[104457] trunk/dports/python/py-numpy
stromnov at macports.org
stromnov at macports.org
Mon Mar 25 14:37:07 PDT 2013
Revision: 104457
https://trac.macports.org/changeset/104457
Author: stromnov at macports.org
Date: 2013-03-25 14:37:07 -0700 (Mon, 25 Mar 2013)
Log Message:
-----------
py-numpy: update to version 1.7.0 (#38438)
Modified Paths:
--------------
trunk/dports/python/py-numpy/Portfile
trunk/dports/python/py-numpy/files/patch-f2py_setup.py.diff
trunk/dports/python/py-numpy/files/patch-fcompiler_g95.diff
Removed Paths:
-------------
trunk/dports/python/py-numpy/files/patch-python33-methods.diff
trunk/dports/python/py-numpy/files/patch-python33-mtrand.diff
trunk/dports/python/py-numpy/files/patch-python33-shape.diff
trunk/dports/python/py-numpy/files/patch-python33-unicode.diff
Modified: trunk/dports/python/py-numpy/Portfile
===================================================================
--- trunk/dports/python/py-numpy/Portfile 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/Portfile 2013-03-25 21:37:07 UTC (rev 104457)
@@ -5,9 +5,9 @@
PortGroup python 1.0
PortGroup github 1.0
-github.setup numpy numpy 1.6.2 v
+github.setup numpy numpy 1.7.0 v
name py-numpy
-revision 2
+revision 0
dist_subdir ${name}/${version}_1
categories-append math
@@ -17,8 +17,8 @@
description The core utilities for the scientific library scipy for Python
long_description ${description}
-checksums rmd160 9643c04a2e8fbb99cdb047281eedbbfb99423553 \
- sha256 0992d326147d0ed83bd059519897e7a8ee52dea5ee66bbe04c0ea1c502cd8618
+checksums rmd160 f64215a07b35b7791a20ca236cdce45d4747473e \
+ sha256 757b91f89e2b9946a325ea4ae6955a7629c65a68e10ed751b98a50e21c13ff5e
python.versions 24 25 26 27 31 32 33
@@ -26,13 +26,6 @@
patchfiles patch-f2py_setup.py.diff \
patch-fcompiler_g95.diff
- if {${python.version} == 33} {
- patchfiles-append patch-python33-unicode.diff \
- patch-python33-mtrand.diff \
- patch-python33-shape.diff \
- patch-python33-methods.diff
- }
-
depends_lib-append port:fftw-3 \
port:py${python.version}-nose
@@ -193,14 +186,3 @@
} else {
livecheck.regex archive/[join ${github.tag_prefix} ""](\[\\d+(?:\\.\\d+)*"\]+)${extract.suffix}"
}
-
-subport py32-numpy {
- pre-activate {
- set regref [registry_open $subport $version $revision $portvariants ""]
- foreach f [registry_prop_retr $regref imagefiles] {
- if {[file extension $f] == ".pyc" && [file exists $f] && [registry_file_registered $f] == "0"} {
- file delete -force $f
- }
- }
- }
-}
Modified: trunk/dports/python/py-numpy/files/patch-f2py_setup.py.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-f2py_setup.py.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-f2py_setup.py.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,5 +1,14 @@
---- numpy/f2py/setup.py 2011-02-27 23:40:29.000000000 -0600
-+++ numpy/f2py/setup.py 2011-05-15 09:18:40.000000000 -0500
+--- numpy/f2py/setup.py.orig 2013-02-10 00:51:36.000000000 +0400
++++ numpy/f2py/setup.py 2013-03-19 15:27:15.000000000 +0400
+@@ -41,7 +41,7 @@
+ config.make_svn_version_py()
+
+ def generate_f2py_py(build_dir):
+- f2py_exe = 'f2py'+os.path.basename(sys.executable)[6:]
++ f2py_exe = 'f2py'
+ if f2py_exe[-4:]=='.exe':
+ f2py_exe = f2py_exe[:-4] + '.py'
+ if 'bdist_wininst' in sys.argv and f2py_exe[-3:] != '.py':
@@ -51,7 +51,7 @@
log.info('Creating %s', target)
f = open(target,'w')
Modified: trunk/dports/python/py-numpy/files/patch-fcompiler_g95.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-fcompiler_g95.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-fcompiler_g95.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,11 +1,11 @@
---- numpy/distutils/fcompiler/__init__.py 2011-03-15 00:22:25.000000000 -0500
-+++ numpy/distutils/fcompiler/__init__.py 2011-05-15 09:21:14.000000000 -0500
-@@ -698,7 +698,7 @@
+--- numpy/distutils/fcompiler/__init__.py.orig 2013-03-19 13:35:03.000000000 +0400
++++ numpy/distutils/fcompiler/__init__.py 2013-03-19 13:35:27.000000000 +0400
+@@ -708,7 +708,7 @@
('cygwin.*', ('gnu','intelv','absoft','compaqv','intelev','gnu95','g95')),
- ('linux.*', ('gnu','intel','lahey','pg','absoft','nag','vast','compaq',
- 'intele','intelem','gnu95','g95','pathf95')),
-- ('darwin.*', ('nag', 'absoft', 'ibm', 'intel', 'gnu', 'gnu95', 'g95', 'pg')),
-+ ('darwin.*', ('nag', 'absoft', 'ibm', 'intel', 'gnu', 'gnu95', 'pg')),
+ ('linux.*', ('gnu95','intel','lahey','pg','absoft','nag','vast','compaq',
+ 'intele','intelem','gnu','g95','pathf95')),
+- ('darwin.*', ('gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'g95', 'pg')),
++ ('darwin.*', ('gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'pg')),
('sunos.*', ('sun','gnu','gnu95','g95')),
('irix.*', ('mips','gnu','gnu95',)),
('aix.*', ('ibm','gnu','gnu95',)),
Deleted: trunk/dports/python/py-numpy/files/patch-python33-methods.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-python33-methods.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-python33-methods.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,11 +0,0 @@
---- numpy/core/src/multiarray/methods.old.c 2012-12-06 16:49:23.000000000 -0600
-+++ numpy/core/src/multiarray/methods.c 2012-12-06 16:48:07.000000000 -0600
-@@ -1476,7 +1476,7 @@
- if (!PyDataType_FLAGCHK(typecode, NPY_LIST_PICKLE)) {
- int swap=!PyArray_ISNOTSWAPPED(self);
- self->data = datastr;
-- if (!_IsAligned(self) || swap) {
-+ if (!_IsAligned(self) || swap || (len <= 1000)) {
- intp num = PyArray_NBYTES(self);
- self->data = PyDataMem_NEW(num);
- if (self->data == NULL) {
Deleted: trunk/dports/python/py-numpy/files/patch-python33-mtrand.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-python33-mtrand.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-python33-mtrand.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,12300 +0,0 @@
---- numpy/random/mtrand/mtrand.old.c 2012-05-19 08:41:41.000000000 -0500
-+++ numpy/random/mtrand/mtrand.c 2012-12-06 15:02:43.000000000 -0600
-@@ -1,16 +1,16 @@
--/* Generated by Cython 0.15.1 on Tue May 1 15:44:50 2012 */
-+/* Generated by Cython 0.17.1 on Thu Dec 6 15:02:42 2012 */
-
- #define PY_SSIZE_T_CLEAN
- #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)
- #ifndef __stdcall
- #define __stdcall
-@@ -22,36 +22,44 @@
- #define __fastcall
- #endif
- #endif
--
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- #endif
- #ifndef DL_EXPORT
- #define DL_EXPORT(t) t
- #endif
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- #endif
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-- #define METH_COEXIST 0
-- #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
-- #define PyDict_Contains(d,o) PySequence_Contains(d,o)
-+#ifndef Py_HUGE_VAL
-+ #define Py_HUGE_VAL HUGE_VAL
-+#endif
-+#ifdef PYPY_VERSION
-+#define CYTHON_COMPILING_IN_PYPY 1
-+#define CYTHON_COMPILING_IN_CPYTHON 0
-+#else
-+#define CYTHON_COMPILING_IN_PYPY 0
-+#define CYTHON_COMPILING_IN_CPYTHON 1
- #endif
--
- #if PY_VERSION_HEX < 0x02050000
- typedef int Py_ssize_t;
- #define PY_SSIZE_T_MAX INT_MAX
- #define PY_SSIZE_T_MIN INT_MIN
- #define PY_FORMAT_SIZE_T ""
-+ #define CYTHON_FORMAT_SSIZE_T ""
- #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
- #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o)
-- #define PyNumber_Index(o) PyNumber_Int(o)
-- #define PyIndex_Check(o) PyNumber_Check(o)
-+ #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
-+ (PyErr_Format(PyExc_TypeError, \
-+ "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
-+ (PyObject*)0))
-+ #define PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && !PyComplex_Check(o))
- #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
-+ #define __PYX_BUILD_PY_SSIZE_T "i"
-+#else
-+ #define __PYX_BUILD_PY_SSIZE_T "n"
-+ #define CYTHON_FORMAT_SSIZE_T "z"
- #endif
--
- #if PY_VERSION_HEX < 0x02060000
- #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
- #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type)
-@@ -59,7 +67,6 @@
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- PyObject_HEAD_INIT(type) size,
- #define PyType_Modified(t)
--
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- PyObject *obj;
-@@ -73,7 +80,6 @@
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- void *internal;
