[packages/python-numpy-compat] - rel 9; fix build with python3.7
arekm
arekm at pld-linux.org
Mon Jul 2 10:32:24 CEST 2018
commit 026ffe0968f41286df3f4311624734cd9a540642
Author: Arkadiusz Miśkiewicz <arekm at maven.pl>
Date: Mon Jul 2 10:32:09 2018 +0200
- rel 9; fix build with python3.7
numpy-mtrand-regenerated.patch | 38810 +++++++++++++++++++++++++++++++++++++++
python-numpy-compat.spec | 4 +-
2 files changed, 38813 insertions(+), 1 deletion(-)
---
diff --git a/python-numpy-compat.spec b/python-numpy-compat.spec
index ae0e903..6243731 100644
--- a/python-numpy-compat.spec
+++ b/python-numpy-compat.spec
@@ -7,13 +7,14 @@ Summary: Python 2 numerical facilities - deprecated packages
Summary(pl.UTF-8): Moduły do obliczeń numerycznych dla języka Python 2 - przestarzałe pakiety
Name: python-numpy-compat
Version: 1.8.2
-Release: 8
+Release: 9
Epoch: 1
License: BSD
Group: Libraries/Python
Source0: http://downloads.sourceforge.net/numpy/numpy-%{version}.tar.gz
# Source0-md5: dd8eece8f6fda3a13836de4adbafb0cb
Patch0: python-numpy-fortran-version.patch
+Patch1: numpy-mtrand-regenerated.patch
URL: http://sourceforge.net/projects/numpy/
BuildRequires: rpmbuild(macros) >= 1.710
%if %{with python2}
@@ -190,6 +191,7 @@ pakietami Numeric.
%prep
%setup -q -n numpy-%{version}
%patch0 -p1
+%patch1 -p1
%build
# numpy.distutils uses CFLAGS/LDFLAGS as its own flags replacements,
diff --git a/numpy-mtrand-regenerated.patch b/numpy-mtrand-regenerated.patch
new file mode 100644
index 0000000..733311e
--- /dev/null
+++ b/numpy-mtrand-regenerated.patch
@@ -0,0 +1,38810 @@
+diff -urN numpy-1.8.2.org/numpy/random/mtrand/mtrand.c numpy-1.8.2/numpy/random/mtrand/mtrand.c
+--- numpy-1.8.2.org/numpy/random/mtrand/mtrand.c 2014-08-05 20:04:19.000000000 +0200
++++ numpy-1.8.2/numpy/random/mtrand/mtrand.c 2018-07-02 10:14:25.845222580 +0200
+@@ -1,27 +1,17 @@
+-/* Generated by Cython 0.19 on Thu Feb 27 20:15:06 2014 */
++/* Generated by Cython 0.27.3 */
+
+ #define PY_SSIZE_T_CLEAN
+-#ifndef CYTHON_USE_PYLONG_INTERNALS
+-#ifdef PYLONG_BITS_IN_DIGIT
+-#define CYTHON_USE_PYLONG_INTERNALS 0
+-#else
+-#include "pyconfig.h"
+-#ifdef PYLONG_BITS_IN_DIGIT
+-#define CYTHON_USE_PYLONG_INTERNALS 1
+-#else
+-#define CYTHON_USE_PYLONG_INTERNALS 0
+-#endif
+-#endif
+-#endif
+ #include "Python.h"
+ #ifndef Py_PYTHON_H
+ #error Python headers needed to compile C extensions, please install development version of Python.
+-#elif PY_VERSION_HEX < 0x02040000
+- #error Cython requires Python 2.4+.
++#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)
++ #error Cython requires Python 2.6+ or Python 3.3+.
+ #else
+-#include <stddef.h> /* For offsetof */
++#define CYTHON_ABI "0_27_3"
++#define CYTHON_FUTURE_DIVISION 0
++#include <stddef.h>
+ #ifndef offsetof
+-#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
++ #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+ #endif
+ #if !defined(WIN32) && !defined(MS_WINDOWS)
+ #ifndef __stdcall
+@@ -40,6 +30,12 @@
+ #ifndef DL_EXPORT
+ #define DL_EXPORT(t) t
+ #endif
++#define __PYX_COMMA ,
++#ifndef HAVE_LONG_LONG
++ #if PY_VERSION_HEX >= 0x02070000
++ #define HAVE_LONG_LONG
++ #endif
++#endif
+ #ifndef PY_LONG_LONG
+ #define PY_LONG_LONG LONG_LONG
+ #endif
+@@ -47,103 +43,277 @@
+ #define Py_HUGE_VAL HUGE_VAL
+ #endif
+ #ifdef PYPY_VERSION
+-#define CYTHON_COMPILING_IN_PYPY 1
+-#define CYTHON_COMPILING_IN_CPYTHON 0
++ #define CYTHON_COMPILING_IN_PYPY 1
++ #define CYTHON_COMPILING_IN_PYSTON 0
++ #define CYTHON_COMPILING_IN_CPYTHON 0
++ #undef CYTHON_USE_TYPE_SLOTS
++ #define CYTHON_USE_TYPE_SLOTS 0
++ #undef CYTHON_USE_PYTYPE_LOOKUP
++ #define CYTHON_USE_PYTYPE_LOOKUP 0
++ #if PY_VERSION_HEX < 0x03050000
++ #undef CYTHON_USE_ASYNC_SLOTS
++ #define CYTHON_USE_ASYNC_SLOTS 0
++ #elif !defined(CYTHON_USE_ASYNC_SLOTS)
++ #define CYTHON_USE_ASYNC_SLOTS 1
++ #endif
++ #undef CYTHON_USE_PYLIST_INTERNALS
++ #define CYTHON_USE_PYLIST_INTERNALS 0
++ #undef CYTHON_USE_UNICODE_INTERNALS
++ #define CYTHON_USE_UNICODE_INTERNALS 0
++ #undef CYTHON_USE_UNICODE_WRITER
++ #define CYTHON_USE_UNICODE_WRITER 0
++ #undef CYTHON_USE_PYLONG_INTERNALS
++ #define CYTHON_USE_PYLONG_INTERNALS 0
++ #undef CYTHON_AVOID_BORROWED_REFS
++ #define CYTHON_AVOID_BORROWED_REFS 1
++ #undef CYTHON_ASSUME_SAFE_MACROS
++ #define CYTHON_ASSUME_SAFE_MACROS 0
++ #undef CYTHON_UNPACK_METHODS
++ #define CYTHON_UNPACK_METHODS 0
++ #undef CYTHON_FAST_THREAD_STATE
++ #define CYTHON_FAST_THREAD_STATE 0
++ #undef CYTHON_FAST_PYCALL
++ #define CYTHON_FAST_PYCALL 0
++ #undef CYTHON_PEP489_MULTI_PHASE_INIT
++ #define CYTHON_PEP489_MULTI_PHASE_INIT 0
++ #undef CYTHON_USE_TP_FINALIZE
++ #define CYTHON_USE_TP_FINALIZE 0
++#elif defined(PYSTON_VERSION)
++ #define CYTHON_COMPILING_IN_PYPY 0
++ #define CYTHON_COMPILING_IN_PYSTON 1
++ #define CYTHON_COMPILING_IN_CPYTHON 0
++ #ifndef CYTHON_USE_TYPE_SLOTS
++ #define CYTHON_USE_TYPE_SLOTS 1
++ #endif
++ #undef CYTHON_USE_PYTYPE_LOOKUP
++ #define CYTHON_USE_PYTYPE_LOOKUP 0
++ #undef CYTHON_USE_ASYNC_SLOTS
++ #define CYTHON_USE_ASYNC_SLOTS 0
++ #undef CYTHON_USE_PYLIST_INTERNALS
++ #define CYTHON_USE_PYLIST_INTERNALS 0
++ #ifndef CYTHON_USE_UNICODE_INTERNALS
++ #define CYTHON_USE_UNICODE_INTERNALS 1
++ #endif
++ #undef CYTHON_USE_UNICODE_WRITER
++ #define CYTHON_USE_UNICODE_WRITER 0
++ #undef CYTHON_USE_PYLONG_INTERNALS
++ #define CYTHON_USE_PYLONG_INTERNALS 0
++ #ifndef CYTHON_AVOID_BORROWED_REFS
++ #define CYTHON_AVOID_BORROWED_REFS 0
++ #endif
++ #ifndef CYTHON_ASSUME_SAFE_MACROS
++ #define CYTHON_ASSUME_SAFE_MACROS 1
++ #endif
++ #ifndef CYTHON_UNPACK_METHODS
++ #define CYTHON_UNPACK_METHODS 1
++ #endif
++ #undef CYTHON_FAST_THREAD_STATE
++ #define CYTHON_FAST_THREAD_STATE 0
++ #undef CYTHON_FAST_PYCALL
++ #define CYTHON_FAST_PYCALL 0
++ #undef CYTHON_PEP489_MULTI_PHASE_INIT
++ #define CYTHON_PEP489_MULTI_PHASE_INIT 0
++ #undef CYTHON_USE_TP_FINALIZE
++ #define CYTHON_USE_TP_FINALIZE 0
+ #else
+-#define CYTHON_COMPILING_IN_PYPY 0
+-#define CYTHON_COMPILING_IN_CPYTHON 1
++ #define CYTHON_COMPILING_IN_PYPY 0
++ #define CYTHON_COMPILING_IN_PYSTON 0
++ #define CYTHON_COMPILING_IN_CPYTHON 1
++ #ifndef CYTHON_USE_TYPE_SLOTS
++ #define CYTHON_USE_TYPE_SLOTS 1
++ #endif
++ #if PY_VERSION_HEX < 0x02070000
++ #undef CYTHON_USE_PYTYPE_LOOKUP
++ #define CYTHON_USE_PYTYPE_LOOKUP 0
++ #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)
++ #define CYTHON_USE_PYTYPE_LOOKUP 1
++ #endif
++ #if PY_MAJOR_VERSION < 3
++ #undef CYTHON_USE_ASYNC_SLOTS
++ #define CYTHON_USE_ASYNC_SLOTS 0
++ #elif !defined(CYTHON_USE_ASYNC_SLOTS)
++ #define CYTHON_USE_ASYNC_SLOTS 1
++ #endif
++ #if PY_VERSION_HEX < 0x02070000
++ #undef CYTHON_USE_PYLONG_INTERNALS
++ #define CYTHON_USE_PYLONG_INTERNALS 0
++ #elif !defined(CYTHON_USE_PYLONG_INTERNALS)
++ #define CYTHON_USE_PYLONG_INTERNALS 1
++ #endif
++ #ifndef CYTHON_USE_PYLIST_INTERNALS
++ #define CYTHON_USE_PYLIST_INTERNALS 1
++ #endif
++ #ifndef CYTHON_USE_UNICODE_INTERNALS
++ #define CYTHON_USE_UNICODE_INTERNALS 1
++ #endif
++ #if PY_VERSION_HEX < 0x030300F0
++ #undef CYTHON_USE_UNICODE_WRITER
++ #define CYTHON_USE_UNICODE_WRITER 0
++ #elif !defined(CYTHON_USE_UNICODE_WRITER)
++ #define CYTHON_USE_UNICODE_WRITER 1
++ #endif
++ #ifndef CYTHON_AVOID_BORROWED_REFS
++ #define CYTHON_AVOID_BORROWED_REFS 0
++ #endif
++ #ifndef CYTHON_ASSUME_SAFE_MACROS
++ #define CYTHON_ASSUME_SAFE_MACROS 1
++ #endif
++ #ifndef CYTHON_UNPACK_METHODS
++ #define CYTHON_UNPACK_METHODS 1
++ #endif
++ #ifndef CYTHON_FAST_THREAD_STATE
++ #define CYTHON_FAST_THREAD_STATE 1
++ #endif
++ #ifndef CYTHON_FAST_PYCALL
++ #define CYTHON_FAST_PYCALL 1
++ #endif
++ #ifndef CYTHON_PEP489_MULTI_PHASE_INIT
++ #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000)
++ #endif
++ #ifndef CYTHON_USE_TP_FINALIZE
++ #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)
++ #endif
+ #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_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 __Pyx_PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && \
+- !PyComplex_Check(o))
+- #define PyIndex_Check __Pyx_PyIndex_Check
+- #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"
+- #define __Pyx_PyIndex_Check PyIndex_Check
+-#endif
+-#if PY_VERSION_HEX < 0x02060000
+- #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
+- #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type)
+- #define Py_SIZE(ob) (((PyVarObject*)(ob))->ob_size)
+- #define PyVarObject_HEAD_INIT(type, size) \
+- PyObject_HEAD_INIT(type) size,
+- #define PyType_Modified(t)
+- typedef struct {
+- void *buf;
+- PyObject *obj;
+- Py_ssize_t len;
+- Py_ssize_t itemsize;
+- int readonly;
+- int ndim;
+- char *format;
+- Py_ssize_t *shape;
+- Py_ssize_t *strides;
+- Py_ssize_t *suboffsets;
+- void *internal;
+- } Py_buffer;
+- #define PyBUF_SIMPLE 0
+- #define PyBUF_WRITABLE 0x0001
+- #define PyBUF_FORMAT 0x0004
+- #define PyBUF_ND 0x0008
+- #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
+- #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
+- #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
+- #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 *);
++#if !defined(CYTHON_FAST_PYCCALL)
++#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
+ #endif
++#if CYTHON_USE_PYLONG_INTERNALS
++ #include "longintrepr.h"
++ #undef SHIFT
++ #undef BASE
++ #undef MASK
++#endif
++#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
++ #define Py_OptimizeFlag 0
++#endif
++#define __PYX_BUILD_PY_SSIZE_T "n"
++#define CYTHON_FORMAT_SSIZE_T "z"
+ #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)
++ #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)
++ #define __Pyx_DefaultClassType PyClass_Type
+ #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) \
++ #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)
++ #define __Pyx_DefaultClassType PyType_Type
+ #endif
+-#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
+- #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
+-#endif
+-#if PY_MAJOR_VERSION >= 3
++#ifndef Py_TPFLAGS_CHECKTYPES
+ #define Py_TPFLAGS_CHECKTYPES 0
++#endif
++#ifndef Py_TPFLAGS_HAVE_INDEX
+ #define Py_TPFLAGS_HAVE_INDEX 0
+ #endif
+-#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
++#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
+ #define Py_TPFLAGS_HAVE_NEWBUFFER 0
+ #endif
+-#if PY_VERSION_HEX < 0x02060000
+- #define Py_TPFLAGS_HAVE_VERSION_TAG 0
++#ifndef Py_TPFLAGS_HAVE_FINALIZE
++ #define Py_TPFLAGS_HAVE_FINALIZE 0
++#endif
++#if PY_VERSION_HEX < 0x030700A0 || !defined(METH_FASTCALL)
++ #ifndef METH_FASTCALL
++ #define METH_FASTCALL 0x80
++ #endif
++ typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, Py_ssize_t nargs);
++ typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject **args,
++ Py_ssize_t nargs, PyObject *kwnames);
++#else
++ #define __Pyx_PyCFunctionFast _PyCFunctionFast
++ #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords
++#endif
++#if CYTHON_FAST_PYCCALL
++#define __Pyx_PyFastCFunction_Check(func)\
++ ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)))))
++#else
++#define __Pyx_PyFastCFunction_Check(func) 0
++#endif
++#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000
++ #define __Pyx_PyThreadState_Current PyThreadState_GET()
++#elif PY_VERSION_HEX >= 0x03060000
++ #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()
++#elif PY_VERSION_HEX >= 0x03000000
++ #define __Pyx_PyThreadState_Current PyThreadState_GET()
++#else
++ #define __Pyx_PyThreadState_Current _PyThreadState_Current
++#endif
++#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)
++#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))
++#else
++#define __Pyx_PyDict_NewPresized(n) PyDict_New()
++#endif
++#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION
++ #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
+ #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
+ #define CYTHON_PEP393_ENABLED 1
+- #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \
++ #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_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u)
++ #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u)
++ #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u)
+ #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i)
++ #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch)
++ #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
+ #else
+ #define CYTHON_PEP393_ENABLED 0
++ #define PyUnicode_1BYTE_KIND 1
++ #define PyUnicode_2BYTE_KIND 2
++ #define PyUnicode_4BYTE_KIND 4
+ #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]))
++ #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
++ #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE))
++ #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u))
++ #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
++ #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
++ #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u))
++#endif
++#if CYTHON_COMPILING_IN_PYPY
++ #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b)
++ #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b)
++#else
++ #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b)
++ #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
++ PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))
++#endif
++#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
++ #define PyUnicode_Contains(u, s) PySequence_Contains(u, s)
++#endif
++#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
++ #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type)
++#endif
++#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
++ #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt)
++#endif
++#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
++ #define PyObject_Malloc(s) PyMem_Malloc(s)
++ #define PyObject_Free(p) PyMem_Free(p)
++ #define PyObject_Realloc(p) PyMem_Realloc(p)
++#endif
++#if CYTHON_COMPILING_IN_PYSTON
++ #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co)
++ #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
++#else
++ #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0)
++ #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno)
++#endif
++#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
++#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
++#if PY_MAJOR_VERSION >= 3
++ #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b)
++#else
++ #define __Pyx_PyString_Format(a, b) PyString_Format(a, b)
++#endif
++#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
++ #define PyObject_ASCII(o) PyObject_Repr(o)
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ #define PyBaseString_Type PyUnicode_Type
+@@ -152,40 +322,17 @@
+ #define PyString_Check PyUnicode_Check
+ #define PyString_CheckExact PyUnicode_CheckExact
+ #endif
+-#if PY_VERSION_HEX < 0x02060000
+- #define PyBytesObject PyStringObject
+- #define PyBytes_Type PyString_Type
+- #define PyBytes_Check PyString_Check
+- #define PyBytes_CheckExact PyString_CheckExact
+- #define PyBytes_FromString PyString_FromString
+- #define PyBytes_FromStringAndSize PyString_FromStringAndSize
+- #define PyBytes_FromFormat PyString_FromFormat
+- #define PyBytes_DecodeEscape PyString_DecodeEscape
+- #define PyBytes_AsString PyString_AsString
+- #define PyBytes_AsStringAndSize PyString_AsStringAndSize
+- #define PyBytes_Size PyString_Size
+- #define PyBytes_AS_STRING PyString_AS_STRING
+- #define PyBytes_GET_SIZE PyString_GET_SIZE
+- #define PyBytes_Repr PyString_Repr
+- #define PyBytes_Concat PyString_Concat
+- #define PyBytes_ConcatAndDel PyString_ConcatAndDel
+-#endif
+ #if PY_MAJOR_VERSION >= 3
+ #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)
+ #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)
+ #else
+- #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
+- PyString_Check(obj) || PyUnicode_Check(obj))
+- #define __Pyx_PyBaseString_CheckExact(obj) (Py_TYPE(obj) == &PyBaseString_Type)
+-#endif
+-#if PY_VERSION_HEX < 0x02060000
+- #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type)
+- #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type)
++ #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))
++ #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
+ #endif
+ #ifndef PySet_CheckExact
+ #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type)
+ #endif
+-#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
++#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
+ #if PY_MAJOR_VERSION >= 3
+ #define PyIntObject PyLongObject
+ #define PyInt_Type PyLong_Type
+@@ -201,11 +348,17 @@
+ #define PyInt_AsSsize_t PyLong_AsSsize_t
+ #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask
+ #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
++ #define PyNumber_Int PyNumber_Long
+ #endif
+ #if PY_MAJOR_VERSION >= 3
+ #define PyBoolObject PyLongObject
+ #endif
+-#if PY_VERSION_HEX < 0x03020000
++#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
++ #ifndef PyUnicode_InternFromString
++ #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
++ #endif
++#endif
++#if PY_VERSION_HEX < 0x030200A4
+ typedef long Py_hash_t;
+ #define __Pyx_PyInt_FromHash_t PyInt_FromLong
+ #define __Pyx_PyInt_AsHash_t PyInt_AsLong
+@@ -213,45 +366,115 @@
+ #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
+ #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t
+ #endif
+-#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
+- #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)
+- #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
+-#else
+- #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
+- (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
+- (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
+- (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
+- #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
+- (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+- (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
+- (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
+- #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
+- (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+- (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
+- (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
+-#endif
+ #if PY_MAJOR_VERSION >= 3
+- #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
++ #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
++#else
++ #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
++#endif
++#ifndef __has_attribute
++ #define __has_attribute(x) 0
+ #endif
+-#if PY_VERSION_HEX < 0x02050000
+- #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n)))
+- #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
+- #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),((char *)(n)))
+-#else
+- #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),(n))
+- #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
+- #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n))
+-#endif
+-#if PY_VERSION_HEX < 0x02050000
+- #define __Pyx_NAMESTR(n) ((char *)(n))
+- #define __Pyx_DOCSTR(n) ((char *)(n))
++#ifndef __has_cpp_attribute
++ #define __has_cpp_attribute(x) 0
++#endif
++#if CYTHON_USE_ASYNC_SLOTS
++ #if PY_VERSION_HEX >= 0x030500B1
++ #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
++ #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
++ #else
++ #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
++ #endif
+ #else
+- #define __Pyx_NAMESTR(n) (n)
+- #define __Pyx_DOCSTR(n) (n)
++ #define __Pyx_PyType_AsAsync(obj) NULL
+ #endif
+-#ifndef CYTHON_INLINE
++#ifndef __Pyx_PyAsyncMethodsStruct
++ typedef struct {
++ unaryfunc am_await;
++ unaryfunc am_aiter;
++ unaryfunc am_anext;
++ } __Pyx_PyAsyncMethodsStruct;
++#endif
++#ifndef CYTHON_RESTRICT
+ #if defined(__GNUC__)
++ #define CYTHON_RESTRICT __restrict__
++ #elif defined(_MSC_VER) && _MSC_VER >= 1400
++ #define CYTHON_RESTRICT __restrict
++ #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
++ #define CYTHON_RESTRICT restrict
++ #else
++ #define CYTHON_RESTRICT
++ #endif
++#endif
++#ifndef CYTHON_UNUSED
++# if defined(__GNUC__)
++# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
++# define CYTHON_UNUSED __attribute__ ((__unused__))
++# else
++# define CYTHON_UNUSED
++# endif
++# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
++# define CYTHON_UNUSED __attribute__ ((__unused__))
++# else
++# define CYTHON_UNUSED
++# endif
++#endif
++#ifndef CYTHON_MAYBE_UNUSED_VAR
++# if defined(__cplusplus)
++ template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
++# else
++# define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
++# endif
++#endif
++#ifndef CYTHON_NCP_UNUSED
++# if CYTHON_COMPILING_IN_CPYTHON
++# define CYTHON_NCP_UNUSED
++# else
++# define CYTHON_NCP_UNUSED CYTHON_UNUSED
++# endif
++#endif
++#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)
++#ifdef _MSC_VER
++ #ifndef _MSC_STDINT_H_
++ #if _MSC_VER < 1300
++ typedef unsigned char uint8_t;
++ typedef unsigned int uint32_t;
++ #else
++ typedef unsigned __int8 uint8_t;
++ typedef unsigned __int32 uint32_t;
++ #endif
++ #endif
++#else
++ #include <stdint.h>
++#endif
++#ifndef CYTHON_FALLTHROUGH
++ #if defined(__cplusplus) && __cplusplus >= 201103L
++ #if __has_cpp_attribute(fallthrough)
++ #define CYTHON_FALLTHROUGH [[fallthrough]]
++ #elif __has_cpp_attribute(clang::fallthrough)
++ #define CYTHON_FALLTHROUGH [[clang::fallthrough]]
++ #elif __has_cpp_attribute(gnu::fallthrough)
++ #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]
++ #endif
++ #endif
++ #ifndef CYTHON_FALLTHROUGH
++ #if __has_attribute(fallthrough)
++ #define CYTHON_FALLTHROUGH __attribute__((fallthrough))
++ #else
++ #define CYTHON_FALLTHROUGH
++ #endif
++ #endif
++ #if defined(__clang__ ) && defined(__apple_build_version__)
++ #if __apple_build_version__ < 7000000
++ #undef CYTHON_FALLTHROUGH
++ #define CYTHON_FALLTHROUGH
++ #endif
++ #endif
++#endif
++
++#ifndef CYTHON_INLINE
++ #if defined(__clang__)
++ #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
++ #elif defined(__GNUC__)
+ #define CYTHON_INLINE __inline__
+ #elif defined(_MSC_VER)
+ #define CYTHON_INLINE __inline
+@@ -261,28 +484,32 @@
+ #define CYTHON_INLINE
+ #endif
+ #endif
++
++#if defined(WIN32) || defined(MS_WINDOWS)
++ #define _USE_MATH_DEFINES
++#endif
++#include <math.h>
+ #ifdef NAN
+ #define __PYX_NAN() ((float) NAN)
+ #else
+ static CYTHON_INLINE float __PYX_NAN() {
+- /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
+- a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
+- a quiet NaN. */
+ float value;
+ memset(&value, 0xFF, sizeof(value));
+ return value;
+ }
+ #endif
+-
+-
+-#if PY_MAJOR_VERSION >= 3
+- #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y)
+- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y)
++#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
++#define __Pyx_truncl trunc
+ #else
+- #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y)
+- #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y)
++#define __Pyx_truncl truncl
+ #endif
+
++
++#define __PYX_ERR(f_index, lineno, Ln_error) \
++{ \
++ __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
++}
++
+ #ifndef __PYX_EXTERN_C
+ #ifdef __cplusplus
+ #define __PYX_EXTERN_C extern "C"
+@@ -291,10 +518,6 @@
+ #endif
+ #endif
+
+-#if defined(WIN32) || defined(MS_WINDOWS)
+-#define _USE_MATH_DEFINES
+-#endif
+-#include <math.h>
+ #define __PYX_HAVE__mtrand
+ #define __PYX_HAVE_API__mtrand
+ #include "string.h"
+@@ -309,36 +532,53 @@
+ #include <omp.h>
+ #endif /* _OPENMP */
+
+-#ifdef PYREX_WITHOUT_ASSERTIONS
++#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)
+ #define CYTHON_WITHOUT_ASSERTIONS
+ #endif
+
+-#ifndef CYTHON_UNUSED
+-# if defined(__GNUC__)
+-# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
+-# define CYTHON_UNUSED __attribute__ ((__unused__))
+-# else
+-# define CYTHON_UNUSED
+-# endif
+-# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
+-# define CYTHON_UNUSED __attribute__ ((__unused__))
+-# else
+-# define CYTHON_UNUSED
+-# endif
+-#endif
+-typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
+- const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
++typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
++ const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;
+
+ #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
+ #define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
+ #define __PYX_DEFAULT_STRING_ENCODING ""
+ #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
+ #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+-static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
+-static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
++#define __Pyx_uchar_cast(c) ((unsigned char)c)
