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| author | Charles Harris <charlesr.harris@gmail.com> | 2018-05-14 10:21:36 -0600 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-05-14 10:21:36 -0600 |
| commit | 5469af202699bd30c78789f2fb04d3c10c105614 (patch) | |
| tree | d3bb85f7e99ec15fa7ca295a24bbfb085a1a63b6 | |
| parent | a786915c4e9f6f2a4b4e8f95b9db6f3538343509 (diff) | |
| parent | 671ba6e9fc4f4d199bed3ad7671c009f91a1565b (diff) | |
| download | numpy-5469af202699bd30c78789f2fb04d3c10c105614.tar.gz | |
Merge branch 'master' into linalg-move-matrix-power
52 files changed, 920 insertions, 719 deletions
diff --git a/.circleci/config.yml b/.circleci/config.yml index 230871ce1..e2eb01b04 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -22,7 +22,7 @@ jobs: . venv/bin/activate pip install cython sphinx matplotlib sudo apt-get update - sudo apt-get install -y graphviz + sudo apt-get install -y graphviz texlive-fonts-recommended texlive-latex-recommended texlive-latex-extra texlive-generic-extra latexmk texlive-xetex - run: name: build numpy diff --git a/.travis.yml b/.travis.yml index 168a7a385..4632fbffe 100644 --- a/.travis.yml +++ b/.travis.yml @@ -54,7 +54,6 @@ matrix: - cython3-dbg - python3-dbg - python3-dev - - python3-nose - python3-setuptools - python: 3.6 env: USE_WHEEL=1 RUN_FULL_TESTS=1 @@ -1,4 +1,4 @@ -# <img alt="NumPy" src="branding/icons/numpylogo.svg" height="60"> +# <img alt="NumPy" src="https://cdn.rawgit.com/numpy/numpy/master/branding/icons/numpylogo.svg" height="60"> [](https://travis-ci.org/numpy/numpy) [](https://ci.appveyor.com/project/charris/numpy) diff --git a/benchmarks/benchmarks/bench_random.py b/benchmarks/benchmarks/bench_random.py index 7ed3e2fa1..9d84d83d3 100644 --- a/benchmarks/benchmarks/bench_random.py +++ b/benchmarks/benchmarks/bench_random.py @@ -65,3 +65,18 @@ class Randint_dtype(Benchmark): high = self.high[name] np.random.randint(0, high + 1, size=10**5, dtype=name) + +class Permutation(Benchmark): + def setup(self): + self.n = 10000 + self.a_1d = np.random.random_sample(self.n) + self.a_2d = np.random.random_sample((self.n, 2)) + + def time_permutation_1d(self): + np.random.permutation(self.a_1d) + + def time_permutation_2d(self): + np.random.permutation(self.a_2d) + + def time_permutation_int(self): + np.random.permutation(self.n) diff --git a/doc/RELEASE_WALKTHROUGH.rst.txt b/doc/RELEASE_WALKTHROUGH.rst.txt index 81e15f91f..9a2b71d7e 100644 --- a/doc/RELEASE_WALKTHROUGH.rst.txt +++ b/doc/RELEASE_WALKTHROUGH.rst.txt @@ -1,12 +1,12 @@ -This file contains a walkthrough of the NumPy 1.12.0 release on Fedora Linux. +This file contains a walkthrough of the NumPy 1.14.3 release on Linux. The commands can be copied into the command line, but be sure to -replace 1.12.0 by the correct version. +replace 1.14.3 by the correct version. Release Walkthrough ==================== -Building the release --------------------- +Prepare the release commit +-------------------------- Checkout the branch for the release, make sure it is up to date, and clean the repository:: @@ -16,44 +16,39 @@ repository:: $ git submodule update $ git clean -xdf -Look at the git log to get the hash of the last commit in the release, then -check it out:: - - $ git log - $ git checkout 7849751173fb47a5f17761b3515b42b4d8ce1197 - Edit pavement.py and setup.py as detailed in HOWTO_RELEASE:: $ gvim pavement.py setup.py - $ git commit -a -m"REL: NumPy 1.14.1 release." + $ git commit -a -m"REL: NumPy 1.14.3 release." Sanity check:: $ python runtests.py -m "full" $ python3 runtests.py -m "full" -Tag it,and build the source distribution archives:: +Push this release directly onto the end of the maintenance branch. This +requires write permission to the numpy repository:: - $ git tag -s v1.14.1 - $ paver sdist # sdist will do a git clean -xdf, so we omit that + $ git push upstream maintenance/1.14.x -Check that the files in ``release/installers`` have the correct versions, then -push the tag upstream; generation of the wheels for PyPI needs it:: +As an example, see the 1.14.3 REL commit: `<https://github.com/numpy/numpy/commit/73299826729be58cec179b52c656adfcaefada93>`_. - $ git push upstream v1.14.1 +Build wheels +------------ -Trigger the wheels build. This can take a while. The numpy-wheels repository is -cloned from `<https://github.com/MacPython/numpy-wheels>`_. Start with a pull -as the repo may have been accessed and changed by someone else and a push will -fail. +Trigger the wheels build by pointing the numpy-wheels repository at this +commit. This can take a while. The numpy-wheels repository is cloned from +`<https://github.com/MacPython/numpy-wheels>`_. Start with a pull as the repo +may have been accessed and changed by someone else and a push will fail:: $ cd ../numpy-wheels $ git pull origin master $ git branch <new version> # only when starting new numpy version - $ git checkout v1.14.x # v1.14.x already existed for the 1.14.1 release + $ git checkout v1.14.x # v1.14.x already existed for the 1.14.3 release -The ``.travis.yml`` and ``appveyor.yml`` files need to be edited to make -sure they have the correct version, search for ``BUILD_COMMIT``. +Edit the ``.travis.yml`` and ``appveyor.yml`` files to make sure they have the +correct version, and put in the commit hash for the ``REL`` commit created +above for ``BUILD_COMMIT``. See `<https://github.com/MacPython/numpy-wheels/commit/fed9c04629c155e7804282eb803d81097244598d>`_ for an example:: $ gvim .travis.yml appveyor.yml $ git commit -a @@ -66,7 +61,6 @@ and appveyor build status. Check if all the needed wheels have been built and uploaded before proceeding. There should currently be 22 of them at `<https://wheels.scipy.org>`_, 4 for Mac, 8 for Windows, and 10 for Linux. - Download wheels --------------- @@ -75,7 +69,7 @@ in the ``terryfy`` repository. The terryfy repository may be cloned from `<https://github.com/MacPython/terryfy>`_ if you don't already have it. The wheels can also be uploaded using the ``wheel-uploader``, but we prefer to download all the wheels to the ``../numpy/release/installers`` directory and -upload later using ``twine``. +upload later using ``twine``:: $ cd ../terryfy $ git pull origin master @@ -88,14 +82,56 @@ upload later using ``twine``. If you do this often, consider making CDN_URL and NPY_WHLS part of your default environment. +Tag the release +--------------- + +Once the wheels have been built and downloaded without errors, go back to your +numpy repository in the maintenance branch and tag the ``REL`` commit, signing +it with your gpg key, and build the source distribution archives:: + + $ git tag -s v1.14.3 + $ paver sdist # sdist will do a git clean -xdf, so we omit that + +You should upload your public gpg key to github, so that the tag will appear +"verified" there. + +Check that the files in ``release/installers`` have the correct versions, then +push the tag upstream:: + + $ git push upstream v1.14.3 + +We wait until this point to push the tag because it is very difficult to change +the tag after it has been pushed. + +Reset the maintenance branch into a development state +----------------------------------------------------- + +Add another ``REL`` commit to the numpy maintenance branch, which resets the +``ISREALEASED`` flag to ``False`` and increments the version counter:: + + $ gvim pavement.py setup.py + $ git commit -a -m"REL: prepare 1.14.x for further development" + $ git push upstream maintenance/1.14.x + +This strategy is copied from the scipy release procedure and was used in numpy +for the first time in 1.14.3. It needed to be modified a little since numpy +has more strict requirements for the version number. It was acheived in two +commits: +`<https://github.com/numpy/numpy/commit/b8df705bdcce92d3e2c6f050eb4414192cf0df04>`_ +`<https://github.com/numpy/numpy/commit/29e175269624493114f77cceff93486271f9efff>`_. Upload to PyPI -------------- -Upload to PyPI using ``twine``. The choice here is to sign the files, so will -need to sign every file separately when they are uploaded, keeping the gpg pass -phrase in the clipboard and pasting it in will make that easier. We may chose -to forgo the signing in the future:: +Upload to PyPI using ``twine``. + +In the past, we signed the wheels files, but after 1.14.3 wheels should no +longer support or need signing. The instructions below still sign. + +For the 1.14.3 release we signed every file when it was uploaded. On systems +which do not cache the gpg passphrase for a few minutes, keeping the it in the +clipboard and pasting it in will make that easier. We may chose to forgo the +signing in the future:: $ cd ../numpy $ twine upload -s release/installers/*.whl @@ -120,15 +156,15 @@ Generate the ``release/README`` files:: $ rm release/installers/*.asc $ paver write_release_and_log -Go to `<https://github.com/numpy/numpy/releases>`_, there should be a ``v1.14.1 +Go to `<https://github.com/numpy/numpy/releases>`_, there should be a ``v1.14.3 tag``, click on it and hit the edit button for that tag. There are two ways to add files, using an editable text window and as binary uploads. - Cut and paste the ``release/README.md`` file contents into the text window. -- Upload ``release/installers/numpy-1.12.0.tar.gz`` as a binary file. -- Upload ``release/installers/numpy-1.12.0.zip`` as a binary file. +- Upload ``release/installers/numpy-1.14.3.tar.gz`` as a binary file. +- Upload ``release/installers/numpy-1.14.3.zip`` as a binary file. - Upload ``release/README`` as a binary file. -- Upload ``doc/changelog/1.14.1-changelog.rst`` as a binary file. +- Upload ``doc/changelog/1.14.3-changelog.rst`` as a binary file. - Check the pre-release button if this is a pre-releases. - Hit the ``{Publish,Update} release`` button at the bottom. @@ -143,7 +179,7 @@ upload the documentation. Otherwise:: $ pushd doc $ make dist - $ make upload USERNAME=<yourname> RELEASE=v1.14.1 + $ make upload USERNAME=<yourname> RELEASE=v1.14.3 $ popd If the release series is a new one, you will need to rebuild and upload the @@ -164,7 +200,7 @@ This assumes that you have forked `<https://github.com/scipy/scipy.org>`_:: $ cd ../scipy.org $ git checkout master $ git pull upstream master - $ git checkout -b numpy-1.14.1 + $ git checkout -b numpy-1.14.3 $ gvim www/index.rst # edit the News section $ git commit -a $ git push origin HEAD @@ -180,7 +216,7 @@ announcements for the basic template. The contributor list can be generated as follows:: $ cd ../numpy - $ ./tools/changelog.py $GITHUB v1.14.0..v1.14.1 > tmp.rst + $ ./tools/changelog.py $GITHUB v1.14.2..v1.14.3 > tmp.rst The contents of ``tmp.rst`` can then be cut and pasted into the announcement email. diff --git a/doc/changelog/1.14.3-changelog.rst b/doc/changelog/1.14.3-changelog.rst new file mode 100644 index 000000000..784a9177f --- /dev/null +++ b/doc/changelog/1.14.3-changelog.rst @@ -0,0 +1,27 @@ + +Contributors +============ + +A total of 6 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Allan Haldane +* Charles Harris +* Jonathan March + +* Malcolm Smith + +* Matti Picus +* Pauli Virtanen + +Pull requests merged +==================== + +A total of 8 pull requests were merged for this release. + +* `#10862 <https://github.com/numpy/numpy/pull/10862>`__: BUG: floating types should override tp_print (1.14 backport) +* `#10905 <https://github.com/numpy/numpy/pull/10905>`__: BUG: for 1.14 back-compat, accept list-of-lists in fromrecords +* `#10947 <https://github.com/numpy/numpy/pull/10947>`__: BUG: 'style' arg to array2string broken in legacy mode (1.14... +* `#10959 <https://github.com/numpy/numpy/pull/10959>`__: BUG: test, fix for missing flags['WRITEBACKIFCOPY'] key +* `#10960 <https://github.com/numpy/numpy/pull/10960>`__: BUG: Add missing underscore to prototype in check_embedded_lapack +* `#10961 <https://github.com/numpy/numpy/pull/10961>`__: BUG: Fix encoding regression in ma/bench.py (Issue #10868) +* `#10962 <https://github.com/numpy/numpy/pull/10962>`__: BUG: core: fix NPY_TITLE_KEY macro on pypy +* `#10974 <https://github.com/numpy/numpy/pull/10974>`__: BUG: test, fix PyArray_DiscardWritebackIfCopy... diff --git a/doc/release/1.14.3-notes.rst b/doc/release/1.14.3-notes.rst new file mode 100644 index 000000000..60b631168 --- /dev/null +++ b/doc/release/1.14.3-notes.rst @@ -0,0 +1,41 @@ +========================== +NumPy 1.14.3 Release Notes +========================== + +This is a bugfix release for a few bugs reported following the 1.14.2 release: + +* np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15 +* In python2, float types use the new print style when printing to a file +* style arg in "legacy" print mode now works for 0d arrays + +The Python versions supported in this release are 2.7 and 3.4 - 3.6. The Python +3.6 wheels available from PIP are built with Python 3.6.2 and should be +compatible with all previous versions of Python 3.6. The source releases were +cythonized with Cython 0.28.2. + +Contributors +============ + +A total of 6 people contributed to