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--
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- #define PyBUF_WRITABLE 0x0001
- #define PyBUF_FORMAT 0x0004
-@@ -83,24 +89,44 @@
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- #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
- #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
--
-+ #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
-+ #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
-+ typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
-+ typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
- #endif
--
- #if PY_MAJOR_VERSION < 3
- #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
-+ #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-+ PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
- #else
- #define __Pyx_BUILTIN_MODULE_NAME "builtins"
-+ #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
-+ PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
-+#endif
-+#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
-+ #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
- #endif
--
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- #define Py_TPFLAGS_CHECKTYPES 0
- #define Py_TPFLAGS_HAVE_INDEX 0
- #endif
--
- #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
- #define Py_TPFLAGS_HAVE_NEWBUFFER 0
- #endif
--
-+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
-+ #define CYTHON_PEP393_ENABLED 1
-+ #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \
-+ 0 : _PyUnicode_Ready((PyObject *)(op)))
-+ #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
-+ #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
-+ #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
-+#else
-+ #define CYTHON_PEP393_ENABLED 0
-+ #define __Pyx_PyUnicode_READY(op) (0)
-+ #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
-+ #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-+ #define __Pyx_PyUnicode_READ(k, d, i) ((k=k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
-+#endif
- #if PY_MAJOR_VERSION >= 3
- #define PyBaseString_Type PyUnicode_Type
- #define PyStringObject PyUnicodeObject
-@@ -108,7 +134,6 @@
- #define PyString_Check PyUnicode_Check
- #define PyString_CheckExact PyUnicode_CheckExact
- #endif
--
- #if PY_VERSION_HEX < 0x02060000
- #define PyBytesObject PyStringObject
- #define PyBytes_Type PyString_Type
-@@ -127,7 +152,6 @@
- #define PyBytes_Concat PyString_Concat
- #define PyBytes_ConcatAndDel PyString_ConcatAndDel
- #endif
--
- #if PY_VERSION_HEX < 0x02060000
- #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type)
- #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type)
-@@ -135,9 +159,7 @@
- #ifndef PySet_CheckExact
- #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
- #endif
--
- #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
--
- #if PY_MAJOR_VERSION >= 3
- #define PyIntObject PyLongObject
- #define PyInt_Type PyLong_Type
-@@ -154,11 +176,9 @@
- #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
- #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
- #endif
--
- #if PY_MAJOR_VERSION >= 3
- #define PyBoolObject PyLongObject
- #endif
--
- #if PY_VERSION_HEX < 0x03020000
- typedef long Py_hash_t;
- #define __Pyx_PyInt_FromHash_t PyInt_FromLong
-@@ -167,16 +187,6 @@
- #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
- #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
- #endif
--
--
--#if PY_MAJOR_VERSION >= 3
-- #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
-- #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
--
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- #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
- #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
-@@ -195,11 +205,9 @@
- (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
--
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- #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
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- #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n)))
- #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
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- #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n))
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- #define __Pyx_DOCSTR(n) ((char *)(n))
-@@ -218,6 +225,15 @@
- #define __Pyx_DOCSTR(n) (n)
- #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
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- #ifndef __PYX_EXTERN_C
- #ifdef __cplusplus
- #define __PYX_EXTERN_C extern "C"
-@@ -269,7 +285,7 @@
- # else
- # define CYTHON_UNUSED
- # endif
--# elif defined(__ICC) || defined(__INTEL_COMPILER)
-+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
- # define CYTHON_UNUSED __attribute__ ((__unused__))
- # else
- # define CYTHON_UNUSED
-@@ -293,8 +309,12 @@
- 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))
-
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-@@ -461,6 +492,8 @@
- #define __Pyx_XGOTREF(r)
- #define __Pyx_XGIVEREF(r)
- #endif /* CYTHON_REFNANNY */
-+#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
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--static void __Pyx_RaiseDoubleKeywordsError(
-- const char* func_name, PyObject* kw_name); /*proto*/
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--static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, const char* function_name); /*proto*/
-+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
-+ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
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- static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
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- Py_DECREF(j);
- return r;
- }
--
--
- #define __Pyx_GetItemInt_List(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_List_Fast(o, i) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
--
- static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i) {
-- if (likely(o != Py_None)) {
-- if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-- PyObject *r = PyList_GET_ITEM(o, i);
-- Py_INCREF(r);
-- return r;
-- }
-- else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) {
-- PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i);
-- Py_INCREF(r);
-- return r;
-- }
-+#if CYTHON_COMPILING_IN_CPYTHON
-+ if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-+ PyObject *r = PyList_GET_ITEM(o, i);
-+ Py_INCREF(r);
-+ return r;
-+ }
-+ else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) {
-+ PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i);
-+ Py_INCREF(r);
-+ return r;
- }
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-+#else
-+ return PySequence_GetItem(o, i);
-+#endif
- }
--
- #define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_Tuple_Fast(o, i) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
--
- static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i) {
-- if (likely(o != Py_None)) {
-- if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-- PyObject *r = PyTuple_GET_ITEM(o, i);
-- Py_INCREF(r);
-- return r;
-- }
-- else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) {
-- PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i);
-- Py_INCREF(r);
-- return r;
-- }
-+#if CYTHON_COMPILING_IN_CPYTHON
-+ if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-+ PyObject *r = PyTuple_GET_ITEM(o, i);
-+ Py_INCREF(r);
-+ return r;
-+ }
-+ else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) {
-+ PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i);
-+ Py_INCREF(r);
-+ return r;
- }
- return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-+#else
-+ return PySequence_GetItem(o, i);
-+#endif
- }
--
--
- #define __Pyx_GetItemInt(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
- __Pyx_GetItemInt_Fast(o, i) : \
- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
--
- static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i) {
-- PyObject *r;
-- if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-- r = PyList_GET_ITEM(o, i);
-- Py_INCREF(r);
-- }
-- else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-- r = PyTuple_GET_ITEM(o, i);
-- Py_INCREF(r);
-+#if CYTHON_COMPILING_IN_CPYTHON
