CYT: Regenerate mtrand.c file.

1 files changed, 6516 insertions, 5900 deletions

diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index 909f743b8..eda815fdd 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,18 +1,39 @@
-/* Generated by Cython 0.12.1 on Wed Mar  2 12:31:18 2011 */
+/* Generated by Cython 0.14.1 on Thu Mar 10 10:20:54 2011 */
 
 #define PY_SSIZE_T_CLEAN
 #include "Python.h"
-#include "structmember.h"
 #ifndef Py_PYTHON_H
     #error Python headers needed to compile C extensions, please install development version of Python.
 #else
 
-#ifndef PY_LONG_LONG
-  #define PY_LONG_LONG LONG_LONG
+#include <stddef.h> /* For offsetof */
+#ifndef offsetof
+#define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
+#endif
+
+#if !defined(WIN32) && !defined(MS_WINDOWS)
+  #ifndef __stdcall
+    #define __stdcall
+  #endif
+  #ifndef __cdecl
+    #define __cdecl
+  #endif
+  #ifndef __fastcall
+    #define __fastcall
+  #endif
+#endif
+
+#ifndef DL_IMPORT
+  #define DL_IMPORT(t) t
 #endif
 #ifndef DL_EXPORT
   #define DL_EXPORT(t) t
 #endif
+
+#ifndef PY_LONG_LONG
+  #define PY_LONG_LONG LONG_LONG
+#endif
+
 #if PY_VERSION_HEX < 0x02040000
   #define METH_COEXIST 0
   #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
@@ -82,14 +103,43 @@
 
 #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
-#else
+#endif
+
+#if PY_VERSION_HEX < 0x02060000
+  #define PyBytesObject                PyStringObject
   #define PyBytes_Type                 PyString_Type
+  #define PyBytes_Check                PyString_Check
   #define PyBytes_CheckExact           PyString_CheckExact
+  #define PyBytes_FromString           PyString_FromString
+  #define PyBytes_FromStringAndSize    PyString_FromStringAndSize
+  #define PyBytes_FromFormat           PyString_FromFormat
+  #define PyBytes_DecodeEscape         PyString_DecodeEscape
+  #define PyBytes_AsString             PyString_AsString
+  #define PyBytes_AsStringAndSize      PyString_AsStringAndSize
+  #define PyBytes_Size                 PyString_Size
+  #define PyBytes_AS_STRING            PyString_AS_STRING
+  #define PyBytes_GET_SIZE             PyString_GET_SIZE
+  #define PyBytes_Repr                 PyString_Repr
+  #define PyBytes_Concat               PyString_Concat
+  #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
+#endif
+
+#if PY_VERSION_HEX < 0x02060000
+  #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
+  #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
+#endif
+#ifndef PySet_CheckExact
+  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
 #endif
 
+#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
+
 #if PY_MAJOR_VERSION >= 3
+  #define PyIntObject                  PyLongObject
   #define PyInt_Type                   PyLong_Type
   #define PyInt_Check(op)              PyLong_Check(op)
   #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
@@ -103,30 +153,42 @@
   #define PyInt_AsSsize_t              PyLong_AsSsize_t
   #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
   #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+#endif
+
+#if PY_MAJOR_VERSION >= 3
+  #define PyBoolObject                 PyLongObject
+#endif
+
+
+#if PY_MAJOR_VERSION >= 3
   #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
   #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
 #else
   #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
   #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-
 #endif
 
-#if PY_MAJOR_VERSION >= 3
-  #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func)
+#if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
+  #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
+  #define __Pyx_PySequence_DelSlice(obj, a, b) PySequence_DelSlice(obj, a, b)
+#else
+  #define __Pyx_PySequence_GetSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), (PyObject*)0) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_GetSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object is unsliceable", (obj)->ob_type->tp_name), (PyObject*)0)))
+  #define __Pyx_PySequence_SetSlice(obj, a, b, value) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_SetSlice(obj, a, b, value)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice assignment", (obj)->ob_type->tp_name), -1)))
+  #define __Pyx_PySequence_DelSlice(obj, a, b) (unlikely(!(obj)) ? \
+        (PyErr_SetString(PyExc_SystemError, "null argument to internal routine"), -1) : \
+        (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
+            (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
 #endif
 
-#if !defined(WIN32) && !defined(MS_WINDOWS)
-  #ifndef __stdcall
-    #define __stdcall
-  #endif
-  #ifndef __cdecl
-    #define __cdecl
-  #endif
-  #ifndef __fastcall
-    #define __fastcall
-  #endif
-#else
-  #define _USE_MATH_DEFINES
+#if PY_MAJOR_VERSION >= 3
+  #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
 #endif
 
 #if PY_VERSION_HEX < 0x02050000
@@ -146,11 +208,16 @@
   #define __Pyx_NAMESTR(n) (n)
   #define __Pyx_DOCSTR(n)  (n)
 #endif
+
 #ifdef __cplusplus
 #define __PYX_EXTERN_C extern "C"
 #else
 #define __PYX_EXTERN_C extern
 #endif
+
+#if defined(WIN32) || defined(MS_WINDOWS)
+#define _USE_MATH_DEFINES
+#endif
 #include <math.h>
 #define __PYX_HAVE_API__mtrand
 #include "string.h"
@@ -161,100 +228,51 @@
 #include "distributions.h"
 #include "initarray.h"
 
+#ifdef PYREX_WITHOUT_ASSERTIONS
+#define CYTHON_WITHOUT_ASSERTIONS
+#endif
+
+
+/* inline attribute */
 #ifndef CYTHON_INLINE
   #if defined(__GNUC__)
     #define CYTHON_INLINE __inline__
   #elif defined(_MSC_VER)
     #define CYTHON_INLINE __inline
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_INLINE inline
   #else
-    #define CYTHON_INLINE 
+    #define CYTHON_INLINE
   #endif
 #endif
 
+/* unused attribute */
+#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)
+#   define CYTHON_UNUSED __attribute__ ((__unused__))
+# else
+#   define CYTHON_UNUSED
+# endif
+#endif
+
 typedef struct {PyObject **p; char *s; const long n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; /*proto*/
 
 
 /* Type Conversion Predeclarations */
 
-#if PY_MAJOR_VERSION < 3
-#define __Pyx_PyBytes_FromString          PyString_FromString
-#define __Pyx_PyBytes_FromStringAndSize   PyString_FromStringAndSize
-#define __Pyx_PyBytes_AsString            PyString_AsString
-#else
-#define __Pyx_PyBytes_FromString          PyBytes_FromString
-#define __Pyx_PyBytes_FromStringAndSize   PyBytes_FromStringAndSize
-#define __Pyx_PyBytes_AsString            PyBytes_AsString
-#endif
-
-#define __Pyx_PyBytes_FromUString(s)      __Pyx_PyBytes_FromString((char*)s)
-#define __Pyx_PyBytes_AsUString(s)        ((unsigned char*) __Pyx_PyBytes_AsString(s))
+#define __Pyx_PyBytes_FromUString(s) PyBytes_FromString((char*)s)
+#define __Pyx_PyBytes_AsUString(s)   ((unsigned char*) PyBytes_AsString(s))
 
 #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
 static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
 static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
 
-#if !defined(T_PYSSIZET)
-#if PY_VERSION_HEX < 0x02050000
-#define T_PYSSIZET T_INT
-#elif !defined(T_LONGLONG)
-#define T_PYSSIZET \
-        ((sizeof(Py_ssize_t) == sizeof(int))  ? T_INT  : \
-        ((sizeof(Py_ssize_t) == sizeof(long)) ? T_LONG : -1))
-#else
-#define T_PYSSIZET \
-        ((sizeof(Py_ssize_t) == sizeof(int))          ? T_INT      : \
-        ((sizeof(Py_ssize_t) == sizeof(long))         ? T_LONG     : \
-        ((sizeof(Py_ssize_t) == sizeof(PY_LONG_LONG)) ? T_LONGLONG : -1)))
-#endif
-#endif
-
-
-#if !defined(T_ULONGLONG)
-#define __Pyx_T_UNSIGNED_INT(x) \
-        ((sizeof(x) == sizeof(unsigned char))  ? T_UBYTE : \
-        ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
-        ((sizeof(x) == sizeof(unsigned int))   ? T_UINT : \
-        ((sizeof(x) == sizeof(unsigned long))  ? T_ULONG : -1))))
-#else
-#define __Pyx_T_UNSIGNED_INT(x) \
-        ((sizeof(x) == sizeof(unsigned char))  ? T_UBYTE : \
-        ((sizeof(x) == sizeof(unsigned short)) ? T_USHORT : \
-        ((sizeof(x) == sizeof(unsigned int))   ? T_UINT : \
-        ((sizeof(x) == sizeof(unsigned long))  ? T_ULONG : \
-        ((sizeof(x) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1)))))
-#endif
-#if !defined(T_LONGLONG)
-#define __Pyx_T_SIGNED_INT(x) \
-        ((sizeof(x) == sizeof(char))  ? T_BYTE : \
-        ((sizeof(x) == sizeof(short)) ? T_SHORT : \
-        ((sizeof(x) == sizeof(int))   ? T_INT : \
-        ((sizeof(x) == sizeof(long))  ? T_LONG : -1))))
-#else
-#define __Pyx_T_SIGNED_INT(x) \
-        ((sizeof(x) == sizeof(char))  ? T_BYTE : \
-        ((sizeof(x) == sizeof(short)) ? T_SHORT : \
-        ((sizeof(x) == sizeof(int))   ? T_INT : \
-        ((sizeof(x) == sizeof(long))  ? T_LONG : \
-        ((sizeof(x) == sizeof(PY_LONG_LONG))   ? T_LONGLONG : -1)))))
-#endif
-
-#define __Pyx_T_FLOATING(x) \
-        ((sizeof(x) == sizeof(float)) ? T_FLOAT : \
-        ((sizeof(x) == sizeof(double)) ? T_DOUBLE : -1))
-
-#if !defined(T_SIZET)
-#if !defined(T_ULONGLONG)
-#define T_SIZET \
-        ((sizeof(size_t) == sizeof(unsigned int))  ? T_UINT  : \
-        ((sizeof(size_t) == sizeof(unsigned long)) ? T_ULONG : -1))
-#else
-#define T_SIZET \
-        ((sizeof(size_t) == sizeof(unsigned int))          ? T_UINT      : \
-        ((sizeof(size_t) == sizeof(unsigned long))         ? T_ULONG     : \
-        ((sizeof(size_t) == sizeof(unsigned PY_LONG_LONG)) ? T_ULONGLONG : -1)))
-#endif
-#endif
-
 static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
 static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
 static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
@@ -264,7 +282,7 @@ static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
 
 #ifdef __GNUC__
 /* Test for GCC > 2.95 */
-#if __GNUC__ > 2 ||               (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) 
+#if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))
 #define likely(x)   __builtin_expect(!!(x), 1)
 #define unlikely(x) __builtin_expect(!!(x), 0)
 #else /* __GNUC__ > 2 ... */
@@ -284,9 +302,13 @@ 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;
 
 
+static const char *__pyx_f[] = {
+  "mtrand.pyx",
+  "numpy.pxi",
+};
+
 /* Type declarations */
 
 typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *);
@@ -366,14 +388,21 @@ struct __pyx_obj_6mtrand_RandomState {
 #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0)
 #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0)
 
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
+
+static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+
 static void __Pyx_RaiseDoubleKeywordsError(
     const char* func_name, PyObject* kw_name); /*proto*/
 
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],     PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,     const char* function_name); /*proto*/
+
 static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
     Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
 
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],     PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,     const char* function_name); /*proto*/
-
 
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
     PyObject *r;
@@ -384,11 +413,11 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j
 }
 
 
-#define __Pyx_GetItemInt_List(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
-                                                    __Pyx_GetItemInt_List_Fast(o, i, size <= sizeof(long)) : \
+#define __Pyx_GetItemInt_List(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
+                                                    __Pyx_GetItemInt_List_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
 
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i) {
     if (likely(o != Py_None)) {
         if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
             PyObject *r = PyList_GET_ITEM(o, i);
@@ -401,14 +430,14 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_
             return r;
         }
     }
-    return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
-#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
-                                                    __Pyx_GetItemInt_Tuple_Fast(o, i, size <= sizeof(long)) : \
+#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
+                                                    __Pyx_GetItemInt_Tuple_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
 
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i) {
     if (likely(o != Py_None)) {
         if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
             PyObject *r = PyTuple_GET_ITEM(o, i);
@@ -421,15 +450,15 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize
             return r;
         }
     }
-    return __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
 
-#define __Pyx_GetItemInt(o, i, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
-                                                    __Pyx_GetItemInt_Fast(o, i, size <= sizeof(long)) : \
+#define __Pyx_GetItemInt(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
+                                                    __Pyx_GetItemInt_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
 
-static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int fits_long) {
+static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i) {
     PyObject *r;
     if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
         r = PyList_GET_ITEM(o, i);
@@ -443,17 +472,17 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
         r = PySequence_GetItem(o, i);
     }
     else {
-        r = __Pyx_GetItemInt_Generic(o, fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i));
+        r = __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
     }
     return r;
 }
 
 static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
 
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(void);
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
 
 static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/
-static int __Pyx_EndUnpack(PyObject *); /*proto*/
+static int __Pyx_EndUnpack(PyObject *, Py_ssize_t expected); /*proto*/
 
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
 
@@ -462,24 +491,8 @@ static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict,
 
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
 
-static CYTHON_INLINE PyObject* __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
-    if (likely(PyList_CheckExact(L))) {
-        if (PyList_Append(L, x) < 0) return NULL;
-        Py_INCREF(Py_None);
-        return Py_None; /* this is just to have an accurate signature */
-    }
-    else {
-        PyObject *r, *m;
-        m = __Pyx_GetAttrString(L, "append");
-        if (!m) return NULL;
-        r = PyObject_CallFunctionObjArgs(m, x, NULL);
-        Py_DECREF(m);
-        return r;
-    }
-}
-
-#define __Pyx_SetItemInt(o, i, v, size, to_py_func) ((size <= sizeof(Py_ssize_t)) ? \
-                                                    __Pyx_SetItemInt_Fast(o, i, v, size <= sizeof(long)) : \
+#define __Pyx_SetItemInt(o, i, v, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
+                                                    __Pyx_SetItemInt_Fast(o, i, v) : \
                                                     __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
 
 static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
@@ -490,7 +503,7 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyOb
     return r;
 }
 
-static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int fits_long) {
+static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v) {
     if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
         Py_INCREF(v);
         Py_DECREF(PyList_GET_ITEM(o, i));
@@ -500,7 +513,7 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObje
     else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0)))
         return PySequence_SetItem(o, i, v);
     else {
-        PyObject *j = fits_long ? PyInt_FromLong(i) : PyLong_FromLongLong(i);
+        PyObject *j = PyInt_FromSsize_t(i);
         return __Pyx_SetItemInt_Generic(o, j, v);
     }
 }
@@ -510,13 +523,6 @@ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb);
 
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
 
-static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
-
-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-
 static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
 
 static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *);
@@ -535,6 +541,8 @@ static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *);
 
 static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *);
 
+static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject *);
+
 static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *);
 
 static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *);
@@ -581,107 +589,147 @@ static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *, __pyx_t_6mtrand_rk_d
 static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *); /*proto*/
 static double __pyx_f_6mtrand_kahan_sum(double *, long); /*proto*/
 #define __Pyx_MODULE_NAME "mtrand"
-int __pyx_module_is_main_mtrand = 0;
+static int __pyx_module_is_main_mtrand = 0;
 
