/* Generated by Cython 0.25.2 */

#define PY_SSIZE_T_CLEAN
#include "Python.h"
#ifndef Py_PYTHON_H
    #error Python headers needed to compile C extensions, please install development version of Python.
#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000)
    #error Cython requires Python 2.6+ or Python 3.2+.
#else
#define CYTHON_ABI "0_25_2"
#include <stddef.h>
#ifndef offsetof
  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
#endif
#if !defined(WIN32) && !defined(MS_WINDOWS)
  #ifndef __stdcall
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#ifndef DL_IMPORT
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#ifndef DL_EXPORT
  #define DL_EXPORT(t) t
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#ifndef HAVE_LONG_LONG
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    #define HAVE_LONG_LONG
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#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_PYSTON 0
  #define CYTHON_COMPILING_IN_CPYTHON 0
  #undef CYTHON_USE_TYPE_SLOTS
  #define CYTHON_USE_TYPE_SLOTS 0
  #undef CYTHON_USE_ASYNC_SLOTS
  #define CYTHON_USE_ASYNC_SLOTS 0
  #undef CYTHON_USE_PYLIST_INTERNALS
  #define CYTHON_USE_PYLIST_INTERNALS 0
  #undef CYTHON_USE_UNICODE_INTERNALS
  #define CYTHON_USE_UNICODE_INTERNALS 0
  #undef CYTHON_USE_UNICODE_WRITER
  #define CYTHON_USE_UNICODE_WRITER 0
  #undef CYTHON_USE_PYLONG_INTERNALS
  #define CYTHON_USE_PYLONG_INTERNALS 0
  #undef CYTHON_AVOID_BORROWED_REFS
  #define CYTHON_AVOID_BORROWED_REFS 1
  #undef CYTHON_ASSUME_SAFE_MACROS
  #define CYTHON_ASSUME_SAFE_MACROS 0
  #undef CYTHON_UNPACK_METHODS
  #define CYTHON_UNPACK_METHODS 0
  #undef CYTHON_FAST_THREAD_STATE
  #define CYTHON_FAST_THREAD_STATE 0
  #undef CYTHON_FAST_PYCALL
  #define CYTHON_FAST_PYCALL 0
#elif defined(PYSTON_VERSION)
  #define CYTHON_COMPILING_IN_PYPY 0
  #define CYTHON_COMPILING_IN_PYSTON 1
  #define CYTHON_COMPILING_IN_CPYTHON 0
  #ifndef CYTHON_USE_TYPE_SLOTS
    #define CYTHON_USE_TYPE_SLOTS 1
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  #undef CYTHON_USE_ASYNC_SLOTS
  #define CYTHON_USE_ASYNC_SLOTS 0
  #undef CYTHON_USE_PYLIST_INTERNALS
  #define CYTHON_USE_PYLIST_INTERNALS 0
  #ifndef CYTHON_USE_UNICODE_INTERNALS
    #define CYTHON_USE_UNICODE_INTERNALS 1
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  #undef CYTHON_USE_UNICODE_WRITER
  #define CYTHON_USE_UNICODE_WRITER 0
  #undef CYTHON_USE_PYLONG_INTERNALS
  #define CYTHON_USE_PYLONG_INTERNALS 0
  #ifndef CYTHON_AVOID_BORROWED_REFS
    #define CYTHON_AVOID_BORROWED_REFS 0
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  #ifndef CYTHON_ASSUME_SAFE_MACROS
    #define CYTHON_ASSUME_SAFE_MACROS 1
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  #ifndef CYTHON_UNPACK_METHODS
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  #undef CYTHON_FAST_THREAD_STATE
  #define CYTHON_FAST_THREAD_STATE 0
  #undef CYTHON_FAST_PYCALL
  #define CYTHON_FAST_PYCALL 0
#else
  #define CYTHON_COMPILING_IN_PYPY 0
  #define CYTHON_COMPILING_IN_PYSTON 0
  #define CYTHON_COMPILING_IN_CPYTHON 1
  #ifndef CYTHON_USE_TYPE_SLOTS
    #define CYTHON_USE_TYPE_SLOTS 1
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  #if PY_MAJOR_VERSION < 3
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    #define CYTHON_USE_ASYNC_SLOTS 0
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  #if PY_VERSION_HEX < 0x02070000
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    #define CYTHON_USE_PYLONG_INTERNALS 0
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    #define CYTHON_USE_PYLONG_INTERNALS 1
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    #define CYTHON_USE_PYLIST_INTERNALS 1
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    #define CYTHON_USE_UNICODE_WRITER 0
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    #define CYTHON_USE_UNICODE_WRITER 1
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  #ifndef CYTHON_AVOID_BORROWED_REFS
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  #ifndef CYTHON_ASSUME_SAFE_MACROS
    #define CYTHON_ASSUME_SAFE_MACROS 1
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  #ifndef CYTHON_UNPACK_METHODS
    #define CYTHON_UNPACK_METHODS 1
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  #ifndef CYTHON_FAST_THREAD_STATE
    #define CYTHON_FAST_THREAD_STATE 1
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  #ifndef CYTHON_FAST_PYCALL
    #define CYTHON_FAST_PYCALL 1
  #endif
#endif
#if !defined(CYTHON_FAST_PYCCALL)
#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)
#endif
#if CYTHON_USE_PYLONG_INTERNALS
  #include "longintrepr.h"
  #undef SHIFT
  #undef BASE
  #undef MASK
#endif
#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)
  #define Py_OptimizeFlag 0
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#define __PYX_BUILD_PY_SSIZE_T "n"
#define CYTHON_FORMAT_SSIZE_T "z"
#if PY_MAJOR_VERSION < 3
  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\
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  #define __Pyx_DefaultClassType PyClass_Type
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  #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)\
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  #define __Pyx_DefaultClassType PyType_Type
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#ifndef Py_TPFLAGS_CHECKTYPES
  #define Py_TPFLAGS_CHECKTYPES 0
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#ifndef Py_TPFLAGS_HAVE_INDEX
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#ifndef Py_TPFLAGS_HAVE_NEWBUFFER
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#endif
#ifndef Py_TPFLAGS_HAVE_FINALIZE
  #define Py_TPFLAGS_HAVE_FINALIZE 0
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#ifndef METH_FASTCALL
  #define METH_FASTCALL 0x80
  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args,
                                              Py_ssize_t nargs, PyObject *kwnames);
#else
  #define __Pyx_PyCFunctionFast _PyCFunctionFast
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#if CYTHON_FAST_PYCCALL
#define __Pyx_PyFastCFunction_Check(func)\
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#define __Pyx_PyFastCFunction_Check(func) 0
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#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_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)
  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)
  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))
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  #define CYTHON_PEP393_ENABLED 0
  #define PyUnicode_1BYTE_KIND  1
  #define PyUnicode_2BYTE_KIND  2
  #define PyUnicode_4BYTE_KIND  4
  #define __Pyx_PyUnicode_READY(op)       (0)
  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)
  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)
  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))
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#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)
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  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)
  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\
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#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)
  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)
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#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)
  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)
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#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)
  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, "__format__", "O", fmt)
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#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)
  #define PyObject_Malloc(s)   PyMem_Malloc(s)
  #define PyObject_Free(p)     PyMem_Free(p)
  #define PyObject_Realloc(p)  PyMem_Realloc(p)
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#if CYTHON_COMPILING_IN_PYSTON
  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)
  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)
#else
  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)
  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)
#endif
#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
#else
  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
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#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)
  #define PyObject_ASCII(o)            PyObject_Repr(o)
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#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_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_Check(obj) || PyUnicode_Check(obj))
  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
#endif
#ifndef PySet_CheckExact
  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
#endif
#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)
#if PY_MAJOR_VERSION >= 3
  #define PyIntObject                  PyLongObject
  #define PyInt_Type                   PyLong_Type
  #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_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY
  #ifndef PyUnicode_InternFromString
    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)
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#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
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#if PY_MAJOR_VERSION >= 3
  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
#else
  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)
#endif
#if CYTHON_USE_ASYNC_SLOTS
  #if PY_VERSION_HEX >= 0x030500B1
    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods
    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)
  #else
    typedef struct {
        unaryfunc am_await;
        unaryfunc am_aiter;
        unaryfunc am_anext;
    } __Pyx_PyAsyncMethodsStruct;
    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))
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#else
  #define __Pyx_PyType_AsAsync(obj) NULL
#endif
#ifndef CYTHON_RESTRICT
  #if defined(__GNUC__)
    #define CYTHON_RESTRICT __restrict__
  #elif defined(_MSC_VER) && _MSC_VER >= 1400
    #define CYTHON_RESTRICT __restrict
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_RESTRICT restrict
  #else
    #define CYTHON_RESTRICT
  #endif
#endif
#ifndef CYTHON_UNUSED
# if defined(__GNUC__)
#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))
#     define CYTHON_UNUSED __attribute__ ((__unused__))
#   else
#     define CYTHON_UNUSED
#   endif
# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
#   define CYTHON_UNUSED __attribute__ ((__unused__))
# else
#   define CYTHON_UNUSED
# endif
#endif
#ifndef CYTHON_MAYBE_UNUSED_VAR
#  if defined(__cplusplus)
     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }
#  else
#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)
#  endif
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#ifndef CYTHON_NCP_UNUSED
# if CYTHON_COMPILING_IN_CPYTHON
#  define CYTHON_NCP_UNUSED
# else
#  define CYTHON_NCP_UNUSED CYTHON_UNUSED
# endif
#endif
#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)

#ifndef CYTHON_INLINE
  #if defined(__clang__)
    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))
  #elif defined(__GNUC__)
    #define CYTHON_INLINE __inline__
  #elif defined(_MSC_VER)
    #define CYTHON_INLINE __inline
  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define CYTHON_INLINE inline
  #else
    #define CYTHON_INLINE
  #endif
#endif

#if defined(WIN32) || defined(MS_WINDOWS)
  #define _USE_MATH_DEFINES
#endif
#include <math.h>
#ifdef NAN
#define __PYX_NAN() ((float) NAN)
#else
static CYTHON_INLINE float __PYX_NAN() {
  float value;
  memset(&value, 0xFF, sizeof(value));
  return value;
}
#endif
#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)
#define __Pyx_truncl trunc
#else
#define __Pyx_truncl truncl
#endif


#define __PYX_ERR(f_index, lineno, Ln_error) \
{ \
  __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \
}

#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

#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 <string.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

typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;
                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;

#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0
#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0
#define __PYX_DEFAULT_STRING_ENCODING ""
#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
#define __Pyx_uchar_cast(c) ((unsigned char)c)
#define __Pyx_long_cast(x) ((long)x)
#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\
    (sizeof(type) < sizeof(Py_ssize_t))  ||\
    (sizeof(type) > sizeof(Py_ssize_t) &&\
          likely(v < (type)PY_SSIZE_T_MAX ||\
                 v == (type)PY_SSIZE_T_MAX)  &&\
          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\
                                v == (type)PY_SSIZE_T_MIN)))  ||\
    (sizeof(type) == sizeof(Py_ssize_t) &&\
          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\
                               v == (type)PY_SSIZE_T_MAX)))  )
#if defined (__cplusplus) && __cplusplus >= 201103L
    #include <cstdlib>
    #define __Pyx_sst_abs(value) std::abs(value)
#elif SIZEOF_INT >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) abs(value)
#elif SIZEOF_LONG >= SIZEOF_SIZE_T
    #define __Pyx_sst_abs(value) labs(value)
#elif defined (_MSC_VER) && defined (_M_X64)
    #define __Pyx_sst_abs(value) _abs64(value)
#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
    #define __Pyx_sst_abs(value) llabs(value)
#elif defined (__GNUC__)
    #define __Pyx_sst_abs(value) __builtin_llabs(value)
#else
    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)
#endif
static CYTHON_INLINE 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_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)
#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)
#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)
#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)
#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)
#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_NewRef(obj) (Py_INCREF(obj), obj)
#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)
#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))
static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(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_ASSUME_SAFE_MACROS
#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
#else
#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
#endif
#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))
#else
#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))
#endif
#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))
#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
static int __Pyx_sys_getdefaultencoding_not_ascii;
static int __Pyx_init_sys_getdefaultencoding_params(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*) "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);
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bad:
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    Py_XDECREF(ascii_chars_u);
    Py_XDECREF(ascii_chars_b);
    return -1;
}
#endif
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)
#else
#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
static char* __PYX_DEFAULT_STRING_ENCODING;
static int __Pyx_init_sys_getdefaultencoding_params(void) {
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    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;
    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));
    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;
    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);
    Py_DECREF(default_encoding);
    return 0;
bad:
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    return -1;
}
#endif
#endif


/* Test for GCC > 2.95 */
#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))
  #define likely(x)   __builtin_expect(!!(x), 1)
  #define unlikely(x) __builtin_expect(!!(x), 0)
#else /* !__GNUC__ or GCC < 2.95 */
  #define likely(x)   (x)
  #define unlikely(x) (x)
#endif /* __GNUC__ */

static PyObject *__pyx_m;
static PyObject *__pyx_d;
static PyObject *__pyx_b;
static PyObject *__pyx_empty_tuple;
static PyObject *__pyx_empty_bytes;
static PyObject *__pyx_empty_unicode;
static int __pyx_lineno;
static int __pyx_clineno = 0;
static const char * __pyx_cfilenm= __FILE__;
static const char *__pyx_filename;


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

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

/* "mtrand.pyx":126
 *     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":127
 * 
 * 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":128
 * 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":129
 * 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":131
 * 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":132
 * 
 * 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":133
 * 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":134
 * 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":135
 * 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":593
 * 
 * 
 * cdef class RandomState:             # <<<<<<<<<<<<<<
 *     """
 *     RandomState(seed=None)
 */
struct __pyx_obj_6mtrand_RandomState {
  PyObject_HEAD
  struct __pyx_vtabstruct_6mtrand_RandomState *__pyx_vtab;
  rk_state *internal_state;
  PyObject *lock;
  PyObject *state_address;
};



struct __pyx_vtabstruct_6mtrand_RandomState {
  PyObject *(*_shuffle_raw)(struct __pyx_obj_6mtrand_RandomState *, npy_intp, npy_intp, npy_intp, char *, char *);
};
static struct __pyx_vtabstruct_6mtrand_RandomState *__pyx_vtabptr_6mtrand_RandomState;
static CYTHON_INLINE PyObject *__pyx_f_6mtrand_11RandomState__shuffle_raw(struct __pyx_obj_6mtrand_RandomState *, npy_intp, npy_intp, npy_intp, char *, char *);

/* --- Runtime support code (head) --- */
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  #define __Pyx_GIVEREF(r)
  #define __Pyx_XINCREF(r) Py_XINCREF(r)
  #define __Pyx_XDECREF(r) Py_XDECREF(r)
  #define __Pyx_XGOTREF(r)
  #define __Pyx_XGIVEREF(r)
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#define __Pyx_XDECREF_SET(r, v) do {\
        PyObject *tmp = (PyObject *) r;\
        r = v; __Pyx_XDECREF(tmp);\
    } while (0)
#define __Pyx_DECREF_SET(r, v) do {\
        PyObject *tmp = (PyObject *) r;\
        r = v; __Pyx_DECREF(tmp);\
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#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)

/* PyObjectGetAttrStr.proto */
#if CYTHON_USE_TYPE_SLOTS
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {
    PyTypeObject* tp = Py_TYPE(obj);
    if (likely(tp->tp_getattro))
        return tp->tp_getattro(obj, attr_name);
#if PY_MAJOR_VERSION < 3
    if (likely(tp->tp_getattr))
        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));
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    return PyObject_GetAttr(obj, attr_name);
}
#else
#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n)
#endif

/* GetBuiltinName.proto */
static PyObject *__Pyx_GetBuiltinName(PyObject *name);

/* RaiseArgTupleInvalid.proto */
static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
    Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found);

/* RaiseDoubleKeywords.proto */
static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name);

/* ParseKeywords.proto */
static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\
    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\
    const char* function_name);

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

/* PyCFunctionFastCall.proto */
#if CYTHON_FAST_PYCCALL
static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs);
#else
#define __Pyx_PyCFunction_FastCall(func, args, nargs)  (assert(0), NULL)
#endif

/* PyFunctionFastCall.proto */
#if CYTHON_FAST_PYCALL
#define __Pyx_PyFunction_FastCall(func, args, nargs)\
    __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL)
#if 1 || PY_VERSION_HEX < 0x030600B1
static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs);
#else
#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs)
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/* PyObjectCall.proto */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw);
#else
#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw)
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/* PyObjectCallMethO.proto */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg);
#endif

/* PyObjectCallOneArg.proto */
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg);

/* PyObjectLookupSpecial.proto */
#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x02070000
static CYTHON_INLINE PyObject* __Pyx_PyObject_LookupSpecial(PyObject* obj, PyObject* attr_name) {
    PyObject *res;
    PyTypeObject *tp = Py_TYPE(obj);
#if PY_MAJOR_VERSION < 3
    if (unlikely(PyInstance_Check(obj)))
        return __Pyx_PyObject_GetAttrStr(obj, attr_name);
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    res = _PyType_Lookup(tp, attr_name);
    if (likely(res)) {
        descrgetfunc f = Py_TYPE(res)->tp_descr_get;
        if (!f) {
            Py_INCREF(res);
        } else {
            res = f(res, obj, (PyObject *)tp);
        }
    } else {
        PyErr_SetObject(PyExc_AttributeError, attr_name);
    }
    return res;
}
#else
#define __Pyx_PyObject_LookupSpecial(o,n) __Pyx_PyObject_GetAttrStr(o,n)
#endif

/* PyObjectCallNoArg.proto */
#if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func);
#else
#define __Pyx_PyObject_CallNoArg(func) __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL)
#endif

/* PyThreadStateGet.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_PyThreadState_declare  PyThreadState *__pyx_tstate;
#define __Pyx_PyThreadState_assign  __pyx_tstate = PyThreadState_GET();
#else
#define __Pyx_PyThreadState_declare
#define __Pyx_PyThreadState_assign
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/* PyErrFetchRestore.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_ErrRestoreWithState(type, value, tb)  __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb)
#define __Pyx_ErrFetchWithState(type, value, tb)    __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb)
#define __Pyx_ErrRestore(type, value, tb)  __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb)
#define __Pyx_ErrFetch(type, value, tb)    __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
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#define __Pyx_ErrRestoreWithState(type, value, tb)  PyErr_Restore(type, value, tb)
#define __Pyx_ErrFetchWithState(type, value, tb)  PyErr_Fetch(type, value, tb)
#define __Pyx_ErrRestore(type, value, tb)  PyErr_Restore(type, value, tb)
#define __Pyx_ErrFetch(type, value, tb)  PyErr_Fetch(type, value, tb)
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/* RaiseException.proto */
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause);

/* SaveResetException.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);
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#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)
#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)
#endif

/* PyErrExceptionMatches.proto */
#if CYTHON_FAST_THREAD_STATE
#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);
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#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)
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/* GetException.proto */
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#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);
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static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);
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/* ExtTypeTest.proto */
static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type);

/* GetItemInt.proto */
#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
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    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\
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#define __Pyx_GetItemInt_List(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,
                                                              int wraparound, int boundscheck);
#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\
    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                              int wraparound, int boundscheck);
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
                                                     int is_list, int wraparound, int boundscheck);

/* IncludeStringH.proto */
#include <string.h>

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

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

/* StrEquals.proto */
#if PY_MAJOR_VERSION >= 3
#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
#else
#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
#endif

/* SliceObject.proto */
static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
        PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
        int has_cstart, int has_cstop, int wraparound);

/* RaiseTooManyValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);

/* RaiseNeedMoreValuesToUnpack.proto */
static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);

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

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

/* PySequenceContains.proto */
static CYTHON_INLINE int __Pyx_PySequence_ContainsTF(PyObject* item, PyObject* seq, int eq) {
    int result = PySequence_Contains(seq, item);
    return unlikely(result < 0) ? result : (result == (eq == Py_EQ));
}

/* PyIntBinop.proto */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
#else
#define __Pyx_PyInt_SubtractObjC(op1, op2, intval, inplace)\
    (inplace ? PyNumber_InPlaceSubtract(op1, op2) : PyNumber_Subtract(op1, op2))
#endif

/* PyIntBinop.proto */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
#else
#define __Pyx_PyInt_EqObjC(op1, op2, intval, inplace)\
    PyObject_RichCompare(op1, op2, Py_EQ)
    #endif

/* SliceObject.proto */
#define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)\
    __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
        PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
        PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
        int has_cstart, int has_cstop, int wraparound);

/* PyObjectSetAttrStr.proto */
#if CYTHON_USE_TYPE_SLOTS
#define __Pyx_PyObject_DelAttrStr(o,n) __Pyx_PyObject_SetAttrStr(o,n,NULL)
static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr_name, PyObject* value) {
    PyTypeObject* tp = Py_TYPE(obj);
    if (likely(tp->tp_setattro))
        return tp->tp_setattro(obj, attr_name, value);
#if PY_MAJOR_VERSION < 3
    if (likely(tp->tp_setattr))
        return tp->tp_setattr(obj, PyString_AS_STRING(attr_name), value);
#endif
    return PyObject_SetAttr(obj, attr_name, value);
}
#else
#define __Pyx_PyObject_DelAttrStr(o,n)   PyObject_DelAttr(o,n)
#define __Pyx_PyObject_SetAttrStr(o,n,v) PyObject_SetAttr(o,n,v)
#endif

