/* Generated by Cython 0.20.2 (Debian 0.20.2-1) on Mon Dec 14 19:51:22 2015 */

#define PY_SSIZE_T_CLEAN
#ifndef CYTHON_USE_PYLONG_INTERNALS
#ifdef PYLONG_BITS_IN_DIGIT
#define CYTHON_USE_PYLONG_INTERNALS 0
#else
#include "pyconfig.h"
#ifdef PYLONG_BITS_IN_DIGIT
#define CYTHON_USE_PYLONG_INTERNALS 1
#else
#define CYTHON_USE_PYLONG_INTERNALS 0
#endif
#endif
#endif
#include "Python.h"
#ifndef Py_PYTHON_H
    #error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02040000
    #error Cython requires Python 2.4+.
#else
#define CYTHON_ABI "0_20_2"
#include <stddef.h> /* For offsetof */
#ifndef offsetof
#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
#endif
#if !defined(WIN32) && !defined(MS_WINDOWS)
  #ifndef __stdcall
    #define __stdcall
  #endif
  #ifndef __cdecl
    #define __cdecl
  #endif
  #ifndef __fastcall
    #define __fastcall
  #endif
#endif
#ifndef DL_IMPORT
  #define DL_IMPORT(t) t
#endif
#ifndef DL_EXPORT
  #define DL_EXPORT(t) t
#endif
#ifndef PY_LONG_LONG
  #define PY_LONG_LONG LONG_LONG
#endif
#ifndef Py_HUGE_VAL
  #define Py_HUGE_VAL HUGE_VAL
#endif
#ifdef PYPY_VERSION
#define CYTHON_COMPILING_IN_PYPY 1
#define CYTHON_COMPILING_IN_CPYTHON 0
#else
#define CYTHON_COMPILING_IN_PYPY 0
#define CYTHON_COMPILING_IN_CPYTHON 1
#endif
#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600
#define Py_OptimizeFlag 0
#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_As_int(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 *);
#endif
#if PY_MAJOR_VERSION < 3
  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
          PyCode_New(a+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) \
          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_VERSION_HEX < 0x02060000
  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
#endif
#if PY_MAJOR_VERSION >= 3
  #define Py_TPFLAGS_CHECKTYPES 0
  #define Py_TPFLAGS_HAVE_INDEX 0
#endif
#if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
  #define Py_TPFLAGS_HAVE_NEWBUFFER 0
#endif
#if PY_VERSION_HEX < 0x02060000
  #define Py_TPFLAGS_HAVE_VERSION_TAG 0
#endif
#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
  #define Py_TPFLAGS_IS_ABSTRACT 0
#endif
#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
  #define Py_TPFLAGS_HAVE_FINALIZE 0
#endif
#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
  #define CYTHON_PEP393_ENABLED 1
  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
                                              0 : _PyUnicode_Ready((PyObject *)(op)))
  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
  #define __Pyx_PyUnicode_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)
#else
  #define CYTHON_PEP393_ENABLED 0
  #define __Pyx_PyUnicode_READY(op)       (0)
  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
  #define __Pyx_PyUnicode_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]))
#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
#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
  #define PyBaseString_Type            PyUnicode_Type
  #define PyStringObject               PyUnicodeObject
  #define PyString_Type                PyUnicode_Type
  #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) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
#endif
#if PY_VERSION_HEX < 0x02060000
  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
#endif
#ifndef PySet_CheckExact
  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
#endif
#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
#if PY_MAJOR_VERSION >= 3
  #define PyIntObject                  PyLongObject
  #define PyInt_Type                   PyLong_Type
  #define PyInt_Check(op)              PyLong_Check(op)
  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
  #define PyInt_FromString             PyLong_FromString
  #define PyInt_FromUnicode            PyLong_FromUnicode
  #define PyInt_FromLong               PyLong_FromLong
  #define PyInt_FromSize_t             PyLong_FromSize_t
  #define PyInt_FromSsize_t            PyLong_FromSsize_t
  #define PyInt_AsLong                 PyLong_AsLong
  #define PyInt_AS_LONG                PyLong_AS_LONG
  #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 < 0x030200A4
  typedef long Py_hash_t;
  #define __Pyx_PyInt_FromHash_t PyInt_FromLong
  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong
#else
  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
#endif
#if (PY_MAJOR_VERSION < 3) || (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))
#endif
#if PY_VERSION_HEX < 0x02050000
  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),((char *)(n)))
#else
  #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),(n))
  #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
  #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
#endif
#if PY_VERSION_HEX < 0x02050000
  #define __Pyx_NAMESTR(n) ((char *)(n))
  #define __Pyx_DOCSTR(n)  ((char *)(n))
#else
  #define __Pyx_NAMESTR(n) (n)
  #define __Pyx_DOCSTR(n)  (n)
#endif
#ifndef CYTHON_INLINE
  #if defined(__GNUC__)
    #define CYTHON_INLINE __inline__
  #elif defined(_MSC_VER)
    #define CYTHON_INLINE __inline
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_INLINE inline
  #else
    #define CYTHON_INLINE
  #endif
#endif
#ifndef CYTHON_RESTRICT
  #if defined(__GNUC__)
    #define CYTHON_RESTRICT __restrict__
  #elif defined(_MSC_VER) && _MSC_VER >= 1400
    #define CYTHON_RESTRICT __restrict
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_RESTRICT restrict
  #else
    #define CYTHON_RESTRICT
  #endif
#endif
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
#else
static CYTHON_INLINE float __PYX_NAN() {
  /* Initialize NaN. The sign is irrelevant, an exponent with all bits 1 and
   a nonzero mantissa means NaN. If the first bit in the mantissa is 1, it is
   a quiet NaN. */
  float value;
  memset(&value, 0xFF, sizeof(value));
  return value;
}
#endif
#ifdef __cplusplus
template<typename T>
void __Pyx_call_destructor(T* x) {
    x->~T();
}
#endif


#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
#else
  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
#endif

#ifndef __PYX_EXTERN_C
  #ifdef __cplusplus
    #define __PYX_EXTERN_C extern "C"
  #else
    #define __PYX_EXTERN_C extern
  #endif
#endif

#if defined(WIN32) || defined(MS_WINDOWS)
#define _USE_MATH_DEFINES
#endif
#include <math.h>
#define __PYX_HAVE__mtrand
#define __PYX_HAVE_API__mtrand
#include "string.h"
#include "math.h"
#include "numpy/npy_no_deprecated_api.h"
#include "numpy/arrayobject.h"
#include "numpy/npy_math.h"
#include "mtrand_py_helper.h"
#include "randomkit.h"
#include "distributions.h"
#include "initarray.h"
#ifdef _OPENMP
#include <omp.h>
#endif /* _OPENMP */

#ifdef PYREX_WITHOUT_ASSERTIONS
#define CYTHON_WITHOUT_ASSERTIONS
#endif

#ifndef CYTHON_UNUSED
# if defined(__GNUC__)
#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
#     define CYTHON_UNUSED __attribute__ ((__unused__))
#   else
#     define CYTHON_UNUSED
#   endif
# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
#   define CYTHON_UNUSED __attribute__ ((__unused__))
# else
#   define CYTHON_UNUSED
# endif
#endif
typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/

#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
#define __PYX_DEFAULT_STRING_ENCODING ""
#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#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)))  )
static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
static CYTHON_INLINE 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(const char*);
#if PY_MAJOR_VERSION < 3
    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#else
    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
#endif
#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
#define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((const char*)s)
#if PY_MAJOR_VERSION < 3
static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u)
{
    const Py_UNICODE *u_end = u;
    while (*u_end++) ;
    return (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))
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
#if CYTHON_COMPILING_IN_CPYTHON
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(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) goto bad;
    default_encoding = PyObject_CallMethod(sys, (char*) (const 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 {
        char ascii_chars[128];
        int c;
        for (c = 0; c < 128; c++) {
            ascii_chars[c] = c;
        }
        __Pyx_sys_getdefaultencoding_not_ascii = 1;
        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);
        if (!ascii_chars_u) goto bad;
        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);
        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 '%.200s' is not a superset of ascii.",
                default_encoding_c);
            goto bad;
        }
        Py_DECREF(ascii_chars_u);
        Py_DECREF(ascii_chars_b);
    }
    Py_DECREF(default_encoding);
    return 0;
bad:
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/* 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)
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static PyObject *__pyx_m;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;


static const char *__pyx_f[] = {
  "mtrand.pyx",
  "numpy.pxd",
};

/*--- Type declarations ---*/
struct __pyx_obj_6mtrand_RandomState;

/* "mtrand.pyx":111
 *     long rk_logseries(rk_state *state, double p) nogil
 * 
 * ctypedef double (* rk_cont0)(rk_state *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* rk_cont1)(rk_state *state, double a) nogil
 * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) nogil
 */
typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *);

/* "mtrand.pyx":112
 * 
 * ctypedef double (* rk_cont0)(rk_state *state) nogil
 * ctypedef double (* rk_cont1)(rk_state *state, double a) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) nogil
 * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) nogil
 */
typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *, double);

/* "mtrand.pyx":113
 * ctypedef double (* rk_cont0)(rk_state *state) nogil
 * ctypedef double (* rk_cont1)(rk_state *state, double a) nogil
 * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) nogil             # <<<<<<<<<<<<<<
 * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) nogil
 * 
 */
typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double);

/* "mtrand.pyx":114
 * ctypedef double (* rk_cont1)(rk_state *state, double a) nogil
 * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) nogil
 * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) nogil             # <<<<<<<<<<<<<<
 * 
 * ctypedef long (* rk_disc0)(rk_state *state) nogil
 */
typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double);

/* "mtrand.pyx":116
 * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) nogil
 * 
 * ctypedef long (* rk_disc0)(rk_state *state) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) nogil
 * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) nogil
 */
typedef long (*__pyx_t_6mtrand_rk_disc0)(rk_state *);

/* "mtrand.pyx":117
 * 
 * ctypedef long (* rk_disc0)(rk_state *state) nogil
 * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) nogil
 * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) nogil
 */
typedef long (*__pyx_t_6mtrand_rk_discnp)(rk_state *, long, double);

/* "mtrand.pyx":118
 * ctypedef long (* rk_disc0)(rk_state *state) nogil
 * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) nogil
 * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) nogil
 * ctypedef long (* rk_discd)(rk_state *state, double a) nogil
 */
typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *, double, double);

/* "mtrand.pyx":119
 * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) nogil
 * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) nogil
 * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) nogil             # <<<<<<<<<<<<<<
 * ctypedef long (* rk_discd)(rk_state *state, double a) nogil
 * 
 */
typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long);

/* "mtrand.pyx":120
 * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) nogil
 * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) nogil
 * ctypedef long (* rk_discd)(rk_state *state, double a) nogil             # <<<<<<<<<<<<<<
 * 
 * 
 */
typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double);

/* "mtrand.pyx":577
 *     return shape
 * 
 * cdef class RandomState:             # <<<<<<<<<<<<<<
 *     """
 *     RandomState(seed=None)
 */
struct __pyx_obj_6mtrand_RandomState {
  PyObject_HEAD
  rk_state *internal_state;
  PyObject *lock;
};

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static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
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static PyObject *__Pyx_GetBuiltinName(PyObject *name); /*proto*/

static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/

#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); /*proto*/
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#if CYTHON_COMPILING_IN_CPYTHON && (PY_VERSION_HEX >= 0x03020000 || PY_MAJOR_VERSION < 3 && PY_VERSION_HEX >= 0x02070000)
static CYTHON_INLINE PyObject* __Pyx_PyObject_LookupSpecial(PyObject* obj, PyObject* attr_name) {
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#if PY_MAJOR_VERSION < 3
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static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/

static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/

static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
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static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/

static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
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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 int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/

static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/

#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
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static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
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static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
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static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
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#include <string.h>

static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/

static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/

#if PY_MAJOR_VERSION >= 3
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static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
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static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);

static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);

static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/

static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/

#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
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static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
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static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /*proto*/

#if CYTHON_COMPILING_IN_CPYTHON
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#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
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static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);

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static void __Pyx_AddTraceback(const char *funcname, int c_line,
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static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/


/* Module declarations from 'numpy' */

/* Module declarations from 'cython' */

/* Module declarations from 'mtrand' */
static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0;
static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0;
static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0;
static PyTypeObject *__pyx_ptype_6mtrand_broadcast = 0;
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static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *, __pyx_t_6mtrand_rk_discnp, PyObject *, long, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *, __pyx_t_6mtrand_rk_discnp, PyObject *, PyArrayObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discdd_array_sc(rk_state *, __pyx_t_6mtrand_rk_discdd, PyObject *, double, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discdd_array(rk_state *, __pyx_t_6mtrand_rk_discdd, PyObject *, PyArrayObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *, __pyx_t_6mtrand_rk_discnmN, PyObject *, long, long, long, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *, __pyx_t_6mtrand_rk_discnmN, PyObject *, PyArrayObject *, PyArrayObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *, PyObject *); /*proto*/
static double __pyx_f_6mtrand_kahan_sum(double *, npy_intp); /*proto*/
#define __Pyx_MODULE_NAME "mtrand"
int __pyx_module_is_main_mtrand = 0;

