ANN: NumPy 1.7.0b1 release
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
========================= NumPy 1.7.0 Release Notes =========================
This release includes several new features as well as numerous bug fixes and refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.2.
Highlights ==========
* ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose where a computation should be done) * ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general cleanup and bug fixes) * ``numpy.random.choice`` (random sample generating function)
Compatibility notes ===================
In a future version of numpy, the functions np.diag, np.diagonal, and the diagonal method of ndarrays will return a view onto the original array, instead of producing a copy as they do now. This makes a difference if you write to the array returned by any of these functions. To facilitate this transition, numpy 1.7 produces a FutureWarning if it detects that you may be attempting to write to such an array. See the documentation for np.diagonal for details.
Similar to np.diagonal above, in a future version of numpy, indexing a record array by a list of field names will return a view onto the original array, instead of producing a copy as they do now. As with np.diagonal, numpy 1.7 produces a FutureWarning if it detects that you may be attemping to write to such an array. See the documentation for array indexing for details.
The default casting rule for UFunc out= parameters has been changed from 'unsafe' to 'same_kind'. Most usages which violate the 'same_kind' rule are likely bugs, so this change may expose previously undetected errors in projects that depend on NumPy.
Full-array boolean indexing has been optimized to use a different, optimized code path. This code path should produce the same results, but any feedback about changes to your code would be appreciated.
Attempting to write to a read-only array (one with ``arr.flags.writeable`` set to ``False``) used to raise either a RuntimeError, ValueError, or TypeError inconsistently, depending on which code path was taken. It now consistently raises a ValueError.
The <ufunc>.reduce functions evaluate some reductions in a different order than in previous versions of NumPy, generally providing higher performance. Because of the nature of floating-point arithmetic, this may subtly change some results, just as linking NumPy to a different BLAS implementations such as MKL can.
If upgrading from 1.5, then generally in 1.6 and 1.7 there have been substantial code added and some code paths altered, particularly in the areas of type resolution and buffered iteration over universal functions. This might have an impact on your code particularly if you relied on accidental behavior in the past.
New features ============
Reduction UFuncs Generalize axis= Parameter -------------------------------------------
Any ufunc.reduce function call, as well as other reductions like sum, prod, any, all, max and min support the ability to choose a subset of the axes to reduce over. Previously, one could say axis=None to mean all the axes or axis=# to pick a single axis. Now, one can also say axis=(#,#) to pick a list of axes for reduction.
Reduction UFuncs New keepdims= Parameter ----------------------------------------
There is a new keepdims= parameter, which if set to True, doesn't throw away the reduction axes but instead sets them to have size one. When this option is set, the reduction result will broadcast correctly to the original operand which was reduced.
Datetime support ----------------
.. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes in future versions of NumPy.
There have been a lot of fixes and enhancements to datetime64 compared to NumPy 1.6:
* the parser is quite strict about only accepting ISO 8601 dates, with a few convenience extensions * converts between units correctly * datetime arithmetic works correctly * business day functionality (allows the datetime to be used in contexts where only certain days of the week are valid)
The notes in `doc/source/reference/arrays.datetime.rst https://github.com/numpy/numpy/blob/maintenance/1.7.x/doc/source/reference/arrays.datetime.rst`_ (also available in the online docs at `arrays.datetime.html http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html`_) should be consulted for more details.
Custom formatter for printing arrays ------------------------------------
See the new ``formatter`` parameter of the ``numpy.set_printoptions`` function.
New function numpy.random.choice ---------------------------------
A generic sampling function has been added which will generate samples from a given array-like. The samples can be with or without replacement, and with uniform or given non-uniform probabilities.
New function isclose --------------------
Returns a boolean array where two arrays are element-wise equal within a tolerance. Both relative and absolute tolerance can be specified. The function is NA aware.
Preliminary multi-dimensional support in the polynomial package ---------------------------------------------------------------
Axis keywords have been added to the integration and differentiation functions and a tensor keyword was added to the evaluation functions. These additions allow multi-dimensional coefficient arrays to be used in those functions. New functions for evaluating 2-D and 3-D coefficient arrays on grids or sets of points were added together with 2-D and 3-D pseudo-Vandermonde matrices that can be used for fitting.