/* KeywordStringCheck.proto */
static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed);

/* PyIntBinop.proto */
#if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, long intval, int inplace);
#else
#define __Pyx_PyInt_AddObjC(op1, op2, intval, inplace)\
    (inplace ? PyNumber_InPlaceAdd(op1, op2) : PyNumber_Add(op1, op2))
#endif

/* Import.proto */
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);

/* ImportFrom.proto */
static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name);

/* ListAppend.proto */
#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS
static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
    PyListObject* L = (PyListObject*) list;
    Py_ssize_t len = Py_SIZE(list);
    if (likely(L->allocated > len) & likely(len > (L->allocated >> 1))) {
        Py_INCREF(x);
        PyList_SET_ITEM(list, len, x);
        Py_SIZE(list) = len+1;
        return 0;
    }
    return PyList_Append(list, x);
}
#else
#define __Pyx_PyList_Append(L,x) PyList_Append(L,x)
#endif

/* ForceInitThreads.proto */
#ifndef __PYX_FORCE_INIT_THREADS
  #define __PYX_FORCE_INIT_THREADS 0
#endif

/* SetItemInt.proto */
#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\
    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\
    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\
    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) :\
               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
                                               int is_list, int wraparound, int boundscheck);

/* SetVTable.proto */
static int __Pyx_SetVtable(PyObject *dict, void *vtable);

/* CodeObjectCache.proto */
typedef struct {
    PyCodeObject* code_object;
    int code_line;
} __Pyx_CodeObjectCacheEntry;
struct __Pyx_CodeObjectCache {
    int count;
    int max_count;
    __Pyx_CodeObjectCacheEntry* entries;
};
static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
static PyCodeObject *__pyx_find_code_object(int code_line);
static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);

/* AddTraceback.proto */
static void __Pyx_AddTraceback(const char *funcname, int c_line,
                               int py_line, const char *filename);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_bool(npy_bool value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int8(npy_int8 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int16(npy_int16 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint8(npy_uint8 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint16(npy_uint16 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint32(npy_uint32 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint64(npy_uint64 value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);

/* CIntToPy.proto */
static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_bool __Pyx_PyInt_As_npy_bool(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_uint8 __Pyx_PyInt_As_npy_uint8(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_int8 __Pyx_PyInt_As_npy_int8(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_uint16 __Pyx_PyInt_As_npy_uint16(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_int16 __Pyx_PyInt_As_npy_int16(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_uint32 __Pyx_PyInt_As_npy_uint32(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_uint64 __Pyx_PyInt_As_npy_uint64(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE npy_int64 __Pyx_PyInt_As_npy_int64(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *);

/* CIntFromPy.proto */
static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);

/* CheckBinaryVersion.proto */
static int __Pyx_check_binary_version(void);

/* PyIdentifierFromString.proto */
#if !defined(__Pyx_PyIdentifier_FromString)
#if PY_MAJOR_VERSION < 3
  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
#else
  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
#endif
#endif

/* ModuleImport.proto */
static PyObject *__Pyx_ImportModule(const char *name);

/* TypeImport.proto */
static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);

/* InitStrings.proto */
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t);

static CYTHON_INLINE PyObject *__pyx_f_6mtrand_11RandomState__shuffle_raw(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_n, npy_intp __pyx_v_itemsize, npy_intp __pyx_v_stride, char *__pyx_v_data, char *__pyx_v_buf); /* proto*/