++#define __Pyx_long_cast(x) ((long)x)
++#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\
++ (sizeof(type) < sizeof(Py_ssize_t)) ||\
++ (sizeof(type) > sizeof(Py_ssize_t) &&\
++ likely(v < (type)PY_SSIZE_T_MAX ||\
++ v == (type)PY_SSIZE_T_MAX) &&\
++ (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
++ v == (type)PY_SSIZE_T_MIN))) ||\
++ (sizeof(type) == sizeof(Py_ssize_t) &&\
++ (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
++ v == (type)PY_SSIZE_T_MAX))) )
++#if defined (__cplusplus) && __cplusplus >= 201103L
++ #include <cstdlib>
++ #define __Pyx_sst_abs(value) std::abs(value)
++#elif SIZEOF_INT >= SIZEOF_SIZE_T
++ #define __Pyx_sst_abs(value) abs(value)
++#elif SIZEOF_LONG >= SIZEOF_SIZE_T
++ #define __Pyx_sst_abs(value) labs(value)
++#elif defined (_MSC_VER)
++ #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))
++#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
++ #define __Pyx_sst_abs(value) llabs(value)
++#elif defined (__GNUC__)
++ #define __Pyx_sst_abs(value) __builtin_llabs(value)
++#else
++ #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
++#endif
++static CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);
++static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
++#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
++#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
+ #define __Pyx_PyBytes_FromString PyBytes_FromString
+ #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
+-static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*);
++static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);
+ #if PY_MAJOR_VERSION < 3
+ #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString
+ #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+@@ -346,52 +586,69 @@
+ #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString
+ #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
+ #endif
+-#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
+-#define __Pyx_PyObject_FromUString(s) __Pyx_PyObject_FromString((char*)s)
+-#define __Pyx_PyBytes_FromUString(s) __Pyx_PyBytes_FromString((char*)s)
+-#define __Pyx_PyStr_FromUString(s) __Pyx_PyStr_FromString((char*)s)
+-#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s)
+-#if PY_MAJOR_VERSION < 3
+-static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
+-{
++#define __Pyx_PyBytes_AsWritableString(s) ((char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyBytes_AsWritableSString(s) ((signed char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyBytes_AsWritableUString(s) ((unsigned char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyBytes_AsString(s) ((const char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyBytes_AsSString(s) ((const signed char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyBytes_AsUString(s) ((const unsigned char*) PyBytes_AS_STRING(s))
++#define __Pyx_PyObject_AsWritableString(s) ((char*) __Pyx_PyObject_AsString(s))
++#define __Pyx_PyObject_AsWritableSString(s) ((signed char*) __Pyx_PyObject_AsString(s))
++#define __Pyx_PyObject_AsWritableUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s))
++#define __Pyx_PyObject_AsSString(s) ((const signed char*) __Pyx_PyObject_AsString(s))
++#define __Pyx_PyObject_AsUString(s) ((const unsigned char*) __Pyx_PyObject_AsString(s))
++#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s)
++#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s)
++#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s)
++#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s)
++#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
++static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {
+ const Py_UNICODE *u_end = u;
+ while (*u_end++) ;
+- return u_end - u - 1;
++ return (size_t)(u_end - u - 1);
+ }
+-#else
+-#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen
+-#endif
+ #define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))
+ #define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode
+ #define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode
+-#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None)
+-#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
++#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)
++#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
++#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
+ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
+-static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
++static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);
++#define __Pyx_PySequence_Tuple(obj)\
++ (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))
+ static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
+ static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
+-static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
+-#if CYTHON_COMPILING_IN_CPYTHON
++#if CYTHON_ASSUME_SAFE_MACROS
+ #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+ #else
+ #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
+ #endif
+ #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
++#if PY_MAJOR_VERSION >= 3
++#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
++#else
++#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
++#endif
++#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
+ #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
+ static int __Pyx_sys_getdefaultencoding_not_ascii;
+-static int __Pyx_init_sys_getdefaultencoding_params() {
+- PyObject* sys = NULL;
++static int __Pyx_init_sys_getdefaultencoding_params(void) {
++ PyObject* sys;
+ PyObject* default_encoding = NULL;
+ PyObject* ascii_chars_u = NULL;
+ PyObject* ascii_chars_b = NULL;
++ const char* default_encoding_c;
+ sys = PyImport_ImportModule("sys");
+- if (sys == NULL) goto bad;
+- default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+- if (default_encoding == NULL) goto bad;
+- if (strcmp(PyBytes_AsString(default_encoding), "ascii") == 0) {
++ if (!sys) goto bad;
++ default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL);
++ Py_DECREF(sys);
++ if (!default_encoding) goto bad;
++ default_encoding_c = PyBytes_AsString(default_encoding);
++ if (!default_encoding_c) goto bad;
++ if (strcmp(default_encoding_c, "ascii") == 0) {
+ __Pyx_sys_getdefaultencoding_not_ascii = 0;
+ } else {
+- const char* default_encoding_c = PyBytes_AS_STRING(default_encoding);
+ char ascii_chars[128];
+ int c;
+ for (c = 0; c < 128; c++) {
+@@ -399,23 +656,21 @@
+ }
+ __Pyx_sys_getdefaultencoding_not_ascii = 1;
+ ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
+- if (ascii_chars_u == NULL) goto bad;
++ if (!ascii_chars_u) goto bad;
+ ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
+- if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
++ if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
+ PyErr_Format(
+ PyExc_ValueError,
+- "This module compiled with c_string_encoding=ascii, but default encoding '%s' is not a superset of ascii.",
++ "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
+ default_encoding_c);
+ goto bad;
+ }
++ Py_DECREF(ascii_chars_u);
++ Py_DECREF(ascii_chars_b);
+ }
+- Py_XDECREF(sys);
+- Py_XDECREF(default_encoding);
+- Py_XDECREF(ascii_chars_u);
+- Py_XDECREF(ascii_chars_b);
++ Py_DECREF(default_encoding);
+ return 0;
+ bad:
+- Py_XDECREF(sys);
+ Py_XDECREF(default_encoding);
+ Py_XDECREF(ascii_chars_u);
+ Py_XDECREF(ascii_chars_b);
+@@ -428,22 +683,23 @@
+ #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
+ #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
+ static char* __PYX_DEFAULT_STRING_ENCODING;
+-static int __Pyx_init_sys_getdefaultencoding_params() {
+- PyObject* sys = NULL;
++static int __Pyx_init_sys_getdefaultencoding_params(void) {
++ PyObject* sys;
+ PyObject* default_encoding = NULL;
+ char* default_encoding_c;
+ sys = PyImport_ImportModule("sys");
+- if (sys == NULL) goto bad;
++ if (!sys) goto bad;
+ default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL);
+- if (default_encoding == NULL) goto bad;
+- default_encoding_c = PyBytes_AS_STRING(default_encoding);
++ Py_DECREF(sys);
++ if (!default_encoding) goto bad;
++ default_encoding_c = PyBytes_AsString(default_encoding);
++ if (!default_encoding_c) goto bad;
+ __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
++ if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
+ strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
+- Py_DECREF(sys);
+ Py_DECREF(default_encoding);
+ return 0;
+ bad:
+- Py_XDECREF(sys);
+ Py_XDECREF(default_encoding);
+ return -1;
+ }
+@@ -451,25 +707,23 @@
+ #endif
+
+
+-#ifdef __GNUC__
+- /* Test for GCC > 2.95 */
+- #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))
+- #define likely(x) __builtin_expect(!!(x), 1)
+- #define unlikely(x) __builtin_expect(!!(x), 0)
+- #else /* __GNUC__ > 2 ... */
+- #define likely(x) (x)
+- #define unlikely(x) (x)
+- #endif /* __GNUC__ > 2 ... */
+-#else /* __GNUC__ */
++/* Test for GCC > 2.95 */
++#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
++ #define likely(x) __builtin_expect(!!(x), 1)
++ #define unlikely(x) __builtin_expect(!!(x), 0)
++#else /* !__GNUC__ or GCC < 2.95 */
+ #define likely(x) (x)
+ #define unlikely(x) (x)
+ #endif /* __GNUC__ */
++static CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }
+
+-static PyObject *__pyx_m;
++static PyObject *__pyx_m = NULL;
+ static PyObject *__pyx_d;
+ static PyObject *__pyx_b;
++static PyObject *__pyx_cython_runtime;
+ static PyObject *__pyx_empty_tuple;
+ static PyObject *__pyx_empty_bytes;
++static PyObject *__pyx_empty_unicode;
+ static int __pyx_lineno;
+ static int __pyx_clineno = 0;
+ static const char * __pyx_cfilenm= __FILE__;
+@@ -577,6 +831,9 @@
+ rk_state *internal_state;
+ };
+
++
++/* --- Runtime support code (head) --- */
++/* Refnanny.proto */
+ #ifndef CYTHON_REFNANNY
+ #define CYTHON_REFNANNY 0
+ #endif
+@@ -590,22 +847,22 @@
+ void (*FinishContext)(void**);
+ } __Pyx_RefNannyAPIStruct;
+ static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
+- static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
++ static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname);
+ #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
+ #ifdef WITH_THREAD
+- #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+- if (acquire_gil) { \
+- PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
+- __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+- PyGILState_Release(__pyx_gilstate_save); \
+- } else { \
+- __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
++ #define __Pyx_RefNannySetupContext(name, acquire_gil)\
++ if (acquire_gil) {\
++ PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\
++ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\
++ PyGILState_Release(__pyx_gilstate_save);\
++ } else {\
++ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\
+ }
+ #else
+- #define __Pyx_RefNannySetupContext(name, acquire_gil) \
++ #define __Pyx_RefNannySetupContext(name, acquire_gil)\
+ __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+ #endif
+- #define __Pyx_RefNannyFinishContext() \
++ #define __Pyx_RefNannyFinishContext()\
+ __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+ #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+ #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
+@@ -627,11 +884,20 @@
+ #define __Pyx_XDECREF(r) Py_XDECREF(r)
+ #define __Pyx_XGOTREF(r)
+ #define __Pyx_XGIVEREF(r)
+-#endif /* CYTHON_REFNANNY */
++#endif
++#define __Pyx_XDECREF_SET(r, v) do {\
++ PyObject *tmp = (PyObject *) r;\
++ r = v; __Pyx_XDECREF(tmp);\
++ } while (0)
++#define __Pyx_DECREF_SET(r, v) do {\
++ PyObject *tmp = (PyObject *) r;\
++ r = v; __Pyx_DECREF(tmp);\
++ } while (0)
+ #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+ #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
+
+-#if CYTHON_COMPILING_IN_CPYTHON
++/* PyObjectGetAttrStr.proto */
++#if CYTHON_USE_TYPE_SLOTS
+ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
+ PyTypeObject* tp = Py_TYPE(obj);
+ if (likely(tp->tp_getattro))
+@@ -646,67 +912,204 @@
+ #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
+ #endif
+
+-static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/
++/* GetBuiltinName.proto */
++static PyObject *__Pyx_GetBuiltinName(PyObject *name);
++
++/* GetModuleGlobalName.proto */
++static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name);
++
++/* PyFunctionFastCall.proto */
++#if CYTHON_FAST_PYCALL
++#define __Pyx_PyFunction_FastCall(func, args, nargs)\
++ __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
++#if 1 || PY_VERSION_HEX < 0x030600B1
++static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs);
++#else
++#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)
++#endif
++#endif
++
++/* PyCFunctionFastCall.proto */
++#if CYTHON_FAST_PYCCALL
++static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);
++#else
++#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL)
++#endif
++
++/* PyObjectCall.proto */
++#if CYTHON_COMPILING_IN_CPYTHON
++static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
++#else
++#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
++#endif
+
+-static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/
++/* PyThreadStateGet.proto */
++#if CYTHON_FAST_THREAD_STATE
++#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate;
++#define __Pyx_PyThreadState_assign __pyx_tstate = __Pyx_PyThreadState_Current;
++#define __Pyx_PyErr_Occurred() __pyx_tstate->curexc_type
++#else
++#define __Pyx_PyThreadState_declare
++#define __Pyx_PyThreadState_assign
++#define __Pyx_PyErr_Occurred() PyErr_Occurred()
++#endif
+
+-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+-static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
++/* PyErrFetchRestore.proto */
++#if CYTHON_FAST_THREAD_STATE
++#define __Pyx_PyErr_Clear() __Pyx_ErrRestore(NULL, NULL, NULL)
++#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
++#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
++#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
++#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
++static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
++static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
++#if CYTHON_COMPILING_IN_CPYTHON
++#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL))
++#else
++#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
++#endif
++#else
++#define __Pyx_PyErr_Clear() PyErr_Clear()
++#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc)
++#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb)
++#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb)
++#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb)
++#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb)
++#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb)
++#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb)
++#endif
+
+-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
++/* RaiseException.proto */
++static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);
+
++/* RaiseArgTupleInvalid.proto */
+ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
+- Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
++ Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);
++
++/* RaiseDoubleKeywords.proto */
++static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);
++
++/* ParseKeywords.proto */
++static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
++ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\
++ const char* function_name);
+
+-static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
++/* PyObjectCallMethO.proto */
++#if CYTHON_COMPILING_IN_CPYTHON
++static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
++#endif
++
++/* PyObjectCallOneArg.proto */
++static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);
++
++/* SaveResetException.proto */
++#if CYTHON_FAST_THREAD_STATE
++#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
++static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
++#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
++static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
++#else
++#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb)
++#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb)
++#endif
++
++/* PyErrExceptionMatches.proto */
++#if CYTHON_FAST_THREAD_STATE
++#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
++static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
++#else
++#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err)
++#endif
++
++/* GetException.proto */
++#if CYTHON_FAST_THREAD_STATE
++#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb)
++static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
++#else
++static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
++#endif
+
+-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_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+-
+-#define __Pyx_GetItemInt(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
+- (((size) <= sizeof(Py_ssize_t)) ? \
+- __Pyx_GetItemInt_Fast(o, i, is_list, wraparound, boundscheck) : \
+- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
+-#define __Pyx_GetItemInt_List(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
+- (((size) <= sizeof(Py_ssize_t)) ? \
+- __Pyx_GetItemInt_List_Fast(o, i, wraparound, boundscheck) : \
+- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
++/* GetItemInt.proto */
++#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
++ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
++ __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
++ (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\
++ __Pyx_GetItemInt_Generic(o, to_py_func(i))))
++#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
++ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
++ __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
++ (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
+ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck);
+-#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
+- (((size) <= sizeof(Py_ssize_t)) ? \
+- __Pyx_GetItemInt_Tuple_Fast(o, i, wraparound, boundscheck) : \
+- __Pyx_GetItemInt_Generic(o, to_py_func(i)))
++#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
++ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
++ __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
++ (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
+ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
+ int wraparound, int boundscheck);
+-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
++static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
+ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
+ int is_list, int wraparound, int boundscheck);
+
++/* IncludeStringH.proto */
++#include <string.h>
++
++/* BytesEquals.proto */
++static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals);
++
++/* UnicodeEquals.proto */
++static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals);
++
++/* StrEquals.proto */
++#if PY_MAJOR_VERSION >= 3
++#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
++#else
++#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
++#endif
++
++/* SliceObject.proto */
+ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
+ PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
+ PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
+ int has_cstart, int has_cstop, int wraparound);
+
++/* RaiseTooManyValuesToUnpack.proto */
+ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
+
++/* RaiseNeedMoreValuesToUnpack.proto */
+ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
+
+-static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
++/* IterFinish.proto */
++static CYTHON_INLINE int __Pyx_IterFinish(void);
++
++/* UnpackItemEndCheck.proto */
++static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
++
++/* PyObjectCallNoArg.proto */
++#if CYTHON_COMPILING_IN_CPYTHON
++static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
++#else
++#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL)
++#endif
+
+-static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
++/* PyIntBinop.proto */
++#if !CYTHON_COMPILING_IN_PYPY
++static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
++#else
++#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\
++ PyObject_RichCompare(op1, op2, Py_EQ)
++ #endif
+
+-#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
++/* SliceObject.proto */
++#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)\
+ __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
+ static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
+ PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
+ PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
+ int has_cstart, int has_cstop, int wraparound);
+
+-#if CYTHON_COMPILING_IN_CPYTHON
++/* PyObjectSetAttrStr.proto */
++#if CYTHON_USE_TYPE_SLOTS
+ #define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
+ static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
+ PyTypeObject* tp = Py_TYPE(obj);
+@@ -723,11 +1126,22 @@
+ #define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
+ #endif
+
+-static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
++/* KeywordStringCheck.proto */
++static int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed);
++
++/* PyIntBinop.proto */
++#if !CYTHON_COMPILING_IN_PYPY
++static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
++#else
++#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\
++ (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
++#endif
+
+-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
++/* ExtTypeTest.proto */
++static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);
+
+-#if CYTHON_COMPILING_IN_CPYTHON
++/* ListAppend.proto */
++#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
+ static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
+ PyListObject* L = (PyListObject*) list;
+ Py_ssize_t len = Py_SIZE(list);
+@@ -743,6 +1157,7 @@
+ #define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
+ #endif
+
++/* SliceTupleAndList.proto */
+ #if CYTHON_COMPILING_IN_CPYTHON
+ static CYTHON_INLINE PyObject* __Pyx_PyList_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
+ static CYTHON_INLINE PyObject* __Pyx_PyTuple_GetSlice(PyObject* src, Py_ssize_t start, Py_ssize_t stop);
+@@ -751,63 +1166,85 @@
+ #define __Pyx_PyTuple_GetSlice(seq, start, stop) PySequence_GetSlice(seq, start, stop)
+ #endif
+
+-static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /*proto*/
++/* Import.proto */
++static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);
+
+-#ifndef __PYX_FORCE_INIT_THREADS
+- #define __PYX_FORCE_INIT_THREADS 0
+-#endif
++/* ImportFrom.proto */
++static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);
+
+-#define __Pyx_SetItemInt(o, i, v, size, to_py_func, is_list, wraparound, boundscheck) \
+- (((size) <= sizeof(Py_ssize_t)) ? \
+- __Pyx_SetItemInt_Fast(o, i, v, is_list, wraparound, boundscheck) : \
+- __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
+-static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
++/* SetItemInt.proto */
++#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
++ (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
++ __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
++ (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
++ __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
++static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
+ static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
+ int is_list, int wraparound, int boundscheck);
+
+-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+-
+-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/
+-
+-static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
+-
+-static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
+-
+-static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
+-
+-static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *);
+-
+-static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *);
+-
+-static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *);
+-
+-static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *);
+-
+-static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *);
++/* CLineInTraceback.proto */
++#ifdef CYTHON_CLINE_IN_TRACEBACK
++#define __Pyx_CLineForTraceback(tstate, c_line) (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)
++#else
++static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);
++#endif
+
+-static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *);
++/* CodeObjectCache.proto */
++typedef struct {
++ PyCodeObject* code_object;
++ int code_line;
