Merge branch 'master' into release

CHANGES.rst

@@ -79,7 +79,7 @@ Bugs fixed
 * The exception handling in generators and coroutines under CPython 3.7 was adapted
   to the newly introduced exception stack.  Users of Cython 0.28 who want to support
   Python 3.7 are encouraged to upgrade to 0.29 to avoid potentially incorrect error
-  reporting and tracebacks.
+  reporting and tracebacks.  (Github issue #1958)
 
 * Crash when importing a module under Stackless Python that was built for CPython.
   Patch by Anselm Kruis.  (Github issue #2534)

docs/examples/tutorial/numpy/convolve2.pyx

-# tag: numpy
-# You can ignore the previous line.
-# It's for internal testing of the cython documentation.
-
-import numpy as np
-
-# "cimport" is used to import special compile-time information
-# about the numpy module (this is stored in a file numpy.pxd which is
-# currently part of the Cython distribution).
-cimport numpy as np
-
-# We now need to fix a datatype for our arrays. I've used the variable
-# DTYPE for this, which is assigned to the usual NumPy runtime
-# type info object.
-DTYPE = np.int
-
-# "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
-# every type in the numpy module there's a corresponding compile-time
-# type with a _t-suffix.
-ctypedef np.int_t DTYPE_t
-
-# "def" can type its arguments but not have a return type. The type of the
-# arguments for a "def" function is checked at run-time when entering the
-# function.
-#
-# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
-# this has is to a) insert checks that the function arguments really are
-# NumPy arrays, and b) make some attribute access like f.shape[0] much
-# more efficient. (In this example this doesn't matter though.)
-def naive_convolve(np.ndarray f, np.ndarray g):
-    if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
-        raise ValueError("Only odd dimensions on filter supported")
-    assert f.dtype == DTYPE and g.dtype == DTYPE
-
-    # The "cdef" keyword is also used within functions to type variables. It
-    # can only be used at the top indentation level (there are non-trivial
-    # problems with allowing them in other places, though we'd love to see
-    # good and thought out proposals for it).
-    #
-    # For the indices, the "int" type is used. This corresponds to a C int,
-    # other C types (like "unsigned int") could have been used instead.
-    # Purists could use "Py_ssize_t" which is the proper Python type for
-    # array indices.
-    cdef int vmax = f.shape[0]
-    cdef int wmax = f.shape[1]
-    cdef int smax = g.shape[0]
-    cdef int tmax = g.shape[1]
-    cdef int smid = smax // 2
-    cdef int tmid = tmax // 2
-    cdef int xmax = vmax + 2 * smid
-    cdef int ymax = wmax + 2 * tmid
-    cdef np.ndarray h = np.zeros([xmax, ymax], dtype=DTYPE)
-    cdef int x, y, s, t, v, w
-
-    # It is very important to type ALL your variables. You do not get any
-    # warnings if not, only much slower code (they are implicitly typed as
-    # Python objects).
-    cdef int s_from, s_to, t_from, t_to
-
-    # For the value variable, we want to use the same data type as is
-    # stored in the array, so we use "DTYPE_t" as defined above.
-    # NB! An important side-effect of this is that if "value" overflows its
-    # datatype size, it will simply wrap around like in C, rather than raise
-    # an error like in Python.
-    cdef DTYPE_t value
-    for x in range(xmax):
-        for y in range(ymax):
-            s_from = max(smid - x, -smid)
-            s_to = min((xmax - x) - smid, smid + 1)
-            t_from = max(tmid - y, -tmid)
-            t_to = min((ymax - y) - tmid, tmid + 1)
-            value = 0
-            for s in range(s_from, s_to):
-                for t in range(t_from, t_to):
-                    v = x - smid + s
-                    w = y - tmid + t
-                    value += g[smid - s, tmid - t] * f[v, w]
-            h[x, y] = value
-    return h
+# tag: numpy_old
+# You can ignore the previous line.
+# It's for internal testing of the cython documentation.
+
+import numpy as np
+
+# "cimport" is used to import special compile-time information
+# about the numpy module (this is stored in a file numpy.pxd which is
+# currently part of the Cython distribution).
+cimport numpy as np
+
+# We now need to fix a datatype for our arrays. I've used the variable
+# DTYPE for this, which is assigned to the usual NumPy runtime
+# type info object.
+DTYPE = np.int
+
+# "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
+# every type in the numpy module there's a corresponding compile-time
+# type with a _t-suffix.
+ctypedef np.int_t DTYPE_t
+
+# "def" can type its arguments but not have a return type. The type of the
+# arguments for a "def" function is checked at run-time when entering the
+# function.
+#
+# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
+# this has is to a) insert checks that the function arguments really are
+# NumPy arrays, and b) make some attribute access like f.shape[0] much
+# more efficient. (In this example this doesn't matter though.)
+def naive_convolve(np.ndarray f, np.ndarray g):
+    if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
+        raise ValueError("Only odd dimensions on filter supported")
+    assert f.dtype == DTYPE and g.dtype == DTYPE
+
+    # The "cdef" keyword is also used within functions to type variables. It
+    # can only be used at the top indentation level (there are non-trivial
+    # problems with allowing them in other places, though we'd love to see
+    # good and thought out proposals for it).
+    #
+    # For the indices, the "int" type is used. This corresponds to a C int,
+    # other C types (like "unsigned int") could have been used instead.
+    # Purists could use "Py_ssize_t" which is the proper Python type for
+    # array indices.
+    cdef int vmax = f.shape[0]
+    cdef int wmax = f.shape[1]
+    cdef int smax = g.shape[0]
+    cdef int tmax = g.shape[1]
+    cdef int smid = smax // 2
+    cdef int tmid = tmax // 2
+    cdef int xmax = vmax + 2 * smid
+    cdef int ymax = wmax + 2 * tmid
+    cdef np.ndarray h = np.zeros([xmax, ymax], dtype=DTYPE)
+    cdef int x, y, s, t, v, w
+
+    # It is very important to type ALL your variables. You do not get any
+    # warnings if not, only much slower code (they are implicitly typed as
+    # Python objects).
+    cdef int s_from, s_to, t_from, t_to
+
+    # For the value variable, we want to use the same data type as is
+    # stored in the array, so we use "DTYPE_t" as defined above.
+    # NB! An important side-effect of this is that if "value" overflows its
+    # datatype size, it will simply wrap around like in C, rather than raise
+    # an error like in Python.
+    cdef DTYPE_t value
+    for x in range(xmax):
+        for y in range(ymax):
+            s_from = max(smid - x, -smid)
+            s_to = min((xmax - x) - smid, smid + 1)
+            t_from = max(tmid - y, -tmid)
+            t_to = min((ymax - y) - tmid, tmid + 1)
+            value = 0
+            for s in range(s_from, s_to):
+                for t in range(t_from, t_to):
+                    v = x - smid + s
+                    w = y - tmid + t
+                    value += g[smid - s, tmid - t] * f[v, w]
+            h[x, y] = value
+    return h