 /* Implementation of mtrand */
 static PyObject *__pyx_builtin_ValueError;
 static PyObject *__pyx_builtin_TypeError;
 static char __pyx_k_1[] = "size is not compatible with inputs";
-static char __pyx_k_2[] = "algorithm must be 'MT19937'";
-static char __pyx_k_3[] = "state must be 624 longs";
-static char __pyx_k_4[] = "low >= high";
-static char __pyx_k_9[] = "scale <= 0";
-static char __pyx_k_10[] = "a <= 0";
-static char __pyx_k_11[] = "b <= 0";
-static char __pyx_k_13[] = "shape <= 0";
-static char __pyx_k_15[] = "dfnum <= 0";
-static char __pyx_k_16[] = "dfden <= 0";
-static char __pyx_k_17[] = "dfnum <= 1";
-static char __pyx_k_18[] = "nonc < 0";
-static char __pyx_k_19[] = "df <= 0";
-static char __pyx_k_20[] = "nonc <= 0";
-static char __pyx_k_21[] = "df <= 1";
-static char __pyx_k_22[] = "kappa < 0";
-static char __pyx_k_31[] = "sigma <= 0";
-static char __pyx_k_32[] = "sigma <= 0.0";
-static char __pyx_k_34[] = "scale <= 0.0";
-static char __pyx_k_35[] = "mean <= 0";
-static char __pyx_k_36[] = "mean <= 0.0";
-static char __pyx_k_37[] = "left > mode";
-static char __pyx_k_38[] = "mode > right";
-static char __pyx_k_39[] = "left == right";
-static char __pyx_k_40[] = "n <= 0";
-static char __pyx_k_41[] = "p < 0";
-static char __pyx_k_42[] = "p > 1";
-static char __pyx_k_44[] = "lam < 0";
-static char __pyx_k_45[] = "a <= 1.0";
-static char __pyx_k_46[] = "p < 0.0";
-static char __pyx_k_47[] = "p > 1.0";
-static char __pyx_k_48[] = "ngood < 1";
-static char __pyx_k_49[] = "nbad < 1";
-static char __pyx_k_50[] = "nsample < 1";
-static char __pyx_k_51[] = "ngood + nbad < nsample";
-static char __pyx_k_52[] = "p <= 0.0";
-static char __pyx_k_53[] = "p >= 1.0";
-static char __pyx_k_54[] = "mean must be 1 dimensional";
-static char __pyx_k_55[] = "cov must be 2 dimensional and square";
-static char __pyx_k_56[] = "mean and cov must have same length";
-static char __pyx_k_57[] = "numpy.dual";
-static char __pyx_k_58[] = "sum(pvals[:-1]) > 1.0";
-static char __pyx_k_59[] = "standard_exponential";
-static char __pyx_k_60[] = "noncentral_chisquare";
-static char __pyx_k_61[] = "RandomState.seed (line 567)";
-static char __pyx_k_62[] = "RandomState.get_state (line 600)";
-static char __pyx_k_63[] = "RandomState.set_state (line 637)";
-static char __pyx_k_64[] = "RandomState.random_sample (line 718)";
-static char __pyx_k_65[] = "RandomState.tomaxint (line 761)";
-static char __pyx_k_66[] = "RandomState.randint (line 808)";
-static char __pyx_k_67[] = "RandomState.bytes (line 888)";
-static char __pyx_k_68[] = "RandomState.uniform (line 915)";
-static char __pyx_k_69[] = "RandomState.rand (line 1002)";
-static char __pyx_k_70[] = "RandomState.randn (line 1045)";
-static char __pyx_k_71[] = "RandomState.random_integers (line 1101)";
-static char __pyx_k_72[] = "RandomState.standard_normal (line 1179)";
-static char __pyx_k_73[] = "RandomState.normal (line 1211)";
-static char __pyx_k_74[] = "RandomState.beta (line 1311)";
-static char __pyx_k_75[] = "RandomState.exponential (line 1370)";
-static char __pyx_k_76[] = "RandomState.standard_exponential (line 1424)";
-static char __pyx_k_77[] = "RandomState.standard_gamma (line 1452)";
-static char __pyx_k_78[] = "RandomState.gamma (line 1534)";
-static char __pyx_k_79[] = "RandomState.f (line 1625)";
-static char __pyx_k_80[] = "RandomState.noncentral_f (line 1728)";
-static char __pyx_k_81[] = "RandomState.chisquare (line 1823)";
-static char __pyx_k_82[] = "RandomState.noncentral_chisquare (line 1901)";
-static char __pyx_k_83[] = "RandomState.standard_cauchy (line 1993)";
-static char __pyx_k_84[] = "RandomState.standard_t (line 2054)";
-static char __pyx_k_85[] = "RandomState.vonmises (line 2155)";
-static char __pyx_k_86[] = "RandomState.pareto (line 2249)";
-static char __pyx_k_87[] = "RandomState.weibull (line 2345)";
-static char __pyx_k_88[] = "RandomState.power (line 2445)";
-static char __pyx_k_89[] = "RandomState.laplace (line 2554)";
-static char __pyx_k_90[] = "RandomState.gumbel (line 2644)";
-static char __pyx_k_91[] = "RandomState.logistic (line 2775)";
-static char __pyx_k_92[] = "RandomState.lognormal (line 2863)";
-static char __pyx_k_93[] = "RandomState.rayleigh (line 2994)";
-static char __pyx_k_94[] = "RandomState.wald (line 3066)";
-static char __pyx_k_95[] = "RandomState.triangular (line 3152)";
-static char __pyx_k_96[] = "RandomState.binomial (line 3240)";
-static char __pyx_k_97[] = "RandomState.negative_binomial (line 3348)";
-static char __pyx_k_98[] = "RandomState.poisson (line 3443)";
-static char __pyx_k_99[] = "RandomState.zipf (line 3506)";
+static char __pyx_k_9[] = "algorithm must be 'MT19937'";
+static char __pyx_k_11[] = "state must be 624 longs";
+static char __pyx_k_13[] = "low >= high";
+static char __pyx_k_19[] = "scale <= 0";
+static char __pyx_k_22[] = "a <= 0";
+static char __pyx_k_24[] = "b <= 0";
+static char __pyx_k_31[] = "shape <= 0";
+static char __pyx_k_41[] = "dfnum <= 0";
+static char __pyx_k_43[] = "dfden <= 0";
+static char __pyx_k_45[] = "dfnum <= 1";
+static char __pyx_k_48[] = "nonc < 0";
+static char __pyx_k_53[] = "df <= 0";
+static char __pyx_k_57[] = "nonc <= 0";
+static char __pyx_k_59[] = "df <= 1";
+static char __pyx_k_64[] = "kappa < 0";
+static char __pyx_k_87[] = "sigma <= 0";
+static char __pyx_k_89[] = "sigma <= 0.0";
+static char __pyx_k_93[] = "scale <= 0.0";
+static char __pyx_k_95[] = "mean <= 0";
+static char __pyx_k_98[] = "mean <= 0.0";
 static char __pyx_k__a[] = "a";
 static char __pyx_k__b[] = "b";
 static char __pyx_k__f[] = "f";
+static char __pyx_k__l[] = "l";
 static char __pyx_k__n[] = "n";
 static char __pyx_k__p[] = "p";
-static char __pyx_k_100[] = "RandomState.geometric (line 3594)";
-static char __pyx_k_101[] = "RandomState.hypergeometric (line 3660)";
-static char __pyx_k_102[] = "RandomState.logseries (line 3779)";
-static char __pyx_k_103[] = "RandomState.multivariate_normal (line 3874)";
-static char __pyx_k_104[] = "RandomState.multinomial (line 4006)";
-static char __pyx_k_105[] = "RandomState.dirichlet (line 4099)";
-static char __pyx_k_106[] = "RandomState.shuffle (line 4215)";
-static char __pyx_k_107[] = "RandomState.permutation (line 4277)";
+static char __pyx_k_101[] = "left > mode";
+static char __pyx_k_103[] = "mode > right";
+static char __pyx_k_105[] = "left == right";
+static char __pyx_k_110[] = "n <= 0";
+static char __pyx_k_112[] = "p < 0";
+static char __pyx_k_114[] = "p > 1";
+static char __pyx_k_126[] = "lam < 0";
+static char __pyx_k_128[] = "lam value too large";
+static char __pyx_k_131[] = "lam value too large.";
+static char __pyx_k_133[] = "a <= 1.0";
+static char __pyx_k_136[] = "p < 0.0";
+static char __pyx_k_138[] = "p > 1.0";
+static char __pyx_k_142[] = "ngood < 1";
+static char __pyx_k_144[] = "nbad < 1";
+static char __pyx_k_146[] = "nsample < 1";
+static char __pyx_k_148[] = "ngood + nbad < nsample";
+static char __pyx_k_154[] = "p <= 0.0";
+static char __pyx_k_156[] = "p >= 1.0";
+static char __pyx_k_160[] = "mean must be 1 dimensional";
+static char __pyx_k_162[] = "cov must be 2 dimensional and square";
+static char __pyx_k_164[] = "mean and cov must have same length";
+static char __pyx_k_166[] = "numpy.dual";
+static char __pyx_k_167[] = "sum(pvals[:-1]) > 1.0";
+static char __pyx_k_171[] = "standard_exponential";
+static char __pyx_k_172[] = "noncentral_chisquare";
+static char __pyx_k_173[] = "RandomState.random_sample (line 719)";
+static char __pyx_k_174[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Defines the shape of the returned array of random floats. If None\n            (the default), returns a single float.\n\n        Returns\n        -------\n        out : float or ndarray of floats\n            Array of random floats of shape `size` (unless ``size=None``, in which\n            case a single float is returned).\n\n        Examples\n        --------\n        >>> np.random.random_sample()\n        0.47108547995356098\n        >>> type(np.random.random_sample())\n        <type 'float'>\n        >>> np.random.random_sample((5,))\n        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n\n        Three-by-two array of random numbers from [-5, 0):\n\n        >>> 5 * np.random.random_sample((3, 2)) - 5\n        array([[-3.99149989, -0.52338984],\n               [-2.99091858, -0.79479508],\n               [-1.23204345, -1.75224494]])\n\n        ";
+static char __pyx_k_175[] = "RandomState.tomaxint (line 762)";
+static char __pyx_k_176[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
+static char __pyx_k_177[] = "RandomState.randint (line 809)";
+static char __pyx_k_178[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n        then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is\n            returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
+static char __pyx_k_179[] = "RandomState.bytes (line 889)";
+static char __pyx_k_180[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
+static char __pyx_k_181[] = "RandomState.uniform (line 916)";
+static char __pyx_k_182[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Shape of output.  If the given size is, for example, (m,n,k),\n            m*n*k samples are generated.  If no shape is specified, a single sample\n            is returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are w""ithin the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_183[] = "RandomState.rand (line 1003)";
+static char __pyx_k_184[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\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        `random`.\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
+static char __pyx_k_185[] = "RandomState.randn (line 1046)";
+static char __pyx_k_186[] = "\n        randn([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 ``(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        d1, ..., dn : `n` ints, optional\n            The dimensions of the returned array, should be all positive.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n\n        ";
+static char __pyx_k_187[] = "RandomState.random_integers (line 1102)";
+static char __pyx_k_188[] = "\n        random_integers(low, high=None, size=None)\n\n        Return random integers between `low` and `high`, inclusive.\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        closed interval [`low`, `high`].  If `high` is None (the default),\n        then results are from [1, `low`].\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >>> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_189[] = "RandomState.standard_normal (line 1180)";
+static char __pyx_k_190[] = "\n        standard_normal(size=None)\n\n        Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        >>> s = np.random.standard_normal(8000)\n        >>> s\n        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random\n               -0.38672696, -0.4685006 ])                               #random\n        >>> s.shape\n        (8000,)\n        >>> s = np.random.standard_normal(size=(3, 4, 2))\n        >>> s.shape\n        (3, 4, 2)\n\n        ";
+static char __pyx_k_191[] = "RandomState.normal (line 1212)";
+static char __pyx_k_192[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_193[] = "RandomState.standard_exponential (line 1425)";
+static char __pyx_k_194[] = "\n        standard_exponential(size=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        Output a 3x8000 array:\n\n        >>> n = np.random.standard_exponential((3, 8000))\n\n        ";
+static char __pyx_k_195[] = "RandomState.standard_gamma (line 1453)";
+static char __pyx_k_196[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_197[] = "RandomState.gamma (line 1535)";
+static char __pyx_k_198[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_199[] = "RandomState.f (line 1626)";
+static char __pyx_k_200[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
+static char __pyx_k_201[] = "RandomState.noncentral_f (line 1729)";
+static char __pyx_k_202[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : int\n            Parameter, should be > 1.\n        dfden : int\n            Parameter, should be > 1.\n        nonc : float\n            Parameter, should be >= 0.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            Drawn samples.\n\n        Notes\n        -----\n        When calculating the power of an experiment (power = probability of\n        rejecting the null hypothesis when a specific alternative is true) the\n        non-central F statistic becomes important.  When the null hypothesis is\n        true, the F statistic follows a central F distribution. When the null\n        hypothesis is not true, then it follows a non-central F statistic.\n\n        References\n        ----------\n        Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n        Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n        Wikipedia, \"Noncentral F distribution\",\n        http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n        Examples\n        --------\n        In a study, testing for a specific alternative to the null hypothesis\n        requires use of the Noncentral F distribution. We need to calculate the\n        area in the tail of the distribution that exceeds the value of the F\n        distribution for the null hypothesis.  We'll plot the two probability\n        distributions for comp""arison.\n\n        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, normed=True)\n        >>> plt.plot(F[1][1:], F[0])\n        >>> plt.plot(NF[1][1:], NF[0])\n        >>> plt.show()\n\n        ";
+static char __pyx_k_203[] = "RandomState.chisquare (line 1824)";
+static char __pyx_k_204[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : tuple of ints, int, optional\n             Size of the returned array.  By default, a scalar is\n             returned.\n\n        Returns\n        -------\n        output : ndarray\n            Samples drawn from the distribution, packed in a `size`-shaped\n            array.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        `NIST/SEMATECH e-Handbook of Statistical Methods\n        <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
+static char __pyx_k_205[] = "RandomState.noncentral_chisquare (line 1902)";
+static char __pyx_k_206[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_207[] = "RandomState.standard_cauchy (line 1994)";
+static char __pyx_k_208[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        ..[3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<25)""]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_209[] = "RandomState.standard_t (line 2055)";
+static char __pyx_k_210[] = "\n        standard_t(df, size=None)\n\n        Standard Student's t distribution with df degrees of freedom.\n\n        A special case of the hyperbolic distribution.\n        As `df` gets large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be > 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a Normal\n        distribution. The t test provides a way to test whether the sample mean\n        (that is the mean calculated from the data) is a good estimate of the true\n        mean.\n\n        The derivation of the t-distribution was forst published in 1908 by William\n        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n        issues, he had to publish under a pseudonym, and so he used the name\n        Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               http://en.wikipedia.org/wiki/Student's_t-distribution\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in Kj is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Doe""s their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, normed=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
+static char __pyx_k_211[] = "RandomState.vonmises (line 2156)";
+static char __pyx_k_212[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_213[] = "RandomState.pareto (line 2250)";
+static char __pyx_k_214[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_215[] = "RandomState.weibull (line 2346)";
+static char __pyx_k_216[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_217[] = "RandomState.power (line 2446)";
+static char __pyx_k_218[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
+static char __pyx_k_219[] = "RandomState.laplace (line 2555)";
+static char __pyx_k_220[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))\n        >>> plt.plot(x,g)\n\n        ";
+static char __pyx_k_221[] = "RandomState.gumbel (line 2645)";
+static char __pyx_k_222[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_223[] = "RandomState.logistic (line 2776)";
+static char __pyx_k_224[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
+static char __pyx_k_225[] = "RandomState.lognormal (line 2864)";
+static char __pyx_k_226[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean, standard\n        deviation, and shape. Note that the mean and standard deviation are not the\n        values for the distribution itself, but of the underlying normal\n        distribution it is derived from.\n\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, >0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.\n\n        The probability density function for the log-normal distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n        of the normally distributed logarithm of the variable.\n\n        A log-normal distribution results if a random variable is the *product* of\n        a large number of independent, identically-distributed variables in the\n        same way that a normal distribution results if the variable is the *sum*\n        of a large number of independent, identically-distributed variables\n        (see the last example). It is one of the so-called \"fat-tailed\"\n        distributions.\n\n        The log-normal distribution is commonly used to model the lifespan of units\n        with fatigue-stress failure modes. Since thi""s includes\n        most mechanical systems, the log-normal distribution has widespread\n        application.\n\n        It is also commonly used to model oil field sizes, species abundance, and\n        latent periods of infectious diseases.\n\n        References\n        ----------\n        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n               Distributions across the Sciences: Keys and Clues\", May 2001\n               Vol. 51 No. 5 BioScience\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 31-32.\n        .. [3] Wikipedia, \"Lognormal distribution\",\n               http://en.wikipedia.org/wiki/Lognormal_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_227[] = "RandomState.rayleigh (line 2995)";
+static char __pyx_k_228[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        ..[2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
+static char __pyx_k_229[] = "RandomState.wald (line 3067)";
+static char __pyx_k_230[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        ..[3] Wikipedia, \"Wald distributio""n\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_231[] = "RandomState.triangular (line 3153)";
+static char __pyx_k_232[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        ..[1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_233[] = "RandomState.binomial (line 3241)";
+static char __pyx_k_234[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer > 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, > 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n    ""    .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
+static char __pyx_k_235[] = "RandomState.negative_binomial (line 3349)";
+static char __pyx_k_236[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
+static char __pyx_k_237[] = "RandomState.poisson (line 3444)";
+static char __pyx_k_238[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the Binomial\n        distribution for large N.\n\n        Parameters\n        ----------\n        lam : float\n            Expectation of interval, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Notes\n        -----\n        The Poisson distribution\n\n        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n        For events with an expected separation :math:`\\lambda` the Poisson\n        distribution :math:`f(k; \\lambda)` describes the probability of\n        :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n        Because the output is limited to the range of the C long type, a\n        ValueError is raised when `lam` is within 10 sigma of the maximum\n        representable value.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n               Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n        .. [2] Wikipedia, \"Poisson distribution\",\n           http://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> s = np.random.poisson(5, 10000)\n\n        Display histogram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_239[] = "RandomState.zipf (line 3515)";
+static char __pyx_k_240[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float > 1\n            Distribution parameter.\n        size : int or tuple of int, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn; a single integer is equivalent in\n            its result to providing a mono-tuple, i.e., a 1-D array of length\n            *size* is returned.  The default is None, in which case a single\n            scalar is returned.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples are greater than or equal to one.\n\n        See Also\n        --------\n        scipy.stats.distributions.zipf : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n        in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.zipf""(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        Truncate s values at 50 so plot is interesting\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a)/sps.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_241[] = "RandomState.geometric (line 3603)";
+static char __pyx_k_242[] = "\n        geometric(p, size=None)\n\n        Draw samples from the geometric distribution.\n\n        Bernoulli trials are experiments with one of two outcomes:\n        success or failure (an example of such an experiment is flipping\n        a coin).  The geometric distribution models the number of trials\n        that must be run in order to achieve success.  It is therefore\n        supported on the positive integers, ``k = 1, 2, ...``.\n\n        The probability mass function of the geometric distribution is\n\n        .. math:: f(k) = (1 - p)^{k - 1} p\n\n        where `p` is the probability of success of an individual trial.\n\n        Parameters\n        ----------\n        p : float\n            The probability of success of an individual trial.\n        size : tuple of ints\n            Number of values to draw from the distribution.  The output\n            is shaped according to `size`.\n\n        Returns\n        -------\n        out : ndarray\n            Samples from the geometric distribution, shaped according to\n            `size`.\n\n        Examples\n        --------\n        Draw ten thousand values from the geometric distribution,\n        with the probability of an individual success equal to 0.35:\n\n        >>> z = np.random.geometric(p=0.35, size=10000)\n\n        How many trials succeeded after a single run?\n\n        >>> (z == 1).sum() / 10000.\n        0.34889999999999999 #random\n\n        ";
+static char __pyx_k_243[] = "RandomState.hypergeometric (line 3669)";
+static char __pyx_k_244[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn wit""h\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
+static char __pyx_k_245[] = "RandomState.logseries (line 3788)";
+static char __pyx_k_246[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Log""arithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n                     logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_247[] = "RandomState.multivariate_normal (line 3883)";
+static char __pyx_k_248[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution.  Must be symmetric and\n            positive semi-definite for \"physically meaningful\" results.\n        size : tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw ""N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (*cov* is a multiple of the identity matrix)\n          - Diagonal covariance (*cov* has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n        3rd ed., New York: McGraw-Hill, 1991.\n\n        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n        2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
+static char __pyx_k_249[] = "RandomState.multinomial (line 4015)";
+static char __pyx_k_250[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
+static char __pyx_k_251[] = "RandomState.dirichlet (line 4108)";
+static char __pyx_k_252[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
+static char __pyx_k_253[] = "RandomState.shuffle (line 4224)";
+static char __pyx_k_254[] = "\n        shuffle(x)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        Parameters\n        ----------\n        x : array_like\n            The array or list to be shuffled.\n\n        Returns\n        -------\n        None\n\n        Examples\n        --------\n        >>> arr = np.arange(10)\n        >>> np.random.shuffle(arr)\n        >>> arr\n        [1 7 5 2 9 4 3 6 0 8]\n\n        This function only shuffles the array along the first index of a\n        multi-dimensional array:\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.shuffle(arr)\n        >>> arr\n        array([[3, 4, 5],\n               [6, 7, 8],\n               [0, 1, 2]])\n\n        ";
+static char __pyx_k_255[] = "RandomState.permutation (line 4286)";
+static char __pyx_k_256[] = "\n        permutation(x)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        If `x` is a multi-dimensional array, it is only shuffled along its\n        first index.\n\n        Parameters\n        ----------\n        x : int or array_like\n            If `x` is an integer, randomly permute ``np.arange(x)``.\n            If `x` is an array, make a copy and shuffle the elements\n            randomly.\n\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> np.random.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n        >>> np.random.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.permutation(arr)\n        array([[6, 7, 8],\n               [0, 1, 2],\n               [3, 4, 5]])\n\n        ";
 static char __pyx_k__df[] = "df";
 static char __pyx_k__mu[] = "mu";
 static char __pyx_k__nd[] = "nd";
@@ -694,6 +742,7 @@ static char __pyx_k__key[] = "key";
 static char __pyx_k__lam[] = "lam";
 static char __pyx_k__loc[] = "loc";
 static char __pyx_k__low[] = "low";
+static char __pyx_k__max[] = "max";
 static char __pyx_k__pos[] = "pos";
 static char __pyx_k__svd[] = "svd";
 static char __pyx_k__beta[] = "beta";
@@ -723,6 +772,7 @@ static char __pyx_k__empty[] = "empty";
 static char __pyx_k__equal[] = "equal";
 static char __pyx_k__gamma[] = "gamma";
 static char __pyx_k__gauss[] = "gauss";
+static char __pyx_k__iinfo[] = "iinfo";
 static char __pyx_k__kappa[] = "kappa";
 static char __pyx_k__ngood[] = "ngood";
 static char __pyx_k__numpy[] = "numpy";
@@ -734,6 +784,7 @@ static char __pyx_k__scale[] = "scale";
 static char __pyx_k__shape[] = "shape";
 static char __pyx_k__sigma[] = "sigma";
 static char __pyx_k__zeros[] = "zeros";
+static char __pyx_k__append[] = "append";
 static char __pyx_k__arange[] = "arange";
 static char __pyx_k__gumbel[] = "gumbel";
 static char __pyx_k__normal[] = "normal";
@@ -761,7 +812,6 @@ static char __pyx_k__logistic[] = "logistic";
 static char __pyx_k__multiply[] = "multiply";
 static char __pyx_k__rayleigh[] = "rayleigh";
 static char __pyx_k__subtract[] = "subtract";
-static char __pyx_k__tomaxint[] = "tomaxint";
 static char __pyx_k__vonmises[] = "vonmises";
 static char __pyx_k__TypeError[] = "TypeError";
 static char __pyx_k__chisquare[] = "chisquare";
@@ -777,7 +827,6 @@ static char __pyx_k__dimensions[] = "dimensions";
 static char __pyx_k__less_equal[] = "less_equal";
 static char __pyx_k__standard_t[] = "standard_t";
 static char __pyx_k__triangular[] = "triangular";
-static char __pyx_k__RandomState[] = "RandomState";
 static char __pyx_k__exponential[] = "exponential";
 static char __pyx_k__multinomial[] = "multinomial";
 static char __pyx_k__permutation[] = "permutation";
@@ -787,6 +836,7 @@ static char __pyx_k__random_sample[] = "random_sample";
 static char __pyx_k__hypergeometric[] = "hypergeometric";
 static char __pyx_k__internal_state[] = "internal_state";
 static char __pyx_k__standard_gamma[] = "standard_gamma";
+static char __pyx_k__poisson_lam_max[] = "poisson_lam_max";
 static char __pyx_k__random_integers[] = "random_integers";
 static char __pyx_k__standard_cauchy[] = "standard_cauchy";
 static char __pyx_k__standard_normal[] = "standard_normal";
@@ -794,98 +844,136 @@ static char __pyx_k__negative_binomial[] = "negative_binomial";
 static char __pyx_k____RandomState_ctor[] = "__RandomState_ctor";
 static char __pyx_k__multivariate_normal[] = "multivariate_normal";
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-static PyObject *__pyx_kp_u_97;
-static PyObject *__pyx_kp_u_98;
-static PyObject *__pyx_kp_u_99;
+static PyObject *__pyx_kp_s_93;
+static PyObject *__pyx_kp_s_95;
+static PyObject *__pyx_kp_s_98;
 static PyObject *__pyx_n_s__MT19937;
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 static PyObject *__pyx_n_s__TypeError;
 static PyObject *__pyx_n_s__ValueError;
 static PyObject *__pyx_n_s____RandomState_ctor;
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 static PyObject *__pyx_n_s__add;
 static PyObject *__pyx_n_s__alpha;
 static PyObject *__pyx_n_s__any;
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 static PyObject *__pyx_n_s__arange;
 static PyObject *__pyx_n_s__array;
 static PyObject *__pyx_n_s__asarray;
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 static PyObject *__pyx_n_s__has_gauss;
 static PyObject *__pyx_n_s__high;
 static PyObject *__pyx_n_s__hypergeometric;
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 static PyObject *__pyx_n_s__integer;
 static PyObject *__pyx_n_s__internal_state;
 static PyObject *__pyx_n_s__kappa;
 static PyObject *__pyx_n_s__key;
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 static PyObject *__pyx_n_s__lam;
 static PyObject *__pyx_n_s__laplace;
 static PyObject *__pyx_n_s__left;
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 static PyObject *__pyx_n_s__lognormal;
 static PyObject *__pyx_n_s__logseries;
 static PyObject *__pyx_n_s__low;
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 static PyObject *__pyx_n_s__mean;
 static PyObject *__pyx_n_s__mode;
 static PyObject *__pyx_n_s__mu;
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 static PyObject *__pyx_n_s__pareto;
 static PyObject *__pyx_n_s__permutation;
 static PyObject *__pyx_n_s__poisson;
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 static PyObject *__pyx_n_s__pos;
 static PyObject *__pyx_n_s__power;
 static PyObject *__pyx_n_s__pvals;
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 static PyObject *__pyx_n_s__subtract;
 static PyObject *__pyx_n_s__svd;
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 static PyObject *__pyx_n_s__uint;
 static PyObject *__pyx_n_s__uint32;
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 static PyObject *__pyx_n_s__zipf;
 static PyObject *__pyx_int_0;
 static PyObject *__pyx_int_1;
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 static PyObject *__pyx_int_624;
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-static PyObject *__pyx_k_24;
-static PyObject *__pyx_k_25;
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-static PyObject *__pyx_k_30;
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+static PyObject *__pyx_k_82;
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@@ -5030,7 +5127,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_get_state(PyObject *__pyx_v_self
   return __pyx_r;
 }
 
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-static PyObject *__pyx_pf_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_4set_state[] = "\n        set_state(state)\n\n        Set the internal state of the generator from a tuple.\n\n        For use if one has reason to manually (re-)set the internal state of the\n        \"Mersenne Twister\"[1]_ pseudo-random number generating algorithm.\n\n        Parameters\n        ----------\n        state : tuple(str, ndarray of 624 uints, int, int, float)\n            The `state` tuple has the following items:\n\n            1. the string 'MT19937', specifying the Mersenne Twister algorithm.\n            2. a 1-D array of 624 unsigned integers ``keys``.\n            3. an integer ``pos``.\n            4. an integer ``has_gauss``.\n            5. a float ``cached_gaussian``.\n\n        Returns\n        -------\n        out : None\n            Returns 'None' on success.\n\n        See Also\n        --------\n        get_state\n\n        Notes\n        -----\n        `set_state` and `get_state` are not needed to work with any of the\n        random distributions in NumPy. If the internal state is manually altered,\n        the user should know exactly what he/she is doing.\n\n        For backwards compatibility, the form (str, array of 624 uints, int) is\n        also accepted although it is missing some information about the cached\n        Gaussian value: ``state = ('MT19937', keys, pos)``.\n\n        References\n        ----------\n        .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n           623-dimensionally equidistributed uniform pseudorandom number\n           generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n           Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
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@@ -5057,112 +5154,104 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self
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   double __pyx_t_8;
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-  /* "mtrand.pyx":686
+  /* "mtrand.pyx":687
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+  /* "mtrand.pyx":688
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+  /* "mtrand.pyx":690
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-  /* "mtrand.pyx":690
+  /* "mtrand.pyx":691
  *             raise ValueError("algorithm must be 'MT19937'")
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+  __pyx_t_2 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__get_state); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 716; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_2);
-  __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 715; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 716; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_3);
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __pyx_t_2 = PyTuple_New(3); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 715; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_t_2 = PyTuple_New(3); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 716; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
   PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_1);
   __Pyx_GIVEREF(__pyx_t_1);
   __Pyx_INCREF(((PyObject *)__pyx_empty_tuple));
@@ -5550,7 +5631,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState___reduce__(PyObject *__pyx_v_sel
   __Pyx_GIVEREF(__pyx_t_3);
   __pyx_t_1 = 0;
   __pyx_t_3 = 0;
-  __pyx_r = __pyx_t_2;
+  __pyx_r = ((PyObject *)__pyx_t_2);
   __pyx_t_2 = 0;
   goto __pyx_L0;
 
@@ -5568,7 +5649,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState___reduce__(PyObject *__pyx_v_sel
   return __pyx_r;
 }
 
-/* "mtrand.pyx":718
+/* "mtrand.pyx":719
  * 
  *     # Basic distributions:
  *     def random_sample(self, size=None):             # <<<<<<<<<<<<<<
@@ -5576,9 +5657,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState___reduce__(PyObject *__pyx_v_sel
  *         random_sample(size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_random_sample[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Defines the shape of the returned array of random floats. If None\n            (the default), returns a single float.\n\n        Returns\n        -------\n        out : float or ndarray of floats\n            Array of random floats of shape `size` (unless ``size=None``, in which\n            case a single float is returned).\n\n        Examples\n        --------\n        >>> np.random.random_sample()\n        0.47108547995356098\n        >>> type(np.random.random_sample())\n        <type 'float'>\n        >>> np.random.random_sample((5,))\n        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n\n        Three-by-two array of random numbers from [-5, 0):\n\n        >>> 5 * np.random.random_sample((3, 2)) - 5\n        array([[-3.99149989, -0.52338984],\n               [-2.99091858, -0.79479508],\n               [-1.23204345, -1.75224494]])\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_8random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_8random_sample[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Defines the shape of the returned array of random floats. If None\n            (the default), returns a single float.\n\n        Returns\n        -------\n        out : float or ndarray of floats\n            Array of random floats of shape `size` (unless ``size=None``, in which\n            case a single float is returned).\n\n        Examples\n        --------\n        >>> np.random.random_sample()\n        0.47108547995356098\n        >>> type(np.random.random_sample())\n        <type 'float'>\n        >>> np.random.random_sample((5,))\n        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n\n        Three-by-two array of random numbers from [-5, 0):\n\n        >>> 5 * np.random.random_sample((3, 2)) - 5\n        array([[-3.99149989, -0.52338984],\n               [-2.99091858, -0.79479508],\n               [-1.23204345, -1.75224494]])\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_8random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_size = 0;
   PyObject *__pyx_r = NULL;
   PyObject *__pyx_t_1 = NULL;
@@ -5595,13 +5676,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_
     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  0:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
+        if (value) { values[0] = value; kw_args--; }
       }
     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "random_sample") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "random_sample") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
     }
     __pyx_v_size = values[0];
   } else {
@@ -5614,13 +5695,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("random_sample", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 718; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("random_sample", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 719; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.random_sample");
+  __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
 
-  /* "mtrand.pyx":759
+  /* "mtrand.pyx":760
  * 
  *         """
  *         return cont0_array(self.internal_state, rk_double, size)             # <<<<<<<<<<<<<<
@@ -5628,7 +5710,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_
  *     def tomaxint(self, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_double, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 759; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_double, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 760; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
@@ -5646,7 +5728,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_
   return __pyx_r;
 }
 
-/* "mtrand.pyx":761
+/* "mtrand.pyx":762
  *         return cont0_array(self.internal_state, rk_double, size)
  * 
  *     def tomaxint(self, size=None):             # <<<<<<<<<<<<<<
@@ -5654,9 +5736,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_
  *         tomaxint(size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_tomaxint[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_9tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_9tomaxint[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_9tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_size = 0;
   PyObject *__pyx_r = NULL;
   PyObject *__pyx_t_1 = NULL;
@@ -5673,13 +5755,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self,
     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  0:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
+        if (value) { values[0] = value; kw_args--; }
       }
     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "tomaxint") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 761; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "tomaxint") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 762; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
     }
     __pyx_v_size = values[0];
   } else {
@@ -5692,13 +5774,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self,
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("tomaxint", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 761; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("tomaxint", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 762; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.tomaxint");
+  __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
 
-  /* "mtrand.pyx":806
+  /* "mtrand.pyx":807
  * 
  *         """
  *         return disc0_array(self.internal_state, rk_long, size)             # <<<<<<<<<<<<<<
@@ -5706,7 +5789,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self,
  *     def randint(self, low, high=None, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_f_6mtrand_disc0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_long, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 806; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = __pyx_f_6mtrand_disc0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_long, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 807; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
@@ -5724,7 +5807,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self,
   return __pyx_r;
 }
 