++} __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 CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *);
++/* AddTraceback.proto */
++static void __Pyx_AddTraceback(const char *funcname, int c_line,
++ int py_line, const char *filename);
+
+-static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *);
++/* CIntToPy.proto */
++static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
+
+-static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject *);
++/* CIntToPy.proto */
++static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
+
+-static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *);
++/* CIntToPy.proto */
++static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value);
+
+-static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *);
++/* CIntFromPy.proto */
++static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *);
+
+-static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *);
++/* CIntFromPy.proto */
++static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *);
+
+-static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *);
++/* CIntFromPy.proto */
++static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
+
+-static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *);
++/* CIntFromPy.proto */
++static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
+
+-static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *);
++/* FastTypeChecks.proto */
++#if CYTHON_COMPILING_IN_CPYTHON
++#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)
++static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);
++static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);
++static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);
++#else
++#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
++#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)
++#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))
++#endif
+
++/* CheckBinaryVersion.proto */
+ static int __Pyx_check_binary_version(void);
+
++/* PyIdentifierFromString.proto */
+ #if !defined(__Pyx_PyIdentifier_FromString)
+ #if PY_MAJOR_VERSION < 3
+ #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
+@@ -816,28 +1253,14 @@
+ #endif
+ #endif
+
+-static PyObject *__Pyx_ImportModule(const char *name); /*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);
++/* ModuleImport.proto */
++static PyObject *__Pyx_ImportModule(const char *name);
+
+-static void __Pyx_AddTraceback(const char *funcname, int c_line,
+- int py_line, const char *filename); /*proto*/
++/* TypeImport.proto */
++static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);
+
+-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
++/* InitStrings.proto */
++static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);
+
+
+ /* Module declarations from 'numpy' */
+@@ -866,11 +1289,578 @@
+ static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *); /*proto*/
+ static double __pyx_f_6mtrand_kahan_sum(double *, npy_intp); /*proto*/
+ #define __Pyx_MODULE_NAME "mtrand"
++extern int __pyx_module_is_main_mtrand;
+ int __pyx_module_is_main_mtrand = 0;
+
+ /* Implementation of 'mtrand' */
+ static PyObject *__pyx_builtin_ValueError;
+ static PyObject *__pyx_builtin_TypeError;
++static const char __pyx_k_a[] = "a";
++static const char __pyx_k_b[] = "b";
++static const char __pyx_k_d[] = "d";
++static const char __pyx_k_f[] = "f";
++static const char __pyx_k_l[] = "l";
++static const char __pyx_k_n[] = "n";
++static const char __pyx_k_p[] = "p";
++static const char __pyx_k_df[] = "df";
++static const char __pyx_k_mu[] = "mu";
++static const char __pyx_k_np[] = "np";
++static const char __pyx_k_a_0[] = "a <= 0";
++static const char __pyx_k_add[] = "add";
++static const char __pyx_k_any[] = "any";
++static const char __pyx_k_b_0[] = "b <= 0";
++static const char __pyx_k_cov[] = "cov";
++static const char __pyx_k_dot[] = "dot";
++static const char __pyx_k_int[] = "int";
++static const char __pyx_k_lam[] = "lam";
++static const char __pyx_k_loc[] = "loc";
++static const char __pyx_k_low[] = "low";
++static const char __pyx_k_max[] = "max";
++static const char __pyx_k_n_0[] = "n < 0";
++static const char __pyx_k_p_0[] = "p < 0";
++static const char __pyx_k_p_1[] = "p > 1";
++static const char __pyx_k_sum[] = "sum";
++static const char __pyx_k_svd[] = "svd";
++static const char __pyx_k_beta[] = "beta";
++static const char __pyx_k_copy[] = "copy";
++static const char __pyx_k_df_0[] = "df <= 0";
++static const char __pyx_k_df_1[] = "df <= 1";
++static const char __pyx_k_high[] = "high";
++static const char __pyx_k_intp[] = "intp";
++static const char __pyx_k_item[] = "item";
++static const char __pyx_k_left[] = "left";
++static const char __pyx_k_less[] = "less";
++static const char __pyx_k_main[] = "__main__";
++static const char __pyx_k_mean[] = "mean";
++static const char __pyx_k_mode[] = "mode";
++static const char __pyx_k_nbad[] = "nbad";
++static const char __pyx_k_ndim[] = "ndim";
++static const char __pyx_k_nonc[] = "nonc";
++static const char __pyx_k_prod[] = "prod";
++static const char __pyx_k_rand[] = "rand";
++static const char __pyx_k_seed[] = "seed";
++static const char __pyx_k_side[] = "side";
++static const char __pyx_k_size[] = "size";
++static const char __pyx_k_sort[] = "sort";
++static const char __pyx_k_sqrt[] = "sqrt";
++static const char __pyx_k_take[] = "take";
++static const char __pyx_k_test[] = "__test__";
++static const char __pyx_k_uint[] = "uint";
++static const char __pyx_k_wald[] = "wald";
++static const char __pyx_k_zipf[] = "zipf";
++static const char __pyx_k_a_1_0[] = "a <= 1.0";
++static const char __pyx_k_alpha[] = "alpha";
++static const char __pyx_k_array[] = "array";
++static const char __pyx_k_bytes[] = "bytes";
++static const char __pyx_k_dfden[] = "dfden";
++static const char __pyx_k_dfnum[] = "dfnum";
++static const char __pyx_k_dtype[] = "dtype";
++static const char __pyx_k_empty[] = "empty";
++static const char __pyx_k_equal[] = "equal";
++static const char __pyx_k_gamma[] = "gamma";
++static const char __pyx_k_iinfo[] = "iinfo";
++static const char __pyx_k_index[] = "index";
++static const char __pyx_k_kappa[] = "kappa";
++static const char __pyx_k_lam_0[] = "lam < 0";
++static const char __pyx_k_n_0_2[] = "n <= 0";
++static const char __pyx_k_ndmin[] = "ndmin";
++static const char __pyx_k_ngood[] = "ngood";
++static const char __pyx_k_numpy[] = "numpy";
++static const char __pyx_k_p_0_0[] = "p < 0.0";
++static const char __pyx_k_p_1_0[] = "p > 1.0";
++static const char __pyx_k_power[] = "power";
++static const char __pyx_k_pvals[] = "pvals";
++static const char __pyx_k_randn[] = "randn";
++static const char __pyx_k_ravel[] = "ravel";
++static const char __pyx_k_right[] = "right";
++static const char __pyx_k_scale[] = "scale";
++static const char __pyx_k_shape[] = "shape";
++static const char __pyx_k_sigma[] = "sigma";
++static const char __pyx_k_zeros[] = "zeros";
++static const char __pyx_k_arange[] = "arange";
++static const char __pyx_k_choice[] = "choice";
++static const char __pyx_k_cumsum[] = "cumsum";
++static const char __pyx_k_double[] = "double";
++static const char __pyx_k_fields[] = "fields";
++static const char __pyx_k_gumbel[] = "gumbel";
++static const char __pyx_k_import[] = "__import__";
++static const char __pyx_k_mean_0[] = "mean <= 0";
++static const char __pyx_k_mtrand[] = "mtrand";
++static const char __pyx_k_nbad_0[] = "nbad < 0";
++static const char __pyx_k_nonc_0[] = "nonc < 0";
++static const char __pyx_k_normal[] = "normal";
++static const char __pyx_k_pareto[] = "pareto";
++static const char __pyx_k_rand_2[] = "_rand";
++static const char __pyx_k_random[] = "random";
++static const char __pyx_k_reduce[] = "reduce";
++static const char __pyx_k_uint32[] = "uint32";
++static const char __pyx_k_unique[] = "unique";
++static const char __pyx_k_MT19937[] = "MT19937";
++static const char __pyx_k_asarray[] = "asarray";
++static const char __pyx_k_dfden_0[] = "dfden <= 0";
++static const char __pyx_k_dfnum_0[] = "dfnum <= 0";
++static const char __pyx_k_dfnum_1[] = "dfnum <= 1";
++static const char __pyx_k_float64[] = "float64";
++static const char __pyx_k_greater[] = "greater";
++static const char __pyx_k_integer[] = "integer";
++static const char __pyx_k_kappa_0[] = "kappa < 0";
++static const char __pyx_k_laplace[] = "laplace";
++static const char __pyx_k_ndarray[] = "ndarray";
++static const char __pyx_k_ngood_0[] = "ngood < 0";
++static const char __pyx_k_nsample[] = "nsample";
++static const char __pyx_k_p_0_0_2[] = "p <= 0.0";
++static const char __pyx_k_p_1_0_2[] = "p >= 1.0";
++static const char __pyx_k_poisson[] = "poisson";
++static const char __pyx_k_randint[] = "randint";
++static const char __pyx_k_replace[] = "replace";
++static const char __pyx_k_scale_0[] = "scale <= 0";
++static const char __pyx_k_shape_0[] = "shape <= 0";
++static const char __pyx_k_shuffle[] = "shuffle";
++static const char __pyx_k_sigma_0[] = "sigma <= 0";
++static const char __pyx_k_uniform[] = "uniform";
++static const char __pyx_k_weibull[] = "weibull";
++static const char __pyx_k_allclose[] = "allclose";
++static const char __pyx_k_binomial[] = "binomial";
++static const char __pyx_k_logistic[] = "logistic";
++static const char __pyx_k_low_high[] = "low >= high";
++static const char __pyx_k_mean_0_0[] = "mean <= 0.0";
++static const char __pyx_k_multiply[] = "multiply";
++static const char __pyx_k_nonc_0_2[] = "nonc <= 0";
++static const char __pyx_k_operator[] = "operator";
++static const char __pyx_k_rayleigh[] = "rayleigh";
++static const char __pyx_k_subtract[] = "subtract";
++static const char __pyx_k_vonmises[] = "vonmises";
++static const char __pyx_k_TypeError[] = "TypeError";
++static const char __pyx_k_chisquare[] = "chisquare";
++static const char __pyx_k_dirichlet[] = "dirichlet";
++static const char __pyx_k_geometric[] = "geometric";
++static const char __pyx_k_get_state[] = "get_state";
++static const char __pyx_k_left_mode[] = "left > mode";
++static const char __pyx_k_lognormal[] = "lognormal";
++static const char __pyx_k_logseries[] = "logseries";
++static const char __pyx_k_nsample_1[] = "nsample < 1";
++static const char __pyx_k_scale_0_0[] = "scale <= 0.0";
++static const char __pyx_k_set_state[] = "set_state";
++static const char __pyx_k_sigma_0_0[] = "sigma <= 0.0";
++static const char __pyx_k_ValueError[] = "ValueError";
++static const char __pyx_k_empty_like[] = "empty_like";
++static const char __pyx_k_left_right[] = "left == right";
++static const char __pyx_k_less_equal[] = "less_equal";
++static const char __pyx_k_mode_right[] = "mode > right";
++static const char __pyx_k_mtrand_pyx[] = "mtrand.pyx";
++static const char __pyx_k_numpy_dual[] = "numpy.dual";
++static const char __pyx_k_standard_t[] = "standard_t";
++static const char __pyx_k_triangular[] = "triangular";
++static const char __pyx_k_exponential[] = "exponential";
++static const char __pyx_k_multinomial[] = "multinomial";
++static const char __pyx_k_permutation[] = "permutation";
++static const char __pyx_k_noncentral_f[] = "noncentral_f";
++static const char __pyx_k_return_index[] = "return_index";
++static const char __pyx_k_searchsorted[] = "searchsorted";
++static const char __pyx_k_greater_equal[] = "greater_equal";
++static const char __pyx_k_random_sample[] = "random_sample";
++static const char __pyx_k_hypergeometric[] = "hypergeometric";
++static const char __pyx_k_standard_gamma[] = "standard_gamma";
++static const char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
++static const char __pyx_k_random_integers[] = "random_integers";
++static const char __pyx_k_shape_from_size[] = "_shape_from_size";
++static const char __pyx_k_standard_cauchy[] = "standard_cauchy";
++static const char __pyx_k_standard_normal[] = "standard_normal";
++static const char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
++static const char __pyx_k_RandomState_ctor[] = "__RandomState_ctor";
++static const char __pyx_k_negative_binomial[] = "negative_binomial";
++static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback";
++static const char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
++static const char __pyx_k_a_must_be_non_empty[] = "a must be non-empty";
++static const char __pyx_k_lam_value_too_large[] = "lam value too large";
++static const char __pyx_k_multivariate_normal[] = "multivariate_normal";
++static const char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
++static const char __pyx_k_standard_exponential[] = "standard_exponential";
++static const char __pyx_k_lam_value_too_large_2[] = "lam value too large.";
++static const char __pyx_k_RandomState_f_line_1812[] = "RandomState.f (line 1812)";
++static const char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
++static const char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
++static const char __pyx_k_state_must_be_624_longs[] = "state must be 624 longs";
++static const char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
++static const char __pyx_k_algorithm_must_be_MT19937[] = "algorithm must be 'MT19937'";
++static const char __pyx_k_RandomState_bytes_line_900[] = "RandomState.bytes (line 900)";
++static const char __pyx_k_RandomState_rand_line_1187[] = "RandomState.rand (line 1187)";
++static const char __pyx_k_RandomState_wald_line_3242[] = "RandomState.wald (line 3242)";
++static const char __pyx_k_RandomState_zipf_line_3690[] = "RandomState.zipf (line 3690)";
++static const char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
++static const char __pyx_k_RandomState_choice_line_928[] = "RandomState.choice (line 928)";
++static const char __pyx_k_RandomState_gamma_line_1721[] = "RandomState.gamma (line 1721)";
++static const char __pyx_k_RandomState_power_line_2631[] = "RandomState.power (line 2631)";
++static const char __pyx_k_RandomState_randn_line_1231[] = "RandomState.randn (line 1231)";
++static const char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
++static const char __pyx_k_RandomState_gumbel_line_2830[] = "RandomState.gumbel (line 2830)";
++static const char __pyx_k_RandomState_normal_line_1398[] = "RandomState.normal (line 1398)";
++static const char __pyx_k_RandomState_pareto_line_2435[] = "RandomState.pareto (line 2435)";
++static const char __pyx_k_RandomState_randint_line_820[] = "RandomState.randint (line 820)";
++static const char __pyx_k_RandomState_laplace_line_2740[] = "RandomState.laplace (line 2740)";
++static const char __pyx_k_RandomState_poisson_line_3619[] = "RandomState.poisson (line 3619)";
++static const char __pyx_k_RandomState_shuffle_line_4389[] = "RandomState.shuffle (line 4389)";
++static const char __pyx_k_RandomState_tomaxint_line_773[] = "RandomState.tomaxint (line 773)";
++static const char __pyx_k_RandomState_uniform_line_1100[] = "RandomState.uniform (line 1100)";
++static const char __pyx_k_RandomState_weibull_line_2531[] = "RandomState.weibull (line 2531)";
++static const char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
++static const char __pyx_k_RandomState_binomial_line_3416[] = "RandomState.binomial (line 3416)";
++static const char __pyx_k_RandomState_logistic_line_2961[] = "RandomState.logistic (line 2961)";
++static const char __pyx_k_RandomState_rayleigh_line_3170[] = "RandomState.rayleigh (line 3170)";
++static const char __pyx_k_RandomState_vonmises_line_2341[] = "RandomState.vonmises (line 2341)";
++static const char __pyx_k_dirichlet_alpha_size_None_Draw[] = "\n dirichlet(alpha, size=None)\n\n Draw samples from the Dirichlet distribution.\n\n Draw `size` samples of dimension k from a Dirichlet distribution. A\n Dirichlet-distributed random variable can be seen as a multivariate\n generalization of a Beta distribution. Dirichlet pdf is the conjugate\n prior of a multinomial in Bayesian inference.\n\n Parameters\n ----------\n alpha : array\n Parameter of the distribution (k dimension for sample of\n dimension k).\n size : array\n Number of samples to draw.\n\n Returns\n -------\n samples : ndarray,\n The drawn samples, of shape (alpha.ndim, size).\n\n Notes\n -----\n .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n Uses the following property for computation: for each dimension,\n draw a random sample y_i from a standard gamma generator of shape\n `alpha_i`, then\n :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n Dirichlet distributed.\n\n References\n ----------\n .. [1] David McKay, \"Information Theory, Inference and Learning\n Algorithms,\" chapter 23,\n http://www.inference.phy.cam.ac.uk/mackay/\n .. [2] Wikipedia, \"Dirichlet distribution\",\n http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n Examples\n --------\n Taking an example cited in Wikipedia, this distribution can be used if\n one wanted to cut strings (each of initial length 1.0) into K pieces\n with different lengths, where each piece had, on average, a designated\n average length, but allowing some variation in the relative sizes of the\n pieces.\n\n >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n >>> plt.barh(range(20), s[0])\n >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n >>> plt.title(\"Lengths of Strings\")\n\n ";
++static const char __pyx_k_laplace_loc_0_0_scale_1_0_size[] = "\n laplace(loc=0.0, scale=1.0, size=None)\n\n Draw samples from the Laplace or double exponential distribution with\n specified location (or mean) and scale (decay).\n\n The Laplace distribution is similar to the Gaussian/normal distribution,\n but is sharper at the peak and has fatter tails. It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. [4] Wikipedia, \"Laplace distribution\",\n http://en.wikipedia.org/wik""i/Laplace_distribution\n\n Examples\n --------\n Draw samples from the distribution\n\n >>> loc, scale = 0., 1.\n >>> s = np.random.laplace(loc, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> x = np.arange(-8., 8., .01)\n >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n >>> plt.plot(x, pdf)\n\n Plot Gaussian for comparison:\n\n >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n ... np.exp( - (x - loc)**2 / (2 * scale**2) ))\n >>> plt.plot(x,g)\n\n ";
++static const char __pyx_k_permutation_x_Randomly_permute[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n ";
++static const char __pyx_k_poisson_lam_1_0_size_None_Draw[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n ";
++static const char __pyx_k_rand_d0_d1_dn_Random_values_in[] = "\n rand(d0, d1, ..., dn)\n\n Random values in a given shape.\n\n Create an array of the given shape and propagate it with\n random samples from a uniform distribution\n over ``[0, 1)``.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should all be positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n out : ndarray, shape ``(d0, d1, ..., dn)``\n Random values.\n\n See Also\n --------\n random\n\n Notes\n -----\n This is a convenience function. If you want an interface that\n takes a shape-tuple as the first argument, refer to\n np.random.random_sample .\n\n Examples\n --------\n >>> np.random.rand(3,2)\n array([[ 0.14022471, 0.96360618], #random\n [ 0.37601032, 0.25528411], #random\n [ 0.49313049, 0.94909878]]) #random\n\n ";
++static const char __pyx_k_randn_d0_d1_dn_Return_a_sample[] = "\n randn(d0, d1, ..., dn)\n\n Return a sample (or samples) from the \"standard normal\" distribution.\n\n If positive, int_like or int-convertible arguments are provided,\n `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n with random floats sampled from a univariate \"normal\" (Gaussian)\n distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n floats, they are first converted to integers by truncation). A single\n float randomly sampled from the distribution is returned if no\n argument is provided.\n\n This is a convenience function. If you want an interface that takes a\n tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should be all positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n Z : ndarray or float\n A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n the standard normal distribution, or a single such float if\n no parameters were supplied.\n\n See Also\n --------\n random.standard_normal : Similar, but takes a tuple as its argument.\n\n Notes\n -----\n For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n ``sigma * np.random.randn(...) + mu``\n\n Examples\n --------\n >>> np.random.randn()\n 2.1923875335537315 #random\n\n Two-by-four array of samples from N(3, 6.25):\n\n >>> 2.5 * np.random.randn(2, 4) + 3\n array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n\n ";
++static const char __pyx_k_random_sample_size_None_Return[] = "\n random_sample(size=None)\n\n Return random floats in the half-open interval [0.0, 1.0).\n\n Results are from the \"continuous uniform\" distribution over the\n stated interval. To sample :math:`Unif[a, b), b > a` multiply\n the output of `random_sample` by `(b-a)` and add `a`::\n\n (b - a) * random_sample() + a\n\n Parameters\n ----------\n size : int or tuple of ints, optional\n Defines the shape of the returned array of random floats. If None\n (the default), returns a single float.\n\n Returns\n -------\n out : float or ndarray of floats\n Array of random floats of shape `size` (unless ``size=None``, in which\n case a single float is returned).\n\n Examples\n --------\n >>> np.random.random_sample()\n 0.47108547995356098\n >>> type(np.random.random_sample())\n <type 'float'>\n >>> np.random.random_sample((5,))\n array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])\n\n Three-by-two array of random numbers from [-5, 0):\n\n >>> 5 * np.random.random_sample((3, 2)) - 5\n array([[-3.99149989, -0.52338984],\n [-2.99091858, -0.79479508],\n [-1.23204345, -1.75224494]])\n\n ";
++static const char __pyx_k_shuffle_x_Modify_a_sequence_in[] = "\n shuffle(x)\n\n Modify a sequence in-place by shuffling its contents.\n\n Parameters\n ----------\n x : array_like\n The array or list to be shuffled.\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> arr = np.arange(10)\n >>> np.random.shuffle(arr)\n >>> arr\n [1 7 5 2 9 4 3 6 0 8]\n\n This function only shuffles the array along the first index of a\n multi-dimensional array:\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.shuffle(arr)\n >>> arr\n array([[3, 4, 5],\n [6, 7, 8],\n [0, 1, 2]])\n\n ";
++static const char __pyx_k_standard_exponential_size_None[] = "\n standard_exponential(size=None)\n\n Draw samples from the standard exponential distribution.\n\n `standard_exponential` is identical to the exponential distribution\n with a scale parameter of 1.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n Output a 3x8000 array:\n\n >>> n = np.random.standard_exponential((3, 8000))\n\n ";
++static const char __pyx_k_standard_gamma_shape_size_None[] = "\n standard_gamma(shape, size=None)\n\n Draw samples from a Standard Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n shape (sometimes designated \"k\") and scale=1.\n\n Parameters\n ----------\n shape : float\n Parameter, should be > 0.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2., 1. # mean and width\n >>> s = np.random.standard_gamma(shape, 1000000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt""\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n ... (sps.gamma(shape) * scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_wald_mean_scale_size_None_Draw[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n .. [3] Wikipedia, \"Wald distribu""tion\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n ";
++static const char __pyx_k_RandomState_chisquare_line_2009[] = "RandomState.chisquare (line 2009)";
++static const char __pyx_k_RandomState_dirichlet_line_4278[] = "RandomState.dirichlet (line 4278)";
++static const char __pyx_k_RandomState_geometric_line_3778[] = "RandomState.geometric (line 3778)";
++static const char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 3844)";
++static const char __pyx_k_RandomState_lognormal_line_3049[] = "RandomState.lognormal (line 3049)";
++static const char __pyx_k_RandomState_logseries_line_3963[] = "RandomState.logseries (line 3963)";
++static const char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4058)";
++static const char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1639)";
++static const char __pyx_k_binomial_n_p_size_None_Draw_sam[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer >= 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, >= 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.""\n .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n ";