/* Module declarations from 'numpy' */

/* Module declarations from 'libc' */

/* Module declarations from 'libc.string' */

/* 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;
static PyTypeObject *__pyx_ptype_6mtrand_RandomState = 0;
static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *, __pyx_t_6mtrand_rk_cont0, PyObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont1, PyObject *, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *, __pyx_t_6mtrand_rk_cont1, PyObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont2, PyObject *, double, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont2_array(rk_state *, __pyx_t_6mtrand_rk_cont2, PyObject *, PyArrayObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont3, PyObject *, double, double, double, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *, __pyx_t_6mtrand_rk_cont3, PyObject *, PyArrayObject *, PyArrayObject *, PyArrayObject *, PyObject *); /*proto*/
static PyObject *__pyx_f_6mtrand_disc0_array(rk_state *, __pyx_t_6mtrand_rk_disc0, PyObject *, PyObject *); /*proto*/
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_range;
static PyObject *__pyx_builtin_TypeError;
static PyObject *__pyx_builtin_OverflowError;
static PyObject *__pyx_builtin_DeprecationWarning;
static PyObject *__pyx_builtin_RuntimeWarning;
static PyObject *__pyx_builtin_reversed;
static const char __pyx_k_L[] = "L";
static const char __pyx_k_T[] = "T";
static const char __pyx_k_a[] = "a";
static const char __pyx_k_b[] = "b";
static const char __pyx_k_d[] = "d";
static const char __pyx_k_f[] = "f";
static const char __pyx_k_l[] = "l";
static const char __pyx_k_n[] = "n";
static const char __pyx_k_p[] = "p";
static const char __pyx_k_df[] = "df";
static const char __pyx_k_mu[] = "mu";
static const char __pyx_k_np[] = "np";
static const char __pyx_k_a_0[] = "a <= 0";
static const char __pyx_k_add[] = "add";
static const char __pyx_k_all[] = "all";
static const char __pyx_k_any[] = "any";
static const char __pyx_k_b_0[] = "b <= 0";
static const char __pyx_k_buf[] = "buf";
static const char __pyx_k_cnt[] = "cnt";
static const char __pyx_k_cov[] = "cov";
static const char __pyx_k_dot[] = "dot";
static const char __pyx_k_eps[] = "eps";
static const char __pyx_k_int[] = "int";
static const char __pyx_k_lam[] = "lam";
static const char __pyx_k_loc[] = "loc";
static const char __pyx_k_low[] = "low";
static const char __pyx_k_max[] = "max";
static const char __pyx_k_n_0[] = "n < 0";
static const char __pyx_k_off[] = "off";
static const char __pyx_k_out[] = "out";
static const char __pyx_k_p_0[] = "p < 0";
static const char __pyx_k_p_1[] = "p > 1";
static const char __pyx_k_rng[] = "rng";
static const char __pyx_k_svd[] = "svd";
static const char __pyx_k_tol[] = "tol";
static const char __pyx_k_Lock[] = "Lock";
static const char __pyx_k_atol[] = "atol";
static const char __pyx_k_beta[] = "beta";
static const char __pyx_k_bool[] = "bool_";
static const char __pyx_k_copy[] = "copy";
static const char __pyx_k_data[] = "data";
static const char __pyx_k_df_0[] = "df <= 0";
static const char __pyx_k_exit[] = "__exit__";
static const char __pyx_k_high[] = "high";
static const char __pyx_k_int8[] = "int8";
static const char __pyx_k_intp[] = "intp";
static const char __pyx_k_item[] = "item";
static const char __pyx_k_left[] = "left";
static const char __pyx_k_less[] = "less";
static const char __pyx_k_long[] = "long";
static const char __pyx_k_main[] = "__main__";
static const char __pyx_k_mean[] = "mean";
static const char __pyx_k_mode[] = "mode";
static const char __pyx_k_name[] = "name";
static const char __pyx_k_nbad[] = "nbad";
static const char __pyx_k_ndim[] = "ndim";
static const char __pyx_k_nonc[] = "nonc";
static const char __pyx_k_prod[] = "prod";
static const char __pyx_k_rand[] = "rand";
static const char __pyx_k_rtol[] = "rtol";
static const char __pyx_k_safe[] = "safe";
static const char __pyx_k_seed[] = "seed";
static const char __pyx_k_side[] = "side";
static const char __pyx_k_size[] = "size";
static const char __pyx_k_sort[] = "sort";
static const char __pyx_k_sqrt[] = "sqrt";
static const char __pyx_k_take[] = "take";
static const char __pyx_k_test[] = "__test__";
static const char __pyx_k_uint[] = "uint";
static const char __pyx_k_wald[] = "wald";
static const char __pyx_k_warn[] = "warn";
static const char __pyx_k_zipf[] = "zipf";
static const char __pyx_k_a_0_2[] = "a < 0";
static const char __pyx_k_a_1_0[] = "a <= 1.0";
static const char __pyx_k_alpha[] = "alpha";
static const char __pyx_k_array[] = "array";
static const char __pyx_k_bytes[] = "bytes";
static const char __pyx_k_dfden[] = "dfden";
static const char __pyx_k_dfnum[] = "dfnum";
static const char __pyx_k_dtype[] = "dtype";
static const char __pyx_k_empty[] = "empty";
static const char __pyx_k_enter[] = "__enter__";
static const char __pyx_k_equal[] = "equal";
static const char __pyx_k_finfo[] = "finfo";
static const char __pyx_k_gamma[] = "gamma";
static const char __pyx_k_iinfo[] = "iinfo";
static const char __pyx_k_index[] = "index";
static const char __pyx_k_int16[] = "int16";
static const char __pyx_k_int32[] = "int32";
static const char __pyx_k_int64[] = "int64";
static const char __pyx_k_isnan[] = "isnan";
static const char __pyx_k_kappa[] = "kappa";
static const char __pyx_k_lam_0[] = "lam < 0";
static const char __pyx_k_n_0_2[] = "n <= 0";
static const char __pyx_k_ngood[] = "ngood";
static const char __pyx_k_numpy[] = "numpy";
static const char __pyx_k_p_0_0[] = "p < 0.0";
static const char __pyx_k_p_1_0[] = "p > 1.0";
static const char __pyx_k_power[] = "power";
static const char __pyx_k_pvals[] = "pvals";
static const char __pyx_k_raise[] = "raise";
static const char __pyx_k_randn[] = "randn";
static const char __pyx_k_range[] = "range";
static const char __pyx_k_ravel[] = "ravel";
static const char __pyx_k_right[] = "right";
static const char __pyx_k_scale[] = "scale";
static const char __pyx_k_shape[] = "shape";
static const char __pyx_k_sigma[] = "sigma";
static const char __pyx_k_state[] = "state";
static const char __pyx_k_uint8[] = "uint8";
static const char __pyx_k_zeros[] = "zeros";
static const char __pyx_k_arange[] = "arange";
static const char __pyx_k_astype[] = "astype";
static const char __pyx_k_bool_2[] = "bool";
static const char __pyx_k_choice[] = "choice";
static const char __pyx_k_ctypes[] = "ctypes";
static const char __pyx_k_cumsum[] = "cumsum";
static const char __pyx_k_format[] = "format";
static const char __pyx_k_gumbel[] = "gumbel";
static const char __pyx_k_ignore[] = "ignore";
static const char __pyx_k_import[] = "__import__";
static const char __pyx_k_mean_0[] = "mean <= 0";
static const char __pyx_k_mtrand[] = "mtrand";
static const char __pyx_k_nbad_0[] = "nbad < 0";
static const char __pyx_k_nonc_0[] = "nonc < 0";
static const char __pyx_k_normal[] = "normal";
static const char __pyx_k_pareto[] = "pareto";
static const char __pyx_k_rand_2[] = "_rand";
static const char __pyx_k_random[] = "random";
static const char __pyx_k_reduce[] = "reduce";
static const char __pyx_k_uint16[] = "uint16";
static const char __pyx_k_uint32[] = "uint32";
static const char __pyx_k_uint64[] = "uint64";
static const char __pyx_k_unique[] = "unique";
static const char __pyx_k_unsafe[] = "unsafe";
static const char __pyx_k_MT19937[] = "MT19937";
static const char __pyx_k_asarray[] = "asarray";
static const char __pyx_k_casting[] = "casting";
static const char __pyx_k_dfden_0[] = "dfden <= 0";
static const char __pyx_k_dfnum_0[] = "dfnum <= 0";
static const char __pyx_k_dfnum_1[] = "dfnum <= 1";
static const char __pyx_k_float64[] = "float64";
static const char __pyx_k_greater[] = "greater";
static const char __pyx_k_integer[] = "integer";
static const char __pyx_k_kappa_0[] = "kappa < 0";
static const char __pyx_k_laplace[] = "laplace";
static const char __pyx_k_ndarray[] = "ndarray";
static const char __pyx_k_ngood_0[] = "ngood < 0";
static const char __pyx_k_nsample[] = "nsample";
static const char __pyx_k_p_0_0_2[] = "p <= 0.0";
static const char __pyx_k_p_1_0_2[] = "p >= 1.0";
static const char __pyx_k_poisson[] = "poisson";
static const char __pyx_k_randint[] = "randint";
static const char __pyx_k_replace[] = "replace";
static const char __pyx_k_reshape[] = "reshape";
static const char __pyx_k_scale_0[] = "scale < 0";
static const char __pyx_k_shape_0[] = "shape < 0";
static const char __pyx_k_shuffle[] = "shuffle";
static const char __pyx_k_sigma_0[] = "sigma < 0";
static const char __pyx_k_signbit[] = "signbit";
static const char __pyx_k_strides[] = "strides";
static const char __pyx_k_uniform[] = "uniform";
static const char __pyx_k_weibull[] = "weibull";
static const char __pyx_k_allclose[] = "allclose";
static const char __pyx_k_binomial[] = "binomial";
static const char __pyx_k_floating[] = "floating";
static const char __pyx_k_isfinite[] = "isfinite";
static const char __pyx_k_itemsize[] = "itemsize";
static const char __pyx_k_logistic[] = "logistic";
static const char __pyx_k_low_high[] = "low >= high";
static const char __pyx_k_mean_0_0[] = "mean <= 0.0";
static const char __pyx_k_operator[] = "operator";
static const char __pyx_k_p_is_nan[] = "p is nan";
static const char __pyx_k_rayleigh[] = "rayleigh";
static const char __pyx_k_reversed[] = "reversed";
static const char __pyx_k_rngstate[] = "rngstate";
static const char __pyx_k_subtract[] = "subtract";
static const char __pyx_k_vonmises[] = "vonmises";
static const char __pyx_k_warnings[] = "warnings";
static const char __pyx_k_TypeError[] = "TypeError";
static const char __pyx_k_broadcast[] = "broadcast";
static const char __pyx_k_chisquare[] = "chisquare";
static const char __pyx_k_dirichlet[] = "dirichlet";
static const char __pyx_k_geometric[] = "geometric";
static const char __pyx_k_get_state[] = "get_state";
static const char __pyx_k_left_mode[] = "left > mode";
static const char __pyx_k_lognormal[] = "lognormal";
static const char __pyx_k_logseries[] = "logseries";
static const char __pyx_k_nsample_1[] = "nsample < 1";
static const char __pyx_k_rand_bool[] = "_rand_bool";
static const char __pyx_k_rand_int8[] = "_rand_int8";
static const char __pyx_k_scale_0_0[] = "scale < 0.0";
static const char __pyx_k_scale_0_2[] = "scale <= 0";
static const char __pyx_k_set_state[] = "set_state";
static const char __pyx_k_sigma_0_0[] = "sigma < 0.0";
static const char __pyx_k_threading[] = "threading";
static const char __pyx_k_ValueError[] = "ValueError";
static const char __pyx_k_array_data[] = "array_data";
static const char __pyx_k_empty_like[] = "empty_like";
static const char __pyx_k_issubdtype[] = "issubdtype";
static const char __pyx_k_left_right[] = "left == right";
static const char __pyx_k_less_equal[] = "less_equal";
static const char __pyx_k_logical_or[] = "logical_or";
static const char __pyx_k_mode_right[] = "mode > right";
static const char __pyx_k_numpy_dual[] = "numpy.dual";
static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__";
static const char __pyx_k_rand_int16[] = "_rand_int16";
static const char __pyx_k_rand_int32[] = "_rand_int32";
static const char __pyx_k_rand_int64[] = "_rand_int64";
static const char __pyx_k_rand_uint8[] = "_rand_uint8";
static const char __pyx_k_standard_t[] = "standard_t";
static const char __pyx_k_triangular[] = "triangular";
static const char __pyx_k_ImportError[] = "ImportError";
static const char __pyx_k_check_valid[] = "check_valid";
static const char __pyx_k_exponential[] = "exponential";
static const char __pyx_k_multinomial[] = "multinomial";
static const char __pyx_k_permutation[] = "permutation";
static const char __pyx_k_rand_uint16[] = "_rand_uint16";
static const char __pyx_k_rand_uint32[] = "_rand_uint32";
static const char __pyx_k_rand_uint64[] = "_rand_uint64";
static const char __pyx_k_scale_0_0_2[] = "scale <= 0.0";
static const char __pyx_k_noncentral_f[] = "noncentral_f";
static const char __pyx_k_randint_type[] = "_randint_type";
static const char __pyx_k_return_index[] = "return_index";
static const char __pyx_k_searchsorted[] = "searchsorted";
static const char __pyx_k_OverflowError[] = "OverflowError";
static const char __pyx_k_count_nonzero[] = "count_nonzero";
static const char __pyx_k_greater_equal[] = "greater_equal";
static const char __pyx_k_random_sample[] = "random_sample";
static const char __pyx_k_RuntimeWarning[] = "RuntimeWarning";
static const char __pyx_k_hypergeometric[] = "hypergeometric";
static const char __pyx_k_standard_gamma[] = "standard_gamma";
static const char __pyx_k_dummy_threading[] = "dummy_threading";
static const char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
static const char __pyx_k_random_integers[] = "random_integers";
static const char __pyx_k_shape_from_size[] = "_shape_from_size";
static const char __pyx_k_standard_cauchy[] = "standard_cauchy";
static const char __pyx_k_standard_normal[] = "standard_normal";
static const char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
static const char __pyx_k_RandomState_ctor[] = "__RandomState_ctor";
static const char __pyx_k_negative_binomial[] = "negative_binomial";
static const char __pyx_k_DeprecationWarning[] = "DeprecationWarning";
static const char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
static const char __pyx_k_a_must_be_non_empty[] = "a must be non-empty";
static const char __pyx_k_lam_value_too_large[] = "lam value too large";
static const char __pyx_k_multivariate_normal[] = "multivariate_normal";
static const char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
static const char __pyx_k_standard_exponential[] = "standard_exponential";
static const char __pyx_k_lam_value_too_large_2[] = "lam value too large.";
static const char __pyx_k_RandomState_f_line_1992[] = "RandomState.f (line 1992)";
static const char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
static const char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
static const char __pyx_k_state_must_be_624_longs[] = "state must be 624 longs";
static const char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
static const char __pyx_k_algorithm_must_be_MT19937[] = "algorithm must be 'MT19937'";
static const char __pyx_k_RandomState_bytes_line_999[] = "RandomState.bytes (line 999)";
static const char __pyx_k_RandomState_rand_line_1316[] = "RandomState.rand (line 1316)";
static const char __pyx_k_RandomState_wald_line_3505[] = "RandomState.wald (line 3505)";
static const char __pyx_k_RandomState_zipf_line_3991[] = "RandomState.zipf (line 3991)";
static const char __pyx_k_Range_exceeds_valid_bounds[] = "Range exceeds valid bounds";
static const char __pyx_k_low_is_out_of_bounds_for_s[] = "low is out of bounds for %s";
static const char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
static const char __pyx_k_RandomState_gamma_line_1896[] = "RandomState.gamma (line 1896)";
static const char __pyx_k_RandomState_power_line_2869[] = "RandomState.power (line 2869)";
static const char __pyx_k_RandomState_randn_line_1360[] = "RandomState.randn (line 1360)";
static const char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
static const char __pyx_k_high_is_out_of_bounds_for_s[] = "high is out of bounds for %s";
static const char __pyx_k_RandomState_choice_line_1028[] = "RandomState.choice (line 1028)";
static const char __pyx_k_RandomState_gumbel_line_3078[] = "RandomState.gumbel (line 3078)";
static const char __pyx_k_RandomState_normal_line_1547[] = "RandomState.normal (line 1547)";
static const char __pyx_k_RandomState_pareto_line_2649[] = "RandomState.pareto (line 2649)";
static const char __pyx_k_RandomState_randint_line_905[] = "RandomState.randint (line 905)";
static const char __pyx_k_RandomState_laplace_line_2980[] = "RandomState.laplace (line 2980)";
static const char __pyx_k_RandomState_poisson_line_3903[] = "RandomState.poisson (line 3903)";
static const char __pyx_k_RandomState_shuffle_line_4759[] = "RandomState.shuffle (line 4759)";
static const char __pyx_k_RandomState_tomaxint_line_858[] = "RandomState.tomaxint (line 858)";
static const char __pyx_k_RandomState_uniform_line_1210[] = "RandomState.uniform (line 1210)";
static const char __pyx_k_RandomState_weibull_line_2759[] = "RandomState.weibull (line 2759)";
static const char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
static const char __pyx_k_RandomState_binomial_line_3686[] = "RandomState.binomial (line 3686)";
static const char __pyx_k_RandomState_logistic_line_3209[] = "RandomState.logistic (line 3209)";
static const char __pyx_k_RandomState_rayleigh_line_3426[] = "RandomState.rayleigh (line 3426)";
static const char __pyx_k_RandomState_vonmises_line_2551[] = "RandomState.vonmises (line 2551)";
static const char __pyx_k_dirichlet_alpha_size_None_Draw[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : 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 const char __pyx_k_laplace_loc_0_0_scale_1_0_size[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float or array_like of floats, optional\n            The position, :math:`\\mu`, of the distribution peak. Default is 0.\n        scale : float or array_like of floats, optional\n            :math:`\\lambda`, the exponential decay. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Laplace distribution.\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.). \"Han""dbook 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, \" Birkhauser, 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 const char __pyx_k_permutation_x_Randomly_permute[] = "\n        permutation(x)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        If `x` is a multi-dimensional array, it is only shuffled along its\n        first index.\n\n        Parameters\n        ----------\n        x : int or array_like\n            If `x` is an integer, randomly permute ``np.arange(x)``.\n            If `x` is an array, make a copy and shuffle the elements\n            randomly.\n\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> np.random.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n        >>> np.random.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.permutation(arr)\n        array([[6, 7, 8],\n               [0, 1, 2],\n               [3, 4, 5]])\n\n        ";
static const char __pyx_k_poisson_lam_1_0_size_None_Draw[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the binomial distribution\n        for large N.\n\n        Parameters\n        ----------\n        lam : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``lam`` is a scalar. Otherwise,\n            ``np.array(lam).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Poisson distribution.\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 histo""gram 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 const char __pyx_k_rand_d0_d1_dn_Random_values_in[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and populate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should all be positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample .\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
static const char __pyx_k_randn_d0_d1_dn_Return_a_sample[] = "\n        randn(d0, d1, ..., dn)\n\n        Return a sample (or samples) from the \"standard normal\" distribution.\n\n        If positive, int_like or int-convertible arguments are provided,\n        `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n        with random floats sampled from a univariate \"normal\" (Gaussian)\n        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n        floats, they are first converted to integers by truncation). A single\n        float randomly sampled from the distribution is returned if no\n        argument is provided.\n\n        This is a convenience function.  If you want an interface that takes a\n        tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should be all positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n\n        ";
static const char __pyx_k_random_sample_size_None_Return[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            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 const char __pyx_k_shuffle_x_Modify_a_sequence_in[] = "\n        shuffle(x)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        This function only shuffles the array along the first axis of a\n        multi-dimensional array. The order of sub-arrays is changed but\n        their contents remains the same.\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        Multi-dimensional arrays are only shuffled along the first axis:\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.shuffle(arr)\n        >>> arr\n        array([[3, 4, 5],\n               [6, 7, 8],\n               [0, 1, 2]])\n\n        ";
static const char __pyx_k_standard_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 const char __pyx_k_standard_exponential_size_None[] = "\n        standard_exponential(size=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints, 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 const char __pyx_k_standard_gamma_shape_size_None[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``shape`` is a scalar.  Otherwise,\n            ``np.array(shape).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized standard gamma distribution.\n\n        See Also\n        --------\n        scipy.stats.gamma : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma distribution\",\n               http://en.wikipedia.org/wiki/Gamma_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2""., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_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 const char __pyx_k_wald_mean_scale_size_None_Draw[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian. 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 : float or array_like of floats\n            Distribution mean, should be > 0.\n        scale : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``mean`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Wald distribution.\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 : Methodology, 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 const char __pyx_k_RandomState_chisquare_line_2196[] = "RandomState.chisquare (line 2196)";
static const char __pyx_k_RandomState_dirichlet_line_4643[] = "RandomState.dirichlet (line 4643)";
static const char __pyx_k_RandomState_geometric_line_4082[] = "RandomState.geometric (line 4082)";
static const char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 4150)";
static const char __pyx_k_RandomState_lognormal_line_3302[] = "RandomState.lognormal (line 3302)";
static const char __pyx_k_RandomState_logseries_line_4272[] = "RandomState.logseries (line 4272)";
static const char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4369)";
static const char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1810)";
static const char __pyx_k_Seed_must_be_between_0_and_2_32[] = "Seed must be between 0 and 2**32 - 1";
static const char __pyx_k_Unsupported_dtype_s_for_randint[] = "Unsupported dtype \"%s\" for randint";
static const char __pyx_k_binomial_n_p_size_None_Draw_sam[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer >= 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : int or array_like of ints\n            Parameter of the distribution, >= 0. Floats are also accepted,\n            but they will be truncated to integers.\n        p : float or array_like of floats\n            Parameter of the distribution, >= 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.  If size is ``None`` (default),\n            a single value is returned if ``n`` and ``p`` are both scalars.\n            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized binomial distribution, where\n            each sample is equal to the number of successes over the n trials.\n\n        See Also\n        --------\n        scipy.stats.binom : probability density function, distribution or\n            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 binom""ial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5  # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.\n        # answer = 0.38885, or 38%.\n\n        ";
static const char __pyx_k_bytes_length_Return_random_byte[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
static const char __pyx_k_chisquare_df_size_None_Draw_sam[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized chi-square distribution.\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 const char __pyx_k_choice_a_size_None_replace_True[] = "\n        choice(a, size=None, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n                .. versionadded:: 1.7.0\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a were np.arange(a)\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 : single item or ndarray\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 const 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 : int or array_like of ints\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : int or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``dfnum`` and ``dfden`` are both scalars.\n            Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.f : probability density function, distribution or\n            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 const char __pyx_k_gamma_shape_scale_1_0_size_None[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : float or array_like of floats\n            The shape of the gamma distribution. Should be greater than zero.\n        scale : float or array_like of floats, optional\n            The scale of the gamma distribution. Should be greater than zero.\n            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.  If size is ``None`` (default),\n            a single value is returned if ``shape`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized gamma distribution.\n\n        See Also\n        --------\n        scipy.stats.gamma : probability density function, distribution or\n            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 Resourc""e.\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=4, std=2*sqrt(2)\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_geometric_p_size_None_Draw_samp[] = "\n        geometric(p, size=None)\n\n        Draw samples from the geometric distribution.\n\n        Bernoulli trials are experiments with one of two outcomes:\n        success or failure (an example of such an experiment is flipping\n        a coin).  The geometric distribution models the number of trials\n        that must be run in order to achieve success.  It is therefore\n        supported on the positive integers, ``k = 1, 2, ...``.\n\n        The probability mass function of the geometric distribution is\n\n        .. math:: f(k) = (1 - p)^{k - 1} p\n\n        where `p` is the probability of success of an individual trial.\n\n        Parameters\n        ----------\n        p : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``p`` is a scalar.  Otherwise,\n            ``np.array(p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized geometric distribution.\n\n        Examples\n        --------\n        Draw ten thousand values from the geometric distribution,\n        with the probability of an individual success equal to 0.35:\n\n        >>> z = np.random.geometric(p=0.35, size=10000)\n\n        How many trials succeeded after a single run?\n\n        >>> (z == 1).sum() / 10000.\n        0.34889999999999999 #random\n\n        ";
static const char __pyx_k_gumbel_loc_0_0_scale_1_0_size_N[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        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 or array_like of floats, optional\n            The location of the mode of the distribution. Default is 0.\n        scale : float or array_like of floats, optional\n            The scale parameter of the distribution. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Gumbel distribution.\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 event 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.sqrt(6) / np.pi\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_home_charris_Workspace_numpy_gi[] = "/home/charris/Workspace/numpy.git/numpy/random/mtrand/randint_helpers.pxi";
static const char __pyx_k_hypergeometric_ngood_nbad_nsamp[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : int or array_like of ints\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like of ints\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``ngood``, ``nbad``, and ``nsample``\n            are all scalars.  Otherwise, ``np.broadcast(ngood, nbad, nsample).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized hypergeometric distribution.\n\n        See Also\n        --------\n        scipy.stats.hypergeom : probability density function, distribution or\n            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 const char __pyx_k_logistic_loc_0_0_scale_1_0_size[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a logistic distribution.\n\n        Samples are drawn from a logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float or array_like of floats, optional\n            Parameter of the distribution. Default is 0.\n        scale : float or array_like of floats, optional\n            Parameter of the distribution. Should be greater than zero.\n            Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized logistic distribution.\n\n        See Also\n        --------\n        scipy.stats.logistic : probability density function, distribution or\n            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, Financ""e, 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 const 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 or array_like of floats, optional\n            Mean value of the underlying normal distribution. Default is 0.\n        sigma : float or array_like of floats, optional\n            Standard deviation of the underlying normal distribution. Should\n            be greater than zero. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``mean`` and ``sigma`` are both scalars.\n            Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized log-normal distribution.\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 *produc""t*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        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.h""ist(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 const char __pyx_k_logseries_p_size_None_Draw_samp[] = "\n        logseries(p, size=None)\n\n        Draw samples from a logarithmic series distribution.\n\n        Samples are drawn from a log series distribution with specified\n        shape parameter, 0 < ``p`` < 1.\n\n        Parameters\n        ----------\n        p : float or array_like of floats\n            Shape parameter for the distribution.  Must be in the range (0, 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.  If size is ``None`` (default),\n            a single value is returned if ``p`` is a scalar.  Otherwise,\n            ``np.array(p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized logarithmic series distribution.\n\n        See Also\n        --------\n        scipy.stats.logser : probability density function, distribution or\n            cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The log series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random ""sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"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 const char __pyx_k_multinomial_n_pvals_size_None_D[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the\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 die is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])\n        array([11, 16, 14, 17, 16, 26])\n\n        The probability inputs should be normalized. As an implementation\n        detail, the value of the last entry is ignored and assumed to take\n        up any leftover probability mass, but this should not be relied on.\n        A biased coin which has twice as much weight on one side as on the\n        other should be sampled like so:\n\n        >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])  # RIGHT\n        array([38, 62])\n\n        not like:\n\n        >>> np.random.multinomial(100, [1.0, 2.0])  # WRONG\n        array([100,   0])\n\n        ";
static const char __pyx_k_multivariate_normal_mean_cov_si[] = "\n        multivariate_normal(mean, cov[, size, check_valid, tol])\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        check_valid : { 'warn', 'raise', 'ignore' }, optional\n            Behavior when the covariance matrix is not positive semidefinite.\n        tol : float, optional\n            Tolerance when checking the singular values in covariance matrix.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This ""is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (`cov` is a multiple of the identity matrix)\n          - Diagonal covariance (`cov` has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0, 0]\n        >>> cov = [[1, 0], [0, 100]]  # diagonal covariance\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 const char __pyx_k_negative_binomial_n_p_size_None[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative binomial distribution.\n\n        Samples are drawn from a negative binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int or array_like of ints\n            Parameter of the distribution, > 0. Floats are also accepted,\n            but they will be truncated to integers.\n        p : float or array_like of floats\n            Parameter of the distribution, >= 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.  If size is ``None`` (default),\n            a single value is returned if ``n`` and ``p`` are both scalars.\n            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized negative binomial distribution,\n            where each sample is equal to N, the number of trials it took to\n            achieve n - 1 successes, N - (n - 1) failures, and a success on the,\n            (N + n)th trial.\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 distribution:\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 const char __pyx_k_noncentral_chisquare_df_nonc_si[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int or array_like of ints\n            Degrees of freedom, should be > 0 as of NumPy 1.10.0,\n            should be > 1 for earlier versions.\n        nonc : float or array_like of floats\n            Non-centrality, should be non-negative.\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` and ``nonc`` are both scalars.\n            Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized noncentral chi-square distribution.\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        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n        ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
static const char __pyx_k_noncentral_f_dfnum_dfden_nonc_s[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : int or array_like of ints\n            Parameter, should be > 1.\n        dfden : int or array_like of ints\n            Parameter, should be > 1.\n        nonc : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``dfnum``, ``dfden``, and ``nonc``\n            are all scalars.  Otherwise, ``np.broadcast(dfnum, dfden, nonc).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized noncentral Fisher distribution.\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-dis""tribution\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 const char __pyx_k_normal_loc_0_0_scale_1_0_size_N[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float or array_like of floats\n            Mean (\"centre\") of the distribution.\n        scale : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized normal distribution.\n\n        See Also\n        --------\n        scipy.stats.norm : probability density function, distribution or\n            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 v""ariance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to\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 const char __pyx_k_pareto_a_size_None_Draw_samples[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with\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        a : float or array_like of floats\n            Shape of the distribution. 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.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Pareto distribution.\n\n        See Also\n        --------\n        scipy.stats.lomax : probability density function, distribution or\n            cumulative density function, etc.\n        scipy.stats.genpareto : probability density function, distribution or\n            cumulative densit""y 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 const char __pyx_k_power_a_size_None_Draws_samples[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Parameter of the distribution. 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.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized power distribution.\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 = 100""0\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
static const char __pyx_k_randint_low_high_None_size_None[] = "\n        randint(low, high=None, size=None, dtype='l')\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution of\n        the specified dtype in the \"half-open\" interval [`low`, `high`). If\n        `high` is None (the default), 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 one above the\n            *highest* such 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        dtype : dtype, optional\n            Desired dtype of the result. All dtypes are determined by their\n            name, i.e., 'int64', 'int', etc, so byteorder is not available\n            and a specific precision may have different C types depending\n            on the platform. The default value is 'np.int'.\n\n            .. versionadded:: 1.11.0\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        ---""-----\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
static const char __pyx_k_random_integers_low_high_None_s[] = "\n        random_integers(low, high=None, size=None)\n\n        Random integers of type np.int between `low` and `high`, inclusive.\n\n        Return random integers of type np.int from the \"discrete uniform\"\n        distribution in the closed interval [`low`, `high`].  If `high` is\n        None (the default), then results are from [1, `low`]. The np.int\n        type translates to the C long type used by Python 2 for \"short\"\n        integers and its precision is platform dependent.\n\n        This function has been deprecated. Use randint instead.\n\n        .. deprecated:: 1.11.0\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 const char __pyx_k_rayleigh_scale_1_0_size_None_Dr[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : float or array_like of floats, optional\n            Scale, also equals the mode. Should be >= 0. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``scale`` is a scalar.  Otherwise,\n            ``np.array(scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Rayleigh distribution.\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        >>> mo""devalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
static const char __pyx_k_standard_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 or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized standard Student's t distribution.\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 Gosset 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-distributio""n\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 const char __pyx_k_tomaxint_size_None_Random_integ[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : 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 const char __pyx_k_triangular_left_mode_right_size[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution over the\n        interval ``[left, right]``.\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 : float or array_like of floats\n            Lower limit.\n        mode : float or array_like of floats\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``left``, ``mode``, and ``right``\n            are all scalars.  Otherwise, ``np.broadcast(left, mode, right).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized triangular distribution.\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(r-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 const 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 or array_like of floats, 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 or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``low`` and ``high`` are both scalars.\n            Otherwise, ``np.broadcast(low, high).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized uniform distribution.\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        anywhe""re within the interval ``[a, b)``, and zero elsewhere.\n\n        When ``high`` == ``low``, values of ``low`` will be returned.\n        If ``high`` < ``low``, the results are officially undefined\n        and may eventually raise an error, i.e. do not rely on this\n        function to behave when passed arguments satisfying that\n        inequality condition.\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 const char __pyx_k_vonmises_mu_kappa_size_None_Dra[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float or array_like of floats\n            Mode (\"center\") of the distribution.\n        kappa : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``mu`` and ``kappa`` are both scalars.\n            Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized von Mises distribution.\n\n        See Also\n        --------\n        scipy.stats.vonmises : probability density function, distribution, or\n            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, aero""dynamics, 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 const 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 or array_like of floats\n            Shape of the distribution. 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.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Weibull distribution.\n\n        See Also\n        --------\n        scipy.stats.weibull_max\n        scipy.stats.weibull_min\n        scipy.stats.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 di""stribution 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 const char __pyx_k_zipf_a_size_None_Draw_samples_f[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float or array_like of floats\n            Distribution parameter. Should be greater than 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.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar. Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Zipf distribution.\n\n        See Also\n        --------\n        scipy.stats.zipf : probability density function, distribution, or\n            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        >>> from scipy import special\n\n        Truncate s values at 50 so plot is interesting:\n\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a) / special.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
static const char __pyx_k_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=False'";
static const char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
static const char __pyx_k_RandomState_multinomial_line_453[] = "RandomState.multinomial (line 4530)";
static const char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3802)";
static const char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2277)";
static const char __pyx_k_RandomState_noncentral_f_line_20[] = "RandomState.noncentral_f (line 2099)";
static const char __pyx_k_RandomState_permutation_line_484[] = "RandomState.permutation (line 4847)";
static const char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1417)";
static const char __pyx_k_RandomState_random_sample_line_8[] = "RandomState.random_sample (line 814)";
static const char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2381)";
static const char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1779)";
static const char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1514)";
static const char __pyx_k_RandomState_standard_t_line_2445[] = "RandomState.standard_t (line 2445)";
static const char __pyx_k_RandomState_triangular_line_3592[] = "RandomState.triangular (line 3592)";
static const char __pyx_k_This_function_is_deprecated_Plea[] = "This function is deprecated. Please call randint(1, {low} + 1) instead";
static const char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
static const char __pyx_k_check_valid_must_equal_warn_rais[] = "check_valid must equal 'warn', 'raise', or 'ignore'";
static const char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
static const char __pyx_k_covariance_is_not_positive_semid[] = "covariance is not positive-semidefinite.";
static const char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
static const char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
static const char __pyx_k_size_is_not_compatible_with_inpu[] = "size is not compatible with inputs";
static const char __pyx_k_home_charris_Workspace_numpy_gi_2[] = "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx";
static const char __pyx_k_This_function_is_deprecated_Plea_2[] = "This function is deprecated. Please call randint({low}, {high} + 1) instead";
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static PyObject *__pyx_n_s_equal;
static PyObject *__pyx_n_s_exit;
static PyObject *__pyx_n_s_exponential;
static PyObject *__pyx_n_s_f;
static PyObject *__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa;
static PyObject *__pyx_n_s_finfo;
static PyObject *__pyx_n_s_float64;
static PyObject *__pyx_n_s_floating;
static PyObject *__pyx_n_s_format;
static PyObject *__pyx_n_s_gamma;
static PyObject *__pyx_kp_u_gamma_shape_scale_1_0_size_None;
static PyObject *__pyx_n_s_geometric;
static PyObject *__pyx_kp_u_geometric_p_size_None_Draw_samp;
static PyObject *__pyx_n_s_get_state;
static PyObject *__pyx_n_s_greater;
static PyObject *__pyx_n_s_greater_equal;
static PyObject *__pyx_n_s_gumbel;
static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_high;
static PyObject *__pyx_kp_s_high_is_out_of_bounds_for_s;
static PyObject *__pyx_kp_s_home_charris_Workspace_numpy_gi;
static PyObject *__pyx_kp_s_home_charris_Workspace_numpy_gi_2;
static PyObject *__pyx_n_s_hypergeometric;
static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
static PyObject *__pyx_n_s_ignore;
static PyObject *__pyx_n_s_iinfo;
static PyObject *__pyx_n_s_import;
static PyObject *__pyx_n_s_index;
static PyObject *__pyx_n_s_int;
static PyObject *__pyx_n_s_int16;
static PyObject *__pyx_n_s_int32;
static PyObject *__pyx_n_s_int64;
static PyObject *__pyx_n_s_int8;
static PyObject *__pyx_n_s_integer;
static PyObject *__pyx_n_s_intp;
static PyObject *__pyx_n_s_isfinite;
static PyObject *__pyx_n_s_isnan;
static PyObject *__pyx_n_s_issubdtype;
static PyObject *__pyx_n_s_item;
static PyObject *__pyx_n_s_itemsize;
static PyObject *__pyx_n_s_kappa;
static PyObject *__pyx_kp_s_kappa_0;
static PyObject *__pyx_n_s_l;
static PyObject *__pyx_n_s_lam;
static PyObject *__pyx_kp_s_lam_0;
static PyObject *__pyx_kp_s_lam_value_too_large;
static PyObject *__pyx_kp_s_lam_value_too_large_2;
static PyObject *__pyx_n_s_laplace;
static PyObject *__pyx_kp_u_laplace_loc_0_0_scale_1_0_size;
static PyObject *__pyx_n_s_left;
static PyObject *__pyx_kp_s_left_mode;
static PyObject *__pyx_kp_s_left_right;
static PyObject *__pyx_n_s_less;
static PyObject *__pyx_n_s_less_equal;
static PyObject *__pyx_n_s_loc;
static PyObject *__pyx_n_s_logical_or;
static PyObject *__pyx_n_s_logistic;
static PyObject *__pyx_kp_u_logistic_loc_0_0_scale_1_0_size;
static PyObject *__pyx_n_s_lognormal;
static PyObject *__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si;
static PyObject *__pyx_n_s_logseries;
static PyObject *__pyx_kp_u_logseries_p_size_None_Draw_samp;
static PyObject *__pyx_n_s_long;
static PyObject *__pyx_n_s_low;
static PyObject *__pyx_kp_s_low_high;
static PyObject *__pyx_kp_s_low_is_out_of_bounds_for_s;
static PyObject *__pyx_n_s_main;
static PyObject *__pyx_n_s_max;
static PyObject *__pyx_n_s_mean;
static PyObject *__pyx_kp_s_mean_0;
static PyObject *__pyx_kp_s_mean_0_0;
static PyObject *__pyx_kp_s_mean_and_cov_must_have_same_leng;
static PyObject *__pyx_kp_s_mean_must_be_1_dimensional;
static PyObject *__pyx_n_s_mode;
static PyObject *__pyx_kp_s_mode_right;
static PyObject *__pyx_n_s_mtrand;
static PyObject *__pyx_n_s_mu;
static PyObject *__pyx_n_s_multinomial;
static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
static PyObject *__pyx_n_s_multivariate_normal;
static PyObject *__pyx_kp_u_multivariate_normal_mean_cov_si;
static PyObject *__pyx_n_s_n;
static PyObject *__pyx_kp_s_n_0;
static PyObject *__pyx_kp_s_n_0_2;
static PyObject *__pyx_n_s_name;
static PyObject *__pyx_n_s_nbad;
static PyObject *__pyx_kp_s_nbad_0;
static PyObject *__pyx_n_s_ndarray;
static PyObject *__pyx_n_s_ndim;
static PyObject *__pyx_n_s_negative_binomial;
static PyObject *__pyx_kp_u_negative_binomial_n_p_size_None;
static PyObject *__pyx_n_s_ngood;
static PyObject *__pyx_kp_s_ngood_0;
static PyObject *__pyx_kp_s_ngood_nbad_nsample;
static PyObject *__pyx_n_s_nonc;
static PyObject *__pyx_kp_s_nonc_0;
static PyObject *__pyx_n_s_noncentral_chisquare;
static PyObject *__pyx_kp_u_noncentral_chisquare_df_nonc_si;
static PyObject *__pyx_n_s_noncentral_f;
static PyObject *__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s;
static PyObject *__pyx_n_s_normal;
static PyObject *__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N;
static PyObject *__pyx_n_s_np;
static PyObject *__pyx_n_s_nsample;
static PyObject *__pyx_kp_s_nsample_1;
static PyObject *__pyx_n_s_numpy;
static PyObject *__pyx_n_s_numpy_dual;
static PyObject *__pyx_n_s_off;
static PyObject *__pyx_n_s_operator;
static PyObject *__pyx_n_s_out;
static PyObject *__pyx_n_s_p;
static PyObject *__pyx_kp_s_p_0;
static PyObject *__pyx_kp_s_p_0_0;
static PyObject *__pyx_kp_s_p_0_0_2;
static PyObject *__pyx_kp_s_p_1;
static PyObject *__pyx_kp_s_p_1_0;
static PyObject *__pyx_kp_s_p_1_0_2;
static PyObject *__pyx_kp_s_p_is_nan;
static PyObject *__pyx_kp_s_p_must_be_1_dimensional;
static PyObject *__pyx_n_s_pareto;
static PyObject *__pyx_kp_u_pareto_a_size_None_Draw_samples;
static PyObject *__pyx_n_s_permutation;
static PyObject *__pyx_kp_u_permutation_x_Randomly_permute;
static PyObject *__pyx_n_s_poisson;
static PyObject *__pyx_kp_u_poisson_lam_1_0_size_None_Draw;
static PyObject *__pyx_n_s_poisson_lam_max;
static PyObject *__pyx_n_s_power;
static PyObject *__pyx_kp_u_power_a_size_None_Draws_samples;
static PyObject *__pyx_kp_s_probabilities_are_not_non_negati;
static PyObject *__pyx_kp_s_probabilities_do_not_sum_to_1;
static PyObject *__pyx_n_s_prod;
static PyObject *__pyx_n_s_pvals;
static PyObject *__pyx_n_s_pyx_vtable;
static PyObject *__pyx_n_s_raise;
static PyObject *__pyx_n_s_rand;
static PyObject *__pyx_n_s_rand_2;
static PyObject *__pyx_n_s_rand_bool;
static PyObject *__pyx_kp_u_rand_d0_d1_dn_Random_values_in;
static PyObject *__pyx_n_s_rand_int16;
static PyObject *__pyx_n_s_rand_int32;
static PyObject *__pyx_n_s_rand_int64;
static PyObject *__pyx_n_s_rand_int8;
static PyObject *__pyx_n_s_rand_uint16;
static PyObject *__pyx_n_s_rand_uint32;
static PyObject *__pyx_n_s_rand_uint64;
static PyObject *__pyx_n_s_rand_uint8;
static PyObject *__pyx_n_s_randint;
static PyObject *__pyx_kp_u_randint_low_high_None_size_None;
static PyObject *__pyx_n_s_randint_type;
static PyObject *__pyx_n_s_randn;
static PyObject *__pyx_kp_u_randn_d0_d1_dn_Return_a_sample;
static PyObject *__pyx_n_s_random;
static PyObject *__pyx_n_s_random_integers;
static PyObject *__pyx_kp_u_random_integers_low_high_None_s;
static PyObject *__pyx_n_s_random_sample;
static PyObject *__pyx_kp_u_random_sample_size_None_Return;
static PyObject *__pyx_n_s_range;
static PyObject *__pyx_n_s_ravel;
static PyObject *__pyx_n_s_rayleigh;
static PyObject *__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr;
static PyObject *__pyx_n_s_reduce;
static PyObject *__pyx_n_s_replace;
static PyObject *__pyx_n_s_reshape;
static PyObject *__pyx_n_s_return_index;
static PyObject *__pyx_n_s_reversed;
static PyObject *__pyx_n_s_right;
static PyObject *__pyx_n_s_rng;
static PyObject *__pyx_n_s_rngstate;
static PyObject *__pyx_n_s_rtol;
static PyObject *__pyx_n_s_safe;
static PyObject *__pyx_n_s_scale;
static PyObject *__pyx_kp_s_scale_0;
static PyObject *__pyx_kp_s_scale_0_0;
static PyObject *__pyx_kp_s_scale_0_0_2;
static PyObject *__pyx_kp_s_scale_0_2;
static PyObject *__pyx_n_s_searchsorted;
static PyObject *__pyx_n_s_seed;
static PyObject *__pyx_n_s_set_state;
static PyObject *__pyx_n_s_shape;
static PyObject *__pyx_kp_s_shape_0;
static PyObject *__pyx_n_s_shape_from_size;
static PyObject *__pyx_n_s_shuffle;
static PyObject *__pyx_kp_u_shuffle_x_Modify_a_sequence_in;
static PyObject *__pyx_n_s_side;
static PyObject *__pyx_n_s_sigma;
static PyObject *__pyx_kp_s_sigma_0;
static PyObject *__pyx_kp_s_sigma_0_0;
static PyObject *__pyx_n_s_signbit;
static PyObject *__pyx_n_s_size;
static PyObject *__pyx_kp_s_size_is_not_compatible_with_inpu;
static PyObject *__pyx_n_s_sort;
static PyObject *__pyx_n_s_sqrt;
static PyObject *__pyx_n_s_standard_cauchy;
static PyObject *__pyx_kp_u_standard_cauchy_size_None_Draw;
static PyObject *__pyx_n_s_standard_exponential;
static PyObject *__pyx_kp_u_standard_exponential_size_None;
static PyObject *__pyx_n_s_standard_gamma;
static PyObject *__pyx_kp_u_standard_gamma_shape_size_None;
static PyObject *__pyx_n_s_standard_normal;
static PyObject *__pyx_kp_u_standard_normal_size_None_Draw;
static PyObject *__pyx_n_s_standard_t;
static PyObject *__pyx_kp_u_standard_t_df_size_None_Draw_sa;
static PyObject *__pyx_n_s_state;
static PyObject *__pyx_kp_s_state_must_be_624_longs;
static PyObject *__pyx_n_s_strides;
static PyObject *__pyx_n_s_subtract;
static PyObject *__pyx_kp_s_sum_pvals_1_1_0;
static PyObject *__pyx_n_s_svd;
static PyObject *__pyx_n_s_take;
static PyObject *__pyx_n_s_test;
static PyObject *__pyx_n_s_threading;
static PyObject *__pyx_n_s_tol;
static PyObject *__pyx_kp_u_tomaxint_size_None_Random_integ;
static PyObject *__pyx_n_s_triangular;
static PyObject *__pyx_kp_u_triangular_left_mode_right_size;
static PyObject *__pyx_n_s_uint;
static PyObject *__pyx_n_s_uint16;
static PyObject *__pyx_n_s_uint32;
static PyObject *__pyx_n_s_uint64;
static PyObject *__pyx_n_s_uint8;
static PyObject *__pyx_n_s_uniform;
static PyObject *__pyx_kp_u_uniform_low_0_0_high_1_0_size_N;
static PyObject *__pyx_n_s_unique;
static PyObject *__pyx_n_s_unsafe;
static PyObject *__pyx_n_s_vonmises;
static PyObject *__pyx_kp_u_vonmises_mu_kappa_size_None_Dra;
static PyObject *__pyx_n_s_wald;
static PyObject *__pyx_kp_u_wald_mean_scale_size_None_Draw;
static PyObject *__pyx_n_s_warn;
static PyObject *__pyx_n_s_warnings;
static PyObject *__pyx_n_s_weibull;
static PyObject *__pyx_kp_u_weibull_a_size_None_Draw_sample;
static PyObject *__pyx_n_s_zeros;
static PyObject *__pyx_n_s_zipf;
static PyObject *__pyx_kp_u_zipf_a_size_None_Draw_samples_f;
static PyObject *__pyx_pf_6mtrand__rand_bool(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_2_rand_int8(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_4_rand_int16(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_6_rand_int32(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_8_rand_int64(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_10_rand_uint8(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_12_rand_uint16(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_14_rand_uint32(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_16_rand_uint64(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size, PyObject *__pyx_v_rngstate); /* proto */
static PyObject *__pyx_pf_6mtrand_18_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, PyObject *__pyx_v_dtype); /* 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 */
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static PyObject *__pyx_pf_6mtrand_11RandomState_88zipf(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
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static PyObject *__pyx_pf_6mtrand_11RandomState_94logseries(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
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/* "randint_helpers.pxi":5
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 * 
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static char __pyx_doc_6mtrand_11RandomState_46gamma[] = "\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 : float or array_like of floats\n            The shape of the gamma distribution. Should be greater than zero.\n        scale : float or array_like of floats, optional\n            The scale of the gamma distribution. Should be greater than zero.\n            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.  If size is ``None`` (default),\n            a single value is returned if ``shape`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized gamma distribution.\n\n        See Also\n        --------\n        scipy.stats.gamma : probability density function, distribution or\n            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 Resourc""e.\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=4, std=2*sqrt(2)\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        ";
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static char __pyx_doc_6mtrand_11RandomState_48f[] = "\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 : int or array_like of ints\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : int or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``dfnum`` and ``dfden`` are both scalars.\n            Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.f : probability density function, distribution or\n            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        ";
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static char __pyx_doc_6mtrand_11RandomState_52chisquare[] = "\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 or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized chi-square distribution.\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        ";
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static char __pyx_doc_6mtrand_11RandomState_54noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int or array_like of ints\n            Degrees of freedom, should be > 0 as of NumPy 1.10.0,\n            should be > 1 for earlier versions.\n        nonc : float or array_like of floats\n            Non-centrality, should be non-negative.\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` and ``nonc`` are both scalars.\n            Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized noncentral chi-square distribution.\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        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n        ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
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static char __pyx_doc_6mtrand_11RandomState_58standard_t[] = "\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 or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``df`` is a scalar.  Otherwise,\n            ``np.array(df).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized standard Student's t distribution.\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 Gosset 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-distributio""n\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        ";
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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 or array_like of floats\n            Mode (\"center\") of the distribution.\n        kappa : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``mu`` and ``kappa`` are both scalars.\n            Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized von Mises distribution.\n\n        See Also\n        --------\n        scipy.stats.vonmises : probability density function, distribution, or\n            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, aero""dynamics, 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        a : float or array_like of floats\n            Shape of the distribution. 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.  If size is ``None`` (default),\n            a single value is returned if ``a`` is a scalar.  Otherwise,\n            ``np.array(a).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Pareto distribution.\n\n        See Also\n        --------\n        scipy.stats.lomax : probability density function, distribution or\n            cumulative density function, etc.\n        scipy.stats.genpareto : probability density function, distribution or\n            cumulative densit""y 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_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 or array_like of floats, optional\n            The location of the mode of the distribution. Default is 0.\n        scale : float or array_like of floats, optional\n            The scale parameter of the distribution. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``loc`` and ``scale`` are both scalars.\n            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized Gumbel distribution.\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 event 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.sqrt(6) / np.pi\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 or array_like of floats, optional\n            Mean value of the underlying normal distribution. Default is 0.\n        sigma : float or array_like of floats, optional\n            Standard deviation of the underlying normal distribution. Should\n            be greater than zero. Default is 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.  If size is ``None`` (default),\n            a single value is returned if ``mean`` and ``sigma`` are both scalars.\n            Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized log-normal distribution.\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 *produc""t*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        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.h""ist(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 over the\n        interval ``[left, right]``.\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 : float or array_like of floats\n            Lower limit.\n        mode : float or array_like of floats\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : float or array_like of floats\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.  If size is ``None`` (default),\n            a single value is returned if ``left``, ``mode``, and ``right``\n            are all scalars.  Otherwise, ``np.broadcast(left, mode, right).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized triangular distribution.\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(r-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 or array_like of ints\n            Parameter of the distribution, > 0. Floats are also accepted,\n            but they will be truncated to integers.\n        p : float or array_like of floats\n            Parameter of the distribution, >= 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.  If size is ``None`` (default),\n            a single value is returned if ``n`` and ``p`` are both scalars.\n            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized negative binomial distribution,\n            where each sample is equal to N, the number of trials it took to\n            achieve n - 1 successes, N - (n - 1) failures, and a success on the,\n            (N + n)th trial.\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 distribution:\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_92hypergeometric[] = "\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 of ints\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like of ints\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like of ints\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.  If size is ``None`` (default),\n            a single value is returned if ``ngood``, ``nbad``, and ``nsample``\n            are all scalars.  Otherwise, ``np.broadcast(ngood, nbad, nsample).size``\n            samples are drawn.\n\n        Returns\n        -------\n        out : ndarray or scalar\n            Drawn samples from the parameterized hypergeometric distribution.\n\n        See Also\n        --------\n        scipy.stats.hypergeom : probability density function, distribution or\n            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        ";
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static char __pyx_doc_6mtrand_11RandomState_96multivariate_normal[] = "\n        multivariate_normal(mean, cov[, size, check_valid, tol])\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        check_valid : { 'warn', 'raise', 'ignore' }, optional\n            Behavior when the covariance matrix is not positive semidefinite.\n        tol : float, optional\n            Tolerance when checking the singular values in covariance matrix.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This ""is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (`cov` is a multiple of the identity matrix)\n          - Diagonal covariance (`cov` has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0, 0]\n        >>> cov = [[1, 0], [0, 100]]  # diagonal covariance\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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static struct __pyx_vtabstruct_6mtrand_RandomState __pyx_vtable_6mtrand_RandomState;