-+ if (PyList_CheckExact(o)) {
-+ Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-+ if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
-+ PyObject *r = PyList_GET_ITEM(o, n);
-+ Py_INCREF(r);
-+ return r;
-+ }
- }
-- else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) {
-- r = PySequence_GetItem(o, i);
-+ else if (PyTuple_CheckExact(o)) {
-+ Py_ssize_t n = (likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
-+ if (likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
-+ PyObject *r = PyTuple_GET_ITEM(o, n);
-+ Py_INCREF(r);
-+ return r;
-+ }
-+ } else { /* inlined PySequence_GetItem() */
-+ PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-+ if (likely(m && m->sq_item)) {
-+ if (unlikely(i < 0) && likely(m->sq_length)) {
-+ Py_ssize_t l = m->sq_length(o);
-+ if (unlikely(l < 0)) return NULL;
-+ i += l;
-+ }
-+ return m->sq_item(o, i);
-+ }
- }
-- else {
-- r = __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
-+#else
-+ if (PySequence_Check(o)) {
-+ return PySequence_GetItem(o, i);
- }
-- return r;
-+#endif
-+ return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
- }
-
-+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-+
- static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
-
--static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
-+static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
-
- static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
-
- static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
--static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict,
-- const char* function_name, int kw_allowed); /*proto*/
-+static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
-
- static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
-
- #define __Pyx_SetItemInt(o, i, v, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
- __Pyx_SetItemInt_Fast(o, i, v) : \
- __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
--
- static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
- int r;
- if (!j) return -1;
-@@ -575,20 +621,38 @@
- Py_DECREF(j);
- return r;
- }
--
- static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v) {
-- if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-- Py_INCREF(v);
-- Py_DECREF(PyList_GET_ITEM(o, i));
-- PyList_SET_ITEM(o, i, v);
-- return 1;
-+#if CYTHON_COMPILING_IN_CPYTHON
-+ if (PyList_CheckExact(o)) {
-+ Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
-+ if (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 { /* inlined PySequence_SetItem() */
-+ PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
-+ if (likely(m && m->sq_ass_item)) {
-+ if (unlikely(i < 0) && likely(m->sq_length)) {
-+ Py_ssize_t l = m->sq_length(o);
-+ if (unlikely(l < 0)) return -1;
-+ i += l;
-+ }
-+ return m->sq_ass_item(o, i, v);
-+ }
- }
-- else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0)))
-+#else
-+#if CYTHON_COMPILING_IN_PYPY
-+ if (PySequence_Check(o) && !PyDict_Check(o)) {
-+#else
-+ if (PySequence_Check(o)) {
-+#endif
- return PySequence_SetItem(o, i, v);
-- else {
-- PyObject *j = PyInt_FromSsize_t(i);
-- return __Pyx_SetItemInt_Generic(o, j, v);
- }
-+#endif
-+ return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
- }
-
- static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-@@ -596,17 +660,9 @@
-
- static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level); /*proto*/
-
--#include <string.h>
--
--static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
--
--static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
-+static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
-
--#if PY_MAJOR_VERSION >= 3
--#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
--#else
--#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
--#endif
-+static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name);
-
- static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
-
-@@ -642,19 +698,40 @@
-
- static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *);
-
--static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
--
- static int __Pyx_check_binary_version(void);
-
--static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/
-+#if !defined(__Pyx_PyIdentifier_FromString)
-+#if PY_MAJOR_VERSION < 3
-+ #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
-+#else
-+ #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
-+#endif
-+#endif
-
- static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
-
--static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
-- int __pyx_lineno, const char *__pyx_filename); /*proto*/
-+static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*proto*/
-+
-+typedef struct {
-+ int code_line;
-+ PyCodeObject* code_object;
-+} __Pyx_CodeObjectCacheEntry;
-+struct __Pyx_CodeObjectCache {
-+ int count;
-+ int max_count;
-+ __Pyx_CodeObjectCacheEntry* entries;
-+};
-+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
-+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
-+static PyCodeObject *__pyx_find_code_object(int code_line);
-+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
-+
-+static void __Pyx_AddTraceback(const char *funcname, int c_line,
-+ int py_line, const char *filename); /*proto*/
-
- static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-
-+
- /* Module declarations from 'numpy' */
-
- /* Module declarations from 'mtrand' */
-@@ -686,6 +763,58 @@
- /* Implementation of 'mtrand' */
- static PyObject *__pyx_builtin_ValueError;
- static PyObject *__pyx_builtin_TypeError;
-+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_24uniform(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_26rand(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_28randn(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_30random_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_32standard_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_34normal(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_36beta(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_38exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_40standard_exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_42standard_gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_44gamma(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_46f(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_48noncentral_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_50chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_52noncentral_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_54standard_cauchy(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_56standard_t(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_58vonmises(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_60pareto(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_62weibull(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_64power(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_66laplace(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_68gumbel(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_70logistic(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_72lognormal(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_74rayleigh(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_76wald(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_78triangular(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_80binomial(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_82negative_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_84poisson(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_86zipf(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_88geometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_90hypergeometric(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_92logseries(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_94multivariate_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_96multinomial(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_98dirichlet(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_100shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
-+static PyObject *__pyx_pf_6mtrand_11RandomState_102permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
