.path.abspath(os.path.normpath(
os.path.join(os.path.dirname(__file__), os.path.pardir, 'Includes')))
class CompilationData(object):
# Bundles the information that is passed from transform to transform.
# (For now, this is only)
......@@ -74,6 +70,8 @@ class Context(object):
self.pxds = {} # full name -> node tree
standard_include_path = os.path.abspath(os.path.normpath(
os.path.join(os.path.dirname(__file__), os.path.pardir, 'Includes')))
self.include_directories = include_directories + [standard_include_path]
self.set_language_level(language_level)
......
# The hacks that are specific for NumPy. These were introduced because
# the NumPy ABI changed so that the shape, ndim, strides, etc. fields were
# no longer available, however the use of these were so entrenched in
# Cython codes
import os
from StringEncoding import EncodedString
def should_apply_numpy_hack(obj_type):
if not obj_type.is_extension_type or obj_type.objstruct_cname != 'PyArrayObject':
return False
from Scanning import FileSourceDescriptor
from Main import standard_include_path
type_source = obj_type.pos[0]
if isinstance(type_source, FileSourceDescriptor):
type_source_path = os.path.abspath(os.path.normpath(type_source.filename))
return type_source_path == os.path.join(standard_include_path, 'numpy.pxd')
else:
return False
def numpy_transform_attribute_node(node):
import PyrexTypes
import ExprNodes
assert isinstance(node, ExprNodes.AttributeNode)
if node.obj.type.objstruct_cname != 'PyArrayObject':
return node
pos = node.pos
numpy_pxd_scope = node.obj.type.scope.parent_scope
def macro_call_node(numpy_macro_name):
array_node = node.obj
func_entry = numpy_pxd_scope.entries[numpy_macro_name]
function_name_node = ExprNodes.NameNode(
name=EncodedString(numpy_macro_name),
pos=pos,
entry=func_entry,
is_called=1,
type=func_entry.type,
cf_maybe_null=False,
cf_is_null=False)
call_node = ExprNodes.SimpleCallNode(
pos=pos,
function=function_name_node,
name=EncodedString(numpy_macro_name),
args=[array_node],
type=func_entry.type.return_type,
analysed=True)
return call_node
if node.attribute == u'ndim':
result = macro_call_node(u'PyArray_NDIM')
elif node.attribute == u'data':
call_node = macro_call_node(u'PyArray_DATA')
cast_node = ExprNodes.TypecastNode(pos,
type=PyrexTypes.c_char_ptr_type,
operand=call_node)
result = cast_node
elif node.attribute == u'shape':
result = macro_call_node(u'PyArray_DIMS')
elif node.attribute == u'strides':
result = macro_call_node(u'PyArray_STRIDES')
else:
result = node
return result
......@@ -18,8 +18,7 @@ from Cython.Compiler.TreeFragment import TreeFragment
from Cython.Compiler.StringEncoding import EncodedString
from Cython.Compiler.Errors import error, warning, CompileError, InternalError
from Cython.Compiler.Code import UtilityCode
from Cython.Compiler.NumpySupport import (should_apply_numpy_hack,
numpy_transform_attribute_node)
import copy
......@@ -1748,7 +1747,6 @@ if VALUE is not None:
class AnalyseExpressionsTransform(CythonTransform):
# Also handles NumPy
def visit_ModuleNode(self, node):
self.env_stack = [node.scope]
......@@ -1790,21 +1788,10 @@ class AnalyseExpressionsTransform(CythonTransform):
elif node.memslice_ellipsis_noop:
# memoryviewslice[...] expression, drop the IndexNode
node = node.base
return node
def visit_AttributeNode(self, node):
# Note: Expression analysis for attributes has already happened
# at this point (by recursive calls starting from FuncDefNode)
#print node.dump()
#return node
type = node.obj.type
if (not node.type.is_error and type.is_extension_type and
should_apply_numpy_hack(type)):
node = numpy_transform_attribute_node(node)
self.visitchildren(node)
return node
class FindInvalidUseOfFusedTypes(CythonTransform):
def visit_FuncDefNode(self, node):
......
......@@ -188,7 +188,6 @@ def create_pipeline(context, mode, exclude_classes=()):
_check_c_declarations,
InlineDefNodeCalls(context),
AnalyseExpressionsTransform(context),
# AnalyseExpressionsTransform also contains the NumPy-specific support
FindInvalidUseOfFusedTypes(context),
CreateClosureClasses(context), ## After all lookups and type inference
ExpandInplaceOperators(context),
......
......@@ -151,9 +151,6 @@ cdef extern from "numpy/arrayobject.h":
ctypedef void (*PyArray_VectorUnaryFunc)(void *, void *, npy_intp, void *, void *)
ctypedef struct PyArray_Descr:
pass
ctypedef class numpy.dtype [object PyArray_Descr]:
# Use PyDataType_* macros when possible, however there are no macros
# for accessing some of the fields, so some are defined. Please
......@@ -180,11 +177,15 @@ cdef extern from "numpy/arrayobject.h":
ctypedef class numpy.ndarray [object PyArrayObject]:
cdef __cythonbufferdefaults__ = {"mode": "strided"}
# Note: The fields are no longer defined, please use accessor
# functions. Cython special-cases/hacks the data, ndim, shape
# and stride attributes of the ndarray to use accessor
# functions for backwards compatability and convenience.
cdef:
# Only taking a few of the most commonly used and stable fields.
# One should use PyArray_* macros instead to access the C fields.
char *data
int ndim "nd"
npy_intp *shape "dimensions"
npy_intp *strides
dtype descr
PyObject* base
# Note: This syntax (function definition in pxd files) is an
# experimental exception made for __getbuffer__ and __releasebuffer__
......@@ -235,7 +236,7 @@ cdef extern from "numpy/arrayobject.h":
cdef int t
cdef char* f = NULL
cdef dtype descr = get_array_dtype(self)
cdef dtype descr = self.descr
cdef list stack
cdef int offset
......@@ -375,29 +376,20 @@ cdef extern from "numpy/arrayobject.h":
bint PyArray_ISWRITEABLE(ndarray m)
bint PyArray_ISALIGNED(ndarray m)
int PyArray_NDIM(ndarray) nogil
int PyArray_NDIM(ndarray)
bint PyArray_ISONESEGMENT(ndarray)
bint PyArray_ISFORTRAN(ndarray)
int PyArray_FORTRANIF(ndarray)
void* PyArray_DATA(ndarray) nogil
char* PyArray_BYTES(ndarray) nogil
npy_intp* PyArray_DIMS(ndarray) nogil
npy_intp* PyArray_STRIDES(ndarray) nogil
npy_intp PyArray_DIM(ndarray, size_t) nogil
npy_intp PyArray_STRIDE(ndarray, size_t) nogil
# The two functions below return borrowed references and should
# be used with care; often you will want to use get_array_base
# or get_array_dtype (define below) instead from Cython.
PyObject* PyArray_BASE(ndarray)
# Cython API of the function below might change! PyArray_DESCR
# actually returns PyArray_Descr* == pointer-version of dtype,
# which appears to be difficult to declare properly in Cython;
# protect it with trailing underscore for now just to avoid having
# user code depend on it without reading this note.
PyArray_Descr * PyArray_DESCR_ "PyArray_DESCR"(ndarray)
void* PyArray_DATA(ndarray)
char* PyArray_BYTES(ndarray)
npy_intp* PyArray_DIMS(ndarray)
npy_intp* PyArray_STRIDES(ndarray)
npy_intp PyArray_DIM(ndarray, size_t)
npy_intp PyArray_STRIDE(ndarray, size_t)
# object PyArray_BASE(ndarray) wrong refcount semantics
# dtype PyArray_DESCR(ndarray) wrong refcount semantics
int PyArray_FLAGS(ndarray)
npy_intp PyArray_ITEMSIZE(ndarray)
int PyArray_TYPE(ndarray arr)
......@@ -969,34 +961,18 @@ cdef extern from "numpy/ufuncobject.h":
void import_ufunc()
# The ability to set the base field of an ndarray seems to be
# deprecated in NumPy 1.7 (no PyArray_SET_BASE seems to be
# available). Remove this support and see who complains and how their
# case could be fixed in 1.7...
#
#cdef inline void set_array_base(ndarray arr, object base):
# cdef PyObject* baseptr
# if base is None:
# baseptr = NULL
# else:
# Py_INCREF(base) # important to do this before decref below!
# baseptr = <PyObject*>base
# Py_XDECREF(arr.base)
# arr.base = baseptr
cdef inline void set_array_base(ndarray arr, object base):
cdef PyObject* baseptr
if base is None:
baseptr = NULL
else:
Py_INCREF(base) # important to do this before decref below!
baseptr = <PyObject*>base
Py_XDECREF(arr.base)
arr.base = baseptr
cdef inline object get_array_base(ndarray arr):
cdef PyObject *pobj = PyArray_BASE(arr)
if pobj != NULL:
obj = <object>pobj
Py_INCREF(obj)
return obj
else:
if arr.base is NULL:
return None
cdef inline dtype get_array_dtype(ndarray arr):
if PyArray_DESCR_(arr) != NULL:
obj = <object>PyArray_DESCR_(arr)
Py_INCREF(obj)
return obj
else:
return None
return <object>arr.base
# tag: numpy
import numpy as np
cimport numpy as np
int64_array = np.ones((3, 2), dtype=np.int64)
def f():
"""
>>> f()
ndim 2
data 1
shape 3 2
shape[1] 2
strides 16 8
"""
cdef np.ndarray x = int64_array
cdef int i
cdef Py_ssize_t j, k
cdef char *p
# todo: int * p: 23:13: Cannot assign type 'char *' to 'int *'
with nogil:
i = x.ndim
print 'ndim', i
with nogil:
p = x.data
print 'data', (<np.int64_t*>p)[0]
with nogil:
j = x.shape[0]
k = x.shape[1]
print 'shape', j, k
# Check that non-typical uses still work
cdef np.npy_intp *shape
with nogil:
shape = x.shape + 1
print 'shape[1]', shape[0]
with nogil:
j = x.strides[0]
k = x.strides[1]
print 'strides', j, k
def test_non_namenode_attribute_access(obj):
"""
>>> test_non_namenode_attribute_access(int64_array)
data 1
"""
# Try casting, resulting in an AttributeNode with a TypeCastNode as object
# and 'data' as attribute
print "data", (<np.int64_t *> (<np.ndarray> obj).data)[0]
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