diff options
113 files changed, 9850 insertions, 7461 deletions
diff --git a/.gitignore b/.gitignore index f54e63b89..416299a1f 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,8 @@ .*.sw[nop] .sw[nop] *.tmp +*.vim +tags # Compiled source # ################### @@ -90,3 +92,29 @@ numpy/core/include/numpy/_numpyconfig.h numpy/version.py site.cfg .tox +numpy/core/include/numpy/__multiarray_api.c +numpy/core/include/numpy/__ufunc_api.c +numpy/core/include/numpy/__umath_generated.c +numpy/core/include/numpy/config.h +numpy/core/include/numpy/multiarray_api.txt +numpy/core/include/numpy/ufunc_api.txt +numpy/core/lib/ +numpy/core/src/multiarray/arraytypes.c +numpy/core/src/multiarray/einsum.c +numpy/core/src/multiarray/lowlevel_strided_loops.c +numpy/core/src/multiarray/multiarray_tests.c +numpy/core/src/multiarray/nditer_templ.c +numpy/core/src/multiarray/scalartypes.c +numpy/core/src/npymath/ieee754.c +numpy/core/src/npymath/npy_math.c +numpy/core/src/npymath/npy_math_complex.c +numpy/core/src/npysort/heapsort.c +numpy/core/src/npysort/mergesort.c +numpy/core/src/npysort/quicksort.c +numpy/core/src/npysort/sort.c +numpy/core/src/scalarmathmodule.c +numpy/core/src/umath/funcs.inc +numpy/core/src/umath/loops.c +numpy/core/src/umath/umath_tests.c +numpy/distutils/__config__.py +doc/source/reference/generated
\ No newline at end of file diff --git a/.travis.yml b/.travis.yml index 2c15d9dbc..d310f8d2a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -23,6 +23,10 @@ matrix: include: - python: 2.5 env: TEST_PY24="true" + - python: 2.7 + env: NPY_SEPARATE_COMPILATION=1 + - python: 3.2 + env: NPY_SEPARATE_COMPILATION=1 before_install: - mkdir builds - pushd builds diff --git a/doc/HOWTO_RELEASE.rst.txt b/doc/HOWTO_RELEASE.rst.txt index 6fd33e868..b933cbecd 100644 --- a/doc/HOWTO_RELEASE.rst.txt +++ b/doc/HOWTO_RELEASE.rst.txt @@ -38,14 +38,14 @@ Python 2.4-2.7 and >=3.1 are the currently supported versions on all platforms. OS X ---- -OS X 10.4 (Tiger), 10.5 (Leopard) and 10.6 (Snow Leopard) are supported. -Note that only the Python from `python.org <http://python.org>`_ is -supported. Binaries do *not* support Apple Python. +OS X versions 10.4 - 10.7 are supported. Note that there are currently still +issues with compiling on 10.7, due to Apple moving to gcc-llvm. +Only the Python from `python.org <http://python.org>`_ is supported. Binaries +do *not* support Apple Python. Windows ------- -Windows XP and Windows Vista are supported by the 1.3.0 release, the 1.4.x -releases also support Windows 7. +Windows XP, Vista and 7 are supported. Linux ----- @@ -66,10 +66,15 @@ Compilers The same gcc version is used as the one with which Python itself is built on each platform. At the moment this means: -* OS X uses gcc-4.0 (since that is what Python itself is built with). -* Windows build uses the latest released version from MinGW. Note that NumPy - releases until 1.6.x and SciPy releases until 0.10.x were made with GCC - 3.4.5. For NumPy 1.7.x ad later GCC 4.x is required. +* OS X uses gcc-4.0 (since that is what Python itself is built with) up to + Python 2.6. Python 2.7 comes in two flavors; the 32-bit version is built with + gcc-4.0 and the 64-bit version with gcc-4.2. The "release.sh" script + sets environment variables to pick the right compiler. + All binaries should be built on OS X 10.5, with the exception of the 64-bit + Python 2.7 one which should be built on 10.6. +* Windows builds use MinGW 3.4.5. Updating this to a more recent MinGW with + GCC 4.x is desired, but there are still practical difficulties in building + the binary installers. Cython is not needed for building the binaries, because generated C files from Cython sources are checked in at the moment. It is worth keeping an eye on what @@ -85,7 +90,8 @@ Python * Python(s) from `python.org <http://python.org>`_ * virtualenv * paver -* bdist_mpkg +* bdist_mpkg from https://github.com/rgommers/bdist_mpkg (has a necessary + patch, don't use the unsupported version on PyPi). Python itself should be installed multiple times - each version a binary is built for should be installed. The other dependencies only have to be installed @@ -150,10 +156,7 @@ add adding a PATH variable in HKEY_CURRENT_USER/Environment. Virtualenv ---------- Virtualenv is a very useful tool to keep several versions of packages around. -It is also used in the Paver script to build the docs. The use of the -``--no-site-packages`` option in the Paver script is probably not necessary, -and may prevent successful building of the docs. If doc building fails because -Matplotlib can not be found, remove this option. +It is also used in the Paver script to build the docs. What is released @@ -187,6 +190,15 @@ Check the buildbot ------------------ The buildbot is located at `<http://buildbot.scipy.org/>`_. +Agree on a release schedule +--------------------------- +A typical release schedule is one beta, two release candidates and a final +release. It's best to discuss the timing on the mailing list first, in order +for people to get their commits in on time, get doc wiki edits merged, etc. +After a date is set, create a new maintenance/x.y.z branch, add new empty +release notes for the next version in the master branch and update the Trac +Milestones. + Handle test warnings -------------------- The default behavior of the test suite in the master branch is to report errors @@ -230,7 +242,7 @@ Check deprecations ------------------ Before the release branch is made, it should be checked that all deprecated code that should be removed is actually removed, and all new deprecations say -in the dostring or deprecation warning at what version the code will be +in the docstring or deprecation warning at what version the code will be removed. Check the C API version number @@ -247,11 +259,12 @@ Check the release notes ----------------------- Check that the release notes are up-to-date, and mention at least the following: -- major new features -- deprecated and removed features -- supported Python versions -- for SciPy, supported NumPy version(s) -- outlook for the near future + + - major new features + - deprecated and removed features + - supported Python versions + - for SciPy, supported NumPy version(s) + - outlook for the near future Also make sure that as soon as the branch is made, there is a new release notes file in trunk for the next release. diff --git a/doc/TESTS.rst.txt b/doc/TESTS.rst.txt index 9bde1849f..92f236f5a 100644 --- a/doc/TESTS.rst.txt +++ b/doc/TESTS.rst.txt @@ -107,6 +107,11 @@ whether a certain assumption is valid. If the assertion fails, the test fails. Note that the Python builtin ``assert`` should not be used, because it is stripped during compilation with ``-O``. +Note that ``test_`` functions or methods should not have a docstring, because +that makes it hard to identify the test from the output of running the test +suite with ``verbose=2`` (or similar verbosity setting). Use plain comments +(``#``) if necessary. + Sometimes it is convenient to run ``test_yyy.py`` by itself, so we add :: @@ -174,7 +179,7 @@ nose decorators:: @nose.with_setup(setup_func, teardown_func) def test_with_extras(): - """This test uses the setup/teardown functions.""" + # This test uses the setup/teardown functions. global helpful_variable print " In test_with_extras" print " Helpful is %s" % helpful_variable @@ -198,6 +203,15 @@ Note that 'check_even' is not itself a test (no 'test' in the name), but 'test_evens' is a generator that returns a series of tests, using 'check_even', across a range of inputs. +A problem with generator tests can be that if a test is failing, it's +hard to see for which parameters. To avoid this problem, ensure that: + + - No computation related to the features tested is done in the + ``test_*`` generator function, but delegated to a corresponding + ``check_*`` function (can be inside the generator, to share namespace). + - The generators are used *solely* for loops over parameters. + - Those parameters are *not* arrays. + .. warning:: Parametric tests cannot be implemented on classes derived from diff --git a/doc/release/1.7.0-notes.rst b/doc/release/1.7.0-notes.rst new file mode 100644 index 000000000..e8b1de72d --- /dev/null +++ b/doc/release/1.7.0-notes.rst @@ -0,0 +1,279 @@ +========================= +NumPy 1.7.0 Release Notes +========================= + +This release includes several new features as well as numerous bug fixes and +refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.2. + +Highlights +========== + +* ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose + where a computation should be done) +* ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general + cleanup and bug fixes) +* ``numpy.random.choice`` (random sample generating function) + + +Compatibility notes +=================== + +In a future version of numpy, the functions np.diag, np.diagonal, and +the diagonal method of ndarrays will return a view onto the original +array, instead of producing a copy as they do now. This makes a +difference if you write to the array returned by any of these +functions. To facilitate this transition, numpy 1.7 produces a +FutureWarning if it detects that you may be attempting to write to +such an array. See the documentation for np.diagonal for details. + +The default casting rule for UFunc out= parameters has been changed from +'unsafe' to 'same_kind'. Most usages which violate the 'same_kind' +rule are likely bugs, so this change may expose previously undetected +errors in projects that depend on NumPy. + +Full-array boolean indexing has been optimized to use a different, +optimized code path. This code path should produce the same results, +but any feedback about changes to your code would be appreciated. + +Attempting to write to a read-only array (one with +``arr.flags.writeable`` set to ``False``) used to raise either a +RuntimeError, ValueError, or TypeError inconsistently, depending on +which code path was taken. It now consistently raises a ValueError. + +The <ufunc>.reduce functions evaluate some reductions in a different +order than in previous versions of NumPy, generally providing higher +performance. Because of the nature of floating-point arithmetic, this +may subtly change some results, just as linking NumPy to a different +BLAS implementations such as MKL can. + +If upgrading from 1.5, then generally in 1.6 and 1.7 there have been +substantial code added and some code paths altered, particularly in +the areas of type resolution and buffered iteration over universal +functions. This might have an impact on your code particularly if +you relied on accidental behavior in the past. + +New features +============ + +Mask-based NA missing values +---------------------------- + +Preliminary support for NA missing values similar to those in R has +been implemented. This was done by adding optional NA masks to the core +array object. + +.. note:: The NA API is *experimental*, and may undergo changes in future + versions of NumPy. The current implementation based on masks will likely be + supplemented by a second one based on bit-patterns, and it is possible that + a difference will be made between missing and ignored data. + +While a significant amount of the NumPy functionality has been extended to +support NA masks, not everything is yet supported. Here is an (incomplete) +list of things that do and do not work with NA values: + +What works with NA: + * Basic indexing and slicing, as well as full boolean mask indexing. + * All element-wise ufuncs. + * All UFunc.reduce methods, with a new skipna parameter. + * np.all and np.any satisfy the rules NA | True == True and + NA & False == False + * The nditer object. + * Array methods: + + ndarray.clip, ndarray.min, ndarray.max, ndarray.sum, ndarray.prod, + ndarray.conjugate, ndarray.diagonal, ndarray.flatten + + numpy.concatenate, numpy.column_stack, numpy.hstack, + numpy.vstack, numpy.dstack, numpy.squeeze, numpy.mean, numpy.std, + numpy.var + +What doesn't work with NA: + * Fancy indexing, such as with lists and partial boolean masks. + * ndarray.flat and any other methods that use the old iterator + mechanism instead of the newer nditer. + * Struct dtypes, which will have corresponding struct masks with + one mask value per primitive field of the struct dtype. + * UFunc.accumulate, UFunc.reduceat. + * Ufunc calls with both NA masks and a where= mask at the same time. + * File I/O has not been extended to support NA-masks. + * np.logical_and and np.logical_or don't satisfy the + rules NA | True == True and NA & False == False yet. + * Array methods: + + ndarray.argmax, ndarray.argmin, + + numpy.repeat, numpy.delete (relies on fancy indexing), + numpy.append, numpy.insert (relies on fancy indexing), + numpy.where, + +Differences with R: + * R's parameter rm.na=T is spelled skipna=True in NumPy. + * np.isna(nan) is False, but R's is.na(nan) is TRUE. This is because + NumPy's NA is treated independently of the underlying data type. + * Boolean indexing, where the result is compressed to just + the elements with true in the mask, raises if the boolean mask + has an NA value in it. This is because that value could be either + True or False, meaning the count of the output array is actually + NA. R treats this case in a manner inconsistent with the NA model, + returning NA values in the spots where the boolean index has NA. + This may have a practical advantage in spite of violating the + NA theoretical model, so NumPy could adopt the behavior if necessary + +For more information, see `doc/neps/missing-data.rst <https://github.com/numpy/numpy/blob/maintenance/1.7.x/doc/neps/missing-data.rst>`_. + +Reduction UFuncs Generalize axis= Parameter +------------------------------------------- + +Any ufunc.reduce function call, as well as other reductions like +sum, prod, any, all, max and min support the ability to choose +a subset of the axes to reduce over. Previously, one could say +axis=None to mean all the axes or axis=# to pick a single axis. +Now, one can also say axis=(#,#) to pick a list of axes for reduction. + +Reduction UFuncs New keepdims= Parameter +---------------------------------------- + +There is a new keepdims= parameter, which if set to True, doesn't +throw away the reduction axes but instead sets them to have size one. +When this option is set, the reduction result will broadcast correctly +to the original operand which was reduced. + +Datetime support +---------------- + +.. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes + in future versions of NumPy. + +There have been a lot of fixes and enhancements to datetime64 compared +to NumPy 1.6: + +* the parser is quite strict about only accepting ISO 8601 dates, with a few + convenience extensions +* converts between units correctly +* datetime arithmetic works correctly +* business day functionality (allows the datetime to be used in contexts where + only certain days of the week are valid) + +The notes in `doc/source/reference/arrays.datetime.rst <https://github.com/numpy/numpy/blob/maintenance/1.7.x/doc/source/reference/arrays.datetime.rst>`_ +(also available in the online docs at `arrays.datetime.html +<http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html>`_) should be +consulted for more details. + +Custom formatter for printing arrays +------------------------------------ + +See the new ``formatter`` parameter of the ``numpy.set_printoptions`` +function. + +New function numpy.random.choice +--------------------------------- + +A generic sampling function has been added which will generate samples from +a given array-like. The samples can be with or without replacement, and +with uniform or given non-uniform probabilities. + +New function isclose +-------------------- + +Returns a boolean array where two arrays are element-wise equal within a +tolerance. Both relative and absolute tolerance can be specified. The +function is NA aware. + +Preliminary multi-dimensional support in the polynomial package +--------------------------------------------------------------- + +Axis keywords have been added to the integration and differentiation +functions and a tensor keyword was added to the evaluation functions. +These additions allow multi-dimensional coefficient arrays to be used in +those functions. New functions for evaluating 2-D and 3-D coefficient +arrays on grids or sets of points were added together with 2-D and 3-D +pseudo-Vandermonde matrices that can be used for fitting. + +Support for mask-based NA values in the polynomial package fits +--------------------------------------------------------------- + +The fitting functions recognize and remove masked data from the fit. + +Ability to pad rank-n arrays +---------------------------- + +A pad module containing functions for padding n-dimensional arrays has +been added. The various private padding functions are exposed as options to +a public 'pad' function. Example:: + + pad(a, 5, mode='mean') + +Current modes are ``constant``, ``edge``, ``linear_ramp``, ``maximum``, +``mean``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``, and +``<function>``. + + +New argument to searchsorted +---------------------------- + +The function searchsorted now accepts a 'sorter' argument that is a +permuation array that sorts the array to search. + +C API +----- + +New function ``PyArray_RequireWriteable`` provides a consistent +interface for checking array writeability -- any C code which works +with arrays whose WRITEABLE flag is not known to be True a priori, +should make sure to call this function before writing. + +NumPy C Style Guide added (``doc/C_STYLE_GUIDE.rst.txt``). + +Changes +======= + +General +------- + +The function np.concatenate tries to match the layout of its input +arrays. Previously, the layout did not follow any particular reason, +and depended in an undesirable way on the particular axis chosen for +concatenation. A bug was also fixed which silently allowed out of bounds +axis arguments. + +The ufuncs logical_or, logical_and, and logical_not now follow Python's +behavior with object arrays, instead of trying to call methods on the +objects. For example the expression (3 and 'test') produces the string +'test', and now np.logical_and(np.array(3, 'O'), np.array('test', 'O')) +produces 'test' as well. + +C-API +----- + +The following macros now require trailing semicolons:: + + NPY_BEGIN_THREADS_DEF + NPY_BEGIN_THREADS + NPY_ALLOW_C_API + NPY_ALLOW_C_API_DEF + NPY_DISABLE_C_API + + +Deprecations +============ + +General +------- + +Specifying a custom string formatter with a `_format` array attribute is +deprecated. The new `formatter` keyword in ``numpy.set_printoptions`` or +``numpy.array2string`` can be used instead. + +The deprecated imports in the polynomial package have been removed. + +C-API +----- + +Direct access to the fields of PyArrayObject* has been deprecated. Direct +access has been recommended against for many releases. Expect similar +deprecations for PyArray_Descr* and other core objects in the future as +preparation for NumPy 2.0. + +The macros in old_defines.h are deprecated and will be removed in the next +minor release (>= 1.8). The sed script tools/replace_old_macros.sed can +be used to replace these macros with the newer versions. + +You can test your code against the deprecated C API by #defining +NPY_NO_DEPRECATED_API to the target version number, for example +NPY_1_7_API_VERSION, before including any NumPy headers. diff --git a/doc/source/dev/gitwash/development_setup.rst b/doc/source/dev/gitwash/development_setup.rst index 489a2e75d..0cff3c214 100644 --- a/doc/source/dev/gitwash/development_setup.rst +++ b/doc/source/dev/gitwash/development_setup.rst @@ -38,8 +38,8 @@ Create your own forked copy of NumPy_ .. image:: forking_button.png - Now, after a short pause and some 'Hardcore forking action', you - should find yourself at the home page for your own forked copy of NumPy_. + After a short pause, you should find yourself at the home page for + your own forked copy of NumPy_. .. include:: git_links.inc @@ -49,7 +49,7 @@ Create your own forked copy of NumPy_ Set up your fork ################ -First you follow the instructions for :ref:`forking`. +First you follow the instructions for :ref:`forking`. Overview ======== @@ -110,4 +110,3 @@ Just for your own satisfaction, show yourself that you now have a new origin git@github.com:your-user-name/numpy.git (push) .. include:: git_links.inc - diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 8736cbc3f..bf8077a69 100644 --- a/doc/source/reference/c-api.array.rst +++ b/doc/source/reference/c-api.array.rst @@ -289,6 +289,7 @@ From scratch Fill the array pointed to by *obj* ---which must be a (subclass of) bigndarray---with the contents of *val* (evaluated as a byte). + This macro calls memset, so obj must be contiguous. .. cfunction:: PyObject* PyArray_Zeros(int nd, npy_intp* dims, PyArray_Descr* dtype, int fortran) diff --git a/doc/source/reference/index.rst b/doc/source/reference/index.rst index 2e881542e..128339e16 100644 --- a/doc/source/reference/index.rst +++ b/doc/source/reference/index.rst @@ -7,6 +7,7 @@ NumPy Reference :Release: |version| :Date: |today| + .. module:: numpy This reference manual details functions, modules, and objects @@ -20,7 +21,7 @@ For learning how to use NumPy, see also :ref:`user`. arrays ufuncs routines - ctypes + ctypeslib distutils c-api internals diff --git a/doc/source/reference/routines.matlib.rst b/doc/source/reference/routines.matlib.rst index 7f8a9eabb..a35eaec78 100644 --- a/doc/source/reference/routines.matlib.rst +++ b/doc/source/reference/routines.matlib.rst @@ -7,5 +7,30 @@ This module contains all functions in the :mod:`numpy` namespace, with the following replacement functions that return :class:`matrices <matrix>` instead of :class:`ndarrays <ndarray>`. -.. automodule:: numpy.matlib +.. currentmodule:: numpy + +Functions that are also in the numpy namespace and return matrices + +.. autosummary:: + + mat + matrix + asmatrix + bmat + + +Replacement functions in `matlib` + +.. currentmodule:: numpy.matlib + +.. autosummary:: + :toctree: generated/ + empty + zeros + ones + eye + identity + repmat + rand + randn diff --git a/doc/source/reference/routines.polynomials.rst b/doc/source/reference/routines.polynomials.rst index 3e9b2603f..e85d0549b 100644 --- a/doc/source/reference/routines.polynomials.rst +++ b/doc/source/reference/routines.polynomials.rst @@ -1,12 +1,22 @@ Polynomials *********** -The polynomial package is newer and more complete than poly1d and the -convenience classes are better behaved in the numpy environment. When -backwards compatibility is not an issue it should be the package of choice. -Note that the various routines in the polynomial package all deal with -series whose coefficients go from degree zero upward, which is the reverse -of the poly1d convention. The easy way to remember this is that indexes +Polynomials in NumPy can be *created*, *manipulated*, and even *fitted* using +the :doc:`routines.polynomials.classes` +of the `numpy.polynomial` package, introduced in NumPy 1.4. + +Prior to NumPy 1.4, `numpy.poly1d` was the class of choice and it is still +available in order to maintain backward compatibility. +However, the newer Polynomial package is more complete than `numpy.poly1d` +and its convenience classes are better behaved in the numpy environment. +Therefore Polynomial is recommended for new coding. + +Transition notice +----------------- +The various routines in the Polynomial package all deal with +series whose coefficients go from degree zero upward, +which is the *reverse order* of the Poly1d convention. +The easy way to remember this is that indexes correspond to degree, i.e., coef[i] is the coefficient of the term of degree i. @@ -14,10 +24,9 @@ degree i. .. toctree:: :maxdepth: 2 - routines.polynomials.poly1d - + routines.polynomials.package .. toctree:: :maxdepth: 2 - routines.polynomials.package + routines.polynomials.poly1d diff --git a/doc/source/reference/routines.random.rst b/doc/source/reference/routines.random.rst index 84765c035..c8b097d7d 100644 --- a/doc/source/reference/routines.random.rst +++ b/doc/source/reference/routines.random.rst @@ -37,7 +37,7 @@ Distributions beta binomial chisquare - mtrand.dirichlet + dirichlet exponential f gamma @@ -75,7 +75,7 @@ Random generator .. autosummary:: :toctree: generated/ - mtrand.RandomState + RandomState seed get_state set_state diff --git a/doc/source/reference/routines.statistics.rst b/doc/source/reference/routines.statistics.rst index b41b62839..c420705af 100644 --- a/doc/source/reference/routines.statistics.rst +++ b/doc/source/reference/routines.statistics.rst @@ -4,17 +4,18 @@ Statistics .. currentmodule:: numpy -Extremal values ---------------- +Order statistics +---------------- .. autosummary:: :toctree: generated/ amin amax - nanmax nanmin + nanmax ptp + percentile Averages and variances ---------------------- diff --git a/numpy/add_newdocs.py b/numpy/add_newdocs.py index f550e2d41..a416559db 100644 --- a/numpy/add_newdocs.py +++ b/numpy/add_newdocs.py @@ -1822,10 +1822,21 @@ add_newdoc('numpy.core.multiarray', 'result_type', """) -add_newdoc('numpy.core.multiarray','newbuffer', - """newbuffer(size) +add_newdoc('numpy.core.multiarray', 'newbuffer', + """ + newbuffer(size) + + Return a new uninitialized buffer object. - Return a new uninitialized buffer object of size bytes + Parameters + ---------- + size : int + Size in bytes of returned buffer object. + + Returns + ------- + newbuffer : buffer object + Returned, uninitialized buffer object of `size` bytes. """) @@ -3717,8 +3728,9 @@ add_newdoc('numpy.core.multiarray', 'copyto', """ copyto(dst, src, casting='same_kind', where=None, preservena=False) - Copies values from `src` into `dst`, broadcasting as necessary. - Raises a TypeError if the casting rule is violated, and if + Copies values from one array to another, broadcasting as necessary. + + Raises a TypeError if the `casting` rule is violated, and if `where` is provided, it selects which elements to copy. .. versionadded:: 1.7.0 diff --git a/numpy/core/SConscript b/numpy/core/SConscript index 6f4484b6f..952544a25 100644 --- a/numpy/core/SConscript +++ b/numpy/core/SConscript @@ -381,8 +381,6 @@ arraytypes_src = env.GenerateFromTemplate( pjoin('src', 'multiarray', 'arraytypes.c.src')) nditer_src = env.GenerateFromTemplate( pjoin('src', 'multiarray', 'nditer_templ.c.src')) -boolean_ops_src = env.GenerateFromTemplate( - pjoin('src', 'multiarray', 'boolean_ops.c.src')) lowlevel_strided_loops_src = env.GenerateFromTemplate( pjoin('src', 'multiarray', 'lowlevel_strided_loops.c.src')) einsum_src = env.GenerateFromTemplate(pjoin('src', 'multiarray', 'einsum.c.src')) @@ -431,7 +429,9 @@ env.Install('$distutils_installdir/lib/npy-pkg-config', mlib_ini) env.Install('$distutils_installdir/lib/npy-pkg-config', npymath_ini) # npysort core lib -npysort_src = [env.GenerateFromTemplate(pjoin('src', 'npysort', 'sort.c.src'))] +npysort_src = [env.GenerateFromTemplate(pjoin('src', 'npysort', 'quicksort.c.src')), + env.GenerateFromTemplate(pjoin('src', 'npysort', 'mergesort.c.src')), + env.GenerateFromTemplate(pjoin('src', 'npysort', 'heapsort.c.src'))] env.StaticExtLibrary("npysort", npysort_src) env.Prepend(LIBS=["npysort"]) env.Prepend(LIBPATH=["."]) @@ -446,7 +446,6 @@ if ENABLE_SEPARATE_COMPILATION: pjoin('src', 'multiarray', 'array_assign.c'), pjoin('src', 'multiarray', 'array_assign_scalar.c'), pjoin('src', 'multiarray', 'array_assign_array.c'), - pjoin('src', 'multiarray', 'boolean_ops.c'), pjoin('src', 'multiarray', 'datetime.c'), pjoin('src', 'multiarray', 'datetime_strings.c'), pjoin('src', 'multiarray', 'datetime_busday.c'), @@ -456,8 +455,6 @@ if ENABLE_SEPARATE_COMPILATION: pjoin('src', 'multiarray', 'descriptor.c'), pjoin('src', 'multiarray', 'iterators.c'), pjoin('src', 'multiarray', 'mapping.c'), - pjoin('src', 'multiarray', 'na_mask.c'), - pjoin('src', 'multiarray', 'na_object.c'), pjoin('src', 'multiarray', 'number.c'), pjoin('src', 'multiarray', 'getset.c'), pjoin('src', 'multiarray', 'sequence.c'), @@ -483,7 +480,6 @@ if ENABLE_SEPARATE_COMPILATION: multiarray_src.extend(arraytypes_src) multiarray_src.extend(scalartypes_src) multiarray_src.extend(lowlevel_strided_loops_src) - multiarray_src.extend(boolean_ops_src) multiarray_src.extend(nditer_src) multiarray_src.extend(einsum_src) else: diff --git a/numpy/core/bento.info b/numpy/core/bento.info index 04401f991..bf8ae3a81 100644 --- a/numpy/core/bento.info +++ b/numpy/core/bento.info @@ -10,7 +10,9 @@ Library: src/npymath/halffloat.c CompiledLibrary: npysort Sources: - src/npysort/sort.c.src + src/npysort/quicksort.c.src, + src/npysort/mergesort.c.src, + src/npysort/heapsort.c.src Extension: multiarray Sources: src/multiarray/multiarraymodule_onefile.c diff --git a/numpy/core/bscript b/numpy/core/bscript index 3db659529..fcbfcb3e0 100644 --- a/numpy/core/bscript +++ b/numpy/core/bscript @@ -30,7 +30,11 @@ from setup_common \ C99_FUNCS_SINGLE, C99_COMPLEX_TYPES, C99_COMPLEX_FUNCS, \ MANDATORY_FUNCS, C_ABI_VERSION, C_API_VERSION -ENABLE_SEPARATE_COMPILATION = False +try: + val = os.environ['NPY_SEPARATE_COMPILATION'] + ENABLE_SEPARATE_COMPILATION = (val != "0") +except KeyError: + ENABLE_SEPARATE_COMPILATION = False NUMPYCONFIG_SYM = [] @@ -424,13 +428,11 @@ def pre_build(context): "src/multiarray/arraytypes.c.src", "src/multiarray/nditer_templ.c.src", "src/multiarray/lowlevel_strided_loops.c.src", - "src/multiarray/einsum.c.src", - "src/multiarray/boolean_ops.c.src"] + "src/multiarray/einsum.c.src"] bld(target="multiarray_templates", source=multiarray_templates) if ENABLE_SEPARATE_COMPILATION: sources = [pjoin('src', 'multiarray', 'arrayobject.c'), pjoin('src', 'multiarray', 'arraytypes.c.src'), - pjoin('src', 'multiarray', 'boolean_ops.c.src'), pjoin('src', 'multiarray', 'buffer.c'), pjoin('src', 'multiarray', 'calculation.c'), pjoin('src', 'multiarray', 'common.c'), diff --git a/numpy/core/code_generators/cversions.txt b/numpy/core/code_generators/cversions.txt index 99ea072ca..5bdb33c94 100644 --- a/numpy/core/code_generators/cversions.txt +++ b/numpy/core/code_generators/cversions.txt @@ -10,4 +10,4 @@ # PyArray_CountNonzero, PyArray_NewLikeArray and PyArray_MatrixProduct2. 0x00000006 = e61d5dc51fa1c6459328266e215d6987 # Version 7 (NumPy 1.7) improved datetime64, misc utilities. -0x00000007 = 1768b6c404a3d5a2a6bfe7c68f89e3aa +0x00000007 = e396ba3912dcf052eaee1b0b203a7724 diff --git a/numpy/core/code_generators/numpy_api.py b/numpy/core/code_generators/numpy_api.py index fd2b9628e..c49c3c346 100644 --- a/numpy/core/code_generators/numpy_api.py +++ b/numpy/core/code_generators/numpy_api.py @@ -324,6 +324,10 @@ multiarray_funcs_api = { 'PyArray_DebugPrint': 285, 'PyArray_FailUnlessWriteable': 286, 'PyArray_SetUpdateIfCopyBase': 287, + 'PyDataMem_NEW': 288, + 'PyDataMem_FREE': 289, + 'PyDataMem_RENEW': 290, + 'PyDataMem_SetEventHook': 291, } ufunc_types_api = { diff --git a/numpy/core/defchararray.py b/numpy/core/defchararray.py index b2deb6318..1f4b3bef8 100644 --- a/numpy/core/defchararray.py +++ b/numpy/core/defchararray.py @@ -259,19 +259,22 @@ def str_len(a): def add(x1, x2): """ - Return (x1 + x2), that is string concatenation, element-wise for a - pair of array_likes of str or unicode. + Return element-wise string concatenation for two arrays of str or unicode. + + Arrays `x1` and `x2` must have the same shape. Parameters ---------- x1 : array_like of str or unicode - + Input array. x2 : array_like of str or unicode + Input array. Returns ------- add : ndarray Output array of `string_` or `unicode_`, depending on input types + of the same shape as `x1` and `x2`. """ arr1 = numpy.asarray(x1) @@ -346,6 +349,7 @@ def capitalize(a): Parameters ---------- a : array_like of str or unicode + Input array of strings to capitalize. Returns ------- @@ -365,6 +369,7 @@ def capitalize(a): >>> np.char.capitalize(c) array(['A1b2', '1b2a', 'B2a1', '2a1b'], dtype='|S4') + """ a_arr = numpy.asarray(a) return _vec_string(a_arr, a_arr.dtype, 'capitalize') @@ -960,7 +965,7 @@ else: def lower(a): """ - Return an array with the elements of `a` converted to lowercase. + Return an array with the elements converted to lowercase. Call `str.lower` element-wise. @@ -968,11 +973,12 @@ def lower(a): Parameters ---------- - a : array-like of str or unicode + a : array_like, {str, unicode} + Input array. Returns ------- - out : ndarray, str or unicode + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also @@ -987,6 +993,7 @@ def lower(a): >>> np.char.lower(c) array(['a1b c', '1bca', 'bca1'], dtype='|S5') + """ a_arr = numpy.asarray(a) return _vec_string(a_arr, a_arr.dtype, 'lower') @@ -1000,18 +1007,19 @@ def lstrip(a, chars=None): Parameters ---------- - a : array-like of str or unicode + a : array-like, {str, unicode} + Input array. - chars : str or unicode, optional - The `chars` argument is a string specifying the set of - characters to be removed. If omitted or None, the `chars` - argument defaults to removing whitespace. The `chars` argument - is not a prefix; rather, all combinations of its values are - stripped. + chars : {str, unicode}, optional + The `chars` argument is a string specifying the set of + characters to be removed. If omitted or None, the `chars` + argument defaults to removing whitespace. The `chars` argument + is not a prefix; rather, all combinations of its values are + stripped. Returns ------- - out : ndarray, str or unicode + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also @@ -1024,9 +1032,14 @@ def lstrip(a, chars=None): >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='|S7') - >>> np.char.lstrip(c, 'a') # 'a' unstripped from c[1] because whitespace leading + + The 'a' variable is unstripped from c[1] because whitespace leading. + + >>> np.char.lstrip(c, 'a') array(['AaAaA', ' aA ', 'bBABba'], dtype='|S7') + + >>> np.char.lstrip(c, 'A') # leaves c unchanged array(['aAaAaA', ' aA ', 'abBABba'], dtype='|S7') @@ -1056,12 +1069,14 @@ if sys.version_info >= (2, 5): Parameters ---------- - a : array-like of str or unicode - sep : str or unicode + a : array_like, {str, unicode} + Input array + sep : {str, unicode} + Separator to split each string element in `a`. Returns ------- - out : ndarray + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type. The output array will have an extra dimension with 3 elements per input element. @@ -1069,6 +1084,7 @@ if sys.version_info >= (2, 5): See also -------- str.partition + """ return _to_string_or_unicode_array( _vec_string(a, object_, 'partition', (sep,))) @@ -1229,7 +1245,7 @@ else: if sys.version_info >= (2, 5): def rpartition(a, sep): """ - Partition each element in `a` around `sep`. + Partition (split) each element around the right-most separator. Calls `str.rpartition` element-wise. @@ -1241,8 +1257,10 @@ if sys.version_info >= (2, 5): Parameters ---------- - a : array-like of str or unicode + a : array_like of str or unicode + Input array sep : str or unicode + Right-most separator to split each element in array. Returns ------- @@ -1254,6 +1272,7 @@ if sys.version_info >= (2, 5): See also -------- str.rpartition + """ return _to_string_or_unicode_array( _vec_string(a, object_, 'rpartition', (sep,))) @@ -1478,7 +1497,7 @@ def strip(a, chars=None): def swapcase(a): """ - For each element in `a`, return a copy of the string with + Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa. Calls `str.swapcase` element-wise. @@ -1487,11 +1506,12 @@ def swapcase(a): Parameters ---------- - a : array-like of str or unicode + a : array_like, {str, unicode} + Input array. Returns ------- - out : ndarray + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also @@ -1506,14 +1526,16 @@ def swapcase(a): >>> np.char.swapcase(c) array(['A1b C', '1B cA', 'B cA1', 'Ca1B'], dtype='|S5') + """ a_arr = numpy.asarray(a) return _vec_string(a_arr, a_arr.dtype, 'swapcase') def title(a): """ - For each element in `a`, return a titlecased version of the - string: words start with uppercase characters, all remaining cased + Return element-wise title cased version of string or unicode. + + Title case words start with uppercase characters, all remaining cased characters are lowercase. Calls `str.title` element-wise. @@ -1522,7 +1544,8 @@ def title(a): Parameters ---------- - a : array-like of str or unicode + a : array_like, {str, unicode} + Input array. Returns ------- @@ -1541,6 +1564,7 @@ def title(a): >>> np.char.title(c) array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'], dtype='|S5') + """ a_arr = numpy.asarray(a) return _vec_string(a_arr, a_arr.dtype, 'title') @@ -1582,7 +1606,7 @@ def translate(a, table, deletechars=None): def upper(a): """ - Return an array with the elements of `a` converted to uppercase. + Return an array with the elements converted to uppercase. Calls `str.upper` element-wise. @@ -1590,11 +1614,12 @@ def upper(a): Parameters ---------- - a : array-like of str or unicode + a : array_like, {str, unicode} + Input array. Returns ------- - out : ndarray + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also @@ -1609,30 +1634,33 @@ def upper(a): >>> np.char.upper(c) array(['A1B C', '1BCA', 'BCA1'], dtype='|S5') + """ a_arr = numpy.asarray(a) return _vec_string(a_arr, a_arr.dtype, 'upper') def zfill(a, width): """ - Return the numeric string left-filled with zeros in a string of - length `width`. + Return the numeric string left-filled with zeros Calls `str.zfill` element-wise. Parameters ---------- - a : array-like of str or unicode + a : array_like, {str, unicode} + Input array. width : int + Width of string to left-fill elements in `a`. Returns ------- - out : ndarray + out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See also -------- str.zfill + """ a_arr = numpy.asarray(a) width_arr = numpy.asarray(width) @@ -1642,7 +1670,7 @@ def zfill(a, width): def isnumeric(a): """ - For each element in `a`, return True if there are only numeric + For each element, return True if there are only numeric characters in the element. Calls `unicode.isnumeric` element-wise. @@ -1653,16 +1681,18 @@ def isnumeric(a): Parameters ---------- - a : array-like of unicode + a : array_like, unicode + Input array. Returns ------- - out : ndarray - Array of booleans + out : ndarray, bool + Array of booleans of same shape as `a`. See also -------- unicode.isnumeric + """ if _use_unicode(a) != unicode_: raise TypeError("isnumeric is only available for Unicode strings and arrays") @@ -1670,7 +1700,7 @@ def isnumeric(a): def isdecimal(a): """ - For each element in `a`, return True if there are only decimal + For each element, return True if there are only decimal characters in the element. Calls `unicode.isdecimal` element-wise. @@ -1681,16 +1711,18 @@ def isdecimal(a): Parameters ---------- - a : array-like of unicode + a : array_like, unicode + Input array. Returns ------- - out : ndarray - Array of booleans + out : ndarray, bool + Array of booleans identical in shape to `a`. See also -------- unicode.isdecimal + """ if _use_unicode(a) != unicode_: raise TypeError("isnumeric is only available for Unicode strings and arrays") diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 3e89079dd..d73f1313c 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -937,7 +937,7 @@ def diagonal(a, offset=0, axis1=0, axis2=1): In NumPy 1.7, it continues to return a copy of the diagonal, but depending on this fact is deprecated. Writing to the resulting array continues to - work as it used to, but a DeprecationWarning will be issued. + work as it used to, but a FutureWarning will be issued. In NumPy 1.8, it will switch to returning a read-only view on the original array. Attempting to write to the resulting array will produce an error. diff --git a/numpy/core/include/numpy/ndarrayobject.h b/numpy/core/include/numpy/ndarrayobject.h index 74943d535..f00dd7744 100644 --- a/numpy/core/include/numpy/ndarrayobject.h +++ b/numpy/core/include/numpy/ndarrayobject.h @@ -229,8 +229,10 @@ PyArray_XDECREF_ERR(PyArrayObject *arr) #if PY_VERSION_HEX >= 0x02050000 #define DEPRECATE(msg) PyErr_WarnEx(PyExc_DeprecationWarning,msg,1) +#define DEPRECATE_FUTUREWARNING(msg) PyErr_WarnEx(PyExc_FutureWarning,msg,1) #else #define DEPRECATE(msg) PyErr_Warn(PyExc_DeprecationWarning,msg) +#define DEPRECATE_FUTUREWARNING(msg) PyErr_Warn(PyExc_FutureWarning,msg) #endif diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index 354669d56..954303352 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.h @@ -327,10 +327,7 @@ struct NpyAuxData_tag { * allocated. */ - /* Data buffer */ -#define PyDataMem_NEW(size) ((char *)malloc(size)) -#define PyDataMem_FREE(ptr) free(ptr) -#define PyDataMem_RENEW(ptr,size) ((char *)realloc(ptr,size)) + /* Data buffer - PyDataMem_NEW/FREE/RENEW are in multiarraymodule.c */ #define NPY_USE_PYMEM 1 @@ -1714,6 +1711,13 @@ typedef struct { */ } PyArrayInterface; +/* + * This is a function for hooking into the PyDataMem_NEW/FREE/RENEW functions. + * See the documentation for PyDataMem_SetEventHook. + */ +typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size, + void *user_data); + #if !