- static char __pyx_k_1[] = "size is not compatible with inputs";
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-+static char __pyx_doc_6mtrand_11RandomState_34normal[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_46f[] = "\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\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 ";
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-- goto __pyx_L7;
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--static char __pyx_doc_6mtrand_11RandomState_25chisquare[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_26noncentral_chisquare[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_27standard_cauchy[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n ..[3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<25)""] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n ";
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-+static char __pyx_doc_6mtrand_11RandomState_56standard_t[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_28standard_t[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_29vonmises[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_58vonmises[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_60pareto[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_30pareto[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_62weibull[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull : probability density function,\n distribution or cumulative density function, etc.\n\n gumbel, scipy.stats.distributions.genextreme\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n Generalstabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n ";
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--static char __pyx_doc_6mtrand_11RandomState_31weibull[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull : probability density function,\n distribution or cumulative density function, etc.\n\n gumbel, scipy.stats.distributions.genextreme\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n Generalstabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n ";
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-+static char __pyx_doc_6mtrand_11RandomState_64power[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_32power[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_34gumbel[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_68gumbel[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_35logistic[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_36lognormal[] = "\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, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\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 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.\n\n The probability density function for the log-normal 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 deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\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\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\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, color='r', linewidth=2)\n >>> plt.show()\n\n ";
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-+static char __pyx_doc_6mtrand_11RandomState_72lognormal[] = "\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, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\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 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.\n\n The probability density function for the log-normal 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 deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since thi""s includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\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\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\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, color='r', linewidth=2)\n >>> plt.show()\n\n ";
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--static char __pyx_doc_6mtrand_11RandomState_38wald[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n ..[1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n ..[3] Wikipedia, \"Wald distributio""n\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n ";
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--static PyObject *__pyx_pf_6mtrand_11RandomState_39triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-+static char __pyx_doc_6mtrand_11RandomState_80binomial[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_41negative_binomial[] = "\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_82negative_binomial[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_45hypergeometric[] = "\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 : float (but truncated to an integer)\n parameter, > 0.\n nbad : float\n parameter, >= 0.\n nsample : float\n parameter, > 0 and <= ngood+nbad\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.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 distribution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn wit""h\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_90hypergeometric[] = "\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 : float (but truncated to an integer)\n parameter, > 0.\n nbad : float\n parameter, >= 0.\n nsample : float\n parameter, > 0 and <= ngood+nbad\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.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 distribution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn wit""h\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 ";
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-+static char __pyx_doc_6mtrand_11RandomState_92logseries[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_46logseries[] = "\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 ";
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--static char __pyx_doc_6mtrand_11RandomState_49dirichlet[] = "\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 ";
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-- {__Pyx_NAMESTR("rand"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_13rand, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_13rand)},
-- {__Pyx_NAMESTR("randn"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_14randn, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_14randn)},
-- {__Pyx_NAMESTR("random_integers"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_15random_integers, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_15random_integers)},
-- {__Pyx_NAMESTR("standard_normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_16standard_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_16standard_normal)},
-- {__Pyx_NAMESTR("normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_17normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_17normal)},
-- {__Pyx_NAMESTR("beta"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_18beta, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_18beta)},
-- {__Pyx_NAMESTR("exponential"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_19exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_19exponential)},
-- {__Pyx_NAMESTR("standard_exponential"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_20standard_exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_20standard_exponential)},
-- {__Pyx_NAMESTR("standard_gamma"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_21standard_gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_21standard_gamma)},
-- {__Pyx_NAMESTR("gamma"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_22gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_22gamma)},
-- {__Pyx_NAMESTR("f"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_23f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_23f)},
-- {__Pyx_NAMESTR("noncentral_f"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_24noncentral_f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_24noncentral_f)},
-- {__Pyx_NAMESTR("chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_25chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_25chisquare)},
