diff options
86 files changed, 1709 insertions, 28408 deletions
diff --git a/.gitignore b/.gitignore index 78a960f59..abba1fdc0 100644 --- a/.gitignore +++ b/.gitignore @@ -132,3 +132,6 @@ numpy/core/src/umath/umath_tests.c numpy/distutils/__config__.py numpy/linalg/umath_linalg.c doc/source/reference/generated +# cythonized files +cythonize.dat +numpy/random/mtrand/mtrand.c diff --git a/.travis.yml b/.travis.yml index 6d87767f8..944818428 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,6 +32,8 @@ before_install: - pip install nose # pip install coverage - python -V + - pip install --upgrade pip setuptools + - pip install --find-links http://wheels.astropy.org/ --find-links http://wheels2.astropy.org/ --use-wheel Cython - sudo apt-get install -qq libatlas-dev libatlas-base-dev gfortran - popd @@ -61,6 +61,16 @@ def _register_metadata(context): context.register_metadata("is_released", _SETUP_PY.ISRELEASED) context.register_metadata("full_version", full_version) +def _generate_cython(): + print("Cythonizing sources") + cwd = os.path.abspath(os.path.dirname(__file__)) + p = subprocess.call([sys.executable, + os.path.join(cwd, 'tools', 'cythonize.py'), + 'numpy'], + cwd=cwd) + if p != 0: + raise RuntimeError("Running cythonize failed!") + @hooks.post_configure def post_configure(context): conf = context.waf_context @@ -76,6 +86,8 @@ def post_configure(context): blas_lapack.check_blas_lapack(context) + _generate_cython() + @hooks.pre_build def pre_build(context): _register_metadata(context) diff --git a/doc/neps/deferred-ufunc-evaluation.rst b/doc/neps/deferred-ufunc-evaluation.rst index 634a1f238..b00c0dd2d 100644 --- a/doc/neps/deferred-ufunc-evaluation.rst +++ b/doc/neps/deferred-ufunc-evaluation.rst @@ -1,4 +1,7 @@ -:Title: Deferred UFunc Evaluation +========================= +Deferred UFunc Evaluation +========================= + :Author: Mark Wiebe <mwwiebe@gmail.com> :Content-Type: text/x-rst :Created: 30-Nov-2010 diff --git a/doc/neps/math_config_clean.txt b/doc/neps/math_config_clean.rst index 26511d7bf..26511d7bf 100644 --- a/doc/neps/math_config_clean.txt +++ b/doc/neps/math_config_clean.rst diff --git a/doc/neps/missing-data.rst b/doc/neps/missing-data.rst index 0d03d7774..f561a88b8 100644 --- a/doc/neps/missing-data.rst +++ b/doc/neps/missing-data.rst @@ -1,4 +1,7 @@ -:Title: Missing Data Functionality in NumPy +=================================== +Missing Data Functionality in NumPy +=================================== + :Author: Mark Wiebe <mwwiebe@gmail.com> :Copyright: Copyright 2011 by Enthought, Inc :License: CC By-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0/) diff --git a/doc/neps/new-iterator-ufunc.rst b/doc/neps/new-iterator-ufunc.rst index b253e874b..6d132f9a5 100644 --- a/doc/neps/new-iterator-ufunc.rst +++ b/doc/neps/new-iterator-ufunc.rst @@ -1,4 +1,7 @@ -:Title: Optimizing Iterator/UFunc Performance +===================================== +Optimizing Iterator/UFunc Performance +===================================== + :Author: Mark Wiebe <mwwiebe@gmail.com> :Content-Type: text/x-rst :Created: 25-Nov-2010 diff --git a/doc/neps/newbugtracker.rst b/doc/neps/newbugtracker.rst index 8bee7ad13..f4b029b47 100644 --- a/doc/neps/newbugtracker.rst +++ b/doc/neps/newbugtracker.rst @@ -1,3 +1,9 @@ +=========================================== +Replacing Trac with a different bug tracker +=========================================== + +:Author: David Cournapeau, Stefan van der Walt + Some release managers of both numpy and scipy are becoming more and more disatisfied with the current development workflow, in particular for bug tracking. This document is a tentative to explain some problematic scenario, diff --git a/doc/neps/npy-format.rst b/doc/neps/npy-format.rst new file mode 100644 index 000000000..f7bb799f2 --- /dev/null +++ b/doc/neps/npy-format.rst @@ -0,0 +1,292 @@ +===================================== +A Simple File Format for NumPy Arrays +===================================== + +Author: Robert Kern <robert.kern@gmail.com> +Status: Draft +Created: 20-Dec-2007 + + +Abstract +-------- + +We propose a standard binary file format (NPY) for persisting +a single arbitrary NumPy array on disk. The format stores all of +the shape and dtype information necessary to reconstruct the array +correctly even on another machine with a different architecture. +The format is designed to be as simple as possible while achieving +its limited goals. The implementation is intended to be pure +Python and distributed as part of the main numpy package. + + +Rationale +--------- + +A lightweight, omnipresent system for saving NumPy arrays to disk +is a frequent need. Python in general has pickle [1] for saving +most Python objects to disk. This often works well enough with +NumPy arrays for many purposes, but it has a few drawbacks: + +- Dumping or loading a pickle file require the duplication of the + data in memory. For large arrays, this can be a showstopper. + +- The array data is not directly accessible through + memory-mapping. Now that numpy has that capability, it has + proved very useful for loading large amounts of data (or more to + the point: avoiding loading large amounts of data when you only + need a small part). + +Both of these problems can be addressed by dumping the raw bytes +to disk using ndarray.tofile() and numpy.fromfile(). However, +these have their own problems: + +- The data which is written has no information about the shape or + dtype of the array. + +- It is incapable of handling object arrays. + +The NPY file format is an evolutionary advance over these two +approaches. Its design is mostly limited to solving the problems +with pickles and tofile()/fromfile(). It does not intend to solve +more complicated problems for which more complicated formats like +HDF5 [2] are a better solution. + + +Use Cases +--------- + +- Neville Newbie has just started to pick up Python and NumPy. He + has not installed many packages, yet, nor learned the standard + library, but he has been playing with NumPy at the interactive + prompt to do small tasks. He gets a result that he wants to + save. + +- Annie Analyst has been using large nested record arrays to + represent her statistical data. She wants to convince her + R-using colleague, David Doubter, that Python and NumPy are + awesome by sending him her analysis code and data. She needs + the data to load at interactive speeds. Since David does not + use Python usually, needing to install large packages would turn + him off. + +- Simon Seismologist is developing new seismic processing tools. + One of his algorithms requires large amounts of intermediate + data to be written to disk. The data does not really fit into + the industry-standard SEG-Y schema, but he already has a nice + record-array dtype for using it internally. + +- Polly Parallel wants to split up a computation on her multicore + machine as simply as possible. Parts of the computation can be + split up among different processes without any communication + between processes; they just need to fill in the appropriate + portion of a large array with their results. Having several + child processes memory-mapping a common array is a good way to + achieve this. + + +Requirements +------------ + +The format MUST be able to: + +- Represent all NumPy arrays including nested record + arrays and object arrays. + +- Represent the data in its native binary form. + +- Be contained in a single file. + +- Support Fortran-contiguous arrays directly. + +- Store all of the necessary information to reconstruct the array + including shape and dtype on a machine of a different + architecture. Both little-endian and big-endian arrays must be + supported and a file with little-endian numbers will yield + a little-endian array on any machine reading the file. The + types must be described in terms of their actual sizes. For + example, if a machine with a 64-bit C "long int" writes out an + array with "long ints", a reading machine with 32-bit C "long + ints" will yield an array with 64-bit integers. + +- Be reverse engineered. Datasets often live longer than the + programs that created them. A competent developer should be + able to create a solution in his preferred programming language to + read most NPY files that he has been given without much + documentation. + +- Allow memory-mapping of the data. + +- Be read from a filelike stream object instead of an actual file. + This allows the implementation to be tested easily and makes the + system more flexible. NPY files can be stored in ZIP files and + easily read from a ZipFile object. + +- Store object arrays. Since general Python objects are + complicated and can only be reliably serialized by pickle (if at + all), many of the other requirements are waived for files + containing object arrays. Files with object arrays do not have + to be mmapable since that would be technically impossible. We + cannot expect the pickle format to be reverse engineered without + knowledge of pickle. However, one should at least be able to + read and write object arrays with the same generic interface as + other arrays. + +- Be read and written using APIs provided in the numpy package + itself without any other libraries. The implementation inside + numpy may be in C if necessary. + +The format explicitly *does not* need to: + +- Support multiple arrays in a file. Since we require filelike + objects to be supported, one could use the API to build an ad + hoc format that supported multiple arrays. However, solving the + general problem and use cases is beyond the scope of the format + and the API for numpy. + +- Fully handle arbitrary subclasses of numpy.ndarray. Subclasses + will be accepted for writing, but only the array data will be + written out. A regular numpy.ndarray object will be created + upon reading the file. The API can be used to build a format + for a particular subclass, but that is out of scope for the + general NPY format. + + +Format Specification: Version 1.0 +--------------------------------- + +The first 6 bytes are a magic string: exactly "\x93NUMPY". + +The next 1 byte is an unsigned byte: the major version number of +the file format, e.g. \x01. + +The next 1 byte is an unsigned byte: the minor version number of +the file format, e.g. \x00. Note: the version of the file format +is not tied to the version of the numpy package. + +The next 2 bytes form a little-endian unsigned short int: the +length of the header data HEADER_LEN. + +The next HEADER_LEN bytes form the header data describing the +array's format. It is an ASCII string which contains a Python +literal expression of a dictionary. It is terminated by a newline +('\n') and padded with spaces ('\x20') to make the total length of +the magic string + 4 + HEADER_LEN be evenly divisible by 16 for +alignment purposes. + +The dictionary contains three keys: + + "descr" : dtype.descr + An object that can be passed as an argument to the + numpy.dtype() constructor to create the array's dtype. + + "fortran_order" : bool + Whether the array data is Fortran-contiguous or not. + Since Fortran-contiguous arrays are a common form of + non-C-contiguity, we allow them to be written directly to + disk for efficiency. + + "shape" : tuple of int + The shape of the array. + +For repeatability and readability, this dictionary is formatted +using pprint.pformat() so the keys are in alphabetic order. + +Following the header comes the array data. If the dtype contains +Python objects (i.e. dtype.hasobject is True), then the data is +a Python pickle of the array. Otherwise the data is the +contiguous (either C- or Fortran-, depending on fortran_order) +bytes of the array. Consumers can figure out the number of bytes +by multiplying the number of elements given by the shape (noting +that shape=() means there is 1 element) by dtype.itemsize. + + +Conventions +----------- + +We recommend using the ".npy" extension for files following this +format. This is by no means a requirement; applications may wish +to use this file format but use an extension specific to the +application. In the absence of an obvious alternative, however, +we suggest using ".npy". + +For a simple way to combine multiple arrays into a single file, +one can use ZipFile to contain multiple ".npy" files. We +recommend using the file extension ".npz" for these archives. + + +Alternatives +------------ + +The author believes that this system (or one along these lines) is +about the simplest system that satisfies all of the requirements. +However, one must always be wary of introducing a new binary +format to the world. + +HDF5 [2] is a very flexible format that should be able to +represent all of NumPy's arrays in some fashion. It is probably +the only widely-used format that can faithfully represent all of +NumPy's array features. It has seen substantial adoption by the +scientific community in general and the NumPy community in +particular. It is an excellent solution for a wide variety of +array storage problems with or without NumPy. + +HDF5 is a complicated format that more or less implements +a hierarchical filesystem-in-a-file. This fact makes satisfying +some of the Requirements difficult. To the author's knowledge, as +of this writing, there is no application or library that reads or +writes even a subset of HDF5 files that does not use the canonical +libhdf5 implementation. This implementation is a large library +that is not always easy to build. It would be infeasible to +include it in numpy. + +It might be feasible to target an extremely limited subset of +HDF5. Namely, there would be only one object in it: the array. +Using contiguous storage for the data, one should be able to +implement just enough of the format to provide the same metadata +that the proposed format does. One could still meet all of the +technical requirements like mmapability. + +We would accrue a substantial benefit by being able to generate +files that could be read by other HDF5 software. Furthermore, by +providing the first non-libhdf5 implementation of HDF5, we would +be able to encourage more adoption of simple HDF5 in applications +where it was previously infeasible because of the size of the +library. The basic work may encourage similar dead-simple +implementations in other languages and further expand the +community. + +The remaining concern is about reverse engineerability of the +format. Even the simple subset of HDF5 would be very difficult to +reverse engineer given just a file by itself. However, given the +prominence of HDF5, this might not be a substantial concern. + +In conclusion, we are going forward with the design laid out in +this document. If someone writes code to handle the simple subset +of HDF5 that would be useful to us, we may consider a revision of +the file format. + + +Implementation +-------------- + +The current implementation is included in the 1.0.5 release of numpy. + + http://github.com/numpy/numpy/blob/v1.5.0/numpy/lib/format.py + +Specifically, the file format.py in this directory implements the +format as described here. + + +References +---------- + +[1] http://docs.python.org/lib/module-pickle.html + +[2] http://hdf.ncsa.uiuc.edu/products/hdf5/index.html + + +Copyright +--------- + +This document has been placed in the public domain. + diff --git a/doc/neps/npy-format.txt b/doc/neps/npy-format.txt deleted file mode 100644 index 98a0c1aaf..000000000 --- a/doc/neps/npy-format.txt +++ /dev/null @@ -1,293 +0,0 @@ -Title: A Simple File Format for NumPy Arrays -Discussions-To: numpy-discussion@mail.scipy.org -Version: $Revision$ -Last-Modified: $Date$ -Author: Robert Kern <robert.kern@gmail.com> -Status: Draft -Type: Standards Track -Content-Type: text/plain -Created: 20-Dec-2007 - - -Abstract - - We propose a standard binary file format (NPY) for persisting - a single arbitrary NumPy array on disk. The format stores all of - the shape and dtype information necessary to reconstruct the array - correctly even on another machine with a different architecture. - The format is designed to be as simple as possible while achieving - its limited goals. The implementation is intended to be pure - Python and distributed as part of the main numpy package. - - -Rationale - - A lightweight, omnipresent system for saving NumPy arrays to disk - is a frequent need. Python in general has pickle [1] for saving - most Python objects to disk. This often works well enough with - NumPy arrays for many purposes, but it has a few drawbacks: - - - Dumping or loading a pickle file require the duplication of the - data in memory. For large arrays, this can be a showstopper. - - - The array data is not directly accessible through - memory-mapping. Now that numpy has that capability, it has - proved very useful for loading large amounts of data (or more to - the point: avoiding loading large amounts of data when you only - need a small part). - - Both of these problems can be addressed by dumping the raw bytes - to disk using ndarray.tofile() and numpy.fromfile(). However, - these have their own problems: - - - The data which is written has no information about the shape or - dtype of the array. - - - It is incapable of handling object arrays. - - The NPY file format is an evolutionary advance over these two - approaches. Its design is mostly limited to solving the problems - with pickles and tofile()/fromfile(). It does not intend to solve - more complicated problems for which more complicated formats like - HDF5 [2] are a better solution. - - -Use Cases - - - Neville Newbie has just started to pick up Python and NumPy. He - has not installed many packages, yet, nor learned the standard - library, but he has been playing with NumPy at the interactive - prompt to do small tasks. He gets a result that he wants to - save. - - - Annie Analyst has been using large nested record arrays to - represent her statistical data. She wants to convince her - R-using colleague, David Doubter, that Python and NumPy are - awesome by sending him her analysis code and data. She needs - the data to load at interactive speeds. Since David does not - use Python usually, needing to install large packages would turn - him off. - - - Simon Seismologist is developing new seismic processing tools. - One of his algorithms requires large amounts of intermediate - data to be written to disk. The data does not really fit into - the industry-standard SEG-Y schema, but he already has a nice - record-array dtype for using it internally. - - - Polly Parallel wants to split up a computation on her multicore - machine as simply as possible. Parts of the computation can be - split up among different processes without any communication - between processes; they just need to fill in the appropriate - portion of a large array with their results. Having several - child processes memory-mapping a common array is a good way to - achieve this. - - -Requirements - - The format MUST be able to: - - - Represent all NumPy arrays including nested record - arrays and object arrays. - - - Represent the data in its native binary form. - - - Be contained in a single file. - - - Support Fortran-contiguous arrays directly. - - - Store all of the necessary information to reconstruct the array - including shape and dtype on a machine of a different - architecture. Both little-endian and big-endian arrays must be - supported and a file with little-endian numbers will yield - a little-endian array on any machine reading the file. The - types must be described in terms of their actual sizes. For - example, if a machine with a 64-bit C "long int" writes out an - array with "long ints", a reading machine with 32-bit C "long - ints" will yield an array with 64-bit integers. - - - Be reverse engineered. Datasets often live longer than the - programs that created them. A competent developer should be - able to create a solution in his preferred programming language to - read most NPY files that he has been given without much - documentation. - - - Allow memory-mapping of the data. - - - Be read from a filelike stream object instead of an actual file. - This allows the implementation to be tested easily and makes the - system more flexible. NPY files can be stored in ZIP files and - easily read from a ZipFile object. - - - Store object arrays. Since general Python objects are - complicated and can only be reliably serialized by pickle (if at - all), many of the other requirements are waived for files - containing object arrays. Files with object arrays do not have - to be mmapable since that would be technically impossible. We - cannot expect the pickle format to be reverse engineered without - knowledge of pickle. However, one should at least be able to - read and write object arrays with the same generic interface as - other arrays. - - - Be read and written using APIs provided in the numpy package - itself without any other libraries. The implementation inside - numpy may be in C if necessary. - - The format explicitly *does not* need to: - - - Support multiple arrays in a file. Since we require filelike - objects to be supported, one could use the API to build an ad - hoc format that supported multiple arrays. However, solving the - general problem and use cases is beyond the scope of the format - and the API for numpy. - - - Fully handle arbitrary subclasses of numpy.ndarray. Subclasses - will be accepted for writing, but only the array data will be - written out. A regular numpy.ndarray object will be created - upon reading the file. The API can be used to build a format - for a particular subclass, but that is out of scope for the - general NPY format. - - -Format Specification: Version 1.0 - - The first 6 bytes are a magic string: exactly "\x93NUMPY". - - The next 1 byte is an unsigned byte: the major version number of - the file format, e.g. \x01. - - The next 1 byte is an unsigned byte: the minor version number of - the file format, e.g. \x00. Note: the version of the file format - is not tied to the version of the numpy package. - - The next 2 bytes form a little-endian unsigned short int: the - length of the header data HEADER_LEN. - - The next HEADER_LEN bytes form the header data describing the - array's format. It is an ASCII string which contains a Python - literal expression of a dictionary. It is terminated by a newline - ('\n') and padded with spaces ('\x20') to make the total length of - the magic string + 4 + HEADER_LEN be evenly divisible by 16 for - alignment purposes. - - The dictionary contains three keys: - - "descr" : dtype.descr - An object that can be passed as an argument to the - numpy.dtype() constructor to create the array's dtype. - - "fortran_order" : bool - Whether the array data is Fortran-contiguous or not. - Since Fortran-contiguous arrays are a common form of - non-C-contiguity, we allow them to be written directly to - disk for efficiency. - - "shape" : tuple of int - The shape of the array. - - For repeatability and readability, this dictionary is formatted - using pprint.pformat() so the keys are in alphabetic order. - - Following the header comes the array data. If the dtype contains - Python objects (i.e. dtype.hasobject is True), then the data is - a Python pickle of the array. Otherwise the data is the - contiguous (either C- or Fortran-, depending on fortran_order) - bytes of the array. Consumers can figure out the number of bytes - by multiplying the number of elements given by the shape (noting - that shape=() means there is 1 element) by dtype.itemsize. - - -Conventions - - We recommend using the ".npy" extension for files following this - format. This is by no means a requirement; applications may wish - to use this file format but use an extension specific to the - application. In the absence of an obvious alternative, however, - we suggest using ".npy". - - For a simple way to combine multiple arrays into a single file, - one can use ZipFile to contain multiple ".npy" files. We - recommend using the file extension ".npz" for these archives. - - -Alternatives - - The author believes that this system (or one along these lines) is - about the simplest system that satisfies all of the requirements. - However, one must always be wary of introducing a new binary - format to the world. - - HDF5 [2] is a very flexible format that should be able to - represent all of NumPy's arrays in some fashion. It is probably - the only widely-used format that can faithfully represent all of - NumPy's array features. It has seen substantial adoption by the - scientific community in general and the NumPy community in - particular. It is an excellent solution for a wide variety of - array storage problems with or without NumPy. - - HDF5 is a complicated format that more or less implements - a hierarchical filesystem-in-a-file. This fact makes satisfying - some of the Requirements difficult. To the author's knowledge, as - of this writing, there is no application or library that reads or - writes even a subset of HDF5 files that does not use the canonical - libhdf5 implementation. This implementation is a large library - that is not always easy to build. It would be infeasible to - include it in numpy. - - It might be feasible to target an extremely limited subset of - HDF5. Namely, there would be only one object in it: the array. - Using contiguous storage for the data, one should be able to - implement just enough of the format to provide the same metadata - that the proposed format does. One could still meet all of the - technical requirements like mmapability. - - We would accrue a substantial benefit by being able to generate - files that could be read by other HDF5 software. Furthermore, by - providing the first non-libhdf5 implementation of HDF5, we would - be able to encourage more adoption of simple HDF5 in applications - where it was previously infeasible because of the size of the - library. The basic work may encourage similar dead-simple - implementations in other languages and further expand the - community. - - The remaining concern is about reverse engineerability of the - format. Even the simple subset of HDF5 would be very difficult to - reverse engineer given just a file by itself. However, given the - prominence of HDF5, this might not be a substantial concern. - - In conclusion, we are going forward with the design laid out in - this document. If someone writes code to handle the simple subset - of HDF5 that would be useful to us, we may consider a revision of - the file format. - - -Implementation - - The current implementation is included in the 1.0.5 release of numpy. - - http://github.com/numpy/numpy/blob/v1.5.0/numpy/lib/format.py - - Specifically, the file format.py in this directory implements the - format as described here. - - -References - - [1] http://docs.python.org/lib/module-pickle.html - - [2] http://hdf.ncsa.uiuc.edu/products/hdf5/index.html - - -Copyright - - This document has been placed in the public domain. - - - -Local Variables: -mode: indented-text -indent-tabs-mode: nil -sentence-end-double-space: t -fill-column: 70 -coding: utf-8 -End: diff --git a/doc/neps/pep_buffer.txt b/doc/neps/pep_buffer.txt deleted file mode 100644 index a154d2792..000000000 --- a/doc/neps/pep_buffer.txt +++ /dev/null @@ -1,869 +0,0 @@ -:PEP: 3118 -:Title: Revising the buffer protocol -:Version: $Revision$ -:Last-Modified: $Date$ -:Authors: Travis Oliphant <oliphant@ee.byu.edu>, Carl Banks <pythondev@aerojockey.com> -:Status: Draft -:Type: Standards Track -:Content-Type: text/x-rst -:Created: 28-Aug-2006 -:Python-Version: 3000 - -Abstract -======== - -This PEP proposes re-designing the buffer interface (PyBufferProcs -function pointers) to improve the way Python allows memory sharing -in Python 3.0 - -In particular, it is proposed that the character buffer portion -of the API be elminated and the multiple-segment portion be -re-designed in conjunction with allowing for strided memory -to be shared. In addition, the new buffer interface will -allow the sharing of any multi-dimensional nature of the -memory and what data-format the memory contains. - -This interface will allow any extension module to either -create objects that share memory or create algorithms that -use and manipulate raw memory from arbitrary objects that -export the interface. - - -Rationale -========= - -The Python 2.X buffer protocol allows different Python types to -exchange a pointer to a sequence of internal buffers. This -functionality is *extremely* useful for sharing large segments of -memory between different high-level objects, but it is too limited and -has issues: - -1. There is the little used "sequence-of-segments" option - (bf_getsegcount) that is not well motivated. - -2. There is the apparently redundant character-buffer option - (bf_getcharbuffer) - -3. There is no way for a consumer to tell the buffer-API-exporting - object it is "finished" with its view of the memory and - therefore no way for the exporting object to be sure that it is - safe to reallocate the pointer to the memory that it owns (for - example, the array object reallocating its memory after sharing - it with the buffer object which held the original pointer led - to the infamous buffer-object problem). - -4. Memory is just a pointer with a length. There is no way to - describe what is "in" the memory (float, int, C-structure, etc.) - -5. There is no shape information provided for the memory. But, - several array-like Python types could make use of a standard - way to describe the shape-interpretation of the memory - (wxPython, GTK, pyQT, CVXOPT, PyVox, Audio and Video - Libraries, ctypes, NumPy, data-base interfaces, etc.) - -6. There is no way to share discontiguous memory (except through - the sequence of segments notion). - - There are two widely used libraries that use the concept of - discontiguous memory: PIL and NumPy. Their view of discontiguous - arrays is different, though. The proposed buffer interface allows - sharing of either memory model. Exporters will use only one - approach and consumers may choose to support discontiguous - arrays of each type however they choose. - - NumPy uses the notion of constant striding in each dimension as its - basic concept of an array. With this concept, a simple sub-region - of a larger array can be described without copying the data. - Thus, stride information is the additional information that must be - shared. - - The PIL uses a more opaque memory representation. Sometimes an - image is contained in a contiguous segment of memory, but sometimes - it is contained in an array of pointers to the contiguous segments - (usually lines) of the image. The PIL is where the idea of multiple - buffer segments in the original buffer interface came from. - - NumPy's strided memory model is used more often in computational - libraries and because it is so simple it makes sense to support - memory sharing using this model. The PIL memory model is sometimes - used in C-code where a 2-d array can be then accessed using double - pointer indirection: e.g. image[i][j]. - - The buffer interface should allow the object to export either of these - memory models. Consumers are free to either require contiguous memory - or write code to handle one or both of these memory models. - -Proposal Overview -================= - -* Eliminate the char-buffer and multiple-segment sections of the - buffer-protocol. - -* Unify the read/write versions of getting the buffer. - -* Add a new function to the interface that should be called when - the consumer object is "done" with the memory area. - -* Add a new variable to allow the interface