Ability to pad rank-n arrays ----------------------------
A pad module containing functions for padding n-dimensional arrays has been added. The various private padding functions are exposed as options to a public 'pad' function. Example::
pad(a, 5, mode='mean')
Current modes are ``constant``, ``edge``, ``linear_ramp``, ``maximum``, ``mean``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``, and ``<function>``.
New argument to searchsorted ----------------------------
The function searchsorted now accepts a 'sorter' argument that is a permuation array that sorts the array to search.
C API -----
New function ``PyArray_RequireWriteable`` provides a consistent interface for checking array writeability -- any C code which works with arrays whose WRITEABLE flag is not known to be True a priori, should make sure to call this function before writing.
NumPy C Style Guide added (``doc/C_STYLE_GUIDE.rst.txt``).
Changes =======
General -------
The function np.concatenate tries to match the layout of its input arrays. Previously, the layout did not follow any particular reason, and depended in an undesirable way on the particular axis chosen for concatenation. A bug was also fixed which silently allowed out of bounds axis arguments.
The ufuncs logical_or, logical_and, and logical_not now follow Python's behavior with object arrays, instead of trying to call methods on the objects. For example the expression (3 and 'test') produces the string 'test', and now np.logical_and(np.array(3, 'O'), np.array('test', 'O')) produces 'test' as well.
Deprecations ============
General -------
Specifying a custom string formatter with a `_format` array attribute is deprecated. The new `formatter` keyword in ``numpy.set_printoptions`` or ``numpy.array2string`` can be used instead.
The deprecated imports in the polynomial package have been removed.
C-API -----
Direct access to the fields of PyArrayObject* has been deprecated. Direct access has been recommended against for many releases. Expect similar deprecations for PyArray_Descr* and other core objects in the future as preparation for NumPy 2.0.
The macros in old_defines.h are deprecated and will be removed in the next major release (>= 2.0). The sed script tools/replace_old_macros.sed can be used to replace these macros with the newer versions.
You can test your code against the deprecated C API by #defining NPY_NO_DEPRECATED_API to the target version number, for example NPY_1_7_API_VERSION, before including any NumPy headers.
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/. For comparison, the tests against numpy-MKL-1.6.2 are at http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/.
Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions.
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.
Hope it helps.
Christoph
On Tue, Aug 21, 2012 at 3:59 PM, Christoph Gohlke cgohlke@uci.edu wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at < http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-num...
.
For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/
.
Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions.
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
Most (all?) of the statsmodels issues are due to structured / record array view changes discussed in the thread "view of recarray issue." I.e., rec_array.view((float, 3)) no longer works, though I thought this was fixed.
Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.
Hope it helps.
Christoph
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Tue, Aug 21, 2012 at 3:59 PM, Christoph Gohlke cgohlke@uci.edu wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/. For comparison, the tests against numpy-MKL-1.6.2 are at http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/.
Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions.
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
Thanks Christoph,
All the statsmodels errors (14) look like http://projects.scipy.org/numpy/ticket/2187 recarray/structured dtype view
Josef
Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.
Hope it helps.
Christoph
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Tue, Aug 21, 2012 at 9:59 PM, Christoph Gohlke cgohlke@uci.edu wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at < http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-num...
.
For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/
.
Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning
Flipped the release switch on those in commit ea23de8, errors should be gone now.
, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions.
This looks really bad. Last discussion I could find on this topic on the Cython list: http://grokbase.com/t/python/cython-devel/121ygkvfhc/cython-upcoming-issues-...
At this point I think it would be a good idea to install a warning filter for this on import of numpy and leave it in place. And also in the test runner. Even if Cython would fix it now, that doesn't help for all binaries of existing releases of projects that depend on numpy.
Ralf
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.
Hope it helps.
Christoph
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Tue, Aug 21, 2012 at 9:59 PM, Christoph Gohlke cgohlke@uci.edu wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at < http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-num...
.
For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/
.
...
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
None of the scipy ones are serious or due to changes in numpy as far as I can tell. The one thing to do in numpy is silence this warning: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.
Thanks for all those tests Christoph, extremely useful!
Ralf
On Tue, Aug 21, 2012 at 1:59 PM, Christoph Gohlke cgohlke@uci.edu wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
Hi Ondrej,
will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well.
I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from http://www.lfd.uci.edu/~gohlke/pythonlibs/.