(defined(NPY_NO_DEPRECATED_API) && (NPY_API_VERSION <= NPY_NO_DEPRECATED_API)) #include "npy_deprecated_api.h" #endif diff --git a/numpy/core/include/numpy/npy_3kcompat.h b/numpy/core/include/numpy/npy_3kcompat.h index d3d5404f5..4b9d0ea32 100644 --- a/numpy/core/include/numpy/npy_3kcompat.h +++ b/numpy/core/include/numpy/npy_3kcompat.h @@ -137,17 +137,6 @@ PyUnicode_Concat2(PyObject **left, PyObject *right) *left = newobj; } - -/* - * Accessing items of ob_base - */ - -#if (PY_VERSION_HEX < 0x02060000) -#define Py_TYPE(o) (((PyObject*)(o))->ob_type) -#define Py_REFCNT(o) (((PyObject*)(o))->ob_refcnt) -#define Py_SIZE(o) (((PyVarObject*)(o))->ob_size) -#endif - /* * PyFile_* compatibility */ @@ -204,7 +193,7 @@ npy_PyFile_Dup(PyObject *file, char *mode) fclose(handle); return NULL; } - fseek(handle, pos, SEEK_SET); + npy_fseek(handle, pos, SEEK_SET); return handle; } @@ -215,11 +204,11 @@ static NPY_INLINE int npy_PyFile_DupClose(PyObject *file, FILE* handle) { PyObject *ret; - long position; - position = ftell(handle); + Py_ssize_t position; + position = npy_ftell(handle); fclose(handle); - ret = PyObject_CallMethod(file, "seek", "li", position, 0); + ret = PyObject_CallMethod(file, "seek", NPY_SSIZE_T_PYFMT "i", position, 0); if (ret == NULL) { return -1; } diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h index 4f895e0a7..7fca7e220 100644 --- a/numpy/core/include/numpy/npy_common.h +++ b/numpy/core/include/numpy/npy_common.h @@ -16,6 +16,15 @@ #define NPY_INLINE #endif +/* Enable 64 bit file position support on win-amd64. Ticket #1660 */ +#if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) + #define npy_fseek _fseeki64 + #define npy_ftell _ftelli64 +#else + #define npy_fseek fseek + #define npy_ftell ftell +#endif + /* enums for detected endianness */ enum { NPY_CPU_UNKNOWN_ENDIAN, @@ -48,9 +57,7 @@ typedef Py_uintptr_t npy_uintp; #define PY_SSIZE_T_MIN INT_MIN #endif #define NPY_SSIZE_T_PYFMT "i" -#undef PyIndex_Check #define constchar const char -#define PyIndex_Check(op) 0 #else #define NPY_SSIZE_T_PYFMT "n" #define constchar char diff --git a/numpy/core/memmap.py b/numpy/core/memmap.py index fe52b3a09..f068c7b6c 100644 --- a/numpy/core/memmap.py +++ b/numpy/core/memmap.py @@ -4,7 +4,7 @@ import warnings from numeric import uint8, ndarray, dtype import sys -from numpy.compat import asbytes +import numpy as np dtypedescr = dtype valid_filemodes = ["r", "c", "r+", "w+"] @@ -235,7 +235,7 @@ class memmap(ndarray): if mode == 'w+' or (mode == 'r+' and flen < bytes): fid.seek(bytes - 1, 0) - fid.write(asbytes('\0')) + fid.write(np.compat.asbytes('\0')) fid.flush() if mode == 'c': @@ -271,13 +271,16 @@ class memmap(ndarray): return self def __array_finalize__(self, obj): - if hasattr(obj, '_mmap'): + if hasattr(obj, '_mmap') and np.may_share_memory(self, obj): self._mmap = obj._mmap self.filename = obj.filename self.offset = obj.offset self.mode = obj.mode else: self._mmap = None + self.filename = None + self.offset = None + self.mode = None def flush(self): """ diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py index a104db19c..45b998b61 100644 --- a/numpy/core/numeric.py +++ b/numpy/core/numeric.py @@ -696,9 +696,10 @@ def correlate(a, v, mode='valid', old_behavior=False): Refer to the `convolve` docstring. Note that the default is `valid`, unlike `convolve`, which uses `full`. old_behavior : bool - If True, uses the old behavior from Numeric, (correlate(a,v) == correlate(v, - a), and the conjugate is not taken for complex arrays). If False, uses - the conventional signal processing definition (see note). + If True, uses the old behavior from Numeric, + (correlate(a,v) == correlate(v,a), and the conjugate is not taken + for complex arrays). If False, uses the conventional signal + processing definition. See Also -------- @@ -2344,7 +2345,14 @@ def setbufsize(size): return old def getbufsize(): - """Return the size of the buffer used in ufuncs. + """ + Return the size of the buffer used in ufuncs. + + Returns + ------- + getbufsize : int + Size of ufunc buffer in bytes. + """ return umath.geterrobj()[0] diff --git a/numpy/core/setup.py b/numpy/core/setup.py index 92d352e9a..a1000ae0b 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -13,8 +13,8 @@ from setup_common import * # Set to True to enable multiple file compilations (experimental) try: - os.environ['NPY_SEPARATE_COMPILATION'] - ENABLE_SEPARATE_COMPILATION = True + val = os.environ['NPY_SEPARATE_COMPILATION'] + ENABLE_SEPARATE_COMPILATION = (val != "0") except KeyError: ENABLE_SEPARATE_COMPILATION = False @@ -671,7 +671,10 @@ def configuration(parent_package='',top_path=None): # This library is created for the build but it is not installed config.add_library('npysort', - sources = [join('src', 'npysort', 'sort.c.src')]) + sources = [join('src', 'npysort', 'quicksort.c.src'), + join('src', 'npysort', 'mergesort.c.src'), + join('src', 'npysort', 'heapsort.c.src')]) + ####################################################################### # multiarray module # diff --git a/numpy/core/shape_base.py b/numpy/core/shape_base.py index 8a4c80e27..261379077 100644 --- a/numpy/core/shape_base.py +++ b/numpy/core/shape_base.py @@ -12,7 +12,7 @@ def atleast_1d(*arys): Parameters ---------- - array1, array2, ... : array_like + arys1, arys2, ... : array_like One or more input arrays. Returns @@ -61,7 +61,7 @@ def atleast_2d(*arys): Parameters ---------- - array1, array2, ... : array_like + arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have two or more dimensions are preserved. @@ -113,7 +113,7 @@ def atleast_3d(*arys): Parameters ---------- - array1, array2, ... : array_like + arys1, arys2, ... : array_like One or more array-like sequences. Non-array inputs are converted to arrays. Arrays that already have three or more dimensions are preserved. diff --git a/numpy/core/src/dummymodule.c b/numpy/core/src/dummymodule.c index eca6edb54..1d48709e9 100644 --- a/numpy/core/src/dummymodule.c +++ b/numpy/core/src/dummymodule.c @@ -9,7 +9,7 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #include <Python.h> -#include <numpy/npy_3kcompat.h> +#include <npy_pycompat.h> static struct PyMethodDef methods[] = { {NULL, NULL, 0, NULL} diff --git a/numpy/core/src/multiarray/_datetime.h b/numpy/core/src/multiarray/_datetime.h index b359a9f17..7ec3cd907 100644 --- a/numpy/core/src/multiarray/_datetime.h +++ b/numpy/core/src/multiarray/_datetime.h @@ -1,9 +1,13 @@ #ifndef _NPY_PRIVATE__DATETIME_H_ #define _NPY_PRIVATE__DATETIME_H_ +#ifdef NPY_ENABLE_SEPARATE_COMPILATION +extern NPY_NO_EXPORT char *_datetime_strings[NPY_DATETIME_NUMUNITS]; +extern NPY_NO_EXPORT int _days_per_month_table[2][12]; +#else NPY_NO_EXPORT char *_datetime_strings[NPY_DATETIME_NUMUNITS]; - NPY_NO_EXPORT int _days_per_month_table[2][12]; +#endif NPY_NO_EXPORT void numpy_pydatetime_import(); diff --git a/numpy/core/src/multiarray/array_assign.c b/numpy/core/src/multiarray/array_assign.c index 03f81f21c..9c1685c16 100644 --- a/numpy/core/src/multiarray/array_assign.c +++ b/numpy/core/src/multiarray/array_assign.c @@ -16,7 +16,7 @@ #include <numpy/ndarraytypes.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "shape.h" diff --git a/numpy/core/src/multiarray/array_assign_array.c b/numpy/core/src/multiarray/array_assign_array.c index 98c2b19ff..4f462cb05 100644 --- a/numpy/core/src/multiarray/array_assign_array.c +++ b/numpy/core/src/multiarray/array_assign_array.c @@ -15,7 +15,7 @@ #include <numpy/ndarraytypes.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "convert_datatype.h" #include "methods.h" diff --git a/numpy/core/src/multiarray/array_assign_scalar.c b/numpy/core/src/multiarray/array_assign_scalar.c index c59e6752d..57f8b9074 100644 --- a/numpy/core/src/multiarray/array_assign_scalar.c +++ b/numpy/core/src/multiarray/array_assign_scalar.c @@ -15,7 +15,7 @@ #include <numpy/ndarraytypes.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "convert_datatype.h" #include "methods.h" diff --git a/numpy/core/src/multiarray/arrayobject.c b/numpy/core/src/multiarray/arrayobject.c index 3cba466dd..e8bc6b7b6 100644 --- a/numpy/core/src/multiarray/arrayobject.c +++ b/numpy/core/src/multiarray/arrayobject.c @@ -32,7 +32,7 @@ maintainer email: oliphant.travis@ieee.org #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" @@ -698,7 +698,7 @@ array_might_be_written(PyArrayObject *obj) "release -- see numpy.diagonal docs for details. The quick fix is\n" "to make an explicit copy (e.g., do arr.diagonal().copy())."; if (PyArray_FLAGS(obj) & NPY_ARRAY_WARN_ON_WRITE) { - if (DEPRECATE(msg) < 0) { + if (DEPRECATE_FUTUREWARNING(msg) < 0) { return -1; } /* Only warn once per array */ diff --git a/numpy/core/src/multiarray/arraytypes.c.src b/numpy/core/src/multiarray/arraytypes.c.src index c446619b6..ad45cdb9e 100644 --- a/numpy/core/src/multiarray/arraytypes.c.src +++ b/numpy/core/src/multiarray/arraytypes.c.src @@ -7,7 +7,7 @@ #define _MULTIARRAYMODULE #include "numpy/arrayobject.h" #include "numpy/arrayscalars.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/npy_math.h" #include "numpy/halffloat.h" diff --git a/numpy/core/src/multiarray/buffer.c b/numpy/core/src/multiarray/buffer.c index f9a7dddc0..530edbb1a 100644 --- a/numpy/core/src/multiarray/buffer.c +++ b/numpy/core/src/multiarray/buffer.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "buffer.h" #include "numpyos.h" diff --git a/numpy/core/src/multiarray/calculation.c b/numpy/core/src/multiarray/calculation.c index d8d02ef0c..618a8714c 100644 --- a/numpy/core/src/multiarray/calculation.c +++ b/numpy/core/src/multiarray/calculation.c @@ -8,7 +8,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "number.h" diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c index 9281a6d84..f62b37b4d 100644 --- a/numpy/core/src/multiarray/common.c +++ b/numpy/core/src/multiarray/common.c @@ -6,7 +6,7 @@ #include "numpy/arrayobject.h" #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "usertypes.h" diff --git a/numpy/core/src/multiarray/conversion_utils.c b/numpy/core/src/multiarray/conversion_utils.c index ec759b85b..914bc274d 100644 --- a/numpy/core/src/multiarray/conversion_utils.c +++ b/numpy/core/src/multiarray/conversion_utils.c @@ -9,7 +9,7 @@ #include "numpy/arrayobject.h" #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "arraytypes.h" diff --git a/numpy/core/src/multiarray/convert.c b/numpy/core/src/multiarray/convert.c index 042aea07b..8b646e59b 100644 --- a/numpy/core/src/multiarray/convert.c +++ b/numpy/core/src/multiarray/convert.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "arrayobject.h" #include "mapping.h" diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index 6fdb10bb2..de7468c51 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "scalartypes.h" diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c index c9cdffb23..ae29add6e 100644 --- a/numpy/core/src/multiarray/ctors.c +++ b/numpy/core/src/multiarray/ctors.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "ctors.h" @@ -3000,41 +3000,21 @@ array_fromfile_binary(FILE *fp, PyArray_Descr *dtype, npy_intp num, size_t *nrea if (num < 0) { int fail = 0; - -#if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) - /* Workaround Win64 fwrite() bug. Ticket #1660 */ - start = (npy_intp )_ftelli64(fp); - if (start < 0) { - fail = 1; - } - if (_fseeki64(fp, 0, SEEK_END) < 0) { - fail = 1; - } - numbytes = (npy_intp) _ftelli64(fp); - if (numbytes < 0) { - fail = 1; - } - numbytes -= start; - if (_fseeki64(fp, start, SEEK_SET) < 0) { - fail = 1; - } -#else - start = (npy_intp)ftell(fp); + start = (npy_intp) npy_ftell(fp); if (start < 0) { fail = 1; } - if (fseek(fp, 0, SEEK_END) < 0) { + if (npy_fseek(fp, 0, SEEK_END) < 0) { fail = 1; } - numbytes = (npy_intp) ftell(fp); + numbytes = (npy_intp) npy_ftell(fp); if (numbytes < 0) { fail = 1; } numbytes -= start; - if (fseek(fp, start, SEEK_SET) < 0) { + if (npy_fseek(fp, start, SEEK_SET) < 0) { fail = 1; } -#endif if (fail) { PyErr_SetString(PyExc_IOError, "could not seek in file"); diff --git a/numpy/core/src/multiarray/datetime.c b/numpy/core/src/multiarray/datetime.c index 55fb3e7ed..6f70fc2ff 100644 --- a/numpy/core/src/multiarray/datetime.c +++ b/numpy/core/src/multiarray/datetime.c @@ -18,7 +18,7 @@ #include <numpy/arrayobject.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/arrayscalars.h" #include "methods.h" diff --git a/numpy/core/src/multiarray/datetime_busday.c b/numpy/core/src/multiarray/datetime_busday.c index aa38594cb..331e10496 100644 --- a/numpy/core/src/multiarray/datetime_busday.c +++ b/numpy/core/src/multiarray/datetime_busday.c @@ -15,7 +15,7 @@ #include <numpy/arrayobject.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/arrayscalars.h" #include "lowlevel_strided_loops.h" diff --git a/numpy/core/src/multiarray/datetime_busdaycal.c b/numpy/core/src/multiarray/datetime_busdaycal.c index b5b74dd14..82eea9f20 100644 --- a/numpy/core/src/multiarray/datetime_busdaycal.c +++ b/numpy/core/src/multiarray/datetime_busdaycal.c @@ -16,7 +16,7 @@ #include <numpy/arrayobject.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/arrayscalars.h" #include "lowlevel_strided_loops.h" diff --git a/numpy/core/src/multiarray/datetime_busdaycal.h b/numpy/core/src/multiarray/datetime_busdaycal.h index 8401f38b6..5d7325733 100644 --- a/numpy/core/src/multiarray/datetime_busdaycal.h +++ b/numpy/core/src/multiarray/datetime_busdaycal.h @@ -26,7 +26,12 @@ typedef struct { npy_bool weekmask[7]; } NpyBusDayCalendar; +#ifdef NPY_ENABLE_SEPARATE_COMPILATION +extern NPY_NO_EXPORT PyTypeObject NpyBusDayCalendar_Type; +#else NPY_NO_EXPORT PyTypeObject NpyBusDayCalendar_Type; +#endif + /* * Converts a Python input into a 7-element weekmask, where 0 means diff --git a/numpy/core/src/multiarray/datetime_strings.c b/numpy/core/src/multiarray/datetime_strings.c index cfeabc783..32009bb79 100644 --- a/numpy/core/src/multiarray/datetime_strings.c +++ b/numpy/core/src/multiarray/datetime_strings.c @@ -17,7 +17,7 @@ #include <numpy/arrayobject.h> #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/arrayscalars.h" #include "methods.h" diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index 5090dc39c..596e9b837 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "_datetime.h" #include "common.h" diff --git a/numpy/core/src/multiarray/dtype_transfer.c b/numpy/core/src/multiarray/dtype_transfer.c index b99dd5ff4..138275c4c 100644 --- a/numpy/core/src/multiarray/dtype_transfer.c +++ b/numpy/core/src/multiarray/dtype_transfer.c @@ -19,7 +19,7 @@ #include <numpy/arrayobject.h> #include <numpy/npy_cpu.h> -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "convert_datatype.h" #include "_datetime.h" diff --git a/numpy/core/src/multiarray/einsum.c.src b/numpy/core/src/multiarray/einsum.c.src index 03def17b4..2c59533f8 100644 --- a/numpy/core/src/multiarray/einsum.c.src +++ b/numpy/core/src/multiarray/einsum.c.src @@ -16,7 +16,7 @@ #define _MULTIARRAYMODULE #include <numpy/arrayobject.h> #include <numpy/halffloat.h> -#include <numpy/npy_3kcompat.h> +#include <npy_pycompat.h> #include <ctype.h> diff --git a/numpy/core/src/multiarray/flagsobject.c b/numpy/core/src/multiarray/flagsobject.c index 6e5455c01..faaf264e0 100644 --- a/numpy/core/src/multiarray/flagsobject.c +++ b/numpy/core/src/multiarray/flagsobject.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" diff --git a/numpy/core/src/multiarray/getset.c b/numpy/core/src/multiarray/getset.c index 51caba240..32212ebc4 100644 --- a/numpy/core/src/multiarray/getset.c +++ b/numpy/core/src/multiarray/getset.c @@ -10,7 +10,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "scalartypes.h" diff --git a/numpy/core/src/multiarray/hashdescr.c b/numpy/core/src/multiarray/hashdescr.c index 71bc2adac..bff266415 100644 --- a/numpy/core/src/multiarray/hashdescr.c +++ b/numpy/core/src/multiarray/hashdescr.c @@ -6,7 +6,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "hashdescr.h" diff --git a/numpy/core/src/multiarray/item_selection.c b/numpy/core/src/multiarray/item_selection.c index 977580d70..8c80c26f1 100644 --- a/numpy/core/src/multiarray/item_selection.c +++ b/numpy/core/src/multiarray/item_selection.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "arrayobject.h" @@ -19,6 +19,7 @@ #include "lowlevel_strided_loops.h" #include "item_selection.h" +#include "npy_sort.h" /*NUMPY_API * Take @@ -890,7 +891,7 @@ _new_argsort(PyArrayObject *op, int axis, NPY_SORTKIND which) static PyArrayObject *global_obj; static int -qsortCompare (const void *a, const void *b) +sortCompare (const void *a, const void *b) { return PyArray_DESCR(global_obj)->f->compare(a,b,global_obj); } @@ -954,7 +955,9 @@ PyArray_Sort(PyArrayObject *op, int axis, NPY_SORTKIND which) PyArrayObject *ap = NULL, *store_arr = NULL; char *ip; int i, n, m, elsize, orign; - int axis_orig=axis; + int res = 0; + int axis_orig = axis; + int (*sort)(void *, size_t, size_t, npy_comparator); n = PyArray_NDIM(op); if ((n == 0) || (PyArray_SIZE(op) == 1)) { @@ -975,13 +978,30 @@ PyArray_Sort(PyArrayObject *op, int axis, NPY_SORTKIND which) if (PyArray_DESCR(op)->f->sort[which] != NULL) { return _new_sort(op, axis, which); } - if ((which != NPY_QUICKSORT) - || PyArray_DESCR(op)->f->compare == NULL) { + + if (PyArray_DESCR(op)->f->compare == NULL) { PyErr_SetString(PyExc_TypeError, - "desired sort not supported for this type"); + "type does not have compare function"); return -1; } + switch (which) { + case NPY_QUICKSORT : + sort = npy_quicksort; + break; + case NPY_HEAPSORT : + sort = npy_heapsort; + break; + case NPY_MERGESORT : + sort = npy_mergesort; + break; + default: + PyErr_SetString(PyExc_TypeError, + "requested sort kind is not supported"); + goto fail; + } + + SWAPAXES2(op); ap = (PyArrayObject *)PyArray_FromAny((PyObject *)op, @@ -1001,13 +1021,26 @@ PyArray_Sort(PyArrayObject *op, int axis, NPY_SORTKIND which) store_arr = global_obj; global_obj = ap; for (ip = PyArray_DATA(ap), i = 0; i < n; i++, ip += elsize*m) { - qsort(ip, m, elsize, qsortCompare); + res = sort(ip, m, elsize, sortCompare); + if (res < 0) { + break; + } } global_obj = store_arr; if (PyErr_Occurred()) { goto fail; } + else if (res == -NPY_ENOMEM) { + PyErr_NoMemory(); + goto fail; + } + else if (res == -NPY_ECOMP) { + PyErr_SetString(PyExc_TypeError, + "sort comparison failed"); + goto fail; + } + finish: Py_DECREF(ap); /* Should update op if needed */ @@ -1045,6 +1078,8 @@ PyArray_ArgSort(PyArrayObject *op, int axis, NPY_SORTKIND which) npy_intp i, j, n, m, orign; int argsort_elsize; char *store_ptr; + int res = 0; + int (*sort)(void *, size_t, size_t, npy_comparator); n = PyArray_NDIM(op); if ((n == 0) || (PyArray_SIZE(op) == 1)) { @@ -1071,14 +1106,32 @@ PyArray_ArgSort(PyArrayObject *op, int axis, NPY_SORTKIND which) return (PyObject *)ret; } - if ((which != NPY_QUICKSORT) || PyArray_DESCR(op2)->f->compare == NULL) { + if (PyArray_DESCR(op2)->f->compare == NULL) { PyErr_SetString(PyExc_TypeError, - "requested sort not available for type"); + "type does not have compare function"); Py_DECREF(op2); op = NULL; goto fail; } + switch (which) { + case NPY_QUICKSORT : + sort = npy_quicksort; + break; + case NPY_HEAPSORT : + sort = npy_heapsort; + break; + case NPY_MERGESORT : + sort = npy_mergesort; + break; + default: + PyErr_SetString(PyExc_TypeError, + "requested sort kind is not supported"); + Py_DECREF(op2); + op = NULL; + goto fail; + } + /* ap will contain the reference to op2 */ SWAPAXES(ap, op2); op = (PyArrayObject *)PyArray_ContiguousFromAny((PyObject *)ap, @@ -1109,11 +1162,27 @@ PyArray_ArgSort(PyArrayObject *op, int axis, NPY_SORTKIND which) for (j = 0; j < m; j++) { ip[j] = j; } - qsort((char *)ip, m, sizeof(npy_intp), argsort_static_compare); + res = sort((char *)ip, m, sizeof(npy_intp), argsort_static_compare); + if (res < 0) { + break; + } } global_data = store_ptr; global_obj = store; + if (PyErr_Occurred()) { + goto fail; + } + else if (res == -NPY_ENOMEM) { + PyErr_NoMemory(); + goto fail; + } + else if (res == -NPY_ECOMP) { + PyErr_SetString(PyExc_TypeError, + "sort comparison failed"); + goto fail; + } + finish: Py_DECREF(op); SWAPBACK(op, ret); @@ -1123,7 +1192,6 @@ PyArray_ArgSort(PyArrayObject *op, int axis, NPY_SORTKIND which) Py_XDECREF(op); Py_XDECREF(ret); return NULL; - } diff --git a/numpy/core/src/multiarray/iterators.c b/numpy/core/src/multiarray/iterators.c index af0d0d3c2..2e464a3db 100644 --- a/numpy/core/src/multiarray/iterators.c +++ b/numpy/core/src/multiarray/iterators.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "arrayobject.h" #include "iterators.h" diff --git a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src index c57e1ccc1..78818b31d 100644 --- a/numpy/core/src/multiarray/lowlevel_strided_loops.c.src +++ b/numpy/core/src/multiarray/lowlevel_strided_loops.c.src @@ -151,7 +151,7 @@ static void #else /* unaligned copy and swap */ - memcpy(dst, src, @elsize@); + memmove(dst, src, @elsize@); # if @is_swap@ == 1 @swap@@elsize@(dst); # elif @is_swap@ == 2 @@ -239,7 +239,7 @@ _strided_to_strided(char *dst, npy_intp dst_stride, NpyAuxData *NPY_UNUSED(data)) { while (N > 0) { - memcpy(dst, src, src_itemsize); + memmove(dst, src, src_itemsize); dst += dst_stride; src += src_stride; --N; @@ -255,7 +255,7 @@ _swap_strided_to_strided(char *dst, npy_intp dst_stride, char *a, *b, c; while (N > 0) { - memcpy(dst, src, src_itemsize); + memmove(dst, src, src_itemsize); /* general in-place swap */ a = dst; b = dst + src_itemsize - 1; @@ -281,7 +281,7 @@ _swap_pair_strided_to_strided(char *dst, npy_intp dst_stride, npy_intp itemsize_half = src_itemsize / 2; while (N > 0) { - memcpy(dst, src, src_itemsize); + memmove(dst, src, src_itemsize); /* general in-place swap */ a = dst; b = dst + itemsize_half - 1; @@ -312,7 +312,7 @@ _contig_to_contig(char *dst, npy_intp NPY_UNUSED(dst_stride), npy_intp N, npy_intp src_itemsize, NpyAuxData *NPY_UNUSED(data)) { - memcpy(dst, src, src_itemsize*N); + memmove(dst, src, src_itemsize*N); } @@ -802,7 +802,7 @@ static void src_value = *((_TYPE1 *)src); # endif #else - memcpy(&src_value, src, sizeof(src_value)); + memmove(&src_value, src, sizeof(src_value)); #endif /* Do the cast */ @@ -838,7 +838,7 @@ static void *((_TYPE2 *)dst) = dst_value; # endif #else - memcpy(dst, &dst_value, sizeof(dst_value)); + memmove(dst, &dst_value, sizeof(dst_value)); #endif #if @contig@ diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c index 7abb3ff7f..cdefb9982 100644 --- a/numpy/core/src/multiarray/mapping.c +++ b/numpy/core/src/multiarray/mapping.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "iterators.h" diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index 93d246671..c4147eff2 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -10,7 +10,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "ctors.h" diff --git a/numpy/core/src/multiarray/multiarray_tests.c.src b/numpy/core/src/multiarray/multiarray_tests.c.src index 15d4c871e..0768ec0ca 100644 --- a/numpy/core/src/multiarray/multiarray_tests.c.src +++ b/numpy/core/src/multiarray/multiarray_tests.c.src @@ -2,7 +2,7 @@ #include <Python.h> #include "numpy/arrayobject.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" /* * TODO: @@ -375,6 +375,59 @@ clean_ax: return NULL; } +/* PyDataMem_SetHook tests */ +static int malloc_free_counts[2]; +static PyDataMem_EventHookFunc *old_hook = NULL; +static void *old_data; + +static void test_hook(void *old, void *new, size_t size, void *user_data) +{ + int* counters = (int *) user_data; + if (old == NULL) { + counters[0]++; /* malloc counter */ + } + if (size == 0) { + counters[1]++; /* free counter */ + } +} + +static PyObject* +test_pydatamem_seteventhook_start(PyObject* NPY_UNUSED(self), PyObject* NPY_UNUSED(args)) +{ + malloc_free_counts[0] = malloc_free_counts[1] = 0; + old_hook = PyDataMem_SetEventHook(test_hook, (void *) malloc_free_counts, &old_data); + Py_INCREF(Py_None); + return Py_None; +} + +static PyObject* +test_pydatamem_seteventhook_end(PyObject* NPY_UNUSED(self), PyObject* NPY_UNUSED(args)) +{ + PyDataMem_EventHookFunc *my_hook; + void *my_data; + + my_hook = PyDataMem_SetEventHook(old_hook, old_data, &my_data); + if ((my_hook != test_hook) || (my_data != (void *) malloc_free_counts)) { + PyErr_SetString(PyExc_ValueError, + "hook/data was not the expected test hook"); + return NULL; + } + + if (malloc_free_counts[0] == 0) { + PyErr_SetString(PyExc_ValueError, + "malloc count is zero after test"); + return NULL; + } + if (malloc_free_counts[1] == 0) { + PyErr_SetString(PyExc_ValueError, + "free count is zero after test"); + return NULL; + } + + Py_INCREF(Py_None); + return Py_None; +} + static PyMethodDef Multiarray_TestsMethods[] = { {"test_neighborhood_iterator", test_neighborhood_iterator, @@ -382,6 +435,12 @@ static PyMethodDef Multiarray_TestsMethods[] = { {"test_neighborhood_iterator_oob", test_neighborhood_iterator_oob, METH_VARARGS, NULL}, + {"test_pydatamem_seteventhook_start", + test_pydatamem_seteventhook_start, + METH_NOARGS, NULL}, + {"test_pydatamem_seteventhook_end", + test_pydatamem_seteventhook_end, + METH_NOARGS, NULL}, {NULL, NULL, 0, NULL} /* Sentinel */ }; diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index 1ab7823ad..1b9be3cd9 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -27,7 +27,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" NPY_NO_EXPORT int NPY_NUMUSERTYPES = 0; @@ -3430,6 +3430,102 @@ test_interrupt(PyObject *NPY_UNUSED(self), PyObject *args) return PyInt_FromLong(a); } +/* malloc/free/realloc hook */ +NPY_NO_EXPORT PyDataMem_EventHookFunc *_PyDataMem_eventhook; +NPY_NO_EXPORT void *_PyDataMem_eventhook_user_data; + +/*NUMPY_API + * Sets the allocation event hook for numpy array data. + * Takes a PyDataMem_EventHookFunc *, which has the signature: + * void hook(void *old, void *new, size_t size, void *user_data). + * Also takes a void *user_data, and void **old_data. + * + * Returns a pointer to the previous hook or NULL. If old_data is + * non-NULL, the previous user_data pointer will be copied to it. + * + * If not NULL, hook will be called at the end of each PyDataMem_NEW/FREE/RENEW: + * result = PyDataMem_NEW(size) -> (*hook)(NULL, result, size, user_data) + * PyDataMem_FREE(ptr) -> (*hook)(ptr, NULL, 0, user_data) + * result = PyDataMem_RENEW(ptr, size) -> (*hook)(ptr, result, size, user_data) + * + * When the hook is called, the GIL will be held by the calling + * thread. The hook should be written to be reentrant, if it performs + * operations that might cause new allocation events (such as the + * creation/descruction numpy objects, or creating/destroying Python + * objects which might cause a gc) + */ +NPY_NO_EXPORT PyDataMem_EventHookFunc * +PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, + void *user_data, void **old_data) +{ + PyGILState_STATE gilstate = PyGILState_Ensure(); + PyDataMem_EventHookFunc *temp = _PyDataMem_eventhook; + _PyDataMem_eventhook = newhook; + if (old_data != NULL) { + *old_data = _PyDataMem_eventhook_user_data; + } + _PyDataMem_eventhook_user_data = user_data; + PyGILState_Release(gilstate); + return temp; +} + +/*NUMPY_API + * Allocates memory for array data. + */ +NPY_NO_EXPORT void * +PyDataMem_NEW(size_t size) +{ + void *result; + + result = malloc(size); + if (_PyDataMem_eventhook != NULL) { + PyGILState_STATE gilstate = PyGILState_Ensure(); + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(NULL, result, size, + _PyDataMem_eventhook_user_data); + } + PyGILState_Release(gilstate); + } + return (char *)result; +} + +/*NUMPY_API + * Free memory for array data. + */ +NPY_NO_EXPORT void +PyDataMem_FREE(void *ptr) +{ + free(ptr); + if (_PyDataMem_eventhook != NULL) { + PyGILState_STATE gilstate = PyGILState_Ensure(); + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(ptr, NULL, 0, + _PyDataMem_eventhook_user_data); + } + PyGILState_Release(gilstate); + } +} + +/*NUMPY_API + * Reallocate/resize memory for array data. + */ +NPY_NO_EXPORT void * +PyDataMem_RENEW(void *ptr, size_t size) +{ + void *result; + + result = realloc(ptr, size); + if (_PyDataMem_eventhook != NULL) { + PyGILState_STATE gilstate = PyGILState_Ensure(); + if (_PyDataMem_eventhook != NULL) { + (*_PyDataMem_eventhook)(ptr, result, size, + _PyDataMem_eventhook_user_data); + } + PyGILState_Release(gilstate); + } + return (char *)result; +} + static struct PyMethodDef array_module_methods[] = { {"_get_ndarray_c_version", (PyCFunction)array__get_ndarray_c_version, diff --git a/numpy/core/src/multiarray/nditer_impl.h b/numpy/core/src/multiarray/nditer_impl.h index bc4d01ff9..1251baa6e 100644 --- a/numpy/core/src/multiarray/nditer_impl.h +++ b/numpy/core/src/multiarray/nditer_impl.h @@ -17,7 +17,7 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE #include <numpy/arrayobject.h> -#include <numpy/npy_3kcompat.h> +#include <npy_pycompat.h> #include "convert_datatype.h" #include "lowlevel_strided_loops.h" diff --git a/numpy/core/src/multiarray/nditer_pywrap.c b/numpy/core/src/multiarray/nditer_pywrap.c index d6029e814..fd88cdbc7 100644 --- a/numpy/core/src/multiarray/nditer_pywrap.c +++ b/numpy/core/src/multiarray/nditer_pywrap.c @@ -13,11 +13,8 @@ #define NPY_NO_DEPRECATED_API NPY_API_VERSION #define _MULTIARRAYMODULE #include <numpy/arrayobject.h> -#include <numpy/npy_3kcompat.h> - #include "npy_config.h" - -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" typedef struct NewNpyArrayIterObject_tag NewNpyArrayIterObject; diff --git a/numpy/core/src/multiarray/number.c b/numpy/core/src/multiarray/number.c index 4cbf80df9..c37a232f4 100644 --- a/numpy/core/src/multiarray/number.c +++ b/numpy/core/src/multiarray/number.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "number.h" diff --git a/numpy/core/src/multiarray/numpymemoryview.c b/numpy/core/src/multiarray/numpymemoryview.c index eda28e1af..991321a8c 100644 --- a/numpy/core/src/multiarray/numpymemoryview.c +++ b/numpy/core/src/multiarray/numpymemoryview.c @@ -16,7 +16,7 @@ #include "numpy/arrayscalars.h" #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpymemoryview.h" diff --git a/numpy/core/src/multiarray/numpyos.c b/numpy/core/src/multiarray/numpyos.c index 9307a2473..cef170108 100644 --- a/numpy/core/src/multiarray/numpyos.c +++ b/numpy/core/src/multiarray/numpyos.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" /* * From the C99 standard, section 7.19.6: The exponent always contains at least diff --git a/numpy/core/src/multiarray/refcount.c b/numpy/core/src/multiarray/refcount.c index 2ba907f59..b067cd948 100644 --- a/numpy/core/src/multiarray/refcount.c +++ b/numpy/core/src/multiarray/refcount.c @@ -14,7 +14,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" static void _fillobject(char *optr, PyObject *obj, PyArray_Descr *dtype); diff --git a/numpy/core/src/multiarray/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index dd0e0158b..00c71f913 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "ctors.h" #include "descriptor.h" diff --git a/numpy/core/src/multiarray/scalartypes.c.src b/numpy/core/src/multiarray/scalartypes.c.src index 276937be1..e547071cc 100644 --- a/numpy/core/src/multiarray/scalartypes.c.src +++ b/numpy/core/src/multiarray/scalartypes.c.src @@ -13,7 +13,7 @@ #include "numpy/halffloat.h" #include "numpy/arrayscalars.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "npy_config.h" #include "mapping.h" diff --git a/numpy/core/src/multiarray/sequence.c b/numpy/core/src/multiarray/sequence.c index 5a43f0565..e5f74251e 100644 --- a/numpy/core/src/multiarray/sequence.c +++ b/numpy/core/src/multiarray/sequence.c @@ -9,7 +9,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "common.h" #include "mapping.h" diff --git a/numpy/core/src/multiarray/shape.c b/numpy/core/src/multiarray/shape.c index 3ed46eac4..067232632 100644 --- a/numpy/core/src/multiarray/shape.c +++ b/numpy/core/src/multiarray/shape.c @@ -11,7 +11,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "ctors.h" diff --git a/numpy/core/src/multiarray/ucsnarrow.c b/numpy/core/src/multiarray/ucsnarrow.c index 406b776bc..b0afdc6d9 100644 --- a/numpy/core/src/multiarray/ucsnarrow.c +++ b/numpy/core/src/multiarray/ucsnarrow.c @@ -12,7 +12,7 @@ #include "npy_config.