-- {__Pyx_NAMESTR("noncentral_chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_26noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_26noncentral_chisquare)},
-- {__Pyx_NAMESTR("standard_cauchy"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_27standard_cauchy, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_27standard_cauchy)},
-- {__Pyx_NAMESTR("standard_t"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_28standard_t, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_28standard_t)},
-- {__Pyx_NAMESTR("vonmises"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_29vonmises, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_29vonmises)},
-- {__Pyx_NAMESTR("pareto"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_30pareto, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_30pareto)},
-- {__Pyx_NAMESTR("weibull"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_31weibull, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_31weibull)},
-- {__Pyx_NAMESTR("power"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_32power, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_32power)},
-- {__Pyx_NAMESTR("laplace"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_33laplace, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_33laplace)},
-- {__Pyx_NAMESTR("gumbel"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_34gumbel, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_34gumbel)},
-- {__Pyx_NAMESTR("logistic"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_35logistic, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_35logistic)},
-- {__Pyx_NAMESTR("lognormal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_36lognormal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_36lognormal)},
-- {__Pyx_NAMESTR("rayleigh"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_37rayleigh, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_37rayleigh)},
-- {__Pyx_NAMESTR("wald"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_38wald, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_38wald)},
-- {__Pyx_NAMESTR("triangular"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_39triangular, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_39triangular)},
-- {__Pyx_NAMESTR("binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_40binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_40binomial)},
-- {__Pyx_NAMESTR("negative_binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_41negative_binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_41negative_binomial)},
-- {__Pyx_NAMESTR("poisson"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_42poisson, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_42poisson)},
-- {__Pyx_NAMESTR("zipf"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_43zipf, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_43zipf)},
-- {__Pyx_NAMESTR("geometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_44geometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_44geometric)},
-- {__Pyx_NAMESTR("hypergeometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_45hypergeometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_45hypergeometric)},
-- {__Pyx_NAMESTR("logseries"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_46logseries, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_46logseries)},
-- {__Pyx_NAMESTR("multivariate_normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_47multivariate_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_47multivariate_normal)},
-- {__Pyx_NAMESTR("multinomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_48multinomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_48multinomial)},
-- {__Pyx_NAMESTR("dirichlet"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_49dirichlet, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_49dirichlet)},
-- {__Pyx_NAMESTR("shuffle"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_50shuffle, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_50shuffle)},
-- {__Pyx_NAMESTR("permutation"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_51permutation, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_51permutation)},
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-@@ -20501,7 +21154,7 @@
- * raise ValueError("cov must be 2 dimensional and square")
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-@@ -20515,7 +21168,7 @@
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-- __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_163));
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- __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_162));
-@@ -20529,7 +21182,7 @@
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-- __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_165));
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- __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_164));
-@@ -20543,7 +21196,7 @@
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- __pyx_k_tuple_168 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_168)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-- __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_168));
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- __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_167));
-@@ -20557,13 +21210,13 @@
- * def __init__(self, seed=None):
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-- __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_169));
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- PyTuple_SET_ITEM(__pyx_k_tuple_169, 0, ((PyObject *)__pyx_n_s__l));
- __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l));
- __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_169));
- __pyx_k_tuple_170 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_170)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-- __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_170));
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- __Pyx_INCREF(((PyObject *)__pyx_n_s__l));
- PyTuple_SET_ITEM(__pyx_k_tuple_170, 0, ((PyObject *)__pyx_n_s__l));
- __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l));
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- Py_FatalError("failed to import 'refnanny' module");
- }
- #endif
-- __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_mtrand(void)");
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- if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-- if (__pyx_binding_PyCFunctionType_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-+ #ifdef __Pyx_CyFunction_USED
-+ if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-+ #endif
-+ #ifdef __Pyx_FusedFunction_USED
-+ if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-+ #endif
-+ #ifdef __Pyx_Generator_USED
-+ if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
- #endif
- /*--- Library function declarations ---*/
- /*--- Threads initialization code ---*/
-@@ -20624,16 +21283,15 @@
- #endif
- /*--- Module creation code ---*/
- #if PY_MAJOR_VERSION < 3
-- __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION);
-+ __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
- #else
- __pyx_m = PyModule_Create(&__pyx_moduledef);
- #endif
-- if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-- #if PY_MAJOR_VERSION < 3
-- Py_INCREF(__pyx_m);
-+ if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-+ __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-+ #if CYTHON_COMPILING_IN_PYPY
-+ Py_INCREF(__pyx_b);
- #endif
-- __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME));
-- if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
- if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
- /*--- Initialize various global constants etc. ---*/
- if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-@@ -20716,7 +21374,7 @@
- __Pyx_GOTREF(__pyx_t_4);
- __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
- __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-- __Pyx_GOTREF(((PyObject *)__pyx_t_1));
-+ __Pyx_GOTREF(__pyx_t_1);
- PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_4);
- __Pyx_GIVEREF(__pyx_t_4);
- __pyx_t_4 = 0;
-@@ -21667,7 +22325,6 @@
- }
-
- /* Runtime support code */
--