The test results are at < http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-num...
.
What version of Numpy were the test packages compiled against?
For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/
.
Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions.
There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status).
Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.
Chuck
On 8/22/2012 6:26 AM, Charles R Harris wrote:
On Tue, Aug 21, 2012 at 1:59 PM, Christoph Gohlke <cgohlke@uci.edu mailto:cgohlke@uci.edu> wrote:
On 8/21/2012 9:24 AM, Ondřej Čertík wrote: > Hi, > > I'm pleased to announce the availability of the first beta release of > NumPy 1.7.0b1. > > Sources and binary installers can be found at >https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/ > > Please test this release and report any issues on the numpy-discussion > mailing list. The following problems are known and > we'll work on fixing them before the final release: > >http://projects.scipy.org/numpy/ticket/2187 >http://projects.scipy.org/numpy/ticket/2185 >http://projects.scipy.org/numpy/ticket/2066 >http://projects.scipy.org/numpy/ticket/1588 >http://projects.scipy.org/numpy/ticket/2076 >http://projects.scipy.org/numpy/ticket/2101 >http://projects.scipy.org/numpy/ticket/2108 >http://projects.scipy.org/numpy/ticket/2150 >http://projects.scipy.org/numpy/ticket/2189 > > I would like to thank Ralf for a lot of help with creating binaries > and other help for this release. > > Cheers, > Ondrej > > Hi Ondrej, will numpy 1.7.0 final support Python 3.3? The recent patch in the master branch seems to work well. I tested a win-amd64-py2.7\msvc9\MKL build of the numpy maintenance/1.7.x branch against a number of package binaries from <http://www.lfd.uci.edu/~gohlke/pythonlibs/ <http://www.lfd.uci.edu/%7Egohlke/pythonlibs/>>. The test results are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/ <http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20120821-win-amd64-py2.7-numpy-MKL-1.7.0rc1.dev-28ffac7/>>.What version of Numpy were the test packages compiled against?
numpy-MKL-1.6.x
Christoph
For comparison, the tests against numpy-MKL-1.6.2 are at <http://www.lfd.uci.edu/~gohlke/pythonlibs/tests/20120821-win-amd64-py2.7/ <http://www.lfd.uci.edu/%7Egohlke/pythonlibs/tests/20120821-win-amd64-py2.7/>>. Besides some numpy 1.7.x test errors due to RuntimeWarning and DeprecationWarning, there are hundreds of "RuntimeWarning (numpy.dtype size changed, may indicate binary incompatibility)" when loading Cython extensions. There are additional test failures in scipy, statsmodels, bottleneck, skimage, vigra, and mahotas. I did not check in detail or with existing tickets (http://projects.scipy.org/ is timing out or responding with HTTP 500 status). Other packages test OK against numpy 1.7.x, e.g. PIL, PyGame, matplotlib, Pandas, tables, and numexpr.Chuck
On 08/21/2012 10:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Currently in trying to support python 3.3 in Fedora Rawhide (F19) and Fedora 18 we are doing:
# Regenerate Cython c sources # This is needed with numpy-1.6.2.tar.gz with python 3.3 to avoid an exception # with an import call in the generated .c file in the tarball that uses the # old default of -1: # File "mtrand.pyx", line 126, in init mtrand (numpy/random/mtrand/mtrand.c:20679) # ValueError: level must be >= 0 # due to the changes in import in 3.3 # Regenerating with a newer Cython fixes it: pushd numpy/random/mtrand/ rm -v mtrand.c cython mtrand.pyx popd
However with 1.7.0b1 and Cython 0.16 we get:
+ cython mtrand.pyx Error compiling Cython file: ------------------------------------------------------------ ... PyArray_DIMS(oa) , NPY_DOUBLE) length = PyArray_SIZE(array) array_data = <double *>PyArray_DATA(array) itera = <flatiter>PyArray_IterNew(<object>oa) for i from 0 <= i < length: array_data[i] = func(state, (<double *>(itera.dataptr))[0]) ^ ------------------------------------------------------------ mtrand.pyx:177:41: Python objects cannot be cast to pointers of primitive types Error compiling Cython file: ------------------------------------------------------------ ... cdef npy_intp i cdef broadcast multi if size is None: multi = <broadcast> PyArray_MultiIterNew(2, <void *>oa, <void *>ob) array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE) ^ ------------------------------------------------------------ mtrand.pyx:222:59: Cannot convert Python object to 'npy_intp *' Error compiling Cython file: ------------------------------------------------------------ ... cdef npy_intp i cdef broadcast multi if size is None: multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc) array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE) ^ ------------------------------------------------------------ mtrand.pyx:275:59: Cannot convert Python object to 'npy_intp *' Error compiling Cython file: ------------------------------------------------------------ ... cdef long *on_data cdef broadcast multi if size is None: multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op) array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG) ^ ------------------------------------------------------------ mtrand.pyx:341:59: Cannot convert Python object to 'npy_intp *' Error compiling Cython file: ------------------------------------------------------------ ... cdef double *on_data cdef broadcast multi if size is None: multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op) array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG) ^ ------------------------------------------------------------ mtrand.pyx:390:59: Cannot convert Python object to 'npy_intp *' Error compiling Cython file: ------------------------------------------------------------ ... cdef npy_intp i cdef broadcast multi if size is None: multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN) array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG) ^ ------------------------------------------------------------ mtrand.pyx:442:59: Cannot convert Python object to 'npy_intp *' Error compiling Cython file: ------------------------------------------------------------ ... PyArray_DIMS(oa), NPY_LONG) length = PyArray_SIZE(array) array_data = <long *>PyArray_DATA(array) itera = <flatiter>PyArray_IterNew(<object>oa) for i from 0 <= i < length: array_data[i] = func(state, (<double *>(itera.dataptr))[0]) ^ ------------------------------------------------------------ mtrand.pyx:498:41: Python objects cannot be cast to pointers of primitive types Error compiling Cython file: ------------------------------------------------------------ ... flow = PyFloat_AsDouble(low) fhigh = PyFloat_AsDouble(high) if not PyErr_Occurred(): return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow) PyErr_Clear() olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ARRAY_ALIGNED) ^ ------------------------------------------------------------ mtrand.pyx:1140:75: undeclared name not builtin: NPY_ARRAY_ALIGNED
I'm afraid I know nothing about Cython. Looks like there were a lot of changes between 1.6.2 and 1.7.0b1 in mtrand.pyx. I'll try not rebuilding and see if we see the original problem.
On 08/22/2012 09:55 AM, Orion Poplawski wrote:
On 08/21/2012 10:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Currently in trying to support python 3.3 in Fedora Rawhide (F19) and Fedora 18 we are doing:
# Regenerate Cython c sources # This is needed with numpy-1.6.2.tar.gz with python 3.3 to avoid an exception # with an import call in the generated .c file in the tarball that uses the # old default of -1: # File "mtrand.pyx", line 126, in init mtrand (numpy/random/mtrand/mtrand.c:20679) # ValueError: level must be >= 0 # due to the changes in import in 3.3 # Regenerating with a newer Cython fixes it: pushd numpy/random/mtrand/ rm -v mtrand.c cython mtrand.pyx popd
If I drop the cython generation it builds, but the python 3 test failure I get now is:
+ /usr/bin/python3 -c 'import pkg_resources, numpy ; numpy.test()' /usr/lib/python3.3/site-packages/nose/core.py:247: ResourceWarning: unclosed file <_io.TextIOWrapper name='/usr/lib/python3.3/site-packages/nose/usage.txt' mode='r' encoding='ANSI_X3.4-1968'> os.path.dirname(__file__), 'usage.txt'), 'r').read() ........................S....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................! .......... ......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................K......................................................................................................................................................................................................................................................! .......... ......................K...................................................E................................................................................................................................................................K....................................................................................................K......................K..........................................................................................................S.............................................................................................................................................................................................................................................................E............................................................................................................................................................................................................................................................................! .......... ............................................................................................................................................./usr/lib64/python3.3/zipfile.py:1513: ResourceWarning: unclosed file <_io.BufferedReader name='/tmp/tmpemcede.npz'> self.fp = None .............