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" /* * Functions only needed on narrow builds of Python for converting back and diff --git a/numpy/core/src/multiarray/usertypes.c b/numpy/core/src/multiarray/usertypes.c index 92c8a1243..f69abcc6b 100644 --- a/numpy/core/src/multiarray/usertypes.c +++ b/numpy/core/src/multiarray/usertypes.c @@ -34,7 +34,7 @@ maintainer email: oliphant.travis@ieee.org #include "common.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "usertypes.h" diff --git a/numpy/core/src/npysort/heapsort.c.src b/numpy/core/src/npysort/heapsort.c.src new file mode 100644 index 000000000..6a8c79f7a --- /dev/null +++ b/numpy/core/src/npysort/heapsort.c.src @@ -0,0 +1,341 @@ +/* -*- c -*- */ + +/* + * The purpose of this module is to add faster sort functions + * that are type-specific. This is done by altering the + * function table for the builtin descriptors. + * + * These sorting functions are copied almost directly from numarray + * with a few modifications (complex comparisons compare the imaginary + * part if the real parts are equal, for example), and the names + * are changed. + * + * The original sorting code is due to Charles R. Harris who wrote + * it for numarray. + */ + +/* + * Quick sort is usually the fastest, but the worst case scenario can + * be slower than the merge and heap sorts. The merge sort requires + * extra memory and so for large arrays may not be useful. + * + * The merge sort is *stable*, meaning that equal components + * are unmoved from their entry versions, so it can be used to + * implement lexigraphic sorting on multiple keys. + * + * The heap sort is included for completeness. + */ + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#include <stdlib.h> +#include "npy_sort.h" +#include "npysort_common.h" + +#define NOT_USED NPY_UNUSED(unused) +#define PYA_QS_STACK 100 +#define SMALL_QUICKSORT 15 +#define SMALL_MERGESORT 20 +#define SMALL_STRING 16 + + +/* + ***************************************************************************** + ** NUMERIC SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = BOOL, BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, + * LONGLONG, ULONGLONG, HALF, FLOAT, DOUBLE, LONGDOUBLE, + * CFLOAT, CDOUBLE, CLONGDOUBLE# + * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, + * longlong, ulonglong, half, float, double, longdouble, + * cfloat, cdouble, clongdouble# + * #type = npy_bool, npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, + * npy_uint, npy_long, npy_ulong, npy_longlong, npy_ulonglong, + * npy_ushort, npy_float, npy_double, npy_longdouble, npy_cfloat, + * npy_cdouble, npy_clongdouble# + */ + +int +heapsort_@suff@(@type@ *start, npy_intp n, void *NOT_USED) +{ + @type@ tmp, *a; + npy_intp i,j,l; + + /* The array needs to be offset by one for heapsort indexing */ + a = start - 1; + + for (l = n>>1; l > 0; --l) { + tmp = a[l]; + for (i = l, j = l<<1; j <= n;) { + if (j < n && @TYPE@_LT(a[j], a[j+1])) { + j += 1; + } + if (@TYPE@_LT(tmp, a[j])) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + for (; n > 1;) { + tmp = a[n]; + a[n] = a[1]; + n -= 1; + for (i = 1, j = 2; j <= n;) { + if (j < n && @TYPE@_LT(a[j], a[j+1])) { + j++; + } + if (@TYPE@_LT(tmp, a[j])) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + return 0; +} + + +int +aheapsort_@suff@(@type@ *v, npy_intp *tosort, npy_intp n, void *NOT_USED) +{ + npy_intp *a, i,j,l, tmp; + /* The arrays need to be offset by one for heapsort indexing */ + a = tosort - 1; + + for (l = n>>1; l > 0; --l) { + tmp = a[l]; + for (i = l, j = l<<1; j <= n;) { + if (j < n && @TYPE@_LT(v[a[j]], v[a[j+1]])) { + j += 1; + } + if (@TYPE@_LT(v[tmp], v[a[j]])) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + for (; n > 1;) { + tmp = a[n]; + a[n] = a[1]; + n -= 1; + for (i = 1, j = 2; j <= n;) { + if (j < n && @TYPE@_LT(v[a[j]], v[a[j+1]])) { + j++; + } + if (@TYPE@_LT(v[tmp], v[a[j]])) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** STRING SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = STRING, UNICODE# + * #suff = string, unicode# + * #type = npy_char, npy_ucs4# + */ + +int +heapsort_@suff@(@type@ *start, npy_intp n, PyArrayObject *arr) +{ + size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); + @type@ *tmp = malloc(PyArray_ITEMSIZE(arr)); + @type@ *a = start - len; + npy_intp i, j, l; + + for (l = n>>1; l > 0; --l) { + @TYPE@_COPY(tmp, a + l*len, len); + for (i = l, j = l<<1; j <= n;) { + if (j < n && @TYPE@_LT(a + j*len, a + (j+1)*len, len)) + j += 1; + if (@TYPE@_LT(tmp, a + j*len, len)) { + @TYPE@_COPY(a + i*len, a + j*len, len); + i = j; + j += j; + } + else { + break; + } + } + @TYPE@_COPY(a + i*len, tmp, len); + } + + for (; n > 1;) { + @TYPE@_COPY(tmp, a + n*len, len); + @TYPE@_COPY(a + n*len, a + len, len); + n -= 1; + for (i = 1, j = 2; j <= n;) { + if (j < n && @TYPE@_LT(a + j*len, a + (j+1)*len, len)) + j++; + if (@TYPE@_LT(tmp, a + j*len, len)) { + @TYPE@_COPY(a + i*len, a + j*len, len); + i = j; + j += j; + } + else { + break; + } + } + @TYPE@_COPY(a + i*len, tmp, len); + } + + free(tmp); + return 0; +} + + +int +aheapsort_@suff@(@type@ *v, npy_intp *tosort, npy_intp n, PyArrayObject *arr) +{ + size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); + npy_intp *a, i,j,l, tmp; + + /* The array needs to be offset by one for heapsort indexing */ + a = tosort - 1; + + for (l = n>>1; l > 0; --l) { + tmp = a[l]; + for (i = l, j = l<<1; j <= n;) { + if (j < n && @TYPE@_LT(v + a[j]*len, v + a[j+1]*len, len)) + j += 1; + if (@TYPE@_LT(v + tmp*len, v + a[j]*len, len)) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + for (; n > 1;) { + tmp = a[n]; + a[n] = a[1]; + n -= 1; + for (i = 1, j = 2; j <= n;) { + if (j < n && @TYPE@_LT(v + a[j]*len, v + a[j+1]*len, len)) + j++; + if (@TYPE@_LT(v + tmp*len, v + a[j]*len, len)) { + a[i] = a[j]; + i = j; + j += j; + } + else { + break; + } + } + a[i] = tmp; + } + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** GENERIC SORT ** + ***************************************************************************** + */ + + +/* + * This sort has almost the same signature as libc qsort and is intended to + * provide a heapsort for array types that don't have type specific sorts. + * The difference in the signature is an error return, as it might be the + * case that a memory allocation fails. + */ +int +npy_heapsort(void *base, size_t num, size_t size, npy_comparator cmp) +{ + char *tmp = (char *) malloc(size); + char *a = (char *) base - size; + size_t i, j, l; + + if (tmp == NULL) { + return -NPY_ENOMEM; + } + + for (l = num >> 1; l > 0; --l) { + GENERIC_COPY(tmp, a + l*size, size); + for (i = l, j = l << 1; j <= num;) { + if (j < num && GENERIC_LT(a + j*size, a + (j+1)*size, cmp)) + j += 1; + if (GENERIC_LT(tmp, a + j*size, cmp)) { + GENERIC_COPY(a + i*size, a + j*size, size); + i = j; + j += j; + } + else { + break; + } + } + GENERIC_COPY(a + i*size, tmp, size); + } + + for (; num > 1;) { + GENERIC_COPY(tmp, a + num*size, size); + GENERIC_COPY(a + num*size, a + size, size); + num -= 1; + for (i = 1, j = 2; j <= num;) { + if (j < num && GENERIC_LT(a + j*size, a + (j+1)*size, cmp)) + j++; + if (GENERIC_LT(tmp, a + j*size, cmp)) { + GENERIC_COPY(a + i*size, a + j*size, size); + i = j; + j += j; + } + else { + break; + } + } + GENERIC_COPY(a + i*size, tmp, size); + } + + free(tmp); + return 0; +} diff --git a/numpy/core/src/npysort/mergesort.c.src b/numpy/core/src/npysort/mergesort.c.src new file mode 100644 index 000000000..1003b95d1 --- /dev/null +++ b/numpy/core/src/npysort/mergesort.c.src @@ -0,0 +1,433 @@ +/* -*- c -*- */ + +/* + * The purpose of this module is to add faster sort functions + * that are type-specific. This is done by altering the + * function table for the builtin descriptors. + * + * These sorting functions are copied almost directly from numarray + * with a few modifications (complex comparisons compare the imaginary + * part if the real parts are equal, for example), and the names + * are changed. + * + * The original sorting code is due to Charles R. Harris who wrote + * it for numarray. + */ + +/* + * Quick sort is usually the fastest, but the worst case scenario can + * be slower than the merge and heap sorts. The merge sort requires + * extra memory and so for large arrays may not be useful. + * + * The merge sort is *stable*, meaning that equal components + * are unmoved from their entry versions, so it can be used to + * implement lexigraphic sorting on multiple keys. + * + * The heap sort is included for completeness. + */ + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#include <stdlib.h> +#include "npy_sort.h" +#include "npysort_common.h" + +#define NOT_USED NPY_UNUSED(unused) +#define PYA_QS_STACK 100 +#define SMALL_QUICKSORT 15 +#define SMALL_MERGESORT 20 +#define SMALL_STRING 16 + + +/* + ***************************************************************************** + ** NUMERIC SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = BOOL, BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, + * LONGLONG, ULONGLONG, HALF, FLOAT, DOUBLE, LONGDOUBLE, + * CFLOAT, CDOUBLE, CLONGDOUBLE# + * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, + * longlong, ulonglong, half, float, double, longdouble, + * cfloat, cdouble, clongdouble# + * #type = npy_bool, npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, + * npy_uint, npy_long, npy_ulong, npy_longlong, npy_ulonglong, + * npy_ushort, npy_float, npy_double, npy_longdouble, npy_cfloat, + * npy_cdouble, npy_clongdouble# + */ + +static void +mergesort0_@suff@(@type@ *pl, @type@ *pr, @type@ *pw) +{ + @type@ vp, *pi, *pj, *pk, *pm; + + if (pr - pl > SMALL_MERGESORT) { + /* merge sort */ + pm = pl + ((pr - pl) >> 1); + mergesort0_@suff@(pl, pm, pw); + mergesort0_@suff@(pm, pr, pw); + for (pi = pw, pj = pl; pj < pm;) { + *pi++ = *pj++; + } + pi = pw + (pm - pl); + pj = pw; + pk = pl; + while (pj < pi && pm < pr) { + if (@TYPE@_LT(*pm, *pj)) { + *pk++ = *pm++; + } + else { + *pk++ = *pj++; + } + } + while(pj < pi) { + *pk++ = *pj++; + } + } + else { + /* insertion sort */ + for (pi = pl + 1; pi < pr; ++pi) { + vp = *pi; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, *pk)) { + *pj-- = *pk--; + } + *pj = vp; + } + } +} + + +int +mergesort_@suff@(@type@ *start, npy_intp num, void *NOT_USED) +{ + @type@ *pl, *pr, *pw; + + pl = start; + pr = pl + num; + pw = (@type@ *) malloc((num/2) * sizeof(@type@)); + if (pw == NULL) { + PyErr_NoMemory(); + return -NPY_ENOMEM; + } + mergesort0_@suff@(pl, pr, pw); + + free(pw); + return 0; +} + + +static void +amergesort0_@suff@(npy_intp *pl, npy_intp *pr, @type@ *v, npy_intp *pw) +{ + @type@ vp; + npy_intp vi, *pi, *pj, *pk, *pm; + + if (pr - pl > SMALL_MERGESORT) { + /* merge sort */ + pm = pl + ((pr - pl) >> 1); + amergesort0_@suff@(pl, pm, v, pw); + amergesort0_@suff@(pm, pr, v, pw); + for (pi = pw, pj = pl; pj < pm;) { + *pi++ = *pj++; + } + pi = pw + (pm - pl); + pj = pw; + pk = pl; + while (pj < pi && pm < pr) { + if (@TYPE@_LT(v[*pm], v[*pj])) { + *pk++ = *pm++; + } + else { + *pk++ = *pj++; + } + } + while(pj < pi) { + *pk++ = *pj++; + } + } + else { + /* insertion sort */ + for (pi = pl + 1; pi < pr; ++pi) { + vi = *pi; + vp = v[vi]; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, v[*pk])) { + *pj-- = *pk--; + } + *pj = vi; + } + } +} + + +int +amergesort_@suff@(@type@ *v, npy_intp *tosort, npy_intp num, void *NOT_USED) +{ + npy_intp *pl, *pr, *pw; + + pl = tosort; + pr = pl + num; + pw = (npy_intp *) malloc((num/2) * sizeof(npy_intp)); + if (pw == NULL) { + PyErr_NoMemory(); + return -NPY_ENOMEM; + } + amergesort0_@suff@(pl, pr, v, pw); + free(pw); + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** STRING SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = STRING, UNICODE# + * #suff = string, unicode# + * #type = npy_char, npy_ucs4# + */ + +static void +mergesort0_@suff@(@type@ *pl, @type@ *pr, @type@ *pw, @type@ *vp, size_t len) +{ + @type@ *pi, *pj, *pk, *pm; + + if ((size_t)(pr - pl) > SMALL_MERGESORT*len) { + /* merge sort */ + pm = pl + (((pr - pl)/len) >> 1)*len; + mergesort0_@suff@(pl, pm, pw, vp, len); + mergesort0_@suff@(pm, pr, pw, vp, len); + @TYPE@_COPY(pw, pl, pm - pl); + pi = pw + (pm - pl); + pj = pw; + pk = pl; + while (pj < pi && pm < pr) { + if (@TYPE@_LT(pm, pj, len)) { + @TYPE@_COPY(pk, pm, len); + pm += len; + pk += len; + } + else { + @TYPE@_COPY(pk, pj, len); + pj += len; + pk += len; + } + } + @TYPE@_COPY(pk, pj, pi - pj); + } + else { + /* insertion sort */ + for (pi = pl + len; pi < pr; pi += len) { + @TYPE@_COPY(vp, pi, len); + pj = pi; + pk = pi - len; + while (pj > pl && @TYPE@_LT(vp, pk, len)) { + @TYPE@_COPY(pj, pk, len); + pj -= len; + pk -= len; + } + @TYPE@_COPY(pj, vp, len); + } + } +} + + +int +mergesort_@suff@(@type@ *start, npy_intp num, PyArrayObject *arr) +{ + size_t elsize = PyArray_ITEMSIZE(arr); + size_t len = elsize / sizeof(@type@); + @type@ *pl, *pr, *pw, *vp; + int err = 0; + + pl = start; + pr = pl + num*len; + pw = (@type@ *) malloc((num/2) * elsize); + if (pw == NULL) { + PyErr_NoMemory(); + err = -NPY_ENOMEM; + goto fail_0; + } + vp = (@type@ *) malloc(elsize); + if (vp == NULL) { + PyErr_NoMemory(); + err = -NPY_ENOMEM; + goto fail_1; + } + mergesort0_@suff@(pl, pr, pw, vp, len); + + free(vp); +fail_1: + free(pw); +fail_0: + return err; +} + + +static void +amergesort0_@suff@(npy_intp *pl, npy_intp *pr, @type@ *v, npy_intp *pw, size_t len) +{ + @type@ *vp; + npy_intp vi, *pi, *pj, *pk, *pm; + + if (pr - pl > SMALL_MERGESORT) { + /* merge sort */ + pm = pl + ((pr - pl) >> 1); + amergesort0_@suff@(pl, pm, v, pw, len); + amergesort0_@suff@(pm, pr, v, pw, len); + for (pi = pw, pj = pl; pj < pm;) { + *pi++ = *pj++; + } + pi = pw + (pm - pl); + pj = pw; + pk = pl; + while (pj < pi && pm < pr) { + if (@TYPE@_LT(v + (*pm)*len, v + (*pj)*len, len)) { + *pk++ = *pm++; + } + else { + *pk++ = *pj++; + } + } + while (pj < pi) { + *pk++ = *pj++; + } + } + else { + /* insertion sort */ + for (pi = pl + 1; pi < pr; ++pi) { + vi = *pi; + vp = v + vi*len; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, v + (*pk)*len, len)) { + *pj-- = *pk--; + } + *pj = vi; + } + } +} + + +int +amergesort_@suff@(@type@ *v, npy_intp *tosort, npy_intp num, PyArrayObject *arr) +{ + size_t elsize = PyArray_ITEMSIZE(arr); + size_t len = elsize / sizeof(@type@); + npy_intp *pl, *pr, *pw; + + pl = tosort; + pr = pl + num; + pw = (npy_intp *) malloc((num/2) * sizeof(npy_intp)); + if (pw == NULL) { + PyErr_NoMemory(); + return -NPY_ENOMEM; + } + amergesort0_@suff@(pl, pr, v, pw, len); + free(pw); + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** GENERIC SORT ** + ***************************************************************************** + */ + + +static void +npy_mergesort0(char *pl, char *pr, char *pw, char *vp, size_t size, npy_comparator cmp) +{ + char *pi, *pj, *pk, *pm; + + if ((size_t)(pr - pl) > SMALL_MERGESORT*size) { + /* merge sort */ + pm = pl + (((pr - pl)/size) >> 1)*size; + npy_mergesort0(pl, pm, pw, vp, size, cmp); + npy_mergesort0(pm, pr, pw, vp, size, cmp); + GENERIC_COPY(pw, pl, pm - pl); + pi = pw + (pm - pl); + pj = pw; + pk = pl; + while (pj < pi && pm < pr) { + if (GENERIC_LT(pm, pj, cmp)) { + GENERIC_COPY(pk, pm, size); + pm += size; + pk += size; + } + else { + GENERIC_COPY(pk, pj, size); + pj += size; + pk += size; + } + } + GENERIC_COPY(pk, pj, pi - pj); + } + else { + /* insertion sort */ + for (pi = pl + size; pi < pr; pi += size) { + GENERIC_COPY(vp, pi, size); + pj = pi; + pk = pi - size; + while (pj > pl && GENERIC_LT(vp, pk, cmp)) { + GENERIC_COPY(pj, pk, size); + pj -= size; + pk -= size; + } + GENERIC_COPY(pj, vp, size); + } + } +} + + +/* + * This sort has almost the same signature as libc qsort and is intended to + * provide a mergesort for array types that don't have type specific sorts. + * The difference in the signature is an error return, as it might be the + * case that a memory allocation fails. + */ +int +npy_mergesort(void *base, size_t num, size_t size, npy_comparator cmp) +{ + char *pl, *pr, *pw, *vp; + int err = 0; + + pl = (char *)base; + pr = pl + num*size; + pw = (char *) malloc((num/2) * size); + if (pw == NULL) { + err = -NPY_ENOMEM; + goto fail_0; + } + vp = (char *) malloc(size); + if (vp == NULL) { + err = -NPY_ENOMEM; + goto fail_1; + } + npy_mergesort0(pl, pr, pw, vp, size, cmp); + + free(vp); +fail_1: + free(pw); +fail_0: + return err; +} diff --git a/numpy/core/src/npysort/npysort_common.h b/numpy/core/src/npysort/npysort_common.h index bc1d278b0..318a80222 100644 --- a/numpy/core/src/npysort/npysort_common.h +++ b/numpy/core/src/npysort/npysort_common.h @@ -1,15 +1,9 @@ #ifndef __NPY_SORT_COMMON_H__ #define __NPY_SORT_COMMON_H__ -/* -#include "Python.h" -#include "numpy/npy_common.h" -#include "numpy/npy_math.h" -#include "npy_config.h" -*/ +#include <stdlib.h> #include <numpy/ndarraytypes.h> - /* ***************************************************************************** ** SWAP MACROS ** @@ -155,14 +149,17 @@ npy_half_lt_nonan(npy_half h1, npy_half h2) if (h1&0x8000u) { if (h2&0x8000u) { return (h1&0x7fffu) > (h2&0x7fffu); - } else { + } + else { /* Signed zeros are equal, have to check for it */ return (h1 != 0x8000u) || (h2 != 0x0000u); } - } else { + } + else { if (h2&0x8000u) { return 0; - } else { + } + else { return (h1&0x7fffu) < (h2&0x7fffu); } } @@ -176,7 +173,8 @@ HALF_LT(npy_half a, npy_half b) if (npy_half_isnan(b)) { ret = !npy_half_isnan(a); - } else { + } + else { ret = !npy_half_isnan(a) && npy_half_lt_nonan(a, b); } @@ -254,14 +252,6 @@ CLONGDOUBLE_LT(npy_clongdouble a, npy_clongdouble b) } -/* The PyObject functions are stubs for later use */ -NPY_INLINE static int -PyObject_LT(PyObject *pa, PyObject *pb) -{ - return 0; -} - - NPY_INLINE static void STRING_COPY(char *s1, char *s2, size_t len) { @@ -333,4 +323,29 @@ UNICODE_LT(npy_ucs4 *s1, npy_ucs4 *s2, size_t len) return ret; } + +NPY_INLINE static void +GENERIC_COPY(char *a, char *b, size_t len) +{ + memcpy(a, b, len); +} + + +NPY_INLINE static void +GENERIC_SWAP(char *a, char *b, size_t len) +{ + while(len--) { + const char t = *a; + *a++ = *b; + *b++ = t; + } +} + + +NPY_INLINE static int +GENERIC_LT(char *a, char *b, int (*cmp)(const void *, const void *)) +{ + return cmp(a, b) < 0; +} + #endif diff --git a/numpy/core/src/npysort/quicksort.c.src b/numpy/core/src/npysort/quicksort.c.src new file mode 100644 index 000000000..2225887b1 --- /dev/null +++ b/numpy/core/src/npysort/quicksort.c.src @@ -0,0 +1,363 @@ +/* -*- c -*- */ + +/* + * The purpose of this module is to add faster sort functions + * that are type-specific. This is done by altering the + * function table for the builtin descriptors. + * + * These sorting functions are copied almost directly from numarray + * with a few modifications (complex comparisons compare the imaginary + * part if the real parts are equal, for example), and the names + * are changed. + * + * The original sorting code is due to Charles R. Harris who wrote + * it for numarray. + */ + +/* + * Quick sort is usually the fastest, but the worst case scenario can + * be slower than the merge and heap sorts. The merge sort requires + * extra memory and so for large arrays may not be useful. + * + * The merge sort is *stable*, meaning that equal components + * are unmoved from their entry versions, so it can be used to + * implement lexigraphic sorting on multiple keys. + * + * The heap sort is included for completeness. + */ + +#define NPY_NO_DEPRECATED_API NPY_API_VERSION + +#include <stdlib.h> +#include "npy_sort.h" +#include "npysort_common.h" + +#define NOT_USED NPY_UNUSED(unused) +#define PYA_QS_STACK 100 +#define SMALL_QUICKSORT 15 +#define SMALL_MERGESORT 20 +#define SMALL_STRING 16 + + +/* + ***************************************************************************** + ** NUMERIC SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = BOOL, BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, + * LONGLONG, ULONGLONG, HALF, FLOAT, DOUBLE, LONGDOUBLE, + * CFLOAT, CDOUBLE, CLONGDOUBLE# + * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, + * longlong, ulonglong, half, float, double, longdouble, + * cfloat, cdouble, clongdouble# + * #type = npy_bool, npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, + * npy_uint, npy_long, npy_ulong, npy_longlong, npy_ulonglong, + * npy_ushort, npy_float, npy_double, npy_longdouble, npy_cfloat, + * npy_cdouble, npy_clongdouble# + */ + +int +quicksort_@suff@(@type@ *start, npy_intp num, void *NOT_USED) +{ + @type@ vp; + @type@ *pl = start; + @type@ *pr = start + num - 1; + @type@ *stack[PYA_QS_STACK]; + @type@ **sptr = stack; + @type@ *pm, *pi, *pj, *pk; + + for (;;) { + while ((pr - pl) > SMALL_QUICKSORT) { + /* quicksort partition */ + pm = pl + ((pr - pl) >> 1); + if (@TYPE@_LT(*pm, *pl)) @TYPE@_SWAP(*pm, *pl); + if (@TYPE@_LT(*pr, *pm)) @TYPE@_SWAP(*pr, *pm); + if (@TYPE@_LT(*pm, *pl)) @TYPE@_SWAP(*pm, *pl); + vp = *pm; + pi = pl; + pj = pr - 1; + @TYPE@_SWAP(*pm, *pj); + for (;;) { + do ++pi; while (@TYPE@_LT(*pi, vp)); + do --pj; while (@TYPE@_LT(vp, *pj)); + if (pi >= pj) { + break; + } + @TYPE@_SWAP(*pi,*pj); + } + pk = pr - 1; + @TYPE@_SWAP(*pi, *pk); + /* push largest partition on stack */ + if (pi - pl < pr - pi) { + *sptr++ = pi + 1; + *sptr++ = pr; + pr = pi - 1; + } + else { + *sptr++ = pl; + *sptr++ = pi - 1; + pl = pi + 1; + } + } + + /* insertion sort */ + for (pi = pl + 1; pi <= pr; ++pi) { + vp = *pi; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, *pk)) { + *pj-- = *pk--; + } + *pj = vp; + } + if (sptr == stack) { + break; + } + pr = *(--sptr); + pl = *(--sptr); + } + + return 0; +} + + +int +aquicksort_@suff@(@type@ *v, npy_intp* tosort, npy_intp num, void *NOT_USED) +{ + @type@ vp; + npy_intp *pl = tosort; + npy_intp *pr = tosort + num - 1; + npy_intp *stack[PYA_QS_STACK]; + npy_intp **sptr = stack; + npy_intp *pm, *pi, *pj, *pk, vi; + + for (;;) { + while ((pr - pl) > SMALL_QUICKSORT) { + /* quicksort partition */ + pm = pl + ((pr - pl) >> 1); + if (@TYPE@_LT(v[*pm],v[*pl])) INTP_SWAP(*pm, *pl); + if (@TYPE@_LT(v[*pr],v[*pm])) INTP_SWAP(*pr, *pm); + if (@TYPE@_LT(v[*pm],v[*pl])) INTP_SWAP(*pm, *pl); + vp = v[*pm]; + pi = pl; + pj = pr - 1; + INTP_SWAP(*pm, *pj); + for (;;) { + do ++pi; while (@TYPE@_LT(v[*pi], vp)); + do --pj; while (@TYPE@_LT(vp, v[*pj])); + if (pi >= pj) { + break; + } + INTP_SWAP(*pi, *pj); + } + pk = pr - 1; + INTP_SWAP(*pi,*pk); + /* push largest partition on stack */ + if (pi - pl < pr - pi) { + *sptr++ = pi + 1; + *sptr++ = pr; + pr = pi - 1; + } + else { + *sptr++ = pl; + *sptr++ = pi - 1; + pl = pi + 1; + } + } + + /* insertion sort */ + for (pi = pl + 1; pi <= pr; ++pi) { + vi = *pi; + vp = v[vi]; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, v[*pk])) { + *pj-- = *pk--; + } + *pj = vi; + } + if (sptr == stack) { + break; + } + pr = *(--sptr); + pl = *(--sptr); + } + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** STRING SORTS ** + ***************************************************************************** + */ + + +/**begin repeat + * + * #TYPE = STRING, UNICODE# + * #suff = string, unicode# + * #type = npy_char, npy_ucs4# + */ + +int +quicksort_@suff@(@type@ *start, npy_intp num, PyArrayObject *arr) +{ + const size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); + @type@ *vp = malloc(PyArray_ITEMSIZE(arr)); + @type@ *pl = start; + @type@ *pr = start + (num - 1)*len; + @type@ *stack[PYA_QS_STACK], **sptr = stack, *pm, *pi, *pj, *pk; + + for (;;) { + while ((size_t)(pr - pl) > SMALL_QUICKSORT*len) { + /* quicksort partition */ + pm = pl + (((pr - pl)/len) >> 1)*len; + if (@TYPE@_LT(pm, pl, len)) @TYPE@_SWAP(pm, pl, len); + if (@TYPE@_LT(pr, pm, len)) @TYPE@_SWAP(pr, pm, len); + if (@TYPE@_LT(pm, pl, len)) @TYPE@_SWAP(pm, pl, len); + @TYPE@_COPY(vp, pm, len); + pi = pl; + pj = pr - len; + @TYPE@_SWAP(pm, pj, len); + for (;;) { + do pi += len; while (@TYPE@_LT(pi, vp, len)); + do pj -= len; while (@TYPE@_LT(vp, pj, len)); + if (pi >= pj) { + break; + } + @TYPE@_SWAP(pi, pj, len); + } + pk = pr - len; + @TYPE@_SWAP(pi, pk, len); + /* push largest partition on stack */ + if (pi - pl < pr - pi) { + *sptr++ = pi + len; + *sptr++ = pr; + pr = pi - len; + } + else { + *sptr++ = pl; + *sptr++ = pi - len; + pl = pi + len; + } + } + + /* insertion sort */ + for (pi = pl + len; pi <= pr; pi += len) { + @TYPE@_COPY(vp, pi, len); + pj = pi; + pk = pi - len; + while (pj > pl && @TYPE@_LT(vp, pk, len)) { + @TYPE@_COPY(pj, pk, len); + pj -= len; + pk -= len; + } + @TYPE@_COPY(pj, vp, len); + } + if (sptr == stack) { + break; + } + pr = *(--sptr); + pl = *(--sptr); + } + + free(vp); + return 0; +} + + +int +aquicksort_@suff@(@type@ *v, npy_intp* tosort, npy_intp num, PyArrayObject *arr) +{ + size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); + @type@ *vp; + npy_intp *pl = tosort; + npy_intp *pr = tosort + num - 1; + npy_intp *stack[PYA_QS_STACK]; + npy_intp **sptr=stack; + npy_intp *pm, *pi, *pj, *pk, vi; + + for (;;) { + while ((pr - pl) > SMALL_QUICKSORT) { + /* quicksort partition */ + pm = pl + ((pr - pl) >> 1); + if (@TYPE@_LT(v + (*pm)*len, v + (*pl)*len, len)) INTP_SWAP(*pm, *pl); + if (@TYPE@_LT(v + (*pr)*len, v + (*pm)*len, len)) INTP_SWAP(*pr, *pm); + if (@TYPE@_LT(v + (*pm)*len, v + (*pl)*len, len)) INTP_SWAP(*pm, *pl); + vp = v + (*pm)*len; + pi = pl; + pj = pr - 1; + INTP_SWAP(*pm,*pj); + for (;;) { + do ++pi; while (@TYPE@_LT(v + (*pi)*len, vp, len)); + do --pj; while (@TYPE@_LT(vp, v + (*pj)*len, len)); + if (pi >= pj) { + break; + } + INTP_SWAP(*pi,*pj); + } + pk = pr - 1; + INTP_SWAP(*pi,*pk); + /* push largest partition on stack */ + if (pi - pl < pr - pi) { + *sptr++ = pi + 1; + *sptr++ = pr; + pr = pi - 1; + } + else { + *sptr++ = pl; + *sptr++ = pi - 1; + pl = pi + 1; + } + } + + /* insertion sort */ + for (pi = pl + 1; pi <= pr; ++pi) { + vi = *pi; + vp = v + vi*len; + pj = pi; + pk = pi - 1; + while (pj > pl && @TYPE@_LT(vp, v + (*pk)*len, len)) { + *pj-- = *pk--; + } + *pj = vi; + } + if (sptr == stack) { + break; + } + pr = *(--sptr); + pl = *(--sptr); + } + + return 0; +} + +/**end repeat**/ + + +/* + ***************************************************************************** + ** GENERIC SORT ** + ***************************************************************************** + */ + + +/* + * This sort has almost the same signature as libc qsort and is intended to + * supply an error return for compatibility with the other generic sort + * kinds. + */ +int +npy_quicksort(void *base, size_t num, size_t size, npy_comparator cmp) +{ + qsort(base, num, size, cmp); + return 0; +} diff --git a/numpy/core/src/npysort/sort.c.src b/numpy/core/src/npysort/sort.c.src deleted file mode 100644 index 618259f96..000000000 --- a/numpy/core/src/npysort/sort.c.src +++ /dev/null @@ -1,794 +0,0 @@ -/* -*- c -*- */ - -/* - * The purpose of this module is to add faster sort functions - * that are type-specific. This is done by altering the - * function table for the builtin descriptors. - * - * These sorting functions are copied almost directly from numarray - * with a few modifications (complex comparisons compare the imaginary - * part if the real parts are equal, for example), and the names - * are changed. - * - * The original sorting code is due to Charles R. Harris who wrote - * it for numarray. - */ - -/* - * Quick sort is usually the fastest, but the worst case scenario can - * be slower than the merge and heap sorts. The merge sort requires - * extra memory and so for large arrays may not be useful. - * - * The merge sort is *stable*, meaning that equal components - * are unmoved from their entry versions, so it can be used to - * implement lexigraphic sorting on multiple keys. - * - * The heap sort is included for completeness. - */ - -#define NPY_NO_DEPRECATED_API NPY_API_VERSION - -#include <stdlib.h> -#include "npy_sort.h" -#include "npysort_common.h" - -#define NOT_USED NPY_UNUSED(unused) -#define PYA_QS_STACK 100 -#define SMALL_QUICKSORT 15 -#define SMALL_MERGESORT 20 -#define SMALL_STRING 16 - - -/* - ***************************************************************************** - ** NUMERIC SORTS ** - ***************************************************************************** - */ - - -/**begin repeat - * - * #TYPE = BOOL, BYTE, UBYTE, SHORT, USHORT, INT, UINT, LONG, ULONG, - * LONGLONG, ULONGLONG, HALF, FLOAT, DOUBLE, LONGDOUBLE, - * CFLOAT, CDOUBLE, CLONGDOUBLE# - * #suff = bool, byte, ubyte, short, ushort, int, uint, long, ulong, - * longlong, ulonglong, half, float, double, longdouble, - * cfloat, cdouble, clongdouble# - * #type = npy_bool, npy_byte, npy_ubyte, npy_short, npy_ushort, npy_int, - * npy_uint, npy_long, npy_ulong, npy_longlong, npy_ulonglong, - * npy_ushort, npy_float, npy_double, npy_longdouble, npy_cfloat, - * npy_cdouble, npy_clongdouble# - */ - -int -quicksort_@suff@(@type@ *start, npy_intp num, void *NOT_USED) -{ - @type@ *pl = start; - @type@ *pr = start + num - 1; - @type@ vp; - @type@ *stack[PYA_QS_STACK], **sptr = stack, *pm, *pi, *pj, *pk; - - for (;;) { - while ((pr - pl) > SMALL_QUICKSORT) { - /* quicksort partition */ - pm = pl + ((pr - pl) >> 1); - if (@TYPE@_LT(*pm, *pl)) @TYPE@_SWAP(*pm, *pl); - if (@TYPE@_LT(*pr, *pm)) @TYPE@_SWAP(*pr, *pm); - if (@TYPE@_LT(*pm, *pl)) @TYPE@_SWAP(*pm, *pl); - vp = *pm; - pi = pl; - pj = pr - 1; - @TYPE@_SWAP(*pm, *pj); - for (;;) { - do ++pi; while (@TYPE@_LT(*pi, vp)); - do --pj; while (@TYPE@_LT(vp, *pj)); - if (pi >= pj) { - break; - } - @TYPE@_SWAP(*pi,*pj); - } - pk = pr - 1; - @TYPE@_SWAP(*pi, *pk); - /* push largest partition on stack */ - if (pi - pl < pr - pi) { - *sptr++ = pi + 1; - *sptr++ = pr; - pr = pi - 1; - } - else { - *sptr++ = pl; - *sptr++ = pi - 1; - pl = pi + 1; - } - } - - /* insertion sort */ - for (pi = pl + 1; pi <= pr; ++pi) { - vp = *pi; - pj = pi; - pk = pi - 1; - while (pj > pl && @TYPE@_LT(vp, *pk)) { - *pj-- = *pk--; - } - *pj = vp; - } - if (sptr == stack) { - break; - } - pr = *(--sptr); - pl = *(--sptr); - } - - return 0; -} - -int -aquicksort_@suff@(@type@ *v, npy_intp* tosort, npy_intp num, void *NOT_USED) -{ - @type@ vp; - npy_intp *pl, *pr; - npy_intp *stack[PYA_QS_STACK], **sptr=stack, *pm, *pi, *pj, *pk, vi; - - pl = tosort; - pr = tosort + num - 1; - - for (;;) { - while ((pr - pl) > SMALL_QUICKSORT) { - /* quicksort partition */ - pm = pl + ((pr - pl) >> 1); - if (@TYPE@_LT(v[*pm],v[*pl])) INTP_SWAP(*pm,*pl); - if (@TYPE@_LT(v[*pr],v[*pm])) INTP_SWAP(*pr,*pm); - if (@TYPE@_LT(v[*pm],v[*pl])) INTP_SWAP(*pm,*pl); - vp = v[*pm]; - pi = pl; - pj = pr - 1; - INTP_SWAP(*pm,*pj); - for (;;) { - do ++pi; while (@TYPE@_LT(v[*pi],vp)); - do --pj; while (@TYPE@_LT(vp,v[*pj])); - if (pi >= pj) { - break; - } - INTP_SWAP(*pi,*pj); - } - pk = pr - 1; - INTP_SWAP(*pi,*pk); - /* push largest partition on stack */ - if (pi - pl < pr - pi) { - *sptr++ = pi + 1; - *sptr++ = pr; - pr = pi - 1; - } - else { - *sptr++ = pl; - *sptr++ = pi - 1; - pl = pi + 1; - } - } - - /* insertion sort */ - for (pi = pl + 1; pi <= pr; ++pi) { - vi = *pi; - vp = v[vi]; - pj = pi; - pk = pi - 1; - while (pj > pl && @TYPE@_LT(vp, v[*pk])) { - *pj-- = *pk--; - } - *pj = vi; - } - if (sptr == stack) { - break; - } - pr = *(--sptr); - pl = *(--sptr); - } - - return 0; -} - - -int -heapsort_@suff@(@type@ *start, npy_intp n, void *NOT_USED) -{ - @type@ tmp, *a; - npy_intp i,j,l; - - /* The array needs to be offset by one for heapsort indexing */ - a = start - 1; - - for (l = n>>1; l > 0; --l) { - tmp = a[l]; - for (i = l, j = l<<1; j <= n;) { - if (j < n && @TYPE@_LT(a[j], a[j+1])) { - j += 1; - } - if (@TYPE@_LT(tmp, a[j])) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - for (; n > 1;) { - tmp = a[n]; - a[n] = a[1]; - n -= 1; - for (i = 1, j = 2; j <= n;) { - if (j < n && @TYPE@_LT(a[j], a[j+1])) { - j++; - } - if (@TYPE@_LT(tmp, a[j])) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - return 0; -} - -int -aheapsort_@suff@(@type@ *v, npy_intp *tosort, npy_intp n, void *NOT_USED) -{ - npy_intp *a, i,j,l, tmp; - /* The arrays need to be offset by one for heapsort indexing */ - a = tosort - 1; - - for (l = n>>1; l > 0; --l) { - tmp = a[l]; - for (i = l, j = l<<1; j <= n;) { - if (j < n && @TYPE@_LT(v[a[j]], v[a[j+1]])) { - j += 1; - } - if (@TYPE@_LT(v[tmp], v[a[j]])) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - for (; n > 1;) { - tmp = a[n]; - a[n] = a[1]; - n -= 1; - for (i = 1, j = 2; j <= n;) { - if (j < n && @TYPE@_LT(v[a[j]], v[a[j+1]])) { - j++; - } - if (@TYPE@_LT(v[tmp], v[a[j]])) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - return 0; -} - -static void -mergesort0_@suff@(@type@ *pl, @type@ *pr, @type@ *pw) -{ - @type@ vp, *pi, *pj, *pk, *pm; - - if (pr - pl > SMALL_MERGESORT) { - /* merge sort */ - pm = pl + ((pr - pl) >> 1); - mergesort0_@suff@(pl, pm, pw); - mergesort0_@suff@(pm, pr, pw); - for (pi = pw, pj = pl; pj < pm;) { - *pi++ = *pj++; - } - pj = pw; - pk = pl; - while (pj < pi && pm < pr) { - if (@TYPE@_LT(*pm, *pj)) { - *pk = *pm++; - } - else { - *pk = *pj++; - } - pk++; - } - while(pj < pi) { - *pk++ = *pj++; - } - } - else { - /* insertion sort */ - for (pi = pl + 1; pi < pr; ++pi) { - vp = *pi; - pj = pi; - pk = pi -1; - while (pj > pl && @TYPE@_LT(vp, *pk)) { - *pj-- = *pk--; - } - *pj = vp; - } - } -} - -int -mergesort_@suff@(@type@ *start, npy_intp num, void *NOT_USED) -{ - @type@ *pl, *pr, *pw; - - pl = start; - pr = pl + num; - pw = (@type@ *) PyDataMem_NEW((num/2)*sizeof(@type@)); - if (!pw) { - PyErr_NoMemory(); - return -1; - } - mergesort0_@suff@(pl, pr, pw); - - PyDataMem_FREE(pw); - return 0; -} - -static void -amergesort0_@suff@(npy_intp *pl, npy_intp *pr, @type@ *v, npy_intp *pw) -{ - @type@ vp; - npy_intp vi, *pi, *pj, *pk, *pm; - - if (pr - pl > SMALL_MERGESORT) { - /* merge sort */ - pm = pl + ((pr - pl + 1)>>1); - amergesort0_@suff@(pl, pm-1, v, pw); - amergesort0_@suff@(pm, pr, v, pw); - for (pi = pw, pj = pl; pj < pm; ++pi, ++pj) { - *pi = *pj; - } - for (pk = pw, pm = pl; pk < pi && pj <= pr; ++pm) { - if (@TYPE@_LT(v[*pj], v[*pk])) { - *pm = *pj; - ++pj; - } - else { - *pm = *pk; - ++pk; - } - } - for (; pk < pi; ++pm, ++pk) { - *pm = *pk; - } - } - else { - /* insertion sort */ - for (pi = pl + 1; pi <= pr; ++pi) { - vi = *pi; - vp = v[vi]; - for (pj = pi, pk = pi - 1; pj > pl && @TYPE@_LT(vp, v[*pk]); --pj, --pk) { - *pj = *pk; - } - *pj = vi; - } - } -} - -int -amergesort_@suff@(@type@ *v, npy_intp *tosort, npy_intp num, void *NOT_USED) -{ - npy_intp *pl, *pr, *pw; - - pl = tosort; pr = pl + num - 1; - pw = PyDimMem_NEW((1+num/2)); - - if (!pw) { - PyErr_NoMemory(); - return -1; - } - - amergesort0_@suff@(pl, pr, v, pw); - PyDimMem_FREE(pw); - - return 0; -} - - -/**end repeat**/ - -/* - ***************************************************************************** - ** STRING SORTS ** - ***************************************************************************** - */ - - -/**begin repeat - * - * #TYPE = STRING, UNICODE# - * #suff = string, unicode# - * #type = npy_char, npy_ucs4# - */ - -static void -mergesort0_@suff@(@type@ *pl, @type@ *pr, @type@ *pw, @type@ *vp, size_t len) -{ - @type@ *pi, *pj, *pk, *pm; - - if ((size_t)(pr - pl) > SMALL_MERGESORT*len) { - /* merge sort */ - pm = pl + (((pr - pl)/len) >> 1)*len; - mergesort0_@suff@(pl, pm, pw, vp, len); - mergesort0_@suff@(pm, pr, pw, vp, len); - @TYPE@_COPY(pw, pl, pm - pl); - pi = pw + (pm - pl); - pj = pw; - pk = pl; - while (pj < pi && pm < pr) { - if (@TYPE@_LT(pm, pj, len)) { - @TYPE@_COPY(pk, pm, len); - pm += len; - } - else { - @TYPE@_COPY(pk, pj, len); - pj += len; - } - pk += len; - } - @TYPE@_COPY(pk, pj, pi - pj); - } - else { - /* insertion sort */ - for (pi = pl + len; pi < pr; pi += len) { - @TYPE@_COPY(vp, pi, len); - pj = pi; - pk = pi - len; - while (pj > pl && @TYPE@_LT(vp, pk, len)) { - @TYPE@_COPY(pj, pk, len); - pj -= len; - pk -= len; - } - @TYPE@_COPY(pj, vp, len); - } - } -} - -int -mergesort_@suff@(@type@ *start, npy_intp num, PyArrayObject *arr) -{ - const size_t elsize = PyArray_ITEMSIZE(arr); - const size_t len = elsize / sizeof(@type@); - @type@ *pl, *pr, *pw, *vp; - int err = 0; - - pl = start; - pr = pl + num*len; - pw = (@type@ *) PyDataMem_NEW((num/2)*elsize); - if (!pw) { - PyErr_NoMemory(); - err = -1; - goto fail_0; - } - vp = (@type@ *) PyDataMem_NEW(elsize); - if (!vp) { - PyErr_NoMemory(); - err = -1; - goto fail_1; - } - mergesort0_@suff@(pl, pr, pw, vp, len); - - PyDataMem_FREE(vp); -fail_1: - PyDataMem_FREE(pw); -fail_0: - return err; -} - -int -quicksort_@suff@(@type@ *start, npy_intp num, PyArrayObject *arr) -{ - const size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); - @type@ *vp = malloc(PyArray_ITEMSIZE(arr)); - @type@ *pl = start; - @type@ *pr = start + (num - 1)*len; - @type@ *stack[PYA_QS_STACK], **sptr = stack, *pm, *pi, *pj, *pk; - - for (;;) { - while ((size_t)(pr - pl) > SMALL_QUICKSORT*len) { - /* quicksort partition */ - pm = pl + (((pr - pl)/len) >> 1)*len; - if (@TYPE@_LT(pm, pl, len)) @TYPE@_SWAP(pm, pl, len); - if (@TYPE@_LT(pr, pm, len)) @TYPE@_SWAP(pr, pm, len); - if (@TYPE@_LT(pm, pl, len)) @TYPE@_SWAP(pm, pl, len); - @TYPE@_COPY(vp, pm, len); - pi = pl; - pj = pr - len; - @TYPE@_SWAP(pm, pj, len); - for (;;) { - do pi += len; while (@TYPE@_LT(pi, vp, len)); - do pj -= len; while (@TYPE@_LT(vp, pj, len)); - if (pi >= pj) { - break; - } - @TYPE@_SWAP(pi, pj, len); - } - pk = pr - len; - @TYPE@_SWAP(pi, pk, len); - /* push largest partition on stack */ - if (pi - pl < pr - pi) { - *sptr++ = pi + len; - *sptr++ = pr; - pr = pi - len; - } - else { - *sptr++ = pl; - *sptr++ = pi - len; - pl = pi + len; - } - } - - /* insertion sort */ - for (pi = pl + len; pi <= pr; pi += len) { - @TYPE@_COPY(vp, pi, len); - pj = pi; - pk = pi - len; - while (pj > pl && @TYPE@_LT(vp, pk, len)) { - @TYPE@_COPY(pj, pk, len); - pj -= len; - pk -= len; - } - @TYPE@_COPY(pj, vp, len); - } - if (sptr == stack) { - break; - } - pr = *(--sptr); - pl = *(--sptr); - } - - free(vp); - return 0; -} - - -int -heapsort_@suff@(@type@ *start, npy_intp n, PyArrayObject *arr) -{ - size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); - @type@ *tmp = malloc(PyArray_ITEMSIZE(arr)); - @type@ *a = start - len; - npy_intp i,j,l; - - for (l = n>>1; l > 0; --l) { - @TYPE@_COPY(tmp, a + l*len, len); - for (i = l, j = l<<1; j <= n;) { - if (j < n && @TYPE@_LT(a + j*len, a + (j+1)*len, len)) - j += 1; - if (@TYPE@_LT(tmp, a + j*len, len)) { - @TYPE@_COPY(a + i*len, a + j*len, len); - i = j; - j += j; - } - else { - break; - } - } - @TYPE@_COPY(a + i*len, tmp, len); - } - - for (; n > 1;) { - @TYPE@_COPY(tmp, a + n*len, len); - @TYPE@_COPY(a + n*len, a + len, len); - n -= 1; - for (i = 1, j = 2; j <= n;) { - if (j < n && @TYPE@_LT(a + j*len, a + (j+1)*len, len)) - j++; - if (@TYPE@_LT(tmp, a + j*len, len)) { - @TYPE@_COPY(a + i*len, a + j*len, len); - i = j; - j += j; - } - else { - break; - } - } - @TYPE@_COPY(a + i*len, tmp, len); - } - - free(tmp); - return 0; -} - - -int -aheapsort_@suff@(@type@ *v, npy_intp *tosort, npy_intp n, PyArrayObject *arr) -{ - size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); - npy_intp *a, i,j,l, tmp; - - /* The array needs to be offset by one for heapsort indexing */ - a = tosort - 1; - - for (l = n>>1; l > 0; --l) { - tmp = a[l]; - for (i = l, j = l<<1; j <= n;) { - if (j < n && @TYPE@_LT(v + a[j]*len, v + a[j+1]*len, len)) - j += 1; - if (@TYPE@_LT(v + tmp*len, v + a[j]*len, len)) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - for (; n > 1;) { - tmp = a[n]; - a[n] = a[1]; - n -= 1; - for (i = 1, j = 2; j <= n;) { - if (j < n && @TYPE@_LT(v + a[j]*len, v + a[j+1]*len, len)) - j++; - if (@TYPE@_LT(v + tmp*len, v + a[j]*len, len)) { - a[i] = a[j]; - i = j; - j += j; - } - else { - break; - } - } - a[i] = tmp; - } - - return 0; -} - - -int -aquicksort_@suff@(@type@ *v, npy_intp* tosort, npy_intp num, PyArrayObject *arr) -{ - size_t len = PyArray_ITEMSIZE(arr)/sizeof(@type@); - @type@ *vp; - npy_intp *pl = tosort; - npy_intp *pr = tosort + num - 1; - npy_intp *stack[PYA_QS_STACK]; - npy_intp **sptr=stack; - npy_intp *pm, *pi, *pj, *pk, vi; - - for (;;) { - while ((pr - pl) > SMALL_QUICKSORT) { - /* quicksort partition */ - pm = pl + ((pr - pl) >> 1); - if (@TYPE@_LT(v + (*pm)*len, v + (*pl)*len, len)) INTP_SWAP(*pm, *pl); - if (@TYPE@_LT(v + (*pr)*len, v + (*pm)*len, len)) INTP_SWAP(*pr, *pm); - if (@TYPE@_LT(v + (*pm)*len, v + (*pl)*len, len)) INTP_SWAP(*pm, *pl); - vp = v + (*pm)*len; - pi = pl; - pj = pr - 1; - INTP_SWAP(*pm,*pj); - for (;;) { - do ++pi; while (@TYPE@_LT(v + (*pi)*len, vp, len)); - do --pj; while (@TYPE@_LT(vp, v + (*pj)*len, len)); - if (pi >= pj) { - break; - } - INTP_SWAP(*pi,*pj); - } - pk = pr - 1; - INTP_SWAP(*pi,*pk); - /* push largest partition on stack */ - if (pi - pl < pr - pi) { - *sptr++ = pi + 1; - *sptr++ = pr; - pr = pi - 1; - } - else { - *sptr++ = pl; - *sptr++ = pi - 1; - pl = pi + 1; - } - } - - /* insertion sort */ - for (pi = pl + 1; pi <= pr; ++pi) { - vi = *pi; - vp = v + vi*len; - pj = pi; - pk = pi - 1; - while (pj > pl && @TYPE@_LT(vp, v + (*pk)*len, len)) { - *pj-- = *pk--; - } - *pj = vi; - } - if (sptr == stack) { - break; - } - pr = *(--sptr); - pl = *(--sptr); - } - - return 0; -} - - -static void -amergesort0_@suff@(npy_intp *pl, npy_intp *pr, @type@ *v, npy_intp *pw, int len) -{ - @type@ *vp; - npy_intp vi, *pi, *pj, *pk, *pm; - - if (pr - pl > SMALL_MERGESORT) { - /* merge sort */ - pm = pl + ((pr - pl) >> 1); - amergesort0_@suff@(pl, pm, v, pw, len); - amergesort0_@suff@(pm, pr, v, pw, len); - for (pi = pw, pj = pl; pj < pm;) { - *pi++ = *pj++; - } - pj = pw; - pk = pl; - while (pj < pi && pm < pr) { - if (@TYPE@_LT(v + (*pm)*len, v + (*pj)*len, len)) { - *pk = *pm++; - } else { - *pk = *pj++; - } - pk++; - } - while (pj < pi) { - *pk++ = *pj++; - } - } else { - /* insertion sort */ - for (pi = pl + 1; pi < pr; ++pi) { - vi = *pi; - vp = v + vi*len; - pj = pi; - pk = pi -1; - while (pj > pl && @TYPE@_LT(vp, v + (*pk)*len, len)) { - *pj-- = *pk--; - } - *pj = vi; - } - } -} - - -int -amergesort_@suff@(@type@ *v, npy_intp *tosort, npy_intp num, PyArrayObject *arr) -{ - const size_t elsize = PyArray_ITEMSIZE(arr); - const size_t len = elsize / sizeof(@type@); - npy_intp *pl, *pr, *pw; - - pl = tosort; - pr = pl + num; - pw = PyDimMem_NEW(num/2); - if (!pw) { - PyErr_NoMemory(); - return -1; - } - amergesort0_@suff@(pl, pr, v, pw, len); - - PyDimMem_FREE(pw); - return 0; -} -/**end repeat**/ diff --git a/numpy/core/src/private/npy_pycompat.h b/numpy/core/src/private/npy_pycompat.h new file mode 100644 index 000000000..6b49c7476 --- /dev/null +++ b/numpy/core/src/private/npy_pycompat.h @@ -0,0 +1,24 @@ +#ifndef _NPY_PYCOMPAT_H_ +#define _NPY_PYCOMPAT_H_ + +#include "numpy/npy_3kcompat.h" + +/* + * Accessing items of ob_base + */ + +#if (PY_VERSION_HEX < 0x02060000) +#define Py_TYPE(o) (((PyObject*)(o))->ob_type) +#define Py_REFCNT(o) (((PyObject*)(o))->ob_refcnt) +#define Py_SIZE(o) (((PyVarObject*)(o))->ob_size) +#endif + +/* + * PyIndex_Check + */ +#if (PY_VERSION_HEX < 0x02050000) +#undef PyIndex_Check +#define PyIndex_Check(o) 0 +#endif + +#endif /* _NPY_COMPAT_H_ */ diff --git a/numpy/core/src/private/npy_sort.h b/numpy/core/src/private/npy_sort.h index 94d831f86..120e7efc2 100644 --- a/numpy/core/src/private/npy_sort.h +++ b/numpy/core/src/private/npy_sort.h @@ -6,6 +6,10 @@ #include <numpy/npy_common.h> #include <numpy/ndarraytypes.h> +#define NPY_ENOMEM 1 +#define NPY_ECOMP 2 + +typedef int (*npy_comparator)(const void *, const void *); int quicksort_bool(npy_bool *vec, npy_intp cnt, void *null); int heapsort_bool(npy_bool *vec, npy_intp cnt, void *null); @@ -166,4 +170,9 @@ int aquicksort_unicode(npy_ucs4 *vec, npy_intp *ind, npy_intp cnt, PyArrayObject int aheapsort_unicode(npy_ucs4 *vec, npy_intp *ind, npy_intp cnt, PyArrayObject *arr); int amergesort_unicode(npy_ucs4 *vec, npy_intp *ind, npy_intp cnt, PyArrayObject *arr); + +int npy_quicksort(void *base, size_t num, size_t size, npy_comparator cmp); +int npy_heapsort(void *base, size_t num, size_t size, npy_comparator cmp); +int npy_mergesort(void *base, size_t num, size_t size, npy_comparator cmp); + #endif diff --git a/numpy/core/src/scalarmathmodule.c.src b/numpy/core/src/scalarmathmodule.c.src index d9f7abc4e..4b56c1510 100644 --- a/numpy/core/src/scalarmathmodule.c.src +++ b/numpy/core/src/scalarmathmodule.c.src @@ -13,7 +13,7 @@ #include "numpy/ufuncobject.h" #include "numpy/arrayscalars.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/halffloat.h" diff --git a/numpy/core/src/umath/funcs.inc.src b/numpy/core/src/umath/funcs.inc.src index f3fefcfc5..6d46efc44 100644 --- a/numpy/core/src/umath/funcs.inc.src +++ b/numpy/core/src/umath/funcs.inc.src @@ -7,7 +7,7 @@ */ #define NPY_NO_DEPRECATED_API NPY_API_VERSION -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" /* diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src index 7eede45d6..040486a32 100644 --- a/numpy/core/src/umath/loops.c.src +++ b/numpy/core/src/umath/loops.c.src @@ -16,7 +16,7 @@ #include "numpy/npy_math.h" #include "numpy/halffloat.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "ufunc_object.h" diff --git a/numpy/core/src/umath/reduction.c b/numpy/core/src/umath/reduction.c index be301e26d..529d5523a 100644 --- a/numpy/core/src/umath/reduction.c +++ b/numpy/core/src/umath/reduction.c @@ -6,12 +6,18 @@ * * See LICENSE.txt for the license. */ +#define _UMATHMODULE +#define NPY_NO_DEPRECATED_API NPY_API_VERSION #define PY_SSIZE_T_CLEAN #include <Python.h> -#define NPY_NO_DEPRECATED_API NPY_API_VERSION -#define _MULTIARRAYMODULE +#include "npy_config.h" +#ifdef ENABLE_SEPARATE_COMPILATION +#define PY_ARRAY_UNIQUE_SYMBOL _npy_umathmodule_ARRAY_API +#define NO_IMPORT_ARRAY +#endif + #include <numpy/arrayobject.h> #include "npy_config.h" @@ -239,11 +245,12 @@ check_nonreorderable_axes(int ndim, npy_bool *axis_flags, const char *funcname) * it sees along the reduction axes to result, then return a view of * the operand which excludes that element. * - * If a reduction has an identity, such as 0 or 1, the result should - * be initialized by calling PyArray_AssignZero(result, NULL, NULL) - * or PyArray_AssignOne(result, NULL, NULL), because this - * function raises an exception when there are no elements to reduce. - * + * If a reduction has an identity, such as 0 or 1, the result should be + * initialized by calling PyArray_AssignZero(result, NULL, NULL) or + * PyArray_AssignOne(result, NULL, NULL), because this function raises an + * exception when there are no elements to reduce (which appropriate iff the + * reduction operation has no identity). + * * This means it copies the subarray indexed at zero along each reduction axis * into 'result', then returns a view into 'operand' excluding those copied * elements. @@ -293,14 +300,6 @@ PyArray_InitializeReduceResult( return NULL; } - if (PyArray_SIZE(operand) == 0) { - PyErr_Format(PyExc_ValueError, - "zero-size array to reduction operation %s " - "which has no identity", - funcname); - return NULL; - } - /* Take a view into 'operand' which we can modify. */ op_view = (PyArrayObject *)PyArray_View(operand, NULL, &PyArray_Type); if (op_view == NULL) { @@ -320,6 +319,14 @@ PyArray_InitializeReduceResult( memcpy(shape_orig, shape, ndim * sizeof(npy_intp)); for (idim = 0; idim < ndim; ++idim) { if (axis_flags[idim]) { + if (shape[idim] == 0) { + PyErr_Format(PyExc_ValueError, + "zero-size array to reduction operation %s " + "which has no identity", + funcname); + Py_DECREF(op_view); + return NULL; + } shape[idim] = 1; ++nreduce_axes; } diff --git a/numpy/core/src/umath/ufunc_object.c b/numpy/core/src/umath/ufunc_object.c index 3e0306bd2..2712f0474 100644 --- a/numpy/core/src/umath/ufunc_object.c +++ b/numpy/core/src/umath/ufunc_object.c @@ -34,7 +34,7 @@ #define NO_IMPORT_ARRAY #endif -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" diff --git a/numpy/core/src/umath/ufunc_type_resolution.c b/numpy/core/src/umath/ufunc_type_resolution.c index da1b2e811..372edc07a 100644 --- a/numpy/core/src/umath/ufunc_type_resolution.c +++ b/numpy/core/src/umath/ufunc_type_resolution.c @@ -19,7 +19,7 @@ #define NO_IMPORT_ARRAY #endif -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "numpy/ufuncobject.h" #include "ufunc_type_resolution.h" diff --git a/numpy/core/src/umath/umath_tests.c.src b/numpy/core/src/umath/umath_tests.c.src index 43583d119..0ba13f15f 100644 --- a/numpy/core/src/umath/umath_tests.c.src +++ b/numpy/core/src/umath/umath_tests.c.src @@ -11,7 +11,7 @@ #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" -#include "numpy/npy_3kcompat.h" +#include "npy_pycompat.h" #include "npy_config.h" diff --git a/numpy/core/tests/test_memmap.py b/numpy/core/tests/test_memmap.py index add714f89..7f134ee91 100644 --- a/numpy/core/tests/test_memmap.py +++ b/numpy/core/tests/test_memmap.py @@ -85,5 +85,24 @@ class TestMemmap(TestCase): fp_base[0] = 6 assert_equal(fp_base[0], 6) + def test_arithmetic_drops_references(self): + fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+', + shape=self.shape) + tmp = (fp + 10) + if isinstance(tmp, memmap): + assert tmp._mmap is not fp._mmap + + def test_indexing_drops_references(self): + fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+', + shape=self.shape) + tmp = fp[[(1, 2), (2, 3)]] + if isinstance(tmp, memmap): + assert tmp._mmap is not fp._mmap + + def test_slicing_keeps_references(self): + fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+', + shape=self.shape) + assert fp[:2, :2]._mmap is fp._mmap + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_multiarray.py b/numpy/core/tests/test_multiarray.py index 82ead8958..3427800a2 100644 --- a/numpy/core/tests/test_multiarray.py +++ b/numpy/core/tests/test_multiarray.py @@ -10,7 +10,8 @@ from numpy.testing.utils import WarningManager from numpy.compat import asbytes, getexception, strchar from test_print import in_foreign_locale from numpy.core.multiarray_tests import ( - test_neighborhood_iterator, test_neighborhood_iterator_oob + test_neighborhood_iterator, test_neighborhood_iterator_oob, + test_pydatamem_seteventhook_start, test_pydatamem_seteventhook_end, ) from numpy.testing import ( TestCase, run_module_suite, assert_, assert_raises, @@ -490,7 +491,7 @@ class TestMethods(TestCase): # of sorted items must be greater than ~50 to check the actual # algorithm because quick and merge sort fall over to insertion # sort for small arrays. - a = np.arange(100) + a = np.arange(101) b = a[::-1].copy() for kind in ['q','m','h'] : msg = "scalar sort, kind=%s" % kind @@ -526,7 +527,7 @@ class TestMethods(TestCase): # test string sorts. s = 'aaaaaaaa' - a = np.array([s + chr(i) for i in range(100)]) + a = np.array([s + chr(i) for i in range(101)]) b = a[::-1].copy() for kind in ['q', 'm', 'h'] : msg = "string sort, kind=%s" % kind @@ -537,9 +538,9 @@ class TestMethods(TestCase): c.sort(kind=kind) assert_equal(c, a, msg) - # test unicode sort. + # test unicode sorts. s = 'aaaaaaaa' - a = np.array([s + chr(i) for i in range(100)], dtype=np.unicode) + a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode) b = a[::-1].copy() for kind in ['q', 'm', 'h'] : msg = "unicode sort, kind=%s" % kind @@ -550,7 +551,55 @@ class TestMethods(TestCase): c.sort(kind=kind) assert_equal(c, a, msg) - # todo, check object array sorts. + # test object array sorts. + a = np.empty((101,), dtype=np.object) + a[:] = range(101) + b = a[::-1] + for kind in ['q', 'h', 'm'] : + msg = "object sort, kind=%s" % kind + c = a.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + c = b.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + + # test record array sorts. + dt = np.dtype([('f',float),('i',int)]) + a = array([(i,i) for i in range(101)], dtype = dt) + b = a[::-1] + for kind in ['q', 'h', 'm'] : + msg = "object sort, kind=%s" % kind + c = a.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + c = b.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + + # test datetime64 sorts. + a = np.arange(0, 101, dtype='datetime64[D]') + b = a[::-1] + for kind in ['q', 'h', 'm'] : + msg = "datetime64 sort, kind=%s" % kind + c = a.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + c = b.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + + # test timedelta64 sorts. + a = np.arange(0, 101, dtype='timedelta64[D]') + b = a[::-1] + for kind in ['q', 'h', 'm'] : + msg = "timedelta64 sort, kind=%s" % kind + c = a.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) + c = b.copy(); + c.sort(kind=kind) + assert_equal(c, a, msg) # check axis handling. This should be the same for all type # specific sorts, so we only check it for one type and one kind @@ -566,10 +615,6 @@ class TestMethods(TestCase): d = a.copy() d.sort() assert_equal(d, c, "test sort with default axis") - # using None is known fail at this point - # d = a.copy() - # d.sort(axis=None) - #assert_equal(d, c, "test sort with axis=None") def test_sort_order(self): @@ -612,7 +657,7 @@ class TestMethods(TestCase): # of sorted items must be greater than ~50 to check the actual # algorithm because quick and merge sort fall over to insertion # sort for small arrays. - a = np.arange(100) + a = np.arange(101) b = a[::-1].copy() for kind in ['q','m','h'] : msg = "scalar argsort, kind=%s" % kind @@ -636,10 +681,10 @@ class TestMethods(TestCase): # test string argsorts. s = 'aaaaaaaa' - a = np.array([s + chr(i) for i in range(100)]) + a = np.array([s + chr(i) for i in range(101)]) b = a[::-1].copy() - r = arange(100) - rr = r[::-1].copy() + r = np.arange(101) + rr = r[::-1] for kind in ['q', 'm', 'h'] : msg = "string argsort, kind=%s" % kind assert_equal(a.copy().argsort(kind=kind), r, msg) @@ -647,16 +692,57 @@ class TestMethods(TestCase): # test unicode argsorts. s = 'aaaaaaaa' - a = np.array([s + chr(i) for i in range(100)], dtype=np.unicode) - b = a[::-1].copy() - r = arange(100) - rr = r[::-1].copy() + a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode) + b = a[::-1] + r = np.arange(101) + rr = r[::-1] for kind in ['q', 'm', 'h'] : msg = "unicode argsort, kind=%s" % kind assert_equal(a.copy().argsort(kind=kind), r, msg) assert_equal(b.copy().argsort(kind=kind), rr, msg) - # todo, check object array argsorts. + # test object array argsorts. + a = np.empty((101,), dtype=np.object) + a[:] = range(101) + b = a[::-1] + r = np.arange(101) + rr = r[::-1] + for kind in ['q', 'm', 'h'] : + msg = "object argsort, kind=%s" % kind + assert_equal(a.copy().argsort(kind=kind), r, msg) + assert_equal(b.copy().argsort(kind=kind), rr, msg) + + # test structured array argsorts. + dt = np.dtype([('f',float),('i',int)]) + a = array([(i,i) for i in range(101)], dtype = dt) + b = a[::-1] + r = np.arange(101) + rr = r[::-1] + for kind in ['q', 'm', 'h'] : + msg = "structured array argsort, kind=%s" % kind + assert_equal(a.copy().argsort(kind=kind), r, msg) + assert_equal(b.copy().argsort(kind=kind), rr, msg) + + # test datetime64 argsorts. + a = np.arange(0, 101, dtype='datetime64[D]') + b = a[::-1] + r = np.arange(101) + rr = r[::-1] + for kind in ['q', 'h', 'm'] : + msg = "datetime64 argsort, kind=%s" % kind + assert_equal(a.copy().argsort(kind=kind), r, msg) + assert_equal(b.copy().argsort(kind=kind), rr, msg) + + # test timedelta64 argsorts. + a = np.arange(0, 101, dtype='timedelta64[D]') + b = a[::-1] + r = np.arange(101) + rr = r[::-1] + for kind in ['q', 'h', 'm'] : + msg = "timedelta64 argsort, kind=%s" % kind + assert_equal(a.copy().argsort(kind=kind), r, msg) + assert_equal(b.copy().argsort(kind=kind), rr, msg) + # check axis handling. This should be the same for all type # specific argsorts, so we only check it for one type and one kind @@ -827,19 +913,19 @@ class TestMethods(TestCase): # All the different functions raise a warning, but not an error, and # 'a' is not modified: assert_equal(collect_warning_types(a.diagonal().__setitem__, 0, 10), - [DeprecationWarning]) + [FutureWarning]) assert_equal(a, np.arange(9).reshape(3, 3)) assert_equal(collect_warning_types(np.diagonal(a).__setitem__, 0, 10), - [DeprecationWarning]) + [FutureWarning]) assert_equal(a, np.arange(9).reshape(3, 3)) assert_equal(collect_warning_types(np.diag(a).__setitem__, 0, 10), - [DeprecationWarning]) + [FutureWarning]) assert_equal(a, np.arange(9).reshape(3, 3)) # Views also warn d = np.diagonal(a) d_view = d.view() assert_equal(collect_warning_types(d_view.__setitem__, 0, 10), - [DeprecationWarning]) + [FutureWarning]) # But the write goes through: assert_equal(d[0], 10) # Only one warning per call to diagonal, though (even if there are @@ -861,21 +947,21 @@ class TestMethods(TestCase): buf_or_memoryview[0] = "x" assert_equal(collect_warning_types(get_data_and_write, lambda d: d.data), - [DeprecationWarning]) + [FutureWarning]) if hasattr(np, "getbuffer"): assert_equal(collect_warning_types(get_data_and_write, np.getbuffer), - [DeprecationWarning]) + [FutureWarning]) # PEP 3118: if have_memoryview: assert_equal(collect_warning_types(get_data_and_write, memoryview), - [DeprecationWarning]) + [FutureWarning]) # Void dtypes can give us a read-write buffer, but only in Python 2: import sys if sys.version_info[0] < 3: aV = np.empty((3, 3), dtype="V10") assert_equal(collect_warning_types(aV.diagonal().item, 0), - [DeprecationWarning]) + [FutureWarning]) # XX it seems that direct indexing of a void object returns a void # scalar, which ignores not just WARN_ON_WRITE but even WRITEABLE. # i.e. in this: @@ -887,7 +973,7 @@ class TestMethods(TestCase): # __array_interface also lets a data pointer get away from us log = collect_warning_types(getattr, a.diagonal(), "__array_interface__") - assert_equal(log, [DeprecationWarning]) + assert_equal(log, [FutureWarning]) # ctypeslib goes via __array_interface__: try: # may not exist in python 2.4: @@ -896,10 +982,10 @@ class TestMethods(TestCase): pass else: log = collect_warning_types(np.ctypeslib.as_ctypes, a.diagonal()) - assert_equal(log, [DeprecationWarning]) + assert_equal(log, [FutureWarning]) # __array_struct__ log = collect_warning_types(getattr, a.diagonal(), "__array_struct__") - assert_equal(log, [DeprecationWarning]) + assert_equal(log, [FutureWarning]) # Make sure that our recommendation to silence the warning by copying # the array actually works: @@ -932,7 +1018,7 @@ class TestMethods(TestCase): ro_diag.flags.writeable = False assert_equal(collect_warning_types(getattr, ro_diag, "__array_struct__"), []) - + def test_ravel(self): a = np.array([[0,1],[2,3]]) @@ -1092,7 +1178,7 @@ class TestFancyIndexing(TestCase): x = xorig.copy() x[m3] = 10 assert_array_equal(x, array([[1,10,3,4],[5,6,7,8]])) - + class TestStringCompare(TestCase): def test_string(self): @@ -1700,7 +1786,7 @@ class TestFlat(TestCase): self.b = a[::2,::2] self.a0 = a0 self.b0 = a0[::2,::2] - + def test_contiguous(self): testpassed = False try: @@ -1734,7 +1820,7 @@ class TestFlat(TestCase): assert d.flags.updateifcopy is False assert e.flags.updateifcopy is False assert f.flags.updateifcopy is True - assert f.base is self.b0 + assert f.base is self.b0 class TestResize(TestCase): def test_basic(self): @@ -2649,6 +2735,16 @@ def test_flat_element_deletion(): except: raise AssertionError +class TestMemEventHook(TestCase): + def test_mem_seteventhook(self): + # The actual tests are within the C code in + # multiarray/multiarray_tests.c.src + test_pydatamem_seteventhook_start() + # force an allocation and free of a numpy array + a = np.zeros(10) + del a + test_pydatamem_seteventhook_end() + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_multiarray_assignment.py b/numpy/core/tests/test_multiarray_assignment.py new file mode 100644 index 000000000..29ddcf906 --- /dev/null +++ b/numpy/core/tests/test_multiarray_assignment.py @@ -0,0 +1,78 @@ +import numpy as np +from numpy.testing import TestCase + +ndims = 2 +size = 10 +shape = tuple([size] * ndims) + + +def _indices_for_nelems(nelems): + """Returns slices of length nelems, from start onwards, in direction sign.""" + + if nelems == 0: + return [size // 2] # int index + + res = [] + for step in (1, 2): + for sign in (-1, 1): + start = size // 2 - nelems * step * sign // 2 + stop = start + nelems * step * sign + res.append(slice(start, stop, step * sign)) + + return res + + +def _indices_for_axis(): + """Returns (src, dst) pairs of indices.""" + + res = [] + for nelems in (0, 2, 3): + ind = _indices_for_nelems(nelems) + + # no itertools.product available in Py2.4 + res.extend([(a, b) for a in ind for b in ind]) # all assignments of size "nelems" + + return res + + +def _indices(ndims): + """Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs.""" + + ind = _indices_for_axis() + + # no itertools.product available in Py2.4 + + res = [[]] + for i in xrange(ndims): + newres = [] + for elem in ind: + for others in res: + newres.append([elem] + others) + res = newres + + return res + + +def _check_assignment(srcidx, dstidx): + """Check assignment arr[dstidx] = arr[srcidx] works.""" + + arr = np.arange(np.product(shape)).reshape(shape) + + cpy = arr.copy() + + cpy[dstidx] = arr[srcidx] + arr[dstidx] = arr[srcidx] + + assert np.all(arr == cpy), 'assigning arr[%s] = arr[%s]' % (dstidx, srcidx) + + +def test_overlapping_assignments(): + """Test automatically generated assignments which overlap in memory.""" + + inds = _indices(ndims) + + for ind in inds: + srcidx = tuple([a[0] for a in ind]) + dstidx = tuple([a[1] for a in ind]) + + yield _check_assignment, srcidx, dstidx diff --git a/numpy/core/tests/test_ufunc.py b/numpy/core/tests/test_ufunc.py index eb1da6676..53928129f 100644 --- a/numpy/core/tests/test_ufunc.py +++ b/numpy/core/tests/test_ufunc.py @@ -681,5 +681,66 @@ class TestUfunc(TestCase): assert_raises(ValueError, np.divide.reduce, a, axis=(0,1)) + def test_reduce_zero_axis(self): + # If we have a n x m array and do a reduction with axis=1, then we are + # doing n reductions, and each reduction takes an m-element array. For + # a reduction operation without an identity, then: + # n > 0, m > 0: fine + # n = 0, m > 0: fine, doing 0 reductions of m-element arrays + # n > 0, m = 0: can't reduce a 0-element array, ValueError + # n = 0, m = 0: can't reduce a 0-element array, ValueError (for + # consistency with the above case) + # This test doesn't actually look at return values, it just checks to + # make sure that error we get an error in exactly those cases where we + # expect one, and assumes the calculations themselves are done + # correctly. + def ok(f, *args, **kwargs): + f(*args, **kwargs) + def err(f, *args, **kwargs): + assert_raises(ValueError, f, *args, **kwargs) + def t(expect, func, n, m): + expect(func, np.zeros((n, m)), axis=1) + expect(func, np.zeros((m, n)), axis=0) + expect(func, np.zeros((n // 2, n // 2, m)), axis=2) + expect(func, np.zeros((n // 2, m, n // 2)), axis=1) + expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2)) + expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2)) + expect(func, np.zeros((m // 3, m // 3, m // 3, + n // 2, n //2)), + axis=(0, 1, 2)) + # Check what happens if the inner (resp. outer) dimensions are a + # mix of zero and non-zero: + expect(func, np.zeros((10, m, n)), axis=(0, 1)) + expect(func, np.zeros((10, n, m)), axis=(0, 2)) + expect(func, np.zeros((m, 10, n)), axis=0) + expect(func, np.zeros((10, m, n)), axis=1) + expect(func, np.zeros((10, n, m)), axis=2) + # np.maximum is just an arbitrary ufunc with no reduction identity + assert_equal(np.maximum.identity, None) + t(ok, np.maximum.reduce, 30, 30) + t(ok, np.maximum.reduce, 0, 30) + t(err, np.maximum.reduce, 30, 0) + t(err, np.maximum.reduce, 0, 0) + err(np.maximum.reduce, []) + np.maximum.reduce(np.zeros((0, 0)), axis=()) + + # all of the combinations are fine for a reduction that has an + # identity + t(ok, np.add.reduce, 30, 30) + t(ok, np.add.reduce, 0, 30) + t(ok, np.add.reduce, 30, 0) + t(ok, np.add.reduce, 0, 0) + np.add.reduce([]) + np.add.reduce(np.zeros((0, 0)), axis=()) + + # OTOH, accumulate always makes sense for any combination of n and m, + # because it maps an m-element array to an m-element array. These + # tests are simpler because accumulate doesn't accept multiple axes. + for uf in (np.maximum, np.add): + uf.accumulate(np.zeros((30, 0)), axis=0) + uf.accumulate(np.zeros((0, 30)), axis=0) + uf.accumulate(np.zeros((30, 30)), axis=0) + uf.accumulate(np.zeros((0, 0)), axis=0) + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_umath.py b/numpy/core/tests/test_umath.py index 6aed19ca6..892b1fae6 100644 --- a/numpy/core/tests/test_umath.py +++ b/numpy/core/tests/test_umath.py @@ -1,10 +1,17 @@ import sys +import platform from numpy.testing import * import numpy.core.umath as ncu import numpy as np +def on_powerpc(): + """ True if we are running on a Power PC platform.""" + return platform.processor() == 'powerpc' or \ + platform.machine().startswith('ppc') + + class _FilterInvalids(object): def setUp(self): self.olderr = np.seterr(invalid='ignore') @@ -1118,7 +1125,8 @@ def test_nextafter(): def test_nextafterf(): return _test_nextafter(np.float32) -@dec.knownfailureif(sys.platform == 'win32', "Long double support buggy on win32") +@dec.knownfailureif(sys.platform == 'win32' or on_powerpc(), + "Long double support buggy on win32 and PPC, ticket 1664.") def test_nextafterl(): return _test_nextafter(np.longdouble) @@ -1143,7 +1151,8 @@ def test_spacing(): def test_spacingf(): return _test_spacing(np.float32) -@dec.knownfailureif(sys.platform == 'win32', "Long double support buggy on win32") +@dec.knownfailureif(sys.platform == 'win32' or on_powerpc(), + "Long double support buggy on win32 and PPC, ticket 1664.") def test_spacingl(): return _test_spacing(np.longdouble) diff --git a/numpy/lib/_iotools.py b/numpy/lib/_iotools.py index 7921b4116..2f2a4bc57 100644 --- a/numpy/lib/_iotools.py +++ b/numpy/lib/_iotools.py @@ -203,13 +203,17 @@ class LineSplitter(object): self._handyman = _handyman # def _delimited_splitter(self, line): - line = line.split(self.comments)[0].strip(asbytes(" \r\n")) + if self.comments is not None: + line = line.split(self.comments)[0] + line = line.strip(asbytes(" \r\n")) if not line: return [] return line.split(self.delimiter) # def _fixedwidth_splitter(self, line): - line = line.split(self.comments)[0].strip(asbytes("\r\n")) + if self.comments is not None: + line = line.split(self.comments)[0] + line = line.strip(asbytes("\r\n")) if not line: return [] fixed = self.delimiter @@ -217,7 +221,8 @@ class LineSplitter(object): return [line[s] for s in slices] # def _variablewidth_splitter(self, line): - line = line.split(self.comments)[0] + if self.comments is not None: + line = line.split(self.comments)[0] if not line: return [] slices = self.delimiter diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 91dd96f9c..20a0e7151 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -44,7 +44,7 @@ def ediff1d(ary, to_end=None, to_begin=None): Returns ------- - ed : ndarray + ediff1d : ndarray The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``. See Also @@ -212,7 +212,7 @@ def intersect1d(ar1, ar2, assume_unique=False): Returns ------- - out : ndarray + intersect1d : ndarray Sorted 1D array of common and unique elements. See Also @@ -251,7 +251,7 @@ def setxor1d(ar1, ar2, assume_unique=False): Returns ------- - xor : ndarray + setxor1d : ndarray Sorted 1D array of unique values that are in only one of the input arrays. @@ -287,7 +287,7 @@ def in1d(ar1, ar2, assume_unique=False): Parameters ---------- - ar1 : array_like, shape (M,) + ar1 : (M,) array_like Input array. ar2 : array_like The values against which to test each value of `ar1`. @@ -297,8 +297,8 @@ def in1d(ar1, ar2, assume_unique=False): Returns ------- - mask : ndarray of bools, shape(M,) - The values `ar1[mask]` are in `ar2`. + in1d : (M,) ndarray, bool + The values `ar1[in1d]` are in `ar2`. See Also -------- @@ -365,7 +365,7 @@ def union1d(ar1, ar2): Returns ------- - union : ndarray + union1d : ndarray Unique, sorted union of the input arrays. See Also @@ -399,7 +399,7 @@ def setdiff1d(ar1, ar2, assume_unique=False): Returns ------- - difference : ndarray + setdiff1d : ndarray Sorted 1D array of values in `ar1` that are not in `ar2`. See Also diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index f3df3b96b..a0781ebf9 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -843,7 +843,7 @@ def gradient(f, *varargs): Returns ------- - g : ndarray + gradient : ndarray N arrays of the same shape as `f` giving the derivative of `f` with respect to each dimension. @@ -948,7 +948,7 @@ def diff(a, n=1, axis=-1): Returns ------- - out : ndarray + diff : ndarray The `n` order differences. The shape of the output is the same as `a` except along `axis` where the dimension is smaller by `n`. @@ -1284,6 +1284,11 @@ def extract(condition, arr): arr : array_like Input array of the same size as `condition`. + Returns + ------- + extract : ndarray + Rank 1 array of values from `arr` where `condition` is True. + See Also -------- take, put, copyto, compress @@ -1316,9 +1321,10 @@ def place(arr, mask, vals): """ Change elements of an array based on conditional and input values. - Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` - uses the first N elements of `vals`, where N is the number of True values - in `mask`, while `copyto` uses the elements where `mask` is True. + Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that + `place` uses the first N elements of `vals`, where N is the number of + True values in `mask`, while `copyto` uses the elements where `mask` + is True. Note that `extract` does the exact opposite of `place`. @@ -2713,7 +2719,7 @@ def kaiser(M,beta): A beta value of 14 is probably a good starting point. Note that as beta gets large, the window narrows, and so the number of samples needs to be - large enough to sample the increasingly narrow spike, otherwise nans will + large enough to sample the increasingly narrow spike, otherwise NaNs will get returned. Most references to the Kaiser window come from the signal processing diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index 221529929..b63003f80 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -359,7 +359,6 @@ def load(file, mmap_mode=None): own_fid = True elif isinstance(file, gzip.GzipFile): fid = seek_gzip_factory(file) - own_fid = True else: fid = file @@ -371,7 +370,7 @@ def load(file, mmap_mode=None): fid.seek(-N, 1) # back-up if magic.startswith(_ZIP_PREFIX): # zip-file (assume .npz) own_fid = False - return NpzFile(fid, own_fid=True) + return NpzFile(fid, own_fid=own_fid) elif magic == format.MAGIC_PREFIX: # .npy file if mmap_mode: return format.open_memmap(file, mode=mmap_mode) @@ -471,8 +470,7 @@ def savez(file, *args, **kwds): -------- save : Save a single array to a binary file in NumPy format. savetxt : Save an array to a file as plain text. - numpy.savez_compressed : Save several arrays into a compressed .npz file - format + savez_compressed : Save several arrays into a compressed .npz file format Notes ----- @@ -1293,7 +1291,8 @@ def genfromtxt(fname, dtype=float, comments='#', delimiter=None, """ # Py3 data conversions to bytes, for convenience - comments = asbytes(comments) + if comments is not None: + comments = asbytes(comments) if isinstance(delimiter, unicode): delimiter = asbytes(delimiter) if isinstance(missing, unicode): diff --git a/numpy/lib/src/_compiled_base.c b/numpy/lib/src/_compiled_base.c index c31ee5cd8..d389b7f8e 100644 --- a/numpy/lib/src/_compiled_base.c +++ b/numpy/lib/src/_compiled_base.c @@ -7,6 +7,10 @@ #include "numpy/ufuncobject.h" #include "string.h" +#if (PY_VERSION_HEX < 0x02060000) +#define Py_TYPE(o) (((PyObject*)(o))->ob_type) +#endif + static npy_intp incr_slot_(double x, double *bins, npy_intp lbins) { @@ -670,7 +674,10 @@ arr_interp(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict) /* only pre-calculate slopes if there are relatively few of them. */ if (lenxp <= lenx) { - slopes = (double *) PyDataMem_NEW((lenxp - 1)*sizeof(double)); + slopes = (double *) PyArray_malloc((lenxp - 1)*sizeof(double)); + if (! slopes) { + goto fail; + } NPY_BEGIN_ALLOW_THREADS; for (i = 0; i < lenxp - 1; i++) { slopes[i] = (dy[i + 1] - dy[i])/(dx[i + 1] - dx[i]); @@ -692,7 +699,7 @@ arr_interp(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict) } } NPY_END_ALLOW_THREADS; - PyDataMem_FREE(slopes); + PyArray_free(slopes); } else { NPY_BEGIN_ALLOW_THREADS; diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py index e40c155a4..b0d2ca7c3 100644 --- a/numpy/lib/tests/test_arraysetops.py +++ b/numpy/lib/tests/test_arraysetops.py @@ -8,31 +8,62 @@ from numpy.lib.arraysetops import * import warnings -class TestAso(TestCase): - def test_unique( self ): - a = np.array( [5, 7, 1, 2, 1, 5, 7] ) - - ec = np.array( [1, 2, 5, 7] ) - c = unique( a ) - assert_array_equal( c, ec ) - - vals, indices = unique( a, return_index=True ) +class TestSetOps(TestCase): - ed = np.array( [2, 3, 0, 1] ) - assert_array_equal(vals, ec) - assert_array_equal(indices, ed) - - vals, ind0, ind1 = unique( a, return_index=True, - return_inverse=True ) - + def test_unique( self ): - ee = np.array( [2, 3, 0, 1, 0, 2, 3] ) - assert_array_equal(vals, ec) - assert_array_equal(ind0, ed) - assert_array_equal(ind1, ee) + def check_all(a, b, i1, i2, dt): + msg = "check values failed for type '%s'" % dt + v = unique(a) + assert_array_equal(v, b, msg) + + msg = "check indexes failed for type '%s'" % dt + v, j = unique(a, 1, 0) + assert_array_equal(v, b, msg) + assert_array_equal(j, i1, msg) + + msg = "check reverse indexes failed for type '%s'" % dt + v, j = unique(a, 0, 1) + assert_array_equal(v, b, msg) + assert_array_equal(j, i2, msg) + + msg = "check with all indexes failed for type '%s'" % dt + v, j1, j2 = unique(a, 1, 1) + assert_array_equal(v, b, msg) + assert_array_equal(j1, i1, msg) + assert_array_equal(j2, i2, msg) + + a = [5, 7, 1, 2, 1, 5, 7]*10 + b = [1, 2, 5, 7] + i1 = [2, 3, 0, 1] + i2 = [2, 3, 0, 1, 0, 2, 3]*10 + + # test for numeric arrays + types = [] + types.extend(np.typecodes['AllInteger']) + types.extend(np.typecodes['AllFloat']) + types.append('datetime64[D]') + types.append('timedelta64[D]') + for dt in types: + aa = np.array(a, dt) + bb = np.array(b, dt) + check_all(aa, bb, i1, i2, dt) + + # test for object arrays + dt = 'O' + aa = np.empty(len(a), dt) + aa[:] = a + bb = np.empty(len(b), dt) + bb[:] = b + check_all(aa, bb, i1, i2, dt) + + # test for structured arrays + dt = [('', 'i'), ('', 'i')] + aa = np.array(zip(a,a), dt) + bb = np.array(zip(b,b), dt) + check_all(aa, bb, i1, i2, dt) - assert_array_equal([], unique([])) def test_intersect1d( self ): # unique inputs diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py index 8922070df..f8caeedb6 100644 --- a/numpy/lib/tests/test_io.py +++ b/numpy/lib/tests/test_io.py @@ -167,6 +167,52 @@ class TestSavezLoad(RoundtripTest, TestCase): if errors: raise AssertionError(errors) + def test_not_closing_opened_fid(self): + # Test that issue #2178 is fixed: + # verify could seek on 'loaded' file + + fd, tmp = mkstemp(suffix='.npz') + os.close(fd) + try: + fp = open(tmp, 'wb') + np.savez(fp, data='LOVELY LOAD') + fp.close() + + fp = open(tmp, 'rb', 10000) + fp.seek(0) + assert_(not fp.closed) + _ = np.load(fp)['data'] + assert_(not fp.closed) # must not get closed by .load(opened fp) + fp.seek(0) + assert_(not fp.closed) + + finally: + fp.close() + os.remove(tmp) + + def test_closing_fid(self): + # Test that issue #1517 (too many opened files) remains closed + # It might be a "week" test since failed to get triggered on + # e.g. Debian sid of 2012 Jul 05 but was reported to + # trigger the failure on Ubuntu 10.04: + # http://projects.scipy.org/numpy/ticket/1517#comment:2 + fd, tmp = mkstemp(suffix='.npz') + os.close(fd) + + try: + fp = open(tmp, 'wb') + np.savez(fp, data='LOVELY LOAD') + fp.close() + + for i in range(1, 1025): + try: + np.load(tmp)["data"] + except Exception, e: + raise AssertionError("Failed to load data from a file: %s" % e) + finally: + os.remove(tmp) + + class TestSaveTxt(TestCase): def test_array(self): a = np.array([[1, 2], [3, 4]], float) @@ -1381,8 +1427,6 @@ M 33 21.99 usecols=("A", "C", "E"), names=True) assert_equal(test.dtype.names, ctrl_names) - - def test_fixed_width_names(self): "Test fix-width w/ names" data = " A B C\n 0 1 2.3\n 45 67 9." @@ -1406,6 +1450,14 @@ M 33 21.99 test = np.ndfromtxt(StringIO(data), **kwargs) assert_equal(test, ctrl) + def test_comments_is_none(self): + # Github issue 329 (None was previously being converted to 'None'). + test = np.genfromtxt(StringIO("test1,testNonetherestofthedata"), + dtype=None, comments=None, delimiter=',') + assert_equal(test[1], asbytes('testNonetherestofthedata')) + test = np.genfromtxt(StringIO("test1, testNonetherestofthedata"), + dtype=None, comments=None, delimiter=',') + assert_equal(test[1], asbytes(' testNonetherestofthedata')) def test_recfromtxt(self): # @@ -1426,7 +1478,6 @@ M 33 21.99 assert_equal(test.mask, control.mask) assert_equal(test.A, [0, 2]) - def test_recfromcsv(self): # data = StringIO('A,B\n0,1\n2,3') diff --git a/numpy/lib/type_check.py b/numpy/lib/type_check.py index c116c7e4a..e22d63156 100644 --- a/numpy/lib/type_check.py +++ b/numpy/lib/type_check.py @@ -233,11 +233,10 @@ def isreal(x): def iscomplexobj(x): """ - Return True if x is a complex type or an array of complex numbers. + Check for a complex type or an array of complex numbers. - The type of the input is checked, not the value. So even if the input - has an imaginary part equal to zero, `iscomplexobj` evaluates to True - if the data type is complex. + The type of the input is checked, not the value. Even if the input + has an imaginary part equal to zero, `iscomplexobj` evaluates to True. Parameters ---------- @@ -246,8 +245,9 @@ def iscomplexobj(x): Returns ------- - y : bool - The return value, True if `x` is of a complex type. + iscomplexobj : bool + The return value, True if `x` is of a complex type or has at least + one complex element. See Also -------- diff --git a/numpy/linalg/linalg.py b/numpy/linalg/linalg.py index aba656b5e..a7f1c074d 100644 --- a/numpy/linalg/linalg.py +++ b/numpy/linalg/linalg.py @@ -250,15 +250,15 @@ def solve(a, b): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Coefficient matrix. - b : array_like, shape (M,) or (M, N) + b : {(M,), (M, N)}, array_like Ordinate or "dependent variable" values. Returns ------- - x : ndarray, shape (M,) or (M, N) depending on b - Solution to the system a x = b + x : {(M,), (M, N)} ndarray + Solution to the system a x = b. Returned shape is identical to `b`. Raises ------ @@ -410,12 +410,12 @@ def inv(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Matrix to be inverted. Returns ------- - ainv : ndarray or matrix, shape (M, M) + ainv : (M, M) ndarray or matrix (Multiplicative) inverse of the matrix `a`. Raises @@ -459,14 +459,15 @@ def cholesky(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Hermitian (symmetric if all elements are real), positive-definite input matrix. Returns ------- - L : ndarray, or matrix object if `a` is, shape (M, M) - Lower-triangular Cholesky factor of a. + L : {(M, M) ndarray, (M, M) matrix} + Lower-triangular Cholesky factor of `a`. Returns a matrix object + if `a` is a matrix object. Raises ------ @@ -709,12 +710,12 @@ def eigvals(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex- or real-valued matrix whose eigenvalues will be computed. Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, each repeated according to its multiplicity. They are not necessarily ordered, nor are they necessarily real for real matrices. @@ -815,7 +816,7 @@ def eigvalsh(a, UPLO='L'): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex- or real-valued matrix whose eigenvalues are to be computed. UPLO : {'L', 'U'}, optional @@ -824,7 +825,7 @@ def eigvalsh(a, UPLO='L'): Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, not necessarily ordered, each repeated according to its multiplicity. @@ -910,18 +911,17 @@ def eig(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A square array of real or complex elements. Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, each repeated according to its multiplicity. The eigenvalues are not necessarily ordered, nor are they necessarily real for real arrays (though for real arrays complex-valued eigenvalues should occur in conjugate pairs). - - v : ndarray, shape (M, M) + v : (M, M) ndarray The normalized (unit "length") eigenvectors, such that the column ``v[:,i]`` is the eigenvector corresponding to the eigenvalue ``w[i]``. @@ -1077,7 +1077,7 @@ def eigh(a, UPLO='L'): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like A complex Hermitian or real symmetric matrix. UPLO : {'L', 'U'}, optional Specifies whether the calculation is done with the lower triangular @@ -1085,11 +1085,12 @@ def eigh(a, UPLO='L'): Returns ------- - w : ndarray, shape (M,) + w : (M,) ndarray The eigenvalues, not necessarily ordered. - v : ndarray, or matrix object if `a` is, shape (M, M) + v : {(M, M) ndarray, (M, M) matrix} The column ``v[:, i]`` is the normalized eigenvector corresponding - to the eigenvalue ``w[i]``. + to the eigenvalue ``w[i]``. Will return a matrix object if `a` is + a matrix object. Raises ------ @@ -1338,7 +1339,7 @@ def cond(x, p=None): Parameters ---------- - x : array_like, shape (M, N) + x : (M, N) array_like The matrix whose condition number is sought. p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional Order of the norm: @@ -1419,38 +1420,64 @@ def matrix_rank(M, tol=None): """ Return matrix rank of array using SVD method - Rank of the array is the number of SVD singular values of the - array that are greater than `tol`. + Rank of the array is the number of SVD singular values of the array that are + greater than `tol`. Parameters ---------- - M : array_like + M : {(M,), (M, N)} array_like array of <=2 dimensions - tol : {None, float} + tol : {None, float}, optional threshold below which SVD values are considered zero. If `tol` is None, and ``S`` is an array with singular values for `M`, and ``eps`` is the epsilon value for datatype of ``S``, then `tol` is - set to ``S.max() * eps``. + set to ``S.max() * max(M.shape) * eps``. Notes ----- - Golub and van Loan [1]_ define "numerical rank deficiency" as using - tol=eps*S[0] (where S[0] is the maximum singular value and thus the - 2-norm of the matrix). This is one definition of rank deficiency, - and the one we use here. When floating point roundoff is the main - concern, then "numerical rank deficiency" is a reasonable choice. In - some cases you may prefer other definitions. The most useful measure - of the tolerance depends on the operations you intend to use on your - matrix. For example, if your data come from uncertain measurements - with uncertainties greater than floating point epsilon, choosing a - tolerance near that uncertainty may be preferable. The tolerance - may be absolute if the uncertainties are absolute rather than - relative. + The default threshold to detect rank deficiency is a test on the magnitude + of the singular values of `M`. By default, we identify singular values less + than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with + the symbols defined above). This is the algorithm MATLAB uses [1]. It also + appears in *Numerical recipes* in the discussion of SVD solutions for linear + least squares [2]. + + This default threshold is designed to detect rank deficiency accounting for + the numerical errors of the SVD computation. Imagine that there is a column + in `M` that is an exact (in floating point) linear combination of other + columns in `M`. Computing the SVD on `M` will not produce a singular value + exactly equal to 0 in general: any difference of the smallest SVD value from + 0 will be caused by numerical imprecision in the calculation of the SVD. + Our threshold for small SVD values takes this numerical imprecision into + account, and the default threshold will detect such numerical rank + deficiency. The threshold may declare a matrix `M` rank deficient even if + the linear combination of some columns of `M` is not exactly equal to + another column of `M` but only numerically very close to another column of + `M`. + + We chose our default threshold because it is in wide use. Other thresholds + are possible. For example, elsewhere in the 2007 edition of *Numerical + recipes* there is an alternative threshold of ``S.max() * + np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe + this threshold as being based on "expected roundoff error" (p 71). + + The thresholds above deal with floating point roundoff error in the + calculation of the SVD. However, you may have more information about the + sources of error in `M` that would make you consider other tolerance values + to detect *effective* rank deficiency. The most useful measure of the + tolerance depends on the operations you intend to use on your matrix. For + example, if your data come from uncertain measurements with uncertainties + greater than floating point epsilon, choosing a tolerance near that + uncertainty may be preferable. The tolerance may be absolute if the + uncertainties are absolute rather than relative. References ---------- - .. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*. - Baltimore: Johns Hopkins University Press, 1996. + .. [1] MATLAB reference documention, "Rank" + http://www.mathworks.com/help/techdoc/ref/rank.html + .. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery, + "Numerical Recipes (3rd edition)", Cambridge University Press, 2007, + page 795. Examples -------- @@ -1464,7 +1491,6 @@ def matrix_rank(M, tol=None): 1 >>> matrix_rank(np.zeros((4,))) 0 - """ M = asarray(M) if M.ndim > 2: @@ -1473,7 +1499,7 @@ def matrix_rank(M, tol=None): return int(not all(M==0)) S = svd(M, compute_uv=False) if tol is None: - tol = S.max() * finfo(S.dtype).eps + tol = S.max() * max(M.shape) * finfo(S.dtype).eps return sum(S > tol) @@ -1489,7 +1515,7 @@ def pinv(a, rcond=1e-15 ): Parameters ---------- - a : array_like, shape (M, N) + a : (M, N) array_like Matrix to be pseudo-inverted. rcond : float Cutoff for small singular values. @@ -1499,7 +1525,7 @@ def pinv(a, rcond=1e-15 ): Returns ------- - B : ndarray, shape (N, M) + B : (N, M) ndarray The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so is `B`. @@ -1647,14 +1673,19 @@ def det(a): Parameters ---------- - a : array_like, shape (M, M) + a : (M, M) array_like Input array. Returns ------- - det : ndarray + det : float Determinant of `a`. + See Also + -------- + slogdet : Another way to representing the determinant, more suitable + for large matrices where underflow/overflow may occur. + Notes ----- The determinant is computed via LU factorization using the LAPACK @@ -1668,11 +1699,6 @@ def det(a): >>> np.linalg.det(a) -2.0 - See Also - -------- - slogdet : Another way to representing the determinant, more suitable - for large matrices where underflow/overflow may occur. - """ sign, logdet = slogdet(a) return sign * exp(logdet) @@ -1693,9 +1719,9 @@ def lstsq(a, b, rcond=-1): Parameters ---------- - a : array_like, shape (M, N) + a : (M, N) array_like "Coefficient" matrix. - b : array_like, shape (M,) or (M, K) + b : {(M,), (M, K)} array_like Ordinate or "dependent variable" values. If `b` is two-dimensional, the least-squares solution is calculated for each of the `K` columns of `b`. @@ -1706,18 +1732,18 @@ def lstsq(a, b, rcond=-1): Returns ------- - x : ndarray, shape (N,) or (N, K) + x : {(M,), (M, K)} ndarray Least-squares solution. The shape of `x` depends on the shape of `b`. - residues : ndarray, shape (), (1,), or (K,) - Sums of residues; squared Euclidean 2-norm for each column in + residuals : {(), (1,), (K,)} ndarray + Sums of residuals; squared Euclidean 2-norm for each column in ``b - a*x``. If the rank of `a` is < N or > M, this is an empty array. If `b` is 1-dimensional, this is a (1,) shape array. Otherwise the shape is (K,). rank : int Rank of matrix `a`. - s : ndarray, shape (min(M,N),) + s : (min(M, N),) ndarray Singular values of `a`. Raises @@ -1849,7 +1875,7 @@ def norm(x, ord=None): Parameters ---------- - x : array_like, shape (M,) or (M, N) + x : {(M,), (M, N)} array_like Input array. ord : {non-zero int, inf, -inf, 'fro'}, optional Order of the norm (see table under ``Notes``). inf means numpy's diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 2a2ec3da5..623939863 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -3,7 +3,9 @@ import sys import numpy as np -from numpy.testing import * +from numpy.testing import (TestCase, assert_, assert_equal, assert_raises, + assert_array_equal, assert_almost_equal, + run_module_suite) from numpy import array, single, double, csingle, cdouble, dot, identity from numpy import multiply, atleast_2d, inf, asarray, matrix from numpy import linalg @@ -451,6 +453,19 @@ class TestMatrixRank(object): yield assert_equal, matrix_rank(1), 1 +def test_reduced_rank(): + # Test matrices with reduced rank + rng = np.random.RandomState(20120714) + for i in range(100): + # Make a rank deficient matrix + X = rng.normal(size=(40, 10)) + X[:, 0] = X[:, 1] + X[:, 2] + # Assert that matrix_rank detected deficiency + assert_equal(matrix_rank(X), 9) + X[:, 3] = X[:, 4] + X[:, 5] + assert_equal(matrix_rank(X), 8) + + class TestQR(TestCase): def test_qr_empty(self): a = np.zeros((0,2)) diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c index 003df267a..984acf8cb 100644 --- a/numpy/random/mtrand/mtrand.c +++ b/numpy/random/mtrand/mtrand.c @@ -1,4 +1,4 @@ -/* Generated by Cython 0.15.1 on Sun Apr 1 22:07:06 2012 */ +/* Generated by Cython 0.14.1 on Thu Jul 12 12:48:58 2012 */ #define PY_SSIZE_T_CLEAN #include "Python.h" @@ -46,7 +46,7 @@ #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) - #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) + #define PyInt_AsSsize_t(o) PyInt_AsLong(o) #define PyNumber_Index(o) PyNumber_Int(o) #define PyIndex_Check(o) PyNumber_Check(o) #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message) @@ -159,15 +159,6 @@ #define PyBoolObject PyLongObject #endif -#if PY_VERSION_HEX < 0x03020000 - typedef long Py_hash_t; - #define __Pyx_PyInt_FromHash_t PyInt_FromLong - #define __Pyx_PyInt_AsHash_t PyInt_AsLong -#else - #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t - #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t -#endif - #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) @@ -218,19 +209,16 @@ #define __Pyx_DOCSTR(n) (n) #endif -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif +#ifdef __cplusplus +#define __PYX_EXTERN_C extern "C" +#else +#define __PYX_EXTERN_C extern #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include <math.h> -#define __PYX_HAVE__mtrand #define __PYX_HAVE_API__mtrand #include "string.h" #include "math.h" @@ -240,9 +228,6 @@ #include "randomkit.h" #include "distributions.h" #include "initarray.h" -#ifdef _OPENMP -#include <omp.h> -#endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS @@ -285,7 +270,6 @@ typedef struct {PyObject **p; char *s; const long n; const char* encoding; const #define __Pyx_PyBytes_FromUString(s) PyBytes_FromString((char*)s) #define __Pyx_PyBytes_AsUString(s) ((unsigned char*) PyBytes_AsString(s)) -#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); @@ -298,17 +282,17 @@ static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); #ifdef __GNUC__ - /* Test for GCC > 2.95 */ - #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) - #define likely(x) __builtin_expect(!!(x), 1) - #define unlikely(x) __builtin_expect(!!(x), 0) - #else /* __GNUC__ > 2 ... */ - #define likely(x) (x) - #define unlikely(x) (x) - #endif /* __GNUC__ > 2 ... */ +/* Test for GCC > 2.95 */ +#if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) +#define likely(x) __builtin_expect(!!(x), 1) +#define unlikely(x) __builtin_expect(!!(x), 0) +#else /* __GNUC__ > 2 ... */ +#define likely(x) (x) +#define unlikely(x) (x) +#endif /* __GNUC__ > 2 ... */ #else /* __GNUC__ */ - #define likely(x) (x) - #define unlikely(x) (x) +#define likely(x) (x) +#define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; @@ -326,88 +310,24 @@ static const char *__pyx_f[] = { "numpy.pxd", }; -/*--- Type declarations ---*/ -struct __pyx_obj_6mtrand_RandomState; +/* Type declarations */ -/* "mtrand.pyx":107 - * long rk_logseries(rk_state *state, double p) - * - * ctypedef double (* rk_cont0)(rk_state *state) # <<<<<<<<<<<<<< - * ctypedef double (* rk_cont1)(rk_state *state, double a) - * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) - */ typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *); -/* "mtrand.pyx":108 - * - * ctypedef double (* rk_cont0)(rk_state *state) - * ctypedef double (* rk_cont1)(rk_state *state, double a) # <<<<<<<<<<<<<< - * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) - * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) - */ typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *, double); -/* "mtrand.pyx":109 - * ctypedef double (* rk_cont0)(rk_state *state) - * ctypedef double (* rk_cont1)(rk_state *state, double a) - * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) # <<<<<<<<<<<<<< - * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) - * - */ typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double); -/* "mtrand.pyx":110 - * ctypedef double (* rk_cont1)(rk_state *state, double a) - * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) - * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) # <<<<<<<<<<<<<< - * - * ctypedef long (* rk_disc0)(rk_state *state) - */ typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double); -/* "mtrand.pyx":112 - * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) - * - * ctypedef long (* rk_disc0)(rk_state *state) # <<<<<<<<<<<<<< - * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) - * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) - */ typedef long (*__pyx_t_6mtrand_rk_disc0)(rk_state *); -/* "mtrand.pyx":113 - * - * ctypedef long (* rk_disc0)(rk_state *state) - * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) # <<<<<<<<<<<<<< - * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) - * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) - */ typedef long (*__pyx_t_6mtrand_rk_discnp)(rk_state *, long, double); -/* "mtrand.pyx":114 - * ctypedef long (* rk_disc0)(rk_state *state) - * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) - * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) # <<<<<<<<<<<<<< - * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) - * ctypedef long (* rk_discd)(rk_state *state, double a) - */ typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *, double, double); -/* "mtrand.pyx":115 - * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) - * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) - * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) # <<<<<<<<<<<<<< - * ctypedef long (* rk_discd)(rk_state *state, double a) - * - */ typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long); -/* "mtrand.pyx":116 - * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) - * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) - * ctypedef long (* rk_discd)(rk_state *state, double a) # <<<<<<<<<<<<<< - * - * - */ typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double); /* "mtrand.pyx":524 @@ -417,12 +337,12 @@ typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double); * """ * RandomState(seed=None) */ + struct __pyx_obj_6mtrand_RandomState { PyObject_HEAD rk_state *internal_state; }; - #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif @@ -437,38 +357,44 @@ struct __pyx_obj_6mtrand_RandomState { void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; - static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/ - #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; - #define __Pyx_RefNannySetupContext(name) __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) + static __Pyx_RefNannyAPIStruct * __Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); + end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; + } + #define __Pyx_RefNannySetupContext(name) void *__pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) #define __Pyx_RefNannyFinishContext() __Pyx_RefNanny->FinishContext(&__pyx_refnanny) - #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) - #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) - #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) - #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) - #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) - #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) - #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r);} } while(0) #else - #define __Pyx_RefNannyDeclarations #define __Pyx_RefNannySetupContext(name) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) - #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) - #define __Pyx_XGOTREF(r) - #define __Pyx_XGIVEREF(r) #endif /* CYTHON_REFNANNY */ +#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0) +#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0) static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/ static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name); /*proto*/ @@ -556,12 +482,11 @@ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); -static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/ +static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ +static int __Pyx_EndUnpack(PyObject *, Py_ssize_t expected); /*proto*/ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname); - static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/ @@ -597,19 +522,7 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObje static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level); /*proto*/ - -#include <string.h> - -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/ - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/ - -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals -#else -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals -#endif +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp); @@ -647,20 +560,17 @@ static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject * static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *); -static int __Pyx_check_binary_version(void); - -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/ +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size, int strict); /*proto*/ static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ -static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, - int __pyx_lineno, const char *__pyx_filename); /*proto*/ +static void __Pyx_AddTraceback(const char *funcname); /*proto*/ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/ +/* Module declarations from numpy */ -/* Module declarations from 'numpy' */ +/* Module declarations from mtrand */ -/* Module declarations from 'mtrand' */ static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0; static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0; static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0; @@ -684,9 +594,9 @@ static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *, __pyx_t_6mtrand_rk_d static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *); /*proto*/ static double __pyx_f_6mtrand_kahan_sum(double *, npy_intp); /*proto*/ #define __Pyx_MODULE_NAME "mtrand" -int __pyx_module_is_main_mtrand = 0; +static int __pyx_module_is_main_mtrand = 0; -/* Implementation of 'mtrand' */ +/* Implementation of mtrand */ static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_TypeError; static char __pyx_k_1[] = "size is not compatible with inputs"; @@ -789,7 +699,7 @@ static char __pyx_k_226[] = "\n chisquare(df, size=None)\n\n Draw static char __pyx_k_227[] = "RandomState.noncentral_chisquare (line 2049)"; static char __pyx_k_228[] = "\n noncentral_chisquare(df, nonc, size=None)\n\n Draw samples from a noncentral chi-square distribution.\n\n The noncentral :math:`\\chi^2` distribution is a generalisation of\n the :math:`\\chi^2` distribution.\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be >= 1.\n nonc : float\n Non-centrality, should be > 0.\n size : int or tuple of ints\n Shape of the output.\n\n Notes\n -----\n The probability density function for the noncentral Chi-square distribution\n is\n\n .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n In Delhi (2007), it is noted that the noncentral chi-square is useful in\n bombing and coverage problems, the probability of killing the point target\n given by the noncentral chi-squared distribution.\n\n References\n ----------\n .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n analysis of weapon systems effectiveness\", Metrika, Volume 15,\n Number 1 / December, 1970.\n .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> import matplotlib.pyplot as plt\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n Draw values from a noncentral chisquare with very small noncentrality,\n and compare to a chisquare.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n "" ... bins=np.arange(0., 25, .1), normed=True)\n >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n ... bins=np.arange(0., 25, .1), normed=True)\n >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n >>> plt.show()\n\n Demonstrate how large values of non-centrality lead to a more symmetric\n distribution.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n "; static char __pyx_k_229[] = "RandomState.standard_cauchy (line 2141)"; -static char __pyx_k_230[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n ..[3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<25)""] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n "; +static char __pyx_k_230[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n .. [3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<""25)] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n "; static char __pyx_k_231[] = "RandomState.standard_t (line 2202)"; static char __pyx_k_232[] = "\n standard_t(df, size=None)\n\n Standard Student's t distribution with df degrees of freedom.\n\n A special case of the hyperbolic distribution.\n As `df` gets large, the result resembles that of the standard normal\n distribution (`standard_normal`).\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be > 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn samples.\n\n Notes\n -----\n The probability density function for the t distribution is\n\n .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n The t test is based on an assumption that the data come from a Normal\n distribution. The t test provides a way to test whether the sample mean\n (that is the mean calculated from the data) is a good estimate of the true\n mean.\n\n The derivation of the t-distribution was forst published in 1908 by William\n Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n issues, he had to publish under a pseudonym, and so he used the name\n Student.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n Springer, 2002.\n .. [2] Wikipedia, \"Student's t-distribution\"\n http://en.wikipedia.org/wiki/Student's_t-distribution\n\n Examples\n --------\n From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n women in Kj is:\n\n >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n ... 7515, 8230, 8770])\n\n Doe""s their energy intake deviate systematically from the recommended\n value of 7725 kJ?\n\n We have 10 degrees of freedom, so is the sample mean within 95% of the\n recommended value?\n\n >>> s = np.random.standard_t(10, size=100000)\n >>> np.mean(intake)\n 6753.636363636364\n >>> intake.std(ddof=1)\n 1142.1232221373727\n\n Calculate the t statistic, setting the ddof parameter to the unbiased\n value so the divisor in the standard deviation will be degrees of\n freedom, N-1.\n\n >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(s, bins=100, normed=True)\n\n For a one-sided t-test, how far out in the distribution does the t\n statistic appear?\n\n >>> >>> np.sum(s<t) / float(len(s))\n 0.0090699999999999999 #random\n\n So the p-value is about 0.009, which says the null hypothesis has a\n probability of about 99% of being true.\n\n "; static char __pyx_k_233[] = "RandomState.vonmises (line 2303)"; @@ -797,7 +707,7 @@ static char __pyx_k_234[] = "\n vonmises(mu, kappa, size=None)\n\n static char __pyx_k_235[] = "RandomState.pareto (line 2397)"; static char __pyx_k_236[] = "\n pareto(a, size=None)\n\n Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n classical Pareto distribution can be obtained from the Lomax distribution\n by adding the location parameter m, see below. The smallest value of the\n Lomax distribution is zero while for the classical Pareto distribution it\n is m, where the standard Pareto distribution has location m=1.\n Lomax can also be considered as a simplified version of the Generalized\n Pareto distribution (available in SciPy), with the scale set to one and\n the location set to zero.\n\n The Pareto distribution must be greater than zero, and is unbounded above.\n It is also known as the \"80-20 rule\". In this distribution, 80 percent of\n the weights are in the lowest 20 percent of the range, while the other 20\n percent fill the remaining 80 percent of the range.\n\n Parameters\n ----------\n shape : float, > 0.\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.lomax.pdf : probability density function,\n distribution or cumulative density function, etc.\n scipy.stats.distributions.genpareto.pdf : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Pareto distribution is\n\n .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n where :math:`a` is the shape and :math:`m` the location\n\n The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n is a power law probability distribution useful in many real world probl""ems.\n Outside the field of economics it is generally referred to as the Bradford\n distribution. Pareto developed the distribution to describe the\n distribution of wealth in an economy. It has also found use in insurance,\n web page access statistics, oil field sizes, and many other problems,\n including the download frequency for projects in Sourceforge [1]. It is\n one of the so-called \"fat-tailed\" distributions.\n\n\n References\n ----------\n .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n Sourceforge projects.\n .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 23-30.\n .. [4] Wikipedia, \"Pareto distribution\",\n http://en.wikipedia.org/wiki/Pareto_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a, m = 3., 1. # shape and mode\n >>> s = np.random.pareto(a, 1000) + m\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n >>> fit = a*m**a/bins**(a+1)\n >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n >>> plt.show()\n\n "; static char __pyx_k_237[] = "RandomState.weibull (line 2493)"; -static char __pyx_k_238[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull : probability density function,\n distribution or cumulative density function, etc.\n\n gumbel, scipy.stats.distributions.genextreme\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n Generalstabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n "; +static char __pyx_k_238[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull_max\n scipy.stats.distributions.weibull_min\n scipy.stats.distributions.genextreme\n gumbel\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n General""stabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n "; static char __pyx_k_239[] = "RandomState.power (line 2593)"; static char __pyx_k_240[] = "\n power(a, size=None)\n\n Draws samples in [0, 1] from a power distribution with positive\n exponent a - 1.\n\n Also known as the power function distribution.\n\n Parameters\n ----------\n a : float\n parameter, > 0\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n The returned samples lie in [0, 1].\n\n Raises\n ------\n ValueError\n If a<1.\n\n Notes\n -----\n The probability density function is\n\n .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n The power function distribution is just the inverse of the Pareto\n distribution. It may also be seen as a special case of the Beta\n distribution.\n\n It is used, for example, in modeling the over-reporting of insurance\n claims.\n\n References\n ----------\n .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n in economics and actuarial sciences\", Wiley, 2003.\n .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n Functions\", National Institute of Standards and Technology Handbook\n Series, June 2003.\n http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> samples = 1000\n >>> s = np.random.power(a, samples)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, bins=""30)\n >>> x = np.linspace(0, 1, 100)\n >>> y = a*x**(a-1.)\n >>> normed_y = samples*np.diff(bins)[0]*y\n >>> plt.plot(x, normed_y)\n >>> plt.show()\n\n Compare the power function distribution to the inverse of the Pareto.\n\n >>> from scipy import stats\n >>> rvs = np.random.power(5, 1000000)\n >>> rvsp = np.random.pareto(5, 1000000)\n >>> xx = np.linspace(0,1,100)\n >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n >>> plt.figure()\n >>> plt.hist(rvs, bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('np.random.power(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of stats.pareto(5)')\n\n "; static char __pyx_k_241[] = "RandomState.laplace (line 2702)"; @@ -809,11 +719,11 @@ static char __pyx_k_246[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n static char __pyx_k_247[] = "RandomState.lognormal (line 3011)"; static char __pyx_k_248[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n "; static char __pyx_k_249[] = "RandomState.rayleigh (line 3132)"; -static char __pyx_k_250[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n ..