- #if CYTHON_REFNANNY
- static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
- PyObject *m = NULL, *p = NULL;
-@@ -21700,9 +22357,9 @@
- }
-
- static CYTHON_INLINE void __Pyx_ErrRestore(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->curexc_type;
- tmp_value = tstate->curexc_value;
- tmp_tb = tstate->curexc_traceback;
-@@ -21712,55 +22369,60 @@
- Py_XDECREF(tmp_type);
- Py_XDECREF(tmp_value);
- Py_XDECREF(tmp_tb);
-+#else
-+ PyErr_Restore(type, value, tb);
-+#endif
- }
--
- static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
-+#if CYTHON_COMPILING_IN_CPYTHON
- PyThreadState *tstate = PyThreadState_GET();
- *type = tstate->curexc_type;
- *value = tstate->curexc_value;
- *tb = tstate->curexc_traceback;
--
- tstate->curexc_type = 0;
- tstate->curexc_value = 0;
- tstate->curexc_traceback = 0;
-+#else
-+ PyErr_Fetch(type, value, tb);
-+#endif
- }
-
--
- #if PY_MAJOR_VERSION < 3
--static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-- /* cause is unused */
-+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
-+ CYTHON_UNUSED PyObject *cause) {
- Py_XINCREF(type);
-- Py_XINCREF(value);
-- Py_XINCREF(tb);
-- /* First, check the traceback argument, replacing None with NULL. */
-- if (tb == Py_None) {
-- Py_DECREF(tb);
-- tb = 0;
-- }
-- else if (tb != NULL && !PyTraceBack_Check(tb)) {
-- PyErr_SetString(PyExc_TypeError,
-- "raise: arg 3 must be a traceback or None");
-- goto raise_error;
-- }
-- /* Next, replace a missing value with None */
-- if (value == NULL) {
-- value = Py_None;
-+ if (!value || value == Py_None)
-+ value = NULL;
-+ else
- Py_INCREF(value);
-+ if (!tb || tb == Py_None)
-+ tb = NULL;
-+ else {
-+ Py_INCREF(tb);
-+ if (!PyTraceBack_Check(tb)) {
-+ PyErr_SetString(PyExc_TypeError,
-+ "raise: arg 3 must be a traceback or None");
-+ goto raise_error;
-+ }
- }
- #if PY_VERSION_HEX < 0x02050000
-- if (!PyClass_Check(type))
-+ if (PyClass_Check(type)) {
- #else
-- if (!PyType_Check(type))
-+ if (PyType_Check(type)) {
- #endif
-- {
-- /* Raising an instance. The value should be a dummy. */
-- if (value != Py_None) {
-+#if CYTHON_COMPILING_IN_PYPY
-+ if (!value) {
-+ Py_INCREF(Py_None);
-+ value = Py_None;
-+ }
-+#endif
-+ PyErr_NormalizeException(&type, &value, &tb);
-+ } else {
-+ if (value) {
- PyErr_SetString(PyExc_TypeError,
- "instance exception may not have a separate value");
- goto raise_error;
- }
-- /* Normalize to raise <class>, <instance> */
-- Py_DECREF(value);
- value = type;
- #if PY_VERSION_HEX < 0x02050000
- if (PyInstance_Check(type)) {
-@@ -21783,7 +22445,6 @@
- }
- #endif
- }
--
- __Pyx_ErrRestore(type, value, tb);
- return;
- raise_error:
-@@ -21792,10 +22453,9 @@
- 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)) {
-@@ -21805,7 +22465,6 @@
- }
- if (value == Py_None)
- value = 0;
--
- if (PyExceptionInstance_Check(type)) {
- if (value) {
- PyErr_SetString(PyExc_TypeError,
-@@ -21814,13 +22473,36 @@
- }
- value = type;
- type = (PyObject*) Py_TYPE(value);
-- } else if (!PyExceptionClass_Check(type)) {
-+ } 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 (cause) {
-+ if (cause && cause != Py_None) {
- PyObject *fixed_cause;
- if (PyExceptionClass_Check(cause)) {
- fixed_cause = PyObject_CallObject(cause, NULL);
-@@ -21837,14 +22519,9 @@
- "BaseException");
- goto bad;
- }
-- if (!value) {
-- value = PyObject_CallObject(type, NULL);
-- }
- PyException_SetCause(value, fixed_cause);
- }
--
- PyErr_SetObject(type, value);
--
- if (tb) {
- PyThreadState *tstate = PyThreadState_GET();
- PyObject* tmp_tb = tstate->curexc_traceback;
-@@ -21854,8 +22531,8 @@
- Py_XDECREF(tmp_tb);
- }
- }
--
- bad:
-+ Py_XDECREF(owned_instance);
- return;
- }
- #endif
-@@ -21869,7 +22546,7 @@
- "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
- #else
- "%s() got multiple values for keyword argument '%s'", func_name,
-- PyString_AS_STRING(kw_name));
-+ PyString_AsString(kw_name));
- #endif
- }
-
-@@ -21885,55 +22562,77 @@
- 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;
-- } else {
-- #if PY_MAJOR_VERSION < 3
-- if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) {
-- #else
-- if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) {
-- #endif
-- goto invalid_keyword_type;
-- } else {
-- for (name = first_kw_arg; *name; name++) {
-- #if PY_MAJOR_VERSION >= 3
-- if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) &&
-- PyUnicode_Compare(**name, key) == 0) break;
-- #else
-- if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) &&
-- _PyString_Eq(**name, key)) break;
-- #endif
-- }
-- if (*name) {
-+ 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;
-- } else {
-- /* unexpected keyword found */
-- for (name=argnames; name != first_kw_arg; name++) {
-- if (**name == key) goto arg_passed_twice;
-- #if PY_MAJOR_VERSION >= 3
-- if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) &&
-- PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice;
-- #else
-- if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) &&
-- _PyString_Eq(**name, key)) goto arg_passed_twice;
-- #endif
-- }
-- if (kwds2) {
-- if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
-- } else {
-- goto invalid_keyword;
-+ 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, **name);
-+ __Pyx_RaiseDoubleKeywordsError(function_name, key);
- goto bad;
- invalid_keyword_type:
- PyErr_Format(PyExc_TypeError,
-@@ -21961,7 +22660,6 @@
- {
- Py_ssize_t num_expected;
- const char *more_or_less;
--
- if (num_found < num_min) {
- num_expected = num_min;
- more_or_less = "at least";
-@@ -21973,21 +22671,54 @@
- more_or_less = "exactly";
- }
- PyErr_Format(PyExc_TypeError,
-- "%s() takes %s %"PY_FORMAT_SIZE_T"d positional argument%s (%"PY_FORMAT_SIZE_T"d given)",
-+ "%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 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 %"PY_FORMAT_SIZE_T"d value%s to unpack",
-+ "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack",
- index, (index == 1) ? "" : "s");
- }
-
--static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
-- PyErr_Format(PyExc_ValueError,
-- "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected);
-+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) {
-@@ -21995,19 +22726,15 @@
- Py_DECREF(retval);
- __Pyx_RaiseTooManyValuesError(expected);
- return -1;
-- } else if (PyErr_Occurred()) {
-- if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
-- PyErr_Clear();
-- return 0;
-- } else {
-- return -1;
-- }
-+ } else {
-+ return __Pyx_IterFinish();
- }
- return 0;
- }
-
- 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;
-@@ -22016,19 +22743,27 @@
- 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
-- *type = local_type;
-- *value = local_value;
-- *tb = local_tb;
- 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;
-@@ -22036,10 +22771,13 @@
- tstate->exc_value = local_value;
- tstate->exc_traceback = local_tb;
- /* Make sure tstate is in a consistent state when we XDECREF
-- these objects (XDECREF may run arbitrary code). */
-+ 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;
-@@ -22051,7 +22789,6 @@
- return -1;
- }
-
--
- static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
- PyObject *kwdict,
- const char* function_name,
-@@ -22059,13 +22796,17 @@
- {
- 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)))
-- #else
-- if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key)))
- #endif
-- goto invalid_keyword_type;
-+ if (unlikely(!PyUnicode_Check(key)))
-+ goto invalid_keyword_type;
- }
- if ((!kw_allowed) && unlikely(key))
- goto invalid_keyword;
-@@ -22074,6 +22815,7 @@
- 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
-@@ -22098,8 +22840,8 @@
- return 0;
- }
-
--
- 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;
-@@ -22107,9 +22849,12 @@
- 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;
-@@ -22121,6 +22866,9 @@
- 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, long level) {
-@@ -22149,12 +22897,33 @@
- goto bad;
- #if PY_VERSION_HEX >= 0x02050000
- {
-- PyObject *py_level = PyInt_FromLong(level);
-- if (!py_level)
-- goto bad;
-- module = PyObject_CallFunctionObjArgs(py_import,
-- name, global_dict, empty_dict, list, py_level, NULL);
-- Py_DECREF(py_level);