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................! .......... .....................................................................................................................................................................................................................................K.................................................... ====================================================================== ERROR: Ticket #16 ---------------------------------------------------------------------- Traceback (most recent call last): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/core/tests/test_regression.py", line 41, in test_pickle_transposed b = pickle.load(f) EOFError ====================================================================== ERROR: Failure: ValueError (can't handle version 187 of numpy.ndarray pickle) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python3.3/site-packages/nose/failure.py", line 37, in runTest raise self.exc_class(self.exc_val).with_traceback(self.tb) File "/usr/lib/python3.3/site-packages/nose/loader.py", line 232, in generate for test in g(): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 429, in test_roundtrip arr2 = roundtrip(arr) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 420, in roundtrip arr2 = format.read_array(f2) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/format.py", line 449, in read_array array = pickle.load(fp) ValueError: can't handle version 187 of numpy.ndarray pickle ---------------------------------------------------------------------- Ran 4418 tests in 31.180s FAILED (KNOWNFAIL=6, SKIP=2, errors=2)
On 08/22/2012 10:26 AM, Orion Poplawski wrote:
On 08/22/2012 09:55 AM, Orion Poplawski wrote:
On 08/21/2012 10:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Currently in trying to support python 3.3 in Fedora Rawhide (F19) and Fedora 18 we are doing:
# Regenerate Cython c sources # This is needed with numpy-1.6.2.tar.gz with python 3.3 to avoid an exception # with an import call in the generated .c file in the tarball that uses the # old default of -1: # File "mtrand.pyx", line 126, in init mtrand (numpy/random/mtrand/mtrand.c:20679) # ValueError: level must be >= 0 # due to the changes in import in 3.3 # Regenerating with a newer Cython fixes it: pushd numpy/random/mtrand/ rm -v mtrand.c cython mtrand.pyx popd
If I drop the cython generation it builds, but the python 3 test failure I get now is:
..
ERROR: Ticket #16
Traceback (most recent call last): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/core/tests/test_regression.py", line 41, in test_pickle_transposed b = pickle.load(f) EOFError ====================================================================== ERROR: Failure: ValueError (can't handle version 187 of numpy.ndarray pickle)
Traceback (most recent call last): File "/usr/lib/python3.3/site-packages/nose/failure.py", line 37, in runTest raise self.exc_class(self.exc_val).with_traceback(self.tb) File "/usr/lib/python3.3/site-packages/nose/loader.py", line 232, in generate for test in g(): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 429, in test_roundtrip arr2 = roundtrip(arr) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 420, in roundtrip arr2 = format.read_array(f2) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/format.py", line 449, in read_array array = pickle.load(fp) ValueError: can't handle version 187 of numpy.ndarray pickle
I should note that I'm taking numpy/core/src/multiarray/scalarapi.c and numpy/core/src/multiarray/scalartypes.c.src from git master, which I thought had the fix for this.
Le mercredi 22 août 2012 à 10:59 -0600, Orion Poplawski a écrit :
On 08/22/2012 10:26 AM, Orion Poplawski wrote:
On 08/22/2012 09:55 AM, Orion Poplawski wrote:
On 08/21/2012 10:24 AM, Ondřej Čertík wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Currently in trying to support python 3.3 in Fedora Rawhide (F19) and Fedora 18 we are doing:
# Regenerate Cython c sources # This is needed with numpy-1.6.2.tar.gz with python 3.3 to avoid an exception # with an import call in the generated .c file in the tarball that uses the # old default of -1: # File "mtrand.pyx", line 126, in init mtrand (numpy/random/mtrand/mtrand.c:20679) # ValueError: level must be >= 0 # due to the changes in import in 3.3 # Regenerating with a newer Cython fixes it: pushd numpy/random/mtrand/ rm -v mtrand.c cython mtrand.pyx popd
If I drop the cython generation it builds, but the python 3 test failure I get now is:
..
ERROR: Ticket #16
Traceback (most recent call last): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/core/tests/test_regression.py", line 41, in test_pickle_transposed b = pickle.load(f) EOFError ====================================================================== ERROR: Failure: ValueError (can't handle version 187 of numpy.ndarray pickle)
Traceback (most recent call last): File "/usr/lib/python3.3/site-packages/nose/failure.py", line 37, in runTest raise self.exc_class(self.exc_val).with_traceback(self.tb) File "/usr/lib/python3.3/site-packages/nose/loader.py", line 232, in generate for test in g(): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 429, in test_roundtrip arr2 = roundtrip(arr) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 420, in roundtrip arr2 = format.read_array(f2) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/format.py", line 449, in read_array array = pickle.load(fp) ValueError: can't handle version 187 of numpy.ndarray pickle
I should note that I'm taking numpy/core/src/multiarray/scalarapi.c and numpy/core/src/multiarray/scalartypes.c.src from git master, which I thought had the fix for this.