this release. People with a "+" by their +names contributed a patch for the first time. + +* Allan Haldane +* Charles Harris +* Jonathan March + +* Malcolm Smith + +* Matti Picus +* Pauli Virtanen + +Pull requests merged +==================== + +A total of 8 pull requests were merged for this release. + +* `#10862 <https://github.com/numpy/numpy/pull/10862>`__: BUG: floating types should override tp_print (1.14 backport) +* `#10905 <https://github.com/numpy/numpy/pull/10905>`__: BUG: for 1.14 back-compat, accept list-of-lists in fromrecords +* `#10947 <https://github.com/numpy/numpy/pull/10947>`__: BUG: 'style' arg to array2string broken in legacy mode (1.14... +* `#10959 <https://github.com/numpy/numpy/pull/10959>`__: BUG: test, fix for missing flags['WRITEBACKIFCOPY'] key +* `#10960 <https://github.com/numpy/numpy/pull/10960>`__: BUG: Add missing underscore to prototype in check_embedded_lapack +* `#10961 <https://github.com/numpy/numpy/pull/10961>`__: BUG: Fix encoding regression in ma/bench.py (Issue #10868) +* `#10962 <https://github.com/numpy/numpy/pull/10962>`__: BUG: core: fix NPY_TITLE_KEY macro on pypy +* `#10974 <https://github.com/numpy/numpy/pull/10974>`__: BUG: test, fix PyArray_DiscardWritebackIfCopy... diff --git a/doc/release/1.15.0-notes.rst b/doc/release/1.15.0-notes.rst index 736ec0961..d3f34d2bc 100644 --- a/doc/release/1.15.0-notes.rst +++ b/doc/release/1.15.0-notes.rst @@ -20,15 +20,18 @@ New functions * ``nanquantile`` function, an interface to ``nanpercentile`` without factors of 100 -* `np.printoptions`, the context manager which sets print options temporarily +* `np.printoptions`, a context manager that sets print options temporarily for the scope of the ``with`` block:: >>> with np.printoptions(precision=2): ... print(np.array([2.0]) / 3) [0.67] - * `np.histogram_bin_edges`, a function to get the edges of the bins used by a histogram - without needing to calculate the histogram. +* `np.histogram_bin_edges`, a function to get the edges of the bins used by a histogram + without needing to calculate the histogram. + +* `npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier`` have been added to + deal with compiler optimization changing the order of operations. See below for details. Deprecations ============ @@ -42,6 +45,7 @@ Deprecations * `np.ma.loads`, `np.ma.dumps` * `np.ma.load`, `np.ma.dump` - these functions already failed on python 3, when called with a string. + * Direct imports from the following modules is deprecated. All testing related imports should come from `numpy.testing`. * `np.testing.utils` @@ -116,6 +120,12 @@ longer possible, and objects expecting the old API are respected. The silent suc by removing the interception of an otherwise-normal Exception when ``axis`` was provided to an object using the old API. +unstructured void array's ``.item`` method now returns a bytes object +--------------------------------------------------------------------- +``.item`` now returns a ``bytes`` object instead of a buffer or byte array. +This may affect code which assumed the return value was mutable, which is no +longer the case. + C API changes ============= @@ -123,6 +133,14 @@ C API changes * ``NpyIter_Close`` has been added and should be called before ``NpyIter_Deallocate`` to resolve possible writeback-enabled arrays. +* Functions ``npy_get_floatstatus_barrier`` and ``npy_clear_floatstatus_barrier`` + have been added and should be used in place of the ``npy_get_floatstatus``and + ``npy_clear_status`` functions. Optimizing compilers like GCC 8.1 and Clang + were rearranging the order of operations when the previous functions were + used in the ufunc SIMD functions, resulting in the floatstatus flags being ' + checked before the operation whose status we wanted to check was run. + See `#10339 <https://github.com/numpy/numpy/issues/10370>`__. + New Features ============ @@ -198,6 +216,13 @@ passed explicitly, and are not yet computed automatically. No longer does an IQR of 0 result in `n_bins=1`, rather the number of bins chosen is related to the data size in this situation. +``histogram`` and ``histogramdd`` return edges matching the float type of the data +---------------------------------------------------------------------------------- +When passed ``float16``, ``np.float32``, or ``np.longdouble`` data, the +returned edges are now of the same dtype. Previously, ``histogram`` would only +return the same type if explicit bins were given, and ``histogram`` would +produce ``float64`` bins no matter what the inputs. + ``histogramdd`` allows explicit ranges to be given in a subset of axes ---------------------------------------------------------------------- The ``range`` argument of `histogramdd` can now contain ``None`` values to @@ -295,5 +320,11 @@ of the change, to further improve consistency, the name of the first argument has been changed to ``a`` (from ``M``), and the exceptions for non-square matrices have been changed to ``LinAlgError`` (from ``ValueError``). +Increased performance in ``random.permutation`` for multidimensional arrays +--------------------------------------------------------------------------- +``permutation`` uses the fast path in ``random.shuffle`` for all input +array dimensions. Previously the fast path was only used for 1-d arrays. + + Changes ======= diff --git a/doc/scipy-sphinx-theme b/doc/scipy-sphinx-theme -Subproject c466764e2231ba132c09826b5b138fffa1cfcec +Subproject d990ab9134199f6496b9ac8567f10791f04a720 diff --git a/doc/source/dev/gitwash/development_workflow.rst b/doc/source/dev/gitwash/development_workflow.rst index 5476e3202..c6884a7cf 100644 --- a/doc/source/dev/gitwash/development_workflow.rst +++ b/doc/source/dev/gitwash/development_workflow.rst @@ -396,7 +396,7 @@ collaborator: Now all those people can do:: - git clone git@githhub.com:your-user-name/numpy.git + git clone git@github.com:your-user-name/numpy.git Remember that links starting with ``git@`` use the ssh protocol and are read-write; links starting with ``git://`` are read-only. diff --git a/doc/source/reference/c-api.coremath.rst b/doc/source/reference/c-api.coremath.rst index d3f7fcf75..ad92235da 100644 --- a/doc/source/reference/c-api.coremath.rst +++ b/doc/source/reference/c-api.coremath.rst @@ -183,14 +183,46 @@ Those can be useful for precise floating point comparison. * NPY_FPE_UNDERFLOW * NPY_FPE_INVALID + Note that :c:func:`npy_get_floatstatus_barrier` is preferable as it prevents + agressive compiler optimizations reordering the call relative to + the code setting the status, which could lead to incorrect results. + .. versionadded:: 1.9.0 +.. c:function:: int npy_get_floatstatus_barrier(char*) + + Get floating point status. A pointer to a local variable is passed in to + prevent aggresive compiler optimizations from reodering this function call + relative to the code setting the status, which could lead to incorrect + results. + + Returns a bitmask with following possible flags: + + * NPY_FPE_DIVIDEBYZERO + * NPY_FPE_OVERFLOW + * NPY_FPE_UNDERFLOW + * NPY_FPE_INVALID + + .. versionadded:: 1.15.0 + .. c:function:: int npy_clear_floatstatus() Clears the floating point status. Returns the previous status mask. + Note that :c:func:`npy_clear_floatstatus_barrier` is preferable as it + prevents agressive compiler optimizations reordering the call relative to + the code setting the status, which could lead to incorrect results. + .. versionadded:: 1.9.0 +.. c:function:: int npy_clear_floatstatus_barrier(char*) + + Clears the floating point status. A pointer to a local variable is passed in to + prevent aggresive compiler optimizations from reodering this function call. + Returns the previous status mask. + + .. versionadded:: 1.15.0 +n Complex functions ~~~~~~~~~~~~~~~~~ diff --git a/doc/source/reference/routines.io.rst b/doc/source/reference/routines.io.rst index 573498792..9acd22c56 100644 --- a/doc/source/reference/routines.io.rst +++ b/doc/source/reference/routines.io.rst @@ -14,7 +14,7 @@ NumPy binary files (NPY, NPZ) savez_compressed The format of these binary file types is documented in -http://numpy.github.io/neps/npy-format.html +:ref:`binary-serialization`. Text files ---------- diff --git a/doc/source/reference/swig.testing.rst b/doc/source/reference/swig.testing.rst index 13642a52e..594df952e 100644 --- a/doc/source/reference/swig.testing.rst +++ b/doc/source/reference/swig.testing.rst @@ -22,7 +22,7 @@ typemaps are working as expected. Testing Organization -------------------- -There are three indepedent testing frameworks supported, for one-, +There are three independent testing frameworks supported, for one-, two-, and three-dimensional arrays respectively. For one-dimensional arrays, there are two C++ files, a header and a source, named:: diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py index 7492baf9d..112af9a34 100644 --- a/numpy/core/code_generators/generate_umath.py +++ b/numpy/core/code_generators/generate_umath.py @@ -908,8 +908,8 @@ if sys.version_info[0] >= 3: del defdict['divide'] def indent(st, spaces): - indention = ' '*spaces - indented = indention + st.replace('\n', '\n'+indention) + indentation = ' '*spaces + indented = indentation + st.replace('\n', '\n'+indentation) # trim off any trailing spaces indented = re.sub(r' +$', r'', indented) return indented diff --git a/numpy/core/einsumfunc.py b/numpy/core/einsumfunc.py index 8cd6eae12..bb6767c4f 100644 --- a/numpy/core/einsumfunc.py +++ b/numpy/core/einsumfunc.py @@ -1148,7 +1148,7 @@ def einsum(*operands, **kwargs): # Do the contraction new_view = c_einsum(einsum_str, *tmp_operands, **einsum_kwargs) - # Append new items and derefernce what we can + # Append new items and dereference what we can operands.append(new_view) del tmp_operands, new_view diff --git a/numpy/core/include/numpy/npy_interrupt.h b/numpy/core/include/numpy/npy_interrupt.h index f71fd689e..40cb7ac5e 100644 --- a/numpy/core/include/numpy/npy_interrupt.h +++ b/numpy/core/include/numpy/npy_interrupt.h @@ -55,7 +55,7 @@ Ideas: Simple Interface: -In your C-extension: around a block of code you want to be interruptable +In your C-extension: around a block of code you want to be interruptible with a SIGINT NPY_SIGINT_ON diff --git a/numpy/core/include/numpy/npy_math.h b/numpy/core/include/numpy/npy_math.h index ba32bcdd3..582390cdc 100644 --- a/numpy/core/include/numpy/npy_math.h +++ b/numpy/core/include/numpy/npy_math.h @@ -524,8 +524,17 @@ npy_clongdouble npy_catanhl(npy_clongdouble z); #define NPY_FPE_UNDERFLOW 4 #define NPY_FPE_INVALID 8 -int npy_get_floatstatus(void); +int npy_clear_floatstatus_barrier(char*); +int npy_get_floatstatus_barrier(char*); +/* + * use caution with these - clang and gcc8.1 are known to reorder calls + * to this form of the function which can defeat the check. The _barrier + * form of the call is preferable, where the argument is + * (char*)&local_variable + */ int npy_clear_floatstatus(void); +int npy_get_floatstatus(void); + void npy_set_floatstatus_divbyzero(void); void npy_set_floatstatus_overflow(void); void npy_set_floatstatus_underflow(void); diff --git a/numpy/core/setup.py b/numpy/core/setup.py index 15f6e1522..7d8bab557 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -664,7 +664,7 @@ def configuration(parent_package='',top_path=None): def get_mathlib_info(*args): # Another ugly hack: the mathlib info is known once build_src is run, # but we cannot use add_installed_pkg_config here either, so we only - # update the substition dictionary during npymath build + # update the substitution dictionary during npymath build config_cmd = config.get_config_cmd() # Check that the toolchain works, to fail early if it doesn't diff --git a/numpy/core/src/multiarray/array_assign_scalar.c b/numpy/core/src/multiarray/array_assign_scalar.c index 3d259ae05..17de99cb9 100644 --- a/numpy/core/src/multiarray/array_assign_scalar.c +++ b/numpy/core/src/multiarray/array_assign_scalar.c @@ -245,6 +245,10 @@ PyArray_AssignRawScalar(PyArrayObject *dst, allocated_src_data = 1; } + if (PyDataType_FLAGCHK(PyArray_DESCR(dst), NPY_NEEDS_INIT)) { + memset(tmp_src_data, 0, PyArray_DESCR(dst)->elsize); + } + if (PyArray_CastRawArrays(1, src_data, tmp_src_data, 0, 0, src_dtype, PyArray_DESCR(dst), 0) != NPY_SUCCEED) { src_data = tmp_src_data; diff --git a/numpy/core/src/multiarray/arraytypes.c.src b/numpy/core/src/multiarray/arraytypes.c.src index 5e6804a5c..42f876125 100644 --- a/numpy/core/src/multiarray/arraytypes.c.src +++ b/numpy/core/src/multiarray/arraytypes.c.src @@ -655,9 +655,7 @@ VOID_getitem(void *input, void *vap) { PyArrayObject *ap = vap; char *ip = input; - PyArrayObject *u = NULL; PyArray_Descr* descr; - int itemsize; descr = PyArray_DESCR(ap); if (PyDataType_HASFIELDS(descr)) { @@ -731,68 +729,7 @@ VOID_getitem(void *input, void *vap) return (PyObject *)ret; } - /* 2017-11-26, 1.14 */ - if (DEPRECATE_FUTUREWARNING( - "the `.item()` method of unstructured void types will return an " - "immutable `bytes` object in the near future, the same as " - "returned by `bytes(void_obj)`, instead of the mutable memoryview " - "or integer array returned in numpy 1.13.") < 0) { - return NULL; - } - /* - * In the future all the code below will be replaced by - * - * For unstructured void types like V4, return a bytes object (copy). - * return PyBytes_FromStringAndSize(PyArray_DATA(ap), descr->elsize); - */ - - if (PyDataType_FLAGCHK(descr, NPY_ITEM_HASOBJECT) - || PyDataType_FLAGCHK(descr, NPY_ITEM_IS_POINTER)) { - PyErr_SetString(PyExc_ValueError, - "tried to get void-array with object members as buffer."); - return NULL; - } - itemsize = PyArray_DESCR(ap)->elsize; - -#if defined(NPY_PY3K) - /* - * Return a byte array; there are no plain buffer objects on Py3 - */ - { - npy_intp dims[1], strides[1]; - dims[0] = itemsize; - strides[0] = 1; - descr = PyArray_DescrNewFromType(NPY_BYTE); - u = (PyArrayObject *)PyArray_NewFromDescr(&PyArray_Type, - descr, 1, dims, strides, ip, - PyArray_ISWRITEABLE(ap) ? NPY_ARRAY_WRITEABLE : 0, - NULL); - Py_INCREF(ap); - if (PyArray_SetBaseObject(u, (PyObject *)ap) < 0) { - Py_DECREF(u); - return NULL; - } - } -#else - /* - * default is to return buffer object pointing to - * current item a view of it - */ - if (PyArray_ISWRITEABLE(ap)) { - if (array_might_be_written(ap) < 0) { - return NULL; - } - u = (PyArrayObject *)PyBuffer_FromReadWriteMemory(ip, itemsize); - } - else { - u = (PyArrayObject *)PyBuffer_FromMemory(ip, itemsize); - } -#endif - - if (u == NULL) { - return NULL; - } - return (PyObject *)u; + return PyBytes_FromStringAndSize(PyArray_DATA(ap), descr->elsize); } diff --git a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src index 397aaf209..fa68af19a 100644 --- a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src +++ b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src @@ -1373,7 +1373,7 @@ PyArray_TransferMaskedStridedToNDim(npy_intp ndim, /* * Advanded indexing iteration of arrays when there is a single indexing * array which has the same memory order as the value array and both - * can be trivally iterated (single stride, aligned, no casting necessary). + * can be trivially iterated (single stride, aligned, no casting necessary). */ NPY_NO_EXPORT int mapiter_trivial_@name@(PyArrayObject *self, PyArrayObject *ind, @@ -1747,7 +1747,7 @@ mapiter_@name@(PyArrayMapIterObject *mit) } else { /* - * faster resetting if the subspace iteration is trival. + * faster resetting if the subspace iteration is trivial. * reset_offsets are zero for positive strides, * for negative strides this shifts the pointer to the last * item. diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 7eccb4a4b..6d323dbd8 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -197,7 +197,7 @@ PyArray_CompareLists(npy_intp *l1, npy_intp *l2, int n) } /* - * simulates a C-style 1-3 dimensional array which can be accesed using + * simulates a C-style 1-3 dimensional array which can be accessed using * ptr[i] or ptr[i][j] or ptr[i][j][k] -- requires pointer allocation * for 2-d and 3-d. * @@ -3605,7 +3605,7 @@ as_buffer(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds) /* - * Prints floating-point scalars usign the Dragon4 algorithm, scientific mode. + * Prints floating-point scalars using the Dragon4 algorithm, scientific mode. * See docstring of `np.format_float_scientific` for description of arguments. * The differences is that a value of -1 is valid for pad_left, exp_digits, * precision, which is equivalent to `None`. @@ -3661,7 +3661,7 @@ dragon4_scientific(PyObject *NPY_UNUSED(dummy), PyObject *args, PyObject *kwds) } /* - * Prints floating-point scalars usign the Dragon4 algorithm, positional mode. + * Prints floating-point scalars using the Dragon4 algorithm, positional mode. * See docstring of `np.format_float_positional` for description of arguments. * The differences is that a value of -1 is valid for pad_left, pad_right, * precision, which is equivalent to `None`. diff --git a/numpy/core/src/npymath/ieee754.c.src b/numpy/core/src/npymath/ieee754.c.src index bca690b4d..5405c8fe3 100644 --- a/numpy/core/src/npymath/ieee754.c.src +++ b/numpy/core/src/npymath/ieee754.c.src @@ -6,6 +6,7 @@ */ #include "npy_math_common.h" #include "npy_math_private.h" +#include "numpy/utils.h" #ifndef HAVE_COPYSIGN double npy_copysign(double x, double y) @@ -557,6 +558,15 @@ npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y) } #endif +int npy_clear_floatstatus() { + char x=0; + return npy_clear_floatstatus_barrier(&x); +} +int npy_get_floatstatus() { + char x=0; + return npy_get_floatstatus_barrier(&x); +} + /* * Functions to set the floating point status word. * keep in sync with NO_FLOATING_POINT_SUPPORT in ufuncobject.h @@ -574,18 +584,24 @@ npy_longdouble npy_nextafterl(npy_longdouble x, npy_longdouble y) defined(__NetBSD__) #include <ieeefp.h> -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char * param)) { int fpstatus = fpgetsticky(); + /* + * By using a volatile, the compiler cannot reorder this call + */ + if (param != NULL) { + volatile char NPY_UNUSED(c) = *(char*)param; + } return ((FP_X_DZ & fpstatus) ? NPY_FPE_DIVIDEBYZERO : 0) | ((FP_X_OFL & fpstatus) ? NPY_FPE_OVERFLOW : 0) | ((FP_X_UFL & fpstatus) ? NPY_FPE_UNDERFLOW : 0) | ((FP_X_INV & fpstatus) ? NPY_FPE_INVALID : 0); } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char * param) { - int fpstatus = npy_get_floatstatus(); + int fpstatus = npy_get_floatstatus_barrier(param); fpsetsticky(0); return fpstatus; @@ -617,10 +633,16 @@ void npy_set_floatstatus_invalid(void) (defined(__FreeBSD__) && (__FreeBSD_version >= 502114)) # include <fenv.h> -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char* param) { int fpstatus = fetestexcept(FE_DIVBYZERO | FE_OVERFLOW | FE_UNDERFLOW | FE_INVALID); + /* + * By using a volatile, the compiler cannot reorder this call + */ + if (param != NULL) { + volatile char NPY_UNUSED(c) = *(char*)param; + } return ((FE_DIVBYZERO & fpstatus) ? NPY_FPE_DIVIDEBYZERO : 0) | ((FE_OVERFLOW & fpstatus) ? NPY_FPE_OVERFLOW : 0) | @@ -628,10 +650,10 @@ int npy_get_floatstatus(void) ((FE_INVALID & fpstatus) ? NPY_FPE_INVALID : 0); } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char * param) { /* testing float status is 50-100 times faster than clearing on x86 */ - int fpstatus = npy_get_floatstatus(); + int fpstatus = npy_get_floatstatus_barrier(param); if (fpstatus != 0) { feclearexcept(FE_DIVBYZERO | FE_OVERFLOW | FE_UNDERFLOW | FE_INVALID); @@ -665,18 +687,24 @@ void npy_set_floatstatus_invalid(void) #include <float.h> #include <fpxcp.h> -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char *param) { int fpstatus = fp_read_flag(); + /* + * By using a volatile, the compiler cannot reorder this call + */ + if (param != NULL) { + volatile char NPY_UNUSED(c) = *(char*)param; + } return ((FP_DIV_BY_ZERO & fpstatus) ? NPY_FPE_DIVIDEBYZERO : 0) | ((FP_OVERFLOW & fpstatus) ? NPY_FPE_OVERFLOW : 0) | ((FP_UNDERFLOW & fpstatus) ? NPY_FPE_UNDERFLOW : 0) | ((FP_INVALID & fpstatus) ? NPY_FPE_INVALID : 0); } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char * param) { - int fpstatus = npy_get_floatstatus(); + int fpstatus = npy_get_floatstatus_barrier(param); fp_swap_flag(0); return fpstatus; @@ -710,8 +738,11 @@ void npy_set_floatstatus_invalid(void) #include <float.h> -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char *param) { + /* + * By using a volatile, the compiler cannot reorder this call + */ #if defined(_WIN64) int fpstatus = _statusfp(); #else @@ -720,15 +751,18 @@ int npy_get_floatstatus(void) _statusfp2(&fpstatus, &fpstatus2); fpstatus |= fpstatus2; #endif + if (param != NULL) { + volatile char NPY_UNUSED(c) = *(char*)param; + } return ((SW_ZERODIVIDE & fpstatus) ? NPY_FPE_DIVIDEBYZERO : 0) | ((SW_OVERFLOW & fpstatus) ? NPY_FPE_OVERFLOW : 0) | ((SW_UNDERFLOW & fpstatus) ? NPY_FPE_UNDERFLOW : 0) | ((SW_INVALID & fpstatus) ? NPY_FPE_INVALID : 0); } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char *param) { - int fpstatus = npy_get_floatstatus(); + int fpstatus = npy_get_floatstatus_barrier(param); _clearfp(); return fpstatus; @@ -739,18 +773,24 @@ int npy_clear_floatstatus(void) #include <machine/fpu.h> -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char *param) { unsigned long fpstatus = ieee_get_fp_control(); + /* + * By using a volatile, the compiler cannot reorder this call + */ + if (param != NULL) { + volatile char NPY_UNUSED(c) = *(char*)param; + } return ((IEEE_STATUS_DZE & fpstatus) ? NPY_FPE_DIVIDEBYZERO : 0) | ((IEEE_STATUS_OVF & fpstatus) ? NPY_FPE_OVERFLOW : 0) | ((IEEE_STATUS_UNF & fpstatus) ? NPY_FPE_UNDERFLOW : 0) | ((IEEE_STATUS_INV & fpstatus) ? NPY_FPE_INVALID : 0); } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char *param) { - long fpstatus = npy_get_floatstatus(); + int fpstatus = npy_get_floatstatus_barrier(param); /* clear status bits as well as disable exception mode if on */ ieee_set_fp_control(0); @@ -759,13 +799,14 @@ int npy_clear_floatstatus(void) #else -int npy_get_floatstatus(void) +int npy_get_floatstatus_barrier(char NPY_UNUSED(*param)) { return 0; } -int npy_clear_floatstatus(void) +int npy_clear_floatstatus_barrier(char *param) { + int fpstatus = npy_get_floatstatus_barrier(param); return 0; } diff --git a/numpy/core/src/umath/extobj.c b/numpy/core/src/umath/extobj.c index e44036358..188054e22 100644 --- a/numpy/core/src/umath/extobj.c +++ b/numpy/core/src/umath/extobj.c @@ -284,7 +284,7 @@ _check_ufunc_fperr(int errmask, PyObject *extobj, const char *ufunc_name) { if (!errmask) { return 0; } - fperr = PyUFunc_getfperr(); + fperr = npy_get_floatstatus_barrier((char*)extobj); if (!fperr) { return 0; } diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index 8b1c7e703..1ca298b30 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -1819,7 +1819,7 @@ NPY_NO_EXPORT void *((npy_bool *)op1) = @func@(in1) != 0; } } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat1**/ @@ -1866,6 +1866,9 @@ NPY_NO_EXPORT void const @type@ in2 = *(@type@ *)ip2; io1 = (io1 @OP@ in2 || npy_isnan(io1)) ? io1 : in2; } + if (npy_isnan(io1)) { + npy_set_floatstatus_invalid(); + } *((@type@ *)iop1) = io1; } } @@ -1901,7 +1904,7 @@ NPY_NO_EXPORT void *((@type@ *)op1) = (in1 @OP@ in2 || npy_isnan(in2)) ? in1 : in2; } } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat1**/ @@ -1991,7 +1994,7 @@ NPY_NO_EXPORT void *((@type@ *)op1) = tmp + 0; } } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } NPY_NO_EXPORT void @@ -2177,7 +2180,7 @@ HALF_@kind@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED const npy_half in1 = *(npy_half *)ip1; *((npy_bool *)op1) = @func@(in1) != 0; } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat**/ @@ -2239,7 +2242,7 @@ HALF_@kind@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED const npy_half in2 = *(npy_half *)ip2; *((npy_half *)op1) = (@OP@(in1, in2) || npy_half_isnan(in2)) ? in1 : in2; } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat**/ @@ -2681,7 +2684,7 @@ NPY_NO_EXPORT void const @ftype@ in1i = ((@ftype@ *)ip1)[1]; *((npy_bool *)op1) = @func@(in1r) @OP@ @func@(in1i); } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat1**/ @@ -2790,7 +2793,7 @@ NPY_NO_EXPORT void ((@ftype@ *)op1)[1] = in2i; } } - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)dimensions); } /**end repeat1**/ diff --git a/numpy/core/src/umath/reduction.c b/numpy/core/src/umath/reduction.c index 681d3fefa..5c3a84e21 100644 --- a/numpy/core/src/umath/reduction.c +++ b/numpy/core/src/umath/reduction.c @@ -537,7 +537,7 @@ PyUFunc_ReduceWrapper(PyArrayObject *operand, PyArrayObject *out, } /* Start with the floating-point exception flags cleared */ - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&iter); if (NpyIter_GetIterSize(iter) != 0) { NpyIter_IterNextFunc *iternext; diff --git a/numpy/core/src/umath/scalarmath.c.src b/numpy/core/src/umath/scalarmath.c.src index 6e1fb1ee8..3e29c4b4e 100644 --- a/numpy/core/src/umath/scalarmath.c.src +++ b/numpy/core/src/umath/scalarmath.c.src @@ -848,7 +848,7 @@ static PyObject * } #if @fperr@ - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&out); #endif /* @@ -863,7 +863,7 @@ static PyObject * #if @fperr@ /* Check status flag. If it is set, then look up what to do */ - retstatus = PyUFunc_getfperr(); + retstatus = npy_get_floatstatus_barrier((char*)&out); if (retstatus) { int bufsize, errmask; PyObject *errobj; @@ -993,7 +993,7 @@ static PyObject * return Py_NotImplemented; } - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&out); /* * here we do the actual calculation with arg1 and arg2 @@ -1008,7 +1008,7 @@ static PyObject * } /* Check status flag. If it is set, then look up what to do */ - retstatus = PyUFunc_getfperr(); + retstatus = npy_get_floatstatus_barrier((char*)&out); if (retstatus) { int bufsize, errmask; PyObject *errobj; @@ -1072,7 +1072,7 @@ static PyObject * return Py_NotImplemented; } - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&out); /* * here we do the actual calculation with arg1 and arg2 @@ -1136,7 +1136,7 @@ static PyObject * return Py_NotImplemented; } - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&out); /* * here we do the actual calculation with arg1 and arg2 @@ -1150,7 +1150,7 @@ static PyObject * } /* Check status flag. If it is set, then look up what to do */ - retstatus = PyUFunc_getfperr(); + retstatus = npy_get_floatstatus_barrier((char*)&out); if (retstatus) { int bufsize, errmask; PyObject *errobj; diff --git a/numpy/core/src/umath/simd.inc.src b/numpy/core/src/umath/simd.inc.src index 2241414ac..5c0568c12 100644 --- a/numpy/core/src/umath/simd.inc.src +++ b/numpy/core/src/umath/simd.inc.src @@ -1031,7 +1031,7 @@ sse2_@kind@_@TYPE@(@type@ * ip, @type@ * op, const npy_intp n) i += 2 * stride; /* minps/minpd will set invalid flag if nan is encountered */ - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)&c1); LOOP_BLOCKED(@type@, 32) { @vtype@ v1 = @vpre@_load_@vsuf@((@type@*)&ip[i]); @vtype@ v2 = @vpre@_load_@vsuf@((@type@*)&ip[i + stride]); @@ -1040,7 +1040,7 @@ sse2_@kind@_@TYPE@(@type@ * ip, @type@ * op, const npy_intp n) } c1 = @vpre@_@VOP@_@vsuf@(c1, c2); - if (npy_get_floatstatus() & NPY_FPE_INVALID) { + if (npy_get_floatstatus_barrier((char*)&c1) & NPY_FPE_INVALID) { *op = @nan@; } else { @@ -1051,6 +1051,9 @@ sse2_@kind@_@TYPE@(@type@ * ip, @type@ * op, const npy_intp n) LOOP_BLOCKED_END { *op = (*op @OP@ ip[i] || npy_isnan(*op)) ? *op : ip[i]; } + if (npy_isnan(*op)) { + npy_set_floatstatus_invalid(); + } } /**end repeat1**/ diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 36b77ef03..c1e8e5a77 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -100,7 +100,8 @@ PyUFunc_getfperr(void) * non-clearing get was only added in 1.9 so this function always cleared * keep it so just in case third party code relied on the clearing */ - return npy_clear_floatstatus(); + char param = 0; + return npy_clear_floatstatus_barrier(¶m); } #define HANDLEIT(NAME, str) {if (retstatus & NPY_FPE_##NAME) { \ @@ -133,7 +134,8 @@ NPY_NO_EXPORT int PyUFunc_checkfperr(int errmask, PyObject *errobj, int *first) { /* clearing is done for backward compatibility */ - int retstatus = npy_clear_floatstatus(); + int retstatus; + retstatus = npy_clear_floatstatus_barrier((char*)&retstatus); return PyUFunc_handlefperr(errmask, errobj, retstatus, first); } @@ -144,7 +146,8 @@ PyUFunc_checkfperr(int errmask, PyObject *errobj, int *first) NPY_NO_EXPORT void PyUFunc_clearfperr() { - npy_clear_floatstatus(); + char param = 0; + npy_clear_floatstatus_barrier(¶m); } /* @@ -2537,7 +2540,7 @@ PyUFunc_GeneralizedFunction(PyUFuncObject *ufunc, #endif /* Start with the floating-point exception flags cleared */ - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&iter); NPY_UF_DBG_PRINT("Executing inner loop\n"); @@ -2782,7 +2785,7 @@ PyUFunc_GenericFunction(PyUFuncObject *ufunc, } /* Start with the floating-point exception flags cleared */ - PyUFunc_clearfperr(); + npy_clear_floatstatus_barrier((char*)&ufunc); /* Do the ufunc loop */ if (need_fancy) { @@ -3563,7 +3566,7 @@ PyUFunc_Reduceat(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *ind, op_axes_arrays[2]}; npy_uint32 op_flags[3]; int i, idim, ndim, otype_final; - int need_outer_iterator; + int need_outer_iterator = 0; NpyIter *iter = NULL; @@ -4279,11 +4282,9 @@ static PyObject * ufunc_generic_call(PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) { int i; - PyTupleObject *ret; PyArrayObject *mps[NPY_MAXARGS]; PyObject *retobj[NPY_MAXARGS]; PyObject *wraparr[NPY_MAXARGS]; - PyObject *res; PyObject *override = NULL; ufunc_full_args full_args = {NULL, NULL}; int errval; @@ -4360,13 +4361,17 @@ ufunc_generic_call(PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) int j = ufunc->nin+i; PyObject *wrap = wraparr[i]; - if (wrap != NULL) { + if (wrap == NULL) { + /* default behavior */ + retobj[i] = PyArray_Return(mps[j]); + } + else if (wrap == Py_None) { + Py_DECREF(wrap); + retobj[i] = (PyObject *)mps[j]; + } + else { + PyObject *res; PyObject *args_tup; - if (wrap == Py_None) { - Py_DECREF(wrap); - retobj[i] = (PyObject *)mps[j]; - continue; - } /* Call the method with appropriate context */ args_tup = _get_wrap_prepare_args(full_args); @@ -4386,15 +4391,9 @@ ufunc_generic_call(PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) if (res == NULL) { goto fail; } - else { - Py_DECREF(mps[j]); - retobj[i] = res; - continue; - } - } - else { - /* default behavior */ - retobj[i] = PyArray_Return(mps[j]); + + Py_DECREF(mps[j]); + retobj[i] = res; } } @@ -4405,6 +4404,8 @@ ufunc_generic_call(PyUFuncObject *ufunc, PyObject *args, PyObject *kwds) return retobj[0]; } else { + PyTupleObject *ret; + ret = (PyTupleObject *)PyTuple_New(ufunc->nout); for (i = 0; i < ufunc->nout; i++) { PyTuple_SET_ITEM(ret, i, retobj[i]); diff --git a/numpy/core/tests/test_longdouble.py b/numpy/core/tests/test_longdouble.py index 513a71b99..cf50d5d5c 100644 --- a/numpy/core/tests/test_longdouble.py +++ b/numpy/core/tests/test_longdouble.py @@ -6,7 +6,7 @@ import numpy as np from numpy.testing import ( assert_, assert_equal, assert_raises, assert_array_equal, temppath, ) -from ._locales import CommaDecimalPointLocale +from numpy.core.tests._locales import CommaDecimalPointLocale LD_INFO = np.finfo(np.longdouble) longdouble_longer_than_double = (LD_INFO.eps < np.finfo(np.double).eps) diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index 3c5f90cfc..f3032d394 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -34,7 +34,7 @@ from numpy.testing import ( assert_allclose, IS_PYPY, HAS_REFCOUNT, assert_array_less, runstring, SkipTest, temppath, suppress_warnings ) -from ._locales import CommaDecimalPointLocale +from numpy.core.tests._locales import CommaDecimalPointLocale # Need to test an object that does not fully implement math interface from datetime import timedelta, datetime @@ -7290,7 +7290,7 @@ class TestWritebackIfCopy(object): # after resolve, the two arrays no longer reference each other assert_(arr_wb.ctypes.data != 0) assert_equal(arr_wb.base, None) - # assigning to arr_wb does not get transfered to arr + # assigning to arr_wb does not get transferred to arr arr_wb[...] = 100 assert_equal(arr, -100) @@ -7321,7 +7321,7 @@ class TestWritebackIfCopy(object): assert_equal(arr_wb.base, None) if HAS_REFCOUNT: assert_equal(arr_cnt, sys.getrefcount(arr)) - # assigning to arr_wb does not get transfered to arr + # assigning to arr_wb does not get transferred to arr arr_wb[...] = 100 assert_equal(arr, orig) diff --git a/numpy/core/tests/test_print.py b/numpy/core/tests/test_print.py index 746ad0e4b..433208748 100644 --- a/numpy/core/tests/test_print.py +++ b/numpy/core/tests/test_print.py @@ -4,7 +4,7 @@ import sys import numpy as np from numpy.testing import assert_, assert_equal, SkipTest -from ._locales import CommaDecimalPointLocale +from numpy.core.tests._locales import CommaDecimalPointLocale if sys.version_info[0] >= 3: diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index b3cb3e610..d6dcaa982 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -2325,13 +2325,10 @@ class TestRegression(object): def test_void_item_memview(self): va = np.zeros(10, 'V4') - # for now, there is just a futurewarning - assert_warns(FutureWarning, va[:1].item) - # in the future, test we got a bytes copy: - #x = va[:1].item() - #va[0] = b'\xff\xff\xff\xff' - #del va - #assert_equal(x, b'\x00\x00\x00\x00') + x = va[:1].item() + va[0] = b'\xff\xff\xff\xff' + del va + assert_equal(x, b'\x00\x00\x00\x00') def test_structarray_title(self): # The following used to segfault on pypy, due to NPY_TITLE_KEY diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index ea0be1892..1464a9e9a 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -1328,6 +1328,17 @@ class TestMinMax(object): assert_equal(d.max(), d[0]) assert_equal(d.min(), d[0]) + def test_reduce_warns(self): + # gh 10370, 11029 Some compilers reorder the call to npy_getfloatstatus + # and put it before the call to an intrisic function that causes + # invalid status to be set. Also make sure warnings are emitted + for n in (2, 4, 8, 16, 32): + with suppress_warnings() as sup: + sup.record(RuntimeWarning) + for r in np.diagflat([np.nan] * n): + assert_equal(np.min(r), np.nan) + assert_equal(len(sup.log), n) + class TestAbsoluteNegative(object): def test_abs_neg_blocked(self): diff --git a/numpy/distutils/system_info.py b/numpy/distutils/system_info.py index 2a3ff2e52..65d7de316 100644 --- a/numpy/distutils/system_info.py +++ b/numpy/distutils/system_info.py @@ -385,6 +385,7 @@ def get_info(name, notfound_action=0): 'blis': blis_info, # use blas_opt instead 'lapack_mkl': lapack_mkl_info, # use lapack_opt instead 'blas_mkl': blas_mkl_info, # use blas_opt instead + 'accelerate': accelerate_info, # use blas_opt instead 'x11': x11_info, 'fft_opt': fft_opt_info, 'fftw': fftw_info, @@ -1551,39 +1552,10 @@ class lapack_opt_info(system_info): if not atlas_info: atlas_info = get_info('atlas') - if sys.platform == 'darwin' \ - and not os.getenv('_PYTHON_HOST_PLATFORM', None) \ - and not (atlas_info or openblas_info or - lapack_mkl_info): - # Use the system lapack from Accelerate or vecLib under OSX - args = [] - link_args = [] - if get_platform()[-4:] == 'i386' or 'intel' in get_platform() or \ - 'x86_64' in get_platform() or \ - 'i386' in platform.platform(): - intel = 1 - else: - intel = 0 - if os.path.exists('/System/Library/Frameworks' - '/Accelerate.framework/'): - if intel: - args.extend(['-msse3']) - else: - args.extend(['-faltivec']) - link_args.extend(['-Wl,-framework', '-Wl,Accelerate']) - elif os.path.exists('/System/Library/Frameworks' - '/vecLib.framework/'): - if intel: - args.extend(['-msse3']) - else: - args.extend(['-faltivec']) - link_args.extend(['-Wl,-framework', '-Wl,vecLib']) - if args: - self.set_info(extra_compile_args=args, - extra_link_args=link_args, - define_macros=[('NO_ATLAS_INFO', 3), - ('HAVE_CBLAS', None)]) - return + accelerate_info = get_info('accelerate') + if accelerate_info and not atlas_info: + self.set_info(**accelerate_info) + return need_lapack = 0 need_blas = 0 @@ -1659,43 +1631,10 @@ class blas_opt_info(system_info): if not atlas_info: atlas_info = get_info('atlas_blas') - if sys.platform == 'darwin' \ - and not os.getenv('_PYTHON_HOST_PLATFORM', None) \ - and not (atlas_info or openblas_info or - blas_mkl_info or blis_info): - # Use the system BLAS from Accelerate or vecLib under OSX - args = [] - link_args = [] - if get_platform()[-4:] == 'i386' or 'intel' in get_platform() or \ - 'x86_64' in get_platform() or \ - 'i386' in platform.platform(): - intel = 1 - else: - intel = 0 - if os.path.exists('/System/Library/Frameworks' - '/Accelerate.framework/'): - if intel: - args.extend(['-msse3']) - else: - args.extend(['-faltivec']) - args.extend([ - '-I/System/Library/Frameworks/vecLib.framework/Headers']) - link_args.extend(['-Wl,-framework', '-Wl,Accelerate']) - elif os.path.exists('/System/Library/Frameworks' - '/vecLib.framework/'): - if intel: - args.extend(['-msse3']) - else: - args.extend(['-faltivec']) - args.extend([ - '-I/System/Library/Frameworks/vecLib.framework/Headers']) - link_args.extend(['-Wl,-framework', '-Wl,vecLib']) - if args: - self.set_info(extra_compile_args=args, - extra_link_args=link_args, - define_macros=[('NO_ATLAS_INFO', 3), - ('HAVE_CBLAS', None)]) - return + accelerate_info = get_info('accelerate') + if accelerate_info and not atlas_info: + self.set_info(**accelerate_info) + return need_blas = 0 info = {} @@ -1939,6 +1878,58 @@ class blis_info(blas_info): include_dirs=incl_dirs) self.set_info(**info) +class accelerate_info(system_info): + section = 'accelerate' + notfounderror = BlasNotFoundError + + def calc_info(self): + # Make possible to enable/disable from config file/env var + libraries = os.environ.get('ACCELERATE') + if libraries: + libraries = [libraries] + else: + libraries = self.get_libs('libraries', ['accelerate', 'veclib']) + libraries = [lib.strip().lower() for lib in libraries] + + if (sys.platform == 'darwin' and + not os.getenv('_PYTHON_HOST_PLATFORM', None)): + # Use the system BLAS from Accelerate or vecLib under OSX + args = [] + link_args = [] + if get_platform()[-4:] == 'i386' or 'intel' in get_platform() or \ + 'x86_64' in get_platform() or \ + 'i386' in platform.platform(): + intel = 1 + else: + intel = 0 + if (os.path.exists('/System/Library/Frameworks' + '/Accelerate.framework/') and + 'accelerate' in libraries): + if intel: + args.extend(['-msse3']) + else: + args.extend(['-faltivec']) + args.extend([ + '-I/System/Library/Frameworks/vecLib.framework/Headers']) + link_args.extend(['-Wl,-framework', '-Wl,Accelerate']) + elif (os.path.exists('/System/Library/Frameworks' + '/vecLib.framework/') and + 'veclib' in libraries): + if intel: + args.extend(['-msse3']) + else: + args.extend(['-faltivec']) + args.extend([ + '-I/System/Library/Frameworks/vecLib.framework/Headers']) + link_args.extend(['-Wl,-framework', '-Wl,vecLib']) + + if args: + self.set_info(extra_compile_args=args, + extra_link_args=link_args, + define_macros=[('NO_ATLAS_INFO', 3), + ('HAVE_CBLAS', None)]) + + return class blas_src_info(system_info): section = 'blas_src' diff --git a/numpy/f2py/src/fortranobject.c b/numpy/f2py/src/fortranobject.c index dd2484eb4..78b06f066 100644 --- a/numpy/f2py/src/fortranobject.c +++ b/numpy/f2py/src/fortranobject.c @@ -539,7 +539,7 @@ void f2py_report_on_exit(int exit_flag,void *name) { fprintf(stderr,"(d) f2py call-back interface, %6d calls : %8d msec\n", cb_passed_counter,cb_passed_time); - fprintf(stderr,"(e) wrapped (Fortran/C) functions (acctual) : %8d msec\n\n", + fprintf(stderr,"(e) wrapped (Fortran/C) functions (actual) : %8d msec\n\n", passed_call_time-cb_passed_call_time-cb_passed_time); fprintf(stderr,"Use -DF2PY_REPORT_ATEXIT_DISABLE to disable this message.