++static const char __pyx_k_bytes_length_Return_random_byte[] = "\n bytes(length)\n\n Return random bytes.\n\n Parameters\n ----------\n length : int\n Number of random bytes.\n\n Returns\n -------\n out : str\n String of length `length`.\n\n Examples\n --------\n >>> np.random.bytes(10)\n ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n ";
++static const char __pyx_k_chisquare_df_size_None_Draw_sam[] = "\n chisquare(df, size=None)\n\n Draw samples from a chi-square distribution.\n\n When `df` independent random variables, each with standard normal\n distributions (mean 0, variance 1), are squared and summed, the\n resulting distribution is chi-square (see Notes). This distribution\n is often used in hypothesis testing.\n\n Parameters\n ----------\n df : int\n Number of degrees of freedom.\n size : tuple of ints, int, optional\n Size of the returned array. By default, a scalar is\n returned.\n\n Returns\n -------\n output : ndarray\n Samples drawn from the distribution, packed in a `size`-shaped\n array.\n\n Raises\n ------\n ValueError\n When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n is given.\n\n Notes\n -----\n The variable obtained by summing the squares of `df` independent,\n standard normally distributed random variables:\n\n .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n is chi-square distributed, denoted\n\n .. math:: Q \\sim \\chi^2_k.\n\n The probability density function of the chi-squared distribution is\n\n .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n x^{k/2 - 1} e^{-x/2},\n\n where :math:`\\Gamma` is the gamma function,\n\n .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n References\n ----------\n `NIST/SEMATECH e-Handbook of Statistical Methods\n <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n Examples\n --------\n >>> np.random.chisquare(2,4)\n array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])\n\n ";
++static const char __pyx_k_choice_a_size_None_replace_True[] = "\n choice(a, size=None, replace=True, p=None)\n\n Generates a random sample from a given 1-D array\n\n .. versionadded:: 1.7.0\n\n Parameters\n -----------\n a : 1-D array-like or int\n If an ndarray, a random sample is generated from its elements.\n If an int, the random sample is generated as if a was np.arange(n)\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n replace : boolean, optional\n Whether the sample is with or without replacement\n p : 1-D array-like, optional\n The probabilities associated with each entry in a.\n If not given the sample assumes a uniform distribtion over all\n entries in a.\n\n Returns\n --------\n samples : 1-D ndarray, shape (size,)\n The generated random samples\n\n Raises\n -------\n ValueError\n If a is an int and less than zero, if a or p are not 1-dimensional,\n if a is an array-like of size 0, if p is not a vector of\n probabilities, if a and p have different lengths, or if\n replace=False and the sample size is greater than the population\n size\n\n See Also\n ---------\n randint, shuffle, permutation\n\n Examples\n ---------\n Generate a uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3)\n array([0, 3, 4])\n >>> #This is equivalent to np.random.randint(0,5,3)\n\n Generate a non-uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n array([3, 3, 0])\n\n Generate a uniform random sample from np.arange(5) of size 3 without\n replacement:\n\n >>> np.random.choice(5, 3, replace=False)\n array([3,1,0])\n "" >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n Generate a non-uniform random sample from np.arange(5) of size\n 3 without replacement:\n\n >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n array([2, 3, 0])\n\n Any of the above can be repeated with an arbitrary array-like\n instead of just integers. For instance:\n\n >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n dtype='|S11')\n\n ";
++static const char __pyx_k_f_dfnum_dfden_size_None_Draw_sa[] = "\n f(dfnum, dfden, size=None)\n\n Draw samples from a F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n in denominator), where both parameters should be greater than zero.\n\n The random variate of the F distribution (also known as the\n Fisher distribution) is a continuous probability distribution\n that arises in ANOVA tests, and is the ratio of two chi-square\n variates.\n\n Parameters\n ----------\n dfnum : float\n Degrees of freedom in numerator. Should be greater than zero.\n dfden : float\n Degrees of freedom in denominator. Should be greater than zero.\n size : {tuple, int}, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``,\n then ``m * n * k`` samples are drawn. By default only one sample\n is returned.\n\n Returns\n -------\n samples : {ndarray, scalar}\n Samples from the Fisher distribution.\n\n See Also\n --------\n scipy.stats.distributions.f : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The F statistic is used to compare in-group variances to between-group\n variances. Calculating the distribution depends on the sampling, and\n so it is a function of the respective degrees of freedom in the\n problem. The variable `dfnum` is the number of samples minus one, the\n between-groups degrees of freedom, while `dfden` is the within-groups\n degrees of freedom, the sum of the number of samples in each group\n minus the number of groups.\n\n References\n ----------\n .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.""\n .. [2] Wikipedia, \"F-distribution\",\n http://en.wikipedia.org/wiki/F-distribution\n\n Examples\n --------\n An example from Glantz[1], pp 47-40.\n Two groups, children of diabetics (25 people) and children from people\n without diabetes (25 controls). Fasting blood glucose was measured,\n case group had a mean value of 86.1, controls had a mean value of\n 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n data consistent with the null hypothesis that the parents diabetic\n status does not affect their children's blood glucose levels?\n Calculating the F statistic from the data gives a value of 36.01.\n\n Draw samples from the distribution:\n\n >>> dfnum = 1. # between group degrees of freedom\n >>> dfden = 48. # within groups degrees of freedom\n >>> s = np.random.f(dfnum, dfden, 1000)\n\n The lower bound for the top 1% of the samples is :\n\n >>> sort(s)[-10]\n 7.61988120985\n\n So there is about a 1% chance that the F statistic will exceed 7.62,\n the measured value is 36, so the null hypothesis is rejected at the 1%\n level.\n\n ";
++static const char __pyx_k_gamma_shape_scale_1_0_size_None[] = "\n gamma(shape, scale=1.0, size=None)\n\n Draw samples from a Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n \"theta\"), where both parameters are > 0.\n\n Parameters\n ----------\n shape : scalar > 0\n The shape of the gamma distribution.\n scale : scalar > 0, optional\n The scale of the gamma distribution. Default is equal to 1.\n size : shape_tuple, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray, float\n Returns one sample unless `size` parameter is specified.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2.,"" 2. # mean and dispersion\n >>> s = np.random.gamma(shape, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n ... (sps.gamma(shape)*scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_geometric_p_size_None_Draw_samp[] = "\n geometric(p, size=None)\n\n Draw samples from the geometric distribution.\n\n Bernoulli trials are experiments with one of two outcomes:\n success or failure (an example of such an experiment is flipping\n a coin). The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. The output\n is shaped according to `size`.\n\n Returns\n -------\n out : ndarray\n Samples from the geometric distribution, shaped according to\n `size`.\n\n Examples\n --------\n Draw ten thousand values from the geometric distribution,\n with the probability of an individual success equal to 0.35:\n\n >>> z = np.random.geometric(p=0.35, size=10000)\n\n How many trials succeeded after a single run?\n\n >>> (z == 1).sum() / 10000.\n 0.34889999999999999 #random\n\n ";
++static const char __pyx_k_gumbel_loc_0_0_scale_1_0_size_N[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n Gumbel distribution.\n\n Draw samples from a Gumbel distribution with specified location and scale.\n For more information on the Gumbel distribution, see Notes and References\n below.\n\n Parameters\n ----------\n loc : float\n The location of the mode of the distribution.\n scale : float\n The scale parameter of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray\n The samples\n\n See Also\n --------\n scipy.stats.gumbel_l\n scipy.stats.gumbel_r\n scipy.stats.genextreme\n probability density function, distribution, or cumulative density\n function, etc. for each of the above\n weibull\n\n Notes\n -----\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails.\n\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the ""probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n Press, 1958.\n\n Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n 2001.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, beta = 0, 0.1 # location and scale\n >>> s = np.random.gumbel(mu, beta, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n Show how an extreme value distribution can arise from a Gaussian process\n and compare to a Gaussian:\n\n >>> means = []\n >>> maxima = []\n >>> for i in range(0,1000) :\n ... a = np.random.normal(mu, beta, 1000)\n ... means.append(a.mean())\n ... maxima.append(a.max())\n >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n >>> mu = np.mean(maxima) - 0.57721*beta\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp(-np.exp(-(bins - mu)/beta)),\n ... linewidth=2, color='r')\n >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n ... * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n ... linewidth=2, color='g')\n >>> plt.show()\n\n ";
++static const char __pyx_k_hypergeometric_ngood_nbad_nsamp[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : int or array_like\n Number of ways to make a good selection. Must be nonnegative.\n nbad : int or array_like\n Number of ways to make a bad selection. Must be nonnegative.\n nsample : int or array_like\n Number of items sampled. Must be at least 1 and at most\n ``ngood + nbad``.\n size : int or tuple of int\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or scalar\n The values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distrib""ution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn with\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n ";
++static const char __pyx_k_logistic_loc_0_0_scale_1_0_size[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n ";
++static const char __pyx_k_lognormal_mean_0_0_sigma_1_0_si[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n ";
++static const char __pyx_k_logseries_p_size_None_Draw_samp[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_multinomial_n_pvals_size_None_D[] = "\n multinomial(n, pvals, size=None)\n\n Draw samples from a multinomial distribution.\n\n The multinomial distribution is a multivariate generalisation of the\n binomial distribution. Take an experiment with one of ``p``\n possible outcomes. An example of such an experiment is throwing a dice,\n where the outcome can be 1 through 6. Each sample drawn from the\n distribution represents `n` such experiments. Its values,\n ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n was ``i``.\n\n Parameters\n ----------\n n : int\n Number of experiments.\n pvals : sequence of floats, length p\n Probabilities of each of the ``p`` different outcomes. These\n should sum to 1 (however, the last element is always assumed to\n account for the remaining probability, as long as\n ``sum(pvals[:-1]) <= 1)``.\n size : tuple of ints\n Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n and the output shape becomes ``(M, N, K, p)``, since each sample\n has shape ``(p,)``.\n\n Examples\n --------\n Throw a dice 20 times:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=1)\n array([[4, 1, 7, 5, 2, 1]])\n\n It landed 4 times on 1, once on 2, etc.\n\n Now, throw the dice 20 times, and 20 times again:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=2)\n array([[3, 4, 3, 3, 4, 3],\n [2, 4, 3, 4, 0, 7]])\n\n For the first run, we threw 3 times 1, 4 times 2, etc. For the second,\n we threw 2 times 1, 4 times 2, etc.\n\n A loaded dice is more likely to land on number 6:\n\n >>> np.random.multinomial(100, [1/7.]*5)\n array([13, 16, 13, 16, 42])\n\n ";
++static const char __pyx_k_multivariate_normal_mean_cov_si[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : int or tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, w""e draw N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n ";
++static const char __pyx_k_negative_binomial_n_p_size_None[] = "\n negative_binomial(n, p, size=None)\n\n Draw samples from a negative_binomial distribution.\n\n Samples are drawn from a negative_Binomial distribution with specified\n parameters, `n` trials and `p` probability of success where `n` is an\n integer > 0 and `p` is in the interval [0, 1].\n\n Parameters\n ----------\n n : int\n Parameter, > 0.\n p : float\n Parameter, >= 0 and <=1.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. What is the probability\n of having one success for each successive well, that is what is the\n probability of a single success after drilling 5 wells, after 6 wells,\n etc.?\n\n >>> s = np.random.negative_binomial(1, 0.1, 100000)\n >>> for i in range(1, 11):\n ... probability = sum(s<i) / 100000.\n ... print i, \"wells drilled, probability of one success =\", probability\n\n ";
++static const char __pyx_k_noncentral_chisquare_df_nonc_si[] = "\n noncentral_chisquare(df, nonc, size=None)\n\n Draw samples from a noncentral chi-square distribution.\n\n The noncentral :math:`\\chi^2` distribution is a generalisation of\n the :math:`\\chi^2` distribution.\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be >= 1.\n nonc : float\n Non-centrality, should be > 0.\n size : int or tuple of ints\n Shape of the output.\n\n Notes\n -----\n The probability density function for the noncentral Chi-square distribution\n is\n\n .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n In Delhi (2007), it is noted that the noncentral chi-square is useful in\n bombing and coverage problems, the probability of killing the point target\n given by the noncentral chi-squared distribution.\n\n References\n ----------\n .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n analysis of weapon systems effectiveness\", Metrika, Volume 15,\n Number 1 / December, 1970.\n .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> import matplotlib.pyplot as plt\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n Draw values from a noncentral chisquare with very small noncentrality,\n and compare to a chisquare.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n "" ... bins=np.arange(0., 25, .1), normed=True)\n >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n ... bins=np.arange(0., 25, .1), normed=True)\n >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n >>> plt.show()\n\n Demonstrate how large values of non-centrality lead to a more symmetric\n distribution.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n ";
++static const char __pyx_k_noncentral_f_dfnum_dfden_nonc_s[] = "\n noncentral_f(dfnum, dfden, nonc, size=None)\n\n Draw samples from the noncentral F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n freedom in denominator), where both parameters > 1.\n `nonc` is the non-centrality parameter.\n\n Parameters\n ----------\n dfnum : int\n Parameter, should be > 1.\n dfden : int\n Parameter, should be > 1.\n nonc : float\n Parameter, should be >= 0.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : scalar or ndarray\n Drawn samples.\n\n Notes\n -----\n When calculating the power of an experiment (power = probability of\n rejecting the null hypothesis when a specific alternative is true) the\n non-central F statistic becomes important. When the null hypothesis is\n true, the F statistic follows a central F distribution. When the null\n hypothesis is not true, then it follows a non-central F statistic.\n\n References\n ----------\n Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n Wikipedia, \"Noncentral F distribution\",\n http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n Examples\n --------\n In a study, testing for a specific alternative to the null hypothesis\n requires use of the Noncentral F distribution. We need to calculate the\n area in the tail of the distribution that exceeds the value of the F\n distribution for the null hypothesis. We'll plot the two probability\n distributions for comp""arison.\n\n >>> dfnum = 3 # between group deg of freedom\n >>> dfden = 20 # within groups degrees of freedom\n >>> nonc = 3.0\n >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n >>> F = np.histogram(c_vals, bins=50, normed=True)\n >>> plt.plot(F[1][1:], F[0])\n >>> plt.plot(NF[1][1:], NF[0])\n >>> plt.show()\n\n ";
++static const char __pyx_k_normal_loc_0_0_scale_1_0_size_N[] = "\n normal(loc=0.0, scale=1.0, size=None)\n\n Draw random samples from a normal (Gaussian) distribution.\n\n The probability density function of the normal distribution, first\n derived by De Moivre and 200 years later by both Gauss and Laplace\n independently [2]_, is often called the bell curve because of\n its characteristic shape (see the example below).\n\n The normal distributions occurs often in nature. For example, it\n describes the commonly occurring distribution of samples influenced\n by a large number of tiny, random disturbances, each with its own\n unique distribution [2]_.\n\n Parameters\n ----------\n loc : float\n Mean (\"centre\") of the distribution.\n scale : float\n Standard deviation (spread or \"width\") of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.norm : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gaussian distribution is\n\n .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n The square of the standard deviation, :math:`\\sigma^2`, is called the\n variance.\n\n The function has its peak at the mean, and its \"spread\" increases with\n the standard deviation (the function reaches 0.607 times its maximum at\n :math:`x + \\sigma` and :math:`x - \\sigma` [2]_). This implies that\n `numpy.random.normal` is more likely to return samples lying close to the\n mean, rather than those far away.\n""\n References\n ----------\n .. [1] Wikipedia, \"Normal distribution\",\n http://en.wikipedia.org/wiki/Normal_distribution\n .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n Variables and Random Signal Principles\", 4th ed., 2001,\n pp. 51, 51, 125.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 0, 0.1 # mean and standard deviation\n >>> s = np.random.normal(mu, sigma, 1000)\n\n Verify the mean and the variance:\n\n >>> abs(mu - np.mean(s)) < 0.01\n True\n\n >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n True\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_pareto_a_size_None_Draw_samples[] = "\n pareto(a, size=None)\n\n Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n classical Pareto distribution can be obtained from the Lomax distribution\n by adding the location parameter m, see below. The smallest value of the\n Lomax distribution is zero while for the classical Pareto distribution it\n is m, where the standard Pareto distribution has location m=1.\n Lomax can also be considered as a simplified version of the Generalized\n Pareto distribution (available in SciPy), with the scale set to one and\n the location set to zero.\n\n The Pareto distribution must be greater than zero, and is unbounded above.\n It is also known as the \"80-20 rule\". In this distribution, 80 percent of\n the weights are in the lowest 20 percent of the range, while the other 20\n percent fill the remaining 80 percent of the range.\n\n Parameters\n ----------\n shape : float, > 0.\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.lomax.pdf : probability density function,\n distribution or cumulative density function, etc.\n scipy.stats.distributions.genpareto.pdf : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Pareto distribution is\n\n .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n where :math:`a` is the shape and :math:`m` the location\n\n The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n is a power law probability distribution useful in many real world probl""ems.\n Outside the field of economics it is generally referred to as the Bradford\n distribution. Pareto developed the distribution to describe the\n distribution of wealth in an economy. It has also found use in insurance,\n web page access statistics, oil field sizes, and many other problems,\n including the download frequency for projects in Sourceforge [1]. It is\n one of the so-called \"fat-tailed\" distributions.\n\n\n References\n ----------\n .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n Sourceforge projects.\n .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 23-30.\n .. [4] Wikipedia, \"Pareto distribution\",\n http://en.wikipedia.org/wiki/Pareto_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a, m = 3., 1. # shape and mode\n >>> s = np.random.pareto(a, 1000) + m\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n >>> fit = a*m**a/bins**(a+1)\n >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_power_a_size_None_Draws_samples[] = "\n power(a, size=None)\n\n Draws samples in [0, 1] from a power distribution with positive\n exponent a - 1.\n\n Also known as the power function distribution.\n\n Parameters\n ----------\n a : float\n parameter, > 0\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n The returned samples lie in [0, 1].\n\n Raises\n ------\n ValueError\n If a<1.\n\n Notes\n -----\n The probability density function is\n\n .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n The power function distribution is just the inverse of the Pareto\n distribution. It may also be seen as a special case of the Beta\n distribution.\n\n It is used, for example, in modeling the over-reporting of insurance\n claims.\n\n References\n ----------\n .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n in economics and actuarial sciences\", Wiley, 2003.\n .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n Functions\", National Institute of Standards and Technology Handbook\n Series, June 2003.\n http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> samples = 1000\n >>> s = np.random.power(a, samples)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, bins=""30)\n >>> x = np.linspace(0, 1, 100)\n >>> y = a*x**(a-1.)\n >>> normed_y = samples*np.diff(bins)[0]*y\n >>> plt.plot(x, normed_y)\n >>> plt.show()\n\n Compare the power function distribution to the inverse of the Pareto.\n\n >>> from scipy import stats\n >>> rvs = np.random.power(5, 1000000)\n >>> rvsp = np.random.pareto(5, 1000000)\n >>> xx = np.linspace(0,1,100)\n >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n >>> plt.figure()\n >>> plt.hist(rvs, bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('np.random.power(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of stats.pareto(5)')\n\n ";
++static const char __pyx_k_randint_low_high_None_size_None[] = "\n randint(low, high=None, size=None)\n\n Return random integers from `low` (inclusive) to `high` (exclusive).\n\n Return random integers from the \"discrete uniform\" distribution in the\n \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n then results are from [0, `low`).\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, one above the largest (signed) integer to be drawn\n from the distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is\n returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.random_integers : similar to `randint`, only for the closed\n interval [`low`, `high`], and 1 is the lowest value if `high` is\n omitted. In particular, this other one is the one to use to generate\n uniformly distributed discrete non-integers.\n\n Examples\n --------\n >>> np.random.randint(2, size=10)\n array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n >>> np.random.randint(1, size=10)\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n >>> np.random.randint(5, size=(2, 4))\n array([[4, 0, 2, 1],\n [3, 2, 2, 0]])\n\n ";