[2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n "; +static char __pyx_k_250[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n .. [2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n "; static char __pyx_k_251[] = "RandomState.wald (line 3204)"; -static char __pyx_k_252[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n ..[3] Wikipedia, \"Wald distributio""n\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n "; +static char __pyx_k_252[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n .. [3] Wikipedia, \"Wald distribu""tion\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n "; static char __pyx_k_253[] = "RandomState.triangular (line 3290)"; -static char __pyx_k_254[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n ..[1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n ... normed=True)\n >>> plt.show()\n\n "; +static char __pyx_k_254[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n .. [1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n ... normed=True)\n >>> plt.show()\n\n "; static char __pyx_k_255[] = "RandomState.binomial (line 3378)"; static char __pyx_k_256[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer > 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n "" .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n "; static char __pyx_k_257[] = "RandomState.negative_binomial (line 3486)"; @@ -836,20 +746,23 @@ static char __pyx_k_273[] = "RandomState.dirichlet (line 4245)"; static char __pyx_k_274[] = "\n dirichlet(alpha, size=None)\n\n Draw samples from the Dirichlet distribution.\n\n Draw `size` samples of dimension k from a Dirichlet distribution. A\n Dirichlet-distributed random variable can be seen as a multivariate\n generalization of a Beta distribution. Dirichlet pdf is the conjugate\n prior of a multinomial in Bayesian inference.\n\n Parameters\n ----------\n alpha : array\n Parameter of the distribution (k dimension for sample of\n dimension k).\n size : array\n Number of samples to draw.\n\n Returns\n -------\n samples : ndarray,\n The drawn samples, of shape (alpha.ndim, size).\n\n Notes\n -----\n .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n Uses the following property for computation: for each dimension,\n draw a random sample y_i from a standard gamma generator of shape\n `alpha_i`, then\n :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n Dirichlet distributed.\n\n References\n ----------\n .. [1] David McKay, \"Information Theory, Inference and Learning\n Algorithms,\" chapter 23,\n http://www.inference.phy.cam.ac.uk/mackay/\n .. [2] Wikipedia, \"Dirichlet distribution\",\n http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n Examples\n --------\n Taking an example cited in Wikipedia, this distribution can be used if\n one wanted to cut strings (each of initial length 1.0) into K pieces\n with different lengths, where each piece had, on average, a designated\n average length, but allowing some variation in the relative sizes of the\n pieces.\n\n >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n >>> plt.barh(range(20), s[0])\n >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n >>> plt.title(\"Lengths of Strings\")\n\n "; static char __pyx_k_275[] = "RandomState.shuffle (line 4361)"; static char __pyx_k_276[] = "\n shuffle(x)\n\n Modify a sequence in-place by shuffling its contents.\n\n Parameters\n ----------\n x : array_like\n The array or list to be shuffled.\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> arr = np.arange(10)\n >>> np.random.shuffle(arr)\n >>> arr\n [1 7 5 2 9 4 3 6 0 8]\n\n This function only shuffles the array along the first index of a\n multi-dimensional array:\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.shuffle(arr)\n >>> arr\n array([[3, 4, 5],\n [6, 7, 8],\n [0, 1, 2]])\n\n "; -static char __pyx_k_277[] = "RandomState.permutation (line 4423)"; +static char __pyx_k_277[] = "RandomState.permutation (line 4419)"; static char __pyx_k_278[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n "; static char __pyx_k__df[] = "df"; static char __pyx_k__mu[] = "mu"; +static char __pyx_k__nd[] = "nd"; static char __pyx_k__np[] = "np"; static char __pyx_k__add[] = "add"; static char __pyx_k__any[] = "any"; static char __pyx_k__cov[] = "cov"; static char __pyx_k__dot[] = "dot"; static char __pyx_k__int[] = "int"; +static char __pyx_k__key[] = "key"; static char __pyx_k__lam[] = "lam"; static char __pyx_k__loc[] = "loc"; static char __pyx_k__low[] = "low"; static char __pyx_k__max[] = "max"; +static char __pyx_k__pos[] = "pos"; static char __pyx_k__sum[] = "sum"; static char __pyx_k__svd[] = "svd"; static char __pyx_k__beta[] = "beta"; @@ -882,6 +795,7 @@ static char __pyx_k__dtype[] = "dtype"; static char __pyx_k__empty[] = "empty"; static char __pyx_k__equal[] = "equal"; static char __pyx_k__gamma[] = "gamma"; +static char __pyx_k__gauss[] = "gauss"; static char __pyx_k__iinfo[] = "iinfo"; static char __pyx_k__kappa[] = "kappa"; static char __pyx_k__ndmin[] = "ndmin"; @@ -895,6 +809,7 @@ static char __pyx_k__scale[] = "scale"; static char __pyx_k__shape[] = "shape"; static char __pyx_k__sigma[] = "sigma"; static char __pyx_k__zeros[] = "zeros"; +static char __pyx_k__append[] = "append"; static char __pyx_k__arange[] = "arange"; static char __pyx_k__choice[] = "choice"; static char __pyx_k__cumsum[] = "cumsum"; @@ -908,10 +823,12 @@ static char __pyx_k__uint32[] = "uint32"; static char __pyx_k__unique[] = "unique"; static char __pyx_k__MT19937[] = "MT19937"; static char __pyx_k__asarray[] = "asarray"; +static char __pyx_k__dataptr[] = "dataptr"; static char __pyx_k__float64[] = "float64"; static char __pyx_k__greater[] = "greater"; static char __pyx_k__integer[] = "integer"; static char __pyx_k__laplace[] = "laplace"; +static char __pyx_k__ndarray[] = "ndarray"; static char __pyx_k__nsample[] = "nsample"; static char __pyx_k__poisson[] = "poisson"; static char __pyx_k__randint[] = "randint"; @@ -933,10 +850,12 @@ static char __pyx_k__chisquare[] = "chisquare"; static char __pyx_k__dirichlet[] = "dirichlet"; static char __pyx_k__geometric[] = "geometric"; static char __pyx_k__get_state[] = "get_state"; +static char __pyx_k__has_gauss[] = "has_gauss"; static char __pyx_k__lognormal[] = "lognormal"; static char __pyx_k__logseries[] = "logseries"; static char __pyx_k__set_state[] = "set_state"; static char __pyx_k__ValueError[] = "ValueError"; +static char __pyx_k__dimensions[] = "dimensions"; static char __pyx_k__less_equal[] = "less_equal"; static char __pyx_k__standard_t[] = "standard_t"; static char __pyx_k__triangular[] = "triangular"; @@ -948,6 +867,7 @@ static char __pyx_k__searchsorted[] = "searchsorted"; static char __pyx_k__greater_equal[] = "greater_equal"; static char __pyx_k__random_sample[] = "random_sample"; static char __pyx_k__hypergeometric[] = "hypergeometric"; +static char __pyx_k__internal_state[] = "internal_state"; static char __pyx_k__standard_gamma[] = "standard_gamma"; static char __pyx_k__poisson_lam_max[] = "poisson_lam_max"; static char __pyx_k__random_integers[] = "random_integers"; @@ -1111,6 +1031,7 @@ static PyObject *__pyx_n_s__add; static PyObject *__pyx_n_s__allclose; static PyObject *__pyx_n_s__alpha; 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If the internal state is manually altered,\n the user should know exactly what he/she is doing.\n\n For backwards compatibility, the form (str, array of 624 uints, int) is\n also accepted although it is missing some information about the cached\n Gaussian value: ``state = ('MT19937', keys, pos)``.\n\n References\n ----------\n .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n 623-dimensionally equidistributed uniform pseudorandom number\n generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n\n "; static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) { - PyArrayObject *arrayObject_obj = 0; + PyArrayObject *arrayObject_obj; int __pyx_v_pos; - PyObject *__pyx_v_algorithm_name = NULL; - PyObject *__pyx_v_key = NULL; - PyObject *__pyx_v_has_gauss = NULL; - PyObject *__pyx_v_cached_gaussian = NULL; + PyObject *__pyx_v_algorithm_name; + PyObject *__pyx_v_key; + PyObject *__pyx_v_has_gauss; + PyObject *__pyx_v_cached_gaussian; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; - PyObject *__pyx_t_5 = NULL; - PyObject *(*__pyx_t_6)(PyObject *); - int __pyx_t_7; - Py_ssize_t __pyx_t_8; - PyObject *__pyx_t_9 = NULL; - PyObject *__pyx_t_10 = NULL; 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__pyx_t_1 = 0; @@ -5336,7 +5292,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel * raise ValueError("algorithm must be 'MT19937'") * key, pos = state[1:3] */ - __pyx_t_2 = __Pyx_PyString_Equals(__pyx_v_algorithm_name, ((PyObject *)__pyx_n_s__MT19937), Py_NE); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_RichCompare(__pyx_v_algorithm_name, ((PyObject *)__pyx_n_s__MT19937), Py_NE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; if (__pyx_t_2) { /* "mtrand.pyx":691 @@ -5348,7 +5307,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel */ __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_10), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 691; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0, 0); + __Pyx_Raise(__pyx_t_1, 0, 0); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 691; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L5; @@ -5364,53 +5323,34 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel */ __pyx_t_1 = __Pyx_PySequence_GetSlice(__pyx_v_state, 1, 3); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - if ((likely(PyTuple_CheckExact(__pyx_t_1))) || (PyList_CheckExact(__pyx_t_1))) { - PyObject* sequence = __pyx_t_1; - if (likely(PyTuple_CheckExact(sequence))) { - if (unlikely(PyTuple_GET_SIZE(sequence) != 2)) { - if (PyTuple_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2); - else __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(sequence)); - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); - __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); - } else { - if (unlikely(PyList_GET_SIZE(sequence) != 2)) { - if (PyList_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2); - else __Pyx_RaiseNeedMoreValuesError(PyList_GET_SIZE(sequence)); - {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_t_3 = PyList_GET_ITEM(sequence, 0); - __pyx_t_4 = PyList_GET_ITEM(sequence, 1); - } - __Pyx_INCREF(__pyx_t_3); - __Pyx_INCREF(__pyx_t_4); + if (PyTuple_CheckExact(__pyx_t_1) && likely(PyTuple_GET_SIZE(__pyx_t_1) == 2)) { + PyObject* tuple = __pyx_t_1; + __pyx_t_3 = PyTuple_GET_ITEM(tuple, 0); __Pyx_INCREF(__pyx_t_3); + __pyx_t_4 = PyTuple_GET_ITEM(tuple, 1); __Pyx_INCREF(__pyx_t_4); + __pyx_t_5 = __Pyx_PyInt_AsInt(__pyx_t_4); if (unlikely((__pyx_t_5 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __Pyx_DECREF(__pyx_v_key); + __pyx_v_key = __pyx_t_3; + __pyx_t_3 = 0; + __pyx_v_pos = __pyx_t_5; } else { - Py_ssize_t index = -1; - __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); + __pyx_t_6 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext; - index = 0; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L6_unpacking_failed; + __pyx_t_3 = __Pyx_UnpackItem(__pyx_t_6, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; 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long *__pyx_v_array_data; - PyArrayObject *arrayObject = 0; + PyArrayObject *arrayObject; npy_intp __pyx_v_length; npy_intp __pyx_v_i; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations int __pyx_t_1; long __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; npy_intp __pyx_t_6; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__low,&__pyx_n_s__high,&__pyx_n_s__size,0}; __Pyx_RefNannySetupContext("randint"); - { + if (unlikely(__pyx_kwds)) { + Py_ssize_t kw_args = PyDict_Size(__pyx_kwds); PyObject* values[3] = {0,0,0}; values[1] = ((PyObject *)Py_None); values[2] = ((PyObject *)Py_None); - if (unlikely(__pyx_kwds)) { - Py_ssize_t kw_args; - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); - case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); - case 0: break; 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if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1002; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyEval_CallObjectWithKeywords(__pyx_t_1, ((PyObject *)__pyx_t_2), ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1002; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_v_a); __pyx_v_a = __pyx_t_5; @@ -6617,9 +6539,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __pyx_t_4 = PyObject_RichCompare(__pyx_t_5, __pyx_int_1, Py_NE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - if (__pyx_t_2) { + if (__pyx_t_3) { /* "mtrand.pyx":1004 * a = np.array(a, ndmin=1, copy=0) @@ -6630,7 +6552,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_19), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __Pyx_Raise(__pyx_t_4, 0, 0, 0); + __Pyx_Raise(__pyx_t_4, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; @@ -6646,6 +6568,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_4 = PyObject_GetAttr(__pyx_v_a, __pyx_n_s__size); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1005; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_v_pop_size); __pyx_v_pop_size = __pyx_t_4; __pyx_t_4 = 0; @@ -6656,8 +6579,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * raise ValueError("a must be non-empty") * */ - __pyx_t_2 = (__pyx_v_pop_size == __pyx_int_0); - if (__pyx_t_2) { + __pyx_t_3 = (__pyx_v_pop_size == __pyx_int_0); + if (__pyx_t_3) { /* "mtrand.pyx":1007 * pop_size = a.size @@ -6668,7 +6591,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_21), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1007; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __Pyx_Raise(__pyx_t_4, 0, 0, 0); + __Pyx_Raise(__pyx_t_4, 0, 0); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1007; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L9; @@ -6686,9 +6609,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_4 = PyObject_RichCompare(Py_None, __pyx_v_p, Py_NE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1009; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1009; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1009; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - if (__pyx_t_2) { + if (__pyx_t_3) { /* "mtrand.pyx":1010 * @@ -6707,22 +6630,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __Pyx_INCREF(__pyx_v_p); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_p); __Pyx_GIVEREF(__pyx_v_p); - __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_1)); - __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __pyx_t_6 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__double); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_6 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__double); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_n_s__dtype), __pyx_t_6) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_n_s__dtype), __pyx_t_6) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_n_s__ndmin), __pyx_int_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_n_s__copy), __pyx_int_0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_6 = PyEval_CallObjectWithKeywords(__pyx_t_5, ((PyObject *)__pyx_t_4), ((PyObject *)__pyx_t_1)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_n_s__ndmin), __pyx_int_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_n_s__copy), __pyx_int_0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = PyEval_CallObjectWithKeywords(__pyx_t_5, ((PyObject *)__pyx_t_4), ((PyObject *)__pyx_t_2)); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1010; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __Pyx_DECREF(__pyx_v_p); __pyx_v_p = __pyx_t_6; __pyx_t_6 = 0; @@ -6736,12 +6659,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__ndim); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_1 = PyObject_RichCompare(__pyx_t_6, __pyx_int_1, Py_NE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = PyObject_RichCompare(__pyx_t_6, __pyx_int_1, Py_NE); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (__pyx_t_2) { + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + if (__pyx_t_3) { /* "mtrand.pyx":1012 * p = np.array(p, dtype=np.double, ndmin=1, copy=0) @@ -6750,10 +6673,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * if p.size != pop_size: * raise ValueError("a and p must have same size") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_23), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_23), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L11; } @@ -6766,14 +6689,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * raise ValueError("a and p must have same size") * if np.any(p < 0): */ - __pyx_t_1 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_6 = PyObject_RichCompare(__pyx_t_1, __pyx_v_pop_size, Py_NE); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__size); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_t_6 = PyObject_RichCompare(__pyx_t_2, __pyx_v_pop_size, Py_NE); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - if (__pyx_t_2) { + if (__pyx_t_3) { /* "mtrand.pyx":1014 * raise ValueError("p must be 1-dimensional") @@ -6784,7 +6707,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_25), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_Raise(__pyx_t_6, 0, 0, 0); + __Pyx_Raise(__pyx_t_6, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L12; @@ -6800,8 +6723,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyObject_RichCompare(__pyx_v_p, __pyx_int_0, Py_LT); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); @@ -6810,13 +6733,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_6 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - if (__pyx_t_2) { + if (__pyx_t_3) { /* "mtrand.pyx":1016 * raise ValueError("a and p must have same size") @@ -6827,7 +6750,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_27), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_Raise(__pyx_t_6, 0, 0, 0); + __Pyx_Raise(__pyx_t_6, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L13; @@ -6848,24 +6771,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__sum); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_1 = PyObject_Call(__pyx_t_6, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = PyObject_Call(__pyx_t_6, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_6)); - PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_1); - __Pyx_GIVEREF(__pyx_t_1); + PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_2); + __Pyx_GIVEREF(__pyx_t_2); __Pyx_INCREF(__pyx_int_1); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_1 = 0; - __pyx_t_1 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = 0; + __pyx_t_2 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_7 = (!__pyx_t_2); + __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_7 = (!__pyx_t_3); if (__pyx_t_7) { /* "mtrand.pyx":1018 @@ -6875,10 +6798,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * * # Actual sampling */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_29), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); 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if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__cumsum); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_t_6 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __Pyx_DECREF(__pyx_v_cdf); __pyx_v_cdf = __pyx_t_6; __pyx_t_6 = 0; @@ -6934,12 +6858,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = __Pyx_GetItemInt(__pyx_v_cdf, -1, sizeof(long), PyInt_FromLong); if (!__pyx_t_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); 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__pyx_v_uniform_samples = __pyx_t_4; __pyx_t_4 = 0; @@ -6982,14 +6907,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __Pyx_INCREF(__pyx_v_uniform_samples); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_v_uniform_samples); __Pyx_GIVEREF(__pyx_v_uniform_samples); - __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_1)); - if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_n_s__side), ((PyObject *)__pyx_n_s__right)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_5 = PyEval_CallObjectWithKeywords(__pyx_t_4, ((PyObject *)__pyx_t_6), ((PyObject *)__pyx_t_1)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyDict_New(); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + if (PyDict_SetItem(__pyx_t_2, ((PyObject *)__pyx_n_s__side), ((PyObject *)__pyx_n_s__right)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyEval_CallObjectWithKeywords(__pyx_t_4, ((PyObject *)__pyx_t_6), ((PyObject *)__pyx_t_2)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __Pyx_DECREF(__pyx_v_idx); __pyx_v_idx = __pyx_t_5; __pyx_t_5 = 0; goto __pyx_L16; @@ -7005,22 +6931,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_5 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__randint); 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if (PyDict_SetItem(__pyx_t_6, ((PyObject *)__pyx_n_s__size), __pyx_v_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_4 = PyEval_CallObjectWithKeywords(__pyx_t_5, ((PyObject *)__pyx_t_1), ((PyObject *)__pyx_t_6)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyEval_CallObjectWithKeywords(__pyx_t_5, ((PyObject *)__pyx_t_2), ((PyObject *)__pyx_t_6)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; + __Pyx_DECREF(__pyx_v_idx); __pyx_v_idx = __pyx_t_4; __pyx_t_4 = 0; } @@ -7059,24 +6986,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __Pyx_INCREF(((PyObject *)__pyx_kp_s_32)); 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; - __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_1)); - PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_6); + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_t_1), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; - __Pyx_Raise(__pyx_t_6, 0, 0, 0); + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __Pyx_Raise(__pyx_t_6, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L17; @@ -7105,8 +7032,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1035; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__sum); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1035; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); 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} @@ -8642,8 +8615,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_19beta(PyObject *__pyx_v_self, P * raise ValueError("b <= 0") * return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb) */ - __pyx_t_1 = (__pyx_v_fb <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fb <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":1505 * raise ValueError("a <= 0") @@ -8652,10 +8625,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_19beta(PyObject *__pyx_v_self, P * return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_45), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1505; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_45), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1505; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1505; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -8669,10 +8642,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_19beta(PyObject *__pyx_v_self, P * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_beta, __pyx_v_size, __pyx_v_fa, __pyx_v_fb); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1506; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_cont2_array_sc(__pyx_t_3, rk_beta, __pyx_t_1, __pyx_v_fa, __pyx_v_fb); 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goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_r = __pyx_t_3; + __pyx_t_3 = 0; goto __pyx_L0; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_1); - __Pyx_AddTraceback("mtrand.RandomState.standard_exponential", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_XDECREF(__pyx_t_2); + __Pyx_XDECREF(__pyx_t_3); + __Pyx_AddTraceback("mtrand.RandomState.standard_exponential"); __pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); @@ -9207,65 +9209,61 @@ static char __pyx_doc_6mtrand_11RandomState_22standard_gamma[] = "\n stan static PyObject *__pyx_pf_6mtrand_11RandomState_22standard_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_shape = 0; PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_oshape = 0; + PyArrayObject *__pyx_v_oshape; double __pyx_v_fshape; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations - int __pyx_t_1; - PyObject *__pyx_t_2 = NULL; 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goto __pyx_L1_error;} goto __pyx_L8; } @@ -10370,8 +10433,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * raise ValueError("nonc < 0") * return cont3_array_sc(self.internal_state, rk_noncentral_f, size, */ - __pyx_t_1 = (__pyx_v_fnonc < 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fnonc < 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":1952 * raise ValueError("dfden <= 0") @@ -10380,10 +10443,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * return cont3_array_sc(self.internal_state, rk_noncentral_f, size, * fdfnum, fdfden, fnonc) */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_69), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1952; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_69), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1952; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1952; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L9; } @@ -10397,6 +10460,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * */ __Pyx_XDECREF(__pyx_r); + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); /* "mtrand.pyx":1954 * raise ValueError("nonc < 0") @@ -10405,10 +10471,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * * PyErr_Clear() */ - __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden, __pyx_v_fnonc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2124; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2124; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -11075,8 +11174,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * raise ValueError("nonc <= 0") * return cont2_array_sc(self.internal_state, rk_noncentral_chisquare, */ - __pyx_t_1 = (__pyx_v_fnonc <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fnonc <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":2126 * raise ValueError("df <= 0") @@ -11085,10 +11184,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * return cont2_array_sc(self.internal_state, rk_noncentral_chisquare, * size, fdf, fnonc) */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_78), NULL); 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- __Pyx_XDECREF((PyObject *)__pyx_v_ononc); + __Pyx_DECREF((PyObject *)__pyx_v_odf); + __Pyx_DECREF((PyObject *)__pyx_v_ononc); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; @@ -11322,52 +11437,48 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject */ static PyObject *__pyx_pf_6mtrand_11RandomState_28standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_28standard_cauchy[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n ..[3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<25)""] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n "; +static char __pyx_doc_6mtrand_11RandomState_28standard_cauchy[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n .. 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- __Pyx_XDECREF((PyObject *)__pyx_v_oa); + __Pyx_DECREF((PyObject *)__pyx_v_oa); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; @@ -12183,69 +12342,65 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_31pareto(PyObject *__pyx_v_self, */ static PyObject *__pyx_pf_6mtrand_11RandomState_32weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_32weibull[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull : probability density function,\n distribution or cumulative density function, etc.\n\n gumbel, scipy.stats.distributions.genextreme\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n Generalstabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n "; +static char __pyx_doc_6mtrand_11RandomState_32weibull[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull_max\n scipy.stats.distributions.weibull_min\n scipy.stats.distributions.genextreme\n gumbel\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n General""stabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n "; static PyObject *__pyx_pf_6mtrand_11RandomState_32weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_a = 0; PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_oa = 0; + PyArrayObject *__pyx_v_oa; double __pyx_v_fa; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations - int __pyx_t_1; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -12547,10 +12716,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_33power(PyObject *__pyx_v_self, * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_power, __pyx_v_size, __pyx_v_fa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2693; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_cont1_array_sc(__pyx_t_3, rk_power, __pyx_t_1, __pyx_v_fa); 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -13385,10 +13613,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_sel * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_logistic, __pyx_v_size, __pyx_v_floc, __pyx_v_fscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3002; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_cont2_array_sc(__pyx_t_3, rk_logistic, __pyx_t_1, __pyx_v_floc, __pyx_v_fscale); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3002; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_1); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + if (__pyx_t_2) { /* "mtrand.pyx":3129 * osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ARRAY_ALIGNED) @@ -13764,10 +14025,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se * return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_110), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3129; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_110), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3129; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_Raise(__pyx_t_4, 0, 0); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3129; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -13781,24 +14042,28 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se * def rayleigh(self, scale=1.0, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_lognormal, __pyx_v_size, __pyx_v_omean, __pyx_v_osigma); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); 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- __Pyx_XDECREF((PyObject *)__pyx_v_osigma); + __Pyx_DECREF((PyObject *)__pyx_v_omean); + __Pyx_DECREF((PyObject *)__pyx_v_osigma); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; @@ -13813,71 +14078,68 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se */ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_38rayleigh[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n ..[2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n "; +static char __pyx_doc_6mtrand_11RandomState_38rayleigh[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n .. [2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. 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if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3200; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3201 * oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED) @@ -14014,10 +14287,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel * return cont1_array(self.internal_state, rk_rayleigh, size, oscale) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_114), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3201; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_114), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3201; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3201; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -14031,23 +14304,27 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel * def wald(self, mean, scale, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_6 = __pyx_f_6mtrand_cont1_array(__pyx_t_3, rk_rayleigh, __pyx_t_1, __pyx_v_oscale); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3202; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_r = __pyx_t_6; + __pyx_t_6 = 0; goto __pyx_L0; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_2); - __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); - __Pyx_AddTraceback("mtrand.RandomState.rayleigh", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_XDECREF(__pyx_t_6); + __Pyx_AddTraceback("mtrand.RandomState.rayleigh"); __pyx_r = NULL; __pyx_L0:; - __Pyx_XDECREF((PyObject *)__pyx_v_oscale); + __Pyx_DECREF((PyObject *)__pyx_v_oscale); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; @@ -14062,81 +14339,80 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_38rayleigh(PyObject *__pyx_v_sel */ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_39wald[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n ..[3] Wikipedia, \"Wald distributio""n\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n "; +static char __pyx_doc_6mtrand_11RandomState_39wald[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n .. [3] Wikipedia, \"Wald distribu""tion\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n "; static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_mean = 0; PyObject *__pyx_v_scale = 0; PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_omean = 0; - PyArrayObject *__pyx_v_oscale = 0; + PyArrayObject *__pyx_v_omean; + PyArrayObject *__pyx_v_oscale; double __pyx_v_fmean; double __pyx_v_fscale; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations - int __pyx_t_1; - PyObject *__pyx_t_2 = NULL; - PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_1 = NULL; + int __pyx_t_2; + rk_state *__pyx_t_3; PyObject *__pyx_t_4 = NULL; PyObject *__pyx_t_5 = NULL; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; + PyObject *__pyx_t_6 = NULL; static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__mean,&__pyx_n_s__scale,&__pyx_n_s__size,0}; __Pyx_RefNannySetupContext("wald"); - { + if (unlikely(__pyx_kwds)) { + Py_ssize_t kw_args = PyDict_Size(__pyx_kwds); PyObject* values[3] = {0,0,0}; values[2] = ((PyObject *)Py_None); - if (unlikely(__pyx_kwds)) { - Py_ssize_t kw_args; - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); - case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); - case 0: break; - default: goto __pyx_L5_argtuple_error; - } - kw_args = PyDict_Size(__pyx_kwds); - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 0: - values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mean); - if (likely(values[0])) kw_args--; - else goto __pyx_L5_argtuple_error; - case 1: - values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__scale); - if (likely(values[1])) kw_args--; - else { - __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} - } - case 2: - if (kw_args > 0) { - PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); - if (value) { values[2] = value; kw_args--; } - } - } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "wald") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + case 0: break; + default: goto __pyx_L5_argtuple_error; + } + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 0: + values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mean); + if (likely(values[0])) kw_args--; + else goto __pyx_L5_argtuple_error; + case 1: + values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__scale); + if (likely(values[1])) kw_args--; + else { + __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } - } else { - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); - values[0] = PyTuple_GET_ITEM(__pyx_args, 0); - break; - default: goto __pyx_L5_argtuple_error; + case 2: + if (kw_args > 0) { + PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); + if (value) { values[2] = value; kw_args--; } } } + if (unlikely(kw_args > 0)) { + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "wald") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } __pyx_v_mean = values[0]; __pyx_v_scale = values[1]; __pyx_v_size = values[2]; + } else { + __pyx_v_size = ((PyObject *)Py_None); + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 3: + __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: + __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 1); + __pyx_v_mean = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; + } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.wald", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("mtrand.RandomState.wald"); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; + __pyx_v_omean = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_v_oscale = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); /* "mtrand.pyx":3270 * cdef double fmean, fscale @@ -14145,7 +14421,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * fscale = PyFloat_AsDouble(scale) * if not PyErr_Occurred(): */ - __pyx_v_fmean = PyFloat_AsDouble(__pyx_v_mean); + __pyx_t_1 = __pyx_v_mean; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fmean = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3271 * @@ -14154,7 +14433,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if not PyErr_Occurred(): * if fmean <= 0: */ - __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale); + __pyx_t_1 = __pyx_v_scale; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fscale = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3272 * fmean = PyFloat_AsDouble(mean) @@ -14163,8 +14445,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if fmean <= 0: * raise ValueError("mean <= 0") */ - __pyx_t_1 = (!PyErr_Occurred()); - if (__pyx_t_1) { + __pyx_t_2 = (!PyErr_Occurred()); + if (__pyx_t_2) { /* "mtrand.pyx":3273 * fscale = PyFloat_AsDouble(scale) @@ -14173,8 +14455,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * raise ValueError("mean <= 0") * if fscale <= 0: */ - __pyx_t_1 = (__pyx_v_fmean <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fmean <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":3274 * if not PyErr_Occurred(): @@ -14183,10 +14465,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if fscale <= 0: * raise ValueError("scale <= 0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -14199,8 +14481,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * raise ValueError("scale <= 0") * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) */ - __pyx_t_1 = (__pyx_v_fscale <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fscale <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":3276 * raise ValueError("mean <= 0") @@ -14209,10 +14491,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -14226,10 +14508,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_wald, __pyx_v_size, __pyx_v_fmean, __pyx_v_fscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3277; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_cont2_array_sc(__pyx_t_3, rk_wald, __pyx_t_1, __pyx_v_fmean, __pyx_v_fscale); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3277; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_r = __pyx_t_4; + __pyx_t_4 = 0; goto __pyx_L0; goto __pyx_L6; } @@ -14251,11 +14537,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED) * if np.any(np.less_equal(omean,0.0)): */ - __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_mean, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_v_omean = ((PyArrayObject *)__pyx_t_2); - __pyx_t_2 = 0; + __pyx_t_4 = __pyx_v_mean; 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Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n ..