to describe what is in - memory (unifying what is currently done now in struct and - array) - -* Add a new variable to allow the protocol to share shape information - -* Add a new variable for sharing stride information - -* Add a new mechanism for sharing arrays that must - be accessed using pointer indirection. - -* Fix all objects in the core and the standard library to conform - to the new interface - -* Extend the struct module to handle more format specifiers - -* Extend the buffer object into a new memory object which places - a Python veneer around the buffer interface. - -* Add a few functions to make it easy to copy contiguous data - in and out of object supporting the buffer interface. - -Specification -============= - -While the new specification allows for complicated memory sharing. -Simple contiguous buffers of bytes can still be obtained from an -object. In fact, the new protocol allows a standard mechanism for -doing this even if the original object is not represented as a -contiguous chunk of memory. - -The easiest way to obtain a simple contiguous chunk of memory is -to use the provided C-API to obtain a chunk of memory. - - -Change the PyBufferProcs structure to - -:: - - typedef struct { - getbufferproc bf_getbuffer; - releasebufferproc bf_releasebuffer; - } - - -:: - - typedef int (*getbufferproc)(PyObject *obj, PyBuffer *view, int flags) - -This function returns 0 on success and -1 on failure (and raises an -error). The first variable is the "exporting" object. The second -argument is the address to a bufferinfo structure. If view is NULL, -then no information is returned but a lock on the memory is still -obtained. In this case, the corresponding releasebuffer should also -be called with NULL. - -The third argument indicates what kind of buffer the exporter is -allowed to return. It essentially tells the exporter what kind of -memory area the consumer can deal with. It also indicates what -members of the PyBuffer structure the consumer is going to care about. - -The exporter can use this information to simplify how much of the PyBuffer -structure is filled in and/or raise an error if the object can't support -a simpler view of its memory. - -Thus, the caller can request a simple "view" and either receive it or -have an error raised if it is not possible. - -All of the following assume that at least buf, len, and readonly -will always be utilized by the caller. - -Py_BUF_SIMPLE - - The returned buffer will be assumed to be readable (the object may - or may not have writeable memory). Only the buf, len, and readonly - variables may be accessed. The format will be assumed to be - unsigned bytes . This is a "stand-alone" flag constant. It never - needs to be \|'d to the others. The exporter will raise an - error if it cannot provide such a contiguous buffer. - -Py_BUF_WRITEABLE - - The returned buffer must be writeable. If it is not writeable, - then raise an error. - -Py_BUF_READONLY - - The returned buffer must be readonly. If the object is already - read-only or it can make its memory read-only (and there are no - other views on the object) then it should do so and return the - buffer information. If the object does not have read-only memory - (or cannot make it read-only), then an error should be raised. - -Py_BUF_FORMAT - - The returned buffer must have true format information. This would - be used when the consumer is going to be checking for what 'kind' - of data is actually stored. An exporter should always be able - to provide this information if requested. - -Py_BUF_SHAPE - - The returned buffer must have shape information. The memory will - be assumed C-style contiguous (last dimension varies the fastest). - The exporter may raise an error if it cannot provide this kind - of contiguous buffer. - -Py_BUF_STRIDES (implies Py_BUF_SHAPE) - - The returned buffer must have strides information. This would be - used when the consumer can handle strided, discontiguous arrays. - Handling strides automatically assumes you can handle shape. - The exporter may raise an error if cannot provide a strided-only - representation of the data (i.e. without the suboffsets). - -Py_BUF_OFFSETS (implies Py_BUF_STRIDES) - - The returned buffer must have suboffsets information. This would - be used when the consumer can handle indirect array referencing - implied by these suboffsets. - -Py_BUF_FULL (Py_BUF_OFFSETS | Py_BUF_WRITEABLE | Py_BUF_FORMAT) - -Thus, the consumer simply wanting a contiguous chunk of bytes from -the object would use Py_BUF_SIMPLE, while a consumer that understands -how to make use of the most complicated cases could use Py_BUF_INDIRECT. - -If format information is going to be probed, then Py_BUF_FORMAT must -be \|'d to the flags otherwise the consumer assumes it is unsigned -bytes. - -There is a C-API that simple exporting objects can use to fill-in the -buffer info structure correctly according to the provided flags if a -contiguous chunk of "unsigned bytes" is all that can be exported. - - -The bufferinfo structure is:: - - struct bufferinfo { - void *buf; - Py_ssize_t len; - int readonly; - const char *format; - int ndims; - Py_ssize_t *shape; - Py_ssize_t *strides; - Py_ssize_t *suboffsets; - int itemsize; - void *internal; - } PyBuffer; - -Before calling this function, the bufferinfo structure can be filled -with whatever. Upon return from getbufferproc, the bufferinfo -structure is filled in with relevant information about the buffer. -This same bufferinfo structure must be passed to bf_releasebuffer (if -available) when the consumer is done with the memory. The caller is -responsible for keeping a reference to obj until releasebuffer is -called (i.e. this call does not alter the reference count of obj). - -The members of the bufferinfo structure are: - -buf - a pointer to the start of the memory for the object - -len - the total bytes of memory the object uses. This should be the - same as the product of the shape array multiplied by the number of - bytes per item of memory. - -readonly - an integer variable to hold whether or not the memory is - readonly. 1 means the memory is readonly, zero means the - memory is writeable. - -format - a NULL-terminated format-string (following the struct-style syntax - including extensions) indicating what is in each element of - memory. The number of elements is len / itemsize, where itemsize - is the number of bytes implied by the format. For standard - unsigned bytes use a format string of "B". - -ndims - a variable storing the number of dimensions the memory represents. - Must be >=0. - -shape - an array of ``Py_ssize_t`` of length ``ndims`` indicating the - shape of the memory as an N-D array. Note that ``((*shape)[0] * - ... * (*shape)[ndims-1])*itemsize = len``. If ndims is 0 (indicating - a scalar), then this must be NULL. - -strides - address of a ``Py_ssize_t*`` variable that will be filled with a - pointer to an array of ``Py_ssize_t`` of length ``ndims`` (or NULL - if ndims is 0). indicating the number of bytes to skip to get to - the next element in each dimension. If this is not requested by - the caller (BUF_STRIDES is not set), then this member of the - structure will not be used and the consumer is assuming the array - is C-style contiguous. If this is not the case, then an error - should be raised. If this member is requested by the caller - (BUF_STRIDES is set), then it must be filled in. - - -suboffsets - address of a ``Py_ssize_t *`` variable that will be filled with a - pointer to an array of ``Py_ssize_t`` of length ``*ndims``. If - these suboffset numbers are >=0, then the value stored along the - indicated dimension is a pointer and the suboffset value dictates - how many bytes to add to the pointer after de-referencing. A - suboffset value that it negative indicates that no de-referencing - should occur (striding in a contiguous memory block). If all - suboffsets are negative (i.e. no de-referencing is needed, then - this must be NULL. - - For clarity, here is a function that returns a pointer to the - element in an N-D array pointed to by an N-dimesional index when - there are both strides and suboffsets.:: - - void* get_item_pointer(int ndim, void* buf, Py_ssize_t* strides, - Py_ssize_t* suboffsets, Py_ssize_t *indices) { - char* pointer = (char*)buf; - int i; - for (i = 0; i < ndim; i++) { - pointer += strides[i]*indices[i]; - if (suboffsets[i] >=0 ) { - pointer = *((char**)pointer) + suboffsets[i]; - } - } - return (void*)pointer; - } - - Notice the suboffset is added "after" the dereferencing occurs. - Thus slicing in the ith dimension would add to the suboffsets in - the (i-1)st dimension. Slicing in the first dimension would change - the location of the starting pointer directly (i.e. buf would - be modified). - -itemsize - This is a storage for the itemsize of each element of the shared - memory. It can be obtained using PyBuffer_SizeFromFormat but an - exporter may know it without making this call and thus storing it - is more convenient and faster. - -internal - This is for use internally by the exporting object. For example, - this might be re-cast as an integer by the exporter and used to - store flags about whether or not the shape, strides, and suboffsets - arrays must be freed when the buffer is released. The consumer - should never touch this value. - - -The exporter is responsible for making sure the memory pointed to by -buf, format, shape, strides, and suboffsets is valid until -releasebuffer is called. If the exporter wants to be able to change -shape, strides, and/or suboffsets before releasebuffer is called then -it should allocate those arrays when getbuffer is called (pointing to -them in the buffer-info structure provided) and free them when -releasebuffer is called. - - -The same bufferinfo struct should be used in the release-buffer -interface call. The caller is responsible for the memory of the -bufferinfo structure itself. - -``typedef int (*releasebufferproc)(PyObject *obj, PyBuffer *view)`` - Callers of getbufferproc must make sure that this function is - called when memory previously acquired from the object is no - longer needed. The exporter of the interface must make sure that - any memory pointed to in the bufferinfo structure remains valid - until releasebuffer is called. - - Both of these routines are optional for a type object - - If the releasebuffer function is not provided then it does not ever - need to be called. - -Exporters will need to define a releasebuffer function if they can -re-allocate their memory, strides, shape, suboffsets, or format -variables which they might share through the struct bufferinfo. -Several mechanisms could be used to keep track of how many getbuffer -calls have been made and shared. Either a single variable could be -used to keep track of how many "views" have been exported, or a -linked-list of bufferinfo structures filled in could be maintained in -each object. - -All that is specifically required by the exporter, however, is to -ensure that any memory shared through the bufferinfo structure remains -valid until releasebuffer is called on the bufferinfo structure. - - -New C-API calls are proposed -============================ - -:: - - int PyObject_CheckBuffer(PyObject *obj) - -Return 1 if the getbuffer function is available otherwise 0. - -:: - - int PyObject_GetBuffer(PyObject *obj, PyBuffer *view, - int flags) - -This is a C-API version of the getbuffer function call. It checks to -make sure object has the required function pointer and issues the -call. Returns -1 and raises an error on failure and returns 0 on -success. - -:: - - int PyObject_ReleaseBuffer(PyObject *obj, PyBuffer *view) - -This is a C-API version of the releasebuffer function call. It checks -to make sure the object has the required function pointer and issues -the call. Returns 0 on success and -1 (with an error raised) on -failure. This function always succeeds if there is no releasebuffer -function for the object. - -:: - - PyObject *PyObject_GetMemoryView(PyObject *obj) - -Return a memory-view object from an object that defines the buffer interface. - -A memory-view object is an extended buffer object that could replace -the buffer object (but doesn't have to). It's C-structure is - -:: - - typedef struct { - PyObject_HEAD - PyObject *base; - int ndims; - Py_ssize_t *starts; /* slice starts */ - Py_ssize_t *stops; /* slice stops */ - Py_ssize_t *steps; /* slice steps */ - } PyMemoryViewObject; - -This is functionally similar to the current buffer object except only -a reference to base is kept. The actual memory for base must be -re-grabbed using the buffer-protocol, whenever it is needed. - -The getbuffer and releasebuffer for this object use the underlying -base object (adjusted using the slice information). If the number of -dimensions of the base object (or the strides or the size) has changed -when a new view is requested, then the getbuffer will trigger an error. - -This memory-view object will support mult-dimensional slicing. Slices -of the memory-view object are other memory-view objects. When an -"element" from the memory-view is returned it is always a tuple of -bytes object + format string which can then be interpreted using the -struct module if desired. - -:: - - int PyBuffer_SizeFromFormat(const char *) - -Return the implied itemsize of the data-format area from a struct-style -description. - -:: - - int PyObject_GetContiguous(PyObject *obj, void **buf, Py_ssize_t *len, - char **format, char fortran) - -Return a contiguous chunk of memory representing the buffer. If a -copy is made then return 1. If no copy was needed return 0. If an -error occurred in probing the buffer interface, then return -1. The -contiguous chunk of memory is pointed to by ``*buf`` and the length of -that memory is ``*len``. If the object is multi-dimensional, then if -fortran is 'F', the first dimension of the underlying array will vary -the fastest in the buffer. If fortran is 'C', then the last dimension -will vary the fastest (C-style contiguous). If fortran is 'A', then it -does not matter and you will get whatever the object decides is more -efficient. - -:: - - int PyObject_CopyToObject(PyObject *obj, void *buf, Py_ssize_t len, - char fortran) - -Copy ``len`` bytes of data pointed to by the contiguous chunk of -memory pointed to by ``buf`` into the buffer exported by obj. Return -0 on success and return -1 and raise an error on failure. If the -object does not have a writeable buffer, then an error is raised. If -fortran is 'F', then if the object is multi-dimensional, then the data -will be copied into the array in Fortran-style (first dimension varies -the fastest). If fortran is 'C', then the data will be copied into the -array in C-style (last dimension varies the fastest). If fortran is 'A', then -it does not matter and the copy will be made in whatever way is more -efficient. - -:: - - void PyBuffer_FreeMem(void *buf) - -This function frees the memory returned by PyObject_GetContiguous if a -copy was made. Do not call this function unless -PyObject_GetContiguous returns a 1 indicating that new memory was -created. - - -These last three C-API calls allow a standard way of getting data in and -out of Python objects into contiguous memory areas no matter how it is -actually stored. These calls use the extended buffer interface to perform -their work. - -:: - - int PyBuffer_IsContiguous(PyBuffer *view, char fortran); - -Return 1 if the memory defined by the view object is C-style (fortran = 'C') -or Fortran-style (fortran = 'A') contiguous. Return 0 otherwise. - -:: - - void PyBuffer_FillContiguousStrides(int *ndims, Py_ssize_t *shape, - int itemsize, - Py_ssize_t *strides, char fortran) - -Fill the strides array with byte-strides of a contiguous (C-style if -fortran is 0 or Fortran-style if fortran is 1) array of the given -shape with the given number of bytes per element. - -:: - - int PyBuffer_FillInfo(PyBuffer *view, void *buf, - Py_ssize_t len, int readonly, int infoflags) - -Fills in a buffer-info structure correctly for an exporter that can -only share a contiguous chunk of memory of "unsigned bytes" of the -given length. Returns 0 on success and -1 (with raising an error) on -error. - - -Additions to the struct string-syntax -===================================== - -The struct string-syntax is missing some characters to fully -implement data-format descriptions already available elsewhere (in -ctypes and NumPy for example). The Python 2.5 specification is -at http://docs.python.org/lib/module-struct.html - -Here are the proposed additions: - - -================ =========== -Character Description -================ =========== -'t' bit (number before states how many bits) -'?' platform _Bool type -'g' long double -'c' ucs-1 (latin-1) encoding -'u' ucs-2 -'w' ucs-4 -'O' pointer to Python Object -'Z' complex (whatever the next specifier is) -'&' specific pointer (prefix before another charater) -'T{}' structure (detailed layout inside {}) -'(k1,k2,...,kn)' multi-dimensional array of whatever follows -':name:' optional name of the preceeding element -'X{}' pointer to a function (optional function - signature inside {}) -' \n\t' ignored (allow better readability) - -- this may already be true -================ =========== - -The struct module will be changed to understand these as well and -return appropriate Python objects on unpacking. Unpacking a -long-double will return a decimal object or a ctypes long-double. -Unpacking 'u' or 'w' will return Python unicode. Unpacking a -multi-dimensional array will return a list (of lists if >1d). -Unpacking a pointer will return a ctypes pointer object. Unpacking a -function pointer will return a ctypes call-object (perhaps). Unpacking -a bit will return a Python Bool. White-space in the struct-string -syntax will be ignored if it isn't already. Unpacking a named-object -will return some kind of named-tuple-like object that acts like a -tuple but whose entries can also be accessed by name. Unpacking a -nested structure will return a nested tuple. - -Endian-specification ('!', '@','=','>','<', '^') is also allowed -inside the string so that it can change if needed. The -previously-specified endian string is in force until changed. The -default endian is '@' which means native data-types and alignment. If -un-aligned, native data-types are requested, then the endian -specification is '^'. - -According to the struct-module, a number can preceed a character -code to specify how many of that type there are. The -(k1,k2,...,kn) extension also allows specifying if the data is -supposed to be viewed as a (C-style contiguous, last-dimension -varies the fastest) multi-dimensional array of a particular format. - -Functions should be added to ctypes to create a ctypes object from -a struct description, and add long-double, and ucs-2 to ctypes. - -Examples of Data-Format Descriptions -==================================== - -Here are some examples of C-structures and how they would be -represented using the struct-style syntax. - -<named> is the constructor for a named-tuple (not-specified yet). - -float - 'f' <--> Python float -complex double - 'Zd' <--> Python complex -RGB Pixel data - 'BBB' <--> (int, int, int) - 'B:r: B:g: B:b:' <--> <named>((int, int, int), ('r','g','b')) - -Mixed endian (weird but possible) - '>i:big: <i:little:' <--> <named>((int, int), ('big', 'little')) - -Nested structure - :: - - struct { - int ival; - struct { - unsigned short sval; - unsigned char bval; - unsigned char cval; - } sub; - } - """i:ival: - T{ - H:sval: - B:bval: - B:cval: - }:sub: - """ -Nested array - :: - - struct { - int ival; - double data[16*4]; - } - """i:ival: - (16,4)d:data: - """ - - -Code to be affected -=================== - -All objects and modules in Python that export or consume the old -buffer interface will be modified. Here is a partial list. - -* buffer object -* bytes object -* string object -* array module -* struct module -* mmap module -* ctypes module - -Anything else using the buffer API. - - -Issues and Details -================== - -It is intended that this PEP will be back-ported to Python 2.6 by -adding the C-API and the two functions to the existing buffer -protocol. - -The proposed locking mechanism relies entirely on the exporter object -to not invalidate any of the memory pointed to by the buffer structure -until a corresponding releasebuffer is called. If it wants to be able -to change its own shape and/or strides arrays, then it needs to create -memory for these in the bufferinfo structure and copy information -over. - -The sharing of strided memory and suboffsets is new and can be seen as -a modification of the multiple-segment interface. It is motivated by -NumPy and the PIL. NumPy objects should be able to share their -strided memory with code that understands how to manage strided memory -because strided memory is very common when interfacing with compute -libraries. - -Also, with this approach it should be possible to write generic code -that works with both kinds of memory. - -Memory management of the format string, the shape array, the strides -array, and the suboffsets array in the bufferinfo structure is always -the responsibility of the exporting object. The consumer should not -set these pointers to any other memory or try to free them. - -Several ideas were discussed and rejected: - - Having a "releaser" object whose release-buffer was called. This - was deemed unacceptable because it caused the protocol to be - asymmetric (you called release on something different than you - "got" the buffer from). It also complicated the protocol without - providing a real benefit. - - Passing all the struct variables separately into the function. - This had the advantage that it allowed one to set NULL to - variables that were not of interest, but it also made the function - call more difficult. The flags variable allows the same - ability of consumers to be "simple" in how they call the protocol. - -Code -======== - -The authors of the PEP promise to contribute and maintain the code for -this proposal but will welcome any help. - - - - -Examples -========= - -Ex. 1 ------------ - -This example shows how an image object that uses contiguous lines might expose its buffer. - -:: - - struct rgba { - unsigned char r, g, b, a; - }; - - struct ImageObject { - PyObject_HEAD; - ... - struct rgba** lines; - Py_ssize_t height; - Py_ssize_t width; - Py_ssize_t shape_array[2]; - Py_ssize_t stride_array[2]; - Py_ssize_t view_count; - }; - -"lines" points to malloced 1-D array of (struct rgba*). Each pointer -in THAT block points to a seperately malloced array of (struct rgba). - -In order to access, say, the red value of the pixel at x=30, y=50, you'd use "lines[50][30].r". - -So what does ImageObject's getbuffer do? Leaving error checking out:: - - int Image_getbuffer(PyObject *self, PyBuffer *view, int flags) { - - static Py_ssize_t suboffsets[2] = { -1, 0 }; - - view->buf = self->lines; - view->len = self->height*self->width; - view->readonly = 0; - view->ndims = 2; - self->shape_array[0] = height; - self->shape_array[1] = width; - view->shape = &self->shape_array; - self->stride_array[0] = sizeof(struct rgba*); - self->stride_array[1] = sizeof(struct rgba); - view->strides = &self->stride_array; - view->suboffsets = suboffsets; - - self->view_count ++; - - return 0; - } - - - int Image_releasebuffer(PyObject *self, PyBuffer *view) { - self->view_count--; - return 0; - } - - -Ex. 2 ------------ - -This example shows how an object that wants to expose a contiguous -chunk of memory (which will never be re-allocated while the object is -alive) would do that. - -:: - - int myobject_getbuffer(PyObject *self, PyBuffer *view, int flags) { - - void *buf; - Py_ssize_t len; - int readonly=0; - - buf = /* Point to buffer */ - len = /* Set to size of buffer */ - readonly = /* Set to 1 if readonly */ - - return PyObject_FillBufferInfo(view, buf, len, readonly, flags); - } - -No releasebuffer is necessary because the memory will never -be re-allocated so the locking mechanism is not needed. - -Ex. 3 ------------ - -A consumer that wants to only get a simple contiguous chunk of bytes -from a Python object, obj would do the following: - -:: - - PyBuffer view; - int ret; - - if (PyObject_GetBuffer(obj, &view, Py_BUF_SIMPLE) < 0) { - /* error return */ - } - - /* Now, view.buf is the pointer to memory - view.len is the length - view.readonly is whether or not the memory is read-only. - */ - - - /* After using the information and you don't need it anymore */ - - if (PyObject_ReleaseBuffer(obj, &view) < 0) { - /* error return */ - } - - -Ex. 4 ------------ - -A consumer that wants to be able to use any object's memory but is -writing an algorithm that only handle contiguous memory could do the following: - -:: - - void *buf; - Py_ssize_t len; - char *format; - - if (PyObject_GetContiguous(obj, &buf, &len, &format, 0) < 0) { - /* error return */ - } - - /* process memory pointed to by buffer if format is correct */ - - /* Optional: - - if, after processing, we want to copy data from buffer back - into the the object - - we could do - */ - - if (PyObject_CopyToObject(obj, buf, len, 0) < 0) { - /* error return */ - } - - -Copyright -========= - -This PEP is placed in the public domain diff --git a/doc/neps/structured_array_extensions.txt b/doc/neps/structured_array_extensions.rst index 716b98a76..a4248362c 100644 --- a/doc/neps/structured_array_extensions.txt +++ b/doc/neps/structured_array_extensions.rst @@ -1,3 +1,6 @@ +=========================== +Structured array extensions +=========================== 1. Create with-style context that makes "named-columns" available as names in the namespace. diff --git a/doc/neps/warnfix.txt b/doc/neps/warnfix.rst index b6f307bcb..93ef26488 100644 --- a/doc/neps/warnfix.txt +++ b/doc/neps/warnfix.rst @@ -1,7 +1,6 @@ -=========================================================== -A proposal to build numpy without warning with a big set of -warning flags -=========================================================== +========================================================================= +A proposal to build numpy without warning with a big set of warning flags +========================================================================= :Author: David Cournapeau :Contact: david@ar.media.kyoto-u.ac.jp @@ -12,7 +11,7 @@ Executive summary When building numpy and scipy, we are limited to a quite restricted set of warning compilers, thus missing a large class of potential bugs which could be -detected with stronger warning flags. The goal of this PEP is present the +detected with stronger warning flags. The goal of this NEP is present the various methods used to clean the code and implement some policy to make numpy buildable with a bigger set of warning flags, while keeping the build warnings free. diff --git a/doc/release/1.9.0-notes.rst b/doc/release/1.9.0-notes.rst index 5849c2129..4b1e094ad 100644 --- a/doc/release/1.9.0-notes.rst +++ b/doc/release/1.9.0-notes.rst @@ -129,6 +129,9 @@ final two axes instead of raising an exception. for ``tostring`` which exports arrays as ``bytes``. This is more consistent in Python 3 where ``str`` and ``bytes`` are not the same. +Build system +~~~~~~~~~~~~ +Added experimental support for the ppc64le architecture. Compatibility to python ``numbers`` module ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -140,6 +143,11 @@ the python ``numbers`` module. The ordering of the columns of the Vandermonde matrix can be specified with this new boolean argument. +``unique_counts`` parameter added to ``np.unique`` +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +The number of times each unique item comes up in the input can now be +obtained as an optional return value. + Improvements ============ @@ -194,6 +202,11 @@ Several more functions now release the Global Interpreter Lock allowing more efficient parallization using the ``threading`` module. Most notably the GIL is now released for fancy indexing and ``np.where``. +MaskedArray support for more complicated base classes +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +Built-in assumptions that the baseclass behaved like a plain array are being +removed. In particalur, ``repr`` and ``str`` should now work more reliably. + Changes ======= @@ -315,7 +328,7 @@ This change was already applied to the 1.8.1 release. `npyio.recfromcsv` no longer accepts the undocumented `update` keyword, which used to override the `dtype` keyword. - + C-API ~~~~~ @@ -334,6 +347,11 @@ The integer and empty input to ``select`` is deprecated. In the future only boolean arrays will be valid conditions and an empty ``condlist`` will be considered an input error instead of returning the default. +``rank`` function +~~~~~~~~~~~~~~~~~ +The ``rank`` function has been deprecated to avoid confusion with +``numpy.linalg.matrix_rank``. + C-API ~~~~~ diff --git a/doc/source/_templates/indexcontent.html b/doc/source/_templates/indexcontent.html index fb753e7b7..e3fe3ac9b 100644 --- a/doc/source/_templates/indexcontent.html +++ b/doc/source/_templates/indexcontent.html @@ -34,6 +34,7 @@ <td width="50%"> <p class="biglink"><a class="biglink" href="{{ pathto("bugs") }}">Reporting bugs</a></p> <p class="biglink"><a class="biglink" href="{{ pathto("about") }}">About NumPy</a></p> + <p class="biglink"><a class="biglink" href="{{ pathto("neps/index") }}">Numpy Enhancement Proposals</a><br/> </td><td width="50%"> <p class="biglink"><a class="biglink" href="{{ pathto("release") }}">Release Notes</a></p> <p class="biglink"><a class="biglink" href="{{ pathto("license") }}">License of Numpy</a></p> @@ -47,12 +48,14 @@ Public Domain in August 2008). The reference documentation for many of the functions are written by numerous contributors and developers of Numpy, both prior to and during the - <a href="http://scipy.org/Developer_Zone/DocMarathon2008">Numpy Documentation Marathon</a>. + <a href="http://docs.scipy.org/numpy/">Numpy Documentation Marathon</a>. </p> <p> - The Documentation Marathon is still ongoing. Please help us write - better documentation for Numpy by joining it! Instructions on how to - join and what to do can be found - <a href="http://scipy.org/Developer_Zone/DocMarathon2008">on the scipy.org website</a>. + The preferred way to update the documentation is by submitting a pull + request on Github (see the + <a href="http://docs.scipy.org/doc/numpy-dev/dev/">Developer Guide</a>. + The <a href="http://docs.scipy.org/numpy/">Numpy Documentation Wiki</a> + can also still be used to submit documentation fixes. + Please help us to further improve the Numpy documentation! </p> {% endblock %} diff --git a/doc/source/contents.rst b/doc/source/contents.rst index 023ebb8a0..1d46e8c7b 100644 --- a/doc/source/contents.rst +++ b/doc/source/contents.rst @@ -8,6 +8,7 @@ Numpy manual contents reference/index f2py/index dev/index + neps/index release about bugs diff --git a/doc/source/f2py/python-usage.rst b/doc/source/f2py/python-usage.rst index 83454c440..c7c7e3fd0 100644 --- a/doc/source/f2py/python-usage.rst +++ b/doc/source/f2py/python-usage.rst @@ -17,20 +17,14 @@ computational part of such functions is implemented in C or Fortran and wrapped with F2PY (or any other tool capable of providing CObject of a function). -.. example:: - - Consider a `Fortran 77 file`__ ``ftype.f``: +Consider a Fortran 77 file ``ftype.f``: .. include:: ftype.f :literal: - and build a wrapper using:: - - f2py -c ftype.f -m ftype +and build a wrapper using ``f2py -c ftype.f -m ftype``. - __ ftype.f - - In Python: +In