\n"); fprintf(stderr,"Exit status: %d\n",exit_flag); diff --git a/numpy/lib/arraypad.py b/numpy/lib/arraypad.py index daaa68d06..600301c56 100644 --- a/numpy/lib/arraypad.py +++ b/numpy/lib/arraypad.py @@ -100,12 +100,8 @@ def _prepend_const(arr, pad_amt, val, axis=-1): return arr padshape = tuple(x if i != axis else pad_amt for (i, x) in enumerate(arr.shape)) - if val == 0: - return np.concatenate((np.zeros(padshape, dtype=arr.dtype), arr), - axis=axis) - else: - return np.concatenate(((np.zeros(padshape) + val).astype(arr.dtype), - arr), axis=axis) + return np.concatenate((np.full(padshape, val, dtype=arr.dtype), arr), + axis=axis) def _append_const(arr, pad_amt, val, axis=-1): @@ -134,12 +130,8 @@ def _append_const(arr, pad_amt, val, axis=-1): return arr padshape = tuple(x if i != axis else pad_amt for (i, x) in enumerate(arr.shape)) - if val == 0: - return np.concatenate((arr, np.zeros(padshape, dtype=arr.dtype)), - axis=axis) - else: - return np.concatenate( - (arr, (np.zeros(padshape) + val).astype(arr.dtype)), axis=axis) + return np.concatenate((arr, np.full(padshape, val, dtype=arr.dtype)), + axis=axis) def _prepend_edge(arr, pad_amt, axis=-1): @@ -164,13 +156,9 @@ def _prepend_edge(arr, pad_amt, axis=-1): if pad_amt == 0: return arr - edge_slice = tuple(slice(None) if i != axis else 0 + edge_slice = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - edge_arr = arr[edge_slice].reshape(pad_singleton) + edge_arr = arr[edge_slice] return np.concatenate((edge_arr.repeat(pad_amt, axis=axis), arr), axis=axis) @@ -198,13 +186,9 @@ def _append_edge(arr, pad_amt, axis=-1): if pad_amt == 0: return arr - edge_slice = tuple(slice(None) if i != axis else arr.shape[axis] - 1 + edge_slice = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - edge_arr = arr[edge_slice].reshape(pad_singleton) + edge_arr = arr[edge_slice] return np.concatenate((arr, edge_arr.repeat(pad_amt, axis=axis)), axis=axis) @@ -244,15 +228,11 @@ def _prepend_ramp(arr, pad_amt, end, axis=-1): reverse=True).astype(np.float64) # Appropriate slicing to extract n-dimensional edge along `axis` - edge_slice = tuple(slice(None) if i != axis else 0 + edge_slice = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract edge, reshape to original rank, and extend along `axis` - edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis) + # Extract edge, and extend along `axis` + edge_pad = arr[edge_slice].repeat(pad_amt, axis) # Linear ramp slope = (end - edge_pad) / float(pad_amt) @@ -299,15 +279,11 @@ def _append_ramp(arr, pad_amt, end, axis=-1): reverse=False).astype(np.float64) # Slice a chunk from the edge to calculate stats on - edge_slice = tuple(slice(None) if i != axis else -1 + edge_slice = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract edge, reshape to original rank, and extend along `axis` - edge_pad = arr[edge_slice].reshape(pad_singleton).repeat(pad_amt, axis) + # Extract edge, and extend along `axis` + edge_pad = arr[edge_slice].repeat(pad_amt, axis) # Linear ramp slope = (end - edge_pad) / float(pad_amt) @@ -359,12 +335,8 @@ def _prepend_max(arr, pad_amt, num, axis=-1): max_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate max, reshape to add singleton dimension back - max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton) + # Extract slice, calculate max + max_chunk = arr[max_slice].max(axis=axis, keepdims=True) # Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt` return np.concatenate((max_chunk.repeat(pad_amt, axis=axis), arr), @@ -415,12 +387,8 @@ def _append_max(arr, pad_amt, num, axis=-1): else: max_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate max, reshape to add singleton dimension back - max_chunk = arr[max_slice].max(axis=axis).reshape(pad_singleton) + # Extract slice, calculate max + max_chunk = arr[max_slice].max(axis=axis, keepdims=True) # Concatenate `arr` with `max_chunk`, extended along `axis` by `pad_amt` return np.concatenate((arr, max_chunk.repeat(pad_amt, axis=axis)), @@ -466,12 +434,8 @@ def _prepend_mean(arr, pad_amt, num, axis=-1): mean_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate mean, reshape to add singleton dimension back - mean_chunk = arr[mean_slice].mean(axis).reshape(pad_singleton) + # Extract slice, calculate mean + mean_chunk = arr[mean_slice].mean(axis, keepdims=True) _round_ifneeded(mean_chunk, arr.dtype) # Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt` @@ -523,12 +487,8 @@ def _append_mean(arr, pad_amt, num, axis=-1): else: mean_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate mean, reshape to add singleton dimension back - mean_chunk = arr[mean_slice].mean(axis=axis).reshape(pad_singleton) + # Extract slice, calculate mean + mean_chunk = arr[mean_slice].mean(axis=axis, keepdims=True) _round_ifneeded(mean_chunk, arr.dtype) # Concatenate `arr` with `mean_chunk`, extended along `axis` by `pad_amt` @@ -575,12 +535,8 @@ def _prepend_med(arr, pad_amt, num, axis=-1): med_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate median, reshape to add singleton dimension back - med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton) + # Extract slice, calculate median + med_chunk = np.median(arr[med_slice], axis=axis, keepdims=True) _round_ifneeded(med_chunk, arr.dtype) # Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt` @@ -632,12 +588,8 @@ def _append_med(arr, pad_amt, num, axis=-1): else: med_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate median, reshape to add singleton dimension back - med_chunk = np.median(arr[med_slice], axis=axis).reshape(pad_singleton) + # Extract slice, calculate median + med_chunk = np.median(arr[med_slice], axis=axis, keepdims=True) _round_ifneeded(med_chunk, arr.dtype) # Concatenate `arr` with `med_chunk`, extended along `axis` by `pad_amt` @@ -685,12 +637,8 @@ def _prepend_min(arr, pad_amt, num, axis=-1): min_slice = tuple(slice(None) if i != axis else slice(num) for (i, x) in enumerate(arr.shape)) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate min, reshape to add singleton dimension back - min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton) + # Extract slice, calculate min + min_chunk = arr[min_slice].min(axis=axis, keepdims=True) # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt` return np.concatenate((min_chunk.repeat(pad_amt, axis=axis), arr), @@ -741,12 +689,8 @@ def _append_min(arr, pad_amt, num, axis=-1): else: min_slice = tuple(slice(None) for x in arr.shape) - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - - # Extract slice, calculate min, reshape to add singleton dimension back - min_chunk = arr[min_slice].min(axis=axis).reshape(pad_singleton) + # Extract slice, calculate min + min_chunk = arr[min_slice].min(axis=axis, keepdims=True) # Concatenate `arr` with `min_chunk`, extended along `axis` by `pad_amt` return np.concatenate((arr, min_chunk.repeat(pad_amt, axis=axis)), @@ -798,17 +742,11 @@ def _pad_ref(arr, pad_amt, method, axis=-1): ref_chunk1 = arr[ref_slice] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - ref_chunk1 = ref_chunk1.reshape(pad_singleton) - # Memory/computationally more expensive, only do this if `method='odd'` if 'odd' in method and pad_amt[0] > 0: - edge_slice1 = tuple(slice(None) if i != axis else 0 + edge_slice1 = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice1].reshape(pad_singleton) + edge_chunk = arr[edge_slice1] ref_chunk1 = 2 * edge_chunk - ref_chunk1 del edge_chunk @@ -824,13 +762,10 @@ def _pad_ref(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) ref_chunk2 = arr[ref_slice][rev_idx] - if pad_amt[1] == 1: - ref_chunk2 = ref_chunk2.reshape(pad_singleton) - if 'odd' in method: - edge_slice2 = tuple(slice(None) if i != axis else -1 + edge_slice2 = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice2].reshape(pad_singleton) + edge_chunk = arr[edge_slice2] ref_chunk2 = 2 * edge_chunk - ref_chunk2 del edge_chunk @@ -884,17 +819,11 @@ def _pad_sym(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) sym_chunk1 = arr[sym_slice][rev_idx] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - sym_chunk1 = sym_chunk1.reshape(pad_singleton) - # Memory/computationally more expensive, only do this if `method='odd'` if 'odd' in method and pad_amt[0] > 0: - edge_slice1 = tuple(slice(None) if i != axis else 0 + edge_slice1 = tuple(slice(None) if i != axis else slice(0, 1) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice1].reshape(pad_singleton) + edge_chunk = arr[edge_slice1] sym_chunk1 = 2 * edge_chunk - sym_chunk1 del edge_chunk @@ -908,13 +837,10 @@ def _pad_sym(arr, pad_amt, method, axis=-1): for (i, x) in enumerate(arr.shape)) sym_chunk2 = arr[sym_slice][rev_idx] - if pad_amt[1] == 1: - sym_chunk2 = sym_chunk2.reshape(pad_singleton) - if 'odd' in method: - edge_slice2 = tuple(slice(None) if i != axis else -1 + edge_slice2 = tuple(slice(None) if i != axis else slice(x - 1, x) for (i, x) in enumerate(arr.shape)) - edge_chunk = arr[edge_slice2].reshape(pad_singleton) + edge_chunk = arr[edge_slice2] sym_chunk2 = 2 * edge_chunk - sym_chunk2 del edge_chunk @@ -965,12 +891,6 @@ def _pad_wrap(arr, pad_amt, axis=-1): for (i, x) in enumerate(arr.shape)) wrap_chunk1 = arr[wrap_slice] - # Shape to restore singleton dimension after slicing - pad_singleton = tuple(x if i != axis else 1 - for (i, x) in enumerate(arr.shape)) - if pad_amt[0] == 1: - wrap_chunk1 = wrap_chunk1.reshape(pad_singleton) - ########################################################################## # Appended region @@ -979,9 +899,6 @@ def _pad_wrap(arr, pad_amt, axis=-1): for (i, x) in enumerate(arr.shape)) wrap_chunk2 = arr[wrap_slice] - if pad_amt[1] == 1: - wrap_chunk2 = wrap_chunk2.reshape(pad_singleton) - # Concatenate `arr` with both chunks, extending along `axis` return np.concatenate((wrap_chunk1, arr, wrap_chunk2), axis=axis) diff --git a/numpy/lib/format.py b/numpy/lib/format.py index 363bb2101..a0ec55f01 100644 --- a/numpy/lib/format.py +++ b/numpy/lib/format.py @@ -1,5 +1,11 @@ """ -Define a simple format for saving numpy arrays to disk with the full + +.. _binary-serialization: + +Binary Serialization +==================== + +A simple format for saving numpy arrays to disk with the full information about them. The ``.npy`` format is the standard binary file format in NumPy for @@ -143,8 +149,10 @@ data HEADER_LEN." Notes ----- -The ``.npy`` format, including reasons for creating it and a comparison of -alternatives, is described fully in the "npy-format" NEP. +The ``.npy`` format, including motivation for creating it and a comparison of +alternatives, is described in the `"npy-format" NEP +<http://www.numpy.org/neps/nep-0001-npy-format.html>`_, however details have +evolved with time and this document is more current. """ from __future__ import division, absolute_import, print_function diff --git a/numpy/lib/histograms.py b/numpy/lib/histograms.py index 90e19769e..2922b3a86 100644 --- a/numpy/lib/histograms.py +++ b/numpy/lib/histograms.py @@ -877,12 +877,6 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # bins is an integer bins = D*[bins] - # avoid rounding issues for comparisons when dealing with inexact types - if np.issubdtype(sample.dtype, np.inexact): - edge_dt = sample.dtype - else: - edge_dt = float - # normalize the range argument if range is None: range = (None,) * D @@ -896,13 +890,12 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): raise ValueError( '`bins[{}]` must be positive, when an integer'.format(i)) smin, smax = _get_outer_edges(sample[:,i], range[i]) - edges[i] = np.linspace(smin, smax, bins[i] + 1, dtype=edge_dt) + edges[i] = np.linspace(smin, smax, bins[i] + 1) elif np.ndim(bins[i]) == 1: - edges[i] = np.asarray(bins[i], edge_dt) - # not just monotonic, due to the use of mindiff below - if np.any(edges[i][:-1] >= edges[i][1:]): + edges[i] = np.asarray(bins[i]) + if np.any(edges[i][:-1] > edges[i][1:]): raise ValueError( - '`bins[{}]` must be strictly increasing, when an array' + '`bins[{}]` must be monotonically increasing, when an array' .format(i)) else: raise ValueError( @@ -913,7 +906,8 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # Compute the bin number each sample falls into. Ncount = tuple( - np.digitize(sample[:, i], edges[i]) + # avoid np.digitize to work around gh-11022 + np.searchsorted(edges[i], sample[:, i], side='right') for