[1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n ... normed=True)\n >>> plt.show()\n\n "; +static char __pyx_doc_6mtrand_11RandomState_40triangular[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n .. 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+ __pyx_t_1 = __pyx_v_left; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fleft = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3351 * @@ -14533,7 +14834,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * fmode = PyFloat_AsDouble(mode) * if not PyErr_Occurred(): */ - __pyx_v_fright = PyFloat_AsDouble(__pyx_v_right); + __pyx_t_1 = __pyx_v_right; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fright = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3352 * fleft = PyFloat_AsDouble(left) @@ -14542,7 +14846,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if not PyErr_Occurred(): * if fleft > fmode: */ - __pyx_v_fmode = PyFloat_AsDouble(__pyx_v_mode); + __pyx_t_1 = __pyx_v_mode; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fmode = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3353 * fright = PyFloat_AsDouble(right) @@ -14551,8 +14858,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fleft > fmode: * raise ValueError("left > mode") */ - __pyx_t_1 = (!PyErr_Occurred()); - if (__pyx_t_1) { + __pyx_t_2 = (!PyErr_Occurred()); + if (__pyx_t_2) { /* "mtrand.pyx":3354 * fmode = PyFloat_AsDouble(mode) @@ -14561,8 +14868,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * raise ValueError("left > mode") * if fmode > fright: */ - __pyx_t_1 = (__pyx_v_fleft > __pyx_v_fmode); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fleft > __pyx_v_fmode); + if (__pyx_t_2) { /* "mtrand.pyx":3355 * if not PyErr_Occurred(): @@ -14571,10 +14878,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fmode > fright: * raise ValueError("mode > right") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -14587,8 +14894,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * raise ValueError("mode > right") * if fleft == fright: */ - __pyx_t_1 = (__pyx_v_fmode > __pyx_v_fright); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fmode > __pyx_v_fright); + if (__pyx_t_2) { /* "mtrand.pyx":3357 * raise ValueError("left > mode") @@ -14597,10 +14904,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fleft == fright: * raise ValueError("left == right") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_124), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; 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/* "mtrand.pyx":3558 * fp = PyFloat_AsDouble(p) @@ -15476,8 +15829,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * if fn <= 0: * raise ValueError("n <= 0") */ - __pyx_t_1 = (!PyErr_Occurred()); - if (__pyx_t_1) { + __pyx_t_2 = (!PyErr_Occurred()); + if (__pyx_t_2) { /* "mtrand.pyx":3559 * fn = PyFloat_AsDouble(n) @@ -15486,8 +15839,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * raise ValueError("n <= 0") * if fp < 0: */ - __pyx_t_1 = (__pyx_v_fn <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fn <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":3560 * if not PyErr_Occurred(): @@ -15496,10 +15849,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * if fp < 0: * raise ValueError("p < 0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_139), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_139), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -15512,8 +15865,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * raise ValueError("p < 0") * elif fp > 1: */ - __pyx_t_1 = (__pyx_v_fp < 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fp < 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":3562 * raise ValueError("n <= 0") @@ -15522,10 +15875,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * elif fp > 1: * raise ValueError("p > 1") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_140), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_140), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -15537,8 +15890,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * raise ValueError("p > 1") * return discdd_array_sc(self.internal_state, rk_negative_binomial, */ - __pyx_t_1 = (__pyx_v_fp > 1.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fp > 1.0); + if (__pyx_t_2) { /* "mtrand.pyx":3564 * raise ValueError("p < 0") @@ -15547,10 +15900,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * return discdd_array_sc(self.internal_state, rk_negative_binomial, * size, fn, fp) */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_141), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3564; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_141), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3564; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3564; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -15564,6 +15917,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * */ __Pyx_XDECREF(__pyx_r); + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; /* "mtrand.pyx":3566 * raise ValueError("p > 1") @@ -15572,10 +15926,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_42negative_binomial(PyObject *__ * * PyErr_Clear() */ - __pyx_t_2 = __pyx_f_6mtrand_discdd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_negative_binomial, __pyx_v_size, __pyx_v_fn, __pyx_v_fp); 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goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __pyx_t_4 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; + __pyx_t_6 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __pyx_t_1 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__less); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; + __pyx_t_6 = PyTuple_New(2); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_6)); __Pyx_INCREF(__pyx_v_p); - PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_v_p); 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + __pyx_t_1 = PyObject_RichCompare(__pyx_v_lam, __pyx_int_0, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3638 * if not PyErr_Occurred(): @@ -15943,10 +16312,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * if lam > self.poisson_lam_max: * raise ValueError("lam value too large") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_147), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3638; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_147), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3638; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3638; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -15959,14 +16328,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * raise ValueError("lam value too large") * return discd_array_sc(self.internal_state, rk_poisson, size, flam) */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__poisson_lam_max); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_RichCompare(__pyx_v_lam, __pyx_t_2, Py_GT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__poisson_lam_max); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_3 = PyObject_RichCompare(__pyx_v_lam, __pyx_t_1, Py_GT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - if (__pyx_t_1) { + if (__pyx_t_2) { /* "mtrand.pyx":3640 * raise ValueError("lam < 0") @@ -15977,7 +16346,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self */ __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_149), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3640; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_Raise(__pyx_t_3, 0, 0, 0); + __Pyx_Raise(__pyx_t_3, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3640; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; @@ -15992,10 +16361,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = __pyx_f_6mtrand_discd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_poisson, __pyx_v_size, __pyx_v_flam); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3641; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __pyx_r = __pyx_t_3; - __pyx_t_3 = 0; + __pyx_t_4 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_3 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_3); + __pyx_t_1 = __pyx_f_6mtrand_discd_array_sc(__pyx_t_4, rk_poisson, __pyx_t_3, __pyx_v_flam); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3641; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __pyx_r = __pyx_t_1; + __pyx_t_1 = 0; goto __pyx_L0; goto __pyx_L6; } @@ -16017,9 +16390,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * if np.any(np.less(olam, 0)): * raise ValueError("lam < 0") */ - __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_lam, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3645; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __pyx_v_lam; 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__pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); @@ -16048,22 +16425,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self __Pyx_INCREF(__pyx_int_0); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); - __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __pyx_t_6 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); - PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_5); - __Pyx_GIVEREF(__pyx_t_5); - __pyx_t_5 = 0; - __pyx_t_5 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_6); + __Pyx_GIVEREF(__pyx_t_6); + __pyx_t_6 = 0; + __pyx_t_6 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - if (__pyx_t_1) { + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3646; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3647 * olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ARRAY_ALIGNED) @@ -16072,10 +16449,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * if np.any(np.greater(olam, self.poisson_lam_max)): * raise ValueError("lam value too large.") */ - __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_150), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3647; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_Raise(__pyx_t_5, 0, 0, 0); - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; + __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_150), NULL); 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__pyx_lineno = 3793; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3793; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -16570,10 +16961,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_discd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_geometric, __pyx_v_size, __pyx_v_fp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3794; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_discd_array_sc(__pyx_t_3, rk_geometric, __pyx_t_1, __pyx_v_fp); 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__pyx_lineno = 3802; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; + __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3802; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(((PyObject *)__pyx_t_5)); + PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_1); + __Pyx_GIVEREF(__pyx_t_1); + __pyx_t_1 = 0; + __pyx_t_1 = PyObject_Call(__pyx_t_6, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3802; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3802; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3803 * raise ValueError("p < 0.0") @@ -16712,10 +17111,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * return discd_array(self.internal_state, rk_geometric, size, op) * */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_161), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3803; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_161), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3803; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3803; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L10; } @@ -16729,23 +17128,27 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * def hypergeometric(self, ngood, nbad, nsample, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_geometric, __pyx_v_size, __pyx_v_op); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_5 = __pyx_f_6mtrand_discd_array(__pyx_t_3, rk_geometric, __pyx_t_1, __pyx_v_op); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_5); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_r = __pyx_t_5; + __pyx_t_5 = 0; goto __pyx_L0; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_2); - __Pyx_XDECREF(__pyx_t_3); + __Pyx_XDECREF(__pyx_t_1); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); - __Pyx_AddTraceback("mtrand.RandomState.geometric", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_XDECREF(__pyx_t_6); + __Pyx_AddTraceback("mtrand.RandomState.geometric"); __pyx_r = NULL; __pyx_L0:; - __Pyx_XDECREF((PyObject *)__pyx_v_op); + __Pyx_DECREF((PyObject *)__pyx_v_op); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; @@ -16766,88 +17169,88 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx PyObject *__pyx_v_nbad = 0; PyObject *__pyx_v_nsample = 0; PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_ongood = 0; - PyArrayObject *__pyx_v_onbad = 0; - PyArrayObject *__pyx_v_onsample = 0; + PyArrayObject *__pyx_v_ongood; + PyArrayObject *__pyx_v_onbad; + PyArrayObject *__pyx_v_onsample; long __pyx_v_lngood; long __pyx_v_lnbad; long __pyx_v_lnsample; PyObject *__pyx_r = NULL; - __Pyx_RefNannyDeclarations - int __pyx_t_1; - PyObject *__pyx_t_2 = NULL; + PyObject *__pyx_t_1 = NULL; + int __pyx_t_2; PyObject *__pyx_t_3 = NULL; - PyObject *__pyx_t_4 = NULL; + rk_state *__pyx_t_4; PyObject *__pyx_t_5 = NULL; PyObject *__pyx_t_6 = NULL; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; + PyObject *__pyx_t_7 = NULL; static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__ngood,&__pyx_n_s__nbad,&__pyx_n_s__nsample,&__pyx_n_s__size,0}; __Pyx_RefNannySetupContext("hypergeometric"); - { + if (unlikely(__pyx_kwds)) { + Py_ssize_t kw_args = PyDict_Size(__pyx_kwds); PyObject* values[4] = {0,0,0,0}; values[3] = ((PyObject *)Py_None); - if (unlikely(__pyx_kwds)) { - Py_ssize_t kw_args; - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); 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kw_args--; } - } + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + case 0: break; + default: goto __pyx_L5_argtuple_error; + } + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 0: + values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__ngood); + if (likely(values[0])) kw_args--; + else goto __pyx_L5_argtuple_error; + case 1: + values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nbad); + if (likely(values[1])) kw_args--; + else { + __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3806; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "hypergeometric") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3806; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + case 2: + values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nsample); + if (likely(values[2])) kw_args--; + else { + __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3806; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } - } else { - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3); - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - values[1] = PyTuple_GET_ITEM(__pyx_args, 1); - values[0] = PyTuple_GET_ITEM(__pyx_args, 0); - break; - default: goto __pyx_L5_argtuple_error; + case 3: + if (kw_args > 0) { + PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); + if (value) { values[3] = value; kw_args--; } } } + if (unlikely(kw_args > 0)) { + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "hypergeometric") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3806; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } __pyx_v_ngood = values[0]; __pyx_v_nbad = values[1]; __pyx_v_nsample = values[2]; __pyx_v_size = values[3]; + } else { + __pyx_v_size = ((PyObject *)Py_None); + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 4: + __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 3); + case 3: + __pyx_v_nsample = PyTuple_GET_ITEM(__pyx_args, 2); + __pyx_v_nbad = PyTuple_GET_ITEM(__pyx_args, 1); + __pyx_v_ngood = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; + } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3806; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.hypergeometric", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("mtrand.RandomState.hypergeometric"); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; + __pyx_v_ongood = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_v_onbad = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_v_onsample = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); /* "mtrand.pyx":3893 * cdef long lngood, lnbad, lnsample @@ -16856,7 +17259,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * lnbad = PyInt_AsLong(nbad) * lnsample = PyInt_AsLong(nsample) */ - __pyx_v_lngood = PyInt_AsLong(__pyx_v_ngood); + __pyx_t_1 = __pyx_v_ngood; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_lngood = PyInt_AsLong(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3894 * @@ -16865,7 +17271,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * lnsample = PyInt_AsLong(nsample) * if not PyErr_Occurred(): */ - __pyx_v_lnbad = PyInt_AsLong(__pyx_v_nbad); + __pyx_t_1 = __pyx_v_nbad; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_lnbad = PyInt_AsLong(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3895 * lngood = PyInt_AsLong(ngood) @@ -16874,7 +17283,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if not PyErr_Occurred(): * if ngood < 1: */ - __pyx_v_lnsample = PyInt_AsLong(__pyx_v_nsample); + __pyx_t_1 = __pyx_v_nsample; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_lnsample = PyInt_AsLong(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3896 * lnbad = PyInt_AsLong(nbad) @@ -16883,8 +17295,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if ngood < 1: * raise ValueError("ngood < 1") */ - __pyx_t_1 = (!PyErr_Occurred()); - if (__pyx_t_1) { + __pyx_t_2 = (!PyErr_Occurred()); + if (__pyx_t_2) { /* "mtrand.pyx":3897 * lnsample = PyInt_AsLong(nsample) @@ -16893,11 +17305,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("ngood < 1") * if nbad < 1: */ - __pyx_t_2 = PyObject_RichCompare(__pyx_v_ngood, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3897; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3897; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + __pyx_t_1 = PyObject_RichCompare(__pyx_v_ngood, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3897; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3897; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3898 * if not PyErr_Occurred(): @@ -16906,10 +17318,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if nbad < 1: * raise ValueError("nbad < 1") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_163), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_163), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L7; } @@ -16922,11 +17334,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("nbad < 1") * if nsample < 1: */ - __pyx_t_2 = PyObject_RichCompare(__pyx_v_nbad, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + __pyx_t_1 = PyObject_RichCompare(__pyx_v_nbad, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3900 * raise ValueError("ngood < 1") @@ -16935,10 +17347,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if nsample < 1: * raise ValueError("nsample < 1") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_165), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_165), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L8; } @@ -16951,11 +17363,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("nsample < 1") * if ngood + nbad < nsample: */ - __pyx_t_2 = PyObject_RichCompare(__pyx_v_nsample, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + __pyx_t_1 = PyObject_RichCompare(__pyx_v_nsample, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3902 * raise ValueError("nbad < 1") @@ -16964,10 +17376,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if ngood + nbad < nsample: * raise ValueError("ngood + nbad < nsample") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_167), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_167), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L9; } @@ -16980,14 +17392,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("ngood + nbad < nsample") * return discnmN_array_sc(self.internal_state, rk_hypergeometric, size, */ - __pyx_t_2 = PyNumber_Add(__pyx_v_ngood, __pyx_v_nbad); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_3 = PyObject_RichCompare(__pyx_t_2, __pyx_v_nsample, Py_LT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyNumber_Add(__pyx_v_ngood, __pyx_v_nbad); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_3 = PyObject_RichCompare(__pyx_t_1, __pyx_v_nsample, Py_LT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - if (__pyx_t_1) { + if (__pyx_t_2) { /* "mtrand.pyx":3904 * raise ValueError("nsample < 1") @@ -16998,7 +17410,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx */ __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_169), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3904; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_Raise(__pyx_t_3, 0, 0, 0); + __Pyx_Raise(__pyx_t_3, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3904; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L10; @@ -17013,6 +17425,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * */ __Pyx_XDECREF(__pyx_r); + __pyx_t_4 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_3 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_3); /* "mtrand.pyx":3906 * raise ValueError("ngood + nbad < nsample") @@ -17021,10 +17436,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * * */ - __pyx_t_3 = __pyx_f_6mtrand_discnmN_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_hypergeometric, __pyx_v_size, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __pyx_r = __pyx_t_3; - __pyx_t_3 = 0; + __pyx_t_1 = __pyx_f_6mtrand_discnmN_array_sc(__pyx_t_4, rk_hypergeometric, __pyx_t_3, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __pyx_r = __pyx_t_1; + __pyx_t_1 = 0; goto __pyx_L0; goto __pyx_L6; } @@ -17046,9 +17462,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED) * onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED) */ - __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_ngood, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3911; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __pyx_v_ngood; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_3 = PyArray_FROM_OTF(__pyx_t_1, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3911; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); + __Pyx_DECREF(((PyObject *)__pyx_v_ongood)); __pyx_v_ongood = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; @@ -17059,11 +17479,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED) * if np.any(np.less(ongood, 1)): */ - __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nbad, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); - __pyx_v_onbad = ((PyArrayObject *)__pyx_t_3); + __pyx_t_3 = __pyx_v_nbad; + __Pyx_INCREF(__pyx_t_3); + __pyx_t_1 = PyArray_FROM_OTF(__pyx_t_3, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_1))); + __Pyx_DECREF(((PyObject *)__pyx_v_onbad)); + __pyx_v_onbad = ((PyArrayObject *)__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3913 * ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ARRAY_ALIGNED) @@ -17072,9 +17496,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if np.any(np.less(ongood, 1)): * raise ValueError("ngood < 1") */ - __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nsample, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __pyx_v_nsample; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_3 = PyArray_FROM_OTF(__pyx_t_1, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); + __Pyx_DECREF(((PyObject *)__pyx_v_onsample)); __pyx_v_onsample = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; @@ -17087,13 +17515,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx */ __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_2 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__any); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__less); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_4); + __pyx_t_5 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__less); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); @@ -17103,22 +17531,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx __Pyx_INCREF(__pyx_int_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __pyx_t_6 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_3)); - PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_5); - __Pyx_GIVEREF(__pyx_t_5); - __pyx_t_5 = 0; - __pyx_t_5 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_6); + __Pyx_GIVEREF(__pyx_t_6); + __pyx_t_6 = 0; + __pyx_t_6 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - if (__pyx_t_1) { + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":3915 * onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED) @@ -17127,10 +17555,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if np.any(np.less(onbad, 1)): * raise ValueError("nbad < 1") */ - __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_170), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_Raise(__pyx_t_5, 0, 0, 0); - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; + __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_170), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __Pyx_Raise(__pyx_t_6, 0, 0); + __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3915; __pyx_clineno = __LINE__; goto __pyx_L1_error;} goto __pyx_L11; } @@ -17143,40 +17571,40 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("nbad < 1") * if np.any(np.less(onsample, 1)): */ - __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __pyx_t_3 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_6); + __pyx_t_3 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __pyx_t_5 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __pyx_t_2 = PyObject_GetAttr(__pyx_t_5, __pyx_n_s__less); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; 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__pyx_lineno = 3925; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } __pyx_v_p = values[0]; __pyx_v_size = values[1]; + } else { + __pyx_v_size = ((PyObject *)Py_None); + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 2: __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: __pyx_v_p = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; + } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("logseries", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3925; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.logseries", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("mtrand.RandomState.logseries"); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; + __pyx_v_op = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); /* "mtrand.pyx":4002 * cdef double fp @@ -17448,7 +17876,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * if not PyErr_Occurred(): * if fp <= 0.0: */ - __pyx_v_fp = PyFloat_AsDouble(__pyx_v_p); + __pyx_t_1 = __pyx_v_p; + __Pyx_INCREF(__pyx_t_1); + __pyx_v_fp = PyFloat_AsDouble(__pyx_t_1); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":4003 * @@ -17457,8 +17888,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * if fp <= 0.0: * raise ValueError("p <= 0.0") */ - __pyx_t_1 = (!PyErr_Occurred()); - if (__pyx_t_1) { + __pyx_t_2 = (!PyErr_Occurred()); + if (__pyx_t_2) { /* "mtrand.pyx":4004 * fp = PyFloat_AsDouble(p) @@ -17467,8 +17898,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * raise ValueError("p <= 0.0") * if fp >= 1.0: */ - __pyx_t_1 = (__pyx_v_fp <= 0.0); - if (__pyx_t_1) { + __pyx_t_2 = (__pyx_v_fp <= 0.0); + if (__pyx_t_2) { /* "mtrand.pyx":4005 * if not PyErr_Occurred(): @@ -17477,10 +17908,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * if fp >= 1.0: * raise ValueError("p >= 1.0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_175), NULL); 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__pyx_lineno = 4008; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_r = __pyx_t_2; - __pyx_t_2 = 0; + __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; + __pyx_t_1 = __pyx_v_size; + __Pyx_INCREF(__pyx_t_1); + __pyx_t_4 = __pyx_f_6mtrand_discd_array_sc(__pyx_t_3, rk_logseries, __pyx_t_1, __pyx_v_fp); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4008; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_r = __pyx_t_4; + __pyx_t_4 = 0; goto __pyx_L0; goto __pyx_L6; } @@ -17545,11 +17980,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * if np.any(np.less_equal(op, 0.0)): * raise ValueError("p <= 0.0") */ - __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_p, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); 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__pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_5)); - PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2); - __Pyx_GIVEREF(__pyx_t_2); - __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; - __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_1) { + __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":4014 * op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED) @@ -17602,10 +18041,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_se * if np.any(np.greater_equal(op, 1.0)): * raise ValueError("p >= 1.0") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_178), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_178), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; 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if (PyObject_SetAttr(__pyx_m, __pyx_n_s__permutation, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4516; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyObject_SetAttr(__pyx_m, __pyx_n_s__permutation, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4512; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":1 @@ -22993,7 +23365,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); if (__pyx_m) { - __Pyx_AddTraceback("init mtrand", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("init mtrand"); Py_DECREF(__pyx_m); __pyx_m = 0; } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init mtrand"); @@ -23009,34 +23381,11 @@ PyMODINIT_FUNC PyInit_mtrand(void) /* Runtime support code */ -#if CYTHON_REFNANNY -static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { - PyObject *m = NULL, *p = NULL; - void *r = NULL; - m = PyImport_ImportModule((char *)modname); - if (!m) goto end; - p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); - if (!p) goto end; - r = PyLong_AsVoidPtr(p); -end: - Py_XDECREF(p); - Py_XDECREF(m); - return (__Pyx_RefNannyAPIStruct *)r; -} -#endif /* CYTHON_REFNANNY */ - static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); - if (!result) { - if (dict != __pyx_b) { - PyErr_Clear(); - result = PyObject_GetAttr(__pyx_b, name); - } - if (!result) { - PyErr_SetObject(PyExc_NameError, name); - } - } + if (!result) + PyErr_SetObject(PyExc_NameError, name); return result; } @@ -23068,8 +23417,7 @@ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyOb #if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - /* cause is unused */ +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { Py_XINCREF(type); Py_XINCREF(value); Py_XINCREF(tb); @@ -23136,7 +23484,7 @@ raise_error: #else /* Python 3+ */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { @@ -23161,29 +23509,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject goto bad; } - if (cause) { - PyObject *fixed_cause; - if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } - else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } - else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - if (!value) { - value = PyObject_CallObject(type, NULL); - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); if (tb) { @@ -23301,7 +23626,7 @@ static void __Pyx_RaiseArgtupleInvalid( Py_ssize_t num_found) { Py_ssize_t num_expected; - const char *more_or_less; + const char *number, *more_or_less; if (num_found < num_min) { num_expected = num_min; @@ -23313,38 +23638,57 @@ static void __Pyx_RaiseArgtupleInvalid( if (exact) { more_or_less = "exactly"; } + number = (num_expected == 1) ? "" : "s"; PyErr_Format(PyExc_TypeError, - "%s() takes %s %"PY_FORMAT_SIZE_T"d positional argument%s (%"PY_FORMAT_SIZE_T"d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); + #if PY_VERSION_HEX < 0x02050000 + "%s() takes %s %d positional argument%s (%d given)", + #else + "%s() takes %s %zd positional argument%s (%zd given)", + #endif + func_name, more_or_less, num_expected, number, num_found); } static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, - "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack", - index, (index == 1) ? "" : "s"); + #if PY_VERSION_HEX < 0x02050000 + "need more than %d value%s to unpack", (int)index, + #else + "need more than %zd value%s to unpack", index, + #endif + (index == 1) ? "" : "s"); } static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected); + #if PY_VERSION_HEX < 0x02050000 + "too many values to unpack (expected %d)", (int)expected); + #else + "too many values to unpack (expected %zd)", expected); + #endif +} + +static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { + PyObject *item; + if (!