/* Implementation of 'mtrand' */
static PyObject *__pyx_builtin_ImportError;
static PyObject *__pyx_builtin_ValueError;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_OverflowError;
static PyObject *__pyx_builtin_RuntimeWarning;
static PyObject *__pyx_pf_6mtrand__shape_from_size(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_size, PyObject *__pyx_v_d); /* proto */
static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_4seed(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_10__getstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_12__setstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_14__reduce__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_16random_sample(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_18tomaxint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_20randint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_length); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_24choice(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size, PyObject *__pyx_v_replace, PyObject *__pyx_v_p); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_26uniform(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_28rand(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_30randn(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_32random_integers(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_34standard_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_36normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_38beta(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_40exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_42standard_exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_44standard_gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_46gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_48f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_52chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_54noncentral_chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_56standard_cauchy(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_58standard_t(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_60vonmises(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mu, PyObject *__pyx_v_kappa, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_62pareto(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_64weibull(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_66power(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_68laplace(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_70gumbel(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_72logistic(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_74lognormal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_sigma, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_76rayleigh(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_78wald(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_left, PyObject *__pyx_v_mode, PyObject *__pyx_v_right, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_82binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_84negative_binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_86poisson(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_88zipf(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_90geometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_ngood, PyObject *__pyx_v_nbad, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_94logseries(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_96multivariate_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_cov, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_n, PyObject *__pyx_v_pvals, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_100dirichlet(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_102shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
static char __pyx_k_L[] = "L";
static char __pyx_k_T[] = "T";
static char __pyx_k_a[] = "a";
static char __pyx_k_b[] = "b";
static char __pyx_k_d[] = "d";
static char __pyx_k_f[] = "f";
static char __pyx_k_l[] = "l";
static char __pyx_k_n[] = "n";
static char __pyx_k_p[] = "p";
static char __pyx_k_df[] = "df";
static char __pyx_k_mu[] = "mu";
static char __pyx_k_np[] = "np";
static char __pyx_k_a_0[] = "a <= 0";
static char __pyx_k_add[] = "add";
static char __pyx_k_any[] = "any";
static char __pyx_k_b_0[] = "b <= 0";
static char __pyx_k_cov[] = "cov";
static char __pyx_k_dot[] = "dot";
static char __pyx_k_eps[] = "eps";
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_n_0[] = "n < 0";
static char __pyx_k_p_0[] = "p < 0";
static char __pyx_k_p_1[] = "p > 1";
static char __pyx_k_sum[] = "sum";
static char __pyx_k_svd[] = "svd";
static char __pyx_k_Lock[] = "Lock";
static char __pyx_k_axis[] = "axis";
static char __pyx_k_beta[] = "beta";
static char __pyx_k_copy[] = "copy";
static char __pyx_k_df_0[] = "df <= 0";
static char __pyx_k_exit[] = "__exit__";
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_main[] = "__main__";
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_safe[] = "safe";
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_test[] = "__test__";
static char __pyx_k_uint[] = "uint";
static char __pyx_k_wald[] = "wald";
static char __pyx_k_warn[] = "warn";
static char __pyx_k_zipf[] = "zipf";
static char __pyx_k_a_1_0[] = "a <= 1.0";
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_enter[] = "__enter__";
static char __pyx_k_equal[] = "equal";
static char __pyx_k_finfo[] = "finfo";
static char __pyx_k_gamma[] = "gamma";
static char __pyx_k_iinfo[] = "iinfo";
static char __pyx_k_index[] = "index";
static char __pyx_k_int64[] = "int64";
static char __pyx_k_isnan[] = "isnan";
static char __pyx_k_kappa[] = "kappa";
static char __pyx_k_lam_0[] = "lam < 0";
static char __pyx_k_n_0_2[] = "n <= 0";
static char __pyx_k_ngood[] = "ngood";
static char __pyx_k_numpy[] = "numpy";
static char __pyx_k_p_0_0[] = "p < 0.0";
static char __pyx_k_p_1_0[] = "p > 1.0";
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_astype[] = "astype";
static char __pyx_k_choice[] = "choice";
static char __pyx_k_cumsum[] = "cumsum";
static char __pyx_k_fields[] = "fields";
static char __pyx_k_gumbel[] = "gumbel";
static char __pyx_k_import[] = "__import__";
static char __pyx_k_mean_0[] = "mean <= 0";
static char __pyx_k_mtrand[] = "mtrand";
static char __pyx_k_nbad_0[] = "nbad < 0";
static char __pyx_k_nonc_0[] = "nonc < 0";
static char __pyx_k_normal[] = "normal";
static char __pyx_k_pareto[] = "pareto";
static char __pyx_k_rand_2[] = "_rand";
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_unsafe[] = "unsafe";
static char __pyx_k_MT19937[] = "MT19937";
static char __pyx_k_asarray[] = "asarray";
static char __pyx_k_casting[] = "casting";
static char __pyx_k_dfden_0[] = "dfden <= 0";
static char __pyx_k_dfnum_0[] = "dfnum <= 0";
static char __pyx_k_dfnum_1[] = "dfnum <= 1";
static char __pyx_k_float64[] = "float64";
static char __pyx_k_greater[] = "greater";
static char __pyx_k_integer[] = "integer";
static char __pyx_k_kappa_0[] = "kappa < 0";
static char __pyx_k_laplace[] = "laplace";
static char __pyx_k_ndarray[] = "ndarray";
static char __pyx_k_ngood_0[] = "ngood < 0";
static char __pyx_k_nsample[] = "nsample";
static char __pyx_k_p_0_0_2[] = "p <= 0.0";
static char __pyx_k_p_1_0_2[] = "p >= 1.0";
static char __pyx_k_poisson[] = "poisson";
static char __pyx_k_randint[] = "randint";
static char __pyx_k_replace[] = "replace";
static char __pyx_k_reshape[] = "reshape";
static char __pyx_k_scale_0[] = "scale <= 0";
static char __pyx_k_shape_0[] = "shape <= 0";
static char __pyx_k_shuffle[] = "shuffle";
static char __pyx_k_sigma_0[] = "sigma <= 0";
static char __pyx_k_uniform[] = "uniform";
static char __pyx_k_weibull[] = "weibull";
static char __pyx_k_binomial[] = "binomial";
static char __pyx_k_floating[] = "floating";
static char __pyx_k_logistic[] = "logistic";
static char __pyx_k_low_high[] = "low >= high";
static char __pyx_k_mean_0_0[] = "mean <= 0.0";
static char __pyx_k_nonc_0_2[] = "nonc <= 0";
static char __pyx_k_operator[] = "operator";
static char __pyx_k_p_is_nan[] = "p is nan";
static char __pyx_k_rayleigh[] = "rayleigh";
static char __pyx_k_subtract[] = "subtract";
static char __pyx_k_vonmises[] = "vonmises";
static char __pyx_k_warnings[] = "warnings";
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_left_mode[] = "left > mode";
static char __pyx_k_lognormal[] = "lognormal";
static char __pyx_k_logseries[] = "logseries";
static char __pyx_k_nsample_1[] = "nsample < 1";
static char __pyx_k_scale_0_0[] = "scale <= 0.0";
static char __pyx_k_set_state[] = "set_state";
static char __pyx_k_sigma_0_0[] = "sigma <= 0.0";
static char __pyx_k_threading[] = "threading";
static char __pyx_k_ValueError[] = "ValueError";
static char __pyx_k_empty_like[] = "empty_like";
static char __pyx_k_issubdtype[] = "issubdtype";
static char __pyx_k_left_right[] = "left == right";
static char __pyx_k_less_equal[] = "less_equal";
static char __pyx_k_logical_or[] = "logical_or";
static char __pyx_k_mode_right[] = "mode > right";
static char __pyx_k_numpy_dual[] = "numpy.dual";
static char __pyx_k_standard_t[] = "standard_t";
static char __pyx_k_triangular[] = "triangular";
static char __pyx_k_ImportError[] = "ImportError";
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_OverflowError[] = "OverflowError";
static char __pyx_k_count_nonzero[] = "count_nonzero";
static char __pyx_k_greater_equal[] = "greater_equal";
static char __pyx_k_random_sample[] = "random_sample";
static char __pyx_k_RuntimeWarning[] = "RuntimeWarning";
static char __pyx_k_hypergeometric[] = "hypergeometric";
static char __pyx_k_standard_gamma[] = "standard_gamma";
static char __pyx_k_dummy_threading[] = "dummy_threading";
static char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
static char __pyx_k_random_integers[] = "random_integers";
static char __pyx_k_shape_from_size[] = "_shape_from_size";
static char __pyx_k_standard_cauchy[] = "standard_cauchy";
static char __pyx_k_standard_normal[] = "standard_normal";
static char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
static char __pyx_k_RandomState_ctor[] = "__RandomState_ctor";
static char __pyx_k_negative_binomial[] = "negative_binomial";
static char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
static char __pyx_k_a_must_be_non_empty[] = "a must be non-empty";
static char __pyx_k_lam_value_too_large[] = "lam value too large";
static char __pyx_k_multivariate_normal[] = "multivariate_normal";
static char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
static char __pyx_k_standard_exponential[] = "standard_exponential";
static char __pyx_k_lam_value_too_large_2[] = "lam value too large.";
static char __pyx_k_RandomState_f_line_1916[] = "RandomState.f (line 1916)";
static char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
static char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
static char __pyx_k_state_must_be_624_longs[] = "state must be 624 longs";
static char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
static char __pyx_k_algorithm_must_be_MT19937[] = "algorithm must be 'MT19937'";
static char __pyx_k_RandomState_bytes_line_962[] = "RandomState.bytes (line 962)";
static char __pyx_k_RandomState_rand_line_1268[] = "RandomState.rand (line 1268)";
static char __pyx_k_RandomState_wald_line_3403[] = "RandomState.wald (line 3403)";
static char __pyx_k_RandomState_zipf_line_3874[] = "RandomState.zipf (line 3874)";
static char __pyx_k_Range_exceeds_valid_bounds[] = "Range exceeds valid bounds";
static char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
static char __pyx_k_RandomState_choice_line_991[] = "RandomState.choice (line 991)";
static char __pyx_k_RandomState_gamma_line_1822[] = "RandomState.gamma (line 1822)";
static char __pyx_k_RandomState_power_line_2776[] = "RandomState.power (line 2776)";
static char __pyx_k_RandomState_randn_line_1312[] = "RandomState.randn (line 1312)";
static char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
static char __pyx_k_RandomState_gumbel_line_2983[] = "RandomState.gumbel (line 2983)";
static char __pyx_k_RandomState_normal_line_1482[] = "RandomState.normal (line 1482)";
static char __pyx_k_RandomState_pareto_line_2563[] = "RandomState.pareto (line 2563)";
static char __pyx_k_RandomState_randint_line_879[] = "RandomState.randint (line 879)";
static char __pyx_k_RandomState_laplace_line_2887[] = "RandomState.laplace (line 2887)";
static char __pyx_k_RandomState_poisson_line_3788[] = "RandomState.poisson (line 3788)";
static char __pyx_k_RandomState_shuffle_line_4613[] = "RandomState.shuffle (line 4613)";
static char __pyx_k_RandomState_tomaxint_line_832[] = "RandomState.tomaxint (line 832)";
static char __pyx_k_RandomState_uniform_line_1173[] = "RandomState.uniform (line 1173)";
static char __pyx_k_RandomState_weibull_line_2667[] = "RandomState.weibull (line 2667)";
static char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
static char __pyx_k_RandomState_binomial_line_3579[] = "RandomState.binomial (line 3579)";
static char __pyx_k_RandomState_logistic_line_3112[] = "RandomState.logistic (line 3112)";
static char __pyx_k_RandomState_rayleigh_line_3328[] = "RandomState.rayleigh (line 3328)";
static char __pyx_k_RandomState_vonmises_line_2465[] = "RandomState.vonmises (line 2465)";
static 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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (size, alpha.ndim).\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\n        th""e 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_home_vagrant_repos_numpy_numpy[] = "/home/vagrant/repos/numpy/numpy/random/mtrand/mtrand.pyx";
static 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, optional\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float, optional\n            :math:`\\lambda`, the exponential decay.\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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or float\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\n        of an error can be expressed as an exponential function of the\n        absolute magnitude of the error, which leads to the Laplace\n        distribution. For many problems in economics and health\n        sciences, this distribution seems to model the data better\n        than the standard Gaussian distribution.\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] Kotz, Samuel, et. al. \"The Laplace Distribution and\n               Generalizations, \" Birkhaus""er, 2001.\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n        .. [4] Wikipedia, \"Laplace Distribution\",\n               http://en.wikipedia.org/wiki/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_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 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 distribution\n        for large N.\n\n        Parameters\n        ----------\n        lam : float or sequence of float\n            Expectation of interval, should be >= 0. A sequence of expectation\n            intervals must be broadcastable over the requested size.\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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples, of shape *size*, if it was provided.\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\n        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.\"\n               From MathWorld--A Wolfram Web Resource.\n               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        Draw each 100 values for lambda 100 and 500:\n\n        >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))\n\n        ";
static 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 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 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            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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_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 char __pyx_k_standard_cauchy_size_None_Draw[] = "\n        standard_cauchy(size=None)\n\n        Draw samples from a 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, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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/C""auchy_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_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, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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_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, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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_standard_normal_size_None_Draw[] = "\n        standard_normal(size=None)\n\n        Draw 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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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_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. Some references claim that the Wald is an inverse Gaussian\n        with mean equal to 1, but 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\n        unit 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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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\n        from attempts to model Brownian motion. It is also a\n        competitor to the Weibull for use in reliability modeling and\n        modeling stock returns and interest rate 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 : Methodolog""y, and Applications\", CRC Press,\n               1988.\n        .. [3] Wikipedia, \"Wald distribution\"\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_RandomState_chisquare_line_2118[] = "RandomState.chisquare (line 2118)";
static char __pyx_k_RandomState_dirichlet_line_4497[] = "RandomState.dirichlet (line 4497)";
static char __pyx_k_RandomState_geometric_line_3962[] = "RandomState.geometric (line 3962)";
static char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 4031)";
static char __pyx_k_RandomState_lognormal_line_3203[] = "RandomState.lognormal (line 3203)";
static char __pyx_k_RandomState_logseries_line_4152[] = "RandomState.logseries (line 4152)";
static char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4250)";
static char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1736)";
static 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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or 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] Dalg""aard, 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_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 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 : 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.  Default is None, in which case a\n            single value is 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        .. [1] NIST \"Engineering Statistics Handbook\"\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_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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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 distribution over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3)\n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n     ""   replacement:\n\n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n        >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]\n\n        Generate a non-uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n              dtype='|S11')\n\n        ";
static char __pyx_k_f_dfnum_dfden_size_None_Draw_sa[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from an 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 should be greater than\n        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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or 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\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_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 : 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.  Default is None, in which case a\n            single value is returned.\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_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 : 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.  Default is None, in which case a\n            single value is returned.\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_gumbel_loc_0_0_scale_1_0_size_N[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and\n        scale.  For more information on the Gumbel distribution, see\n        Notes and References 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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme\n        Value Type I) distribution is one of a class of Generalized Extreme\n        Value (GEV) distributions used in modeling extreme value problems.\n        The Gumbel is a special case of the Extreme Value Type I distribution\n        for maximums from 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\n        :math:`\\beta` is 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\n        rainfall rates.  It is a \"fat-tailed\" distribution - the probability of\n        an eve""nt in the tail of the distribution is larger than if one used a\n        Gaussian, hence the surprisingly frequent occurrence of 100-year\n        floods. Floods were initially modeled as a Gaussian process, which\n        underestimated the frequency of extreme events.\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\n        of :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        .. [1] Gumbel, E. J., \"Statistics of Extremes,\"\n               New York: Columbia University Press, 1958.\n        .. [2] Reiss, R.-D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values from Insurance, Finance, Hydrology and Other Fields,\"\n               Basel: Birkhauser Verlag, 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_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 ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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        distribution, 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_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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or 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\n               Extreme Values, from Insurance, Finance, Hydrology and Other\n               Fields,\" 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_lognormal_mean_0_0_sigma_1_0_si[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Draw samples 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 : 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.  Default is None, in which case a\n            single value is returned.\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 numbe""r of independent, identically-distributed\n        variables.\n\n        References\n        ----------\n        .. [1] Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal\n               Distributions across the Sciences: Keys and Clues,\"\n               BioScience, Vol. 51, No. 5, May, 2001.\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values,\" 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\n        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='mid')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (""np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
static 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        shape parameter, 0 < ``p`` < 1.\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or 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 o""f Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Logarithmic-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_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\n        outcome 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 : 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.  Default is None, in which case a\n            single value 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        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.  Fo""r 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_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. It must be symmetric and\n            positive-semidefinite for proper sampling.\n        size : int or tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensio""nal\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\n\n        Diagonal covariance means that points are oriented along 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')\n        >>> plt.axis('equal')\n        >>> plt.show()\n\n        Note that the covariance matrix must be positive semidefinite (a.k.a.\n        nonnegative-definite). Otherwise, the behavior of this method is\n        undefined and backwards compatibility is not guaranteed.\n\n        References\n        ----------\n        .. [1] Papoulis, A., \"Probability, Random Variables, and Stochastic\n               Processes,\" 3rd ed., New York: McGraw-Hill, 1991.\n        .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., \"Pattern\n               Classification,\" 2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1, 2)\n        >>> cov = [[1, 0], [0, 1]]\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        >>> list((x[0,0,:] - mean) < 0.6)\n        [True, True]\n\n        ";
static 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, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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\n        probability of success, and :math:`N+n-1` is the number of trials.\n        The negative binomial distribution gives the probability of n-1\n        successes and N failures in N+n-1 trials, and success on the (N+n)th\n        trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears,\n        then the probability distribution of the number of non-\"1\"s that\n        appear before the 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 di""stribution:\n\n        A real world example. A company drills wild-cat oil\n        exploration wells, each with an estimated probability of\n        success of 0.1.  What is the probability of having one success\n        for each successive well, that is what is the probability of a\n        single success after drilling 5 wells, after 6 wells, 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_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 > 0 as of Numpy 1.10,\n            should be > 1 for earlier versions.\n        nonc : float\n            Non-centrality, 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.  Default is None, in which case a\n            single value is returned.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square\n        distribution is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}\n                               \\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\n        useful in bombing and coverage problems, the probability of\n        killing the point target given by the noncentral chi-squared\n        distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in\n               the analysis of weapon systems effectiveness\", Metrika,\n               Volume 15, 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        ...                   bin""s=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_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, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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        .. [1] Weisstein, Eric W. \"Noncentral F-Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NoncentralF-Distribution.html\n        .. [2] 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 comparison.\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_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 : 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.  Default is None, in which case a\n            single value is returned.\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\n        deviation. The square of the standard deviation, :math:`\\sigma^2`,\n        is called the 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 l""ikely to return samples lying close to\n        the 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,\n               Random 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_pareto_a_size_None_Draw_samples[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with\n        specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto\n        distribution. The classical Pareto distribution can be\n        obtained from the Lomax distribution by adding 1 and\n        multiplying by the scale parameter ``m`` (see Notes).  The\n        smallest value of the Lomax distribution is zero while for the\n        classical Pareto distribution it is ``mu``, where the standard\n        Pareto distribution has location ``mu = 1``.  Lomax can also\n        be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set\n        to one and the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is\n        unbounded above.  It is also known as the \"80-20 rule\".  In\n        this distribution, 80 percent of the weights are in the lowest\n        20 percent of the range, while the other 20 percent fill the\n        remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\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.  Default is None, in which case a\n            single value is returned.\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 scale.\n""\n        The Pareto distribution, named after the Italian economist\n        Vilfredo Pareto, is a power law probability distribution\n        useful in many real world problems.  Outside the field of\n        economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe\n        the distribution of wealth in an economy.  It has also found\n        use in insurance, web page access statistics, oil field sizes,\n        and many other problems, including the download frequency for\n        projects in Sourceforge [1]_.  It is one of the so-called\n        \"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., 2.  # shape and mode\n        >>> s = (np.random.pareto(a, 1000) + 1) * m\n\n        Display the histogram of the samples, along with the probability\n        density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, _ = plt.hist(s, 100, normed=True)\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_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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or 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. \"NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology\n               Handbook 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_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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value 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.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_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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value 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_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            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single 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 would arise, for example, if the East\n        and North components of the wind velocity had identical zero-mean\n        Gaussian distributions.  Then the wind speed would have a Rayleigh\n        distribution.\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_standard_t_df_size_None_Draw_sa[] = "\n        standard_t(df, size=None)\n\n        Draw samples from a standard Student's t distribution with `df` degrees\n        of freedom.\n\n        A special case of the hyperbolic distribution.  As `df` gets\n        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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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\n        Normal distribution. The t test provides a way to test whether\n        the sample mean (that is the mean calculated from the data) is\n        a good estimate of the true mean.\n\n        The derivation of the t-distribution was first published in\n        1908 by William Gisset while working for the Guinness Brewery\n        in Dublin. Due to proprietary issues, he had to publish under\n        a pseudonym, and so he used the name 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        Does 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_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 : 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.  Default is None, in which case a\n            single value is 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_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\n        distribution with lower limit left, peak at mode, and upper\n        limit right. Unlike the other distributions, these parameters\n        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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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\n        problems where the underlying distribution is not known, but\n        some knowledge of the limits and mode exists. Often it is used\n        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_uniform_low_0_0_high_1_0_size_N[] = "\n        uniform(low=0.0, high=1.0, size=None)\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            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value 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 within 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_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 ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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        .. [1] Abramowitz, M. and Stegun, I"". A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] von Mises, R., \"Mathematical Theory of Probability\n               and Statistics\", 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        >>> from scipy.special import i0\n        >>> plt.hist(s, 50, normed=True)\n        >>> x = np.linspace(-np.pi, np.pi, num=51)\n        >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))\n        >>> plt.plot(x, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static char __pyx_k_weibull_a_size_None_Draw_sample[] = "\n        weibull(a, size=None)\n\n        Draw samples from a 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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray\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\n        for smallest values, SEV Type III, or Rosin-Rammler\n        distribution) is one of a class of Generalized Extreme Value\n        (GEV) distributions used in modeling extreme value problems.\n        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, Royal Technical University, Stockholm,""\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, \"A Statistical Distribution Function of\n               Wide Applicability\", Journal Of Applied Mechanics ASME Paper\n               1951.\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_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 ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value 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        .. [1] Zipf, G. K., \"Selected Studies of the Principle of Relative\n               Frequency in Language,\" Cambridge, MA: Harvard Univ. Press,\n               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_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=False'";