-+ #if PY_MAJOR_VERSION >= 3
-+ if (level == -1) {
-+ if (strchr(__Pyx_MODULE_NAME, '.')) {
-+ /* try package relative import first */
-+ 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);
-+ if (!module) {
-+ if (!PyErr_ExceptionMatches(PyExc_ImportError))
-+ goto bad;
-+ PyErr_Clear();
-+ }
-+ }
-+ level = 0; /* try absolute import on failure */
-+ }
-+ #endif
-+ if (!module) {
-+ PyObject *py_level = PyInt_FromLong(level);
-+ if (!py_level)
-+ goto bad;
-+ module = PyObject_CallFunctionObjArgs(py_import,
-+ name, global_dict, empty_dict, list, py_level, NULL);
-+ Py_DECREF(py_level);
-+ }
- }
- #else
- if (level>0) {
-@@ -22171,66 +22940,65 @@
- return module;
- }
-
--static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-- if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
-- return (equals == Py_EQ);
-- } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
-- if (PyBytes_GET_SIZE(s1) != PyBytes_GET_SIZE(s2)) {
-- return (equals == Py_NE);
-- } else if (PyBytes_GET_SIZE(s1) == 1) {
-- if (equals == Py_EQ)
-- return (PyBytes_AS_STRING(s1)[0] == PyBytes_AS_STRING(s2)[0]);
-- else
-- return (PyBytes_AS_STRING(s1)[0] != PyBytes_AS_STRING(s2)[0]);
-- } else {
-- int result = memcmp(PyBytes_AS_STRING(s1), PyBytes_AS_STRING(s2), (size_t)PyBytes_GET_SIZE(s1));
-- return (equals == Py_EQ) ? (result == 0) : (result != 0);
-+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 {
-+ npy_intp val;
-+ PyObject *v = __Pyx_PyNumber_Int(x);
-+ #if PY_VERSION_HEX < 0x03000000
-+ if (likely(v) && !PyLong_Check(v)) {
-+ PyObject *tmp = v;
-+ v = PyNumber_Long(tmp);
-+ Py_DECREF(tmp);
- }
-- } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
-- return (equals == Py_NE);
-- } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
-- return (equals == Py_NE);
-- } else {
-- int result;
-- PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-- if (!py_result)
-- return -1;
-- result = __Pyx_PyObject_IsTrue(py_result);
-- Py_DECREF(py_result);
-- return result;
-+ #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;
-+ }
-+ return (npy_intp)-1;
- }
- }
-
--static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-- if (s1 == s2) { /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
-- return (equals == Py_EQ);
-- } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) {
-- if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) {
-- return (equals == Py_NE);
-- } else if (PyUnicode_GET_SIZE(s1) == 1) {
-- if (equals == Py_EQ)
-- return (PyUnicode_AS_UNICODE(s1)[0] == PyUnicode_AS_UNICODE(s2)[0]);
-- else
-- return (PyUnicode_AS_UNICODE(s1)[0] != PyUnicode_AS_UNICODE(s2)[0]);
-- } else {
-- int result = PyUnicode_Compare(s1, s2);
-- if ((result == -1) && unlikely(PyErr_Occurred()))
-- return -1;
-- return (equals == Py_EQ) ? (result == 0) : (result != 0);
-- }
-- } else if ((s1 == Py_None) & PyUnicode_CheckExact(s2)) {
-- return (equals == Py_NE);
-- } else if ((s2 == Py_None) & PyUnicode_CheckExact(s1)) {
-- return (equals == Py_NE);
-- } else {
-- int result;
-- PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
-- if (!py_result)
-- return -1;
-- result = __Pyx_PyObject_IsTrue(py_result);
-- Py_DECREF(py_result);
-- return result;
-- }
-+static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) {
-+#if PY_MAJOR_VERSION < 3
-+ PyErr_Format(PyExc_ImportError, "cannot import name %.230s",
-+ PyString_AsString(name));
-+#else
-+ PyErr_Format(PyExc_ImportError, "cannot import name %S", name);
-+#endif
- }
-
- static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) {
-@@ -22658,58 +23426,6 @@
- }
- }
-
--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 {
-- npy_intp val;
-- PyObject *v = __Pyx_PyNumber_Int(x);
-- #if PY_VERSION_HEX < 0x03000000
-- 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;
-- }
-- return (npy_intp)-1;
-- }
--}
--
- static int __Pyx_check_binary_version(void) {
- char ctversion[4], rtversion[4];
- PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
-@@ -22729,6 +23445,23 @@
- 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,
-@@ -22738,15 +23471,10 @@
- PyObject *result = 0;
- PyObject *py_name = 0;
- char warning[200];
--
- py_module = __Pyx_ImportModule(module_name);
- if (!py_module)
- goto bad;
-- #if PY_MAJOR_VERSION < 3
-- py_name = PyString_FromString(class_name);
-- #else
-- py_name = PyUnicode_FromString(class_name);
-- #endif
-+ py_name = __Pyx_PyIdentifier_FromString(class_name);
- if (!py_name)
- goto bad;
- result = PyObject_GetAttr(py_module, py_name);
-@@ -22762,7 +23490,7 @@
- module_name, class_name);
- goto bad;
- }
-- if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) {
-+ if (!strict && (size_t)((PyTypeObject *)result)->tp_basicsize > size) {
- PyOS_snprintf(warning, sizeof(warning),
- "%s.%s size changed, may indicate binary incompatibility",
- module_name, class_name);
-@@ -22772,7 +23500,7 @@
- if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
- #endif
- }
-- else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) {
-+ else if ((size_t)((PyTypeObject *)result)->tp_basicsize != size) {
- PyErr_Format(PyExc_ValueError,
- "%s.%s has the wrong size, try recompiling",
- module_name, class_name);
-@@ -22786,51 +23514,105 @@
- }
- #endif
-
--#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;
--
-- #if PY_MAJOR_VERSION < 3
-- py_name = PyString_FromString(name);
-- #else
-- py_name = PyUnicode_FromString(name);
-- #endif
-- 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;
-+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);
- }
--#endif
-
- #include "compile.h"
- #include "frameobject.h"
- #include "traceback.h"
--
--static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
-- int __pyx_lineno, const char *__pyx_filename) {
-+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;
-- PyObject *py_globals = 0;
-- PyCodeObject *py_code = 0;
-- PyFrameObject *py_frame = 0;
--
- #if PY_MAJOR_VERSION < 3
-- py_srcfile = PyString_FromString(__pyx_filename);
-+ py_srcfile = PyString_FromString(filename);
- #else
-- py_srcfile = PyUnicode_FromString(__pyx_filename);
-+ py_srcfile = PyUnicode_FromString(filename);
- #endif
- if (!py_srcfile) goto bad;
-- if (__pyx_clineno) {
-+ if (c_line) {
- #if PY_MAJOR_VERSION < 3
-- py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
-+ py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #else
-- py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
-+ py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
- #endif
- }
- else {
-@@ -22841,28 +23623,45 @@
- #endif
- }
- if (!py_funcname) goto bad;
-- py_globals = PyModule_GetDict(__pyx_m);
-- if (!py_globals) goto bad;
-- py_code = PyCode_New(
-+ py_code = __Pyx_PyCode_New(
- 0, /*int argcount,*/
-- #if PY_MAJOR_VERSION >= 3
- 0, /*int kwonlyargcount,*/
-- #endif
- 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,*/
-+ __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,*/
-- __pyx_lineno, /*int firstlineno,*/
-+ py_line, /*int firstlineno,*/
- __pyx_empty_bytes /*PyObject *lnotab*/
- );
-- if (!py_code) goto bad;
-+ 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,*/
-@@ -22870,11 +23669,9 @@
- 0 /*PyObject *locals*/
- );
- if (!py_frame) goto bad;
-- py_frame->f_lineno = __pyx_lineno;
-+ py_frame->f_lineno = py_line;
- PyTraceBack_Here(py_frame);
- bad:
-- Py_XDECREF(py_srcfile);
-- Py_XDECREF(py_funcname);
- Py_XDECREF(py_code);
- Py_XDECREF(py_frame);
- }
-@@ -22909,6 +23706,7 @@
- return 0;
- }
-
-+
- /* Type Conversion Functions */
-
- static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
Deleted: trunk/dports/python/py-numpy/files/patch-python33-shape.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-python33-shape.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-python33-shape.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,88 +0,0 @@
---- numpy/core/tests/test_multiarray.py
-+++ numpy/core/tests/test_multiarray.py
-@@ -11,10 +11,19 @@
-
- from numpy.compat import asbytes, getexception, strchar
-
- from test_print import in_foreign_locale
-
-+if sys.version_info[:2] > (3, 2):
-+ # In Python 3.3 the representation of empty shape, strides and suboffsets
-+ # is an empty tuple instead of None.