Note that the fix is actually in numpy/core/src/multiarray/methods.c, see https://github.com/numpy/numpy/pull/371/
On 08/22/2012 12:36 PM, Ronan Lamy wrote:
Le mercredi 22 août 2012 à 10:59 -0600, Orion Poplawski a écrit :
If I drop the cython generation it builds, but the python 3 test failure I get now is:
..
ERROR: Ticket #16
Traceback (most recent call last): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/core/tests/test_regression.py", line 41, in test_pickle_transposed b = pickle.load(f) EOFError ====================================================================== ERROR: Failure: ValueError (can't handle version 187 of numpy.ndarray pickle)
Traceback (most recent call last): File "/usr/lib/python3.3/site-packages/nose/failure.py", line 37, in runTest raise self.exc_class(self.exc_val).with_traceback(self.tb) File "/usr/lib/python3.3/site-packages/nose/loader.py", line 232, in generate for test in g(): File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 429, in test_roundtrip arr2 = roundtrip(arr) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/tests/test_format.py", line 420, in roundtrip arr2 = format.read_array(f2) File "/builddir/build/BUILDROOT/numpy-1.7.0-0.2.b1.fc19.x86_64/usr/lib64/python3.3/site-packages/numpy/lib/format.py", line 449, in read_array array = pickle.load(fp) ValueError: can't handle version 187 of numpy.ndarray pickle
I should note that I'm taking numpy/core/src/multiarray/scalarapi.c and numpy/core/src/multiarray/scalartypes.c.src from git master, which I thought had the fix for this.
Note that the fix is actually in numpy/core/src/multiarray/methods.c, see https://github.com/numpy/numpy/pull/371/
Thanks!
On Tue, Aug 21, 2012 at 12:24 PM, Ondřej Čertík ondrej.certik@gmail.comwrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
Sources and binary installers can be found at https://sourceforge.net/projects/numpy/files/NumPy/1.7.0b1/
Please test this release and report any issues on the numpy-discussion mailing list. The following problems are known and we'll work on fixing them before the final release:
http://projects.scipy.org/numpy/ticket/2187 http://projects.scipy.org/numpy/ticket/2185 http://projects.scipy.org/numpy/ticket/2066 http://projects.scipy.org/numpy/ticket/1588 http://projects.scipy.org/numpy/ticket/2076 http://projects.scipy.org/numpy/ticket/2101 http://projects.scipy.org/numpy/ticket/2108 http://projects.scipy.org/numpy/ticket/2150 http://projects.scipy.org/numpy/ticket/2189
I would like to thank Ralf for a lot of help with creating binaries and other help for this release.
Cheers, Ondrej
At http://docs.scipy.org/doc/numpy/contents.html, it looks like the TOC tree is a bit messed up. For example, I see that masked arrays are listed multiple times, and I think some of the sub-entries for masked arrays show up multiple times within an entry for masked arrays. Some of the bullets appear as ">" instead of dots.
Don't know what version that page is generated from, but we might want to double-check that 1.7.0's docs don't have the same problem.
Cheers! Ben Root
Hello,
On Tue, Aug 21, 2012 at 6:24 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
I've just uploaded it to Debian experimental, so we can give it a run while in freeze. Some of the buildds are already building[1] the package, so we should get results asap (either failures or successes).
[1] https://buildd.debian.org/status/package.php?p=python-numpy&suite=experi...
If tests fail, it won't stop the build, and indeed I got at least 2 errors (actually 1 error and 1 crash), when running tests for python 2.7 and 3.2 with debug enabled:
2.7 dbg
====================================================================== ERROR: test_power_zero (test_umath.TestPower) ---------------------------------------------------------------------- Traceback (most recent call last): File "/tmp/buildd/python-numpy-1.7.0~b1/debian/tmp/usr/lib/python2.7/dist-packages/numpy/core/tests/test_umath.py", line 139, in test_power_zero assert_complex_equal(np.power(zero, 0+1j), cnan) RuntimeWarning: invalid value encountered in power
----------------------------------------------------------------------
3.2 dbg
python3.2-dbg: numpy/core/src/multiarray/common.c:161: PyArray_DTypeFromObjectHelper: Assertion `((((((PyObject*)(temp))->ob_type))->tp_flags & ((1L<<27))) != 0)' failed. Aborted
I'm reporting them here since you asked so, dunno if you want an issue on github to track them. I'll look at the buildds logs and report additional failures if they come up.