-/* "mtrand.pyx":808
+/* "mtrand.pyx":809
  *         return disc0_array(self.internal_state, rk_long, size)
  * 
  *     def randint(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
@@ -5732,9 +5815,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self,
  *         randint(low, high=None, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_randint[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n        then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is\n            returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_10randint[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n        then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is\n            returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_low = 0;
   PyObject *__pyx_v_high = 0;
   PyObject *__pyx_v_size = 0;
@@ -5772,18 +5855,18 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randint(PyObject *__pyx_v_self,
       if (likely(values[0])) kw_args--;
       else goto __pyx_L5_argtuple_error;
       case  1:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__high);
-        if (unlikely(value)) { values[1] = value; kw_args--; }
+        if (value) { values[1] = value; kw_args--; }
       }
       case  2:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[2] = value; kw_args--; }
+        if (value) { values[2] = value; kw_args--; }
       }
     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "randint") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 808; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "randint") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 809; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-static char __pyx_doc_6mtrand_11RandomState_uniform[] = "\n""        uniform(low=0.0, high=1.0, size=1)\n""\n""        Draw samples from a uniform distribution.\n""\n""        Samples are uniformly distributed over the half-open interval\n""        ``[low, high)`` (includes low, but excludes high).  In other words,\n""        any value within the given interval is equally likely to be drawn\n""        by `uniform`.\n""\n""        Parameters\n""        ----------\n""        low : float, optional\n""            Lower boundary of the output interval.  All values generated will be\n""            greater than or equal to low.  The default value is 0.\n""        high : float\n""            Upper boundary of the output interval.  All values generated will be\n""            less than high.  The default value is 1.0.\n""        size : int or tuple of ints, optional\n""            Shape of output.  If the given size is, for example, (m,n,k),\n""            m*n*k samples are generated.  If no shape is specified, a single sample\n""            is returned.\n""\n""        Returns\n""        -------\n""        out : ndarray\n""            Drawn samples, with shape `size`.\n""\n""        See Also\n""        --------\n""        randint : Discrete uniform distribution, yielding integers.\n""        random_integers : Discrete uniform distribution over the closed\n""                          interval ``[low, high]``.\n""        random_sample : Floats uniformly distributed over ``[0, 1)``.\n""        random : Alias for `random_sample`.\n""        rand : Convenience function that accepts dimensions as input, e.g.,\n""               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n""               uniformly distributed over ``[0, 1)``.\n""\n""        Notes\n""        -----\n""        The probability density function of the uniform distribution is\n""\n""        .. math:: p(x) = \\frac{1}{b - a}\n""\n""        anywhere within the interval ``[a, b)``, and zero elsewhere.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> s = np.random.uniform(-1,0,1000)\n""\n""        All values are 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 PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_12uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_12uniform[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Shape of output.  If the given size is, for example, (m,n,k),\n            m*n*k samples are generated.  If no shape is specified, a single sample\n            is returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are w""ithin the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_12uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -6156,8 +6227,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self,
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+  __Pyx_RaiseArgtupleInvalid("uniform", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 916; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  /* "mtrand.pyx":989
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+  /* "mtrand.pyx":991
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-  /* "mtrand.pyx":991
+  /* "mtrand.pyx":992
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-    /* "mtrand.pyx":992
+    /* "mtrand.pyx":993
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@@ -6253,7 +6321,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self,
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+    __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_uniform, __pyx_v_size, __pyx_v_flow, (__pyx_v_fhigh - __pyx_v_flow)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *         PyErr_Clear()             # <<<<<<<<<<<<<<
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  *         olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
  *         ohigh = <ndarray>PyArray_FROM_OTF(high, NPY_DOUBLE, NPY_ALIGNED)
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+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_low, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 995; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":997
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@@ -6336,21 +6404,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self,
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-  __pyx_t_4 = PyArray_EnsureArray(__pyx_v_temp); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 999; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_4 = PyArray_EnsureArray(__pyx_v_temp); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1000; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-/* "mtrand.pyx":1002
+/* "mtrand.pyx":1003
  *         return cont2_array(self.internal_state, rk_uniform, size, olow, odiff)
  * 
  *     def rand(self, *args):             # <<<<<<<<<<<<<<
@@ -6394,9 +6458,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self,
  *         rand(d0, d1, ..., dn)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\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        `random`.\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 PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_13rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_13rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\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        `random`.\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 PyObject *__pyx_pf_6mtrand_11RandomState_13rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_args = 0;
   PyObject *__pyx_r = NULL;
   Py_ssize_t __pyx_t_1;
@@ -6408,20 +6472,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
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-  __Pyx_INCREF((PyObject *)__pyx_v_self);
 
-  /* "mtrand.pyx":1040
+  /* "mtrand.pyx":1041
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  *             return self.random_sample()
  *         else:
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+  if (unlikely(__pyx_v_args == Py_None)) {
+    PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1041; __pyx_clineno = __LINE__; goto __pyx_L1_error;} 
+  }
+  __pyx_t_1 = PyTuple_GET_SIZE(((PyObject *)__pyx_v_args)); 
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-    /* "mtrand.pyx":1041
+    /* "mtrand.pyx":1042
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  *             return self.random_sample()             # <<<<<<<<<<<<<<
@@ -6429,9 +6495,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
  *             return self.random_sample(size=args)
  */
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+    __pyx_t_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__random_sample); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1042; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1042; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6441,7 +6507,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
   }
   /*else*/ {
 
-    /* "mtrand.pyx":1043
+    /* "mtrand.pyx":1044
  *             return self.random_sample()
  *         else:
  *             return self.random_sample(size=args)             # <<<<<<<<<<<<<<
@@ -6449,12 +6515,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
  *     def randn(self, *args):
  */
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+    __pyx_t_4 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__random_sample); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_3 = PyDict_New(); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    if (PyDict_SetItem(__pyx_t_3, ((PyObject *)__pyx_n_s__size), ((PyObject *)__pyx_v_args)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_5 = PyEval_CallObjectWithKeywords(__pyx_t_4, ((PyObject *)__pyx_empty_tuple), ((PyObject *)__pyx_t_3)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6474,13 +6540,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
   __pyx_r = NULL;
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   __Pyx_DECREF(__pyx_v_args);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
   __Pyx_XGIVEREF(__pyx_r);
   __Pyx_RefNannyFinishContext();
   return __pyx_r;
 }
 
-/* "mtrand.pyx":1045
+/* "mtrand.pyx":1046
  *             return self.random_sample(size=args)
  * 
  *     def randn(self, *args):             # <<<<<<<<<<<<<<
@@ -6488,9 +6553,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rand(PyObject *__pyx_v_self, PyO
  *         randn([d1, ..., dn])
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_randn[] = "\n        randn([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 ``(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        d1, ..., dn : `n` ints, optional\n            The dimensions of the returned array, should be all positive.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(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 PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_14randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_14randn[] = "\n        randn([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 ``(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        d1, ..., dn : `n` ints, optional\n            The dimensions of the returned array, should be all positive.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(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 PyObject *__pyx_pf_6mtrand_11RandomState_14randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -6502,20 +6567,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, Py
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-  /* "mtrand.pyx":1096
+  /* "mtrand.pyx":1097
  * 
  *         """
  *         if len(args) == 0:             # <<<<<<<<<<<<<<
  *             return self.standard_normal()
  *         else:
  */
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+  if (unlikely(__pyx_v_args == Py_None)) {
+    PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1097; __pyx_clineno = __LINE__; goto __pyx_L1_error;} 
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-    /* "mtrand.pyx":1097
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  *         if len(args) == 0:
  *             return self.standard_normal()             # <<<<<<<<<<<<<<
@@ -6523,9 +6590,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, Py
  *             return self.standard_normal(args)
  */
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+    __pyx_t_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1098; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1098; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6535,7 +6602,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, Py
   }
   /*else*/ {
 
-    /* "mtrand.pyx":1099
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  *             return self.standard_normal()
  *         else:
  *             return self.standard_normal(args)             # <<<<<<<<<<<<<<
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+    __pyx_t_4 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1100; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+    __Pyx_INCREF(((PyObject *)__pyx_v_args));
+    PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_args));
+    __Pyx_GIVEREF(((PyObject *)__pyx_v_args));
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-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0;
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@@ -6570,13 +6637,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, Py
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-/* "mtrand.pyx":1101
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  *             return self.standard_normal(args)
  * 
  *     def random_integers(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
@@ -6584,9 +6650,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_randn(PyObject *__pyx_v_self, Py
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-static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_random_integers[] = "\n""        random_integers(low, high=None, size=None)\n""\n""        Return random integers between `low` and `high`, inclusive.\n""\n""        Return random integers from the \"discrete uniform\" distribution in the\n""        closed interval [`low`, `high`].  If `high` is None (the default),\n""        then results are from [1, `low`].\n""\n""        Parameters\n""        ----------\n""        low : int\n""            Lowest (signed) integer to be drawn from the distribution (unless\n""            ``high=None``, in which case this parameter is the *highest* such\n""            integer).\n""        high : int, optional\n""            If provided, the largest (signed) integer to be drawn from the\n""            distribution (see above for behavior if ``high=None``).\n""        size : int or tuple of ints, optional\n""            Output shape. Default is None, in which case a single int is returned.\n""\n""        Returns\n""        -------\n""        out : int or ndarray of ints\n""            `size`-shaped array of random integers from the appropriate\n""            distribution, or a single such random int if `size` not provided.\n""\n""        See Also\n""        --------\n""        random.randint : Similar to `random_integers`, only for the half-open\n""            interval [`low`, `high`), and 0 is the lowest value if `high` is\n""            omitted.\n""\n""        Notes\n""        -----\n""        To sample from N evenly spaced floating-point numbers between a and b,\n""        use::\n""\n""          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n""\n""        Examples\n""        --------\n""        >>> np.random.random_integers(5)\n""        4\n""        >>> type(np.random.random_integers(5))\n""        <type 'int'>\n""        >>> np.random.random_integers(5, size=(3.,2.))\n""        array([[5, 4],\n""               [3, 3],\n""               [4, 5]])\n""\n""        Choose five random numbers from the set of five evenly-spaced\n""        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n""        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n""        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n""        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n""\n""        Roll two six sided dice 1000 times and sum the results:\n""\n""        >>> d1 = np.random.random_integers(1, 6, 1000)\n""        >>> d2 = np.random.random_integers(1, 6, 1000)\n""        >>> dsums = d1 + d2\n""\n""        Display results as a histogram:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_15random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_15random_integers[] = "\n        random_integers(low, high=None, size=None)\n\n        Return random integers between `low` and `high`, inclusive.\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        closed interval [`low`, `high`].  If `high` is None (the default),\n        then results are from [1, `low`].\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >>> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_15random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  __Pyx_INCREF(__pyx_v_size);
 
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  *         """
  *         if high is None:             # <<<<<<<<<<<<<<
@@ -6664,7 +6729,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
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   if (__pyx_t_1) {
 
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@@ -6675,7 +6740,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
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-    /* "mtrand.pyx":1175
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  *             high = low
  *             low = 1             # <<<<<<<<<<<<<<
@@ -6689,7 +6754,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
   }
   __pyx_L6:;
 
-  /* "mtrand.pyx":1176
+  /* "mtrand.pyx":1177
  *             high = low
  *             low = 1
  *         return self.randint(low, high+1, size)             # <<<<<<<<<<<<<<
@@ -6697,12 +6762,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
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+  __pyx_t_2 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__randint); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_3 = PyNumber_Add(__pyx_v_high, __pyx_int_1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1176; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyNumber_Add(__pyx_v_high, __pyx_int_1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1176; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_4);
+  __pyx_t_4 = PyTuple_New(3); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
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   PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_low);
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+  __pyx_t_3 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
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@@ -6729,16 +6794,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
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-/* "mtrand.pyx":1179
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  *     # Complicated, continuous distributions:
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@@ -6746,9 +6809,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_random_integers(PyObject *__pyx_
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-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_16standard_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_16standard_normal[] = "\n        standard_normal(size=None)\n\n        Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        >>> s = np.random.standard_normal(8000)\n        >>> s\n        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random\n               -0.38672696, -0.4685006 ])                               #random\n        >>> s.shape\n        (8000,)\n        >>> s = np.random.standard_normal(size=(3, 4, 2))\n        >>> s.shape\n        (3, 4, 2)\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_16standard_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  0:
-      if (kw_args > 1) {
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         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
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@@ -6784,13 +6847,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_normal(PyObject *__pyx_
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("standard_normal", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1179; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("standard_normal", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1180; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
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-  /* "mtrand.pyx":1209
+  /* "mtrand.pyx":1210
  * 
  *         """
  *         return cont0_array(self.internal_state, rk_gauss, size)             # <<<<<<<<<<<<<<
@@ -6798,7 +6862,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_normal(PyObject *__pyx_
  *     def normal(self, loc=0.0, scale=1.0, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_gauss, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1209; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_gauss, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1210; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -6816,7 +6880,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_normal(PyObject *__pyx_
   return __pyx_r;
 }
 
-/* "mtrand.pyx":1211
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  *     def normal(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
@@ -6824,9 +6888,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_normal(PyObject *__pyx_
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-static PyObject *__pyx_pf_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_normal[] = "\n""        normal(loc=0.0, scale=1.0, size=None)\n""\n""        Draw random samples from a normal (Gaussian) distribution.\n""\n""        The probability density function of the normal distribution, first\n""        derived by De Moivre and 200 years later by both Gauss and Laplace\n""        independently [2]_, is often called the bell curve because of\n""        its characteristic shape (see the example below).\n""\n""        The normal distributions occurs often in nature.  For example, it\n""        describes the commonly occurring distribution of samples influenced\n""        by a large number of tiny, random disturbances, each with its own\n""        unique distribution [2]_.\n""\n""        Parameters\n""        ----------\n""        loc : float\n""            Mean (\"centre\") of the distribution.\n""        scale : float\n""            Standard deviation (spread or \"width\") of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.norm : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Gaussian distribution is\n""\n""        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n""                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n""\n""        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n""        The square of the standard deviation, :math:`\\sigma^2`, is called the\n""        variance.\n""\n""        The function has its peak at the mean, and its \"spread\" increases with\n""        the standard deviation (the function reaches 0.607 times its maximum at\n""        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n""        `numpy.random.normal` is more likely to return samples lying close to the\n""        mean, rather than those far away.\n""\n""        References\n""        ----------\n""        .. [1] Wikipedia, \"Normal distribution\",\n""               http://en.wikipedia.org/wiki/Normal_distribution\n""        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n""               Variables and Random Signal Principles\", 4th ed., 2001,\n""               pp. 51, 51, 125.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n""        >>> s = np.random.normal(mu, sigma, 1000)\n""\n""        Verify the mean and the variance:\n""\n""        >>> abs(mu - np.mean(s)) < 0.01\n""        True\n""\n""        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n""        True\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n""        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n""        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n""        ...          linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_17normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_17normal[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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   __pyx_t_1 = (!PyErr_Occurred());
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-    /* "mtrand.pyx":1354
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-    __pyx_t_1 = (__pyx_v_fa <= 0);
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-      /* "mtrand.pyx":1355
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1355; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1356
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  *                 raise ValueError("b <= 0")
  *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
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-    __pyx_t_1 = (__pyx_v_fb <= 0);
+    __pyx_t_1 = (__pyx_v_fb <= 0.0);
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-      /* "mtrand.pyx":1357
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1357; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":1358
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-  /* "mtrand.pyx":1362
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 }
 
-/* "mtrand.pyx":1370
+/* "mtrand.pyx":1371
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  * 
  *     def exponential(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
@@ -7520,9 +7534,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_beta(PyObject *__pyx_v_self, PyO
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-static char __pyx_doc_6mtrand_11RandomState_exponential[] = "\n        exponential(scale=1.0, size=None)\n\n        Exponential distribution.\n\n        Its probability density function is\n\n        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n        The rate parameter is an alternative, widely used parameterization\n        of the exponential distribution [3]_.\n\n        The exponential distribution is a continuous analogue of the\n        geometric distribution.  It describes many common situations, such as\n        the size of raindrops measured over many rainstorms [1]_, or the time\n        between page requests to Wikipedia [2]_.\n\n        Parameters\n        ----------\n        scale : float\n            The scale parameter, :math:`\\beta = 1/\\lambda`.\n        size : tuple of ints\n            Number of samples to draw.  The output is shaped\n            according to `size`.\n\n        References\n        ----------\n        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n               Random Signal Principles\", 4th ed, 2001, p. 57.\n        .. [2] \"Poisson Process\", Wikipedia,\n               http://en.wikipedia.org/wiki/Poisson_process\n        .. [3] \"Exponential Distribution, Wikipedia,\n               http://en.wikipedia.org/wiki/Exponential_distribution\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_19exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_19exponential[] = "\n        exponential(scale=1.0, size=None)\n\n        Exponential distribution.\n\n        Its probability density function is\n\n        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n        The rate parameter is an alternative, widely used parameterization\n        of the exponential distribution [3]_.\n\n        The exponential distribution is a continuous analogue of the\n        geometric distribution.  It describes many common situations, such as\n        the size of raindrops measured over many rainstorms [1]_, or the time\n        between page requests to Wikipedia [2]_.\n\n        Parameters\n        ----------\n        scale : float\n            The scale parameter, :math:`\\beta = 1/\\lambda`.\n        size : tuple of ints\n            Number of samples to draw.  The output is shaped\n            according to `size`.\n\n        References\n        ----------\n        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n               Random Signal Principles\", 4th ed, 2001, p. 57.\n        .. [2] \"Poisson Process\", Wikipedia,\n               http://en.wikipedia.org/wiki/Poisson_process\n        .. [3] \"Exponential Distribution, Wikipedia,\n               http://en.wikipedia.org/wiki/Exponential_distribution\n\n        ";
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+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1421; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1422; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":1422
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  *         return cont1_array(self.internal_state, rk_exponential, size, oscale)             # <<<<<<<<<<<<<<
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
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   __Pyx_DECREF((PyObject *)__pyx_v_oscale);
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-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_20standard_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_20standard_exponential[] = "\n        standard_exponential(size=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        Output a 3x8000 array:\n\n        >>> n = np.random.standard_exponential((3, 8000))\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_20standard_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -7802,13 +7799,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_exponential(PyObject *_
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     switch (PyTuple_GET_SIZE(__pyx_args)) {
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-      if (kw_args > 1) {
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+  __Pyx_RaiseArgtupleInvalid("standard_exponential", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1425; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -7835,7 +7833,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_exponential(PyObject *_
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 }
 
-/* "mtrand.pyx":1452
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-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_21standard_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_21standard_gamma[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_21standard_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -7892,13 +7890,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_gamma(PyObject *__pyx_v
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+  __Pyx_RaiseArgtupleInvalid("standard_gamma", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1453; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-static char __pyx_doc_6mtrand_11RandomState_gamma[] = "\n""        gamma(shape, scale=1.0, size=None)\n""\n""        Draw samples from a Gamma distribution.\n""\n""        Samples are drawn from a Gamma distribution with specified parameters,\n""        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n""        \"theta\"), where both parameters are > 0.\n""\n""        Parameters\n""        ----------\n""        shape : scalar > 0\n""            The shape of the gamma distribution.\n""        scale : scalar > 0, optional\n""            The scale of the gamma distribution.  Default is equal to 1.\n""        size : shape_tuple, optional\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        out : ndarray, float\n""            Returns one sample unless `size` parameter is specified.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.gamma : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Gamma distribution is\n""\n""        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n""\n""        where :math:`k` is the shape and :math:`\\theta` the scale,\n""        and :math:`\\Gamma` is the Gamma function.\n""\n""        The Gamma distribution is often used to model the times to failure of\n""        electronic components, and arises naturally in processes for which the\n""        waiting times between Poisson distributed events are relevant.\n""\n""        References\n""        ----------\n""        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n""               Wolfram Web Resource.\n""               http://mathworld.wolfram.com/GammaDistribution.html\n""        .. [2] Wikipedia, \"Gamma-distribution\",\n""               http://en.wikipedia.org/wiki/Gamma-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> shape, scale = 2., 2. # mean and dispersion\n""        >>> s = np.random.gamma(shape, scale, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> import scipy.special as sps\n""        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n""        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n""        ...                      (sps.gamma(shape)*scale**shape))\n""        >>> plt.plot(bins, y, linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_22gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_22gamma[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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-static PyObject *__pyx_pf_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+static char __pyx_doc_6mtrand_11RandomState_23f[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
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-    /* "mtrand.pyx":1725
+    /* "mtrand.pyx":1726
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_44), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1726; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1726; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":1726
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  *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)             # <<<<<<<<<<<<<<
@@ -8883,10 +8806,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObje
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-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_r = __pyx_t_4;
-  __pyx_t_4 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_f, __pyx_v_size, __pyx_v_odfnum, __pyx_v_odfden); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1727; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  * 
  *     def noncentral_f(self, dfnum, dfden, nonc, size=None):             # <<<<<<<<<<<<<<
@@ -8918,9 +8837,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObje
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-static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_24noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+static PyObject *__pyx_pf_6mtrand_11RandomState_24noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_nonc = 0;
@@ -8960,22 +8879,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_s
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+        __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+        __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
       case  3:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
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-        if (unlikely(value)) { values[3] = value; kw_args--; }
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+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -8996,21 +8915,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_s
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+  __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.noncentral_f");
+  __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
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-  __Pyx_INCREF(__pyx_v_dfnum);
-  __Pyx_INCREF(__pyx_v_dfden);
-  __Pyx_INCREF(__pyx_v_nonc);
-  __Pyx_INCREF(__pyx_v_size);
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-  /* "mtrand.pyx":1795
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@@ -9019,7 +8934,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_s
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-  /* "mtrand.pyx":1796
+  /* "mtrand.pyx":1797
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-  /* "mtrand.pyx":1797
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@@ -9037,7 +8952,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_s
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-  /* "mtrand.pyx":1798
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-    /* "mtrand.pyx":1799
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  *             if fdfden <= 0:
  */
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+    __pyx_t_1 = (__pyx_v_fdfnum <= 1.0);
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-      /* "mtrand.pyx":1800
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+  /* "mtrand.pyx":1822
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  * 
  *     def chisquare(self, df, size=None):
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+  __pyx_t_2 = __pyx_f_6mtrand_cont3_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_odfnum, __pyx_v_odfden, __pyx_v_ononc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1821; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_DECREF(__pyx_v_dfnum);
-  __Pyx_DECREF(__pyx_v_dfden);
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-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":1823
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  *     def chisquare(self, df, size=None):             # <<<<<<<<<<<<<<
@@ -9470,9 +9344,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_f(PyObject *__pyx_v_s
  *         chisquare(df, size=None)
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-static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_25chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_25chisquare[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : tuple of ints, int, optional\n             Size of the returned array.  By default, a scalar is\n             returned.\n\n        Returns\n        -------\n        output : ndarray\n            Samples drawn from the distribution, packed in a `size`-shaped\n            array.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        `NIST/SEMATECH e-Handbook of Statistical Methods\n        <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_25chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1891; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
+  PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2);
+  __Pyx_GIVEREF(__pyx_t_2);
+  __pyx_t_2 = 0;
+  __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
   __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0;
+  __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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-    /* "mtrand.pyx":1898
+    /* "mtrand.pyx":1899
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-    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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-    __Pyx_Raise(__pyx_t_5, 0, 0);
-    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1898; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_55), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1899; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1899; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     goto __pyx_L8;
   }
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-  /* "mtrand.pyx":1899
+  /* "mtrand.pyx":1900
  *         if np.any(np.less_equal(odf, 0.0)):
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  *         return cont1_array(self.internal_state, rk_chisquare, size, odf)             # <<<<<<<<<<<<<<
@@ -9697,10 +9557,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self
  *     def noncentral_chisquare(self, df, nonc, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_odf); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
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   __pyx_r = Py_None; __Pyx_INCREF(Py_None);
@@ -9714,15 +9574,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self
   __pyx_r = NULL;
   __pyx_L0:;
   __Pyx_DECREF((PyObject *)__pyx_v_odf);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_df);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":1901
+/* "mtrand.pyx":1902
  *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
  * 
  *     def noncentral_chisquare(self, df, nonc, size=None):             # <<<<<<<<<<<<<<
@@ -9730,9 +9587,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self
  *         noncentral_chisquare(df, nonc, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_noncentral_chisquare[] = "\n""        noncentral_chisquare(df, nonc, size=None)\n""\n""        Draw samples from a noncentral chi-square distribution.\n""\n""        The noncentral :math:`\\chi^2` distribution is a generalisation of\n""        the :math:`\\chi^2` distribution.\n""\n""        Parameters\n""        ----------\n""        df : int\n""            Degrees of freedom, should be >= 1.\n""        nonc : float\n""            Non-centrality, should be > 0.\n""        size : int or tuple of ints\n""            Shape of the output.\n""\n""        Notes\n""        -----\n""        The probability density function for the noncentral Chi-square distribution\n""        is\n""\n""        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n""                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n""\n""        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n""\n""        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n""        bombing and coverage problems, the probability of killing the point target\n""        given by the noncentral chi-squared distribution.\n""\n""        References\n""        ----------\n""        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n""               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n""               Number 1 / December, 1970.\n""        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n""               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n""\n""        Examples\n""        --------\n""        Draw values from the distribution and plot the histogram\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n""        ...                   bins=200, normed=True)\n""        >>> plt.show()\n""\n""        Draw values from a noncentral chisquare with very small noncentrality,\n""        and compare to a chisquare.\n""\n""        >>> plt.figure()\n""        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n""        ...                   bins=np.arange(0., 25, .1), normed=True)\n""        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n""        ...                    bins=np.arange(0., 25, .1), normed=True)\n""        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n""        >>> plt.show()\n""\n""        Demonstrate how large values of non-centrality lead to a more symmetric\n""        distribution.\n""\n""        >>> plt.figure()\n""        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n""        ...                   bins=200, normed=True)\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_26noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_26noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_26noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_nonc = 0;
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@@ -9768,16 +9625,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
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       else {
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+        __Pyx_RaiseArgtupleInvalid("noncentral_chisquare", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1902; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
       case  2:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[2] = value; kw_args--; }
+        if (value) { values[2] = value; kw_args--; }
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-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "noncentral_chisquare") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1901; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "noncentral_chisquare") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1902; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -9796,19 +9653,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
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+  __Pyx_RaiseArgtupleInvalid("noncentral_chisquare", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1902; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
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+  __Pyx_RefNannyFinishContext();
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-  __Pyx_INCREF((PyObject *)__pyx_v_self);
-  __Pyx_INCREF(__pyx_v_df);
-  __Pyx_INCREF(__pyx_v_nonc);
-  __Pyx_INCREF(__pyx_v_size);
   __pyx_v_odf = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
   __pyx_v_ononc = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
 