static char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
static char __pyx_k_RandomState_multinomial_line_439[] = "RandomState.multinomial (line 4398)";
static char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3692)";
static char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2199)";
static char __pyx_k_RandomState_noncentral_f_line_20[] = "RandomState.noncentral_f (line 2022)";
static char __pyx_k_RandomState_permutation_line_467[] = "RandomState.permutation (line 4674)";
static char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1369)";
static char __pyx_k_RandomState_random_sample_line_7[] = "RandomState.random_sample (line 788)";
static char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2296)";
static char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1705)";
static char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1449)";
static char __pyx_k_RandomState_standard_t_line_2360[] = "RandomState.standard_t (line 2360)";
static char __pyx_k_RandomState_triangular_line_3488[] = "RandomState.triangular (line 3488)";
static char __pyx_k_Seed_must_be_between_0_and_42949[] = "Seed must be between 0 and 4294967295";
static char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
static char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
static char __pyx_k_covariance_is_not_positive_semid[] = "covariance is not positive-semidefinite.";
static char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
static char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
static 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_ImportError;
static PyObject *__pyx_n_s_L;
static PyObject *__pyx_n_s_Lock;
static PyObject *__pyx_n_s_MT19937;
static PyObject *__pyx_n_s_OverflowError;
static PyObject *__pyx_kp_u_RandomState_binomial_line_3579;
static PyObject *__pyx_kp_u_RandomState_bytes_line_962;
static PyObject *__pyx_kp_u_RandomState_chisquare_line_2118;
static PyObject *__pyx_kp_u_RandomState_choice_line_991;
static PyObject *__pyx_n_s_RandomState_ctor;
static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4497;
static PyObject *__pyx_kp_u_RandomState_f_line_1916;
static PyObject *__pyx_kp_u_RandomState_gamma_line_1822;
static PyObject *__pyx_kp_u_RandomState_geometric_line_3962;
static PyObject *__pyx_kp_u_RandomState_gumbel_line_2983;
static PyObject *__pyx_kp_u_RandomState_hypergeometric_line;
static PyObject *__pyx_kp_u_RandomState_laplace_line_2887;
static PyObject *__pyx_kp_u_RandomState_logistic_line_3112;
static PyObject *__pyx_kp_u_RandomState_lognormal_line_3203;
static PyObject *__pyx_kp_u_RandomState_logseries_line_4152;
static PyObject *__pyx_kp_u_RandomState_multinomial_line_439;
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_20;
static PyObject *__pyx_kp_u_RandomState_normal_line_1482;
static PyObject *__pyx_kp_u_RandomState_pareto_line_2563;
static PyObject *__pyx_kp_u_RandomState_permutation_line_467;
static PyObject *__pyx_kp_u_RandomState_poisson_line_3788;
static PyObject *__pyx_kp_u_RandomState_power_line_2776;
static PyObject *__pyx_kp_u_RandomState_rand_line_1268;
static PyObject *__pyx_kp_u_RandomState_randint_line_879;
static PyObject *__pyx_kp_u_RandomState_randn_line_1312;
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_3328;
static PyObject *__pyx_kp_u_RandomState_shuffle_line_4613;
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_2360;
static PyObject *__pyx_kp_u_RandomState_tomaxint_line_832;
static PyObject *__pyx_kp_u_RandomState_triangular_line_3488;
static PyObject *__pyx_kp_u_RandomState_uniform_line_1173;
static PyObject *__pyx_kp_u_RandomState_vonmises_line_2465;
static PyObject *__pyx_kp_u_RandomState_wald_line_3403;
static PyObject *__pyx_kp_u_RandomState_weibull_line_2667;
static PyObject *__pyx_kp_u_RandomState_zipf_line_3874;
static PyObject *__pyx_kp_s_Range_exceeds_valid_bounds;
static PyObject *__pyx_n_s_RuntimeWarning;
static PyObject *__pyx_kp_s_Seed_must_be_between_0_and_42949;
static PyObject *__pyx_n_s_T;
static PyObject *__pyx_n_s_TypeError;
static PyObject *__pyx_n_s_ValueError;
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static PyObject *__pyx_kp_s_a_0;
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static PyObject *__pyx_kp_s_a_and_p_must_have_same_size;
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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_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_astype;
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static PyObject *__pyx_n_s_b;
static PyObject *__pyx_kp_s_b_0;
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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_casting;
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_copy;
static PyObject *__pyx_n_s_count_nonzero;
static PyObject *__pyx_n_s_cov;
static PyObject *__pyx_kp_s_cov_must_be_2_dimensional_and_sq;
static PyObject *__pyx_kp_s_covariance_is_not_positive_semid;
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;
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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;
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static char __pyx_doc_6mtrand_11RandomState_60vonmises[] = "\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 ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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        .. [1] Abramowitz, M. and Stegun, I"". A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] von Mises, R., \"Mathematical Theory of Probability\n               and Statistics\", 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        >>> from scipy.special import i0\n        >>> plt.hist(s, 50, normed=True)\n        >>> x = np.linspace(-np.pi, np.pi, num=51)\n        >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))\n        >>> plt.plot(x, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_62pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with\n        specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto\n        distribution. The classical Pareto distribution can be\n        obtained from the Lomax distribution by adding 1 and\n        multiplying by the scale parameter ``m`` (see Notes).  The\n        smallest value of the Lomax distribution is zero while for the\n        classical Pareto distribution it is ``mu``, where the standard\n        Pareto distribution has location ``mu = 1``.  Lomax can also\n        be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set\n        to one and the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is\n        unbounded above.  It is also known as the \"80-20 rule\".  In\n        this distribution, 80 percent of the weights are in the lowest\n        20 percent of the range, while the other 20 percent fill the\n        remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\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.  Default is None, in which case a\n            single value is returned.\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 scale.\n""\n        The Pareto distribution, named after the Italian economist\n        Vilfredo Pareto, is a power law probability distribution\n        useful in many real world problems.  Outside the field of\n        economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe\n        the distribution of wealth in an economy.  It has also found\n        use in insurance, web page access statistics, oil field sizes,\n        and many other problems, including the download frequency for\n        projects in Sourceforge [1]_.  It is one of the so-called\n        \"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., 2.  # shape and mode\n        >>> s = (np.random.pareto(a, 1000) + 1) * m\n\n        Display the histogram of the samples, along with the probability\n        density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, _ = plt.hist(s, 100, normed=True)\n        >>> fit = a*m**a / bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_68laplace[] = "\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, optional\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float, optional\n            :math:`\\lambda`, the exponential decay.\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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or float\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\n        of an error can be expressed as an exponential function of the\n        absolute magnitude of the error, which leads to the Laplace\n        distribution. For many problems in economics and health\n        sciences, this distribution seems to model the data better\n        than the standard Gaussian distribution.\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). \"Handbook of\n               Mathematical Functions with Formulas, Graphs, and Mathematical\n               Tables, 9th printing,\" New York: Dover, 1972.\n        .. [2] Kotz, Samuel, et. al. \"The Laplace Distribution and\n               Generalizations, \" Birkhaus""er, 2001.\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n        .. [4] Wikipedia, \"Laplace Distribution\",\n               http://en.wikipedia.org/wiki/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        ";
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static char __pyx_doc_6mtrand_11RandomState_70gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and\n        scale.  For more information on the Gumbel distribution, see\n        Notes and References 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 : 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.  Default is None, in which case a\n            single value is returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme\n        Value Type I) distribution is one of a class of Generalized Extreme\n        Value (GEV) distributions used in modeling extreme value problems.\n        The Gumbel is a special case of the Extreme Value Type I distribution\n        for maximums from 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\n        :math:`\\beta` is 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\n        rainfall rates.  It is a \"fat-tailed\" distribution - the probability of\n        an eve""nt in the tail of the distribution is larger than if one used a\n        Gaussian, hence the surprisingly frequent occurrence of 100-year\n        floods. Floods were initially modeled as a Gaussian process, which\n        underestimated the frequency of extreme events.\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\n        of :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        .. [1] Gumbel, E. J., \"Statistics of Extremes,\"\n               New York: Columbia University Press, 1958.\n        .. [2] Reiss, R.-D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values from Insurance, Finance, Hydrology and Other Fields,\"\n               Basel: Birkhauser Verlag, 2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt""(6)\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_74lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Draw samples 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 : 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.  Default is None, in which case a\n            single value is returned.\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 numbe""r of independent, identically-distributed\n        variables.\n\n        References\n        ----------\n        .. [1] Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal\n               Distributions across the Sciences: Keys and Clues,\"\n               BioScience, Vol. 51, No. 5, May, 2001.\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D. and Thomas, M., \"Statistical Analysis of Extreme\n               Values,\" 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\n        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='mid')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (""np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_80triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability\n        distribution with lower limit left, peak at mode, and upper\n        limit right. Unlike the other distributions, these parameters\n        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.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is 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\n        problems where the underlying distribution is not known, but\n        some knowledge of the limits and mode exists. Often it is used\n        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        ";
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static char __pyx_doc_6mtrand_11RandomState_84negative_binomial[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative binomial distribution.\n\n        Samples are drawn from a negative binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.  Default is None, in which case a\n            single value is returned.\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\n        probability of success, and :math:`N+n-1` is the number of trials.\n        The negative binomial distribution gives the probability of n-1\n        successes and N failures in N+n-1 trials, and success on the (N+n)th\n        trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears,\n        then the probability distribution of the number of non-\"1\"s that\n        appear before the 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 di""stribution:\n\n        A real world example. A company drills wild-cat oil\n        exploration wells, each with an estimated probability of\n        success of 0.1.  What is the probability of having one success\n        for each successive well, that is what is the probability of a\n        single success after drilling 5 wells, after 6 wells, etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_96multivariate_normal[] = "\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. It must be symmetric and\n            positive-semidefinite for proper sampling.\n        size : int or tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensio""nal\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\n\n        Diagonal covariance means that points are oriented along 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')\n        >>> plt.axis('equal')\n        >>> plt.show()\n\n        Note that the covariance matrix must be positive semidefinite (a.k.a.\n        nonnegative-definite). Otherwise, the behavior of this method is\n        undefined and backwards compatibility is not guaranteed.\n\n        References\n        ----------\n        .. [1] Papoulis, A., \"Probability, Random Variables, and Stochastic\n               Processes,\" 3rd ed., New York: McGraw-Hill, 1991.\n        .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., \"Pattern\n               Classification,\" 2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1, 2)\n        >>> cov = [[1, 0], [0, 1]]\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        >>> list((x[0,0,:] - mean) < 0.6)\n        [True, True]\n\n        ";
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  {&__pyx_kp_s_dfnum_1, __pyx_k_dfnum_1, sizeof(__pyx_k_dfnum_1), 0, 0, 1, 0},
  {&__pyx_n_s_dirichlet, __pyx_k_dirichlet, sizeof(__pyx_k_dirichlet), 0, 0, 1, 1},
  {&__pyx_kp_u_dirichlet_alpha_size_None_Draw, __pyx_k_dirichlet_alpha_size_None_Draw, sizeof(__pyx_k_dirichlet_alpha_size_None_Draw), 0, 1, 0, 0},
  {&__pyx_n_s_dot, __pyx_k_dot, sizeof(__pyx_k_dot), 0, 0, 1, 1},
  {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1},
  {&__pyx_n_s_dummy_threading, __pyx_k_dummy_threading, sizeof(__pyx_k_dummy_threading), 0, 0, 1, 1},
  {&__pyx_n_s_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 0, 0, 1, 1},
  {&__pyx_n_s_empty_like, __pyx_k_empty_like, sizeof(__pyx_k_empty_like), 0, 0, 1, 1},
  {&__pyx_n_s_enter, __pyx_k_enter, sizeof(__pyx_k_enter), 0, 0, 1, 1},
  {&__pyx_n_s_eps, __pyx_k_eps, sizeof(__pyx_k_eps), 0, 0, 1, 1},
  {&__pyx_n_s_equal, __pyx_k_equal, sizeof(__pyx_k_equal), 0, 0, 1, 1},
  {&__pyx_n_s_exit, __pyx_k_exit, sizeof(__pyx_k_exit), 0, 0, 1, 1},
  {&__pyx_n_s_exponential, __pyx_k_exponential, sizeof(__pyx_k_exponential), 0, 0, 1, 1},
  {&__pyx_n_s_f, __pyx_k_f, sizeof(__pyx_k_f), 0, 0, 1, 1},
  {&__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa, __pyx_k_f_dfnum_dfden_size_None_Draw_sa, sizeof(__pyx_k_f_dfnum_dfden_size_None_Draw_sa), 0, 1, 0, 0},
  {&__pyx_n_s_fields, __pyx_k_fields, sizeof(__pyx_k_fields), 0, 0, 1, 1},
  {&__pyx_n_s_finfo, __pyx_k_finfo, sizeof(__pyx_k_finfo), 0, 0, 1, 1},
  {&__pyx_n_s_float64, __pyx_k_float64, sizeof(__pyx_k_float64), 0, 0, 1, 1},
  {&__pyx_n_s_floating, __pyx_k_floating, sizeof(__pyx_k_floating), 0, 0, 1, 1},
  {&__pyx_n_s_gamma, __pyx_k_gamma, sizeof(__pyx_k_gamma), 0, 0, 1, 1},
  {&__pyx_kp_u_gamma_shape_scale_1_0_size_None, __pyx_k_gamma_shape_scale_1_0_size_None, sizeof(__pyx_k_gamma_shape_scale_1_0_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_geometric, __pyx_k_geometric, sizeof(__pyx_k_geometric), 0, 0, 1, 1},
  {&__pyx_kp_u_geometric_p_size_None_Draw_samp, __pyx_k_geometric_p_size_None_Draw_samp, sizeof(__pyx_k_geometric_p_size_None_Draw_samp), 0, 1, 0, 0},
  {&__pyx_n_s_get_state, __pyx_k_get_state, sizeof(__pyx_k_get_state), 0, 0, 1, 1},
  {&__pyx_n_s_greater, __pyx_k_greater, sizeof(__pyx_k_greater), 0, 0, 1, 1},
  {&__pyx_n_s_greater_equal, __pyx_k_greater_equal, sizeof(__pyx_k_greater_equal), 0, 0, 1, 1},
  {&__pyx_n_s_gumbel, __pyx_k_gumbel, sizeof(__pyx_k_gumbel), 0, 0, 1, 1},
  {&__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N, __pyx_k_gumbel_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_gumbel_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
  {&__pyx_n_s_high, __pyx_k_high, sizeof(__pyx_k_high), 0, 0, 1, 1},
  {&__pyx_kp_s_home_vagrant_repos_numpy_numpy, __pyx_k_home_vagrant_repos_numpy_numpy, sizeof(__pyx_k_home_vagrant_repos_numpy_numpy), 0, 0, 1, 0},
  {&__pyx_n_s_hypergeometric, __pyx_k_hypergeometric, sizeof(__pyx_k_hypergeometric), 0, 0, 1, 1},
  {&__pyx_kp_u_hypergeometric_ngood_nbad_nsamp, __pyx_k_hypergeometric_ngood_nbad_nsamp, sizeof(__pyx_k_hypergeometric_ngood_nbad_nsamp), 0, 1, 0, 0},
  {&__pyx_n_s_iinfo, __pyx_k_iinfo, sizeof(__pyx_k_iinfo), 0, 0, 1, 1},
  {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
  {&__pyx_n_s_index, __pyx_k_index, sizeof(__pyx_k_index), 0, 0, 1, 1},
  {&__pyx_n_s_int, __pyx_k_int, sizeof(__pyx_k_int), 0, 0, 1, 1},
  {&__pyx_n_s_int64, __pyx_k_int64, sizeof(__pyx_k_int64), 0, 0, 1, 1},
  {&__pyx_n_s_integer, __pyx_k_integer, sizeof(__pyx_k_integer), 0, 0, 1, 1},
  {&__pyx_n_s_intp, __pyx_k_intp, sizeof(__pyx_k_intp), 0, 0, 1, 1},
  {&__pyx_n_s_isnan, __pyx_k_isnan, sizeof(__pyx_k_isnan), 0, 0, 1, 1},
  {&__pyx_n_s_issubdtype, __pyx_k_issubdtype, sizeof(__pyx_k_issubdtype), 0, 0, 1, 1},
  {&__pyx_n_s_item, __pyx_k_item, sizeof(__pyx_k_item), 0, 0, 1, 1},
  {&__pyx_n_s_kappa, __pyx_k_kappa, sizeof(__pyx_k_kappa), 0, 0, 1, 1},
  {&__pyx_kp_s_kappa_0, __pyx_k_kappa_0, sizeof(__pyx_k_kappa_0), 0, 0, 1, 0},
  {&__pyx_n_s_l, __pyx_k_l, sizeof(__pyx_k_l), 0, 0, 1, 1},
  {&__pyx_n_s_lam, __pyx_k_lam, sizeof(__pyx_k_lam), 0, 0, 1, 1},
  {&__pyx_kp_s_lam_0, __pyx_k_lam_0, sizeof(__pyx_k_lam_0), 0, 0, 1, 0},
  {&__pyx_kp_s_lam_value_too_large, __pyx_k_lam_value_too_large, sizeof(__pyx_k_lam_value_too_large), 0, 0, 1, 0},
  {&__pyx_kp_s_lam_value_too_large_2, __pyx_k_lam_value_too_large_2, sizeof(__pyx_k_lam_value_too_large_2), 0, 0, 1, 0},
  {&__pyx_n_s_laplace, __pyx_k_laplace, sizeof(__pyx_k_laplace), 0, 0, 1, 1},
  {&__pyx_kp_u_laplace_loc_0_0_scale_1_0_size, __pyx_k_laplace_loc_0_0_scale_1_0_size, sizeof(__pyx_k_laplace_loc_0_0_scale_1_0_size), 0, 1, 0, 0},
  {&__pyx_n_s_left, __pyx_k_left, sizeof(__pyx_k_left), 0, 0, 1, 1},
  {&__pyx_kp_s_left_mode, __pyx_k_left_mode, sizeof(__pyx_k_left_mode), 0, 0, 1, 0},
  {&__pyx_kp_s_left_right, __pyx_k_left_right, sizeof(__pyx_k_left_right), 0, 0, 1, 0},
  {&__pyx_n_s_less, __pyx_k_less, sizeof(__pyx_k_less), 0, 0, 1, 1},
  {&__pyx_n_s_less_equal, __pyx_k_less_equal, sizeof(__pyx_k_less_equal), 0, 0, 1, 1},
  {&__pyx_n_s_loc, __pyx_k_loc, sizeof(__pyx_k_loc), 0, 0, 1, 1},
  {&__pyx_n_s_logical_or, __pyx_k_logical_or, sizeof(__pyx_k_logical_or), 0, 0, 1, 1},
  {&__pyx_n_s_logistic, __pyx_k_logistic, sizeof(__pyx_k_logistic), 0, 0, 1, 1},
  {&__pyx_kp_u_logistic_loc_0_0_scale_1_0_size, __pyx_k_logistic_loc_0_0_scale_1_0_size, sizeof(__pyx_k_logistic_loc_0_0_scale_1_0_size), 0, 1, 0, 0},
  {&__pyx_n_s_lognormal, __pyx_k_lognormal, sizeof(__pyx_k_lognormal), 0, 0, 1, 1},
  {&__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si, __pyx_k_lognormal_mean_0_0_sigma_1_0_si, sizeof(__pyx_k_lognormal_mean_0_0_sigma_1_0_si), 0, 1, 0, 0},
  {&__pyx_n_s_logseries, __pyx_k_logseries, sizeof(__pyx_k_logseries), 0, 0, 1, 1},
  {&__pyx_kp_u_logseries_p_size_None_Draw_samp, __pyx_k_logseries_p_size_None_Draw_samp, sizeof(__pyx_k_logseries_p_size_None_Draw_samp), 0, 1, 0, 0},
  {&__pyx_n_s_low, __pyx_k_low, sizeof(__pyx_k_low), 0, 0, 1, 1},
  {&__pyx_kp_s_low_high, __pyx_k_low_high, sizeof(__pyx_k_low_high), 0, 0, 1, 0},
  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
  {&__pyx_n_s_max, __pyx_k_max, sizeof(__pyx_k_max), 0, 0, 1, 1},
  {&__pyx_n_s_mean, __pyx_k_mean, sizeof(__pyx_k_mean), 0, 0, 1, 1},
  {&__pyx_kp_s_mean_0, __pyx_k_mean_0, sizeof(__pyx_k_mean_0), 0, 0, 1, 0},
  {&__pyx_kp_s_mean_0_0, __pyx_k_mean_0_0, sizeof(__pyx_k_mean_0_0), 0, 0, 1, 0},
  {&__pyx_kp_s_mean_and_cov_must_have_same_leng, __pyx_k_mean_and_cov_must_have_same_leng, sizeof(__pyx_k_mean_and_cov_must_have_same_leng), 0, 0, 1, 0},
  {&__pyx_kp_s_mean_must_be_1_dimensional, __pyx_k_mean_must_be_1_dimensional, sizeof(__pyx_k_mean_must_be_1_dimensional), 0, 0, 1, 0},
  {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
  {&__pyx_kp_s_mode_right, __pyx_k_mode_right, sizeof(__pyx_k_mode_right), 0, 0, 1, 0},
  {&__pyx_n_s_mtrand, __pyx_k_mtrand, sizeof(__pyx_k_mtrand), 0, 0, 1, 1},
  {&__pyx_n_s_mu, __pyx_k_mu, sizeof(__pyx_k_mu), 0, 0, 1, 1},
  {&__pyx_n_s_multinomial, __pyx_k_multinomial, sizeof(__pyx_k_multinomial), 0, 0, 1, 1},
  {&__pyx_kp_u_multinomial_n_pvals_size_None_D, __pyx_k_multinomial_n_pvals_size_None_D, sizeof(__pyx_k_multinomial_n_pvals_size_None_D), 0, 1, 0, 0},
  {&__pyx_n_s_multivariate_normal, __pyx_k_multivariate_normal, sizeof(__pyx_k_multivariate_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_multivariate_normal_mean_cov_si, __pyx_k_multivariate_normal_mean_cov_si, sizeof(__pyx_k_multivariate_normal_mean_cov_si), 0, 1, 0, 0},
  {&__pyx_n_s_n, __pyx_k_n, sizeof(__pyx_k_n), 0, 0, 1, 1},
  {&__pyx_kp_s_n_0, __pyx_k_n_0, sizeof(__pyx_k_n_0), 0, 0, 1, 0},
  {&__pyx_kp_s_n_0_2, __pyx_k_n_0_2, sizeof(__pyx_k_n_0_2), 0, 0, 1, 0},
  {&__pyx_n_s_nbad, __pyx_k_nbad, sizeof(__pyx_k_nbad), 0, 0, 1, 1},
  {&__pyx_kp_s_nbad_0, __pyx_k_nbad_0, sizeof(__pyx_k_nbad_0), 0, 0, 1, 0},
  {&__pyx_n_s_ndarray, __pyx_k_ndarray, sizeof(__pyx_k_ndarray), 0, 0, 1, 1},
  {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
  {&__pyx_n_s_negative_binomial, __pyx_k_negative_binomial, sizeof(__pyx_k_negative_binomial), 0, 0, 1, 1},
  {&__pyx_kp_u_negative_binomial_n_p_size_None, __pyx_k_negative_binomial_n_p_size_None, sizeof(__pyx_k_negative_binomial_n_p_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_ngood, __pyx_k_ngood, sizeof(__pyx_k_ngood), 0, 0, 1, 1},
  {&__pyx_kp_s_ngood_0, __pyx_k_ngood_0, sizeof(__pyx_k_ngood_0), 0, 0, 1, 0},
  {&__pyx_kp_s_ngood_nbad_nsample, __pyx_k_ngood_nbad_nsample, sizeof(__pyx_k_ngood_nbad_nsample), 0, 0, 1, 0},
  {&__pyx_n_s_nonc, __pyx_k_nonc, sizeof(__pyx_k_nonc), 0, 0, 1, 1},
  {&__pyx_kp_s_nonc_0, __pyx_k_nonc_0, sizeof(__pyx_k_nonc_0), 0, 0, 1, 0},
  {&__pyx_kp_s_nonc_0_2, __pyx_k_nonc_0_2, sizeof(__pyx_k_nonc_0_2), 0, 0, 1, 0},
  {&__pyx_n_s_noncentral_chisquare, __pyx_k_noncentral_chisquare, sizeof(__pyx_k_noncentral_chisquare), 0, 0, 1, 1},
  {&__pyx_kp_u_noncentral_chisquare_df_nonc_si, __pyx_k_noncentral_chisquare_df_nonc_si, sizeof(__pyx_k_noncentral_chisquare_df_nonc_si), 0, 1, 0, 0},
  {&__pyx_n_s_noncentral_f, __pyx_k_noncentral_f, sizeof(__pyx_k_noncentral_f), 0, 0, 1, 1},
  {&__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s, __pyx_k_noncentral_f_dfnum_dfden_nonc_s, sizeof(__pyx_k_noncentral_f_dfnum_dfden_nonc_s), 0, 1, 0, 0},
  {&__pyx_n_s_normal, __pyx_k_normal, sizeof(__pyx_k_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N, __pyx_k_normal_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_normal_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
  {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1},
  {&__pyx_n_s_nsample, __pyx_k_nsample, sizeof(__pyx_k_nsample), 0, 0, 1, 1},
  {&__pyx_kp_s_nsample_1, __pyx_k_nsample_1, sizeof(__pyx_k_nsample_1), 0, 0, 1, 0},
  {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
  {&__pyx_n_s_numpy_dual, __pyx_k_numpy_dual, sizeof(__pyx_k_numpy_dual), 0, 0, 1, 1},
  {&__pyx_n_s_operator, __pyx_k_operator, sizeof(__pyx_k_operator), 0, 0, 1, 1},
  {&__pyx_n_s_p, __pyx_k_p, sizeof(__pyx_k_p), 0, 0, 1, 1},
  {&__pyx_kp_s_p_0, __pyx_k_p_0, sizeof(__pyx_k_p_0), 0, 0, 1, 0},
  {&__pyx_kp_s_p_0_0, __pyx_k_p_0_0, sizeof(__pyx_k_p_0_0), 0, 0, 1, 0},
  {&__pyx_kp_s_p_0_0_2, __pyx_k_p_0_0_2, sizeof(__pyx_k_p_0_0_2), 0, 0, 1, 0},
  {&__pyx_kp_s_p_1, __pyx_k_p_1, sizeof(__pyx_k_p_1), 0, 0, 1, 0},
  {&__pyx_kp_s_p_1_0, __pyx_k_p_1_0, sizeof(__pyx_k_p_1_0), 0, 0, 1, 0},
  {&__pyx_kp_s_p_1_0_2, __pyx_k_p_1_0_2, sizeof(__pyx_k_p_1_0_2), 0, 0, 1, 0},
  {&__pyx_kp_s_p_is_nan, __pyx_k_p_is_nan, sizeof(__pyx_k_p_is_nan), 0, 0, 1, 0},
  {&__pyx_kp_s_p_must_be_1_dimensional, __pyx_k_p_must_be_1_dimensional, sizeof(__pyx_k_p_must_be_1_dimensional), 0, 0, 1, 0},
  {&__pyx_n_s_pareto, __pyx_k_pareto, sizeof(__pyx_k_pareto), 0, 0, 1, 1},
  {&__pyx_kp_u_pareto_a_size_None_Draw_samples, __pyx_k_pareto_a_size_None_Draw_samples, sizeof(__pyx_k_pareto_a_size_None_Draw_samples), 0, 1, 0, 0},
  {&__pyx_n_s_permutation, __pyx_k_permutation, sizeof(__pyx_k_permutation), 0, 0, 1, 1},
  {&__pyx_kp_u_permutation_x_Randomly_permute, __pyx_k_permutation_x_Randomly_permute, sizeof(__pyx_k_permutation_x_Randomly_permute), 0, 1, 0, 0},
  {&__pyx_n_s_poisson, __pyx_k_poisson, sizeof(__pyx_k_poisson), 0, 0, 1, 1},
  {&__pyx_kp_u_poisson_lam_1_0_size_None_Draw, __pyx_k_poisson_lam_1_0_size_None_Draw, sizeof(__pyx_k_poisson_lam_1_0_size_None_Draw), 0, 1, 0, 0},
  {&__pyx_n_s_poisson_lam_max, __pyx_k_poisson_lam_max, sizeof(__pyx_k_poisson_lam_max), 0, 0, 1, 1},
  {&__pyx_n_s_power, __pyx_k_power, sizeof(__pyx_k_power), 0, 0, 1, 1},
  {&__pyx_kp_u_power_a_size_None_Draws_samples, __pyx_k_power_a_size_None_Draws_samples, sizeof(__pyx_k_power_a_size_None_Draws_samples), 0, 1, 0, 0},
  {&__pyx_kp_s_probabilities_are_not_non_negati, __pyx_k_probabilities_are_not_non_negati, sizeof(__pyx_k_probabilities_are_not_non_negati), 0, 0, 1, 0},
  {&__pyx_kp_s_probabilities_do_not_sum_to_1, __pyx_k_probabilities_do_not_sum_to_1, sizeof(__pyx_k_probabilities_do_not_sum_to_1), 0, 0, 1, 0},
  {&__pyx_n_s_prod, __pyx_k_prod, sizeof(__pyx_k_prod), 0, 0, 1, 1},
  {&__pyx_n_s_pvals, __pyx_k_pvals, sizeof(__pyx_k_pvals), 0, 0, 1, 1},
  {&__pyx_n_s_rand, __pyx_k_rand, sizeof(__pyx_k_rand), 0, 0, 1, 1},
  {&__pyx_n_s_rand_2, __pyx_k_rand_2, sizeof(__pyx_k_rand_2), 0, 0, 1, 1},
  {&__pyx_kp_u_rand_d0_d1_dn_Random_values_in, __pyx_k_rand_d0_d1_dn_Random_values_in, sizeof(__pyx_k_rand_d0_d1_dn_Random_values_in), 0, 1, 0, 0},
  {&__pyx_n_s_randint, __pyx_k_randint, sizeof(__pyx_k_randint), 0, 0, 1, 1},
  {&__pyx_kp_u_randint_low_high_None_size_None, __pyx_k_randint_low_high_None_size_None, sizeof(__pyx_k_randint_low_high_None_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_randn, __pyx_k_randn, sizeof(__pyx_k_randn), 0, 0, 1, 1},
  {&__pyx_kp_u_randn_d0_d1_dn_Return_a_sample, __pyx_k_randn_d0_d1_dn_Return_a_sample, sizeof(__pyx_k_randn_d0_d1_dn_Return_a_sample), 0, 1, 0, 0},
  {&__pyx_n_s_random, __pyx_k_random, sizeof(__pyx_k_random), 0, 0, 1, 1},
  {&__pyx_n_s_random_integers, __pyx_k_random_integers, sizeof(__pyx_k_random_integers), 0, 0, 1, 1},
  {&__pyx_kp_u_random_integers_low_high_None_s, __pyx_k_random_integers_low_high_None_s, sizeof(__pyx_k_random_integers_low_high_None_s), 0, 1, 0, 0},
  {&__pyx_n_s_random_sample, __pyx_k_random_sample, sizeof(__pyx_k_random_sample), 0, 0, 1, 1},
  {&__pyx_kp_u_random_sample_size_None_Return, __pyx_k_random_sample_size_None_Return, sizeof(__pyx_k_random_sample_size_None_Return), 0, 1, 0, 0},
  {&__pyx_n_s_ravel, __pyx_k_ravel, sizeof(__pyx_k_ravel), 0, 0, 1, 1},
  {&__pyx_n_s_rayleigh, __pyx_k_rayleigh, sizeof(__pyx_k_rayleigh), 0, 0, 1, 1},
  {&__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr, __pyx_k_rayleigh_scale_1_0_size_None_Dr, sizeof(__pyx_k_rayleigh_scale_1_0_size_None_Dr), 0, 1, 0, 0},
  {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1},
  {&__pyx_n_s_replace, __pyx_k_replace, sizeof(__pyx_k_replace), 0, 0, 1, 1},
  {&__pyx_n_s_reshape, __pyx_k_reshape, sizeof(__pyx_k_reshape), 0, 0, 1, 1},
  {&__pyx_n_s_return_index, __pyx_k_return_index, sizeof(__pyx_k_return_index), 0, 0, 1, 1},
  {&__pyx_n_s_right, __pyx_k_right, sizeof(__pyx_k_right), 0, 0, 1, 1},
  {&__pyx_n_s_safe, __pyx_k_safe, sizeof(__pyx_k_safe), 0, 0, 1, 1},
  {&__pyx_n_s_scale, __pyx_k_scale, sizeof(__pyx_k_scale), 0, 0, 1, 1},
  {&__pyx_kp_s_scale_0, __pyx_k_scale_0, sizeof(__pyx_k_scale_0), 0, 0, 1, 0},
  {&__pyx_kp_s_scale_0_0, __pyx_k_scale_0_0, sizeof(__pyx_k_scale_0_0), 0, 0, 1, 0},
  {&__pyx_n_s_searchsorted, __pyx_k_searchsorted, sizeof(__pyx_k_searchsorted), 0, 0, 1, 1},
  {&__pyx_n_s_seed, __pyx_k_seed, sizeof(__pyx_k_seed), 0, 0, 1, 1},
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  __Pyx_GOTREF(__pyx_tuple__21);
  __Pyx_GIVEREF(__pyx_tuple__21);