-+ # http://docs.python.org/dev/whatsnew/3.3.html#api-changes
-+ EMPTY = ()
-+else:
-+ EMPTY = None
-+
-+
- class TestFlags(TestCase):
- def setUp(self):
- self.a = arange(10)
-
- def test_writeable(self):
-@@ -2160,31 +2169,31 @@
- y = memoryview(x)
- assert_equal(y.format, 'i')
- assert_equal(y.shape, (5,))
- assert_equal(y.ndim, 1)
- assert_equal(y.strides, (4,))
-- assert_equal(y.suboffsets, None)
-+ assert_equal(y.suboffsets, EMPTY)
- assert_equal(y.itemsize, 4)
-
- def test_export_simple_nd(self):
- x = np.array([[1,2],[3,4]], dtype=np.float64)
- y = memoryview(x)
- assert_equal(y.format, 'd')
- assert_equal(y.shape, (2, 2))
- assert_equal(y.ndim, 2)
- assert_equal(y.strides, (16, 8))
-- assert_equal(y.suboffsets, None)
-+ assert_equal(y.suboffsets, EMPTY)
- assert_equal(y.itemsize, 8)
-
- def test_export_discontiguous(self):
- x = np.zeros((3,3,3), dtype=np.float32)[:,0,:]
- y = memoryview(x)
- assert_equal(y.format, 'f')
- assert_equal(y.shape, (3, 3))
- assert_equal(y.ndim, 2)
- assert_equal(y.strides, (36, 4))
-- assert_equal(y.suboffsets, None)
-+ assert_equal(y.suboffsets, EMPTY)
- assert_equal(y.itemsize, 4)
-
- def test_export_record(self):
- dt = [('a', 'b'),
- ('b', 'h'),
-@@ -2212,11 +2221,11 @@
- asbytes('aaaa'), 'bbbb', asbytes(' '), True, 1.0)],
- dtype=dt)
- y = memoryview(x)
- assert_equal(y.shape, (1,))
- assert_equal(y.ndim, 1)
-- assert_equal(y.suboffsets, None)
-+ assert_equal(y.suboffsets, EMPTY)
-
- sz = sum([dtype(b).itemsize for a, b in dt])
- if dtype('l').itemsize == 4:
- assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:^q:dx:B:e:@H:f:=I:g:L:h:^Q:hx:=f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
- else:
-@@ -2226,14 +2235,14 @@
-
- def test_export_subarray(self):
- x = np.array(([[1,2],[3,4]],), dtype=[('a', ('i', (2,2)))])
- y = memoryview(x)
- assert_equal(y.format, 'T{(2,2)i:a:}')
-- assert_equal(y.shape, None)
-+ assert_equal(y.shape, EMPTY)
- assert_equal(y.ndim, 0)
-- assert_equal(y.strides, None)
-- assert_equal(y.suboffsets, None)
-+ assert_equal(y.strides, EMPTY)
-+ assert_equal(y.suboffsets, EMPTY)
- assert_equal(y.itemsize, 16)
-
- def test_export_endian(self):
- x = np.array([1,2,3], dtype='>i')
- y = memoryview(x)
Deleted: trunk/dports/python/py-numpy/files/patch-python33-unicode.diff
===================================================================
--- trunk/dports/python/py-numpy/files/patch-python33-unicode.diff 2013-03-25 21:14:49 UTC (rev 104456)
+++ trunk/dports/python/py-numpy/files/patch-python33-unicode.diff 2013-03-25 21:37:07 UTC (rev 104457)
@@ -1,105 +0,0 @@
---- numpy/core/src/multiarray/scalarapi.c
-+++ numpy/core/src/multiarray/scalarapi.c
-@@ -650,10 +650,39 @@
- * so round up to nearest multiple
- */
- itemsize = (((itemsize - 1) >> 2) + 1) << 2;
- }
- }
-+#if PY_VERSION_HEX >= 0x03030000
-+ if (type_num == NPY_UNICODE) {
-+ PyObject *u, *args;
-+ int byteorder;
-+
-+#if NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
-+ byteorder = -1;
-+#elif NPY_BYTE_ORDER == NPY_BIG_ENDIAN
-+ byteorder = +1;
-+#else
-+ #error Endianness undefined ?
-+#endif
-+ if (swap) byteorder *= -1;
-+
-+ u = PyUnicode_DecodeUTF32(data, itemsize, NULL, &byteorder);
-+ if (u == NULL) {
-+ return NULL;
-+ }
-+ args = Py_BuildValue("(O)", u);
-+ if (args == NULL) {
-+ Py_DECREF(u);
-+ return NULL;
-+ }
-+ obj = type->tp_new(type, args, NULL);
-+ Py_DECREF(u);
-+ Py_DECREF(args);
-+ return obj;
-+ }
-+#endif
- if (type->tp_itemsize != 0) {
- /* String type */
- obj = type->tp_alloc(type, itemsize);
- }
- else {
-@@ -686,10 +715,11 @@
- ((PyStringObject *)obj)->ob_sstate = SSTATE_NOT_INTERNED;
- #endif
- memcpy(destptr, data, itemsize);
- return obj;
- }
-+#if PY_VERSION_HEX < 0x03030000
- else if (type_num == PyArray_UNICODE) {
- /* tp_alloc inherited from Python PyBaseObject_Type */
- PyUnicodeObject *uni = (PyUnicodeObject*)obj;
- size_t length = itemsize >> 2;
- Py_UNICODE *dst;
-@@ -757,10 +787,11 @@
- uni->str[length] = 0;
- uni->length = length;
- #endif
- return obj;
- }
-+#endif /* PY_VERSION_HEX < 0x03030000 */
- else {
- PyVoidScalarObject *vobj = (PyVoidScalarObject *)obj;
- vobj->base = NULL;
- vobj->descr = descr;
- Py_INCREF(descr);
---- numpy/core/src/multiarray/scalartypes.c.src
-+++ numpy/core/src/multiarray/scalartypes.c.src
-@@ -2321,11 +2321,15 @@
- Py_DECREF(typecode);
- #if @default@ == 0
- *((npy_ at name@ *)dest) = *((npy_ at name@ *)src);
- #elif @default@ == 1 /* unicode and strings */
- if (itemsize == 0) { /* unicode */
-+#if PY_VERSION_HEX >= 0x03030000
-+ itemsize = PyUnicode_GetLength(robj) * PyUnicode_KIND(robj);
-+#else
- itemsize = ((PyUnicodeObject *)robj)->length * sizeof(Py_UNICODE);
-+#endif
- }
- memcpy(dest, src, itemsize);
- /* @default@ == 2 won't get here */
- #endif
- Py_DECREF(robj);
---- numpy/core/tests/test_unicode.py
-+++ numpy/core/tests/test_unicode.py
-@@ -24,14 +24,16 @@
- def buffer_length(arr):
- if isinstance(arr, ndarray):
- return len(arr.data)
- return len(buffer(arr))
-
-+# In both cases below we need to make sure that the byte swapped value (as
-+# UCS4) is still a valid unicode:
- # Value that can be represented in UCS2 interpreters
--ucs2_value = u'\uFFFF'
-+ucs2_value = u'\u0900'
- # Value that cannot be represented in UCS2 interpreters (but can in UCS4)
--ucs4_value = u'\U0010FFFF'
-+ucs4_value = u'\U00100900'
-
-
- ############################################################
- # Creation tests
- ############################################################
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