Python: .. include:: ftype_session.dat :literal: @@ -54,18 +48,14 @@ type-casting only the real part of a complex number is used. order to *in situ* changes to be effective. It is recommended to use arrays with proper type but also other types work. -.. example:: - - Consider the following `Fortran 77 code`__: +Consider the following Fortran 77 code: .. include:: scalar.f :literal: - and wrap it using ``f2py -c -m scalar scalar.f``. +and wrap it using ``f2py -c -m scalar scalar.f``. - __ scalar.f - - In Python: +In Python: .. include:: scalar_session.dat :literal: @@ -88,18 +78,14 @@ Because Python strings are immutable, an ``intent(inout)`` argument expects an array version of a string in order to *in situ* changes to be effective. -.. example:: - - Consider the following `Fortran 77 code`__: +Consider the following Fortran 77 code: .. include:: string.f :literal: - and wrap it using ``f2py -c -m mystring string.f``. +and wrap it using ``f2py -c -m mystring string.f``. - __ string.f - - Python session: +Python session: .. include:: string_session.dat :literal: @@ -164,18 +150,14 @@ them to Fortran routines, use a function ``as_column_major_storage(<array>)`` that is provided by all F2PY generated extension modules. -.. example:: - - Consider `Fortran 77 code`__: +Consider Fortran 77 code: .. include:: array.f :literal: - and wrap it using ``f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1``. +and wrap it using ``f2py -c -m arr array.f -DF2PY_REPORT_ON_ARRAY_COPY=1``. - __ array.f - - In Python: +In Python: .. include:: array_session.dat :literal: @@ -187,19 +169,14 @@ Call-back arguments F2PY supports calling Python functions from Fortran or C codes. - -.. example:: - - Consider the following `Fortran 77 code`__ +Consider the following Fortran 77 code: .. include:: callback.f :literal: - and wrap it using ``f2py -c -m callback callback.f``. - - __ callback.f +and wrap it using ``f2py -c -m callback callback.f``. - In Python: +In Python: .. include:: callback_session.dat :literal: @@ -217,53 +194,41 @@ block, use ``use`` statement as illustrated below. The same signature of a callback argument can be referred in different routine signatures. -.. example:: - - We use the same `Fortran 77 code`__ as in previous example but now - we'll pretend that F2PY was not able to guess the signatures of - call-back arguments correctly. First, we create an initial signature - file ``callback2.pyf`` using F2PY:: +We use the same Fortran 77 code as in previous example but now +we'll pretend that F2PY was not able to guess the signatures of +call-back arguments correctly. First, we create an initial signature +file ``callback2.pyf`` using F2PY:: f2py -m callback2 -h callback2.pyf callback.f - Then modify it as follows +Then modify it as follows .. include:: callback2.pyf :literal: - Finally, build the extension module using:: - - f2py -c callback2.pyf callback.f - - An example Python session would be identical to the previous example - except that argument names would differ. +Finally, build the extension module using ``f2py -c callback2.pyf callback.f``. - __ callback.f +An example Python session would be identical to the previous example +except that argument names would differ. Sometimes a Fortran package may require that users provide routines that the package will use. F2PY can construct an interface to such routines so that Python functions could be called from Fortran. -.. example:: - - Consider the following `Fortran 77 subroutine`__ that takes an array - and applies a function ``func`` to its elements. +Consider the following `Fortran 77 subroutine`__ that takes an array +and applies a function ``func`` to its elements. .. include:: calculate.f :literal: - __ calculate.f - - It is expected that function ``func`` has been defined - externally. In order to use a Python function as ``func``, it must - have an attribute ``intent(callback)`` (it must be specified before - the ``external`` statement). +It is expected that function ``func`` has been defined +externally. In order to use a Python function as ``func``, it must +have an attribute ``intent(callback)`` (it must be specified before +the ``external`` statement). - Finally, build an extension module using:: +Finally, build an extension module using ``f2py -c -m foo calculate.f`` - f2py -c -m foo calculate.f - - In Python: +In Python: .. include:: calculate_session.dat :literal: @@ -278,18 +243,14 @@ Then it is not necessary to pass the function in the argument list to the Fortran function. This may be desired if the Fortran function calling the python callback function is itself called by another Fortran function. -.. example:: - - Consider the following `Fortran 77 subroutine`__. +Consider the following Fortran 77 subroutine: .. include:: extcallback.f :literal: - __ extcallback.f +and wrap it using ``f2py -c -m pfromf extcallback.f``. - and wrap it using ``f2py -c -m pfromf extcallback.f``. - - In Python: +In Python: .. include:: extcallback_session.dat :literal: @@ -343,7 +304,6 @@ following rules are applied: * If ``k > l``, then only ``x_1, ..., x_l`` are set. - Common blocks ============== @@ -359,61 +319,51 @@ directly link to data members in common blocks. Data members can be changed by direct assignment or by in-place changes to the corresponding array objects. -.. example:: - - Consider the following `Fortran 77 code`__ +Consider the following Fortran 77 code: .. include:: common.f :literal: - and wrap it using ``f2py -c -m common common.f``. +and wrap it using ``f2py -c -m common common.f``. - __ common.f - - In Python: +In Python: .. include:: common_session.dat :literal: + Fortran 90 module data ======================= The F2PY interface to Fortran 90 module data is similar to Fortran 77 common blocks. -.. example:: - - Consider the following `Fortran 90 code`__ +Consider the following Fortran 90 code: .. include:: moddata.f90 :literal: - and wrap it using ``f2py -c -m moddata moddata.f90``. - - __ moddata.f90 +and wrap it using ``f2py -c -m moddata moddata.f90``. - In Python: +In Python: .. include:: moddata_session.dat :literal: + Allocatable arrays ------------------- F2PY has basic support for Fortran 90 module allocatable arrays. -.. example:: - - Consider the following `Fortran 90 code`__ +Consider the following Fortran 90 code: .. include:: allocarr.f90 :literal: - and wrap it using ``f2py -c -m allocarr allocarr.f90``. - - __ allocarr.f90 +and wrap it using ``f2py -c -m allocarr allocarr.f90``. - In Python: +In Python: .. include:: allocarr_session.dat :literal: diff --git a/doc/source/neps/datetime-proposal.rst b/doc/source/neps/datetime-proposal.rst new file mode 100644 index 000000000..05f0182b7 --- /dev/null +++ b/doc/source/neps/datetime-proposal.rst @@ -0,0 +1 @@ +.. include:: ../../neps/datetime-proposal.rst diff --git a/doc/source/neps/datetime-proposal3.rst b/doc/source/neps/datetime-proposal3.rst new file mode 100644 index 000000000..fa9102a96 --- /dev/null +++ b/doc/source/neps/datetime-proposal3.rst @@ -0,0 +1 @@ +.. include:: ../../neps/datetime-proposal3.rst diff --git a/doc/source/neps/deferred-ufunc-evaluation.rst b/doc/source/neps/deferred-ufunc-evaluation.rst new file mode 100644 index 000000000..b4a7a457d --- /dev/null +++ b/doc/source/neps/deferred-ufunc-evaluation.rst @@ -0,0 +1 @@ +.. include:: ../../neps/deferred-ufunc-evaluation.rst diff --git a/doc/source/neps/generalized-ufuncs.rst b/doc/source/neps/generalized-ufuncs.rst new file mode 100644 index 000000000..8b28f0224 --- /dev/null +++ b/doc/source/neps/generalized-ufuncs.rst @@ -0,0 +1 @@ +.. include:: ../../neps/generalized-ufuncs.rst diff --git a/doc/source/neps/groupby_additions.rst b/doc/source/neps/groupby_additions.rst new file mode 100644 index 000000000..61abc951e --- /dev/null +++ b/doc/source/neps/groupby_additions.rst @@ -0,0 +1 @@ +.. include:: ../../neps/groupby_additions.rst diff --git a/doc/source/neps/index.rst b/doc/source/neps/index.rst new file mode 100644 index 000000000..94cf563a4 --- /dev/null +++ b/doc/source/neps/index.rst @@ -0,0 +1,37 @@ +=========================== +Numpy Enhancement Proposals +=========================== + +Numpy Enhancement Proposals (NEPs) describe proposed changes to Numpy. +NEPs are modeled on Python Enhancement Proposals (PEPs), and are typically +written up when large changes to Numpy are proposed. + +This page provides an overview of all NEPs, making only a distinction between +the ones that have been implemented and those that have not been implemented. + +Implemented NEPs +---------------- + +.. toctree:: + :maxdepth: 1 + + ufunc-overrides + generalized-ufuncs + new-iterator-ufunc + npy-format + +Other NEPs +---------- + +.. toctree:: + :maxdepth: 1 + + missing-data + math_config_clean + groupby_additions + warnfix + newbugtracker + deferred-ufunc-evaluation + structured_array_extensions + datetime-proposal + datetime-proposal3 diff --git a/doc/source/neps/math_config_clean.rst b/doc/source/neps/math_config_clean.rst new file mode 100644 index 000000000..25b340e51 --- /dev/null +++ b/doc/source/neps/math_config_clean.rst @@ -0,0 +1 @@ +.. include:: ../../neps/math_config_clean.rst diff --git a/doc/source/neps/missing-data.rst b/doc/source/neps/missing-data.rst new file mode 100644 index 000000000..f9899f1b0 --- /dev/null +++ b/doc/source/neps/missing-data.rst @@ -0,0 +1 @@ +.. include:: ../../neps/missing-data.rst diff --git a/doc/source/neps/new-iterator-ufunc.rst b/doc/source/neps/new-iterator-ufunc.rst new file mode 100644 index 000000000..7e06aa8ae --- /dev/null +++ b/doc/source/neps/new-iterator-ufunc.rst @@ -0,0 +1 @@ +.. include:: ../../neps/new-iterator-ufunc.rst diff --git a/doc/source/neps/newbugtracker.rst b/doc/source/neps/newbugtracker.rst new file mode 100644 index 000000000..70ea21f8c --- /dev/null +++ b/doc/source/neps/newbugtracker.rst @@ -0,0 +1 @@ +.. include:: ../../neps/newbugtracker.rst diff --git a/doc/source/neps/npy-format.rst b/doc/source/neps/npy-format.rst new file mode 100644 index 000000000..bd1f2bb5c --- /dev/null +++ b/doc/source/neps/npy-format.rst @@ -0,0 +1 @@ +.. include:: ../../neps/npy-format.rst diff --git a/doc/source/neps/structured_array_extensions.rst b/doc/source/neps/structured_array_extensions.rst new file mode 100644 index 000000000..341e6c955 --- /dev/null +++ b/doc/source/neps/structured_array_extensions.rst @@ -0,0 +1 @@ +.. include:: ../../neps/structured_array_extensions.rst diff --git a/doc/source/neps/ufunc-overrides.rst b/doc/source/neps/ufunc-overrides.rst new file mode 100644 index 000000000..2e293ec44 --- /dev/null +++ b/doc/source/neps/ufunc-overrides.rst @@ -0,0 +1 @@ +.. include:: ../../neps/ufunc-overrides.rst diff --git a/doc/source/neps/warnfix.rst b/doc/source/neps/warnfix.rst new file mode 100644 index 000000000..1b9b1b87b --- /dev/null +++ b/doc/source/neps/warnfix.rst @@ -0,0 +1 @@ +.. include:: ../../neps/warnfix.rst diff --git a/doc/source/reference/arrays.indexing.rst b/doc/source/reference/arrays.indexing.rst index e759b6ff8..d319b5d80 100644 --- a/doc/source/reference/arrays.indexing.rst +++ b/doc/source/reference/arrays.indexing.rst @@ -170,9 +170,9 @@ concepts to remember include: .. data:: newaxis - The :const:`newaxis` object can be used in all slicing operations - as discussed above. :const:`None` can also be used instead of - :const:`newaxis`. + The :const:`newaxis` object can be used in all slicing operations to + create an axis of length one. :const: :const:`newaxis` is an alias for + 'None', and 'None' can be used in place of this with the same result. Advanced indexing diff --git a/doc/source/reference/c-api.array.rst b/doc/source/reference/c-api.array.rst index 2ce43b2be..23355bc91 100644 --- a/doc/source/reference/c-api.array.rst +++ b/doc/source/reference/c-api.array.rst @@ -190,7 +190,7 @@ From scratch .. cfunction:: PyObject* PyArray_NewFromDescr(PyTypeObject* subtype, PyArray_Descr* descr, int nd, npy_intp* dims, npy_intp* strides, void* data, int flags, PyObject* obj) - This function steals a reference to *descr* if it is not NULL. + This function steals a reference to *descr*. This is the main array creation function. Most new arrays are created with this flexible function. diff --git a/doc/source/reference/routines.rst b/doc/source/reference/routines.rst index 37b16de59..c2f091d83 100644 --- a/doc/source/reference/routines.rst +++ b/doc/source/reference/routines.rst @@ -36,8 +36,6 @@ indentation. routines.ma routines.math routines.matlib - routines.numarray - routines.oldnumeric routines.other routines.padding routines.polynomials diff --git a/doc/source/reference/routines.testing.rst b/doc/source/reference/routines.testing.rst index 834d8bbe3..c43aeeed9 100644 --- a/doc/source/reference/routines.testing.rst +++ b/doc/source/reference/routines.testing.rst @@ -11,7 +11,7 @@ work right away. Asserts -======= +------- .. autosummary:: :toctree: generated/ diff --git a/numpy/__init__.py b/numpy/__init__.py index 1029652c8..772c75b63 100644 --- a/numpy/__init__.py +++ b/numpy/__init__.py @@ -121,6 +121,17 @@ class ModuleDeprecationWarning(DeprecationWarning): pass +class VisibleDeprecationWarning(UserWarning): + """Visible deprecation warning. + + By default, python will not show deprecation warnings, so this class + can be used when a very visible warning is helpful, for example because + the usage is most likely a user bug. + + """ + pass + + # oldnumeric and numarray were removed in 1.9. In case some packages import # but do not use them, we define them here for backward compatibility. oldnumeric = 'removed' @@ -145,7 +156,7 @@ else: except ImportError: msg = """Error importing numpy: you should not try to import numpy from its source directory; please exit the numpy source tree, and relaunch - your python intepreter from there.""" + your python interpreter from there.""" raise ImportError(msg) from .version import git_revision as __git_revision__ from .version import version as __version__ @@ -157,7 +168,9 @@ else: return loader(*packages, **options) from . import add_newdocs - __all__ = ['add_newdocs', 'ModuleDeprecationWarning'] + __all__ = ['add_newdocs', + 'ModuleDeprecationWarning', + 'VisibleDeprecationWarning'] pkgload.__doc__ = PackageLoader.__call__.__doc__ diff --git a/numpy/core/code_generators/genapi.py b/numpy/core/code_generators/genapi.py index ad054920a..c73209d04 100644 --- a/numpy/core/code_generators/genapi.py +++ b/numpy/core/code_generators/genapi.py @@ -74,6 +74,29 @@ def remove_whitespace(s): def _repl(str): return str.replace('Bool', 'npy_bool') + +class StealRef: + def __init__(self, arg): + self.arg = arg # counting from 1 + + def __str__(self): + try: + return ' '.join('NPY_STEALS_REF_TO_ARG(%d)' % x for x in self.arg) + except TypeError: + return 'NPY_STEALS_REF_TO_ARG(%d)' % self.arg + + +class NonNull: + def __init__(self, arg): + self.arg = arg # counting from 1 + + def __str__(self): + try: + return ' '.join('NPY_GCC_NONNULL(%d)' % x for x in self.arg) + except TypeError: + return 'NPY_GCC_NONNULL(%d)' % self.arg + + class Function(object): def __init__(self, name, return_type, args, doc=''): self.name = name @@ -350,9 +373,10 @@ NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2]; return astr class FunctionApi(object): - def __init__(self, name, index, return_type, args, api_name): + def __init__(self, name, index, annotations, return_type, args, api_name): self.name = name self.index = index + self.annotations = annotations self.return_type = return_type self.args = args self.api_name = api_name @@ -377,17 +401,21 @@ class FunctionApi(object): return " (void *) %s" % self.name def internal_define(self): + annstr = [] + for a in self.annotations: + annstr.append(str(a)) + annstr = ' '.join(annstr) astr = """\ -NPY_NO_EXPORT %s %s \\\n (%s);""" % (self.return_type, - self.name, - self._argtypes_string()) +NPY_NO_EXPORT %s %s %s \\\n (%s);""" % (annstr, self.return_type, + self.name, + self._argtypes_string()) return astr def order_dict(d): """Order dict by its values.""" o = list(d.items()) def _key(x): - return (x[1], x[0]) + return x[1] + (x[0],) return sorted(o, key=_key) def merge_api_dicts(dicts): @@ -419,7 +447,7 @@ Same index has been used twice in api definition: %s raise ValueError(msg) # No 'hole' in the indexes may be allowed, and it must starts at 0 - indexes = set(d.values()) + indexes = set(v[0] for v in d.values()) expected = set(range(len(indexes))) if not indexes == expected: diff = expected.symmetric_difference(indexes) @@ -434,7 +462,7 @@ def get_api_functions(tagname, api_dict): functions.extend(find_functions(f, tagname)) dfunctions = [] for func in functions: - o = api_dict[func.name] + o = api_dict[func.name][0] dfunctions.append( (o, func) ) dfunctions.sort() return [a[1] for a in dfunctions] @@ -444,11 +472,7 @@ def fullapi_hash(api_dicts): of the list of items in the API (as a string).""" a = [] for d in api_dicts: - def sorted_by_values(d): - """Sort a dictionary by its values. Assume the dictionary items is of - the form func_name -> order""" - return sorted(d.items(), key=lambda x_y: (x_y[1], x_y[0])) - for name, index in sorted_by_values(d): + for name, index in order_dict(d): a.extend(name) a.extend(str(index)) diff --git a/numpy/core/code_generators/generate_numpy_api.py b/numpy/core/code_generators/generate_numpy_api.py index ce270a6a0..a590cfb48 100644 --- a/numpy/core/code_generators/generate_numpy_api.py +++ b/numpy/core/code_generators/generate_numpy_api.py @@ -183,13 +183,11 @@ def do_generate_api(targets, sources): doc_file = targets[2] global_vars = sources[0] - global_vars_types = sources[1] - scalar_bool_values = sources[2] - types_api = sources[3] - multiarray_funcs = sources[4] + scalar_bool_values = sources[1] + types_api = sources[2] + multiarray_funcs = sources[3] - # Remove global_vars_type: not a api dict - multiarray_api = sources[:1] + sources[2:] + multiarray_api = sources[:] module_list = [] extension_list = [] @@ -208,18 +206,22 @@ def do_generate_api(targets, sources): multiarray_api_dict = {} for f in numpyapi_list: name = f.name - index = multiarray_funcs[name] - multiarray_api_dict[f.name] = FunctionApi(f.name, index, f.return_type, + index = multiarray_funcs[name][0] + annotations = multiarray_funcs[name][1:] + multiarray_api_dict[f.name] = FunctionApi(f.name, index, annotations, + f.return_type, f.args, api_name) - for name, index in global_vars.items(): - type = global_vars_types[name] + for name, val in global_vars.items(): + index, type = val multiarray_api_dict[name] = GlobalVarApi(name, index, type, api_name) - for name, index in scalar_bool_values.items(): + for name, val in scalar_bool_values.items(): + index = val[0] multiarray_api_dict[name] = BoolValuesApi(name, index, api_name) - for name, index in types_api.items(): + for name, val in types_api.items(): + index = val[0] multiarray_api_dict[name] = TypeApi(name, index, 'PyTypeObject', api_name) if len(multiarray_api_dict) != len(multiarray_api_index): diff --git a/numpy/core/code_generators/generate_ufunc_api.py b/numpy/core/code_generators/generate_ufunc_api.py index 6305385af..b594fccf7 100644 --- a/numpy/core/code_generators/generate_ufunc_api.py +++ b/numpy/core/code_generators/generate_ufunc_api.py @@ -172,11 +172,13 @@ def do_generate_api(targets, sources): api_name = 'PyUFunc_API' for f in ufunc_api_list: name = f.name - index = ufunc_api_index[name] - ufunc_api_dict[name] = FunctionApi(f.name, index, f.return_type, - f.args, api_name) + index = ufunc_api_index[name][0] + annotations = ufunc_api_index[name][1:] + ufunc_api_dict[name] = FunctionApi(f.name, index, annotations, + f.return_type, f.args, api_name) - for name, index in numpy_api.ufunc_types_api.items(): + for name, val in numpy_api.ufunc_types_api.items(): + index = val[0] ufunc_api_dict[name] = TypeApi(name, index, 'PyTypeObject', api_name) # set up object API diff --git a/numpy/core/code_generators/numpy_api.py b/numpy/core/code_generators/numpy_api.py index 791bc6ffc..cd6d2f176 100644 --- a/numpy/core/code_generators/numpy_api.py +++ b/numpy/core/code_generators/numpy_api.py @@ -14,64 +14,64 @@ exception, so it should hopefully not get unnoticed). """ from __future__ import division, absolute_import, print_function -multiarray_global_vars = { - 'NPY_NUMUSERTYPES': 7, - 'NPY_DEFAULT_ASSIGN_CASTING': 292, -} +from code_generators.genapi import StealRef, NonNull -multiarray_global_vars_types = { - 'NPY_NUMUSERTYPES': 'int', - 'NPY_DEFAULT_ASSIGN_CASTING': 'NPY_CASTING', +# index, type +multiarray_global_vars = { + 'NPY_NUMUSERTYPES': (7, 'int'), + 'NPY_DEFAULT_ASSIGN_CASTING': (292, 'NPY_CASTING'), } multiarray_scalar_bool_values = { - '_PyArrayScalar_BoolValues': 9 + '_PyArrayScalar_BoolValues': (9,) } +# index, annotations +# please mark functions that have been checked to not need any annotations multiarray_types_api = { - 'PyBigArray_Type': 1, - 'PyArray_Type': 2, - 'PyArrayDescr_Type': 3, - 'PyArrayFlags_Type': 4, - 'PyArrayIter_Type': 5, - 'PyArrayMultiIter_Type': 6, - 'PyBoolArrType_Type': 8, - 'PyGenericArrType_Type': 10, - 'PyNumberArrType_Type': 11, - 'PyIntegerArrType_Type': 12, - 'PySignedIntegerArrType_Type': 13, - 'PyUnsignedIntegerArrType_Type': 14, - 'PyInexactArrType_Type': 15, - 'PyFloatingArrType_Type': 16, - 'PyComplexFloatingArrType_Type': 17, - 'PyFlexibleArrType_Type': 18, - 'PyCharacterArrType_Type': 19, - 'PyByteArrType_Type': 20, - 'PyShortArrType_Type': 21, - 'PyIntArrType_Type': 22, - 'PyLongArrType_Type': 23, - 'PyLongLongArrType_Type': 24, - 'PyUByteArrType_Type': 25, - 'PyUShortArrType_Type': 26, - 'PyUIntArrType_Type': 27, - 'PyULongArrType_Type': 28, - 'PyULongLongArrType_Type': 29, - 'PyFloatArrType_Type': 30, - 'PyDoubleArrType_Type': 31, - 'PyLongDoubleArrType_Type': 32, - 'PyCFloatArrType_Type': 33, - 'PyCDoubleArrType_Type': 34, - 'PyCLongDoubleArrType_Type': 35, - 'PyObjectArrType_Type': 36, - 'PyStringArrType_Type': 37, - 'PyUnicodeArrType_Type': 38, - 'PyVoidArrType_Type': 39, + 'PyBigArray_Type': (1,), + 'PyArray_Type': (2,), + 'PyArrayDescr_Type': (3,), + 'PyArrayFlags_Type': (4,), + 'PyArrayIter_Type': (5,), + 'PyArrayMultiIter_Type': (6,), + 'PyBoolArrType_Type': (8,), + 'PyGenericArrType_Type': (10,), + 'PyNumberArrType_Type': (11,), + 'PyIntegerArrType_Type': (12,), + 'PySignedIntegerArrType_Type': (13,), + 'PyUnsignedIntegerArrType_Type': (14,), + 'PyInexactArrType_Type': (15,), + 'PyFloatingArrType_Type': (16,), + 'PyComplexFloatingArrType_Type': (17,), + 'PyFlexibleArrType_Type': (18,), + 'PyCharacterArrType_Type': (19,), + 'PyByteArrType_Type': (20,), + 'PyShortArrType_Type': (21,), + 'PyIntArrType_Type': (22,), + 'PyLongArrType_Type': (23,), + 'PyLongLongArrType_Type': (24,), + 'PyUByteArrType_Type': (25,), + 'PyUShortArrType_Type': (26,), + 'PyUIntArrType_Type': (27,), + 'PyULongArrType_Type': (28,), + 'PyULongLongArrType_Type': (29,), + 'PyFloatArrType_Type': (30,), + 'PyDoubleArrType_Type': (31,), + 'PyLongDoubleArrType_Type': (32,), + 'PyCFloatArrType_Type': (33,), + 'PyCDoubleArrType_Type': (34,), + 'PyCLongDoubleArrType_Type': (35,), + 'PyObjectArrType_Type': (36,), + 'PyStringArrType_Type': (37,), + 'PyUnicodeArrType_Type': (38,), + 'PyVoidArrType_Type': (39,), # End 1.5 API - 'PyTimeIntegerArrType_Type': 214, - 'PyDatetimeArrType_Type': 215, - 'PyTimedeltaArrType_Type': 216, - 'PyHalfArrType_Type': 217, - 'NpyIter_Type': 218, + 'PyTimeIntegerArrType_Type': (214,), + 'PyDatetimeArrType_Type': (215,), + 'PyTimedeltaArrType_Type': (216,), + 'PyHalfArrType_Type': (217,), + 'NpyIter_Type': (218,), # End 1.6 API } @@ -80,318 +80,318 @@ multiarray_types_api = { #define _PyArrayScalar_BoolValues ((PyBoolScalarObject *)PyArray_API[8]) multiarray_funcs_api = { - 'PyArray_GetNDArrayCVersion': 0, - 'PyArray_SetNumericOps': 40, - 'PyArray_GetNumericOps': 41, - 'PyArray_INCREF': 42, - 'PyArray_XDECREF': 43, - 'PyArray_SetStringFunction': 44, - 'PyArray_DescrFromType': 45, - 'PyArray_TypeObjectFromType': 46, - 'PyArray_Zero': 47, - 'PyArray_One': 48, - 'PyArray_CastToType': 49, - 'PyArray_CastTo': 50, - 'PyArray_CastAnyTo': 51, - 'PyArray_CanCastSafely': 52, - 'PyArray_CanCastTo': 53, - 'PyArray_ObjectType': 54, - 'PyArray_DescrFromObject': 55, - 'PyArray_ConvertToCommonType': 56, - 'PyArray_DescrFromScalar': 57, - 'PyArray_DescrFromTypeObject': 58, - 'PyArray_Size': 59, - 'PyArray_Scalar': 60, - 'PyArray_FromScalar': 61, - 'PyArray_ScalarAsCtype': 62, - 'PyArray_CastScalarToCtype': 63, - 'PyArray_CastScalarDirect': 64, - 'PyArray_ScalarFromObject': 65, - 'PyArray_GetCastFunc': 66, - 'PyArray_FromDims': 67, - 'PyArray_FromDimsAndDataAndDescr': 68, - 'PyArray_FromAny': 69, - 'PyArray_EnsureArray': 70, - 'PyArray_EnsureAnyArray': 71, - 'PyArray_FromFile': 72, - 'PyArray_FromString': 73, - 'PyArray_FromBuffer': 74, - 'PyArray_FromIter': 75, - 'PyArray_Return': 76, - 'PyArray_GetField': 77, - 'PyArray_SetField': 78, - 'PyArray_Byteswap': 79, - 'PyArray_Resize': 80, - 'PyArray_MoveInto': 81, - 'PyArray_CopyInto': 82, - 'PyArray_CopyAnyInto': 83, - 'PyArray_CopyObject': 84, - 'PyArray_NewCopy': 85, - 'PyArray_ToList': 86, - 'PyArray_ToString': 87, - 'PyArray_ToFile': 88, - 'PyArray_Dump': 89, - 'PyArray_Dumps': 90, - 'PyArray_ValidType': 91, - 'PyArray_UpdateFlags': 92, - 'PyArray_New': 93, - 'PyArray_NewFromDescr': 94, - 'PyArray_DescrNew': 95, - 'PyArray_DescrNewFromType': 96, - 'PyArray_GetPriority': 97, - 'PyArray_IterNew': 98, - 'PyArray_MultiIterNew': 99, - 'PyArray_PyIntAsInt': 100, - 'PyArray_PyIntAsIntp': 101, - 'PyArray_Broadcast': 102, - 'PyArray_FillObjectArray': 103, - 'PyArray_FillWithScalar': 104, - 'PyArray_CheckStrides': 105, - 'PyArray_DescrNewByteorder': 106, - 'PyArray_IterAllButAxis': 107, - 'PyArray_CheckFromAny': 108, - 'PyArray_FromArray': 109, - 'PyArray_FromInterface': 110, - 'PyArray_FromStructInterface': 111, - 'PyArray_FromArrayAttr': 112, - 'PyArray_ScalarKind': 113, - 'PyArray_CanCoerceScalar': 114, - 'PyArray_NewFlagsObject': 115, - 'PyArray_CanCastScalar': 116, - 'PyArray_CompareUCS4': 117, - 'PyArray_RemoveSmallest': 118, - 'PyArray_ElementStrides': 119, - 'PyArray_Item_INCREF': 120, - 'PyArray_Item_XDECREF': 121, - 'PyArray_FieldNames': 122, - 'PyArray_Transpose': 123, - 'PyArray_TakeFrom': 124, - 'PyArray_PutTo': 125, - 'PyArray_PutMask': 126, - 'PyArray_Repeat': 127, - 'PyArray_Choose': 128, - 'PyArray_Sort': 129, - 'PyArray_ArgSort': 130, - 'PyArray_SearchSorted': 131, - 'PyArray_ArgMax': 132, - 'PyArray_ArgMin': 133, - 'PyArray_Reshape': 134, - 'PyArray_Newshape': 135, - 'PyArray_Squeeze': 136, - 'PyArray_View': 137, - 'PyArray_SwapAxes': 138, - 'PyArray_Max': 139, - 'PyArray_Min': 140, - 'PyArray_Ptp': 141, - 'PyArray_Mean': 142, - 'PyArray_Trace': 143, - 'PyArray_Diagonal': 144, - 'PyArray_Clip': 145, - 'PyArray_Conjugate': 146, - 'PyArray_Nonzero': 147, - 'PyArray_Std': 148, - 'PyArray_Sum': 149, - 'PyArray_CumSum': 150, - 'PyArray_Prod': 151, - 'PyArray_CumProd': 152, - 'PyArray_All': 153, - 'PyArray_Any': 154, - 'PyArray_Compress': 155, - 'PyArray_Flatten': 156, - 'PyArray_Ravel': 157, - 'PyArray_MultiplyList': 158, - 'PyArray_MultiplyIntList': 159, - 'PyArray_GetPtr': 160, - 'PyArray_CompareLists': 161, - 'PyArray_AsCArray': 162, - 'PyArray_As1D': 163, - 'PyArray_As2D': 164, - 'PyArray_Free': 165, - 'PyArray_Converter': 166, - 'PyArray_IntpFromSequence': 167, - 'PyArray_Concatenate': 168, - 'PyArray_InnerProduct': 169, - 'PyArray_MatrixProduct': 170, - 'PyArray_CopyAndTranspose': 171, - 'PyArray_Correlate': 172, - 'PyArray_TypestrConvert': 173, - 'PyArray_DescrConverter': 174, - 'PyArray_DescrConverter2': 175, - 'PyArray_IntpConverter': 176, - 'PyArray_BufferConverter': 177, - 'PyArray_AxisConverter': 178, - 'PyArray_BoolConverter': 179, - 'PyArray_ByteorderConverter': 180, - 'PyArray_OrderConverter': 181, - 'PyArray_EquivTypes': 182, - 'PyArray_Zeros': 183, - 'PyArray_Empty': 184, - 'PyArray_Where': 185, - 'PyArray_Arange': 186, - 'PyArray_ArangeObj': 187, - 'PyArray_SortkindConverter': 188, - 'PyArray_LexSort': 189, - 'PyArray_Round': 190, - 'PyArray_EquivTypenums': 191, - 'PyArray_RegisterDataType': 192, - 'PyArray_RegisterCastFunc': 193, - 'PyArray_RegisterCanCast': 194, - 'PyArray_InitArrFuncs': 195, - 'PyArray_IntTupleFromIntp': 196, - 'PyArray_TypeNumFromName': 197, - 'PyArray_ClipmodeConverter': 198, - 'PyArray_OutputConverter': 199, - 'PyArray_BroadcastToShape': 200, - '_PyArray_SigintHandler': 201, - '_PyArray_GetSigintBuf': 202, - 'PyArray_DescrAlignConverter': 203, - 'PyArray_DescrAlignConverter2': 204, - 'PyArray_SearchsideConverter': 205, - 'PyArray_CheckAxis': 206, - 'PyArray_OverflowMultiplyList': 207, - 'PyArray_CompareString': 208, - 'PyArray_MultiIterFromObjects': 209, - 'PyArray_GetEndianness': 210, - 'PyArray_GetNDArrayCFeatureVersion': 211, - 'PyArray_Correlate2': 212, - 'PyArray_NeighborhoodIterNew': 213, + 'PyArray_GetNDArrayCVersion': (0,), + 'PyArray_SetNumericOps': (40,), + 'PyArray_GetNumericOps': (41,), + 'PyArray_INCREF': (42,), + 'PyArray_XDECREF': (43,), + 'PyArray_SetStringFunction': (44,), + 'PyArray_DescrFromType': (45,), + 'PyArray_TypeObjectFromType': (46,), + 'PyArray_Zero': (47,), + 'PyArray_One': (48,), + 'PyArray_CastToType': (49, StealRef(2), NonNull(2)), + 'PyArray_CastTo': (50,), + 'PyArray_CastAnyTo': (51,), + 'PyArray_CanCastSafely': (52,), + 'PyArray_CanCastTo': (53,), + 'PyArray_ObjectType': (54,), + 'PyArray_DescrFromObject': (55,), + 'PyArray_ConvertToCommonType': (56,), + 'PyArray_DescrFromScalar': (57,), + 'PyArray_DescrFromTypeObject': (58,), + 'PyArray_Size': (59,), + 'PyArray_Scalar': (60,), + 'PyArray_FromScalar': (61, StealRef(2)), + 'PyArray_ScalarAsCtype': (62,), + 'PyArray_CastScalarToCtype': (63,), + 'PyArray_CastScalarDirect': (64,), + 'PyArray_ScalarFromObject': (65,), + 'PyArray_GetCastFunc': (66,), + 'PyArray_FromDims': (67,), + 'PyArray_FromDimsAndDataAndDescr': (68, StealRef(3)), + 'PyArray_FromAny': (69, StealRef(2)), + 'PyArray_EnsureArray': (70, StealRef(1)), + 'PyArray_EnsureAnyArray': (71, StealRef(1)), + 'PyArray_FromFile': (72,), + 'PyArray_FromString': (73,), + 'PyArray_FromBuffer': (74,), + 'PyArray_FromIter': (75, StealRef(2)), + 'PyArray_Return': (76, StealRef(1)), + 'PyArray_GetField': (77, StealRef(2), NonNull(2)), + 'PyArray_SetField': (78, StealRef(2), NonNull(2)), + 'PyArray_Byteswap': (79,), + 'PyArray_Resize': (80,), + 'PyArray_MoveInto': (81,), + 'PyArray_CopyInto': (82,), + 'PyArray_CopyAnyInto': (83,), + 'PyArray_CopyObject': (84,), + 'PyArray_NewCopy': (85, NonNull(1)), + 'PyArray_ToList': (86,), + 'PyArray_ToString': (87,), + 'PyArray_ToFile': (88,), + 'PyArray_Dump': (89,), + 'PyArray_Dumps': (90,), + 'PyArray_ValidType': (91,), + 'PyArray_UpdateFlags': (92,), + 'PyArray_New': (93, NonNull(1)), + 'PyArray_NewFromDescr': (94, StealRef(2), NonNull([1, 2])), + 'PyArray_DescrNew': (95,), + 'PyArray_DescrNewFromType': (96,), + 'PyArray_GetPriority': (97,), + 'PyArray_IterNew': (98,), + 'PyArray_MultiIterNew': (99,), + 'PyArray_PyIntAsInt': (100,), + 'PyArray_PyIntAsIntp': (101,), + 'PyArray_Broadcast': (102,), + 'PyArray_FillObjectArray': (103,), + 'PyArray_FillWithScalar': (104,), + 'PyArray_CheckStrides': (105,), + 'PyArray_DescrNewByteorder': (106,), + 'PyArray_IterAllButAxis': (107,), + 'PyArray_CheckFromAny': (108, StealRef(2)), + 'PyArray_FromArray': (109, StealRef(2)), + 'PyArray_FromInterface': (110,), + 'PyArray_FromStructInterface': (111,), + 'PyArray_FromArrayAttr': (112,), + 'PyArray_ScalarKind': (113,), + 'PyArray_CanCoerceScalar': (114,), + 'PyArray_NewFlagsObject': (115,), + 'PyArray_CanCastScalar': (116,), + 'PyArray_CompareUCS4': (117,), + 'PyArray_RemoveSmallest': (118,), + 'PyArray_ElementStrides': (119,), + 'PyArray_Item_INCREF': (120,), + 