++static const char __pyx_k_random_integers_low_high_None_s[] = "\n random_integers(low, high=None, size=None)\n\n Return random integers between `low` and `high`, inclusive.\n\n Return random integers from the \"discrete uniform\" distribution in the\n closed interval [`low`, `high`]. If `high` is None (the default),\n then results are from [1, `low`].\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, the largest (signed) integer to be drawn from the\n distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.randint : Similar to `random_integers`, only for the half-open\n interval [`low`, `high`), and 0 is the lowest value if `high` is\n omitted.\n\n Notes\n -----\n To sample from N evenly spaced floating-point numbers between a and b,\n use::\n\n a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n Examples\n --------\n >>> np.random.random_integers(5)\n 4\n >>> type(np.random.random_integers(5))\n <type 'int'>\n >>> np.random.random_integers(5, size=(3.,2.))\n array([[5, 4],\n [3, 3],\n [4, 5]])\n\n Choose five random numbers from the set of five evenly-spaced\n numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])\n\n Roll two six sided dice 1000 times and sum the results:\n\n >>> d1 = np.random.random_integers(1, 6, 1000)\n >>> d2 = np.random.random_integers(1, 6, 1000)\n >>> dsums = d1 + d2\n\n Display results as a histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n >>> plt.show()\n\n ";
++static const char __pyx_k_rayleigh_scale_1_0_size_None_Dr[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n .. [2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n ";
++static const char __pyx_k_standard_cauchy_size_None_Stand[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n .. [3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<""25)] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n ";
++static const char __pyx_k_standard_normal_size_None_Retur[] = "\n standard_normal(size=None)\n\n Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n Parameters\n ----------\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n >>> s = np.random.standard_normal(8000)\n >>> s\n array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, #random\n -0.38672696, -0.4685006 ]) #random\n >>> s.shape\n (8000,)\n >>> s = np.random.standard_normal(size=(3, 4, 2))\n >>> s.shape\n (3, 4, 2)\n\n ";
++static const char __pyx_k_standard_t_df_size_None_Standar[] = "\n standard_t(df, size=None)\n\n Standard Student's t distribution with df degrees of freedom.\n\n A special case of the hyperbolic distribution.\n As `df` gets large, the result resembles that of the standard normal\n distribution (`standard_normal`).\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be > 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn samples.\n\n Notes\n -----\n The probability density function for the t distribution is\n\n .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n The t test is based on an assumption that the data come from a Normal\n distribution. The t test provides a way to test whether the sample mean\n (that is the mean calculated from the data) is a good estimate of the true\n mean.\n\n The derivation of the t-distribution was forst published in 1908 by William\n Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n issues, he had to publish under a pseudonym, and so he used the name\n Student.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n Springer, 2002.\n .. [2] Wikipedia, \"Student's t-distribution\"\n http://en.wikipedia.org/wiki/Student's_t-distribution\n\n Examples\n --------\n From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n women in Kj is:\n\n >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n ... 7515, 8230, 8770])\n\n Doe""s their energy intake deviate systematically from the recommended\n value of 7725 kJ?\n\n We have 10 degrees of freedom, so is the sample mean within 95% of the\n recommended value?\n\n >>> s = np.random.standard_t(10, size=100000)\n >>> np.mean(intake)\n 6753.636363636364\n >>> intake.std(ddof=1)\n 1142.1232221373727\n\n Calculate the t statistic, setting the ddof parameter to the unbiased\n value so the divisor in the standard deviation will be degrees of\n freedom, N-1.\n\n >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(s, bins=100, normed=True)\n\n For a one-sided t-test, how far out in the distribution does the t\n statistic appear?\n\n >>> >>> np.sum(s<t) / float(len(s))\n 0.0090699999999999999 #random\n\n So the p-value is about 0.009, which says the null hypothesis has a\n probability of about 99% of being true.\n\n ";
++static const char __pyx_k_tomaxint_size_None_Random_integ[] = "\n tomaxint(size=None)\n\n Random integers between 0 and ``sys.maxint``, inclusive.\n\n Return a sample of uniformly distributed random integers in the interval\n [0, ``sys.maxint``].\n\n Parameters\n ----------\n size : tuple of ints, int, optional\n Shape of output. If this is, for example, (m,n,k), m*n*k samples\n are generated. If no shape is specified, a single sample is\n returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Uniform sampling over a given half-open interval of integers.\n random_integers : Uniform sampling over a given closed interval of\n integers.\n\n Examples\n --------\n >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n >>> RS.tomaxint((2,2,2))\n array([[[1170048599, 1600360186],\n [ 739731006, 1947757578]],\n [[1871712945, 752307660],\n [1601631370, 1479324245]]])\n >>> import sys\n >>> sys.maxint\n 2147483647\n >>> RS.tomaxint((2,2,2)) < sys.maxint\n array([[[ True, True],\n [ True, True]],\n [[ True, True],\n [ True, True]]], dtype=bool)\n\n ";
++static const char __pyx_k_triangular_left_mode_right_size[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n .. [1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n ... normed=True)\n >>> plt.show()\n\n ";
++static const char __pyx_k_uniform_low_0_0_high_1_0_size_1[] = "\n uniform(low=0.0, high=1.0, size=1)\n\n Draw samples from a uniform distribution.\n\n Samples are uniformly distributed over the half-open interval\n ``[low, high)`` (includes low, but excludes high). In other words,\n any value within the given interval is equally likely to be drawn\n by `uniform`.\n\n Parameters\n ----------\n low : float, optional\n Lower boundary of the output interval. All values generated will be\n greater than or equal to low. The default value is 0.\n high : float\n Upper boundary of the output interval. All values generated will be\n less than high. The default value is 1.0.\n size : int or tuple of ints, optional\n Shape of output. If the given size is, for example, (m,n,k),\n m*n*k samples are generated. If no shape is specified, a single sample\n is returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Discrete uniform distribution, yielding integers.\n random_integers : Discrete uniform distribution over the closed\n interval ``[low, high]``.\n random_sample : Floats uniformly distributed over ``[0, 1)``.\n random : Alias for `random_sample`.\n rand : Convenience function that accepts dimensions as input, e.g.,\n ``rand(2,2)`` would generate a 2-by-2 array of floats,\n uniformly distributed over ``[0, 1)``.\n\n Notes\n -----\n The probability density function of the uniform distribution is\n\n .. math:: p(x) = \\frac{1}{b - a}\n\n anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> s = np.random.uniform(-1,0,1000)\n\n All values are w""ithin the given interval:\n\n >>> np.all(s >= -1)\n True\n >>> np.all(s < 0)\n True\n\n Display the histogram of the samples, along with the\n probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_vonmises_mu_kappa_size_None_Dra[] = "\n vonmises(mu, kappa, size=None)\n\n Draw samples from a von Mises distribution.\n\n Samples are drawn from a von Mises distribution with specified mode\n (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n The von Mises distribution (also known as the circular normal\n distribution) is a continuous probability distribution on the unit\n circle. It may be thought of as the circular analogue of the normal\n distribution.\n\n Parameters\n ----------\n mu : float\n Mode (\"center\") of the distribution.\n kappa : float\n Dispersion of the distribution, has to be >=0.\n size : int or tuple of int\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples, which are in the interval [-pi, pi].\n\n See Also\n --------\n scipy.stats.distributions.vonmises : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the von Mises distribution is\n\n .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n The von Mises is named for Richard Edler von Mises, who was born in\n Austria-Hungary, in what is now the Ukraine. He fled to the United\n States in 1939 and became a professor at Harvard. He worked in\n probability theory, aerodynamics, fluid mechanics, and philosophy of\n science.\n\n References\n ----------\n Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n Functions*, New York: Dover, 1965.\n\n "" von Mises, R., *Mathematical Theory of Probability and Statistics*,\n New York: Academic Press, 1964.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n >>> s = np.random.vonmises(mu, kappa, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_weibull_a_size_None_Weibull_dis[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull_max\n scipy.stats.distributions.weibull_min\n scipy.stats.distributions.genextreme\n gumbel\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n General""stabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n ";
++static const char __pyx_k_zipf_a_size_None_Draw_samples_f[] = "\n zipf(a, size=None)\n\n Draw samples from a Zipf distribution.\n\n Samples are drawn from a Zipf distribution with specified parameter\n `a` > 1.\n\n The Zipf distribution (also known as the zeta distribution) is a\n continuous probability distribution that satisfies Zipf's law: the\n frequency of an item is inversely proportional to its rank in a\n frequency table.\n\n Parameters\n ----------\n a : float > 1\n Distribution parameter.\n size : int or tuple of int, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 2. # parameter\n >>> s = np.random.zipf""(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n Truncate s values at 50 so plot is interesting\n >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n >>> x = np.arange(1., 50.)\n >>> y = x**(-a)/sps.zetac(a)\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n ";
++static const char __pyx_k_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=False'";
++static const char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
++static const char __pyx_k_RandomState_multinomial_line_419[] = "RandomState.multinomial (line 4190)";
++static const char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3524)";
++static const char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2087)";
++static const char __pyx_k_RandomState_noncentral_f_line_19[] = "RandomState.noncentral_f (line 1914)";
++static const char __pyx_k_RandomState_permutation_line_444[] = "RandomState.permutation (line 4448)";
++static const char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1288)";
++static const char __pyx_k_RandomState_random_sample_line_7[] = "RandomState.random_sample (line 730)";
++static const char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2179)";
++static const char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1611)";
++static const char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1366)";
++static const char __pyx_k_RandomState_standard_t_line_2240[] = "RandomState.standard_t (line 2240)";
++static const char __pyx_k_RandomState_triangular_line_3328[] = "RandomState.triangular (line 3328)";
++static const char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
++static const char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
++static const char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
++static const char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
++static const char __pyx_k_size_is_not_compatible_with_inpu[] = "size is not compatible with inputs";
++static PyObject *__pyx_kp_s_Cannot_take_a_larger_sample_than;
++static PyObject *__pyx_kp_s_Fewer_non_zero_entries_in_p_than;
++static PyObject *__pyx_n_s_MT19937;
++static PyObject *__pyx_kp_u_RandomState_binomial_line_3416;
++static PyObject *__pyx_kp_u_RandomState_bytes_line_900;
++static PyObject *__pyx_kp_u_RandomState_chisquare_line_2009;
++static PyObject *__pyx_kp_u_RandomState_choice_line_928;
++static PyObject *__pyx_n_s_RandomState_ctor;
++static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4278;
++static PyObject *__pyx_kp_u_RandomState_f_line_1812;
++static PyObject *__pyx_kp_u_RandomState_gamma_line_1721;
++static PyObject *__pyx_kp_u_RandomState_geometric_line_3778;
++static PyObject *__pyx_kp_u_RandomState_gumbel_line_2830;
++static PyObject *__pyx_kp_u_RandomState_hypergeometric_line;
++static PyObject *__pyx_kp_u_RandomState_laplace_line_2740;
++static PyObject *__pyx_kp_u_RandomState_logistic_line_2961;
++static PyObject *__pyx_kp_u_RandomState_lognormal_line_3049;
++static PyObject *__pyx_kp_u_RandomState_logseries_line_3963;
++static PyObject *__pyx_kp_u_RandomState_multinomial_line_419;
++static PyObject *__pyx_kp_u_RandomState_multivariate_normal;
++static PyObject *__pyx_kp_u_RandomState_negative_binomial_li;
++static PyObject *__pyx_kp_u_RandomState_noncentral_chisquare;
++static PyObject *__pyx_kp_u_RandomState_noncentral_f_line_19;
++static PyObject *__pyx_kp_u_RandomState_normal_line_1398;
++static PyObject *__pyx_kp_u_RandomState_pareto_line_2435;
++static PyObject *__pyx_kp_u_RandomState_permutation_line_444;
++static PyObject *__pyx_kp_u_RandomState_poisson_line_3619;
++static PyObject *__pyx_kp_u_RandomState_power_line_2631;
++static PyObject *__pyx_kp_u_RandomState_rand_line_1187;
++static PyObject *__pyx_kp_u_RandomState_randint_line_820;
++static PyObject *__pyx_kp_u_RandomState_randn_line_1231;
++static PyObject *__pyx_kp_u_RandomState_random_integers_line;
++static PyObject *__pyx_kp_u_RandomState_random_sample_line_7;
++static PyObject *__pyx_kp_u_RandomState_rayleigh_line_3170;
++static PyObject *__pyx_kp_u_RandomState_shuffle_line_4389;
++static PyObject *__pyx_kp_u_RandomState_standard_cauchy_line;
++static PyObject *__pyx_kp_u_RandomState_standard_exponential;
++static PyObject *__pyx_kp_u_RandomState_standard_gamma_line;
++static PyObject *__pyx_kp_u_RandomState_standard_normal_line;
++static PyObject *__pyx_kp_u_RandomState_standard_t_line_2240;
++static PyObject *__pyx_kp_u_RandomState_tomaxint_line_773;
++static PyObject *__pyx_kp_u_RandomState_triangular_line_3328;
++static PyObject *__pyx_kp_u_RandomState_uniform_line_1100;
++static PyObject *__pyx_kp_u_RandomState_vonmises_line_2341;
++static PyObject *__pyx_kp_u_RandomState_wald_line_3242;
++static PyObject *__pyx_kp_u_RandomState_weibull_line_2531;
++static PyObject *__pyx_kp_u_RandomState_zipf_line_3690;
++static PyObject *__pyx_n_s_TypeError;
++static PyObject *__pyx_n_s_ValueError;
++static PyObject *__pyx_n_s_a;
++static PyObject *__pyx_kp_s_a_0;
++static PyObject *__pyx_kp_s_a_1_0;
++static PyObject *__pyx_kp_s_a_and_p_must_have_same_size;
++static PyObject *__pyx_kp_s_a_must_be_1_dimensional;
++static PyObject *__pyx_kp_s_a_must_be_1_dimensional_or_an_in;
++static PyObject *__pyx_kp_s_a_must_be_greater_than_0;
++static PyObject *__pyx_kp_s_a_must_be_non_empty;
++static PyObject *__pyx_n_s_add;
++static PyObject *__pyx_kp_s_algorithm_must_be_MT19937;
++static PyObject *__pyx_n_s_allclose;
++static PyObject *__pyx_n_s_alpha;
++static PyObject *__pyx_n_s_any;
++static PyObject *__pyx_n_s_arange;
++static PyObject *__pyx_n_s_array;
++static PyObject *__pyx_n_s_asarray;
++static PyObject *__pyx_n_s_b;
++static PyObject *__pyx_kp_s_b_0;
++static PyObject *__pyx_n_s_beta;
++static PyObject *__pyx_n_s_binomial;
++static PyObject *__pyx_kp_u_binomial_n_p_size_None_Draw_sam;
++static PyObject *__pyx_n_s_bytes;
++static PyObject *__pyx_kp_u_bytes_length_Return_random_byte;
++static PyObject *__pyx_n_s_chisquare;
++static PyObject *__pyx_kp_u_chisquare_df_size_None_Draw_sam;
++static PyObject *__pyx_n_s_choice;
++static PyObject *__pyx_kp_u_choice_a_size_None_replace_True;
++static PyObject *__pyx_n_s_cline_in_traceback;
++static PyObject *__pyx_n_s_copy;
++static PyObject *__pyx_n_s_cov;
++static PyObject *__pyx_kp_s_cov_must_be_2_dimensional_and_sq;
++static PyObject *__pyx_n_s_cumsum;
++static PyObject *__pyx_n_s_d;
++static PyObject *__pyx_n_s_df;
++static PyObject *__pyx_kp_s_df_0;
++static PyObject *__pyx_kp_s_df_1;
++static PyObject *__pyx_n_s_dfden;
++static PyObject *__pyx_kp_s_dfden_0;
++static PyObject *__pyx_n_s_dfnum;
++static PyObject *__pyx_kp_s_dfnum_0;
++static PyObject *__pyx_kp_s_dfnum_1;
++static PyObject *__pyx_n_s_dirichlet;
++static PyObject *__pyx_kp_u_dirichlet_alpha_size_None_Draw;
++static PyObject *__pyx_n_s_dot;
++static PyObject *__pyx_n_s_double;
++static PyObject *__pyx_n_s_dtype;
++static PyObject *__pyx_n_s_empty;
++static PyObject *__pyx_n_s_empty_like;
++static PyObject *__pyx_n_s_equal;
++static PyObject *__pyx_n_s_exponential;
++static PyObject *__pyx_n_s_f;
++static PyObject *__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa;
++static PyObject *__pyx_n_s_fields;
++static PyObject *__pyx_n_s_float64;
++static PyObject *__pyx_n_s_gamma;
++static PyObject *__pyx_kp_u_gamma_shape_scale_1_0_size_None;
++static PyObject *__pyx_n_s_geometric;
++static PyObject *__pyx_kp_u_geometric_p_size_None_Draw_samp;
++static PyObject *__pyx_n_s_get_state;
++static PyObject *__pyx_n_s_greater;
++static PyObject *__pyx_n_s_greater_equal;
++static PyObject *__pyx_n_s_gumbel;
++static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
++static PyObject *__pyx_n_s_high;
++static PyObject *__pyx_n_s_hypergeometric;
++static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
++static PyObject *__pyx_n_s_iinfo;
++static PyObject *__pyx_n_s_import;
++static PyObject *__pyx_n_s_index;
++static PyObject *__pyx_n_s_int;
++static PyObject *__pyx_n_s_integer;
++static PyObject *__pyx_n_s_intp;
++static PyObject *__pyx_n_s_item;
++static PyObject *__pyx_n_s_kappa;
++static PyObject *__pyx_kp_s_kappa_0;
++static PyObject *__pyx_n_s_l;
++static PyObject *__pyx_n_s_lam;
++static PyObject *__pyx_kp_s_lam_0;
++static PyObject *__pyx_kp_s_lam_value_too_large;
++static PyObject *__pyx_kp_s_lam_value_too_large_2;
++static PyObject *__pyx_n_s_laplace;
++static PyObject *__pyx_kp_u_laplace_loc_0_0_scale_1_0_size;
++static PyObject *__pyx_n_s_left;
++static PyObject *__pyx_kp_s_left_mode;
++static PyObject *__pyx_kp_s_left_right;
++static PyObject *__pyx_n_s_less;
++static PyObject *__pyx_n_s_less_equal;
++static PyObject *__pyx_n_s_loc;
++static PyObject *__pyx_n_s_logistic;
++static PyObject *__pyx_kp_u_logistic_loc_0_0_scale_1_0_size;
++static PyObject *__pyx_n_s_lognormal;
++static PyObject *__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si;
++static PyObject *__pyx_n_s_logseries;
++static PyObject *__pyx_kp_u_logseries_p_size_None_Draw_samp;
++static PyObject *__pyx_n_s_low;
++static PyObject *__pyx_kp_s_low_high;
++static PyObject *__pyx_n_s_main;
++static PyObject *__pyx_n_s_max;
++static PyObject *__pyx_n_s_mean;
++static PyObject *__pyx_kp_s_mean_0;
++static PyObject *__pyx_kp_s_mean_0_0;
++static PyObject *__pyx_kp_s_mean_and_cov_must_have_same_leng;
++static PyObject *__pyx_kp_s_mean_must_be_1_dimensional;
++static PyObject *__pyx_n_s_mode;
++static PyObject *__pyx_kp_s_mode_right;
++static PyObject *__pyx_n_s_mtrand;
++static PyObject *__pyx_kp_s_mtrand_pyx;
++static PyObject *__pyx_n_s_mu;
++static PyObject *__pyx_n_s_multinomial;
++static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
++static PyObject *__pyx_n_s_multiply;
++static PyObject *__pyx_n_s_multivariate_normal;
++static PyObject *__pyx_kp_u_multivariate_normal_mean_cov_si;
++static PyObject *__pyx_n_s_n;
++static PyObject *__pyx_kp_s_n_0;
++static PyObject *__pyx_kp_s_n_0_2;
++static PyObject *__pyx_n_s_nbad;
++static PyObject *__pyx_kp_s_nbad_0;
++static PyObject *__pyx_n_s_ndarray;
++static PyObject *__pyx_n_s_ndim;
++static PyObject *__pyx_n_s_ndmin;
++static PyObject *__pyx_n_s_negative_binomial;
++static PyObject *__pyx_kp_u_negative_binomial_n_p_size_None;
++static PyObject *__pyx_n_s_ngood;
++static PyObject *__pyx_kp_s_ngood_0;
++static PyObject *__pyx_kp_s_ngood_nbad_nsample;
++static PyObject *__pyx_n_s_nonc;
++static PyObject *__pyx_kp_s_nonc_0;
++static PyObject *__pyx_kp_s_nonc_0_2;
++static PyObject *__pyx_n_s_noncentral_chisquare;
++static PyObject *__pyx_kp_u_noncentral_chisquare_df_nonc_si;
++static PyObject *__pyx_n_s_noncentral_f;
++static PyObject *__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s;
++static PyObject *__pyx_n_s_normal;
++static PyObject *__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N;
++static PyObject *__pyx_n_s_np;
++static PyObject *__pyx_n_s_nsample;
++static PyObject *__pyx_kp_s_nsample_1;
++static PyObject *__pyx_n_s_numpy;
++static PyObject *__pyx_n_s_numpy_dual;
++static PyObject *__pyx_n_s_operator;
++static PyObject *__pyx_n_s_p;
++static PyObject *__pyx_kp_s_p_0;
++static PyObject *__pyx_kp_s_p_0_0;
++static PyObject *__pyx_kp_s_p_0_0_2;
++static PyObject *__pyx_kp_s_p_1;
++static PyObject *__pyx_kp_s_p_1_0;
++static PyObject *__pyx_kp_s_p_1_0_2;
++static PyObject *__pyx_kp_s_p_must_be_1_dimensional;
++static PyObject *__pyx_n_s_pareto;
++static PyObject *__pyx_kp_u_pareto_a_size_None_Draw_samples;
++static PyObject *__pyx_n_s_permutation;
++static PyObject *__pyx_kp_u_permutation_x_Randomly_permute;
++static PyObject *__pyx_n_s_poisson;
++static PyObject *__pyx_kp_u_poisson_lam_1_0_size_None_Draw;
++static PyObject *__pyx_n_s_poisson_lam_max;
++static PyObject *__pyx_n_s_power;
++static PyObject *__pyx_kp_u_power_a_size_None_Draws_samples;
++static PyObject *__pyx_kp_s_probabilities_are_not_non_negati;
++static PyObject *__pyx_kp_s_probabilities_do_not_sum_to_1;
++static PyObject *__pyx_n_s_prod;
++static PyObject *__pyx_n_s_pvals;
++static PyObject *__pyx_n_s_rand;
++static PyObject *__pyx_n_s_rand_2;
++static PyObject *__pyx_kp_u_rand_d0_d1_dn_Random_values_in;
++static PyObject *__pyx_n_s_randint;
++static PyObject *__pyx_kp_u_randint_low_high_None_size_None;
++static PyObject *__pyx_n_s_randn;
++static PyObject *__pyx_kp_u_randn_d0_d1_dn_Return_a_sample;
++static PyObject *__pyx_n_s_random;
++static PyObject *__pyx_n_s_random_integers;
++static PyObject *__pyx_kp_u_random_integers_low_high_None_s;
++static PyObject *__pyx_n_s_random_sample;
++static PyObject *__pyx_kp_u_random_sample_size_None_Return;
++static PyObject *__pyx_n_s_ravel;
++static PyObject *__pyx_n_s_rayleigh;
++static PyObject *__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr;
++static PyObject *__pyx_n_s_reduce;
++static PyObject *__pyx_n_s_replace;
++static PyObject *__pyx_n_s_return_index;
++static PyObject *__pyx_n_s_right;
++static PyObject *__pyx_n_s_scale;
++static PyObject *__pyx_kp_s_scale_0;
++static PyObject *__pyx_kp_s_scale_0_0;
++static PyObject *__pyx_n_s_searchsorted;
++static PyObject *__pyx_n_s_seed;
++static PyObject *__pyx_n_s_set_state;
++static PyObject *__pyx_n_s_shape;
++static PyObject *__pyx_kp_s_shape_0;
++static PyObject *__pyx_n_s_shape_from_size;
++static PyObject *__pyx_n_s_shuffle;
++static PyObject *__pyx_kp_u_shuffle_x_Modify_a_sequence_in;
++static PyObject *__pyx_n_s_side;
++static PyObject *__pyx_n_s_sigma;
++static PyObject *__pyx_kp_s_sigma_0;
++static PyObject *__pyx_kp_s_sigma_0_0;
++static PyObject *__pyx_n_s_size;
++static PyObject *__pyx_kp_s_size_is_not_compatible_with_inpu;
++static PyObject *__pyx_n_s_sort;
++static PyObject *__pyx_n_s_sqrt;
++static PyObject *__pyx_n_s_standard_cauchy;
++static PyObject *__pyx_kp_u_standard_cauchy_size_None_Stand;
++static PyObject *__pyx_n_s_standard_exponential;
++static PyObject *__pyx_kp_u_standard_exponential_size_None;
++static PyObject *__pyx_n_s_standard_gamma;
++static PyObject *__pyx_kp_u_standard_gamma_shape_size_None;
++static PyObject *__pyx_n_s_standard_normal;
++static PyObject *__pyx_kp_u_standard_normal_size_None_Retur;
++static PyObject *__pyx_n_s_standard_t;
++static PyObject *__pyx_kp_u_standard_t_df_size_None_Standar;
++static PyObject *__pyx_kp_s_state_must_be_624_longs;
++static PyObject *__pyx_n_s_subtract;
++static PyObject *__pyx_n_s_sum;
++static PyObject *__pyx_kp_s_sum_pvals_1_1_0;
++static PyObject *__pyx_n_s_svd;
++static PyObject *__pyx_n_s_take;
++static PyObject *__pyx_n_s_test;
++static PyObject *__pyx_kp_u_tomaxint_size_None_Random_integ;
++static PyObject *__pyx_n_s_triangular;
++static PyObject *__pyx_kp_u_triangular_left_mode_right_size;
++static PyObject *__pyx_n_s_uint;
++static PyObject *__pyx_n_s_uint32;
++static PyObject *__pyx_n_s_uniform;
++static PyObject *__pyx_kp_u_uniform_low_0_0_high_1_0_size_1;
++static PyObject *__pyx_n_s_unique;
++static PyObject *__pyx_n_s_vonmises;