static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, CYTHON_UNUSED PyObject *a, CYTHON_UNUSED PyObject *k) {
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static void __pyx_tp_dealloc_6mtrand_RandomState(PyObject *o) {
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    PyErr_Restore(etype, eval, etb);
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}

static int __pyx_tp_traverse_6mtrand_RandomState(PyObject *o, visitproc v, void *a) {
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static int __pyx_tp_clear_6mtrand_RandomState(PyObject *o) {
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  p->lock = Py_None; Py_INCREF(Py_None);
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  {"bytes", (PyCFunction)__pyx_pw_6mtrand_11RandomState_23bytes, METH_O, __pyx_doc_6mtrand_11RandomState_22bytes},
  {"choice", (PyCFunction)__pyx_pw_6mtrand_11RandomState_25choice, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_24choice},
  {"uniform", (PyCFunction)__pyx_pw_6mtrand_11RandomState_27uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_26uniform},
  {"rand", (PyCFunction)__pyx_pw_6mtrand_11RandomState_29rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_28rand},
  {"randn", (PyCFunction)__pyx_pw_6mtrand_11RandomState_31randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_30randn},
  {"random_integers", (PyCFunction)__pyx_pw_6mtrand_11RandomState_33random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_32random_integers},
  {"standard_normal", (PyCFunction)__pyx_pw_6mtrand_11RandomState_35standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_34standard_normal},
  {"normal", (PyCFunction)__pyx_pw_6mtrand_11RandomState_37normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_36normal},
  {"beta", (PyCFunction)__pyx_pw_6mtrand_11RandomState_39beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_38beta},
  {"exponential", (PyCFunction)__pyx_pw_6mtrand_11RandomState_41exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_40exponential},
  {"standard_exponential", (PyCFunction)__pyx_pw_6mtrand_11RandomState_43standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_42standard_exponential},
  {"standard_gamma", (PyCFunction)__pyx_pw_6mtrand_11RandomState_45standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_44standard_gamma},
  {"gamma", (PyCFunction)__pyx_pw_6mtrand_11RandomState_47gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_46gamma},
  {"f", (PyCFunction)__pyx_pw_6mtrand_11RandomState_49f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_48f},
  {"noncentral_f", (PyCFunction)__pyx_pw_6mtrand_11RandomState_51noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_50noncentral_f},
  {"chisquare", (PyCFunction)__pyx_pw_6mtrand_11RandomState_53chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_52chisquare},
  {"noncentral_chisquare", (PyCFunction)__pyx_pw_6mtrand_11RandomState_55noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_54noncentral_chisquare},
  {"standard_cauchy", (PyCFunction)__pyx_pw_6mtrand_11RandomState_57standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_56standard_cauchy},
  {"standard_t", (PyCFunction)__pyx_pw_6mtrand_11RandomState_59standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_58standard_t},
  {"vonmises", (PyCFunction)__pyx_pw_6mtrand_11RandomState_61vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_60vonmises},
  {"pareto", (PyCFunction)__pyx_pw_6mtrand_11RandomState_63pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_62pareto},
  {"weibull", (PyCFunction)__pyx_pw_6mtrand_11RandomState_65weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_64weibull},
  {"power", (PyCFunction)__pyx_pw_6mtrand_11RandomState_67power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_66power},
  {"laplace", (PyCFunction)__pyx_pw_6mtrand_11RandomState_69laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_68laplace},
  {"gumbel", (PyCFunction)__pyx_pw_6mtrand_11RandomState_71gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_70gumbel},
  {"logistic", (PyCFunction)__pyx_pw_6mtrand_11RandomState_73logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_72logistic},
  {"lognormal", (PyCFunction)__pyx_pw_6mtrand_11RandomState_75lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_74lognormal},
  {"rayleigh", (PyCFunction)__pyx_pw_6mtrand_11RandomState_77rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_76rayleigh},
  {"wald", (PyCFunction)__pyx_pw_6mtrand_11RandomState_79wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_78wald},
  {"triangular", (PyCFunction)__pyx_pw_6mtrand_11RandomState_81triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_80triangular},
  {"binomial", (PyCFunction)__pyx_pw_6mtrand_11RandomState_83binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_82binomial},
  {"negative_binomial", (PyCFunction)__pyx_pw_6mtrand_11RandomState_85negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_84negative_binomial},
  {"poisson", (PyCFunction)__pyx_pw_6mtrand_11RandomState_87poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_86poisson},
  {"zipf", (PyCFunction)__pyx_pw_6mtrand_11RandomState_89zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_88zipf},
  {"geometric", (PyCFunction)__pyx_pw_6mtrand_11RandomState_91geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_90geometric},
  {"hypergeometric", (PyCFunction)__pyx_pw_6mtrand_11RandomState_93hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_92hypergeometric},
  {"logseries", (PyCFunction)__pyx_pw_6mtrand_11RandomState_95logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_94logseries},
  {"multivariate_normal", (PyCFunction)__pyx_pw_6mtrand_11RandomState_97multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_96multivariate_normal},
  {"multinomial", (PyCFunction)__pyx_pw_6mtrand_11RandomState_99multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_98multinomial},
  {"dirichlet", (PyCFunction)__pyx_pw_6mtrand_11RandomState_101dirichlet, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_100dirichlet},
  {"shuffle", (PyCFunction)__pyx_pw_6mtrand_11RandomState_103shuffle, METH_O, __pyx_doc_6mtrand_11RandomState_102shuffle},
  {"permutation", (PyCFunction)__pyx_pw_6mtrand_11RandomState_105permutation, METH_O, __pyx_doc_6mtrand_11RandomState_104permutation},
  {0, 0, 0, 0}
};