-  /* "mtrand.pyx":1972
+  /* "mtrand.pyx":1973
  *         cdef ndarray odf, ononc
  *         cdef double fdf, fnonc
  *         fdf = PyFloat_AsDouble(df)             # <<<<<<<<<<<<<<
@@ -9817,7 +9671,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
  */
   __pyx_v_fdf = PyFloat_AsDouble(__pyx_v_df);
 
-  /* "mtrand.pyx":1973
+  /* "mtrand.pyx":1974
  *         cdef double fdf, fnonc
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  *         fnonc = PyFloat_AsDouble(nonc)             # <<<<<<<<<<<<<<
@@ -9826,7 +9680,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
  */
   __pyx_v_fnonc = PyFloat_AsDouble(__pyx_v_nonc);
 
-  /* "mtrand.pyx":1974
+  /* "mtrand.pyx":1975
  *         fdf = PyFloat_AsDouble(df)
  *         fnonc = PyFloat_AsDouble(nonc)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -9836,71 +9690,59 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
   __pyx_t_1 = (!PyErr_Occurred());
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":1975
+    /* "mtrand.pyx":1976
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  *         if not PyErr_Occurred():
  *             if fdf <= 1:             # <<<<<<<<<<<<<<
  *                 raise ValueError("df <= 0")
  *             if fnonc <= 0:
  */
-    __pyx_t_1 = (__pyx_v_fdf <= 1);
+    __pyx_t_1 = (__pyx_v_fdf <= 1.0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":1976
+      /* "mtrand.pyx":1977
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  *             if fdf <= 1:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")
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-      __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1976; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_56), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1977; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
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-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      __Pyx_Raise(__pyx_t_3, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1976; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1977; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L7;
     }
     __pyx_L7:;
 
-    /* "mtrand.pyx":1977
+    /* "mtrand.pyx":1978
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  *             if fnonc <= 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("nonc <= 0")
  *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
  */
-    __pyx_t_1 = (__pyx_v_fnonc <= 0);
+    __pyx_t_1 = (__pyx_v_fnonc <= 0.0);
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-      /* "mtrand.pyx":1978
+      /* "mtrand.pyx":1979
  *                 raise ValueError("df <= 0")
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
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-      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1978; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      __Pyx_GOTREF(__pyx_t_3);
-      __Pyx_INCREF(((PyObject *)__pyx_kp_s_20));
-      PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_kp_s_20));
-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_20));
-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1978; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_58), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1979; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1978; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1979; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L8;
     }
     __pyx_L8:;
 
-    /* "mtrand.pyx":1979
+    /* "mtrand.pyx":1980
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  *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,             # <<<<<<<<<<<<<<
@@ -9909,14 +9751,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
  */
     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":1980
+    /* "mtrand.pyx":1981
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  * 
  *         PyErr_Clear()
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+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1990; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     goto __pyx_L10;
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   __pyx_L10:;
 
-  /* "mtrand.pyx":1990
+  /* "mtrand.pyx":1991
  *         if np.any(np.less_equal(ononc, 0.0)):
  *             raise ValueError("nonc < 0")
  *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,             # <<<<<<<<<<<<<<
@@ -10103,17 +9933,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
  */
   __Pyx_XDECREF(__pyx_r);
 
-  /* "mtrand.pyx":1991
+  /* "mtrand.pyx":1992
  *             raise ValueError("nonc < 0")
  *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
  *             odf, ononc)             # <<<<<<<<<<<<<<
  * 
  *     def standard_cauchy(self, size=None):
  */
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-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_r = __pyx_t_4;
-  __pyx_t_4 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_noncentral_chisquare, __pyx_v_size, __pyx_v_odf, __pyx_v_ononc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
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   __pyx_r = Py_None; __Pyx_INCREF(Py_None);
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   __pyx_L0:;
   __Pyx_DECREF((PyObject *)__pyx_v_odf);
   __Pyx_DECREF((PyObject *)__pyx_v_ononc);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_df);
-  __Pyx_DECREF(__pyx_v_nonc);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":1993
+/* "mtrand.pyx":1994
  *             odf, ononc)
  * 
  *     def standard_cauchy(self, size=None):             # <<<<<<<<<<<<<<
@@ -10145,9 +9971,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *_
  *         standard_cauchy(size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_standard_cauchy[] = "\n""        standard_cauchy(size=None)\n""\n""        Standard Cauchy distribution with mode = 0.\n""\n""        Also known as the Lorentz distribution.\n""\n""        Parameters\n""        ----------\n""        size : int or tuple of ints\n""            Shape of the output.\n""\n""        Returns\n""        -------\n""        samples : ndarray or scalar\n""            The drawn samples.\n""\n""        Notes\n""        -----\n""        The probability density function for the full Cauchy distribution is\n""\n""        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n""                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n""\n""        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n""        :math:`\\gamma=1`\n""\n""        The Cauchy distribution arises in the solution to the driven harmonic\n""        oscillator problem, and also describes spectral line broadening. It\n""        also describes the distribution of values at which a line tilted at\n""        a random angle will cut the x axis.\n""\n""        When studying hypothesis tests that assume normality, seeing how the\n""        tests perform on data from a Cauchy distribution is a good indicator of\n""        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n""        very much like a Gaussian distribution, but with heavier tails.\n""\n""        References\n""        ----------\n""        ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n""              Distribution\",\n""              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n""        ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n""              Wolfram Web Resource.\n""              http://mathworld.wolfram.com/CauchyDistribution.html\n""        ..[3] Wikipedia, \"Cauchy distribution\"\n""              http://en.wikipedia.org/wiki/Cauchy_distribution\n""\n""        Examples\n""        --------\n""        Draw samples and plot the distribution:\n""\n""        >>> s = np.random.standard_cauchy(1000000)\n""        >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well\n""        >>> plt.hist(s, bins=100)\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_27standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_27standard_cauchy[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        ..[3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<25)""]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_27standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_r = NULL;
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@@ -10164,13 +9990,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_
     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  0:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
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@@ -10183,13 +10009,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("standard_cauchy", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("standard_cauchy", 0, 0, 1, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1994; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
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-  /* "mtrand.pyx":2052
+  /* "mtrand.pyx":2053
  * 
  *         """
  *         return cont0_array(self.internal_state, rk_standard_cauchy, size)             # <<<<<<<<<<<<<<
@@ -10197,7 +10024,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_
  *     def standard_t(self, df, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_standard_cauchy, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2052; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_standard_cauchy, __pyx_v_size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2053; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_1);
   __pyx_r = __pyx_t_1;
   __pyx_t_1 = 0;
@@ -10215,7 +10042,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_
   return __pyx_r;
 }
 
-/* "mtrand.pyx":2054
+/* "mtrand.pyx":2055
  *         return cont0_array(self.internal_state, rk_standard_cauchy, size)
  * 
  *     def standard_t(self, df, size=None):             # <<<<<<<<<<<<<<
@@ -10223,9 +10050,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_cauchy(PyObject *__pyx_
  *         standard_t(df, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_standard_t(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_standard_t[] = "\n""        standard_t(df, size=None)\n""\n""        Standard Student's t distribution with df degrees of freedom.\n""\n""        A special case of the hyperbolic distribution.\n""        As `df` gets large, the result resembles that of the standard normal\n""        distribution (`standard_normal`).\n""\n""        Parameters\n""        ----------\n""        df : int\n""            Degrees of freedom, should be > 0.\n""        size : int or tuple of ints, optional\n""            Output shape. Default is None, in which case a single value is\n""            returned.\n""\n""        Returns\n""        -------\n""        samples : ndarray or scalar\n""            Drawn samples.\n""\n""        Notes\n""        -----\n""        The probability density function for the t distribution is\n""\n""        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n""                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n""\n""        The t test is based on an assumption that the data come from a Normal\n""        distribution. The t test provides a way to test whether the sample mean\n""        (that is the mean calculated from the data) is a good estimate of the true\n""        mean.\n""\n""        The derivation of the t-distribution was forst published in 1908 by William\n""        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n""        issues, he had to publish under a pseudonym, and so he used the name\n""        Student.\n""\n""        References\n""        ----------\n""        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n""               Springer, 2002.\n""        .. [2] Wikipedia, \"Student's t-distribution\"\n""               http://en.wikipedia.org/wiki/Student's_t-distribution\n""\n""        Examples\n""        --------\n""        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n""        women in Kj is:\n""\n""        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n""        ...                    7515, 8230, 8770])\n""\n""        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 PyObject *__pyx_pf_6mtrand_11RandomState_standard_t(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_28standard_t(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_28standard_t[] = "\n        standard_t(df, size=None)\n\n        Standard Student's t distribution with df degrees of freedom.\n\n        A special case of the hyperbolic distribution.\n        As `df` gets large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be > 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a Normal\n        distribution. The t test provides a way to test whether the sample mean\n        (that is the mean calculated from the data) is a good estimate of the true\n        mean.\n\n        The derivation of the t-distribution was forst published in 1908 by William\n        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n        issues, he had to publish under a pseudonym, and so he used the name\n        Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               http://en.wikipedia.org/wiki/Student's_t-distribution\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in Kj is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Doe""s their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, normed=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_28standard_t(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_size = 0;
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@@ -10254,13 +10081,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_t(PyObject *__pyx_v_sel
       if (likely(values[0])) kw_args--;
       else goto __pyx_L5_argtuple_error;
       case  1:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[1] = value; kw_args--; }
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+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "standard_t") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2055; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
     }
     __pyx_v_df = values[0];
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@@ -10275,17 +10102,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_t(PyObject *__pyx_v_sel
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
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+  __Pyx_RaiseArgtupleInvalid("standard_t", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2055; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.standard_t");
+  __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
-  __Pyx_INCREF((PyObject *)__pyx_v_self);
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-  /* "mtrand.pyx":2142
+  /* "mtrand.pyx":2143
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  * 
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   __pyx_v_fdf = PyFloat_AsDouble(__pyx_v_df);
 
-  /* "mtrand.pyx":2143
+  /* "mtrand.pyx":2144
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  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -10304,39 +10129,33 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_standard_t(PyObject *__pyx_v_sel
   __pyx_t_1 = (!PyErr_Occurred());
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":2144
+    /* "mtrand.pyx":2145
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  *         if not PyErr_Occurred():
  *             if fdf <= 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("df <= 0")
  *             return cont1_array_sc(self.internal_state, rk_standard_t, size, fdf)
  */
-    __pyx_t_1 = (__pyx_v_fdf <= 0);
+    __pyx_t_1 = (__pyx_v_fdf <= 0.0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":2145
+      /* "mtrand.pyx":2146
  *         if not PyErr_Occurred():
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-static char __pyx_doc_6mtrand_11RandomState_vonmises[] = "\n""        vonmises(mu, kappa, size=None)\n""\n""        Draw samples from a von Mises distribution.\n""\n""        Samples are drawn from a von Mises distribution with specified mode\n""        (mu) and dispersion (kappa), on the interval [-pi, pi].\n""\n""        The von Mises distribution (also known as the circular normal\n""        distribution) is a continuous probability distribution on the unit\n""        circle.  It may be thought of as the circular analogue of the normal\n""        distribution.\n""\n""        Parameters\n""        ----------\n""        mu : float\n""            Mode (\"center\") of the distribution.\n""        kappa : float\n""            Dispersion of the distribution, has to be >=0.\n""        size : int or tuple of int\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : scalar or ndarray\n""            The returned samples, which are in the interval [-pi, pi].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.vonmises : probability density function,\n""            distribution, or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the von Mises distribution is\n""\n""        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n""\n""        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n""        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n""\n""        The von Mises is named for Richard Edler von Mises, who was born in\n""        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n""        States in 1939 and became a professor at Harvard.  He worked in\n""        probability theory, aerodynamics, fluid mechanics, and philosophy of\n""        science.\n""\n""        References\n""        ----------\n""        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n""        Functions*, New York: Dover, 1965.\n""\n""        von Mises, R., *Mathematical Theory of Probability and Statistics*,\n""        New York: Academic Press, 1964.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n""        >>> s = np.random.vonmises(mu, kappa, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> import scipy.special as sps\n""        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n""        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n""        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n""        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
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+static char __pyx_doc_6mtrand_11RandomState_29vonmises[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_29vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -10521,16 +10331,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self,
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+        __Pyx_RaiseArgtupleInvalid("vonmises", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2156; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RaiseArgtupleInvalid("vonmises", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2156; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -10570,7 +10377,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self,
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  */
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+  __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0;
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+    /* "mtrand.pyx":2247
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2246; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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-  /* "mtrand.pyx":2247
+  /* "mtrand.pyx":2248
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  *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)             # <<<<<<<<<<<<<<
@@ -10749,10 +10544,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self,
  *     def pareto(self, a, size=None):
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
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+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
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-  __Pyx_DECREF(__pyx_v_mu);
-  __Pyx_DECREF(__pyx_v_kappa);
-  __Pyx_DECREF(__pyx_v_size);
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  *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
  * 
  *     def pareto(self, a, size=None):             # <<<<<<<<<<<<<<
@@ -10784,9 +10575,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self,
  *         pareto(a, size=None)
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-static PyObject *__pyx_pf_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_pareto[] = "\n""        pareto(a, size=None)\n""\n""        Draw samples from a Pareto II or Lomax distribution with specified shape.\n""\n""        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n""        classical Pareto distribution can be obtained from the Lomax distribution\n""        by adding the location parameter m, see below. The smallest value of the\n""        Lomax distribution is zero while for the classical Pareto distribution it\n""        is m, where the standard Pareto distribution has location m=1.\n""        Lomax can also be considered as a simplified version of the Generalized\n""        Pareto distribution (available in SciPy), with the scale set to one and\n""        the location set to zero.\n""\n""        The Pareto distribution must be greater than zero, and is unbounded above.\n""        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n""        the weights are in the lowest 20 percent of the range, while the other 20\n""        percent fill the remaining 80 percent of the range.\n""\n""        Parameters\n""        ----------\n""        shape : float, > 0.\n""            Shape of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.lomax.pdf : probability density function,\n""            distribution or cumulative density function, etc.\n""        scipy.stats.distributions.genpareto.pdf : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Pareto distribution is\n""\n""        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n""\n""        where :math:`a` is the shape and :math:`m` the location\n""\n""        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n""        is a power law probability distribution useful in many real world problems.\n""        Outside the field of economics it is generally referred to as the Bradford\n""        distribution. Pareto developed the distribution to describe the\n""        distribution of wealth in an economy.  It has also found use in insurance,\n""        web page access statistics, oil field sizes, and many other problems,\n""        including the download frequency for projects in Sourceforge [1].  It is\n""        one of the so-called \"fat-tailed\" distributions.\n""\n""\n""        References\n""        ----------\n""        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n""               Sourceforge projects.\n""        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n""        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n""               Values, Birkhauser Verlag, Basel, pp 23-30.\n""        .. [4] Wikipedia, \"Pareto distribution\",\n""               http://en.wikipedia.org/wiki/Pareto_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> a, m = 3., 1. # shape and mode\n""        >>> s = np.random.pareto(a, 1000) + m\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n""        >>> fit = a*m**a/bins**(a+1)\n""        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_30pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_30pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_30pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+  __Pyx_RaiseArgtupleInvalid("pareto", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2250; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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  */
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2335; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-static char __pyx_doc_6mtrand_11RandomState_weibull[] = "\n""        weibull(a, size=None)\n""\n""        Weibull distribution.\n""\n""        Draw samples from a 1-parameter Weibull distribution with the given\n""        shape parameter `a`.\n""\n""        .. math:: X = (-ln(U))^{1/a}\n""\n""        Here, U is drawn from the uniform distribution over (0,1].\n""\n""        The more common 2-parameter Weibull, including a scale parameter\n""        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n""\n""        Parameters\n""        ----------\n""        a : float\n""            Shape of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.weibull : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        gumbel, scipy.stats.distributions.genextreme\n""\n""        Notes\n""        -----\n""        The Weibull (or Type III asymptotic extreme value distribution for smallest\n""        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n""        Generalized Extreme Value (GEV) distributions used in modeling extreme\n""        value problems.  This class includes the Gumbel and Frechet distributions.\n""\n""        The probability density for the Weibull distribution is\n""\n""        .. math:: p(x) = \\frac{a}\n""                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n""\n""        where :math:`a` is the shape and :math:`\\lambda` the scale.\n""\n""        The function has its peak (the mode) at\n""        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n""\n""        When ``a = 1``, the Weibull distribution reduces to the exponential\n""        distribution.\n""\n""        References\n""        ----------\n""        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n""               1939 \"A Statistical Theory Of The Strength Of Materials\",\n""               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n""               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n""        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n""               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n""        .. [3] Wikipedia, \"Weibull distribution\",\n""               http://en.wikipedia.org/wiki/Weibull_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> a = 5. # shape\n""        >>> s = np.random.weibull(a, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> x = np.arange(1,100.)/50.\n""        >>> def weib(x,n,a):\n""        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n""\n""        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n""        >>> x = np.arange(1,100.)/50.\n""        >>> scale = count.max()/weib(x, 1., 5.).max()\n""        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n""        >>> plt.show()\n""\n""        ";
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+static char __pyx_doc_6mtrand_11RandomState_31weibull[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
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+static char __pyx_doc_6mtrand_11RandomState_32power[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
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+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2552; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     goto __pyx_L8;
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-  /* "mtrand.pyx":2552
+  /* "mtrand.pyx":2553
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  *         return cont1_array(self.internal_state, rk_power, size, oa)             # <<<<<<<<<<<<<<
@@ -11531,10 +11274,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_power(PyObject *__pyx_v_self, Py
  *     def laplace(self, loc=0.0, scale=1.0, size=None):
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_power, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; __Pyx_INCREF(Py_None);
@@ -11548,15 +11291,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_power(PyObject *__pyx_v_self, Py
   __pyx_r = NULL;
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   __Pyx_DECREF((PyObject *)__pyx_v_oa);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_a);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":2554
+/* "mtrand.pyx":2555
  *         return cont1_array(self.internal_state, rk_power, size, oa)
  * 
  *     def laplace(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
@@ -11564,9 +11304,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_power(PyObject *__pyx_v_self, Py
  *         laplace(loc=0.0, scale=1.0, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_laplace[] = "\n""        laplace(loc=0.0, scale=1.0, size=None)\n""\n""        Draw samples from the Laplace or double exponential distribution with\n""        specified location (or mean) and scale (decay).\n""\n""        The Laplace distribution is similar to the Gaussian/normal distribution,\n""        but is sharper at the peak and has fatter tails. It represents the\n""        difference between two independent, identically distributed exponential\n""        random variables.\n""\n""        Parameters\n""        ----------\n""        loc : float\n""            The position, :math:`\\mu`, of the distribution peak.\n""        scale : float\n""            :math:`\\lambda`, the exponential decay.\n""\n""        Notes\n""        -----\n""        It has the probability density function\n""\n""        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n""                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n""\n""        The first law of Laplace, from 1774, states that the frequency of an error\n""        can be expressed as an exponential function of the absolute magnitude of\n""        the error, which leads to the Laplace distribution. For many problems in\n""        Economics and Health sciences, this distribution seems to model the data\n""        better than the standard Gaussian distribution\n""\n""\n""        References\n""        ----------\n""        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n""               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n""               printing.  New York: Dover, 1972.\n""\n""        .. [2] The Laplace distribution and generalizations\n""               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n""               Birkhauser, 2001.\n""\n""        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n""               From MathWorld--A Wolfram Web Resource.\n""               http://mathworld.wolfram.com/LaplaceDistribution.html\n""\n""        .. [4] Wikipedia, \"Laplace distribution\",\n""               http://en.wikipedia.org/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 PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_33laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_33laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))\n        >>> plt.plot(x,g)\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_33laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_loc = 0;
   PyObject *__pyx_v_scale = 0;
   PyObject *__pyx_v_size = 0;
@@ -11585,8 +11325,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
   if (unlikely(__pyx_kwds)) {
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     PyObject* values[3] = {0,0,0};
-    values[0] = __pyx_k_23;
-    values[1] = __pyx_k_24;
+    values[0] = __pyx_k_73;
+    values[1] = __pyx_k_74;
     values[2] = ((PyObject *)Py_None);
     switch (PyTuple_GET_SIZE(__pyx_args)) {
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@@ -11597,30 +11337,30 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  0:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__loc);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
+        if (value) { values[0] = value; kw_args--; }
       }
       case  1:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__scale);
-        if (unlikely(value)) { values[1] = value; kw_args--; }
+        if (value) { values[1] = value; kw_args--; }
       }
       case  2:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[2] = value; kw_args--; }
+        if (value) { values[2] = value; kw_args--; }
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     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "laplace") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2554; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "laplace") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2555; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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     __pyx_v_size = values[2];
   } else {
-    __pyx_v_loc = __pyx_k_23;
-    __pyx_v_scale = __pyx_k_24;
+    __pyx_v_loc = __pyx_k_73;
+    __pyx_v_scale = __pyx_k_74;
     __pyx_v_size = ((PyObject *)Py_None);
     switch (PyTuple_GET_SIZE(__pyx_args)) {
       case  3: __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
@@ -11632,19 +11372,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("laplace", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2554; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("laplace", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2555; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.laplace");
+  __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
-  __Pyx_INCREF((PyObject *)__pyx_v_self);
-  __Pyx_INCREF(__pyx_v_loc);
-  __Pyx_INCREF(__pyx_v_scale);
-  __Pyx_INCREF(__pyx_v_size);
   __pyx_v_oloc = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
   __pyx_v_oscale = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
 
-  /* "mtrand.pyx":2630
+  /* "mtrand.pyx":2631
  *         cdef double floc, fscale
  * 
  *         floc = PyFloat_AsDouble(loc)             # <<<<<<<<<<<<<<
@@ -11653,7 +11390,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
  */
   __pyx_v_floc = PyFloat_AsDouble(__pyx_v_loc);
 
-  /* "mtrand.pyx":2631
+  /* "mtrand.pyx":2632
  * 
  *         floc = PyFloat_AsDouble(loc)
  *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
@@ -11662,7 +11399,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
  */
   __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
 
-  /* "mtrand.pyx":2632
+  /* "mtrand.pyx":2633
  *         floc = PyFloat_AsDouble(loc)
  *         fscale = PyFloat_AsDouble(scale)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -11672,39 +11409,33 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
   __pyx_t_1 = (!PyErr_Occurred());
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":2633
+    /* "mtrand.pyx":2634
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  *         if not PyErr_Occurred():
  *             if fscale <= 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("scale <= 0")
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
  */
-    __pyx_t_1 = (__pyx_v_fscale <= 0);
+    __pyx_t_1 = (__pyx_v_fscale <= 0.0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":2634
+      /* "mtrand.pyx":2635
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
  * 
  */
-      __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2634; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_75), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
-      __Pyx_INCREF(((PyObject *)__pyx_kp_s_9));
-      PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_kp_s_9));
-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_9));
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      __Pyx_Raise(__pyx_t_3, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2634; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L7;
     }
     __pyx_L7:;
 
-    /* "mtrand.pyx":2635
+    /* "mtrand.pyx":2636
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)             # <<<<<<<<<<<<<<
@@ -11712,16 +11443,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
  *         PyErr_Clear()
  */
     __Pyx_XDECREF(__pyx_r);
-    __pyx_t_3 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_laplace, __pyx_v_size, __pyx_v_floc, __pyx_v_fscale); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2635; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_r = __pyx_t_3;
-    __pyx_t_3 = 0;
+    __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_laplace, __pyx_v_size, __pyx_v_floc, __pyx_v_fscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2636; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_r = __pyx_t_2;
+    __pyx_t_2 = 0;
     goto __pyx_L0;
     goto __pyx_L6;
   }
   __pyx_L6:;
 
-  /* "mtrand.pyx":2637
+  /* "mtrand.pyx":2638
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  * 
  *         PyErr_Clear()             # <<<<<<<<<<<<<<
@@ -11730,101 +11461,95 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
  */
   PyErr_Clear();
 
-  /* "mtrand.pyx":2638
+  /* "mtrand.pyx":2639
  * 
  *         PyErr_Clear()
  *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
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-  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_loc, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_loc, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2639; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2639; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(((PyObject *)__pyx_v_oloc));
-  __pyx_v_oloc = ((PyArrayObject *)__pyx_t_3);
-  __pyx_t_3 = 0;
+  __pyx_v_oloc = ((PyArrayObject *)__pyx_t_2);
+  __pyx_t_2 = 0;
 
-  /* "mtrand.pyx":2639
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  *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
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  *             raise ValueError("scale <= 0")
  */
-  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2639; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  if (!(likely(((__pyx_t_3) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_3, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2639; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2640; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2640; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_DECREF(((PyObject *)__pyx_v_oscale));
-  __pyx_v_oscale = ((PyArrayObject *)__pyx_t_3);
-  __pyx_t_3 = 0;
+  __pyx_v_oscale = ((PyArrayObject *)__pyx_t_2);
+  __pyx_t_2 = 0;
 