++static PyObject *__pyx_kp_u_vonmises_mu_kappa_size_None_Dra;
++static PyObject *__pyx_n_s_wald;
++static PyObject *__pyx_kp_u_wald_mean_scale_size_None_Draw;
++static PyObject *__pyx_n_s_weibull;
++static PyObject *__pyx_kp_u_weibull_a_size_None_Weibull_dis;
++static PyObject *__pyx_n_s_zeros;
++static PyObject *__pyx_n_s_zipf;
++static PyObject *__pyx_kp_u_zipf_a_size_None_Draw_samples_f;
+ static PyObject *__pyx_pf_6mtrand__shape_from_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_size, PyObject *__pyx_v_d); /* proto */
+ static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
+ static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
+@@ -926,720 +1916,141 @@
+ static PyObject *__pyx_pf_6mtrand_11RandomState_102shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
+ static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
+ static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
+-static char __pyx_k_1[] = "size is not compatible with inputs";
+-static char __pyx_k_9[] = "algorithm must be 'MT19937'";
+-static char __pyx_k_13[] = "state must be 624 longs";
+-static char __pyx_k_15[] = "low >= high";
+-static char __pyx_k_18[] = "a must be 1-dimensional or an integer";
+-static char __pyx_k_20[] = "a must be greater than 0";
+-static char __pyx_k_22[] = "a must be 1-dimensional";
+-static char __pyx_k_24[] = "a must be non-empty";
+-static char __pyx_k_26[] = "p must be 1-dimensional";
+-static char __pyx_k_28[] = "a and p must have same size";
+-static char __pyx_k_30[] = "probabilities are not non-negative";
+-static char __pyx_k_32[] = "probabilities do not sum to 1";
+-static char __pyx_k_34[] = "Cannot take a larger sample than population when 'replace=False'";
+-static char __pyx_k_36[] = "Fewer non-zero entries in p than size";
+-static char __pyx_k_44[] = "scale <= 0";
+-static char __pyx_k_47[] = "a <= 0";
+-static char __pyx_k_49[] = "b <= 0";
+-static char __pyx_k_56[] = "shape <= 0";
+-static char __pyx_k_66[] = "dfnum <= 0";
+-static char __pyx_k_68[] = "dfden <= 0";
+-static char __pyx_k_70[] = "dfnum <= 1";
+-static char __pyx_k_73[] = "nonc < 0";
+-static char __pyx_k_78[] = "df <= 0";
+-static char __pyx_k_82[] = "nonc <= 0";
+-static char __pyx_k_84[] = "df <= 1";
+-static char __pyx_k_89[] = "kappa < 0";
+-static char __pyx_k__a[] = "a";
+-static char __pyx_k__b[] = "b";
+-static char __pyx_k__d[] = "d";
+-static char __pyx_k__f[] = "f";
+-static char __pyx_k__l[] = "l";
+-static char __pyx_k__n[] = "n";
+-static char __pyx_k__p[] = "p";
+-static char __pyx_k_112[] = "sigma <= 0";
+-static char __pyx_k_114[] = "sigma <= 0.0";
+-static char __pyx_k_118[] = "scale <= 0.0";
+-static char __pyx_k_120[] = "mean <= 0";
+-static char __pyx_k_123[] = "mean <= 0.0";
+-static char __pyx_k_126[] = "left > mode";
+-static char __pyx_k_128[] = "mode > right";
+-static char __pyx_k_130[] = "left == right";
+-static char __pyx_k_135[] = "n < 0";
+-static char __pyx_k_137[] = "p < 0";
+-static char __pyx_k_139[] = "p > 1";
+-static char __pyx_k_144[] = "n <= 0";
+-static char __pyx_k_152[] = "lam < 0";
+-static char __pyx_k_154[] = "lam value too large";
+-static char __pyx_k_157[] = "lam value too large.";
+-static char __pyx_k_159[] = "a <= 1.0";
+-static char __pyx_k_162[] = "p < 0.0";
+-static char __pyx_k_164[] = "p > 1.0";
+-static char __pyx_k_168[] = "ngood < 0";
+-static char __pyx_k_170[] = "nbad < 0";
+-static char __pyx_k_172[] = "nsample < 1";
+-static char __pyx_k_174[] = "ngood + nbad < nsample";
+-static char __pyx_k_180[] = "p <= 0.0";
+-static char __pyx_k_182[] = "p >= 1.0";
+-static char __pyx_k_186[] = "mean must be 1 dimensional";
+-static char __pyx_k_188[] = "cov must be 2 dimensional and square";
+-static char __pyx_k_190[] = "mean and cov must have same length";
+-static char __pyx_k_193[] = "numpy.dual";
+-static char __pyx_k_194[] = "sum(pvals[:-1]) > 1.0";
+-static char __pyx_k_198[] = "/home/jtaylor/prog/numpy/numpy/random/mtrand/mtrand.pyx";
+-static char __pyx_k_201[] = "standard_exponential";
+-static char __pyx_k_202[] = "noncentral_chisquare";
+-static char __pyx_k_203[] = "RandomState.random_sample (line 730)";
+-static char __pyx_k_204[] = "\n random_sample(size=None)\n\n Return random floats in the half-open interval [0.0, 1.0).\n\n Results are from the \"continuous uniform\" distribution over the\n stated interval. To sample :math:`Unif[a, b), b > a` multiply\n the output of `random_sample` by `(b-a)` and add `a`::\n\n (b - a) * random_sample() + a\n\n Parameters\n ----------\n size : int or tuple of ints, optional\n Defines the shape of the returned array of random floats. If None\n (the default), returns a single float.\n\n Returns\n -------\n out : float or ndarray of floats\n Array of random floats of shape `size` (unless ``size=None``, in which\n case a single float is returned).\n\n Examples\n --------\n >>> np.random.random_sample()\n 0.47108547995356098\n >>> type(np.random.random_sample())\n <type 'float'>\n >>> np.random.random_sample((5,))\n array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])\n\n Three-by-two array of random numbers from [-5, 0):\n\n >>> 5 * np.random.random_sample((3, 2)) - 5\n array([[-3.99149989, -0.52338984],\n [-2.99091858, -0.79479508],\n [-1.23204345, -1.75224494]])\n\n ";
+-static char __pyx_k_205[] = "RandomState.tomaxint (line 773)";
+-static char __pyx_k_206[] = "\n tomaxint(size=None)\n\n Random integers between 0 and ``sys.maxint``, inclusive.\n\n Return a sample of uniformly distributed random integers in the interval\n [0, ``sys.maxint``].\n\n Parameters\n ----------\n size : tuple of ints, int, optional\n Shape of output. If this is, for example, (m,n,k), m*n*k samples\n are generated. If no shape is specified, a single sample is\n returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Uniform sampling over a given half-open interval of integers.\n random_integers : Uniform sampling over a given closed interval of\n integers.\n\n Examples\n --------\n >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n >>> RS.tomaxint((2,2,2))\n array([[[1170048599, 1600360186],\n [ 739731006, 1947757578]],\n [[1871712945, 752307660],\n [1601631370, 1479324245]]])\n >>> import sys\n >>> sys.maxint\n 2147483647\n >>> RS.tomaxint((2,2,2)) < sys.maxint\n array([[[ True, True],\n [ True, True]],\n [[ True, True],\n [ True, True]]], dtype=bool)\n\n ";
+-static char __pyx_k_207[] = "RandomState.randint (line 820)";
+-static char __pyx_k_208[] = "\n randint(low, high=None, size=None)\n\n Return random integers from `low` (inclusive) to `high` (exclusive).\n\n Return random integers from the \"discrete uniform\" distribution in the\n \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n then results are from [0, `low`).\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, one above the largest (signed) integer to be drawn\n from the distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is\n returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.random_integers : similar to `randint`, only for the closed\n interval [`low`, `high`], and 1 is the lowest value if `high` is\n omitted. In particular, this other one is the one to use to generate\n uniformly distributed discrete non-integers.\n\n Examples\n --------\n >>> np.random.randint(2, size=10)\n array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n >>> np.random.randint(1, size=10)\n array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n >>> np.random.randint(5, size=(2, 4))\n array([[4, 0, 2, 1],\n [3, 2, 2, 0]])\n\n ";
+-static char __pyx_k_209[] = "RandomState.bytes (line 900)";
+-static char __pyx_k_210[] = "\n bytes(length)\n\n Return random bytes.\n\n Parameters\n ----------\n length : int\n Number of random bytes.\n\n Returns\n -------\n out : str\n String of length `length`.\n\n Examples\n --------\n >>> np.random.bytes(10)\n ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n ";
+-static char __pyx_k_211[] = "RandomState.choice (line 928)";
+-static char __pyx_k_212[] = "\n choice(a, size=None, replace=True, p=None)\n\n Generates a random sample from a given 1-D array\n\n .. versionadded:: 1.7.0\n\n Parameters\n -----------\n a : 1-D array-like or int\n If an ndarray, a random sample is generated from its elements.\n If an int, the random sample is generated as if a was np.arange(n)\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n replace : boolean, optional\n Whether the sample is with or without replacement\n p : 1-D array-like, optional\n The probabilities associated with each entry in a.\n If not given the sample assumes a uniform distribtion over all\n entries in a.\n\n Returns\n --------\n samples : 1-D ndarray, shape (size,)\n The generated random samples\n\n Raises\n -------\n ValueError\n If a is an int and less than zero, if a or p are not 1-dimensional,\n if a is an array-like of size 0, if p is not a vector of\n probabilities, if a and p have different lengths, or if\n replace=False and the sample size is greater than the population\n size\n\n See Also\n ---------\n randint, shuffle, permutation\n\n Examples\n ---------\n Generate a uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3)\n array([0, 3, 4])\n >>> #This is equivalent to np.random.randint(0,5,3)\n\n Generate a non-uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n array([3, 3, 0])\n\n Generate a uniform random sample from np.arange(5) of size 3 without\n replacement:\n\n >>> np.random.choice(5, 3, replace=False)\n array([3,1,0])\n "" >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n Generate a non-uniform random sample from np.arange(5) of size\n 3 without replacement:\n\n >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n array([2, 3, 0])\n\n Any of the above can be repeated with an arbitrary array-like\n instead of just integers. For instance:\n\n >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n dtype='|S11')\n\n ";
+-static char __pyx_k_213[] = "RandomState.uniform (line 1100)";
+-static char __pyx_k_214[] = "\n uniform(low=0.0, high=1.0, size=1)\n\n Draw samples from a uniform distribution.\n\n Samples are uniformly distributed over the half-open interval\n ``[low, high)`` (includes low, but excludes high). In other words,\n any value within the given interval is equally likely to be drawn\n by `uniform`.\n\n Parameters\n ----------\n low : float, optional\n Lower boundary of the output interval. All values generated will be\n greater than or equal to low. The default value is 0.\n high : float\n Upper boundary of the output interval. All values generated will be\n less than high. The default value is 1.0.\n size : int or tuple of ints, optional\n Shape of output. If the given size is, for example, (m,n,k),\n m*n*k samples are generated. If no shape is specified, a single sample\n is returned.\n\n Returns\n -------\n out : ndarray\n Drawn samples, with shape `size`.\n\n See Also\n --------\n randint : Discrete uniform distribution, yielding integers.\n random_integers : Discrete uniform distribution over the closed\n interval ``[low, high]``.\n random_sample : Floats uniformly distributed over ``[0, 1)``.\n random : Alias for `random_sample`.\n rand : Convenience function that accepts dimensions as input, e.g.,\n ``rand(2,2)`` would generate a 2-by-2 array of floats,\n uniformly distributed over ``[0, 1)``.\n\n Notes\n -----\n The probability density function of the uniform distribution is\n\n .. math:: p(x) = \\frac{1}{b - a}\n\n anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> s = np.random.uniform(-1,0,1000)\n\n All values are w""ithin the given interval:\n\n >>> np.all(s >= -1)\n True\n >>> np.all(s < 0)\n True\n\n Display the histogram of the samples, along with the\n probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_215[] = "RandomState.rand (line 1187)";
+-static char __pyx_k_216[] = "\n rand(d0, d1, ..., dn)\n\n Random values in a given shape.\n\n Create an array of the given shape and propagate it with\n random samples from a uniform distribution\n over ``[0, 1)``.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should all be positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n out : ndarray, shape ``(d0, d1, ..., dn)``\n Random values.\n\n See Also\n --------\n random\n\n Notes\n -----\n This is a convenience function. If you want an interface that\n takes a shape-tuple as the first argument, refer to\n np.random.random_sample .\n\n Examples\n --------\n >>> np.random.rand(3,2)\n array([[ 0.14022471, 0.96360618], #random\n [ 0.37601032, 0.25528411], #random\n [ 0.49313049, 0.94909878]]) #random\n\n ";
+-static char __pyx_k_217[] = "RandomState.randn (line 1231)";
+-static char __pyx_k_218[] = "\n randn(d0, d1, ..., dn)\n\n Return a sample (or samples) from the \"standard normal\" distribution.\n\n If positive, int_like or int-convertible arguments are provided,\n `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n with random floats sampled from a univariate \"normal\" (Gaussian)\n distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n floats, they are first converted to integers by truncation). A single\n float randomly sampled from the distribution is returned if no\n argument is provided.\n\n This is a convenience function. If you want an interface that takes a\n tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should be all positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n Z : ndarray or float\n A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n the standard normal distribution, or a single such float if\n no parameters were supplied.\n\n See Also\n --------\n random.standard_normal : Similar, but takes a tuple as its argument.\n\n Notes\n -----\n For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n ``sigma * np.random.randn(...) + mu``\n\n Examples\n --------\n >>> np.random.randn()\n 2.1923875335537315 #random\n\n Two-by-four array of samples from N(3, 6.25):\n\n >>> 2.5 * np.random.randn(2, 4) + 3\n array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n\n ";
+-static char __pyx_k_219[] = "RandomState.random_integers (line 1288)";
+-static char __pyx_k_220[] = "\n random_integers(low, high=None, size=None)\n\n Return random integers between `low` and `high`, inclusive.\n\n Return random integers from the \"discrete uniform\" distribution in the\n closed interval [`low`, `high`]. If `high` is None (the default),\n then results are from [1, `low`].\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, the largest (signed) integer to be drawn from the\n distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.randint : Similar to `random_integers`, only for the half-open\n interval [`low`, `high`), and 0 is the lowest value if `high` is\n omitted.\n\n Notes\n -----\n To sample from N evenly spaced floating-point numbers between a and b,\n use::\n\n a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n Examples\n --------\n >>> np.random.random_integers(5)\n 4\n >>> type(np.random.random_integers(5))\n <type 'int'>\n >>> np.random.random_integers(5, size=(3.,2.))\n array([[5, 4],\n [3, 3],\n [4, 5]])\n\n Choose five random numbers from the set of five evenly-spaced\n numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])\n\n Roll two six sided dice 1000 times and sum the results:\n\n >>> d1 = np.random.random_integers(1, 6, 1000)\n >>> d2 = np.random.random_integers(1, 6, 1000)\n >>> dsums = d1 + d2\n\n Display results as a histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n >>> plt.show()\n\n ";
+-static char __pyx_k_221[] = "RandomState.standard_normal (line 1366)";
+-static char __pyx_k_222[] = "\n standard_normal(size=None)\n\n Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n Parameters\n ----------\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n >>> s = np.random.standard_normal(8000)\n >>> s\n array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, #random\n -0.38672696, -0.4685006 ]) #random\n >>> s.shape\n (8000,)\n >>> s = np.random.standard_normal(size=(3, 4, 2))\n >>> s.shape\n (3, 4, 2)\n\n ";
+-static char __pyx_k_223[] = "RandomState.normal (line 1398)";
+-static char __pyx_k_224[] = "\n normal(loc=0.0, scale=1.0, size=None)\n\n Draw random samples from a normal (Gaussian) distribution.\n\n The probability density function of the normal distribution, first\n derived by De Moivre and 200 years later by both Gauss and Laplace\n independently [2]_, is often called the bell curve because of\n its characteristic shape (see the example below).\n\n The normal distributions occurs often in nature. For example, it\n describes the commonly occurring distribution of samples influenced\n by a large number of tiny, random disturbances, each with its own\n unique distribution [2]_.\n\n Parameters\n ----------\n loc : float\n Mean (\"centre\") of the distribution.\n scale : float\n Standard deviation (spread or \"width\") of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.norm : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gaussian distribution is\n\n .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n The square of the standard deviation, :math:`\\sigma^2`, is called the\n variance.\n\n The function has its peak at the mean, and its \"spread\" increases with\n the standard deviation (the function reaches 0.607 times its maximum at\n :math:`x + \\sigma` and :math:`x - \\sigma` [2]_). This implies that\n `numpy.random.normal` is more likely to return samples lying close to the\n mean, rather than those far away.\n""\n References\n ----------\n .. [1] Wikipedia, \"Normal distribution\",\n http://en.wikipedia.org/wiki/Normal_distribution\n .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n Variables and Random Signal Principles\", 4th ed., 2001,\n pp. 51, 51, 125.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 0, 0.1 # mean and standard deviation\n >>> s = np.random.normal(mu, sigma, 1000)\n\n Verify the mean and the variance:\n\n >>> abs(mu - np.mean(s)) < 0.01\n True\n\n >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n True\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_225[] = "RandomState.standard_exponential (line 1611)";
+-static char __pyx_k_226[] = "\n standard_exponential(size=None)\n\n Draw samples from the standard exponential distribution.\n\n `standard_exponential` is identical to the exponential distribution\n with a scale parameter of 1.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n out : float or ndarray\n Drawn samples.\n\n Examples\n --------\n Output a 3x8000 array:\n\n >>> n = np.random.standard_exponential((3, 8000))\n\n ";
+-static char __pyx_k_227[] = "RandomState.standard_gamma (line 1639)";
+-static char __pyx_k_228[] = "\n standard_gamma(shape, size=None)\n\n Draw samples from a Standard Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n shape (sometimes designated \"k\") and scale=1.\n\n Parameters\n ----------\n shape : float\n Parameter, should be > 0.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2., 1. # mean and width\n >>> s = np.random.standard_gamma(shape, 1000000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt""\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n ... (sps.gamma(shape) * scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_229[] = "RandomState.gamma (line 1721)";
+-static char __pyx_k_230[] = "\n gamma(shape, scale=1.0, size=None)\n\n Draw samples from a Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n \"theta\"), where both parameters are > 0.\n\n Parameters\n ----------\n shape : scalar > 0\n The shape of the gamma distribution.\n scale : scalar > 0, optional\n The scale of the gamma distribution. Default is equal to 1.\n size : shape_tuple, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray, float\n Returns one sample unless `size` parameter is specified.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2.,"" 2. # mean and dispersion\n >>> s = np.random.gamma(shape, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n ... (sps.gamma(shape)*scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_231[] = "RandomState.f (line 1812)";
+-static char __pyx_k_232[] = "\n f(dfnum, dfden, size=None)\n\n Draw samples from a F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n in denominator), where both parameters should be greater than zero.\n\n The random variate of the F distribution (also known as the\n Fisher distribution) is a continuous probability distribution\n that arises in ANOVA tests, and is the ratio of two chi-square\n variates.\n\n Parameters\n ----------\n dfnum : float\n Degrees of freedom in numerator. Should be greater than zero.\n dfden : float\n Degrees of freedom in denominator. Should be greater than zero.\n size : {tuple, int}, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``,\n then ``m * n * k`` samples are drawn. By default only one sample\n is returned.\n\n Returns\n -------\n samples : {ndarray, scalar}\n Samples from the Fisher distribution.\n\n See Also\n --------\n scipy.stats.distributions.f : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The F statistic is used to compare in-group variances to between-group\n variances. Calculating the distribution depends on the sampling, and\n so it is a function of the respective degrees of freedom in the\n problem. The variable `dfnum` is the number of samples minus one, the\n between-groups degrees of freedom, while `dfden` is the within-groups\n degrees of freedom, the sum of the number of samples in each group\n minus the number of groups.\n\n References\n ----------\n .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.""\n .. [2] Wikipedia, \"F-distribution\",\n http://en.wikipedia.org/wiki/F-distribution\n\n Examples\n --------\n An example from Glantz[1], pp 47-40.\n Two groups, children of diabetics (25 people) and children from people\n without diabetes (25 controls). Fasting blood glucose was measured,\n case group had a mean value of 86.1, controls had a mean value of\n 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n data consistent with the null hypothesis that the parents diabetic\n status does not affect their children's blood glucose levels?\n Calculating the F statistic from the data gives a value of 36.01.\n\n Draw samples from the distribution:\n\n >>> dfnum = 1. # between group degrees of freedom\n >>> dfden = 48. # within groups degrees of freedom\n >>> s = np.random.f(dfnum, dfden, 1000)\n\n The lower bound for the top 1% of the samples is :\n\n >>> sort(s)[-10]\n 7.61988120985\n\n So there is about a 1% chance that the F statistic will exceed 7.62,\n the measured value is 36, so the null hypothesis is rejected at the 1%\n level.\n\n ";
+-static char __pyx_k_233[] = "RandomState.noncentral_f (line 1914)";
+-static char __pyx_k_234[] = "\n noncentral_f(dfnum, dfden, nonc, size=None)\n\n Draw samples from the noncentral F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n freedom in denominator), where both parameters > 1.\n `nonc` is the non-centrality parameter.\n\n Parameters\n ----------\n dfnum : int\n Parameter, should be > 1.\n dfden : int\n Parameter, should be > 1.\n nonc : float\n Parameter, should be >= 0.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : scalar or ndarray\n Drawn samples.\n\n Notes\n -----\n When calculating the power of an experiment (power = probability of\n rejecting the null hypothesis when a specific alternative is true) the\n non-central F statistic becomes important. When the null hypothesis is\n true, the F statistic follows a central F distribution. When the null\n hypothesis is not true, then it follows a non-central F statistic.\n\n References\n ----------\n Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n Wikipedia, \"Noncentral F distribution\",\n http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n Examples\n --------\n In a study, testing for a specific alternative to the null hypothesis\n requires use of the Noncentral F distribution. We need to calculate the\n area in the tail of the distribution that exceeds the value of the F\n distribution for the null hypothesis. We'll plot the two probability\n distributions for comp""arison.\n\n >>> dfnum = 3 # between group deg of freedom\n >>> dfden = 20 # within groups degrees of freedom\n >>> nonc = 3.0\n >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n >>> F = np.histogram(c_vals, bins=50, normed=True)\n >>> plt.plot(F[1][1:], F[0])\n >>> plt.plot(NF[1][1:], NF[0])\n >>> plt.show()\n\n ";
+-static char __pyx_k_235[] = "RandomState.chisquare (line 2009)";