  /* "mtrand.pyx":437
 *         if (multi.size != PyArray_SIZE(array)):
 *             raise ValueError("size is not compatible with inputs")
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < multi.size:
 *                 on_data = <double *>PyArray_MultiIter_DATA(multi, 1)
 */
  __pyx_tuple__22 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 437; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__22);
  __Pyx_GIVEREF(__pyx_tuple__22);

  /* "mtrand.pyx":460
 *         length = PyArray_SIZE(array)
 *         array_data = <long *>PyArray_DATA(array)
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < length:
 *                 array_data[i] = func(state, n, m, N)
 */
  __pyx_tuple__23 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 460; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__23);
  __Pyx_GIVEREF(__pyx_tuple__23);

  /* "mtrand.pyx":480
 *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
 *         array_data = <long *>PyArray_DATA(array)
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < multi.size:
 *                 on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
 */
  __pyx_tuple__24 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 480; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__24);
  __Pyx_GIVEREF(__pyx_tuple__24);

  /* "mtrand.pyx":493
 *                                                 <void *>oN)
 *         if (multi.size != PyArray_SIZE(array)):
 *             raise ValueError("size is not compatible with inputs")             # <<<<<<<<<<<<<<
 *         with lock, nogil:
 *             for i from 0 <= i < multi.size:
 */
  __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 493; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__25);
  __Pyx_GIVEREF(__pyx_tuple__25);

  /* "mtrand.pyx":494
 *         if (multi.size != PyArray_SIZE(array)):
 *             raise ValueError("size is not compatible with inputs")
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < multi.size:
 *                 on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
 */
  __pyx_tuple__26 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__26)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 494; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__26);
  __Pyx_GIVEREF(__pyx_tuple__26);

  /* "mtrand.pyx":517
 *         length = PyArray_SIZE(array)
 *         array_data = <long *>PyArray_DATA(array)
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < length:
 *                 array_data[i] = func(state, a)
 */
  __pyx_tuple__27 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 517; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__27);
  __Pyx_GIVEREF(__pyx_tuple__27);

  /* "mtrand.pyx":538
 *         array_data = <long *>PyArray_DATA(array)
 *         itera = <flatiter>PyArray_IterNew(<object>oa)
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < length:
 *                 array_data[i] = func(state, (<double *>(itera.dataptr))[0])
 */
  __pyx_tuple__28 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__28)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 538; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__28);
  __Pyx_GIVEREF(__pyx_tuple__28);

  /* "mtrand.pyx":547
 *         multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
 *         if (multi.size != PyArray_SIZE(array)):
 *             raise ValueError("size is not compatible with inputs")             # <<<<<<<<<<<<<<
 *         with lock, nogil:
 *             for i from 0 <= i < multi.size:
 */
  __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 547; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__29);
  __Pyx_GIVEREF(__pyx_tuple__29);

  /* "mtrand.pyx":548
 *         if (multi.size != PyArray_SIZE(array)):
 *             raise ValueError("size is not compatible with inputs")
 *         with lock, nogil:             # <<<<<<<<<<<<<<
 *             for i from 0 <= i < multi.size:
 *                 oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
 */
  __pyx_tuple__30 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__30)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 548; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__30);
  __Pyx_GIVEREF(__pyx_tuple__30);

  /* "mtrand.pyx":649
 *         try:
 *             if seed is None:
 *                 with self.lock:             # <<<<<<<<<<<<<<
 *                     errcode = rk_randomseed(self.internal_state)
 *             else:
 */
  __pyx_tuple__31 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 649; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__31);
  __Pyx_GIVEREF(__pyx_tuple__31);

  /* "mtrand.pyx":654
 *                 idx = operator.index(seed)
 *                 if idx > int(2**32 - 1) or idx < 0:
 *                     raise ValueError("Seed must be between 0 and 4294967295")             # <<<<<<<<<<<<<<
 *                 with self.lock:
 *                     rk_seed(idx, self.internal_state)
 */
  __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_Seed_must_be_between_0_and_42949); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__32);
  __Pyx_GIVEREF(__pyx_tuple__32);

  /* "mtrand.pyx":655
 *                 if idx > int(2**32 - 1) or idx < 0:
 *                     raise ValueError("Seed must be between 0 and 4294967295")
 *                 with self.lock:             # <<<<<<<<<<<<<<
 *                     rk_seed(idx, self.internal_state)
 *         except TypeError:
 */
  __pyx_tuple__33 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 655; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__33);
  __Pyx_GIVEREF(__pyx_tuple__33);

  /* "mtrand.pyx":660
 *             obj = np.asarray(seed).astype(np.int64, casting='safe')
 *             if ((obj > int(2**32 - 1)) | (obj < 0)).any():
 *                 raise ValueError("Seed must be between 0 and 4294967295")             # <<<<<<<<<<<<<<
 *             obj = obj.astype('L', casting='unsafe')
 *             with self.lock:
 */
  __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_Seed_must_be_between_0_and_42949); if (unlikely(!__pyx_tuple__34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 660; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__34);
  __Pyx_GIVEREF(__pyx_tuple__34);

  /* "mtrand.pyx":661
 *             if ((obj > int(2**32 - 1)) | (obj < 0)).any():
 *                 raise ValueError("Seed must be between 0 and 4294967295")
 *             obj = obj.astype('L', casting='unsafe')             # <<<<<<<<<<<<<<
 *             with self.lock:
 *                 init_by_array(self.internal_state, <unsigned long *>PyArray_DATA(obj),
 */
  __pyx_tuple__35 = PyTuple_Pack(1, __pyx_n_s_L); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 661; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__35);
  __Pyx_GIVEREF(__pyx_tuple__35);

  /* "mtrand.pyx":662
 *                 raise ValueError("Seed must be between 0 and 4294967295")
 *             obj = obj.astype('L', casting='unsafe')
 *             with self.lock:             # <<<<<<<<<<<<<<
 *                 init_by_array(self.internal_state, <unsigned long *>PyArray_DATA(obj),
 *                     PyArray_DIM(obj, 0))
 */
  __pyx_tuple__36 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 662; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__36);
  __Pyx_GIVEREF(__pyx_tuple__36);

  /* "mtrand.pyx":698
 *         cdef ndarray state "arrayObject_state"
 *         state = <ndarray>np.empty(624, np.uint)
 *         with self.lock:             # <<<<<<<<<<<<<<
 *             memcpy(<void*>PyArray_DATA(state), <void*>(self.internal_state.key), 624*sizeof(long))
 *             has_gauss = self.internal_state.has_gauss
 */
  __pyx_tuple__37 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__37);
  __Pyx_GIVEREF(__pyx_tuple__37);

  /* "mtrand.pyx":757
 *         algorithm_name = state[0]
 *         if algorithm_name != 'MT19937':
 *             raise ValueError("algorithm must be 'MT19937'")             # <<<<<<<<<<<<<<
 *         key, pos = state[1:3]
 *         if len(state) == 3:
 */
  __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_algorithm_must_be_MT19937); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 757; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__38);
  __Pyx_GIVEREF(__pyx_tuple__38);

  /* "mtrand.pyx":758
 *         if algorithm_name != 'MT19937':
 *             raise ValueError("algorithm must be 'MT19937'")
 *         key, pos = state[1:3]             # <<<<<<<<<<<<<<
 *         if len(state) == 3:
 *             has_gauss = 0
 */
  __pyx_slice__39 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 758; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_slice__39);
  __Pyx_GIVEREF(__pyx_slice__39);

  /* "mtrand.pyx":763
 *             cached_gaussian = 0.0
 *         else:
 *             has_gauss, cached_gaussian = state[3:5]             # <<<<<<<<<<<<<<
 *         try:
 *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_ULONG, 1, 1)
 */
  __pyx_slice__40 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_slice__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_slice__40);
  __Pyx_GIVEREF(__pyx_slice__40);

  /* "mtrand.pyx":770
 *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_LONG, 1, 1)
 *         if PyArray_DIM(obj, 0) != 624:
 *             raise ValueError("state must be 624 longs")             # <<<<<<<<<<<<<<
 *         with self.lock:
 *             memcpy(<void*>(self.internal_state.key), <void*>PyArray_DATA(obj), 624*sizeof(long))
 */
  __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_state_must_be_624_longs); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 770; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__41);
  __Pyx_GIVEREF(__pyx_tuple__41);

  /* "mtrand.pyx":771
 *         if PyArray_DIM(obj, 0) != 624:
 *             raise ValueError("state must be 624 longs")
 *         with self.lock:             # <<<<<<<<<<<<<<
 *             memcpy(<void*>(self.internal_state.key), <void*>PyArray_DATA(obj), 624*sizeof(long))
 *             self.internal_state.pos = pos
 */
  __pyx_tuple__42 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__42);
  __Pyx_GIVEREF(__pyx_tuple__42);

  /* "mtrand.pyx":945
 * 
 *         if lo >= hi :
 *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
 * 
 *         diff = <unsigned long>hi - <unsigned long>lo - 1UL
 */
  __pyx_tuple__43 = PyTuple_Pack(1, __pyx_kp_s_low_high); if (unlikely(!__pyx_tuple__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 945; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__43);
  __Pyx_GIVEREF(__pyx_tuple__43);

  /* "mtrand.pyx":949
 *         diff = <unsigned long>hi - <unsigned long>lo - 1UL
 *         if size is None:
 *             with self.lock:             # <<<<<<<<<<<<<<
 *                 rv = lo + <long>rk_interval(diff, self. internal_state)
 *             return rv
 */
  __pyx_tuple__44 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 949; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__44);
  __Pyx_GIVEREF(__pyx_tuple__44);

  /* "mtrand.pyx":956
 *             length = PyArray_SIZE(array)
 *             array_data = <long *>PyArray_DATA(array)
 *             with self.lock, nogil:             # <<<<<<<<<<<<<<
 *                 for i from 0 <= i < length:
 *                     rv = lo + <long>rk_interval(diff, self. internal_state)
 */
  __pyx_tuple__45 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 956; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__45);
  __Pyx_GIVEREF(__pyx_tuple__45);

  /* "mtrand.pyx":986
 *         cdef void *bytes
 *         bytestring = empty_py_bytes(length, &bytes)
 *         with self.lock, nogil:             # <<<<<<<<<<<<<<
 *             rk_fill(bytes, length, self.internal_state)
 *         return bytestring
 */
  __pyx_tuple__46 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 986; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__46);
  __Pyx_GIVEREF(__pyx_tuple__46);