static PyTypeObject __pyx_type_6mtrand_RandomState = {
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  "\n    RandomState(seed=None)\n\n    Container for the Mersenne Twister pseudo-random number generator.\n\n    `RandomState` exposes a number of methods for generating random numbers\n    drawn from a variety of probability distributions. In addition to the\n    distribution-specific arguments, each method takes a keyword argument\n    `size` that defaults to ``None``. If `size` is ``None``, then a single\n    value is generated and returned. If `size` is an integer, then a 1-D\n    array filled with generated values is returned. If `size` is a tuple,\n    then an array with that shape is filled and returned.\n\n    *Compatibility Guarantee*\n    A fixed seed and a fixed series of calls to 'RandomState' methods using\n    the same parameters will always produce the same results up to roundoff\n    error except when the values were incorrect. Incorrect values will be\n    fixed and the NumPy version in which the fix was made will be noted in\n    the relevant docstring. Extension of existing parameter ranges and the\n    addition of new parameters is allowed as long the previous behavior\n    remains unchanged.\n\n    Parameters\n    ----------\n    seed : {None, int, array_like}, optional\n        Random seed used to initialize the pseudo-random number generator.  Can\n        be any integer between 0 and 2**32 - 1 inclusive, an array (or other\n        sequence) of such integers, or ``None`` (the default).  If `seed` is\n        ``None``, then `RandomState` will try to read data from\n        ``/dev/urandom`` (or the Windows analogue) if available or seed from\n        the clock otherwise.\n\n    Notes\n    -----\n    The Python stdlib module \"random\" also contains a Mersenne Twister\n    pseudo-random number generator with a number of methods that are similar\n    to the ones available in `RandomState`. `RandomState`, besides being\n    NumPy-aware, has the advantage that it provides a much larger number\n    of probability distributions to choose from.\n\n    ", /*tp_doc*/
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  {&__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_long, __pyx_k_long, sizeof(__pyx_k_long), 0, 0, 1, 1},
  {&__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_kp_s_low_is_out_of_bounds_for_s, __pyx_k_low_is_out_of_bounds_for_s, sizeof(__pyx_k_low_is_out_of_bounds_for_s), 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_name, __pyx_k_name, sizeof(__pyx_k_name), 0, 0, 1, 1},
  {&__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_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_off, __pyx_k_off, sizeof(__pyx_k_off), 0, 0, 1, 1},
  {&__pyx_n_s_operator, __pyx_k_operator, sizeof(__pyx_k_operator), 0, 0, 1, 1},
  {&__pyx_n_s_out, __pyx_k_out, sizeof(__pyx_k_out), 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_pyx_vtable, __pyx_k_pyx_vtable, sizeof(__pyx_k_pyx_vtable), 0, 0, 1, 1},
  {&__pyx_n_s_raise, __pyx_k_raise, sizeof(__pyx_k_raise), 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_n_s_rand_bool, __pyx_k_rand_bool, sizeof(__pyx_k_rand_bool), 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_rand_int16, __pyx_k_rand_int16, sizeof(__pyx_k_rand_int16), 0, 0, 1, 1},
  {&__pyx_n_s_rand_int32, __pyx_k_rand_int32, sizeof(__pyx_k_rand_int32), 0, 0, 1, 1},
  {&__pyx_n_s_rand_int64, __pyx_k_rand_int64, sizeof(__pyx_k_rand_int64), 0, 0, 1, 1},
  {&__pyx_n_s_rand_int8, __pyx_k_rand_int8, sizeof(__pyx_k_rand_int8), 0, 0, 1, 1},
  {&__pyx_n_s_rand_uint16, __pyx_k_rand_uint16, sizeof(__pyx_k_rand_uint16), 0, 0, 1, 1},
  {&__pyx_n_s_rand_uint32, __pyx_k_rand_uint32, sizeof(__pyx_k_rand_uint32), 0, 0, 1, 1},
  {&__pyx_n_s_rand_uint64, __pyx_k_rand_uint64, sizeof(__pyx_k_rand_uint64), 0, 0, 1, 1},
  {&__pyx_n_s_rand_uint8, __pyx_k_rand_uint8, sizeof(__pyx_k_rand_uint8), 0, 0, 1, 1},
  {&__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_randint_type, __pyx_k_randint_type, sizeof(__pyx_k_randint_type), 0, 0, 1, 1},
  {&__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_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1},
  {&__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_reversed, __pyx_k_reversed, sizeof(__pyx_k_reversed), 0, 0, 1, 1},
  {&__pyx_n_s_right, __pyx_k_right, sizeof(__pyx_k_right), 0, 0, 1, 1},
  {&__pyx_n_s_rng, __pyx_k_rng, sizeof(__pyx_k_rng), 0, 0, 1, 1},
  {&__pyx_n_s_rngstate, __pyx_k_rngstate, sizeof(__pyx_k_rngstate), 0, 0, 1, 1},
  {&__pyx_n_s_rtol, __pyx_k_rtol, sizeof(__pyx_k_rtol), 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_kp_s_scale_0_0_2, __pyx_k_scale_0_0_2, sizeof(__pyx_k_scale_0_0_2), 0, 0, 1, 0},
  {&__pyx_kp_s_scale_0_2, __pyx_k_scale_0_2, sizeof(__pyx_k_scale_0_2), 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},
  {&__pyx_n_s_set_state, __pyx_k_set_state, sizeof(__pyx_k_set_state), 0, 0, 1, 1},
  {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
  {&__pyx_kp_s_shape_0, __pyx_k_shape_0, sizeof(__pyx_k_shape_0), 0, 0, 1, 0},
  {&__pyx_n_s_shape_from_size, __pyx_k_shape_from_size, sizeof(__pyx_k_shape_from_size), 0, 0, 1, 1},
  {&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
  {&__pyx_kp_u_shuffle_x_Modify_a_sequence_in, __pyx_k_shuffle_x_Modify_a_sequence_in, sizeof(__pyx_k_shuffle_x_Modify_a_sequence_in), 0, 1, 0, 0},
  {&__pyx_n_s_side, __pyx_k_side, sizeof(__pyx_k_side), 0, 0, 1, 1},
  {&__pyx_n_s_sigma, __pyx_k_sigma, sizeof(__pyx_k_sigma), 0, 0, 1, 1},
  {&__pyx_kp_s_sigma_0, __pyx_k_sigma_0, sizeof(__pyx_k_sigma_0), 0, 0, 1, 0},
  {&__pyx_kp_s_sigma_0_0, __pyx_k_sigma_0_0, sizeof(__pyx_k_sigma_0_0), 0, 0, 1, 0},
  {&__pyx_n_s_signbit, __pyx_k_signbit, sizeof(__pyx_k_signbit), 0, 0, 1, 1},
  {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1},
  {&__pyx_kp_s_size_is_not_compatible_with_inpu, __pyx_k_size_is_not_compatible_with_inpu, sizeof(__pyx_k_size_is_not_compatible_with_inpu), 0, 0, 1, 0},
  {&__pyx_n_s_sort, __pyx_k_sort, sizeof(__pyx_k_sort), 0, 0, 1, 1},
  {&__pyx_n_s_sqrt, __pyx_k_sqrt, sizeof(__pyx_k_sqrt), 0, 0, 1, 1},
  {&__pyx_n_s_standard_cauchy, __pyx_k_standard_cauchy, sizeof(__pyx_k_standard_cauchy), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_cauchy_size_None_Draw, __pyx_k_standard_cauchy_size_None_Draw, sizeof(__pyx_k_standard_cauchy_size_None_Draw), 0, 1, 0, 0},
  {&__pyx_n_s_standard_exponential, __pyx_k_standard_exponential, sizeof(__pyx_k_standard_exponential), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_exponential_size_None, __pyx_k_standard_exponential_size_None, sizeof(__pyx_k_standard_exponential_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_standard_gamma, __pyx_k_standard_gamma, sizeof(__pyx_k_standard_gamma), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_gamma_shape_size_None, __pyx_k_standard_gamma_shape_size_None, sizeof(__pyx_k_standard_gamma_shape_size_None), 0, 1, 0, 0},
  {&__pyx_n_s_standard_normal, __pyx_k_standard_normal, sizeof(__pyx_k_standard_normal), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_normal_size_None_Draw, __pyx_k_standard_normal_size_None_Draw, sizeof(__pyx_k_standard_normal_size_None_Draw), 0, 1, 0, 0},
  {&__pyx_n_s_standard_t, __pyx_k_standard_t, sizeof(__pyx_k_standard_t), 0, 0, 1, 1},
  {&__pyx_kp_u_standard_t_df_size_None_Draw_sa, __pyx_k_standard_t_df_size_None_Draw_sa, sizeof(__pyx_k_standard_t_df_size_None_Draw_sa), 0, 1, 0, 0},
  {&__pyx_n_s_state, __pyx_k_state, sizeof(__pyx_k_state), 0, 0, 1, 1},
  {&__pyx_kp_s_state_must_be_624_longs, __pyx_k_state_must_be_624_longs, sizeof(__pyx_k_state_must_be_624_longs), 0, 0, 1, 0},
  {&__pyx_n_s_strides, __pyx_k_strides, sizeof(__pyx_k_strides), 0, 0, 1, 1},
  {&__pyx_n_s_subtract, __pyx_k_subtract, sizeof(__pyx_k_subtract), 0, 0, 1, 1},
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 *         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__33 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__33)) __PYX_ERR(0, 546, __pyx_L1_error)
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  /* "mtrand.pyx":547
 *         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)
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  /* "mtrand.pyx":675
 *         try:
 *             if seed is None:
 *                 with self.lock:             # <<<<<<<<<<<<<<
 *                     errcode = rk_randomseed(self.internal_state)
 *             else:
 */
  __pyx_tuple__35 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__35)) __PYX_ERR(0, 675, __pyx_L1_error)
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  /* "mtrand.pyx":680
 *                 idx = operator.index(seed)
 *                 if idx > int(2**32 - 1) or idx < 0:
 *                     raise ValueError("Seed must be between 0 and 2**32 - 1")             # <<<<<<<<<<<<<<
 *                 with self.lock:
 *                     rk_seed(idx, self.internal_state)
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  __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s_Seed_must_be_between_0_and_2_32); if (unlikely(!__pyx_tuple__36)) __PYX_ERR(0, 680, __pyx_L1_error)
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  /* "mtrand.pyx":681
 *                 if idx > int(2**32 - 1) or idx < 0:
 *                     raise ValueError("Seed must be between 0 and 2**32 - 1")
 *                 with self.lock:             # <<<<<<<<<<<<<<
 *                     rk_seed(idx, self.internal_state)
 *         except TypeError:
 */
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  /* "mtrand.pyx":686
 *             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 2**32 - 1")             # <<<<<<<<<<<<<<
 *             obj = obj.astype('L', casting='unsafe')
 *             with self.lock:
 */
  __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_Seed_must_be_between_0_and_2_32); if (unlikely(!__pyx_tuple__38)) __PYX_ERR(0, 686, __pyx_L1_error)
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  /* "mtrand.pyx":687
 *             if ((obj > int(2**32 - 1)) | (obj < 0)).any():
 *                 raise ValueError("Seed must be between 0 and 2**32 - 1")
 *             obj = obj.astype('L', casting='unsafe')             # <<<<<<<<<<<<<<
 *             with self.lock:
 *                 init_by_array(self.internal_state, <unsigned long *>PyArray_DATA(obj),
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  /* "mtrand.pyx":688
 *                 raise ValueError("Seed must be between 0 and 2**32 - 1")
 *             obj = obj.astype('L', casting='unsafe')
 *             with self.lock:             # <<<<<<<<<<<<<<
 *                 init_by_array(self.internal_state, <unsigned long *>PyArray_DATA(obj),
 *                     PyArray_DIM(obj, 0))
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  /* "mtrand.pyx":724
 *         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))
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  /* "mtrand.pyx":783
 *         algorithm_name = state[0]
 *         if algorithm_name != 'MT19937':
 *             raise ValueError("algorithm must be 'MT19937'")             # <<<<<<<<<<<<<<
 *         key, pos = state[1:3]
 *         if len(state) == 3:
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  __pyx_tuple__42 = PyTuple_Pack(1, __pyx_kp_s_algorithm_must_be_MT19937); if (unlikely(!__pyx_tuple__42)) __PYX_ERR(0, 783, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__42);

  /* "mtrand.pyx":784
 *         if algorithm_name != 'MT19937':
 *             raise ValueError("algorithm must be 'MT19937'")
 *         key, pos = state[1:3]             # <<<<<<<<<<<<<<
 *         if len(state) == 3:
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  __pyx_slice__43 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__43)) __PYX_ERR(0, 784, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_slice__43);
  __Pyx_GIVEREF(__pyx_slice__43);

  /* "mtrand.pyx":789
 *             cached_gaussian = 0.0
 *         else:
 *             has_gauss, cached_gaussian = state[3:5]             # <<<<<<<<<<<<<<
 *         try:
 *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_ULONG, 1, 1)
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  __pyx_slice__44 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_slice__44)) __PYX_ERR(0, 789, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_slice__44);

  /* "mtrand.pyx":796
 *             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__45 = PyTuple_Pack(1, __pyx_kp_s_state_must_be_624_longs); if (unlikely(!__pyx_tuple__45)) __PYX_ERR(0, 796, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__45);

  /* "mtrand.pyx":797
 *         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__46 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__46)) __PYX_ERR(0, 797, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__46);
  __Pyx_GIVEREF(__pyx_tuple__46);

  /* "mtrand.pyx":988
 *             raise ValueError("high is out of bounds for %s" % (key,))
 *         if ilow >= ihigh:
 *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
 * 
 *         with self.lock:
 */
  __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_low_high); if (unlikely(!__pyx_tuple__48)) __PYX_ERR(0, 988, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__48);
  __Pyx_GIVEREF(__pyx_tuple__48);

  /* "mtrand.pyx":990
 *             raise ValueError("low >= high")
 * 
 *         with self.lock:             # <<<<<<<<<<<<<<
 *             ret = randfunc(ilow, ihigh - 1, size, self.state_address)
 * 
 */
  __pyx_tuple__49 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__49)) __PYX_ERR(0, 990, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__50);

  /* "mtrand.pyx":1023
 *         cdef void *bytes
 *         bytestring = empty_py_bytes(length, &bytes)
 *         with self.lock, nogil:             # <<<<<<<<<<<<<<
 *             rk_fill(bytes, length, self.internal_state)
 *         return bytestring
 */
  __pyx_tuple__51 = PyTuple_Pack(3, Py_None, Py_None, Py_None); if (unlikely(!__pyx_tuple__51)) __PYX_ERR(0, 1023, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__51);

  /* "mtrand.pyx":1113
 *                 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__52 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional_or_an_in); if (unlikely(!__pyx_tuple__52)) __PYX_ERR(0, 1113, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__52);

  /* "mtrand.pyx":1115
 *                 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__53 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_greater_than_0); if (unlikely(!__pyx_tuple__53)) __PYX_ERR(0, 1115, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__53);

  /* "mtrand.pyx":1117
 *                 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__54 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional); if (unlikely(!__pyx_tuple__54)) __PYX_ERR(0, 1117, __pyx_L1_error)
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  /* "mtrand.pyx":1121
 *             pop_size = a.shape[0]
 *             if pop_size is 0:
 *                 raise ValueError("a must be non-empty")             # <<<<<<<<<<<<<<
 * 
 *         if p is not None:
 */
  __pyx_tuple__55 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_non_empty); if (unlikely(!__pyx_tuple__55)) __PYX_ERR(0, 1121, __pyx_L1_error)
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  /* "mtrand.pyx":1135
 * 
 *             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__56 = PyTuple_Pack(1, __pyx_kp_s_p_must_be_1_dimensional); if (unlikely(!__pyx_tuple__56)) __PYX_ERR(0, 1135, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__56);

  /* "mtrand.pyx":1137
 *                 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__57 = PyTuple_Pack(1, __pyx_kp_s_a_and_p_must_have_same_size); if (unlikely(!__pyx_tuple__57)) __PYX_ERR(0, 1137, __pyx_L1_error)
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  /* "mtrand.pyx":1139
 *                 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__58 = PyTuple_Pack(1, __pyx_kp_s_probabilities_are_not_non_negati); if (unlikely(!__pyx_tuple__58)) __PYX_ERR(0, 1139, __pyx_L1_error)
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  /* "mtrand.pyx":1141
 *                 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__59 = PyTuple_Pack(1, __pyx_kp_s_probabilities_do_not_sum_to_1); if (unlikely(!__pyx_tuple__59)) __PYX_ERR(0, 1141, __pyx_L1_error)
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  /* "mtrand.pyx":1161
 *         else:
 *             if size > pop_size:
 *                 raise ValueError("Cannot take a larger sample than "             # <<<<<<<<<<<<<<
 *                                  "population when 'replace=False'")
 * 
 */
  __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_Cannot_take_a_larger_sample_than); if (unlikely(!__pyx_tuple__60)) __PYX_ERR(0, 1161, __pyx_L1_error)
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  /* "mtrand.pyx":1166
 *             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__61 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__61)) __PYX_ERR(0, 1166, __pyx_L1_error)
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  /* "mtrand.pyx":1191
 *         if shape is None and isinstance(idx, np.ndarray):
 *             # In most cases a scalar will have been made an array
 *             idx = idx.item(0)             # <<<<<<<<<<<<<<
 * 
 *         #Use samples as indices for a if a is array-like
 */
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  /* "mtrand.pyx":1203
 *             # array, taking into account that np.array(item) may not work
 *             # for object arrays.
 *             res = np.empty((), dtype=a.dtype)             # <<<<<<<<<<<<<<
 *             res[()] = a[idx]
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  /* "mtrand.pyx":1300
 * 
 *             if not npy_isfinite(fscale):
 *                 raise OverflowError('Range exceeds valid bounds')             # <<<<<<<<<<<<<<
 * 
 *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow,
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  __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_Range_exceeds_valid_bounds); if (unlikely(!__pyx_tuple__64)) __PYX_ERR(0, 1300, __pyx_L1_error)
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  /* "mtrand.pyx":1311
 * 
 *         if not np.all(np.isfinite(odiff)):
 *             raise OverflowError('Range exceeds valid bounds')             # <<<<<<<<<<<<<<
 * 
 *         return cont2_array(self.internal_state, rk_uniform, size, olow, odiff,
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  __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_Range_exceeds_valid_bounds); if (unlikely(!__pyx_tuple__65)) __PYX_ERR(0, 1311, __pyx_L1_error)
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  /* "mtrand.pyx":1646
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
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 *                                   fscale, self.lock)
 */
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  /* "mtrand.pyx":1651
 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__67)) __PYX_ERR(0, 1651, __pyx_L1_error)
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  /* "mtrand.pyx":1704
 * 
 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             if fb <= 0:
 *                 raise ValueError("b <= 0")
 */
  __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__68)) __PYX_ERR(0, 1704, __pyx_L1_error)
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  /* "mtrand.pyx":1706
 *                 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__69 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__69)) __PYX_ERR(0, 1706, __pyx_L1_error)
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  /* "mtrand.pyx":1711
 * 
 *         if np.any(np.less_equal(oa, 0)):
 *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less_equal(ob, 0)):
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 */
  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__70)) __PYX_ERR(0, 1711, __pyx_L1_error)
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  /* "mtrand.pyx":1713
 *             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__71 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__71)) __PYX_ERR(0, 1713, __pyx_L1_error)
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  /* "mtrand.pyx":1770
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_exponential, size,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__72)) __PYX_ERR(0, 1770, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__72);