+-static char __pyx_k_236[] = "\n chisquare(df, size=None)\n\n Draw samples from a chi-square distribution.\n\n When `df` independent random variables, each with standard normal\n distributions (mean 0, variance 1), are squared and summed, the\n resulting distribution is chi-square (see Notes). This distribution\n is often used in hypothesis testing.\n\n Parameters\n ----------\n df : int\n Number of degrees of freedom.\n size : tuple of ints, int, optional\n Size of the returned array. By default, a scalar is\n returned.\n\n Returns\n -------\n output : ndarray\n Samples drawn from the distribution, packed in a `size`-shaped\n array.\n\n Raises\n ------\n ValueError\n When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n is given.\n\n Notes\n -----\n The variable obtained by summing the squares of `df` independent,\n standard normally distributed random variables:\n\n .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n is chi-square distributed, denoted\n\n .. math:: Q \\sim \\chi^2_k.\n\n The probability density function of the chi-squared distribution is\n\n .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n x^{k/2 - 1} e^{-x/2},\n\n where :math:`\\Gamma` is the gamma function,\n\n .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n References\n ----------\n `NIST/SEMATECH e-Handbook of Statistical Methods\n <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n Examples\n --------\n >>> np.random.chisquare(2,4)\n array([ 1.89920014, 9.00867716, 3.13710533, 5.62318272])\n\n ";
+-static char __pyx_k_237[] = "RandomState.noncentral_chisquare (line 2087)";
+-static char __pyx_k_238[] = "\n noncentral_chisquare(df, nonc, size=None)\n\n Draw samples from a noncentral chi-square distribution.\n\n The noncentral :math:`\\chi^2` distribution is a generalisation of\n the :math:`\\chi^2` distribution.\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be >= 1.\n nonc : float\n Non-centrality, should be > 0.\n size : int or tuple of ints\n Shape of the output.\n\n Notes\n -----\n The probability density function for the noncentral Chi-square distribution\n is\n\n .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n In Delhi (2007), it is noted that the noncentral chi-square is useful in\n bombing and coverage problems, the probability of killing the point target\n given by the noncentral chi-squared distribution.\n\n References\n ----------\n .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n analysis of weapon systems effectiveness\", Metrika, Volume 15,\n Number 1 / December, 1970.\n .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> import matplotlib.pyplot as plt\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n Draw values from a noncentral chisquare with very small noncentrality,\n and compare to a chisquare.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n "" ... bins=np.arange(0., 25, .1), normed=True)\n >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n ... bins=np.arange(0., 25, .1), normed=True)\n >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n >>> plt.show()\n\n Demonstrate how large values of non-centrality lead to a more symmetric\n distribution.\n\n >>> plt.figure()\n >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n ... bins=200, normed=True)\n >>> plt.show()\n\n ";
+-static char __pyx_k_239[] = "RandomState.standard_cauchy (line 2179)";
+-static char __pyx_k_240[] = "\n standard_cauchy(size=None)\n\n Standard Cauchy distribution with mode = 0.\n\n Also known as the Lorentz distribution.\n\n Parameters\n ----------\n size : int or tuple of ints\n Shape of the output.\n\n Returns\n -------\n samples : ndarray or scalar\n The drawn samples.\n\n Notes\n -----\n The probability density function for the full Cauchy distribution is\n\n .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n and the Standard Cauchy distribution just sets :math:`x_0=0` and\n :math:`\\gamma=1`\n\n The Cauchy distribution arises in the solution to the driven harmonic\n oscillator problem, and also describes spectral line broadening. It\n also describes the distribution of values at which a line tilted at\n a random angle will cut the x axis.\n\n When studying hypothesis tests that assume normality, seeing how the\n tests perform on data from a Cauchy distribution is a good indicator of\n their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n very much like a Gaussian distribution, but with heavier tails.\n\n References\n ----------\n .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n Distribution\",\n http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/CauchyDistribution.html\n .. [3] Wikipedia, \"Cauchy distribution\"\n http://en.wikipedia.org/wiki/Cauchy_distribution\n\n Examples\n --------\n Draw samples and plot the distribution:\n\n >>> s = np.random.standard_cauchy(1000000)\n >>> s = s[(s>-25) & (s<""25)] # truncate distribution so it plots well\n >>> plt.hist(s, bins=100)\n >>> plt.show()\n\n ";
+-static char __pyx_k_241[] = "RandomState.standard_t (line 2240)";
+-static char __pyx_k_242[] = "\n standard_t(df, size=None)\n\n Standard Student's t distribution with df degrees of freedom.\n\n A special case of the hyperbolic distribution.\n As `df` gets large, the result resembles that of the standard normal\n distribution (`standard_normal`).\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be > 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn samples.\n\n Notes\n -----\n The probability density function for the t distribution is\n\n .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n The t test is based on an assumption that the data come from a Normal\n distribution. The t test provides a way to test whether the sample mean\n (that is the mean calculated from the data) is a good estimate of the true\n mean.\n\n The derivation of the t-distribution was forst published in 1908 by William\n Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n issues, he had to publish under a pseudonym, and so he used the name\n Student.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n Springer, 2002.\n .. [2] Wikipedia, \"Student's t-distribution\"\n http://en.wikipedia.org/wiki/Student's_t-distribution\n\n Examples\n --------\n From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n women in Kj is:\n\n >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n ... 7515, 8230, 8770])\n\n Doe""s their energy intake deviate systematically from the recommended\n value of 7725 kJ?\n\n We have 10 degrees of freedom, so is the sample mean within 95% of the\n recommended value?\n\n >>> s = np.random.standard_t(10, size=100000)\n >>> np.mean(intake)\n 6753.636363636364\n >>> intake.std(ddof=1)\n 1142.1232221373727\n\n Calculate the t statistic, setting the ddof parameter to the unbiased\n value so the divisor in the standard deviation will be degrees of\n freedom, N-1.\n\n >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(s, bins=100, normed=True)\n\n For a one-sided t-test, how far out in the distribution does the t\n statistic appear?\n\n >>> >>> np.sum(s<t) / float(len(s))\n 0.0090699999999999999 #random\n\n So the p-value is about 0.009, which says the null hypothesis has a\n probability of about 99% of being true.\n\n ";
+-static char __pyx_k_243[] = "RandomState.vonmises (line 2341)";
+-static char __pyx_k_244[] = "\n vonmises(mu, kappa, size=None)\n\n Draw samples from a von Mises distribution.\n\n Samples are drawn from a von Mises distribution with specified mode\n (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n The von Mises distribution (also known as the circular normal\n distribution) is a continuous probability distribution on the unit\n circle. It may be thought of as the circular analogue of the normal\n distribution.\n\n Parameters\n ----------\n mu : float\n Mode (\"center\") of the distribution.\n kappa : float\n Dispersion of the distribution, has to be >=0.\n size : int or tuple of int\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples, which are in the interval [-pi, pi].\n\n See Also\n --------\n scipy.stats.distributions.vonmises : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the von Mises distribution is\n\n .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n The von Mises is named for Richard Edler von Mises, who was born in\n Austria-Hungary, in what is now the Ukraine. He fled to the United\n States in 1939 and became a professor at Harvard. He worked in\n probability theory, aerodynamics, fluid mechanics, and philosophy of\n science.\n\n References\n ----------\n Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n Functions*, New York: Dover, 1965.\n\n "" von Mises, R., *Mathematical Theory of Probability and Statistics*,\n New York: Academic Press, 1964.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n >>> s = np.random.vonmises(mu, kappa, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_245[] = "RandomState.pareto (line 2435)";
+-static char __pyx_k_246[] = "\n pareto(a, size=None)\n\n Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n classical Pareto distribution can be obtained from the Lomax distribution\n by adding the location parameter m, see below. The smallest value of the\n Lomax distribution is zero while for the classical Pareto distribution it\n is m, where the standard Pareto distribution has location m=1.\n Lomax can also be considered as a simplified version of the Generalized\n Pareto distribution (available in SciPy), with the scale set to one and\n the location set to zero.\n\n The Pareto distribution must be greater than zero, and is unbounded above.\n It is also known as the \"80-20 rule\". In this distribution, 80 percent of\n the weights are in the lowest 20 percent of the range, while the other 20\n percent fill the remaining 80 percent of the range.\n\n Parameters\n ----------\n shape : float, > 0.\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.lomax.pdf : probability density function,\n distribution or cumulative density function, etc.\n scipy.stats.distributions.genpareto.pdf : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Pareto distribution is\n\n .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n where :math:`a` is the shape and :math:`m` the location\n\n The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n is a power law probability distribution useful in many real world probl""ems.\n Outside the field of economics it is generally referred to as the Bradford\n distribution. Pareto developed the distribution to describe the\n distribution of wealth in an economy. It has also found use in insurance,\n web page access statistics, oil field sizes, and many other problems,\n including the download frequency for projects in Sourceforge [1]. It is\n one of the so-called \"fat-tailed\" distributions.\n\n\n References\n ----------\n .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n Sourceforge projects.\n .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 23-30.\n .. [4] Wikipedia, \"Pareto distribution\",\n http://en.wikipedia.org/wiki/Pareto_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a, m = 3., 1. # shape and mode\n >>> s = np.random.pareto(a, 1000) + m\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n >>> fit = a*m**a/bins**(a+1)\n >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_247[] = "RandomState.weibull (line 2531)";
+-static char __pyx_k_248[] = "\n weibull(a, size=None)\n\n Weibull distribution.\n\n Draw samples from a 1-parameter Weibull distribution with the given\n shape parameter `a`.\n\n .. math:: X = (-ln(U))^{1/a}\n\n Here, U is drawn from the uniform distribution over (0,1].\n\n The more common 2-parameter Weibull, including a scale parameter\n :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n Parameters\n ----------\n a : float\n Shape of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.distributions.weibull_max\n scipy.stats.distributions.weibull_min\n scipy.stats.distributions.genextreme\n gumbel\n\n Notes\n -----\n The Weibull (or Type III asymptotic extreme value distribution for smallest\n values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n Generalized Extreme Value (GEV) distributions used in modeling extreme\n value problems. This class includes the Gumbel and Frechet distributions.\n\n The probability density for the Weibull distribution is\n\n .. math:: p(x) = \\frac{a}\n {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n The function has its peak (the mode) at\n :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n When ``a = 1``, the Weibull distribution reduces to the exponential\n distribution.\n\n References\n ----------\n .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n 1939 \"A Statistical Theory Of The Strength Of Materials\",\n Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n General""stabens Litografiska Anstalts Forlag, Stockholm.\n .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n Applicability\", Journal Of Applied Mechanics ASME Paper.\n .. [3] Wikipedia, \"Weibull distribution\",\n http://en.wikipedia.org/wiki/Weibull_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> s = np.random.weibull(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> x = np.arange(1,100.)/50.\n >>> def weib(x,n,a):\n ... return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n >>> x = np.arange(1,100.)/50.\n >>> scale = count.max()/weib(x, 1., 5.).max()\n >>> plt.plot(x, weib(x, 1., 5.)*scale)\n >>> plt.show()\n\n ";
+-static char __pyx_k_249[] = "RandomState.power (line 2631)";
+-static char __pyx_k_250[] = "\n power(a, size=None)\n\n Draws samples in [0, 1] from a power distribution with positive\n exponent a - 1.\n\n Also known as the power function distribution.\n\n Parameters\n ----------\n a : float\n parameter, > 0\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n The returned samples lie in [0, 1].\n\n Raises\n ------\n ValueError\n If a<1.\n\n Notes\n -----\n The probability density function is\n\n .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n The power function distribution is just the inverse of the Pareto\n distribution. It may also be seen as a special case of the Beta\n distribution.\n\n It is used, for example, in modeling the over-reporting of insurance\n claims.\n\n References\n ----------\n .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n in economics and actuarial sciences\", Wiley, 2003.\n .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n Functions\", National Institute of Standards and Technology Handbook\n Series, June 2003.\n http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 5. # shape\n >>> samples = 1000\n >>> s = np.random.power(a, samples)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, bins=""30)\n >>> x = np.linspace(0, 1, 100)\n >>> y = a*x**(a-1.)\n >>> normed_y = samples*np.diff(bins)[0]*y\n >>> plt.plot(x, normed_y)\n >>> plt.show()\n\n Compare the power function distribution to the inverse of the Pareto.\n\n >>> from scipy import stats\n >>> rvs = np.random.power(5, 1000000)\n >>> rvsp = np.random.pareto(5, 1000000)\n >>> xx = np.linspace(0,1,100)\n >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n >>> plt.figure()\n >>> plt.hist(rvs, bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('np.random.power(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n >>> plt.figure()\n >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n >>> plt.plot(xx,powpdf,'r-')\n >>> plt.title('inverse of stats.pareto(5)')\n\n ";
+-static char __pyx_k_251[] = "RandomState.laplace (line 2740)";
+-static char __pyx_k_252[] = "\n laplace(loc=0.0, scale=1.0, size=None)\n\n Draw samples from the Laplace or double exponential distribution with\n specified location (or mean) and scale (decay).\n\n The Laplace distribution is similar to the Gaussian/normal distribution,\n but is sharper at the peak and has fatter tails. It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. [4] Wikipedia, \"Laplace distribution\",\n http://en.wikipedia.org/wik""i/Laplace_distribution\n\n Examples\n --------\n Draw samples from the distribution\n\n >>> loc, scale = 0., 1.\n >>> s = np.random.laplace(loc, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> x = np.arange(-8., 8., .01)\n >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n >>> plt.plot(x, pdf)\n\n Plot Gaussian for comparison:\n\n >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n ... np.exp( - (x - loc)**2 / (2 * scale**2) ))\n >>> plt.plot(x,g)\n\n ";
+-static char __pyx_k_253[] = "RandomState.gumbel (line 2830)";
+-static char __pyx_k_254[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n Gumbel distribution.\n\n Draw samples from a Gumbel distribution with specified location and scale.\n For more information on the Gumbel distribution, see Notes and References\n below.\n\n Parameters\n ----------\n loc : float\n The location of the mode of the distribution.\n scale : float\n The scale parameter of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray\n The samples\n\n See Also\n --------\n scipy.stats.gumbel_l\n scipy.stats.gumbel_r\n scipy.stats.genextreme\n probability density function, distribution, or cumulative density\n function, etc. for each of the above\n weibull\n\n Notes\n -----\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails.\n\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the ""probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n Press, 1958.\n\n Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n 2001.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, beta = 0, 0.1 # location and scale\n >>> s = np.random.gumbel(mu, beta, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n Show how an extreme value distribution can arise from a Gaussian process\n and compare to a Gaussian:\n\n >>> means = []\n >>> maxima = []\n >>> for i in range(0,1000) :\n ... a = np.random.normal(mu, beta, 1000)\n ... means.append(a.mean())\n ... maxima.append(a.max())\n >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n >>> mu = np.mean(maxima) - 0.57721*beta\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp(-np.exp(-(bins - mu)/beta)),\n ... linewidth=2, color='r')\n >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n ... * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n ... linewidth=2, color='g')\n >>> plt.show()\n\n ";
+-static char __pyx_k_255[] = "RandomState.logistic (line 2961)";
+-static char __pyx_k_256[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n ";
+-static char __pyx_k_257[] = "RandomState.lognormal (line 3049)";
+-static char __pyx_k_258[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean,\n standard deviation, and array shape. Note that the mean and standard\n deviation are not the values for the distribution itself, but of the\n underlying normal distribution it is derived from.\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, > 0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or float\n The desired samples. An array of the same shape as `size` if given,\n if `size` is None a float is returned.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed. The probability density function for the log-normal\n distribution is:\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n deviation of the normally distributed logarithm of the variable.\n A log-normal distribution results if a random variable is the *product*\n of a large number of independent, identically-distributed variables in\n the same way that a normal distribution results if the variable is the\n *sum* of a large number of independent, identically-distributed\n variables.\n\n Reference""s\n ----------\n Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n May, 2001. http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color='r')\n >>> plt.axis('tight')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n >>> plt.show()\n\n ";
+-static char __pyx_k_259[] = "RandomState.rayleigh (line 3170)";
+-static char __pyx_k_260[] = "\n rayleigh(scale=1.0, size=None)\n\n Draw samples from a Rayleigh distribution.\n\n The :math:`\\chi` and Weibull distributions are generalizations of the\n Rayleigh.\n\n Parameters\n ----------\n scale : scalar\n Scale, also equals the mode. Should be >= 0.\n size : int or tuple of ints, optional\n Shape of the output. Default is None, in which case a single\n value is returned.\n\n Notes\n -----\n The probability density function for the Rayleigh distribution is\n\n .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n The Rayleigh distribution arises if the wind speed and wind direction are\n both gaussian variables, then the vector wind velocity forms a Rayleigh\n distribution. The Rayleigh distribution is used to model the expected\n output from wind turbines.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n .. [2] Wikipedia, \"Rayleigh distribution\"\n http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram\n\n >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n Wave heights tend to follow a Rayleigh distribution. If the mean wave\n height is 1 meter, what fraction of waves are likely to be larger than 3\n meters?\n\n >>> meanvalue = 1\n >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n >>> s = np.random.rayleigh(modevalue, 1000000)\n\n The percentage of waves larger than 3 meters is:\n\n >>> 100.*sum(s>3)/1000000.\n 0.087300000000000003\n\n ";
+-static char __pyx_k_261[] = "RandomState.wald (line 3242)";
+-static char __pyx_k_262[] = "\n wald(mean, scale, size=None)\n\n Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n As the scale approaches infinity, the distribution becomes more like a\n Gaussian.\n\n Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n this is by no means universal.\n\n The Inverse Gaussian distribution was first studied in relationship to\n Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n because there is an inverse relationship between the time to cover a unit\n distance and distance covered in unit time.\n\n Parameters\n ----------\n mean : scalar\n Distribution mean, should be > 0.\n scale : scalar\n Scale parameter, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn sample, all greater than zero.\n\n Notes\n -----\n The probability density function for the Wald distribution is\n\n .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n As noted above the Inverse Gaussian distribution first arise from attempts\n to model Brownian Motion. It is also a competitor to the Weibull for use in\n reliability modeling and modeling stock returns and interest rate\n processes.\n\n References\n ----------\n .. [1] Brighton Webs Ltd., Wald Distribution,\n http://www.brighton-webs.co.uk/distributions/wald.asp\n .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n Distribution: Theory : Methodology, and Applications\", CRC Press,\n 1988.\n .. [3] Wikipedia, \"Wald distribu""tion\"\n http://en.wikipedia.org/wiki/Wald_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n >>> plt.show()\n\n ";
+-static char __pyx_k_263[] = "RandomState.triangular (line 3328)";
+-static char __pyx_k_264[] = "\n triangular(left, mode, right, size=None)\n\n Draw samples from the triangular distribution.\n\n The triangular distribution is a continuous probability distribution with\n lower limit left, peak at mode, and upper limit right. Unlike the other\n distributions, these parameters directly define the shape of the pdf.\n\n Parameters\n ----------\n left : scalar\n Lower limit.\n mode : scalar\n The value where the peak of the distribution occurs.\n The value should fulfill the condition ``left <= mode <= right``.\n right : scalar\n Upper limit, should be larger than `left`.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n The returned samples all lie in the interval [left, right].\n\n Notes\n -----\n The probability density function for the Triangular distribution is\n\n .. math:: P(x;l, m, r) = \\begin{cases}\n \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n 0& \\text{otherwise}.\n \\end{cases}\n\n The triangular distribution is often used in ill-defined problems where the\n underlying distribution is not known, but some knowledge of the limits and\n mode exists. Often it is used in simulations.\n\n References\n ----------\n .. [1] Wikipedia, \"Triangular distribution\"\n http://en.wikipedia.org/wiki/Triangular_distribution\n\n Examples\n --------\n Draw values from the distribution and plot the histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n ... normed=True)\n >>> plt.show()\n\n ";
+-static char __pyx_k_265[] = "RandomState.binomial (line 3416)";
+-static char __pyx_k_266[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer >= 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, >= 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.""\n .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n ";