-  /* "mtrand.pyx":2640
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  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
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@@ -11867,9 +11588,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self,
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-static PyObject *__pyx_pf_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_gumbel[] = "\n""        gumbel(loc=0.0, scale=1.0, size=None)\n""\n""        Gumbel distribution.\n""\n""        Draw samples from a Gumbel distribution with specified location and scale.\n""        For more information on the Gumbel distribution, see Notes and References\n""        below.\n""\n""        Parameters\n""        ----------\n""        loc : float\n""            The location of the mode of the distribution.\n""        scale : float\n""            The scale parameter of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        out : ndarray\n""            The samples\n""\n""        See Also\n""        --------\n""        scipy.stats.gumbel_l\n""        scipy.stats.gumbel_r\n""        scipy.stats.genextreme\n""            probability density function, distribution, or cumulative density\n""            function, etc. for each of the above\n""        weibull\n""\n""        Notes\n""        -----\n""        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n""        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n""        distributions used in modeling extreme value problems.  The Gumbel is a\n""        special case of the Extreme Value Type I distribution for maximums from\n""        distributions with \"exponential-like\" tails.\n""\n""        The probability density for the Gumbel distribution is\n""\n""        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n""                  \\beta}},\n""\n""        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n""        the scale parameter.\n""\n""        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n""        very early in the hydrology literature, for modeling the occurrence of\n""        flood events. It is also used for modeling maximum wind speed and rainfall\n""        rates.  It is a \"fat-tailed\" distribution - the probability of an event in\n""        the tail of the distribution is larger than if one used a Gaussian, hence\n""        the surprisingly frequent occurrence of 100-year floods. Floods were\n""        initially modeled as a Gaussian process, which underestimated the frequency\n""        of extreme events.\n""\n""\n""        It is one of a class of extreme value distributions, the Generalized\n""        Extreme Value (GEV) distributions, which also includes the Weibull and\n""        Frechet.\n""\n""        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n""        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n""\n""        References\n""        ----------\n""        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n""        Press, 1958.\n""\n""        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n""        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n""        2001.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, beta = 0, 0.1 # location and scale\n""        >>> s = np.random.gumbel(mu, beta, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n""        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n""        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n""        ...          linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        Show how an extreme value distribution can arise from a Gaussian process\n""        and compare to a Gaussian:\n""\n""        >>> means = []\n""        >>> maxima = []\n""        >>> for i in range(0,1000) :\n""        ...    a = np.random.normal(mu, beta, 1000)\n""        ...    means.append(a.mean())\n""        ...    maxima.append(a.max())\n""        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n""        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)\n""        >>> mu = np.mean(maxima) - 0.57721*beta\n""        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n""        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n""        ...          linewidth=2, color='r')\n""        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n""        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n""        ...          linewidth=2, color='g')\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_34gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_34gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_34gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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     }
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+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_80), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2773; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2773; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2773
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_gumbel, __pyx_v_size, __pyx_v_oloc, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2774; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
+  __pyx_t_2 = 0;
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   __pyx_r = Py_None; __Pyx_INCREF(Py_None);
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-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_loc);
-  __Pyx_DECREF(__pyx_v_scale);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":2775
+/* "mtrand.pyx":2776
  *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
  * 
  *     def logistic(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
@@ -12170,9 +11872,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, P
  *         logistic(loc=0.0, scale=1.0, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_logistic[] = "\n""        logistic(loc=0.0, scale=1.0, size=None)\n""\n""        Draw samples from a Logistic distribution.\n""\n""        Samples are drawn from a Logistic distribution with specified\n""        parameters, loc (location or mean, also median), and scale (>0).\n""\n""        Parameters\n""        ----------\n""        loc : float\n""\n""        scale : float > 0.\n""\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.logistic : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Logistic distribution is\n""\n""        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n""\n""        where :math:`\\mu` = location and :math:`s` = scale.\n""\n""        The Logistic distribution is used in Extreme Value problems where it\n""        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n""        the World Chess Federation (FIDE) where it is used in the Elo ranking\n""        system, assuming the performance of each player is a logistically\n""        distributed random variable.\n""\n""        References\n""        ----------\n""        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n""               Values, from Insurance, Finance, Hydrology and Other Fields,\n""               Birkhauser Verlag, Basel, pp 132-133.\n""        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n""               MathWorld--A Wolfram Web Resource.\n""               http://mathworld.wolfram.com/LogisticDistribution.html\n""        .. [3] Wikipedia, \"Logistic-distribution\",\n""               http://en.wikipedia.org/wiki/Logistic-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> loc, scale = 10, 1\n""        >>> s = np.random.logistic(loc, scale, 10000)\n""        >>> count, bins, ignored = plt.hist(s, bins=50)\n""\n""        #   plot against distribution\n""\n""        >>> def logist(x, loc, scale):\n""        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n""        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n""        ... logist(bins, loc, scale).max())\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_35logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_35logistic[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_35logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_size = 0;
@@ -12191,8 +11893,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self,
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@@ -12203,30 +11905,30 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self,
     }
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         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__loc);
-        if (unlikely(value)) { values[0] = value; kw_args--; }
+        if (value) { values[0] = value; kw_args--; }
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       case  1:
-      if (kw_args > 1) {
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-        if (unlikely(value)) { values[1] = value; kw_args--; }
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       case  2:
-      if (kw_args > 1) {
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-        if (unlikely(value)) { values[2] = value; kw_args--; }
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+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "logistic") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2776; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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   }
   goto __pyx_L4_argument_unpacking_done;
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+  __Pyx_RaiseArgtupleInvalid("logistic", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2776; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  __Pyx_INCREF(__pyx_v_scale);
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-  /* "mtrand.pyx":2849
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@@ -12259,7 +11958,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self,
  */
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-  /* "mtrand.pyx":2850
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-  /* "mtrand.pyx":2851
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   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":2852
+    /* "mtrand.pyx":2853
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  */
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-      /* "mtrand.pyx":2853
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2853; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2854; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -12318,16 +12011,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self,
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+  __Pyx_GOTREF(__pyx_t_2);
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-static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_lognormal[] = "\n""        lognormal(mean=0.0, sigma=1.0, size=None)\n""\n""        Return samples drawn from a log-normal distribution.\n""\n""        Draw samples from a log-normal distribution with specified mean, standard\n""        deviation, and shape. Note that the mean and standard deviation are not the\n""        values for the distribution itself, but of the underlying normal\n""        distribution it is derived from.\n""\n""\n""        Parameters\n""        ----------\n""        mean : float\n""            Mean value of the underlying normal distribution\n""        sigma : float, >0.\n""            Standard deviation of the underlying normal distribution\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.lognorm : probability density function, distribution,\n""            cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        A variable `x` has a log-normal distribution if `log(x)` is normally\n""        distributed.\n""\n""        The probability density function for the log-normal distribution is\n""\n""        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n""                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n""\n""        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n""        of the normally distributed logarithm of the variable.\n""\n""        A log-normal distribution results if a random variable is the *product* of\n""        a large number of independent, identically-distributed variables in the\n""        same way that a normal distribution results if the variable is the *sum*\n""        of a large number of independent, identically-distributed variables\n""        (see the last example). It is one of the so-called \"fat-tailed\"\n""        distributions.\n""\n""        The log-normal distribution is commonly used to model the lifespan of units\n""        with fatigue-stress failure modes. Since this includes\n""        most mechanical systems, the log-normal distribution has widespread\n""        application.\n""\n""        It is also commonly used to model oil field sizes, species abundance, and\n""        latent periods of infectious diseases.\n""\n""        References\n""        ----------\n""        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n""               Distributions across the Sciences: Keys and Clues\", May 2001\n""               Vol. 51 No. 5 BioScience\n""               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n""        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n""               Values, Birkhauser Verlag, Basel, pp 31-32.\n""        .. [3] Wikipedia, \"Lognormal distribution\",\n""               http://en.wikipedia.org/wiki/Lognormal_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, sigma = 3., 1. # mean and standard deviation\n""        >>> s = np.random.lognormal(mu, sigma, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n""\n""        >>> x = np.linspace(min(bins), max(bins), 10000)\n""        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n""        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n""\n""        >>> plt.plot(x, pdf, linewidth=2, color='r')\n""        >>> plt.axis('tight')\n""        >>> plt.show()\n""\n""        Demonstrate that taking the products of random samples from a uniform\n""        distribution can be fit well by a log-normal probability density function.\n""\n""        >>> # Generate a thousand samples: each is the product of 100 random\n""        >>> # values, drawn from a normal distribution.\n""        >>> b = []\n""        >>> for i in range(1000):\n""        ...    a = 10. + np.random.random(100)\n""        ...    b.append(np.product(a))\n""\n""        >>> b = np.array(b) / np.min(b) # scale values to be positive\n""\n""        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n""\n""        >>> sigma = np.std(np.log(b))\n""        >>> mu = np.mean(np.log(b))\n""\n""        >>> x = np.linspace(min(bins), max(bins), 10000)\n""        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n""        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n""\n""        >>> plt.plot(x, pdf, color='r', linewidth=2)\n""        >>> plt.show()\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_36lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_36lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean, standard\n        deviation, and shape. Note that the mean and standard deviation are not the\n        values for the distribution itself, but of the underlying normal\n        distribution it is derived from.\n\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, >0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.\n\n        The probability density function for the log-normal distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n        of the normally distributed logarithm of the variable.\n\n        A log-normal distribution results if a random variable is the *product* of\n        a large number of independent, identically-distributed variables in the\n        same way that a normal distribution results if the variable is the *sum*\n        of a large number of independent, identically-distributed variables\n        (see the last example). It is one of the so-called \"fat-tailed\"\n        distributions.\n\n        The log-normal distribution is commonly used to model the lifespan of units\n        with fatigue-stress failure modes. Since thi""s includes\n        most mechanical systems, the log-normal distribution has widespread\n        application.\n\n        It is also commonly used to model oil field sizes, species abundance, and\n        latent periods of infectious diseases.\n\n        References\n        ----------\n        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n               Distributions across the Sciences: Keys and Clues\", May 2001\n               Vol. 51 No. 5 BioScience\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 31-32.\n        .. [3] Wikipedia, \"Lognormal distribution\",\n               http://en.wikipedia.org/wiki/Lognormal_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
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@@ -12506,30 +12189,30 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self
     }
     switch (PyTuple_GET_SIZE(__pyx_args)) {
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-      if (kw_args > 1) {
+      if (kw_args > 0) {
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-        if (unlikely(value)) { values[0] = value; kw_args--; }
+        if (value) { values[0] = value; kw_args--; }
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       case  1:
-      if (kw_args > 1) {
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-        if (unlikely(value)) { values[1] = value; kw_args--; }
+        if (value) { values[1] = value; kw_args--; }
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-      if (kw_args > 1) {
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-    __pyx_v_sigma = __pyx_k_30;
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+  __Pyx_RaiseArgtupleInvalid("lognormal", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2864; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2991; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_90), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2992; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2992; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":2992
+  /* "mtrand.pyx":2993
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  *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)             # <<<<<<<<<<<<<<
@@ -12741,10 +12409,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self
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-  __Pyx_GOTREF(__pyx_t_5);
-  __pyx_r = __pyx_t_5;
-  __pyx_t_5 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_lognormal, __pyx_v_size, __pyx_v_omean, __pyx_v_osigma); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
+  __pyx_r = __pyx_t_2;
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-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_mean);
-  __Pyx_DECREF(__pyx_v_sigma);
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 }
 
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  *     def rayleigh(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
@@ -12776,9 +12440,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self
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-static PyObject *__pyx_pf_6mtrand_11RandomState_rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_37rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_37rayleigh[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        ..[2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_37rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -12794,7 +12458,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rayleigh(PyObject *__pyx_v_self,
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@@ -12804,23 +12468,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rayleigh(PyObject *__pyx_v_self,
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+        if (value) { values[0] = value; kw_args--; }
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       case  1:
-      if (kw_args > 1) {
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+  __Pyx_RaiseArgtupleInvalid("rayleigh", 0, 0, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2995; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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   __pyx_t_1 = (!PyErr_Occurred());
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":3055
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  */
-    __pyx_t_1 = (__pyx_v_fscale <= 0);
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-      /* "mtrand.pyx":3056
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  * 
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3056; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3057; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -12900,16 +12556,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_rayleigh(PyObject *__pyx_v_self,
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-  __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3)));
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+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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-  /* "mtrand.pyx":3064
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-static PyObject *__pyx_pf_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_38wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_38wald[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        ..[3] Wikipedia, \"Wald distributio""n\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
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+  __pyx_t_2 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0;
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-    /* "mtrand.pyx":3147
+    /* "mtrand.pyx":3148
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3147; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_100), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3148; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3148; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     goto __pyx_L9;
   }
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-  /* "mtrand.pyx":3148
+  /* "mtrand.pyx":3149
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  *         return cont2_array(self.internal_state, rk_wald, size, omean, oscale)             # <<<<<<<<<<<<<<
@@ -13402,10 +13022,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyO
  * 
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-  __Pyx_GOTREF(__pyx_t_4);
-  __pyx_r = __pyx_t_4;
-  __pyx_t_4 = 0;
+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_wald, __pyx_v_size, __pyx_v_omean, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_r = __pyx_t_2;
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@@ -13437,9 +13053,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyO
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-static PyObject *__pyx_pf_6mtrand_11RandomState_triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_39triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_39triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        ..[1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_39triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_mode = 0;
   PyObject *__pyx_v_right = 0;
@@ -13479,22 +13095,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_triangular(PyObject *__pyx_v_sel
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+        __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-        __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3152; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-      if (kw_args > 1) {
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-        if (unlikely(value)) { values[3] = value; kw_args--; }
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@@ -13515,21 +13131,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_triangular(PyObject *__pyx_v_sel
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+  __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  /* "mtrand.pyx":3212
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-  /* "mtrand.pyx":3213
+  /* "mtrand.pyx":3214
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  *         fleft = PyFloat_AsDouble(left)
  *         fright = PyFloat_AsDouble(right)             # <<<<<<<<<<<<<<
@@ -13547,7 +13159,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_triangular(PyObject *__pyx_v_sel
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-  /* "mtrand.pyx":3214
+  /* "mtrand.pyx":3215
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  *         fright = PyFloat_AsDouble(right)
  *         fmode = PyFloat_AsDouble(mode)             # <<<<<<<<<<<<<<
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-  /* "mtrand.pyx":3215
+  /* "mtrand.pyx":3216
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  *         fmode = PyFloat_AsDouble(mode)
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-    /* "mtrand.pyx":3216
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-      /* "mtrand.pyx":3217
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_102), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3217; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-/* "mtrand.pyx":3240
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-static char __pyx_doc_6mtrand_11RandomState_binomial[] = "\n""        binomial(n, p, size=None)\n""\n""        Draw samples from a binomial distribution.\n""\n""        Samples are drawn from a Binomial distribution with specified\n""        parameters, n trials and p probability of success where\n""        n an integer > 0 and p is in the interval [0,1]. (n may be\n""        input as a float, but it is truncated to an integer in use)\n""\n""        Parameters\n""        ----------\n""        n : float (but truncated to an integer)\n""                parameter, > 0.\n""        p : float\n""                parameter, >= 0 and <=1.\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.binom : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Binomial distribution is\n""\n""        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n""\n""        where :math:`n` is the number of trials, :math:`p` is the probability\n""        of success, and :math:`N` is the number of successes.\n""\n""        When estimating the standard error of a proportion in a population by\n""        using a random sample, the normal distribution works well unless the\n""        product p*n <=5, where p = population proportion estimate, and n =\n""        number of samples, in which case the binomial distribution is used\n""        instead. For example, a sample of 15 people shows 4 who are left\n""        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n""        so the binomial distribution should be used in this case.\n""\n""        References\n""        ----------\n""        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n""               Springer-Verlag, 2002.\n""        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n""               Fifth Edition, 2002.\n""        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n""               and Quigley, 1972.\n""        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n""               Wolfram Web Resource.\n""               http://mathworld.wolfram.com/BinomialDistribution.html\n""        .. [5] Wikipedia, \"Binomial-distribution\",\n""               http://en.wikipedia.org/wiki/Binomial_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> n, p = 10, .5 # number of trials, probability of each trial\n""        >>> s = np.random.binomial(n, p, 1000)\n""        # result of flipping a coin 10 times, tested 1000 times.\n""\n""        A real world example. A company drills 9 wild-cat oil exploration\n""        wells, each with an estimated probability of success of 0.1. All nine\n""        wells fail. What is the probability of that happening?\n""\n""        Let's do 20,000 trials of the model, and count the number that\n""        generate zero positive results.\n""\n""        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n""        answer = 0.38885, or 38%.\n""\n""        ";
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+static char __pyx_doc_6mtrand_11RandomState_40binomial[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer > 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, > 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n    ""    .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
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+        __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3241; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3241; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -14192,16 +13742,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self,
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-  /* "mtrand.pyx":3346
+  /* "mtrand.pyx":3347
  *         if np.any(np.greater(p, 1)):
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  *         return discnp_array(self.internal_state, rk_binomial, size, on, op)             # <<<<<<<<<<<<<<
@@ -14438,7 +13970,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self,
  *     def negative_binomial(self, n, p, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_3 = __pyx_f_6mtrand_discnp_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_binomial, __pyx_v_size, __pyx_v_on, __pyx_v_op); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3346; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __pyx_f_6mtrand_discnp_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_binomial, __pyx_v_size, __pyx_v_on, __pyx_v_op); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3347; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -14456,16 +13988,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self,
   __pyx_L0:;
   __Pyx_DECREF((PyObject *)__pyx_v_on);
   __Pyx_DECREF((PyObject *)__pyx_v_op);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_n);
-  __Pyx_DECREF(__pyx_v_p);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
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  * 
  *     def negative_binomial(self, n, p, size=None):             # <<<<<<<<<<<<<<
@@ -14473,9 +14001,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self,
  *         negative_binomial(n, p, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_negative_binomial[] = "\n""        negative_binomial(n, p, size=None)\n""\n""        Draw samples from a negative_binomial distribution.\n""\n""        Samples are drawn from a negative_Binomial distribution with specified\n""        parameters, `n` trials and `p` probability of success where `n` is an\n""        integer > 0 and `p` is in the interval [0, 1].\n""\n""        Parameters\n""        ----------\n""        n : int\n""            Parameter, > 0.\n""        p : float\n""            Parameter, >= 0 and <=1.\n""        size : int or tuple of ints\n""            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : int or ndarray of ints\n""            Drawn samples.\n""\n""        Notes\n""        -----\n""        The probability density for the Negative Binomial distribution is\n""\n""        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n""\n""        where :math:`n-1` is the number of successes, :math:`p` is the probability\n""        of success, and :math:`N+n-1` is the number of trials.\n""\n""        The negative binomial distribution gives the probability of n-1 successes\n""        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n""\n""        If one throws a die repeatedly until the third time a \"1\" appears, then the\n""        probability distribution of the number of non-\"1\"s that appear before the\n""        third \"1\" is a negative binomial distribution.\n""\n""        References\n""        ----------\n""        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n""               MathWorld--A Wolfram Web Resource.\n""               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n""        .. [2] Wikipedia, \"Negative binomial distribution\",\n""               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        A real world example. A company drills wild-cat oil exploration wells, each\n""        with an estimated probability of success of 0.1.  What is the probability\n""        of having one success for each successive well, that is what is the\n""        probability of a single success after drilling 5 wells, after 6 wells,\n""        etc.?\n""\n""        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n""        >>> for i in range(1, 11):\n""        ...    probability = sum(s<i) / 100000.\n""        ...    print i, \"wells drilled, probability of one success =\", probability\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_41negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_41negative_binomial[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_41negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -14511,16 +14039,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
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+        __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3349; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
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-        if (unlikely(value)) { values[2] = value; kw_args--; }
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@@ -14539,19 +14067,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
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+  __Pyx_RaiseArgtupleInvalid("negative_binomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3349; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  __Pyx_INCREF((PyObject *)__pyx_v_self);
-  __Pyx_INCREF(__pyx_v_n);
-  __Pyx_INCREF(__pyx_v_p);
-  __Pyx_INCREF(__pyx_v_size);
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   __pyx_v_op = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None);
 
-  /* "mtrand.pyx":3418
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@@ -14560,7 +14085,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
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-  /* "mtrand.pyx":3419
+  /* "mtrand.pyx":3420
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  *         fn = PyFloat_AsDouble(n)             # <<<<<<<<<<<<<<
@@ -14569,7 +14094,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
  */
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-  /* "mtrand.pyx":3420
+  /* "mtrand.pyx":3421
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  *         fn = PyFloat_AsDouble(n)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -14579,102 +14104,84 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
   __pyx_t_1 = (!PyErr_Occurred());
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-    /* "mtrand.pyx":3421
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  *                 raise ValueError("n <= 0")
  *             if fp < 0:
  */
-    __pyx_t_1 = (__pyx_v_fn <= 0);
+    __pyx_t_1 = (__pyx_v_fn <= 0.0);
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-      /* "mtrand.pyx":3422
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-      __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3422; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_119), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3423; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_kp_s_40));
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      __Pyx_Raise(__pyx_t_3, 0, 0);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3422; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3423; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L7;
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  *                 raise ValueError("p < 0")
  *             elif fp > 1:
  */
-    __pyx_t_1 = (__pyx_v_fp < 0);
+    __pyx_t_1 = (__pyx_v_fp < 0.0);
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-      /* "mtrand.pyx":3424
+      /* "mtrand.pyx":3425
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  *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
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-      __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3424; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __Pyx_INCREF(((PyObject *)__pyx_kp_s_41));
-      PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_kp_s_41));
-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_41));
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_120), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3425; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
-      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3424; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3425; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L8;
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-    /* "mtrand.pyx":3425
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  *             return discdd_array_sc(self.internal_state, rk_negative_binomial,
  */
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+    __pyx_t_1 = (__pyx_v_fp > 1.0);
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-      /* "mtrand.pyx":3426
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  *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_121), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_kp_s_42));
-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42));
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3426; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       goto __pyx_L8;
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-    /* "mtrand.pyx":3427
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@@ -14683,23 +14190,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
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     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":3428
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-    __Pyx_GOTREF(__pyx_t_3);
-    __pyx_r = __pyx_t_3;
-    __pyx_t_3 = 0;
+    __pyx_t_2 = __pyx_f_6mtrand_discdd_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_negative_binomial, __pyx_v_size, __pyx_v_fn, __pyx_v_fp); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3428; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_2);
+    __pyx_r = __pyx_t_2;
+    __pyx_t_2 = 0;
     goto __pyx_L0;
     goto __pyx_L6;
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   __pyx_L6:;
 
-  /* "mtrand.pyx":3430
+  /* "mtrand.pyx":3431
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  * 
  *         PyErr_Clear()             # <<<<<<<<<<<<<<
@@ -14708,227 +14215,209 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_negative_binomial(PyObject *__py
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   PyErr_Clear();
 