'PyArray_Item_XDECREF': (121,), + 'PyArray_FieldNames': (122,), + 'PyArray_Transpose': (123,), + 'PyArray_TakeFrom': (124,), + 'PyArray_PutTo': (125,), + 'PyArray_PutMask': (126,), + 'PyArray_Repeat': (127,), + 'PyArray_Choose': (128,), + 'PyArray_Sort': (129,), + 'PyArray_ArgSort': (130,), + 'PyArray_SearchSorted': (131,), + 'PyArray_ArgMax': (132,), + 'PyArray_ArgMin': (133,), + 'PyArray_Reshape': (134,), + 'PyArray_Newshape': (135,), + 'PyArray_Squeeze': (136,), + 'PyArray_View': (137, StealRef(2)), + 'PyArray_SwapAxes': (138,), + 'PyArray_Max': (139,), + 'PyArray_Min': (140,), + 'PyArray_Ptp': (141,), + 'PyArray_Mean': (142,), + 'PyArray_Trace': (143,), + 'PyArray_Diagonal': (144,), + 'PyArray_Clip': (145,), + 'PyArray_Conjugate': (146,), + 'PyArray_Nonzero': (147,), + 'PyArray_Std': (148,), + 'PyArray_Sum': (149,), + 'PyArray_CumSum': (150,), + 'PyArray_Prod': (151,), + 'PyArray_CumProd': (152,), + 'PyArray_All': (153,), + 'PyArray_Any': (154,), + 'PyArray_Compress': (155,), + 'PyArray_Flatten': (156,), + 'PyArray_Ravel': (157,), + 'PyArray_MultiplyList': (158,), + 'PyArray_MultiplyIntList': (159,), + 'PyArray_GetPtr': (160,), + 'PyArray_CompareLists': (161,), + 'PyArray_AsCArray': (162, StealRef(5)), + 'PyArray_As1D': (163,), + 'PyArray_As2D': (164,), + 'PyArray_Free': (165,), + 'PyArray_Converter': (166,), + 'PyArray_IntpFromSequence': (167,), + 'PyArray_Concatenate': (168,), + 'PyArray_InnerProduct': (169,), + 'PyArray_MatrixProduct': (170,), + 'PyArray_CopyAndTranspose': (171,), + 'PyArray_Correlate': (172,), + 'PyArray_TypestrConvert': (173,), + 'PyArray_DescrConverter': (174,), + 'PyArray_DescrConverter2': (175,), + 'PyArray_IntpConverter': (176,), + 'PyArray_BufferConverter': (177,), + 'PyArray_AxisConverter': (178,), + 'PyArray_BoolConverter': (179,), + 'PyArray_ByteorderConverter': (180,), + 'PyArray_OrderConverter': (181,), + 'PyArray_EquivTypes': (182,), + 'PyArray_Zeros': (183, StealRef(3)), + 'PyArray_Empty': (184, StealRef(3)), + 'PyArray_Where': (185,), + 'PyArray_Arange': (186,), + 'PyArray_ArangeObj': (187,), + 'PyArray_SortkindConverter': (188,), + 'PyArray_LexSort': (189,), + 'PyArray_Round': (190,), + 'PyArray_EquivTypenums': (191,), + 'PyArray_RegisterDataType': (192,), + 'PyArray_RegisterCastFunc': (193,), + 'PyArray_RegisterCanCast': (194,), + 'PyArray_InitArrFuncs': (195,), + 'PyArray_IntTupleFromIntp': (196,), + 'PyArray_TypeNumFromName': (197,), + 'PyArray_ClipmodeConverter': (198,), + 'PyArray_OutputConverter': (199,), + 'PyArray_BroadcastToShape': (200,), + '_PyArray_SigintHandler': (201,), + '_PyArray_GetSigintBuf': (202,), + 'PyArray_DescrAlignConverter': (203,), + 'PyArray_DescrAlignConverter2': (204,), + 'PyArray_SearchsideConverter': (205,), + 'PyArray_CheckAxis': (206,), + 'PyArray_OverflowMultiplyList': (207,), + 'PyArray_CompareString': (208,), + 'PyArray_MultiIterFromObjects': (209,), + 'PyArray_GetEndianness': (210,), + 'PyArray_GetNDArrayCFeatureVersion': (211,), + 'PyArray_Correlate2': (212,), + 'PyArray_NeighborhoodIterNew': (213,), # End 1.5 API - 'PyArray_SetDatetimeParseFunction': 219, - 'PyArray_DatetimeToDatetimeStruct': 220, - 'PyArray_TimedeltaToTimedeltaStruct': 221, - 'PyArray_DatetimeStructToDatetime': 222, - 'PyArray_TimedeltaStructToTimedelta': 223, + 'PyArray_SetDatetimeParseFunction': (219,), + 'PyArray_DatetimeToDatetimeStruct': (220,), + 'PyArray_TimedeltaToTimedeltaStruct': (221,), + 'PyArray_DatetimeStructToDatetime': (222,), + 'PyArray_TimedeltaStructToTimedelta': (223,), # NDIter API - 'NpyIter_New': 224, - 'NpyIter_MultiNew': 225, - 'NpyIter_AdvancedNew': 226, - 'NpyIter_Copy': 227, - 'NpyIter_Deallocate': 228, - 'NpyIter_HasDelayedBufAlloc': 229, - 'NpyIter_HasExternalLoop': 230, - 'NpyIter_EnableExternalLoop': 231, - 'NpyIter_GetInnerStrideArray': 232, - 'NpyIter_GetInnerLoopSizePtr': 233, - 'NpyIter_Reset': 234, - 'NpyIter_ResetBasePointers': 235, - 'NpyIter_ResetToIterIndexRange': 236, - 'NpyIter_GetNDim': 237, - 'NpyIter_GetNOp': 238, - 'NpyIter_GetIterNext': 239, - 'NpyIter_GetIterSize': 240, - 'NpyIter_GetIterIndexRange': 241, - 'NpyIter_GetIterIndex': 242, - 'NpyIter_GotoIterIndex': 243, - 'NpyIter_HasMultiIndex': 244, - 'NpyIter_GetShape': 245, - 'NpyIter_GetGetMultiIndex': 246, - 'NpyIter_GotoMultiIndex': 247, - 'NpyIter_RemoveMultiIndex': 248, - 'NpyIter_HasIndex': 249, - 'NpyIter_IsBuffered': 250, - 'NpyIter_IsGrowInner': 251, - 'NpyIter_GetBufferSize': 252, - 'NpyIter_GetIndexPtr': 253, - 'NpyIter_GotoIndex': 254, - 'NpyIter_GetDataPtrArray': 255, - 'NpyIter_GetDescrArray': 256, - 'NpyIter_GetOperandArray': 257, - 'NpyIter_GetIterView': 258, - 'NpyIter_GetReadFlags': 259, - 'NpyIter_GetWriteFlags': 260, - 'NpyIter_DebugPrint': 261, - 'NpyIter_IterationNeedsAPI': 262, - 'NpyIter_GetInnerFixedStrideArray': 263, - 'NpyIter_RemoveAxis': 264, - 'NpyIter_GetAxisStrideArray': 265, - 'NpyIter_RequiresBuffering': 266, - 'NpyIter_GetInitialDataPtrArray': 267, - 'NpyIter_CreateCompatibleStrides': 268, + 'NpyIter_New': (224,), + 'NpyIter_MultiNew': (225,), + 'NpyIter_AdvancedNew': (226,), + 'NpyIter_Copy': (227,), + 'NpyIter_Deallocate': (228,), + 'NpyIter_HasDelayedBufAlloc': (229,), + 'NpyIter_HasExternalLoop': (230,), + 'NpyIter_EnableExternalLoop': (231,), + 'NpyIter_GetInnerStrideArray': (232,), + 'NpyIter_GetInnerLoopSizePtr': (233,), + 'NpyIter_Reset': (234,), + 'NpyIter_ResetBasePointers': (235,), + 'NpyIter_ResetToIterIndexRange': (236,), + 'NpyIter_GetNDim': (237,), + 'NpyIter_GetNOp': (238,), + 'NpyIter_GetIterNext': (239,), + 'NpyIter_GetIterSize': (240,), + 'NpyIter_GetIterIndexRange': (241,), + 'NpyIter_GetIterIndex': (242,), + 'NpyIter_GotoIterIndex': (243,), + 'NpyIter_HasMultiIndex': (244,), + 'NpyIter_GetShape': (245,), + 'NpyIter_GetGetMultiIndex': (246,), + 'NpyIter_GotoMultiIndex': (247,), + 'NpyIter_RemoveMultiIndex': (248,), + 'NpyIter_HasIndex': (249,), + 'NpyIter_IsBuffered': (250,), + 'NpyIter_IsGrowInner': (251,), + 'NpyIter_GetBufferSize': (252,), + 'NpyIter_GetIndexPtr': (253,), + 'NpyIter_GotoIndex': (254,), + 'NpyIter_GetDataPtrArray': (255,), + 'NpyIter_GetDescrArray': (256,), + 'NpyIter_GetOperandArray': (257,), + 'NpyIter_GetIterView': (258,), + 'NpyIter_GetReadFlags': (259,), + 'NpyIter_GetWriteFlags': (260,), + 'NpyIter_DebugPrint': (261,), + 'NpyIter_IterationNeedsAPI': (262,), + 'NpyIter_GetInnerFixedStrideArray': (263,), + 'NpyIter_RemoveAxis': (264,), + 'NpyIter_GetAxisStrideArray': (265,), + 'NpyIter_RequiresBuffering': (266,), + 'NpyIter_GetInitialDataPtrArray': (267,), + 'NpyIter_CreateCompatibleStrides': (268,), # - 'PyArray_CastingConverter': 269, - 'PyArray_CountNonzero': 270, - 'PyArray_PromoteTypes': 271, - 'PyArray_MinScalarType': 272, - 'PyArray_ResultType': 273, - 'PyArray_CanCastArrayTo': 274, - 'PyArray_CanCastTypeTo': 275, - 'PyArray_EinsteinSum': 276, - 'PyArray_NewLikeArray': 277, - 'PyArray_GetArrayParamsFromObject': 278, - 'PyArray_ConvertClipmodeSequence': 279, - 'PyArray_MatrixProduct2': 280, + 'PyArray_CastingConverter': (269,), + 'PyArray_CountNonzero': (270,), + 'PyArray_PromoteTypes': (271,), + 'PyArray_MinScalarType': (272,), + 'PyArray_ResultType': (273,), + 'PyArray_CanCastArrayTo': (274,), + 'PyArray_CanCastTypeTo': (275,), + 'PyArray_EinsteinSum': (276,), + 'PyArray_NewLikeArray': (277, StealRef(3), NonNull(1)), + 'PyArray_GetArrayParamsFromObject': (278,), + 'PyArray_ConvertClipmodeSequence': (279,), + 'PyArray_MatrixProduct2': (280,), # End 1.6 API - 'NpyIter_IsFirstVisit': 281, - 'PyArray_SetBaseObject': 282, - 'PyArray_CreateSortedStridePerm': 283, - 'PyArray_RemoveAxesInPlace': 284, - 'PyArray_DebugPrint': 285, - 'PyArray_FailUnlessWriteable': 286, - 'PyArray_SetUpdateIfCopyBase': 287, - 'PyDataMem_NEW': 288, - 'PyDataMem_FREE': 289, - 'PyDataMem_RENEW': 290, - 'PyDataMem_SetEventHook': 291, - 'PyArray_MapIterSwapAxes': 293, - 'PyArray_MapIterArray': 294, - 'PyArray_MapIterNext': 295, + 'NpyIter_IsFirstVisit': (281,), + 'PyArray_SetBaseObject': (282, StealRef(2)), + 'PyArray_CreateSortedStridePerm': (283,), + 'PyArray_RemoveAxesInPlace': (284,), + 'PyArray_DebugPrint': (285,), + 'PyArray_FailUnlessWriteable': (286,), + 'PyArray_SetUpdateIfCopyBase': (287, StealRef(2)), + 'PyDataMem_NEW': (288,), + 'PyDataMem_FREE': (289,), + 'PyDataMem_RENEW': (290,), + 'PyDataMem_SetEventHook': (291,), + 'PyArray_MapIterSwapAxes': (293,), + 'PyArray_MapIterArray': (294,), + 'PyArray_MapIterNext': (295,), # End 1.7 API - 'PyArray_Partition': 296, - 'PyArray_ArgPartition': 297, - 'PyArray_SelectkindConverter': 298, - 'PyDataMem_NEW_ZEROED': 299, + 'PyArray_Partition': (296,), + 'PyArray_ArgPartition': (297,), + 'PyArray_SelectkindConverter': (298,), + 'PyDataMem_NEW_ZEROED': (299,), # End 1.8 API } ufunc_types_api = { - 'PyUFunc_Type': 0 + 'PyUFunc_Type': (0,) } ufunc_funcs_api = { - 'PyUFunc_FromFuncAndData': 1, - 'PyUFunc_RegisterLoopForType': 2, - 'PyUFunc_GenericFunction': 3, - 'PyUFunc_f_f_As_d_d': 4, - 'PyUFunc_d_d': 5, - 'PyUFunc_f_f': 6, - 'PyUFunc_g_g': 7, - 'PyUFunc_F_F_As_D_D': 8, - 'PyUFunc_F_F': 9, - 'PyUFunc_D_D': 10, - 'PyUFunc_G_G': 11, - 'PyUFunc_O_O': 12, - 'PyUFunc_ff_f_As_dd_d': 13, - 'PyUFunc_ff_f': 14, - 'PyUFunc_dd_d': 15, - 'PyUFunc_gg_g': 16, - 'PyUFunc_FF_F_As_DD_D': 17, - 'PyUFunc_DD_D': 18, - 'PyUFunc_FF_F': 19, - 'PyUFunc_GG_G': 20, - 'PyUFunc_OO_O': 21, - 'PyUFunc_O_O_method': 22, - 'PyUFunc_OO_O_method': 23, - 'PyUFunc_On_Om': 24, - 'PyUFunc_GetPyValues': 25, - 'PyUFunc_checkfperr': 26, - 'PyUFunc_clearfperr': 27, - 'PyUFunc_getfperr': 28, - 'PyUFunc_handlefperr': 29, - 'PyUFunc_ReplaceLoopBySignature': 30, - 'PyUFunc_FromFuncAndDataAndSignature': 31, - 'PyUFunc_SetUsesArraysAsData': 32, + 'PyUFunc_FromFuncAndData': (1,), + 'PyUFunc_RegisterLoopForType': (2,), + 'PyUFunc_GenericFunction': (3,), + 'PyUFunc_f_f_As_d_d': (4,), + 'PyUFunc_d_d': (5,), + 'PyUFunc_f_f': (6,), + 'PyUFunc_g_g': (7,), + 'PyUFunc_F_F_As_D_D': (8,), + 'PyUFunc_F_F': (9,), + 'PyUFunc_D_D': (10,), + 'PyUFunc_G_G': (11,), + 'PyUFunc_O_O': (12,), + 'PyUFunc_ff_f_As_dd_d': (13,), + 'PyUFunc_ff_f': (14,), + 'PyUFunc_dd_d': (15,), + 'PyUFunc_gg_g': (16,), + 'PyUFunc_FF_F_As_DD_D': (17,), + 'PyUFunc_DD_D': (18,), + 'PyUFunc_FF_F': (19,), + 'PyUFunc_GG_G': (20,), + 'PyUFunc_OO_O': (21,), + 'PyUFunc_O_O_method': (22,), + 'PyUFunc_OO_O_method': (23,), + 'PyUFunc_On_Om': (24,), + 'PyUFunc_GetPyValues': (25,), + 'PyUFunc_checkfperr': (26,), + 'PyUFunc_clearfperr': (27,), + 'PyUFunc_getfperr': (28,), + 'PyUFunc_handlefperr': (29,), + 'PyUFunc_ReplaceLoopBySignature': (30,), + 'PyUFunc_FromFuncAndDataAndSignature': (31,), + 'PyUFunc_SetUsesArraysAsData': (32,), # End 1.5 API - 'PyUFunc_e_e': 33, - 'PyUFunc_e_e_As_f_f': 34, - 'PyUFunc_e_e_As_d_d': 35, - 'PyUFunc_ee_e': 36, - 'PyUFunc_ee_e_As_ff_f': 37, - 'PyUFunc_ee_e_As_dd_d': 38, + 'PyUFunc_e_e': (33,), + 'PyUFunc_e_e_As_f_f': (34,), + 'PyUFunc_e_e_As_d_d': (35,), + 'PyUFunc_ee_e': (36,), + 'PyUFunc_ee_e_As_ff_f': (37,), + 'PyUFunc_ee_e_As_dd_d': (38,), # End 1.6 API - 'PyUFunc_DefaultTypeResolver': 39, - 'PyUFunc_ValidateCasting': 40, + 'PyUFunc_DefaultTypeResolver': (39,), + 'PyUFunc_ValidateCasting': (40,), # End 1.7 API - 'PyUFunc_RegisterLoopForDescr': 41, + 'PyUFunc_RegisterLoopForDescr': (41,), # End 1.8 API } @@ -399,7 +399,6 @@ ufunc_funcs_api = { # XXX: DO NOT CHANGE THE ORDER OF TUPLES BELOW ! multiarray_api = ( multiarray_global_vars, - multiarray_global_vars_types, multiarray_scalar_bool_values, multiarray_types_api, multiarray_funcs_api, diff --git a/numpy/core/fromnumeric.py b/numpy/core/fromnumeric.py index 3de81305d..a2d2f3100 100644 --- a/numpy/core/fromnumeric.py +++ b/numpy/core/fromnumeric.py @@ -4,7 +4,9 @@ from __future__ import division, absolute_import, print_function import types +import warnings +from .. import VisibleDeprecationWarning from . import multiarray as mu from . import umath as um from . import numerictypes as nt @@ -1771,7 +1773,7 @@ def any(a, axis=None, out=None, keepdims=False): Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical OR reduction is performed. - The default (`axis` = `None`) is perform a logical OR over all + The default (`axis` = `None`) is to perform a logical OR over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. @@ -1849,7 +1851,7 @@ def all(a, axis=None, out=None, keepdims=False): Input array or object that can be converted to an array. axis : None or int or tuple of ints, optional Axis or axes along which a logical AND reduction is performed. - The default (`axis` = `None`) is perform a logical OR over all + The default (`axis` = `None`) is to perform a logical AND over all the dimensions of the input array. `axis` may be negative, in which case it counts from the last to the first axis. @@ -2453,6 +2455,11 @@ def rank(a): If `a` is not already an array, a conversion is attempted. Scalars are zero dimensional. + .. note:: + This function is deprecated in NumPy 1.9 to avoid confusion with + `numpy.linalg.matrix_rank`. The ``ndim`` attribute or function + should be used instead. + Parameters ---------- a : array_like @@ -2486,6 +2493,10 @@ def rank(a): 0 """ + warnings.warn( + "`rank` is deprecated; use the `ndim` attribute or function instead. " + "To find the rank of a matrix see `numpy.linalg.matrix_rank`.", + VisibleDeprecationWarning) try: return a.ndim except AttributeError: diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h index ce348ed11..21ff8cd1a 100644 --- a/numpy/core/include/numpy/ndarraytypes.h +++ b/numpy/core/include/numpy/ndarraytypes.h @@ -1492,13 +1492,13 @@ PyArray_STRIDE(const PyArrayObject *arr, int istride) return ((PyArrayObject_fields *)arr)->strides[istride]; } -static NPY_INLINE PyObject * +static NPY_INLINE NPY_RETURNS_BORROWED_REF PyObject * PyArray_BASE(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->base; } -static NPY_INLINE PyArray_Descr * +static NPY_INLINE NPY_RETURNS_BORROWED_REF PyArray_Descr * PyArray_DESCR(PyArrayObject *arr) { return ((PyArrayObject_fields *)arr)->descr; diff --git a/numpy/core/include/numpy/npy_common.h b/numpy/core/include/numpy/npy_common.h index 46c745c99..6244783f5 100644 --- a/numpy/core/include/numpy/npy_common.h +++ b/numpy/core/include/numpy/npy_common.h @@ -25,6 +25,16 @@ #define NPY_GCC_OPT_3 #endif +/* + * mark an argument (starting from 1) that must not be NULL and is not checked + * DO NOT USE IF FUNCTION CHECKS FOR NULL!! the compiler will remove the check + */ +#ifdef HAVE_ATTRIBUTE_NONNULL +#define NPY_GCC_NONNULL(n) __attribute__((nonnull(n))) +#else +#define NPY_GCC_NONNULL(n) +#endif + #if defined HAVE_XMMINTRIN_H && defined HAVE__MM_LOAD_PS #define NPY_HAVE_SSE_INTRINSICS #endif @@ -63,6 +73,20 @@ #define NPY_INLINE #endif +#ifdef WITH_CPYCHECKER_RETURNS_BORROWED_REF_ATTRIBUTE + #define NPY_RETURNS_BORROWED_REF \ + __attribute__((cpychecker_returns_borrowed_ref)) +#else + #define NPY_RETURNS_BORROWED_REF +#endif + +#ifdef WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE + #define NPY_STEALS_REF_TO_ARG(n) \ + __attribute__((cpychecker_steals_reference_to_arg(n))) +#else + #define NPY_STEALS_REF_TO_ARG(n) +#endif + /* 64 bit file position support, also on win-amd64. Ticket #1660 */ #if defined(_MSC_VER) && defined(_WIN64) && (_MSC_VER > 1400) || \ defined(__MINGW32__) || defined(__MINGW64__) diff --git a/numpy/core/include/numpy/npy_cpu.h b/numpy/core/include/numpy/npy_cpu.h index 80bd5fc56..31668afc7 100644 --- a/numpy/core/include/numpy/npy_cpu.h +++ b/numpy/core/include/numpy/npy_cpu.h @@ -5,6 +5,7 @@ * NPY_CPU_AMD64 * NPY_CPU_PPC * NPY_CPU_PPC64 + * NPY_CPU_PPC64LE * NPY_CPU_SPARC * NPY_CPU_S390 * NPY_CPU_IA64 @@ -41,6 +42,8 @@ * _ARCH_PPC is used by at least gcc on AIX */ #define NPY_CPU_PPC +#elif defined(__ppc64le__) + #define NPY_CPU_PPC64LE #elif defined(__ppc64__) #define NPY_CPU_PPC64 #elif defined(__sparc__) || defined(__sparc) diff --git a/numpy/core/include/numpy/npy_endian.h b/numpy/core/include/numpy/npy_endian.h index 36d09232f..44d4326b1 100644 --- a/numpy/core/include/numpy/npy_endian.h +++ b/numpy/core/include/numpy/npy_endian.h @@ -27,7 +27,8 @@ || defined(NPY_CPU_ARMEL) \ || defined(NPY_CPU_AARCH64) \ || defined(NPY_CPU_SH_LE) \ - || defined(NPY_CPU_MIPSEL) + || defined(NPY_CPU_MIPSEL) \ + || defined(NPY_CPU_PPC64LE) #define NPY_BYTE_ORDER NPY_LITTLE_ENDIAN #elif defined(NPY_CPU_PPC) \ || defined(NPY_CPU_SPARC) \ diff --git a/numpy/core/setup.py b/numpy/core/setup.py index a93311ec5..c28de7c11 100644 --- a/numpy/core/setup.py +++ b/numpy/core/setup.py @@ -176,9 +176,8 @@ def check_math_capabilities(config, moredefs, mathlibs): moredefs.append((fname2def(f), 1)) for dec, fn in OPTIONAL_GCC_ATTRIBUTES: - if config.check_funcs_once([fn], - decl=dict((('%s %s' % (dec, fn), True),)), - call=False): + if config.check_func(fn, decl='int %s %s(void *);' % (dec, fn), + call=False): moredefs.append((fname2def(fn), 1)) # C99 functions: float and long double versions @@ -466,7 +465,7 @@ def configuration(parent_package='',top_path=None): 'MOTOROLA_EXTENDED_12_BYTES_BE', 'IEEE_QUAD_LE', 'IEEE_QUAD_BE', 'IEEE_DOUBLE_LE', 'IEEE_DOUBLE_BE', - 'DOUBLE_DOUBLE_BE']: + 'DOUBLE_DOUBLE_BE', 'DOUBLE_DOUBLE_LE']: moredefs.append(('HAVE_LDOUBLE_%s' % rep, 1)) else: raise ValueError("Unrecognized long double format: %s" % rep) diff --git a/numpy/core/setup_common.py b/numpy/core/setup_common.py index 24d480ae7..4188f4c3f 100644 --- a/numpy/core/setup_common.py +++ b/numpy/core/setup_common.py @@ -127,6 +127,8 @@ OPTIONAL_GCC_ATTRIBUTES = [('__attribute__((optimize("unroll-loops")))', 'attribute_optimize_unroll_loops'), ('__attribute__((optimize("O3")))', 'attribute_optimize_opt_3'), + ('__attribute__((nonnull (1)))', + 'attribute_nonnull'), ] # Subset of OPTIONAL_STDFUNCS which may alreay have HAVE_* defined by Python.h @@ -258,6 +260,8 @@ _IEEE_QUAD_PREC_BE = ['300', '031', '326', '363', '105', '100', '000', '000', _IEEE_QUAD_PREC_LE = _IEEE_QUAD_PREC_BE[::-1] _DOUBLE_DOUBLE_BE = ['301', '235', '157', '064', '124', '000', '000', '000'] + \ ['000'] * 8 +_DOUBLE_DOUBLE_LE = ['000', '000', '000', '124', '064', '157', '235', '301'] + \ + ['000'] * 8 def long_double_representation(lines): """Given a binary dump as given by GNU od -b, look for long double @@ -297,6 +301,8 @@ def long_double_representation(lines): return 'IEEE_QUAD_LE' elif read[8:-8] == _DOUBLE_DOUBLE_BE: return 'DOUBLE_DOUBLE_BE' + elif read[8:-8] == _DOUBLE_DOUBLE_LE: + return 'DOUBLE_DOUBLE_LE' elif read[:16] == _BEFORE_SEQ: if read[16:-8] == _IEEE_DOUBLE_LE: return 'IEEE_DOUBLE_LE' diff --git a/numpy/core/src/multiarray/arrayobject.c b/numpy/core/src/multiarray/arrayobject.c index 1f6c3e3d5..a09e0e3b9 100644 --- a/numpy/core/src/multiarray/arrayobject.c +++ b/numpy/core/src/multiarray/arrayobject.c @@ -1435,7 +1435,11 @@ array_richcompare(PyArrayObject *self, PyObject *other, int cmp_op) return result; } array_other = (PyArrayObject *)PyArray_FromObject(other, - NPY_NOTYPE, 0, 0); + NPY_NOTYPE, 0, 0); + if (array_other == NULL) { + PyErr_Clear(); + return result; + } if (PyArray_ISSTRING(self) && PyArray_ISSTRING(array_other)) { Py_DECREF(result); result = _strings_richcompare(self, (PyArrayObject *) diff --git a/numpy/core/src/multiarray/common.c b/numpy/core/src/multiarray/common.c index b89a6b778..2b3d3c3d2 100644 --- a/numpy/core/src/multiarray/common.c +++ b/numpy/core/src/multiarray/common.c @@ -781,29 +781,6 @@ offset_bounds_from_strides(const int itemsize, const int nd, } -NPY_NO_EXPORT int -_is_basic_python_type(PyObject * obj) -{ - if (obj == Py_None || - /* Basic number types */ -#if !defined(NPY_PY3K) - PyInt_CheckExact(obj) || -#endif - PyLong_CheckExact(obj) || - PyFloat_CheckExact(obj) || - PyComplex_CheckExact(obj) || - /* Basic sequence types */ - PyList_CheckExact(obj) || - PyTuple_CheckExact(obj) || - PyDict_CheckExact(obj) || - PyAnySet_CheckExact(obj)) { - return 1; - } - - return 0; -} - - /** * Convert an array shape to a string such as "(1, 2)". * diff --git a/numpy/core/src/multiarray/common.h b/numpy/core/src/multiarray/common.h index d64411115..6b49d6b4c 100644 --- a/numpy/core/src/multiarray/common.h +++ b/numpy/core/src/multiarray/common.h @@ -64,9 +64,6 @@ _IsAligned(PyArrayObject *ap); NPY_NO_EXPORT npy_bool _IsWriteable(PyArrayObject *ap); -NPY_NO_EXPORT int -_is_basic_python_type(PyObject * obj); - NPY_NO_EXPORT void offset_bounds_from_strides(const int itemsize, const int nd, const npy_intp *dims, const npy_intp *strides, @@ -183,6 +180,34 @@ npy_memchr(char * haystack, char needle, return p; } +static NPY_INLINE int +_is_basic_python_type(PyObject * obj) +{ + if (obj == Py_None || + PyBool_Check(obj) || + /* Basic number types */ +#if !defined(NPY_PY3K) + PyInt_CheckExact(obj) || + PyString_CheckExact(obj) || +#endif + PyLong_CheckExact(obj) || + PyFloat_CheckExact(obj) || + PyComplex_CheckExact(obj) || + /* Basic sequence types */ + PyList_CheckExact(obj) || + PyTuple_CheckExact(obj) || + PyDict_CheckExact(obj) || + PyAnySet_CheckExact(obj) || + PyUnicode_CheckExact(obj) || + PyBytes_CheckExact(obj) || + PySlice_Check(obj)) { + + return 1; + } + + return 0; +} + #include "ucsnarrow.h" #endif diff --git a/numpy/core/src/multiarray/convert_datatype.c b/numpy/core/src/multiarray/convert_datatype.c index b58d8e9fb..1db3bfe85 100644 --- a/numpy/core/src/multiarray/convert_datatype.c +++ b/numpy/core/src/multiarray/convert_datatype.c @@ -36,7 +36,7 @@ NPY_NO_EXPORT npy_intp REQUIRED_STR_LEN[] = {0, 3, 5, 10, 10, 20, 20, 20, 20}; * For backward compatibility * * Cast an array using typecode structure. - * steals reference to at --- cannot be NULL + * steals reference to dtype --- cannot be NULL * * This function always makes a copy of arr, even if the dtype * doesn't change. @@ -642,7 +642,13 @@ NPY_NO_EXPORT npy_bool PyArray_CanCastTypeTo(PyArray_Descr *from, PyArray_Descr *to, NPY_CASTING casting) { - if (casting == NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND) { + /* fast path for basic types */ + if (NPY_LIKELY(from->type_num < NPY_OBJECT) && + NPY_LIKELY(from->type_num == to->type_num) && + NPY_LIKELY(from->byteorder == to->byteorder)) { + return 1; + } + else if (casting == NPY_INTERNAL_UNSAFE_CASTING_BUT_WARN_UNLESS_SAME_KIND) { npy_bool unsafe_ok, same_kind_ok; unsafe_ok = PyArray_CanCastTypeTo_impl(from, to, NPY_UNSAFE_CASTING); same_kind_ok = PyArray_CanCastTypeTo_impl(from, to, diff --git a/numpy/core/src/multiarray/ctors.c b/numpy/core/src/multiarray/ctors.c index 3f135ebf8..5f8fbc4dc 100644 --- a/numpy/core/src/multiarray/ctors.c +++ b/numpy/core/src/multiarray/ctors.c @@ -26,6 +26,7 @@ #include "array_assign.h" #include "mapping.h" /* for array_item_asarray */ #include "scalarmathmodule.h" /* for npy_mul_with_overflow_intp */ +#include <assert.h> /* * Reading from a file or a string. @@ -885,12 +886,15 @@ PyArray_NewFromDescr_int(PyTypeObject *subtype, PyArray_Descr *descr, int nd, int i; size_t sd; npy_intp size; + assert(dims != NULL || (nd == 0)); if (descr->subarray) { PyObject *ret; npy_intp newdims[2*NPY_MAXDIMS]; npy_intp *newstrides = NULL; - memcpy(newdims, dims, nd*sizeof(npy_intp)); + if (nd > 0) { + memcpy(newdims, dims, nd * sizeof(npy_intp)); + } if (strides) { newstrides = newdims + NPY_MAXDIMS; memcpy(newstrides, strides, nd*sizeof(npy_intp)); @@ -1307,7 +1311,9 @@ _array_from_buffer_3118(PyObject *obj, PyObject **out) else { d = view->len; for (k = 0; k < nd; ++k) { - d /= view->shape[k]; + if (view->shape[k] != 0) { + d /= view->shape[k]; + } strides[k] = d; } } diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c index 46419e48f..8b55c9fbd 100644 --- a/numpy/core/src/multiarray/descriptor.c +++ b/numpy/core/src/multiarray/descriptor.c @@ -410,7 +410,8 @@ _convert_from_array_descr(PyObject *obj, int align) PyObject *nameslist; PyArray_Descr *new; PyArray_Descr *conv; - char dtypeflags = 0; + /* Types with fields need the Python C API for field access */ + char dtypeflags = NPY_NEEDS_PYAPI; int maxalign = 0; n = PyList_GET_SIZE(obj); @@ -599,7 +600,8 @@ _convert_from_list(PyObject *obj, int align) PyObject *nameslist = NULL; int ret; int maxalign = 0; - char dtypeflags = 0; + /* Types with fields need the Python C API for field access */ + char dtypeflags = NPY_NEEDS_PYAPI; n = PyList_GET_SIZE(obj); /* @@ -935,7 +937,8 @@ _convert_from_dict(PyObject *obj, int align) int n, i; int totalsize, itemsize; int maxalign = 0; - char dtypeflags = 0; + /* Types with fields need the Python C API for field access */ + char dtypeflags = NPY_NEEDS_PYAPI; int has_out_of_order_fields = 0; fields = PyDict_New(); diff --git a/numpy/core/src/multiarray/mapping.c b/numpy/core/src/multiarray/mapping.c index 1436c9abb..e2b8ef700 100644 --- a/numpy/core/src/multiarray/mapping.c +++ b/numpy/core/src/multiarray/mapping.c @@ -1646,8 +1646,10 @@ array_assign_item(PyArrayObject *self, Py_ssize_t i, PyObject *op) return -1; } if (PyArray_CopyObject(view, op) < 0) { + Py_DECREF(view); return -1; } + Py_DECREF(view); } return 0; } diff --git a/numpy/core/src/multiarray/methods.c b/numpy/core/src/multiarray/methods.c index abfa04cb6..9104ffd03 100644 --- a/numpy/core/src/multiarray/methods.c +++ b/numpy/core/src/multiarray/methods.c @@ -352,9 +352,9 @@ array_swapaxes(PyArrayObject *self, PyObject *args) } -/* steals typed reference */ /*NUMPY_API Get a subset of bytes from each element of the array + steals reference to typed, must not be NULL */ NPY_NO_EXPORT PyObject * PyArray_GetField(PyArrayObject *self, PyArray_Descr *typed, int offset) @@ -410,6 +410,7 @@ array_getfield(PyArrayObject *self, PyObject *args, PyObject *kwds) /*NUMPY_API Set a subset of bytes from each element of the array + steals reference to dtype, must not be NULL */ NPY_NO_EXPORT int PyArray_SetField(PyArrayObject *self, PyArray_Descr *dtype, diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c index c6d3c632f..a05e87e84 100644 --- a/numpy/core/src/multiarray/multiarraymodule.c +++ b/numpy/core/src/multiarray/multiarraymodule.c @@ -1540,7 +1540,7 @@ PyArray_EquivTypenums(int typenum1, int typenum2) * NPY_RELAXED_STRIDES_CHECKING: If the strides logic is changed, the * order specific stride setting is not necessary. */ -static PyObject * +static NPY_STEALS_REF_TO_ARG(1) PyObject * _prepend_ones(PyArrayObject *arr, int nd, int ndmin, NPY_ORDER order) { npy_intp newdims[NPY_MAXDIMS]; diff --git a/numpy/core/src/multiarray/scalarapi.c b/numpy/core/src/multiarray/scalarapi.c index 8bbfb41fa..d71823566 100644 --- a/numpy/core/src/multiarray/scalarapi.c +++ b/numpy/core/src/multiarray/scalarapi.c @@ -831,8 +831,9 @@ PyArray_Scalar(void *data, PyArray_Descr *descr, PyObject *base) /*NUMPY_API * - *Return either an array or the appropriate Python object if the array - *is 0d and matches a Python type. + * Return either an array or the appropriate Python object if the array + * is 0d and matches a Python type. + * steals reference to mp */ NPY_NO_EXPORT PyObject * PyArray_Return(PyArrayObject *mp) diff --git a/numpy/core/src/npymath/ieee754.c.src b/numpy/core/src/npymath/ieee754.c.src index 5a60bc049..b8f6889e1 100644 --- a/numpy/core/src/npymath/ieee754.c.src +++ b/numpy/core/src/npymath/ieee754.c.src @@ -133,7 +133,8 @@ static float _nextf(float x, int p) return x; } -#ifdef HAVE_LDOUBLE_DOUBLE_DOUBLE_BE +#if defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_BE) || \ + defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_LE) /* * FIXME: this is ugly and untested. The asm part only works with gcc, and we diff --git a/numpy/core/src/npymath/npy_math_private.h b/numpy/core/src/npymath/npy_math_private.h index 2bca6bf60..b3b1690be 100644 --- a/numpy/core/src/npymath/npy_math_private.h +++ b/numpy/core/src/npymath/npy_math_private.h @@ -435,7 +435,8 @@ do { \ typedef npy_uint32 ldouble_sign_t; #endif -#ifndef HAVE_LDOUBLE_DOUBLE_DOUBLE_BE +#if !defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_BE) && \ + !defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_LE) /* Get the sign bit of x. x should be of type IEEEl2bitsrep */ #define GET_LDOUBLE_SIGN(x) \ (((x).a[LDBL_SIGN_INDEX] & LDBL_SIGN_MASK) >> LDBL_SIGN_SHIFT) diff --git a/numpy/core/src/private/npy_fpmath.h b/numpy/core/src/private/npy_fpmath.h index 8a120cab7..86b9cf3da 100644 --- a/numpy/core/src/private/npy_fpmath.h +++ b/numpy/core/src/private/npy_fpmath.h @@ -29,6 +29,8 @@ #define HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE #elif defined(NPY_CPU_PPC) || defined(NPY_CPU_PPC64) #define HAVE_LDOUBLE_IEEE_DOUBLE_16_BYTES_BE + #elif defined(NPY_CPU_PPC64LE) + #define HAVE_LDOUBLE_IEEE_DOUBLE_16_BYTES_LE #endif #endif #endif @@ -41,7 +43,8 @@ defined(HAVE_LDOUBLE_INTEL_EXTENDED_16_BYTES_LE) || \ defined(HAVE_LDOUBLE_INTEL_EXTENDED_12_BYTES_LE) || \ defined(HAVE_LDOUBLE_MOTOROLA_EXTENDED_12_BYTES_BE) || \ - defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_BE)) + defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_BE) || \ + defined(HAVE_LDOUBLE_DOUBLE_DOUBLE_LE)) #error No long double representation defined #endif diff --git a/numpy/core/src/private/ufunc_override.h b/numpy/core/src/private/ufunc_override.h index 8ef29d1fd..ce2836834 100644 --- a/numpy/core/src/private/ufunc_override.h +++ b/numpy/core/src/private/ufunc_override.h @@ -60,7 +60,12 @@ PyUFunc_CheckOverride(PyUFuncObject *ufunc, char *method, for (i = 0; i < nargs; ++i) { obj = PyTuple_GET_ITEM(args, i); - if (PyArray_CheckExact(obj) || PyArray_IsAnyScalar(obj)) { + /* + * TODO: could use PyArray_GetAttrString_SuppressException if it + * weren't private to multiarray.so + */ + if (PyArray_CheckExact(obj) || PyArray_IsScalar(obj, Generic) || + _is_basic_python_type(obj)) { continue; } if (PyObject_HasAttrString(obj, "__numpy_ufunc__")) { diff --git a/numpy/core/src/umath/umathmodule.c b/numpy/core/src/umath/umathmodule.c index b1fc2f7be..3ed7ee771 100644 --- a/numpy/core/src/umath/umathmodule.c +++ b/numpy/core/src/umath/umathmodule.c @@ -390,13 +390,13 @@ NPY_VISIBILITY_HIDDEN PyObject * npy_um_str_pyvals_name = NULL; static int intern_strings(void) { - npy_um_str_out = PyUString_FromString("out"); - npy_um_str_subok = PyUString_FromString("subok"); - npy_um_str_array_prepare = PyUString_FromString("__array_prepare__"); - npy_um_str_array_wrap = PyUString_FromString("__array_wrap__"); - npy_um_str_array_finalize = PyUString_FromString("__array_finalize__"); - npy_um_str_ufunc = PyUString_FromString("__numpy_ufunc__"); - npy_um_str_pyvals_name = PyUString_FromString(UFUNC_PYVALS_NAME); + npy_um_str_out = PyUString_InternFromString("out"); + npy_um_str_subok = PyUString_InternFromString("subok"); + npy_um_str_array_prepare = PyUString_InternFromString("__array_prepare__"); + npy_um_str_array_wrap = PyUString_InternFromString("__array_wrap__"); + npy_um_str_array_finalize = PyUString_InternFromString("__array_finalize__"); + npy_um_str_ufunc = PyUString_InternFromString("__numpy_ufunc__"); + npy_um_str_pyvals_name = PyUString_InternFromString(UFUNC_PYVALS_NAME); return npy_um_str_out && npy_um_str_subok && npy_um_str_array_prepare && npy_um_str_array_wrap && npy_um_str_array_finalize && npy_um_str_ufunc; diff --git a/numpy/core/tests/test_deprecations.py b/numpy/core/tests/test_deprecations.py index f7fbb94e5..6e559ab26 100644 --- a/numpy/core/tests/test_deprecations.py +++ b/numpy/core/tests/test_deprecations.py @@ -366,5 +366,14 @@ class TestBooleanSubtractDeprecations(_DeprecationTestCase): self.assert_deprecated(operator.neg, args=(generic,)) +class TestRankDeprecation(_DeprecationTestCase): + """Test that np.rank is deprecated. The function should simply be + removed. The VisibleDeprecationWarning may become unnecessary. + """ + def test(self): + a = np.arange(10) + assert_warns(np.VisibleDeprecationWarning, np.rank, a) + + if __name__ == "__main__": run_module_suite() diff --git a/numpy/core/tests/test_regression.py b/numpy/core/tests/test_regression.py index e4feff372..f3e34c109 100644 --- a/numpy/core/tests/test_regression.py +++ b/numpy/core/tests/test_regression.py @@ -154,7 +154,8 @@ class TestRegression(TestCase): {'names':['a'],'formats':['foo']}, align=1) @dec.knownfailureif((sys.version_info[0] >= 3) or - (sys.platform == "win32" and platform.architecture()[0] == "64bit"), + (sys.platform == "win32" and + platform.architecture()[0] == "64bit"), "numpy.intp('0xff', 16) not supported on Py3, " "as it does not inherit from Python int") def test_intp(self,level=rlevel): @@ -1972,6 +1973,26 @@ class TestRegression(TestCase): del masked, c base.dtype + def test_richcompare_crash(self): + # gh-4613 + import operator as op + + # dummy class where __array__ throws exception + class Foo(object): + __array_priority__ = 1002 + def __array__(self,*args,**kwargs): + raise Exception() + + rhs = Foo() + lhs = np.array(1) + for f in [op.lt, op.le, op.gt, op.ge]: + if sys.version_info[0] >= 3: + assert_raises(TypeError, f, lhs, rhs) + else: + f(lhs, rhs) + assert_(not op.eq(lhs, rhs)) + assert_(op.ne(lhs, rhs)) + if __name__ == "__main__": run_module_suite() diff --git a/numpy/distutils/command/config.py b/numpy/distutils/command/config.py index cadcc1dde..0086e3632 100644 --- a/numpy/distutils/command/config.py +++ b/numpy/distutils/command/config.py @@ -307,7 +307,10 @@ int main () self._check_compiler() body = [] if decl: - body.append("int %s (void);" % func) + if type(decl) == str: + body.append(decl) + else: + body.append("int %s (void);" % func) # Handle MSVC intrinsics: force MS compiler to make a function call. # Useful to test for some functions when built with optimization on, to # avoid build error because the intrinsic and our 'fake' test diff --git a/numpy/distutils/fcompiler/intel.py b/numpy/distutils/fcompiler/intel.py index 21a3c5eaf..a80e525e3 100644 --- a/numpy/distutils/fcompiler/intel.py +++ b/numpy/distutils/fcompiler/intel.py @@ -68,7 +68,7 @@ class IntelFCompiler(BaseIntelFCompiler): opt.remove('-shared') except ValueError: idx = 0 - opt[idx:idx] = ['-dynamiclib', '-Wl,-undefined,dynamic_lookup', '-Wl,-framework,Python'] + opt[idx:idx] = ['-dynamiclib', '-Wl,-undefined,dynamic_lookup'] return opt class IntelItaniumFCompiler(IntelFCompiler): @@ -162,7 +162,7 @@ class IntelVisualFCompiler(BaseIntelFCompiler): return ['/4Yb', '/d2'] def get_flags_opt(self): - return ['/O2'] + return ['/O1'] # Scipy test failures with /O2 def get_flags_arch(self): return ["/arch:IA-32", "/QaxSSE3"] diff --git a/numpy/doc/misc.py b/numpy/doc/misc.py index 311e6e0c8..1709ad66d 100644 --- a/numpy/doc/misc.py +++ b/numpy/doc/misc.py @@ -3,8 +3,8 @@ Miscellaneous ============= -IEEE 754 Floating Point Special Values: ------------------------------------------------ +IEEE 754 Floating Point Special Values +-------------------------------------- Special values defined in numpy: nan, inf, @@ -46,8 +46,8 @@ from the results: :: >>> np.nansum(x) 42.0 -How numpy handles numerical exceptions: ------------------------------------------- +How numpy handles numerical exceptions +-------------------------------------- The default is to ``'warn'`` for ``invalid``, ``divide``, and ``overflow`` and ``'ignore'`` for ``underflow``. But this can be changed, and it can be @@ -72,8 +72,8 @@ These behaviors can be set for all kinds of errors or specific ones: Note that integer divide-by-zero is handled by the same machinery. These behaviors are set on a per-thread basis. -Examples: ------------- +Examples +-------- :: @@ -96,8 +96,8 @@ Examples: >>> j = np.seterr(**oldsettings) # restore previous ... # error-handling settings -Interfacing to C: ------------------ +Interfacing to C +---------------- Only a survey of the choices. Little detail on how each works. 