i in _range(D) ) @@ -921,16 +915,10 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # For the rightmost bin, we want values equal to the right edge to be # counted in the last bin, and not as an outlier. for i in _range(D): - # Rounding precision - mindiff = dedges[i].min() - if not np.isinf(mindiff): - decimal = int(-np.log10(mindiff)) + 6 - # Find which points are on the rightmost edge. - not_smaller_than_edge = (sample[:, i] >= edges[i][-1]) - on_edge = (np.around(sample[:, i], decimal) == - np.around(edges[i][-1], decimal)) - # Shift these points one bin to the left. - Ncount[i][on_edge & not_smaller_than_edge] -= 1 + # Find which points are on the rightmost edge. + on_edge = (sample[:, i] == edges[i][-1]) + # Shift these points one bin to the left. + Ncount[i][on_edge] -= 1 # Compute the sample indices in the flattened histogram matrix. # This raises an error if the array is too large. diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 97f50b5d8..95804be7f 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -475,9 +475,7 @@ def save(file, arr, allow_pickle=True, fix_imports=True): Notes ----- - For a description of the ``.npy`` format, see the module docstring - of `numpy.lib.format` or the NumPy Enhancement Proposal - http://numpy.github.io/neps/npy-format.html + For a description of the ``.npy`` format, see :ref:`binary-serialization`. Examples -------- @@ -561,9 +559,7 @@ def savez(file, *args, **kwds): The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is not compressed and each file in the archive contains one variable in ``.npy`` format. For a - description of the ``.npy`` format, see `numpy.lib.format` or the - NumPy Enhancement Proposal - http://numpy.github.io/neps/npy-format.html + description of the ``.npy`` format, see :ref:`binary-serialization`. When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for @@ -642,9 +638,9 @@ def savez_compressed(file, *args, **kwds): The ``.npz`` file format is a zipped archive of files named after the variables they contain. The archive is compressed with ``zipfile.ZIP_DEFLATED`` and each file in the archive contains one variable - in ``.npy`` format. For a description of the ``.npy`` format, see - `numpy.lib.format` or the NumPy Enhancement Proposal - http://numpy.github.io/neps/npy-format.html + in ``.npy`` format. For a description of the ``.npy`` format, see + :ref:`binary-serialization`. + When opening the saved ``.npz`` file with `load` a `NpzFile` object is returned. This is a dictionary-like object which can be queried for @@ -791,8 +787,8 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, the data-type. comments : str or sequence of str, optional The characters or list of characters used to indicate the start of a - comment. For backwards compatibility, byte strings will be decoded as - 'latin1'. The default is '#'. + comment. None implies no comments. For backwards compatibility, byte + strings will be decoded as 'latin1'. The default is '#'. delimiter : str, optional The string used to separate values. For backwards compatibility, byte strings will be decoded as 'latin1'. The default is whitespace. diff --git a/numpy/lib/tests/test_arraypad.py b/numpy/lib/tests/test_arraypad.py index 8be49ce67..8ba0370b0 100644 --- a/numpy/lib/tests/test_arraypad.py +++ b/numpy/lib/tests/test_arraypad.py @@ -489,6 +489,19 @@ class TestConstant(object): ) assert_allclose(test, expected) + def test_check_large_integers(self): + uint64_max = 2 ** 64 - 1 + arr = np.full(5, uint64_max, dtype=np.uint64) + test = np.pad(arr, 1, mode="constant", constant_values=arr.min()) + expected = np.full(7, uint64_max, dtype=np.uint64) + assert_array_equal(test, expected) + + int64_max = 2 ** 63 - 1 + arr = np.full(5, int64_max, dtype=np.int64) + test = np.pad(arr, 1, mode="constant", constant_values=arr.min()) + expected = np.full(7, int64_max, dtype=np.int64) + assert_array_equal(test, expected) + class TestLinearRamp(object): def test_check_simple(self): diff --git a/numpy/lib/tests/test_function_base.py b/numpy/lib/tests/test_function_base.py index 43d62a7ff..5dc96775b 100644 --- a/numpy/lib/tests/test_function_base.py +++ b/numpy/lib/tests/test_function_base.py @@ -1525,9 +1525,9 @@ class TestDigitize(object): class TestUnwrap(object): def test_simple(self): - # check that unwrap removes jumps greather that 2*pi + # check that unwrap removes jumps greater that 2*pi assert_array_equal(unwrap([1, 1 + 2 * np.pi]), [1, 1]) - # check that unwrap maintans continuity + # check that unwrap maintains continuity assert_(np.all(diff(unwrap(rand(10) * 100)) < np.pi)) @@ -2759,7 +2759,7 @@ class TestQuantile(object): assert_equal(np.quantile(x, 0.5), 1.75) def test_no_p_overwrite(self): - # this is worth retesting, beause quantile does not make a copy + # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() np.quantile(np.arange(100.), p, interpolation="midpoint") diff --git a/numpy/lib/tests/test_histograms.py b/numpy/lib/tests/test_histograms.py index 06daacbdc..e16ae12c2 100644 --- a/numpy/lib/tests/test_histograms.py +++ b/numpy/lib/tests/test_histograms.py @@ -253,7 +253,7 @@ class TestHistogram(object): one_nan = np.array([0, 1, np.nan]) all_nan = np.array([np.nan, np.nan]) - # the internal commparisons with NaN give warnings + # the internal comparisons with NaN give warnings sup = suppress_warnings() sup.filter(RuntimeWarning) with sup: @@ -613,8 +613,6 @@ class TestHistogramdd(object): assert_raises(ValueError, np.histogramdd, x, bins=[-1, 2, 4, 5]) assert_raises(ValueError, np.histogramdd, x, bins=[1, 0.99, 1, 1]) assert_raises( - ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 2, 3]]) - assert_raises( ValueError, np.histogramdd, x, bins=[1, 1, 1, [1, 2, 3, -3]]) assert_(np.histogramdd(x, bins=[1, 1, 1, [1, 2, 3, 4]])) @@ -646,7 +644,7 @@ class TestHistogramdd(object): bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) assert_(hist[0] == 0.0) - assert_(hist[1] == 1.) + assert_(hist[1] == 0.0) x = [1.0001] bins = [[0., 0.5, 1.0]] hist, _ = histogramdd(x, bins=bins) @@ -660,3 +658,40 @@ class TestHistogramdd(object): range=[[0.0, 1.0], [0.25, 0.75], [0.25, np.inf]]) assert_raises(ValueError, histogramdd, vals, range=[[0.0, 1.0], [np.nan, 0.75], [0.25, 0.5]]) + + def test_equal_edges(self): + """ Test that adjacent entries in an edge array can be equal """ + x = np.array([0, 1, 2]) + y = np.array([0, 1, 2]) + x_edges = np.array([0, 2, 2]) + y_edges = 1 + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + hist_expected = np.array([ + [2.], + [1.], # x == 2 falls in the final bin + ]) + assert_equal(hist, hist_expected) + + def test_edge_dtype(self): + """ Test that if an edge array is input, its type is preserved """ + x = np.array([0, 10, 20]) + y = x / 10 + x_edges = np.array([0, 5, 15, 20]) + y_edges = x_edges / 10 + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + assert_equal(edges[0].dtype, x_edges.dtype) + assert_equal(edges[1].dtype, y_edges.dtype) + + def test_large_integers(self): + big = 2**60 # Too large to represent with a full precision float + + x = np.array([0], np.int64) + x_edges = np.array([-1, +1], np.int64) + y = big + x + y_edges = big + x_edges + + hist, edges = histogramdd((x, y), bins=(x_edges, y_edges)) + + assert_equal(hist[0, 0], 1) diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index e69d9dd7d..42e221506 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -912,7 +912,7 @@ class TestNanFunctions_Quantile(object): assert_equal(np.nanquantile(x, 0.5), 1.75) def test_no_p_overwrite(self): - # this is worth retesting, beause quantile does not make a copy + # this is worth retesting, because quantile does not make a copy p0 = np.array([0, 0.75, 0.25, 0.5, 1.0]) p = p0.copy() np.nanquantile(np.arange(100.), p, interpolation="midpoint") diff --git a/numpy/lib/tests/test_shape_base.py b/numpy/lib/tests/test_shape_base.py index 080fd066d..0e86fbb19 100644 --- a/numpy/lib/tests/test_shape_base.py +++ b/numpy/lib/tests/test_shape_base.py @@ -79,7 +79,7 @@ class TestApplyAlongAxis(object): def test_axis_insertion(self, cls=np.ndarray): def f1to2(x): - """produces an assymmetric non-square matrix from x""" + """produces an asymmetric non-square matrix from x""" assert_equal(x.ndim, 1) return (x[::-1] * x[1:,None]).view(cls) @@ -123,7 +123,7 @@ class TestApplyAlongAxis(object): def test_axis_insertion_ma(self): def f1to2(x): - """produces an assymmetric non-square matrix from x""" + """produces an asymmetric non-square matrix from x""" assert_equal(x.ndim, 1) res = x[::-1] * x[1:,None] return np.ma.masked_where(res%5==0, res) diff --git a/numpy/lib/twodim_base.py b/numpy/lib/twodim_base.py index 402c18850..cca316e9a 100644 --- a/numpy/lib/twodim_base.py +++ b/numpy/lib/twodim_base.py @@ -650,7 +650,7 @@ def histogram2d(x, y, bins=10, range=None, normed=False, weights=None): N = 1 if N != 1 and N != 2: - xedges = yedges = asarray(bins, float) + xedges = yedges = asarray(bins) bins = [xedges, yedges] hist, edges = histogramdd([x, y], bins, range, normed, weights) return hist, edges[0], edges[1] diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 00baa52bc..5ed1ff1c0 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -7,7 +7,6 @@ import os import sys import itertools import traceback -import warnings import pytest import numpy as np @@ -22,12 +21,11 @@ from numpy.testing import ( ) -def ifthen(a, b): - return not a or b - - -def imply(a, b): - return not a or b +def consistent_subclass(out, in_): + # For ndarray subclass input, our output should have the same subclass + # (non-ndarray input gets converted to ndarray). + return type(out) is (type(in_) if isinstance(in_, np.ndarray) + else np.ndarray) old_assert_almost_equal = assert_almost_equal @@ -65,6 +63,7 @@ all_tags = { 'generalized', 'size-0', 'strided' # optional additions } + class LinalgCase(object): def __init__(self, name, a, b, tags=set()): """ @@ -86,6 +85,7 @@ class LinalgCase(object): def __repr__(self): return "<LinalgCase: %s>" % (self.name,) + def apply_tag(tag, cases): """ Add the given tag (a string) to each of the cases (a list of LinalgCase @@ -129,10 +129,6 @@ CASES += apply_tag('square', [ np.empty((0, 0), dtype=double), np.empty((0,), dtype=double), tags={'size-0'}), - LinalgCase("0x0_matrix", - np.empty((0, 0), dtype=double).view(np.matrix), - np.empty((0, 1), dtype=double).view(np.matrix), - tags={'size-0'}), LinalgCase("8x8", np.random.rand(8, 8), np.random.rand(8)), @@ -142,12 +138,6 @@ CASES += apply_tag('square', [ LinalgCase("nonarray", [[1, 2], [3, 4]], [2, 1]), - LinalgCase("matrix_b_only", - array([[1., 2.], [3., 4.]]), - matrix([2., 1.]).T), - LinalgCase("matrix_a_and_b", - matrix([[1., 2.], [3., 4.]]), - matrix([2., 1.]).T), ]) # non-square test-cases @@ -231,9 +221,6 @@ CASES += apply_tag('hermitian', [ LinalgCase("matrix_b_only", array([[1., 2.], [2., 1.]]), None), - LinalgCase("hmatrix_a_and_b", - matrix([[1., 2.], [2., 1.]]), - None), LinalgCase("hmatrix_1x1", np.random.rand(1, 1), None), @@ -270,12 +257,13 @@ def _make_generalized_cases(): return new_cases + CASES += _make_generalized_cases() + # # Generate stride combination variations of the above # - def _stride_comb_iter(x): """ Generate cartesian product of strides for all axes @@ -323,6 +311,7 @@ def _stride_comb_iter(x): xi = np.lib.stride_tricks.as_strided(x, strides=s) yield xi, "stride_xxx_0_0" + def _make_strided_cases(): new_cases = [] for case in CASES: @@ -333,94 +322,104 @@ def _make_strided_cases(): new_cases.append(new_case) return new_cases + CASES += _make_strided_cases() # # Test different routines against the above cases # +class LinalgTestCase(object): + TEST_CASES = CASES -def _check_cases(func, require=set(), exclude=set()): - """ - Run func on each of the cases with all of the tags in require, and none - of the tags in exclude - """ - for case in CASES: - # filter by require and exclude - if case.tags & require != require: - continue - if case.tags & exclude: - continue + def check_cases(self, require=set(), exclude=set()): + """ + Run func on each of the cases with all of the tags in require, and none + of the tags in exclude + """ + for case in self.TEST_CASES: + # filter by require and exclude + if case.tags & require != require: + continue + if case.tags & exclude: + continue - try: - case.check(func) - except Exception: - msg = "In test case: %r\n\n" % case - msg += traceback.format_exc() - raise AssertionError(msg) + try: + case.check(self.do) + except Exception: + msg = "In test case: %r\n\n" % case + msg += traceback.format_exc() + raise AssertionError(msg) -class LinalgSquareTestCase(object): +class LinalgSquareTestCase(LinalgTestCase): def test_sq_cases(self): - _check_cases(self.do, require={'square'}, exclude={'generalized', 'size-0'}) + self.check_cases(require={'square'}, + exclude={'generalized', 'size-0'}) def test_empty_sq_cases(self): - _check_cases(self.do, require={'square', 'size-0'}, exclude={'generalized'}) + self.check_cases(require={'square', 