+-static char __pyx_k_267[] = "RandomState.negative_binomial (line 3524)";
+-static char __pyx_k_268[] = "\n negative_binomial(n, p, size=None)\n\n Draw samples from a negative_binomial distribution.\n\n Samples are drawn from a negative_Binomial distribution with specified\n parameters, `n` trials and `p` probability of success where `n` is an\n integer > 0 and `p` is in the interval [0, 1].\n\n Parameters\n ----------\n n : int\n Parameter, > 0.\n p : float\n Parameter, >= 0 and <=1.\n size : int or tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. What is the probability\n of having one success for each successive well, that is what is the\n probability of a single success after drilling 5 wells, after 6 wells,\n etc.?\n\n >>> s = np.random.negative_binomial(1, 0.1, 100000)\n >>> for i in range(1, 11):\n ... probability = sum(s<i) / 100000.\n ... print i, \"wells drilled, probability of one success =\", probability\n\n ";
+-static char __pyx_k_269[] = "RandomState.poisson (line 3619)";
+-static char __pyx_k_270[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n ";
+-static char __pyx_k_271[] = "RandomState.zipf (line 3690)";
+-static char __pyx_k_272[] = "\n zipf(a, size=None)\n\n Draw samples from a Zipf distribution.\n\n Samples are drawn from a Zipf distribution with specified parameter\n `a` > 1.\n\n The Zipf distribution (also known as the zeta distribution) is a\n continuous probability distribution that satisfies Zipf's law: the\n frequency of an item is inversely proportional to its rank in a\n frequency table.\n\n Parameters\n ----------\n a : float > 1\n Distribution parameter.\n size : int or tuple of int, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = 2. # parameter\n >>> s = np.random.zipf""(a, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n Truncate s values at 50 so plot is interesting\n >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n >>> x = np.arange(1., 50.)\n >>> y = x**(-a)/sps.zetac(a)\n >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_273[] = "RandomState.geometric (line 3778)";
+-static char __pyx_k_274[] = "\n geometric(p, size=None)\n\n Draw samples from the geometric distribution.\n\n Bernoulli trials are experiments with one of two outcomes:\n success or failure (an example of such an experiment is flipping\n a coin). The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. The output\n is shaped according to `size`.\n\n Returns\n -------\n out : ndarray\n Samples from the geometric distribution, shaped according to\n `size`.\n\n Examples\n --------\n Draw ten thousand values from the geometric distribution,\n with the probability of an individual success equal to 0.35:\n\n >>> z = np.random.geometric(p=0.35, size=10000)\n\n How many trials succeeded after a single run?\n\n >>> (z == 1).sum() / 10000.\n 0.34889999999999999 #random\n\n ";
+-static char __pyx_k_275[] = "RandomState.hypergeometric (line 3844)";
+-static char __pyx_k_276[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : int or array_like\n Number of ways to make a good selection. Must be nonnegative.\n nbad : int or array_like\n Number of ways to make a bad selection. Must be nonnegative.\n nsample : int or array_like\n Number of items sampled. Must be at least 1 and at most\n ``ngood + nbad``.\n size : int or tuple of int\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : ndarray or scalar\n The values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distrib""ution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn with\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n ";
+-static char __pyx_k_277[] = "RandomState.logseries (line 3963)";
+-static char __pyx_k_278[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n ";
+-static char __pyx_k_279[] = "RandomState.multivariate_normal (line 4058)";
+-static char __pyx_k_280[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : int or tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, w""e draw N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n ";
+-static char __pyx_k_281[] = "RandomState.multinomial (line 4190)";
+-static char __pyx_k_282[] = "\n multinomial(n, pvals, size=None)\n\n Draw samples from a multinomial distribution.\n\n The multinomial distribution is a multivariate generalisation of the\n binomial distribution. Take an experiment with one of ``p``\n possible outcomes. An example of such an experiment is throwing a dice,\n where the outcome can be 1 through 6. Each sample drawn from the\n distribution represents `n` such experiments. Its values,\n ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n was ``i``.\n\n Parameters\n ----------\n n : int\n Number of experiments.\n pvals : sequence of floats, length p\n Probabilities of each of the ``p`` different outcomes. These\n should sum to 1 (however, the last element is always assumed to\n account for the remaining probability, as long as\n ``sum(pvals[:-1]) <= 1)``.\n size : tuple of ints\n Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n and the output shape becomes ``(M, N, K, p)``, since each sample\n has shape ``(p,)``.\n\n Examples\n --------\n Throw a dice 20 times:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=1)\n array([[4, 1, 7, 5, 2, 1]])\n\n It landed 4 times on 1, once on 2, etc.\n\n Now, throw the dice 20 times, and 20 times again:\n\n >>> np.random.multinomial(20, [1/6.]*6, size=2)\n array([[3, 4, 3, 3, 4, 3],\n [2, 4, 3, 4, 0, 7]])\n\n For the first run, we threw 3 times 1, 4 times 2, etc. For the second,\n we threw 2 times 1, 4 times 2, etc.\n\n A loaded dice is more likely to land on number 6:\n\n >>> np.random.multinomial(100, [1/7.]*5)\n array([13, 16, 13, 16, 42])\n\n ";
+-static char __pyx_k_283[] = "RandomState.dirichlet (line 4278)";
+-static char __pyx_k_284[] = "\n dirichlet(alpha, size=None)\n\n Draw samples from the Dirichlet distribution.\n\n Draw `size` samples of dimension k from a Dirichlet distribution. A\n Dirichlet-distributed random variable can be seen as a multivariate\n generalization of a Beta distribution. Dirichlet pdf is the conjugate\n prior of a multinomial in Bayesian inference.\n\n Parameters\n ----------\n alpha : array\n Parameter of the distribution (k dimension for sample of\n dimension k).\n size : array\n Number of samples to draw.\n\n Returns\n -------\n samples : ndarray,\n The drawn samples, of shape (alpha.ndim, size).\n\n Notes\n -----\n .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n Uses the following property for computation: for each dimension,\n draw a random sample y_i from a standard gamma generator of shape\n `alpha_i`, then\n :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n Dirichlet distributed.\n\n References\n ----------\n .. [1] David McKay, \"Information Theory, Inference and Learning\n Algorithms,\" chapter 23,\n http://www.inference.phy.cam.ac.uk/mackay/\n .. [2] Wikipedia, \"Dirichlet distribution\",\n http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n Examples\n --------\n Taking an example cited in Wikipedia, this distribution can be used if\n one wanted to cut strings (each of initial length 1.0) into K pieces\n with different lengths, where each piece had, on average, a designated\n average length, but allowing some variation in the relative sizes of the\n pieces.\n\n >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n >>> plt.barh(range(20), s[0])\n >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n >>> plt.title(\"Lengths of Strings\")\n\n ";
+-static char __pyx_k_285[] = "RandomState.shuffle (line 4389)";
+-static char __pyx_k_286[] = "\n shuffle(x)\n\n Modify a sequence in-place by shuffling its contents.\n\n Parameters\n ----------\n x : array_like\n The array or list to be shuffled.\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> arr = np.arange(10)\n >>> np.random.shuffle(arr)\n >>> arr\n [1 7 5 2 9 4 3 6 0 8]\n\n This function only shuffles the array along the first index of a\n multi-dimensional array:\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.shuffle(arr)\n >>> arr\n array([[3, 4, 5],\n [6, 7, 8],\n [0, 1, 2]])\n\n ";
+-static char __pyx_k_287[] = "RandomState.permutation (line 4448)";
+-static char __pyx_k_288[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n ";
+-static char __pyx_k__df[] = "df";
+-static char __pyx_k__mu[] = "mu";
+-static char __pyx_k__np[] = "np";
+-static char __pyx_k__add[] = "add";
+-static char __pyx_k__any[] = "any";
+-static char __pyx_k__cov[] = "cov";
+-static char __pyx_k__dot[] = "dot";
+-static char __pyx_k__int[] = "int";
+-static char __pyx_k__lam[] = "lam";
+-static char __pyx_k__loc[] = "loc";
+-static char __pyx_k__low[] = "low";
+-static char __pyx_k__max[] = "max";
+-static char __pyx_k__sum[] = "sum";
+-static char __pyx_k__svd[] = "svd";
+-static char __pyx_k__beta[] = "beta";
+-static char __pyx_k__copy[] = "copy";
+-static char __pyx_k__high[] = "high";
+-static char __pyx_k__intp[] = "intp";
+-static char __pyx_k__item[] = "item";
+-static char __pyx_k__left[] = "left";
+-static char __pyx_k__less[] = "less";
+-static char __pyx_k__mean[] = "mean";
+-static char __pyx_k__mode[] = "mode";
+-static char __pyx_k__nbad[] = "nbad";
+-static char __pyx_k__ndim[] = "ndim";
+-static char __pyx_k__nonc[] = "nonc";
+-static char __pyx_k__prod[] = "prod";
+-static char __pyx_k__rand[] = "rand";
+-static char __pyx_k__seed[] = "seed";
+-static char __pyx_k__side[] = "side";
+-static char __pyx_k__size[] = "size";
+-static char __pyx_k__sort[] = "sort";
+-static char __pyx_k__sqrt[] = "sqrt";
+-static char __pyx_k__take[] = "take";
+-static char __pyx_k__uint[] = "uint";
+-static char __pyx_k__wald[] = "wald";
+-static char __pyx_k__zipf[] = "zipf";
+-static char __pyx_k___rand[] = "_rand";
+-static char __pyx_k__alpha[] = "alpha";
+-static char __pyx_k__array[] = "array";
+-static char __pyx_k__bytes[] = "bytes";
+-static char __pyx_k__dfden[] = "dfden";
+-static char __pyx_k__dfnum[] = "dfnum";
+-static char __pyx_k__dtype[] = "dtype";
+-static char __pyx_k__empty[] = "empty";
+-static char __pyx_k__equal[] = "equal";
+-static char __pyx_k__gamma[] = "gamma";
+-static char __pyx_k__iinfo[] = "iinfo";
+-static char __pyx_k__index[] = "index";
+-static char __pyx_k__kappa[] = "kappa";
+-static char __pyx_k__ndmin[] = "ndmin";
+-static char __pyx_k__ngood[] = "ngood";
+-static char __pyx_k__numpy[] = "numpy";
+-static char __pyx_k__power[] = "power";
+-static char __pyx_k__pvals[] = "pvals";
+-static char __pyx_k__randn[] = "randn";
+-static char __pyx_k__ravel[] = "ravel";
+-static char __pyx_k__right[] = "right";
+-static char __pyx_k__scale[] = "scale";
+-static char __pyx_k__shape[] = "shape";
+-static char __pyx_k__sigma[] = "sigma";
+-static char __pyx_k__zeros[] = "zeros";
+-static char __pyx_k__arange[] = "arange";
+-static char __pyx_k__choice[] = "choice";
+-static char __pyx_k__cumsum[] = "cumsum";
+-static char __pyx_k__double[] = "double";
+-static char __pyx_k__fields[] = "fields";
+-static char __pyx_k__gumbel[] = "gumbel";
+-static char __pyx_k__mtrand[] = "mtrand";
+-static char __pyx_k__normal[] = "normal";
+-static char __pyx_k__pareto[] = "pareto";
+-static char __pyx_k__random[] = "random";
+-static char __pyx_k__reduce[] = "reduce";
+-static char __pyx_k__uint32[] = "uint32";
+-static char __pyx_k__unique[] = "unique";
+-static char __pyx_k__MT19937[] = "MT19937";
+-static char __pyx_k__asarray[] = "asarray";
+-static char __pyx_k__float64[] = "float64";
+-static char __pyx_k__greater[] = "greater";
+-static char __pyx_k__integer[] = "integer";
+-static char __pyx_k__laplace[] = "laplace";
+-static char __pyx_k__ndarray[] = "ndarray";
+-static char __pyx_k__nsample[] = "nsample";
+-static char __pyx_k__poisson[] = "poisson";
+-static char __pyx_k__randint[] = "randint";
+-static char __pyx_k__replace[] = "replace";
+-static char __pyx_k__shuffle[] = "shuffle";
+-static char __pyx_k__uniform[] = "uniform";
+-static char __pyx_k__weibull[] = "weibull";
+-static char __pyx_k____main__[] = "__main__";
+-static char __pyx_k____test__[] = "__test__";
+-static char __pyx_k__allclose[] = "allclose";
+-static char __pyx_k__binomial[] = "binomial";
+-static char __pyx_k__logistic[] = "logistic";
+-static char __pyx_k__multiply[] = "multiply";
+-static char __pyx_k__operator[] = "operator";
+-static char __pyx_k__rayleigh[] = "rayleigh";
+-static char __pyx_k__subtract[] = "subtract";
+-static char __pyx_k__vonmises[] = "vonmises";
+-static char __pyx_k__TypeError[] = "TypeError";
+-static char __pyx_k__chisquare[] = "chisquare";
+-static char __pyx_k__dirichlet[] = "dirichlet";
+-static char __pyx_k__geometric[] = "geometric";
+-static char __pyx_k__get_state[] = "get_state";
+-static char __pyx_k__lognormal[] = "lognormal";
+-static char __pyx_k__logseries[] = "logseries";
+-static char __pyx_k__set_state[] = "set_state";
+-static char __pyx_k__ValueError[] = "ValueError";
+-static char __pyx_k____import__[] = "__import__";
+-static char __pyx_k__empty_like[] = "empty_like";
+-static char __pyx_k__less_equal[] = "less_equal";
+-static char __pyx_k__standard_t[] = "standard_t";
+-static char __pyx_k__triangular[] = "triangular";
+-static char __pyx_k__exponential[] = "exponential";
+-static char __pyx_k__multinomial[] = "multinomial";
+-static char __pyx_k__permutation[] = "permutation";
+-static char __pyx_k__noncentral_f[] = "noncentral_f";
+-static char __pyx_k__return_index[] = "return_index";
+-static char __pyx_k__searchsorted[] = "searchsorted";
+-static char __pyx_k__greater_equal[] = "greater_equal";
+-static char __pyx_k__random_sample[] = "random_sample";
+-static char __pyx_k__hypergeometric[] = "hypergeometric";
+-static char __pyx_k__standard_gamma[] = "standard_gamma";
+-static char __pyx_k__poisson_lam_max[] = "poisson_lam_max";
+-static char __pyx_k__random_integers[] = "random_integers";
+-static char __pyx_k__standard_cauchy[] = "standard_cauchy";
+-static char __pyx_k__standard_normal[] = "standard_normal";
+-static char __pyx_k___shape_from_size[] = "_shape_from_size";
+-static char __pyx_k__negative_binomial[] = "negative_binomial";
+-static char __pyx_k____RandomState_ctor[] = "__RandomState_ctor";
+-static char __pyx_k__multivariate_normal[] = "multivariate_normal";
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+-static PyObject *__pyx_kp_s_82;
+-static PyObject *__pyx_kp_s_84;
+-static PyObject *__pyx_kp_s_89;
+-static PyObject *__pyx_kp_s_9;
+-static PyObject *__pyx_n_s__MT19937;
+-static PyObject *__pyx_n_s__TypeError;
+-static PyObject *__pyx_n_s__ValueError;
+-static PyObject *__pyx_n_s____RandomState_ctor;
+-static PyObject *__pyx_n_s____import__;
+-static PyObject *__pyx_n_s____main__;
+-static PyObject *__pyx_n_s____test__;
+-static PyObject *__pyx_n_s___rand;
+-static PyObject *__pyx_n_s___shape_from_size;
+-static PyObject *__pyx_n_s__a;
+-static PyObject *__pyx_n_s__add;
+-static PyObject *__pyx_n_s__allclose;
+-static PyObject *__pyx_n_s__alpha;
+-static PyObject *__pyx_n_s__any;
+-static PyObject *__pyx_n_s__arange;
+-static PyObject *__pyx_n_s__array;
+-static PyObject *__pyx_n_s__asarray;
+-static PyObject *__pyx_n_s__b;
+-static PyObject *__pyx_n_s__beta;
+-static PyObject *__pyx_n_s__binomial;
+-static PyObject *__pyx_n_s__bytes;
+-static PyObject *__pyx_n_s__chisquare;
+-static PyObject *__pyx_n_s__choice;
+-static PyObject *__pyx_n_s__copy;
+-static PyObject *__pyx_n_s__cov;
+-static PyObject *__pyx_n_s__cumsum;
+-static PyObject *__pyx_n_s__d;
+-static PyObject *__pyx_n_s__df;
+-static PyObject *__pyx_n_s__dfden;
+-static PyObject *__pyx_n_s__dfnum;
+-static PyObject *__pyx_n_s__dirichlet;
+-static PyObject *__pyx_n_s__dot;
+-static PyObject *__pyx_n_s__double;
+-static PyObject *__pyx_n_s__dtype;
+-static PyObject *__pyx_n_s__empty;
+-static PyObject *__pyx_n_s__empty_like;
+-static PyObject *__pyx_n_s__equal;
+-static PyObject *__pyx_n_s__exponential;
+-static PyObject *__pyx_n_s__f;
+-static PyObject *__pyx_n_s__fields;
+-static PyObject *__pyx_n_s__float64;
+-static PyObject *__pyx_n_s__gamma;
+-static PyObject *__pyx_n_s__geometric;
+-static PyObject *__pyx_n_s__get_state;
+-static PyObject *__pyx_n_s__greater;
+-static PyObject *__pyx_n_s__greater_equal;
+-static PyObject *__pyx_n_s__gumbel;
+-static PyObject *__pyx_n_s__high;
+-static PyObject *__pyx_n_s__hypergeometric;
+-static PyObject *__pyx_n_s__iinfo;
+-static PyObject *__pyx_n_s__index;
+-static PyObject *__pyx_n_s__int;
+-static PyObject *__pyx_n_s__integer;
+-static PyObject *__pyx_n_s__intp;
+-static PyObject *__pyx_n_s__item;
+-static PyObject *__pyx_n_s__kappa;
+-static PyObject *__pyx_n_s__l;
+-static PyObject *__pyx_n_s__lam;
+-static PyObject *__pyx_n_s__laplace;
+-static PyObject *__pyx_n_s__left;
+-static PyObject *__pyx_n_s__less;
+-static PyObject *__pyx_n_s__less_equal;
+-static PyObject *__pyx_n_s__loc;
+-static PyObject *__pyx_n_s__logistic;
+-static PyObject *__pyx_n_s__lognormal;
+-static PyObject *__pyx_n_s__logseries;
+-static PyObject *__pyx_n_s__low;
+-static PyObject *__pyx_n_s__max;
+-static PyObject *__pyx_n_s__mean;
+-static PyObject *__pyx_n_s__mode;
+-static PyObject *__pyx_n_s__mtrand;
+-static PyObject *__pyx_n_s__mu;
+-static PyObject *__pyx_n_s__multinomial;
+-static PyObject *__pyx_n_s__multiply;
+-static PyObject *__pyx_n_s__multivariate_normal;
+-static PyObject *__pyx_n_s__n;
+-static PyObject *__pyx_n_s__nbad;
+-static PyObject *__pyx_n_s__ndarray;
+-static PyObject *__pyx_n_s__ndim;
+-static PyObject *__pyx_n_s__ndmin;
+-static PyObject *__pyx_n_s__negative_binomial;
+-static PyObject *__pyx_n_s__ngood;
+-static PyObject *__pyx_n_s__nonc;
+-static PyObject *__pyx_n_s__noncentral_f;
+-static PyObject *__pyx_n_s__normal;
+-static PyObject *__pyx_n_s__np;
+-static PyObject *__pyx_n_s__nsample;
+-static PyObject *__pyx_n_s__numpy;
+-static PyObject *__pyx_n_s__operator;
+-static PyObject *__pyx_n_s__p;
+-static PyObject *__pyx_n_s__pareto;
+-static PyObject *__pyx_n_s__permutation;
+-static PyObject *__pyx_n_s__poisson;
+-static PyObject *__pyx_n_s__poisson_lam_max;
+-static PyObject *__pyx_n_s__power;
+-static PyObject *__pyx_n_s__prod;
+-static PyObject *__pyx_n_s__pvals;
+-static PyObject *__pyx_n_s__rand;
+-static PyObject *__pyx_n_s__randint;
+-static PyObject *__pyx_n_s__randn;
+-static PyObject *__pyx_n_s__random;
+-static PyObject *__pyx_n_s__random_integers;
+-static PyObject *__pyx_n_s__random_sample;
+-static PyObject *__pyx_n_s__ravel;
+-static PyObject *__pyx_n_s__rayleigh;
+-static PyObject *__pyx_n_s__reduce;
+-static PyObject *__pyx_n_s__replace;
+-static PyObject *__pyx_n_s__return_index;
+-static PyObject *__pyx_n_s__right;
+-static PyObject *__pyx_n_s__scale;
+-static PyObject *__pyx_n_s__searchsorted;
+-static PyObject *__pyx_n_s__seed;
+-static PyObject *__pyx_n_s__set_state;
+-static PyObject *__pyx_n_s__shape;
+-static PyObject *__pyx_n_s__shuffle;
+-static PyObject *__pyx_n_s__side;
+-static PyObject *__pyx_n_s__sigma;
+-static PyObject *__pyx_n_s__size;
+-static PyObject *__pyx_n_s__sort;
+-static PyObject *__pyx_n_s__sqrt;
+-static PyObject *__pyx_n_s__standard_cauchy;
+-static PyObject *__pyx_n_s__standard_gamma;
+-static PyObject *__pyx_n_s__standard_normal;
+-static PyObject *__pyx_n_s__standard_t;
+-static PyObject *__pyx_n_s__subtract;
+-static PyObject *__pyx_n_s__sum;
+-static PyObject *__pyx_n_s__svd;
+-static PyObject *__pyx_n_s__take;
+-static PyObject *__pyx_n_s__triangular;
+-static PyObject *__pyx_n_s__uint;
+-static PyObject *__pyx_n_s__uint32;
+-static PyObject *__pyx_n_s__uniform;
+-static PyObject *__pyx_n_s__unique;
+-static PyObject *__pyx_n_s__vonmises;
+-static PyObject *__pyx_n_s__wald;
+-static PyObject *__pyx_n_s__weibull;
+-static PyObject *__pyx_n_s__zeros;
+-static PyObject *__pyx_n_s__zipf;
++static PyObject *__pyx_float_0_0;
++static PyObject *__pyx_float_1_0;
+ static PyObject *__pyx_int_0;
+ static PyObject *__pyx_int_1;
+ static PyObject *__pyx_int_3;
+ static PyObject *__pyx_int_5;
+ static PyObject *__pyx_int_10;
+ static PyObject *__pyx_int_624;
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+-static PyObject *__pyx_k_40;
+-static PyObject *__pyx_k_41;
+-static PyObject *__pyx_k_42;
+-static PyObject *__pyx_k_43;
+-static PyObject *__pyx_k_53;
+-static PyObject *__pyx_k_59;
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+-static PyObject *__pyx_k_99;
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+-static PyObject *__pyx_k_103;
+-static PyObject *__pyx_k_106;
+-static PyObject *__pyx_k_107;
+-static PyObject *__pyx_k_110;
+-static PyObject *__pyx_k_111;
+-static PyObject *__pyx_k_116;
+-static PyObject *__pyx_k_151;
+-static PyObject *__pyx_k_tuple_2;
+-static PyObject *__pyx_k_tuple_3;
+-static PyObject *__pyx_k_tuple_4;
+-static PyObject *__pyx_k_tuple_5;
+-static PyObject *__pyx_k_tuple_6;
+-static PyObject *__pyx_k_tuple_7;
+-static PyObject *__pyx_k_tuple_8;
+-static PyObject *__pyx_k_slice_11;
+-static PyObject *__pyx_k_slice_12;
+-static PyObject *__pyx_k_tuple_10;
+-static PyObject *__pyx_k_tuple_14;
+-static PyObject *__pyx_k_tuple_16;
+-static PyObject *__pyx_k_tuple_19;
+-static PyObject *__pyx_k_tuple_21;
+-static PyObject *__pyx_k_tuple_23;
+-static PyObject *__pyx_k_tuple_25;
+-static PyObject *__pyx_k_tuple_27;
+-static PyObject *__pyx_k_tuple_29;
+-static PyObject *__pyx_k_tuple_31;
+-static PyObject *__pyx_k_tuple_33;
+-static PyObject *__pyx_k_tuple_35;
+-static PyObject *__pyx_k_tuple_37;
+-static PyObject *__pyx_k_tuple_38;
+-static PyObject *__pyx_k_tuple_39;
+-static PyObject *__pyx_k_tuple_45;
+-static PyObject *__pyx_k_tuple_46;
+-static PyObject *__pyx_k_tuple_48;
+-static PyObject *__pyx_k_tuple_50;
+-static PyObject *__pyx_k_tuple_51;
+-static PyObject *__pyx_k_tuple_52;
+-static PyObject *__pyx_k_tuple_54;
+-static PyObject *__pyx_k_tuple_55;
+-static PyObject *__pyx_k_tuple_57;
+-static PyObject *__pyx_k_tuple_58;
+-static PyObject *__pyx_k_tuple_60;
+-static PyObject *__pyx_k_tuple_61;
+-static PyObject *__pyx_k_tuple_62;
+-static PyObject *__pyx_k_tuple_63;
+-static PyObject *__pyx_k_tuple_64;
+-static PyObject *__pyx_k_tuple_65;
+-static PyObject *__pyx_k_tuple_67;
+-static PyObject *__pyx_k_tuple_69;
+-static PyObject *__pyx_k_tuple_71;
+-static PyObject *__pyx_k_tuple_72;
+-static PyObject *__pyx_k_tuple_74;
+-static PyObject *__pyx_k_tuple_75;
+-static PyObject *__pyx_k_tuple_76;
+-static PyObject *__pyx_k_tuple_77;
+-static PyObject *__pyx_k_tuple_79;
+-static PyObject *__pyx_k_tuple_80;
+-static PyObject *__pyx_k_tuple_81;
+-static PyObject *__pyx_k_tuple_83;
+-static PyObject *__pyx_k_tuple_85;
+-static PyObject *__pyx_k_tuple_86;
+-static PyObject *__pyx_k_tuple_87;
+-static PyObject *__pyx_k_tuple_88;
+-static PyObject *__pyx_k_tuple_90;
+-static PyObject *__pyx_k_tuple_91;
+-static PyObject *__pyx_k_tuple_92;
+-static PyObject *__pyx_k_tuple_93;
+-static PyObject *__pyx_k_tuple_94;
+-static PyObject *__pyx_k_tuple_95;
+-static PyObject *__pyx_k_tuple_96;
+-static PyObject *__pyx_k_tuple_97;
+-static PyObject *__pyx_k_slice_192;
+-static PyObject *__pyx_k_tuple_100;
+-static PyObject *__pyx_k_tuple_101;
+-static PyObject *__pyx_k_tuple_104;
+-static PyObject *__pyx_k_tuple_105;
+-static PyObject *__pyx_k_tuple_108;
+-static PyObject *__pyx_k_tuple_109;
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+-static PyObject *__pyx_k_tuple_155;
+-static PyObject *__pyx_k_tuple_156;
+-static PyObject *__pyx_k_tuple_158;
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+-static PyObject *__pyx_k_tuple_161;
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+-static PyObject *__pyx_k_tuple_166;
+-static PyObject *__pyx_k_tuple_167;
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+-static PyObject *__pyx_k_tuple_171;
+-static PyObject *__pyx_k_tuple_173;
+-static PyObject *__pyx_k_tuple_175;
+-static PyObject *__pyx_k_tuple_176;
+-static PyObject *__pyx_k_tuple_177;
+-static PyObject *__pyx_k_tuple_178;
+-static PyObject *__pyx_k_tuple_179;
+-static PyObject *__pyx_k_tuple_181;
+-static PyObject *__pyx_k_tuple_183;
+-static PyObject *__pyx_k_tuple_184;
+-static PyObject *__pyx_k_tuple_185;
+-static PyObject *__pyx_k_tuple_187;
+-static PyObject *__pyx_k_tuple_189;
+-static PyObject *__pyx_k_tuple_191;
+-static PyObject *__pyx_k_tuple_195;
+-static PyObject *__pyx_k_tuple_196;
+-static PyObject *__pyx_k_tuple_199;
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================================================================
---- gitweb:
http://git.pld-linux.org/gitweb.cgi/packages/python-numpy-compat.git/commitdiff/026ffe0968f41286df3f4311624734cd9a540642
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