-  /* "mtrand.pyx":3432
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  *         on = <ndarray>PyArray_FROM_OTF(n, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
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-  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_n, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3432; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_3);
-  __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3)));
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_n, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3433; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_2);
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3439; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-static PyObject *__pyx_pf_6mtrand_11RandomState_poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_42poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_42poisson[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the Binomial\n        distribution for large N.\n\n        Parameters\n        ----------\n        lam : float\n            Expectation of interval, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Notes\n        -----\n        The Poisson distribution\n\n        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n        For events with an expected separation :math:`\\lambda` the Poisson\n        distribution :math:`f(k; \\lambda)` describes the probability of\n        :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n        Because the output is limited to the range of the C long type, a\n        ValueError is raised when `lam` is within 10 sigma of the maximum\n        representable value.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n               Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n        .. [2] Wikipedia, \"Poisson distribution\",\n           http://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> s = np.random.poisson(5, 10000)\n\n        Display histogram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n        >>> plt.show()\n\n        ";
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-static PyObject *__pyx_pf_6mtrand_11RandomState_zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_43zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_43zipf[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float > 1\n            Distribution parameter.\n        size : int or tuple of int, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn; a single integer is equivalent in\n            its result to providing a mono-tuple, i.e., a 1-D array of length\n            *size* is returned.  The default is None, in which case a single\n            scalar is returned.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples are greater than or equal to one.\n\n        See Also\n        --------\n        scipy.stats.distributions.zipf : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n        in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.zipf""(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        Truncate s values at 50 so plot is interesting\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a)/sps.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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-static PyObject *__pyx_pf_6mtrand_11RandomState_geometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_44geometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+static PyObject *__pyx_pf_6mtrand_11RandomState_44geometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3645; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "mtrand.pyx":3647
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-static char __pyx_doc_6mtrand_11RandomState_hypergeometric[] = "\n""        hypergeometric(ngood, nbad, nsample, size=None)\n""\n""        Draw samples from a Hypergeometric distribution.\n""\n""        Samples are drawn from a Hypergeometric distribution with specified\n""        parameters, ngood (ways to make a good selection), nbad (ways to make\n""        a bad selection), and nsample = number of items sampled, which is less\n""        than or equal to the sum ngood + nbad.\n""\n""        Parameters\n""        ----------\n""        ngood : float (but truncated to an integer)\n""                parameter, > 0.\n""        nbad  : float\n""                parameter, >= 0.\n""        nsample  : float\n""                   parameter, > 0 and <= ngood+nbad\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.hypergeom : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Hypergeometric distribution is\n""\n""        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n""\n""        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n""\n""        for P(x) the probability of x successes, n = ngood, m = nbad, and\n""        N = number of samples.\n""\n""        Consider an urn with black and white marbles in it, ngood of them\n""        black and nbad are white. If you draw nsample balls without\n""        replacement, then the Hypergeometric distribution describes the\n""        distribution of black balls in the drawn sample.\n""\n""        Note that this distribution is very similar to the Binomial\n""        distribution, except that in this case, samples are drawn without\n""        replacement, whereas in the Binomial case samples are drawn 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_45hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn wit""h\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_45hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -15904,22 +15418,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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+        __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3669; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -15940,21 +15454,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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+  __Pyx_RaiseArgtupleInvalid("hypergeometric", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3669; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  __Pyx_INCREF(__pyx_v_nbad);
-  __Pyx_INCREF(__pyx_v_nsample);
-  __Pyx_INCREF(__pyx_v_size);
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-  /* "mtrand.pyx":3747
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@@ -15963,7 +15473,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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-  /* "mtrand.pyx":3748
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  *         lnbad = PyInt_AsLong(nbad)             # <<<<<<<<<<<<<<
@@ -15972,7 +15482,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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  *         lnbad = PyInt_AsLong(nbad)
  *         lnsample = PyInt_AsLong(nsample)             # <<<<<<<<<<<<<<
@@ -15981,7 +15491,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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  *         lnsample = PyInt_AsLong(nsample)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -15991,150 +15501,126 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
   __pyx_t_1 = (!PyErr_Occurred());
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  *                 raise ValueError("ngood < 1")             # <<<<<<<<<<<<<<
  *             if nbad < 1:
  *                 raise ValueError("nbad < 1")
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_143), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3761; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __Pyx_GOTREF(__pyx_t_3);
+      __Pyx_Raise(__pyx_t_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3752; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *                 raise ValueError("ngood < 1")
  *             if nbad < 1:             # <<<<<<<<<<<<<<
  *                 raise ValueError("nbad < 1")
  *             if nsample < 1:
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-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+    __pyx_t_2 = PyObject_RichCompare(__pyx_v_nbad, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3762; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *             if nbad < 1:
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  *             if nsample < 1:
  *                 raise ValueError("nsample < 1")
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-      __Pyx_INCREF(((PyObject *)__pyx_kp_s_49));
-      PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_kp_s_49));
-      __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_49));
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-    /* "mtrand.pyx":3755
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  *             if nbad < 1:
  *                 raise ValueError("nbad < 1")
  *             if nsample < 1:             # <<<<<<<<<<<<<<
  *                 raise ValueError("nsample < 1")
  *             if ngood + nbad < nsample:
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+    __pyx_t_2 = PyObject_RichCompare(__pyx_v_nsample, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3764; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3764; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "mtrand.pyx":3756
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  *                 raise ValueError("nbad < 1")
  *             if nsample < 1:
  *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *             if ngood + nbad < nsample:
  *                 raise ValueError("ngood + nbad < nsample")
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_147), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3765; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3758; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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+  __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0;
+  __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3783; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_153), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __Pyx_GOTREF(__pyx_t_3);
+    __Pyx_Raise(__pyx_t_6, 0, 0);
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-    __Pyx_Raise(__pyx_t_3, 0, 0);
-    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3775; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     goto __pyx_L14;
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  *         return discnmN_array(self.internal_state, rk_hypergeometric, size,             # <<<<<<<<<<<<<<
@@ -16492,17 +15954,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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-  /* "mtrand.pyx":3777
+  /* "mtrand.pyx":3786
  *             raise ValueError("ngood + nbad < nsample")
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-  __Pyx_GOTREF(__pyx_t_3);
-  __pyx_r = __pyx_t_3;
-  __pyx_t_3 = 0;
+  __pyx_t_6 = __pyx_f_6mtrand_discnmN_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_hypergeometric, __pyx_v_size, __pyx_v_ongood, __pyx_v_onbad, __pyx_v_onsample); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_6);
+  __pyx_r = __pyx_t_6;
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   __pyx_r = Py_None; __Pyx_INCREF(Py_None);
@@ -16519,17 +15981,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
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-  __Pyx_DECREF(__pyx_v_nsample);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
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  *     def logseries(self, p, size=None):             # <<<<<<<<<<<<<<
@@ -16537,9 +15994,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v
  *         logseries(p, size=None)
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-static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_logseries[] = "\n""        logseries(p, size=None)\n""\n""        Draw samples from a Logarithmic Series distribution.\n""\n""        Samples are drawn from a Log Series distribution with specified\n""        parameter, p (probability, 0 < p < 1).\n""\n""        Parameters\n""        ----------\n""        loc : float\n""\n""        scale : float > 0.\n""\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.logser : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Log Series distribution is\n""\n""        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n""\n""        where p = probability.\n""\n""        The Log Series distribution is frequently used to represent species\n""        richness and occurrence, first proposed by Fisher, Corbet, and\n""        Williams in 1943 [2].  It may also be used to model the numbers of\n""        occupants seen in cars [3].\n""\n""        References\n""        ----------\n""        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n""               species diversity through the log series distribution of\n""               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n""               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n""        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n""               relation between the number of species and the number of\n""               individuals in a random sample of an animal population.\n""               Journal of Animal Ecology, 12:42-58.\n""        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n""               Data Sets, CRC Press, 1994.\n""        .. [4] Wikipedia, \"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 PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_46logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_46logseries[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Log""arithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n                     logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_46logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -16568,13 +16025,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self
       if (likely(values[0])) kw_args--;
       else goto __pyx_L5_argtuple_error;
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-        if (unlikely(value)) { values[1] = value; kw_args--; }
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@@ -16589,17 +16046,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self
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+  __Pyx_RaiseArgtupleInvalid("logseries", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3788; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-  /* "mtrand.pyx":3856
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@@ -16608,7 +16063,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self
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-  /* "mtrand.pyx":3857
+  /* "mtrand.pyx":3866
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  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
@@ -16618,7 +16073,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self
   __pyx_t_1 = (!PyErr_Occurred());
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":3858
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-      /* "mtrand.pyx":3859
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-      __Pyx_GOTREF(__pyx_t_3);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3859; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3861; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3862
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@@ -16690,7 +16133,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self
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-static char __pyx_doc_6mtrand_11RandomState_multivariate_normal[] = "\n""        multivariate_normal(mean, cov[, size])\n""\n""        Draw random samples from a multivariate normal distribution.\n""\n""        The multivariate normal, multinormal or Gaussian distribution is a\n""        generalization of the one-dimensional normal distribution to higher\n""        dimensions.  Such a distribution is specified by its mean and\n""        covariance matrix.  These parameters are analogous to the mean\n""        (average or \"center\") and variance (standard deviation, or \"width,\"\n""        squared) of the one-dimensional normal distribution.\n""\n""        Parameters\n""        ----------\n""        mean : 1-D array_like, of length N\n""            Mean of the N-dimensional distribution.\n""        cov : 2-D array_like, of shape (N, N)\n""            Covariance matrix of the distribution.  Must be symmetric and\n""            positive semi-definite for \"physically meaningful\" results.\n""        size : tuple of ints, optional\n""            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n""            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n""            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n""            If no shape is specified, a single (`N`-D) sample is returned.\n""\n""        Returns\n""        -------\n""        out : ndarray\n""            The drawn samples, of shape *size*, if that was provided.  If not,\n""            the shape is ``(N,)``.\n""\n""            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n""            value drawn from the distribution.\n""\n""        Notes\n""        -----\n""        The mean is a coordinate in N-dimensional space, which represents the\n""        location where samples are most likely to be generated.  This is\n""        analogous to the peak of the bell curve for the one-dimensional or\n""        univariate normal distribution.\n""\n""        Covariance indicates the level to which two variables vary together.\n""        From the multivariate normal distribution, we draw N-dimensional\n""        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n""        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n""        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n""        \"spread\").\n""\n""        Instead of specifying the full covariance matrix, popular\n""        approximations include:\n""\n""          - Spherical covariance (*cov* is a multiple of the identity matrix)\n""          - Diagonal covariance (*cov* has non-negative elements, and only on\n""            the diagonal)\n""\n""        This geometrical property can be seen in two dimensions by plotting\n""        generated data-points:\n""\n""        >>> mean = [0,0]\n""        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n""        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n""\n""        Note that the covariance matrix must be non-negative definite.\n""\n""        References\n""        ----------\n""        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n""        3rd ed., New York: McGraw-Hill, 1991.\n""\n""        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n""        2nd ed., New York: Wiley, 2001.\n""\n""        Examples\n""        --------\n""        >>> mean = (1,2)\n""        >>> cov = [[1,0],[1,0]]\n""        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n""        >>> x.shape\n""        (3, 3, 2)\n""\n""        The following is probably true, given that 0.6 is roughly twice the\n""        standard deviation:\n""\n""        >>> print list( (x[0,0,:] - mean) < 0.6 )\n""        [True, True]\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_47multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_47multivariate_normal[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution.  Must be symmetric and\n            positive semi-definite for \"physically meaningful\" results.\n        size : tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw ""N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (*cov* is a multiple of the identity matrix)\n          - Diagonal covariance (*cov* has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n        3rd ed., New York: McGraw-Hill, 1991.\n\n        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n        2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
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-static char __pyx_doc_6mtrand_11RandomState_multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_48multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_48multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_48multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   long __pyx_v_n;
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   PyObject *__pyx_v_size = 0;
@@ -17641,18 +17050,18 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
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-        __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4006; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
       case  2:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[2] = value; kw_args--; }
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+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "multinomial") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+    __pyx_v_n = __Pyx_PyInt_AsLong(values[0]); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -17662,51 +17071,49 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
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+      __pyx_v_n = __Pyx_PyInt_AsLong(PyTuple_GET_ITEM(__pyx_args, 0)); if (unlikely((__pyx_v_n == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RaiseArgtupleInvalid("multinomial", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RefNannyFinishContext();
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-  /* "mtrand.pyx":4065
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  *         cdef double Sum
  * 
  *         d = len(pvals)             # <<<<<<<<<<<<<<
  *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
  *         pix = <double*>parr.data
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-  __pyx_t_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4065; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4074; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":4066
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  *         pix = <double*>parr.data
  * 
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+  __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_pvals, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4075; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *         d = len(pvals)
  *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
  *         pix = <double*>parr.data             # <<<<<<<<<<<<<<
@@ -17715,39 +17122,33 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
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  * 
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  * 
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-  __pyx_t_3 = (__pyx_f_6mtrand_kahan_sum(__pyx_v_pix, (__pyx_v_d - 1)) > (1.0 + 9.9999999999999998e-13));
+  __pyx_t_3 = (__pyx_f_6mtrand_kahan_sum(__pyx_v_pix, (__pyx_v_d - 1)) > (1.0 + 1e-12));
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  *             raise ValueError("sum(pvals[:-1]) > 1.0")             # <<<<<<<<<<<<<<
  * 
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+    __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_168), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __Pyx_GOTREF(__pyx_t_4);
+    __Pyx_Raise(__pyx_t_2, 0, 0);
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-    __Pyx_Raise(__pyx_t_4, 0, 0);
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4070; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "mtrand.pyx":4072
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  *         if size is None:             # <<<<<<<<<<<<<<
@@ -17757,112 +17158,112 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
   __pyx_t_3 = (__pyx_v_size == Py_None);
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-    __Pyx_GIVEREF(__pyx_t_4);
-    __pyx_t_4 = 0;
-    __Pyx_DECREF(__pyx_v_shape);
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+    __pyx_t_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4086; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__zeros); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_4);
+  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
+  __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
   __Pyx_INCREF(__pyx_v_shape);
-  PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_v_shape);
+  PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape);
   __Pyx_GIVEREF(__pyx_v_shape);
-  __Pyx_INCREF(((PyObject *)((PyObject*)&PyInt_Type)));
-  PyTuple_SET_ITEM(__pyx_t_4, 1, ((PyObject *)((PyObject*)&PyInt_Type)));
-  __Pyx_GIVEREF(((PyObject *)((PyObject*)&PyInt_Type)));
-  __pyx_t_5 = PyObject_Call(__pyx_t_2, __pyx_t_4, NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_INCREF(((PyObject *)((PyObject*)(&PyInt_Type))));
+  PyTuple_SET_ITEM(__pyx_t_2, 1, ((PyObject *)((PyObject*)(&PyInt_Type))));
+  __Pyx_GIVEREF(((PyObject *)((PyObject*)(&PyInt_Type))));
+  __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_5);
-  __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
   __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
+  __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0;
   __Pyx_DECREF(__pyx_v_multin);
   __pyx_v_multin = __pyx_t_5;
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-  /* "mtrand.pyx":4080
+  /* "mtrand.pyx":4089
  * 
  *         multin = np.zeros(shape, int)
  *         mnarr = <ndarray>multin             # <<<<<<<<<<<<<<
@@ -17873,7 +17274,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
   __Pyx_DECREF(((PyObject *)arrayObject_mnarr));
   arrayObject_mnarr = ((PyArrayObject *)__pyx_v_multin);
 
-  /* "mtrand.pyx":4081
+  /* "mtrand.pyx":4090
  *         multin = np.zeros(shape, int)
  *         mnarr = <ndarray>multin
  *         mnix = <long*>mnarr.data             # <<<<<<<<<<<<<<
@@ -17882,7 +17283,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  */
   __pyx_v_mnix = ((long *)arrayObject_mnarr->data);
 
-  /* "mtrand.pyx":4082
+  /* "mtrand.pyx":4091
  *         mnarr = <ndarray>multin
  *         mnix = <long*>mnarr.data
  *         i = 0             # <<<<<<<<<<<<<<
@@ -17891,7 +17292,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  */
   __pyx_v_i = 0;
 
-  /* "mtrand.pyx":4083
+  /* "mtrand.pyx":4092
  *         mnix = <long*>mnarr.data
  *         i = 0
  *         while i < PyArray_SIZE(mnarr):             # <<<<<<<<<<<<<<
@@ -17902,7 +17303,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     __pyx_t_3 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr));
     if (!__pyx_t_3) break;
 
-    /* "mtrand.pyx":4084
+    /* "mtrand.pyx":4093
  *         i = 0
  *         while i < PyArray_SIZE(mnarr):
  *             Sum = 1.0             # <<<<<<<<<<<<<<
@@ -17911,7 +17312,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  */
     __pyx_v_Sum = 1.0;
 
-    /* "mtrand.pyx":4085
+    /* "mtrand.pyx":4094
  *         while i < PyArray_SIZE(mnarr):
  *             Sum = 1.0
  *             dn = n             # <<<<<<<<<<<<<<
@@ -17920,7 +17321,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  */
     __pyx_v_dn = __pyx_v_n;
 
-    /* "mtrand.pyx":4086
+    /* "mtrand.pyx":4095
  *             Sum = 1.0
  *             dn = n
  *             for j from 0 <= j < d-1:             # <<<<<<<<<<<<<<
@@ -17930,7 +17331,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     __pyx_t_6 = (__pyx_v_d - 1);
     for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
 
-      /* "mtrand.pyx":4087
+      /* "mtrand.pyx":4096
  *             dn = n
  *             for j from 0 <= j < d-1:
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)             # <<<<<<<<<<<<<<
@@ -17940,11 +17341,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
       __pyx_t_7 = (__pyx_v_pix[__pyx_v_j]);
       if (unlikely(__pyx_v_Sum == 0)) {
         PyErr_Format(PyExc_ZeroDivisionError, "float division");
-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4087; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4096; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       }
       (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, __pyx_v_dn, (__pyx_t_7 / __pyx_v_Sum));
 
-      /* "mtrand.pyx":4088
+      /* "mtrand.pyx":4097
  *             for j from 0 <= j < d-1:
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]             # <<<<<<<<<<<<<<
@@ -17953,7 +17354,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  */
       __pyx_v_dn = (__pyx_v_dn - (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]));
 
-      /* "mtrand.pyx":4089
+      /* "mtrand.pyx":4098
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]
  *                 if dn <= 0:             # <<<<<<<<<<<<<<
@@ -17963,7 +17364,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
       __pyx_t_3 = (__pyx_v_dn <= 0);
       if (__pyx_t_3) {
 
-        /* "mtrand.pyx":4090
+        /* "mtrand.pyx":4099
  *                 dn = dn - mnix[i+j]
  *                 if dn <= 0:
  *                     break             # <<<<<<<<<<<<<<
@@ -17975,7 +17376,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
       }
       __pyx_L12:;
 
-      /* "mtrand.pyx":4091
+      /* "mtrand.pyx":4100
  *                 if dn <= 0:
  *                     break
  *                 Sum = Sum - pix[j]             # <<<<<<<<<<<<<<
@@ -17986,7 +17387,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     }
     __pyx_L11_break:;
 
-    /* "mtrand.pyx":4092
+    /* "mtrand.pyx":4101
  *                     break
  *                 Sum = Sum - pix[j]
  *             if dn > 0:             # <<<<<<<<<<<<<<
@@ -17996,7 +17397,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     __pyx_t_3 = (__pyx_v_dn > 0);
     if (__pyx_t_3) {
 
-      /* "mtrand.pyx":4093
+      /* "mtrand.pyx":4102
  *                 Sum = Sum - pix[j]
  *             if dn > 0:
  *                 mnix[i+d-1] = dn             # <<<<<<<<<<<<<<
@@ -18008,7 +17409,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     }
     __pyx_L13:;
 
-    /* "mtrand.pyx":4095
+    /* "mtrand.pyx":4104
  *                 mnix[i+d-1] = dn
  * 
  *             i = i + d             # <<<<<<<<<<<<<<
@@ -18018,7 +17419,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
     __pyx_v_i = (__pyx_v_i + __pyx_v_d);
   }
 
-  /* "mtrand.pyx":4097
+  /* "mtrand.pyx":4106
  *             i = i + d
  * 
  *         return multin             # <<<<<<<<<<<<<<
@@ -18043,15 +17444,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
   __Pyx_DECREF((PyObject *)arrayObject_mnarr);
   __Pyx_DECREF(__pyx_v_shape);
   __Pyx_DECREF(__pyx_v_multin);
-  __Pyx_DECREF((PyObject *)__pyx_v_self);
-  __Pyx_DECREF(__pyx_v_pvals);
-  __Pyx_DECREF(__pyx_v_size);
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 }
 
-/* "mtrand.pyx":4099
+/* "mtrand.pyx":4108
  *         return multin
  * 
  *     def dirichlet(self, object alpha, size=None):             # <<<<<<<<<<<<<<
@@ -18059,9 +17457,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_multinomial(PyObject *__pyx_v_se
  *         dirichlet(alpha, size=None)
  */
 
-static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_dirichlet[] = "\n""        dirichlet(alpha, size=None)\n""\n""        Draw samples from the Dirichlet distribution.\n""\n""        Draw `size` samples of dimension k from a Dirichlet distribution. A\n""        Dirichlet-distributed random variable can be seen as a multivariate\n""        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n""        prior of a multinomial in Bayesian inference.\n""\n""        Parameters\n""        ----------\n""        alpha : array\n""            Parameter of the distribution (k dimension for sample of\n""            dimension k).\n""        size : array\n""            Number of samples to draw.\n""\n""        Returns\n""        -------\n""        samples : ndarray,\n""            The drawn samples, of shape (alpha.ndim, size).\n""\n""        Notes\n""        -----\n""        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n""\n""        Uses the following property for computation: for each dimension,\n""        draw a random sample y_i from a standard gamma generator of shape\n""        `alpha_i`, then\n""        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n""        Dirichlet distributed.\n""\n""        References\n""        ----------\n""        .. [1] David McKay, \"Information Theory, Inference and Learning\n""               Algorithms,\" chapter 23,\n""               http://www.inference.phy.cam.ac.uk/mackay/\n""        .. [2] Wikipedia, \"Dirichlet distribution\",\n""               http://en.wikipedia.org/wiki/Dirichlet_distribution\n""\n""        Examples\n""        --------\n""        Taking an example cited in Wikipedia, this distribution can be used if\n""        one wanted to cut strings (each of initial length 1.0) into K pieces\n""        with different lengths, where each piece had, on average, a designated\n""        average length, but allowing some variation in the relative sizes of the\n""        pieces.\n""\n""        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n""\n""        >>> plt.barh(range(20), s[0])\n""        >>> plt.barh(range(20), s[1], left=s[0], color='g')\n""        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n""        >>> plt.title(\"Lengths of Strings\")\n""\n""        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_49dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_49dirichlet[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_49dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_alpha = 0;
   PyObject *__pyx_v_size = 0;
   long __pyx_v_k;
@@ -18101,13 +17499,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self
       if (likely(values[0])) kw_args--;
       else goto __pyx_L5_argtuple_error;
       case  1:
-      if (kw_args > 1) {
+      if (kw_args > 0) {
         PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
-        if (unlikely(value)) { values[1] = value; kw_args--; }
+        if (value) { values[1] = value; kw_args--; }
       }
     }
     if (unlikely(kw_args > 0)) {
-      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4099; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+      if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4108; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -18122,44 +17520,42 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self
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+  __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4108; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.dirichlet");
+  __Pyx_RefNannyFinishContext();
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-  __Pyx_INCREF(__pyx_v_alpha);
-  __Pyx_INCREF(__pyx_v_size);
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-  /* "mtrand.pyx":4185
+  /* "mtrand.pyx":4194
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  *         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
  *         alpha_data  = <double*>alpha_arr.data
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-  __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4185; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_1 = PyObject_Length(__pyx_v_alpha); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4194; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_v_k = __pyx_t_1;
 
-  /* "mtrand.pyx":4186
+  /* "mtrand.pyx":4195
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  *         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)             # <<<<<<<<<<<<<<
  *         alpha_data  = <double*>alpha_arr.data
  * 
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-  __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_ContiguousFromObject(__pyx_v_alpha, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4195; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __Pyx_GOTREF(__pyx_t_2);
   __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_2)));
   __Pyx_DECREF(((PyObject *)__pyx_v_alpha_arr));
   __pyx_v_alpha_arr = ((PyArrayObject *)__pyx_t_2);
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
 
-  /* "mtrand.pyx":4187
+  /* "mtrand.pyx":4196
  *         k           = len(alpha)
  *         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
  *         alpha_data  = <double*>alpha_arr.data             # <<<<<<<<<<<<<<
@@ -18168,7 +17564,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self
  */
   __pyx_v_alpha_data = ((double *)__pyx_v_alpha_arr->data);
 
-  /* "mtrand.pyx":4189
+  /* "mtrand.pyx":4198
  *         alpha_data  = <double*>alpha_arr.data
  * 
  *         if size is None:             # <<<<<<<<<<<<<<
@@ -18178,47 +17574,47 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self
   __pyx_t_3 = (__pyx_v_size == Py_None);
   if (__pyx_t_3) {
 
-    /* "mtrand.pyx":4190
+    /* "mtrand.pyx":4199
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  *         if size is None:
  *             shape = (k,)             # <<<<<<<<<<<<<<
  *         elif type(size) is int:
  *             shape = (size, k)
  */
-    __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4190; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4199; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_3 = (((PyObject *)Py_TYPE(__pyx_v_size)) == ((PyObject *)((PyObject*)&PyInt_Type)));
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-static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self, PyObject *__pyx_v_x) {
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+      __pyx_t_2 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4262; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
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-      __pyx_t_1 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4253; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
+      __pyx_t_1 = PyObject_Length(__pyx_t_2); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4262; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
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@@ -18521,7 +17911,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-    /* "mtrand.pyx":4254
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@@ -18530,12 +17920,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-      if (__Pyx_GetException(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4254; __pyx_clineno = __LINE__; goto __pyx_L7_except_error;}
+      if (__Pyx_GetException(&__pyx_t_2, &__pyx_t_3, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4263; __pyx_clineno = __LINE__; goto __pyx_L7_except_error;}
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-      /* "mtrand.pyx":4255
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@@ -18562,7 +17952,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-  /* "mtrand.pyx":4257
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@@ -18572,7 +17962,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
   __pyx_t_5 = (__pyx_v_j == 0);
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-    /* "mtrand.pyx":4259
+    /* "mtrand.pyx":4268
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@@ -18583,7 +17973,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-      /* "mtrand.pyx":4260
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-      /* "mtrand.pyx":4261
+      /* "mtrand.pyx":4270
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+      __pyx_t_4 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_j, sizeof(long), PyInt_FromLong); if (!__pyx_t_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4270; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_i, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_i, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4270; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_i, __pyx_t_4, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_i, __pyx_t_4, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4270; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_t_3, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4261; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_t_3, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4270; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "mtrand.pyx":4262
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@@ -18621,30 +18011,29 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-    /* "mtrand.pyx":4265
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+    __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4274; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_5 = PyObject_HasAttr(__pyx_t_3, ((PyObject *)__pyx_n_s__copy)); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4274; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":4266
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-    __pyx_t_6 = __pyx_v_copy;
-    if (__pyx_t_6) {
+    if (__pyx_v_copy) {
 