'size-0'}, + exclude={'generalized'}) -class LinalgNonsquareTestCase(object): +class LinalgNonsquareTestCase(LinalgTestCase): def test_nonsq_cases(self): - _check_cases(self.do, require={'nonsquare'}, exclude={'generalized', 'size-0'}) + self.check_cases(require={'nonsquare'}, + exclude={'generalized', 'size-0'}) def test_empty_nonsq_cases(self): - _check_cases(self.do, require={'nonsquare', 'size-0'}, exclude={'generalized'}) + self.check_cases(require={'nonsquare', 'size-0'}, + exclude={'generalized'}) -class HermitianTestCase(object): + +class HermitianTestCase(LinalgTestCase): def test_herm_cases(self): - _check_cases(self.do, require={'hermitian'}, exclude={'generalized', 'size-0'}) + self.check_cases(require={'hermitian'}, + exclude={'generalized', 'size-0'}) def test_empty_herm_cases(self): - _check_cases(self.do, require={'hermitian', 'size-0'}, exclude={'generalized'}) + self.check_cases(require={'hermitian', 'size-0'}, + exclude={'generalized'}) -class LinalgGeneralizedSquareTestCase(object): +class LinalgGeneralizedSquareTestCase(LinalgTestCase): @pytest.mark.slow def test_generalized_sq_cases(self): - _check_cases(self.do, require={'generalized', 'square'}, exclude={'size-0'}) + self.check_cases(require={'generalized', 'square'}, + exclude={'size-0'}) @pytest.mark.slow def test_generalized_empty_sq_cases(self): - _check_cases(self.do, require={'generalized', 'square', 'size-0'}) + self.check_cases(require={'generalized', 'square', 'size-0'}) -class LinalgGeneralizedNonsquareTestCase(object): +class LinalgGeneralizedNonsquareTestCase(LinalgTestCase): @pytest.mark.slow def test_generalized_nonsq_cases(self): - _check_cases(self.do, require={'generalized', 'nonsquare'}, exclude={'size-0'}) + self.check_cases(require={'generalized', 'nonsquare'}, + exclude={'size-0'}) @pytest.mark.slow def test_generalized_empty_nonsq_cases(self): - _check_cases(self.do, require={'generalized', 'nonsquare', 'size-0'}) + self.check_cases(require={'generalized', 'nonsquare', 'size-0'}) -class HermitianGeneralizedTestCase(object): +class HermitianGeneralizedTestCase(LinalgTestCase): @pytest.mark.slow def test_generalized_herm_cases(self): - _check_cases(self.do, - require={'generalized', 'hermitian'}, - exclude={'size-0'}) + self.check_cases(require={'generalized', 'hermitian'}, + exclude={'size-0'}) @pytest.mark.slow def test_generalized_empty_herm_cases(self): - _check_cases(self.do, - require={'generalized', 'hermitian', 'size-0'}, - exclude={'none'}) + self.check_cases(require={'generalized', 'hermitian', 'size-0'}, + exclude={'none'}) def dot_generalized(a, b): @@ -452,13 +451,15 @@ def identity_like_generalized(a): return identity(a.shape[0]) -class TestSolve(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): - +class SolveCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): + # kept apart from TestSolve for use for testing with matrices. def do(self, a, b, tags): x = linalg.solve(a, b) assert_almost_equal(b, dot_generalized(a, x)) - assert_(imply(isinstance(b, matrix), isinstance(x, matrix))) + assert_(consistent_subclass(x, b)) + +class TestSolve(SolveCases): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -518,14 +519,16 @@ class TestSolve(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(isinstance(result, ArraySubclass)) -class TestInv(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class InvCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): a_inv = linalg.inv(a) assert_almost_equal(dot_generalized(a, a_inv), identity_like_generalized(a)) - assert_(imply(isinstance(a, matrix), isinstance(a_inv, matrix))) + assert_(consistent_subclass(a_inv, a)) + +class TestInv(InvCases): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -550,13 +553,15 @@ class TestInv(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(isinstance(res, ArraySubclass)) -class TestEigvals(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class EigvalsCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): ev = linalg.eigvals(a) evalues, evectors = linalg.eig(a) assert_almost_equal(ev, evalues) + +class TestEigvals(EigvalsCases): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -585,15 +590,17 @@ class TestEigvals(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(isinstance(res, np.ndarray)) -class TestEig(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class EigCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): evalues, evectors = linalg.eig(a) assert_allclose(dot_generalized(a, evectors), np.asarray(evectors) * np.asarray(evalues)[..., None, :], rtol=get_rtol(evalues.dtype)) - assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix))) + assert_(consistent_subclass(evectors, a)) + +class TestEig(EigCases): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -632,7 +639,7 @@ class TestEig(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(isinstance(a, np.ndarray)) -class TestSVD(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class SVDCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): if 'size-0' in tags: @@ -643,9 +650,11 @@ class TestSVD(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_allclose(a, dot_generalized(np.asarray(u) * np.asarray(s)[..., None, :], np.asarray(vt)), rtol=get_rtol(u.dtype)) - assert_(imply(isinstance(a, matrix), isinstance(u, matrix))) - assert_(imply(isinstance(a, matrix), isinstance(vt, matrix))) + assert_(consistent_subclass(u, a)) + assert_(consistent_subclass(vt, a)) + +class TestSVD(SVDCases): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -670,7 +679,7 @@ class TestSVD(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_raises(linalg.LinAlgError, linalg.svd, a) -class TestCond(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class CondCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): # cond(x, p) for p in (None, 2, -2) def do(self, a, b, tags): @@ -715,6 +724,8 @@ class TestCond(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): * (abs(cinv)**2).sum(-1).sum(-1)), single_decimal=5, double_decimal=11) + +class TestCond(CondCases): def test_basic_nonsvd(self): # Smoketest the non-svd norms A = array([[1., 0, 1], [0, -2., 0], [0, 0, 3.]]) @@ -778,20 +789,24 @@ class TestCond(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(np.isfinite(c[1,0])) -class TestPinv(LinalgSquareTestCase, - LinalgNonsquareTestCase, - LinalgGeneralizedSquareTestCase, - LinalgGeneralizedNonsquareTestCase): +class PinvCases(LinalgSquareTestCase, + LinalgNonsquareTestCase, + LinalgGeneralizedSquareTestCase, + LinalgGeneralizedNonsquareTestCase): def do(self, a, b, tags): a_ginv = linalg.pinv(a) # `a @ a_ginv == I` does not hold if a is singular dot = dot_generalized assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11) - assert_(imply(isinstance(a, matrix), isinstance(a_ginv, matrix))) + assert_(consistent_subclass(a_ginv, a)) + + +class TestPinv(PinvCases): + pass -class TestDet(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): +class DetCases(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): def do(self, a, b, tags): d = linalg.det(a) @@ -810,6 +825,8 @@ class TestDet(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_almost_equal(np.abs(s[m]), 1) assert_equal(ld[~m], -inf) + +class TestDet(DetCases): def test_zero(self): assert_equal(linalg.det([[0.0]]), 0.0) assert_equal(type(linalg.det([[0.0]])), double) @@ -853,7 +870,7 @@ class TestDet(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase): assert_(res[1].dtype.type is np.float64) -class TestLstsq(LinalgSquareTestCase, LinalgNonsquareTestCase): +class LstsqCases(LinalgSquareTestCase, LinalgNonsquareTestCase): def do(self, a, b, tags): if 'size-0' in tags: @@ -881,9 +898,11 @@ class TestLstsq(LinalgSquareTestCase, LinalgNonsquareTestCase): expect_resids = np.array([]).view(type(x)) assert_almost_equal(residuals, expect_resids) assert_(np.issubdtype(residuals.dtype, np.floating)) - assert_(imply(isinstance(b, matrix), isinstance(x, matrix))) - assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix))) + assert_(consistent_subclass(x, b)) + assert_(consistent_subclass(residuals, b)) + +class TestLstsq(LstsqCases): def test_future_rcond(self): a = np.array([[0., 1., 0., 1., 2., 0.], [0., 2., 0., 0., 1., 0.], @@ -902,6 +921,7 @@ class TestLstsq(LinalgSquareTestCase, LinalgNonsquareTestCase): # Warning should be raised exactly once (first command) assert_(len(w) == 1) + class TestMatrixPower(object): R90 = array([[0, 1], [-1, 0]]) Arb22 = array([[4, -7], [-2, 10]]) @@ -975,7 +995,7 @@ class TestBoolPower(object): assert_equal(matrix_power(A, 2), A) -class TestEigvalsh(HermitianTestCase, HermitianGeneralizedTestCase): +class TestEigvalshCases(HermitianTestCase, HermitianGeneralizedTestCase): def do(self, a, b, tags): # note that eigenvalue arrays returned by eig must be sorted since @@ -988,6 +1008,8 @@ class TestEigvalsh(HermitianTestCase, HermitianGeneralizedTestCase): ev2 = linalg.eigvalsh(a, 'U') assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype)) + +class TestEigvalsh(object): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -1043,7 +1065,7 @@ class TestEigvalsh(HermitianTestCase, HermitianGeneralizedTestCase): assert_(isinstance(res, np.ndarray)) -class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase): +class TestEighCases(HermitianTestCase, HermitianGeneralizedTestCase): def do(self, a, b, tags): # note that eigenvalue arrays returned by eig must be sorted since @@ -1064,6 +1086,8 @@ class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase): np.asarray(ev2)[..., None, :] * np.asarray(evc2), rtol=get_rtol(ev.dtype), err_msg=repr(a)) + +class TestEigh(object): def test_types(self): def check(dtype): x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype) @@ -1124,11 +1148,13 @@ class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase): assert_(isinstance(a, np.ndarray)) -class _TestNorm(object): - +class _TestNormBase(object): dt = None dec = None + +class _TestNormGeneral(_TestNormBase): + def test_empty(self): assert_equal(norm([]), 0.0) assert_equal(norm(array([], dtype=self.dt)), 0.0) @@ -1175,57 +1201,6 @@ class _TestNorm(object): assert_(issubclass(an.dtype.type, np.floating)) assert_almost_equal(an, 1.0) - def test_matrix_return_type(self): - a = np.array([[1, 0, 1], [0, 1, 1]]) - - exact_types = np.typecodes['AllInteger'] - - # float32, complex64, float64, complex128 types are the only types - # allowed by `linalg`, which performs the matrix operations used - # within `norm`. - inexact_types = 'fdFD' - - all_types = exact_types + inexact_types - - for each_inexact_types in all_types: - at = a.astype(each_inexact_types) - - an = norm(at, -np.inf) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 2.0) - - with suppress_warnings() as sup: - sup.filter(RuntimeWarning, "divide by zero encountered") - an = norm(at, -1) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 1.0) - - an = norm(at, 1) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 2.0) - - an = norm(at, 2) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 3.0**(1.0/2.0)) - - an = norm(at, -2) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 1.0) - - an = norm(at, np.inf) - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 2.0) - - an = norm(at, 'fro') - assert_(issubclass(an.dtype.type, np.floating)) - assert_almost_equal(an, 2.0) - - an = norm(at, 'nuc') - assert_(issubclass(an.dtype.type, np.floating)) - # Lower bar needed to support low precision floats. - # They end up being off by 1 in the 7th place. - old_assert_almost_equal(an, 2.7320508075688772, decimal=6) - def test_vector(self): a = [1, 2, 3, 4] b = [-1, -2, -3, -4] @@ -1256,39 +1231,6 @@ class _TestNorm(object): array(c, dtype=self.dt)): _test(v) - def test_matrix_2x2(self): - A = matrix([[1, 3], [5, 7]], dtype=self.dt) - assert_almost_equal(norm(A), 84 ** 0.5) - assert_almost_equal(norm(A, 'fro'), 84 ** 0.5) - assert_almost_equal(norm(A, 'nuc'), 10.0) - assert_almost_equal(norm(A, inf), 12.0) - assert_almost_equal(norm(A, -inf), 4.0) - assert_almost_equal(norm(A, 1), 10.0) - assert_almost_equal(norm(A, -1), 6.0) - assert_almost_equal(norm(A, 2), 9.1231056256176615) - assert_almost_equal(norm(A, -2), 0.87689437438234041) - - assert_raises(ValueError, norm, A, 'nofro') - assert_raises(ValueError, norm, A, -3) - assert_raises(ValueError, norm, A, 0) - - def test_matrix_3x3(self): - # This test has been added because the 2x2 example - # happened to have equal nuclear norm and induced 1-norm. - # The 1/10 scaling factor accommodates the absolute tolerance - # used in assert_almost_equal. - A = (1 / 10) * \ - np.