  /* "mtrand.pyx":1775
 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_exponential, size, oscale,
 *                            self.lock)
 */
  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__73)) __PYX_ERR(0, 1775, __pyx_L1_error)
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  /* "mtrand.pyx":1887
 *             fshape = PyFloat_AsDouble(shape)
 *             if np.signbit(fshape):
 *                 raise ValueError("shape < 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_standard_gamma,
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 * 
 *         if np.any(np.signbit(oshape)):
 *             raise ValueError("shape < 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_standard_gamma, size,
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  /* "mtrand.pyx":1979
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fshape):
 *                 raise ValueError("shape < 0")             # <<<<<<<<<<<<<<
 *             if np.signbit(fscale):
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  /* "mtrand.pyx":1981
 *                 raise ValueError("shape < 0")
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_gamma, size, fshape,
 *                                   fscale, self.lock)
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 * 
 *         if np.any(np.signbit(oshape)):
 *             raise ValueError("shape < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.signbit(oscale)):
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 *             raise ValueError("shape < 0")
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale,
 *                            self.lock)
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 * 
 *             if fdfnum <= 0:
 *                 raise ValueError("dfnum <= 0")             # <<<<<<<<<<<<<<
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")
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  /* "mtrand.pyx":2088
 *                 raise ValueError("dfnum <= 0")
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum,
 *                                   fdfden, self.lock)
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 * 
 *         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")
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  /* "mtrand.pyx":2095
 *             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)
 */
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  /* "mtrand.pyx":2179
 * 
 *             if fdfnum <= 1:
 *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")
 */
  __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__84)) __PYX_ERR(0, 2179, __pyx_L1_error)
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  /* "mtrand.pyx":2181
 *                 raise ValueError("dfnum <= 1")
 *             if fdfden <= 0:
 *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
 *             if fnonc < 0:
 *                 raise ValueError("nonc < 0")
 */
  __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__85)) __PYX_ERR(0, 2181, __pyx_L1_error)
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  /* "mtrand.pyx":2183
 *                 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)
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  __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__86)) __PYX_ERR(0, 2183, __pyx_L1_error)
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  /* "mtrand.pyx":2188
 * 
 *         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")
 */
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  /* "mtrand.pyx":2190
 *             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")
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  __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__88)) __PYX_ERR(0, 2190, __pyx_L1_error)
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  /* "mtrand.pyx":2192
 *             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__89 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__89)) __PYX_ERR(0, 2192, __pyx_L1_error)
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  /* "mtrand.pyx":2268
 * 
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf,
 *                                   self.lock)
 */
  __pyx_tuple__90 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__90)) __PYX_ERR(0, 2268, __pyx_L1_error)
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  /* "mtrand.pyx":2273
 * 
 *         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__91 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__91)) __PYX_ERR(0, 2273, __pyx_L1_error)
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  /* "mtrand.pyx":2368
 * 
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             if fnonc < 0:
 *                 raise ValueError("nonc < 0")
 */
  __pyx_tuple__92 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__92)) __PYX_ERR(0, 2368, __pyx_L1_error)
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  /* "mtrand.pyx":2370
 *                 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)
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  /* "mtrand.pyx":2375
 * 
 *         if np.any(np.less_equal(odf, 0.0)):
 *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(ononc, 0.0)):
 *             raise ValueError("nonc < 0")
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 *             raise ValueError("df <= 0")
 *         if np.any(np.less(ononc, 0.0)):
 *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
 *                            odf, ononc, self.lock)
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  /* "mtrand.pyx":2542
 * 
 *             if fdf <= 0:
 *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_standard_t, size,
 *                                   fdf, self.lock)
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  __pyx_tuple__96 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__96)) __PYX_ERR(0, 2542, __pyx_L1_error)
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 * 
 *         if np.any(np.less_equal(odf, 0.0)):
 *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_standard_t, size, odf,
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  __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__97)) __PYX_ERR(0, 2547, __pyx_L1_error)
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 * 
 *             if fkappa < 0:
 *                 raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu,
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  __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__98)) __PYX_ERR(0, 2640, __pyx_L1_error)
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 * 
 *         if np.any(np.less(okappa, 0.0)):
 *             raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa,
 *                            self.lock)
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 * 
 *             if fa <= 0:
 *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_pareto, size, fa,
 *                                   self.lock)
 */
  __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__100)) __PYX_ERR(0, 2751, __pyx_L1_error)
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 * 
 *         if np.any(np.less_equal(oa, 0.0)):
 *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_pareto, size, oa, self.lock)
 * 
 */
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 *             fa = PyFloat_AsDouble(a)
 *             if np.signbit(fa):
 *                 raise ValueError("a < 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_weibull, size, fa,
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  __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_a_0_2); if (unlikely(!__pyx_tuple__102)) __PYX_ERR(0, 2860, __pyx_L1_error)
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 * 
 *         if np.any(np.signbit(oa)):
 *             raise ValueError("a < 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_weibull, size, oa,
 *                            self.lock)
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  __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_a_0_2); if (unlikely(!__pyx_tuple__103)) __PYX_ERR(0, 2865, __pyx_L1_error)
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 *             fa = PyFloat_AsDouble(a)
 *             if np.signbit(fa):
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 *                                   self.lock)
 */
  __pyx_tuple__104 = PyTuple_Pack(1, __pyx_kp_s_a_0_2); if (unlikely(!__pyx_tuple__104)) __PYX_ERR(0, 2972, __pyx_L1_error)
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  /* "mtrand.pyx":2977
 * 
 *         if np.any(np.signbit(oa)):
 *             raise ValueError("a < 0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_power, size, oa, self.lock)
 * 
 */
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  /* "mtrand.pyx":3069
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc,
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  __pyx_tuple__106 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__106)) __PYX_ERR(0, 3069, __pyx_L1_error)
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 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__107 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__107)) __PYX_ERR(0, 3074, __pyx_L1_error)
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  /* "mtrand.pyx":3200
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc,
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  __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__108)) __PYX_ERR(0, 3200, __pyx_L1_error)
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  /* "mtrand.pyx":3205
 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale,
 *                            self.lock)
 */
  __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__109)) __PYX_ERR(0, 3205, __pyx_L1_error)
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  /* "mtrand.pyx":3293
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc,
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  __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__110)) __PYX_ERR(0, 3293, __pyx_L1_error)
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  /* "mtrand.pyx":3298
 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_logistic, size, oloc,
 *                            oscale, self.lock)
 */
  __pyx_tuple__111 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__111)) __PYX_ERR(0, 3298, __pyx_L1_error)
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  /* "mtrand.pyx":3417
 *             fsigma = PyFloat_AsDouble(sigma)
 *             if np.signbit(fsigma):
 *                 raise ValueError("sigma < 0")             # <<<<<<<<<<<<<<
 *             return cont2_array_sc(self.internal_state, rk_lognormal, size,
 *                                   fmean, fsigma, self.lock)
 */
  __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_sigma_0); if (unlikely(!__pyx_tuple__112)) __PYX_ERR(0, 3417, __pyx_L1_error)
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  /* "mtrand.pyx":3422
 * 
 *         if np.any(np.signbit(osigma)):
 *             raise ValueError("sigma < 0.0")             # <<<<<<<<<<<<<<
 *         return cont2_array(self.internal_state, rk_lognormal, size, omean,
 *                            osigma, self.lock)
 */
  __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_sigma_0_0); if (unlikely(!__pyx_tuple__113)) __PYX_ERR(0, 3422, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__113);
  __Pyx_GIVEREF(__pyx_tuple__113);

  /* "mtrand.pyx":3496
 *             fscale = PyFloat_AsDouble(scale)
 *             if np.signbit(fscale):
 *                 raise ValueError("scale < 0")             # <<<<<<<<<<<<<<
 *             return cont1_array_sc(self.internal_state, rk_rayleigh, size,
 *                                   fscale, self.lock)
 */
  __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__114)) __PYX_ERR(0, 3496, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__114);
  __Pyx_GIVEREF(__pyx_tuple__114);

  /* "mtrand.pyx":3501
 * 
 *         if np.any(np.signbit(oscale)):
 *             raise ValueError("scale < 0.0")             # <<<<<<<<<<<<<<
 *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale,
 *                            self.lock)
 */
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  /* "mtrand.pyx":3579
 * 
 *             if fmean <= 0:
 *                 raise ValueError("mean <= 0")             # <<<<<<<<<<<<<<
 *             if fscale <= 0:
 *                 raise ValueError("scale <= 0")
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  __Pyx_GIVEREF(__pyx_tuple__116);

  /* "mtrand.pyx":3581
 *                 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__117 = PyTuple_Pack(1, __pyx_kp_s_scale_0_2); if (unlikely(!__pyx_tuple__117)) __PYX_ERR(0, 3581, __pyx_L1_error)
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 * 
 *         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")
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  __pyx_tuple__118 = PyTuple_Pack(1, __pyx_kp_s_mean_0_0); if (unlikely(!__pyx_tuple__118)) __PYX_ERR(0, 3586, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__118);

  /* "mtrand.pyx":3588
 *             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)
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  __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0_2); if (unlikely(!__pyx_tuple__119)) __PYX_ERR(0, 3588, __pyx_L1_error)
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  /* "mtrand.pyx":3668
 * 
 *             if fleft > fmode:
 *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
 *             if fmode > fright:
 *                 raise ValueError("mode > right")
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  __Pyx_GIVEREF(__pyx_tuple__120);

  /* "mtrand.pyx":3670
 *                 raise ValueError("left > mode")
 *             if fmode > fright:
 *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
 *             if fleft == fright:
 *                 raise ValueError("left == right")
 */
  __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__121)) __PYX_ERR(0, 3670, __pyx_L1_error)
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  /* "mtrand.pyx":3672
 *                 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)
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  __pyx_tuple__122 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__122)) __PYX_ERR(0, 3672, __pyx_L1_error)
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  /* "mtrand.pyx":3677
 * 
 *         if np.any(np.greater(oleft, omode)):
 *             raise ValueError("left > mode")             # <<<<<<<<<<<<<<
 *         if np.any(np.greater(omode, oright)):
 *             raise ValueError("mode > right")
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  __pyx_tuple__123 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__123)) __PYX_ERR(0, 3677, __pyx_L1_error)
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  /* "mtrand.pyx":3679
 *             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__124 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__124)) __PYX_ERR(0, 3679, __pyx_L1_error)
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  /* "mtrand.pyx":3681
 *             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__125 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__125)) __PYX_ERR(0, 3681, __pyx_L1_error)
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  /* "mtrand.pyx":3783
 * 
 *             if ln < 0:
 *                 raise ValueError("n < 0")             # <<<<<<<<<<<<<<
 *             if fp < 0:
 *                 raise ValueError("p < 0")
 */
  __pyx_tuple__126 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__126)) __PYX_ERR(0, 3783, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__126);

  /* "mtrand.pyx":3785
 *                 raise ValueError("n < 0")
 *             if fp < 0:
 *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *             elif fp > 1:
 *                 raise ValueError("p > 1")
 */
  __pyx_tuple__127 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__127)) __PYX_ERR(0, 3785, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__127);

  /* "mtrand.pyx":3787
 *                 raise ValueError("p < 0")
 *             elif fp > 1:
 *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
 *             elif np.isnan(fp):
 *                 raise ValueError("p is nan")
 */
  __pyx_tuple__128 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__128)) __PYX_ERR(0, 3787, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__128);

  /* "mtrand.pyx":3789
 *                 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__129 = PyTuple_Pack(1, __pyx_kp_s_p_is_nan); if (unlikely(!__pyx_tuple__129)) __PYX_ERR(0, 3789, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__129);

  /* "mtrand.pyx":3794
 * 
 *         if np.any(np.less(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); if (unlikely(!__pyx_tuple__130)) __PYX_ERR(0, 3794, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__130);

  /* "mtrand.pyx":3796
 *             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_ERR(0, 3796, __pyx_L1_error)
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  /* "mtrand.pyx":3798
 *             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__132 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__132)) __PYX_ERR(0, 3798, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__132);
  __Pyx_GIVEREF(__pyx_tuple__132);

  /* "mtrand.pyx":3886
 * 
 *             if fn <= 0:
 *                 raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
 *             if fp < 0:
 *                 raise ValueError("p < 0")
 */
  __pyx_tuple__133 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__133)) __PYX_ERR(0, 3886, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__133);
  __Pyx_GIVEREF(__pyx_tuple__133);

  /* "mtrand.pyx":3888
 *                 raise ValueError("n <= 0")
 *             if fp < 0:
 *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
 *             elif fp > 1:
 *                 raise ValueError("p > 1")
 */
  __pyx_tuple__134 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__134)) __PYX_ERR(0, 3888, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__134);

  /* "mtrand.pyx":3890
 *                 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__135 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__135)) __PYX_ERR(0, 3890, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__135);

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 * 
 *         if np.any(np.less_equal(n, 0)):
 *             raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(p, 0)):
 *             raise ValueError("p < 0")
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  __pyx_tuple__136 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__136)) __PYX_ERR(0, 3895, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__136);
  __Pyx_GIVEREF(__pyx_tuple__136);

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 *             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__137 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__137)) __PYX_ERR(0, 3897, __pyx_L1_error)
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 *             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)
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  __pyx_tuple__138 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__138)) __PYX_ERR(0, 3899, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__138);

  /* "mtrand.pyx":3978
 * 
 *             if lam < 0:
 *                 raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
 *             if lam > self.poisson_lam_max:
 *                 raise ValueError("lam value too large")
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  __pyx_tuple__139 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__139)) __PYX_ERR(0, 3978, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__139);

  /* "mtrand.pyx":3980
 *                 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__140 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large); if (unlikely(!__pyx_tuple__140)) __PYX_ERR(0, 3980, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__140);

  /* "mtrand.pyx":3985
 * 
 *         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__141 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__141)) __PYX_ERR(0, 3985, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__141);

  /* "mtrand.pyx":3987
 *             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__142 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large_2); if (unlikely(!__pyx_tuple__142)) __PYX_ERR(0, 3987, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__142);

  /* "mtrand.pyx":4074
 * 
 *             if fa <= 1.0:
 *                 raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
 *             return discd_array_sc(self.internal_state, rk_zipf, size, fa,
 *                                   self.lock)
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  __pyx_tuple__143 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__143)) __PYX_ERR(0, 4074, __pyx_L1_error)
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 * 
 *         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__144 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__144)) __PYX_ERR(0, 4079, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__144);

  /* "mtrand.pyx":4137
 * 
 *             if fp < 0.0:
 *                 raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
 *             if fp > 1.0:
 *                 raise ValueError("p > 1.0")
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  __pyx_tuple__145 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__145)) __PYX_ERR(0, 4137, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__145);
  __Pyx_GIVEREF(__pyx_tuple__145);

  /* "mtrand.pyx":4139
 *                 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__146 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__146)) __PYX_ERR(0, 4139, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__146);

  /* "mtrand.pyx":4144
 * 
 *         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__147 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__147)) __PYX_ERR(0, 4144, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__147);
  __Pyx_GIVEREF(__pyx_tuple__147);

  /* "mtrand.pyx":4146
 *             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__148 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__148)) __PYX_ERR(0, 4146, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__148);
  __Pyx_GIVEREF(__pyx_tuple__148);

  /* "mtrand.pyx":4251
 * 
 *             if lngood < 0:
 *                 raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
 *             if lnbad < 0:
 *                 raise ValueError("nbad < 0")
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  __pyx_tuple__149 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__149)) __PYX_ERR(0, 4251, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__149);
  __Pyx_GIVEREF(__pyx_tuple__149);

  /* "mtrand.pyx":4253
 *                 raise ValueError("ngood < 0")
 *             if lnbad < 0:
 *                 raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
 *             if lnsample < 1:
 *                 raise ValueError("nsample < 1")
 */
  __pyx_tuple__150 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__150)) __PYX_ERR(0, 4253, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__150);

  /* "mtrand.pyx":4255
 *                 raise ValueError("nbad < 0")
 *             if lnsample < 1:
 *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
 *             if lngood + lnbad < lnsample:
 *                 raise ValueError("ngood + nbad < nsample")
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  __pyx_tuple__151 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__151)) __PYX_ERR(0, 4255, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__151);
  __Pyx_GIVEREF(__pyx_tuple__151);

  /* "mtrand.pyx":4257
 *                 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)
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  __pyx_tuple__152 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__152)) __PYX_ERR(0, 4257, __pyx_L1_error)
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  __Pyx_GIVEREF(__pyx_tuple__152);

  /* "mtrand.pyx":4262
 * 
 *         if np.any(np.less(ongood, 0)):
 *             raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
 *         if np.any(np.less(onbad, 0)):
 *             raise ValueError("nbad < 0")
 */
  __pyx_tuple__153 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__153)) __PYX_ERR(0, 4262, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__153);
  __Pyx_GIVEREF(__pyx_tuple__153);

  /* "mtrand.pyx":4264
 *             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__154 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__154)) __PYX_ERR(0, 4264, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__154);
  __Pyx_GIVEREF(__pyx_tuple__154);

  /* "mtrand.pyx":4266
 *             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__155 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__155)) __PYX_ERR(0, 4266, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__155);
  __Pyx_GIVEREF(__pyx_tuple__155);

  /* "mtrand.pyx":4268
 *             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)
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  __pyx_tuple__156 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__156)) __PYX_ERR(0, 4268, __pyx_L1_error)
  __Pyx_GOTREF(__pyx_tuple__156);
  __Pyx_GIVEREF(__pyx_tuple__156);

  /* "mtrand.pyx":4355
 * 
 *             if fp <= 0.0:
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 * 
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 *         if len(mean.shape) != 1:
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 *             psd = np.allclose(np.dot(v.T * s, v), cov, rtol=tol, atol=tol)
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 *                                   RuntimeWarning)
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 *         x = np.dot(x, np.sqrt(s)[:, None] * v)
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 *                     raise ValueError("covariance is not positive-semidefinite.")
 * 
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 * 
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 *         shape = _shape_from_size(size, d)
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 * 
 * def _rand_bool(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * def _rand_int16(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * def _rand_int32(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * def _rand_int64(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * def _rand_uint8(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * 
 * def _rand_uint16(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 *         return array
 * 
 * def _rand_uint32(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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  __pyx_tuple__189 = PyTuple_Pack(12, __pyx_n_s_low, __pyx_n_s_high, __pyx_n_s_size, __pyx_n_s_rngstate, __pyx_n_s_off, __pyx_n_s_rng, __pyx_n_s_buf, __pyx_n_s_out, __pyx_n_s_array, __pyx_n_s_cnt, __pyx_n_s_state, __pyx_n_s_array_data); if (unlikely(!__pyx_tuple__189)) __PYX_ERR(1, 362, __pyx_L1_error)
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 * 
 * def _rand_uint64(low, high, size, rngstate):             # <<<<<<<<<<<<<<
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 * 
 * def _shape_from_size(size, d):             # <<<<<<<<<<<<<<
 *     if size is None:
 *         shape = (d,)
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  /* "mtrand.pyx":638
 *     cdef object lock
 *     cdef object state_address
 *     poisson_lam_max = np.iinfo('l').max - np.sqrt(np.iinfo('l').max)*10             # <<<<<<<<<<<<<<
 * 
 *     def __init__(self, seed=None):
 */
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                }
            }
        } 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;
}

/* GetModuleGlobalName */
static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {
    PyObject *result;
#if !CYTHON_AVOID_BORROWED_REFS
    result = PyDict_GetItem(__pyx_d, name);
    if (likely(result)) {
        Py_INCREF(result);
    } else {
#else
    result = PyObject_GetItem(__pyx_d, name);
    if (!result) {
        PyErr_Clear();
#endif
        result = __Pyx_GetBuiltinName(name);
    }
    return result;
}

/* PyCFunctionFastCall */
  #if CYTHON_FAST_PYCCALL
static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {
    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;
    PyCFunction meth = PyCFunction_GET_FUNCTION(func);
    PyObject *self = PyCFunction_GET_SELF(func);
    assert(PyCFunction_Check(func));
    assert(METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)));
    assert(nargs >= 0);
    assert(nargs == 0 || args != NULL);
    /* _PyCFunction_FastCallDict() must not be called with an exception set,
       because it may clear it (directly or indirectly) and so the
       caller loses its exception */
    assert(!PyErr_Occurred());
    return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL);
}
#endif  // CYTHON_FAST_PYCCALL

/* PyFunctionFastCall */
  #if CYTHON_FAST_PYCALL
#include "frameobject.h"
static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,
                                               PyObject *globals) {
    PyFrameObject *f;
    PyThreadState *tstate = PyThreadState_GET();
    PyObject **fastlocals;
    Py_ssize_t i;
    PyObject *result;
    assert(globals != NULL);
    /* XXX Perhaps we should create a specialized
       PyFrame_New() that doesn't take locals, but does
       take builtins without sanity checking them.
       */
    assert(tstate != NULL);
    f = PyFrame_New(tstate, co, globals, NULL);
    if (f == NULL) {
        return NULL;
    }
    fastlocals = f->f_localsplus;
    for (i = 0; i < na; i++) {
        Py_INCREF(*args);
        fastlocals[i] = *args++;
    }
    result = PyEval_EvalFrameEx(f,0);
    ++tstate->recursion_depth;
    Py_DECREF(f);
    --tstate->recursion_depth;
    return result;
}
#if 1 || PY_VERSION_HEX < 0x030600B1
static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {
    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);
    PyObject *globals = PyFunction_GET_GLOBALS(func);
    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);
    PyObject *closure;
#if PY_MAJOR_VERSION >= 3
    PyObject *kwdefs;
#endif
    PyObject *kwtuple, **k;
    PyObject **d;
    Py_ssize_t nd;
    Py_ssize_t nk;
    PyObject *result;
    assert(kwargs == NULL || PyDict_Check(kwargs));
    nk = kwargs ? PyDict_Size(kwargs) : 0;
    if (Py_EnterRecursiveCall((char*)" while calling a Python object")) {
        return NULL;
    }
    if (
#if PY_MAJOR_VERSION >= 3
            co->co_kwonlyargcount == 0 &&
#endif
            likely(kwargs == NULL || nk == 0) &&
            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {
        if (argdefs == NULL && co->co_argcount == nargs) {
            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);
            goto done;
        }
        else if (nargs == 0 && argdefs != NULL
                 && co->co_argcount == Py_SIZE(argdefs)) {
            /* function called with no arguments, but all parameters have
               a default value: use default values as arguments .*/
            args = &PyTuple_GET_ITEM(argdefs, 0);
            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);
            goto done;
        }
    }
    if (kwargs != NULL) {
        Py_ssize_t pos, i;
        kwtuple = PyTuple_New(2 * nk);
        if (kwtuple == NULL) {
            result = NULL;
            goto done;
        }
        k = &PyTuple_GET_ITEM(kwtuple, 0);
        pos = i = 0;
        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {
            Py_INCREF(k[i]);
            Py_INCREF(k[i+1]);
            i += 2;
        }
        nk = i / 2;
    }
    else {
        kwtuple = NULL;
        k = NULL;
    }
    closure = PyFunction_GET_CLOSURE(func);
#if PY_MAJOR_VERSION >= 3
    kwdefs = PyFunction_GET_KW_DEFAULTS(func);
#endif
    if (argdefs != NULL) {
        d = &PyTuple_GET_ITEM(argdefs, 0);
        nd = Py_SIZE(argdefs);
    }
    else {
        d = NULL;
        nd = 0;
    }
#if PY_MAJOR_VERSION >= 3
    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,
                               args, nargs,
                               k, (int)nk,
                               d, (int)nd, kwdefs, closure);
#else
    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,
                               args, nargs,
                               k, (int)nk,
                               d, (int)nd, closure);
#endif
    Py_XDECREF(kwtuple);
done:
    Py_LeaveRecursiveCall();
    return result;
}
#endif  // CPython < 3.6
#endif  // CYTHON_FAST_PYCALL