  /* "mtrand.pyx":1076
 *                 pop_size = operator.index(a.item())
 *             except TypeError:
 *                 raise ValueError("a must be 1-dimensional or an integer")             # <<<<<<<<<<<<<<
 *             if pop_size <= 0:
 *                 raise ValueError("a must be greater than 0")
 */
  __pyx_tuple__47 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional_or_an_in); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1076; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__47);
  __Pyx_GIVEREF(__pyx_tuple__47);

  /* "mtrand.pyx":1078
 *                 raise ValueError("a must be 1-dimensional or an integer")
 *             if pop_size <= 0:
 *                 raise ValueError("a must be greater than 0")             # <<<<<<<<<<<<<<
 *         elif a.ndim != 1:
 *             raise ValueError("a must be 1-dimensional")
 */
  __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_greater_than_0); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1078; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__48);
  __Pyx_GIVEREF(__pyx_tuple__48);

  /* "mtrand.pyx":1080
 *                 raise ValueError("a must be greater than 0")
 *         elif a.ndim != 1:
 *             raise ValueError("a must be 1-dimensional")             # <<<<<<<<<<<<<<
 *         else:
 *             pop_size = a.shape[0]
 */
  __pyx_tuple__49 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1080; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__49);
  __Pyx_GIVEREF(__pyx_tuple__49);

  /* "mtrand.pyx":1084
 *             pop_size = a.shape[0]
 *             if pop_size is 0:
 *                 raise ValueError("a must be non-empty")             # <<<<<<<<<<<<<<
 * 
 *         if p is not None:
 */
  __pyx_tuple__50 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_non_empty); if (unlikely(!__pyx_tuple__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1084; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__50);
  __Pyx_GIVEREF(__pyx_tuple__50);

  /* "mtrand.pyx":1098
 * 
 *             if p.ndim != 1:
 *                 raise ValueError("p must be 1-dimensional")             # <<<<<<<<<<<<<<
 *             if p.size != pop_size:
 *                 raise ValueError("a and p must have same size")
 */
  __pyx_tuple__51 = PyTuple_Pack(1, __pyx_kp_s_p_must_be_1_dimensional); if (unlikely(!__pyx_tuple__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1098; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__51);
  __Pyx_GIVEREF(__pyx_tuple__51);

  /* "mtrand.pyx":1100
 *                 raise ValueError("p must be 1-dimensional")
 *             if p.size != pop_size:
 *                 raise ValueError("a and p must have same size")             # <<<<<<<<<<<<<<
 *             if np.logical_or.reduce(p < 0):
 *                 raise ValueError("probabilities are not non-negative")
 */
  __pyx_tuple__52 = PyTuple_Pack(1, __pyx_kp_s_a_and_p_must_have_same_size); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__52);
  __Pyx_GIVEREF(__pyx_tuple__52);

  /* "mtrand.pyx":1102
 *                 raise ValueError("a and p must have same size")
 *             if np.logical_or.reduce(p < 0):
 *                 raise ValueError("probabilities are not non-negative")             # <<<<<<<<<<<<<<
 *             if abs(kahan_sum(pix, d) - 1.) > atol:
 *                 raise ValueError("probabilities do not sum to 1")
 */
  __pyx_tuple__53 = PyTuple_Pack(1, __pyx_kp_s_probabilities_are_not_non_negati); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1102; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__53);
  __Pyx_GIVEREF(__pyx_tuple__53);

  /* "mtrand.pyx":1104
 *                 raise ValueError("probabilities are not non-negative")
 *             if abs(kahan_sum(pix, d) - 1.) > atol:
 *                 raise ValueError("probabilities do not sum to 1")             # <<<<<<<<<<<<<<
 * 
 *         shape = size
 */
  __pyx_tuple__54 = PyTuple_Pack(1, __pyx_kp_s_probabilities_do_not_sum_to_1); if (unlikely(!__pyx_tuple__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1104; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__54);
  __Pyx_GIVEREF(__pyx_tuple__54);

  /* "mtrand.pyx":1124
 *         else:
 *             if size > pop_size:
 *                 raise ValueError("Cannot take a larger sample than "             # <<<<<<<<<<<<<<
 *                                  "population when 'replace=False'")
 * 
 */
  __pyx_tuple__55 = PyTuple_Pack(1, __pyx_kp_s_Cannot_take_a_larger_sample_than); if (unlikely(!__pyx_tuple__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1124; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__55);
  __Pyx_GIVEREF(__pyx_tuple__55);

  /* "mtrand.pyx":1129
 *             if p is not None:
 *                 if np.count_nonzero(p > 0) < size:
 *                     raise ValueError("Fewer non-zero entries in p than size")             # <<<<<<<<<<<<<<
 *                 n_uniq = 0
 *                 p = p.copy()
 */
  __pyx_tuple__56 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1129; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__56);
  __Pyx_GIVEREF(__pyx_tuple__56);

  /* "mtrand.pyx":1154
 *         if shape is None and isinstance(idx, np.ndarray):
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 *         #Use samples as indices for a if a is array-like
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  __pyx_tuple__57 = PyTuple_Pack(1, __pyx_int_0); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__57);
  __Pyx_GIVEREF(__pyx_tuple__57);

  /* "mtrand.pyx":1166
 *             # array, taking into account that np.array(item) may not work
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  __pyx_tuple__58 = PyTuple_Pack(1, __pyx_empty_tuple); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1166; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__58);
  __Pyx_GIVEREF(__pyx_tuple__58);

  /* "mtrand.pyx":1252
 *         fscale = fhigh - flow
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 * 
 *         if not PyErr_Occurred():
 */
  __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_Range_exceeds_valid_bounds); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1252; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__59);
  __Pyx_GIVEREF(__pyx_tuple__59);

  /* "mtrand.pyx":1572
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_normal, size, floc,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__60);
  __Pyx_GIVEREF(__pyx_tuple__60);

  /* "mtrand.pyx":1581
 *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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 *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__61 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1581; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__61);
  __Pyx_GIVEREF(__pyx_tuple__61);

  /* "mtrand.pyx":1630
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 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             if fb <= 0:
 *                 raise ValueError("b <= 0")
 */
  __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1630; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__62);
  __Pyx_GIVEREF(__pyx_tuple__62);

  /* "mtrand.pyx":1632
 *                 raise ValueError("a <= 0")
 *             if fb <= 0:
 *                 raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb,
 *                                   self.lock)
 */
  __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1632; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__63);
  __Pyx_GIVEREF(__pyx_tuple__63);

  /* "mtrand.pyx":1641
 *         ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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 *         if np.any(np.less_equal(ob, 0)):
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 */
  __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__64);
  __Pyx_GIVEREF(__pyx_tuple__64);

  /* "mtrand.pyx":1643
 *             raise ValueError("a <= 0")
 *         if np.any(np.less_equal(ob, 0)):
 *             raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_beta, size, oa, ob,
 *                            self.lock)
 */
  __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1643; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__65);
  __Pyx_GIVEREF(__pyx_tuple__65);

  /* "mtrand.pyx":1692
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_exponential, size,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__66 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__66);
  __Pyx_GIVEREF(__pyx_tuple__66);

  /* "mtrand.pyx":1701
 *                                             NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oscale, 0.0)):
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 *         return cont1_array(self.internal_state, rk_exponential, size, oscale,
 *                            self.lock)
 */
  __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__67);
  __Pyx_GIVEREF(__pyx_tuple__67);

  /* "mtrand.pyx":1810
 *         if not PyErr_Occurred():
 *             if fshape <= 0:
 *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_standard_gamma,
 *                                   size, fshape, self.lock)
 */
  __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1810; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__68);
  __Pyx_GIVEREF(__pyx_tuple__68);

  /* "mtrand.pyx":1818
 *                                             NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oshape, 0.0)):
 *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_standard_gamma, size,
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  __pyx_tuple__69 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__69);
  __Pyx_GIVEREF(__pyx_tuple__69);

  /* "mtrand.pyx":1900
 *         if not PyErr_Occurred():
 *             if fshape <= 0:
 *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")
 */
  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__70);
  __Pyx_GIVEREF(__pyx_tuple__70);

  /* "mtrand.pyx":1902
 *                 raise ValueError("shape <= 0")
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_gamma, size, fshape,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__71 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1902; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__71);
  __Pyx_GIVEREF(__pyx_tuple__71);

  /* "mtrand.pyx":1910
 *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oshape, 0.0)):
 *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0")
 */
  __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__72);
  __Pyx_GIVEREF(__pyx_tuple__72);

  /* "mtrand.pyx":1912
 *             raise ValueError("shape <= 0")
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale,
 *                            self.lock)
 */
  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1912; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__73);
  __Pyx_GIVEREF(__pyx_tuple__73);

  /* "mtrand.pyx":2005
 *         if not PyErr_Occurred():
 *             if fdfnum <= 0:
 *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
 *             if fdfden <= 0:
 *                 raise ValueError("scale <= 0")
 */
  __pyx_tuple__74 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2005; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__74);
  __Pyx_GIVEREF(__pyx_tuple__74);

  /* "mtrand.pyx":2007
 *                 raise ValueError("shape <= 0")
 *             if fdfden <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum,
 *                                   fdfden, self.lock)
 */
  __pyx_tuple__75 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__75);
  __Pyx_GIVEREF(__pyx_tuple__75);

  /* "mtrand.pyx":2016
 *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(odfnum, 0.0)):
 *             raise ValueError("dfnum <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less_equal(odfden, 0.0)):
 *             raise ValueError("dfden <= 0")
 */
  __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_dfnum_0); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2016; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__76);
  __Pyx_GIVEREF(__pyx_tuple__76);

  /* "mtrand.pyx":2018
 *             raise ValueError("dfnum <= 0")
 *         if np.any(np.less_equal(odfden, 0.0)):
 *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden,
 *                            self.lock)
 */
  __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2018; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__77);
  __Pyx_GIVEREF(__pyx_tuple__77);

  /* "mtrand.pyx":2095
 *         if not PyErr_Occurred():
 *             if fdfnum <= 1:
 *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")
 */
  __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2095; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__78);
  __Pyx_GIVEREF(__pyx_tuple__78);

  /* "mtrand.pyx":2097
 *                 raise ValueError("dfnum <= 1")
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
 *             if fnonc < 0:
 *                 raise ValueError("nonc < 0")
 */
  __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2097; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__79);
  __Pyx_GIVEREF(__pyx_tuple__79);

  /* "mtrand.pyx":2099
 *                 raise ValueError("dfden <= 0")
 *             if fnonc < 0:
 *                 raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
 *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
 *                                   fdfnum, fdfden, fnonc, self.lock)
 */
  __pyx_tuple__80 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2099; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__80);
  __Pyx_GIVEREF(__pyx_tuple__80);

  /* "mtrand.pyx":2110
 * 
 *         if np.any(np.less_equal(odfnum, 1.0)):
 *             raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
 *         if np.any(np.less_equal(odfden, 0.0)):
 *             raise ValueError("dfden <= 0")
 */
  __pyx_tuple__81 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2110; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__81);
  __Pyx_GIVEREF(__pyx_tuple__81);

  /* "mtrand.pyx":2112
 *             raise ValueError("dfnum <= 1")
 *         if np.any(np.less_equal(odfden, 0.0)):
 *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(ononc, 0.0)):
 *             raise ValueError("nonc < 0")
 */
  __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2112; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__82);
  __Pyx_GIVEREF(__pyx_tuple__82);

  /* "mtrand.pyx":2114
 *             raise ValueError("dfden <= 0")
 *         if np.any(np.less(ononc, 0.0)):
 *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
 *         return cont3_array(self.internal_state, rk_noncentral_f, size, odfnum,
 *                            odfden, ononc, self.lock)
 */
  __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2114; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__83);
  __Pyx_GIVEREF(__pyx_tuple__83);

  /* "mtrand.pyx":2187
 *         if not PyErr_Occurred():
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf,
 *                                   self.lock)
 */
  __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2187; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__84);
  __Pyx_GIVEREF(__pyx_tuple__84);

  /* "mtrand.pyx":2195
 *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(odf, 0.0)):
 *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_chisquare, size, odf,
 *                            self.lock)
 */
  __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2195; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__85);
  __Pyx_GIVEREF(__pyx_tuple__85);

  /* "mtrand.pyx":2279
 *         if not PyErr_Occurred():
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             if fnonc <= 0:
 *                 raise ValueError("nonc <= 0")
 */
  __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2279; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__86);
  __Pyx_GIVEREF(__pyx_tuple__86);

  /* "mtrand.pyx":2281
 *                 raise ValueError("df <= 0")
 *             if fnonc <= 0:
 *                 raise ValueError("nonc <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
 *                                   size, fdf, fnonc, self.lock)
 */
  __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_s_nonc_0_2); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__87);
  __Pyx_GIVEREF(__pyx_tuple__87);

  /* "mtrand.pyx":2290
 *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(odf, 0.0)):
 *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less_equal(ononc, 0.0)):
 *             raise ValueError("nonc < 0")
 */
  __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2290; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__88);
  __Pyx_GIVEREF(__pyx_tuple__88);

  /* "mtrand.pyx":2292
 *             raise ValueError("df <= 0")
 *         if np.any(np.less_equal(ononc, 0.0)):
 *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
 *                            odf, ononc, self.lock)
 */
  __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2292; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__89);
  __Pyx_GIVEREF(__pyx_tuple__89);

  /* "mtrand.pyx":2453
 *         if not PyErr_Occurred():
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_standard_t, size,
 *                                   fdf, self.lock)
 */
  __pyx_tuple__90 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2453; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__90);
  __Pyx_GIVEREF(__pyx_tuple__90);

  /* "mtrand.pyx":2461
 *         odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(odf, 0.0)):
 *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_standard_t, size, odf,
 *                            self.lock)
 */
  __pyx_tuple__91 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2461; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__91);
  __Pyx_GIVEREF(__pyx_tuple__91);

  /* "mtrand.pyx":2549
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 *             if fkappa < 0:
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 */
  __pyx_tuple__92 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__92);
  __Pyx_GIVEREF(__pyx_tuple__92);

  /* "mtrand.pyx":2559
 *                                             NPY_ARRAY_ALIGNED)
 *         if np.any(np.less(okappa, 0.0)):
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 *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa,
 *                            self.lock)
 */
  __pyx_tuple__93 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2559; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__93);
  __Pyx_GIVEREF(__pyx_tuple__93);

  /* "mtrand.pyx":2656
 *         if not PyErr_Occurred():
 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_pareto, size, fa,
 *                                   self.lock)
 */
  __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2656; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__94);
  __Pyx_GIVEREF(__pyx_tuple__94);

  /* "mtrand.pyx":2664
 *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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 *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_pareto, size, oa, self.lock)
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 */
  __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2664; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__95);
  __Pyx_GIVEREF(__pyx_tuple__95);

  /* "mtrand.pyx":2764
 *         if not PyErr_Occurred():
 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_weibull, size, fa,
 *                                   self.lock)
 */
  __pyx_tuple__96 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2764; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__96);
  __Pyx_GIVEREF(__pyx_tuple__96);

  /* "mtrand.pyx":2772
 *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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 *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_weibull, size, oa,
 *                            self.lock)
 */
  __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2772; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__97);
  __Pyx_GIVEREF(__pyx_tuple__97);

  /* "mtrand.pyx":2876
 *         if not PyErr_Occurred():
 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_power, size, fa,
 *                                   self.lock)
 */
  __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2876; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__98);
  __Pyx_GIVEREF(__pyx_tuple__98);

  /* "mtrand.pyx":2884
 *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oa, 0.0)):
 *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_power, size, oa, self.lock)
 * 
 */
  __pyx_tuple__99 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2884; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__99);
  __Pyx_GIVEREF(__pyx_tuple__99);

  /* "mtrand.pyx":2971
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc,
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 */
  __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2971; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__100);
  __Pyx_GIVEREF(__pyx_tuple__100);

  /* "mtrand.pyx":2979
 *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__101 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2979; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__101);
  __Pyx_GIVEREF(__pyx_tuple__101);

  /* "mtrand.pyx":3100
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__102);
  __Pyx_GIVEREF(__pyx_tuple__102);

  /* "mtrand.pyx":3108
 *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3108; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__103);
  __Pyx_GIVEREF(__pyx_tuple__103);

  /* "mtrand.pyx":3191
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__104 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3191; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__104);
  __Pyx_GIVEREF(__pyx_tuple__104);

  /* "mtrand.pyx":3199
 *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_logistic, size, oloc,
 *                            oscale, self.lock)
 */
  __pyx_tuple__105 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3199; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__105);
  __Pyx_GIVEREF(__pyx_tuple__105);

  /* "mtrand.pyx":3315
 *         if not PyErr_Occurred():
 *             if fsigma <= 0:
 *                 raise ValueError("sigma <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_lognormal, size,
 *                                   fmean, fsigma, self.lock)
 */
  __pyx_tuple__106 = PyTuple_Pack(1, __pyx_kp_s_sigma_0); if (unlikely(!__pyx_tuple__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__106);
  __Pyx_GIVEREF(__pyx_tuple__106);

  /* "mtrand.pyx":3324
 *         osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(osigma, 0.0)):
 *             raise ValueError("sigma <= 0.0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_lognormal, size, omean,
 *                            osigma, self.lock)
 */
  __pyx_tuple__107 = PyTuple_Pack(1, __pyx_kp_s_sigma_0_0); if (unlikely(!__pyx_tuple__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3324; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__107);
  __Pyx_GIVEREF(__pyx_tuple__107);

  /* "mtrand.pyx":3391
 *         if not PyErr_Occurred():
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_rayleigh, size,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3391; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__108);
  __Pyx_GIVEREF(__pyx_tuple__108);

  /* "mtrand.pyx":3399
 *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oscale, 0.0)):
 *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale,
 *                            self.lock)
 */
  __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__109);
  __Pyx_GIVEREF(__pyx_tuple__109);

  /* "mtrand.pyx":3472
 *         if not PyErr_Occurred():
 *             if fmean <= 0:
 *                 raise ValueError("mean <= 0")             # <<<<<<<<<<<<<<
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")
 */
  __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_mean_0); if (unlikely(!__pyx_tuple__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3472; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__110);
  __Pyx_GIVEREF(__pyx_tuple__110);

  /* "mtrand.pyx":3474
 *                 raise ValueError("mean <= 0")
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_wald, size, fmean,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__111 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3474; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__111);
  __Pyx_GIVEREF(__pyx_tuple__111);

  /* "mtrand.pyx":3482
 *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(omean,0.0)):
 *             raise ValueError("mean <= 0.0")             # <<<<<<<<<<<<<<
 *         elif np.any(np.less_equal(oscale,0.0)):
 *             raise ValueError("scale <= 0.0")
 */
  __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_mean_0_0); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3482; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__112);
  __Pyx_GIVEREF(__pyx_tuple__112);

  /* "mtrand.pyx":3484
 *             raise ValueError("mean <= 0.0")
 *         elif np.any(np.less_equal(oscale,0.0)):
 *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_wald, size, omean, oscale,
 *                            self.lock)
 */
  __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3484; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__113);
  __Pyx_GIVEREF(__pyx_tuple__113);

  /* "mtrand.pyx":3556
 *         if not PyErr_Occurred():
 *             if fleft > fmode:
 *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
 *             if fmode > fright:
 *                 raise ValueError("mode > right")
 */
  __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__114);
  __Pyx_GIVEREF(__pyx_tuple__114);

  /* "mtrand.pyx":3558
 *                 raise ValueError("left > mode")
 *             if fmode > fright:
 *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
 *             if fleft == fright:
 *                 raise ValueError("left == right")
 */
  __pyx_tuple__115 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3558; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__115);
  __Pyx_GIVEREF(__pyx_tuple__115);

  /* "mtrand.pyx":3560
 *                 raise ValueError("mode > right")
 *             if fleft == fright:
 *                 raise ValueError("left == right")             # <<<<<<<<<<<<<<
 *             return cont3_array_sc(self.internal_state, rk_triangular, size,
 *                                   fleft, fmode, fright, self.lock)
 */
  __pyx_tuple__116 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3560; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__116);
  __Pyx_GIVEREF(__pyx_tuple__116);