-      /* "mtrand.pyx":4267
+      /* "mtrand.pyx":4276
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@@ -18655,7 +18044,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self,
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-        /* "mtrand.pyx":4268
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-        /* "mtrand.pyx":4269
+        /* "mtrand.pyx":4278
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-        __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_j, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_j, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         __Pyx_GOTREF(__pyx_t_3);
-        __pyx_t_4 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__copy); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_4 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__copy); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        __pyx_t_3 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        __pyx_t_4 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_i, sizeof(long), PyInt_FromLong); if (!__pyx_t_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-        __pyx_t_2 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__copy); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_2 = PyObject_GetAttr(__pyx_t_4, __pyx_n_s__copy); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        __pyx_t_4 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_i, __pyx_t_3, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
-        if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_t_4, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4269; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_t_4, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-        /* "mtrand.pyx":4270
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-      /* "mtrand.pyx":4272
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-        /* "mtrand.pyx":4273
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-        /* "mtrand.pyx":4274
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+        __pyx_t_4 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_j, sizeof(long), PyInt_FromLong); if (!__pyx_t_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
         __Pyx_GOTREF(__pyx_t_4);
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+        __pyx_t_3 = __Pyx_PySequence_GetSlice(__pyx_t_4, 0, PY_SSIZE_T_MAX); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        __pyx_t_4 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_i, sizeof(long), PyInt_FromLong); if (!__pyx_t_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-        __pyx_t_2 = PySequence_GetSlice(__pyx_t_4, 0, PY_SSIZE_T_MAX); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4274; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        __pyx_t_2 = __Pyx_PySequence_GetSlice(__pyx_t_4, 0, PY_SSIZE_T_MAX); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_i, __pyx_t_3, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+        if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_t_2, sizeof(long), PyInt_FromLong) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4283; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-        /* "mtrand.pyx":4275
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-  {__Pyx_NAMESTR("standard_cauchy"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_cauchy, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_standard_cauchy)},
-  {__Pyx_NAMESTR("standard_t"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_t, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_standard_t)},
-  {__Pyx_NAMESTR("vonmises"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_vonmises, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_vonmises)},
-  {__Pyx_NAMESTR("pareto"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_pareto, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_pareto)},
-  {__Pyx_NAMESTR("weibull"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_weibull)},
-  {__Pyx_NAMESTR("power"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_power)},
-  {__Pyx_NAMESTR("laplace"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_laplace)},
-  {__Pyx_NAMESTR("gumbel"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_gumbel)},
-  {__Pyx_NAMESTR("logistic"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_logistic)},
-  {__Pyx_NAMESTR("lognormal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_lognormal)},
-  {__Pyx_NAMESTR("rayleigh"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_rayleigh)},
-  {__Pyx_NAMESTR("wald"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_wald)},
-  {__Pyx_NAMESTR("triangular"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_triangular)},
-  {__Pyx_NAMESTR("binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_binomial)},
-  {__Pyx_NAMESTR("negative_binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_negative_binomial)},
-  {__Pyx_NAMESTR("poisson"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_poisson, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_poisson)},
-  {__Pyx_NAMESTR("zipf"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_zipf, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_zipf)},
-  {__Pyx_NAMESTR("geometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_geometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_geometric)},
-  {__Pyx_NAMESTR("hypergeometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_hypergeometric)},
-  {__Pyx_NAMESTR("logseries"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_logseries)},
-  {__Pyx_NAMESTR("multivariate_normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_multivariate_normal)},
-  {__Pyx_NAMESTR("multinomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_multinomial)},
-  {__Pyx_NAMESTR("dirichlet"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_dirichlet, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_dirichlet)},
-  {__Pyx_NAMESTR("shuffle"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_shuffle, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_shuffle)},
-  {__Pyx_NAMESTR("permutation"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_permutation, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_permutation)},
+static PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
+  {__Pyx_NAMESTR("seed"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_2seed, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_2seed)},
+  {__Pyx_NAMESTR("get_state"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_3get_state, METH_NOARGS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_3get_state)},
+  {__Pyx_NAMESTR("set_state"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_4set_state, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_4set_state)},
+  {__Pyx_NAMESTR("__getstate__"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_5__getstate__, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("__setstate__"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_6__setstate__, METH_O, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("__reduce__"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_7__reduce__, METH_NOARGS, __Pyx_DOCSTR(0)},
+  {__Pyx_NAMESTR("random_sample"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_8random_sample, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_8random_sample)},
+  {__Pyx_NAMESTR("tomaxint"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_9tomaxint, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_9tomaxint)},
+  {__Pyx_NAMESTR("randint"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_10randint, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_10randint)},
+  {__Pyx_NAMESTR("bytes"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_11bytes, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_11bytes)},
+  {__Pyx_NAMESTR("uniform"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_12uniform, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_12uniform)},
+  {__Pyx_NAMESTR("rand"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_13rand, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_13rand)},
+  {__Pyx_NAMESTR("randn"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_14randn, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_14randn)},
+  {__Pyx_NAMESTR("random_integers"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_15random_integers, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_15random_integers)},
+  {__Pyx_NAMESTR("standard_normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_16standard_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_16standard_normal)},
+  {__Pyx_NAMESTR("normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_17normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_17normal)},
+  {__Pyx_NAMESTR("beta"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_18beta, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_18beta)},
+  {__Pyx_NAMESTR("exponential"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_19exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_19exponential)},
+  {__Pyx_NAMESTR("standard_exponential"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_20standard_exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_20standard_exponential)},
+  {__Pyx_NAMESTR("standard_gamma"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_21standard_gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_21standard_gamma)},
+  {__Pyx_NAMESTR("gamma"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_22gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_22gamma)},
+  {__Pyx_NAMESTR("f"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_23f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_23f)},
+  {__Pyx_NAMESTR("noncentral_f"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_24noncentral_f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_24noncentral_f)},
+  {__Pyx_NAMESTR("chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_25chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_25chisquare)},
+  {__Pyx_NAMESTR("noncentral_chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_26noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_26noncentral_chisquare)},
+  {__Pyx_NAMESTR("standard_cauchy"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_27standard_cauchy, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_27standard_cauchy)},
+  {__Pyx_NAMESTR("standard_t"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_28standard_t, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_28standard_t)},
+  {__Pyx_NAMESTR("vonmises"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_29vonmises, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_29vonmises)},
+  {__Pyx_NAMESTR("pareto"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_30pareto, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_30pareto)},
+  {__Pyx_NAMESTR("weibull"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_31weibull, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_31weibull)},
+  {__Pyx_NAMESTR("power"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_32power, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_32power)},
+  {__Pyx_NAMESTR("laplace"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_33laplace, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_33laplace)},
+  {__Pyx_NAMESTR("gumbel"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_34gumbel, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_34gumbel)},
+  {__Pyx_NAMESTR("logistic"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_35logistic, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_35logistic)},
+  {__Pyx_NAMESTR("lognormal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_36lognormal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_36lognormal)},
+  {__Pyx_NAMESTR("rayleigh"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_37rayleigh, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_37rayleigh)},
+  {__Pyx_NAMESTR("wald"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_38wald, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_38wald)},
+  {__Pyx_NAMESTR("triangular"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_39triangular, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_39triangular)},
+  {__Pyx_NAMESTR("binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_40binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_40binomial)},
+  {__Pyx_NAMESTR("negative_binomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_41negative_binomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_41negative_binomial)},
+  {__Pyx_NAMESTR("poisson"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_42poisson, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_42poisson)},
+  {__Pyx_NAMESTR("zipf"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_43zipf, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_43zipf)},
+  {__Pyx_NAMESTR("geometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_44geometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_44geometric)},
+  {__Pyx_NAMESTR("hypergeometric"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_45hypergeometric, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_45hypergeometric)},
+  {__Pyx_NAMESTR("logseries"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_46logseries, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_46logseries)},
+  {__Pyx_NAMESTR("multivariate_normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_47multivariate_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_47multivariate_normal)},
+  {__Pyx_NAMESTR("multinomial"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_48multinomial, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_48multinomial)},
+  {__Pyx_NAMESTR("dirichlet"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_49dirichlet, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_49dirichlet)},
+  {__Pyx_NAMESTR("shuffle"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_50shuffle, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_50shuffle)},
+  {__Pyx_NAMESTR("permutation"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_51permutation, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_51permutation)},
   {0, 0, 0, 0}
 };
 
@@ -19027,10 +18410,10 @@ static PyNumberMethods __pyx_tp_as_number_RandomState = {
   0, /*nb_coerce*/
   #endif
   0, /*nb_int*/
-  #if PY_MAJOR_VERSION >= 3
-  0, /*reserved*/
-  #else
+  #if PY_MAJOR_VERSION < 3
   0, /*nb_long*/
+  #else
+  0, /*reserved*/
   #endif
   0, /*nb_float*/
   #if PY_MAJOR_VERSION < 3
@@ -19056,7 +18439,7 @@ static PyNumberMethods __pyx_tp_as_number_RandomState = {
   0, /*nb_true_divide*/
   0, /*nb_inplace_floor_divide*/
   0, /*nb_inplace_true_divide*/
-  #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_INDEX)
+  #if PY_VERSION_HEX >= 0x02050000
   0, /*nb_index*/
   #endif
 };
@@ -19101,7 +18484,7 @@ static PyBufferProcs __pyx_tp_as_buffer_RandomState = {
   #endif
 };
 
-PyTypeObject __pyx_type_6mtrand_RandomState = {
+static PyTypeObject __pyx_type_6mtrand_RandomState = {
   PyVarObject_HEAD_INIT(0, 0)
   __Pyx_NAMESTR("mtrand.RandomState"), /*tp_name*/
   sizeof(struct __pyx_obj_6mtrand_RandomState), /*tp_basicsize*/
@@ -19110,7 +18493,11 @@ PyTypeObject __pyx_type_6mtrand_RandomState = {
   0, /*tp_print*/
   0, /*tp_getattr*/
   0, /*tp_setattr*/
+  #if PY_MAJOR_VERSION < 3
   0, /*tp_compare*/
+  #else
+  0, /*reserved*/
+  #endif
   0, /*tp_repr*/
   &__pyx_tp_as_number_RandomState, /*tp_as_number*/
   &__pyx_tp_as_sequence_RandomState, /*tp_as_sequence*/
@@ -19121,7 +18508,7 @@ PyTypeObject __pyx_type_6mtrand_RandomState = {
   0, /*tp_getattro*/
   0, /*tp_setattro*/
   &__pyx_tp_as_buffer_RandomState, /*tp_as_buffer*/
-  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_NEWBUFFER, /*tp_flags*/
+  Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE, /*tp_flags*/
   __Pyx_DOCSTR("\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    Parameters\n    ----------\n    seed : int or array_like, optional\n        Random seed initializing the pseudo-random number generator.\n        Can be an integer, an array (or other sequence) of integers of\n        any length, or ``None`` (the default).\n        If `seed` is ``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*/
   0, /*tp_traverse*/
   0, /*tp_clear*/
@@ -19153,12 +18540,10 @@ PyTypeObject __pyx_type_6mtrand_RandomState = {
   #endif
 };
 
-static struct PyMethodDef __pyx_methods[] = {
+static PyMethodDef __pyx_methods[] = {
   {0, 0, 0, 0}
 };
 
-static void __pyx_init_filenames(void); /*proto*/
-
 #if PY_MAJOR_VERSION >= 3
 static struct PyModuleDef __pyx_moduledef = {
     PyModuleDef_HEAD_INIT,
@@ -19175,98 +18560,136 @@ static struct PyModuleDef __pyx_moduledef = {
 
 static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_kp_s_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 1, 0},
-  {&__pyx_kp_s_10, __pyx_k_10, sizeof(__pyx_k_10), 0, 0, 1, 0},
-  {&__pyx_kp_u_100, __pyx_k_100, sizeof(__pyx_k_100), 0, 1, 0, 0},
-  {&__pyx_kp_u_101, __pyx_k_101, sizeof(__pyx_k_101), 0, 1, 0, 0},
-  {&__pyx_kp_u_102, __pyx_k_102, sizeof(__pyx_k_102), 0, 1, 0, 0},
-  {&__pyx_kp_u_103, __pyx_k_103, sizeof(__pyx_k_103), 0, 1, 0, 0},
-  {&__pyx_kp_u_104, __pyx_k_104, sizeof(__pyx_k_104), 0, 1, 0, 0},
-  {&__pyx_kp_u_105, __pyx_k_105, sizeof(__pyx_k_105), 0, 1, 0, 0},
-  {&__pyx_kp_u_106, __pyx_k_106, sizeof(__pyx_k_106), 0, 1, 0, 0},
-  {&__pyx_kp_u_107, __pyx_k_107, sizeof(__pyx_k_107), 0, 1, 0, 0},
+  {&__pyx_kp_s_101, __pyx_k_101, sizeof(__pyx_k_101), 0, 0, 1, 0},
+  {&__pyx_kp_s_103, __pyx_k_103, sizeof(__pyx_k_103), 0, 0, 1, 0},
+  {&__pyx_kp_s_105, __pyx_k_105, sizeof(__pyx_k_105), 0, 0, 1, 0},
   {&__pyx_kp_s_11, __pyx_k_11, sizeof(__pyx_k_11), 0, 0, 1, 0},
+  {&__pyx_kp_s_110, __pyx_k_110, sizeof(__pyx_k_110), 0, 0, 1, 0},
+  {&__pyx_kp_s_112, __pyx_k_112, sizeof(__pyx_k_112), 0, 0, 1, 0},
+  {&__pyx_kp_s_114, __pyx_k_114, sizeof(__pyx_k_114), 0, 0, 1, 0},
+  {&__pyx_kp_s_126, __pyx_k_126, sizeof(__pyx_k_126), 0, 0, 1, 0},
+  {&__pyx_kp_s_128, __pyx_k_128, sizeof(__pyx_k_128), 0, 0, 1, 0},
   {&__pyx_kp_s_13, __pyx_k_13, sizeof(__pyx_k_13), 0, 0, 1, 0},
-  {&__pyx_kp_s_15, __pyx_k_15, sizeof(__pyx_k_15), 0, 0, 1, 0},
-  {&__pyx_kp_s_16, __pyx_k_16, sizeof(__pyx_k_16), 0, 0, 1, 0},
-  {&__pyx_kp_s_17, __pyx_k_17, sizeof(__pyx_k_17), 0, 0, 1, 0},
-  {&__pyx_kp_s_18, __pyx_k_18, sizeof(__pyx_k_18), 0, 0, 1, 0},
+  {&__pyx_kp_s_131, __pyx_k_131, sizeof(__pyx_k_131), 0, 0, 1, 0},
+  {&__pyx_kp_s_133, __pyx_k_133, sizeof(__pyx_k_133), 0, 0, 1, 0},
+  {&__pyx_kp_s_136, __pyx_k_136, sizeof(__pyx_k_136), 0, 0, 1, 0},
+  {&__pyx_kp_s_138, __pyx_k_138, sizeof(__pyx_k_138), 0, 0, 1, 0},
+  {&__pyx_kp_s_142, __pyx_k_142, sizeof(__pyx_k_142), 0, 0, 1, 0},
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+  {&__pyx_kp_s_146, __pyx_k_146, sizeof(__pyx_k_146), 0, 0, 1, 0},
+  {&__pyx_kp_s_148, __pyx_k_148, sizeof(__pyx_k_148), 0, 0, 1, 0},
+  {&__pyx_kp_s_154, __pyx_k_154, sizeof(__pyx_k_154), 0, 0, 1, 0},
+  {&__pyx_kp_s_156, __pyx_k_156, sizeof(__pyx_k_156), 0, 0, 1, 0},
+  {&__pyx_kp_s_160, __pyx_k_160, sizeof(__pyx_k_160), 0, 0, 1, 0},
+  {&__pyx_kp_s_162, __pyx_k_162, sizeof(__pyx_k_162), 0, 0, 1, 0},
+  {&__pyx_kp_s_164, __pyx_k_164, sizeof(__pyx_k_164), 0, 0, 1, 0},
+  {&__pyx_n_s_166, __pyx_k_166, sizeof(__pyx_k_166), 0, 0, 1, 1},
+  {&__pyx_kp_s_167, __pyx_k_167, sizeof(__pyx_k_167), 0, 0, 1, 0},
+  {&__pyx_n_s_171, __pyx_k_171, sizeof(__pyx_k_171), 0, 0, 1, 1},
+  {&__pyx_n_s_172, __pyx_k_172, sizeof(__pyx_k_172), 0, 0, 1, 1},
+  {&__pyx_kp_u_173, __pyx_k_173, sizeof(__pyx_k_173), 0, 1, 0, 0},
+  {&__pyx_kp_u_174, __pyx_k_174, sizeof(__pyx_k_174), 0, 1, 0, 0},
+  {&__pyx_kp_u_175, __pyx_k_175, sizeof(__pyx_k_175), 0, 1, 0, 0},
+  {&__pyx_kp_u_176, __pyx_k_176, sizeof(__pyx_k_176), 0, 1, 0, 0},
+  {&__pyx_kp_u_177, __pyx_k_177, sizeof(__pyx_k_177), 0, 1, 0, 0},
+  {&__pyx_kp_u_178, __pyx_k_178, sizeof(__pyx_k_178), 0, 1, 0, 0},
+  {&__pyx_kp_u_179, __pyx_k_179, sizeof(__pyx_k_179), 0, 1, 0, 0},
+  {&__pyx_kp_u_180, __pyx_k_180, sizeof(__pyx_k_180), 0, 1, 0, 0},
+  {&__pyx_kp_u_181, __pyx_k_181, sizeof(__pyx_k_181), 0, 1, 0, 0},
+  {&__pyx_kp_u_182, __pyx_k_182, sizeof(__pyx_k_182), 0, 1, 0, 0},
+  {&__pyx_kp_u_183, __pyx_k_183, sizeof(__pyx_k_183), 0, 1, 0, 0},
+  {&__pyx_kp_u_184, __pyx_k_184, sizeof(__pyx_k_184), 0, 1, 0, 0},
+  {&__pyx_kp_u_185, __pyx_k_185, sizeof(__pyx_k_185), 0, 1, 0, 0},
+  {&__pyx_kp_u_186, __pyx_k_186, sizeof(__pyx_k_186), 0, 1, 0, 0},
+  {&__pyx_kp_u_187, __pyx_k_187, sizeof(__pyx_k_187), 0, 1, 0, 0},
+  {&__pyx_kp_u_188, __pyx_k_188, sizeof(__pyx_k_188), 0, 1, 0, 0},
+  {&__pyx_kp_u_189, __pyx_k_189, sizeof(__pyx_k_189), 0, 1, 0, 0},
   {&__pyx_kp_s_19, __pyx_k_19, sizeof(__pyx_k_19), 0, 0, 1, 0},
-  {&__pyx_kp_s_2, __pyx_k_2, sizeof(__pyx_k_2), 0, 0, 1, 0},
-  {&__pyx_kp_s_20, __pyx_k_20, sizeof(__pyx_k_20), 0, 0, 1, 0},
-  {&__pyx_kp_s_21, __pyx_k_21, sizeof(__pyx_k_21), 0, 0, 1, 0},
+  {&__pyx_kp_u_190, __pyx_k_190, sizeof(__pyx_k_190), 0, 1, 0, 0},
+  {&__pyx_kp_u_191, __pyx_k_191, sizeof(__pyx_k_191), 0, 1, 0, 0},
+  {&__pyx_kp_u_192, __pyx_k_192, sizeof(__pyx_k_192), 0, 1, 0, 0},
+  {&__pyx_kp_u_193, __pyx_k_193, sizeof(__pyx_k_193), 0, 1, 0, 0},
+  {&__pyx_kp_u_194, __pyx_k_194, sizeof(__pyx_k_194), 0, 1, 0, 0},
+  {&__pyx_kp_u_195, __pyx_k_195, sizeof(__pyx_k_195), 0, 1, 0, 0},
+  {&__pyx_kp_u_196, __pyx_k_196, sizeof(__pyx_k_196), 0, 1, 0, 0},
+  {&__pyx_kp_u_197, __pyx_k_197, sizeof(__pyx_k_197), 0, 1, 0, 0},
+  {&__pyx_kp_u_198, __pyx_k_198, sizeof(__pyx_k_198), 0, 1, 0, 0},
+  {&__pyx_kp_u_199, __pyx_k_199, sizeof(__pyx_k_199), 0, 1, 0, 0},
+  {&__pyx_kp_u_200, __pyx_k_200, sizeof(__pyx_k_200), 0, 1, 0, 0},
+  {&__pyx_kp_u_201, __pyx_k_201, sizeof(__pyx_k_201), 0, 1, 0, 0},
+  {&__pyx_kp_u_202, __pyx_k_202, sizeof(__pyx_k_202), 0, 1, 0, 0},
+  {&__pyx_kp_u_203, __pyx_k_203, sizeof(__pyx_k_203), 0, 1, 0, 0},
+  {&__pyx_kp_u_204, __pyx_k_204, sizeof(__pyx_k_204), 0, 1, 0, 0},
+  {&__pyx_kp_u_205, __pyx_k_205, sizeof(__pyx_k_205), 0, 1, 0, 0},
+  {&__pyx_kp_u_206, __pyx_k_206, sizeof(__pyx_k_206), 0, 1, 0, 0},
+  {&__pyx_kp_u_207, __pyx_k_207, sizeof(__pyx_k_207), 0, 1, 0, 0},
+  {&__pyx_kp_u_208, __pyx_k_208, sizeof(__pyx_k_208), 0, 1, 0, 0},
+  {&__pyx_kp_u_209, __pyx_k_209, sizeof(__pyx_k_209), 0, 1, 0, 0},
+  {&__pyx_kp_u_210, __pyx_k_210, sizeof(__pyx_k_210), 0, 1, 0, 0},
+  {&__pyx_kp_u_211, __pyx_k_211, sizeof(__pyx_k_211), 0, 1, 0, 0},
+  {&__pyx_kp_u_212, __pyx_k_212, sizeof(__pyx_k_212), 0, 1, 0, 0},
+  {&__pyx_kp_u_213, __pyx_k_213, sizeof(__pyx_k_213), 0, 1, 0, 0},
+  {&__pyx_kp_u_214, __pyx_k_214, sizeof(__pyx_k_214), 0, 1, 0, 0},
+  {&__pyx_kp_u_215, __pyx_k_215, sizeof(__pyx_k_215), 0, 1, 0, 0},
+  {&__pyx_kp_u_216, __pyx_k_216, sizeof(__pyx_k_216), 0, 1, 0, 0},
+  {&__pyx_kp_u_217, __pyx_k_217, sizeof(__pyx_k_217), 0, 1, 0, 0},
+  {&__pyx_kp_u_218, __pyx_k_218, sizeof(__pyx_k_218), 0, 1, 0, 0},
+  {&__pyx_kp_u_219, __pyx_k_219, sizeof(__pyx_k_219), 0, 1, 0, 0},
   {&__pyx_kp_s_22, __pyx_k_22, sizeof(__pyx_k_22), 0, 0, 1, 0},
-  {&__pyx_kp_s_3, __pyx_k_3, sizeof(__pyx_k_3), 0, 0, 1, 0},
+  {&__pyx_kp_u_220, __pyx_k_220, sizeof(__pyx_k_220), 0, 1, 0, 0},
+  {&__pyx_kp_u_221, __pyx_k_221, sizeof(__pyx_k_221), 0, 1, 0, 0},
+  {&__pyx_kp_u_222, __pyx_k_222, sizeof(__pyx_k_222), 0, 1, 0, 0},
+  {&__pyx_kp_u_223, __pyx_k_223, sizeof(__pyx_k_223), 0, 1, 0, 0},
+  {&__pyx_kp_u_224, __pyx_k_224, sizeof(__pyx_k_224), 0, 1, 0, 0},
+  {&__pyx_kp_u_225, __pyx_k_225, sizeof(__pyx_k_225), 0, 1, 0, 0},
+  {&__pyx_kp_u_226, __pyx_k_226, sizeof(__pyx_k_226), 0, 1, 0, 0},
+  {&__pyx_kp_u_227, __pyx_k_227, sizeof(__pyx_k_227), 0, 1, 0, 0},
+  {&__pyx_kp_u_228, __pyx_k_228, sizeof(__pyx_k_228), 0, 1, 0, 0},
+  {&__pyx_kp_u_229, __pyx_k_229, sizeof(__pyx_k_229), 0, 1, 0, 0},
+  {&__pyx_kp_u_230, __pyx_k_230, sizeof(__pyx_k_230), 0, 1, 0, 0},
+  {&__pyx_kp_u_231, __pyx_k_231, sizeof(__pyx_k_231), 0, 1, 0, 0},
+  {&__pyx_kp_u_232, __pyx_k_232, sizeof(__pyx_k_232), 0, 1, 0, 0},
+  {&__pyx_kp_u_233, __pyx_k_233, sizeof(__pyx_k_233), 0, 1, 0, 0},
+  {&__pyx_kp_u_234, __pyx_k_234, sizeof(__pyx_k_234), 0, 1, 0, 0},
+  {&__pyx_kp_u_235, __pyx_k_235, sizeof(__pyx_k_235), 0, 1, 0, 0},
+  {&__pyx_kp_u_236, __pyx_k_236, sizeof(__pyx_k_236), 0, 1, 0, 0},
+  {&__pyx_kp_u_237, __pyx_k_237, sizeof(__pyx_k_237), 0, 1, 0, 0},
+  {&__pyx_kp_u_238, __pyx_k_238, sizeof(__pyx_k_238), 0, 1, 0, 0},
+  {&__pyx_kp_u_239, __pyx_k_239, sizeof(__pyx_k_239), 0, 1, 0, 0},
+  {&__pyx_kp_s_24, __pyx_k_24, sizeof(__pyx_k_24), 0, 0, 1, 0},
+  {&__pyx_kp_u_240, __pyx_k_240, sizeof(__pyx_k_240), 0, 1, 0, 0},
+  {&__pyx_kp_u_241, __pyx_k_241, sizeof(__pyx_k_241), 0, 1, 0, 0},
+  {&__pyx_kp_u_242, __pyx_k_242, sizeof(__pyx_k_242), 0, 1, 0, 0},
+  {&__pyx_kp_u_243, __pyx_k_243, sizeof(__pyx_k_243), 0, 1, 0, 0},
+  {&__pyx_kp_u_244, __pyx_k_244, sizeof(__pyx_k_244), 0, 1, 0, 0},
+  {&__pyx_kp_u_245, __pyx_k_245, sizeof(__pyx_k_245), 0, 1, 0, 0},
+  {&__pyx_kp_u_246, __pyx_k_246, sizeof(__pyx_k_246), 0, 1, 0, 0},
+  {&__pyx_kp_u_247, __pyx_k_247, sizeof(__pyx_k_247), 0, 1, 0, 0},
+  {&__pyx_kp_u_248, __pyx_k_248, sizeof(__pyx_k_248), 0, 1, 0, 0},
+  {&__pyx_kp_u_249, __pyx_k_249, sizeof(__pyx_k_249), 0, 1, 0, 0},
+  {&__pyx_kp_u_250, __pyx_k_250, sizeof(__pyx_k_250), 0, 1, 0, 0},
+  {&__pyx_kp_u_251, __pyx_k_251, sizeof(__pyx_k_251), 0, 1, 0, 0},
+  {&__pyx_kp_u_252, __pyx_k_252, sizeof(__pyx_k_252), 0, 1, 0, 0},
+  {&__pyx_kp_u_253, __pyx_k_253, sizeof(__pyx_k_253), 0, 1, 0, 0},
+  {&__pyx_kp_u_254, __pyx_k_254, sizeof(__pyx_k_254), 0, 1, 0, 0},
+  {&__pyx_kp_u_255, __pyx_k_255, sizeof(__pyx_k_255), 0, 1, 0, 0},
+  {&__pyx_kp_u_256, __pyx_k_256, sizeof(__pyx_k_256), 0, 1, 0, 0},
   {&__pyx_kp_s_31, __pyx_k_31, sizeof(__pyx_k_31), 0, 0, 1, 0},
-  {&__pyx_kp_s_32, __pyx_k_32, sizeof(__pyx_k_32), 0, 0, 1, 0},
-  {&__pyx_kp_s_34, __pyx_k_34, sizeof(__pyx_k_34), 0, 0, 1, 0},
-  {&__pyx_kp_s_35, __pyx_k_35, sizeof(__pyx_k_35), 0, 0, 1, 0},
-  {&__pyx_kp_s_36, __pyx_k_36, sizeof(__pyx_k_36), 0, 0, 1, 0},
-  {&__pyx_kp_s_37, __pyx_k_37, sizeof(__pyx_k_37), 0, 0, 1, 0},
-  {&__pyx_kp_s_38, __pyx_k_38, sizeof(__pyx_k_38), 0, 0, 1, 0},
-  {&__pyx_kp_s_39, __pyx_k_39, sizeof(__pyx_k_39), 0, 0, 1, 0},
-  {&__pyx_kp_s_4, __pyx_k_4, sizeof(__pyx_k_4), 0, 0, 1, 0},
-  {&__pyx_kp_s_40, __pyx_k_40, sizeof(__pyx_k_40), 0, 0, 1, 0},
   {&__pyx_kp_s_41, __pyx_k_41, sizeof(__pyx_k_41), 0, 0, 1, 0},
-  {&__pyx_kp_s_42, __pyx_k_42, sizeof(__pyx_k_42), 0, 0, 1, 0},
-  {&__pyx_kp_s_44, __pyx_k_44, sizeof(__pyx_k_44), 0, 0, 1, 0},
+  {&__pyx_kp_s_43, __pyx_k_43, sizeof(__pyx_k_43), 0, 0, 1, 0},
   {&__pyx_kp_s_45, __pyx_k_45, sizeof(__pyx_k_45), 0, 0, 1, 0},
-  {&__pyx_kp_s_46, __pyx_k_46, sizeof(__pyx_k_46), 0, 0, 1, 0},
-  {&__pyx_kp_s_47, __pyx_k_47, sizeof(__pyx_k_47), 0, 0, 1, 0},
   {&__pyx_kp_s_48, __pyx_k_48, sizeof(__pyx_k_48), 0, 0, 1, 0},
-  {&__pyx_kp_s_49, __pyx_k_49, sizeof(__pyx_k_49), 0, 0, 1, 0},
-  {&__pyx_kp_s_50, __pyx_k_50, sizeof(__pyx_k_50), 0, 0, 1, 0},
-  {&__pyx_kp_s_51, __pyx_k_51, sizeof(__pyx_k_51), 0, 0, 1, 0},
-  {&__pyx_kp_s_52, __pyx_k_52, sizeof(__pyx_k_52), 0, 0, 1, 0},
   {&__pyx_kp_s_53, __pyx_k_53, sizeof(__pyx_k_53), 0, 0, 1, 0},
-  {&__pyx_kp_s_54, __pyx_k_54, sizeof(__pyx_k_54), 0, 0, 1, 0},
-  {&__pyx_kp_s_55, __pyx_k_55, sizeof(__pyx_k_55), 0, 0, 1, 0},
-  {&__pyx_kp_s_56, __pyx_k_56, sizeof(__pyx_k_56), 0, 0, 1, 0},
-  {&__pyx_n_s_57, __pyx_k_57, sizeof(__pyx_k_57), 0, 0, 1, 1},
-  {&__pyx_kp_s_58, __pyx_k_58, sizeof(__pyx_k_58), 0, 0, 1, 0},
-  {&__pyx_n_s_59, __pyx_k_59, sizeof(__pyx_k_59), 0, 0, 1, 1},
-  {&__pyx_n_s_60, __pyx_k_60, sizeof(__pyx_k_60), 0, 0, 1, 1},
-  {&__pyx_kp_u_61, __pyx_k_61, sizeof(__pyx_k_61), 0, 1, 0, 0},
-  {&__pyx_kp_u_62, __pyx_k_62, sizeof(__pyx_k_62), 0, 1, 0, 0},
-  {&__pyx_kp_u_63, __pyx_k_63, sizeof(__pyx_k_63), 0, 1, 0, 0},
-  {&__pyx_kp_u_64, __pyx_k_64, sizeof(__pyx_k_64), 0, 1, 0, 0},
-  {&__pyx_kp_u_65, __pyx_k_65, sizeof(__pyx_k_65), 0, 1, 0, 0},
-  {&__pyx_kp_u_66, __pyx_k_66, sizeof(__pyx_k_66), 0, 1, 0, 0},
-  {&__pyx_kp_u_67, __pyx_k_67, sizeof(__pyx_k_67), 0, 1, 0, 0},
-  {&__pyx_kp_u_68, __pyx_k_68, sizeof(__pyx_k_68), 0, 1, 0, 0},
-  {&__pyx_kp_u_69, __pyx_k_69, sizeof(__pyx_k_69), 0, 1, 0, 0},
-  {&__pyx_kp_u_70, __pyx_k_70, sizeof(__pyx_k_70), 0, 1, 0, 0},
-  {&__pyx_kp_u_71, __pyx_k_71, sizeof(__pyx_k_71), 0, 1, 0, 0},
-  {&__pyx_kp_u_72, __pyx_k_72, sizeof(__pyx_k_72), 0, 1, 0, 0},
-  {&__pyx_kp_u_73, __pyx_k_73, sizeof(__pyx_k_73), 0, 1, 0, 0},
-  {&__pyx_kp_u_74, __pyx_k_74, sizeof(__pyx_k_74), 0, 1, 0, 0},
-  {&__pyx_kp_u_75, __pyx_k_75, sizeof(__pyx_k_75), 0, 1, 0, 0},
-  {&__pyx_kp_u_76, __pyx_k_76, sizeof(__pyx_k_76), 0, 1, 0, 0},
-  {&__pyx_kp_u_77, __pyx_k_77, sizeof(__pyx_k_77), 0, 1, 0, 0},
-  {&__pyx_kp_u_78, __pyx_k_78, sizeof(__pyx_k_78), 0, 1, 0, 0},
-  {&__pyx_kp_u_79, __pyx_k_79, sizeof(__pyx_k_79), 0, 1, 0, 0},
-  {&__pyx_kp_u_80, __pyx_k_80, sizeof(__pyx_k_80), 0, 1, 0, 0},
-  {&__pyx_kp_u_81, __pyx_k_81, sizeof(__pyx_k_81), 0, 1, 0, 0},
-  {&__pyx_kp_u_82, __pyx_k_82, sizeof(__pyx_k_82), 0, 1, 0, 0},
-  {&__pyx_kp_u_83, __pyx_k_83, sizeof(__pyx_k_83), 0, 1, 0, 0},
-  {&__pyx_kp_u_84, __pyx_k_84, sizeof(__pyx_k_84), 0, 1, 0, 0},
-  {&__pyx_kp_u_85, __pyx_k_85, sizeof(__pyx_k_85), 0, 1, 0, 0},
-  {&__pyx_kp_u_86, __pyx_k_86, sizeof(__pyx_k_86), 0, 1, 0, 0},
-  {&__pyx_kp_u_87, __pyx_k_87, sizeof(__pyx_k_87), 0, 1, 0, 0},
-  {&__pyx_kp_u_88, __pyx_k_88, sizeof(__pyx_k_88), 0, 1, 0, 0},
-  {&__pyx_kp_u_89, __pyx_k_89, sizeof(__pyx_k_89), 0, 1, 0, 0},
+  {&__pyx_kp_s_57, __pyx_k_57, sizeof(__pyx_k_57), 0, 0, 1, 0},
+  {&__pyx_kp_s_59, __pyx_k_59, sizeof(__pyx_k_59), 0, 0, 1, 0},
+  {&__pyx_kp_s_64, __pyx_k_64, sizeof(__pyx_k_64), 0, 0, 1, 0},
+  {&__pyx_kp_s_87, __pyx_k_87, sizeof(__pyx_k_87), 0, 0, 1, 0},
+  {&__pyx_kp_s_89, __pyx_k_89, sizeof(__pyx_k_89), 0, 0, 1, 0},
   {&__pyx_kp_s_9, __pyx_k_9, sizeof(__pyx_k_9), 0, 0, 1, 0},
-  {&__pyx_kp_u_90, __pyx_k_90, sizeof(__pyx_k_90), 0, 1, 0, 0},
-  {&__pyx_kp_u_91, __pyx_k_91, sizeof(__pyx_k_91), 0, 1, 0, 0},
-  {&__pyx_kp_u_92, __pyx_k_92, sizeof(__pyx_k_92), 0, 1, 0, 0},
-  {&__pyx_kp_u_93, __pyx_k_93, sizeof(__pyx_k_93), 0, 1, 0, 0},
-  {&__pyx_kp_u_94, __pyx_k_94, sizeof(__pyx_k_94), 0, 1, 0, 0},
-  {&__pyx_kp_u_95, __pyx_k_95, sizeof(__pyx_k_95), 0, 1, 0, 0},
-  {&__pyx_kp_u_96, __pyx_k_96, sizeof(__pyx_k_96), 0, 1, 0, 0},
-  {&__pyx_kp_u_97, __pyx_k_97, sizeof(__pyx_k_97), 0, 1, 0, 0},
-  {&__pyx_kp_u_98, __pyx_k_98, sizeof(__pyx_k_98), 0, 1, 0, 0},
-  {&__pyx_kp_u_99, __pyx_k_99, sizeof(__pyx_k_99), 0, 1, 0, 0},
+  {&__pyx_kp_s_93, __pyx_k_93, sizeof(__pyx_k_93), 0, 0, 1, 0},
+  {&__pyx_kp_s_95, __pyx_k_95, sizeof(__pyx_k_95), 0, 0, 1, 0},
+  {&__pyx_kp_s_98, __pyx_k_98, sizeof(__pyx_k_98), 0, 0, 1, 0},
   {&__pyx_n_s__MT19937, __pyx_k__MT19937, sizeof(__pyx_k__MT19937), 0, 0, 1, 1},
-  {&__pyx_n_s__RandomState, __pyx_k__RandomState, sizeof(__pyx_k__RandomState), 0, 0, 1, 1},
   {&__pyx_n_s__TypeError, __pyx_k__TypeError, sizeof(__pyx_k__TypeError), 0, 0, 1, 1},
   {&__pyx_n_s__ValueError, __pyx_k__ValueError, sizeof(__pyx_k__ValueError), 0, 0, 1, 1},
   {&__pyx_n_s____RandomState_ctor, __pyx_k____RandomState_ctor, sizeof(__pyx_k____RandomState_ctor), 0, 0, 1, 1},
@@ -19277,6 +18700,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = {
   {&__pyx_n_s__add, __pyx_k__add, sizeof(__pyx_k__add), 0, 0, 1, 1},
   {&__pyx_n_s__alpha, __pyx_k__alpha, sizeof(__pyx_k__alpha), 0, 0, 1, 1},
   {&__pyx_n_s__any, __pyx_k__any, sizeof(__pyx_k__any), 0, 0, 1, 1},
+  {&__pyx_n_s__append, __pyx_k__append, sizeof(__pyx_k__append), 0, 0, 1, 1},
   {&__pyx_n_s__arange, __pyx_k__arange, sizeof(__pyx_k__arange), 0, 0, 1, 1},
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   {&__pyx_n_s__hypergeometric, __pyx_k__hypergeometric, sizeof(__pyx_k__hypergeometric), 0, 0, 1, 1},
+  {&__pyx_n_s__iinfo, __pyx_k__iinfo, sizeof(__pyx_k__iinfo), 0, 0, 1, 1},
   {&__pyx_n_s__integer, __pyx_k__integer, sizeof(__pyx_k__integer), 0, 0, 1, 1},
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   {&__pyx_n_s__key, __pyx_k__key, sizeof(__pyx_k__key), 0, 0, 1, 1},
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   {&__pyx_n_s__lam, __pyx_k__lam, sizeof(__pyx_k__lam), 0, 0, 1, 1},
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   {&__pyx_n_s__low, __pyx_k__low, sizeof(__pyx_k__low), 0, 0, 1, 1},
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   {&__pyx_n_s__mean, __pyx_k__mean, sizeof(__pyx_k__mean), 0, 0, 1, 1},
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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+ */
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_32));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_33));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
+  PyTuple_SET_ITEM(__pyx_k_tuple_35, 0, ((PyObject *)__pyx_kp_s_31));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_36));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_37));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_39));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_40));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_41));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_42));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_43));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_45));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_46));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_64));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_64));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_65));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_64));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_71));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_103));
+  PyTuple_SET_ITEM(__pyx_k_tuple_104, 0, ((PyObject *)__pyx_kp_s_103));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_103));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_104));
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+ *                                   fmode, fright)
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+  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_106));
+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_105));
+  PyTuple_SET_ITEM(__pyx_k_tuple_106, 0, ((PyObject *)__pyx_kp_s_105));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_105));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_106));
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+ *             raise ValueError("mode > right")
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_101));
+  PyTuple_SET_ITEM(__pyx_k_tuple_107, 0, ((PyObject *)__pyx_kp_s_101));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_101));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_107));
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+ *             raise ValueError("left == right")
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_103));
+  PyTuple_SET_ITEM(__pyx_k_tuple_108, 0, ((PyObject *)__pyx_kp_s_103));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_103));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_108));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_105));
+  PyTuple_SET_ITEM(__pyx_k_tuple_109, 0, ((PyObject *)__pyx_kp_s_105));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_105));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_109));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
+  PyTuple_SET_ITEM(__pyx_k_tuple_111, 0, ((PyObject *)__pyx_kp_s_110));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_111));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_113));
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+ * 
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
+  PyTuple_SET_ITEM(__pyx_k_tuple_115, 0, ((PyObject *)__pyx_kp_s_114));
+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_115));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_116));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
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+  __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_126));
+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_127));
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+  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_129));
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+  __pyx_t_4 = __Pyx_GetName(__pyx_m, __pyx_n_s___rand); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __Pyx_XDECREF(__pyx_t_1);
   __Pyx_XDECREF(__pyx_t_2);
   __Pyx_XDECREF(__pyx_t_3);
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@@ -20850,16 +21447,152 @@ PyMODINIT_FUNC PyInit_mtrand(void)
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-
 /* Runtime support code */
 