1) Bare metal, wrap your own C-code manually. @@ -121,29 +121,22 @@ Only a survey of the choices. Little detail on how each works. - API will change for Python 3.0! -2) pyrex +2) Cython - Plusses: - avoid learning C API's - no dealing with reference counting - - can code in psuedo python and generate C code + - can code in pseudo python and generate C code - can also interface to existing C code - should shield you from changes to Python C api - - become pretty popular within Python community + - has become the de-facto standard within the scientific Python community + - fast indexing support for arrays - Minuses: - Can write code in non-standard form which may become obsolete - Not as flexible as manual wrapping - - Maintainers not easily adaptable to new features - -Thus: - -3) cython - fork of pyrex to allow needed features for SAGE - - - being considered as the standard scipy/numpy wrapping tool - - fast indexing support for arrays 4) ctypes @@ -180,16 +173,15 @@ Thus: - generates lots of code between Python and the C code - can cause performance problems that are nearly impossible to optimize - out + out - interface files can be hard to write - doesn't necessarily avoid reference counting issues or needing to know API's -7) Weave +7) scipy.weave - Plusses: - - Phenomenal tool - can turn many numpy expressions into C code - dynamic compiling and loading of generated C code - can embed pure C code in Python module and have weave extract, generate @@ -197,7 +189,8 @@ Thus: - Minuses: - - Future uncertain--lacks a champion + - Future very uncertain: it's the only part of Scipy not ported to Python 3 + and is effectively deprecated in favor of Cython. 8) Psyco @@ -214,15 +207,19 @@ Thus: Interfacing to Fortran: ----------------------- -Fortran: Clear choice is f2py. (Pyfort is an older alternative, but not -supported any longer) +The clear choice to wrap Fortran code is +`f2py <http://docs.scipy.org/doc/numpy-dev/f2py/>`_. + +Pyfort is an older alternative, but not supported any longer. +Fwrap is a newer project that looked promising but isn't being developed any +longer. Interfacing to C++: ------------------- - 1) CXX - 2) Boost.python - 3) SWIG - 4) Sage has used cython to wrap C++ (not pretty, but it can be done) + 1) Cython + 2) CXX + 3) Boost.python + 4) SWIG 5) SIP (used mainly in PyQT) """ diff --git a/numpy/lib/arraysetops.py b/numpy/lib/arraysetops.py index 691550579..0755fffd1 100644 --- a/numpy/lib/arraysetops.py +++ b/numpy/lib/arraysetops.py @@ -90,7 +90,7 @@ def ediff1d(ary, to_end=None, to_begin=None): return ed -def unique(ar, return_index=False, return_inverse=False): +def unique(ar, return_index=False, return_inverse=False, return_counts=False): """ Find the unique elements of an array. @@ -109,6 +109,10 @@ def unique(ar, return_index=False, return_inverse=False): return_inverse : bool, optional If True, also return the indices of the unique array that can be used to reconstruct `ar`. + return_counts : bool, optional + .. versionadded:: 1.9.0 + If True, also return the number of times each unique value comes up + in `ar`. Returns ------- @@ -120,6 +124,10 @@ def unique(ar, return_index=False, return_inverse=False): unique_inverse : ndarray, optional The indices to reconstruct the (flattened) original array from the unique array. Only provided if `return_inverse` is True. + unique_counts : ndarray, optional + .. versionadded:: 1.9.0 + The number of times each of the unique values comes up in the + original array. Only provided if `return_counts` is True. See Also -------- @@ -162,41 +170,49 @@ def unique(ar, return_index=False, return_inverse=False): try: ar = ar.flatten() except AttributeError: - if not return_inverse and not return_index: - return np.sort(list(set(ar))) + if not return_inverse and not return_index and not return_counts: + return np.sort(list((set(ar)))) else: ar = np.asanyarray(ar).flatten() + optional_indices = return_index or return_inverse + optional_returns = optional_indices or return_counts + if ar.size == 0: - if return_inverse and return_index: - return ar, np.empty(0, np.bool), np.empty(0, np.bool) - elif return_inverse or return_index: - return ar, np.empty(0, np.bool) + if not optional_returns: + ret = ar else: - return ar + ret = (ar,) + if return_index: + ret += (np.empty(0, np.bool),) + if return_inverse: + ret += (np.empty(0, np.bool),) + if return_counts: + ret += (np.empty(0, np.intp),) + return ret + + if optional_indices: + perm = ar.argsort(kind='mergesort' if return_index else 'quicksort') + aux = ar[perm] + else: + ar.sort() + aux = ar + flag = np.concatenate(([True], aux[1:] != aux[:-1])) - if return_inverse or return_index: + if not optional_returns: + ret = aux[flag] + else: + ret = (aux[flag],) if return_index: - perm = ar.argsort(kind='mergesort') - else: - perm = ar.argsort() - aux = ar[perm] - flag = np.concatenate(([True], aux[1:] != aux[:-1])) + ret += (perm[flag],) if return_inverse: iflag = np.cumsum(flag) - 1 iperm = perm.argsort() - if return_index: - return aux[flag], perm[flag], iflag[iperm] - else: - return aux[flag], iflag[iperm] - else: - return aux[flag], perm[flag] - - else: - ar.sort() - flag = np.concatenate(([True], ar[1:] != ar[:-1])) - return ar[flag] - + ret += (np.take(iflag, iperm),) + if return_counts: + idx = np.concatenate(np.nonzero(flag) + ([ar.size],)) + ret += (np.diff(idx),) + return ret def intersect1d(ar1, ar2, assume_unique=False): """ diff --git a/numpy/lib/nanfunctions.py b/numpy/lib/nanfunctions.py index 5766084ab..badba32da 100644 --- a/numpy/lib/nanfunctions.py +++ b/numpy/lib/nanfunctions.py @@ -228,7 +228,7 @@ def nanmin(a, axis=None, out=None, keepdims=False): # Check for all-NaN axis mask = np.all(mask, axis=axis, keepdims=keepdims) if np.any(mask): - res = _copyto(res, mask, np.nan) + res = _copyto(res, np.nan, mask) warnings.warn("All-NaN axis encountered", RuntimeWarning) return res @@ -327,7 +327,7 @@ def nanmax(a, axis=None, out=None, keepdims=False): # Check for all-NaN axis mask = np.all(mask, axis=axis, keepdims=keepdims) if np.any(mask): - res = _copyto(res, mask, np.nan) + res = _copyto(res, np.nan, mask) warnings.warn("All-NaN axis encountered", RuntimeWarning) return res diff --git a/numpy/lib/npyio.py b/numpy/lib/npyio.py index f69ca0c73..98b4b6e35 100644 --- a/numpy/lib/npyio.py +++ b/numpy/lib/npyio.py @@ -845,6 +845,11 @@ def loadtxt(fname, dtype=float, comments='#', delimiter=None, continue if usecols: vals = [vals[i] for i in usecols] + if len(vals) != N: + line_num = i + skiprows + 1 + raise ValueError("Wrong number of columns at line %d" + % line_num) + # Convert each value according to its column and store items = [conv(val) for (conv, val) in zip(converters, vals)] # Then pack it according to the dtype's nesting diff --git a/numpy/lib/tests/test_arraysetops.py b/numpy/lib/tests/test_arraysetops.py index e44ccd12b..41d77c07f 100644 --- a/numpy/lib/tests/test_arraysetops.py +++ b/numpy/lib/tests/test_arraysetops.py @@ -14,31 +14,59 @@ class TestSetOps(TestCase): def test_unique(self): - def check_all(a, b, i1, i2, dt): - msg = "check values failed for type '%s'" % dt + def check_all(a, b, i1, i2, c, dt): + base_msg = 'check {0} failed for type {1}' + + msg = base_msg.format('values', dt) v = unique(a) assert_array_equal(v, b, msg) - msg = "check indexes failed for type '%s'" % dt - v, j = unique(a, 1, 0) + msg = base_msg.format('return_index', dt) + v, j = unique(a, 1, 0, 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) + msg = base_msg.format('return_inverse', dt) + v, j = unique(a, 0, 1, 0) 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) + msg = base_msg.format('return_counts', dt) + v, j = unique(a, 0, 0, 1) + assert_array_equal(v, b, msg) + assert_array_equal(j, c, msg) + + msg = base_msg.format('return_index and return_inverse', dt) + v, j1, j2 = unique(a, 1, 1, 0) + assert_array_equal(v, b, msg) + assert_array_equal(j1, i1, msg) + assert_array_equal(j2, i2, msg) + + msg = base_msg.format('return_index and return_counts', dt) + v, j1, j2 = unique(a, 1, 0, 1) + assert_array_equal(v, b, msg) + assert_array_equal(j1, i1, msg) + assert_array_equal(j2, c, msg) + + msg = base_msg.format('return_inverse and return_counts', dt) + v, j1, j2 = unique(a, 0, 1, 1) + assert_array_equal(v, b, msg) + assert_array_equal(j1, i2, msg) + assert_array_equal(j2, c, msg) + + msg = base_msg.format(('return_index, return_inverse ' + 'and return_counts'), dt) + v, j1, j2, j3 = unique(a, 1, 1, 1) assert_array_equal(v, b, msg) assert_array_equal(j1, i1, msg) assert_array_equal(j2, i2, msg) + assert_array_equal(j3, c, 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 + c = np.multiply([2, 1, 2, 2], 10) # test for numeric arrays types = [] @@ -49,7 +77,7 @@ class TestSetOps(TestCase): for dt in types: aa = np.array(a, dt) bb = np.array(b, dt) - check_all(aa, bb, i1, i2, dt) + check_all(aa, bb, i1, i2, c, dt) # test for object arrays dt = 'O' @@ -57,13 +85,13 @@ class TestSetOps(TestCase): aa[:] = a bb = np.empty(len(b), dt) bb[:] = b - check_all(aa, bb, i1, i2, dt) + check_all(aa, bb, i1, i2, c, dt) # test for structured arrays dt = [('', 'i'), ('', 'i')] aa = np.array(list(zip(a, a)), dt) bb = np.array(list(zip(b, b)), dt) - check_all(aa, bb, i1, i2, dt) + check_all(aa, bb, i1, i2, c, dt) # test for ticket #2799 aa = [1.+0.j, 1- 1.j, 1] diff --git a/numpy/lib/tests/test_io.py b/numpy/lib/tests/test_io.py index 418386e35..d0f81bde3 100644 --- a/numpy/lib/tests/test_io.py +++ b/numpy/lib/tests/test_io.py @@ -19,10 +19,13 @@ from numpy.lib._iotools import (ConverterError, ConverterLockError, ConversionWarning) from numpy.compat import asbytes, asbytes_nested, bytes, asstr from nose import SkipTest -from numpy.ma.testutils import (TestCase, assert_equal, assert_array_equal, - assert_raises, run_module_suite) +from numpy.ma.testutils import ( + TestCase, assert_equal, assert_array_equal, + assert_raises, assert_raises_regex, run_module_suite +) from numpy.testing import assert_warns, assert_, build_err_msg + @contextlib.contextmanager def tempdir(change_dir=False): tmpdir = mkdtemp() @@ -759,6 +762,14 @@ class TestLoadTxt(TestCase): res = np.loadtxt(count()) assert_array_equal(res, np.arange(10)) + def test_bad_line(self): + c = TextIO() + c.write('1 2 3\n4 5 6\n2 3') + c.seek(0) + + # Check for exception and that exception contains line number + assert_raises_regex(ValueError, "3", np.loadtxt, c) + class Testfromregex(TestCase): # np.fromregex expects files opened in binary mode. diff --git a/numpy/lib/tests/test_nanfunctions.py b/numpy/lib/tests/test_nanfunctions.py index df332dfb1..f00aa0165 100644 --- a/numpy/lib/tests/test_nanfunctions.py +++ b/numpy/lib/tests/test_nanfunctions.py @@ -114,6 +114,31 @@ class TestNanFunctions_MinMax(TestCase): assert_(res.shape == (3, 1)) res = f(mat) assert_(np.isscalar(res)) + # check that rows of nan are dealt with for subclasses (#4628) + mat[1] = np.nan + for f in self.nanfuncs: + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat, axis=0) + assert_(isinstance(res, np.matrix)) + assert_(not np.any(np.isnan(res))) + assert_(len(w) == 0) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat, axis=1) + assert_(isinstance(res, np.matrix)) + assert_(np.isnan(res[1, 0]) and not np.isnan(res[0, 0]) + and not np.isnan(res[2, 0])) + assert_(len(w) == 1, 'no warning raised') + assert_(issubclass(w[0].category, RuntimeWarning)) + + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter('always') + res = f(mat) + assert_(np.isscalar(res)) + assert_(res != np.nan) + assert_(len(w) == 0) class TestNanFunctions_ArgminArgmax(TestCase): diff --git a/numpy/ma/core.py b/numpy/ma/core.py index faf9896a5..fe3c03789 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -2470,7 +2470,6 @@ class _arraymethod(object): return result - class MaskedIterator(object): """ Flat iterator object to iterate over masked arrays. @@ -2534,8 +2533,14 @@ class MaskedIterator(object): result = self.dataiter.__getitem__(indx).view(type(self.ma)) if self.maskiter is not None: _mask = self.maskiter.__getitem__(indx) - _mask.shape = result.shape - result._mask = _mask + if isinstance(_mask, ndarray): + # set shape to match that of data; this is needed for matrices + _mask.shape = result.shape + result._mask = _mask + elif isinstance(_mask, np.void): + return mvoid(result, mask=_mask, hardmask=self.ma._hardmask) + elif _mask: # Just a scalar, masked + return masked return result ### This won't work is ravel makes a copy @@ -2567,8 +2572,12 @@ class MaskedIterator(object): """ d = next(self.dataiter) - if self.maskiter is not None and next(self.maskiter): - d = masked + if self.maskiter is not None: + m = next(self.maskiter) + if isinstance(m, np.void): + return mvoid(d, mask=m, hardmask=self.ma._hardmask) + elif m: # Just a scalar, masked + return masked return d next = __next__ @@ -3585,9 +3594,8 @@ class MaskedArray(ndarray): if m.dtype.names: m = m.view((bool, len(m.dtype))) if m.any(): - r = np.array(self._data.tolist(), dtype=object) - np.copyto(r, f, where=m) - return str(tuple(r)) + return str(tuple((f if _m else _d) for _d, _m in + zip(self._data.tolist(), m))) else: return str(self._data) elif m: @@ -3598,7 +3606,7 @@ class MaskedArray(ndarray): names = self.dtype.names if names is None: res = self._data.astype("O") - res[m] = f + res.view(ndarray)[m] = f else: rdtype = _recursive_make_descr(self.dtype, "O") res = self._data.astype(rdtype) @@ -3612,19 +3620,22 @@ class MaskedArray(ndarray): """ n = len(self.shape) - name = repr(self._data).split('(')[0] + if self._baseclass is np.ndarray: + name = 'array' + else: + name = self._baseclass.__name__ + parameters = dict(name=name, nlen=" " * len(name), - data=str(self), mask=str(self._mask), - fill=str(self.fill_value), dtype=str(self.dtype)) + data=str(self), mask=str(self._mask), + fill=str(self.fill_value), dtype=str(self.dtype)) if self.dtype.names: if n <= 1: return _print_templates['short_flx'] % parameters - return _print_templates['long_flx'] % parameters + return _print_templates['long_flx'] % parameters elif n <= 1: return _print_templates['short_std'] % parameters return _print_templates['long_std'] % parameters - def __eq__(self, other): "Check whether other equals self elementwise" if self is masked: diff --git a/numpy/ma/extras.py b/numpy/ma/extras.py index 7182bf4ae..f53d9c7e5 100644 --- a/numpy/ma/extras.py +++ b/numpy/ma/extras.py @@ -416,26 +416,53 @@ def apply_over_axes(func, a, axes): """ (This docstring will be overwritten) """ - val = np.asarray(a) - msk = getmaskarray(a) + val = asarray(a) N = a.ndim if array(axes).ndim == 0: axes = (axes,) for axis in axes: if axis < 0: axis = N + axis args = (val, axis) - res = ma.array(func(*(val, axis)), mask=func(*(msk, axis))) + res = func(*args) if res.ndim == val.ndim: - (val, msk) = (res._data, res._mask) + val = res else: res = ma.expand_dims(res, axis) if res.ndim == val.ndim: - (val, msk) = (res._data, res._mask) + val = res else: - raise ValueError("Function is not returning"\ - " an array of correct shape") + raise ValueError("function is not returning " + "an array of the correct shape") return val -apply_over_axes.__doc__ = np.apply_over_axes.__doc__ +apply_over_axes.__doc__ = np.apply_over_axes.__doc__[ + :np.apply_over_axes.__doc__.find('Notes')].rstrip() + \ + """ + + Examples + -------- + >>> a = ma.arange(24).reshape(2,3,4) + >>> a[:,0,1] = ma.masked + >>> a[:,1,:] = ma.masked + >>> print a + [[[0 -- 2 3] + [-- -- -- --] + [8 9 10 11]] + + [[12 -- 14 15] + [-- -- -- --] + [20 21 22 23]]] + >>> print ma.apply_over_axes(ma.sum, a, [0,2]) + [[[46] + [--] + [124]]] + + Tuple axis arguments to ufuncs are equivalent: + + >>> print ma.sum(a, axis=(0,2)).reshape((1,-1,1)) + [[[46] + [--] + [124]]] +""" def average(a, axis=None, weights=None, returned=False): diff --git a/numpy/ma/tests/test_core.py b/numpy/ma/tests/test_core.py index 6bb8d5b2e..4a39103b2 100644 --- a/numpy/ma/tests/test_core.py +++ b/numpy/ma/tests/test_core.py @@ -369,6 +369,13 @@ class TestMaskedArray(TestCase): assert_equal(copied.mask, [0, 0, 0]) assert_equal(a.mask, [0, 1, 0]) + def test_str_repr(self): + a = array([0, 1, 2], mask=[False, True, False]) + assert_equal(str(a), '[0 -- 2]') + assert_equal(repr(a), 'masked_array(data = [0 -- 2],\n' + ' mask = [False True False],\n' + ' fill_value = 999999)\n') + def test_pickling(self): # Tests pickling a = arange(10) @@ -1298,16 +1305,57 @@ class TestMaskedArrayAttributes(TestCase): assert_equal(a.mask, nomask) def test_flat(self): + # Test that flat can return all types of items [#4585, #4615] + # test simple access + test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) + assert_equal(test.flat[1], 2) + assert_equal(test.flat[2], masked) + self.assertTrue(np.all(test.flat[0:2] == test[0, 0:2])) # Test flat on masked_matrices test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) test.flat = masked_array([3, 2, 1], mask=[1, 0, 0]) control = masked_array(np.matrix([[3, 2, 1]]), mask=[1, 0, 0]) assert_equal(test, control) - # + # Test setting test = masked_array(np.matrix([[1, 2, 3]]), mask=[0, 0, 1]) testflat = test.flat testflat[:] = testflat[[2, 1, 0]] assert_equal(test, control) + testflat[0] = 9 + assert_equal(test[0, 0], 9) + # test 2-D record array + # ... on structured array w/ masked records + x = array([[(1, 1.1, 'one'), (2, 2.2, 'two'), (3, 3.3, 'thr')], + [(4, 4.4, 'fou'), (5, 5.5, 'fiv'), (6, 6.6, 'six')]], + dtype=[('a', int), ('b', float), ('c', '|S8')]) + x['a'][0, 1] = masked + x['b'][1, 0] = masked + x['c'][0, 2] = masked + x[-1, -1] = masked + xflat = x.flat + assert_equal(xflat[0], x[0, 0]) + assert_equal(xflat[1], x[0, 1]) + assert_equal(xflat[2], x[0, 2]) + assert_equal(xflat[:3], x[0]) + assert_equal(xflat[3], x[1, 0]) + assert_equal(xflat[4], x[1, 1]) + assert_equal(xflat[5], x[1, 2]) + assert_equal(xflat[3:], x[1]) + assert_equal(xflat[-1], x[-1, -1]) + i = 0 + j = 0 + for xf in xflat: + assert_equal(xf, x[j, i]) + i += 1 + if i >= x.shape[-1]: + i = 0 + j += 1 + # test that matrices keep the correct shape (#4615) + a = masked_array(np.matrix(np.eye(2)), mask=0) + b = a.flat + b01 = b[:2] + assert_equal(b01.data, array([[1., 0.]])) + assert_equal(b01.mask, array([[False, False]])) #------------------------------------------------------------------------------ @@ -3624,7 +3672,7 @@ def test_append_masked_array(): def test_append_masked_array_along_axis(): a = np.ma.masked_equal([1,2,3], value=2) b = np.ma.masked_values([[4, 5, 6], [7, 8, 9]], 7) - + # When `axis` is specified, `values` must have the correct shape. assert_raises(ValueError, np.ma.append, a, b, axis=0) @@ -3634,7 +3682,7 @@ def test_append_masked_array_along_axis(): expected = expected.reshape((3,3)) assert_array_equal(result.data, expected.data) assert_array_equal(result.mask, expected.mask) - + ############################################################################### if __name__ == "__main__": diff --git a/numpy/ma/tests/test_extras.py b/numpy/ma/tests/test_extras.py index dc0f87b92..fa7503392 100644 --- a/numpy/ma/tests/test_extras.py +++ b/numpy/ma/tests/test_extras.py @@ -489,7 +489,8 @@ class TestApplyOverAxes(TestCase): assert_equal(test, ctrl) a[(a % 2).astype(np.bool)] = masked test = apply_over_axes(np.sum, a, [0, 2]) - ctrl = np.array([[[30], [44], [60]]]) + ctrl = np.array([[[28], [44], [60]]]) + assert_equal(test, ctrl) class TestMedian(TestCase): diff --git a/numpy/ma/tests/test_subclassing.py b/numpy/ma/tests/test_subclassing.py index c2c9b8ec9..ade5c59da 100644 --- a/numpy/ma/tests/test_subclassing.py +++ b/numpy/ma/tests/test_subclassing.py @@ -82,6 +82,24 @@ class MMatrix(MaskedArray, np.matrix,): mmatrix = MMatrix +# also a subclass that overrides __str__, __repr__ and __setitem__, disallowing +# setting to non-class values (and thus np.ma.core.masked_print_option) +class ComplicatedSubArray(SubArray): + def __str__(self): + return 'myprefix {0} mypostfix'.format( + super(ComplicatedSubArray, self).__str__()) + + def __repr__(self): + # Return a repr that does not start with 'name(' + return '<{0} {1}>'.format(self.__class__.__name__, self) + + def __setitem__(self, item, value): + # this ensures direct assignment to masked_print_option will fail + if not isinstance(value, ComplicatedSubArray): + raise ValueError("Can only set to MySubArray values") + super(ComplicatedSubArray, self).__setitem__(item, value) + + class TestSubclassing(TestCase): # Test suite for masked subclasses of ndarray. @@ -187,6 +205,31 @@ class TestSubclassing(TestCase): assert_equal(mxsub.info, xsub.info) assert_equal(mxsub._mask, m) + def test_subclass_repr(self): + """test that repr uses the name of the subclass + and 'array' for np.ndarray""" + x = np.arange(5) + mx = masked_array(x, mask=[True, False, True, False, False]) + self.assertTrue(repr(mx).startswith('masked_array')) + xsub = SubArray(x) + mxsub = masked_array(xsub, mask=[True, False, True, False, False]) + self.assertTrue(repr(mxsub).startswith( + 'masked_{0}(data = [-- 1 -- 3 4]'.format(SubArray.__name__))) + + def test_subclass_str(self): + """test str with subclass that has overridden str, setitem""" + # first without override + x = np.arange(5) + xsub = SubArray(x) + mxsub = masked_array(xsub, mask=[True, False, True, False, False]) + self.assertTrue(str(mxsub) == '[-- 1 -- 3 4]') + + xcsub = ComplicatedSubArray(x) + assert_raises(ValueError, xcsub.__setitem__, 0, + np.ma.core.masked_print_option) + mxcsub = masked_array(xcsub, mask=[True, False, True, False, False]) + self.assertTrue(str(mxcsub) == 'myprefix [-- 1 -- 3 4] mypostfix') + ############################################################################### if __name__ == '__main__': diff --git a/numpy/polynomial/hermite.py b/numpy/polynomial/hermite.py index 713c780d3..43ede58ac 100644 --- a/numpy/polynomial/hermite.py +++ b/numpy/polynomial/hermite.py @@ -110,7 +110,7 @@ def poly2herm(pol) : Examples -------- - >>> from numpy.polynomial.hermite_e import poly2herme + >>> from numpy.polynomial.hermite import poly2herm >>> poly2herm(np.arange(4)) array([ 1. , 2.75 , 0.5 , 0.375]) diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c deleted file mode 100644 index eac1f34d7..000000000 --- a/numpy/random/mtrand/mtrand.c +++ /dev/null @@ -1,26501 +0,0 @@ -/* Generated by Cython 0.19.2 on Thu Mar 13 15:49:44 2014 */ - -#define PY_SSIZE_T_CLEAN -#ifndef CYTHON_USE_PYLONG_INTERNALS -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 0 -#else -#include "pyconfig.h" -#ifdef PYLONG_BITS_IN_DIGIT -#define CYTHON_USE_PYLONG_INTERNALS 1 -#else -#define CYTHON_USE_PYLONG_INTERNALS 0 -#endif -#endif -#endif -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02040000 - #error Cython requires Python 2.4+. -#else -#include <stddef.h> /* For offsetof */ -#ifndef offsetof -#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - 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\ - (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \ - (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \ - (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0))) - #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \ - (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ - (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \ - (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1))) - #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \ - (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \ - (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ - (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) -#endif -#if PY_MAJOR_VERSION >= 3 - #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) -#endif -#if PY_VERSION_HEX < 0x02050000 - #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) - #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) - #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n))) -#else - #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n)) - #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) - #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) -#endif -#if PY_VERSION_HEX < 0x02050000 - #define __Pyx_NAMESTR(n) ((char *)(n)) - #define __Pyx_DOCSTR(n) ((char *)(n)) -#else - #define __Pyx_NAMESTR(n) (n) - #define __Pyx_DOCSTR(n) (n) -#endif -#ifndef CYTHON_INLINE - #if defined(__GNUC__) - #define CYTHON_INLINE __inline__ - #elif defined(_MSC_VER) - #define CYTHON_INLINE __inline - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_INLINE inline - #else - #define CYTHON_INLINE - #endif -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and - a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is - a quiet NaN. */ - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif - - -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#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" -#include "numpy/npy_no_deprecated_api.h" -#include "numpy/arrayobject.h" -#include "mtrand_py_helper.h" -#include "randomkit.h" -#include "distributions.h" -#include "initarray.h" -#ifdef _OPENMP -#include <omp.h> -#endif /* _OPENMP */ - -#ifdef PYREX_WITHOUT_ASSERTIONS -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/ - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s) -#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s) -#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s) -#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s) -#if PY_MAJOR_VERSION < 3 -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return u_end - u - 1; -} -#else -#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#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); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); -#if CYTHON_COMPILING_IN_CPYTHON -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params() { - PyObject* sys = NULL; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - sys = PyImport_ImportModule("sys"); - if (sys == NULL) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - if (default_encoding == NULL) goto bad; - if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - const char* default_encoding_c = PyBytes_AS_STRING(default_encoding); - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (ascii_chars_u == NULL) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - } - Py_XDECREF(sys); - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return 0; -bad: - Py_XDECREF(sys); - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; -static int __Pyx_init_sys_getdefaultencoding_params() { - PyObject* sys = NULL; - PyObject* default_encoding = NULL; - char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (sys == NULL) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - if (default_encoding == NULL) goto bad; - default_encoding_c = PyBytes_AS_STRING(default_encoding); - __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); - strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); - Py_DECREF(sys); - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(sys); - Py_XDECREF(default_encoding); - return -1; -} -#endif -#endif - - -#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 ... */ -#else /* __GNUC__ */ - #define likely(x) (x) - #define unlikely(x) (x) -#endif /* __GNUC__ */ - -static PyObject *__pyx_m; -static PyObject *__pyx_d; -static PyObject *__pyx_b; -static PyObject *__pyx_empty_tuple; -static PyObject *__pyx_empty_bytes; -static int __pyx_lineno; -static int __pyx_clineno = 0; -static const char * __pyx_cfilenm= __FILE__; -static const char *__pyx_filename; - - -static const char *__pyx_f[] = { - "mtrand.pyx", - "numpy.pxd", -}; - -/*--- Type declarations ---*/ -struct __pyx_obj_6mtrand_RandomState; - -/* "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); 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/*proto*/ -#define __Pyx_MODULE_NAME "mtrand" -int __pyx_module_is_main_mtrand = 0; - -/* Implementation of 'mtrand' */ -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_RuntimeWarning; -static PyObject *__pyx_pf_6mtrand__shape_from_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_size, PyObject *__pyx_v_d); /* proto */ -static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */ -static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_4seed(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_10__getstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_12__setstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_14__reduce__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_16random_sample(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_18tomaxint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_20randint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_length); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_24choice(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size, PyObject *__pyx_v_replace, PyObject *__pyx_v_p); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_26uniform(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_28rand(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_30randn(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_32random_integers(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_34standard_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_36normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_38beta(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_40exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_42standard_exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_44standard_gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_46gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_48f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_52chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_54noncentral_chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_56standard_cauchy(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_58standard_t(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_60vonmises(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mu, PyObject *__pyx_v_kappa, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_62pareto(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_64weibull(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_66power(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_68laplace(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_70gumbel(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_72logistic(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_74lognormal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_sigma, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_76rayleigh(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_78wald(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_left, PyObject *__pyx_v_mode, PyObject *__pyx_v_right, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_82binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_84negative_binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_86poisson(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_88zipf(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_90geometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_ngood, PyObject *__pyx_v_nbad, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_94logseries(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_96multivariate_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_cov, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_n, PyObject *__pyx_v_pvals, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_100dirichlet(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_102shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */ -static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */ -static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/ -static char __pyx_k_1[] = "size is not compatible with inputs"; -static char __pyx_k_9[] = "algorithm must be 'MT19937'"; -static char __pyx_k_13[] = "state must be 624 longs"; -static char __pyx_k_15[] = "low >= high"; -static char __pyx_k_18[] = "a must be 1-dimensional or an integer"; -static char __pyx_k_20[] = "a must be greater than 0"; -static char __pyx_k_22[] = "a must be 1-dimensional"; -static char __pyx_k_24[] = "a must be non-empty"; -static char __pyx_k_26[] = "p must be 1-dimensional"; -static char __pyx_k_28[] = "a and p must have same size"; -static char __pyx_k_30[] = "probabilities are not non-negative"; -static char __pyx_k_32[] = "probabilities do not sum to 1"; -static char __pyx_k_34[] = "Cannot take a larger sample than population when 'replace=False'"; -static char __pyx_k_36[] = "Fewer non-zero entries in p than size"; -static char __pyx_k_44[] = "scale <= 0"; -static char __pyx_k_47[] = "a <= 0"; -static char __pyx_k_49[] = "b <= 0"; -static char __pyx_k_56[] = "shape <= 0"; -static char __pyx_k_66[] = "dfnum <= 0"; -static char __pyx_k_68[] = "dfden <= 0"; -static char __pyx_k_70[] = "dfnum <= 1"; -static char __pyx_k_73[] = "nonc < 0"; -static char __pyx_k_78[] = "df <= 0"; -static char __pyx_k_82[] = "nonc <= 0"; -static char __pyx_k_84[] = "df <= 1"; -static char __pyx_k_89[] = "kappa < 0"; -static char __pyx_k__T[] = "T"; -static char __pyx_k__a[] = "a"; -static char __pyx_k__b[] = "b"; -static char __pyx_k__d[] = "d"; -static char __pyx_k__f[] = "f"; -static char __pyx_k__l[] = "l"; -static char __pyx_k__n[] = "n"; -static char __pyx_k__p[] = "p"; -static char __pyx_k_112[] = "sigma <= 0"; -static char __pyx_k_114[] = "sigma <= 0.0"; -static char __pyx_k_118[] = "scale <= 0.0"; -static char __pyx_k_120[] = "mean <= 0"; -static char __pyx_k_123[] = "mean <= 0.0"; -static char __pyx_k_126[] = "left > mode"; -static char __pyx_k_128[] = "mode > right"; -static char __pyx_k_130[] = "left == right"; -static char __pyx_k_135[] = "n < 0"; -static char __pyx_k_137[] = "p < 0"; -static char __pyx_k_139[] = "p > 1"; -static char __pyx_k_144[] = "n <= 0"; -static char __pyx_k_152[] = "lam < 0"; -static char __pyx_k_154[] = "lam value too large"; -static char __pyx_k_157[] = "lam value too large."; -static char __pyx_k_159[] = "a <= 1.0"; -static char __pyx_k_162[] = "p < 0.0"; -static char __pyx_k_164[] = "p > 1.0"; -static char __pyx_k_168[] = "ngood < 0"; -static char __pyx_k_170[] = "nbad < 0"; -static char __pyx_k_172[] = "nsample < 1"; -static char __pyx_k_174[] = "ngood + nbad < nsample"; -static char __pyx_k_180[] = "p <= 0.0"; -static char __pyx_k_182[] = "p >= 1.0"; -static char __pyx_k_186[] = "numpy.dual"; -static char __pyx_k_187[] = "mean must be 1 dimensional"; -static char __pyx_k_189[] = "cov must be 2 dimensional and square"; -static char __pyx_k_191[] = "mean and cov must have same length"; -static char __pyx_k_194[] = "covariance is not positive-semidefinite."; -static char __pyx_k_197[] = "sum(pvals[:-1]) > 1.0"; -static char __pyx_k_201[] = "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx"; -static char __pyx_k_204[] = "standard_exponential"; -static char __pyx_k_205[] = "noncentral_chisquare"; -static char __pyx_k_206[] = "RandomState.random_sample (line 731)"; -static char __pyx_k_207[] = "\n random_sample(size=None)\n\n Return random floats in the half-open interval [0.0, 1.0).\n\n Results are from the \"continuous uniform\" distribution over the\n stated interval. To sample :math:`Unif[a, b), b > a` multiply\n the output of `random_sample` by `(b-a)` and add `a`::\n\n (b - a) * random_sample() + a\n\n Parameters\n ----------\n size : int or tuple of ints, optional\n Defines the shape of the returned array of random floats. If None\n (the default), returns a single float.\n\n Returns\n -------\n out : float or ndarray of floats\n Array of random floats of shape `size` (unless ``size=None``, in which\n case a single float is returned).\n\n Examples\n --------\n >>> np.random.random_sample()\n 0.47108547995356098\n >>> type(np.random.random_sample())\n <type 'float'>\n >>> np.random.random_sample((5,))\n array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])\n\n Three-by-two array of random numbers from [-5, 0):\n\n >>> 5 * np.random.random_sample((3, 2)) - 5\n array([[-3.99149989, -0.52338984],\n [-2.99091858, -0.79479508],\n [-1.23204345, -1.75224494]])\n\n "; -static char __pyx_k_208[] = "RandomState.tomaxint (line 774)"; -static char __pyx_k_209[] = "\n tomaxint(size=None)\n\n Random integers between 0 and ``sys.maxint``, inclusive.\n\n Return a sample of uniformly distributed random integers in the interval\n [0, ``sys.maxint``].\n\n Parameters\n ----------\n size : tuple of ints, int, optional\n Shape of output. If this is, for example, (m,n,k), m*n*k samples\n are generated. If no shape is specified, a single sample is\n returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Uniform sampling over a given half-open interval of integers.\n random_integers : Uniform sampling over a given closed interval of\n integers.\n\n Examples\n --------\n >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n >>> RS.tomaxint((2,2,2))\n array([[[1170048599, 1600360186],\n [ 739731006, 1947757578]],\n [[1871712945, 752307660],\n [1601631370, 1479324245]]])\n >>> import sys\n >>> sys.maxint\n 2147483647\n >>> RS.tomaxint((2,2,2)) < sys.maxint\n array([[[ True, True],\n [ True, True]],\n [[ True, True],\n [ True, True]]], dtype=bool)\n\n "; -static char __pyx_k_210[] = "RandomState.randint (line 821)"; -static char __pyx_k_211[] = "\n randint(low, high=None, size=None)\n\n Return random integers from `low` (inclusive) to `high` (exclusive).\n\n Return random integers from the \"discrete uniform\" distribution in the\n \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n then results are from [0, `low`).\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, one above the largest (signed) integer to be drawn\n from the distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is\n returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.random_integers : similar to `randint`, only for the closed\n interval [`low`, `high`], and 1 is the lowest value if `high` is\n omitted. In particular, this other one is the one to use to generate\n uniformly distributed discrete non-integers.\n\n Examples\n --------\n >>> np.random.randint(2, size=10)\n array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n >>> np.random.randint(1, size=10)\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n >>> np.random.randint(5, size=(2, 4))\n array([[4, 0, 2, 1],\n [3, 2, 2, 0]])\n\n "; -static char __pyx_k_212[] = "RandomState.bytes (line 901)"; -static char __pyx_k_213[] = "\n bytes(length)\n\n Return random bytes.\n\n Parameters\n ----------\n length : int\n Number of random bytes.\n\n Returns\n -------\n out : str\n String of length `length`.\n\n Examples\n --------\n >>> np.random.bytes(10)\n ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n "; -static char __pyx_k_214[] = "RandomState.choice (line 929)"; -static char __pyx_k_215[] = "\n choice(a, size=None, replace=True, p=None)\n\n Generates a random sample from a given 1-D array\n\n .. versionadded:: 1.7.0\n\n Parameters\n -----------\n a : 1-D array-like or int\n If an ndarray, a random sample is generated from its elements.\n If an int, the random sample is generated as if a was np.arange(n)\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n replace : boolean, optional\n Whether the sample is with or without replacement\n p : 1-D array-like, optional\n The probabilities associated with each entry in a.\n If not given the sample assumes a uniform distribtion over all\n entries in a.\n\n Returns\n --------\n samples : 1-D ndarray, shape (size,)\n The generated random samples\n\n Raises\n -------\n ValueError\n If a is an int and less than zero, if a or p are not 1-dimensional,\n if a is an array-like of size 0, if p is not a vector of\n probabilities, if a and p have different lengths, or if\n replace=False and the sample size is greater than the population\n size\n\n See Also\n ---------\n randint, shuffle, permutation\n\n Examples\n ---------\n Generate a uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3)\n array([0, 3, 4])\n >>> #This is equivalent to np.random.randint(0,5,3)\n\n Generate a non-uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n array([3, 3, 0])\n\n Generate a uniform random sample from np.arange(5) of size 3 without\n replacement:\n\n >>> np.random.choice(5, 3, replace=False)\n array([3,1,0])\n "" >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]\n\n Generate a non-uniform random sample from np.arange(5) of size\n 3 without replacement:\n\n >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n array([2, 3, 0])\n\n Any of the above can be repeated with an arbitrary array-like\n instead of just integers. For instance:\n\n >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n dtype='|S11')\n\n "; -static char __pyx_k_216[] = "RandomState.uniform (line 1104)"; -static char __pyx_k_217[] = "\n uniform(low=0.0, high=1.0, size=1)\n\n Draw samples from a uniform distribution.\n\n Samples are uniformly distributed over the half-open interval\n ``[low, high)`` (includes low, but excludes high). In other words,\n any value within the given interval is equally likely to be drawn\n by `uniform`.\n\n Parameters\n ----------\n low : float, optional\n Lower boundary of the output interval. All values generated will be\n greater than or equal to low. The default value is 0.\n high : float\n Upper boundary of the output interval. All values generated will be\n less than high. The default value is 1.0.\n size : int or tuple of ints, optional\n Shape of output. If the given size is, for example, (m,n,k),\n m*n*k samples are generated. If no shape is specified, a single sample\n is returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Discrete uniform distribution, yielding integers.\n random_integers : Discrete uniform distribution over the closed\n interval ``[low, high]``.\n random_sample : Floats uniformly distributed over ``[0, 1)``.\n random : Alias for `random_sample`.\n rand : Convenience function that accepts dimensions as input, e.g.,\n ``rand(2,2)`` would generate a 2-by-2 array of floats,\n uniformly distributed over ``[0, 1)``.\n\n Notes\n -----\n The probability density function of the uniform distribution is\n\n .. math:: p(x) = \\frac{1}{b - a}\n\n anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> s = np.random.uniform(-1,0,1000)\n\n All values are w""ithin the given interval:\n\n >>> np.all(s >= -1)\n True\n >>> np.all(s < 0)\n True\n\n Display the histogram of the samples, along with the\n probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_218[] = "RandomState.rand (line 1191)"; -static char __pyx_k_219[] = "\n rand(d0, d1, ..., dn)\n\n Random values in a given shape.\n\n Create an array of the given shape and propagate it with\n random samples from a uniform distribution\n over ``[0, 1)``.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should all be positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n out : ndarray, shape ``(d0, d1, ..., dn)``\n Random values.\n\n See Also\n --------\n random\n\n Notes\n -----\n This is a convenience function. If you want an interface that\n takes a shape-tuple as the first argument, refer to\n np.random.random_sample .\n\n Examples\n --------\n >>> np.random.rand(3,2)\n array([[ 0.14022471, 0.96360618], #random\n [ 0.37601032, 0.25528411], #random\n [ 0.49313049, 0.94909878]]) #random\n\n "; -static char __pyx_k_220[] = "RandomState.randn (line 1235)"; -static char __pyx_k_221[] = "\n randn(d0, d1, ..., dn)\n\n Return a sample (or samples) from the \"standard normal\" distribution.\n\n If positive, int_like or int-convertible arguments are provided,\n `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n with random floats sampled from a univariate \"normal\" (Gaussian)\n distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n floats, they are first converted to integers by truncation). A single\n float randomly sampled from the distribution is returned if no\n argument is provided.\n\n This is a convenience function. If you want an interface that takes a\n tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should be all positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n Z : ndarray or float\n A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n the standard normal distribution, or a single such float if\n no parameters were supplied.\n\n See Also\n --------\n random.standard_normal : Similar, but takes a tuple as its argument.\n\n Notes\n -----\n For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n ``sigma * np.random.randn(...) + mu``\n\n Examples\n --------\n >>> np.random.randn()\n 2.1923875335537315 #random\n\n Two-by-four array of samples from N(3, 6.25):\n\n >>> 2.5 * np.random.randn(2, 4) + 3\n array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n\n "; -static char __pyx_k_222[] = "RandomState.random_integers (line 1292)"; -static char __pyx_k_223[] = "\n random_integers(low, high=None, size=None)\n\n Return random integers between `low` and `high`, inclusive.\n\n Return random integers from the \"discrete uniform\" distribution in the\n closed interval [`low`, `high`]. If `high` is None (the default),\n then results are from [1, `low`].\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, the largest (signed) integer to be drawn from the\n distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.randint : Similar to `random_integers`, only for the half-open\n interval [`low`, `high`), and 0 is the lowest value if `high` is\n omitted.\n\n Notes\n -----\n To sample from N evenly spaced floating-point numbers between a and b,\n use::\n\n a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n Examples\n --------\n >>> np.random.random_integers(5)\n 4\n >>> type(np.random.random_integers(5))\n <type 'int'>\n >>> np.random.random_integers(5, size=(3.,2.))\n array([[5, 4],\n [3, 3],\n [4, 5]])\n\n Choose five random numbers from the set of five evenly-spaced\n numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])\n\n Roll two six sided dice 1000 times and sum the results:\n\n >>> d1 = np.random.random_integers(1, 6, 1000)\n >>> d2 = np.random.random_integers(1, 6, 1000)\n >>> dsums = d1 + d2\n\n Display results as a histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n >>> plt.show()\n\n "; -static char __pyx_k_224[] = "RandomState.standard_normal (line 1370)"; -static char __pyx_k_225[] = "\n standard_normal(size=None)\n\n Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n Parameters\n ----------\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 out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n >>> s = np.random.standard_normal(8000)\n >>> s\n array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, #random\n -0.38672696, -0.4685006 ]) #random\n >>> s.shape\n (8000,)\n >>> s = np.random.standard_normal(size=(3, 4, 2))\n >>> s.shape\n (3, 4, 2)\n\n "; -static char __pyx_k_226[] = "RandomState.normal (line 1402)"; -static char __pyx_k_227[] = "\n normal(loc=0.0, scale=1.0, size=None)\n\n Draw random samples from a normal (Gaussian) distribution.\n\n The probability density function of the normal distribution, first\n derived by De Moivre and 200 years later by both Gauss and Laplace\n independently [2]_, is often called the bell curve because of\n its characteristic shape (see the example below).\n\n The normal distributions occurs often in nature. For example, it\n describes the commonly occurring distribution of samples influenced\n by a large number of tiny, random disturbances, each with its own\n unique distribution [2]_.\n\n Parameters\n ----------\n loc : float\n Mean (\"centre\") of the distribution.\n scale : float\n Standard deviation (spread or \"width\") 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.norm : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gaussian distribution is\n\n .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n The square of the standard deviation, :math:`\\sigma^2`, is called the\n variance.\n\n The function has its peak at the mean, and its \"spread\" increases with\n the standard deviation (the function reaches 0.607 times its maximum at\n :math:`x + \\sigma` and :math:`x - \\sigma` [2]_). This implies that\n `numpy.random.normal` is more likely to return samples lying close to the\n mean, rather than those far away.\n""\n References\n ----------\n .. [1] Wikipedia, \"Normal distribution\",\n http://en.wikipedia.org/wiki/Normal_distribution\n .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n Variables and Random Signal Principles\", 4th ed., 2001,\n pp. 51, 51, 125.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 0, 0.1 # mean and standard deviation\n >>> s = np.random.normal(mu, sigma, 1000)\n\n Verify the mean and the variance:\n\n >>> abs(mu - np.mean(s)) < 0.01\n True\n\n >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n True\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, 30, normed=True)\n >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_228[] = "RandomState.standard_exponential (line 1615)"; -static char __pyx_k_229[] = "\n standard_exponential(size=None)\n\n Draw samples from the standard exponential distribution.\n\n `standard_exponential` is identical to the exponential distribution\n with a scale parameter of 1.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n Output a 3x8000 array:\n\n >>> n = np.random.standard_exponential((3, 8000))\n\n "; -static char __pyx_k_230[] = "RandomState.standard_gamma (line 1643)"; -static char __pyx_k_231[] = "\n standard_gamma(shape, size=None)\n\n Draw samples from a Standard Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n shape (sometimes designated \"k\") and scale=1.\n\n Parameters\n ----------\n shape : float\n Parameter, should be > 0.\n size : int or 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 scalar\n The drawn samples.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2., 1. # mean and width\n >>> s = np.random.standard_gamma(shape, 1000000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt""\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n ... (sps.gamma(shape) * scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_232[] = "RandomState.gamma (line 1725)"; -static char __pyx_k_233[] = "\n gamma(shape, scale=1.0, size=None)\n\n Draw samples from a Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n \"theta\"), where both parameters are > 0.\n\n Parameters\n ----------\n shape : scalar > 0\n The shape of the gamma distribution.\n scale : scalar > 0, optional\n The scale of the gamma distribution. Default is equal to 1.\n size : shape_tuple, optional\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 out : ndarray, float\n Returns one sample unless `size` parameter is specified.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2.,"" 2. # mean and dispersion\n >>> s = np.random.gamma(shape, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n ... (sps.gamma(shape)*scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_234[] = "RandomState.f (line 1816)"; -static char __pyx_k_235[] = "\n f(dfnum, dfden, size=None)\n\n Draw samples from a F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n in denominator), where both parameters should be greater than zero.\n\n The random variate of the F distribution (also known as the\n Fisher distribution) is a continuous probability distribution\n that arises in ANOVA tests, and is the ratio of two chi-square\n variates.\n\n Parameters\n ----------\n dfnum : float\n Degrees of freedom in numerator. Should be greater than zero.\n dfden : float\n Degrees of freedom in denominator. Should be greater than zero.\n size : {tuple, int}, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``,\n then ``m * n * k`` samples are drawn. By default only one sample\n is returned.\n\n Returns\n -------\n samples : {ndarray, scalar}\n Samples from the Fisher distribution.\n\n See Also\n --------\n scipy.stats.distributions.f : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The F statistic is used to compare in-group variances to between-group\n variances. Calculating the distribution depends on the sampling, and\n so it is a function of the respective degrees of freedom in the\n problem. The variable `dfnum` is the number of samples minus one, the\n between-groups degrees of freedom, while `dfden` is the within-groups\n degrees of freedom, the sum of the number of samples in each group\n minus the number of groups.\n\n References\n ----------\n .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.""\n .. [2] Wikipedia, \"F-distribution\",\n http://en.wikipedia.org/wiki/F-distribution\n\n Examples\n --------\n An example from Glantz[1], pp 47-40.\n Two groups, children of diabetics (25 people) and children from people\n without diabetes (25 controls). Fasting blood glucose was measured,\n case group had a mean value of 86.1, controls had a mean value of\n 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n data consistent with the null hypothesis that the parents diabetic\n status does not affect their children's blood glucose levels?\n Calculating the F statistic from the data gives a value of 36.01.\n\n Draw samples from the distribution:\n\n >>> dfnum = 1. # between group degrees of freedom\n >>> dfden = 48. # within groups degrees of freedom\n >>> s = np.random.f(dfnum, dfden, 1000)\n\n The lower bound for the top 1% of the samples is :\n\n >>> sort(s)[-10]\n 7.61988120985\n\n So there is about a 1% chance that the F statistic will exceed 7.62,\n the measured value is 36, so the null hypothesis is rejected at the 1%\n level.\n\n "; -static char __pyx_k_236[] = "RandomState.noncentral_f (line 1918)"; -static char __pyx_k_237[] = "\n noncentral_f(dfnum, dfden, nonc, size=None)\n\n Draw samples from the noncentral F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n freedom in denominator), where both parameters > 1.\n `nonc` is the non-centrality parameter.\n\n Parameters\n ----------\n dfnum : int\n Parameter, should be > 1.\n dfden : int\n Parameter, should be > 1.\n nonc : float\n Parameter, should be >= 0.\n size : int or 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 : scalar or ndarray\n Drawn samples.\n\n Notes\n -----\n When calculating the power of an experiment (power = probability of\n rejecting the null hypothesis when a specific alternative is true) the\n non-central F statistic becomes important. When the null hypothesis is\n true, the F statistic follows a central F distribution. When the null\n hypothesis is not true, then it follows a non-central F statistic.\n\n References\n ----------\n Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n Wikipedia, \"Noncentral F distribution\",\n http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n Examples\n --------\n In a study, testing for a specific alternative to the null hypothesis\n requires use of the Noncentral F distribution. We need to calculate the\n area in the tail of the distribution that exceeds the value of the F\n distribution for the null hypothesis. We'll plot the two probability\n distributions for comp""arison.\n\n >>> dfnum = 3 # between group deg of freedom\n >>> dfden = 20 # within groups degrees of freedom\n >>> nonc = 3.0\n >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n >>> F = np.histogram(c_vals, bins=50, normed=True)\n >>> plt.plot(F[1][1:], F[0])\n >>> plt.plot(NF[1][1:], NF[0])\n >>> plt.show()\n\n "; -static char __pyx_k_238[] = "RandomState.chisquare (line 2013)"; -static char __pyx_k_239[] = "\n chisquare(df, size=None)\n\n Draw samples from a chi-square distribution.\n\n When `df` independent random variables, each with standard normal\n distributions (mean 0, variance 1), are squared and summed, the\n resulting distribution is chi-square (see Notes). This distribution\n is often used in hypothesis testing.\n\n Parameters\n ----------\n df : int\n Number of degrees of freedom.\n size : tuple of ints, int, optional\n Size of the returned array. By default, a scalar is\n returned.\n\n Returns\n -------\n output : ndarray\n Samples drawn from the distribution, packed in a `size`-shaped\n array.\n\n Raises\n ------\n ValueError\n When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n is given.\n\n Notes\n -----\n The variable obtained by summing the squares of `df` independent,\n standard normally distributed random variables:\n\n .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n is chi-square distributed, denoted\n\n .. math:: Q \\sim \\chi^2_k.\n\n The probability density function of the chi-squared distribution is\n\n .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n x^{k/2 - 1} e^{-x/2},\n\n where :math:`\\Gamma` is the gamma function,\n\n .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n References\n ----------\n `NIST/SEMATECH e-Handbook of Statistical Methods\n <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n Examples\n --------\n >>> np.random.chisquare(2,4)\n array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])\n\n "; -static char __pyx_k_240[] = "RandomState.noncentral_chisquare (line 2091)"; -static char __pyx_k_241[] = "\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_242[] = "RandomState.standard_cauchy (line 2183)"; -static char __pyx_k_243[] = "\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_244[] = "RandomState.standard_t (line 2244)"; -static char __pyx_k_245[] = "\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_246[] = "RandomState.vonmises (line 2345)"; -static char __pyx_k_247[] = "\n vonmises(mu, kappa, size=None)\n\n Draw samples from a von Mises distribution.\n\n Samples are drawn from a von Mises distribution with specified mode\n (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n The von Mises distribution (also known as the circular normal\n distribution) is a continuous probability distribution on the unit\n circle. It may be thought of as the circular analogue of the normal\n distribution.\n\n Parameters\n ----------\n mu : float\n Mode (\"center\") of the distribution.\n kappa : float\n Dispersion of the distribution, has to be >=0.\n size : int or tuple of 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 : scalar or ndarray\n The returned samples, which are in the interval [-pi, pi].\n\n See Also\n --------\n scipy.stats.distributions.vonmises : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the von Mises distribution is\n\n .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n The von Mises is named for Richard Edler von Mises, who was born in\n Austria-Hungary, in what is now the Ukraine. He fled to the United\n States in 1939 and became a professor at Harvard. He worked in\n probability theory, aerodynamics, fluid mechanics, and philosophy of\n science.\n\n References\n ----------\n Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n Functions*, New York: Dover, 1965.\n\n "" von Mises, R., *Mathematical Theory of Probability and Statistics*,\n New York: Academic Press, 1964.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n >>> s = np.random.vonmises(mu, kappa, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_248[] = "RandomState.pareto (line 2439)"; -static char __pyx_k_249[] = "\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_250[] = "RandomState.weibull (line 2535)"; -static char __pyx_k_251[] = "\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_252[] = "RandomState.power (line 2635)"; -static char __pyx_k_253[] = "\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_254[] = "RandomState.laplace (line 2744)"; -static char __pyx_k_255[] = "\n laplace(loc=0.0, scale=1.0, size=None)\n\n Draw samples from the Laplace or double exponential distribution with\n specified location (or mean) and scale (decay).