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt) - assert_almost_equal(norm(A), (1 / 10) * 89 ** 0.5) - assert_almost_equal(norm(A, 'fro'), (1 / 10) * 89 ** 0.5) - assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836) - assert_almost_equal(norm(A, inf), 1.1) - assert_almost_equal(norm(A, -inf), 0.6) - assert_almost_equal(norm(A, 1), 1.0) - assert_almost_equal(norm(A, -1), 0.4) - assert_almost_equal(norm(A, 2), 0.88722940323461277) - assert_almost_equal(norm(A, -2), 0.19456584790481812) - def test_axis(self): # Vector norms. # Compare the use of `axis` with computing the norm of each row @@ -1368,10 +1310,103 @@ class _TestNorm(object): assert_(found.shape == expected_shape, shape_err.format(found.shape, expected_shape, order, k)) + +class _TestNorm2D(_TestNormBase): + # Define the part for 2d arrays separately, so we can subclass this + # and run the tests using np.matrix in matrixlib.tests.test_matrix_linalg. + array = np.array + + def test_matrix_empty(self): + assert_equal(norm(self.array([[]], dtype=self.dt)), 0.0) + + def test_matrix_return_type(self): + a = self.array([[1, 0, 1], [0, 1, 1]]) + + exact_types = np.typecodes['AllInteger'] + + # float32, complex64, float64, complex128 types are the only types + # allowed by `linalg`, which performs the matrix operations used + # within `norm`. + inexact_types = 'fdFD' + + all_types = exact_types + inexact_types + + for each_inexact_types in all_types: + at = a.astype(each_inexact_types) + + an = norm(at, -np.inf) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 2.0) + + with suppress_warnings() as sup: + sup.filter(RuntimeWarning, "divide by zero encountered") + an = norm(at, -1) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 1.0) + + an = norm(at, 1) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 2.0) + + an = norm(at, 2) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 3.0**(1.0/2.0)) + + an = norm(at, -2) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 1.0) + + an = norm(at, np.inf) + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 2.0) + + an = norm(at, 'fro') + assert_(issubclass(an.dtype.type, np.floating)) + assert_almost_equal(an, 2.0) + + an = norm(at, 'nuc') + assert_(issubclass(an.dtype.type, np.floating)) + # Lower bar needed to support low precision floats. + # They end up being off by 1 in the 7th place. + np.testing.assert_almost_equal(an, 2.7320508075688772, decimal=6) + + def test_matrix_2x2(self): + A = self.array([[1, 3], [5, 7]], dtype=self.dt) + assert_almost_equal(norm(A), 84 ** 0.5) + assert_almost_equal(norm(A, 'fro'), 84 ** 0.5) + assert_almost_equal(norm(A, 'nuc'), 10.0) + assert_almost_equal(norm(A, inf), 12.0) + assert_almost_equal(norm(A, -inf), 4.0) + assert_almost_equal(norm(A, 1), 10.0) + assert_almost_equal(norm(A, -1), 6.0) + assert_almost_equal(norm(A, 2), 9.1231056256176615) + assert_almost_equal(norm(A, -2), 0.87689437438234041) + + assert_raises(ValueError, norm, A, 'nofro') + assert_raises(ValueError, norm, A, -3) + assert_raises(ValueError, norm, A, 0) + + def test_matrix_3x3(self): + # This test has been added because the 2x2 example + # happened to have equal nuclear norm and induced 1-norm. + # The 1/10 scaling factor accommodates the absolute tolerance + # used in assert_almost_equal. + A = (1 / 10) * \ + self.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt) + assert_almost_equal(norm(A), (1 / 10) * 89 ** 0.5) + assert_almost_equal(norm(A, 'fro'), (1 / 10) * 89 ** 0.5) + assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836) + assert_almost_equal(norm(A, inf), 1.1) + assert_almost_equal(norm(A, -inf), 0.6) + assert_almost_equal(norm(A, 1), 1.0) + assert_almost_equal(norm(A, -1), 0.4) + assert_almost_equal(norm(A, 2), 0.88722940323461277) + assert_almost_equal(norm(A, -2), 0.19456584790481812) + def test_bad_args(self): # Check that bad arguments raise the appropriate exceptions. - A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) + A = self.array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) # Using `axis=<integer>` or passing in a 1-D array implies vector @@ -1395,6 +1430,10 @@ class _TestNorm(object): assert_raises(ValueError, norm, B, None, (0, 1, 2)) +class _TestNorm(_TestNorm2D, _TestNormGeneral): + pass + + class TestNorm_NonSystematic(object): def test_longdouble_norm(self): @@ -1422,21 +1461,34 @@ class TestNorm_NonSystematic(object): old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=5) -class TestNormDouble(_TestNorm): +# Separate definitions so we can use them for matrix tests. +class _TestNormDoubleBase(_TestNormBase): dt = np.double dec = 12 -class TestNormSingle(_TestNorm): +class _TestNormSingleBase(_TestNormBase): dt = np.float32 dec = 6 -class TestNormInt64(_TestNorm): +class _TestNormInt64Base(_TestNormBase): dt = np.int64 dec = 12 +class TestNormDouble(_TestNorm, _TestNormDoubleBase): + pass + + +class TestNormSingle(_TestNorm, _TestNormSingleBase): + pass + + +class TestNormInt64(_TestNorm, _TestNormInt64Base): + pass + + class TestMatrixRank(object): def test_matrix_rank(self): @@ -1487,6 +1539,8 @@ def test_reduced_rank(): class TestQR(object): + # Define the array class here, so run this on matrices elsewhere. + array = np.array def check_qr(self, a): # This test expects the argument `a` to be an ndarray or @@ -1537,7 +1591,7 @@ class TestQR(object): # of the functions in lapack_lite. Consequently, this test is # very limited in scope. Note that the results are in FORTRAN # order, hence the h arrays are transposed. - a = array([[1, 2], [3, 4], [5, 6]], dtype=np.double) + a = self.array([[1, 2], [3, 4], [5, 6]], dtype=np.double) # Test double h, tau = linalg.qr(a, mode='raw') @@ -1553,22 +1607,21 @@ class TestQR(object): assert_(tau.shape == (2,)) def test_mode_all_but_economic(self): - a = array([[1, 2], [3, 4]]) - b = array([[1, 2], [3, 4], [5, 6]]) + a = self.array([[1, 2], [3, 4]]) + b = self.array([[1, 2], [3, 4], [5, 6]]) for dt in "fd": m1 = a.astype(dt) m2 = b.astype(dt) self.check_qr(m1) self.check_qr(m2) self.check_qr(m2.T) - self.check_qr(matrix(m1)) + for dt in "fd": m1 = 1 + 1j * a.astype(dt) m2 = 1 + 1j * b.astype(dt) self.check_qr(m1) self.check_qr(m2) self.check_qr(m2.T) - self.check_qr(matrix(m1)) def test_0_size(self): # There may be good ways to do (some of this) reasonably: diff --git a/numpy/linalg/umath_linalg.c.src b/numpy/linalg/umath_linalg.c.src index 03fdd387a..3a5ad7250 100644 --- a/numpy/linalg/umath_linalg.c.src +++ b/numpy/linalg/umath_linalg.c.src @@ -382,17 +382,11 @@ typedef f2c_doublecomplex fortran_doublecomplex; ***************************************************************************** */ -static NPY_INLINE void * -offset_ptr(void* ptr, ptrdiff_t offset) -{ - return (void*)((npy_uint8*)ptr + offset); -} - static NPY_INLINE int get_fp_invalid_and_clear(void) { int status; - status = npy_clear_floatstatus(); + status = npy_clear_floatstatus_barrier((char*)&status); return !!(status & NPY_FPE_INVALID); } @@ -403,7 +397,7 @@ set_fp_invalid_or_clear(int error_occurred) npy_set_floatstatus_invalid(); } else { - npy_clear_floatstatus(); + npy_clear_floatstatus_barrier((char*)&error_occurred); } } @@ -577,104 +571,6 @@ dump_linearize_data(const char* name, const LINEARIZE_DATA_t* params) params->row_strides, params->column_strides); } - -static NPY_INLINE float -FLOAT_add(float op1, float op2) -{ - return op1 + op2; -} - -static NPY_INLINE double -DOUBLE_add(double op1, double op2) -{ - return op1 + op2; -} - -static NPY_INLINE COMPLEX_t -CFLOAT_add(COMPLEX_t op1, COMPLEX_t op2) -{ - COMPLEX_t result; - result.array[0] = op1.array[0] + op2.array[0]; - result.array[1] = op1.array[1] + op2.array[1]; - - return result; -} - -static NPY_INLINE DOUBLECOMPLEX_t -CDOUBLE_add(DOUBLECOMPLEX_t op1, DOUBLECOMPLEX_t op2) -{ - DOUBLECOMPLEX_t result; - result.array[0] = op1.array[0] + op2.array[0]; - result.array[1] = op1.array[1] + op2.array[1]; - - return result; -} - -static NPY_INLINE float -FLOAT_mul(float op1, float op2) -{ - return op1*op2; -} - -static NPY_INLINE double -DOUBLE_mul(double op1, double op2) -{ - return op1*op2; -} - - -static NPY_INLINE COMPLEX_t -CFLOAT_mul(COMPLEX_t op1, COMPLEX_t op2) -{ - COMPLEX_t result; - result.array[0] = op1.array[0]*op2.array[0] - op1.array[1]*op2.array[1]; - result.array[1] = op1.array[1]*op2.array[0] + op1.array[0]*op2.array[1]; - - return result; -} - -static NPY_INLINE DOUBLECOMPLEX_t -CDOUBLE_mul(DOUBLECOMPLEX_t op1, DOUBLECOMPLEX_t op2) -{ - DOUBLECOMPLEX_t result; - result.array[0] = op1.array[0]*op2.array[0] - op1.array[1]*op2.array[1]; - result.array[1] = op1.array[1]*op2.array[0] + op1.array[0]*op2.array[1]; - - return result; -} - -static NPY_INLINE float -FLOAT_mulc(float op1, float op2) -{ - return op1*op2; -} - -static NPY_INLINE double -DOUBLE_mulc(float op1, float op2) -{ - return op1*op2; -} - -static NPY_INLINE COMPLEX_t -CFLOAT_mulc(COMPLEX_t op1, COMPLEX_t op2) -{ - COMPLEX_t result; - result.array[0] = op1.array[0]*op2.array[0] + op1.array[1]*op2.array[1]; - result.array[1] = op1.array[0]*op2.array[1] - op1.array[1]*op2.array[0]; - - return result; -} - -static NPY_INLINE DOUBLECOMPLEX_t -CDOUBLE_mulc(DOUBLECOMPLEX_t op1, DOUBLECOMPLEX_t op2) -{ - DOUBLECOMPLEX_t result; - result.array[0] = op1.array[0]*op2.array[0] + op1.array[1]*op2.array[1]; - result.array[1] = op1.array[0]*op2.array[1] - op1.array[1]*op2.array[0]; - - return result; -} - static NPY_INLINE void print_FLOAT(npy_float s) { diff --git a/numpy/matrixlib/tests/test_matrix_linalg.py b/numpy/matrixlib/tests/test_matrix_linalg.py new file mode 100644 index 000000000..6fc733c2e --- /dev/null +++ b/numpy/matrixlib/tests/test_matrix_linalg.py @@ -0,0 +1,95 @@ +""" Test functions for linalg module using the matrix class.""" +from __future__ import division, absolute_import, print_function + +import numpy as np + +from numpy.linalg.tests.test_linalg import ( + LinalgCase, apply_tag, TestQR as _TestQR, LinalgTestCase, + _TestNorm2D, _TestNormDoubleBase, _TestNormSingleBase, _TestNormInt64Base, + SolveCases, InvCases, EigvalsCases, EigCases, SVDCases, CondCases, + PinvCases, DetCases, LstsqCases) + + +CASES = [] + +# square test cases +CASES += apply_tag('square', [ + LinalgCase("0x0_matrix", + np.empty((0, 0), dtype=np.double).view(np.matrix), + np.empty((0, 1), dtype=np.double).view(np.matrix), + tags={'size-0'}), + LinalgCase("matrix_b_only", + np.array([[1., 2.], [3., 4.]]), + np.matrix([2., 1.]).T), + LinalgCase("matrix_a_and_b", + np.matrix([[1., 2.], [3., 4.]]), + np.matrix([2., 1.]).T), +]) + +# hermitian test-cases +CASES += apply_tag('hermitian', [ + LinalgCase("hmatrix_a_and_b", + np.matrix([[1., 2.], [2., 1.]]), + None), +]) +# No need to make generalized or strided cases for matrices. + + +class MatrixTestCase(LinalgTestCase): + TEST_CASES = CASES + + +class TestSolveMatrix(SolveCases, MatrixTestCase): + pass + + +class TestInvMatrix(InvCases, MatrixTestCase): + pass + + +class TestEigvalsMatrix(EigvalsCases, MatrixTestCase): + pass + + +class TestEigMatrix(EigCases, MatrixTestCase): + pass + + +class TestSVDMatrix(SVDCases, MatrixTestCase): + pass + + +class TestCondMatrix(CondCases, MatrixTestCase): + pass + + +class TestPinvMatrix(PinvCases, MatrixTestCase): + pass + + +class TestDetMatrix(DetCases, MatrixTestCase): + pass + + +class TestLstsqMatrix(LstsqCases, MatrixTestCase): + pass + + +class _TestNorm2DMatrix(_TestNorm2D): + array = np.matrix + + +class TestNormDoubleMatrix(_TestNorm2DMatrix, _TestNormDoubleBase): + pass + + +class TestNormSingleMatrix(_TestNorm2DMatrix, _TestNormSingleBase): + pass + + +class TestNormInt64Matrix(_TestNorm2DMatrix, _TestNormInt64Base): + pass + + +class TestQRMatrix(_TestQR): + array = np.matrix diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index 8ef153c15..b45b3146f 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -4901,10 +4901,24 @@ cdef class RandomState: """ if isinstance(x, (int, long, np.integer)): arr = np.arange(x) - else: - arr = np.array(x) - self.shuffle(arr) - return arr + self.shuffle(arr) + return arr + + arr = np.asarray(x) + + # shuffle has fast-path for 1-d + if arr.ndim == 1: + # must return a copy + if arr is x: + arr = np.array(arr) + self.shuffle(arr) + return arr + + # Shuffle index array, dtype to ensure fast path + idx = np.arange(arr.shape[0], dtype=np.intp) + self.shuffle(idx) + return arr[idx] + _rand = RandomState() seed = _rand.seed diff --git a/numpy/testing/_private/decorators.py b/numpy/testing/_private/decorators.py index 60d3f968f..24c4e385d 100644 --- a/numpy/testing/_private/decorators.py +++ b/numpy/testing/_private/decorators.py @@ -34,7 +34,7 @@ def slow(t): The exact definition of a slow test is obviously both subjective and hardware-dependent, but in general any individual test that requires more - than a second or two should be labeled as slow (the whole suite consits of + than a second or two should be labeled as slow (the whole suite consists of thousands of tests, so even a second is significant). Parameters diff --git a/site.cfg.example b/site.cfg.example index 645b48543..21609a332 100644 --- a/site.cfg.example +++ b/site.cfg.example @@ -180,6 +180,14 @@ # mkl_libs = mkl_rt # lapack_libs = +# ACCELERATE +# ---------- +# Accelerate/vecLib is an OSX framework providing a BLAS and LAPACK implementations. +# +# [accelerate] +# libraries = Accelerate, vecLib +# #libraries = None + # UMFPACK # ------- # The UMFPACK library is used in scikits.umfpack to factor large sparse matrices. |