  /* "mtrand.pyx":3570
 * 
 *         if np.any(np.greater(oleft, omode)):
 *             raise ValueError("left > mode")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(omode, oright)):
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 */
  __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3570; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__117);
  __Pyx_GIVEREF(__pyx_tuple__117);

  /* "mtrand.pyx":3572
 *             raise ValueError("left > mode")
 *         if np.any(np.greater(omode, oright)):
 *             raise ValueError("mode > right")             # <<<<<<<<<<<<<<
 *         if np.any(np.equal(oleft, oright)):
 *             raise ValueError("left == right")
 */
  __pyx_tuple__118 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__118);
  __Pyx_GIVEREF(__pyx_tuple__118);

  /* "mtrand.pyx":3574
 *             raise ValueError("mode > right")
 *         if np.any(np.equal(oleft, oright)):
 *             raise ValueError("left == right")             # <<<<<<<<<<<<<<
 *         return cont3_array(self.internal_state, rk_triangular, size, oleft,
 *                            omode, oright, self.lock)
 */
  __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3574; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__119);
  __Pyx_GIVEREF(__pyx_tuple__119);

  /* "mtrand.pyx":3669
 *         if not PyErr_Occurred():
 *             if ln < 0:
 *                 raise ValueError("n < 0")             # <<<<<<<<<<<<<<
 *             if fp < 0:
 *                 raise ValueError("p < 0")
 */
  __pyx_tuple__120 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3669; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__120);
  __Pyx_GIVEREF(__pyx_tuple__120);

  /* "mtrand.pyx":3671
 *                 raise ValueError("n < 0")
 *             if fp < 0:
 *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *             elif fp > 1:
 *                 raise ValueError("p > 1")
 */
  __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3671; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__121);
  __Pyx_GIVEREF(__pyx_tuple__121);

  /* "mtrand.pyx":3673
 *                 raise ValueError("p < 0")
 *             elif fp > 1:
 *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
 *             elif np.isnan(fp):
 *                 raise ValueError("p is nan")
 */
  __pyx_tuple__122 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3673; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__122);
  __Pyx_GIVEREF(__pyx_tuple__122);

  /* "mtrand.pyx":3675
 *                 raise ValueError("p > 1")
 *             elif np.isnan(fp):
 *                 raise ValueError("p is nan")             # <<<<<<<<<<<<<<
 *             return discnp_array_sc(self.internal_state, rk_binomial, size, ln,
 *                                    fp, self.lock)
 */
  __pyx_tuple__123 = PyTuple_Pack(1, __pyx_kp_s_p_is_nan); if (unlikely(!__pyx_tuple__123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3675; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__123);
  __Pyx_GIVEREF(__pyx_tuple__123);

  /* "mtrand.pyx":3684
 *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less(n, 0)):
 *             raise ValueError("n < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(p, 0)):
 *             raise ValueError("p < 0")
 */
  __pyx_tuple__124 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3684; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__124);
  __Pyx_GIVEREF(__pyx_tuple__124);

  /* "mtrand.pyx":3686
 *             raise ValueError("n < 0")
 *         if np.any(np.less(p, 0)):
 *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(p, 1)):
 *             raise ValueError("p > 1")
 */
  __pyx_tuple__125 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__125)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3686; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__125);
  __Pyx_GIVEREF(__pyx_tuple__125);

  /* "mtrand.pyx":3688
 *             raise ValueError("p < 0")
 *         if np.any(np.greater(p, 1)):
 *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
 *         return discnp_array(self.internal_state, rk_binomial, size, on, op,
 *                             self.lock)
 */
  __pyx_tuple__126 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__126)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3688; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__126);
  __Pyx_GIVEREF(__pyx_tuple__126);

  /* "mtrand.pyx":3767
 *         if not PyErr_Occurred():
 *             if fn <= 0:
 *                 raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
 *             if fp < 0:
 *                 raise ValueError("p < 0")
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  __pyx_tuple__127 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3767; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__127);
  __Pyx_GIVEREF(__pyx_tuple__127);

  /* "mtrand.pyx":3769
 *                 raise ValueError("n <= 0")
 *             if fp < 0:
 *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *             elif fp > 1:
 *                 raise ValueError("p > 1")
 */
  __pyx_tuple__128 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__128)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3769; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__128);
  __Pyx_GIVEREF(__pyx_tuple__128);

  /* "mtrand.pyx":3771
 *                 raise ValueError("p < 0")
 *             elif fp > 1:
 *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
 *             return discdd_array_sc(self.internal_state, rk_negative_binomial,
 *                                    size, fn, fp, self.lock)
 */
  __pyx_tuple__129 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__129)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3771; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__129);
  __Pyx_GIVEREF(__pyx_tuple__129);

  /* "mtrand.pyx":3780
 *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(n, 0)):
 *             raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(p, 0)):
 *             raise ValueError("p < 0")
 */
  __pyx_tuple__130 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__130)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3780; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__130);
  __Pyx_GIVEREF(__pyx_tuple__130);

  /* "mtrand.pyx":3782
 *             raise ValueError("n <= 0")
 *         if np.any(np.less(p, 0)):
 *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(p, 1)):
 *             raise ValueError("p > 1")
 */
  __pyx_tuple__131 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__131)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3782; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__131);
  __Pyx_GIVEREF(__pyx_tuple__131);

  /* "mtrand.pyx":3784
 *             raise ValueError("p < 0")
 *         if np.any(np.greater(p, 1)):
 *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
 *         return discdd_array(self.internal_state, rk_negative_binomial, size,
 *                             on, op, self.lock)
 */
  __pyx_tuple__132 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__132)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__132);
  __Pyx_GIVEREF(__pyx_tuple__132);

  /* "mtrand.pyx":3858
 *         if not PyErr_Occurred():
 *             if lam < 0:
 *                 raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
 *             if lam > self.poisson_lam_max:
 *                 raise ValueError("lam value too large")
 */
  __pyx_tuple__133 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__133)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3858; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__133);
  __Pyx_GIVEREF(__pyx_tuple__133);

  /* "mtrand.pyx":3860
 *                 raise ValueError("lam < 0")
 *             if lam > self.poisson_lam_max:
 *                 raise ValueError("lam value too large")             # <<<<<<<<<<<<<<
 *             return discd_array_sc(self.internal_state, rk_poisson, size, flam,
 *                                   self.lock)
 */
  __pyx_tuple__134 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large); if (unlikely(!__pyx_tuple__134)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3860; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__134);
  __Pyx_GIVEREF(__pyx_tuple__134);

  /* "mtrand.pyx":3868
 *         olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less(olam, 0)):
 *             raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(olam, self.poisson_lam_max)):
 *             raise ValueError("lam value too large.")
 */
  __pyx_tuple__135 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__135)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3868; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__135);
  __Pyx_GIVEREF(__pyx_tuple__135);

  /* "mtrand.pyx":3870
 *             raise ValueError("lam < 0")
 *         if np.any(np.greater(olam, self.poisson_lam_max)):
 *             raise ValueError("lam value too large.")             # <<<<<<<<<<<<<<
 *         return discd_array(self.internal_state, rk_poisson, size, olam,
 *                            self.lock)
 */
  __pyx_tuple__136 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large_2); if (unlikely(!__pyx_tuple__136)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3870; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__136);
  __Pyx_GIVEREF(__pyx_tuple__136);

  /* "mtrand.pyx":3951
 *         if not PyErr_Occurred():
 *             if fa <= 1.0:
 *                 raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
 *             return discd_array_sc(self.internal_state, rk_zipf, size, fa,
 *                                   self.lock)
 */
  __pyx_tuple__137 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__137)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3951; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__137);
  __Pyx_GIVEREF(__pyx_tuple__137);

  /* "mtrand.pyx":3959
 *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(oa, 1.0)):
 *             raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
 *         return discd_array(self.internal_state, rk_zipf, size, oa, self.lock)
 * 
 */
  __pyx_tuple__138 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__138)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3959; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__138);
  __Pyx_GIVEREF(__pyx_tuple__138);

  /* "mtrand.pyx":4014
 *         if not PyErr_Occurred():
 *             if fp < 0.0:
 *                 raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
 *             if fp > 1.0:
 *                 raise ValueError("p > 1.0")
 */
  __pyx_tuple__139 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__139)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4014; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__139);
  __Pyx_GIVEREF(__pyx_tuple__139);

  /* "mtrand.pyx":4016
 *                 raise ValueError("p < 0.0")
 *             if fp > 1.0:
 *                 raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
 *             return discd_array_sc(self.internal_state, rk_geometric, size, fp,
 *                                   self.lock)
 */
  __pyx_tuple__140 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__140)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4016; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__140);
  __Pyx_GIVEREF(__pyx_tuple__140);

  /* "mtrand.pyx":4025
 *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less(op, 0.0)):
 *             raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(op, 1.0)):
 *             raise ValueError("p > 1.0")
 */
  __pyx_tuple__141 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__141)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4025; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__141);
  __Pyx_GIVEREF(__pyx_tuple__141);

  /* "mtrand.pyx":4027
 *             raise ValueError("p < 0.0")
 *         if np.any(np.greater(op, 1.0)):
 *             raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
 *         return discd_array(self.internal_state, rk_geometric, size, op,
 *                            self.lock)
 */
  __pyx_tuple__142 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__142)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4027; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__142);
  __Pyx_GIVEREF(__pyx_tuple__142);

  /* "mtrand.pyx":4125
 *         if not PyErr_Occurred():
 *             if lngood < 0:
 *                 raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
 *             if lnbad < 0:
 *                 raise ValueError("nbad < 0")
 */
  __pyx_tuple__143 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__143)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4125; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__143);
  __Pyx_GIVEREF(__pyx_tuple__143);

  /* "mtrand.pyx":4127
 *                 raise ValueError("ngood < 0")
 *             if lnbad < 0:
 *                 raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
 *             if lnsample < 1:
 *                 raise ValueError("nsample < 1")
 */
  __pyx_tuple__144 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__144)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4127; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__144);
  __Pyx_GIVEREF(__pyx_tuple__144);

  /* "mtrand.pyx":4129
 *                 raise ValueError("nbad < 0")
 *             if lnsample < 1:
 *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
 *             if lngood + lnbad < lnsample:
 *                 raise ValueError("ngood + nbad < nsample")
 */
  __pyx_tuple__145 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__145)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4129; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__145);
  __Pyx_GIVEREF(__pyx_tuple__145);

  /* "mtrand.pyx":4131
 *                 raise ValueError("nsample < 1")
 *             if lngood + lnbad < lnsample:
 *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
 *             return discnmN_array_sc(self.internal_state, rk_hypergeometric,
 *                                     size, lngood, lnbad, lnsample, self.lock)
 */
  __pyx_tuple__146 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__146)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4131; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__146);
  __Pyx_GIVEREF(__pyx_tuple__146);

  /* "mtrand.pyx":4142
 *                                              NPY_ARRAY_ALIGNED)
 *         if np.any(np.less(ongood, 0)):
 *             raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(onbad, 0)):
 *             raise ValueError("nbad < 0")
 */
  __pyx_tuple__147 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__147)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__147);
  __Pyx_GIVEREF(__pyx_tuple__147);

  /* "mtrand.pyx":4144
 *             raise ValueError("ngood < 0")
 *         if np.any(np.less(onbad, 0)):
 *             raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(onsample, 1)):
 *             raise ValueError("nsample < 1")
 */
  __pyx_tuple__148 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__148)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4144; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__148);
  __Pyx_GIVEREF(__pyx_tuple__148);

  /* "mtrand.pyx":4146
 *             raise ValueError("nbad < 0")
 *         if np.any(np.less(onsample, 1)):
 *             raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(np.add(ongood, onbad),onsample)):
 *             raise ValueError("ngood + nbad < nsample")
 */
  __pyx_tuple__149 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__149)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4146; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__149);
  __Pyx_GIVEREF(__pyx_tuple__149);

  /* "mtrand.pyx":4148
 *             raise ValueError("nsample < 1")
 *         if np.any(np.less(np.add(ongood, onbad),onsample)):
 *             raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
 *         return discnmN_array(self.internal_state, rk_hypergeometric, size,
 *                              ongood, onbad, onsample, self.lock)
 */
  __pyx_tuple__150 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__150)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4148; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__150);
  __Pyx_GIVEREF(__pyx_tuple__150);

  /* "mtrand.pyx":4233
 *         if not PyErr_Occurred():
 *             if fp <= 0.0:
 *                 raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
 *             if fp >= 1.0:
 *                 raise ValueError("p >= 1.0")
 */
  __pyx_tuple__151 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__151)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4233; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__151);
  __Pyx_GIVEREF(__pyx_tuple__151);

  /* "mtrand.pyx":4235
 *                 raise ValueError("p <= 0.0")
 *             if fp >= 1.0:
 *                 raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
 *             return discd_array_sc(self.internal_state, rk_logseries, size, fp,
 *                                   self.lock)
 */
  __pyx_tuple__152 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__152)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__152);
  __Pyx_GIVEREF(__pyx_tuple__152);

  /* "mtrand.pyx":4243
 *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
 *         if np.any(np.less_equal(op, 0.0)):
 *             raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater_equal(op, 1.0)):
 *             raise ValueError("p >= 1.0")
 */
  __pyx_tuple__153 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__153)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4243; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__153);
  __Pyx_GIVEREF(__pyx_tuple__153);

  /* "mtrand.pyx":4245
 *             raise ValueError("p <= 0.0")
 *         if np.any(np.greater_equal(op, 1.0)):
 *             raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
 *         return discd_array(self.internal_state, rk_logseries, size, op,
 *                            self.lock)
 */
  __pyx_tuple__154 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__154)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4245; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__154);
  __Pyx_GIVEREF(__pyx_tuple__154);

  /* "mtrand.pyx":4359
 * 
 *         if len(mean.shape) != 1:
 *                raise ValueError("mean must be 1 dimensional")             # <<<<<<<<<<<<<<
 *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
 *                raise ValueError("cov must be 2 dimensional and square")
 */
  __pyx_tuple__155 = PyTuple_Pack(1, __pyx_kp_s_mean_must_be_1_dimensional); if (unlikely(!__pyx_tuple__155)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4359; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__155);
  __Pyx_GIVEREF(__pyx_tuple__155);

  /* "mtrand.pyx":4361
 *                raise ValueError("mean must be 1 dimensional")
 *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
 *                raise ValueError("cov must be 2 dimensional and square")             # <<<<<<<<<<<<<<
 *         if mean.shape[0] != cov.shape[0]:
 *                raise ValueError("mean and cov must have same length")
 */
  __pyx_tuple__156 = PyTuple_Pack(1, __pyx_kp_s_cov_must_be_2_dimensional_and_sq); if (unlikely(!__pyx_tuple__156)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4361; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__156);
  __Pyx_GIVEREF(__pyx_tuple__156);

  /* "mtrand.pyx":4363
 *                raise ValueError("cov must be 2 dimensional and square")
 *         if mean.shape[0] != cov.shape[0]:
 *                raise ValueError("mean and cov must have same length")             # <<<<<<<<<<<<<<
 * 
 *         # Compute shape of output and create a matrix of independent
 */
  __pyx_tuple__157 = PyTuple_Pack(1, __pyx_kp_s_mean_and_cov_must_have_same_leng); if (unlikely(!__pyx_tuple__157)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__157);
  __Pyx_GIVEREF(__pyx_tuple__157);

  /* "mtrand.pyx":4369
 *         # with the same length as mean and as many rows are necessary to
 *         # form a matrix of shape final_shape.
 *         final_shape = list(shape[:])             # <<<<<<<<<<<<<<
 *         final_shape.append(mean.shape[0])
 *         x = self.standard_normal(final_shape).reshape(-1, mean.shape[0])
 */
  __pyx_slice__158 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__158)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4369; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_slice__158);
  __Pyx_GIVEREF(__pyx_slice__158);

  /* "mtrand.pyx":4393
 *                           RuntimeWarning)
 * 
 *         x = np.dot(x, np.sqrt(s)[:, None] * v)             # <<<<<<<<<<<<<<
 *         x += mean
 *         x.shape = tuple(final_shape)
 */
  __pyx_slice__159 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__159)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4393; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_slice__159);
  __Pyx_GIVEREF(__pyx_slice__159);
  __pyx_tuple__160 = PyTuple_Pack(2, __pyx_slice__159, Py_None); if (unlikely(!__pyx_tuple__160)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4393; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
  __Pyx_GOTREF(__pyx_tuple__160);
  __Pyx_GIVEREF(__pyx_tuple__160);

  /* "mtrand.pyx":4471
 * 
 *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
 *             raise ValueError("sum(pvals[:-1]) > 1.0")             # <<<<<<<<<<<<<<
 * 
 *         shape = _shape_from_size(size, d)
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#endif
        PyObject *fixed_cause;
        if (cause == Py_None) {
            fixed_cause = NULL;
        } else if (PyExceptionClass_Check(cause)) {
            fixed_cause = PyObject_CallObject(cause, NULL);
            if (fixed_cause == NULL)
                goto bad;
        } else if (PyExceptionInstance_Check(cause)) {
            fixed_cause = cause;
            Py_INCREF(fixed_cause);
        } else {
            PyErr_SetString(PyExc_TypeError,
                            "exception causes must derive from "
                            "BaseException");
            goto bad;
        }
        PyException_SetCause(value, fixed_cause);
    }
    PyErr_SetObject(type, value);
    if (tb) {
        PyThreadState *tstate = PyThreadState_GET();
        PyObject* tmp_tb = tstate->curexc_traceback;
        if (tb != tmp_tb) {
            Py_INCREF(tb);
            tstate->curexc_traceback = tb;
            Py_XDECREF(tmp_tb);
        }
    }
bad:
    Py_XDECREF(owned_instance);
    return;
}
#endif

static void __Pyx_RaiseArgtupleInvalid(
    const char* func_name,
    int exact,
    Py_ssize_t num_min,
    Py_ssize_t num_max,
    Py_ssize_t num_found)
{
    Py_ssize_t num_expected;
    const char *more_or_less;
    if (num_found < num_min) {
        num_expected = num_min;
        more_or_less = "at least";
    } else {
        num_expected = num_max;
        more_or_less = "at most";
    }
    if (exact) {
        more_or_less = "exactly";
    }
    PyErr_Format(PyExc_TypeError,
                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
                 func_name, more_or_less, num_expected,
                 (num_expected == 1) ? "" : "s", num_found);
}

static void __Pyx_RaiseDoubleKeywordsError(
    const char* func_name,
    PyObject* kw_name)
{
    PyErr_Format(PyExc_TypeError,
        #if PY_MAJOR_VERSION >= 3
        "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
        #else
        "%s() got multiple values for keyword argument '%s'", func_name,
        PyString_AsString(kw_name));
        #endif
}