\n\n The Laplace distribution is similar to the Gaussian/normal distribution,\n but is sharper at the peak and has fatter tails. It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. [4] Wikipedia, \"Laplace distribution\",\n http://en.wikipedia.org/wik""i/Laplace_distribution\n\n Examples\n --------\n Draw samples from the distribution\n\n >>> loc, scale = 0., 1.\n >>> s = np.random.laplace(loc, scale, 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, 30, normed=True)\n >>> x = np.arange(-8., 8., .01)\n >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)\n >>> plt.plot(x, pdf)\n\n Plot Gaussian for comparison:\n\n >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n ... np.exp(-(x - loc)**2 / (2 * scale**2)))\n >>> plt.plot(x,g)\n\n "; -static char __pyx_k_256[] = "RandomState.gumbel (line 2834)"; -static char __pyx_k_257[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n Gumbel distribution.\n\n Draw samples from a Gumbel distribution with specified location and scale.\n For more information on the Gumbel distribution, see Notes and References\n below.\n\n Parameters\n ----------\n loc : float\n The location of the mode of the distribution.\n scale : float\n The scale parameter 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 Returns\n -------\n out : ndarray\n The samples\n\n See Also\n --------\n scipy.stats.gumbel_l\n scipy.stats.gumbel_r\n scipy.stats.genextreme\n probability density function, distribution, or cumulative density\n function, etc. for each of the above\n weibull\n\n Notes\n -----\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails.\n\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the ""probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n Press, 1958.\n\n Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n 2001.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, beta = 0, 0.1 # location and scale\n >>> s = np.random.gumbel(mu, beta, 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, 30, normed=True)\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n Show how an extreme value distribution can arise from a Gaussian process\n and compare to a Gaussian:\n\n >>> means = []\n >>> maxima = []\n >>> for i in range(0,1000) :\n ... a = np.random.normal(mu, beta, 1000)\n ... means.append(a.mean())\n ... maxima.append(a.max())\n >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n >>> mu = np.mean(maxima) - 0.57721*beta\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp(-np.exp(-(bins - mu)/beta)),\n ... linewidth=2, color='r')\n >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n ... * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n ... linewidth=2, color='g')\n >>> plt.show()\n\n "; -static char __pyx_k_258[] = "RandomState.logistic (line 2965)"; -static char __pyx_k_259[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\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.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n "; -static char __pyx_k_260[] = "RandomState.lognormal (line 3053)"; -static char __pyx_k_261[] = "\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_262[] = "RandomState.rayleigh (line 3174)"; -static char __pyx_k_263[] = "\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_264[] = "RandomState.wald (line 3246)"; -static char __pyx_k_265[] = "\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_266[] = "RandomState.triangular (line 3332)"; -static char __pyx_k_267[] = "\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_268[] = "RandomState.binomial (line 3420)"; -static char __pyx_k_269[] = "\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_270[] = "RandomState.negative_binomial (line 3528)"; -static char __pyx_k_271[] = "\n negative_binomial(n, p, size=None)\n\n Draw samples from a negative_binomial distribution.\n\n Samples are drawn from a negative_Binomial distribution with specified\n parameters, `n` trials and `p` probability of success where `n` is an\n integer > 0 and `p` is in the interval [0, 1].\n\n Parameters\n ----------\n n : int\n Parameter, > 0.\n p : float\n Parameter, >= 0 and <=1.\n size : int or 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 : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. What is the probability\n of having one success for each successive well, that is what is the\n probability of a single success after drilling 5 wells, after 6 wells,\n etc.?\n\n >>> s = np.random.negative_binomial(1, 0.1, 100000)\n >>> for i in range(1, 11):\n ... probability = sum(s<i) / 100000.\n ... print i, \"wells drilled, probability of one success =\", probability\n\n "; -static char __pyx_k_272[] = "RandomState.poisson (line 3623)"; -static char __pyx_k_273[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n "; -static char __pyx_k_274[] = "RandomState.zipf (line 3694)"; -static char __pyx_k_275[] = "\n zipf(a, size=None)\n\n Draw samples from a Zipf distribution.\n\n Samples are drawn from a Zipf distribution with specified parameter\n `a` > 1.\n\n The Zipf distribution (also known as the zeta distribution) is a\n continuous probability distribution that satisfies Zipf's law: the\n frequency of an item is inversely proportional to its rank in a\n frequency table.\n\n Parameters\n ----------\n a : float > 1\n Distribution parameter.\n size : int or tuple of int, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 2. # parameter\n >>> s = np.random.zipf""(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 >>> import scipy.special as sps\n Truncate s values at 50 so plot is interesting\n >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n >>> x = np.arange(1., 50.)\n >>> y = x**(-a)/sps.zetac(a)\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n "; -static char __pyx_k_276[] = "RandomState.geometric (line 3782)"; -static char __pyx_k_277[] = "\n geometric(p, size=None)\n\n Draw samples from the geometric distribution.\n\n Bernoulli trials are experiments with one of two outcomes:\n success or failure (an example of such an experiment is flipping\n a coin). The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. The output\n is shaped according to `size`.\n\n Returns\n -------\n out : ndarray\n Samples from the geometric distribution, shaped according to\n `size`.\n\n Examples\n --------\n Draw ten thousand values from the geometric distribution,\n with the probability of an individual success equal to 0.35:\n\n >>> z = np.random.geometric(p=0.35, size=10000)\n\n How many trials succeeded after a single run?\n\n >>> (z == 1).sum() / 10000.\n 0.34889999999999999 #random\n\n "; -static char __pyx_k_278[] = "RandomState.hypergeometric (line 3848)"; -static char __pyx_k_279[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : int or array_like\n Number of ways to make a good selection. Must be nonnegative.\n nbad : int or array_like\n Number of ways to make a bad selection. Must be nonnegative.\n nsample : int or array_like\n Number of items sampled. Must be at least 1 and at most\n ``ngood + nbad``.\n size : int or tuple of 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 or scalar\n The values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distrib""ution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn with\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n "; -static char __pyx_k_280[] = "RandomState.logseries (line 3967)"; -static char __pyx_k_281[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\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.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n "; -static char __pyx_k_282[] = "RandomState.multivariate_normal (line 4062)"; -static char __pyx_k_283[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive-semidefinite for \"physically meaningful\" results.\n size : int or tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we"" draw N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n "; -static char __pyx_k_284[] = "RandomState.multinomial (line 4205)"; -static char __pyx_k_285[] = "\n multinomial(n, pvals, size=None)\n\n Draw samples from a multinomial distribution.\n\n The multinomial distribution is a multivariate generalisation of the\n binomial distribution. Take an experiment with one of ``p``\n possible outcomes. An example of such an experiment is throwing a dice,\n where the outcome can be 1 through 6. Each sample drawn from the\n distribution represents `n` such experiments. Its values,\n ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n was ``i``.\n\n Parameters\n ----------\n n : int\n Number of experiments.\n pvals : sequence of floats, length p\n Probabilities of each of the ``p`` different outcomes. These\n should sum to 1 (however, the last element is always assumed to\n account for the remaining probability, as long as\n ``sum(pvals[:-1]) <= 1)``.\n size : tuple of ints\n Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n and the output shape becomes ``(M, N, K, p)``, since each sample\n has shape ``(p,)``.\n\n Examples\n --------\n Throw a dice 20 times:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=1)\n array([[4, 1, 7, 5, 2, 1]])\n\n It landed 4 times on 1, once on 2, etc.\n\n Now, throw the dice 20 times, and 20 times again:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=2)\n array([[3, 4, 3, 3, 4, 3],\n [2, 4, 3, 4, 0, 7]])\n\n For the first run, we threw 3 times 1, 4 times 2, etc. For the second,\n we threw 2 times 1, 4 times 2, etc.\n\n A loaded dice is more likely to land on number 6:\n\n >>> np.random.multinomial(100, [1/7.]*5)\n array([13, 16, 13, 16, 42])\n\n "; -static char __pyx_k_286[] = "RandomState.dirichlet (line 4293)"; -static char __pyx_k_287[] = "\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_288[] = "RandomState.shuffle (line 4405)"; -static char __pyx_k_289[] = "\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 "; 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Default is equal to 1.\n size : shape_tuple, optional\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 out : ndarray, float\n Returns one sample unless `size` parameter is specified.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. 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This distribution\n is often used in hypothesis testing.\n\n Parameters\n ----------\n df : int\n Number of degrees of freedom.\n size : tuple of ints, int, optional\n Size of the returned array. 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[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 "; 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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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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 .. 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It may be thought of as the circular analogue of the normal\n distribution.\n\n Parameters\n ----------\n mu : float\n Mode (\"center\") of the distribution.\n kappa : float\n Dispersion of the distribution, has to be >=0.\n size : int or tuple of int\n Output shape. 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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. 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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 .. 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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 "; 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It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray\n The samples\n\n See Also\n --------\n scipy.stats.gumbel_l\n scipy.stats.gumbel_r\n scipy.stats.genextreme\n probability density function, distribution, or cumulative density\n function, etc. for each of the above\n weibull\n\n Notes\n -----\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails.\n\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the ""probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n Gumbel, E. 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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.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. 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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 "; 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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 .. 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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. 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(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. 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. 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Must be nonnegative.\n nbad : int or array_like\n Number of ways to make a bad selection. Must be nonnegative.\n nsample : int or array_like\n Number of items sampled. Must be at least 1 and at most\n ``ngood + nbad``.\n size : int or tuple of 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 or scalar\n The values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distrib""ution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn with\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. 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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.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. 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Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive-semidefinite for \"physically meaningful\" results.\n size : int or tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we"" draw N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. 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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 .. 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__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_19)); - - /* "mtrand.pyx":1015 - * raise ValueError("a must be 1-dimensional or an integer") - * if pop_size <= 0: - * raise ValueError("a must be greater than 0") # <<<<<<<<<<<<<< - * elif a.ndim != 1: - * raise ValueError("a must be 1-dimensional") - */ - __pyx_k_tuple_21 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_20)); if (unlikely(!__pyx_k_tuple_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_21); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_21)); - - /* "mtrand.pyx":1017 - * raise ValueError("a must be greater than 0") - * elif a.ndim != 1: - * raise ValueError("a must be 1-dimensional") # <<<<<<<<<<<<<< - * else: - * pop_size = a.shape[0] - */ - __pyx_k_tuple_23 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_22)); if (unlikely(!__pyx_k_tuple_23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_23); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_23)); - - /* "mtrand.pyx":1021 - * pop_size = a.shape[0] - * if pop_size is 0: - * raise ValueError("a must be non-empty") # <<<<<<<<<<<<<< - * - * if None != p: - */ - __pyx_k_tuple_25 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_24)); if (unlikely(!__pyx_k_tuple_25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1021; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_25); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_25)); - - /* "mtrand.pyx":1029 - * - * if p.ndim != 1: - * raise ValueError("p must be 1-dimensional") # <<<<<<<<<<<<<< - * if p.size != pop_size: - * raise ValueError("a and p must have same size") - */ - __pyx_k_tuple_27 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_26)); if (unlikely(!__pyx_k_tuple_27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1029; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_27); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_27)); - - /* "mtrand.pyx":1031 - * raise ValueError("p must be 1-dimensional") - 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__Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_31)); - - /* "mtrand.pyx":1035 - * raise ValueError("probabilities are not non-negative") - * if abs(kahan_sum(pix, d) - 1.) > 1e-8: - * raise ValueError("probabilities do not sum to 1") # <<<<<<<<<<<<<< - * - * shape = size - */ - __pyx_k_tuple_33 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_32)); if (unlikely(!__pyx_k_tuple_33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1035; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_33); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_33)); - - /* "mtrand.pyx":1055 - * else: - * if size > pop_size: - * raise ValueError("Cannot take a larger sample than " # <<<<<<<<<<<<<< - * "population when 'replace=False'") - * - */ - __pyx_k_tuple_35 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_34)); if (unlikely(!__pyx_k_tuple_35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1055; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_35); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_35)); - 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__Pyx_GOTREF(__pyx_k_tuple_86); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_86)); - - /* "mtrand.pyx":2335 - * if not PyErr_Occurred(): - * if fdf <= 0: - * raise ValueError("df <= 0") # <<<<<<<<<<<<<< - * return cont1_array_sc(self.internal_state, rk_standard_t, size, fdf) - * - */ - __pyx_k_tuple_87 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2335; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_87); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_87)); - - /* "mtrand.pyx":2342 - * odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED) - * if np.any(np.less_equal(odf, 0.0)): - * raise ValueError("df <= 0") # <<<<<<<<<<<<<< - * return cont1_array(self.internal_state, rk_standard_t, size, odf) - * - */ - __pyx_k_tuple_88 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2342; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_88); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_88)); - - /* "mtrand.pyx":2428 - * if not PyErr_Occurred(): - * if fkappa < 0: - * raise ValueError("kappa < 0") # <<<<<<<<<<<<<< - * return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu, fkappa) - * - */ - __pyx_k_tuple_90 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_89)); if (unlikely(!__pyx_k_tuple_90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2428; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_90); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_90)); - - /* "mtrand.pyx":2436 - * okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ARRAY_ALIGNED) - * if np.any(np.less(okappa, 0.0)): - * raise ValueError("kappa < 0") # <<<<<<<<<<<<<< - * return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa) - * - */ - __pyx_k_tuple_91 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_89)); if (unlikely(!__pyx_k_tuple_91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2436; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_91); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_91)); - - /* "mtrand.pyx":2525 - * if not PyErr_Occurred(): - * if fa <= 0: - * raise ValueError("a <= 0") # <<<<<<<<<<<<<< - * return cont1_array_sc(self.internal_state, rk_pareto, size, fa) - * - */ - __pyx_k_tuple_92 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2525; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_k_tuple_92); - __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_92)); - - /* "mtrand.pyx":2532 - * oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED) - * if np.any(np.less_equal(oa, 0.0)): - * raise ValueError("a <= 0") # <<<<<<<<<<<<<< - * return cont1_array(self.internal_state, rk_pareto, size, oa) - * - */ - __pyx_k_tuple_93 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2532; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - 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if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - #endif - } - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else /* Python 3+ */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyEval_CallObject(type, args); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else 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; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -static void __Pyx_RaiseArgtupleInvalid( - const char* func_name, - int exact, - Py_ssize_t num_min, - Py_ssize_t num_max, - Py_ssize_t num_found) -{ - Py_ssize_t num_expected; - const char *more_or_less; - if (num_found < num_min) { - num_expected = num_min; - more_or_less = "at least"; - } else { - num_expected = num_max; - more_or_less = "at most"; - } - if (exact) { - more_or_less = "exactly"; - } - PyErr_Format(PyExc_TypeError, - "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" CYTHON_FORMAT_SSIZE_T "d given)", - func_name, more_or_less, num_expected, - (num_expected == 1) ? "" : "s", num_found); -} - -static void __Pyx_RaiseDoubleKeywordsError( - const char* func_name, - PyObject* kw_name) -{ - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION >= 3 - "%s() got multiple values for keyword argument '%U'", func_name, kw_name); - #else - "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AsString(kw_name)); - #endif -} - -static int __Pyx_ParseOptionalKeywords( - PyObject *kwds, - PyObject **argnames[], - PyObject *kwds2, - PyObject *values[], - Py_ssize_t num_pos_args, - const char* function_name) -{ - PyObject *key = 0, *value = 0; - Py_ssize_t pos = 0; - PyObject*** name; - PyObject*** first_kw_arg = argnames + num_pos_args; - while (PyDict_Next(kwds, &pos, &key, &value)) { - name = first_kw_arg; - while (*name && (**name != key)) name++; - if (*name) { - values[name-argnames] = value; - continue; - } - name = first_kw_arg; - #if PY_MAJOR_VERSION < 3 - if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { - while (*name) { - if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) - && _PyString_Eq(**name, key)) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - if ((**argname == key) || ( - (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) - && _PyString_Eq(**argname, key))) { - goto arg_passed_twice; - } - argname++; - } - } - } else - #endif - if (likely(PyUnicode_Check(key))) { - while (*name) { - int cmp = (**name == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**name, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) { - values[name-argnames] = value; - break; - } - name++; - } - if (*name) continue; - else { - PyObject*** argname = argnames; - while (argname != first_kw_arg) { - int cmp = (**argname == key) ? 0 : - #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 - (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : - #endif - PyUnicode_Compare(**argname, key); - if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; - if (cmp == 0) goto arg_passed_twice; - argname++; - } - } - } else - goto invalid_keyword_type; - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; - } - } - return 0; -arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, key); - goto bad; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%s() keywords must be strings", function_name); - goto bad; -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%s() got an unexpected keyword argument '%s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - #endif - Py_INCREF(local_type); - Py_INCREF(local_value); - Py_INCREF(local_tb); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_COMPILING_IN_CPYTHON - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - /* Make sure tstate is in a consistent state when we XDECREF - these objects (DECREF may run arbitrary code). */ - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (!j) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - return NULL; - } - } - return m->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice( - PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop, - PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, - int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { -#if CYTHON_COMPILING_IN_CPYTHON - PyMappingMethods* mp; -#if PY_MAJOR_VERSION < 3 - PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; - if (likely(ms && ms->sq_slice)) { - if (!has_cstart) { - if (_py_start && (*_py_start != Py_None)) { - cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); - if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstart = 0; - } - if (!has_cstop) { - if (_py_stop && (*_py_stop != Py_None)) { - cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); - if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstop = PY_SSIZE_T_MAX; - } - if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { - Py_ssize_t l = ms->sq_length(obj); - if (likely(l >= 0)) { - if (cstop < 0) { - cstop += l; - if (cstop < 0) cstop = 0; - } - if (cstart < 0) { - cstart += l; - if (cstart < 0) cstart = 0; - } - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - goto bad; - } - } - return ms->sq_slice(obj, cstart, cstop); - } -#endif - mp = Py_TYPE(obj)->tp_as_mapping; - if (likely(mp && mp->mp_subscript)) -#endif - { - PyObject* result; - PyObject *py_slice, *py_start, *py_stop; - if (_py_slice) { - py_slice = *_py_slice; - } else { - PyObject* owned_start = NULL; - PyObject* owned_stop = NULL; - if (_py_start) { - py_start = *_py_start; - } else { - if (has_cstart) { - owned_start = py_start = PyInt_FromSsize_t(cstart); - if (unlikely(!py_start)) goto bad; - } else - py_start = Py_None; - } - if (_py_stop) { - py_stop = *_py_stop; - } else { - if (has_cstop) { - owned_stop = py_stop = PyInt_FromSsize_t(cstop); - if (unlikely(!py_stop)) { - Py_XDECREF(owned_start); - goto bad; - } - } else - py_stop = Py_None; - } - py_slice = PySlice_New(py_start, py_stop, Py_None); - Py_XDECREF(owned_start); - Py_XDECREF(owned_stop); - if (unlikely(!py_slice)) goto bad; - } -#if CYTHON_COMPILING_IN_CPYTHON - result = mp->mp_subscript(obj, py_slice); -#else - result = PyObject_GetItem(obj, py_slice); -#endif - if (!_py_slice) { - Py_DECREF(py_slice); - } - return result; - } - PyErr_Format(PyExc_TypeError, - "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name); -bad: - return NULL; -} - -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack", - index, (index == 1) ? "" : "s"); -} - -static CYTHON_INLINE int __Pyx_IterFinish(void) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - PyObject* exc_type = tstate->curexc_type; - if (unlikely(exc_type)) { - if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { - PyObject *exc_value, *exc_tb; - exc_value = tstate->curexc_value; - exc_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; - Py_DECREF(exc_type); - Py_XDECREF(exc_value); - Py_XDECREF(exc_tb); - return 0; - } else { - return -1; - } - } - return 0; -#else - if (unlikely(PyErr_Occurred())) { - if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { - PyErr_Clear(); - return 0; - } else { - return -1; - } - } - return 0; -#endif -} - -static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { - if (unlikely(retval)) { - Py_DECREF(retval); - __Pyx_RaiseTooManyValuesError(expected); - return -1; - } else { - return __Pyx_IterFinish(); - } - return 0; -} - -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_Format(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -static CYTHON_INLINE int __Pyx_PyObject_SetSlice( - PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop, - PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice, - int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) { -#if CYTHON_COMPILING_IN_CPYTHON - PyMappingMethods* mp; -#if PY_MAJOR_VERSION < 3 - PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence; - if (likely(ms && ms->sq_ass_slice)) { - if (!has_cstart) { - if (_py_start && (*_py_start != Py_None)) { - cstart = __Pyx_PyIndex_AsSsize_t(*_py_start); - if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstart = 0; - } - if (!has_cstop) { - if (_py_stop && (*_py_stop != Py_None)) { - cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop); - if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; - } else - cstop = PY_SSIZE_T_MAX; - } - if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) { - Py_ssize_t l = ms->sq_length(obj); - if (likely(l >= 0)) { - if (cstop < 0) { - cstop += l; - if (cstop < 0) cstop = 0; - } - if (cstart < 0) { - cstart += l; - if (cstart < 0) cstart = 0; - } - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - goto bad; - } - } - return ms->sq_ass_slice(obj, cstart, cstop, value); - } -#endif - mp = Py_TYPE(obj)->tp_as_mapping; - if (likely(mp && mp->mp_ass_subscript)) -#endif - { - int result; - PyObject *py_slice, *py_start, *py_stop; - if (_py_slice) { - py_slice = *_py_slice; - } else { - PyObject* owned_start = NULL; - PyObject* owned_stop = NULL; - if (_py_start) { - py_start = *_py_start; - } else { - if (has_cstart) { - owned_start = py_start = PyInt_FromSsize_t(cstart); - if (unlikely(!py_start)) goto bad; - } else - py_start = Py_None; - } - if (_py_stop) { - py_stop = *_py_stop; - } else { - if (has_cstop) { - owned_stop = py_stop = PyInt_FromSsize_t(cstop); - if (unlikely(!py_stop)) { - Py_XDECREF(owned_start); - goto bad; - } - } else - py_stop = Py_None; - } - py_slice = PySlice_New(py_start, py_stop, Py_None); - Py_XDECREF(owned_start); - Py_XDECREF(owned_stop); - if (unlikely(!py_slice)) goto bad; - } -#if CYTHON_COMPILING_IN_CPYTHON - result = mp->mp_ass_subscript(obj, py_slice, value); -#else - result = value ? PyObject_SetItem(obj, py_slice, value) : PyObject_DelItem(obj, py_slice); -#endif - if (!