/* PyObjectCall */
  #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {
    PyObject *result;
    ternaryfunc call = func->ob_type->tp_call;
    if (unlikely(!call))
        return PyObject_Call(func, arg, kw);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = (*call)(func, arg, kw);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* PyObjectCallMethO */
  #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {
    PyObject *self, *result;
    PyCFunction cfunc;
    cfunc = PyCFunction_GET_FUNCTION(func);
    self = PyCFunction_GET_SELF(func);
    if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object")))
        return NULL;
    result = cfunc(self, arg);
    Py_LeaveRecursiveCall();
    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {
        PyErr_SetString(
            PyExc_SystemError,
            "NULL result without error in PyObject_Call");
    }
    return result;
}
#endif

/* PyObjectCallOneArg */
  #if CYTHON_COMPILING_IN_CPYTHON
static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_New(1);
    if (unlikely(!args)) return NULL;
    Py_INCREF(arg);
    PyTuple_SET_ITEM(args, 0, arg);
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, &arg, 1);
    }
#endif
#ifdef __Pyx_CyFunction_USED
    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
    if (likely(PyCFunction_Check(func))) {
#endif
        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {
            return __Pyx_PyObject_CallMethO(func, arg);
#if CYTHON_FAST_PYCCALL
        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {
            return __Pyx_PyCFunction_FastCall(func, &arg, 1);
#endif
        }
    }
    return __Pyx__PyObject_CallOneArg(func, arg);
}
#else
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {
    PyObject *result;
    PyObject *args = PyTuple_Pack(1, arg);
    if (unlikely(!args)) return NULL;
    result = __Pyx_PyObject_Call(func, args, NULL);
    Py_DECREF(args);
    return result;
}
#endif

/* PyObjectCallNoArg */
    #if CYTHON_COMPILING_IN_CPYTHON
static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {
#if CYTHON_FAST_PYCALL
    if (PyFunction_Check(func)) {
        return __Pyx_PyFunction_FastCall(func, NULL, 0);
    }
#endif
#ifdef __Pyx_CyFunction_USED
    if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) {
#else
    if (likely(PyCFunction_Check(func))) {
#endif
        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {
            return __Pyx_PyObject_CallMethO(func, NULL);
        }
    }
    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);
}
#endif

/* PyErrFetchRestore */
      #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    tmp_type = tstate->curexc_type;
    tmp_value = tstate->curexc_value;
    tmp_tb = tstate->curexc_traceback;
    tstate->curexc_type = type;
    tstate->curexc_value = value;
    tstate->curexc_traceback = tb;
    Py_XDECREF(tmp_type);
    Py_XDECREF(tmp_value);
    Py_XDECREF(tmp_tb);
}
static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->curexc_type;
    *value = tstate->curexc_value;
    *tb = tstate->curexc_traceback;
    tstate->curexc_type = 0;
    tstate->curexc_value = 0;
    tstate->curexc_traceback = 0;
}
#endif

/* RaiseException */
      #if PY_MAJOR_VERSION < 3
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
                        CYTHON_UNUSED PyObject *cause) {
    __Pyx_PyThreadState_declare
    Py_XINCREF(type);
    if (!value || value == Py_None)
        value = NULL;
    else
        Py_INCREF(value);
    if (!tb || tb == Py_None)
        tb = NULL;
    else {
        Py_INCREF(tb);
        if (!PyTraceBack_Check(tb)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: arg 3 must be a traceback or None");
            goto raise_error;
        }
    }
    if (PyType_Check(type)) {
#if CYTHON_COMPILING_IN_PYPY
        if (!value) {
            Py_INCREF(Py_None);
            value = Py_None;
        }
#endif
        PyErr_NormalizeException(&type, &value, &tb);
    } else {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto raise_error;
        }
        value = type;
        type = (PyObject*) Py_TYPE(type);
        Py_INCREF(type);
        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
            PyErr_SetString(PyExc_TypeError,
                "raise: exception class must be a subclass of BaseException");
            goto raise_error;
        }
    }
    __Pyx_PyThreadState_assign
    __Pyx_ErrRestore(type, value, tb);
    return;
raise_error:
    Py_XDECREF(value);
    Py_XDECREF(type);
    Py_XDECREF(tb);
    return;
}
#else
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
    PyObject* owned_instance = NULL;
    if (tb == Py_None) {
        tb = 0;
    } else if (tb && !PyTraceBack_Check(tb)) {
        PyErr_SetString(PyExc_TypeError,
            "raise: arg 3 must be a traceback or None");
        goto bad;
    }
    if (value == Py_None)
        value = 0;
    if (PyExceptionInstance_Check(type)) {
        if (value) {
            PyErr_SetString(PyExc_TypeError,
                "instance exception may not have a separate value");
            goto bad;
        }
        value = type;
        type = (PyObject*) Py_TYPE(value);
    } else if (PyExceptionClass_Check(type)) {
        PyObject *instance_class = NULL;
        if (value && PyExceptionInstance_Check(value)) {
            instance_class = (PyObject*) Py_TYPE(value);
            if (instance_class != type) {
                int is_subclass = PyObject_IsSubclass(instance_class, type);
                if (!is_subclass) {
                    instance_class = NULL;
                } else if (unlikely(is_subclass == -1)) {
                    goto bad;
                } else {
                    type = instance_class;
                }
            }
        }
        if (!instance_class) {
            PyObject *args;
            if (!value)
                args = PyTuple_New(0);
            else if (PyTuple_Check(value)) {
                Py_INCREF(value);
                args = value;
            } else
                args = PyTuple_Pack(1, value);
            if (!args)
                goto bad;
            owned_instance = PyObject_Call(type, args, NULL);
            Py_DECREF(args);
            if (!owned_instance)
                goto bad;
            value = owned_instance;
            if (!PyExceptionInstance_Check(value)) {
                PyErr_Format(PyExc_TypeError,
                             "calling %R should have returned an instance of "
                             "BaseException, not %R",
                             type, Py_TYPE(value));
                goto bad;
            }
        }
    } else {
        PyErr_SetString(PyExc_TypeError,
            "raise: exception class must be a subclass of BaseException");
        goto bad;
    }
#if PY_VERSION_HEX >= 0x03030000
    if (cause) {
#else
    if (cause && cause != Py_None) {
#endif
        PyObject *fixed_cause;
        if (cause == Py_None) {
            fixed_cause = NULL;
        } else if (PyExceptionClass_Check(cause)) {
            fixed_cause = PyObject_CallObject(cause, NULL);
            if (fixed_cause == NULL)
                goto bad;
        } else if (PyExceptionInstance_Check(cause)) {
            fixed_cause = cause;
            Py_INCREF(fixed_cause);
        } else {
            PyErr_SetString(PyExc_TypeError,
                            "exception causes must derive from "
                            "BaseException");
            goto bad;
        }
        PyException_SetCause(value, fixed_cause);
    }
    PyErr_SetObject(type, value);
    if (tb) {
#if CYTHON_COMPILING_IN_PYPY
        PyObject *tmp_type, *tmp_value, *tmp_tb;
        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);
        Py_INCREF(tb);
        PyErr_Restore(tmp_type, tmp_value, tb);
        Py_XDECREF(tmp_tb);
#else
        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);
        }
#endif
    }
bad:
    Py_XDECREF(owned_instance);
    return;
}
#endif

/* SaveResetException */
        #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
    *type = tstate->exc_type;
    *value = tstate->exc_value;
    *tb = tstate->exc_traceback;
    Py_XINCREF(*type);
    Py_XINCREF(*value);
    Py_XINCREF(*tb);
}
static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    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);
}
#endif

/* PyErrExceptionMatches */
        #if CYTHON_FAST_THREAD_STATE
static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {
    PyObject *exc_type = tstate->curexc_type;
    if (exc_type == err) return 1;
    if (unlikely(!exc_type)) return 0;
    return PyErr_GivenExceptionMatches(exc_type, err);
}
#endif

/* GetException */
        #if CYTHON_FAST_THREAD_STATE
static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {
#else
static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
#endif
    PyObject *local_type, *local_value, *local_tb;
#if CYTHON_FAST_THREAD_STATE
    PyObject *tmp_type, *tmp_value, *tmp_tb;
    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_FAST_THREAD_STATE
    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_FAST_THREAD_STATE
    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;
}

/* ExtTypeTest */
          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;
}

/* GetItemInt */
          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,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    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,
                                                              CYTHON_NCP_UNUSED int wraparound,
                                                              CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS
    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,
                                                     CYTHON_NCP_UNUSED int wraparound,
                                                     CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
    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))
                        return NULL;
                    PyErr_Clear();
                }
            }
            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));
}

/* BytesEquals */
          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
}

/* UnicodeEquals */
          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 (unlikely(__Pyx_PyUnicode_READY(s1) < 0) || unlikely(__Pyx_PyUnicode_READY(s2) < 0))
            return -1;
        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
}

/* SliceObject */
          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_USE_TYPE_SLOTS
    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))
                    goto bad;
                PyErr_Clear();
            }
        }
        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_USE_TYPE_SLOTS
        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;
}

/* RaiseTooManyValuesToUnpack */
          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);
}

/* RaiseNeedMoreValuesToUnpack */
          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");
}

/* IterFinish */
          static CYTHON_INLINE int __Pyx_IterFinish(void) {
#if CYTHON_FAST_THREAD_STATE
    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
}

/* UnpackItemEndCheck */
          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;
}

/* PyIntBinop */
          #if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_SubtractObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long x;
        long a = PyInt_AS_LONG(op1);
            x = (long)((unsigned long)a - b);
            if (likely((x^a) >= 0 || (x^~b) >= 0))
                return PyInt_FromLong(x);
            return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a, x;
#ifdef HAVE_LONG_LONG
        const PY_LONG_LONG llb = intval;
        PY_LONG_LONG lla, llx;
#endif
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        const Py_ssize_t size = Py_SIZE(op1);
        if (likely(__Pyx_sst_abs(size) <= 1)) {
            a = likely(size) ? digits[0] : 0;
            if (size == -1) a = -a;
        } else {
            switch (size) {
                case -2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case -3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case -4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                default: return PyLong_Type.tp_as_number->nb_subtract(op1, op2);
            }
        }
                x = a - b;
            return PyLong_FromLong(x);
#ifdef HAVE_LONG_LONG
        long_long:
                llx = lla - llb;
            return PyLong_FromLongLong(llx);
#endif
        
        
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
            double result;
            PyFPE_START_PROTECT("subtract", return NULL)
            result = ((double)a) - (double)b;
            PyFPE_END_PROTECT(result)
            return PyFloat_FromDouble(result);
    }
    return (inplace ? PyNumber_InPlaceSubtract : PyNumber_Subtract)(op1, op2);
}
#endif

/* PyIntBinop */
          #if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_EqObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
    if (op1 == op2) {
        Py_RETURN_TRUE;
    }
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long a = PyInt_AS_LONG(op1);
        if (a == b) {
            Py_RETURN_TRUE;
        } else {
            Py_RETURN_FALSE;
        }
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a;
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        const Py_ssize_t size = Py_SIZE(op1);
        if (likely(__Pyx_sst_abs(size) <= 1)) {
            a = likely(size) ? digits[0] : 0;
            if (size == -1) a = -a;
        } else {
            switch (size) {
                case -2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                case -3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                case 3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                case -4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                case 4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
                    }
                #if PyLong_SHIFT < 30 && PyLong_SHIFT != 15
                default: return PyLong_Type.tp_richcompare(op1, op2, Py_EQ);
                #else
                default: Py_RETURN_FALSE;
                #endif
            }
        }
            if (a == b) {
                Py_RETURN_TRUE;
            } else {
                Py_RETURN_FALSE;
            }
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
            if ((double)a == (double)b) {
                Py_RETURN_TRUE;
            } else {
                Py_RETURN_FALSE;
            }
    }
    return PyObject_RichCompare(op1, op2, Py_EQ);
}
#endif

/* SliceObject */
          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_USE_TYPE_SLOTS
    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))
                    goto bad;
                PyErr_Clear();
            }
        }
        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_USE_TYPE_SLOTS
        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;
}

/* KeywordStringCheck */
          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;
}

/* PyIntBinop */
          #if !CYTHON_COMPILING_IN_PYPY
static PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {
    #if PY_MAJOR_VERSION < 3
    if (likely(PyInt_CheckExact(op1))) {
        const long b = intval;
        long x;
        long a = PyInt_AS_LONG(op1);
            x = (long)((unsigned long)a + b);
            if (likely((x^a) >= 0 || (x^b) >= 0))
                return PyInt_FromLong(x);
            return PyLong_Type.tp_as_number->nb_add(op1, op2);
    }
    #endif
    #if CYTHON_USE_PYLONG_INTERNALS
    if (likely(PyLong_CheckExact(op1))) {
        const long b = intval;
        long a, x;
#ifdef HAVE_LONG_LONG
        const PY_LONG_LONG llb = intval;
        PY_LONG_LONG lla, llx;
#endif
        const digit* digits = ((PyLongObject*)op1)->ob_digit;
        const Py_ssize_t size = Py_SIZE(op1);
        if (likely(__Pyx_sst_abs(size) <= 1)) {
            a = likely(size) ? digits[0] : 0;
            if (size == -1) a = -a;
        } else {
            switch (size) {
                case -2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 2:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case -3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 3:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case -4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                case 4:
                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        break;
#ifdef HAVE_LONG_LONG
                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {
                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));
                        goto long_long;
#endif
                    }
                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);
            }
        }
                x = a + b;
            return PyLong_FromLong(x);
#ifdef HAVE_LONG_LONG
        long_long:
                llx = lla + llb;
            return PyLong_FromLongLong(llx);
#endif
        
        
    }
    #endif
    if (PyFloat_CheckExact(op1)) {
        const long b = intval;
        double a = PyFloat_AS_DOUBLE(op1);
            double result;
            PyFPE_START_PROTECT("add", return NULL)
            result = ((double)a) + (double)b;
            PyFPE_END_PROTECT(result)
            return PyFloat_FromDouble(result);
    }
    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);
}
#endif

/* Import */
          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_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;
        }
        #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
        }
    }
bad:
    #if PY_VERSION_HEX < 0x03030000
    Py_XDECREF(py_import);
    #endif
    Py_XDECREF(empty_list);
    Py_XDECREF(empty_dict);
    return module;
}

/* ImportFrom */
          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;
}

/* SetItemInt */
          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,
                                               CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) {
#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS
    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))
                        return -1;
                    PyErr_Clear();
                }
            }
            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);
}

/* SetVTable */
            static int __Pyx_SetVtable(PyObject *dict, void *vtable) {
#if PY_VERSION_HEX >= 0x02070000
    PyObject *ob = PyCapsule_New(vtable, 0, 0);
#else
    PyObject *ob = PyCObject_FromVoidPtr(vtable, 0);
#endif
    if (!ob)
        goto bad;
    if (PyDict_SetItem(dict, __pyx_n_s_pyx_vtable, ob) < 0)
        goto bad;
    Py_DECREF(ob);
    return 0;
bad:
    Py_XDECREF(ob);
    return -1;
}

/* CodeObjectCache */
            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 - start) / 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);
}

/* AddTraceback */
            #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,
        0,
        0,
        0,
        0,
        __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,
        __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;
    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_frame = PyFrame_New(
        PyThreadState_GET(), /*PyThreadState *tstate,*/
        py_code,             /*PyCodeObject *code,*/
        __pyx_d,      /*PyObject *globals,*/
        0                    /*PyObject *locals*/
    );
    if (!py_frame) goto bad;
    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);
    PyTraceBack_Here(py_frame);
bad:
    Py_XDECREF(py_code);
    Py_XDECREF(py_frame);
}

/* CIntFromPyVerify */
            #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)
#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\
    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)
#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\
    {\
        func_type value = func_value;\
        if (sizeof(target_type) < sizeof(func_type)) {\
            if (unlikely(value != (func_type) (target_type) value)) {\
                func_type zero = 0;\
                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\
                    return (target_type) -1;\
                if (is_unsigned && unlikely(value < zero))\
                    goto raise_neg_overflow;\
                else\
                    goto raise_overflow;\
            }\
        }\
        return (target_type) value;\
    }

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_bool(npy_bool value) {
    const npy_bool neg_one = (npy_bool) -1, const_zero = (npy_bool) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_bool) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_bool) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_bool) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_bool) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_bool) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_bool),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int8(npy_int8 value) {
    const npy_int8 neg_one = (npy_int8) -1, const_zero = (npy_int8) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_int8) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_int8) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int8) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_int8) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int8) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_int8),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int16(npy_int16 value) {
    const npy_int16 neg_one = (npy_int16) -1, const_zero = (npy_int16) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_int16) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_int16) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int16) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_int16) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int16) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_int16),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int32(npy_int32 value) {
    const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_int32) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_int32) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_int32) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_int32),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_int64(npy_int64 value) {
    const npy_int64 neg_one = (npy_int64) -1, const_zero = (npy_int64) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_int64) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_int64) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int64) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_int64) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_int64) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_int64),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint8(npy_uint8 value) {
    const npy_uint8 neg_one = (npy_uint8) -1, const_zero = (npy_uint8) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_uint8) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_uint8) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint8) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_uint8) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint8) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_uint8),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint16(npy_uint16 value) {
    const npy_uint16 neg_one = (npy_uint16) -1, const_zero = (npy_uint16) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_uint16) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_uint16) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint16) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_uint16) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint16) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_uint16),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint32(npy_uint32 value) {
    const npy_uint32 neg_one = (npy_uint32) -1, const_zero = (npy_uint32) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_uint32) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_uint32) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint32) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_uint32) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint32) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_uint32),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_uint64(npy_uint64 value) {
    const npy_uint64 neg_one = (npy_uint64) -1, const_zero = (npy_uint64) 0;
    const int is_unsigned = neg_one > const_zero;
    if (is_unsigned) {
        if (sizeof(npy_uint64) < sizeof(long)) {
            return PyInt_FromLong((long) value);
        } else if (sizeof(npy_uint64) <= sizeof(unsigned long)) {
            return PyLong_FromUnsignedLong((unsigned long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint64) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_uint64) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_uint64) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(npy_uint64),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value) {
    const npy_intp neg_one = (npy_intp) -1, const_zero = (npy_intp) 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);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_intp) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(npy_intp) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(npy_intp) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        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);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
    const long neg_one = (long) -1, const_zero = (long) 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);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(long) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(long),
                                     little, !is_unsigned);
    }
}

/* CIntToPy */
            static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
    const int neg_one = (int) -1, const_zero = (int) 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);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);
#endif
        }
    } else {
        if (sizeof(int) <= sizeof(long)) {
            return PyInt_FromLong((long) value);
#ifdef HAVE_LONG_LONG
        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
            return PyLong_FromLongLong((PY_LONG_LONG) value);
#endif
        }
    }
    {
        int one = 1; int little = (int)*(unsigned char *)&one;
        unsigned char *bytes = (unsigned char *)&value;
        return _PyLong_FromByteArray(bytes, sizeof(int),
                                     little, !is_unsigned);
    }
}