static int __Pyx_ParseOptionalKeywords(
    PyObject *kwds,
    PyObject **argnames[],
    PyObject *kwds2,
    PyObject *values[],
    Py_ssize_t num_pos_args,
    const char* function_name)
{
    PyObject *key = 0, *value = 0;
    Py_ssize_t pos = 0;
    PyObject*** name;
    PyObject*** first_kw_arg = argnames + num_pos_args;
    while (PyDict_Next(kwds, &pos, &key, &value)) {
        name = first_kw_arg;
        while (*name && (**name != key)) name++;
        if (*name) {
            values[name-argnames] = value;
            continue;
        }
        name = first_kw_arg;
        #if PY_MAJOR_VERSION < 3
        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
            while (*name) {
                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
                        && _PyString_Eq(**name, key)) {
                    values[name-argnames] = value;
                    break;
                }
                name++;
            }
            if (*name) continue;
            else {
                PyObject*** argname = argnames;
                while (argname != first_kw_arg) {
                    if ((**argname == key) || (
                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
                             && _PyString_Eq(**argname, key))) {
                        goto arg_passed_twice;
                    }
                    argname++;
                }
            }
        } else
        #endif
        if (likely(PyUnicode_Check(key))) {
            while (*name) {
                int cmp = (**name == key) ? 0 :
                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
                #endif
                    PyUnicode_Compare(**name, key);
                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
                if (cmp == 0) {
                    values[name-argnames] = value;
                    break;
                }
                name++;
            }
            if (*name) continue;
            else {
                PyObject*** argname = argnames;
                while (argname != first_kw_arg) {
                    int cmp = (**argname == key) ? 0 :
                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
                    #endif
                        PyUnicode_Compare(**argname, key);
                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
                    if (cmp == 0) goto arg_passed_twice;
                    argname++;
                }
            }
        } else
            goto invalid_keyword_type;
        if (kwds2) {
            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
        } else {
            goto invalid_keyword;
        }
    }
    return 0;
arg_passed_twice:
    __Pyx_RaiseDoubleKeywordsError(function_name, key);
    goto bad;
invalid_keyword_type:
    PyErr_Format(PyExc_TypeError,
        "%.200s() keywords must be strings", function_name);
    goto bad;
invalid_keyword:
    PyErr_Format(PyExc_TypeError,
    #if PY_MAJOR_VERSION < 3
        "%.200s() got an unexpected keyword argument '%.200s'",
        function_name, PyString_AsString(key));
    #else
        "%s() got an unexpected keyword argument '%U'",
        function_name, key);
    #endif
bad:
    return -1;
}

static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyThreadState *tstate = PyThreadState_GET();
    *type = tstate->exc_type;
    *value = tstate->exc_value;
    *tb = tstate->exc_traceback;
    Py_XINCREF(*type);
    Py_XINCREF(*value);
    Py_XINCREF(*tb);
#else
    PyErr_GetExcInfo(type, value, tb);
#endif
}
static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    PyThreadState *tstate = PyThreadState_GET();
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = type;
    tstate->exc_value = value;
    tstate->exc_traceback = tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
#else
    PyErr_SetExcInfo(type, value, tb);
#endif
}

static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
    PyObject *local_type, *local_value, *local_tb;
#if CYTHON_COMPILING_IN_CPYTHON
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    PyThreadState *tstate = PyThreadState_GET();
    local_type = tstate->curexc_type;
    local_value = tstate->curexc_value;
    local_tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
#else
    PyErr_Fetch(&local_type, &local_value, &local_tb);
#endif
    PyErr_NormalizeException(&local_type, &local_value, &local_tb);
#if CYTHON_COMPILING_IN_CPYTHON
    if (unlikely(tstate->curexc_type))
#else
    if (unlikely(PyErr_Occurred()))
#endif
        goto bad;
    #if PY_MAJOR_VERSION >= 3
    if (local_tb) {
        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
            goto bad;
    }
    #endif
    Py_XINCREF(local_tb);
    Py_XINCREF(local_type);
    Py_XINCREF(local_value);
    *type = local_type;
    *value = local_value;
    *tb = local_tb;
#if CYTHON_COMPILING_IN_CPYTHON
    tmp_type = tstate->exc_type;
    tmp_value = tstate->exc_value;
    tmp_tb = tstate->exc_traceback;
    tstate->exc_type = local_type;
    tstate->exc_value = local_value;
    tstate->exc_traceback = local_tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
#else
    PyErr_SetExcInfo(local_type, local_value, local_tb);
#endif
    return 0;
bad:
    *type = 0;
    *value = 0;
    *tb = 0;
    Py_XDECREF(local_type);
    Py_XDECREF(local_value);
    Py_XDECREF(local_tb);
    return -1;
}

static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
    if (unlikely(!type)) {
        PyErr_SetString(PyExc_SystemError, "Missing type object");
        return 0;
    }
    if (likely(PyObject_TypeCheck(obj, type)))
        return 1;
    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
                 Py_TYPE(obj)->tp_name, type->tp_name);
    return 0;
}

static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
    PyObject *r;
    if (!j) return NULL;
    r = PyObject_GetItem(o, j);
    Py_DECREF(j);
    return r;
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
                                                              int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
    if (wraparound & unlikely(i < 0)) i += PyList_GET_SIZE(o);
    if ((!boundscheck) || likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
        PyObject *r = PyList_GET_ITEM(o, i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                              int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
    if (wraparound & unlikely(i < 0)) i += PyTuple_GET_SIZE(o);
    if ((!boundscheck) || likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
        PyObject *r = PyTuple_GET_ITEM(o, i);
        Py_INCREF(r);
        return r;
    }
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
#else
    return PySequence_GetItem(o, i);
#endif
}
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
                                                     int is_list, int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {
            PyObject *r = PyList_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    }
    else if (PyTuple_CheckExact(o)) {
        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
            PyObject *r = PyTuple_GET_ITEM(o, n);
            Py_INCREF(r);
            return r;
        }
    } else {
        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
        if (likely(m && m->sq_item)) {
            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
                Py_ssize_t l = m->sq_length(o);
                if (likely(l >= 0)) {
                    i += l;
                } else {
                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
                        PyErr_Clear();
                    else
                        return NULL;
                }
            }
            return m->sq_item(o, i);
        }
    }
#else
    if (is_list || PySequence_Check(o)) {
        return PySequence_GetItem(o, i);
    }
#endif
    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
}

static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
#if CYTHON_COMPILING_IN_PYPY
    return PyObject_RichCompareBool(s1, s2, equals);
#else
    if (s1 == s2) {
        return (equals == Py_EQ);
    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
        const char *ps1, *ps2;
        Py_ssize_t length = PyBytes_GET_SIZE(s1);
        if (length != PyBytes_GET_SIZE(s2))
            return (equals == Py_NE);
        ps1 = PyBytes_AS_STRING(s1);
        ps2 = PyBytes_AS_STRING(s2);
        if (ps1[0] != ps2[0]) {
            return (equals == Py_NE);
        } else if (length == 1) {
            return (equals == Py_EQ);
        } else {
            int result = memcmp(ps1, ps2, (size_t)length);
            return (equals == Py_EQ) ? (result == 0) : (result != 0);
        }
    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
        return (equals == Py_NE);
    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
        return (equals == Py_NE);
    } else {
        int result;
        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
        if (!py_result)
            return -1;
        result = __Pyx_PyObject_IsTrue(py_result);
        Py_DECREF(py_result);
        return result;
    }
#endif
}

static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
#if CYTHON_COMPILING_IN_PYPY
    return PyObject_RichCompareBool(s1, s2, equals);
#else
#if PY_MAJOR_VERSION < 3
    PyObject* owned_ref = NULL;
#endif
    int s1_is_unicode, s2_is_unicode;
    if (s1 == s2) {
        goto return_eq;
    }
    s1_is_unicode = PyUnicode_CheckExact(s1);
    s2_is_unicode = PyUnicode_CheckExact(s2);
#if PY_MAJOR_VERSION < 3
    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
        owned_ref = PyUnicode_FromObject(s2);
        if (unlikely(!owned_ref))
            return -1;
        s2 = owned_ref;
        s2_is_unicode = 1;
    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
        owned_ref = PyUnicode_FromObject(s1);
        if (unlikely(!owned_ref))
            return -1;
        s1 = owned_ref;
        s1_is_unicode = 1;
    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
        return __Pyx_PyBytes_Equals(s1, s2, equals);
    }
#endif
    if (s1_is_unicode & s2_is_unicode) {
        Py_ssize_t length;
        int kind;
        void *data1, *data2;
        #if CYTHON_PEP393_ENABLED
        if (unlikely(PyUnicode_READY(s1) < 0) || unlikely(PyUnicode_READY(s2) < 0))
            return -1;
        #endif
        length = __Pyx_PyUnicode_GET_LENGTH(s1);
        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
            goto return_ne;
        }
        kind = __Pyx_PyUnicode_KIND(s1);
        if (kind != __Pyx_PyUnicode_KIND(s2)) {
            goto return_ne;
        }
        data1 = __Pyx_PyUnicode_DATA(s1);
        data2 = __Pyx_PyUnicode_DATA(s2);
        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
            goto return_ne;
        } else if (length == 1) {
            goto return_eq;
        } else {
            int result = memcmp(data1, data2, (size_t)(length * kind));
            #if PY_MAJOR_VERSION < 3
            Py_XDECREF(owned_ref);
            #endif
            return (equals == Py_EQ) ? (result == 0) : (result != 0);
        }
    } else if ((s1 == Py_None) & s2_is_unicode) {
        goto return_ne;
    } else if ((s2 == Py_None) & s1_is_unicode) {
        goto return_ne;
    } else {
        int result;
        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
        if (!py_result)
            return -1;
        result = __Pyx_PyObject_IsTrue(py_result);
        Py_DECREF(py_result);
        return result;
    }
return_eq:
    #if PY_MAJOR_VERSION < 3
    Py_XDECREF(owned_ref);
    #endif
    return (equals == Py_EQ);
return_ne:
    #if PY_MAJOR_VERSION < 3
    Py_XDECREF(owned_ref);
    #endif
    return (equals == Py_NE);
#endif
}

static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
    if (likely(ms && ms->sq_slice)) {
        if (!has_cstart) {
            if (_py_start && (*_py_start != Py_None)) {
                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstart = 0;
        }
        if (!has_cstop) {
            if (_py_stop && (*_py_stop != Py_None)) {
                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstop = PY_SSIZE_T_MAX;
        }
        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
            Py_ssize_t l = ms->sq_length(obj);
            if (likely(l >= 0)) {
                if (cstop < 0) {
                    cstop += l;
                    if (cstop < 0) cstop = 0;
                }
                if (cstart < 0) {
                    cstart += l;
                    if (cstart < 0) cstart = 0;
                }
            } else {
                if (PyErr_ExceptionMatches(PyExc_OverflowError))
                    PyErr_Clear();
                else
                    goto bad;
            }
        }
        return ms->sq_slice(obj, cstart, cstop);
    }
#endif
    mp = Py_TYPE(obj)->tp_as_mapping;
    if (likely(mp && mp->mp_subscript))
#endif
    {
        PyObject* result;
        PyObject *py_slice, *py_start, *py_stop;
        if (_py_slice) {
            py_slice = *_py_slice;
        } else {
            PyObject* owned_start = NULL;
            PyObject* owned_stop = NULL;
            if (_py_start) {
                py_start = *_py_start;
            } else {
                if (has_cstart) {
                    owned_start = py_start = PyInt_FromSsize_t(cstart);
                    if (unlikely(!py_start)) goto bad;
                } else
                    py_start = Py_None;
            }
            if (_py_stop) {
                py_stop = *_py_stop;
            } else {
                if (has_cstop) {
                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
                    if (unlikely(!py_stop)) {
                        Py_XDECREF(owned_start);
                        goto bad;
                    }
                } else
                    py_stop = Py_None;
            }
            py_slice = PySlice_New(py_start, py_stop, Py_None);
            Py_XDECREF(owned_start);
            Py_XDECREF(owned_stop);
            if (unlikely(!py_slice)) goto bad;
        }
#if CYTHON_COMPILING_IN_CPYTHON
        result = mp->mp_subscript(obj, py_slice);
#else
        result = PyObject_GetItem(obj, py_slice);
#endif
        if (!_py_slice) {
            Py_DECREF(py_slice);
        }
        return result;
    }
    PyErr_Format(PyExc_TypeError,
        "'%.200s' object is unsliceable", Py_TYPE(obj)->tp_name);
bad:
    return NULL;
}

static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
    PyErr_Format(PyExc_ValueError,
                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
}

static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
    PyErr_Format(PyExc_ValueError,
                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
                 index, (index == 1) ? "" : "s");
}

static CYTHON_INLINE int __Pyx_IterFinish(void) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyThreadState *tstate = PyThreadState_GET();
    PyObject* exc_type = tstate->curexc_type;
    if (unlikely(exc_type)) {
        if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
            PyObject *exc_value, *exc_tb;
            exc_value = tstate->curexc_value;
            exc_tb = tstate->curexc_traceback;
            tstate->curexc_type = 0;
            tstate->curexc_value = 0;
            tstate->curexc_traceback = 0;
            Py_DECREF(exc_type);
            Py_XDECREF(exc_value);
            Py_XDECREF(exc_tb);
            return 0;
        } else {
            return -1;
        }
    }
    return 0;
#else
    if (unlikely(PyErr_Occurred())) {
        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
            PyErr_Clear();
            return 0;
        } else {
            return -1;
        }
    }
    return 0;
#endif
}

static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
    if (unlikely(retval)) {
        Py_DECREF(retval);
        __Pyx_RaiseTooManyValuesError(expected);
        return -1;
    } else {
        return __Pyx_IterFinish();
    }
    return 0;
}

static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
        PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
        int has_cstart, int has_cstop, CYTHON_UNUSED int wraparound) {
#if CYTHON_COMPILING_IN_CPYTHON
    PyMappingMethods* mp;
#if PY_MAJOR_VERSION < 3
    PySequenceMethods* ms = Py_TYPE(obj)->tp_as_sequence;
    if (likely(ms && ms->sq_ass_slice)) {
        if (!has_cstart) {
            if (_py_start && (*_py_start != Py_None)) {
                cstart = __Pyx_PyIndex_AsSsize_t(*_py_start);
                if ((cstart == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstart = 0;
        }
        if (!has_cstop) {
            if (_py_stop && (*_py_stop != Py_None)) {
                cstop = __Pyx_PyIndex_AsSsize_t(*_py_stop);
                if ((cstop == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad;
            } else
                cstop = PY_SSIZE_T_MAX;
        }
        if (wraparound && unlikely((cstart < 0) | (cstop < 0)) && likely(ms->sq_length)) {
            Py_ssize_t l = ms->sq_length(obj);
            if (likely(l >= 0)) {
                if (cstop < 0) {
                    cstop += l;
                    if (cstop < 0) cstop = 0;
                }
                if (cstart < 0) {
                    cstart += l;
                    if (cstart < 0) cstart = 0;
                }
            } else {
                if (PyErr_ExceptionMatches(PyExc_OverflowError))
                    PyErr_Clear();
                else
                    goto bad;
            }
        }
        return ms->sq_ass_slice(obj, cstart, cstop, value);
    }
#endif
    mp = Py_TYPE(obj)->tp_as_mapping;
    if (likely(mp && mp->mp_ass_subscript))
#endif
    {
        int result;
        PyObject *py_slice, *py_start, *py_stop;
        if (_py_slice) {
            py_slice = *_py_slice;
        } else {
            PyObject* owned_start = NULL;
            PyObject* owned_stop = NULL;
            if (_py_start) {
                py_start = *_py_start;
            } else {
                if (has_cstart) {
                    owned_start = py_start = PyInt_FromSsize_t(cstart);
                    if (unlikely(!py_start)) goto bad;
                } else
                    py_start = Py_None;
            }
            if (_py_stop) {
                py_stop = *_py_stop;
            } else {
                if (has_cstop) {
                    owned_stop = py_stop = PyInt_FromSsize_t(cstop);
                    if (unlikely(!py_stop)) {
                        Py_XDECREF(owned_start);
                        goto bad;
                    }
                } else
                    py_stop = Py_None;
            }
            py_slice = PySlice_New(py_start, py_stop, Py_None);
            Py_XDECREF(owned_start);
            Py_XDECREF(owned_stop);
            if (unlikely(!py_slice)) goto bad;
        }
#if CYTHON_COMPILING_IN_CPYTHON
        result = mp->mp_ass_subscript(obj, py_slice, value);
#else
        result = value ? PyObject_SetItem(obj, py_slice, value) : PyObject_DelItem(obj, py_slice);
#endif
        if (!_py_slice) {
            Py_DECREF(py_slice);
        }
        return result;
    }
    PyErr_Format(PyExc_TypeError,
        "'%.200s' object does not support slice %.10s",
        Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion");
bad:
    return -1;
}

static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
    PyObject *kwdict,
    const char* function_name,
    int kw_allowed)
{
    PyObject* key = 0;
    Py_ssize_t pos = 0;
#if CYTHON_COMPILING_IN_PYPY
    if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0))
        goto invalid_keyword;
    return 1;
#else
    while (PyDict_Next(kwdict, &pos, &key, 0)) {
        #if PY_MAJOR_VERSION < 3
        if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key)))
        #endif
            if (unlikely(!PyUnicode_Check(key)))
                goto invalid_keyword_type;
    }
    if ((!kw_allowed) && unlikely(key))
        goto invalid_keyword;
    return 1;
invalid_keyword_type:
    PyErr_Format(PyExc_TypeError,
        "%.200s() keywords must be strings", function_name);
    return 0;
#endif
invalid_keyword:
    PyErr_Format(PyExc_TypeError,
    #if PY_MAJOR_VERSION < 3
        "%.200s() got an unexpected keyword argument '%.200s'",
        function_name, PyString_AsString(key));
    #else
        "%s() got an unexpected keyword argument '%U'",
        function_name, key);
    #endif
    return 0;
}

static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) {
    PyObject* value = __Pyx_PyObject_GetAttrStr(module, name);
    if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) {
        PyErr_Format(PyExc_ImportError,
        #if PY_MAJOR_VERSION < 3
            "cannot import name %.230s", PyString_AS_STRING(name));
        #else
            "cannot import name %S", name);
        #endif
    }
    return value;
}

static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
    int r;
    if (!j) return -1;
    r = PyObject_SetItem(o, j, v);
    Py_DECREF(j);
    return r;
}
static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
                                               int is_list, int wraparound, int boundscheck) {
#if CYTHON_COMPILING_IN_CPYTHON
    if (is_list || PyList_CheckExact(o)) {
        Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o));
        if ((!boundscheck) || likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
            PyObject* old = PyList_GET_ITEM(o, n);
            Py_INCREF(v);
            PyList_SET_ITEM(o, n, v);
            Py_DECREF(old);
            return 1;
        }
    } else {
        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
        if (likely(m && m->sq_ass_item)) {
            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {
                Py_ssize_t l = m->sq_length(o);
                if (likely(l >= 0)) {
                    i += l;
                } else {
                    if (PyErr_ExceptionMatches(PyExc_OverflowError))
                        PyErr_Clear();
                    else
                        return -1;
                }
            }
            return m->sq_ass_item(o, i, v);
        }
    }
#else
#if CYTHON_COMPILING_IN_PYPY
    if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) {
#else
    if (is_list || PySequence_Check(o)) {
#endif
        return PySequence_SetItem(o, i, v);
    }
#endif
    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
}

static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
    PyObject *empty_list = 0;
    PyObject *module = 0;
    PyObject *global_dict = 0;
    PyObject *empty_dict = 0;
    PyObject *list;
    #if PY_VERSION_HEX < 0x03030000
    PyObject *py_import;
    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
    if (!py_import)
        goto bad;
    #endif
    if (from_list)
        list = from_list;
    else {
        empty_list = PyList_New(0);
        if (!empty_list)
            goto bad;
        list = empty_list;
    }
    global_dict = PyModule_GetDict(__pyx_m);
    if (!global_dict)
        goto bad;
    empty_dict = PyDict_New();
    if (!empty_dict)
        goto bad;
    #if PY_VERSION_HEX >= 0x02050000
    {
        #if PY_MAJOR_VERSION >= 3
        if (level == -1) {
            if (strchr(__Pyx_MODULE_NAME, '.')) {
                #if PY_VERSION_HEX < 0x03030000
                PyObject *py_level = PyInt_FromLong(1);
                if (!py_level)
                    goto bad;
                module = PyObject_CallFunctionObjArgs(py_import,
                    name, global_dict, empty_dict, list, py_level, NULL);
                Py_DECREF(py_level);
                #else
                module = PyImport_ImportModuleLevelObject(
                    name, global_dict, empty_dict, list, 1);
                #endif
                if (!module) {
                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
                        goto bad;
                    PyErr_Clear();
                }
            }
            level = 0; /* try absolute import on failure */
        }
        #endif
        if (!module) {
            #if PY_VERSION_HEX < 0x03030000
            PyObject *py_level = PyInt_FromLong(level);
            if (!py_level)
                goto bad;
            module = PyObject_CallFunctionObjArgs(py_import,
                name, global_dict, empty_dict, list, py_level, NULL);
            Py_DECREF(py_level);
            #else
            module = PyImport_ImportModuleLevelObject(
                name, global_dict, empty_dict, list, level);
            #endif
        }
    }
    #else
    if (level>0) {
        PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4.");
        goto bad;
    }
    module = PyObject_CallFunctionObjArgs(py_import,
        name, global_dict, empty_dict, list, NULL);
    #endif
bad:
    #if PY_VERSION_HEX < 0x03030000
    Py_XDECREF(py_import);
    #endif
    Py_XDECREF(empty_list);
    Py_XDECREF(empty_dict);
    return module;
}