_py_slice) { - Py_DECREF(py_slice); - } - return result; - } - PyErr_Format(PyExc_TypeError, - "'%.200s' object does not support slice %s", - Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion"); -bad: - return -1; -} - -static CYTHON_INLINE int __Pyx_CheckKeywordStrings( - PyObject *kwdict, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CPYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - while (PyDict_Next(kwdict, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if ((!kw_allowed) && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - PyErr_Format(PyExc_TypeError, - #if PY_MAJOR_VERSION < 3 - "%s() got an unexpected keyword argument '%s'", - function_name, PyString_AsString(key)); - #else - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { - int r; - if (!j) return -1; - r = PyObject_SetItem(o, j, v); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, - int is_list, int wraparound, int boundscheck) { -#if CYTHON_COMPILING_IN_CPYTHON - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); - if ((!boundscheck) || likely((n >= 0) & (n < PyList_GET_SIZE(o)))) { - PyObject* old = PyList_GET_ITEM(o, n); - Py_INCREF(v); - PyList_SET_ITEM(o, n, v); - Py_DECREF(old); - return 1; - } - } else { - PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; - if (likely(m && m->sq_ass_item)) { - if (wraparound && unlikely(i < 0) && likely(m->sq_length)) { - Py_ssize_t l = m->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (PyErr_ExceptionMatches(PyExc_OverflowError)) - PyErr_Clear(); - else - return -1; - } - } - return m->sq_ass_item(o, i, v); - } - } -#else -#if CYTHON_COMPILING_IN_PYPY - if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) { -#else - if (is_list || PySequence_Check(o)) { -#endif - return PySequence_SetItem(o, i, v); - } -#endif - return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); -} - -static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -#else - PyErr_GetExcInfo(type, value, tb); -#endif -} -static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(type, value, tb); -#endif -} - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s____import__); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - #if PY_VERSION_HEX >= 0x02050000 - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; /* try absolute import on failure */ - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - 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 - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } - #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: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) { - const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0; - const int is_unsigned = const_zero < neg_one; - if (sizeof(npy_intp) == sizeof(char)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedChar(x); - } else if (sizeof(npy_intp) == sizeof(short)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedShort(x); - } else if (sizeof(npy_intp) == sizeof(int)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedInt(x); - } else if (sizeof(npy_intp) == sizeof(long)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedLong(x); - } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x); - } else { - #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); - #else - npy_intp val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } - #endif - return (npy_intp)-1; - } -} - -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; - if ((sizeof(npy_intp) == sizeof(char)) || - (sizeof(npy_intp) == sizeof(short))) { - return PyInt_FromLong((long)val); - } else if ((sizeof(npy_intp) == sizeof(int)) || - (sizeof(npy_intp) == sizeof(long))) { - if (is_unsigned) - return PyLong_FromUnsignedLong((unsigned long)val); - else - return PyInt_FromLong((long)val); - } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) { - if (is_unsigned) - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val); - else - return PyLong_FromLongLong((PY_LONG_LONG)val); - } else { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - return _PyLong_FromByteArray(bytes, sizeof(npy_intp), - little, !is_unsigned); - } -} - -static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) { - const unsigned char neg_one = (unsigned char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned char" : - "value too large to convert to unsigned char"); - } - return (unsigned char)-1; - } - return (unsigned char)val; - } - return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) { - const unsigned short neg_one = (unsigned short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned short" : - "value too large to convert to unsigned short"); - } - return (unsigned short)-1; - } - return (unsigned short)val; - } - return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) { - const unsigned int neg_one = (unsigned int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(unsigned int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(unsigned int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to unsigned int" : - "value too large to convert to unsigned int"); - } - return (unsigned int)-1; - } - return (unsigned int)val; - } - return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x); -} - -static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) { - const char neg_one = (char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to char" : - "value too large to convert to char"); - } - return (char)-1; - } - return (char)val; - } - return (char)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) { - const short neg_one = (short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to short" : - "value too large to convert to short"); - } - return (short)-1; - } - return (short)val; - } - return (short)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject* x) { - const int neg_one = (int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to int" : - "value too large to convert to int"); - } - return (int)-1; - } - return (int)val; - } - return (int)__Pyx_PyInt_AsLong(x); -} - -static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject* x) { - const signed char neg_one = (signed char)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed char) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed char)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed char" : - "value too large to convert to signed char"); - } - return (signed char)-1; - } - return (signed char)val; - } - return (signed char)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) { - const signed short neg_one = (signed short)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed short) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed short)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed short" : - "value too large to convert to signed short"); - } - return (signed short)-1; - } - return (signed short)val; - } - return (signed short)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) { - const signed int neg_one = (signed int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(signed int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(signed int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to signed int" : - "value too large to convert to signed int"); - } - return (signed int)-1; - } - return (signed int)val; - } - return (signed int)__Pyx_PyInt_AsSignedLong(x); -} - -static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) { - const int neg_one = (int)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; - if (sizeof(int) < sizeof(long)) { - long val = __Pyx_PyInt_AsLong(x); - if (unlikely(val != (long)(int)val)) { - if (!unlikely(val == -1 && PyErr_Occurred())) { - PyErr_SetString(PyExc_OverflowError, - (is_unsigned && unlikely(val < 0)) ? - "can't convert negative value to int" : - "value too large to convert to int"); - } - return (int)-1; - } - return (int)val; - } - return (int)__Pyx_PyInt_AsLong(x); -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { - const unsigned long neg_one = (unsigned long)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long)-1; - } - return (unsigned long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned long"); - return (unsigned long)-1; - } - return (unsigned long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (unsigned long)PyLong_AsLong(x); - } - } else { - unsigned long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (unsigned long)-1; - val = __Pyx_PyInt_AsUnsignedLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) { - const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned PY_LONG_LONG"); - return (unsigned PY_LONG_LONG)-1; - } - return (unsigned PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to unsigned PY_LONG_LONG"); - return (unsigned PY_LONG_LONG)-1; - } - return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - unsigned PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (unsigned PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsUnsignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { - const long neg_one = (long)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long)-1; - } - return (long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long)-1; - } - return (long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (long)PyLong_AsLong(x); - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (long)-1; - val = __Pyx_PyInt_AsLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { - const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to PY_LONG_LONG"); - return (PY_LONG_LONG)-1; - } - return (PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to PY_LONG_LONG"); - return (PY_LONG_LONG)-1; - } - return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { - const signed long neg_one = (signed long)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed long"); - return (signed long)-1; - } - return (signed long)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (signed long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed long"); - return (signed long)-1; - } - return (signed long)PyLong_AsUnsignedLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed long)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(signed long) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(signed long) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (signed long)PyLong_AsLong(x); - } - } else { - signed long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (signed long)-1; - val = __Pyx_PyInt_AsSignedLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS -#include "longintrepr.h" -#endif -#endif -static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) { - const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed PY_LONG_LONG"); - return (signed PY_LONG_LONG)-1; - } - return (signed PY_LONG_LONG)val; - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return (signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - if (unlikely(Py_SIZE(x) < 0)) { - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to signed PY_LONG_LONG"); - return (signed PY_LONG_LONG)-1; - } - return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); - } else { -#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -#if CYTHON_USE_PYLONG_INTERNALS - if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) { - switch (Py_SIZE(x)) { - case 0: return 0; - case 1: return +(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - case -1: return -(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0]; - } - } -#endif -#endif - return (signed PY_LONG_LONG)PyLong_AsLongLong(x); - } - } else { - signed PY_LONG_LONG val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (signed PY_LONG_LONG)-1; - val = __Pyx_PyInt_AsSignedLongLong(tmp); - Py_DECREF(tmp); - return val; - } -} - -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_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -#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) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%s.%s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)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; - #else - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - #endif - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%s.%s has the wrong size, try recompiling", - module_name, class_name); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = (start + end) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, /*int argcount,*/ - 0, /*int kwonlyargcount,*/ - 0, /*int nlocals,*/ - 0, /*int stacksize,*/ - 0, /*int flags,*/ - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, /*int firstlineno,*/ - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_globals = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_globals = PyModule_GetDict(__pyx_m); - if (!py_globals) goto bad; - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - py_globals, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - py_frame->f_lineno = py_line; - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else /* Python 3+ has unicode identifiers */ - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/ - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else /* PY_VERSION_HEX < 0x03030000 */ - if (PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_DATA_SIZE(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ - return PyUnicode_AsUTF8AndSize(o, length); -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */ -#endif /* PY_VERSION_HEX < 0x03030000 */ - } else -#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */ - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (r < 0) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { - PyNumberMethods *m; - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (PyInt_Check(x) || PyLong_Check(x)) -#else - if (PyLong_Check(x)) -#endif - return Py_INCREF(x), x; - m = Py_TYPE(x)->tp_as_number; -#if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = PyNumber_Long(x); - } -#else - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Long(x); - } -#endif - if (res) { -#if PY_MAJOR_VERSION < 3 - if (!PyInt_Check(res) && !PyLong_Check(res)) { -#else - if (!PyLong_Check(res)) { -#endif - PyErr_Format(PyExc_TypeError, - "__%s__ returned non-%s (type %.200s)", - name, name, Py_TYPE(res)->tp_name); - Py_DECREF(res); - return NULL; - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject* x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { -#if PY_VERSION_HEX < 0x02050000 - if (ival <= LONG_MAX) - return PyInt_FromLong((long)ival); - else { - unsigned char *bytes = (unsigned char *) &ival; - int one = 1; int little = (int)*(unsigned char*)&one; - return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0); - } -#else - return PyInt_FromSize_t(ival); -#endif -} -static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) { - unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x); - if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) { - if ((val != (unsigned PY_LONG_LONG)-1) || !PyErr_Occurred()) - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to size_t"); - return (size_t)-1; - } - return (size_t)val; -} - - -#endif /* Py_PYTHON_H */ diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx index b33e132a4..fdd82979a 100644 --- a/numpy/random/mtrand/mtrand.pyx +++ b/numpy/random/mtrand/mtrand.pyx @@ -743,8 +743,9 @@ cdef class RandomState: Parameters ---------- size : int or tuple of ints, optional - Defines the shape of the returned array of random floats. If None - (the default), returns a single float. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -782,10 +783,10 @@ cdef class RandomState: Parameters ---------- - size : tuple of ints, int, optional - Shape of output. If this is, for example, (m,n,k), m*n*k samples - are generated. If no shape is specified, a single sample is - returned. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -838,8 +839,9 @@ cdef class RandomState: If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). size : int or tuple of ints, optional - Output shape. Default is None, in which case a single int is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -940,13 +942,14 @@ cdef class RandomState: If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n) size : int or tuple of ints, optional - Output shape. Default is None, in which case a single value is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. replace : boolean, optional Whether the sample is with or without replacement p : 1-D array-like, optional The probabilities associated with each entry in a. - If not given the sample assumes a uniform distribtion over all + If not given the sample assumes a uniform distribution over all entries in a. Returns @@ -1103,7 +1106,7 @@ cdef class RandomState: def uniform(self, low=0.0, high=1.0, size=None): """ - uniform(low=0.0, high=1.0, size=1) + uniform(low=0.0, high=1.0, size=None) Draw samples from a uniform distribution. @@ -1121,9 +1124,9 @@ cdef class RandomState: Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0. size : int or tuple of ints, optional - Shape of output. If the given size is, for example, (m,n,k), - m*n*k samples are generated. If no shape is specified, a single sample - is returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1309,7 +1312,9 @@ cdef class RandomState: If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if ``high=None``). size : int or tuple of ints, optional - Output shape. Default is None, in which case a single int is returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1376,8 +1381,9 @@ cdef class RandomState: Parameters ---------- size : int or tuple of ints, optional - Output shape. Default is None, in which case a single value is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1421,9 +1427,10 @@ cdef class RandomState: Mean ("centre") of the distribution. scale : float Standard deviation (spread or "width") of the distribution. - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. See Also -------- @@ -1525,9 +1532,10 @@ cdef class RandomState: Alpha, non-negative. b : float Beta, non-negative. - size : tuple of ints, optional - The number of samples to draw. The output is packed according to - the size given. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1582,9 +1590,10 @@ cdef class RandomState: ---------- scale : float The scale parameter, :math:`\\beta = 1/\\lambda`. - size : tuple of ints - Number of samples to draw. The output is shaped - according to `size`. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. References ---------- @@ -1623,8 +1632,10 @@ cdef class RandomState: Parameters ---------- - size : int or tuple of ints - Shape of the output. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1653,9 +1664,10 @@ cdef class RandomState: ---------- shape : float Parameter, should be > 0. - size : int or tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1738,9 +1750,10 @@ cdef class RandomState: The shape of the gamma distribution. scale : scalar > 0, optional The scale of the gamma distribution. Default is equal to 1. - size : shape_tuple, optional + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1834,10 +1847,10 @@ cdef class RandomState: Degrees of freedom in numerator. Should be greater than zero. dfden : float Degrees of freedom in denominator. Should be greater than zero. - size : {tuple, int}, optional - Output shape. If the given shape is, e.g., ``(m, n, k)``, - then ``m * n * k`` samples are drawn. By default only one sample - is returned. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -1934,9 +1947,10 @@ cdef class RandomState: Parameter, should be > 1. nonc : float Parameter, should be >= 0. - size : int or tuple of ints - Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2025,9 +2039,10 @@ cdef class RandomState: ---------- df : int Number of degrees of freedom. - size : tuple of ints, int, optional - Size of the returned array. By default, a scalar is - returned. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2103,8 +2118,10 @@ cdef class RandomState: Degrees of freedom, should be >= 1. nonc : float Non-centrality, should be > 0. - size : int or tuple of ints - Shape of the output. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Notes ----- @@ -2190,8 +2207,10 @@ cdef class RandomState: Parameters ---------- - size : int or tuple of ints - Shape of the output. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2256,8 +2275,9 @@ cdef class RandomState: df : int Degrees of freedom, should be > 0. size : int or tuple of ints, optional - Output shape. Default is None, in which case a single value is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2362,9 +2382,10 @@ cdef class RandomState: Mode ("center") of the distribution. kappa : float Dispersion of the distribution, has to be >=0. - size : int or tuple of int + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2460,9 +2481,10 @@ cdef class RandomState: ---------- shape : float, > 0. Shape of the distribution. - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. See Also -------- @@ -2552,9 +2574,10 @@ cdef class RandomState: ---------- a : float Shape of the distribution. - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. See Also -------- @@ -2645,9 +2668,10 @@ cdef class RandomState: ---------- a : float parameter, > 0 - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2759,6 +2783,10 @@ cdef class RandomState: The position, :math:`\\mu`, of the distribution peak. scale : float :math:`\\lambda`, the exponential decay. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Notes ----- @@ -2847,9 +2875,10 @@ cdef class RandomState: The location of the mode of the distribution. scale : float The scale parameter of the distribution. - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -2977,9 +3006,10 @@ cdef class RandomState: scale : float > 0. - size : {tuple, int} + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3067,9 +3097,10 @@ cdef class RandomState: Mean value of the underlying normal distribution sigma : float, > 0. Standard deviation of the underlying normal distribution - size : tuple of ints + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3185,8 +3216,9 @@ cdef class RandomState: scale : scalar Scale, also equals the mode. Should be >= 0. size : int or tuple of ints, optional - Shape of the output. Default is None, in which case a single - value is returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Notes ----- @@ -3267,8 +3299,9 @@ cdef class RandomState: scale : scalar Scale parameter, should be >= 0. size : int or tuple of ints, optional - Output shape. Default is None, in which case a single value is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3327,8 +3360,6 @@ cdef class RandomState: raise ValueError("scale <= 0.0") return cont2_array(self.internal_state, rk_wald, size, omean, oscale) - - def triangular(self, left, mode, right, size=None): """ triangular(left, mode, right, size=None) @@ -3349,8 +3380,9 @@ cdef class RandomState: right : scalar Upper limit, should be larger than `left`. size : int or tuple of ints, optional - Output shape. Default is None, in which case a single value is - returned. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3434,9 +3466,10 @@ cdef class RandomState: parameter, >= 0. p : float parameter, >= 0 and <=1. - size : {tuple, int} + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3511,6 +3544,8 @@ cdef class RandomState: raise ValueError("p < 0") elif fp > 1: raise ValueError("p > 1") + elif np.isnan(fp): + raise ValueError("p is nan") return discnp_array_sc(self.internal_state, rk_binomial, size, ln, fp) PyErr_Clear() @@ -3541,9 +3576,10 @@ cdef class RandomState: Parameter, > 0. p : float Parameter, >= 0 and <=1. - size : int or tuple of ints - Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3634,8 +3670,9 @@ cdef class RandomState: lam : float Expectation of interval, should be >= 0. size : int or tuple of ints, optional - Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Notes ----- @@ -3709,12 +3746,10 @@ cdef class RandomState: ---------- a : float > 1 Distribution parameter. - size : int or tuple of int, optional + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn; a single integer is equivalent in - its result to providing a mono-tuple, i.e., a 1-D array of length - *size* is returned. The default is None, in which case a single - scalar is returned. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3801,9 +3836,10 @@ cdef class RandomState: ---------- p : float The probability of success of an individual trial. - size : tuple of ints - Number of values to draw from the distribution. The output - is shaped according to `size`. + size : int or tuple of ints, optional + Output shape. If the given shape is, e.g., ``(m, n, k)``, then + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3865,9 +3901,10 @@ cdef class RandomState: nsample : int or array_like Number of items sampled. Must be at least 1 and at most ``ngood + nbad``. - size : int or tuple of int + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- @@ -3979,9 +4016,10 @@ cdef class RandomState: scale : float > 0. - size : {tuple, int} + size : int or tuple of ints, optional Output shape. If the given shape is, e.g., ``(m, n, k)``, then - ``m * n * k`` samples are drawn. + ``m * n * k`` samples are drawn. Default is None, in which case a + single value is returned. Returns ------- diff --git a/numpy/random/tests/test_random.py b/numpy/random/tests/test_random.py index c30037e1e..db4093ab4 100644 --- a/numpy/random/tests/test_random.py +++ b/numpy/random/tests/test_random.py @@ -18,6 +18,10 @@ class TestBinomial(TestCase): assert_(random.binomial(0, p) == 0) np.testing.assert_array_equal(random.binomial(zeros, p), zeros) + def test_p_is_nan(self): + # Issue #4571. + assert_raises(ValueError, random.binomial, 1, np.nan) + class TestMultinomial(TestCase): def test_basic(self): diff --git a/numpy/testing/utils.py b/numpy/testing/utils.py index 70357d835..ddf21e2bc 100644 --- a/numpy/testing/utils.py +++ b/numpy/testing/utils.py @@ -1332,7 +1332,6 @@ def assert_array_almost_equal_nulp(x, y, nulp=1): >>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x) >>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x) - ------------------------------------------------------------ Traceback (most recent call last): ... AssertionError: X and Y are not equal to 1 ULP (max is 2) @@ -180,6 +180,16 @@ class sdist_checked(sdist): check_submodules() sdist.run(self) +def generate_cython(): + cwd = os.path.abspath(os.path.dirname(__file__)) + print("Cythonizing sources") + p = subprocess.call([sys.executable, + os.path.join(cwd, 'tools', 'cythonize.py'), + 'numpy/random'], + cwd=cwd) + if p != 0: + raise RuntimeError("Running cythonize failed!") + def setup_package(): src_path = os.path.dirname(os.path.abspath(sys.argv[0])) old_path = os.getcwd() @@ -225,6 +235,10 @@ def setup_package(): metadata['configuration'] = configuration else: from numpy.distutils.core import setup + cwd = os.path.abspath(os.path.dirname(__file__)) + if not os.path.exists(os.path.join(cwd, 'PKG-INFO')): + # Generate Cython sources, unless building from source release + generate_cython() metadata['configuration'] = configuration try: diff --git a/tools/cythonize.py b/tools/cythonize.py new file mode 100755 index 000000000..4ab10a058 --- /dev/null +++ b/tools/cythonize.py @@ -0,0 +1,199 @@ +#!/usr/bin/env python +""" cythonize + +Cythonize pyx files into C files as needed. + +Usage: cythonize [root_dir] + +Default [root_dir] is 'numpy'. + +Checks pyx files to see if they have been changed relative to their +corresponding C files. If they have, then runs cython on these files to +recreate the C files. + +The script thinks that the pyx files have changed relative to the C files +by comparing hashes stored in a database file. + +Simple script to invoke Cython (and Tempita) on all .pyx (.pyx.in) +files; while waiting for a proper build system. Uses file hashes to +figure out if rebuild is needed. + +For now, this script should be run by developers when changing Cython files +only, and the resulting C files checked in, so that end-users (and Python-only +developers) do not get the Cython/Tempita dependencies. + +Originally written by Dag Sverre Seljebotn, and copied here from: + +https://raw.github.com/dagss/private-scipy-refactor/cythonize/cythonize.py + +Note: this script does not check any of the dependent C libraries; it only +operates on the Cython .pyx files. +""" + +from __future__ import division, print_function, absolute_import + +import os +import re +import sys +import hashlib +import subprocess + +HASH_FILE = 'cythonize.dat' +DEFAULT_ROOT = 'numpy' +VENDOR = 'NumPy' + +# WindowsError is not defined on unix systems +try: + WindowsError +except NameError: + WindowsError = None + +# +# Rules +# +def process_pyx(fromfile, tofile): + try: + from Cython.Compiler.Version import version as cython_version + from distutils.version import LooseVersion + if LooseVersion(cython_version) < LooseVersion('0.19'): + raise Exception('Building %s requires Cython >= 0.19' % VENDOR) + + except ImportError: + pass + + flags = ['--fast-fail'] + if tofile.endswith('.cxx'): + flags += ['--cplus'] + + try: + try: + r = subprocess.call(['cython'] + flags + ["-o", tofile, fromfile]) + if r != 0: + raise Exception('Cython failed') + except OSError: + # There are ways of installing Cython that don't result in a cython + # executable on the path, see gh-2397. + r = subprocess.call([sys.executable, '-c', + 'import sys; from Cython.Compiler.Main import ' + 'setuptools_main as main; sys.exit(main())'] + flags + + ["-o", tofile, fromfile]) + if r != 0: + raise Exception('Cython failed') + except OSError: + raise OSError('Cython needs to be installed') + +def process_tempita_pyx(fromfile, tofile): + try: + try: + from Cython import Tempita as tempita + except ImportError: + import tempita + except ImportError: + raise Exception('Building %s requires Tempita: ' + 'pip install --user Tempita' % VENDOR) + with open(fromfile, "r") as f: + tmpl = f.read() + pyxcontent = tempita.sub(tmpl) + assert fromfile.endswith('.pyx.in') + pyxfile = fromfile[:-len('.pyx.in')] + '.pyx' + with open(pyxfile, "w") as f: + f.write(pyxcontent) + process_pyx(pyxfile, tofile) + +rules = { + # fromext : function + '.pyx' : process_pyx, + '.pyx.in' : process_tempita_pyx + } +# +# Hash db +# +def load_hashes(filename): + # Return { filename : (sha1 of input, sha1 of output) } + if os.path.isfile(filename): + hashes = {} + with open(filename, 'r') as f: + for line in f: + filename, inhash, outhash = line.split() + hashes[filename] = (inhash, outhash) + else: + hashes = {} + return hashes + +def save_hashes(hash_db, filename): + with open(filename, 'w') as f: + for key, value in sorted(hash_db.items()): + f.write("%s %s %s\n" % (key, value[0], value[1])) + +def sha1_of_file(filename): + h = hashlib.sha1() + with open(filename, "rb") as f: + h.update(f.read()) + return h.hexdigest() + +# +# Main program +# + +def normpath(path): + path = path.replace(os.sep, '/') + if path.startswith('./'): + path = path[2:] + return path + +def get_hash(frompath, topath): + from_hash = sha1_of_file(frompath) + to_hash = sha1_of_file(topath) if os.path.exists(topath) else None + return (from_hash, to_hash) + +def process(path, fromfile, tofile, processor_function, hash_db): + fullfrompath = os.path.join(path, fromfile) + fulltopath = os.path.join(path, tofile) + current_hash = get_hash(fullfrompath, fulltopath) + if current_hash == hash_db.get(normpath(fullfrompath), None): + print('%s has not changed' % fullfrompath) + return + + orig_cwd = os.getcwd() + try: + os.chdir(path) + print('Processing %s' % fullfrompath) + processor_function(fromfile, tofile) + finally: + os.chdir(orig_cwd) + # changed target file, recompute hash + current_hash = get_hash(fullfrompath, fulltopath) + # store hash in db + hash_db[normpath(fullfrompath)] = current_hash + + +def find_process_files(root_dir): + hash_db = load_hashes(HASH_FILE) + for cur_dir, dirs, files in os.walk(root_dir): + for filename in files: + in_file = os.path.join(cur_dir, filename + ".in") + if filename.endswith('.pyx') and os.path.isfile(in_file): + continue + for fromext, function in rules.items(): + if filename.endswith(fromext): + toext = ".c" + with open(os.path.join(cur_dir, filename), 'rb') as f: + data = f.read() + m = re.search(br"^\s*#\s*distutils:\s*language\s*=\s*c\+\+\s*$", data, re.I|re.M) + if m: + toext = ".cxx" + fromfile = filename + tofile = filename[:-len(fromext)] + toext + process(cur_dir, fromfile, tofile, function, hash_db) + save_hashes(hash_db, HASH_FILE) + +def main(): + try: + root_dir = sys.argv[1] + except IndexError: + root_dir = DEFAULT_ROOT + find_process_files(root_dir) + + +if __name__ == '__main__': + main() diff --git a/tools/travis-test.sh b/tools/travis-test.sh index dfe244dca..f06aea8f6 100755 --- a/tools/travis-test.sh +++ b/tools/travis-test.sh @@ -24,7 +24,7 @@ if [ -z "$USE_DEBUG" ]; then else sysflags="$($PYTHON -c "from distutils import sysconfig; print (sysconfig.get_config_var('CFLAGS'))")" # windows compilers have this requirement - CFLAGS="$sysflags -Werror=declaration-after-statement" $PYTHON setup.py build_ext --inplace + CFLAGS="$sysflags -Werror=declaration-after-statement -Werror=nonnull" $PYTHON setup.py build_ext --inplace fi } @@ -49,7 +49,7 @@ setup_chroot() sudo chroot $DIR bash -c "apt-get update" sudo chroot $DIR bash -c "apt-get install -qq -y --force-yes eatmydata" echo /usr/lib/libeatmydata/libeatmydata.so | sudo tee -a $DIR/etc/ld.so.preload - sudo chroot $DIR bash -c "apt-get install -qq -y --force-yes libatlas-dev libatlas-base-dev gfortran python3-dev python3-nose python3-pip" + sudo chroot $DIR bash -c "apt-get install -qq -y --force-yes libatlas-dev libatlas-base-dev gfortran python3-dev python3-nose python3-pip cython3 cython" } setup_bento() |