Cheers,
Hi Sandro,
On Fri, Aug 31, 2012 at 6:18 AM, Sandro Tosi morph@debian.org wrote:
Hello,
On Tue, Aug 21, 2012 at 6:24 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
Hi,
I'm pleased to announce the availability of the first beta release of NumPy 1.7.0b1.
I've just uploaded it to Debian experimental, so we can give it a run while in freeze. Some of the buildds are already building[1] the package, so we should get results asap (either failures or successes).
This is awesome, thanks you so much for doing this. This should reveal some bugs.
[1] https://buildd.debian.org/status/package.php?p=python-numpy&suite=experi...
If tests fail, it won't stop the build, and indeed I got at least 2 errors (actually 1 error and 1 crash), when running tests for python 2.7 and 3.2 with debug enabled:
2.7 dbg
====================================================================== ERROR: test_power_zero (test_umath.TestPower)
Traceback (most recent call last): File "/tmp/buildd/python-numpy-1.7.0~b1/debian/tmp/usr/lib/python2.7/dist-packages/numpy/core/tests/test_umath.py", line 139, in test_power_zero assert_complex_equal(np.power(zero, 0+1j), cnan) RuntimeWarning: invalid value encountered in power
3.2 dbg
python3.2-dbg: numpy/core/src/multiarray/common.c:161: PyArray_DTypeFromObjectHelper: Assertion `((((((PyObject*)(temp))->ob_type))->tp_flags & ((1L<<27))) != 0)' failed. Aborted
I'm reporting them here since you asked so, dunno if you want an issue on github to track them. I'll look at the buildds logs and report additional failures if they come up.
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I'll add it to the release TODO and try to fix it.
Ondrej
Ond??ej ??ert??k ondrej.certik@gmail.com wrote:
python3.2-dbg: numpy/core/src/multiarray/common.c:161: PyArray_DTypeFromObjectHelper: Assertion `((((((PyObject*)(temp))->ob_type))->tp_flags & ((1L<<27))) != 0)'
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
For the second one there's an issue here:
http://projects.scipy.org/numpy/ticket/2193
Stefan Krah
On Fri, Aug 31, 2012 at 7:17 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I've reported it here: https://github.com/numpy/numpy/issues/402
Cheers,
On Fri, Aug 31, 2012 at 8:07 PM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 7:17 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I've reported it here: https://github.com/numpy/numpy/issues/402
I've just spammed the issue tracker with additional issues, reporting all the test suite failures on Debian architectures; issues are 406 -> 414 .
Don't hesitate to contact me if you need any support or clarification.
Cheers,
On Sat, Sep 1, 2012 at 2:19 AM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 8:07 PM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 7:17 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I've reported it here: https://github.com/numpy/numpy/issues/402
I've just spammed the issue tracker with additional issues, reporting all the test suite failures on Debian architectures; issues are 406 -> 414 .
Don't hesitate to contact me if you need any support or clarification.
Thanks Sandro for reporting it! I put all of them into my release issue:
https://github.com/numpy/numpy/issues/396
most of the failures seem to be caused by these two issues:
https://github.com/numpy/numpy/issues/394 https://github.com/numpy/numpy/issues/426
so I am looking into this now.
Ondrej
All the PyArray_FLOAT*, ... to NPY_FLOAT*.