-static void __pyx_init_filenames(void) {
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+        PyErr_SetObject(PyExc_NameError, name);
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+}
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+static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
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+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
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+        value = type;
+        #if PY_VERSION_HEX < 0x02050000
+            if (PyInstance_Check(type)) {
+                type = (PyObject*) ((PyInstanceObject*)type)->in_class;
+                Py_INCREF(type);
+            }
+            else {
+                type = 0;
+                PyErr_SetString(PyExc_TypeError,
+                    "raise: exception must be an old-style class or instance");
+                goto raise_error;
+            }
+        #else
+            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;
+            }
+        #endif
+    }
+
+    __Pyx_ErrRestore(type, value, tb);
+    return;
+raise_error:
+    Py_XDECREF(value);
+    Py_XDECREF(type);
+    Py_XDECREF(tb);
+    return;
+}
+
+#else /* Python 3+ */
+
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
+    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)) {
+        PyErr_SetString(PyExc_TypeError,
+            "raise: exception class must be a subclass of BaseException");
+        goto bad;
+    }
+
+    PyErr_SetObject(type, value);
+
+    if (tb) {
+        PyThreadState *tstate = PyThreadState_GET();
+        PyObject* tmp_tb = tstate->curexc_traceback;
+        if (tb != tmp_tb) {
+            Py_INCREF(tb);
+            tstate->curexc_traceback = tb;
+            Py_XDECREF(tmp_tb);
+        }
+    }
+
+bad:
+    return;
 }
+#endif
 
 static void __Pyx_RaiseDoubleKeywordsError(
     const char* func_name,
@@ -20874,36 +21607,6 @@ static void __Pyx_RaiseDoubleKeywordsError(
         #endif
 }
 
-static void __Pyx_RaiseArgtupleInvalid(
-    const char* func_name,
-    int exact,
-    Py_ssize_t num_min,
-    Py_ssize_t num_max,
-    Py_ssize_t num_found)
-{
-    Py_ssize_t num_expected;
-    const char *number, *more_or_less;
-
-    if (num_found < num_min) {
-        num_expected = num_min;
-        more_or_less = "at least";
-    } else {
-        num_expected = num_max;
-        more_or_less = "at most";
-    }
-    if (exact) {
-        more_or_less = "exactly";
-    }
-    number = (num_expected == 1) ? "" : "s";
-    PyErr_Format(PyExc_TypeError,
-        #if PY_VERSION_HEX < 0x02050000
-            "%s() takes %s %d positional argument%s (%d given)",
-        #else
-            "%s() takes %s %zd positional argument%s (%zd given)",
-        #endif
-        func_name, more_or_less, num_expected, number, num_found);
-}
-
 static int __Pyx_ParseOptionalKeywords(
     PyObject *kwds,
     PyObject **argnames[],
@@ -20983,6 +21686,36 @@ bad:
     return -1;
 }
 
+static void __Pyx_RaiseArgtupleInvalid(
+    const char* func_name,
+    int exact,
+    Py_ssize_t num_min,
+    Py_ssize_t num_max,
+    Py_ssize_t num_found)
+{
+    Py_ssize_t num_expected;
+    const char *number, *more_or_less;
+
+    if (num_found < num_min) {
+        num_expected = num_min;
+        more_or_less = "at least";
+    } else {
+        num_expected = num_max;
+        more_or_less = "at most";
+    }
+    if (exact) {
+        more_or_less = "exactly";
+    }
+    number = (num_expected == 1) ? "" : "s";
+    PyErr_Format(PyExc_TypeError,
+        #if PY_VERSION_HEX < 0x02050000
+            "%s() takes %s %d positional argument%s (%d given)",
+        #else
+            "%s() takes %s %zd positional argument%s (%zd given)",
+        #endif
+        func_name, more_or_less, num_expected, number, num_found);
+}
+
 
 static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
     PyErr_Format(PyExc_ValueError,
@@ -20994,8 +21727,13 @@ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
                  (index == 1) ? "" : "s");
 }
 
-static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(void) {
-    PyErr_SetString(PyExc_ValueError, "too many values to unpack");
+static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
+    PyErr_Format(PyExc_ValueError,
+        #if PY_VERSION_HEX < 0x02050000
+            "too many values to unpack (expected %d)", (int)expected);
+        #else
+            "too many values to unpack (expected %zd)", expected);
+        #endif
 }
 
 static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) {
@@ -21008,11 +21746,11 @@ static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) {
     return item;
 }
 
-static int __Pyx_EndUnpack(PyObject *iter) {
+static int __Pyx_EndUnpack(PyObject *iter, Py_ssize_t expected) {
     PyObject *item;
     if ((item = PyIter_Next(iter))) {
         Py_DECREF(item);
-        __Pyx_RaiseTooManyValuesError();
+        __Pyx_RaiseTooManyValuesError(expected);
         return -1;
     }
     else if (!PyErr_Occurred())
@@ -21139,14 +21877,14 @@ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb)
 }
 
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
-    PyObject *__import__ = 0;
+    PyObject *py_import = 0;
     PyObject *empty_list = 0;
     PyObject *module = 0;
     PyObject *global_dict = 0;
     PyObject *empty_dict = 0;
     PyObject *list;
-    __import__ = __Pyx_GetAttrString(__pyx_b, "__import__");
-    if (!__import__)
+    py_import = __Pyx_GetAttrString(__pyx_b, "__import__");
+    if (!py_import)
         goto bad;
     if (from_list)
         list = from_list;
@@ -21162,160 +21900,15 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
     empty_dict = PyDict_New();
     if (!empty_dict)
         goto bad;
-    module = PyObject_CallFunctionObjArgs(__import__,
+    module = PyObject_CallFunctionObjArgs(py_import,
         name, global_dict, empty_dict, list, NULL);
 bad:
     Py_XDECREF(empty_list);
-    Py_XDECREF(__import__);
+    Py_XDECREF(py_import);
     Py_XDECREF(empty_dict);
     return module;
 }
 
-static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
-    PyObject *result;
-    result = PyObject_GetAttr(dict, name);
-    if (!result)
-        PyErr_SetObject(PyExc_NameError, name);
-    return result;
-}
-
-static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-
-    tmp_type = tstate->curexc_type;
-    tmp_value = tstate->curexc_value;
-    tmp_tb = tstate->curexc_traceback;
-    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_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
-    PyThreadState *tstate = PyThreadState_GET();
-    *type = tstate->curexc_type;
-    *value = tstate->curexc_value;
-    *tb = tstate->curexc_traceback;
-
-    tstate->curexc_type = 0;
-    tstate->curexc_value = 0;
-    tstate->curexc_traceback = 0;
-}
-
-
-#if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
-    Py_XINCREF(type);
-    Py_XINCREF(value);
-    Py_XINCREF(tb);
-    /* First, check the traceback argument, replacing None with NULL. */
-    if (tb == Py_None) {
-        Py_DECREF(tb);
-        tb = 0;
-    }
-    else if (tb != NULL && !PyTraceBack_Check(tb)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: arg 3 must be a traceback or None");
-        goto raise_error;
-    }
-    /* Next, replace a missing value with None */
-    if (value == NULL) {
-        value = Py_None;
-        Py_INCREF(value);
-    }
-    #if PY_VERSION_HEX < 0x02050000
-    if (!PyClass_Check(type))
-    #else
-    if (!PyType_Check(type))
-    #endif
-    {
-        /* Raising an instance.  The value should be a dummy. */
-        if (value != Py_None) {
-            PyErr_SetString(PyExc_TypeError,
-                "instance exception may not have a separate value");
-            goto raise_error;
-        }
-        /* Normalize to raise <class>, <instance> */
-        Py_DECREF(value);
-        value = type;
-        #if PY_VERSION_HEX < 0x02050000
-            if (PyInstance_Check(type)) {
-                type = (PyObject*) ((PyInstanceObject*)type)->in_class;
-                Py_INCREF(type);
-            }
-            else {
-                type = 0;
-                PyErr_SetString(PyExc_TypeError,
-                    "raise: exception must be an old-style class or instance");
-                goto raise_error;
-            }
-        #else
-            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;
-            }
-        #endif
-    }
-
-    __Pyx_ErrRestore(type, value, tb);
-    return;
-raise_error:
-    Py_XDECREF(value);
-    Py_XDECREF(type);
-    Py_XDECREF(tb);
-    return;
-}
-
-#else /* Python 3+ */
-
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
-    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)) {
-        PyErr_SetString(PyExc_TypeError,
-            "raise: exception class must be a subclass of BaseException");
-        goto bad;
-    }
-
-    PyErr_SetObject(type, value);
-
-    if (tb) {
-        PyThreadState *tstate = PyThreadState_GET();
-        PyObject* tmp_tb = tstate->curexc_traceback;
-        if (tb != tmp_tb) {
-            Py_INCREF(tb);
-            tstate->curexc_traceback = tb;
-            Py_XDECREF(tmp_tb);
-        }
-    }
-
-bad:
-    return;
-}
-#endif
-
 static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject* x) {
     const unsigned char neg_one = (unsigned char)-1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
@@ -21487,6 +22080,25 @@ static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
     return (signed int)__Pyx_PyInt_AsSignedLong(x);
 }
 
+static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject* x) {
+    const int neg_one = (int)-1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (sizeof(int) < sizeof(long)) {
+        long val = __Pyx_PyInt_AsLong(x);
+        if (unlikely(val != (long)(int)val)) {
+            if (!unlikely(val == -1 && PyErr_Occurred())) {
+                PyErr_SetString(PyExc_OverflowError,
+                    (is_unsigned && unlikely(val < 0)) ?
+                    "can't convert negative value to int" :
+                    "value too large to convert to int");
+            }
+            return (int)-1;
+        }
+        return (int)val;
+    }
+    return (int)__Pyx_PyInt_AsLong(x);
+}
+
 static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
     const unsigned long neg_one = (unsigned long)-1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
@@ -21725,19 +22337,23 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
     if (!result)
         goto bad;
     if (!PyType_Check(result)) {
-        PyErr_Format(PyExc_TypeError, 
+        PyErr_Format(PyExc_TypeError,
             "%s.%s is not a type object",
             module_name, class_name);
         goto bad;
     }
     if (!strict && ((PyTypeObject *)result)->tp_basicsize > size) {
-        PyOS_snprintf(warning, sizeof(warning), 
+        PyOS_snprintf(warning, sizeof(warning),
             "%s.%s size changed, may indicate binary incompatibility",
             module_name, class_name);
+        #if PY_VERSION_HEX < 0x02050000
+        PyErr_Warn(NULL, warning);
+        #else
         PyErr_WarnEx(NULL, warning, 0);
+        #endif
     }
     else if (((PyTypeObject *)result)->tp_basicsize != size) {
-        PyErr_Format(PyExc_ValueError, 
+        PyErr_Format(PyExc_ValueError,
             "%s.%s has the wrong size, try recompiling",
             module_name, class_name);
         goto bad;
@@ -21875,8 +22491,8 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
 /* Type Conversion Functions */
 
 static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
-   if (x == Py_True) return 1;
-   else if ((x == Py_False) | (x == Py_None)) return 0;
+   int is_true = x == Py_True;
+   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;
    else return PyObject_IsTrue(x);
 }