(item = PyIter_Next(iter))) { + if (!PyErr_Occurred()) { + __Pyx_RaiseNeedMoreValuesError(index); + } + } + return item; } -static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { - if (unlikely(retval)) { - Py_DECREF(retval); +static int __Pyx_EndUnpack(PyObject *iter, Py_ssize_t expected) { + PyObject *item; + if ((item = PyIter_Next(iter))) { + Py_DECREF(item); __Pyx_RaiseTooManyValuesError(expected); return -1; - } else if (PyErr_Occurred()) { - if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { - PyErr_Clear(); - return 0; - } else { - return -1; - } } - return 0; + else if (!PyErr_Occurred()) + return 0; + else + return -1; } static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { @@ -23393,10 +23737,6 @@ bad: } -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - static CYTHON_INLINE int __Pyx_CheckKeywordStrings( PyObject *kwdict, const char* function_name, @@ -23468,7 +23808,7 @@ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) Py_XDECREF(tmp_tb); } -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; @@ -23492,23 +23832,8 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { empty_dict = PyDict_New(); if (!empty_dict) goto bad; - #if PY_VERSION_HEX >= 0x02050000 - { - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - } - #else - if (level>0) { - PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); - goto bad; - } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); - #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); @@ -23516,68 +23841,6 @@ bad: return module; } -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) { - if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ - return (equals == Py_EQ); - } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) { - if (PyBytes_GET_SIZE(s1) != PyBytes_GET_SIZE(s2)) { - return (equals == Py_NE); - } else if (PyBytes_GET_SIZE(s1) == 1) { - if (equals == Py_EQ) - return (PyBytes_AS_STRING(s1)[0] == PyBytes_AS_STRING(s2)[0]); - else - return (PyBytes_AS_STRING(s1)[0] != PyBytes_AS_STRING(s2)[0]); - } else { - int result = memcmp(PyBytes_AS_STRING(s1), PyBytes_AS_STRING(s2), (size_t)PyBytes_GET_SIZE(s1)); - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -} - -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) { - if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */ - return (equals == Py_EQ); - } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) { - if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) { - return (equals == Py_NE); - } else if (PyUnicode_GET_SIZE(s1) == 1) { - if (equals == Py_EQ) - return (PyUnicode_AS_UNICODE(s1)[0] == PyUnicode_AS_UNICODE(s2)[0]); - else - return (PyUnicode_AS_UNICODE(s1)[0] != PyUnicode_AS_UNICODE(s2)[0]); - } else { - int result = PyUnicode_Compare(s1, s2); - if ((result == -1) && unlikely(PyErr_Occurred())) - return -1; - return (equals == Py_EQ) ? (result == 0) : (result != 0); - } - } else if ((s1 == Py_None) & PyUnicode_CheckExact(s2)) { - return (equals == Py_NE); - } else if ((s2 == Py_None) & PyUnicode_CheckExact(s1)) { - return (equals == Py_NE); - } else { - int result; - PyObject* py_result = PyObject_RichCompare(s1, s2, equals); - if (!py_result) - return -1; - result = __Pyx_PyObject_IsTrue(py_result); - Py_DECREF(py_result); - return result; - } -} - static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) { const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0; const int is_unsigned = const_zero < neg_one; @@ -23598,7 +23861,7 @@ static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) { } else { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; - return _PyLong_FromByteArray(bytes, sizeof(npy_intp), + return _PyLong_FromByteArray(bytes, sizeof(npy_intp), little, !is_unsigned); } } @@ -23814,9 +24077,9 @@ static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { "can't convert negative value to unsigned long"); return (unsigned long)-1; } - return (unsigned long)PyLong_AsUnsignedLong(x); + return PyLong_AsUnsignedLong(x); } else { - return (unsigned long)PyLong_AsLong(x); + return PyLong_AsLong(x); } } else { unsigned long val; @@ -23849,9 +24112,9 @@ static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObje "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } - return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); + return PyLong_AsUnsignedLongLong(x); } else { - return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); + return PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; @@ -23884,9 +24147,9 @@ static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { "can't convert negative value to long"); return (long)-1; } - return (long)PyLong_AsUnsignedLong(x); + return PyLong_AsUnsignedLong(x); } else { - return (long)PyLong_AsLong(x); + return PyLong_AsLong(x); } } else { long val; @@ -23919,9 +24182,9 @@ static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } - return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); + return PyLong_AsUnsignedLongLong(x); } else { - return (PY_LONG_LONG)PyLong_AsLongLong(x); + return PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; @@ -23954,9 +24217,9 @@ static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { "can't convert negative value to signed long"); return (signed long)-1; } - return (signed long)PyLong_AsUnsignedLong(x); + return PyLong_AsUnsignedLong(x); } else { - return (signed long)PyLong_AsLong(x); + return PyLong_AsLong(x); } } else { signed long val; @@ -23989,9 +24252,9 @@ static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } - return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); + return PyLong_AsUnsignedLongLong(x); } else { - return (signed PY_LONG_LONG)PyLong_AsLongLong(x); + return PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; @@ -24055,29 +24318,10 @@ static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) { } } -static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - #if PY_VERSION_HEX < 0x02050000 - return PyErr_Warn(NULL, message); - #else - return PyErr_WarnEx(NULL, message, 1); - #endif - } - return 0; -} - #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) + long size, int strict) { PyObject *py_module = 0; PyObject *result = 0; @@ -24107,17 +24351,17 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class module_name, class_name); goto bad; } - if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) { + if (!strict && ((PyTypeObject *)result)->tp_basicsize > size) { PyOS_snprintf(warning, sizeof(warning), "%s.%s size changed, may indicate binary incompatibility", module_name, class_name); #if PY_VERSION_HEX < 0x02050000 - if (PyErr_Warn(NULL, warning) < 0) goto bad; + PyErr_Warn(NULL, warning); #else - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + PyErr_WarnEx(NULL, warning, 0); #endif } - else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) { + else if (((PyTypeObject *)result)->tp_basicsize != size) { PyErr_Format(PyExc_ValueError, "%s.%s has the wrong size, try recompiling", module_name, class_name); @@ -24127,7 +24371,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class bad: Py_XDECREF(py_module); Py_XDECREF(result); - return NULL; + return 0; } #endif @@ -24157,8 +24401,7 @@ bad: #include "frameobject.h" #include "traceback.h" -static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno, - int __pyx_lineno, const char *__pyx_filename) { +static void __Pyx_AddTraceback(const char *funcname) { PyObject *py_srcfile = 0; PyObject *py_funcname = 0; PyObject *py_globals = 0; diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index f88a1390f..43eeae5a6 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -2178,13 +2178,13 @@ cdef class RandomState: References ---------- - ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy + .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, "Cauchy Distribution", http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm - ..[2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A + .. [2] Weisstein, Eric W. "Cauchy Distribution." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/CauchyDistribution.html - ..[3] Wikipedia, "Cauchy distribution" + .. [3] Wikipedia, "Cauchy distribution" http://en.wikipedia.org/wiki/Cauchy_distribution Examples @@ -2516,10 +2516,10 @@ cdef class RandomState: See Also -------- - scipy.stats.distributions.weibull : probability density function, - distribution or cumulative density function, etc. - - gumbel, scipy.stats.distributions.genextreme + scipy.stats.distributions.weibull_max + scipy.stats.distributions.weibull_min + scipy.stats.distributions.genextreme + gumbel Notes ----- @@ -3159,9 +3159,9 @@ cdef class RandomState: References ---------- - ..[1] Brighton Webs Ltd., Rayleigh Distribution, + .. [1] Brighton Webs Ltd., Rayleigh Distribution, http://www.brighton-webs.co.uk/distributions/rayleigh.asp - ..[2] Wikipedia, "Rayleigh distribution" + .. [2] Wikipedia, "Rayleigh distribution" http://en.wikipedia.org/wiki/Rayleigh_distribution Examples @@ -3247,12 +3247,12 @@ cdef class RandomState: References ---------- - ..[1] Brighton Webs Ltd., Wald Distribution, + .. [1] Brighton Webs Ltd., Wald Distribution, http://www.brighton-webs.co.uk/distributions/wald.asp - ..[2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian + .. [2] Chhikara, Raj S., and Folks, J. Leroy, "The Inverse Gaussian Distribution: Theory : Methodology, and Applications", CRC Press, 1988. - ..[3] Wikipedia, "Wald distribution" + .. [3] Wikipedia, "Wald distribution" http://en.wikipedia.org/wiki/Wald_distribution Examples @@ -3331,7 +3331,7 @@ cdef class RandomState: References ---------- - ..[1] Wikipedia, "Triangular distribution" + .. [1] Wikipedia, "Triangular distribution" http://en.wikipedia.org/wiki/Triangular_distribution Examples @@ -4392,33 +4392,29 @@ cdef class RandomState: """ cdef npy_intp i, j - cdef int copy i = len(x) - 1 - try: - j = len(x[0]) - except: - j = 0 - if (j == 0): - # adaptation of random.shuffle() + # Logic adapted from random.shuffle() + if isinstance(x, np.ndarray) and x.ndim > 1: + # For a multi-dimensional ndarray, indexing returns a view onto + # each row. So we can't just use ordinary assignment to swap the + # rows; we need a bounce buffer. + buf = np.empty(x.shape[1:], dtype=x.dtype) while i > 0: j = rk_interval(i, self.internal_state) - x[i], x[j] = x[j], x[i] + buf[...] = x[j] + x[j] = x[i] + x[i] = buf i = i - 1 else: - # make copies - copy = hasattr(x[0], 'copy') - if copy: - while(i > 0): - j = rk_interval(i, self.internal_state) - x[i], x[j] = x[j].copy(), x[i].copy() - i = i - 1 - else: - while(i > 0): - j = rk_interval(i, self.internal_state) - x[i], x[j] = x[j][:], x[i][:] - i = i - 1 + # For single-dimensional arrays, lists, and any other Python + # sequence types, indexing returns a real object that's + # independent of the array contents, so we can just swap directly. + while i > 0: + j = rk_interval(i, self.internal_state) + x[i], x[j] = x[j], x[i] + i = i - 1 def permutation(self, object x): """ diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py index 2d37e2109..7d9163e61 100644 --- a/numpy/random/tests/test_random.py +++ b/numpy/random/tests/test_random.py @@ -168,12 +168,17 @@ class TestRandomDist(TestCase): np.testing.assert_equal(actual, desired) def test_shuffle(self): - np.random.seed(self.seed) - alist = [1,2,3,4,5,6,7,8,9,0] - np.random.shuffle(alist) - actual = alist - desired = [0, 1, 9, 6, 2, 4, 5, 8, 7, 3] - np.testing.assert_array_equal(actual, desired) + # Test lists, arrays, and multidimensional versions of both: + for conv in [lambda x: x, + np.asarray, + lambda x: [(i, i) for i in x], + lambda x: np.asarray([(i, i) for i in x])]: + np.random.seed(self.seed) + alist = conv([1,2,3,4,5,6,7,8,9,0]) + np.random.shuffle(alist) + actual = alist + desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3]) + np.testing.assert_array_equal(actual, desired) def test_beta(self): np.random.seed(self.seed) diff --git a/numpy/random/tests/test_regression.py b/numpy/random/tests/test_regression.py index f5039c09a..4168ef9a4 100644 --- a/numpy/random/tests/test_regression.py +++ b/numpy/random/tests/test_regression.py @@ -52,6 +52,16 @@ class TestRegression(TestCase): except: raise AssertionError + def test_shuffle_mixed_dimension(self): + """Test for trac ticket #2074""" + for t in [[1, 2, 3, None], + [(1, 1), (2, 2), (3, 3), None], + [1, (2, 2), (3, 3), None], + [(1, 1), 2, 3, None]]: + np.random.seed(12345) + shuffled = list(t) + random.shuffle(shuffled) + assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]]) if __name__ == "__main__": run_module_suite() diff --git a/numpy/testing/decorators.py b/numpy/testing/decorators.py index a7cc9a847..053b99211 100644 --- a/numpy/testing/decorators.py +++ b/numpy/testing/decorators.py @@ -210,7 +210,7 @@ def knownfailureif(fail_condition, msg=None): from noseclasses import KnownFailureTest def knownfailer(*args, **kwargs): if fail_val(): - raise KnownFailureTest, msg + raise KnownFailureTest(msg) else: return f(*args, **kwargs) return nose.tools.make_decorator(f)(knownfailer) diff --git a/numpy/testing/nosetester.py b/numpy/testing/nosetester.py index a776faa43..60fec35d6 100644 --- a/numpy/testing/nosetester.py +++ b/numpy/testing/nosetester.py @@ -119,11 +119,12 @@ class NoseTester(object): Notes ----- - The default for `raise_warnings` is ``(DeprecationWarning, RuntimeWarning)`` - for the master branch of NumPy, and ``()`` for maintenance branches and - released versions. The purpose of this switching behavior is to catch as - many warnings as possible during development, but not give problems for - packaging of released versions. + The default for `raise_warnings` is + ``(DeprecationWarning, RuntimeWarning)`` for the master branch of NumPy, + and ``()`` for maintenance branches and released versions. The purpose + of this switching behavior is to catch as many warnings as possible + during development, but not give problems for packaging of released + versions. """ # Stuff to exclude from tests. These are from numpy.distutils diff --git a/numpy/testing/tests/test_utils.py b/numpy/testing/tests/test_utils.py index 067782dc0..bff8b50ab 100644 --- a/numpy/testing/tests/test_utils.py +++ b/numpy/testing/tests/test_utils.py @@ -369,11 +369,14 @@ class TestAssertAllclose(unittest.TestCase): class TestArrayAlmostEqualNulp(unittest.TestCase): + @dec.knownfailureif(True, "Github issue #347") def test_simple(self): - dev = np.random.randn(10) - x = np.ones(10) - y = x + dev * np.finfo(np.float64).eps - assert_array_almost_equal_nulp(x, y, nulp=2 * np.max(dev)) + np.random.seed(12345) + for i in xrange(100): + dev = np.random.randn(10) + x = np.ones(10) + y = x + dev * np.finfo(np.float64).eps + assert_array_almost_equal_nulp(x, y, nulp=2 * np.max(dev)) def test_simple2(self): x = np.random.randn(10) diff --git a/tools/allocation_tracking/alloc_hook.pyx b/tools/allocation_tracking/alloc_hook.pyx new file mode 100644 index 000000000..d1e656f90 --- /dev/null +++ b/tools/allocation_tracking/alloc_hook.pyx @@ -0,0 +1,42 @@ +# A cython wrapper for using python functions as callbacks for +# PyDataMem_SetEventHook. + +cimport numpy as np + +cdef extern from "Python.h": + object PyLong_FromVoidPtr(void *) + void *PyLong_AsVoidPtr(object) + +ctypedef void PyDataMem_EventHookFunc(void *inp, void *outp, size_t size, + void *user_data) +cdef extern from "numpy/arrayobject.h": + PyDataMem_EventHookFunc * \ + PyDataMem_SetEventHook(PyDataMem_EventHookFunc *newhook, + void *user_data, void **old_data) + +np.import_array() + +cdef void pyhook(void *old, void *new, size_t size, void *user_data): + cdef object pyfunc = <object> user_data + pyfunc(PyLong_FromVoidPtr(old), + PyLong_FromVoidPtr(new), + size) + +class NumpyAllocHook(object): + def __init__(self, callback): + self.callback = callback + + def __enter__(self): + cdef void *old_hook, *old_data + old_hook = <void *> \ + PyDataMem_SetEventHook(<PyDataMem_EventHookFunc *> pyhook, + <void *> self.callback, + <void **> &old_data) + self.old_hook = PyLong_FromVoidPtr(old_hook) + self.old_data = PyLong_FromVoidPtr(old_data) + + def __exit__(self): + PyDataMem_SetEventHook(<PyDataMem_EventHookFunc *> \ + PyLong_AsVoidPtr(self.old_hook), + <void *> PyLong_AsVoidPtr(self.old_data), + <void **> 0) diff --git a/tools/allocation_tracking/setup.py b/tools/allocation_tracking/setup.py new file mode 100644 index 000000000..4462f9f4e --- /dev/null +++ b/tools/allocation_tracking/setup.py @@ -0,0 +1,9 @@ +from distutils.core import setup +from distutils.extension import Extension +from Cython.Distutils import build_ext +import numpy + +setup( + cmdclass = {'build_ext': build_ext}, + ext_modules = [Extension("alloc_hook", ["alloc_hook.pyx"], + include_dirs=[numpy.get_include()])]) diff --git a/tools/allocation_tracking/sorttable.js b/tools/allocation_tracking/sorttable.js new file mode 100644 index 000000000..25bccb2b6 --- /dev/null +++ b/tools/allocation_tracking/sorttable.js @@ -0,0 +1,493 @@ +/* + SortTable + version 2 + 7th April 2007 + Stuart Langridge, http://www.kryogenix.org/code/browser/sorttable/ + + Instructions: + Download this file + Add <script src="sorttable.js"></script> to your HTML + Add class="sortable" to any table you'd like to make sortable + Click on the headers to sort + + Thanks to many, many people for contributions and suggestions. + Licenced as X11: http://www.kryogenix.org/code/browser/licence.html + This basically means: do what you want with it. +*/ + + +var stIsIE = /*@cc_on!@*/false; + +sorttable = { + init: function() { + // quit if this function has already been called + if (arguments.callee.done) return; + // flag this function so we don't do the same thing twice + arguments.callee.done = true; + // kill the timer + if (_timer) clearInterval(_timer); + + if (!document.createElement || !document.getElementsByTagName) return; + + sorttable.DATE_RE = /^(\d\d?)[\/\.-](\d\d?)[\/\.-]((\d\d)?\d\d)$/; + + forEach(document.getElementsByTagName('table'), function(table) { + if (table.className.search(/\bsortable\b/) != -1) { + sorttable.makeSortable(table); + } + }); + + }, + + makeSortable: function(table) { + if (table.getElementsByTagName('thead').length == 0) { + // table doesn't have a tHead. Since it should have, create one and + // put the first table row in it. + the = document.createElement('thead'); + the.appendChild(table.rows[0]); + table.insertBefore(the,table.firstChild); + } + // Safari doesn't support table.tHead, sigh + if (table.tHead == null) table.tHead = table.getElementsByTagName('thead')[0]; + + if (table.tHead.rows.length != 1) return; // can't cope with two header rows + + // Sorttable v1 put rows with a class of "sortbottom" at the bottom (as + // "total" rows, for example). This is B&R, since what you're supposed + // to do is put them in a tfoot. So, if there are sortbottom rows, + // for backwards compatibility, move them to tfoot (creating it if needed). + sortbottomrows = []; + for (var i=0; i<table.rows.length; i++) { + if (table.rows[i].className.search(/\bsortbottom\b/) != -1) { + sortbottomrows[sortbottomrows.length] = table.rows[i]; + } + } + if (sortbottomrows) { + if (table.tFoot == null) { + // table doesn't have a tfoot. Create one. + tfo = document.createElement('tfoot'); + table.appendChild(tfo); + } + for (var i=0; i<sortbottomrows.length; i++) { + tfo.appendChild(sortbottomrows[i]); + } + delete sortbottomrows; + } + + // work through each column and calculate its type + headrow = table.tHead.rows[0].cells; + for (var i=0; i<headrow.length; i++) { + // manually override the type with a sorttable_type attribute + if (!headrow[i].className.match(/\bsorttable_nosort\b/)) { // skip this col + mtch = headrow[i].className.match(/\bsorttable_([a-z0-9]+)\b/); + if (mtch) { override = mtch[1]; } + if (mtch && typeof sorttable["sort_"+override] == 'function') { + headrow[i].sorttable_sortfunction = sorttable["sort_"+override]; + } else { + headrow[i].sorttable_sortfunction = sorttable.guessType(table,i); + } + // make it clickable to sort + headrow[i].sorttable_columnindex = i; + headrow[i].sorttable_tbody = table.tBodies[0]; + dean_addEvent(headrow[i],"click", function(e) { + + if (this.className.search(/\bsorttable_sorted\b/) != -1) { + // if we're already sorted by this column, just + // reverse the table, which is quicker + sorttable.reverse(this.sorttable_tbody); + this.className = this.className.replace('sorttable_sorted', + 'sorttable_sorted_reverse'); + this.removeChild(document.getElementById('sorttable_sortfwdind')); + sortrevind = document.createElement('span'); + sortrevind.id = "sorttable_sortrevind"; + sortrevind.innerHTML = stIsIE ? ' <font face="webdings">5</font>' : ' ▴'; + this.appendChild(sortrevind); + return; + } + if (this.className.search(/\bsorttable_sorted_reverse\b/) != -1) { + // if we're already sorted by this column in reverse, just + // re-reverse the table, which is quicker + sorttable.reverse(this.sorttable_tbody); + this.className = this.className.replace('sorttable_sorted_reverse', + 'sorttable_sorted'); + this.removeChild(document.getElementById('sorttable_sortrevind')); + sortfwdind = document.createElement('span'); + sortfwdind.id = "sorttable_sortfwdind"; + sortfwdind.innerHTML = stIsIE ? ' <font face="webdings">6</font>' : ' ▾'; + this.appendChild(sortfwdind); + return; + } + + // remove sorttable_sorted classes + theadrow = this.parentNode; + forEach(theadrow.childNodes, function(cell) { + if (cell.nodeType == 1) { // an element + cell.className = cell.className.replace('sorttable_sorted_reverse',''); + cell.className = cell.className.replace('sorttable_sorted',''); + } + }); + sortfwdind = document.getElementById('sorttable_sortfwdind'); + if (sortfwdind) { sortfwdind.parentNode.removeChild(sortfwdind); } + sortrevind = document.getElementById('sorttable_sortrevind'); + if (sortrevind) { sortrevind.parentNode.removeChild(sortrevind); } + + this.className += ' sorttable_sorted'; + sortfwdind = document.createElement('span'); + sortfwdind.id = "sorttable_sortfwdind"; + sortfwdind.innerHTML = stIsIE ? ' <font face="webdings">6</font>' : ' ▾'; + this.appendChild(sortfwdind); + + // build an array to sort. This is a Schwartzian transform thing, + // i.e., we "decorate" each row with the actual sort key, + // sort based on the sort keys, and then put the rows back in order + // which is a lot faster because you only do getInnerText once per row + row_array = []; + col = this.sorttable_columnindex; + rows = this.sorttable_tbody.rows; + for (var j=0; j<rows.length; j++) { + row_array[row_array.length] = [sorttable.getInnerText(rows[j].cells[col]), rows[j]]; + } + /* If you want a stable sort, uncomment the following line */ + //sorttable.shaker_sort(row_array, this.sorttable_sortfunction); + /* and comment out this one */ + row_array.sort(this.sorttable_sortfunction); + + tb = this.sorttable_tbody; + for (var j=0; j<row_array.length; j++) { + tb.appendChild(row_array[j][1]); + } + + delete row_array; + }); + } + } + }, + + guessType: function(table, column) { + // guess the type of a column based on its first non-blank row + sortfn = sorttable.sort_alpha; + for (var i=0; i<table.tBodies[0].rows.length; i++) { + text = sorttable.getInnerText(table.tBodies[0].rows[i].cells[column]); + if (text != '') { + if (text.match(/^-?[£$¤]?[\d,.]+%?$/)) { + return sorttable.sort_numeric; + } + // check for a date: dd/mm/yyyy or dd/mm/yy + // can have / or . or - as separator + // can be mm/dd as well + possdate = text.match(sorttable.DATE_RE) + if (possdate) { + // looks like a date + first = parseInt(possdate[1]); + second = parseInt(possdate[2]); + if (first > 12) { + // definitely dd/mm + return sorttable.sort_ddmm; + } else if (second > 12) { + return sorttable.sort_mmdd; + } else { + // looks like a date, but we can't tell which, so assume + // that it's dd/mm (English imperialism!) and keep looking + sortfn = sorttable.sort_ddmm; + } + } + } + } + return sortfn; + }, + + getInnerText: function(node) { + // gets the text we want to use for sorting for a cell. + // strips leading and trailing whitespace. + // this is *not* a generic getInnerText function; it's special to sorttable. + // for example, you can override the cell text with a customkey attribute. + // it also gets .value for <input> fields. + + hasInputs = (typeof node.getElementsByTagName == 'function') && + node.getElementsByTagName('input').length; + + if (node.getAttribute("sorttable_customkey") != null) { + return node.getAttribute("sorttable_customkey"); + } + else if (typeof node.textContent != 'undefined' && !hasInputs) { + return node.textContent.replace(/^\s+|\s+$/g, ''); + } + else if (typeof node.innerText != 'undefined' && !hasInputs) { + return node.innerText.replace(/^\s+|\s+$/g, ''); + } + else if (typeof node.text != 'undefined' && !hasInputs) { + return node.text.replace(/^\s+|\s+$/g, ''); + } + else { + switch (node.nodeType) { + case 3: + if (node.nodeName.toLowerCase() == 'input') { + return node.value.replace(/^\s+|\s+$/g, ''); + } + case 4: + return node.nodeValue.replace(/^\s+|\s+$/g, ''); + break; + case 1: + case 11: + var innerText = ''; + for (var i = 0; i < node.childNodes.length; i++) { + innerText += sorttable.getInnerText(node.childNodes[i]); + } + return innerText.replace(/^\s+|\s+$/g, ''); + break; + default: + return ''; + } + } + }, + + reverse: function(tbody) { + // reverse the rows in a tbody + newrows = []; + for (var i=0; i<tbody.rows.length; i++) { + newrows[newrows.length] = tbody.rows[i]; + } + for (var i=newrows.length-1; i>=0; i--) { + tbody.appendChild(newrows[i]); + } + delete newrows; + }, + + /* sort functions + each sort function takes two parameters, a and b + you are comparing a[0] and b[0] */ + sort_numeric: function(a,b) { + aa = parseFloat(a[0].replace(/[^0-9.-]/g,'')); + if (isNaN(aa)) aa = 0; + bb = parseFloat(b[0].replace(/[^0-9.-]/g,'')); + if (isNaN(bb)) bb = 0; + return aa-bb; + }, + sort_alpha: function(a,b) { + if (a[0]==b[0]) return 0; + if (a[0]<b[0]) return -1; + return 1; + }, + sort_ddmm: function(a,b) { + mtch = a[0].match(sorttable.DATE_RE); + y = mtch[3]; m = mtch[2]; d = mtch[1]; + if (m.length == 1) m = '0'+m; + if (d.length == 1) d = '0'+d; + dt1 = y+m+d; + mtch = b[0].match(sorttable.DATE_RE); + y = mtch[3]; m = mtch[2]; d = mtch[1]; + if (m.length == 1) m = '0'+m; + if (d.length == 1) d = '0'+d; + dt2 = y+m+d; + if (dt1==dt2) return 0; + if (dt1<dt2) return -1; + return 1; + }, + sort_mmdd: function(a,b) { + mtch = a[0].match(sorttable.DATE_RE); + y = mtch[3]; d = mtch[2]; m = mtch[1]; + if (m.length == 1) m = '0'+m; + if (d.length == 1) d = '0'+d; + dt1 = y+m+d; + mtch = b[0].match(sorttable.DATE_RE); + y = mtch[3]; d = mtch[2]; m = mtch[1]; + if (m.length == 1) m = '0'+m; + if (d.length == 1) d = '0'+d; + dt2 = y+m+d; + if (dt1==dt2) return 0; + if (dt1<dt2) return -1; + return 1; + }, + + shaker_sort: function(list, comp_func) { + // A stable sort function to allow multi-level sorting of data + // see: http://en.wikipedia.org/wiki/Cocktail_sort + // thanks to Joseph Nahmias + var b = 0; + var t = list.length - 1; + var swap = true; + + while(swap) { + swap = false; + for(var i = b; i < t; ++i) { + if ( comp_func(list[i], list[i+1]) > 0 ) { + var q = list[i]; list[i] = list[i+1]; list[i+1] = q; + swap = true; + } + } // for + t--; + + if (!swap) break; + + for(var i = t; i > b; --i) { + if ( comp_func(list[i], list[i-1]) < 0 ) { + var q = list[i]; list[i] = list[i-1]; list[i-1] = q; + swap = true; + } + } // for + b++; + + } // while(swap) + } +} + +/* ****************************************************************** + Supporting functions: bundled here to avoid depending on a library + ****************************************************************** */ + +// Dean Edwards/Matthias Miller/John Resig + +/* for Mozilla/Opera9 */ +if (document.addEventListener) { + document.addEventListener("DOMContentLoaded", sorttable.init, false); +} + +/* for Internet Explorer */ +/*@cc_on @*/ +/*@if (@_win32) + document.write("<script id=__ie_onload defer src=javascript:void(0)><\/script>"); + var script = document.getElementById("__ie_onload"); + script.onreadystatechange = function() { + if (this.readyState == "complete") { + sorttable.init(); // call the onload handler + } + }; +/*@end @*/ + +/* for Safari */ +if (/WebKit/i.test(navigator.userAgent)) { // sniff + var _timer = setInterval(function() { + if (/loaded|complete/.test(document.readyState)) { + sorttable.init(); // call the onload handler + } + }, 10); +} + +/* for other browsers */ +window.onload = sorttable.init; + +// written by Dean Edwards, 2005 +// with input from Tino Zijdel, Matthias Miller, Diego Perini + +// http://dean.edwards.name/weblog/2005/10/add-event/ + +function dean_addEvent(element, type, handler) { + if (element.addEventListener) { + element.addEventListener(type, handler, false); + } else { + // assign each event handler a unique ID + if (!handler.$$guid) handler.$$guid = dean_addEvent.guid++; + // create a hash table of event types for the element + if (!element.events) element.events = {}; + // create a hash table of event handlers for each element/event pair + var handlers = element.events[type]; + if (!handlers) { + handlers = element.events[type] = {}; + // store the existing event handler (if there is one) + if (element["on" + type]) { + handlers[0] = element["on" + type]; + } + } + // store the event handler in the hash table + handlers[handler.$$guid] = handler; + // assign a global event handler to do all the work + element["on" + type] = handleEvent; + } +}; +// a counter used to create unique IDs +dean_addEvent.guid = 1; + +function removeEvent(element, type, handler) { + if (element.removeEventListener) { + element.removeEventListener(type, handler, false); + } else { + // delete the event handler from the hash table + if (element.events && element.events[type]) { + delete element.events[type][handler.$$guid]; + } + } +}; + +function handleEvent(event) { + var returnValue = true; + // grab the event object (IE uses a global event object) + event = event || fixEvent(((this.ownerDocument || this.document || this).parentWindow || window).event); + // get a reference to the hash table of event handlers + var handlers = this.events[event.type]; + // execute each event handler + for (var i in handlers) { + this.$$handleEvent = handlers[i]; + if (this.$$handleEvent(event) === false) { + returnValue = false; + } + } + return returnValue; +}; + +function fixEvent(event) { + // add W3C standard event methods + event.preventDefault = fixEvent.preventDefault; + event.stopPropagation = fixEvent.stopPropagation; + return event; +}; +fixEvent.preventDefault = function() { + this.returnValue = false; +}; +fixEvent.stopPropagation = function() { + this.cancelBubble = true; +} + +// Dean's forEach: http://dean.edwards.name/base/forEach.js +/* + forEach, version 1.0 + Copyright 2006, Dean Edwards + License: http://www.opensource.org/licenses/mit-license.php +*/ + +// array-like enumeration +if (!Array.forEach) { // mozilla already supports this + Array.forEach = function(array, block, context) { + for (var i = 0; i < array.length; i++) { + block.call(context, array[i], i, array); + } + }; +} + +// generic enumeration +Function.prototype.forEach = function(object, block, context) { + for (var key in object) { + if (typeof this.prototype[key] == "undefined") { + block.call(context, object[key], key, object); + } + } +}; + +// character enumeration +String.forEach = function(string, block, context) { + Array.forEach(string.split(""), function(chr, index) { + block.call(context, chr, index, string); + }); +}; + +// globally resolve forEach enumeration +var forEach = function(object, block, context) { + if (object) { + var resolve = Object; // default + if (object instanceof Function) { + // functions have a "length" property + resolve = Function; + } else if (object.forEach instanceof Function) { + // the object implements a custom forEach method so use that + object.forEach(block, context); + return; + } else if (typeof object == "string") { + // the object is a string + resolve = String; + } else if (typeof object.length == "number") { + // the object is array-like + resolve = Array; + } + resolve.forEach(object, block, context); + } +}; + diff --git a/tools/allocation_tracking/track_allocations.py b/tools/allocation_tracking/track_allocations.py new file mode 100644 index 000000000..e9d937817 --- /dev/null +++ b/tools/allocation_tracking/track_allocations.py @@ -0,0 +1,131 @@ +import numpy as np +import inspect +from alloc_hook import NumpyAllocHook + +class AllocationTracker(object): + def __init__(self, threshold=0): + '''track numpy allocations of size threshold bytes or more.''' + + self.threshold = threshold + + # The total number of bytes currently allocated with size above + # threshold + self.total_bytes = 0 + + # We buffer requests line by line and move them into the allocation + # trace when a new line occurs + self.current_line = None + self.pending_allocations = [] + + self.blocksizes = {} + + # list of (lineinfo, bytes allocated, bytes freed, # allocations, # + # frees, maximum memory usage, long-lived bytes allocated) + self.allocation_trace = [] + + self.numpy_hook = NumpyAllocHook(self.hook) + + def __enter__(self): + self.numpy_hook.__enter__() + + def __exit__(self, type, value, traceback): + self.check_line_changed() # forces pending events to be handled + self.numpy_hook.__exit__() + + def hook(self, inptr, outptr, size): + if outptr == 0: # it's a free + self.free_cb(inptr) + elif inptr != 0: # realloc + self.realloc_cb(inptr, outptr, size) + else: # malloc + self.alloc_cb(outptr, size) + + def alloc_cb(self, ptr, size): + if size >= self.threshold: + self.check_line_changed() + self.blocksizes[ptr] = size + self.pending_allocations.append(size) + + def free_cb(self, ptr): + size = self.blocksizes.pop(ptr, 0) + if size: + self.check_line_changed() + self.pending_allocations.append(-size) + + def realloc_cb(self, newptr, oldptr, size): + if (size >= self.threshold) or (oldptr in self.blocksizes): + self.check_line_changed() + oldsize = self.blocksizes.pop(oldptr, 0) + self.pending_allocations.append(size - oldsize) + self.blocksizes[newptr] = size + + def get_code_line(self): + # first frame is this line, then check_line_changed(), then 2 callbacks, + # then actual code. + try: + return inspect.stack()[4][1:] + except: + return inspect.stack()[0][1:] + + def check_line_changed(self): + line = self.get_code_line() + if line != self.current_line and (self.current_line is not None): + # move pending events into the allocation_trace + max_size = self.total_bytes + bytes_allocated = 0 + bytes_freed = 0 + num_allocations = 0 + num_frees = 0 + before_size = self.total_bytes + for allocation in self.pending_allocations: + self.total_bytes += allocation + if allocation > 0: + bytes_allocated += allocation + num_allocations += 1 + else: + bytes_freed += -allocation + num_frees += 1 + max_size = max(max_size, self.total_bytes) + long_lived = max(self.total_bytes - before_size, 0) + self.allocation_trace.append((self.current_line, bytes_allocated, + bytes_freed, num_allocations, + num_frees, max_size, long_lived)) + # clear pending allocations + self.pending_allocations = [] + # move to the new line + self.current_line = line + + def write_html(self, filename): + f = open(filename, "w") + f.write('<HTML><HEAD><script src="sorttable.js"></script></HEAD><BODY>\n') + f.write('<TABLE class="sortable" width=100%>\n') + f.write("<TR>\n") + cols = "event#,lineinfo,bytes allocated,bytes freed,#allocations,#frees,max memory usage,long lived bytes".split(',') + for header in cols: + f.write(" <TH>{0}</TH>".format(header)) + f.write("\n</TR>\n") + for idx, event in enumerate(self.allocation_trace): + f.write("<TR>\n") + event = [idx] + list(event) + for col, val in zip(cols, event): + if col == 'lineinfo': + # special handling + try: + filename, line, module, code, index = val + val = "{0}({1}): {2}".format(filename, line, code[index]) + except: + # sometimes this info is not available (from eval()?) + val = str(val) + f.write(" <TD>{0}</TD>".format(val)) + f.write("\n</TR>\n") + f.write("</TABLE></BODY></HTML>\n") + f.close() + + +if __name__ == '__main__': + tracker = AllocationTracker(1000) + with tracker: + for i in range(100): + np.zeros(i * 100) + np.zeros(i * 200) + tracker.write_html("allocations.html") @@ -25,7 +25,7 @@ # installed and that they can be run as 'python2.4', 'python2.5', etc. [tox] -envlist = py24,py25,py26,py27,py31,py32 +envlist = py24,py25,py26,py27,py31,py32,py27-separate,py32-separate [testenv] deps= @@ -33,6 +33,14 @@ deps= changedir={envdir} commands=python {toxinidir}/tools/test-installed-numpy.py {posargs:} +[testenv:py27-separate] +basepython=python2.7 +env=NPY_SEPARATE_COMPILATION=1 + +[testenv:py32-separate] +basepython=python3.2 +env=NPY_SEPARATE_COMPILATION=1 + # Not run by default. Set up the way you want then use 'tox -e debug' # if you want it: [testenv:debug] |