#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
    {                                                                     \
        func_type value = func(x);                                        \
        if (sizeof(target_type) < sizeof(func_type)) {                    \
            if (unlikely(value != (func_type) (target_type) value)) {     \
                func_type zero = 0;                                       \
                PyErr_SetString(PyExc_OverflowError,                      \
                    (is_unsigned && unlikely(value < zero)) ?             \
                    "can't convert negative value to " #target_type :     \
                    "value too large to convert to " #target_type);       \
                return (target_type) -1;                                  \
            }                                                             \
        }                                                                 \
        return (target_type) value;                                       \
    }

#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
 #endif
#endif
static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *x) {
    const npy_intp neg_one = (npy_intp) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_intp) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_intp, long, PyInt_AS_LONG)
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to npy_intp");
                return (npy_intp) -1;
            }
            return (npy_intp) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(npy_intp)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return (npy_intp) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (unlikely(Py_SIZE(x) < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to npy_intp");
                return (npy_intp) -1;
            }
            if (sizeof(npy_intp) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, PyLong_AsUnsignedLong)
            } else if (sizeof(npy_intp) <= sizeof(unsigned long long)) {
                __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long long, PyLong_AsUnsignedLongLong)
            }
        } else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(npy_intp)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return +(npy_intp) ((PyLongObject*)x)->ob_digit[0];
                    case -1: return -(npy_intp) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (sizeof(npy_intp) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT(npy_intp, long, PyLong_AsLong)
            } else if (sizeof(npy_intp) <= sizeof(long long)) {
                __PYX_VERIFY_RETURN_INT(npy_intp, long long, PyLong_AsLongLong)
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_intp val;
            PyObject *v = __Pyx_PyNumber_Int(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (npy_intp) -1;
        }
    } else {
        npy_intp val;
        PyObject *tmp = __Pyx_PyNumber_Int(x);
        if (!tmp) return (npy_intp) -1;
        val = __Pyx_PyInt_As_npy_intp(tmp);
        Py_DECREF(tmp);
        return val;
    }
}

static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
    const long neg_one = (long) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(long) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(long) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(long) <= sizeof(unsigned long long)) {
            return PyLong_FromUnsignedLongLong((unsigned long long) value);
        }
    } else {
        if (sizeof(long) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(long) <= sizeof(long long)) {
            return PyLong_FromLongLong((long long) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(long),
                                     little, !is_unsigned);
    }
}

#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
 #endif
#endif
static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {
    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(unsigned long) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(unsigned long, long, PyInt_AS_LONG)
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to unsigned long");
                return (unsigned long) -1;
            }
            return (unsigned long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(unsigned long)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (unlikely(Py_SIZE(x) < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to unsigned long");
                return (unsigned long) -1;
            }
            if (sizeof(unsigned long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, PyLong_AsUnsignedLong)
            } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long long, PyLong_AsUnsignedLongLong)
            }
        } else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(unsigned long)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0];
                    case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (sizeof(unsigned long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT(unsigned long, long, PyLong_AsLong)
            } else if (sizeof(unsigned long) <= sizeof(long long)) {
                __PYX_VERIFY_RETURN_INT(unsigned long, long long, PyLong_AsLongLong)
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            unsigned long val;
            PyObject *v = __Pyx_PyNumber_Int(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (unsigned long) -1;
        }
    } else {
        unsigned long val;
        PyObject *tmp = __Pyx_PyNumber_Int(x);
        if (!tmp) return (unsigned long) -1;
        val = __Pyx_PyInt_As_unsigned_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
}

static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
    const int neg_one = (int) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(int) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(int) <= sizeof(unsigned long long)) {
            return PyLong_FromUnsignedLongLong((unsigned long long) value);
        }
    } else {
        if (sizeof(int) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(int) <= sizeof(long long)) {
            return PyLong_FromLongLong((long long) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int),
                                     little, !is_unsigned);
    }
}

#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
 #endif
#endif
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
    const int neg_one = (int) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(int) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to int");
                return (int) -1;
            }
            return (int) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(int)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (unlikely(Py_SIZE(x) < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to int");
                return (int) -1;
            }
            if (sizeof(int) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
            } else if (sizeof(int) <= sizeof(unsigned long long)) {
                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
            }
        } else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(int)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (sizeof(int) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
            } else if (sizeof(int) <= sizeof(long long)) {
                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            int val;
            PyObject *v = __Pyx_PyNumber_Int(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (int) -1;
        }
    } else {
        int val;
        PyObject *tmp = __Pyx_PyNumber_Int(x);
        if (!tmp) return (int) -1;
        val = __Pyx_PyInt_As_int(tmp);
        Py_DECREF(tmp);
        return val;
    }
}

#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
 #endif
#endif
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
    const long neg_one = (long) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(long) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to long");
                return (long) -1;
            }
            return (long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(long)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (unlikely(Py_SIZE(x) < 0)) {
                PyErr_SetString(PyExc_OverflowError,
                                "can't convert negative value to long");
                return (long) -1;
            }
            if (sizeof(long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
            } else if (sizeof(long) <= sizeof(unsigned long long)) {
                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
            }
        } else {
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
            if (sizeof(digit) <= sizeof(long)) {
                switch (Py_SIZE(x)) {
                    case  0: return 0;
                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
                }
            }
 #endif
#endif
            if (sizeof(long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
            } else if (sizeof(long) <= sizeof(long long)) {
                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            long val;
            PyObject *v = __Pyx_PyNumber_Int(x);
 #if PY_MAJOR_VERSION < 3
            if (likely(v) && !PyLong_Check(v)) {
                PyObject *tmp = v;
                v = PyNumber_Long(tmp);
                Py_DECREF(tmp);
            }
 #endif
            if (likely(v)) {
                int one = 1; int is_little = (int)*(unsigned char *)&one;
                unsigned char *bytes = (unsigned char *)&val;
                int ret = _PyLong_AsByteArray((PyLongObject *)v,
                                              bytes, sizeof(val),
                                              is_little, !is_unsigned);
                Py_DECREF(v);
                if (likely(!ret))
                    return val;
            }
#endif
            return (long) -1;
        }
    } else {
        long val;
        PyObject *tmp = __Pyx_PyNumber_Int(x);
        if (!tmp) return (long) -1;
        val = __Pyx_PyInt_As_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
}

static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value) {
    const npy_intp neg_one = (npy_intp) -1, const_zero = 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_intp) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_intp) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
        } else if (sizeof(npy_intp) <= sizeof(unsigned long long)) {
            return PyLong_FromUnsignedLongLong((unsigned long long) value);
        }
    } else {
        if (sizeof(npy_intp) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_intp) <= sizeof(long long)) {
            return PyLong_FromLongLong((long long) value);
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_intp),
                                     little, !is_unsigned);
    }
}

static int __Pyx_check_binary_version(void) {
    char ctversion[4], rtversion[4];
    PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
    PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion());
    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {
        char message[200];
        PyOS_snprintf(message, sizeof(message),
                      "compiletime version %s of module '%.100s' "
                      "does not match runtime version %s",
                      ctversion, __Pyx_MODULE_NAME, rtversion);
        #if PY_VERSION_HEX < 0x02050000
        return PyErr_Warn(NULL, message);
        #else
        return PyErr_WarnEx(NULL, message, 1);
        #endif
    }
    return 0;
}

#ifndef __PYX_HAVE_RT_ImportModule
#define __PYX_HAVE_RT_ImportModule
static PyObject *__Pyx_ImportModule(const char *name) {
    PyObject *py_name = 0;
    PyObject *py_module = 0;
    py_name = __Pyx_PyIdentifier_FromString(name);
    if (!py_name)
        goto bad;
    py_module = PyImport_Import(py_name);
    Py_DECREF(py_name);
    return py_module;
bad:
    Py_XDECREF(py_name);
    return 0;
}
#endif

#ifndef __PYX_HAVE_RT_ImportType
#define __PYX_HAVE_RT_ImportType
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
    size_t size, int strict)
{
    PyObject *py_module = 0;
    PyObject *result = 0;
    PyObject *py_name = 0;
    char warning[200];
    Py_ssize_t basicsize;
#ifdef Py_LIMITED_API
    PyObject *py_basicsize;
#endif
    py_module = __Pyx_ImportModule(module_name);
    if (!py_module)
        goto bad;
    py_name = __Pyx_PyIdentifier_FromString(class_name);
    if (!py_name)
        goto bad;
    result = PyObject_GetAttr(py_module, py_name);
    Py_DECREF(py_name);
    py_name = 0;
    Py_DECREF(py_module);
    py_module = 0;
    if (!result)
        goto bad;
    if (!PyType_Check(result)) {
        PyErr_Format(PyExc_TypeError,
            "%.200s.%.200s is not a type object",
            module_name, class_name);
        goto bad;
    }
#ifndef Py_LIMITED_API
    basicsize = ((PyTypeObject *)result)->tp_basicsize;
#else
    py_basicsize = PyObject_GetAttrString(result, "__basicsize__");
    if (!py_basicsize)
        goto bad;
    basicsize = PyLong_AsSsize_t(py_basicsize);
    Py_DECREF(py_basicsize);
    py_basicsize = 0;
    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())
        goto bad;
#endif
    if (!strict && (size_t)basicsize > size) {
        PyOS_snprintf(warning, sizeof(warning),
            "%s.%s size changed, may indicate binary incompatibility",
            module_name, class_name);
        #if PY_VERSION_HEX < 0x02050000
        if (PyErr_Warn(NULL, warning) < 0) goto bad;
        #else
        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
        #endif
    }
    else if ((size_t)basicsize != size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s has the wrong size, try recompiling",
            module_name, class_name);
        goto bad;
    }
    return (PyTypeObject *)result;
bad:
    Py_XDECREF(py_module);
    Py_XDECREF(result);
    return NULL;
}
#endif

static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
    int start = 0, mid = 0, end = count - 1;
    if (end >= 0 && code_line > entries[end].code_line) {
        return count;
    }
    while (start < end) {
        mid = (start + end) / 2;
        if (code_line < entries[mid].code_line) {
            end = mid;
        } else if (code_line > entries[mid].code_line) {
             start = mid + 1;
        } else {
            return mid;
        }
    }
    if (code_line <= entries[mid].code_line) {
        return mid;
    } else {
        return mid + 1;
    }
}
static PyCodeObject *__pyx_find_code_object(int code_line) {
    PyCodeObject* code_object;
    int pos;
    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
        return NULL;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
        return NULL;
    }
    code_object = __pyx_code_cache.entries[pos].code_object;
    Py_INCREF(code_object);
    return code_object;
}
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
    int pos, i;
    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
    if (unlikely(!code_line)) {
        return;
    }
    if (unlikely(!entries)) {
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
        if (likely(entries)) {
            __pyx_code_cache.entries = entries;
            __pyx_code_cache.max_count = 64;
            __pyx_code_cache.count = 1;
            entries[0].code_line = code_line;
            entries[0].code_object = code_object;
            Py_INCREF(code_object);
        }
        return;
    }
    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
        PyCodeObject* tmp = entries[pos].code_object;
        entries[pos].code_object = code_object;
        Py_DECREF(tmp);
        return;
    }
    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
        int new_max = __pyx_code_cache.max_count + 64;
        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));
        if (unlikely(!entries)) {
            return;
        }
        __pyx_code_cache.entries = entries;
        __pyx_code_cache.max_count = new_max;
    }
    for (i=__pyx_code_cache.count; i>pos; i--) {
        entries[i] = entries[i-1];
    }
    entries[pos].code_line = code_line;
    entries[pos].code_object = code_object;
    __pyx_code_cache.count++;
    Py_INCREF(code_object);
}

#include "compile.h"
#include "frameobject.h"
#include "traceback.h"
static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
            const char *funcname, int c_line,
            int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyObject *py_srcfile = 0;
    PyObject *py_funcname = 0;
    #if PY_MAJOR_VERSION < 3
    py_srcfile = PyString_FromString(filename);
    #else
    py_srcfile = PyUnicode_FromString(filename);
    #endif
    if (!py_srcfile) goto bad;
    if (c_line) {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #else
        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
        #endif
    }
    else {
        #if PY_MAJOR_VERSION < 3
        py_funcname = PyString_FromString(funcname);
        #else
        py_funcname = PyUnicode_FromString(funcname);
        #endif
    }
    if (!py_funcname) goto bad;
    py_code = __Pyx_PyCode_New(
        0,            /*int argcount,*/
        0,            /*int kwonlyargcount,*/
        0,            /*int nlocals,*/
        0,            /*int stacksize,*/
        0,            /*int flags,*/
        __pyx_empty_bytes, /*PyObject *code,*/
        __pyx_empty_tuple, /*PyObject *consts,*/
        __pyx_empty_tuple, /*PyObject *names,*/
        __pyx_empty_tuple, /*PyObject *varnames,*/
        __pyx_empty_tuple, /*PyObject *freevars,*/
        __pyx_empty_tuple, /*PyObject *cellvars,*/
        py_srcfile,   /*PyObject *filename,*/
        py_funcname,  /*PyObject *name,*/
        py_line,      /*int firstlineno,*/
        __pyx_empty_bytes  /*PyObject *lnotab*/
    );
    Py_DECREF(py_srcfile);
    Py_DECREF(py_funcname);
    return py_code;
bad:
    Py_XDECREF(py_srcfile);
    Py_XDECREF(py_funcname);
    return NULL;
}
static void __Pyx_AddTraceback(const char *funcname, int c_line,
                               int py_line, const char *filename) {
    PyCodeObject *py_code = 0;
    PyObject *py_globals = 0;
    PyFrameObject *py_frame = 0;
    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
    if (!py_code) {
        py_code = __Pyx_CreateCodeObjectForTraceback(
            funcname, c_line, py_line, filename);
        if (!py_code) goto bad;
        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
    }
    py_globals = PyModule_GetDict(__pyx_m);
    if (!py_globals) goto bad;
    py_frame = PyFrame_New(
        PyThreadState_GET(), /*PyThreadState *tstate,*/
        py_code,             /*PyCodeObject *code,*/
        py_globals,          /*PyObject *globals,*/
        0                    /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    py_frame->f_lineno = py_line;
    PyTraceBack_Here(py_frame);
bad:
    Py_XDECREF(py_code);
    Py_XDECREF(py_frame);
}

static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
    while (t->p) {
        #if PY_MAJOR_VERSION < 3
        if (t->is_unicode) {
            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
        } else if (t->intern) {
            *t->p = PyString_InternFromString(t->s);
        } else {
            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
        }
        #else  /* Python 3+ has unicode identifiers */
        if (t->is_unicode | t->is_str) {
            if (t->intern) {
                *t->p = PyUnicode_InternFromString(t->s);
            } else if (t->encoding) {
                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);
            } else {
                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
            }
        } else {
            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
        }
        #endif
        if (!*t->p)
            return -1;
        ++t;
    }
    return 0;
}

static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {
    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));
}
static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) {
    Py_ssize_t ignore;
    return __Pyx_PyObject_AsStringAndSize(o, &ignore);
}
static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
    if (
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
            __Pyx_sys_getdefaultencoding_not_ascii &&
#endif
            PyUnicode_Check(o)) {
#if PY_VERSION_HEX < 0x03030000
        char* defenc_c;
        PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);
        if (!defenc) return NULL;
        defenc_c = PyBytes_AS_STRING(defenc);
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
        {
            char* end = defenc_c + PyBytes_GET_SIZE(defenc);
            char* c;
            for (c = defenc_c; c < end; c++) {
                if ((unsigned char) (*c) >= 128) {
                    PyUnicode_AsASCIIString(o);
                    return NULL;
                }
            }
        }
#endif /*__PYX_DEFAULT_STRING_ENCODING_IS_ASCII*/
        *length = PyBytes_GET_SIZE(defenc);
        return defenc_c;
#else /* PY_VERSION_HEX < 0x03030000 */
        if (PyUnicode_READY(o) == -1) return NULL;
#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
        if (PyUnicode_IS_ASCII(o)) {
            *length = PyUnicode_GET_LENGTH(o);
            return PyUnicode_AsUTF8(o);
        } else {
            PyUnicode_AsASCIIString(o);
            return NULL;
        }
#else /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
        return PyUnicode_AsUTF8AndSize(o, length);
#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII */
#endif /* PY_VERSION_HEX < 0x03030000 */
    } else
#endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
#if !CYTHON_COMPILING_IN_PYPY
#if PY_VERSION_HEX >= 0x02060000
    if (PyByteArray_Check(o)) {
        *length = PyByteArray_GET_SIZE(o);
        return PyByteArray_AS_STRING(o);
    } else
#endif
#endif
    {
        char* result;
        int r = PyBytes_AsStringAndSize(o, &result, length);
        if (unlikely(r < 0)) {
            return NULL;
        } else {
            return result;
        }
    }
}
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
   int is_true = x == Py_True;
   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
   else return PyObject_IsTrue(x);
}
static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
  PyNumberMethods *m;
  const char *name = NULL;
  PyObject *res = NULL;
#if PY_MAJOR_VERSION < 3
  if (PyInt_Check(x) || PyLong_Check(x))
#else
  if (PyLong_Check(x))
#endif
    return Py_INCREF(x), x;
  m = Py_TYPE(x)->tp_as_number;
#if PY_MAJOR_VERSION < 3
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Int(x);
  }
  else if (m && m->nb_long) {
    name = "long";
    res = PyNumber_Long(x);
  }
#else
  if (m && m->nb_int) {
    name = "int";
    res = PyNumber_Long(x);
  }
#endif
  if (res) {
#if PY_MAJOR_VERSION < 3
    if (!PyInt_Check(res) && !PyLong_Check(res)) {
#else
    if (!PyLong_Check(res)) {
#endif
      PyErr_Format(PyExc_TypeError,
                   "__%.4s__ returned non-%.4s (type %.200s)",
                   name, name, Py_TYPE(res)->tp_name);
      Py_DECREF(res);
      return NULL;
    }
  }
  else if (!PyErr_Occurred()) {
    PyErr_SetString(PyExc_TypeError,
                    "an integer is required");
  }
  return res;
}
#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
 #if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
 #endif
#endif
static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
  Py_ssize_t ival;
  PyObject *x;
#if PY_MAJOR_VERSION < 3
  if (likely(PyInt_CheckExact(b)))
      return PyInt_AS_LONG(b);
#endif
  if (likely(PyLong_CheckExact(b))) {
    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
     #if CYTHON_USE_PYLONG_INTERNALS
       switch (Py_SIZE(b)) {
       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
       case  0: return 0;
       case  1: return ((PyLongObject*)b)->ob_digit[0];
       }
     #endif
    #endif
  #if PY_VERSION_HEX < 0x02060000
    return PyInt_AsSsize_t(b);
  #else
    return PyLong_AsSsize_t(b);
  #endif
  }
  x = PyNumber_Index(b);
  if (!x) return -1;
  ival = PyInt_AsSsize_t(x);
  Py_DECREF(x);
  return ival;
}
static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
#if PY_VERSION_HEX < 0x02050000
   if (ival <= LONG_MAX)
       return PyInt_FromLong((long)ival);
   else {
       unsigned char *bytes = (unsigned char *) &ival;
       int one = 1; int little = (int)*(unsigned char*)&one;
       return _PyLong_FromByteArray(bytes, sizeof(size_t), little, 0);
   }
#else
   return PyInt_FromSize_t(ival);
#endif
}


#endif /* Py_PYTHON_H */