runtests.py

@@ -134,7 +134,8 @@ def get_distutils_distro(_cache=[]):
 
 
 EXT_DEP_MODULES = {
-    'tag:numpy':    'numpy',
+    'tag:numpy':     'numpy',
+    'tag:numpy_old': 'numpy',
     'tag:pythran':  'pythran',
     'tag:setuptools':  'setuptools.sandbox',
     'tag:asyncio':  'asyncio',
@@ -254,12 +255,19 @@ def update_linetrace_extension(ext):
     return ext
 
 
-def update_numpy_extension(ext):
+def update_old_numpy_extension(ext):
+    update_numpy_extension(ext, set_api17_macro=False)
+
+
+def update_numpy_extension(ext, set_api17_macro=True):
     import numpy
     from numpy.distutils.misc_util import get_info
 
     ext.include_dirs.append(numpy.get_include())
 
+    if set_api17_macro:
+        ext.define_macros.append(('NPY_NO_DEPRECATED_API', 'NPY_1_7_API_VERSION'))
+
     # We need the npymath library for numpy.math.
     # This is typically a static-only library.
     for attr, value in get_info('npymath').items():
@@ -391,6 +399,7 @@ EXCLUDE_EXT = object()
 
 EXT_EXTRAS = {
     'tag:numpy' : update_numpy_extension,
+    'tag:numpy_old' : update_old_numpy_extension,
     'tag:openmp': update_openmp_extension,
     'tag:cpp11': update_cpp11_extension,
     'tag:trace' : update_linetrace_extension,

tests/run/numpy_bufacc_T155.pyx

 # ticket: 155
-# tag: numpy
+# tag: numpy_old
 
 """
 >>> myfunc()

tests/run/numpy_parallel.pyx

-# tag: numpy
+# tag: numpy_old
 # tag: openmp
 
 cimport cython

tests/run/numpy_subarray.pyx

-# tag: numpy
+# tag: numpy_old
 
 cimport numpy as np
 cimport cython

tests/run/numpy_test.pyx

-# tag: numpy
+# tag: numpy_old
 # cannot be named "numpy" in order to not clash with the numpy module!
 
 cimport numpy as np