/* CIntFromPy */
            static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *x) {
    const npy_intp neg_one = (npy_intp) -1, const_zero = (npy_intp) 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(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_intp) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_intp) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_intp, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_intp) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) >= 2 * PyLong_SHIFT) {
                            return (npy_intp) (((((npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_intp) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) >= 3 * PyLong_SHIFT) {
                            return (npy_intp) (((((((npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_intp) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) >= 4 * PyLong_SHIFT) {
                            return (npy_intp) (((((((((npy_intp)digits[3]) << PyLong_SHIFT) | (npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_intp) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_intp) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_intp, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_intp) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_intp, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_intp) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_intp, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_intp,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_intp) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_intp) (((npy_intp)-1)*(((((npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_intp) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_intp) ((((((npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_intp) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_intp) (((npy_intp)-1)*(((((((npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_intp) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_intp) ((((((((npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_intp) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_intp) (((npy_intp)-1)*(((((((((npy_intp)digits[3]) << PyLong_SHIFT) | (npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_intp) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_intp) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_intp) ((((((((((npy_intp)digits[3]) << PyLong_SHIFT) | (npy_intp)digits[2]) << PyLong_SHIFT) | (npy_intp)digits[1]) << PyLong_SHIFT) | (npy_intp)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_intp) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_intp, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_intp) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_intp, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#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_IntOrLong(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_IntOrLong(x);
        if (!tmp) return (npy_intp) -1;
        val = __Pyx_PyInt_As_npy_intp(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_intp");
    return (npy_intp) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_intp");
    return (npy_intp) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_bool __Pyx_PyInt_As_npy_bool(PyObject *x) {
    const npy_bool neg_one = (npy_bool) -1, const_zero = (npy_bool) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_bool) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_bool, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_bool) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_bool) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_bool, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_bool) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) >= 2 * PyLong_SHIFT) {
                            return (npy_bool) (((((npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_bool) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) >= 3 * PyLong_SHIFT) {
                            return (npy_bool) (((((((npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_bool) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) >= 4 * PyLong_SHIFT) {
                            return (npy_bool) (((((((((npy_bool)digits[3]) << PyLong_SHIFT) | (npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_bool) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_bool) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_bool, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_bool) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_bool, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_bool) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_bool, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_bool,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_bool) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_bool) (((npy_bool)-1)*(((((npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_bool) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_bool) ((((((npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_bool) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_bool) (((npy_bool)-1)*(((((((npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_bool) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_bool) ((((((((npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_bool) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_bool) (((npy_bool)-1)*(((((((((npy_bool)digits[3]) << PyLong_SHIFT) | (npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_bool) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_bool, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_bool) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_bool) ((((((((((npy_bool)digits[3]) << PyLong_SHIFT) | (npy_bool)digits[2]) << PyLong_SHIFT) | (npy_bool)digits[1]) << PyLong_SHIFT) | (npy_bool)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_bool) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_bool, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_bool) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_bool, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_bool val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_bool) -1;
        }
    } else {
        npy_bool val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_bool) -1;
        val = __Pyx_PyInt_As_npy_bool(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_bool");
    return (npy_bool) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_bool");
    return (npy_bool) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_uint8 __Pyx_PyInt_As_npy_uint8(PyObject *x) {
    const npy_uint8 neg_one = (npy_uint8) -1, const_zero = (npy_uint8) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_uint8) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_uint8, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_uint8) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint8) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint8, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_uint8) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) >= 2 * PyLong_SHIFT) {
                            return (npy_uint8) (((((npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint8) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) >= 3 * PyLong_SHIFT) {
                            return (npy_uint8) (((((((npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint8) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) >= 4 * PyLong_SHIFT) {
                            return (npy_uint8) (((((((((npy_uint8)digits[3]) << PyLong_SHIFT) | (npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_uint8) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_uint8) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint8, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint8) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint8, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint8) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_uint8, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint8,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_uint8) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint8) (((npy_uint8)-1)*(((((npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_uint8) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint8) ((((((npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_uint8) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint8) (((npy_uint8)-1)*(((((((npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint8) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint8) ((((((((npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_uint8) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint8) (((npy_uint8)-1)*(((((((((npy_uint8)digits[3]) << PyLong_SHIFT) | (npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint8) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint8, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint8) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint8) ((((((((((npy_uint8)digits[3]) << PyLong_SHIFT) | (npy_uint8)digits[2]) << PyLong_SHIFT) | (npy_uint8)digits[1]) << PyLong_SHIFT) | (npy_uint8)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_uint8) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint8, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint8) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint8, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_uint8 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_uint8) -1;
        }
    } else {
        npy_uint8 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_uint8) -1;
        val = __Pyx_PyInt_As_npy_uint8(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_uint8");
    return (npy_uint8) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_uint8");
    return (npy_uint8) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_int8 __Pyx_PyInt_As_npy_int8(PyObject *x) {
    const npy_int8 neg_one = (npy_int8) -1, const_zero = (npy_int8) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_int8) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_int8, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_int8) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int8) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_int8, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_int8) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) >= 2 * PyLong_SHIFT) {
                            return (npy_int8) (((((npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int8) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) >= 3 * PyLong_SHIFT) {
                            return (npy_int8) (((((((npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int8) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) >= 4 * PyLong_SHIFT) {
                            return (npy_int8) (((((((((npy_int8)digits[3]) << PyLong_SHIFT) | (npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_int8) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_int8) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int8, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int8) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int8, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int8) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_int8, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_int8,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_int8) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int8) (((npy_int8)-1)*(((((npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_int8) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int8) ((((((npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_int8) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int8) (((npy_int8)-1)*(((((((npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int8) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int8) ((((((((npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_int8) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int8) (((npy_int8)-1)*(((((((((npy_int8)digits[3]) << PyLong_SHIFT) | (npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int8) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int8, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int8) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int8) ((((((((((npy_int8)digits[3]) << PyLong_SHIFT) | (npy_int8)digits[2]) << PyLong_SHIFT) | (npy_int8)digits[1]) << PyLong_SHIFT) | (npy_int8)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_int8) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int8, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int8) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int8, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_int8 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_int8) -1;
        }
    } else {
        npy_int8 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_int8) -1;
        val = __Pyx_PyInt_As_npy_int8(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_int8");
    return (npy_int8) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_int8");
    return (npy_int8) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_uint16 __Pyx_PyInt_As_npy_uint16(PyObject *x) {
    const npy_uint16 neg_one = (npy_uint16) -1, const_zero = (npy_uint16) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_uint16) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_uint16, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_uint16) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint16) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint16, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_uint16) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) >= 2 * PyLong_SHIFT) {
                            return (npy_uint16) (((((npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint16) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) >= 3 * PyLong_SHIFT) {
                            return (npy_uint16) (((((((npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint16) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) >= 4 * PyLong_SHIFT) {
                            return (npy_uint16) (((((((((npy_uint16)digits[3]) << PyLong_SHIFT) | (npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_uint16) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_uint16) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint16, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint16) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint16, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint16) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_uint16, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint16,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_uint16) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint16) (((npy_uint16)-1)*(((((npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_uint16) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint16) ((((((npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_uint16) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint16) (((npy_uint16)-1)*(((((((npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint16) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint16) ((((((((npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_uint16) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint16) (((npy_uint16)-1)*(((((((((npy_uint16)digits[3]) << PyLong_SHIFT) | (npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint16) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint16, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint16) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint16) ((((((((((npy_uint16)digits[3]) << PyLong_SHIFT) | (npy_uint16)digits[2]) << PyLong_SHIFT) | (npy_uint16)digits[1]) << PyLong_SHIFT) | (npy_uint16)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_uint16) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint16, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint16) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint16, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_uint16 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_uint16) -1;
        }
    } else {
        npy_uint16 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_uint16) -1;
        val = __Pyx_PyInt_As_npy_uint16(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_uint16");
    return (npy_uint16) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_uint16");
    return (npy_uint16) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_int16 __Pyx_PyInt_As_npy_int16(PyObject *x) {
    const npy_int16 neg_one = (npy_int16) -1, const_zero = (npy_int16) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_int16) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_int16, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_int16) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int16) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_int16, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_int16) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) >= 2 * PyLong_SHIFT) {
                            return (npy_int16) (((((npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int16) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) >= 3 * PyLong_SHIFT) {
                            return (npy_int16) (((((((npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int16) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) >= 4 * PyLong_SHIFT) {
                            return (npy_int16) (((((((((npy_int16)digits[3]) << PyLong_SHIFT) | (npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_int16) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_int16) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int16, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int16) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int16, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int16) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_int16, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_int16,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_int16) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int16) (((npy_int16)-1)*(((((npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_int16) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int16) ((((((npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_int16) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int16) (((npy_int16)-1)*(((((((npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int16) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int16) ((((((((npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_int16) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int16) (((npy_int16)-1)*(((((((((npy_int16)digits[3]) << PyLong_SHIFT) | (npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int16) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int16, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int16) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int16) ((((((((((npy_int16)digits[3]) << PyLong_SHIFT) | (npy_int16)digits[2]) << PyLong_SHIFT) | (npy_int16)digits[1]) << PyLong_SHIFT) | (npy_int16)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_int16) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int16, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int16) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int16, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_int16 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_int16) -1;
        }
    } else {
        npy_int16 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_int16) -1;
        val = __Pyx_PyInt_As_npy_int16(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_int16");
    return (npy_int16) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_int16");
    return (npy_int16) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_uint32 __Pyx_PyInt_As_npy_uint32(PyObject *x) {
    const npy_uint32 neg_one = (npy_uint32) -1, const_zero = (npy_uint32) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_uint32) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_uint32, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_uint32) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint32) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint32, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_uint32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 2 * PyLong_SHIFT) {
                            return (npy_uint32) (((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 3 * PyLong_SHIFT) {
                            return (npy_uint32) (((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) >= 4 * PyLong_SHIFT) {
                            return (npy_uint32) (((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_uint32) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_uint32) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint32) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint32) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_uint32, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint32,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_uint32) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_uint32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_uint32) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((((npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_uint32) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint32) (((npy_uint32)-1)*(((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint32) ((((((((((npy_uint32)digits[3]) << PyLong_SHIFT) | (npy_uint32)digits[2]) << PyLong_SHIFT) | (npy_uint32)digits[1]) << PyLong_SHIFT) | (npy_uint32)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_uint32) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint32) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint32, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_uint32 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_uint32) -1;
        }
    } else {
        npy_uint32 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_uint32) -1;
        val = __Pyx_PyInt_As_npy_uint32(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_uint32");
    return (npy_uint32) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_uint32");
    return (npy_uint32) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) {
    const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_int32) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_int32) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int32) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 2 * PyLong_SHIFT) {
                            return (npy_int32) (((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 3 * PyLong_SHIFT) {
                            return (npy_int32) (((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) >= 4 * PyLong_SHIFT) {
                            return (npy_int32) (((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_int32) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_int32) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int32) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_int32, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_int32,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_int32) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int32) ((((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int32) ((((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int32) (((npy_int32)-1)*(((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int32) ((((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_int32) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_int32 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_int32) -1;
        }
    } else {
        npy_int32 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_int32) -1;
        val = __Pyx_PyInt_As_npy_int32(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_int32");
    return (npy_int32) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_int32");
    return (npy_int32) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_uint64 __Pyx_PyInt_As_npy_uint64(PyObject *x) {
    const npy_uint64 neg_one = (npy_uint64) -1, const_zero = (npy_uint64) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_uint64) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_uint64, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_uint64) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint64) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint64, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_uint64) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) >= 2 * PyLong_SHIFT) {
                            return (npy_uint64) (((((npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint64) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) >= 3 * PyLong_SHIFT) {
                            return (npy_uint64) (((((((npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint64) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) >= 4 * PyLong_SHIFT) {
                            return (npy_uint64) (((((((((npy_uint64)digits[3]) << PyLong_SHIFT) | (npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_uint64) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_uint64) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint64, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint64) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint64, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_uint64) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_uint64, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_uint64,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_uint64) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint64) (((npy_uint64)-1)*(((((npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_uint64) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_uint64) ((((((npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_uint64) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint64) (((npy_uint64)-1)*(((((((npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_uint64) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_uint64) ((((((((npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_uint64) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint64) (((npy_uint64)-1)*(((((((((npy_uint64)digits[3]) << PyLong_SHIFT) | (npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_uint64) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_uint64, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_uint64) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_uint64) ((((((((((npy_uint64)digits[3]) << PyLong_SHIFT) | (npy_uint64)digits[2]) << PyLong_SHIFT) | (npy_uint64)digits[1]) << PyLong_SHIFT) | (npy_uint64)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_uint64) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint64, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_uint64) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_uint64, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_uint64 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_uint64) -1;
        }
    } else {
        npy_uint64 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_uint64) -1;
        val = __Pyx_PyInt_As_npy_uint64(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_uint64");
    return (npy_uint64) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_uint64");
    return (npy_uint64) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE npy_int64 __Pyx_PyInt_As_npy_int64(PyObject *x) {
    const npy_int64 neg_one = (npy_int64) -1, const_zero = (npy_int64) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(npy_int64) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(npy_int64, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (npy_int64) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int64) 0;
                case  1: __PYX_VERIFY_RETURN_INT(npy_int64, digit, digits[0])
                case 2:
                    if (8 * sizeof(npy_int64) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) >= 2 * PyLong_SHIFT) {
                            return (npy_int64) (((((npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int64) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) >= 3 * PyLong_SHIFT) {
                            return (npy_int64) (((((((npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int64) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) >= 4 * PyLong_SHIFT) {
                            return (npy_int64) (((((((((npy_int64)digits[3]) << PyLong_SHIFT) | (npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (npy_int64) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(npy_int64) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int64, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int64) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int64, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (npy_int64) 0;
                case -1: __PYX_VERIFY_RETURN_INT(npy_int64, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(npy_int64,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(npy_int64) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int64) (((npy_int64)-1)*(((((npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(npy_int64) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 2 * PyLong_SHIFT) {
                            return (npy_int64) ((((((npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(npy_int64) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int64) (((npy_int64)-1)*(((((((npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(npy_int64) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 3 * PyLong_SHIFT) {
                            return (npy_int64) ((((((((npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(npy_int64) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int64) (((npy_int64)-1)*(((((((((npy_int64)digits[3]) << PyLong_SHIFT) | (npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(npy_int64) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(npy_int64, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(npy_int64) - 1 > 4 * PyLong_SHIFT) {
                            return (npy_int64) ((((((((((npy_int64)digits[3]) << PyLong_SHIFT) | (npy_int64)digits[2]) << PyLong_SHIFT) | (npy_int64)digits[1]) << PyLong_SHIFT) | (npy_int64)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(npy_int64) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int64, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(npy_int64) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(npy_int64, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            npy_int64 val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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_int64) -1;
        }
    } else {
        npy_int64 val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (npy_int64) -1;
        val = __Pyx_PyInt_As_npy_int64(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to npy_int64");
    return (npy_int64) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to npy_int64");
    return (npy_int64) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {
    const unsigned long neg_one = (unsigned long) -1, const_zero = (unsigned long) 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(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (unsigned long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (unsigned long) 0;
                case  1: __PYX_VERIFY_RETURN_INT(unsigned long, digit, digits[0])
                case 2:
                    if (8 * sizeof(unsigned long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) >= 2 * PyLong_SHIFT) {
                            return (unsigned long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) >= 3 * PyLong_SHIFT) {
                            return (unsigned long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) >= 4 * PyLong_SHIFT) {
                            return (unsigned long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (unsigned long) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(unsigned long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(unsigned long, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(unsigned long) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(unsigned long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (unsigned long) 0;
                case -1: __PYX_VERIFY_RETURN_INT(unsigned long, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(unsigned long,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(unsigned long) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 2 * PyLong_SHIFT) {
                            return (unsigned long) (((unsigned long)-1)*(((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(unsigned long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 2 * PyLong_SHIFT) {
                            return (unsigned long) ((((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(unsigned long) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 3 * PyLong_SHIFT) {
                            return (unsigned long) (((unsigned long)-1)*(((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 3 * PyLong_SHIFT) {
                            return (unsigned long) ((((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(unsigned long) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 4 * PyLong_SHIFT) {
                            return (unsigned long) (((unsigned long)-1)*(((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(unsigned long) - 1 > 4 * PyLong_SHIFT) {
                            return (unsigned long) ((((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(unsigned long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(unsigned long, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(unsigned long) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(unsigned long, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#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_IntOrLong(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_IntOrLong(x);
        if (!tmp) return (unsigned long) -1;
        val = __Pyx_PyInt_As_unsigned_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to unsigned long");
    return (unsigned long) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to unsigned long");
    return (unsigned long) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
    const int neg_one = (int) -1, const_zero = (int) 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(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (int) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {
                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {
                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {
                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (int) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(int) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (int) 0;
                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {
                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(int) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#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_IntOrLong(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_IntOrLong(x);
        if (!tmp) return (int) -1;
        val = __Pyx_PyInt_As_int(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to int");
    return (int) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to int");
    return (int) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE size_t __Pyx_PyInt_As_size_t(PyObject *x) {
    const size_t neg_one = (size_t) -1, const_zero = (size_t) 0;
    const int is_unsigned = neg_one > const_zero;
#if PY_MAJOR_VERSION < 3
    if (likely(PyInt_Check(x))) {
        if (sizeof(size_t) < sizeof(long)) {
            __PYX_VERIFY_RETURN_INT(size_t, long, PyInt_AS_LONG(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (size_t) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (size_t) 0;
                case  1: __PYX_VERIFY_RETURN_INT(size_t, digit, digits[0])
                case 2:
                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 2 * PyLong_SHIFT) {
                            return (size_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 3 * PyLong_SHIFT) {
                            return (size_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) >= 4 * PyLong_SHIFT) {
                            return (size_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (size_t) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(size_t) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(size_t) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (size_t) 0;
                case -1: __PYX_VERIFY_RETURN_INT(size_t, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(size_t,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(size_t) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(size_t) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                            return (size_t) ((((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(size_t) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(size_t) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                            return (size_t) ((((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(size_t) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
                            return (size_t) (((size_t)-1)*(((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(size_t) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(size_t, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(size_t) - 1 > 4 * PyLong_SHIFT) {
                            return (size_t) ((((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(size_t) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(size_t) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(size_t, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
            PyErr_SetString(PyExc_RuntimeError,
                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
#else
            size_t val;
            PyObject *v = __Pyx_PyNumber_IntOrLong(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 (size_t) -1;
        }
    } else {
        size_t val;
        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);
        if (!tmp) return (size_t) -1;
        val = __Pyx_PyInt_As_size_t(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to size_t");
    return (size_t) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to size_t");
    return (size_t) -1;
}

/* CIntFromPy */
            static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
    const long neg_one = (long) -1, const_zero = (long) 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(x))
        } else {
            long val = PyInt_AS_LONG(x);
            if (is_unsigned && unlikely(val < 0)) {
                goto raise_neg_overflow;
            }
            return (long) val;
        }
    } else
#endif
    if (likely(PyLong_Check(x))) {
        if (is_unsigned) {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {
                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {
                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {
                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));
                        }
                    }
                    break;
            }
#endif
#if CYTHON_COMPILING_IN_CPYTHON
            if (unlikely(Py_SIZE(x) < 0)) {
                goto raise_neg_overflow;
            }
#else
            {
                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);
                if (unlikely(result < 0))
                    return (long) -1;
                if (unlikely(result == 1))
                    goto raise_neg_overflow;
            }
#endif
            if (sizeof(long) <= sizeof(unsigned long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))
#endif
            }
        } else {
#if CYTHON_USE_PYLONG_INTERNALS
            const digit* digits = ((PyLongObject*)x)->ob_digit;
            switch (Py_SIZE(x)) {
                case  0: return (long) 0;
                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))
                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])
                case -2:
                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 2:
                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -3:
                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 3:
                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case -4:
                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
                case 4:
                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {
                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {
                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))
                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {
                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));
                        }
                    }
                    break;
            }
#endif
            if (sizeof(long) <= sizeof(long)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))
#ifdef HAVE_LONG_LONG
            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {
                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))
#endif
            }
        }
        {
#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_IntOrLong(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_IntOrLong(x);
        if (!tmp) return (long) -1;
        val = __Pyx_PyInt_As_long(tmp);
        Py_DECREF(tmp);
        return val;
    }
raise_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "value too large to convert to long");
    return (long) -1;
raise_neg_overflow:
    PyErr_SetString(PyExc_OverflowError,
        "can't convert negative value to long");
    return (long) -1;
}

/* CheckBinaryVersion */
            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);
        return PyErr_WarnEx(NULL, message, 1);
    }
    return 0;
}

/* ModuleImport */
            #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

/* TypeImport */
            #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. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
    }
    else if ((size_t)basicsize != size) {
        PyErr_Format(PyExc_ValueError,
            "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd",
            module_name, class_name, basicsize, size);
        goto bad;
    }
    return (PyTypeObject *)result;
bad:
    Py_XDECREF(py_module);
    Py_XDECREF(result);
    return NULL;
}
#endif

/* InitStrings */
            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
        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 CYTHON_COMPILING_IN_CPYTHON && (__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
        *length = PyBytes_GET_SIZE(defenc);
        return defenc_c;
#else
        if (__Pyx_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
        return PyUnicode_AsUTF8AndSize(o, length);
#endif
#endif
    } else
#endif
#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))
    if (PyByteArray_Check(o)) {
        *length = PyByteArray_GET_SIZE(o);
        return PyByteArray_AS_STRING(o);
    } else
#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_IntOrLong(PyObject* x) {
#if CYTHON_USE_TYPE_SLOTS
  PyNumberMethods *m;
#endif
  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 __Pyx_NewRef(x);
#if CYTHON_USE_TYPE_SLOTS
  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
#else
  res = PyNumber_Int(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;
}
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))) {
    if (sizeof(Py_ssize_t) >= sizeof(long))
        return PyInt_AS_LONG(b);
    else
        return PyInt_AsSsize_t(x);
  }
#endif
  if (likely(PyLong_CheckExact(b))) {
    #if CYTHON_USE_PYLONG_INTERNALS
    const digit* digits = ((PyLongObject*)b)->ob_digit;
    const Py_ssize_t size = Py_SIZE(b);
    if (likely(__Pyx_sst_abs(size) <= 1)) {
        ival = likely(size) ? digits[0] : 0;
        if (size == -1) ival = -ival;
        return ival;
    } else {
      switch (size) {
         case 2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -2:
           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -3:
           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case 4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
         case -4:
           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {
             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));
           }
           break;
      }
    }
    #endif
    return PyLong_AsSsize_t(b);
  }
  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) {
    return PyInt_FromSize_t(ival);
}


#endif /* Py_PYTHON_H */