PyObject *var; PyArrayObject * var_arr; var_arr->{dimensions,strides,nd,descr} to PyArray_{DIMS,...}(var_arr), need macro
PyArray_ISCONTIGUOUS(var) to PyArray_ISCONTIGUOUS(var_arr)
PyArray_{DATA,STRIDES,GETPTR2}(var) to PyArray_{DATA,STRIDES}(var_arr)
The sed script didn't replace NPY_ALIGNED to NPY_ARRAY_ALIGNED. idem for NPY_WRITABLE, NPY_UPDATE_ALL, NPY_C_CONTIGUOUS, NPY_F_CONTIGUOUS,
The sed script did change as well, but I think it should not be deprecated. This flag NPY_ARRAY_ENSURECOPY is a new one. It was not existing in numpy 1.6.0. We try to stay compitible with numpy 1.3 (maybe we will bump to numpy 1.4). This is the info on when this line was introduced:
263df0cc (Mark Wiebe 2011-07-19 17:06:08 -0500 784) #define NPY_ARRAY_ENSURECOPY 0x0020
In the trunk of Theano, I'll define NPY_ARRAY_ENSURECOPY for older version of numpy.
PyArray_SetBaseObject
On Tue, Sep 4, 2012 at 6:31 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
On Sat, Sep 1, 2012 at 2:19 AM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 8:07 PM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 7:17 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I've reported it here: https://github.com/numpy/numpy/issues/402
I've just spammed the issue tracker with additional issues, reporting all the test suite failures on Debian architectures; issues are 406 -> 414 .
Don't hesitate to contact me if you need any support or clarification.
Thanks Sandro for reporting it! I put all of them into my release issue:
https://github.com/numpy/numpy/issues/396
most of the failures seem to be caused by these two issues:
https://github.com/numpy/numpy/issues/394 https://github.com/numpy/numpy/issues/426
so I am looking into this now.
Ondrej _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi,
forget this email, it was sent by error. I'll write the info in a new email in a few minutes.
Fred
On Sun, Sep 9, 2012 at 1:08 PM, Frédéric Bastien nouiz@nouiz.org wrote:
All the PyArray_FLOAT*, ... to NPY_FLOAT*.
PyObject *var; PyArrayObject * var_arr; var_arr->{dimensions,strides,nd,descr} to PyArray_{DIMS,...}(var_arr), need macro
PyArray_ISCONTIGUOUS(var) to PyArray_ISCONTIGUOUS(var_arr)
PyArray_{DATA,STRIDES,GETPTR2}(var) to PyArray_{DATA,STRIDES}(var_arr)
The sed script didn't replace NPY_ALIGNED to NPY_ARRAY_ALIGNED. idem for NPY_WRITABLE, NPY_UPDATE_ALL, NPY_C_CONTIGUOUS, NPY_F_CONTIGUOUS,
The sed script did change as well, but I think it should not be deprecated. This flag NPY_ARRAY_ENSURECOPY is a new one. It was not existing in numpy 1.6.0. We try to stay compitible with numpy 1.3 (maybe we will bump to numpy 1.4). This is the info on when this line was introduced:
263df0cc (Mark Wiebe 2011-07-19 17:06:08 -0500 784) #define NPY_ARRAY_ENSURECOPY 0x0020
In the trunk of Theano, I'll define NPY_ARRAY_ENSURECOPY for older version of numpy.
PyArray_SetBaseObject
On Tue, Sep 4, 2012 at 6:31 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
On Sat, Sep 1, 2012 at 2:19 AM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 8:07 PM, Sandro Tosi morph@debian.org wrote:
On Fri, Aug 31, 2012 at 7:17 PM, Ondřej Čertík ondrej.certik@gmail.com wrote:
If you could create issues at github: https://github.com/numpy/numpy/issues that would be great. If you have time, also with some info about the platform and how to reproduce it. Or at least a link to the build logs.
I've reported it here: https://github.com/numpy/numpy/issues/402
I've just spammed the issue tracker with additional issues, reporting all the test suite failures on Debian architectures; issues are 406 -> 414 .
Don't hesitate to contact me if you need any support or clarification.
Thanks Sandro for reporting it! I put all of them into my release issue:
https://github.com/numpy/numpy/issues/396
most of the failures seem to be caused by these two issues:
https://github.com/numpy/numpy/issues/394 https://github.com/numpy/numpy/issues/426
so I am looking into this now.
Ondrej _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
participants (12)
-
Benjamin Root -
Charles R Harris -
Christoph Gohlke -
Frédéric Bastien -
josef.pktd＠gmail.com -
Ondřej Čertík -
Orion Poplawski -
Ralf Gommers -
Ronan Lamy -
Sandro Tosi -
Skipper Seabold -
Stefan Krah